Journal of Animal Science

Boysen, T. J.;Tetens, J.;Thaller, G.

doi: 10.2527/jas.2009-2565pmid: 20581286

ABSTRACT The distal part of SSC7, which contains the ortholog to the ovine region encompassing the callipyge locus, was analyzed in a Piétrain F2 resource population comprising more than 2,700 offspring. The aim of the study was to map QTL affecting carcass traits comparable with the callipyge phenotype in sheep. We applied an interval mapping approach using 14 microsatellite markers and detected 3 QTL with small effects. The first QTL affects fat thickness on the middle of the back, with no detectable impact on fat sizes at other places on the back, whereas the second QTL affects side fat thickness. The third QTL influences the ham weight and shows a clear parent-of-origin-dependent effect in the form of maternal polar overdominance. It is not located at the position of the imprinting cluster including the callipyge locus, but 7 cM proximal. INTRODUCTION In diploid organisms, gene expression of the 2 parental alleles can be very different, a phenomenon known as allele-specific expression. Genomic imprinting is a prominent underlying mechanism in which the expression of alleles is determined by their parental origin. The intensity ranges from unequal expression to the complete repression of 1 parental allele, equivalent to a functional haploidy. This leads to expression patterns noticeably deviant from the classic Mendelian inheritance models. In mammals, this mechanism is essential for embryogenesis, observable in the failure of parthenogenetic embryos to develop because of the fundamental functional differences of the parental genomes (McGrath and Solter, 1984; Surani et al., 1984). Impairments of mechanisms related to genomic imprinting can result in serious developmental defects. Genomic imprinting is an epigenetic modification that changes the function of the involved DNA without altering the nucleotide sequence, primarily by DNA methylation (Reik et al., 1987; Sapienza et al., 1987; Swain et al., 1987). Although differentially methylated regions are found in almost every imprinted domain, other mechanisms, such as histone and chromatin modification, might be of importance in the parent-of-origin (par)-specific epigenetic marking of the DNA. An example for a special expression pattern caused by this imprinting effect in livestock is the ovine callipyge phenotype, a muscle hypertrophy in the form of enlarged diameter of the fast-twitch myofibers (Carpenter et al., 1996). The resulting enlargement of musculature is not uniform; the muscles of the torso and pelvic limb are considerably increased in size, whereas the thoracic limb is less influenced. This causes a noticeable shift in muscle distribution toward the hindquarters (Jackson et al., 1997). The responsible mutation, CLPG, was identified on Ovis aries chromosome 18 within the intergenic region between the genes DLK1 and GTL2, in a cluster of reciprocally imprinted genes, influencing their expression with unchanged imprinting status (Cockett et al., 1996; Freking et al., 1998, 1999, 2002; Georges et al., 2003; Smit et al., 2003; Bidwell et al., 2004). Only heterozygous animals with the mutation inherited from the sire exhibit the hypertrophy phenotype. This par-dependent pattern of inheritance was termed polar overdominance (Cockett et al., 1996). Whereas the callipyge phenotype is characterized by hypertrophy in several muscles, another ovine phenotype called rib-eye muscling or Carwell is confined to the LM (Nicoll et al., 1998; Walling et al., 2004). The Carwell locus has been mapped to the same chromosomal region on Ovis aries chromosome 18 adjacent to CLPG (McEwan et al., 1998; Nicoll et al., 1998), but no par-dependent effect has been reported (Jopson et al., 2001). Together, these findings support the hypothesis that QTL affecting muscle-related carcass traits might be present in the orthologous chromosomal regions of other mammalian livestock species. In the pig, polar overdominance has been reported for growth and fat deposition traits influenced by a polymorphism in the DLK1 gene (Kim et al., 2004). However, no QTL affecting traits comparable with the ovine callipyge phenotype have yet been mapped to this chromosomal region. The aim of this study was therefore to identify possible QTL for similar traits such as ham weight (HW) in this region by using a very large pig F2 resource population. MATERIALS AND METHODS All procedures involving living animals were approved by the local animal care and use committee and were in agreement with the German animal protection act. Animals and Traits A large 3-generation resource pedigree comprising a total F2 progeny of 2,741 animals was used in this study. The pedigree was established by mating 5 purebred Piétrain boars to 1 Large White, 1 Landrace, and 15 Large White × Landrace crossbred sows. Subsequently, large F2 families (245 to 636 F2 animals) were established by repeatedly mating 14 F1 boars to full-sib sows. With the use of divergent outbred founder breeds and the large F2 full-sib families, a pedigree with an increased detection power for par effects was implemented. Comprehensive phenotypic data, including growth, body composition, carcass and meat quality traits, congenital anomalies, and possible environmental factors, were recorded for the population. Blood samples (3× 9 mL) were collected from all animals and the DNA was extracted with a salt extraction method optimized for frozen blood. All trait data listed in this study were collected on the carcass after routine slaughter in an abattoir without any intervention in the living animal. Methods of measurement and calculations were used according to the performance testing directive of the Central Association for German Pig Production [Zentralverband der Deutschen Schweineproduktion (ZDS), Bonn, Germany]. Carcass weight was determined directly after slaughtering, whereas the HW was obtained after cooling and was therefore corrected by 2% to account for cooling loss. Backfat thickness was measured as the largest fat depth over the withers, the smallest fat depth over the middle of the back (BFmiddle) and the smallest fat depth over the musculus psoas major. Side fat thickness (SF) was determined as the largest meat-free fat depth over the ventral end of the LM. Additionally, fat and meat sizes were measured using the infrared reflection stab-in probe Fat-O-Meat'er (Carometec A/S, Herlev, Denmark) between the third- and second-to-last rib. From these data, meat proportion was calculated calculated according to the following formula (as used for performance testing by the ZDS): {meat proportion (%) = 51,279 + [0.305 × LM area (cm2)] − [0.270 × fat area (cm2)] − [0.406 × SF (cm)] − [0.664 × mean backfat depth (cm)]}. The meat-to-fat ratio (MFR) was calculated from the loin eye muscle area and the area of overlying fat at an incision between the 13th and 14th rib. In this study, we used 2,404 animals with complete phenotypic data sets analyzing 10 traits (Table 1). Table 1. Statistics of the analyzed traits Trait  Unit  Phenotypic mean ± SD  Backfat neck  cm  4.08 ± 0.48  Backfat middle  cm  2.42 ± 0.41  Backfat loin  cm  1.85 ± 0.47  Side fat  cm  3.15 ± 0.68  Meat-to-fat ratio     0.39 ± 0.11  Meat proportion  %  54.0 ± 3.92  Meat measure  mm  60.7 ± 6.00  Fat measure  mm  19.1 ± 4.05  Ham weight  kg  14.1 ± 0.71  Carcass weight  kg  89.2 ± 3.00  Trait  Unit  Phenotypic mean ± SD  Backfat neck  cm  4.08 ± 0.48  Backfat middle  cm  2.42 ± 0.41  Backfat loin  cm  1.85 ± 0.47  Side fat  cm  3.15 ± 0.68  Meat-to-fat ratio     0.39 ± 0.11  Meat proportion  %  54.0 ± 3.92  Meat measure  mm  60.7 ± 6.00  Fat measure  mm  19.1 ± 4.05  Ham weight  kg  14.1 ± 0.71  Carcass weight  kg  89.2 ± 3.00  View Large Table 1. Statistics of the analyzed traits Trait  Unit  Phenotypic mean ± SD  Backfat neck  cm  4.08 ± 0.48  Backfat middle  cm  2.42 ± 0.41  Backfat loin  cm  1.85 ± 0.47  Side fat  cm  3.15 ± 0.68  Meat-to-fat ratio     0.39 ± 0.11  Meat proportion  %  54.0 ± 3.92  Meat measure  mm  60.7 ± 6.00  Fat measure  mm  19.1 ± 4.05  Ham weight  kg  14.1 ± 0.71  Carcass weight  kg  89.2 ± 3.00  Trait  Unit  Phenotypic mean ± SD  Backfat neck  cm  4.08 ± 0.48  Backfat middle  cm  2.42 ± 0.41  Backfat loin  cm  1.85 ± 0.47  Side fat  cm  3.15 ± 0.68  Meat-to-fat ratio     0.39 ± 0.11  Meat proportion  %  54.0 ± 3.92  Meat measure  mm  60.7 ± 6.00  Fat measure  mm  19.1 ± 4.05  Ham weight  kg  14.1 ± 0.71  Carcass weight  kg  89.2 ± 3.00  View Large Genotyping and Linkage Map Construction Fourteen microsatellite markers (Table 2), located in the chromosomal region of SSC7 ortholog to the ovine region encompassing the CLPG locus, were chosen from the results of Karlskov-Mortensen et al. (2007). The markers cover the interval from 109 to 130 Mb of SSC7 (Sscrofa8.52), equivalent to 80 to 102 Mb on human chromosome 14, with an average distance of approximately 1.6 Mb. The PCR primer sequences and approximate marker positions were derived from those of Karlskov-Mortensen et al. (2007) accessed via the pig QTL database (PigQTLdb; http://www.animalgenome.org/QTLdb/pig.html). The marker positions were verified by performing a Basic Local Alignment Search Tool (BLAST) search against the recent assembly of the porcine genome sequence (Sscrofa8.52; pre.ensembl.org), applying an expected threshold value of 0.001 and with an activated RepeatMask option. Based on positional information, the genetic marker distances were calculated using CRI-MAP (Lander and Green, 1987). After checking for possible double recombinants by applying the CHROMPIC option, we calculated the distances using the FIXED option. Additionally, the assumed physical order of the markers was checked by comparing the likelihood of different given marker orders. The marker genotypes were determined by ratio-optimized multiplex-PCR and subsequent fragment length analysis with an ABI 3130xl capillary sequencer (Applied Biosystems, Foster City, CA). Validation of the obtained genotypes and error checking were performed using PEDSTATS (Wigginton and Abecasis, 2005). Table 2. Marker positions and information content1 Marker  GenBank accession  HSA14, Mb  SSC7, Mb  Linkage map, cM  Alleles  Inform. add.  Inform. dom.  Inform. par.  KVL1175  EF131016  80.48  NS  0.0  2  0.13  0.04  0.10  KVL5421  EF131784  84.08  113.7  0.6  4  0.22  0.10  0.12  KVL2777  EF132120  86.94  116.6  4.4  4  0.43  0.37  0.30  KVL2445  EF131796  90.57  120.0  12.6  3  0.20  0.08  0.11  KVL3671  EF133301  93.06  Mult.  20.3  6  0.65  0.61  0.32  KVL0402  EF129812  93.51  123.1  21.6  3  0.51  0.42  0.19  KVL6517  EF132393  94.51  NS  24.4  6  0.72  0.67  0.35  KVL6316  EF132251  96.32  126.3  29.4  5  0.46  0.40  0.27  KVL0201  EF129662  96.56  126.6  30.4  3  0.43  0.28  0.23  KVL3672  EF133302  96.64  NS  31.3  4  0.34  0.20  0.21  KVL5231  EF131640  97.01  127.0  32.4  4  0.40  0.24  0.27  KVL2022  EF128943  97.47  127.6  33.4  6  0.67  0.57  0.49  KVL6583  EF132443  98.95  129.0  36.7  2  0.15  0.04  0.10  KVL0704  EF130016  102.31  130.3  42.5  5  0.52  0.38  0.31  Marker  GenBank accession  HSA14, Mb  SSC7, Mb  Linkage map, cM  Alleles  Inform. add.  Inform. dom.  Inform. par.  KVL1175  EF131016  80.48  NS  0.0  2  0.13  0.04  0.10  KVL5421  EF131784  84.08  113.7  0.6  4  0.22  0.10  0.12  KVL2777  EF132120  86.94  116.6  4.4  4  0.43  0.37  0.30  KVL2445  EF131796  90.57  120.0  12.6  3  0.20  0.08  0.11  KVL3671  EF133301  93.06  Mult.  20.3  6  0.65  0.61  0.32  KVL0402  EF129812  93.51  123.1  21.6  3  0.51  0.42  0.19  KVL6517  EF132393  94.51  NS  24.4  6  0.72  0.67  0.35  KVL6316  EF132251  96.32  126.3  29.4  5  0.46  0.40  0.27  KVL0201  EF129662  96.56  126.6  30.4  3  0.43  0.28  0.23  KVL3672  EF133302  96.64  NS  31.3  4  0.34  0.20  0.21  KVL5231  EF131640  97.01  127.0  32.4  4  0.40  0.24  0.27  KVL2022  EF128943  97.47  127.6  33.4  6  0.67  0.57  0.49  KVL6583  EF132443  98.95  129.0  36.7  2  0.15  0.04  0.10  KVL0704  EF130016  102.31  130.3  42.5  5  0.52  0.38  0.31  1HSA14 = human chromosome 14; NS = no significant Basic Local Alignment Search Tool (BLAST) hit, Mult. = multiple BLAST hits (E = 0.001); Inform. = information content; add. = additive effect; dom. = dominance effect; par. = parent-of-origin effect. View Large Table 2. Marker positions and information content1 Marker  GenBank accession  HSA14, Mb  SSC7, Mb  Linkage map, cM  Alleles  Inform. add.  Inform. dom.  Inform. par.  KVL1175  EF131016  80.48  NS  0.0  2  0.13  0.04  0.10  KVL5421  EF131784  84.08  113.7  0.6  4  0.22  0.10  0.12  KVL2777  EF132120  86.94  116.6  4.4  4  0.43  0.37  0.30  KVL2445  EF131796  90.57  120.0  12.6  3  0.20  0.08  0.11  KVL3671  EF133301  93.06  Mult.  20.3  6  0.65  0.61  0.32  KVL0402  EF129812  93.51  123.1  21.6  3  0.51  0.42  0.19  KVL6517  EF132393  94.51  NS  24.4  6  0.72  0.67  0.35  KVL6316  EF132251  96.32  126.3  29.4  5  0.46  0.40  0.27  KVL0201  EF129662  96.56  126.6  30.4  3  0.43  0.28  0.23  KVL3672  EF133302  96.64  NS  31.3  4  0.34  0.20  0.21  KVL5231  EF131640  97.01  127.0  32.4  4  0.40  0.24  0.27  KVL2022  EF128943  97.47  127.6  33.4  6  0.67  0.57  0.49  KVL6583  EF132443  98.95  129.0  36.7  2  0.15  0.04  0.10  KVL0704  EF130016  102.31  130.3  42.5  5  0.52  0.38  0.31  Marker  GenBank accession  HSA14, Mb  SSC7, Mb  Linkage map, cM  Alleles  Inform. add.  Inform. dom.  Inform. par.  KVL1175  EF131016  80.48  NS  0.0  2  0.13  0.04  0.10  KVL5421  EF131784  84.08  113.7  0.6  4  0.22  0.10  0.12  KVL2777  EF132120  86.94  116.6  4.4  4  0.43  0.37  0.30  KVL2445  EF131796  90.57  120.0  12.6  3  0.20  0.08  0.11  KVL3671  EF133301  93.06  Mult.  20.3  6  0.65  0.61  0.32  KVL0402  EF129812  93.51  123.1  21.6  3  0.51  0.42  0.19  KVL6517  EF132393  94.51  NS  24.4  6  0.72  0.67  0.35  KVL6316  EF132251  96.32  126.3  29.4  5  0.46  0.40  0.27  KVL0201  EF129662  96.56  126.6  30.4  3  0.43  0.28  0.23  KVL3672  EF133302  96.64  NS  31.3  4  0.34  0.20  0.21  KVL5231  EF131640  97.01  127.0  32.4  4  0.40  0.24  0.27  KVL2022  EF128943  97.47  127.6  33.4  6  0.67  0.57  0.49  KVL6583  EF132443  98.95  129.0  36.7  2  0.15  0.04  0.10  KVL0704  EF130016  102.31  130.3  42.5  5  0.52  0.38  0.31  1HSA14 = human chromosome 14; NS = no significant Basic Local Alignment Search Tool (BLAST) hit, Mult. = multiple BLAST hits (E = 0.001); Inform. = information content; add. = additive effect; dom. = dominance effect; par. = parent-of-origin effect. View Large Statistical Analysis The statistical analysis followed the approach of fitting a linear model to the observed phenotypic data and the calculated probabilities for the parental origin of the loci in between the markers, as described by Haley and Knott (1992), to test for the presence of a QTL. This single-locus regression method provides 4 estimates for an effect caused by the existence of each genotype at discrete positions. A QTL will become apparent in a test statistic if a polymorphism segregates in the pedigree, which has a sufficient effect on the recorded traits, in contrast with other sources of phenotypic variance. Included in the analysis of the estimates resulting from these regressions are generally additive (add), dominance (dom), and par effects. They are derived by weighting the estimates with 3 orthogonal contrasts to achieve independent values. The add effect is hereby represented by the phenotypic difference between the 2 homozygous states, dom considered in the offset of the mean of the heterozygous state from the mean of the homozygous state and imprinting as the phenotypic difference between the 2 heterozygous states of 2 alleles, thus a par effect:  The vector [add, dom, par] comprises the assumed genetic effects, and the vector [QQ, Qq, qQ, qq] represents the estimated effects from the regression for the 4 genotypes. A detectable difference between the 2 alternative heterozygous states is proof of a par effect, although it is not sufficient to state the presence of imprinting because other causes, such as maternal effects, cannot be separated from imprinting this way. An imprinting-induced inheritance pattern, such as polar overdominance, would be unrepresentable with add, dom, and par effects alone. This effect would become manifest in both dom and par estimates. Contrasts to reproduce these patterns more appropriately are linear combinations of the contrasts described above, such as dom+par or dom-par for polar dominance and dom+2par or dom-2par for polar overdominance, with the sign depending on whether it is expressed on paternal or maternal origin. The outlined procedure is implemented in the QTL-mapping application GridQTL (Seaton et al., 2006) but without the possibility of including random effects. This software was applied to derive the probabilities for the 4 possible combinations of parental origins in the F2 at equidistant positions of 1 cM across the covered interval. In the following, the usual letters Q and q are assigned to the Piétrain and the Large White-Landrace origin, respectively. In addition, the marker information contents were obtained from GridQTL. Based on these coefficient of origin probabilities, a mixed-effects model was fitted using the REML method, including the fixed effects of year of birth, sex, stable, and family and the carcass weight as covariate. The environmental effect of the litter was included as a random effect. Effects going out from the grandparents were omitted because they are sufficiently captured in the family effects in this pedigree structure. An approximate log-likelihood ratio test described by Haley et al. (1994) was used as the test statistic, and QTL positions are stated as the local maximum. The threshold values for the test statistics were determined empirically by a permutation test at the QTL position following the method of Churchill and Doerge (1994), which provides an approximated probability of the observed effect under the null hypothesis of no QTL. A false positive probability below 5% after 10,000 permutations was assumed to be significant. Confidence intervals were calculated by bootstrapping (Efron, 1979), a related procedure applicable for this task in QTL mapping (Visscher et al., 1996). Aside from the utilization of GridQTL to deduce the line and parental origins and marker information contents, all computations were performed using the statistical software package R version 2.8.0 (R Development Core Team, 2008). RESULTS AND DISCUSSION Within this study, 2,404 animals were genotyped, obtaining 83% of the marker genotypes. Verification of the marker positions by BLAST analysis confirmed the assumed order for 10 of the 14 markers. According to version Sscrofa8.52 of the porcine genome sequence, the marker KVL3671 was mapped to SSC16, whereas for KVL1175, KVL6517, and KVL3672, no significant BLAST hits were obtained. The porcine physical distances were compared with the corresponding distances on human chromosome 14 as derived from BLAST analysis (Table 2). They were in good concordance except for the interval between the markers KVL6583 and KVL0704, which was unexpectedly small in the pig compared with the human. At the same time, the genetic distance between the markers was larger, as expected from the porcine physical distance, indicating a gap in the porcine genome build. The genetic distances on either side of the marker KVL2445 were large in relation to the physical distance. However, no unlikely double recombinants were identified, and testing the marker order by inserting this marker in all possible positions revealed no better order in terms of likelihood. Three significant QTL were detected within the analyzed interval (Figure 1). The first one affected the trait BFmiddle, with the peak of the test statistic located at 10 cM, corresponding to a physical position of 120 Mb according to Sscrofa8.52. The second QTL for SF was located at 12 cM (122 Mb), and both were significant (α = 0.01). The third QTL was located at 30 cM (126.2 Mb), affecting HW and reaching significance (α = 0.05). The estimated QTL effects, as summarized in Table 3, were comparatively small but were significant because the extent of the pedigree used allowed for the detection of minor effects. Figure 1. View largeDownload slide Log-likelihood ratio (LLR) statistics for the traits backfat middle (BFmiddle), side fat (SF), and ham weight (HW). The traits for which no significant QTL were detected are not shown. Marker positions are represented as triangles on the abscissa, and the region corresponding to the DLK1/DIO3 cluster is marked as CLPG. The levels of significance (α = 0.05 for HW, and α = 0.01 for BFmiddle and SF) are illustrated by horizontal lines. Figure 1. View largeDownload slide Log-likelihood ratio (LLR) statistics for the traits backfat middle (BFmiddle), side fat (SF), and ham weight (HW). The traits for which no significant QTL were detected are not shown. Marker positions are represented as triangles on the abscissa, and the region corresponding to the DLK1/DIO3 cluster is marked as CLPG. The levels of significance (α = 0.05 for HW, and α = 0.01 for BFmiddle and SF) are illustrated by horizontal lines. Table 3. Estimated effects at respective QTL positions1 Trait  HW, kg  SF, cm  BFmiddle, cm  Estimate  SE  Estimate  SE  Estimate  SE  Stable  0.066  0.034  −0.083  0.040  −0.118  0.025  Family  0.375  0.022  −0.279  0.026  −0.252  0.016  Yr 1  −0.005  0.028  −0.035  0.034  −0.022  0.020  Yr 2  −0.173  0.058  0.066  0.069  0.031  0.042  BW  0.157  0.004  0.010  0.004  0.012  0.003  Sex  0.093  0.022  −0.520  0.025  −0.179  0.016  Add  0.044  0.033  0.144  0.026  0.068  0.040  Dom  0.093  0.049  −0.050  0.043  −0.089  0.065  Par  −0.107  0.040  0.006  0.031  0.019  0.049  M. Dom  0.200  0.064  −0.056  0.054  −0.108  0.082  P. Dom  −0.014  0.062  −0.044  0.053  −0.070  0.081  M. Overd  0.307  0.095  −0.062  0.077  −0.127  0.118  P. Overd  −0.121  0.092  −0.038  0.076  −0.050  0.116  Trait  HW, kg  SF, cm  BFmiddle, cm  Estimate  SE  Estimate  SE  Estimate  SE  Stable  0.066  0.034  −0.083  0.040  −0.118  0.025  Family  0.375  0.022  −0.279  0.026  −0.252  0.016  Yr 1  −0.005  0.028  −0.035  0.034  −0.022  0.020  Yr 2  −0.173  0.058  0.066  0.069  0.031  0.042  BW  0.157  0.004  0.010  0.004  0.012  0.003  Sex  0.093  0.022  −0.520  0.025  −0.179  0.016  Add  0.044  0.033  0.144  0.026  0.068  0.040  Dom  0.093  0.049  −0.050  0.043  −0.089  0.065  Par  −0.107  0.040  0.006  0.031  0.019  0.049  M. Dom  0.200  0.064  −0.056  0.054  −0.108  0.082  P. Dom  −0.014  0.062  −0.044  0.053  −0.070  0.081  M. Overd  0.307  0.095  −0.062  0.077  −0.127  0.118  P. Overd  −0.121  0.092  −0.038  0.076  −0.050  0.116  1Fixed effects (family as mean) and the contrasts to form the genetic effect estimates additive (Add), dominance (Dom), parent-of-origin (Par), maternal or paternal dominance (M. Dom, P. Dom), and maternal or paternal overdominance (M. Overd, P. Overd). HW = ham weight; SF = side fat thickness; BFmiddle = backfat depth over the middle of the back. View Large Table 3. Estimated effects at respective QTL positions1 Trait  HW, kg  SF, cm  BFmiddle, cm  Estimate  SE  Estimate  SE  Estimate  SE  Stable  0.066  0.034  −0.083  0.040  −0.118  0.025  Family  0.375  0.022  −0.279  0.026  −0.252  0.016  Yr 1  −0.005  0.028  −0.035  0.034  −0.022  0.020  Yr 2  −0.173  0.058  0.066  0.069  0.031  0.042  BW  0.157  0.004  0.010  0.004  0.012  0.003  Sex  0.093  0.022  −0.520  0.025  −0.179  0.016  Add  0.044  0.033  0.144  0.026  0.068  0.040  Dom  0.093  0.049  −0.050  0.043  −0.089  0.065  Par  −0.107  0.040  0.006  0.031  0.019  0.049  M. Dom  0.200  0.064  −0.056  0.054  −0.108  0.082  P. Dom  −0.014  0.062  −0.044  0.053  −0.070  0.081  M. Overd  0.307  0.095  −0.062  0.077  −0.127  0.118  P. Overd  −0.121  0.092  −0.038  0.076  −0.050  0.116  Trait  HW, kg  SF, cm  BFmiddle, cm  Estimate  SE  Estimate  SE  Estimate  SE  Stable  0.066  0.034  −0.083  0.040  −0.118  0.025  Family  0.375  0.022  −0.279  0.026  −0.252  0.016  Yr 1  −0.005  0.028  −0.035  0.034  −0.022  0.020  Yr 2  −0.173  0.058  0.066  0.069  0.031  0.042  BW  0.157  0.004  0.010  0.004  0.012  0.003  Sex  0.093  0.022  −0.520  0.025  −0.179  0.016  Add  0.044  0.033  0.144  0.026  0.068  0.040  Dom  0.093  0.049  −0.050  0.043  −0.089  0.065  Par  −0.107  0.040  0.006  0.031  0.019  0.049  M. Dom  0.200  0.064  −0.056  0.054  −0.108  0.082  P. Dom  −0.014  0.062  −0.044  0.053  −0.070  0.081  M. Overd  0.307  0.095  −0.062  0.077  −0.127  0.118  P. Overd  −0.121  0.092  −0.038  0.076  −0.050  0.116  1Fixed effects (family as mean) and the contrasts to form the genetic effect estimates additive (Add), dominance (Dom), parent-of-origin (Par), maternal or paternal dominance (M. Dom, P. Dom), and maternal or paternal overdominance (M. Overd, P. Overd). HW = ham weight; SF = side fat thickness; BFmiddle = backfat depth over the middle of the back. View Large Although significant, QTL were identified for the 2 fat-related traits SF and BFmiddle; no significant genetic effect on the remaining fat depth measures was found within the analyzed chromosomal region. This indicates a QTL effect restricted to a certain body region. Similar results have been found in other QTL mapping studies, such as in Liu et al. (2007), Geldermann et al. (2003), Nezer et al. (2002), or Malek et al. (2001). In the genome-wide QTL scan conducted by Liu et al. (2007) in a Duroc-Piétrain population, the backfat measures over the shoulder, the 10th rib, and the loin were analyzed. Although QTL were identified that influenced 2 of these traits (shoulder and 10th rib, or 10th rib and loin), no QTL was found that influenced all backfat traits. The QTL detected for HW displayed a noticeable par effect, compared with add and dom effects. However, the dom contrast was misleading when the heterozygote contrast was significant. Because we hypothesized that the QTL might be comparable with the ovine CLPG locus, polar dominance and polar overdominance were considered as additional contrasts. The maximum on HW of 307 g (Table 3) was obtained by applying the contrast resembling maternal overdominance, whereas paternal overdominance has been described for the ovine CLPG locus (Cockett et al., 1996). When an additional ANOVA was performed for the QTL peak position without any contrast, it became apparent that the QTL genotype qQ was the main source of variance within the 4 genotypes. In terms of QTL alleles, this means that the combination of a paternally inherited Large White-Landrace allele (q) and a maternally inherited Piétrain allele (Q) cause the phenotype of increased HW. The peak position of the porcine QTL for HW found in this study was neither coincident with the position of the DLK1-GTL2 intergenic region encompassing the ovine CLPG mutation nor with the porcine DLK1 polymorphism reported by Kim et al. (2004). The QTL at 30 cM was flanked by the markers KVL0402 (21.6 cM) and KVL0201 (30.4 cM). The interval containing the DLK1-GTL2 region was located between 129 and 130 Mb on SSC7, flanked by KVL6583 (36.7 cM) and KVL0704 (42.5 cM). Thus, the peak position of the QTL for HW was located more than 7 cM (approximately 4 Mb) apart from this region. However, the confidence interval covered the entire region. A QTL similar to the ovine Carwell locus seemed unlikely because the corresponding phenotype is confined to the LM and no par effect has been described for this locus (Jopson et al., 2001). The QTL detected in the current study had an effect on HW, comprising mainly the mass of musculus glutaei, musculus biceps femoris, musculus semitendinosus, and musculus semimembranosus. On the other hand, there was no detectable effect on other muscle-related traits such as MFR, meat proportion in the entire carcass, or carcass weight. This would resemble the callipyge phenotype, as described in detail by Jackson et al. (1997). However, the MFR and the meat proportion were derived from fat depths and the measurement of LM only. A change of muscle distribution toward the hindquarter was therefore not detectable and would have required the separate sampling of a sufficient number of muscles across the carcass. The aim of the current study was to map a QTL affecting traits resembling the callipyge phenotype within the region of distal porcine chromosome 7 ortholog to the ovine chromosomal segment encompassing the CLPG locus. Aside from highly significant QTL for BFmiddle and SF, we found evidence of a locus affecting HW, with a clear par-dependent pattern of inheritance in the form of maternal polar overdominance. The peak position of the QTL is approximately 4 Mb apart from the DLK1-GTL2 intergenic region, suggesting a QTL different from CLPG. LITERATURE CITED Bidwell C. A. Kramer L. N. Perkins A. C. Hadfield T. S. Moody D. E. Cockett N. E. 2004. Expression of PEG11 and PEG11AS transcripts in normal and callipyge sheep. BMC Biol.  2: 17. [PubMed] Google Scholar CrossRef Search ADS PubMed  Carpenter C. E. Rice O. D. Cockett N. E. Snowder G. D. 1996. Histology and composition of muscles from normal and callipyge lambs. J. Anim. Sci.  74: 388– 393. [PubMed] Google Scholar CrossRef Search ADS PubMed  Churchill G. A. Doerge R.W. 1994. Empirical threshold values for quantitative trait mapping. Genetics  138: 963– 971. Google Scholar PubMed  Cockett N. E. Jackson S. P. Shay T. L. Farnir F. Berghmans S. Snowder G. D. Nielsen D. M. Georges M. 1996. Polar overdominance at the ovine callipyge locus. Science  273: 236– 238. [PubMed] Google Scholar CrossRef Search ADS PubMed  Efron B. 1979. Bootstrap methods: Another look at the jacknife. Ann. Stat.  7: 1– 26. Google Scholar CrossRef Search ADS   Freking B. A. Keele J. W. Beattie C. W. Kappes S. M. Smith T. P. L. Sonstegard T. S. Nielsen M. K. Leymaster K. A. 1998. Evaluation of the ovine callipyge locus: I. Relative chromosomal position and gene action. J. Anim. Sci.  76: 2062– 2071. [PubMed] Google Scholar CrossRef Search ADS PubMed  Freking B. A. Keele J. W. Nielsen M. K. Leymaster K. A. 1998. Evaluation of the ovine callipyge locus: II. Genotypic effects on growth, slaughter, and carcass traits. J. Anim. Sci.  76: 2549– 2559. [PubMed] Google Scholar CrossRef Search ADS PubMed  Freking B. A. Keele J. W. Shackelford S. D. Wheeler T. L. Koohmaraie M. Nielsen M. K. Leymaster K. A. 1999. Evaluation of the ovine callipyge locus: III. Genotypic effects on meat quality. J. Anim. Sci.  77: 2336– 2344. [PubMed] Google Scholar CrossRef Search ADS PubMed  Freking B. A. Murphy S. K. Wylie A. A. Rhodes S. J. Keele J. W. Leymaster K. A. Jirtle R. L. Smith T. P. 2002. Identification of the single base change causing the callipyge muscle hypertrophy phenotype, the only known example of polar overdominance in mammals. Genome Res.  12: 1496– 1506. [PubMed] Google Scholar CrossRef Search ADS PubMed  Geldermann H. Müller E. Moser G. Reiner G. Bartenschlager H. Cepica S. Stratil A. Kuryl J. Moran C. Davoli R. Brunsch C. 2003. Genome-wide linkage and QTL mapping in porcine F2 families generated from Piétrain, Meishan and Wild Boar crosses. J. Anim. Breed. Genet.  120: 363– 393. Google Scholar CrossRef Search ADS   Georges M. Charlier C. Cockett N. 2003. The callipyge locus: Evidence for the trans interaction of reciprocally imprinted genes. Trends Genet.  19: 248– 252. [PubMed] Google Scholar CrossRef Search ADS PubMed  Haley C. S. Knott S. A. 1992. A simple regression method for mapping quantitative trait loci in line crosses using flanking markers. Heredity  69: 315– 324. [PubMed] Google Scholar CrossRef Search ADS PubMed  Haley C. S. Knott S. A. Elsen J. M. 1994. Mapping quantitative trait loci in crosses between outbred lines using least squares. Genetics  136: 1195– 1207. Google Scholar PubMed  Jackson S. P. Miller M. F. Green R. D. 1997. Phenotypic characterization of Rambouillet sheep expression the callipyge gene: III. Muscle weights and muscle weight distribution. J. Anim. Sci.  75: 133– 138. [PubMed] Google Scholar CrossRef Search ADS PubMed  Jopson N. B. Nicoll G. B. Stevenson-Barry J. M. Duncan S. Greer G. J. Bain W. E. Gerard E. M. Glass B. C. Broad T. E. McEwan J. C. 2001. Mode of inheritance and effects on meat quality of the rib-eye muscling (REM) QTL in sheep. Proc. Assoc. Adv. Anim. Breed. Genet. (Queenstown, NZ)  14: 111– 114. Karlskov-Mortensen P. Hu Z. L. Gorodkin J. Reecy J. M. Fredholm M. 2007. Identification of 10 882 porcine microsatellite sequences and virtual mapping of 4528 of these sequences. Anim. Genet.  38: 401– 405. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kim K.-S. Kim J.-J. Dekkers J. C. M. Rothschild M. F. 2004. Polar overdominant inheritance of a DLK1 polymorphism is associated with growth and fatness in pigs. Mamm. Genome  15: 552– 559. [PubMed] Google Scholar PubMed  Lander E. S. Green P. 1987. Construction of multilocus genetic linkage maps in humans. Proc. Natl. Acad. Sci. USA  84: 2363– 2367. [PubMed] Google Scholar CrossRef Search ADS   Liu G. Jennen D. G. Tholen E. Juengst H. Kleinwächter T. Hölker M. Tesfaye D. Un G. Schreinemachers H. J. Murani E. Ponsuksili S. Kim J. J. Schellander K. Wimmers K. 2007. A genome scan reveals QTL for growth, fatness, leanness and meat quality in a Duroc-Pietrain resource population. Anim. Genet.  38: 241– 252. [PubMed] Google Scholar CrossRef Search ADS PubMed  Malek M. Dekkers J. C. M. Lee H. K. Baas T. J. Rothschild M. F. 2001. A molecular genome scan analysis to identify chromosomal regions influencing economic traits in the pig. I. Growth and body composition. Mamm. Genome  12: 630– 636. [PubMed] Google Scholar CrossRef Search ADS PubMed  McEwan J. C. Gerard E. M. Jopson N. B. Nicoll G. B. Greer G. J. Dodds K. G. Bain W. E. Burkin H. R. Lor E. A. Broad T. E. 1998. Localization of a QTL for rib-eye muscling on OAR18. Anim Genet.  19( Suppl. 1): 66. McGrath J. Solter D. 1984. Completion of mouse embryogenesis requires both the maternal and paternal genomes. Cell  37: 179– 183. [PubMed] Google Scholar CrossRef Search ADS PubMed  Nezer C. Moreau L. Wagenaar D. Georges M. 2002. Results of a whole genome scan targeting QTL for growth and carcass traits in a Piétrain × Large White intercross. Genet. Sel. Evol.  34: 371– 387. [PubMed] Google Scholar CrossRef Search ADS PubMed  Nicoll, G. B., H. R. Burkin, T. E. Broad, N. B. Jopson, G. J. Greer, W. E. Bain, C. S. Wright, K. G. Dodds, P. F. Fennessy, and J. C. McEwan 1998. Genetic linkage of microsatellite markers to the Carwell locus for rib-eye muscling in sheep.  Pages 529– 532 in Proc. 6th World Congr. Genet. Appl. Livest. Prod. Vol. 26. Univ. New England, Armidale, New South Wales, Australia. R Development Core Team 2008. R: A Language and Environment for Statistical Computing.  R Foundation for Statistical Computing, Vienna, Austria. Reik W. Collick A. Norris M. L. Barton S. C. Surani M. A. 1987. Genomic imprinting determines methylation of parental alleles in transgenic mice. Nature  328: 248– 251. https://doi.org/10.1038/328248a0. Google Scholar CrossRef Search ADS PubMed  Sapienza C. Peterson A. C. Rossant J. Balling R. 1987. Degree of methylation of transgenes is dependent on gamete of origin. Nature  328: 251– 254. https://doi.org/10.1038/328251a0. Google Scholar CrossRef Search ADS PubMed  Seaton, G., J. Hernandez, J. A. Grunchec, I. White, J. Allen, D. J. De Koning, W. Wei, D. Berry, C. Haley, and S. Knott 2006. GridQTL: A grid portal for QTL mapping of compute intensive datasets.  Proc. 8th World Congr. Genet. Appl. Livest. Prod., August 13–18, 2006. WCGALP, Belo Horizonte, Brazil. Smit M. Segers K. Carrascosa L. G. Shay T. Baraldi F. Gyapay G. Snowder G. Georges M. Cockett N. Charlier C. 2003. Mosaicism of solid gold supports the causality of a noncoding A-to-G transition in the determinism of the callipyge phenotype. Genetics  163: 453– 456. Google Scholar PubMed  Surani M. A. H. Barton S. C. Norris M. L. 1984. Development of reconstituted mouse eggs suggests imprinting of the genome during gametogenesis. Nature  308: 548– 550. [PubMed] Google Scholar CrossRef Search ADS PubMed  Swain L. Stewart A. Leder P. 1987. Parental legacy determines methylation and expression of an autosomal transgene: A molecular mechanism for parental imprinting. Cell  50: 719– 727. https://doi.org/10.1016/0092-8674(87)90330-8. Google Scholar CrossRef Search ADS PubMed  Visscher P. M. Thompson R. Haley C. S. 1996. Confidence intervals in QTL mapping by bootstrapping. Genetics  143: 1013– 1020. [PubMed] Google Scholar PubMed  Walling G. A. Visscher P. M. Wilson A. D. McTeir B. L. Simm G. Bishop S. C. 2004. Mapping of quantitative trait loci for growth and carcass traits in commercial sheep populations. J. Anim. Sci.  82: 2234– 2245. Google Scholar CrossRef Search ADS PubMed  Wigginton J. E. Abecasis G. R. 2005. PEDSTATS: Descriptive statistics, graphics and quality assessment for gene mapping data. Bioinformatics  21: 3445– 3447. [PubMed] Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Riley, D. G.;Jr., C. C. Chase,;Coleman, S. W.;Olson, T. A.;Randel, R. D.

doi: 10.2527/jas.2009-2573pmid: 20581288

ABSTRACT The objectives of this work were to estimate genetic effects for age and size at estimated time of first conception, and temperament in straightbred and crossbred heifers (n = 554) produced from Romosinuano, Brahman, and Angus cattle, and to evaluate first-parturition performance of heifers, including calf birth weight, occurrence of calving difficulty, occurrence of poor vigor in their newborn calves, and calf mortality. At approximately 7 mo of age, weaned heifers were pastured with Mashona or Tuli bulls until confirmed pregnant. Body weight, hip height, exit velocity (m/s), and chute temperament score (1 = calm, no movement; 5 = continuous movement, struggling) were recorded at 28-d intervals until heifers averaged 19 mo of age. Age at first conception was estimated as age at calving minus 285 d. Regression analyses were used to estimate BW and hip height at age of first conception. Brahman heifers were older, heavier, and had greater hip height than other straightbred groups (P < 0.05) and most crossbred groups. Brahman and reciprocal Brahman-Angus heifers had greater (P < 0.05) exit velocity than Romosinuano and Angus heifers. Brahman sire and dam breed chute temperament scores were greater (P < 0.05) than those of all other breed groups. Estimates of heterosis for age at first conception were −53.7 ± 9.5 (−11%), −56 ± 10.1 (−11%), and −92.9 ± 11 d (−18%) for Romosinuano-Brahman, Romosinuano-Angus, and Brahman-Angus, respectively (P < 0.01). Heterosis was detected (P < 0.04) for Romosinuano-Brahman for BW (12 ± 4.3 kg, 3.7%) and hip height (1.3 ± 0.6 cm, 1%) at first conception. Maternal heterosis for calf birth weight was 3.6 ± 0.5 (12%) and 2.4 ± 0.6 kg (8.6%) for Romosinuano-Angus and Brahman-Angus. In Romosinuano-Brahman and Brahman-Angus, heterosis for exit velocity was 0.23 ± 0.09 (10%) and 0.5 ± 0.1 m/s (21%). The direct breed effect of Romosinuano was to reduce age (−58.2 ± 18.9 d), BW (−57.6 ± 10.5 kg), and hip height (−2.6 ± 1.1 cm) at the time of first conception (P < 0.01), and the direct Brahman effects (P < 0.001) were large and numerically positive for these traits (169.8 ± 20.8 d, 93.3 ± 11.6 kg, and 14 ± 1.2 cm). Use of Romosinuano in crossbreeding programs with Brahman may be useful for decreasing the age at first conception. The larger birth weights of calves born to Romosinuano-Angus cross heifers would not be desirable in southern cow-calf operations. INTRODUCTION Cow-calf producers in the southern United States have used Brahman crossbreds at least in part because of their superior adaptability and excellent reproduction in the harsh environmental conditions of the region. Brahman straightbreds are necessary to produce crossbred females; some of the performance and handling challenges associated with these include late sexual maturation relative to other types of cattle and poor temperament. Adapted Bos taurus females may reach puberty sooner and be more docile than Brahman females; they may be an alternative germplasm source. They may represent a crossbreeding opportunity (with Brahman) to improve these aspects of cow-calf production while still having a female that is adapted to the hot, humid conditions of the region. Investigation of the performance of adapted B. taurus cattle in the United States has been limited, especially for Criollo cattle of South America. A project was designed to evaluate the Criollo breed, Romosinuano, as purebreds and as crossbreds in all stages of beef production (Riley et al., 2007) in Central Florida. The primary objective of this work was to estimate heterosis and breed genetic effects for age, BW, and hip height at the estimated time of first conception, and for assessments of temperament in heifers produced from matings of Romosinuano, Brahman, and Angus cattle. Another objective was to evaluate the performance of these heifers at their first parturition, including birth weight of their calves, occurrence of calving difficulty, occurrence of poor vigor in their newborn calves, and mortality of their calves. MATERIALS AND METHODS All procedures involving animals were approved by the local institutional animal care and use committee. Breeding Design and Procedures The overall project breeding design was detailed previously (Riley et al., 2007). In brief, Romosinuano, Brahman, and Angus bulls were mated to cows of the same breeds in all combinations. The resulting straightbred and crossbred heifers were spring-born from 2002 through 2005. Heifers were weaned at approximately 7 mo of age in September of each year in 3 separate weeks. A total of 13, 13, and 15 Angus, Brahman, and Romosinuano bulls sired daughters with records (average of 13, 15, and 13 heifers per Angus, Brahman, and Romosinuano sire, respectively). Weaned calves were provided a commercial preconditioning concentrate (14% CP, cottonseed- and soybean meal-based ration; 1.8 kg/calf per day; as-fed basis) for a minimum of 21 d and free-choice grass hay. Heifers were divided into 2 management groups and placed with pairs of either Mashona or Tuli bulls in mid-October, with the exception of the first project year, when only Mashona bulls were available. Pairs of bulls were alternated with rested pairs every 2 wk. Body weight, hip height, BCS, exit velocity (m/s), and chute temperament score were recorded on heifers every 28 d through September of the next year, when heifers were approximately 19 mo old. This procedure was modified in the fourth project year: BW, hip height, and BCS were recorded only quarterly through September of the next year. At that time, those heifers that were determined to be pregnant by rectal palpation were removed from the groups, and any open heifers were consolidated into a single group and put with a single Mashona bull, which was replaced every 2 wk. These were maintained in that small group until they were determined to be pregnant. A small group (n = 14, 2.5%) of the heifers in this project never conceived. These included more straightbreds (3 Angus, 5 Brahman, and 1 Romosinuano) than crossbreds (1 Romosinuano-sired heifer out of an Angus dam, 1 Romosinuano-sired heifer out of a Brahman dam, 2 Brahman-sired heifers out of Angus dams, and 1 Angus-sired heifer out of a Brahman dam). To accommodate a limited breeding season, others have projected phenotypes for heifers that did not conceive based on a simulated distribution (Thallman et al., 1999). Because it was possible that in the present study these heifers may never have conceived even if left permanently with bulls, all records from these heifers were excluded from all analyses of this study, and results are based on heifers that calved. Heifers were provided mixed bahiagrass and perennial peanut hay (approximately 11% CP; 55% TDN) free choice, and were supplemented with a mixture of soybean hulls and fortified molasses (16% CP equivalent), each fed at 0.75% of BW (target BW gain was 0.7 kg/d). This feeding schedule commenced when heifers were placed with bulls and was continued through the last week of May in each year. From June through October, heifers grazed bahiagrass pastures. Subsequent winter feeding included hay and fortified molasses, each at a rate of 2.3 kg/animal per day. Calves born to the heifers were weighed and tagged within 24 h after birth, and males were castrated. Calving difficulty for each birth was assessed by assignment of a subjective score from 1 to 4: 1 = no assistance; 2 = minor hand pull; 3 = a moderate to major hand pull; and 4 = major mechanical assistance. A subjective score was assigned as an assessment of calf vigor at birth: 1 = normal, adequate vigor; 2 = a weak calf that nursed without assistance; and 3 = a calf that was too weak to nurse without assistance. Stillbirths (calves dead when found and thought to be dead at birth) were assigned a vigor score of 0. Calves were weaned at an average of approximately 75 d of age (ranging from 60 to 92 d), to facilitate the movement of females into a standard spring breeding season for subsequent evaluation of lifetime reproduction and productivity. Traits Evaluated Age at first conception was estimated by subtracting 285 d from the age of each heifer at calving (Simpson et al., 1998; Thallman et al., 1999). Body weight and hip height at first conception were estimated in a 2-step procedure. First, using the BW and hip height data collected at 28-d intervals for each heifer, each trait was regressed on age in days at weaning (linear) and days since females were placed with bulls. The investigated polynomial regression on days on test began at an order of 7 and was decreased until the F-value of the highest order regression had P = 0.05 or less. Results were similar for breed groups; therefore, this regression was conducted across breed groups. The final regression equation for each trait was used to solve for BW and hip height at the time of first conception for each heifer. Quarterly BW (recorded after the first year postweaning) were also used to estimate BW and hip height for heifers that calved from matings that occurred later than 1 yr after initial exposure to bulls. Temperament traits were measured every 28 d. The amount of time required for a heifer to move approximately 1.8 m when released from the working chute (Burrow et al., 1988) was recorded as an objective measurement and expressed as exit velocity (m/s). Temperament was measured subjectively as chute score on a scale from 1 to 5: 1 = calm, no movement; 2 = restless shifting; 3 = squirming, occasional shaking of squeeze chute; 4 = continuous vigorous movement and shaking of squeeze chute; 5 = rearing, twisting, or struggling violently (Grandin, 1993). Chute score was assessed by a single evaluator. Statistical Analysis Data were analyzed using mixed linear models with MIXED procedures (SAS Inst. Inc., Cary, NC). Fixed effects investigated in all analyses included sire breed, dam breed, year of record, postweaning management group, the age of the dam of the heifer in years (3 levels: 3 and 4 yr olds; 5 to 10 yr olds; and cows older than 10 yr), and their interactions. Management group was a fixed effect nested within year. Calf traits were analyzed as traits of the heifer. Analyses of calf birth weight also included unique effects: calf sex and birth date within year (as a covariate). Order of entry to the chute was investigated as a linear covariate in analyses of temperament traits. Pregnancy status was determined for each record time and was investigated as a 2-level fixed effect in analyses of temperament traits. Fixed effects with F-ratios with P < 0.15 were kept for final analyses. Sire of heifer within sire breed and dam of heifer within dam breed were random terms. The analyses of exit velocity and chute temperament score were repeated-measures analyses; these included the effect of sampling time (which occurred at 28-d intervals). The fixed effects portion of each model was determined while using a simple correlation structure for repeated measures (compound symmetry). For each trait, the fixed portion was subsequently held constant and the covariance between repeated sampling times within each analysis was modeled, investigating various structures for each trait using procedures described by Littell et al. (2002). Information criterion values (particularly the Schwarz Bayesian information criterion values) generated by the MIXED procedures were considered in the determination of best residual covariance structure for each trait. After a covariance structure was selected that best modeled the correlation between repeated measures, the significance of fixed effects was confirmed with that structure. More than one-half of the Angus-sired heifers were by bulls from outside the Brooksville research herd. Because Angus born in the Brooksville herd are visually very different from many unrelated Angus cattle, the source of Angus sire was evaluated in preliminary analyses of all traits by considering the 2 sources as distinct sire breed groups (Riley et al., 2007). Linear contrasts of the sire breed means were constructed to assess Angus sire source differences (outside source minus within-herd source means). When this estimate was different from 0, linear contrasts were similarly constructed to compare Angus sire sources within each dam breed. Calving difficulty, calf vigor, and calf survival to weaning were analyzed as binomial traits using the FREQ procedures of SAS. For calving difficulty, scores of 1 (normal births) were assigned a value of 0, and all other scores (which indicated some difficulty present) were assigned a value of 1. Similarly, calves with adequate vigor at birth (score 1) were assigned a value of 0. Calves with inadequate vigor (vigor scores 2 and 3) were assigned values of 1. Stillbirths (vigor score 0) were not included in analyses of calf vigor. Values of 1 were assigned to deaths, and values of 0 were assigned to calves that survived to weaning. Means represented proportions of difficult births, calves with inadequate vigor, and mortality. Breed group means (sire breed-dam breed combinations) were tested against the χ2 expectation. Sources of Angus sire means were tested against the χ2 expectation, both across and within all dam breeds. Contrasts of appropriate least squares means were constructed based on concepts described by Dickerson (1973) to estimate breed direct and maternal effects, and heterosis effects for each trait. Maternal breed effects were estimated as the average difference between reciprocal crossbred groups {e.g., the Romosinuano maternal effect is 1/2[(AR − RA) + (BR − RB)]}, and pairs of letters indicate calf breed group concentrations in which the first and second letters indicate the breed of sire and dam of calves in the group; and R, B, and A indicate Romosinuano, Brahman, and Angus, respectively. Direct breed effects for each breed were estimated as the purebred mean minus the maternal effect for that breed minus the average of the other 2 pure breeds {e.g., Romosinuano is RR − 1/2[(AR − RA) + (BR − RB)] − 1/2(AA + BB)}. Within a trait, the estimates of breed direct or maternal effects sum to 0. Estimates of heterosis for pairs of breeds for each trait consisted of a contrast between the averages of the crossbred and purebred groups {e.g., the heterosis for Romosinuano and Brahman is 1/2[(RB + BR) − (RR + BB)]}. Calf birth weight was considered a trait of the dam, and thus the results can be considered “maternal heterosis” as has been done by others (e.g., Olson et al., 1993). RESULTS Age at First Conception Across all dam breeds, heifers sired by outside Angus sires were younger at first conception (−34.5 ± 16.6 d; P = 0.04) than heifers sired by Angus sires from the research herd. Although no difference was detected when dam breeds were Brahman or Romosinuano, heifers sired by outside source Angus sires were younger at first conception (−78.8 ± 25.7 d; P = 0.003) when the dam breed was Angus. This difference approached significance (P = 0.09) after Bonferroni correction for multiple comparisons (Table 1). Age of dam of the heifer was excluded from the final models (P = 0.73). Management group met the requirements for inclusion (P = 0.14), but no differences were supported after application of correction for multiple tests. Sire breed-dam breed interaction (P < 0.001) means are shown in Table 1. No other interaction term met the criteria for inclusion in the final models. Brahman heifers had the greatest age at first conception mean, and a greater age than all other breeds except Angus heifers sired by sires from the research herd. Angus-sired F1 groups were the youngest (P < 0.05) age at first conception, but were not different from Angus heifers sired by outside-herd Angus bulls (P > 0.18). Table 1. Least squares means for age, BW, and hip height at estimated time of first conception, and birth weight of first calf by breed group of cow1 Breed group  n  Age, d  BW, kg  Hip height, cm  Birth weight of first calf, kg  RR  83  485.4 ± 9.7x  291.4 ± 5.2z  127.2 ± 0.6x  29.2 ± 1.1x  BB  50  600.6 ± 12.1w  366.0 ± 6.4w  136.3 ± 0.7v  28.3 ± 1.2x  AA  20  537.0 ± 18.6wx  304.5 ± 6.6yz  119.0 ± 0.8z  27.8 ± 1.4x  OA  22  458.2 ± 17.6xy        29.1 ± 1.3x                    RB  54  471.0 ± 11.3x  326.5 ± 5.9xy  131.9 ± 0.7w  29.3 ± 1.1x  BR  85  507.5 ± 10.1x  354.9 ± 5.6wx  134.2 ± 0.6vw  29.8 ± 1.1x                    RA  52  459.2 ± 11.5xy  297.3 ± 6.0z  121.8 ± 0.7yz  32.7 ± 1.1w  AR  28  409.9 ± 16.1y  296.3 ± 5.4z  123.9 ± 0.6y  31.3 ± 1.2wx  OR  50  413.5 ± 12.8y        32.9 ± 1.2w                    BA  62  498.7 ± 11.1x  355.4 ± 6.0wx  127.2 ± 0.7x  31.1 ± 1.1wx  AB  14  427.8 ± 21.8y  333.4 ± 6.4x  127.8 ± 0.7x  30.0 ± 1.5wx  OB  34  399.5 ± 14.6y        31.0 ± 1.2wx  Breed group  n  Age, d  BW, kg  Hip height, cm  Birth weight of first calf, kg  RR  83  485.4 ± 9.7x  291.4 ± 5.2z  127.2 ± 0.6x  29.2 ± 1.1x  BB  50  600.6 ± 12.1w  366.0 ± 6.4w  136.3 ± 0.7v  28.3 ± 1.2x  AA  20  537.0 ± 18.6wx  304.5 ± 6.6yz  119.0 ± 0.8z  27.8 ± 1.4x  OA  22  458.2 ± 17.6xy        29.1 ± 1.3x                    RB  54  471.0 ± 11.3x  326.5 ± 5.9xy  131.9 ± 0.7w  29.3 ± 1.1x  BR  85  507.5 ± 10.1x  354.9 ± 5.6wx  134.2 ± 0.6vw  29.8 ± 1.1x                    RA  52  459.2 ± 11.5xy  297.3 ± 6.0z  121.8 ± 0.7yz  32.7 ± 1.1w  AR  28  409.9 ± 16.1y  296.3 ± 5.4z  123.9 ± 0.6y  31.3 ± 1.2wx  OR  50  413.5 ± 12.8y        32.9 ± 1.2w                    BA  62  498.7 ± 11.1x  355.4 ± 6.0wx  127.2 ± 0.7x  31.1 ± 1.1wx  AB  14  427.8 ± 21.8y  333.4 ± 6.4x  127.8 ± 0.7x  30.0 ± 1.5wx  OB  34  399.5 ± 14.6y        31.0 ± 1.2wx  v–zWithin a column, means that do not share a common superscript differ (P < 0.05). 1Breed group indicated by sire breed (first letter) and dam breed (second letter): A = Angus from the Brooksville, FL, research herd; O = Angus bulls from outside sources; B = Brahman; and R = Romosinuano. For BW and hip height at estimated first conception, A as the sire breed designation letter includes cows sired by all Angus sires. View Large Table 1. Least squares means for age, BW, and hip height at estimated time of first conception, and birth weight of first calf by breed group of cow1 Breed group  n  Age, d  BW, kg  Hip height, cm  Birth weight of first calf, kg  RR  83  485.4 ± 9.7x  291.4 ± 5.2z  127.2 ± 0.6x  29.2 ± 1.1x  BB  50  600.6 ± 12.1w  366.0 ± 6.4w  136.3 ± 0.7v  28.3 ± 1.2x  AA  20  537.0 ± 18.6wx  304.5 ± 6.6yz  119.0 ± 0.8z  27.8 ± 1.4x  OA  22  458.2 ± 17.6xy        29.1 ± 1.3x                    RB  54  471.0 ± 11.3x  326.5 ± 5.9xy  131.9 ± 0.7w  29.3 ± 1.1x  BR  85  507.5 ± 10.1x  354.9 ± 5.6wx  134.2 ± 0.6vw  29.8 ± 1.1x                    RA  52  459.2 ± 11.5xy  297.3 ± 6.0z  121.8 ± 0.7yz  32.7 ± 1.1w  AR  28  409.9 ± 16.1y  296.3 ± 5.4z  123.9 ± 0.6y  31.3 ± 1.2wx  OR  50  413.5 ± 12.8y        32.9 ± 1.2w                    BA  62  498.7 ± 11.1x  355.4 ± 6.0wx  127.2 ± 0.7x  31.1 ± 1.1wx  AB  14  427.8 ± 21.8y  333.4 ± 6.4x  127.8 ± 0.7x  30.0 ± 1.5wx  OB  34  399.5 ± 14.6y        31.0 ± 1.2wx  Breed group  n  Age, d  BW, kg  Hip height, cm  Birth weight of first calf, kg  RR  83  485.4 ± 9.7x  291.4 ± 5.2z  127.2 ± 0.6x  29.2 ± 1.1x  BB  50  600.6 ± 12.1w  366.0 ± 6.4w  136.3 ± 0.7v  28.3 ± 1.2x  AA  20  537.0 ± 18.6wx  304.5 ± 6.6yz  119.0 ± 0.8z  27.8 ± 1.4x  OA  22  458.2 ± 17.6xy        29.1 ± 1.3x                    RB  54  471.0 ± 11.3x  326.5 ± 5.9xy  131.9 ± 0.7w  29.3 ± 1.1x  BR  85  507.5 ± 10.1x  354.9 ± 5.6wx  134.2 ± 0.6vw  29.8 ± 1.1x                    RA  52  459.2 ± 11.5xy  297.3 ± 6.0z  121.8 ± 0.7yz  32.7 ± 1.1w  AR  28  409.9 ± 16.1y  296.3 ± 5.4z  123.9 ± 0.6y  31.3 ± 1.2wx  OR  50  413.5 ± 12.8y        32.9 ± 1.2w                    BA  62  498.7 ± 11.1x  355.4 ± 6.0wx  127.2 ± 0.7x  31.1 ± 1.1wx  AB  14  427.8 ± 21.8y  333.4 ± 6.4x  127.8 ± 0.7x  30.0 ± 1.5wx  OB  34  399.5 ± 14.6y        31.0 ± 1.2wx  v–zWithin a column, means that do not share a common superscript differ (P < 0.05). 1Breed group indicated by sire breed (first letter) and dam breed (second letter): A = Angus from the Brooksville, FL, research herd; O = Angus bulls from outside sources; B = Brahman; and R = Romosinuano. For BW and hip height at estimated first conception, A as the sire breed designation letter includes cows sired by all Angus sires. View Large BW at First Conception A quintic regression of BW on days on test was the highest order regression that was significant, and this was used with the date of first conception of each heifer to predict BW at that time. There was no source of Angus sire difference in BW at first conception (P = 0.45). Age of the dam of each heifer was not important (P = 0.38), nor was management group (P = 0.62). Other than the interaction of sire and dam breeds (P = 0.03), interaction effects were not detected (P > 0.21). Brahman heifers were heavier (P < 0.01) than all breed groups (Table 1) except Brahman-sired F1 heifers (P > 0.73). Romosinuano and reciprocal Romosinuano-Angus crossbred heifers were lighter than all other groups (P < 0.001) except Angus. Hip Height at First Conception A linear regression of hip height on days on test was the highest significant order; therefore, this was used with date of first conception to predict individual hip height at that time. There was no detected difference between Angus sire source for hip height at first conception (P = 0.18). Age of the dam of each heifer did not explain substantial variation in hip height (P > 0.7). No interaction effects, other than the interaction of sire and dam breeds (P = 0.07), were detected (P > 0.26). Brahman heifers were taller (P < 0.001) at estimated first conception than all other breed groups except Brahman-sired heifers out of Romosinuano dams (P = 0.25). Angus heifers had smaller hip height means than all other groups (P < 0.001) except Romosinuano-sired heifers out of Angus dams (P = 0.18). Calf Birth Weight Angus heifers sired by bulls from the research herd had lighter calf birth weights than Angus heifers sired by outside bulls (P = 0.08). Final models included sex of calf, sire breed of calf, year, the interaction of sire and dam breeds (of the heifer as a dam), and calving date within year (P < 0.02). Bull calves (30.9 ± 1.1 kg) were heavier (P < 0.001) at birth than heifer calves (29.5 ± 1.1 kg). Tuli-sired calves (31.0 ± 1.1 kg) were heavier (P < 0.001) than Mashona-sired calves (29.5 ± 1.0 kg). Romosinuano-sired heifers out of Angus dams and outside source Angus-sired heifers out of Romosinuano dams had calves with heavier birth weights than all straightbred groups and reciprocal F1 Brahman-Romosinuano heifers (P < 0.02). Temperament Traits Exit Velocity A difference in mean exit velocity between sources of Angus sires was not detected (P = 0.8). Management group, age of the dam of each heifer, and order of entry into the working chute did not explain substantial variation in exit velocity (P > 0.37). Final models consisted of sire and dam breed and their interaction, year, and month of record (P < 0.001). A compound symmetrical structure best modeled the residual correlation within animals, and that estimated correlation was 0.47. Open cows had a greater (P = 0.07) exit velocity (2.5 ± 0.04 m/s) than pregnant cows (2.4 ± 0.05 m/s). Body weight was investigated as a linear covariate jointly with pregnancy status in models and as an alternative to pregnancy status. Body weight was always important (−0.001 ± 0.0003 m/s; P < 0.001); it appeared to model some of the variation initially thought to be due to pregnancy status (which was not significant when both terms were modeled). Breed group means and differences were minimally affected by the alternative modeling of pregnancy status and BW. The means in Table 2 were generated from the model that fitted BW only. Mean exit velocities of reciprocal Brahman-Angus F1 heifers were greater (P < 0.001) than those for straightbred Romosinuano and Angus, and reciprocal Romosinuano-Angus F1 heifers. Romosinuano heifers also had slower (P < 0.02) exit velocities than reciprocal F1 Brahman-Romosinuano groups and straightbred Brahmans. Sampling month exit velocity means ranged from 2.3 ± 0.1 to 2.6 ± 0.1 m/s (Figure 1). Table 2. Breed group means for exit velocity and sire and dam breed means for chute temperament score1 Breed  Exit velocity, m/s  Effect  Chute score  RR  2.0 ± 0.09w  Sire breed     BB  2.6 ± 0.12yz   R  1.8 ± 0.03z  AA  2.2 ± 0.12wxy   B  2.3 ± 0.03y  RB  2.5 ± 0.10xyz   A  1.9 ± 0.03x  BR  2.7 ± 0.09yz  —     RA  2.1 ± 0.11wx  Dam breed     AR  2.2 ± 0.09wxy   R  1.8 ± 0.02x  BA  2.9 ± 0.10z   B  2.4 ± 0.03y  AB  3.0 ± 0.11z   A  1.9 ± 0.03x  Breed  Exit velocity, m/s  Effect  Chute score  RR  2.0 ± 0.09w  Sire breed     BB  2.6 ± 0.12yz   R  1.8 ± 0.03z  AA  2.2 ± 0.12wxy   B  2.3 ± 0.03y  RB  2.5 ± 0.10xyz   A  1.9 ± 0.03x  BR  2.7 ± 0.09yz  —     RA  2.1 ± 0.11wx  Dam breed     AR  2.2 ± 0.09wxy   R  1.8 ± 0.02x  BA  2.9 ± 0.10z   B  2.4 ± 0.03y  AB  3.0 ± 0.11z   A  1.9 ± 0.03x  w–zMeans in the same column that do not share a common superscript differ (P < 0.05). 1Breed group indicated by sire breed (first letter) and dam breed (second letter): A = Angus; B = Brahman; and R = Romosinuano. View Large Table 2. Breed group means for exit velocity and sire and dam breed means for chute temperament score1 Breed  Exit velocity, m/s  Effect  Chute score  RR  2.0 ± 0.09w  Sire breed     BB  2.6 ± 0.12yz   R  1.8 ± 0.03z  AA  2.2 ± 0.12wxy   B  2.3 ± 0.03y  RB  2.5 ± 0.10xyz   A  1.9 ± 0.03x  BR  2.7 ± 0.09yz  —     RA  2.1 ± 0.11wx  Dam breed     AR  2.2 ± 0.09wxy   R  1.8 ± 0.02x  BA  2.9 ± 0.10z   B  2.4 ± 0.03y  AB  3.0 ± 0.11z   A  1.9 ± 0.03x  Breed  Exit velocity, m/s  Effect  Chute score  RR  2.0 ± 0.09w  Sire breed     BB  2.6 ± 0.12yz   R  1.8 ± 0.03z  AA  2.2 ± 0.12wxy   B  2.3 ± 0.03y  RB  2.5 ± 0.10xyz   A  1.9 ± 0.03x  BR  2.7 ± 0.09yz  —     RA  2.1 ± 0.11wx  Dam breed     AR  2.2 ± 0.09wxy   R  1.8 ± 0.02x  BA  2.9 ± 0.10z   B  2.4 ± 0.03y  AB  3.0 ± 0.11z   A  1.9 ± 0.03x  w–zMeans in the same column that do not share a common superscript differ (P < 0.05). 1Breed group indicated by sire breed (first letter) and dam breed (second letter): A = Angus; B = Brahman; and R = Romosinuano. View Large Figure 1. View largeDownload slide Means for exit velocity (m/s) and chute score by month on test. Figure 1. View largeDownload slide Means for exit velocity (m/s) and chute score by month on test. Chute Score Source of Angus sire was not influential in chute score analyses (P = 0.36). A compound symmetry structure best modeled the residual correlation among repeated measures (r2 = 0.22). Age of the dam of each heifer, year, management group, order of entry into the working chute, and interaction effects were excluded from final analyses (P > 0.23). The final model included sire breed, dam breed, and month of record (P < 0.001). Inclusion of BW as a linear covariate was highly significant in all models (−0.001 ± 0.0002). Open cows had greater (P = 0.02) chute scores (2.1 ± 0.02) than pregnant cows (2.0 ± 0.02); as with exit velocity, pregnancy status was not significant when BW was also modeled. Means in Table 2 were generated from models that included BW, but not pregnancy status. All sire breed chute score means differed (P < 0.01): Brahman-sired heifers had the greatest and Romosinuano-sired heifers had the least means. Heifers out of Brahman dams had greater (P < 0.001) chute scores than heifers out of the other 2 groups, which did not differ (P = 0.64). Monthly chute scores ranged from 1.8 ± 0.04 to 2.2 ± 0.03 (Figure 1). Calving Traits Occurrence of calving difficulty for breed groups differed from the χ2 expectation (P = 0.02; Table 3). Angus heifers and reciprocal Romosinuano-Angus heifers had the largest proportions of difficult births. Heifers out of Brahman dams had almost no occurrence of calving difficulty. Calving difficulty and calf mortality distributions by sire breed of calf and calf sex differed (P < 0.04) from the χ2 expectation, but the occurrence of inadequate newborn vigor did not. A smaller proportion of Mashona-sired calves were born in difficult births than Tuli-sired calves (Table 3). Mashona-sired calves had less mortality than Tuli-sired calves. Bull calves had a larger proportion of difficult births and mortality than heifer calves. One-half of the total calf deaths (16 of 32) were associated with calving difficulty. One calf that died also had inadequate vigor at birth. Table 3. Occurrence and percentage of subgroup totals of calving difficulty, inadequate vigor in newborn calves, and calf mortality before weaning1,2,3,4,5 Breed group  Calving difficulty  Calves with inadequate vigor  Calf mortality  n  %  n  %  n  %  RR  2  2.4  3  3.7  6  7.2  BB  1  2  4  8  1  2  AA  3  15        3  15  OA  2  9.1  1  5.3  3  13.6                       RB        1  1.9        BR  2  2.4  1  1.2  5  5.9                       RA  7  13.5  1  2.0  4  7.7  AR  2  7.1        3  10.7  OR  4  8        2  4                       BA  4  6.5        5  8.1  AB                    OB                    Sire breed of calf                     Mashona  10  2.9  8  1.5  14  2.5   Tuli  16  7.6  3  0.6  18  3.3  Sex of calf                     Male  19  6.9  8  1.5  20  3.6   Female  2  0.1  3  0.6  7  1.3  Breed group  Calving difficulty  Calves with inadequate vigor  Calf mortality  n  %  n  %  n  %  RR  2  2.4  3  3.7  6  7.2  BB  1  2  4  8  1  2  AA  3  15        3  15  OA  2  9.1  1  5.3  3  13.6                       RB        1  1.9        BR  2  2.4  1  1.2  5  5.9                       RA  7  13.5  1  2.0  4  7.7  AR  2  7.1        3  10.7  OR  4  8        2  4                       BA  4  6.5        5  8.1  AB                    OB                    Sire breed of calf                     Mashona  10  2.9  8  1.5  14  2.5   Tuli  16  7.6  3  0.6  18  3.3  Sex of calf                     Male  19  6.9  8  1.5  20  3.6   Female  2  0.1  3  0.6  7  1.3  1Calving difficulty evaluated as 1 for calvings with any occurrence of calving difficulty and as 0 for normal births. 2Inadequate vigor at birth evaluated as 1 for observed inadequate vigor (failed to stand, failed to nurse without assistance, or both) and 0 for normal vigor observed. Calves that were born dead were not included in the evaluation of calf vigor. 3Breed group indicated by sire breed (first letter) and dam breed (second letter): A = Angus from the Brooksville, FL, research herd; O = Angus bulls from outside sources; B = Brahman; and R = Romosinuano. 4Breed group distribution of values differed from the χ2 expectation for calving difficulty (P = 0.02). Sire breed of first calf values and calf sex distribution of values differed (P < 0.04) from the χ2 expectation for calving difficulty and calf mortality. 5Empty cells indicate no occurrence of calving difficulty, inadequate calf vigor, or calf mortality for that breed group. View Large Table 3. Occurrence and percentage of subgroup totals of calving difficulty, inadequate vigor in newborn calves, and calf mortality before weaning1,2,3,4,5 Breed group  Calving difficulty  Calves with inadequate vigor  Calf mortality  n  %  n  %  n  %  RR  2  2.4  3  3.7  6  7.2  BB  1  2  4  8  1  2  AA  3  15        3  15  OA  2  9.1  1  5.3  3  13.6                       RB        1  1.9        BR  2  2.4  1  1.2  5  5.9                       RA  7  13.5  1  2.0  4  7.7  AR  2  7.1        3  10.7  OR  4  8        2  4                       BA  4  6.5        5  8.1  AB                    OB                    Sire breed of calf                     Mashona  10  2.9  8  1.5  14  2.5   Tuli  16  7.6  3  0.6  18  3.3  Sex of calf                     Male  19  6.9  8  1.5  20  3.6   Female  2  0.1  3  0.6  7  1.3  Breed group  Calving difficulty  Calves with inadequate vigor  Calf mortality  n  %  n  %  n  %  RR  2  2.4  3  3.7  6  7.2  BB  1  2  4  8  1  2  AA  3  15        3  15  OA  2  9.1  1  5.3  3  13.6                       RB        1  1.9        BR  2  2.4  1  1.2  5  5.9                       RA  7  13.5  1  2.0  4  7.7  AR  2  7.1        3  10.7  OR  4  8        2  4                       BA  4  6.5        5  8.1  AB                    OB                    Sire breed of calf                     Mashona  10  2.9  8  1.5  14  2.5   Tuli  16  7.6  3  0.6  18  3.3  Sex of calf                     Male  19  6.9  8  1.5  20  3.6   Female  2  0.1  3  0.6  7  1.3  1Calving difficulty evaluated as 1 for calvings with any occurrence of calving difficulty and as 0 for normal births. 2Inadequate vigor at birth evaluated as 1 for observed inadequate vigor (failed to stand, failed to nurse without assistance, or both) and 0 for normal vigor observed. Calves that were born dead were not included in the evaluation of calf vigor. 3Breed group indicated by sire breed (first letter) and dam breed (second letter): A = Angus from the Brooksville, FL, research herd; O = Angus bulls from outside sources; B = Brahman; and R = Romosinuano. 4Breed group distribution of values differed from the χ2 expectation for calving difficulty (P = 0.02). Sire breed of first calf values and calf sex distribution of values differed (P < 0.04) from the χ2 expectation for calving difficulty and calf mortality. 5Empty cells indicate no occurrence of calving difficulty, inadequate calf vigor, or calf mortality for that breed group. View Large Estimates of Genetic Effects Strong heterosis (Table 4) reduced age at first conception for all pairs of breeds (P < 0.001). The estimate for Brahman-Angus was of greater magnitude than either of the other 2 pairs of breeds (P < 0.004). Additionally, heterosis was detected for Romosinuano-Brahman for BW (P = 0.006) and hip height (P = 0.03) at first conception. Calf birth weight (considered a trait of the heifer) heterosis was large (12.4 and 8.6%, respectively; P < 0.001) for Romosinuano-Angus and for Brahman-Angus. Strong, unfavorable heterosis was detected for Romosinuano-Brahman (P = 0.01) and Brahman-Angus (P < 0.001) exit velocity. Table 4. Estimates of heterosis (%), breed direct, and breed maternal effects in cows for age, BW, and hip height at first conception, for birth weight of first calf, and for exit velocity1,2,3,4 Item  Age, d  BW, kg  Hip height, cm  Calf birth weight, kg  Exit velocity, m/s  Heterosis                  RB  −53.7 ± 9.5 (−11%)  12.0 ± 4.3 (3.7%)  1.3 ± 0.6 (1%)     0.23 ± 0.09 (10%)   RA  −56.0 ± 10.1 (−11%)        3.6 ± 0.5 (12.4%)      BA  −92.9 ± 11.0 (−18%)        2.4 ± 0.6 (8.6%)  0.5 ± 0.1 (21%)  Direct effect   R  −58.2 ± 18.9  −57.6 ± 10.5  −2.6 ± 1.1     –0.59 ± 0.18   B  169.8 ± 20.8  93.3 ± 11.6  14.0 ± 1.2     0.6 ± 0.2   A  −111.6 ± 20.9  −35.7 ± 11.4  −11.4 ± 1.2        Maternal effect   R     13.7 ± 6.2  2.2 ± 0.7         B  66.2 ± 12.1  −25.2 ± 6.6            A  −60.7 ± 12.1     −1.4 ± 0.7        Item  Age, d  BW, kg  Hip height, cm  Calf birth weight, kg  Exit velocity, m/s  Heterosis                  RB  −53.7 ± 9.5 (−11%)  12.0 ± 4.3 (3.7%)  1.3 ± 0.6 (1%)     0.23 ± 0.09 (10%)   RA  −56.0 ± 10.1 (−11%)        3.6 ± 0.5 (12.4%)      BA  −92.9 ± 11.0 (−18%)        2.4 ± 0.6 (8.6%)  0.5 ± 0.1 (21%)  Direct effect   R  −58.2 ± 18.9  −57.6 ± 10.5  −2.6 ± 1.1     –0.59 ± 0.18   B  169.8 ± 20.8  93.3 ± 11.6  14.0 ± 1.2     0.6 ± 0.2   A  −111.6 ± 20.9  −35.7 ± 11.4  −11.4 ± 1.2        Maternal effect   R     13.7 ± 6.2  2.2 ± 0.7         B  66.2 ± 12.1  −25.2 ± 6.6            A  −60.7 ± 12.1     −1.4 ± 0.7        1R = Romosinuano; B = Brahman; A = Angus. 2The estimate of age at first conception heterosis in BA was greater than those estimates for RA and RB (P < 0.004). 3Empty cells in this table indicate estimates that did not differ from 0 (P > 0.05). 4Calf birth weight was analyzed as a trait of the cow. View Large Table 4. Estimates of heterosis (%), breed direct, and breed maternal effects in cows for age, BW, and hip height at first conception, for birth weight of first calf, and for exit velocity1,2,3,4 Item  Age, d  BW, kg  Hip height, cm  Calf birth weight, kg  Exit velocity, m/s  Heterosis                  RB  −53.7 ± 9.5 (−11%)  12.0 ± 4.3 (3.7%)  1.3 ± 0.6 (1%)     0.23 ± 0.09 (10%)   RA  −56.0 ± 10.1 (−11%)        3.6 ± 0.5 (12.4%)      BA  −92.9 ± 11.0 (−18%)        2.4 ± 0.6 (8.6%)  0.5 ± 0.1 (21%)  Direct effect   R  −58.2 ± 18.9  −57.6 ± 10.5  −2.6 ± 1.1     –0.59 ± 0.18   B  169.8 ± 20.8  93.3 ± 11.6  14.0 ± 1.2     0.6 ± 0.2   A  −111.6 ± 20.9  −35.7 ± 11.4  −11.4 ± 1.2        Maternal effect   R     13.7 ± 6.2  2.2 ± 0.7         B  66.2 ± 12.1  −25.2 ± 6.6            A  −60.7 ± 12.1     −1.4 ± 0.7        Item  Age, d  BW, kg  Hip height, cm  Calf birth weight, kg  Exit velocity, m/s  Heterosis                  RB  −53.7 ± 9.5 (−11%)  12.0 ± 4.3 (3.7%)  1.3 ± 0.6 (1%)     0.23 ± 0.09 (10%)   RA  −56.0 ± 10.1 (−11%)        3.6 ± 0.5 (12.4%)      BA  −92.9 ± 11.0 (−18%)        2.4 ± 0.6 (8.6%)  0.5 ± 0.1 (21%)  Direct effect   R  −58.2 ± 18.9  −57.6 ± 10.5  −2.6 ± 1.1     –0.59 ± 0.18   B  169.8 ± 20.8  93.3 ± 11.6  14.0 ± 1.2     0.6 ± 0.2   A  −111.6 ± 20.9  −35.7 ± 11.4  −11.4 ± 1.2        Maternal effect   R     13.7 ± 6.2  2.2 ± 0.7         B  66.2 ± 12.1  −25.2 ± 6.6            A  −60.7 ± 12.1     −1.4 ± 0.7        1R = Romosinuano; B = Brahman; A = Angus. 2The estimate of age at first conception heterosis in BA was greater than those estimates for RA and RB (P < 0.004). 3Empty cells in this table indicate estimates that did not differ from 0 (P > 0.05). 4Calf birth weight was analyzed as a trait of the cow. View Large The direct effects of Romosinuano and Angus were to reduce age, BW, and hip height at first conception (P < 0.02). The estimated Brahman direct effects were to increase these traits (P < 0.001), and were of much larger magnitude than those for the other 2 breeds. Estimates of direct effects for exit velocity (P < 0.01) were of the same magnitude but opposite sign for Romosinuano (negative; that is, favorable) and Brahman (positive; that is, unfavorable). The Brahman and Angus maternal effects on age at first conception were consistent in numerical sign with the direct effects, but in both cases were of smaller magnitude. DISCUSSION Heifer Age and Size Age at first estimated date of conception (Simpson et al., 1998; Thallman et al., 1999) is an assessment of first reproductive success rather than documentation of age at puberty. Age at first calving (e.g., McElhenney et al., 1985; Sacco et al., 1990) differs from age at first conception by scale only. A degree of reproductive success is implied by those who considered age at first ovulatory estrus (Stewart et al., 1980; Sacco et al., 1987). Many studies have used limited breeding seasons; year-round exposure to bulls (or marker bulls, as by Sacco et al., 1990) has rarely been used (Grajales et al., 2006). Brahman females reach puberty at older ages than B. taurus females (Tran et al., 1988; Nogueira, 2004), even as crosses with B. taurus (Newman and Deland, 1991; Freetly and Cundiff, 1998). Brahman heifers in this project had an average age at first calving that was on the younger end of the range (690 to 1,800 d) of reviewed reports from the tropics around the world (Abeygunawardena and Dematawewa, 2004). Differences in age and BW at first conception between Romosinuano and Brahman were almost identical to those reported for Romosinuano and Zebu in Colombia (Grajales et al., 2006). Nelore-B. taurus-cross heifers had an older age at first conception than B. taurus crosses on the US Great Plains (Thallman et al., 1999). Estimates of heterosis for age at first conception were of similar magnitude or greater than Brahman-Angus or Brahman-Hereford estimates for either age at first ovulatory estrus or age at first calving, which ranged from −22 to −79.5 d (−6.3 to −8.8%; Stewart et al., 1980; McElhenney et al., 1985; Sacco et al., 1987, 1990). A review of estimates in Bos indicus-B. taurus crosses reported estimates of slightly larger magnitude (−96 to −132 d; −8 to −12%) for age at first calving (Syrstad, 1985). Nelore-Charolais heterosis was −89 d (−12%) for age at puberty (Restle et al., 1999). Estimates involving Romosinuano were similar to Wiltbank et al. (1966) or somewhat greater than those for B. taurus crosses (Laster et al., 1976; Long, 1980; Stewart et al., 1980). Heterosis for age at puberty was detected (3%) in crosses of distinct lines within the Hereford breed (Burfening et al., 1979); results of the present study suggest that there may be line heterotic effects in Angus cattle as well. Romosinuano-sired heifers out of B. taurus dams (Angus and MARC III) were evaluated along with females sired by other tropically adapted breeds in Cycle VIII of the cattle Germplasm Evaluation Program at the US Meat Animal Research Center (Wheeler et al., 2006). Heifers were divided into 2 groups and evaluated in Nebraska and Louisiana. Romosinuano-sired females did not differ in age at observed puberty from Brangus-, Beefmaster-, or Bonsmara-sired heifers (Cundiff and Franke, 2006). The Romosinuano-Brahman estimate of heterosis for BW at first conception was among the smaller estimates for similar BW traits from a published range of −3.7 to 17% in crosses of Brahman with British breeds (Stewart et al., 1980; Sacco et al., 1987, 1990) and in crosses of B. taurus breeds or lines within breeds (Burfening et al., 1979; Stewart et al., 1980). It was less than estimates of heterosis for yearling or 18-mo BW in Brahman-B. taurus heifers (Long et al., 1979; Nelsen et al., 1982; Comerford et al., 1988). The estimate of heterosis for hip height at first conception for Romosinuano-Brahman was consistent with the small estimates for similar traits from Brahman-B. taurus crosses (McElhenney et al., 1985; Sacco et al., 1987, 1990); others were greater and ranged from 3 to 5% (Stewart et al., 1980; Nelsen et al., 1982). Estimates of heterosis for yearling hip height were similarly small (Long et al., 1979; Comerford et al., 1988). Romosinuano-B. taurus heifers were lighter than Brangus-, Beefmaster-, or Bonsmara-sired heifers (Cundiff and Franke, 2006). Temperament Traits The unfavorable heterosis estimates detected for exit velocity were of greater magnitude than estimates for flight time in Sanga-B. taurus and B. indicus-B. taurus crossbreds in Central Queensland, Australia (Prayaga, 2003). Flight time was evaluated in that study as seconds elapsed rather than a rate, and that estimate (−6%) indicated unfavorable heterosis. Prayaga (2003) also reported unfavorable Sanga and B. indicus direct effects and favorable maternal effects for flight time. The Romosinuano direct effect for exit velocity from the present study therefore represents the only favorable direct effect estimated for a temperament trait for a tropically adapted breed or type of cattle. Cattle with poor temperaments might be the last to enter the working chutes in their respective groups. Results did not support this idea for either exit velocity or chute temperament score. This was consistent with the work of Müller and von Keyserlingk (2006), who reported that order of entry through the working chute was random in their study of Angus. There appeared to be no evidence for acclimation effects on exit velocity or chute score associated with multiple workings. This result was consistent with Australian work (Burrow, 1997; Petherick et al., 2002) with similar cattle. Additional handling of other kinds may have little effect on exit velocity (Petherick et al., 2003; Müller and von Keyserlingk, 2006). The estimated within-animal residual correlation of 0.47 for exit velocity appeared to be consistent with the estimate of repeatability of 0.39 for flight time (Prayaga, 2003). Traits at Calving The maternal heterosis estimates for birth weight detected for Romosinuano-Angus and Brahman-Angus were larger than literature estimates (e.g., Wyatt and Franke, 1986; Kress et al., 1992); the closest was Brahman-Angus maternal heterosis (2.9 kg) from Olson et al. (1993). This corresponds with the greater occurrence of calving difficulty, especially for reciprocal Romosinuano-Angus heifers. Negative estimates of maternal heterosis for birth weight were reported for Brahman-Hereford (Cartwright et al., 1964; Arthur et al., 1994), Hereford-Simmental (Kress et al., 1990), and B. taurus crosses (Wyatt and Franke, 1986). Although these data could not be used to estimate genetic effects appropriately, some of the mean proportions for calving difficulty, inadequate vigor at birth, and calf mortality suggested the influence of maternal heterosis. Averages for calves from crossbred heifers appeared to be quite different from averages for calves from straightbred heifers, although not favorably in each case. The occurrence of calving difficulty, inadequate vigor at birth, and mortality for reciprocal Romosinuano-Brahman heifers was 1.4, 1.5, and 5%, respectively, as compared with 2.3, 5.3, and 5.3%, respectively, for the average of straightbred Brahman and Romosinuano heifers. The same values for reciprocal Brahman-Angus crossbred heifers were 3.6, 0, and 4.6% as compared with 6.5, 5.8, and 7.6% for the average of straightbred Brahman and Angus heifers. Others have reported similarly small percentages of calving difficulty and larger percentages of calf survival yet have not detected maternal or direct heterosis in B. taurus composites (Gregory et al., 1991a,b; Newman et al., 1993) or crosses (Reynolds et al., 1986). Gregory et al. (1991a) detected maternal heterosis for calving difficulty in the F2 generation of some composites; the classification of categories differed slightly from that of the present study. Williams et al. (1991) reported 10% maternal heterosis for calf survival for Brahman-Hereford cows; however, they and others (McElhenney et al., 1986; Prayaga, 2004) failed to detect heterosis for this trait in other B. indicus-B. taurus crosses. Results from earlier work indicated low additive genetic control for the occurrence of inadequate vigor in newborn calves and suggested that heterosis may be influential for this trait (Riley et al., 2004), but this could not be confirmed with results from the present study. The relatively greater occurrence of calving difficulty in reciprocal Romosinuano-Angus heifers was unexpected. These groups averaged 10% calving difficulty; the average of the straightbreds was 5.6%. This appeared to be mostly explained by the greater birth weights of calves from these crosses and large heterosis. Romosinuano-sired heifers in Nebraska and Louisiana had a smaller (not significantly) percentage of unassisted births (calves were sired by B. taurus MARC III bulls) than Brangus- and Beefmaster-sired heifers, but a significantly greater percentage of unassisted births than Bonsmara-sired heifers (Franke and Cundiff, 2006). Source of Angus Sires The difference in age at first conception between Angus females that were sired by bulls from within and outside the research herd was surprising. Angus heifers that were sired by bulls from the research herd were older at first conception; this was not consistent with the relative early maturation for Angus noted by others (e.g., Wiltbank et al., 1966; Sacco et al., 1987). However, the difference between age at first calving means of Angus and Brahman heifers of Sacco et al. (1990) appeared to be similar to those from the present study, and the average age at calving of Angus females in that study was almost 80 d greater than that presented herein. Age at puberty is not a function of BW (Laster et al., 1976; Gregory et al., 1991c); however, the absence of differences for BW at first conception between Angus sire sources suggests that age at first reproductive success is influenced by BW. Other potential causes could include inbreeding depression or drift effects because this herd has been mostly isolated in Florida since the 1950s, with minimal introduction of cattle from other sources. During most of that time, heifers were first exposed to bulls as 2 yr olds; there was no selective pressure to reduce age at puberty. If it could be assumed that this Angus herd adapted to Florida conditions during that time, a characteristic of such adaptation might involve delayed initial reproductive success or some aspect of sexual maturation compared with what might be expected from Angus cattle. LITERATURE CITED Abeygunawardena H. Dematawewa C. M. B. 2004. Pre-pubertal and postpartum anestrus in tropical Zebu cattle. Anim. Reprod. Sci.  82–83: 373– 387. [PubMed] Google Scholar CrossRef Search ADS PubMed  Arthur P. F. Hearnshaw H. Kohun P. J. Barlow R. 1994. Evaluation of Bos indicus and Bos taurus straightbreds and crosses. III. Direct and maternal genetic effects on growth traits. Aust. J. Agric. Res.  45: 807– 818. Google Scholar CrossRef Search ADS   Burfening P. J. Kress D. D. Anderson D. C. Blackwell R. L. 1979. Heterosis among closed lines of Hereford cattle. II. Postweaning growth and puberty in heifers. J. Anim. Sci.  49: 958– 963. Google Scholar CrossRef Search ADS   Burrow H. M. 1997. Measurements of temperament and their relationships with performance traits of beef cattle. Anim. Breed. Abstr.  65: 477– 495. Burrow H. M. Siefert G. W. Corbet N. J. 1988. A new technique for measuring temperament in cattle. Proc. Aust. Soc. Anim. Prod.  17: 154– 157. Cartwright, T. C., G. F. Ellis Jr., W. E. Kruse, and E. K. Crouch 1964. Hybrid vigor in Brahman-Hereford crosses.  Tech. Monogr. 1. Tex. Agric. Exp. Stn., College Station. Comerford J. W. Benyshek L. L. Bertrand J. K. Johnson M. H. 1988. Evaluation of performance characteristics in a diallel among Simmental, Limousin, Polled Hereford and Brahman beef cattle. I. Growth, hip height and pelvic size. J. Anim. Sci.  66: 293– 305. [PubMed] Google Scholar CrossRef Search ADS PubMed  Cundiff, L. V., and D. E. Franke 2006. Growth and puberty characteristics of topcross females by tropically adapted sire breeds in temperate and subtropical regions of the United States.  Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, Minas Gerais, Brazil. Comm. 32-18. Dickerson, G. E. 1973. Inbreeding and heterosis in animals. Pages 54– 77 in Proc. Anim. Breed. Genet. Symp.  in Honor of Dr. Jay L. Lush. Am. Soc. Anim. Sci. Champaign, IL. Google Scholar CrossRef Search ADS   Franke, D. E., and L. V. Cundiff 2006. Reproduction and maternal performance of two-year-old topcross females by tropically adapted sire breeds in temperate and subtropical regions of the United States.  Proc. 8th World Congr. Genet. Appl. Livest. Prod., Belo Horizonte, Minas Gerais, Brazil. Comm. 32-18. Freetly H. C. Cundiff L. V. 1998. Reproductive performance, calf growth, and milk production of first-calf heifers sired by seven breeds and raised on different levels of nutrition. J. Anim. Sci.  76: 1513– 1522. [PubMed] Google Scholar CrossRef Search ADS PubMed  Grajales, H., A. Hernández, and E. Prieto 2006. Edad y peso a la pubertad y su relación con la eficiencia reproductiva de grupos raciales bovinos en el trópico Colombiano.  Livest. Res. Rural Dev. Vol. 18. Article 139. http://www.lrrd.org/lrrd18/10/graj18139.htm Accessed Dec. 4, 2008. Grandin T. 1993. Behavioral agitation during handling is persistent over time. Appl. Anim. Behav. Sci.  36: 1– 9. Google Scholar CrossRef Search ADS   Gregory K. E. Cundiff L. V. Koch R. M. 1991 a. Breed effects and heterosis in advanced generations of composite populations for birth weight, birth date, dystocia, and survival as traits of dam in beef cattle. J. Anim. Sci.  69: 3574– 3589. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gregory K. E. Cundiff L. V. Koch R. M. 1991 b. Breed effects and heterosis in advanced generations of composite populations for preweaning traits of beef cattle. J. Anim. Sci.  69: 947– 960. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gregory K. E. Lunstra D. D. Cundiff L. V. Koch R. M. 1991 c. Breed effects and heterosis in advanced generations of composite populations for puberty and scrotal traits of beef cattle. J. Anim. Sci.  69: 2795– 2807. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kress D. D. Doornbos D. E. Anderson D. C. 1990. Performance of crosses among Hereford, Angus and Simmental cattle with different levels of Simmental breeding: IV. Maternal heterosis and calf production by two-year-old dams. J. Anim. Sci.  68: 54– 63. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kress D. D. Doornbos D. E. Anderson D. C. Rossi D. 1992. Performance of crosses among Hereford, Angus, and Simmental cattle with different levels of Simmental breeding: VI. Maternal heterosis of 3- to 8-year-old dams and the dominance model. J. Anim. Sci.  70: 2682– 2687. [PubMed] Google Scholar CrossRef Search ADS PubMed  Laster D. B. Smith G. M. Gregory K. E. 1976. Characterization of biological types of cattle IV. Postweaning growth and puberty of heifers. J. Anim. Sci.  43: 63– 70. [PubMed] Google Scholar CrossRef Search ADS PubMed  Littell, R. C., W. W. Stroup, and R. J. Freund 2002. Mixed model analysis of repeated measures. Pages 280– 301 in SAS for Linear Models.  4th ed. SAS Inst. Inc., Cary, NC. Long C. R. 1980. Crossbreeding for beef production: Experimental results. J. Anim. Sci.  51: 1197– 1223. Google Scholar CrossRef Search ADS   Long C. R. Stewart T. S. Cartwright T. C. Baker J. F. 1979. Characterization of cattle of a five breed diallel: II. Measures of size, condition and growth in heifers. J. Anim. Sci.  49: 432– 447. Google Scholar CrossRef Search ADS   McElhenney W. H. Long C. R. Baker J. F. Cartwright T. C. 1985. Production characters of first-generation cows of a five-breed diallel: Reproduction of young cows and preweaning performance of inter se calves. J. Anim. Sci.  61: 55– 65. [PubMed] Google Scholar CrossRef Search ADS PubMed  McElhenney W. H. Long C. R. Baker J. F. Cartwright T. C. 1986. Production characters of first-generation cows of a five-breed diallel: Reproduction of mature cows and preweaning performance of calves by two third-breed sires. J. Anim. Sci.  63: 59– 67. [PubMed] Google Scholar CrossRef Search ADS PubMed  Müller R. von Keyserlingk M. A. G. 2006. Consistency of flight speed and its correlation to productivity and personality in Bos taurus beef cattle. Appl. Anim. Behav. Sci.  99: 193– 204. Google Scholar CrossRef Search ADS   Nelsen T. C. Long C. R. Cartwright T. C. 1982. Postinflection growth in straightbred and crossbred cattle. I. Heterosis for weight, height and maturing rate. J. Anim. Sci.  55: 280– 292. Google Scholar CrossRef Search ADS   Newman S. Deland M. P. 1991. Lifetime productivity of crossbred cows. 2. Age and weight at first oestrus, calf birth weight, assisted calvings, calving interval and reproduction rate. Aust. J. Exp. Agric.  31: 293– 300. Google Scholar CrossRef Search ADS   Newman S. MacNeil M. D. Reynolds W. L. Knapp B. W. Urick J. J. 1993. Fixed effects in the formation of a composite line of beef cattle. I. Experimental design and reproductive performance. J. Anim. Sci.  71: 2026– 2032. [PubMed] Google Scholar CrossRef Search ADS PubMed  Nogueira G. P. 2004. Puberty in South American Bos indicus (Zebu) cattle. Anim. Reprod. Sci.  82–83: 361– 372. [PubMed] Google Scholar CrossRef Search ADS PubMed  Olson T. A. Peacock F. M. Koger M. 1993. Reproductive and maternal performance of rotational three-breed, and inter se crossbred cows in Florida. J. Anim. Sci.  71: 2322– 2329. [PubMed] Google Scholar CrossRef Search ADS PubMed  Petherick J. C. Holroyd R. G. Doogan V. J. Venus B. K. 2002. Productivity, carcass and meat quality of lot-fed Bos indicus cross steers grouped according to temperament. Aust. J. Exp. Agric.  42: 389– 398. Google Scholar CrossRef Search ADS   Petherick J. C. Holroyd R. G. Swain A. J. 2003. Performance of lot-fed Bos indicus steers exposed to aspects of a feedlot environment before lot-feeding. Aust. J. Exp. Agric.  43: 1181– 1191. Google Scholar CrossRef Search ADS   Prayaga K. C. 2003. Evaluation of beef cattle genotypes and estimation of direct and maternal genetic effects in a tropical environment. 2. Adaptive and temperament traits. Aust. J. Agric. Res.  54: 1027– 1038. Google Scholar CrossRef Search ADS   Prayaga K. C. 2004. Evaluation of beef cattle genotypes and estimation of direct and maternal genetic effects in a tropical environment. 3. Fertility and calf survival traits. Aust. J. Agric. Res.  55: 811– 824. Google Scholar CrossRef Search ADS   Restle J. Polli V. A. Borges de Senna D. 1999. Efeito de grupo genético e heterose sobre a idade e peso à puberdade e sobre o desempenho reprodutivo de novilhas de corte. Pesquisa Agropecu. Bras.  34: 701– 707. Google Scholar CrossRef Search ADS   Reynolds W. L. Bellows R. A. Urick J. J. Knapp B. W. 1986. Crossing beef × beef and beef × Brown Swiss: Pregnancy rate, calf survival, weaning age and rate. J. Anim. Sci.  63: 8– 16. [PubMed] Google Scholar CrossRef Search ADS PubMed  Riley D. G. Chase C. C.Jr. Coleman S. W. Olson T. A. 2007. Evaluation of birth and weaning traits of Romosinuano calves as purebreds and crosses with Brahman and Angus. J. Anim. Sci.  85: 42– 52. [PubMed] Google Scholar CrossRef Search ADS PubMed  Riley D. G. Chase C. C. Olson T. A. Coleman S. W. Hammond A. C. 2004. Genetic and nongenetic influences on vigor at birth and preweaning mortality of purebred and high percentage Brahman calves. J. Anim. Sci.  82: 1581– 1588. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sacco R. E. Baker J. F. Cartwright T. C. 1987. Production characters of primiparous females of a five-breed diallel. J. Anim. Sci.  64: 1612– 1618. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sacco R. E. Baker J. F. Cartwright T. C. Long C. R. Sanders J. O. 1990. Measurements at calving for straightbred and crossbred cows of diverse types. J. Anim. Sci.  68: 3103– 3108. [PubMed] Google Scholar CrossRef Search ADS PubMed  Simpson R. B. Chase C. C.Jr. Hammond A. C. Williams M. J. Olson T. A. 1998. Average daily gain, blood metabolites, and body composition at first conception in Hereford, Senepol, and reciprocal crossbred heifers on two levels of winter nutrition and two summer grazing treatments. J. Anim. Sci.  76: 396– 403. [PubMed] Google Scholar CrossRef Search ADS PubMed  Stewart T. S. Long C. R. Cartwright T. C. 1980. Characterization of cattle of a five-breed diallel. III. Puberty in bulls and heifers. J. Anim. Sci.  50: 808– 820. [PubMed] Google Scholar CrossRef Search ADS PubMed  Syrstad O. 1985. Heterosis in Bos taurus × Bos indicus crosses. Livest. Prod. Sci.  12: 299– 307. Google Scholar CrossRef Search ADS   Thallman R. M. Cundiff L. V. Gregory K. E. Koch R. M. 1999. Germplasm evaluation in beef cattle—Cycle IV: Postweaning growth and puberty of heifers. J. Anim. Sci.  77: 2651– 2659. [PubMed] Google Scholar CrossRef Search ADS PubMed  Tran T. Q. Warnick A. C. Hammond M. E. Koger M. 1988. Reproduction in Brahman cows calving for the first time at two or three years of age. Theriogenology  29: 751– 756. [PubMed] Google Scholar CrossRef Search ADS PubMed  Wheeler, T. L., L. V. Cundiff, L. D. Van Vleck, G. D. Snowder, R. M. Thallman, S. D. Shackelford, and M. Koohmaraie 2006. Preliminary results from Cycle VIII of the cattle Germplasm Evaluation Program at the Roman L. Hruska U.S. Meat Animal Research Center.  http://www.ars.usda.gov/Main/docs.htm?docid=6238 Accessed Apr. 6, 2009. Williams A. R. Franke D. E. Saxton A. M. 1991. Genetic effects for reproductive traits in beef cattle and predicted performance. J. Anim. Sci.  69: 531– 542. [PubMed] Google Scholar CrossRef Search ADS PubMed  Wiltbank J. N. Gregory K. E. Swiger L. A. Ingalls J. E. Rothlisberger J. A. Koch R. M. 1966. Effects of heterosis on age and weight at puberty in beef heifers. J. Anim. Sci.  25: 744– 751. Google Scholar CrossRef Search ADS   Wyatt, W. E., and D. E. Franke 1986. Estimation of direct and maternal additive and heterotic effects for preweaning growth traits in cattle breeds represented in the Southern region. Southern Coop.  Ser. Bull. No. 310. Louis. State Univ. Agric. Center, Baton Rouge. Footnotes 1 Names are necessary to report factually on available data; however, the USDA neither guarantees nor warrants the standard of the product to the exclusion of others that may also be suitable. 2 Appreciation is extended to E. L. Adams, E. J. Bowers, and M. L. Rooks of the Subtropical Agricultural Research Station, V. E. Rooks of the University of Florida, and all of the Subtropical Agricultural Research Station staff for technical assistance and animal care. American Society of Animal Science

Magee, D. A.;Park, S. D. E.;Scraggs, E.;Murphy, A. M.;Doherty, M. L.;Kijas, J. W.;Consortium, International Sheep Genomics;MacHugh, D. E.

doi: 10.2527/jas.2009-2723pmid: 20562352

ABSTRACT Advances in high-throughput genotyping technologies have afforded researchers the opportunity to study ever-increasing numbers of SNP in animal genomes. However, many studies encounter difficulties in obtaining sufficient quantities of high-quality DNA for such analyses, particularly when the source biological material is limited or degraded. The recent development of in vitro whole-genome amplification approaches has permitted researchers to circumvent these challenges by increasing the amount of usable DNA in normally small-quantity samples. Here, we assess the performance of whole-genome amplification products generated from ovine genomic DNA using a high-throughput SNP genotyping platform, the newly developed Illumina ovineSNP50 BeadChip. Our results demonstrate a high genotype call rate for conventional genomic DNA and whole-genome amplified genomic DNA. The data also reveal an exceptionally high concordance rate (≥99%) between the genotypes generated from whole-genome amplified products and their conventional genomic DNA counterparts. This study supports the use of whole-genome amplification as a viable solution for the analysis of high-density SNP genotypic data using compromised or limited starting material. INTRODUCTION Single nucleotide polymorphisms are the most common form of DNA sequence variation in mammalian genomes (Kruglyak and Nickerson, 2001). The abundance and pan-genomic distribution of SNP have resulted in their adoption as the marker of choice for mammalian genome-wide association studies. These surveys have been enabled by the availability of high-throughput genotyping platforms, such as genotyping arrays (Goddard and Hayes, 2009). Although genotyping arrays have greatly reduced the amount of DNA required for large-scale genotyping, insufficient quantities of genetic material can hamper investigations, especially when the source biological material is limited or of poor quality or both (e.g., forensic, ancient, and archival samples). To facilitate the analysis of compromised samples, whole-genome amplification (WGA) technologies have been developed. Indeed, one such WGA method, multiple displacement amplification (MDA), has been particularly successful, generating high-quantity, high-fidelity, and uniformly amplified whole genomic DNA (Lovmar and Syvanen, 2006). Previous studies assessing the performance of WGA-DNA using SNP genotyping arrays have demonstrated ≥98% concordance in genotype calls between nonamplified, conventionally treated genomic DNA and WGA-DNA products generated from the same samples (Croft et al., 2008; Jasmine et al., 2008; Xing et al., 2008). Here, we investigate the performance of WGA sheep DNA using the recently developed Illumina ovine SNP50 BeadChip. To our knowledge, this study represents the first assessment of the performance of WGA sheep DNA using a high-density genotyping platform. MATERIALS AND METHODS This work has been approved by the University College Dublin, Ireland, Animal Research Ethics Committee. The DNA from 5 unrelated Irish Suffolk sheep (4 males and 1 female) was analyzed in this study. The MDA-WGA was performed on all 5 samples using the REPLI-g Midi kit (Qiagen, Crawley, UK) and purified conventional genomic DNA as template according to the manufacturer's instructions (Table 1 and supplemental information; http://jas.fass.org/content/vol88/issue10/). Conventional genomic DNA (herein termed genomic DNA) and WGA-DNA samples (labeled gDNA1–5 and WGA-DNA1–5, respectively) were subsequently analyzed using the Illumina ovineSNP50 BeadChip (Illumina, San Diego, CA), which assays a total of 49,034 evenly spaced SNP loci distributed across the ovine genome. Raw signal intensities were converted into genotype calls using the Genome Studio software package (version 2008.1, Illumina) and a cluster file derived from approximately 3,400 sheep representing more than 70 breeds that form the International Sheep Genomics Consortium's HapMap sample set (http://www.sheephapmap.org). Table 1. The DNA sample information and performance on the Illumina ovineSNP50 BeadChip1 Conventional genomic DNA (gDNA)    gDNA 1  gDNA 2  gDNA 3  gDNA 4  gDNA 5  Mean  SD  SNP genotype performance  Total number of SNP genotypes called  48,956  49,027  49,034  49,028  49,027  49,014  32.78  No-call genotypes, %  0.16  0.01  0.00  0.01  0.01  0.04  0.07  Genotype call rate, %  99.84  99.99  100.00  99.99  99.99  99.96  0.07  AA genotypes called, %  52.64  53.88  54.68  52.74  53.45  53.48  0.85  AB genotypes called, %  32.95  30.79  29.70  33.43  31.74  31.72  1.53  BB genotypes called, %  14.25  15.31  15.61  13.82  14.79  14.76  0.74  Whole-genome amplified DNA (WGA-DNA)  WGA- DNA 1  WGA- DNA 2  WGA- DNA 3  WGA- DNA 4  WGA- DNA 5  Mean  SD   WGA performance  DNA template, µg  0.43  0.15  0.20  0.28  0.30  0.27  0.11  Final yield, µg  21.58  18.75  17.36  18.67  22.88  19.85  2.29  Fold increase in yield  49.76  124.82  87.67  66.22  77.19  81.13  28.17   WGA-DNA SNP genotype call rates  Total number of SNP genotypes called  48,644  48,565  48,956  48,968  48,509  48,728.04  218.61  Total number of no-call SNP  390  469  78  66  525  305.60  218.61  Genotype call rate, %  99.20  99.04  99.84  99.87  98.93  99.38  0.45   Distribution of no-call SNP in WGA-DNA  No-call (gDNA) → no-call (WGA-DNA), %  2.05  1.07  0.00  6.06  0.76  1.99  2.39  AA (gDNA) → no-call (WGA-DNA), %  18.21  10.87  34.62  16.67  13.71  18.82  9.27  AB (gDNA) → no-call (WGA-DNA), %  73.85  85.50  58.97  69.70  81.90  73.98  10.48  BB (gDNA) → no-call (WGA-DNA), %  5.90  2.56  6.41  7.58  3.62  5.21  2.07   Concordance rate  Total discordant SNP  16  10  1  0  9  7.2  6.69  Het (gDNA)→Homo (WGA-DNA)  14  9  1  0  7  6.2  5.81  Homo AA/BB (gDNA)→Homo BB/AA (WGA-DNA)  0  0  0  0  0  0  0  Homo (gDNA)→Het (WGA-DNA)  2  1  0  0  2  1  1  Overall concordance rate, %  99.97  99.98  100.00  100.00  99.98  99.99  0.01  Conventional genomic DNA (gDNA)    gDNA 1  gDNA 2  gDNA 3  gDNA 4  gDNA 5  Mean  SD  SNP genotype performance  Total number of SNP genotypes called  48,956  49,027  49,034  49,028  49,027  49,014  32.78  No-call genotypes, %  0.16  0.01  0.00  0.01  0.01  0.04  0.07  Genotype call rate, %  99.84  99.99  100.00  99.99  99.99  99.96  0.07  AA genotypes called, %  52.64  53.88  54.68  52.74  53.45  53.48  0.85  AB genotypes called, %  32.95  30.79  29.70  33.43  31.74  31.72  1.53  BB genotypes called, %  14.25  15.31  15.61  13.82  14.79  14.76  0.74  Whole-genome amplified DNA (WGA-DNA)  WGA- DNA 1  WGA- DNA 2  WGA- DNA 3  WGA- DNA 4  WGA- DNA 5  Mean  SD   WGA performance  DNA template, µg  0.43  0.15  0.20  0.28  0.30  0.27  0.11  Final yield, µg  21.58  18.75  17.36  18.67  22.88  19.85  2.29  Fold increase in yield  49.76  124.82  87.67  66.22  77.19  81.13  28.17   WGA-DNA SNP genotype call rates  Total number of SNP genotypes called  48,644  48,565  48,956  48,968  48,509  48,728.04  218.61  Total number of no-call SNP  390  469  78  66  525  305.60  218.61  Genotype call rate, %  99.20  99.04  99.84  99.87  98.93  99.38  0.45   Distribution of no-call SNP in WGA-DNA  No-call (gDNA) → no-call (WGA-DNA), %  2.05  1.07  0.00  6.06  0.76  1.99  2.39  AA (gDNA) → no-call (WGA-DNA), %  18.21  10.87  34.62  16.67  13.71  18.82  9.27  AB (gDNA) → no-call (WGA-DNA), %  73.85  85.50  58.97  69.70  81.90  73.98  10.48  BB (gDNA) → no-call (WGA-DNA), %  5.90  2.56  6.41  7.58  3.62  5.21  2.07   Concordance rate  Total discordant SNP  16  10  1  0  9  7.2  6.69  Het (gDNA)→Homo (WGA-DNA)  14  9  1  0  7  6.2  5.81  Homo AA/BB (gDNA)→Homo BB/AA (WGA-DNA)  0  0  0  0  0  0  0  Homo (gDNA)→Het (WGA-DNA)  2  1  0  0  2  1  1  Overall concordance rate, %  99.97  99.98  100.00  100.00  99.98  99.99  0.01  1Illumina ovineSNP50 BeadChip (Illumina, San Diego, CA). A and B represent the major and minor alleles at each locus, respectively. View Large Table 1. The DNA sample information and performance on the Illumina ovineSNP50 BeadChip1 Conventional genomic DNA (gDNA)    gDNA 1  gDNA 2  gDNA 3  gDNA 4  gDNA 5  Mean  SD  SNP genotype performance  Total number of SNP genotypes called  48,956  49,027  49,034  49,028  49,027  49,014  32.78  No-call genotypes, %  0.16  0.01  0.00  0.01  0.01  0.04  0.07  Genotype call rate, %  99.84  99.99  100.00  99.99  99.99  99.96  0.07  AA genotypes called, %  52.64  53.88  54.68  52.74  53.45  53.48  0.85  AB genotypes called, %  32.95  30.79  29.70  33.43  31.74  31.72  1.53  BB genotypes called, %  14.25  15.31  15.61  13.82  14.79  14.76  0.74  Whole-genome amplified DNA (WGA-DNA)  WGA- DNA 1  WGA- DNA 2  WGA- DNA 3  WGA- DNA 4  WGA- DNA 5  Mean  SD   WGA performance  DNA template, µg  0.43  0.15  0.20  0.28  0.30  0.27  0.11  Final yield, µg  21.58  18.75  17.36  18.67  22.88  19.85  2.29  Fold increase in yield  49.76  124.82  87.67  66.22  77.19  81.13  28.17   WGA-DNA SNP genotype call rates  Total number of SNP genotypes called  48,644  48,565  48,956  48,968  48,509  48,728.04  218.61  Total number of no-call SNP  390  469  78  66  525  305.60  218.61  Genotype call rate, %  99.20  99.04  99.84  99.87  98.93  99.38  0.45   Distribution of no-call SNP in WGA-DNA  No-call (gDNA) → no-call (WGA-DNA), %  2.05  1.07  0.00  6.06  0.76  1.99  2.39  AA (gDNA) → no-call (WGA-DNA), %  18.21  10.87  34.62  16.67  13.71  18.82  9.27  AB (gDNA) → no-call (WGA-DNA), %  73.85  85.50  58.97  69.70  81.90  73.98  10.48  BB (gDNA) → no-call (WGA-DNA), %  5.90  2.56  6.41  7.58  3.62  5.21  2.07   Concordance rate  Total discordant SNP  16  10  1  0  9  7.2  6.69  Het (gDNA)→Homo (WGA-DNA)  14  9  1  0  7  6.2  5.81  Homo AA/BB (gDNA)→Homo BB/AA (WGA-DNA)  0  0  0  0  0  0  0  Homo (gDNA)→Het (WGA-DNA)  2  1  0  0  2  1  1  Overall concordance rate, %  99.97  99.98  100.00  100.00  99.98  99.99  0.01  Conventional genomic DNA (gDNA)    gDNA 1  gDNA 2  gDNA 3  gDNA 4  gDNA 5  Mean  SD  SNP genotype performance  Total number of SNP genotypes called  48,956  49,027  49,034  49,028  49,027  49,014  32.78  No-call genotypes, %  0.16  0.01  0.00  0.01  0.01  0.04  0.07  Genotype call rate, %  99.84  99.99  100.00  99.99  99.99  99.96  0.07  AA genotypes called, %  52.64  53.88  54.68  52.74  53.45  53.48  0.85  AB genotypes called, %  32.95  30.79  29.70  33.43  31.74  31.72  1.53  BB genotypes called, %  14.25  15.31  15.61  13.82  14.79  14.76  0.74  Whole-genome amplified DNA (WGA-DNA)  WGA- DNA 1  WGA- DNA 2  WGA- DNA 3  WGA- DNA 4  WGA- DNA 5  Mean  SD   WGA performance  DNA template, µg  0.43  0.15  0.20  0.28  0.30  0.27  0.11  Final yield, µg  21.58  18.75  17.36  18.67  22.88  19.85  2.29  Fold increase in yield  49.76  124.82  87.67  66.22  77.19  81.13  28.17   WGA-DNA SNP genotype call rates  Total number of SNP genotypes called  48,644  48,565  48,956  48,968  48,509  48,728.04  218.61  Total number of no-call SNP  390  469  78  66  525  305.60  218.61  Genotype call rate, %  99.20  99.04  99.84  99.87  98.93  99.38  0.45   Distribution of no-call SNP in WGA-DNA  No-call (gDNA) → no-call (WGA-DNA), %  2.05  1.07  0.00  6.06  0.76  1.99  2.39  AA (gDNA) → no-call (WGA-DNA), %  18.21  10.87  34.62  16.67  13.71  18.82  9.27  AB (gDNA) → no-call (WGA-DNA), %  73.85  85.50  58.97  69.70  81.90  73.98  10.48  BB (gDNA) → no-call (WGA-DNA), %  5.90  2.56  6.41  7.58  3.62  5.21  2.07   Concordance rate  Total discordant SNP  16  10  1  0  9  7.2  6.69  Het (gDNA)→Homo (WGA-DNA)  14  9  1  0  7  6.2  5.81  Homo AA/BB (gDNA)→Homo BB/AA (WGA-DNA)  0  0  0  0  0  0  0  Homo (gDNA)→Het (WGA-DNA)  2  1  0  0  2  1  1  Overall concordance rate, %  99.97  99.98  100.00  100.00  99.98  99.99  0.01  1Illumina ovineSNP50 BeadChip (Illumina, San Diego, CA). A and B represent the major and minor alleles at each locus, respectively. View Large RESULTS AND DISCUSSION The performance of the genomic DNA and WGA-DNA samples on the ovineSNP50 BeadChip are summarized in Table 1. The WGA-DNA products yielded between 17 to 23 µg of DNA, corresponding to a 49- to 124-fold increase in yield. The sex of each animal was also correctly inferred from the resulting SNP genotypes from both sample preparations. To calculate the SNP genotype call rate for all samples, we counted the total number of loci that failed to yield a genotype (i.e., no-call SNP) in the genomic DNA and WGA-DNA products. Across all 10 arrays: 1,624 SNP genotypes from 1,227 different SNP loci were classed as no-call; 1,507 genotypes were classed as no-call in the WGA-DNA samples only; 77 in the genomic DNA samples only; and 20 in genomic DNA and WGA-DNA. Notably, no single SNP locus failed across all 10 assayed DNA samples. The genotype call rates for the 5 genomic DNA samples ranged between 99.84 and 100% [mean call rate of 99.96%, SD of 0.07%]. The mean call rate for the WGA-DNA products was less than the genomic DNA samples, ranging between 98.93 and 99.87% (mean call rate = 99.38%, SD = 0.45%). Overall, the mean call rate for the WGA-DNA products was 0.58% less than the genomic DNA samples. The SNP genotype call rate observed in our study is comparable with that reported for previous studies involving WGA-DNA, which ranged between 92.98 and 100% for genomic DNA-WGA-DNA sample pairs (Croft et al., 2008; Jasmine et al., 2008; Xing et al., 2008). We next investigated possible SNP allele amplification bias induced by the WGA technique used. For this, we selected SNP loci that failed to genotype in WGA-DNA products but were called in the corresponding genomic DNA samples. We found that the proportion of each genotype (AA, AB, BB, where A and B represent the major and minor alleles at each locus, respectively) that failed in the WGA-DNA differed from the proportion of each genotype called for the genomic DNA samples (χ2 test, P < 10−4). A disproportionately large number of SNP classed as no-call in the WGA samples were heterozygous in the genomic DNA samples (2.3× the expected proportion based on the genotypes observed in the genomic DNA samples; Table 1). These findings differ from those of Xing et al. (2008), which show an underrepresentation of AA and AB genotype calls and an overrepresentation of BB genotype calls for SNP loci that failed in WGA-DNA products. It is possible that the discrepancies between our data and those of Xing et al. (2008) reflect differences in the SNP genotyping technology used (Illumina BeadChip vs. Affymetrix SNP array), the SNP genotype imputation algorithms used [Illumina GenomeStudio vs. the Affymetrix Dynamic Model algorithm (Affymetrix, Santa Clara, CA)], or a combination of both. We next investigated the genotype concordance rate between the genomic DNA and WGA-DNA products from the same sheep sample. For this analysis, only those SNP that generated genotypes in genomic DNA and WGA-DNA products were considered. Discordant SNP were classified as those SNP that were (1) heterozygous (AB) in 1 DNA preparation but homozygous for either allele in the other DNA preparation (AA or BB), or (2) homozygous for 1 allele in 1 preparation and homozygous for the alternative allele in the other DNA preparation (AA→BB or BB→AA). In total, 36 discordant SNP genotypes were detected across all 10 genotyping arrays, with 32 SNP genotypes represented by single discordant pairs of samples and 2 SNP genotypes showing discordance in 2 sample pairs. In 31 instances, a SNP locus was detected as being heterozygous in a genomic DNA preparation and homozygous in its WGA-DNA counterpart, and in 5 cases as heterozygous in WGA-DNA and homozygous in genomic DNA. No discordant SNP loci displaying different homozygous calls were detected between the 2 DNA preparations from the same sample. Notably, the WGA-DNA samples display excellent concordance with their genomic DNA counterparts, ranging between 100 and 99.97% (mean concordance rate = 99.99%, SD 0.01%). These concordance rates are similar to previous investigations assessing the performance of high-throughput genotyping analyses of WGA-DNA (Table 2). Table 2. The SNP genotype concordance rate for conventional genomic DNA and whole-genome amplified-DNA (WGA-DNA) samples using high-density genotyping arrays Item  Sample/SNP information  Mean SNP genotype call rate, %  Concordance rate, %  Number of sample pairs  Number of SNP analyzed  Genotyping platform1  Genomic DNA  WGA-DNA  Genomic DNA vs. WGA-DNA  Reference2   Croft et al. (2008)  4  262,264  Affymetrix  97.6  92.98  87.45   Jasmine et al. (2008)  14  224,940  Affymetrix  97.07  97.77  97.74   Xing et al. (2008)  4  262,000  Affymetrix  99.37  97.46  98.65  Overall mean (SD)           98.01 (1.20)  96.07 (2.68)  96.41 (6.22)  Current study (SD)  5  49,034  Illumina  99.96 (0.07)  99.38 (0.45)  99.99 (0.01)  Item  Sample/SNP information  Mean SNP genotype call rate, %  Concordance rate, %  Number of sample pairs  Number of SNP analyzed  Genotyping platform1  Genomic DNA  WGA-DNA  Genomic DNA vs. WGA-DNA  Reference2   Croft et al. (2008)  4  262,264  Affymetrix  97.6  92.98  87.45   Jasmine et al. (2008)  14  224,940  Affymetrix  97.07  97.77  97.74   Xing et al. (2008)  4  262,000  Affymetrix  99.37  97.46  98.65  Overall mean (SD)           98.01 (1.20)  96.07 (2.68)  96.41 (6.22)  Current study (SD)  5  49,034  Illumina  99.96 (0.07)  99.38 (0.45)  99.99 (0.01)  1Illumina ovineSNP50 BeadChip (Illumina, San Diego, CA); Affymetrix (Santa Clara, CA). 2All studies listed involved multiple displacement amplification (MDA)-whole genome analysis (WGA) of human DNA samples, with the exception of the current study (sheep DNA). View Large Table 2. The SNP genotype concordance rate for conventional genomic DNA and whole-genome amplified-DNA (WGA-DNA) samples using high-density genotyping arrays Item  Sample/SNP information  Mean SNP genotype call rate, %  Concordance rate, %  Number of sample pairs  Number of SNP analyzed  Genotyping platform1  Genomic DNA  WGA-DNA  Genomic DNA vs. WGA-DNA  Reference2   Croft et al. (2008)  4  262,264  Affymetrix  97.6  92.98  87.45   Jasmine et al. (2008)  14  224,940  Affymetrix  97.07  97.77  97.74   Xing et al. (2008)  4  262,000  Affymetrix  99.37  97.46  98.65  Overall mean (SD)           98.01 (1.20)  96.07 (2.68)  96.41 (6.22)  Current study (SD)  5  49,034  Illumina  99.96 (0.07)  99.38 (0.45)  99.99 (0.01)  Item  Sample/SNP information  Mean SNP genotype call rate, %  Concordance rate, %  Number of sample pairs  Number of SNP analyzed  Genotyping platform1  Genomic DNA  WGA-DNA  Genomic DNA vs. WGA-DNA  Reference2   Croft et al. (2008)  4  262,264  Affymetrix  97.6  92.98  87.45   Jasmine et al. (2008)  14  224,940  Affymetrix  97.07  97.77  97.74   Xing et al. (2008)  4  262,000  Affymetrix  99.37  97.46  98.65  Overall mean (SD)           98.01 (1.20)  96.07 (2.68)  96.41 (6.22)  Current study (SD)  5  49,034  Illumina  99.96 (0.07)  99.38 (0.45)  99.99 (0.01)  1Illumina ovineSNP50 BeadChip (Illumina, San Diego, CA); Affymetrix (Santa Clara, CA). 2All studies listed involved multiple displacement amplification (MDA)-whole genome analysis (WGA) of human DNA samples, with the exception of the current study (sheep DNA). View Large Finally, we investigated the potential of systematic SNP genotype calling failure in the WGA-DNA products. Of the total 1,157 no-call SNP loci detected in any of the 5 WGA-DNA products, none failed consistently in all 5 array pairs. To analyze further the spatial distribution of both of the no-call (n = 1,227) and discordant SNP loci (n = 34), we plotted the total number of each in 2-Mb nonoverlapping windows across each ovine chromosome. These no-call and discordant loci (n = 1,261) were distributed across the genome with no apparent clustering, indicating no systematic errors with the array design or the differentially prepared DNA samples (Figure S1, supplemental information; http://jas.fass.org/content/vol88/issue10/). This finding is consistent with the observations of Xing et al. (2008). A subsequent study performed by us compared the genotyping performance of 11 compromised WGA-DNA samples with 28 good quality, non-WGA, conventionally treated genomic DNA samples from different individuals belonging to the same pedigree on the Illumina ovineSNP50 BeadChip. Compromised samples had been stored as whole blood at 4°C for a period of 6 mo, then at −20°C for 17 mo before whole genomic DNA extraction using the methods outlined in the supplemental information. The compromised samples showed no increased allelic drop-out as evidenced by Mendelian segregation errors (compromised WGA-DNA mean 0.02%, SD 0.03% vs. genomic DNA mean 0.02%, SD 0.02%) and heterozygosity (compromised WGA-DNA mean 30.76%, SD 2.14% vs. genomic DNA mean 31.79%, SD 2.28%). The compromised WGA-DNA samples did show a reduced call rate compared with the genomic DNA samples, but the difference was not significant (compromised WGA-DNA mean 2.00%, SD 2.16% vs. genomic DNA mean 0.82%, SD 0.75%). In summary, this study demonstrates that the newly developed Illumina ovineSNP50 BeadChip is suitable for the analysis of conventional genomic and WGA sheep DNA samples. This array produces increased SNP genotype call rates and highly concordant SNP genotypes for these DNA sample preparations. Furthermore, this study further supports the use of WGA sheep DNA as a potential viable solution for researchers who require large-scale genotype information from sheep DNA extracted using compromised or limited starting genetic material from sources such as ear punches from tags, hair or wool follicles, saliva, urine, and milk. LITERATURE CITED Croft D. T.Jr. Jordan R. M. Patney H. L. Shriver C. D. Vernalis M. N. Orchard T. J. Ellsworth D. L. 2008. Performance of whole-genome amplified DNA isolated from serum and plasma on high-density single nucleotide polymorphism arrays. J. Mol. Diagn.  10: 249– 257. [PubMed] Google Scholar CrossRef Search ADS PubMed  Goddard M. E. Hayes B. J. 2009. Mapping genes for complex traits in domestic animals and their use in breeding programmes. Nat. Rev. Genet.  10: 381– 391. [PubMed] Google Scholar CrossRef Search ADS PubMed  Jasmine F. Ahsan H. Andrulis I. L. John E. M. Chang-Claude J. Kibriya M. G. 2008. Whole-genome amplification enables accurate genotyping for microarray-based high-density single nucleotide polymorphism array. Cancer Epidemiol. Biomarkers Prev.  17: 3499– 3508. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kruglyak L. Nickerson D. A. 2001. Variation is the spice of life. Nat. Genet.  27: 234– 236. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lovmar L. Syvanen A. C. 2006. Multiple displacement amplification to create a long-lasting source of DNA for genetic studies. Hum. Mutat.  27: 603– 614. [PubMed] Google Scholar CrossRef Search ADS PubMed  Xing J. Watkins W. S. Zhang Y. Witherspoon D. J. Jorde L. B. 2008. High fidelity of whole-genome amplified DNA on high-density single nucleotide polymorphism arrays. Genomics  92: 452– 456. [PubMed] Google Scholar CrossRef Search ADS PubMed  Footnotes 1 This research work was supported by a Research Frontiers Programme grant from Science Foundation Ireland (grant No. 05/RFP/Gen0060). American Society of Animal Science

Cockrum, R. R.;Austin, K. J.;Kim, J. W.;Garbe, J. R.;Fahrenkrug, S. C.;Taylor, J. F.;Cammack, K. M.

doi: 10.2527/jas.2009-2709pmid: 20562356

ABSTRACT Ruminants consuming diets with increased concentrations of nitrate (NO3−) can accumulate nitrite (NO2−) in the blood, resulting in toxicity. In a previous experiment, ewes identified as highly tolerant to subacute dietary NO3− were able to consume greater amounts of NO3− than lowly tolerant ewes without exhibiting signs of toxicity. We hypothesized that highly tolerant and lowly tolerant ewes differ in their ability to metabolize NO3− and thereby differ in the expression of hepatic genes involved in NO3− metabolism. Therefore, our objective was to identify hepatic genes differentially expressed between ewes classified as lowly tolerant and highly tolerant after administration of a subacute quantity of dietary NO3−. Analysis of the Bovine Oligonucleotide Microarray data identified 100 oligonucleotides as differentially expressed (P < 0.05) between lowly tolerant and highly tolerant ewes. Functional analysis of the genes associated with these oligonucleotides revealed 2 response clusters of interest: metabolic and stress. Genes of interest within these 2 clusters (n = 17) and nonclustered genes with the greatest fold changes (FC; n = 5) were selected for validation by real-time reverse-transcription PCR. Relative expression, genomic regulation, and FC agreed between microarray and real-time reverse-transcription-PCR analyses, and FC differences (P < 0.05) between lowly tolerant and highly tolerant ewes were confirmed for 12 genes. Metabolic genes that were downregulated (P ≤ 0.032) in lowly tolerant ewes vs. highly tolerant ewes included aldehyde oxidase 1, argininosuccinate lyase, putative steroid dehydrogenase, 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase1, and sterol carrier protein 2. In contrast, the metabolic gene homeobox was upregulated (P = 0.037) in lowly tolerant ewes. The glutathione peroxidase 3 and inter-α (globulin) inhibitor H4 genes in the stress response cluster were upregulated (P ≤ 0.045) in lowly tolerant ewes. Genes with the greatest FC, but did not cluster within the functional analysis included haptoglobin, which was upregulated (P = 0.024) in lowly tolerant ewes, and fatty acid desaturase 2 and thyroid hormone responsive, both of which were downregulated (P ≤ 0.019) in lowly tolerant ewes. Results from this study indicate that hepatic gene expression differs in ewes identified as lowly tolerant and highly tolerant to increased dietary NO3−. INTRODUCTION Ruminant livestock consuming diets with increased content of intrate (NO3−) can accumulate nitrite (NO2−) in the blood, decreasing the ability of red blood cells to transport O2 to peripheral tissues. Signs associated with subacute NO3− toxicity include lethargy, decreased feed efficiency, impaired immune function, thyroid insufficiency, liver complications, and failure to maintain or gain BW (Yaremcio, 1991). Because the toxicity signs are vague, misdiagnosis or diagnosis failures can occur with subacute and chronic NO3− toxicity. Furthermore, a threshold for dietary NO3− cannot be accurately established because of phenotypic variation among animals (Yaremcio, 1991). This variation can be attributed to a variety of factors including species, dose of NO3−, frequency of exposure, rate of elimination, and age (Eaton and Gilbert, 2008). Although the true economic impact of subacute and chronic NO3− toxicity on livestock species is not known, the signs associated with increased dietary NO3− affect production efficiency, and ultimately the profitability of livestock producers (Nielsen and James, 1992). The combination of nonspecific signs associated with increased dietary NO3− that affect production and the individual variation among animals exposed to subacute dietary NO3− suggests that alternative methods are needed to identify animals lowly tolerant to elevated dietary NO3−, particularly in regions where increased-NO3− forages are prevalent. We hypothesized that the phenotypic variation observed among animals exposed to increased dietary NO3− is, in part, a result of differences in regulation of metabolic genes in the liver. Therefore, our objective was to identify hepatic genes that are differentially expressed between ewes identified as lowly tolerant and highly tolerant to subacute quantity of dietary NO3−. Identification of these genes should provide a basis for future development of biomarkers conferring resistance or susceptibility to NO3− toxicity. MATERIALS AND METHODS All animal procedures were approved by the University of Wyoming Animal Care and Use Committee. Animal Procedures The experimental design for this study was described previously by Cockrum et al. (2010). Briefly, individually penned purebred Suffolk ewes (n = 60; average initial BW = 85.7 ± 46.4 kg) were randomly allotted to 1 of 2 contemporary groups (n = 30) and randomly assigned to 1 of 2 treatments groups: 300 mg of NO3−/kg of BW (NO3−-treated; n = 25 per contemporary group; made into a 1.5 M solution), or 0 mg of NO3−/kg of BW (control; n = 5 per contemporary group; tap water only) for an 8-d period. Both treatments were mixed with a soybean meal/beet pulp supplement fed at 375 g total, 3 times daily, or 125 g/feeding, for 8 d. Bromegrass hay was offered 3 times daily at a rate of 2.5% of initial BW to all ewes. An adjustment period of 3 d was provided to ensure supplement palatability and adjustment to individual pens. Blood was collected via jugular venipuncture into EDTA-lined tubes for plasma collection on d 0, 12 h after initial NO3− exposure, every 24 h for the remaining 8 d of the trial, and 3 d after cessation of treatment. Handling, collection, and blood analyses results for circulating plasma NO3−, urea N, cortisol, glucose, and retinol were previously reported by Cockrum et al. (2010). Liver biopsies were performed on all ewes before treatment and at the end of the 8-d treatment period to obtain tissues for gene expression analyses. Biopsies were conducted using modified procedures of Ferreira et al. (1996) to obtain a sample of approximately 1 g (as-is basis). A bone marrow biopsy punch (Jorgensen Vet Supply, Loveland, CO) modified with a syringe and tubing was used to collect liver tissue samples by vertically inserting the biopsy needle through the intercostal space of the 10th and 11th ribs approximately 9 cm below the processus spinosus. Tissue was rinsed with PBS, snap-frozen on dry ice, and stored at −80°C. An 11% mortality rate typically occurs in sheep when performing liver biopsies as opposed to <0.5% in cattle (Anderson et al., 1962). A total of 120 biopsies (n = 60 ewes; 2 biopsies/ewe) were performed in this study, with an 8.3% death loss (n = 5) as a result of complications associated with the procedure. Ewes remaining for the project included 8 controls and 47 NO3−-treated. Selection for Tolerance The NO3−-treated ewes were identified as highly tolerant and lowly tolerant to NO3− using a 2-stage selection as described previously by Cockrum et al. (2010). First, NO3−-treated ewes were ranked based on NO3− supplement intake. The 20% that consumed the most NO3− supplement (n = 9) and the 20% that consumed the least (n = 9) were initially selected as more tolerant and less tolerant, respectively. From those, 6 highly tolerant and 6 lowly tolerant were chosen for microarray gene expression analyses based on the absence or presence, respectively, of signs associated with NO3− toxicity such as lethargy, head pressing, respiratory congestion potentially attributed to compromised immune function, and decreased feed intake. Selected highly tolerant ewes did not demonstrate any signs associated with subacute NO3− toxicity. Selected lowly tolerant ewes demonstrated at least 2 signs associated with NO3− toxicity. Two controls out of 8 (n = 6) were eliminated from gene expression analyses because of perceived illness (based on lack of feed intake) and the presence of blood clots in the liver biopsy sample. RNA Isolation Liver tissue (0.1 g, as-is basis) from the end of the 8-d treatment period liver biopsy was homogenized in 1 mL of TRI-Reagent (Sigma Aldrich, St. Louis, MO), and RNA was isolated according to the manufacturer's protocol. The mRNA quality and quantity were measured on a NanoDrop spectrophotometer (NanoDrop Technologies Inc., Wilmington, DE). To obtain a sufficient quantity of RNA for microarray analyses, the entire TRI-Reagent RNA pellet was further purified using the RNeasy clean-up protocol (Qiagen, Valencia, CA). The RNeasy purification procedure uses on-column DNase digestion to ensure specificity to target sequence. The RNA sample was eluted with 50 µL of nuclease-free H2O, and the entire elute was used for microarray analysis. A second RNeasy procedure was performed using 20-µg aliquots of TRI-Reagent RNA for real-time reverse-transcription-PCR (RT-PCR) with 2-µg aliquots used for cDNA synthesis. All purified RNA was checked again for quality and quantity using a NanoDrop spectrophotometer. cDNA Synthesis for Microarray Before cDNA synthesis, total RNA was qualified and quantified using an Experion automated electrophoresis system (Bio-Rad, Hercules, CA). Purified RNA (500 ng/µL) diluted with nuclease-free H2O to obtain a total volume of 10 µL of RNA (5-µg aliquots) per reaction was used for cDNA synthesis using the SuperScript II Reverse Transcriptase kit (Invitrogen, Carlsbad, CA). A 3DNA (Genisphere, Hatfield, PA) capture sequence was ligated onto the cDNA. The cDNA was purified using a MiniElute PCR Purification kit according to the manufacturer's protocol (Qiagen) and measured on a NanoDrop spectrophotometer for quantity and quality. Adequate quality was determined by a >1.9 ratio of 260/280 wavelengths. Microarray Analyses Microarray analyses were conducted at the Bovine Genomics Laboratory at the University of Missouri in Columbia using the Bovine Oligonucleotide Microarray (http://www.bovineoligo.org/). This microarray contains 24,000 oligos designed by the Bovine Oligonucleotide Microarray Consortium and includes 16,846 oligo sequences targeting the best ensemble protein matches and gene ontology annotations for each sequence, 704 predicted RefSeq genes with no alignment to the bovine genome, 5,946 reproductive tissue and other expressed sequence tags with a bovine genome alignment, and 504 positive and negative controls. Using a protocol for the 3DNA Array 350 kit (Genisphere) modified by one of the authors (J. W. Kim, unpublished data), labeled cDNA was hybridized to each array using a 2-dye method. A double-loop design was used to control for dye × array effects. Samples from 6 control, 6 lowly tolerant, and 6 highly tolerant ewes were used for the microarray analysis. There were 4 replicates tested per sample; 2 replicates were labeled with CY3 (green) and the other 2 with CY5 (red). The chip allowed the simultaneous analysis of 2 samples, one tagged with CY3 and the other with CY5 dye, simultaneously hybridized to the chip. Microarrays were scanned using a GenePix 4000B scanner and GenePix 6.0 software (Molecular Devices, Sunnyvale, CA). The CY3 signal intensities were measured at a wavelength between 550 to 700 nm and CY5 at a wavelength between 650 to 800 nm. After scanning, cDNA signal intensities for each sample were quantified using BlueFuse (BlueGnome, Cambridge, UK) at the University of Minnesota Functional Genomics Laboratory. Microarray Cross-Species Homology No ovine-specific oligonucleotide microarray is currently available commercially; therefore, a bovine oligonucleotide microarray was used because of high sequence homology between these species. Diez-Tascón et al. (2005) demonstrated that cross-species hybridization between ovine and bovine, using ovine cDNA hybridized to a bovine cDNA microarray, resulted in approximately 96% homology; similar homology would be expected with the use of a bovine oligonucleotide array. The frequency of false positives is expected to be less with the use of an oligonucleotide microarray as opposed to a cDNA microarray. Decker et al. (2009) recently showed that genome-wide hybridization-based single nucleotide polymorphism assays based on 50-base oligonucleotides designed from cattle genomic sequence produced genotypes in sheep with 96.7% fidelity. Gene Annotation Genes identified as differentially expressed (P < 0.05) between lowly tolerant and highly tolerant ewes were clustered based on functional similarities generated by the Database for Annotation, Visualization, and Integrated Discovery or DAVID (Dennis et al., 2003; Huang et al., 2009). Theme discovery was performed with the Expression Analysis Systemic Explorer program. A 1-tailed Fisher's exact test was used within the Expression Analysis Systemic Explorer to identify overrepresented functional themes. Real-Time RT-PCR Real-time RT-PCR was performed on samples from the same control (n = 6), highly tolerant (n = 6), and lowly tolerant ewes (n = 6) as the microarray analysis. Expression of highly tolerant and lowly tolerant ewes was measured relative to control expression (considered to be baseline). Complementary DNA for real-time RT-PCR was synthesized using 2 µg of purified RNA from RNeasy purification (Qiagen). Primers were designed (Table 1) using mRNA sequences and Primer3 Software v. 0.4.0 (Rozen and Skaletsky, 2000) to generate amplicons approximately 150 bp in length. Primers were further verified using the National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/) database by querying forward and reverse primer sequences and validated by presence of a single peak melt-curve in real-time RT-PCR. Forward and reverse primers were diluted to 100 μM, and 1 μL of each was combined with 0.5 μL of nuclease-free H2O and 12.5 μL of SYBR Green (BioRad). Forward and reverse primers, nuclease-free H2O, and SYBR Green mixture (14.5 μL) were mixed with 10 μL of cDNA from the selected samples in a 96-well plate in duplicate. Threshold counts and melt curves were determined using an iQ5 multicolor Real-Time PCR Detection System (BioRad). Complementary DNA was amplified using 40 cycles of 95°C for 10 s and 60°C for 30 s. Melt curve analysis was performed to ensure quality of amplification by incubating the real-time RT-PCR products for 10 s at each 0.5°C interval from 55 to 95°C over 8 cycles. Relative gene expression was measured against the housekeeping gene, glyceraldehyde 3-phosphate dehydrogenase. In total, 22 genes were selected for validation based on their inclusion in the metabolic or stress functional groups (or both) in the DAVID analysis or their greater fold change (FC) in the microarray analyses. Table 1. Forward and reverse primer sequences used in real-time reverse-transcription-PCR Gene1  Forward primer  Reverse primer  AOX1  5′aggccctggaatgaagagat3′  5′tttgcttgcaaggctaggat3′  ASL  5′tccgagcagaactggacttt3′  5′gtttttcttctggggcatca3′  C9  5′caaaatggagggactgtggt3′  5′tccatatgatcccagggaaa3′  CDK9  5′aaggtgaaggacaggctgaa3′  5′ggtgccaggtactcgaacat3′  CSAD  5′ggattgtggtggatgaggtc3′  5′ggttgaagaaacgagggtga3′  CXCR62  5′tatagcgcatgcgttctcac3′  5′tctgtcccagtcccattctc3′  CYP26A1  5′agattcccaaaggctggaat3′  5′tcctccaaatggaatgaagc3′  DDEF22  5′ggagagttgaagcctggttg3′  5′gtcatctctgcacacgcagt3′  EXT2  5′ccgtcctggctattgatgat3′  5′ccccaggcatcttgtaggta3′  FADS2  5′ctctgtcctgaagccagacc3′  5′agggaatccagcacatcatc3′  FGG  5′gaattttggctgggaaatga3′  5′atcatcgccaaaatcgtagc3′  GPX3  5′tgcaaccaatttggaaaaca3′  5′ttcatgggttcccagaaaag3′  HOPX  5′tgtgcaagcacataggaagc3′  5′gctactgggaggtgatggtc3′  HP  5′tggtctcccagcataacctc3′  5′agggtggagaaccaccttct3′  ITIH4  5′ctcagtccccatgaccagtt3′  5′gaggaggatgatgagggtga3′  KIK-I  5′cccgcttgagtaaactgctc3′  5′ggccgcatacactgctaaat3′  PDE4C  5′ctcctggctgacctcaagac3′  5′ggaagaactcggccatgata3′  PDP1  5′caagcaccccaacgattact3′  5′cttgagcctccaaggagatg3′  PFKFB1  5′tgcagaaaacatcaggcaag3′  5′tctggatatggtcctgcaca3′  SCD2  5′ctatgtgaccctgggcaagt3′  5′tcgtggtggacacacttcat3′  SCP2  5′cagataagaaggccgactgc3′  5′ggctgaagctggagattttg3′  THRSP  5′cctcacccatcttaccctga3′  5′aggtagggaggattcccaga3′  Gene1  Forward primer  Reverse primer  AOX1  5′aggccctggaatgaagagat3′  5′tttgcttgcaaggctaggat3′  ASL  5′tccgagcagaactggacttt3′  5′gtttttcttctggggcatca3′  C9  5′caaaatggagggactgtggt3′  5′tccatatgatcccagggaaa3′  CDK9  5′aaggtgaaggacaggctgaa3′  5′ggtgccaggtactcgaacat3′  CSAD  5′ggattgtggtggatgaggtc3′  5′ggttgaagaaacgagggtga3′  CXCR62  5′tatagcgcatgcgttctcac3′  5′tctgtcccagtcccattctc3′  CYP26A1  5′agattcccaaaggctggaat3′  5′tcctccaaatggaatgaagc3′  DDEF22  5′ggagagttgaagcctggttg3′  5′gtcatctctgcacacgcagt3′  EXT2  5′ccgtcctggctattgatgat3′  5′ccccaggcatcttgtaggta3′  FADS2  5′ctctgtcctgaagccagacc3′  5′agggaatccagcacatcatc3′  FGG  5′gaattttggctgggaaatga3′  5′atcatcgccaaaatcgtagc3′  GPX3  5′tgcaaccaatttggaaaaca3′  5′ttcatgggttcccagaaaag3′  HOPX  5′tgtgcaagcacataggaagc3′  5′gctactgggaggtgatggtc3′  HP  5′tggtctcccagcataacctc3′  5′agggtggagaaccaccttct3′  ITIH4  5′ctcagtccccatgaccagtt3′  5′gaggaggatgatgagggtga3′  KIK-I  5′cccgcttgagtaaactgctc3′  5′ggccgcatacactgctaaat3′  PDE4C  5′ctcctggctgacctcaagac3′  5′ggaagaactcggccatgata3′  PDP1  5′caagcaccccaacgattact3′  5′cttgagcctccaaggagatg3′  PFKFB1  5′tgcagaaaacatcaggcaag3′  5′tctggatatggtcctgcaca3′  SCD2  5′ctatgtgaccctgggcaagt3′  5′tcgtggtggacacacttcat3′  SCP2  5′cagataagaaggccgactgc3′  5′ggctgaagctggagattttg3′  THRSP  5′cctcacccatcttaccctga3′  5′aggtagggaggattcccaga3′  1Aldehyde oxidase 1 (AOX1); argininosuccinate lyase (ASL); complement component 9 (C9); cyclin-dependent kinase 9 (CDK9); cysteine sulfinic acid decarboxylase (CSAD); G-protein coupled receptor tymstr (CXCR6); cytochrome P450, family 26, subfamily A, polypeptide 1 (CYP26A1; retinoic acid); development and differentiation-enhancing factor 2 (DDEF2); exostoses (multiple) 2 (EXT2); fatty acid desaturase 2 (FADS2); fibrinogen gamma chain (FGG); glutathione peroxidase 3 (GPX3); homeobox (HOPX); haptoglobin (HP); inter-α (globulin) inhibitor H4 (ITIH4); putative steroid dehydrogenase (KIK-I; predicted); phosphodiesterase 4C (PDE4C; cAMP specific; predicted); pyruvate dehydrogenase phosphatase 1 (PDP1); 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (PFKFB1); stearoyl-CoA desaturase (SCD); sterol carrier protein 2 (SCP2); thyroid hormone responsive (THRSP). 2Did not produce a melt-curve in real-time reverse-transcription-PCR analysis. View Large Table 1. Forward and reverse primer sequences used in real-time reverse-transcription-PCR Gene1  Forward primer  Reverse primer  AOX1  5′aggccctggaatgaagagat3′  5′tttgcttgcaaggctaggat3′  ASL  5′tccgagcagaactggacttt3′  5′gtttttcttctggggcatca3′  C9  5′caaaatggagggactgtggt3′  5′tccatatgatcccagggaaa3′  CDK9  5′aaggtgaaggacaggctgaa3′  5′ggtgccaggtactcgaacat3′  CSAD  5′ggattgtggtggatgaggtc3′  5′ggttgaagaaacgagggtga3′  CXCR62  5′tatagcgcatgcgttctcac3′  5′tctgtcccagtcccattctc3′  CYP26A1  5′agattcccaaaggctggaat3′  5′tcctccaaatggaatgaagc3′  DDEF22  5′ggagagttgaagcctggttg3′  5′gtcatctctgcacacgcagt3′  EXT2  5′ccgtcctggctattgatgat3′  5′ccccaggcatcttgtaggta3′  FADS2  5′ctctgtcctgaagccagacc3′  5′agggaatccagcacatcatc3′  FGG  5′gaattttggctgggaaatga3′  5′atcatcgccaaaatcgtagc3′  GPX3  5′tgcaaccaatttggaaaaca3′  5′ttcatgggttcccagaaaag3′  HOPX  5′tgtgcaagcacataggaagc3′  5′gctactgggaggtgatggtc3′  HP  5′tggtctcccagcataacctc3′  5′agggtggagaaccaccttct3′  ITIH4  5′ctcagtccccatgaccagtt3′  5′gaggaggatgatgagggtga3′  KIK-I  5′cccgcttgagtaaactgctc3′  5′ggccgcatacactgctaaat3′  PDE4C  5′ctcctggctgacctcaagac3′  5′ggaagaactcggccatgata3′  PDP1  5′caagcaccccaacgattact3′  5′cttgagcctccaaggagatg3′  PFKFB1  5′tgcagaaaacatcaggcaag3′  5′tctggatatggtcctgcaca3′  SCD2  5′ctatgtgaccctgggcaagt3′  5′tcgtggtggacacacttcat3′  SCP2  5′cagataagaaggccgactgc3′  5′ggctgaagctggagattttg3′  THRSP  5′cctcacccatcttaccctga3′  5′aggtagggaggattcccaga3′  Gene1  Forward primer  Reverse primer  AOX1  5′aggccctggaatgaagagat3′  5′tttgcttgcaaggctaggat3′  ASL  5′tccgagcagaactggacttt3′  5′gtttttcttctggggcatca3′  C9  5′caaaatggagggactgtggt3′  5′tccatatgatcccagggaaa3′  CDK9  5′aaggtgaaggacaggctgaa3′  5′ggtgccaggtactcgaacat3′  CSAD  5′ggattgtggtggatgaggtc3′  5′ggttgaagaaacgagggtga3′  CXCR62  5′tatagcgcatgcgttctcac3′  5′tctgtcccagtcccattctc3′  CYP26A1  5′agattcccaaaggctggaat3′  5′tcctccaaatggaatgaagc3′  DDEF22  5′ggagagttgaagcctggttg3′  5′gtcatctctgcacacgcagt3′  EXT2  5′ccgtcctggctattgatgat3′  5′ccccaggcatcttgtaggta3′  FADS2  5′ctctgtcctgaagccagacc3′  5′agggaatccagcacatcatc3′  FGG  5′gaattttggctgggaaatga3′  5′atcatcgccaaaatcgtagc3′  GPX3  5′tgcaaccaatttggaaaaca3′  5′ttcatgggttcccagaaaag3′  HOPX  5′tgtgcaagcacataggaagc3′  5′gctactgggaggtgatggtc3′  HP  5′tggtctcccagcataacctc3′  5′agggtggagaaccaccttct3′  ITIH4  5′ctcagtccccatgaccagtt3′  5′gaggaggatgatgagggtga3′  KIK-I  5′cccgcttgagtaaactgctc3′  5′ggccgcatacactgctaaat3′  PDE4C  5′ctcctggctgacctcaagac3′  5′ggaagaactcggccatgata3′  PDP1  5′caagcaccccaacgattact3′  5′cttgagcctccaaggagatg3′  PFKFB1  5′tgcagaaaacatcaggcaag3′  5′tctggatatggtcctgcaca3′  SCD2  5′ctatgtgaccctgggcaagt3′  5′tcgtggtggacacacttcat3′  SCP2  5′cagataagaaggccgactgc3′  5′ggctgaagctggagattttg3′  THRSP  5′cctcacccatcttaccctga3′  5′aggtagggaggattcccaga3′  1Aldehyde oxidase 1 (AOX1); argininosuccinate lyase (ASL); complement component 9 (C9); cyclin-dependent kinase 9 (CDK9); cysteine sulfinic acid decarboxylase (CSAD); G-protein coupled receptor tymstr (CXCR6); cytochrome P450, family 26, subfamily A, polypeptide 1 (CYP26A1; retinoic acid); development and differentiation-enhancing factor 2 (DDEF2); exostoses (multiple) 2 (EXT2); fatty acid desaturase 2 (FADS2); fibrinogen gamma chain (FGG); glutathione peroxidase 3 (GPX3); homeobox (HOPX); haptoglobin (HP); inter-α (globulin) inhibitor H4 (ITIH4); putative steroid dehydrogenase (KIK-I; predicted); phosphodiesterase 4C (PDE4C; cAMP specific; predicted); pyruvate dehydrogenase phosphatase 1 (PDP1); 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (PFKFB1); stearoyl-CoA desaturase (SCD); sterol carrier protein 2 (SCP2); thyroid hormone responsive (THRSP). 2Did not produce a melt-curve in real-time reverse-transcription-PCR analysis. View Large Statistical Analyses Microarrays were analyzed with the statistical analysis program R using linear models for microarray with fixed effects to compare expression levels between lowly tolerant, highly tolerant, and control ewes, and also between NO3−-treated and control ewes. The mathematical linear model assumed for linear models for microarray is  E[Yj] = Xαj,where Yj contains the expression data for the gene j, X is a design matrix, and αj is a vector of coefficients. The contrasts of interest are given by βj = CTαj, where CT is a specified contrast matrix. The coefficients of the fitted model produced by lmFit contain estimated values for the αj. After applying contrasts.fit, estimated values for the βj are produced. Fold changes were obtained after the data were normalized for within-slide print-tip and dye intensity effects. The P-values were adjusted to control false discovery rate according to Benjamini and Hochberg (1995):  Qc = E(Q) = E{V/(V + S)} = E(V/R),where R is an observable random variable, and V and S are unobservable random variables. The proportion of errors committed by falsely rejecting the null hypothesis is defined by V/(V + S) = Q, and Qc is the expectation of Q. All oligonucleotides with a Qc below 0.05 were selected as differentially expressed with an expected false discovery rate of 5%. Only the highly tolerant vs. lowly tolerant contrast results are discussed for the present study in accordance with our hypothesis. Real-time RT-PCR results were analyzed using the 2−∆∆CT method. All samples were measured in duplicate per gene. Estimates of 2−∆∆CT, or the relative gene expression, were obtained by measuring the average threshold cycle difference of the gene of interest relative to the housekeeping gene, glyceraldehyde 3-phosphate dehydrogenase. Sample threshold cycle values were then further normalized to the greatest threshold cycle difference (∆∆CT). Because control expression contents were considered to be baseline and changes in gene expression in highly tolerant and lowly tolerant ewes were of primary interest, the highly tolerant and lowly tolerant relative expression values for each gene were adjusted by subtracting off the mean of the respective control relative expression value. Relative gene expression values were analyzed using GLM procedures (SAS Inst. Inc., Cary, NC) with LSMEANS for mean separation using a Tukey adjustment, assuming α equals 0.05. RESULTS AND DISCUSSION Microarray analysis revealed differences (P < 0.05) in oligonucleotide expression between lowly tolerant and highly tolerant ewes (100 genes; Table 2), lowly tolerant and control ewes (85 genes), highly tolerant and control ewes (5 genes), and NO3−-treated and control ewes (12 genes). The objective of this study, however, was to determine differences in gene expression between lowly tolerant and highly tolerant ewes. Therefore, only the genes differentially expressed between those 2 ewe groups are discussed herein. Table 2. Microarray oligonucleotides and associated genes differentially expressed (P < 0.05) between lowly tolerant and highly tolerant ewes to subacute dietary NO3− Oligonucleotide ID  Accession number  Gene name/ symbol  Fold change1,2  Adjusted P-value  5616:8040_13929797-A:f  NM_001097986  RFTN2  5.20  0.0030  4595:45360_29400233-B:r  NM_001040470  HP  4.46  0.0029  11898:7225_CL1Contig1-A:f  XM_001250068  ADRA1B  4.14  0.0050  NM_001035330.1  NM_001035330  GPA33  3.09  0.0014  14300:9669_CL1Contig1-B:r  XM_605792  LOC527401  2.82  0.0127  19085:4088_CL3Contig2-A:f  BC154381  MC1  2.81  0.0055  4554:4406_45457973-A:f  L18966  PDP1  2.65  0.0030  2216:6490_CL1Contig1-B:r  NM_001038674  LBP  2.65  0.0019  11864:8233_CL1Contig3-B:r  NM_174077  GPX3  2.64  0.0014  1351:30721_CL9Contig1-A:f  XM_870342  DDEF2or ASAP2  2.61  0.0067  18662:7792_12122521-A:f  XM_584164  KIAA0513  2.52  0.0029  11439:36984_CL1Contig1-A:r  BC102635  HOPX  2.25  0.0029  XM_582803.2  NM_001101870  FAM124B  2.21  0.0025  10604:20121_CL1Contig1-A:r  NM_001081623  GATAD1  2.17  0.0112  2120:41375_CL1Contig1-B:r  BC126762  OFST1  2.09  0.0129  XM_580915.2  AC113370  RP11–124O5  2.05  0.0084  18617:12477_16744264-A:r  AC069248  RP11–6I21  2.03  0.0109  16340:3198_CL1Contig2-B:f  NM_001045987  GLYCTK  2.03  0.0067  5691:1380_7032578-A:r  XM_580981  FZD5  2.02  0.0100  14400:6392_CL3Contig1-A:f  XM_001253519  UL16BP3  1.98  0.0075  CV984332.1-B:r  XM_869284  CALM1  1.96  0.0014  10489:5992_49358271-A:f  AC188571  CH251–721D23  1.94  0.0075  DN641059.1-B:r  AL591344  RP11–260H5  1.88  0.0095  CX953715.1-B:r  CR956360  CH242–245E12  1.75  0.0147  11088:9480_CL2Contig1-B:r  XM_001789973  LOC513885  1.71  0.0102  7043:2013_CL1Contig3-B:r  NM_001015590  ITIH4  1.69  0.0084  DN643460.1-A:r  AK151118  I830025I02  1.62  0.0014  CX954105.1-A:r  AC012325  RP11–93H5  1.59  0.0129  20242:3528_CL1Contig1-B:r  XM_591917  C10orf92  1.55  0.0014  2099:7252_37712910-C:f  NM_001077865  CSTF1  1.51  0.0051  11963:2625_42727175-B:r  XM_602953  PDE4C  1.50  0.0124  6638:752_CL3Contig6-B:r  EF153626  SERPINA 3–2  1.50  0.0030  17148:6569_CL1Contig1-A:r  BC133443  TMP176A  1.48  0.0102  XM_585118.2  NM_001101898  ITIH3  1.48  0.0067  18486:7281_CL2Contig2-B:r  NM_001009733  CP  1.46  0.0095  8061:4744_CL1Contig1-B:f  XM_589329  LOC511902  1.44  0.0088  6651:752_CL3Contig3-A:r  NM_001012283  SERPINA 3–7  1.43  0.0088  12785:6356_CL6Contig1-B:r  NM_001098036  SLC39A14  1.42  0.0100  14944:10603_16999762-A:f  NM_001046177  LRG1  1.42  0.0051  NM_001035364.1  NM_001035364  C9  1.41  0.0148  19505:1422_CL3Contig1-A:f  NM_001103301  LGI4  1.41  0.0147  6650:752_CL16Contig1-B:f  NM_001081712  SERPINA 3–8  1.40  0.0113  17125:3659_58763670-B:r  AF327654  PTH  1.40  0.0029  CV976386.1-B:r  XM_001494741  FIGN  1.39  0.0100  17184:7423_CL1Contig1-A:r  AJ276820  c/EBPΔ  1.38  0.0069  3640:5094_CL2Contig3-B:r  BC102629  FGG  1.37  0.0025  CK729578.1-B:r  AC159152  CH243–122B5  1.36  0.0135  8788:19053_29233572-A:f  XM_590780  MGC157906  1.36  0.0067  7308:2727_7426578-A:f  CT956038  CH242–183N12  1.33  0.0115  CV978916.1-B:r  CP000699  TMEM131  1.33  0.0095  CN998180.1-A:r        1.32  0.0095  17667:14668_45064799-B:f  NM_001103285  TUBAL3  1.32  0.0030  2869:45360_CL1446Contig1-A:f  XM_869625  ZNF408  1.31  0.0147  749:5622_CL1Contig1-B:r  XM_001788700  ALKBH1  1.31  0.0147  19215:43797_CL2Contig1-B:f  XM_595526  FBXW5  1.31  0.0030  2394:5511_7055533-A:f  NM_001101282  PTER  1.30  0.0131  5297:38019_46417035-A:f  AY220195  NRRL31156 38Sand28S-18S  1.30  0.0095  2856:9870_60971926-B:f  NM_177496  EXT2  1.30  0.0092  335:45360_CL471Contig1-B:r  NM_001081585  TMPRSS2  1.29  0.0088  DN642204.1-B:r  XM_868603  LOC616557  1.29  0.0088  19497:26873_CL1Contig1-A:f  XM_001255447  NKPD1  1.29  0.0069  XM_586062.2  AF491780  NRG1  1.28  0.0081  18866:46145_CL12Contig1-B:f  XM_001252944  CDH23  1.26  0.0127  12437:2996_CL2Contig2-B:r  XR_027442  LOC533435  −1.30  0.0127  15398:18811_CL1Contig1-B:f  NM_001033990  SCP2  −1.32  0.0088  3690:1682_CL2Contig1-B:r  NM_001045938  AP1B1  −1.33  0.0131  4163:5355_CL1Contig1-A:r  XM_585231  KIK-I  −1.33  0.0086  17215:19283_CL1Contig1-A:f  NM_001101223  LOC613623  −1.37  0.0089  16195:45360_CL359Contig1-A:f  XR_042632  LOC507011  −1.39  0.0100  18782:8424_CL1Contig2-A:r  BT020896  CXCR6  −1.39  0.0085  522:38776_CL9Contig1-B:f  XM_001790250  KALRN  −1.41  0.0131  7923:6333_CL1Contig3-B:f  NM_001034428  ASL  −1.41  0.0088  12605:34974_CL21Contig1-B:f  XM_001249579  MUSK  −1.41  0.0088  4998:2074_CL2Contig1-A:r  AC225592  FOSMID  −1.43  0.0088  12935:6758_29206088-B:f  NM_001101098  SLC22A1  −1.43  0.0030  18637:13261_CL1Contig1-A:r  BC140648  SGPL1  −1.45  0.0030  CV984085.1-B:r  AC006465  RP11–438H20  −1.47  0.0136  CV984377.1-B:r  AC027296  RP11–309L4  −1.49  0.0088  CV978115.1-B:r  NM_001014935  CDK9  −1.49  0.0067  7773:21323_CL1Contig1-A:r  XM_864316  SOCS1  −1.49  0.0067  16873:5307_CL1Contig1-A:r  BC114908     −1.49  0.0030  13631:38423_CL2Contig2-B:f  NM_174572  PFKFB1  −1.52  0.0088  5597:2563_CL1Contig4-B:f  BC105265  AOX1  −1.54  0.0088  DN642743.1-A:r        −1.59  0.0124  7196:446_49407390-A:f  BC144620     −1.59  0.0100  CV984306.1-B:r  NG_008292  IL1RAPL1  −1.59  0.0100  6524:5527_CL1Contig2-B:f  NM_001099364  CYP1A2  −1.59  0.0055  9817:11373_CL1Contig3-  A:f:Mismatch:10  AC027279  RP11–319G9  −1.61  0.0095  5610:2563_CL5Contig1-B:f  DQ153003  XDH  −1.61  0.0088  19708:30757_CL1Contig1-B:f  NM_001075196  ABHD6  −1.61  0.0029  CV981913.1-A:r  CP000076  Pf-5  −1.67  0.0088  11159:14232_CL1Contig1-B:f  XM_001788351  CSAD  −1.69  0.0069  20139:40580_16745911-A:f  NM_001081541  S1PR2  −1.69  0.0067  9110:8577_16350107-A:f  NM_001083444  FADS2  −1.72  0.0073  14053:2011_28296324-A:f  XM_584484  CYP26A1  −1.92  0.0095  CV978512.1-A:r  XM_001491478  HAUS7  −1.96  0.0161  CN998462.1-B:r  DQ069781S3  SCD  −2.17  0.0014  CN654340.1-B:r  NM_001083444  FADS2  −2.22  0.0027  15352:9818_CL1Contig1-A:f  BC114894  THRSP  −2.70  0.0014  8894:4027_CL1Contig1-A:r  NM_001076521  PHLDA2  −3.85  0.0126  Oligonucleotide ID  Accession number  Gene name/ symbol  Fold change1,2  Adjusted P-value  5616:8040_13929797-A:f  NM_001097986  RFTN2  5.20  0.0030  4595:45360_29400233-B:r  NM_001040470  HP  4.46  0.0029  11898:7225_CL1Contig1-A:f  XM_001250068  ADRA1B  4.14  0.0050  NM_001035330.1  NM_001035330  GPA33  3.09  0.0014  14300:9669_CL1Contig1-B:r  XM_605792  LOC527401  2.82  0.0127  19085:4088_CL3Contig2-A:f  BC154381  MC1  2.81  0.0055  4554:4406_45457973-A:f  L18966  PDP1  2.65  0.0030  2216:6490_CL1Contig1-B:r  NM_001038674  LBP  2.65  0.0019  11864:8233_CL1Contig3-B:r  NM_174077  GPX3  2.64  0.0014  1351:30721_CL9Contig1-A:f  XM_870342  DDEF2or ASAP2  2.61  0.0067  18662:7792_12122521-A:f  XM_584164  KIAA0513  2.52  0.0029  11439:36984_CL1Contig1-A:r  BC102635  HOPX  2.25  0.0029  XM_582803.2  NM_001101870  FAM124B  2.21  0.0025  10604:20121_CL1Contig1-A:r  NM_001081623  GATAD1  2.17  0.0112  2120:41375_CL1Contig1-B:r  BC126762  OFST1  2.09  0.0129  XM_580915.2  AC113370  RP11–124O5  2.05  0.0084  18617:12477_16744264-A:r  AC069248  RP11–6I21  2.03  0.0109  16340:3198_CL1Contig2-B:f  NM_001045987  GLYCTK  2.03  0.0067  5691:1380_7032578-A:r  XM_580981  FZD5  2.02  0.0100  14400:6392_CL3Contig1-A:f  XM_001253519  UL16BP3  1.98  0.0075  CV984332.1-B:r  XM_869284  CALM1  1.96  0.0014  10489:5992_49358271-A:f  AC188571  CH251–721D23  1.94  0.0075  DN641059.1-B:r  AL591344  RP11–260H5  1.88  0.0095  CX953715.1-B:r  CR956360  CH242–245E12  1.75  0.0147  11088:9480_CL2Contig1-B:r  XM_001789973  LOC513885  1.71  0.0102  7043:2013_CL1Contig3-B:r  NM_001015590  ITIH4  1.69  0.0084  DN643460.1-A:r  AK151118  I830025I02  1.62  0.0014  CX954105.1-A:r  AC012325  RP11–93H5  1.59  0.0129  20242:3528_CL1Contig1-B:r  XM_591917  C10orf92  1.55  0.0014  2099:7252_37712910-C:f  NM_001077865  CSTF1  1.51  0.0051  11963:2625_42727175-B:r  XM_602953  PDE4C  1.50  0.0124  6638:752_CL3Contig6-B:r  EF153626  SERPINA 3–2  1.50  0.0030  17148:6569_CL1Contig1-A:r  BC133443  TMP176A  1.48  0.0102  XM_585118.2  NM_001101898  ITIH3  1.48  0.0067  18486:7281_CL2Contig2-B:r  NM_001009733  CP  1.46  0.0095  8061:4744_CL1Contig1-B:f  XM_589329  LOC511902  1.44  0.0088  6651:752_CL3Contig3-A:r  NM_001012283  SERPINA 3–7  1.43  0.0088  12785:6356_CL6Contig1-B:r  NM_001098036  SLC39A14  1.42  0.0100  14944:10603_16999762-A:f  NM_001046177  LRG1  1.42  0.0051  NM_001035364.1  NM_001035364  C9  1.41  0.0148  19505:1422_CL3Contig1-A:f  NM_001103301  LGI4  1.41  0.0147  6650:752_CL16Contig1-B:f  NM_001081712  SERPINA 3–8  1.40  0.0113  17125:3659_58763670-B:r  AF327654  PTH  1.40  0.0029  CV976386.1-B:r  XM_001494741  FIGN  1.39  0.0100  17184:7423_CL1Contig1-A:r  AJ276820  c/EBPΔ  1.38  0.0069  3640:5094_CL2Contig3-B:r  BC102629  FGG  1.37  0.0025  CK729578.1-B:r  AC159152  CH243–122B5  1.36  0.0135  8788:19053_29233572-A:f  XM_590780  MGC157906  1.36  0.0067  7308:2727_7426578-A:f  CT956038  CH242–183N12  1.33  0.0115  CV978916.1-B:r  CP000699  TMEM131  1.33  0.0095  CN998180.1-A:r        1.32  0.0095  17667:14668_45064799-B:f  NM_001103285  TUBAL3  1.32  0.0030  2869:45360_CL1446Contig1-A:f  XM_869625  ZNF408  1.31  0.0147  749:5622_CL1Contig1-B:r  XM_001788700  ALKBH1  1.31  0.0147  19215:43797_CL2Contig1-B:f  XM_595526  FBXW5  1.31  0.0030  2394:5511_7055533-A:f  NM_001101282  PTER  1.30  0.0131  5297:38019_46417035-A:f  AY220195  NRRL31156 38Sand28S-18S  1.30  0.0095  2856:9870_60971926-B:f  NM_177496  EXT2  1.30  0.0092  335:45360_CL471Contig1-B:r  NM_001081585  TMPRSS2  1.29  0.0088  DN642204.1-B:r  XM_868603  LOC616557  1.29  0.0088  19497:26873_CL1Contig1-A:f  XM_001255447  NKPD1  1.29  0.0069  XM_586062.2  AF491780  NRG1  1.28  0.0081  18866:46145_CL12Contig1-B:f  XM_001252944  CDH23  1.26  0.0127  12437:2996_CL2Contig2-B:r  XR_027442  LOC533435  −1.30  0.0127  15398:18811_CL1Contig1-B:f  NM_001033990  SCP2  −1.32  0.0088  3690:1682_CL2Contig1-B:r  NM_001045938  AP1B1  −1.33  0.0131  4163:5355_CL1Contig1-A:r  XM_585231  KIK-I  −1.33  0.0086  17215:19283_CL1Contig1-A:f  NM_001101223  LOC613623  −1.37  0.0089  16195:45360_CL359Contig1-A:f  XR_042632  LOC507011  −1.39  0.0100  18782:8424_CL1Contig2-A:r  BT020896  CXCR6  −1.39  0.0085  522:38776_CL9Contig1-B:f  XM_001790250  KALRN  −1.41  0.0131  7923:6333_CL1Contig3-B:f  NM_001034428  ASL  −1.41  0.0088  12605:34974_CL21Contig1-B:f  XM_001249579  MUSK  −1.41  0.0088  4998:2074_CL2Contig1-A:r  AC225592  FOSMID  −1.43  0.0088  12935:6758_29206088-B:f  NM_001101098  SLC22A1  −1.43  0.0030  18637:13261_CL1Contig1-A:r  BC140648  SGPL1  −1.45  0.0030  CV984085.1-B:r  AC006465  RP11–438H20  −1.47  0.0136  CV984377.1-B:r  AC027296  RP11–309L4  −1.49  0.0088  CV978115.1-B:r  NM_001014935  CDK9  −1.49  0.0067  7773:21323_CL1Contig1-A:r  XM_864316  SOCS1  −1.49  0.0067  16873:5307_CL1Contig1-A:r  BC114908     −1.49  0.0030  13631:38423_CL2Contig2-B:f  NM_174572  PFKFB1  −1.52  0.0088  5597:2563_CL1Contig4-B:f  BC105265  AOX1  −1.54  0.0088  DN642743.1-A:r        −1.59  0.0124  7196:446_49407390-A:f  BC144620     −1.59  0.0100  CV984306.1-B:r  NG_008292  IL1RAPL1  −1.59  0.0100  6524:5527_CL1Contig2-B:f  NM_001099364  CYP1A2  −1.59  0.0055  9817:11373_CL1Contig3-  A:f:Mismatch:10  AC027279  RP11–319G9  −1.61  0.0095  5610:2563_CL5Contig1-B:f  DQ153003  XDH  −1.61  0.0088  19708:30757_CL1Contig1-B:f  NM_001075196  ABHD6  −1.61  0.0029  CV981913.1-A:r  CP000076  Pf-5  −1.67  0.0088  11159:14232_CL1Contig1-B:f  XM_001788351  CSAD  −1.69  0.0069  20139:40580_16745911-A:f  NM_001081541  S1PR2  −1.69  0.0067  9110:8577_16350107-A:f  NM_001083444  FADS2  −1.72  0.0073  14053:2011_28296324-A:f  XM_584484  CYP26A1  −1.92  0.0095  CV978512.1-A:r  XM_001491478  HAUS7  −1.96  0.0161  CN998462.1-B:r  DQ069781S3  SCD  −2.17  0.0014  CN654340.1-B:r  NM_001083444  FADS2  −2.22  0.0027  15352:9818_CL1Contig1-A:f  BC114894  THRSP  −2.70  0.0014  8894:4027_CL1Contig1-A:r  NM_001076521  PHLDA2  −3.85  0.0126  1Fold change (FC) values expressed as a ratio between lowly tolerant and highly tolerant ewes. 2FC values >0 represent genes upregulated in lowly tolerant ewes compared with highly tolerant ewes. FC values <0 represent genes downregulated in lowly tolerant ewes compared with highly tolerant ewes. View Large Table 2. Microarray oligonucleotides and associated genes differentially expressed (P < 0.05) between lowly tolerant and highly tolerant ewes to subacute dietary NO3− Oligonucleotide ID  Accession number  Gene name/ symbol  Fold change1,2  Adjusted P-value  5616:8040_13929797-A:f  NM_001097986  RFTN2  5.20  0.0030  4595:45360_29400233-B:r  NM_001040470  HP  4.46  0.0029  11898:7225_CL1Contig1-A:f  XM_001250068  ADRA1B  4.14  0.0050  NM_001035330.1  NM_001035330  GPA33  3.09  0.0014  14300:9669_CL1Contig1-B:r  XM_605792  LOC527401  2.82  0.0127  19085:4088_CL3Contig2-A:f  BC154381  MC1  2.81  0.0055  4554:4406_45457973-A:f  L18966  PDP1  2.65  0.0030  2216:6490_CL1Contig1-B:r  NM_001038674  LBP  2.65  0.0019  11864:8233_CL1Contig3-B:r  NM_174077  GPX3  2.64  0.0014  1351:30721_CL9Contig1-A:f  XM_870342  DDEF2or ASAP2  2.61  0.0067  18662:7792_12122521-A:f  XM_584164  KIAA0513  2.52  0.0029  11439:36984_CL1Contig1-A:r  BC102635  HOPX  2.25  0.0029  XM_582803.2  NM_001101870  FAM124B  2.21  0.0025  10604:20121_CL1Contig1-A:r  NM_001081623  GATAD1  2.17  0.0112  2120:41375_CL1Contig1-B:r  BC126762  OFST1  2.09  0.0129  XM_580915.2  AC113370  RP11–124O5  2.05  0.0084  18617:12477_16744264-A:r  AC069248  RP11–6I21  2.03  0.0109  16340:3198_CL1Contig2-B:f  NM_001045987  GLYCTK  2.03  0.0067  5691:1380_7032578-A:r  XM_580981  FZD5  2.02  0.0100  14400:6392_CL3Contig1-A:f  XM_001253519  UL16BP3  1.98  0.0075  CV984332.1-B:r  XM_869284  CALM1  1.96  0.0014  10489:5992_49358271-A:f  AC188571  CH251–721D23  1.94  0.0075  DN641059.1-B:r  AL591344  RP11–260H5  1.88  0.0095  CX953715.1-B:r  CR956360  CH242–245E12  1.75  0.0147  11088:9480_CL2Contig1-B:r  XM_001789973  LOC513885  1.71  0.0102  7043:2013_CL1Contig3-B:r  NM_001015590  ITIH4  1.69  0.0084  DN643460.1-A:r  AK151118  I830025I02  1.62  0.0014  CX954105.1-A:r  AC012325  RP11–93H5  1.59  0.0129  20242:3528_CL1Contig1-B:r  XM_591917  C10orf92  1.55  0.0014  2099:7252_37712910-C:f  NM_001077865  CSTF1  1.51  0.0051  11963:2625_42727175-B:r  XM_602953  PDE4C  1.50  0.0124  6638:752_CL3Contig6-B:r  EF153626  SERPINA 3–2  1.50  0.0030  17148:6569_CL1Contig1-A:r  BC133443  TMP176A  1.48  0.0102  XM_585118.2  NM_001101898  ITIH3  1.48  0.0067  18486:7281_CL2Contig2-B:r  NM_001009733  CP  1.46  0.0095  8061:4744_CL1Contig1-B:f  XM_589329  LOC511902  1.44  0.0088  6651:752_CL3Contig3-A:r  NM_001012283  SERPINA 3–7  1.43  0.0088  12785:6356_CL6Contig1-B:r  NM_001098036  SLC39A14  1.42  0.0100  14944:10603_16999762-A:f  NM_001046177  LRG1  1.42  0.0051  NM_001035364.1  NM_001035364  C9  1.41  0.0148  19505:1422_CL3Contig1-A:f  NM_001103301  LGI4  1.41  0.0147  6650:752_CL16Contig1-B:f  NM_001081712  SERPINA 3–8  1.40  0.0113  17125:3659_58763670-B:r  AF327654  PTH  1.40  0.0029  CV976386.1-B:r  XM_001494741  FIGN  1.39  0.0100  17184:7423_CL1Contig1-A:r  AJ276820  c/EBPΔ  1.38  0.0069  3640:5094_CL2Contig3-B:r  BC102629  FGG  1.37  0.0025  CK729578.1-B:r  AC159152  CH243–122B5  1.36  0.0135  8788:19053_29233572-A:f  XM_590780  MGC157906  1.36  0.0067  7308:2727_7426578-A:f  CT956038  CH242–183N12  1.33  0.0115  CV978916.1-B:r  CP000699  TMEM131  1.33  0.0095  CN998180.1-A:r        1.32  0.0095  17667:14668_45064799-B:f  NM_001103285  TUBAL3  1.32  0.0030  2869:45360_CL1446Contig1-A:f  XM_869625  ZNF408  1.31  0.0147  749:5622_CL1Contig1-B:r  XM_001788700  ALKBH1  1.31  0.0147  19215:43797_CL2Contig1-B:f  XM_595526  FBXW5  1.31  0.0030  2394:5511_7055533-A:f  NM_001101282  PTER  1.30  0.0131  5297:38019_46417035-A:f  AY220195  NRRL31156 38Sand28S-18S  1.30  0.0095  2856:9870_60971926-B:f  NM_177496  EXT2  1.30  0.0092  335:45360_CL471Contig1-B:r  NM_001081585  TMPRSS2  1.29  0.0088  DN642204.1-B:r  XM_868603  LOC616557  1.29  0.0088  19497:26873_CL1Contig1-A:f  XM_001255447  NKPD1  1.29  0.0069  XM_586062.2  AF491780  NRG1  1.28  0.0081  18866:46145_CL12Contig1-B:f  XM_001252944  CDH23  1.26  0.0127  12437:2996_CL2Contig2-B:r  XR_027442  LOC533435  −1.30  0.0127  15398:18811_CL1Contig1-B:f  NM_001033990  SCP2  −1.32  0.0088  3690:1682_CL2Contig1-B:r  NM_001045938  AP1B1  −1.33  0.0131  4163:5355_CL1Contig1-A:r  XM_585231  KIK-I  −1.33  0.0086  17215:19283_CL1Contig1-A:f  NM_001101223  LOC613623  −1.37  0.0089  16195:45360_CL359Contig1-A:f  XR_042632  LOC507011  −1.39  0.0100  18782:8424_CL1Contig2-A:r  BT020896  CXCR6  −1.39  0.0085  522:38776_CL9Contig1-B:f  XM_001790250  KALRN  −1.41  0.0131  7923:6333_CL1Contig3-B:f  NM_001034428  ASL  −1.41  0.0088  12605:34974_CL21Contig1-B:f  XM_001249579  MUSK  −1.41  0.0088  4998:2074_CL2Contig1-A:r  AC225592  FOSMID  −1.43  0.0088  12935:6758_29206088-B:f  NM_001101098  SLC22A1  −1.43  0.0030  18637:13261_CL1Contig1-A:r  BC140648  SGPL1  −1.45  0.0030  CV984085.1-B:r  AC006465  RP11–438H20  −1.47  0.0136  CV984377.1-B:r  AC027296  RP11–309L4  −1.49  0.0088  CV978115.1-B:r  NM_001014935  CDK9  −1.49  0.0067  7773:21323_CL1Contig1-A:r  XM_864316  SOCS1  −1.49  0.0067  16873:5307_CL1Contig1-A:r  BC114908     −1.49  0.0030  13631:38423_CL2Contig2-B:f  NM_174572  PFKFB1  −1.52  0.0088  5597:2563_CL1Contig4-B:f  BC105265  AOX1  −1.54  0.0088  DN642743.1-A:r        −1.59  0.0124  7196:446_49407390-A:f  BC144620     −1.59  0.0100  CV984306.1-B:r  NG_008292  IL1RAPL1  −1.59  0.0100  6524:5527_CL1Contig2-B:f  NM_001099364  CYP1A2  −1.59  0.0055  9817:11373_CL1Contig3-  A:f:Mismatch:10  AC027279  RP11–319G9  −1.61  0.0095  5610:2563_CL5Contig1-B:f  DQ153003  XDH  −1.61  0.0088  19708:30757_CL1Contig1-B:f  NM_001075196  ABHD6  −1.61  0.0029  CV981913.1-A:r  CP000076  Pf-5  −1.67  0.0088  11159:14232_CL1Contig1-B:f  XM_001788351  CSAD  −1.69  0.0069  20139:40580_16745911-A:f  NM_001081541  S1PR2  −1.69  0.0067  9110:8577_16350107-A:f  NM_001083444  FADS2  −1.72  0.0073  14053:2011_28296324-A:f  XM_584484  CYP26A1  −1.92  0.0095  CV978512.1-A:r  XM_001491478  HAUS7  −1.96  0.0161  CN998462.1-B:r  DQ069781S3  SCD  −2.17  0.0014  CN654340.1-B:r  NM_001083444  FADS2  −2.22  0.0027  15352:9818_CL1Contig1-A:f  BC114894  THRSP  −2.70  0.0014  8894:4027_CL1Contig1-A:r  NM_001076521  PHLDA2  −3.85  0.0126  Oligonucleotide ID  Accession number  Gene name/ symbol  Fold change1,2  Adjusted P-value  5616:8040_13929797-A:f  NM_001097986  RFTN2  5.20  0.0030  4595:45360_29400233-B:r  NM_001040470  HP  4.46  0.0029  11898:7225_CL1Contig1-A:f  XM_001250068  ADRA1B  4.14  0.0050  NM_001035330.1  NM_001035330  GPA33  3.09  0.0014  14300:9669_CL1Contig1-B:r  XM_605792  LOC527401  2.82  0.0127  19085:4088_CL3Contig2-A:f  BC154381  MC1  2.81  0.0055  4554:4406_45457973-A:f  L18966  PDP1  2.65  0.0030  2216:6490_CL1Contig1-B:r  NM_001038674  LBP  2.65  0.0019  11864:8233_CL1Contig3-B:r  NM_174077  GPX3  2.64  0.0014  1351:30721_CL9Contig1-A:f  XM_870342  DDEF2or ASAP2  2.61  0.0067  18662:7792_12122521-A:f  XM_584164  KIAA0513  2.52  0.0029  11439:36984_CL1Contig1-A:r  BC102635  HOPX  2.25  0.0029  XM_582803.2  NM_001101870  FAM124B  2.21  0.0025  10604:20121_CL1Contig1-A:r  NM_001081623  GATAD1  2.17  0.0112  2120:41375_CL1Contig1-B:r  BC126762  OFST1  2.09  0.0129  XM_580915.2  AC113370  RP11–124O5  2.05  0.0084  18617:12477_16744264-A:r  AC069248  RP11–6I21  2.03  0.0109  16340:3198_CL1Contig2-B:f  NM_001045987  GLYCTK  2.03  0.0067  5691:1380_7032578-A:r  XM_580981  FZD5  2.02  0.0100  14400:6392_CL3Contig1-A:f  XM_001253519  UL16BP3  1.98  0.0075  CV984332.1-B:r  XM_869284  CALM1  1.96  0.0014  10489:5992_49358271-A:f  AC188571  CH251–721D23  1.94  0.0075  DN641059.1-B:r  AL591344  RP11–260H5  1.88  0.0095  CX953715.1-B:r  CR956360  CH242–245E12  1.75  0.0147  11088:9480_CL2Contig1-B:r  XM_001789973  LOC513885  1.71  0.0102  7043:2013_CL1Contig3-B:r  NM_001015590  ITIH4  1.69  0.0084  DN643460.1-A:r  AK151118  I830025I02  1.62  0.0014  CX954105.1-A:r  AC012325  RP11–93H5  1.59  0.0129  20242:3528_CL1Contig1-B:r  XM_591917  C10orf92  1.55  0.0014  2099:7252_37712910-C:f  NM_001077865  CSTF1  1.51  0.0051  11963:2625_42727175-B:r  XM_602953  PDE4C  1.50  0.0124  6638:752_CL3Contig6-B:r  EF153626  SERPINA 3–2  1.50  0.0030  17148:6569_CL1Contig1-A:r  BC133443  TMP176A  1.48  0.0102  XM_585118.2  NM_001101898  ITIH3  1.48  0.0067  18486:7281_CL2Contig2-B:r  NM_001009733  CP  1.46  0.0095  8061:4744_CL1Contig1-B:f  XM_589329  LOC511902  1.44  0.0088  6651:752_CL3Contig3-A:r  NM_001012283  SERPINA 3–7  1.43  0.0088  12785:6356_CL6Contig1-B:r  NM_001098036  SLC39A14  1.42  0.0100  14944:10603_16999762-A:f  NM_001046177  LRG1  1.42  0.0051  NM_001035364.1  NM_001035364  C9  1.41  0.0148  19505:1422_CL3Contig1-A:f  NM_001103301  LGI4  1.41  0.0147  6650:752_CL16Contig1-B:f  NM_001081712  SERPINA 3–8  1.40  0.0113  17125:3659_58763670-B:r  AF327654  PTH  1.40  0.0029  CV976386.1-B:r  XM_001494741  FIGN  1.39  0.0100  17184:7423_CL1Contig1-A:r  AJ276820  c/EBPΔ  1.38  0.0069  3640:5094_CL2Contig3-B:r  BC102629  FGG  1.37  0.0025  CK729578.1-B:r  AC159152  CH243–122B5  1.36  0.0135  8788:19053_29233572-A:f  XM_590780  MGC157906  1.36  0.0067  7308:2727_7426578-A:f  CT956038  CH242–183N12  1.33  0.0115  CV978916.1-B:r  CP000699  TMEM131  1.33  0.0095  CN998180.1-A:r        1.32  0.0095  17667:14668_45064799-B:f  NM_001103285  TUBAL3  1.32  0.0030  2869:45360_CL1446Contig1-A:f  XM_869625  ZNF408  1.31  0.0147  749:5622_CL1Contig1-B:r  XM_001788700  ALKBH1  1.31  0.0147  19215:43797_CL2Contig1-B:f  XM_595526  FBXW5  1.31  0.0030  2394:5511_7055533-A:f  NM_001101282  PTER  1.30  0.0131  5297:38019_46417035-A:f  AY220195  NRRL31156 38Sand28S-18S  1.30  0.0095  2856:9870_60971926-B:f  NM_177496  EXT2  1.30  0.0092  335:45360_CL471Contig1-B:r  NM_001081585  TMPRSS2  1.29  0.0088  DN642204.1-B:r  XM_868603  LOC616557  1.29  0.0088  19497:26873_CL1Contig1-A:f  XM_001255447  NKPD1  1.29  0.0069  XM_586062.2  AF491780  NRG1  1.28  0.0081  18866:46145_CL12Contig1-B:f  XM_001252944  CDH23  1.26  0.0127  12437:2996_CL2Contig2-B:r  XR_027442  LOC533435  −1.30  0.0127  15398:18811_CL1Contig1-B:f  NM_001033990  SCP2  −1.32  0.0088  3690:1682_CL2Contig1-B:r  NM_001045938  AP1B1  −1.33  0.0131  4163:5355_CL1Contig1-A:r  XM_585231  KIK-I  −1.33  0.0086  17215:19283_CL1Contig1-A:f  NM_001101223  LOC613623  −1.37  0.0089  16195:45360_CL359Contig1-A:f  XR_042632  LOC507011  −1.39  0.0100  18782:8424_CL1Contig2-A:r  BT020896  CXCR6  −1.39  0.0085  522:38776_CL9Contig1-B:f  XM_001790250  KALRN  −1.41  0.0131  7923:6333_CL1Contig3-B:f  NM_001034428  ASL  −1.41  0.0088  12605:34974_CL21Contig1-B:f  XM_001249579  MUSK  −1.41  0.0088  4998:2074_CL2Contig1-A:r  AC225592  FOSMID  −1.43  0.0088  12935:6758_29206088-B:f  NM_001101098  SLC22A1  −1.43  0.0030  18637:13261_CL1Contig1-A:r  BC140648  SGPL1  −1.45  0.0030  CV984085.1-B:r  AC006465  RP11–438H20  −1.47  0.0136  CV984377.1-B:r  AC027296  RP11–309L4  −1.49  0.0088  CV978115.1-B:r  NM_001014935  CDK9  −1.49  0.0067  7773:21323_CL1Contig1-A:r  XM_864316  SOCS1  −1.49  0.0067  16873:5307_CL1Contig1-A:r  BC114908     −1.49  0.0030  13631:38423_CL2Contig2-B:f  NM_174572  PFKFB1  −1.52  0.0088  5597:2563_CL1Contig4-B:f  BC105265  AOX1  −1.54  0.0088  DN642743.1-A:r        −1.59  0.0124  7196:446_49407390-A:f  BC144620     −1.59  0.0100  CV984306.1-B:r  NG_008292  IL1RAPL1  −1.59  0.0100  6524:5527_CL1Contig2-B:f  NM_001099364  CYP1A2  −1.59  0.0055  9817:11373_CL1Contig3-  A:f:Mismatch:10  AC027279  RP11–319G9  −1.61  0.0095  5610:2563_CL5Contig1-B:f  DQ153003  XDH  −1.61  0.0088  19708:30757_CL1Contig1-B:f  NM_001075196  ABHD6  −1.61  0.0029  CV981913.1-A:r  CP000076  Pf-5  −1.67  0.0088  11159:14232_CL1Contig1-B:f  XM_001788351  CSAD  −1.69  0.0069  20139:40580_16745911-A:f  NM_001081541  S1PR2  −1.69  0.0067  9110:8577_16350107-A:f  NM_001083444  FADS2  −1.72  0.0073  14053:2011_28296324-A:f  XM_584484  CYP26A1  −1.92  0.0095  CV978512.1-A:r  XM_001491478  HAUS7  −1.96  0.0161  CN998462.1-B:r  DQ069781S3  SCD  −2.17  0.0014  CN654340.1-B:r  NM_001083444  FADS2  −2.22  0.0027  15352:9818_CL1Contig1-A:f  BC114894  THRSP  −2.70  0.0014  8894:4027_CL1Contig1-A:r  NM_001076521  PHLDA2  −3.85  0.0126  1Fold change (FC) values expressed as a ratio between lowly tolerant and highly tolerant ewes. 2FC values >0 represent genes upregulated in lowly tolerant ewes compared with highly tolerant ewes. FC values <0 represent genes downregulated in lowly tolerant ewes compared with highly tolerant ewes. View Large Oligonucleotides differentially expressed (P < 0.05; n = 100) between lowly tolerant and highly tolerant ewe groups were blasted in NCBI to identify the corresponding gene. Those genes were submitted to DAVID for functional analysis, 35 of which had a known function and were used for the cluster analysis. The cluster analysis revealed functional categories of metabolic response, stress response, ion binding, hydrolase activity, biological regulation, protein complex, transferase activity, and membrane function (Figure 1). Twenty-two genes were chosen for validation with real-time RT-PCR because they were 1) included in the metabolic response functional category, 2) included in the stress response functional category, or 3) had a FC > |1.5| and a known function of interest to this study. Figure 1. View largeDownload slide Functional analysis of 100 genes associated with oligonucleotides identified as differentially expressed (P < 0.05) in microarray analysis. Numbers within the identified functional groups based on 35 of the 100 accession numbers recognized by the analysis program. Figure 1. View largeDownload slide Functional analysis of 100 genes associated with oligonucleotides identified as differentially expressed (P < 0.05) in microarray analysis. Numbers within the identified functional groups based on 35 of the 100 accession numbers recognized by the analysis program. The metabolic response category was represented by the greatest number of genes (n = 11), indicating that tolerance to high dietary NO3− may be due, at least in part, to metabolic differences among individuals. In addition, the stress response category was moderately represented (n = 6). A stress response was anticipated in the highly tolerant and lowly tolerant ewes because of the rate and duration of NO3− administration, and differences in expression of stress genes between these 2 ewe groups may contribute to the phenotypic differences in NO3− tolerance. Additionally, each of the stress response genes had an overlapping metabolic response function, linking the 2 functional categories of interest together. Genes selected for validation that were involved in metabolic response included aldehyde oxidase 1 (AOX1); argininosuccinate lyase (ASL); cyclin-dependent kinase 9 (CDK9); cytochrome P450, family 26, subfamily A, polypeptide 1 (CYP26A1; retinoic acid); homeobox (HOPX); putative steroid dehydrogenase (KIK-I; predicted); phosphodiesterase 4C (PDE4C; cAMP specific; predicted); 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (PFKFB1); sterol carrier protein 2 (SCP2); G-protein coupled receptor tymstr (CXCR6); and development and differentiation-enhancing factor 2 (DDEF2). Genes selected for validation involved in stress-metabolic response included complement component 9 (C9); exostoses (multiple) 2 (EXT2); fibrinogen gamma chain (FGG); glutathione peroxidase 3 (GPX3); inter-α (globulin) inhibitor H4 (ITIH4); and pyruvate dehydrogenase phosphatase 1 (PDP1). There were 5 genes that were not functionally similar to any other genes but were of interest because of their increased FC and individual functions. Those genes included cysteine sulfinic acid decarboxylase (CSAD); fatty acid desaturase 2 (FADS2); haptoglobin (HP); thyroid hormone responsive (THRSP); and stearoyl-CoA desaturase (SCD). Melt curves were obtained for only 19 of these genes. The 3 genes for which no melt curve was obtained included CXCR6, DDEF2, and SCD. Because available bovine mRNA sequences were used for primer design, nonrecognition of the cDNA or the annealing process or both likely resulted in the nonvalidation of those 3 genes by real-time RT-PCR. Results from microarray and real-time RT-PCR analyses of genes differentially expressed between lowly tolerant and highly tolerant ewes are compared in Table 3, including FC (ratio of lowly tolerant average expression to highly tolerant average expression) and direction of regulation from each analysis. An FC <0 indicated a downregulation in lowly tolerant ewes compared with highly tolerant ewes. Alternatively, an FC >0 indicated an upregulation in lowly tolerant ewes. Real-time RT-PCR relative expression, relative to the controls, are reported in Table 4 for highly tolerant and lowly tolerant ewe groups. In general, FC and regulation patterns of microarray analyses were confirmed by real-time RT-PCR; however, 6 of the 19 genes for which melt-curves were obtained were not differentially expressed (P > 0.05) when analyzed by real-time RT-PCR. Only genes confirmed by real-time RT-PCR as differentially expressed (Table 4; P < 0.05) are discussed further. All FC values reported herein refer to those estimated from real-time RT-PCR analysis. Table 3. Comparison of microarray and real-time reverse-transcription-PCR (RT-PCR) fold change (FC) differences between lowly tolerant and highly tolerant ewes1 Gene2  Microarray  Real-time RT-PCR  FC3 ± SE  FC3 ± SE  Metabolic response   AOX1  −1.54 ± 0.54  −2.22 ± 0.96   ASL  −1.41 ± 0.42  −2.13 ± 0.80   CDK9  −1.49 ± 0.44  1.00 ± 0.25   CYP26A1  −1.92 ± 0.80  −3.33 ± 3.00   HOPX  2.25 ± 0.94  2.67 ± 0.90   KIK-I  −1.33 ± 0.32  −1.69 ± 0.30   PDE4C  1.50 ± 0.53  −1.03 ± 0.45   PFKFB1  −1.52 ± 0.51  −2.17 ± 2.80   SCP2  −1.32 ± 0.33  −1.75 ± 0.38  Stress-metabolic response   C9  1.41 ± 0.37  1.25 ± 0.37   EXT2  1.30 ± 0.31  1.00 ± 0.25   FGG  1.37 ± 0.26  1.43 ± 0.42   GPX3  2.64 ± 1.01  4.23 ± 3.40   ITIH4  1.69 ± 0.66  1.93 ± 0.30   PDP1  2.65 ± 1.07  −1.22 ± 0.50  Nonclustered gene   CSAD  −1.69 ± 0.60  −2.22 ± 1.20   FADS2  −2.22 ± 0.89  −3.70 ± 1.50   HP  4.46 ± 1.74  69.07 ± 566.10   THRSP  −2.70 ± 0.94  −3.85 ± 38.20  Gene2  Microarray  Real-time RT-PCR  FC3 ± SE  FC3 ± SE  Metabolic response   AOX1  −1.54 ± 0.54  −2.22 ± 0.96   ASL  −1.41 ± 0.42  −2.13 ± 0.80   CDK9  −1.49 ± 0.44  1.00 ± 0.25   CYP26A1  −1.92 ± 0.80  −3.33 ± 3.00   HOPX  2.25 ± 0.94  2.67 ± 0.90   KIK-I  −1.33 ± 0.32  −1.69 ± 0.30   PDE4C  1.50 ± 0.53  −1.03 ± 0.45   PFKFB1  −1.52 ± 0.51  −2.17 ± 2.80   SCP2  −1.32 ± 0.33  −1.75 ± 0.38  Stress-metabolic response   C9  1.41 ± 0.37  1.25 ± 0.37   EXT2  1.30 ± 0.31  1.00 ± 0.25   FGG  1.37 ± 0.26  1.43 ± 0.42   GPX3  2.64 ± 1.01  4.23 ± 3.40   ITIH4  1.69 ± 0.66  1.93 ± 0.30   PDP1  2.65 ± 1.07  −1.22 ± 0.50  Nonclustered gene   CSAD  −1.69 ± 0.60  −2.22 ± 1.20   FADS2  −2.22 ± 0.89  −3.70 ± 1.50   HP  4.46 ± 1.74  69.07 ± 566.10   THRSP  −2.70 ± 0.94  −3.85 ± 38.20  1Fold change values expressed as a ratio between lowly tolerant and highly tolerant ewes. 2Aldehyde oxidase 1 (AOX1); argininosuccinate lyase (ASL); cyclin-dependent kinase 9 (CDK9); cytochrome P450, family 26, subfamily A, polypeptide 1 (CYP26A1; retinoic acid); homeobox (HOPX); putative steroid dehydrogenase (KIK-I; predicted); phosphodiesterase 4C (PDE4C; cAMP specific; predicted); 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (PFKFB1); sterol carrier protein 2 (SCP2). Genes selected for validation involved in stress-metabolic response included complement component 9 (C9); exostoses (multiple) 2 (EXT2); fibrinogen gamma chain (FGG); glutathione peroxidase 3 (GPX3); inter-α (globulin) inhibitor H4 (ITIH4); and pyruvate dehydrogenase phosphatase 1 (PDP1). There were 5 genes that were not functionally similar to any other genes but were of interest because of their increased FC and individual functions. Those genes included cysteine sulfinic acid decarboxylase (CSAD); fatty acid desaturase 2 (FADS2); haptoglobin (HP); thyroid hormone responsive (THRSP). 3Fold change values >0 represent genes upregulated in lowly tolerant ewes compared with highly tolerant ewes. FC values <0 represent genes downregulated in lowly tolerant ewes compared with highly tolerant ewes. View Large Table 3. Comparison of microarray and real-time reverse-transcription-PCR (RT-PCR) fold change (FC) differences between lowly tolerant and highly tolerant ewes1 Gene2  Microarray  Real-time RT-PCR  FC3 ± SE  FC3 ± SE  Metabolic response   AOX1  −1.54 ± 0.54  −2.22 ± 0.96   ASL  −1.41 ± 0.42  −2.13 ± 0.80   CDK9  −1.49 ± 0.44  1.00 ± 0.25   CYP26A1  −1.92 ± 0.80  −3.33 ± 3.00   HOPX  2.25 ± 0.94  2.67 ± 0.90   KIK-I  −1.33 ± 0.32  −1.69 ± 0.30   PDE4C  1.50 ± 0.53  −1.03 ± 0.45   PFKFB1  −1.52 ± 0.51  −2.17 ± 2.80   SCP2  −1.32 ± 0.33  −1.75 ± 0.38  Stress-metabolic response   C9  1.41 ± 0.37  1.25 ± 0.37   EXT2  1.30 ± 0.31  1.00 ± 0.25   FGG  1.37 ± 0.26  1.43 ± 0.42   GPX3  2.64 ± 1.01  4.23 ± 3.40   ITIH4  1.69 ± 0.66  1.93 ± 0.30   PDP1  2.65 ± 1.07  −1.22 ± 0.50  Nonclustered gene   CSAD  −1.69 ± 0.60  −2.22 ± 1.20   FADS2  −2.22 ± 0.89  −3.70 ± 1.50   HP  4.46 ± 1.74  69.07 ± 566.10   THRSP  −2.70 ± 0.94  −3.85 ± 38.20  Gene2  Microarray  Real-time RT-PCR  FC3 ± SE  FC3 ± SE  Metabolic response   AOX1  −1.54 ± 0.54  −2.22 ± 0.96   ASL  −1.41 ± 0.42  −2.13 ± 0.80   CDK9  −1.49 ± 0.44  1.00 ± 0.25   CYP26A1  −1.92 ± 0.80  −3.33 ± 3.00   HOPX  2.25 ± 0.94  2.67 ± 0.90   KIK-I  −1.33 ± 0.32  −1.69 ± 0.30   PDE4C  1.50 ± 0.53  −1.03 ± 0.45   PFKFB1  −1.52 ± 0.51  −2.17 ± 2.80   SCP2  −1.32 ± 0.33  −1.75 ± 0.38  Stress-metabolic response   C9  1.41 ± 0.37  1.25 ± 0.37   EXT2  1.30 ± 0.31  1.00 ± 0.25   FGG  1.37 ± 0.26  1.43 ± 0.42   GPX3  2.64 ± 1.01  4.23 ± 3.40   ITIH4  1.69 ± 0.66  1.93 ± 0.30   PDP1  2.65 ± 1.07  −1.22 ± 0.50  Nonclustered gene   CSAD  −1.69 ± 0.60  −2.22 ± 1.20   FADS2  −2.22 ± 0.89  −3.70 ± 1.50   HP  4.46 ± 1.74  69.07 ± 566.10   THRSP  −2.70 ± 0.94  −3.85 ± 38.20  1Fold change values expressed as a ratio between lowly tolerant and highly tolerant ewes. 2Aldehyde oxidase 1 (AOX1); argininosuccinate lyase (ASL); cyclin-dependent kinase 9 (CDK9); cytochrome P450, family 26, subfamily A, polypeptide 1 (CYP26A1; retinoic acid); homeobox (HOPX); putative steroid dehydrogenase (KIK-I; predicted); phosphodiesterase 4C (PDE4C; cAMP specific; predicted); 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (PFKFB1); sterol carrier protein 2 (SCP2). Genes selected for validation involved in stress-metabolic response included complement component 9 (C9); exostoses (multiple) 2 (EXT2); fibrinogen gamma chain (FGG); glutathione peroxidase 3 (GPX3); inter-α (globulin) inhibitor H4 (ITIH4); and pyruvate dehydrogenase phosphatase 1 (PDP1). There were 5 genes that were not functionally similar to any other genes but were of interest because of their increased FC and individual functions. Those genes included cysteine sulfinic acid decarboxylase (CSAD); fatty acid desaturase 2 (FADS2); haptoglobin (HP); thyroid hormone responsive (THRSP). 3Fold change values >0 represent genes upregulated in lowly tolerant ewes compared with highly tolerant ewes. FC values <0 represent genes downregulated in lowly tolerant ewes compared with highly tolerant ewes. View Large Table 4. Relative expression levels from real-time reverse-transcription-PCR in lowly tolerant and highly tolerant ewes, relative to controls Gene1  Lowly tolerant2 (n = 6)  Highly tolerant2 (n = 6)  SE  P-value3  Metabolic response   AOX1  −2.4  2.6  0.96  0.006   ASL  −1  2.7  0.80  0.015   CDK9  0.2  0.2  0.25  0.995   CYP26A1  −14.4  5.9  3.00  <0.001   HOPX  3.9  0.4  0.90  0.037   KIK-I  −0.4  0.9  0.30  0.020   PDE4C  0.8  0.9  0.45  0.974   PFKFB1  −5.1  6.2  2.80  0.032   SCP2  −0.8  1  0.38  0.012  Stress-metabolic response   C9  0.2  −0.3  0.37  0.618   EXT2  0.2  0.2  0.25  0.989   FGG  0.9  0  0.42  0.355   GPX3  7.7  −4.9  3.40  0.045   ITIH4  0.4  −0.9  0.30  0.025   PDP1  0.7  1.6  0.50  0.419  Nonclustered gene   CSAD  −2.5  1.7  1.20  0.066   FADS2  −3.1  6.2  1.50  0.002   HP  1,606.2  −785.66  566.10  0.024   THRSP  −122.7  44.7  38.20  0.019  Gene1  Lowly tolerant2 (n = 6)  Highly tolerant2 (n = 6)  SE  P-value3  Metabolic response   AOX1  −2.4  2.6  0.96  0.006   ASL  −1  2.7  0.80  0.015   CDK9  0.2  0.2  0.25  0.995   CYP26A1  −14.4  5.9  3.00  <0.001   HOPX  3.9  0.4  0.90  0.037   KIK-I  −0.4  0.9  0.30  0.020   PDE4C  0.8  0.9  0.45  0.974   PFKFB1  −5.1  6.2  2.80  0.032   SCP2  −0.8  1  0.38  0.012  Stress-metabolic response   C9  0.2  −0.3  0.37  0.618   EXT2  0.2  0.2  0.25  0.989   FGG  0.9  0  0.42  0.355   GPX3  7.7  −4.9  3.40  0.045   ITIH4  0.4  −0.9  0.30  0.025   PDP1  0.7  1.6  0.50  0.419  Nonclustered gene   CSAD  −2.5  1.7  1.20  0.066   FADS2  −3.1  6.2  1.50  0.002   HP  1,606.2  −785.66  566.10  0.024   THRSP  −122.7  44.7  38.20  0.019  1Aldehyde oxidase 1 (AOX1); argininosuccinate lyase (ASL); cyclin-dependent kinase 9 (CDK9); cytochrome P450, family 26, subfamily A, polypeptide 1 (CYP26A1; retinoic acid); homeobox (HOPX); putative steroid dehydrogenase (KIK-I; predicted); phosphodiesterase 4C (PDE4C; cAMP specific; predicted); 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (PFKFB1); sterol carrier protein 2 (SCP2). Genes selected for validation involved in stress-metabolic response included complement component 9 (C9); exostoses (multiple) 2 (EXT2); fibrinogen gamma chain (FGG); glutathione peroxidase 3 (GPX3); inter-α (globulin) inhibitor H4 (ITIH4); and pyruvate dehydrogenase phosphatase 1 (PDP1). There were 5 genes that were not functionally similar to any other genes but were of interest because of their increased FC and individual functions. Those genes included cysteine sulfinic acid decarboxylase (CSAD); fatty acid desaturase 2 (FADS2); haptoglobin (HP); thyroid hormone responsive (THRSP). 2Relative gene expression adjusted according to control (baseline) expression values. 3Reflects P-value from LSMEANS pairwise comparison with Tukey adjustment. View Large Table 4. Relative expression levels from real-time reverse-transcription-PCR in lowly tolerant and highly tolerant ewes, relative to controls Gene1  Lowly tolerant2 (n = 6)  Highly tolerant2 (n = 6)  SE  P-value3  Metabolic response   AOX1  −2.4  2.6  0.96  0.006   ASL  −1  2.7  0.80  0.015   CDK9  0.2  0.2  0.25  0.995   CYP26A1  −14.4  5.9  3.00  <0.001   HOPX  3.9  0.4  0.90  0.037   KIK-I  −0.4  0.9  0.30  0.020   PDE4C  0.8  0.9  0.45  0.974   PFKFB1  −5.1  6.2  2.80  0.032   SCP2  −0.8  1  0.38  0.012  Stress-metabolic response   C9  0.2  −0.3  0.37  0.618   EXT2  0.2  0.2  0.25  0.989   FGG  0.9  0  0.42  0.355   GPX3  7.7  −4.9  3.40  0.045   ITIH4  0.4  −0.9  0.30  0.025   PDP1  0.7  1.6  0.50  0.419  Nonclustered gene   CSAD  −2.5  1.7  1.20  0.066   FADS2  −3.1  6.2  1.50  0.002   HP  1,606.2  −785.66  566.10  0.024   THRSP  −122.7  44.7  38.20  0.019  Gene1  Lowly tolerant2 (n = 6)  Highly tolerant2 (n = 6)  SE  P-value3  Metabolic response   AOX1  −2.4  2.6  0.96  0.006   ASL  −1  2.7  0.80  0.015   CDK9  0.2  0.2  0.25  0.995   CYP26A1  −14.4  5.9  3.00  <0.001   HOPX  3.9  0.4  0.90  0.037   KIK-I  −0.4  0.9  0.30  0.020   PDE4C  0.8  0.9  0.45  0.974   PFKFB1  −5.1  6.2  2.80  0.032   SCP2  −0.8  1  0.38  0.012  Stress-metabolic response   C9  0.2  −0.3  0.37  0.618   EXT2  0.2  0.2  0.25  0.989   FGG  0.9  0  0.42  0.355   GPX3  7.7  −4.9  3.40  0.045   ITIH4  0.4  −0.9  0.30  0.025   PDP1  0.7  1.6  0.50  0.419  Nonclustered gene   CSAD  −2.5  1.7  1.20  0.066   FADS2  −3.1  6.2  1.50  0.002   HP  1,606.2  −785.66  566.10  0.024   THRSP  −122.7  44.7  38.20  0.019  1Aldehyde oxidase 1 (AOX1); argininosuccinate lyase (ASL); cyclin-dependent kinase 9 (CDK9); cytochrome P450, family 26, subfamily A, polypeptide 1 (CYP26A1; retinoic acid); homeobox (HOPX); putative steroid dehydrogenase (KIK-I; predicted); phosphodiesterase 4C (PDE4C; cAMP specific; predicted); 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 (PFKFB1); sterol carrier protein 2 (SCP2). Genes selected for validation involved in stress-metabolic response included complement component 9 (C9); exostoses (multiple) 2 (EXT2); fibrinogen gamma chain (FGG); glutathione peroxidase 3 (GPX3); inter-α (globulin) inhibitor H4 (ITIH4); and pyruvate dehydrogenase phosphatase 1 (PDP1). There were 5 genes that were not functionally similar to any other genes but were of interest because of their increased FC and individual functions. Those genes included cysteine sulfinic acid decarboxylase (CSAD); fatty acid desaturase 2 (FADS2); haptoglobin (HP); thyroid hormone responsive (THRSP). 2Relative gene expression adjusted according to control (baseline) expression values. 3Reflects P-value from LSMEANS pairwise comparison with Tukey adjustment. View Large Metabolic Response Cluster AOX1 Aldehyde oxidase 1 is a molybdo-flavo enzyme involved in the oxidation of aldehydes and xenobiotic N-heterocyclic compounds (Al-Salmy, 2001; Rivera et al., 2005). Furthermore, decreased expression of AOX1 may contribute to convulsions associated with NO3− toxicity, as AOX1 is oxidized by NO2− and is involved in serotonin degradation (Wood, 1980). In this study, AOX1 was downregulated 2.2-fold (Table 3) in the lowly tolerant ewes, indicating those ewes were more prone to the neurological effects associated with increased dietary NO3−, such as seizures and coma. ASL Argininosuccinate lyase is an enzyme of the urea cycle and is involved in the cleavage of argininosuccinate to arginine and fumarate; argininosuccinate is then reformed for the urea cycle (Sherwin and Natelson, 1975). Plasma urea N (PUN) concentrations were least (20.6 ± 0.90 mg/dL) in the highly tolerant ewes and differed (P = 0.02) from control ewes (24.6 ± 0.90 mg/dL); PUN concentrations were intermediate in lowly tolerant ewes (Cockrum et al., 2010). The 2.1-fold decrease (Table 3) in ASL expression in lowly tolerant ewes may be associated with the greater PUN concentrations. This decreased ASL expression is hypothesized to result from decreased metabolism of ingested NO3−, as lowly tolerant ewes consumed less (0.49% ± 0.05 of DM) NO3− than highly tolerant ewes (1.5% ± 0.04 of DM). Argininosuccinate lyase regulation patterns reflected those observed for PFKFB1 expression; PFKFB1 and ASL are linked by the TCA cycle. CYP26A1 As part of the monoxygenase system, CYP26A1 enzymes metabolize retinoic acid, a known regulator of gene expression and the cell cycle (Thatcher and Isoherranen, 2009). The highly tolerant ewes in the present study consumed approximately 82% of the NO3− supplement offered daily compared with only 23% by the lowly tolerant ewes (Cockrum et al., 2010); the highly tolerant ewes also showed a 3.3-fold greater (Table 3) expression of CYP26A1 compared with the lowly tolerant ewes, which may indicate a retinoid build-up (Nilsson and Hakansson, 2002). Nitrate toxicity has been implicated in decreasing circulating retinol concentrations by inhibition of the thyroid gland. Circulating retinol concentrations were not affected among treatment groups in this trial (Cockrum et al., 2010), but the monoxygenase enzyme involved in retinoic acid metabolism was downregulated in the lowly tolerant ewes. This decrease in retinoic acid metabolism could lead to the accumulation of retinoic acid, and ultimately hepatoxicity (Levin, 1995). HOPX The role of HOPX genes in controlling transcription factors and genetically regulating many biological pathways has been well established. The HOPX gene was expressed 2.6-fold more (Table 3) in lowly tolerant ewes than highly tolerant ewes. Although other genes or pathways potentially affected downstream by the upregulation of HOPX in lowly tolerant ewes, or alternatively the downregulation in highly tolerant ewes, are unknown, translation and posttranscriptional effects of altered expression of HOPX likely affect other metabolic pathways associated with the reduction of NO3− to NO2−. KIK-I Gene ontology of KIK-I includes cellular binding and oxidoreductase activity. The KIK-I gene was downregulated 1.7-fold (Table 3) in the lowly tolerant ewes. Because of limited knowledge about the function of KIK-1, its role in NO3− toxicity is unclear. PFKFB1 Previous research in mice has associated hypoxic regulation with PFKFB1 expression (Minchenko et al., 2003; Bobarykina et al., 2006; Bartrons and Caro, 2007). The expression of PFKFB1 was upregulated 2.2-fold (Table 3) in highly tolerant ewes. Those ewes did not outwardly display signs of hypoxia despite consuming 3 times more dietary NO3− than lowly tolerant ewes. The upregulation of PFKFB1, however, indicates that highly tolerant ewes were showing signs of hypoxia at a molecular level in response to the increased NO3− that may have been limiting O2 availability. SCP2 Sterol carrier protein 2 is involved in hepatic lipid metabolism and homeostasis through intracellular cholesterol transportation (Fuchs et al., 2001). In this study, highly tolerant ewes had a 1.8-fold increase in expression of SCP2 (Table 3), suggesting an increase in lipid metabolism in those animals. The highly tolerant ewes consumed >3.5 times more of the carbohydrate-based supplement offered daily than lowly tolerant ewes (Cockrum et al., 2010), likely contributing to the increased expression of SCP2 in highly tolerant ewes. Stress-Metabolic Response Cluster GPX3 Glutathione peroxidase 3 functions as an extracellular antioxidant, protecting the body from oxidative damage that induces cellular insult. A small decrease in GPX3 expression can affect the ability of a cell to prevent toxicity (Marshall et al., 1999). Vitamin E, selenium, and GPX3 interact to protect the body from oxidative damage (Hoekstra, 1975). Highly tolerant ewes consumed greater NO3− than lowly tolerant ewes, but GPX3 relative gene expression was decreased by 4.2-fold (Table 3) in highly tolerant animals. Lowly tolerant ewes may be more susceptible to oxidative damage induced by NO3−, prompting an increase in GPX3 expression. ITIH4 Inter-α (globulin) inhibitor H4 has been implicated as a positive acute-phase protein in cattle (Pineiro et al., 2004). The ITIH4 gene was upregulated 1.9-fold in lowly tolerant ewes (Table 3), indicating an inflammatory reaction consistent with a toxic response to elevated dietary NO3− intake. In contrast, the highly tolerant ewes did not outwardly exhibit any signs of NO3− toxicity, and their reduced ITIH4 expression indicates a lesser, or perhaps even a lack of, inflammatory response despite consuming greater amounts of dietary NO3−. Nonclustered Genes CSAD Cysteine sulfinic acid decarboxylase is involved in the decarboxylation of cysteine sulfinic acid to form hypotaurine, and ultimately taurine end products (Worden and Stipanuk, 1985; Do and Tappaz, 1996). The CSAD gene was downregulated by 2.2-fold in lowly tolerant ewes (Table 3); however, the role of CSAD in the response to increased dietary NO3− is not known. FADS2 Fatty acid desaturase 2 plays a role in lipid biosynthesis of PUFA into gamma-linoleic acid and stearidonic acid (Glaser et al., 2009). The FADS2 gene was downregulated by 3.7-fold in the lowly tolerant ewes (Table 3). Although the bromegrass hay alone met NRC requirements for nonlactating ewes, the lowly tolerant ewes consumed 58.6% less supplement than the highly tolerant ewes (Cockrum et al., 2010). The decrease in supplement intake of lowly tolerant ewes was paralleled by a decrease in carbohydrate intake, which may have caused the downregulation of FADS2, and ultimately the ability of ewes to tolerate subacute amounts of dietary NO3−. Because the reduction of NO3− to NO2− is the rate-limiting step, insufficient carbohydrate intake can further impede the reduction process and result in NO2− accumulation in the blood (Burrows et al., 1987; Van Soest, 1994). HP Haptoglobin binds to free hemoglobin, allowing apoptotic enzymes to break down hemoglobin. Like ITIH4, HP is an acute-phase protein with increased expression during inflammation caused by severe infection (Dobryszycka, 1997; Wassell, 2000). In ruminants, HP is a major acute-phase protein and has been suggested to serve as a diagnostic marker for acute inflammation such as mastitis and respiratory diseases. Expression of HP was upregulated 4.5-fold in the lowly tolerant ewes in the microarray analysis and 69.1-fold in the real-time RT-PCR analysis (Table 3), the greatest FC detected in either analysis. Variation of expression was also greatest for HP and was observed both across and within treatments, indicating that HP expression varies among individual animals regardless of treatment. Real-time RT-PCR results were confirmed with 2 additional plates performed in duplicate. Within sample variation of HP expression was small, confirming that variation was not due to laboratory error. The substantial upregulation of HP in the lowly tolerant ewes strongly supports the hypothesis that these animals experienced an acute immune response to the increased dietaryNO3−. THRSP The thyroid hormone responsive gene is a positive and negative cofactor to thyroid receptor and is involved in the synthesis of long-chain fatty acids (Chou et al., 2007). The expression of THRSP was downregulated 3.8-fold in lowly tolerant ewes (Table 3), similar to FADS2 and SCP2, indicating that production and metabolism of fatty acids was decreased in the lowly tolerant ewes. In addition, THRSP and CYP26A1 both interact with the thyroid gland and were downregulated in the lowly tolerant ewes, indicating that exposure to subacute dietary NO3− may affect thyroid gland function. Conclusions Changes in gene expression between lowly tolerant and highly tolerant ewes detected by microarray analysis were confirmed by real-time RT-PCR analysis for 11 of 22 genes. Even when differences in expression between lowly tolerant and highly tolerant ewes were not confirmed (P > 0.05), FC and direction of regulation were similar between both analyses. Genes confirmed as differentially regulated in highly tolerant and lowly tolerant ewes included those involved in metabolism and stress response, specifically through thyroid function, immune response and signaling, lipid metabolism and synthesis, glucose regulation, and oxidative functions. The differences in gene expression reflected the phenotypic signs of subacute NO3− toxicity previously reported in lowly tolerant ewes, such as depressed immune function, BW loss, lethargy, and decreased feed intake (Cockrum et al., 2010). Further efforts are needed to fully understand the physiological response to increased dietary NO3−, a critical step for the development of NO3− toxicity treatment and prevention strategies, and ultimately biomarkers conferring resistance to NO3− toxicity. Additionally, the expression of genes identified as differentially regulated after NO3− exposure should be assessed for their a priori expression to identify genes that may confer a latent resistance to NO3− toxicity. LITERATURE CITED Al-Salmy H. S. 2001. Individual variation in hepatic aldehyde oxidase activity. IUBMB Life  51: 249– 253. [PubMed] Google Scholar CrossRef Search ADS PubMed  Anderson T. A. Taylor R. E. Diven R. H. Hubbert F.Jr. Hale W. H. 1962. Reliability of the liver biopsy technique for estimating hepatic vitamin A. J. Anim. Sci.  21: 369– 372. Google Scholar CrossRef Search ADS   Bartrons R. Caro J. 2007. Hypoxia, glucose metabolism and the Warburg's effect. J. Bioenerg. Biomembr.  39: 223– 229. [PubMed] Google Scholar CrossRef Search ADS PubMed  Benjamini Y. Hochberg Y. 1995. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc., B  57: 289– 300. Bobarykina A. Y. Minchenko D. O. Opentanova I. L. Moenner M. Caro J. Esumi H. Minchenko O. H. 2006. Hypoxic regulation of PFKFB-3 and PFKFB-4 gene expression in gastric and pancreatic cancer cell lines and expression of PFKFB genes in gastric cancers. Acta Biochim. Pol.  53: 789– 799. [PubMed] Google Scholar PubMed  Burrows G. E. Horn G. W. McNew R. W. Croy L. I. Keeton R. D. Kyle J. 1987. The prophylactic effect of corn supplementation on experimental nitrate intoxication in cattle. J. Anim. Sci.  64: 1682– 1689. [PubMed] Google Scholar CrossRef Search ADS PubMed  Chou W. Y. Cheng Y. S. Ho C. L. Liu S. T. Liu P. Y. Kuo C. C. Chang H. P. Chen Y. H. Chang G. G. Huang S. M. 2007. Human spot 14 protein interacts physically and functionally with the thyroid receptor. Biochem. Biophys. Res. Commun.  357: 133– 138. [PubMed] Google Scholar CrossRef Search ADS PubMed  Cockrum R. R. Austin K. J. Ludden P. A. Cammack K. M. 2010. Effect of subacute dietary nitrate on production traits and plasma analytes in Suffolk ewes. Animal  4: 702– 708. Google Scholar CrossRef Search ADS PubMed  Decker, J. E., J. C. Pires, G. C. Conant, S. D. McKay, M. P. Heaton, K. Chen, A. Cooper, J. Vilkki, C. M. Seabury, A. R. Caetano, G. S. Johnson, R. A. Brenneman, O. Hanotte, L. S. Eggert, P. Wiener, J. J. Kim, K. S. Kim, T. S. Sonstegard, C. P. Van Tassell, H. L. Neibergs, J. C. McEwan, R. Brauning, L. L. Coutinho, M. E. Babar, G. A. Wilson, M. C. McClure, M. M. Rolf, J. W. Kim, R. D. Schnabel, and J. F. Taylor 2009. Resolving the evolution of extant and extinct ruminants with high-throughput phylogenomics. Proc. Natl. Acad. Sci., USA.  Proc. Natl. Acad. Sci. USA. Oct. 21, 2009. https://doi.org/10.1073/pnas.0904691106. Google Scholar CrossRef Search ADS   Dennis G.Jr. Sherman B. T. Hosack D. A. Yang J. Gao W. Lane H. C. Lempicki R. A. 2003. David: Database for annotation, visualization, and integrated discovery. Genome Biol.  4: 3. [PubMed] Google Scholar CrossRef Search ADS   Diez-Tascón C. Keane O. M. Wilson T. Zadissa A. Hyndman D. L. Baird D. B. McEwan J. C. Crawford A. M. 2005. Microarray analysis of selection lines from outbred populations to identify genes involved with nematode parasite resistance in sheep. Physiol. Genomics  21: 59– 69. [PubMed] Google Scholar CrossRef Search ADS PubMed  Do K. Q. Tappaz M. L. 1996. Specificity of cysteine sulfinate decarboxylase (CSD) for sulfur-containing amino-acids. Neurochem. Int.  28: 363– 371. [PubMed] Google Scholar CrossRef Search ADS PubMed  Dobryszycka W. 1997. Biological functions of haptoglobin—New pieces to an old puzzle. Eur. J. Clin. Chem. Clin. Biochem.  35: 647– 654. [PubMed] Google Scholar PubMed  Eaton, D. L., and S. G. Gilbert 2008. Principles of Toxicology. Pages 11– 43 in Casarett and Doull's Toxicology: The Basic Science of Poisons.  C. D. Klaassen ed. The McGraw-Hill Companies Inc., New York, NY. Ferreira A. V. van der Merwe H. J. Slippers S. C. 1996. A technique for obtaining liver biopsies from mature sheep. Small Rumin. Res.  22: 89– 92. Google Scholar CrossRef Search ADS   Fuchs M. Hafer A. Munch C. Kannenberg F. Teichmann S. Scheibner J. Stange E. F. Seedorf U. 2001. Disruption of the sterol carrier protein 2 gene in mice impairs biliary lipid and hepatic cholesterol metabolism. J. Biol. Chem.  276: 48058– 48065. [PubMed] Google Scholar CrossRef Search ADS PubMed  Glaser C. Heinrich J. Koletzko B. 2009. Role of FADS1 and FADS2 polymorphisms in polyunsaturated fatty acid metabolism. Metabolism  https://doi.org/10.1016/j.metabol.2009.10.022. Hoekstra W. G. 1975. Biochemical function of selenium and its relation to vitamin E. Fed. Proc.  34: 2083– 2089. [PubMed] Google Scholar PubMed  Huang D. W. Sherman B. T. Lempicki R. A. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc.  4: 44– 57. [PubMed] Google Scholar CrossRef Search ADS PubMed  Levin A. A. 1995. Receptors as tools for understanding the toxicity of retinoids. Toxicol. Lett.  82–83: 91– 97. [PubMed] Google Scholar CrossRef Search ADS PubMed  Marshall K. A. Reist M. Jenner P. Halliwell B. 1999. The neuronal toxicity of sulfite plus peroxynitrite is enhanced by glutathione depletion: Implications for Parkinson's disease. Free Radic. Biol. Med.  27: 515– 520. [PubMed] Google Scholar CrossRef Search ADS PubMed  Minchenko O. Opentanova I. Caro J. 2003. Hypoxic regulation of the 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase gene family (PFKFB-1–4) expression in vivo. FEBS Lett.  554: 264– 270. [PubMed] Google Scholar CrossRef Search ADS PubMed  Nielsen, D. B., and L. F. James 1992. Economic impact of poisonous plants on livestock production. Pages 3-10 in The ecology and economic impact of poisonous plants on livestock production.  L. F. James, R. F. Keeler, E. M. Bailey Jr., P. R. Cheeke, and M. P. Hagarty ed. Iowa State Univ. Press, Ames. Nilsson C. B. Hakansson H. 2002. The retinoid signaling system—A target in dioxin toxicity. Crit. Rev. Toxicol.  32: 211– 232. [PubMed] Google Scholar CrossRef Search ADS PubMed  Pineiro M. Andres M. Iturralde M. Carmona S. Hirvonen J. Pyorala S. Heegaard P. M. Tjornehoj K. Lampreave F. Pineiro A. Alava M. A. 2004. ITIH4 (inter-alpha-trypsin inhibitor heavy chain 4) is a new acute-phase protein isolated from cattle during experimental infection. Infect. Immun.  72: 3777– 3782. [PubMed] Google Scholar CrossRef Search ADS PubMed  Rivera S. P. Choi H. H. Chapman B. Whitekus M. J. Terao M. Garattini E. Hankinson O. 2005. Identification of aldehyde oxidase 1 and aldehyde oxidase homologue 1 as dioxin-inducible genes. Toxicology  207: 401– 409. [PubMed] Google Scholar CrossRef Search ADS PubMed  Rozen S. Skaletsky H. 2000. Primer3 on the WWW for general users and for biologist programmers. Methods Mol. Biol.  132: 365– 386. [PubMed] Google Scholar PubMed  Sherwin J. E. Natelson S. 1975. Serum and erythrocyte argininosuccinate lyase assay by NADH fluorescence generated from formed fumarate. Clin. Chem.  21: 230– 234. [PubMed] Google Scholar PubMed  Thatcher J. E. Isoherranen N. 2009. The role of CYP26 enzymes in retinoic acid clearance. Expert Opin. Drug Metab. Toxicol.  5: 875– 886. [PubMed] Google Scholar CrossRef Search ADS PubMed  Van Soest, P. J. 1994. Nutritional Exology of the Ruminant.  2nd ed. Cornell Univ. Press, Ithaca, NY. Wassell J. 2000. Haptoglobin: Function and polymorphism. Clin. Lab.  46: 547– 552. [PubMed] Google Scholar PubMed  Wood P. A. 1980. The molecular pathology of chronic nitrate intoxication in domestic animals: A hypothesis. Vet. Hum. Toxicol.  22: 26– 27. [PubMed] Google Scholar PubMed  Worden J. A. Stipanuk M. H. 1985. A comparison by species, age and sex of cysteine sulfinate decarboxylase activity and taurine concentration in liver and brain of animals. Comp. Biochem. Physiol. B  82: 233– 239. [PubMed] Google Scholar CrossRef Search ADS PubMed  Yaremcio, B. 1991. Nitrate poisoning and feeding nitrate feeds to livestock.  Agric. Facts. Agdex 400/60-1:1–5. Alberta Agric. Food Rural Dev., Alberta, Canada. American Society of Animal Science

Hatcher, S.;Atkins, K. D.;Safari, E.

doi: 10.2527/jas.2009-2461pmid: 20562357

ABSTRACT Direct and maternal components of variance for lamb survival to birth, 7 d, and weaning (110 d) were estimated by REML procedures in a flock of Australian Merino sheep. A total of 14,142 lambs, the progeny of 421 sires and 3,666 dams, born between 1975 and 1983 were available for analysis. The study has produced some of the most precise estimates of genetic parameters for lamb survival in the Australian Merino. Very low heritabilities for lamb viability (0.03) and the performance of the dam or ewe rearing ability (0.07) suggest that genetic solutions to lamb survival are unlikely to be significant. But, despite the low heritabilities, there is still potential for improvement through selective breeding. The estimated repeatability of at least 0.10 shows that multiple records on the rearing ability of a ewe over its lifetime can increase selection accuracy. More importantly, such repeatabilities indicate that current generation improvement can be achieved by culling ewes from the breeding flock with poor rearing ability. Despite maternal bond score and lamb birth weight being highly repeatable and moderately heritable traits, correlations with lamb survival were essentially zero. These traits therefore have no value as indirect selection criteria for Merino lamb survival. INTRODUCTION Lamb loss between birth and weaning in Australian Merino sheep has been estimated to be more than 30% based on discrepancies between reproductive potential based on ultrasound pregnancy scanning and achieved marking percentages in studies of commercial flocks (Kilgour, 1992; Kleemann and Walker, 2005). Given the complexity of lamb survival and the extensive Merino production systems in Australia, selecting sheep with a genetic propensity for lamb survival is a very beneficial and desirable option. Including lamb survival in the breeding objective offers a permanent low-cost solution provided there is genetic variation and effective selection criteria are developed with favorable correlations with survival. Genetic improvement of reproduction has typically focused on selection for the ability of ewes to rear multiples (Atkins, 1980; Cloete and Scholtz, 1998; Cloete et al., 2004). However, improvements in lamb survival are unlikely if litter size is increased through selection without any regard to whether the additional lambs born can be successfully reared (Lindsay, 1982) as multiple-born lambs are more likely to die than singles (Hatcher et al., 2009). When determining the contribution of genetic variation to lamb survival, it is necessary to consider the direct genetic effect due to the genes of the lamb and the maternal effect of the dam, which has genetic and environmental components (Bradford, 1972). Direct heritability of survival in Merino sheep, or lamb viability, has been estimated to be very low (Piper and Bindon, 1977) with low estimates reported in other breeds (Barwick et al., 1990; Gama et al., 1991; Lopez-Villalobos and Garrick, 1999; Morris et al., 2000; Safari et al., 2005b; Riggio et al., 2008). Most reports of the maternal component of survival, or ewe rearing ability, suggest that it may be greater than the direct genetic effect (Lopez-Villalobos and Garrick, 1999; Morris et al., 2000; Riggio et al., 2008), but not all concur (Barwick et al., 1990; Burfening, 1993) due to differences in analytical techniques and models. More recently it has been suggested that genetic variation in lamb viability is influenced by lamb age with estimates declining with time after birth (Southey et al., 2001; Sawalha et al., 2007; Riggio et al., 2008). This study describes genetic analyses undertaken on a mixed bloodline genetic resource flock that represented the major strains and bloodlines within the Australian sheep industry. A detailed description of the data and fixed effects was reported by Hatcher et al. (2009), who identified a nonlinear relationship between birth weight and lamb survival with light and heavy birth weight lambs more likely to die. This paper presents estimates of the magnitude of direct and maternal genetic variation of lamb survival in Australian Merino sheep. Lamb deaths were classified into periods of loss between birth and weaning so that the influence of age on genetic variation could also be investigated. The genetic relationships between lamb birth weight and maternal behavior and lamb survival at each time period were also estimated. MATERIALS AND METHODS Although there was no Industry & Investment NSW ethics committee in existence at the time of this study, all procedures reported in this paper would meet the current guidelines stipulated by the Australian Code of Practice for the Use of Animals for Scientific Research. Data were collected from a flock maintained by New South Wales Department of Primary Industries at the Agricultural Research Centre, Trangie (31°59′ S, 147°57′ E) on the central western plains of New South Wales, Australia. The sheep and the lamb survival records were described by Hatcher et al. (2009). The pasture was predominantly native grasses with some lucerne pastures grown in rotation with a dry land cropping program. General animal husbandry was as described by Morley (1951) and the composition of the 15 subflocks and general management by Mortimer and Atkins (1989). Briefly, the predominant Merino strains in Australia (fine-wool 2 bloodlines, medium-non-Peppin 2 bloodlines, medium Peppin 10 bloodlines, and strong wool or broad micron 1 bloodline) were represented by the 15 subflocks with a total of 14,142 lambs born between 1975 and 1983 available for analysis. These lambs were the progeny of 421 sires and 3,666 dams. The annual size of each subflock was 3 rams and 100 ewes with rams used for 1 mating only with links between subflocks. Ewes were first mated at 2 yr of age and allowed 5 further annual opportunities to lamb with culling only due to extreme physical disability or black-wooled progeny. Lambing occurred during July and August each year with lambing rounds conducted twice daily. During each lambing round, lambs were identified with their dams within 12 h of birth, ear-tagged, and weighed. Any dead lambs were noted at this time. A maternal bond score was assigned to each ewe that coded the behavior of the ewe toward the lamb at tagging and ranged from 1 (good, maintaining close contact with lamb) to 4 (poor, ignoring lamb). A roll call of surviving lambs was made at approximately 7 d of age (range 5 to 8 d) when ewes and lambs were moved from the lambing paddocks, after marking at approximately 30 d of age (range 3 and 5 wk) and at weaning when the average age was 110 d. Variance components for cumulative survival at birth (1 d), 7 d, marking (30 d), and weaning (110 d), and survival within these time periods were obtained by REML procedures with a linear mixed animal model using ASReml (Gilmour et al., 2006). An earlier phenotypic analysis of these data identified low repeatability of dam performance or ewe rearing ability in the 7- to 30-d and 30- to 110-d periods, indicating that lamb survival beyond the first 7 d of age is largely outside the control of the ewe (Hatcher et al., 2009). Therefore these 2 periods were combined into one for the genetic analysis (i.e., 7 to 110 d). Safari et al. (2005a) reported that direct and maternal genetic variances can be underestimated when analyzed using an animal model on the logit scale leading to smaller estimates of heritabilities. However, Lopez-Villalobos and Garrick (1999) reported close agreement between their logit and probit transformed data and untransformed reports from the literature. In our study, data were not transformed before analysis. The univariate models for each lamb survival trait included the overall mean, fixed effects of birth year (9 levels: 1975 to 1983), birth type (3 levels: single, twin, or greater order multiple including 4 litters of quadruplets and 1 litter of quintuplets), flock (15 levels), sex (2 levels), and age of dam (6 levels: 2 to ≥7 yr), which were all significant factors in the earlier phenotypic analysis of these data (Hatcher et al., 2009). The random components included effects for direct genetic (σ2d), maternal genetic (σ2m), and maternal permanent environment (σ2pe) effects and the covariance between additive and dam genetic variances (σdm). The phenotypic variance was calculated as the sum of each of the variance components (σ2p = σ2d + σ2m + σ2pe + σdm + σ2e), where σ2e is the error or remainder. Estimates of the direct and maternal heritability were obtained by dividing the respective variance component by the phenotypic variance. The repeatability of ewe rearing ability was estimated as the proportion of phenotypic variation accounted for by direct genetic and maternal effects, their covariance, and the maternal permanent environment [(σ2d + σ2m + σdm + σ2pe)/σ2p]. Phenotypic and genetic correlations between the various survival traits were estimated using a series of bivariate analyses. The appropriate covariance, phenotypic or genetic, between each pair of survival traits, x and y, was divided by the square root of the product of the phenotypic variance of each trait (rxy = σxy/√(σ2x·σ2y). The maternal genetic and maternal permanent environment correlations were calculated in the same manner. Variation in maternal bond score and lamb birth weight was partitioned using the same univariate model applied to the survival traits with the direct heritability, maternal heritability, and repeatability calculated from the variance components as described above. Phenotypic, genetic, maternal genetic, and maternal permanent environment correlations were estimated between maternal bond score, lamb birth weight, and each of the survival traits as outlined previously. RESULTS Lamb Survival The largest variance component for cumulative survival and survival within time periods was the maternal permanent environment except for survival between 7 and 110 d (Table 1). For cumulative survival, the maternal permanent environment accounted for between 5.8 and 9.5% of the phenotypic variance, but there was no consistent trend with increasing lamb age. The maternal genetic variance was the next largest component with the direct genetic effects accounting for the smallest proportion of the observed variation. Survival within 1 to 7 d of age followed the same trend, but the direct genetic variance was greater than the direct maternal variance between 7 d of age and weaning. There was evidence of significant maternal permanent environmental variance up to 7 d, although it was effectively zero between 7 and 110 d. The significant cumulative maternal permanent environmental variance to 30 and 110 d was carried over from the early postnatal period. The covariance between direct and maternal effects was negative in both time periods and at all ages but not significantly different from zero. The amount of phenotypic variation accounted for by the direct, maternal, and maternal permanent environment components across all ages and time periods shows that selection on lamb survival will result in little genetic gain in the trait. Table 1. Mean lamb survival and estimates of phenotypic variance (σ2p), additive direct genetic variance (σ2d), maternal genetic variance (σ2m), covariance (σdm), and dam permanent environmental variance (σ2pe) Trait  Mean  σ2p  σ2d  σ2m  σdm1  σ2pe  Survival within time periods               1 to 7 d  0.858  0.115 ± 0.001  0.002 ± 0.001  0.005 ± 0.002  −0.001 ± 0.001  0.007 ± 0.002   7 to 110 d  0.893  0.092 ± 0.001  0.005 ± 0.001  0.003 ± 0.001  −0.002 ± 0.002  0.000 ± 0.001  Cumulative survival from birth to               1 d  0.944  0.053 ± 0.001  0.002 ± 0.001  0.004 ± 0.001  −0.002 ± 0.001  0.004 ± 0.001   7 d  0.810  0.148 ± 0.002  0.004 ± 0.002  0.006 ± 0.002  −0.002 ± 0.002  0.014 ± 0.002   30 d  0.785  0.161 ± 0.002  0.003 ± 0.002  0.008 ± 0.003  −0.002 ± 0.002  0.012 ± 0.002   110 d  0.724  0.189 ± 0.002  0.005 ± 0.002  0.006 ± 0.003  −0.001 ± 0.002  0.011 ± 0.002  Trait  Mean  σ2p  σ2d  σ2m  σdm1  σ2pe  Survival within time periods               1 to 7 d  0.858  0.115 ± 0.001  0.002 ± 0.001  0.005 ± 0.002  −0.001 ± 0.001  0.007 ± 0.002   7 to 110 d  0.893  0.092 ± 0.001  0.005 ± 0.001  0.003 ± 0.001  −0.002 ± 0.002  0.000 ± 0.001  Cumulative survival from birth to               1 d  0.944  0.053 ± 0.001  0.002 ± 0.001  0.004 ± 0.001  −0.002 ± 0.001  0.004 ± 0.001   7 d  0.810  0.148 ± 0.002  0.004 ± 0.002  0.006 ± 0.002  −0.002 ± 0.002  0.014 ± 0.002   30 d  0.785  0.161 ± 0.002  0.003 ± 0.002  0.008 ± 0.003  −0.002 ± 0.002  0.012 ± 0.002   110 d  0.724  0.189 ± 0.002  0.005 ± 0.002  0.006 ± 0.003  −0.001 ± 0.002  0.011 ± 0.002  1Covariance between additive and dam genetic variance (σdm) was not significant (P > 0.05). View Large Table 1. Mean lamb survival and estimates of phenotypic variance (σ2p), additive direct genetic variance (σ2d), maternal genetic variance (σ2m), covariance (σdm), and dam permanent environmental variance (σ2pe) Trait  Mean  σ2p  σ2d  σ2m  σdm1  σ2pe  Survival within time periods               1 to 7 d  0.858  0.115 ± 0.001  0.002 ± 0.001  0.005 ± 0.002  −0.001 ± 0.001  0.007 ± 0.002   7 to 110 d  0.893  0.092 ± 0.001  0.005 ± 0.001  0.003 ± 0.001  −0.002 ± 0.002  0.000 ± 0.001  Cumulative survival from birth to               1 d  0.944  0.053 ± 0.001  0.002 ± 0.001  0.004 ± 0.001  −0.002 ± 0.001  0.004 ± 0.001   7 d  0.810  0.148 ± 0.002  0.004 ± 0.002  0.006 ± 0.002  −0.002 ± 0.002  0.014 ± 0.002   30 d  0.785  0.161 ± 0.002  0.003 ± 0.002  0.008 ± 0.003  −0.002 ± 0.002  0.012 ± 0.002   110 d  0.724  0.189 ± 0.002  0.005 ± 0.002  0.006 ± 0.003  −0.001 ± 0.002  0.011 ± 0.002  Trait  Mean  σ2p  σ2d  σ2m  σdm1  σ2pe  Survival within time periods               1 to 7 d  0.858  0.115 ± 0.001  0.002 ± 0.001  0.005 ± 0.002  −0.001 ± 0.001  0.007 ± 0.002   7 to 110 d  0.893  0.092 ± 0.001  0.005 ± 0.001  0.003 ± 0.001  −0.002 ± 0.002  0.000 ± 0.001  Cumulative survival from birth to               1 d  0.944  0.053 ± 0.001  0.002 ± 0.001  0.004 ± 0.001  −0.002 ± 0.001  0.004 ± 0.001   7 d  0.810  0.148 ± 0.002  0.004 ± 0.002  0.006 ± 0.002  −0.002 ± 0.002  0.014 ± 0.002   30 d  0.785  0.161 ± 0.002  0.003 ± 0.002  0.008 ± 0.003  −0.002 ± 0.002  0.012 ± 0.002   110 d  0.724  0.189 ± 0.002  0.005 ± 0.002  0.006 ± 0.003  −0.001 ± 0.002  0.011 ± 0.002  1Covariance between additive and dam genetic variance (σdm) was not significant (P > 0.05). View Large Some ewes repeatedly lose lambs in the early postnatal period leading to dam repeatabilities, ewe rearing ability, greater than 0.10 in the first week of life and the greatest repeatabilities for early cumulative survival at 1 and 7 d (Table 2). But repeatability in the later postnatal period (between 7 and 110 d) was about one-half that of the early postnatal period, indicating that beyond 7 d lamb survival is largely outside the control of the ewe. Table 2. Estimates of direct and maternal heritability and dam repeatability for lamb survival within each time period Trait  Direct heritability  Maternal heritability  Dam repeatability  Survival within time periods   1 to 7 d  0.022 ± 0.010  0.045 ± 0.017  0.120 ± 0.011   7 to 110 d  0.053 ± 0.014  0.029 ± 0.016  0.060 ± 0.013  Cumulative survival from birth to   1 d  0.035 ± 0.011  0.066 ± 0.018  0.140 ± 0.012   7 d  0.028 ± 0.010  0.040 ± 0.017  0.147 ± 0.011   30 d  0.020 ± 0.010  0.048 ± 0.017  0.129 ± 0.011   110 d  0.027 ± 0.010  0.034 ± 0.015  0.110 ± 0.011  Trait  Direct heritability  Maternal heritability  Dam repeatability  Survival within time periods   1 to 7 d  0.022 ± 0.010  0.045 ± 0.017  0.120 ± 0.011   7 to 110 d  0.053 ± 0.014  0.029 ± 0.016  0.060 ± 0.013  Cumulative survival from birth to   1 d  0.035 ± 0.011  0.066 ± 0.018  0.140 ± 0.012   7 d  0.028 ± 0.010  0.040 ± 0.017  0.147 ± 0.011   30 d  0.020 ± 0.010  0.048 ± 0.017  0.129 ± 0.011   110 d  0.027 ± 0.010  0.034 ± 0.015  0.110 ± 0.011  View Large Table 2. Estimates of direct and maternal heritability and dam repeatability for lamb survival within each time period Trait  Direct heritability  Maternal heritability  Dam repeatability  Survival within time periods   1 to 7 d  0.022 ± 0.010  0.045 ± 0.017  0.120 ± 0.011   7 to 110 d  0.053 ± 0.014  0.029 ± 0.016  0.060 ± 0.013  Cumulative survival from birth to   1 d  0.035 ± 0.011  0.066 ± 0.018  0.140 ± 0.012   7 d  0.028 ± 0.010  0.040 ± 0.017  0.147 ± 0.011   30 d  0.020 ± 0.010  0.048 ± 0.017  0.129 ± 0.011   110 d  0.027 ± 0.010  0.034 ± 0.015  0.110 ± 0.011  Trait  Direct heritability  Maternal heritability  Dam repeatability  Survival within time periods   1 to 7 d  0.022 ± 0.010  0.045 ± 0.017  0.120 ± 0.011   7 to 110 d  0.053 ± 0.014  0.029 ± 0.016  0.060 ± 0.013  Cumulative survival from birth to   1 d  0.035 ± 0.011  0.066 ± 0.018  0.140 ± 0.012   7 d  0.028 ± 0.010  0.040 ± 0.017  0.147 ± 0.011   30 d  0.020 ± 0.010  0.048 ± 0.017  0.129 ± 0.011   110 d  0.027 ± 0.010  0.034 ± 0.015  0.110 ± 0.011  View Large The heritability of lamb survival was low for direct (0.02 to 0.05) and maternal genetic (0.03 to 0.07) effects (Table 2). Maternal heritability between 1 and 7 d was double that of the direct heritability, but the reverse occurred between 7 and 110 d when the direct heritability (0.05) was greater than the maternal heritability (0.03). For cumulative survival, the direct heritability was consistently about a one-half to one-third less than the maternal heritability at all lamb ages with no consistent trend evident with increasing lamb age. As expected, the phenotypic and genetic correlations for the direct genetic effect across time periods were all positive and high (Table 3) because of the part-whole nature of these relationships (i.e., only surviving lambs at the start of the preceding time period were considered for the subsequent one). This was also the case for maternal genetic and permanent environmental correlations (Table 4). The inability to estimate the correlation between cumulative lamb survival at 7 and 30 d was due to the large proportion of lambs surviving from 7 d to marking and has been reported previously (Cloete et al., 2009). Table 3. Direct genetic (below diagonal) and phenotypic (above diagonal) correlations for cumulative lamb survival across time periods Cumulative survival to  1 d  7 d  30 d  110 d  1 d    0.512 ± 0.006  0.475 ± 0.007  0.402 ± 0.007  7 d  0.721 ± 0.164    nc1  0.776 ± 0.003  30 d  0.854 ± 0.192  nc    0.841 ± 0.002  110 d  0.487 ± 0.209  0.662 ± 0.136  0.749 ± 0.121    Cumulative survival to  1 d  7 d  30 d  110 d  1 d    0.512 ± 0.006  0.475 ± 0.007  0.402 ± 0.007  7 d  0.721 ± 0.164    nc1  0.776 ± 0.003  30 d  0.854 ± 0.192  nc    0.841 ± 0.002  110 d  0.487 ± 0.209  0.662 ± 0.136  0.749 ± 0.121    1nc = The analysis did not converge for these pairs of estimates. View Large Table 3. Direct genetic (below diagonal) and phenotypic (above diagonal) correlations for cumulative lamb survival across time periods Cumulative survival to  1 d  7 d  30 d  110 d  1 d    0.512 ± 0.006  0.475 ± 0.007  0.402 ± 0.007  7 d  0.721 ± 0.164    nc1  0.776 ± 0.003  30 d  0.854 ± 0.192  nc    0.841 ± 0.002  110 d  0.487 ± 0.209  0.662 ± 0.136  0.749 ± 0.121    Cumulative survival to  1 d  7 d  30 d  110 d  1 d    0.512 ± 0.006  0.475 ± 0.007  0.402 ± 0.007  7 d  0.721 ± 0.164    nc1  0.776 ± 0.003  30 d  0.854 ± 0.192  nc    0.841 ± 0.002  110 d  0.487 ± 0.209  0.662 ± 0.136  0.749 ± 0.121    1nc = The analysis did not converge for these pairs of estimates. View Large Table 4. Maternal genetic (below diagonal) and permanent environmental (above diagonal) correlations for cumulative lamb survival across time periods Cumulative survival to  1 d  7 d  30 d  110 d  1 d     0.874 ± 0.074  0.518 ± 0.174  0.816 ± 0.099  7 d  0.468 ± 0.192     nc1  0.986 ± 0.032  30 d  0.802 ± 0.084  nc     0.997 ± 0.024  110 d  0.559 ± 0.187  0.967 ± 0.063  0.971 ± 0.042     Cumulative survival to  1 d  7 d  30 d  110 d  1 d     0.874 ± 0.074  0.518 ± 0.174  0.816 ± 0.099  7 d  0.468 ± 0.192     nc1  0.986 ± 0.032  30 d  0.802 ± 0.084  nc     0.997 ± 0.024  110 d  0.559 ± 0.187  0.967 ± 0.063  0.971 ± 0.042     1nc = The analysis did not converge for these pairs of estimates. View Large Table 4. Maternal genetic (below diagonal) and permanent environmental (above diagonal) correlations for cumulative lamb survival across time periods Cumulative survival to  1 d  7 d  30 d  110 d  1 d     0.874 ± 0.074  0.518 ± 0.174  0.816 ± 0.099  7 d  0.468 ± 0.192     nc1  0.986 ± 0.032  30 d  0.802 ± 0.084  nc     0.997 ± 0.024  110 d  0.559 ± 0.187  0.967 ± 0.063  0.971 ± 0.042     Cumulative survival to  1 d  7 d  30 d  110 d  1 d     0.874 ± 0.074  0.518 ± 0.174  0.816 ± 0.099  7 d  0.468 ± 0.192     nc1  0.986 ± 0.032  30 d  0.802 ± 0.084  nc     0.997 ± 0.024  110 d  0.559 ± 0.187  0.967 ± 0.063  0.971 ± 0.042     1nc = The analysis did not converge for these pairs of estimates. View Large Maternal Bond Score The average maternal bond score was 1.8, indicating that on average the ewes tended to maintain reasonable contact with their lamb during the tagging, weighing, and scoring activities. The phenotypic variance was 0.46 ± 0.01 with the direct genetic, maternal genetic, and maternal permanent environment estimates each accounting for about 20% of the phenotypic variance (0.09 ± 0.01, 0.10 ± 0.02, and 0.09 ± 0.01, respectively; Table 5). The covariance between the direct and maternal genetic effects was −0.06 ± 0.01. Maternal bond score was a highly repeatable trait (0.49 ± 0.02) with moderate direct (0.20 ± 0.02) and maternal (0.23 ± 0.03) heritability. However, the phenotypic correlations between maternal bond score and lamb survival across ages were low. Although the corresponding genetic correlations were greater, they had large SE (Table 5). The maternal genetic and permanent environmental correlations with lamb survival at each age also had large SE. Table 5. Correlations between maternal bond score, lamb birth weight, and cumulative survival at birth (1 d) and to 7, 30, and 110 d of age Trait  1 d  7 d  30 d  110 d  Maternal bond score               Phenotypic  0.087 ± 0.011  0.020 ± 0.010  0.012 ± 0.010  0.012 ± 0.010   Genetic  −0.128 ± 0.151  0.122 ± 0.149  0.105 ± 0.175  0.161 ± 0.149   Maternal genetic  −0.111 ± 0.188  −0.061 ± 0.206  −0.088 ± 0.191  0.008 ± 0.210   Dam permanent environmental  0.011 ± 0.104  −0.040 ± 0.090  −0.057 ± 0.097  −0.132 ± 0.107  Lamb birth weight               Phenotypic  0.039 ± 0.009  0.141 ± 0.009  0.143 ± 0.009  0.129 ± 0.009   Genetic  0.064 ± 0.136  0.139 ± 0.136  0.098 ± 0.160  0.007 ± 0.140   Maternal genetic  0.085 ± 0.172  0.388 ± 0.177  0.359 ± 0.164  0.297 ± 0.185   Dam permanent environmental  0.171 ± 0.128  0.058 ± 0.114  0.132 ± 0.120  0.229 ± 0.129  Trait  1 d  7 d  30 d  110 d  Maternal bond score               Phenotypic  0.087 ± 0.011  0.020 ± 0.010  0.012 ± 0.010  0.012 ± 0.010   Genetic  −0.128 ± 0.151  0.122 ± 0.149  0.105 ± 0.175  0.161 ± 0.149   Maternal genetic  −0.111 ± 0.188  −0.061 ± 0.206  −0.088 ± 0.191  0.008 ± 0.210   Dam permanent environmental  0.011 ± 0.104  −0.040 ± 0.090  −0.057 ± 0.097  −0.132 ± 0.107  Lamb birth weight               Phenotypic  0.039 ± 0.009  0.141 ± 0.009  0.143 ± 0.009  0.129 ± 0.009   Genetic  0.064 ± 0.136  0.139 ± 0.136  0.098 ± 0.160  0.007 ± 0.140   Maternal genetic  0.085 ± 0.172  0.388 ± 0.177  0.359 ± 0.164  0.297 ± 0.185   Dam permanent environmental  0.171 ± 0.128  0.058 ± 0.114  0.132 ± 0.120  0.229 ± 0.129  View Large Table 5. Correlations between maternal bond score, lamb birth weight, and cumulative survival at birth (1 d) and to 7, 30, and 110 d of age Trait  1 d  7 d  30 d  110 d  Maternal bond score               Phenotypic  0.087 ± 0.011  0.020 ± 0.010  0.012 ± 0.010  0.012 ± 0.010   Genetic  −0.128 ± 0.151  0.122 ± 0.149  0.105 ± 0.175  0.161 ± 0.149   Maternal genetic  −0.111 ± 0.188  −0.061 ± 0.206  −0.088 ± 0.191  0.008 ± 0.210   Dam permanent environmental  0.011 ± 0.104  −0.040 ± 0.090  −0.057 ± 0.097  −0.132 ± 0.107  Lamb birth weight               Phenotypic  0.039 ± 0.009  0.141 ± 0.009  0.143 ± 0.009  0.129 ± 0.009   Genetic  0.064 ± 0.136  0.139 ± 0.136  0.098 ± 0.160  0.007 ± 0.140   Maternal genetic  0.085 ± 0.172  0.388 ± 0.177  0.359 ± 0.164  0.297 ± 0.185   Dam permanent environmental  0.171 ± 0.128  0.058 ± 0.114  0.132 ± 0.120  0.229 ± 0.129  Trait  1 d  7 d  30 d  110 d  Maternal bond score               Phenotypic  0.087 ± 0.011  0.020 ± 0.010  0.012 ± 0.010  0.012 ± 0.010   Genetic  −0.128 ± 0.151  0.122 ± 0.149  0.105 ± 0.175  0.161 ± 0.149   Maternal genetic  −0.111 ± 0.188  −0.061 ± 0.206  −0.088 ± 0.191  0.008 ± 0.210   Dam permanent environmental  0.011 ± 0.104  −0.040 ± 0.090  −0.057 ± 0.097  −0.132 ± 0.107  Lamb birth weight               Phenotypic  0.039 ± 0.009  0.141 ± 0.009  0.143 ± 0.009  0.129 ± 0.009   Genetic  0.064 ± 0.136  0.139 ± 0.136  0.098 ± 0.160  0.007 ± 0.140   Maternal genetic  0.085 ± 0.172  0.388 ± 0.177  0.359 ± 0.164  0.297 ± 0.185   Dam permanent environmental  0.171 ± 0.128  0.058 ± 0.114  0.132 ± 0.120  0.229 ± 0.129  View Large Birth Weight The average birth weight of lambs in this study was 3.63 kg. The additive direct variance accounted for the majority (24%; 0.10 ± 0.01) of the phenotypic variance (0.42 ± 0.01) observed for lamb birth weight, followed by the maternal genetic (14.9%; 0.06 ± 0.01) and dam permanent environmental variance (9.7%; 0.04 ± 0.01). The covariance between the direct and maternal genetic effects was −0.03 ± 0.01. The dam repeatability of lamb birth weight was moderate (0.40 ± 0.02) with the direct heritability being greater than the maternal estimate (0.24 ± 0.02 vs. 0.15 ± 0.02, respectively). The phenotypic and genetic correlations between lamb birth weight and survival were positive but low for all ages (Table 5), and practically they were not significantly different to zero. The smallest phenotypic correlation was observed at birth (0.04) and smallest genetic correlation at 110 d (0.01). The maternal genetic correlation was also low at birth (0.08) but greater at the other lamb ages (0.39, 0.36, and 0.30 at 7, 30, and 110 d, respectively). DISCUSSION Maternal genetic effects (ewe rearing ability) were more important than direct genetic effects (lamb viability) for cumulative lamb survival at birth, 7 d of age, marking and weaning, and for survival between 1 and 7 d of age of those lambs that survived birth. Whereas this result is consistent with earlier work using other breeds of sheep (Burfening, 1993; Lopez-Villalobos and Garrick, 1999; Sawalha et al., 2007; Maxa et al., 2009), few previous studies have further partitioned the maternal effects to separate the permanent environment component (Morris et al., 2000; Southey et al., 2001; Cloete et al., 2009). This is the first study to do so at more than 1 time period between birth and weaning. At each lamb age the maternal permanent environment contributed more to the observed variation than the maternal genetic effect. Comparable results were identified by Morris et al. (2000) and Lopez-Villalobos and Garrick (1999) working with Romney sheep and Barwick et al. (1990) with the US Suffolk. This indicates that the permanent environmental component of the maternal effect is the main determinant of the repeatability of lamb survival. At birth, variation in the maternal permanent environment could be due to physiological differences between dams in their ability to provide nutrients to the developing fetus (Koch and Clark, 1955) and physical differences in pelvic dimensions (Haughey et al., 1985). In addition, behavioral changes at the onset of parturition have impacts on survival, including the ability of a ewe to separate herself from the mob, identify and occupy a birth site conducive to lamb survival, exhibit appropriate maternal behavior (Lindsay et al., 1990), cooperate with the first suckling attempts without aggressive behavior (Alexander, 1988; Cloete et al., 2002), and remain with her lamb(s) for 6 h (Murphy et al., 1994). After parturition it is likely that differences in colostrum production as well as the onset and duration of lactation would contribute to variation in the permanent maternal environment (Bradford, 1972). Interestingly, the maternal permanent environment component for survival between 7 and 110 d of those lambs that were alive at 7 d of age was negligible. Within this time period the direct genetic variance accounted for the largest component of the observed phenotypic variation, indicating that the ability of the lamb to survive on its own is more important than the mothering ability of the ewe between 7 d of age and weaning. Therefore, after 7 d, milk production of the dam has little impact on survival, although it will obviously continue to impact on the growth and physiological development of her progeny. The covariance or correlation between the direct and maternal effects at each lamb age and within each time period in this study, although negative, were essentially zero. Some previous studies (Burfening, 1993; Lopez-Villalobos and Garrick, 1999; Morris et al., 2000; Southey et al., 2001; Everett-Hincks et al., 2005) also report negative correlations between maternal and direct genetic effects on lamb survival, but of a greater magnitude. The conclusion from these studies was that improvements in one component of survival may be associated with reductions in the other, making overall genetic progress in lamb survival slow. Everett-Hincks et al. (2005) and Ch'ang and Rae (1972) proposed that lambs with superior survival to weaning have inferior genes for survival of their own progeny when they become mothers. However, other work (Barwick et al., 1990; Matos et al., 2000; Sawalha et al., 2007) reported positive correlations, albeit with large SE, which suggests that dams with good genetic mothering ability have better direct genetic potential to produce lambs with reduced mortality rate at birth. Variation between studies in the size of the data set and models used for analysis (Burfening, 1993) do have a significant impact on the accuracy of estimating covariance components and genetic parameters and care must be taken to ensure valid comparisons are made between studies. Breed differences may also play a role (Burfening, 1993; Matos et al., 2000; Maxa et al., 2009). Larger data sets are required to estimate the direct maternal correlation with sufficient precision to make conclusions about the biological nature of this relationship. Dam repeatability is a measure of the likely response to selection that can be achieved in the current generation. At all lamb ages and for survival to 7 d of age of those that survived birth, ewe rearing ability had an estimated repeatability of at least 0.11, indicating that multiple records of the rearing ability of a ewe over its lifetime can increase selection accuracy. More importantly, such repeatabilities indicate that current generation improvement can be achieved by culling ewes with poor rearing ability from the breeding flock. However, the estimated repeatability decreased with increasing age of the lamb. For survival between 7 d and weaning of those lambs that survived their first week of life, the repeatability of rearing ability of the dam was about one-half that at all other time periods. This emphasizes the need to record survival as a trait of the dam within 7 d of birth to improve the accuracy for evaluation and selection purposes. Heritability is a measure of future generation genetic response, so the very low heritabilities for lamb survival, less than 0.03, and for ewe rearing ability, 0.07, suggest that genetic solutions to increase lamb survival are unlikely to be significant. Similar estimates of direct and maternal heritability were reported by Safari et al. (2005b) for a range of breeds. But, despite the low heritabilities, there is still potential for improvement through selective breeding. Using assumed figures of 0.75 average survival rate, heritability of 0.08, repeatability of 0.12, and standardized selection differential of 1.3, potential rates of progress of 0.024 in mean survival per generation using a single record of the rearing ability of a ewe or 0.036 per generation using an average of 3 dam records are possible. Such genetic improvement rates would not be attained under practical situations because they refer to single trait selection using complete records on ewe rearing performance. However, these data do indicate that where a breeding program aims to increase reproductive efficiency, ewe rearing ability needs consideration. Of course, such attention would be more practical and economically beneficial if suitable indirect selection criteria for ewe rearing ability were available. Hatcher et al. (2009) identified only small differences between the 15 Merino subflocks represented in the Trangie flock for survival at birth. Significant differences between the subflocks were evident for survival to 7 d of age and to weaning, but these were only substantial for twin-born lambs. To quantify the relative size of the between-flock and within-flock genetic variation, we have used overall survival from birth to weaning. The between-flock variance was 50 and 40% of the within-flock direct genetic and maternal genetic variances, respectively. So, despite the relatively low heritability of the trait, within-flock selection is still a more realistic avenue for improvement in lamb survival than selection among flocks. The genetic and phenotypic correlations for lamb survival across time periods and the maternal genetic and permanent environment correlations were always positive. Survival to 7 d, marking, or weaning were all strongly correlated with each other. This indicates that genes controlling the direct, maternal, or permanent environment that favor early survival may also favor survival at later ages. However the size of the maternal effect decreases substantially once the lamb is 7 d old. These correlations are based on cumulative lamb survival, whereas correlations between independent time periods, if able to be estimated, are likely to remain positive but be less in magnitude. This study has highlighted the need to discriminate between traits that may have some explanatory role in the variation between animals in survival compared with their potential roles as selection or management criteria. Maternal bond score, an assessment based on the movement of the ewe away from her lamb at tagging, has been incorporated with some (O'Connor et al., 1985) or little (Everett-Hincks et al., 2005) success into ewe selection and culling programs in New Zealand. Given the relative importance of ewe rearing ability compared with lamb viability, on early lamb survival in particular, maternal bond score would appear a likely selection criterion for improved lamb survival. However, in the present study, although maternal bond score was a highly repeatable and moderately heritable trait, the genetic and phenotypic correlations with lamb survival were essentially negligible. For this flock at least, maternal bond score was a very poor indirect selection criterion for ewe rearing ability. However, it must be remembered that in addition to assessing the attachment of a dam to her newborn lamb, maternal bond score also gauges ewe temperament, specifically her reaction to the presence of humans (Sawalha et al., 2007). If lamb tagging occurred within the 6 h window after birth before the ewe lamb bond is firmly established (Murphy et al., 1994), it is possible the reaction of the ewe to the human at tagging would be greater than the still forming bond with her lamb. This would be particularly true if the newborn lamb was lethargic and not actively contributing to the formation of the ewe-lamb bond (Lindsay et al., 1990). Lamb birth weight was also highly repeatable and moderately heritable in agreement with previous work (Snyman et al., 1995; Al-Shorepy and Notter, 1998; Safari et al., 2005b, 2007; Riggio et al., 2008; Cloete et al., 2009). The genetic and phenotypic correlations between birth weight and lamb survival were low, particularly for survival to 1 d of age. This is not surprising given that at this age there is a curvilinear relationship between birth weight and lamb survival, with light and heavy lambs more likely to die (Hall et al., 1995; Morris et al., 2000; Sawalha et al., 2007; Hatcher et al., 2009). The phenotypic correlations were slightly greater for cumulative survival to older ages, but the genetic correlations were not significantly different from zero. It is possible that the phenotypic correlations reflect those light birth weight lambs that died due to starvation, mismothering, and exposure, which have no genetic basis. On the other hand, the genetic correlations reflect the heavier birth weight lambs that died due to dystocia, which has both a direct genetic basis (Smith, 1977) and possibly a maternal genetic component (Cloete et al., 2002). Therefore, like maternal bond score, lamb birth weight has limited potential as an indirect selection criterion for lamb survival. The estimated genetic parameters for both lamb viability and ewe rearing ability suggest that genetic improvement in lamb survival will be very slow, but some opportunity exists to identify ewes that are more reliable in rearing their lambs. Survival in the later postnatal period (after 7 d) was less repeatable, which emphasizes the need to record survival within 7 d of birth to improve the accuracy of the trait for evaluation and selection purposes. The key is to identify appropriate selection criteria. Genetic variation in lamb survival has been confirmed as principally arising from genetic variation in ewe rearing ability. The exploitation of this variation, both within and between flocks, would be substantially improved by the identification of highly correlated indirect selection criteria. Unfortunately, no such criteria were identified in our study. Additionally, we need information on the genetic relationships between ewe rearing ability and other traits of commercial importance in the Australian Merino to better predict the likely outcome of breeding programs to increase profitability. Events occurring in the early postnatal period are critical for lamb survival because most lamb deaths occur during this time (Hatcher et al., 2009). Although manipulation of ewe nutrition specifically to increase birth weight is unlikely to increase lamb survival (Hatcher et al., 2009), the ewe must have sufficient body reserves at parturition to facilitate a quick delivery, begin lactogenesis with an adequate quantity of colostrum, and provide satisfactory maternal care to her lamb. The lamb will benefit by having a greater amount of body reserves, particularly brown adipose fat, to metabolize postbirth (Vermorel and Vernet, 1985). Merino producers can facilitate this by ensuring the nutritional requirements of breeding ewes, particularly multiple-bearing ewes, are met during the later stages of pregnancy. Similarly provision of a suitable lambing environment that, first, encourages the ewe to choose a sensible birth site and remain there for at least 6 h and, second, moderates environmental conditions is likely to improve the chance of individual lambs surviving their first week of life. The average survival to weaning in this study was 72.4%, which would have little impact on estimates of genetic parameters using observed or transformed data. The results of Safari et al. (2005a) confirmed that direct and maternal variances can be underestimated when using a logit-transformation and might give unexpected results. Survival rates at either end of the scale, less than 10% or greater then 90%, would require a logit-transformation. For studies with less or greater incidence of survival, threshold analysis methods may be useful. However, Matos et al. (2000) reported no significant differences in the predictive ability of linear and threshold models when analyzing lamb survival in Rambouillet and Finnsheep flocks. The survival rates in their study were comparable with this study, 81.9% for the Rambouillet and 75% for the Finnsheep. Therefore we considered the mixed model analysis procedure used in this study was adequate and had the added benefit of ease of interpretation of results. Despite these data being recorded between 1975 and 1983, these results remain relevant to the present-day Australian sheep and wool industry. Recent studies of the genetics of lamb survival using data from Merino resource flocks in South Australia established in the late 1980s (Brien et al., 2009b) and preliminary results from the Cooperative Research Centre for Sheep Industry Innovation's Information Nucleus flock established in 2007 (Brien et al., 2009a) use similar methods of analyses and report genetic parameters similar to those in this study. LITERATURE CITED Alexander G. 1988. What makes a good mother? Components and comparative aspects of maternal behaviour in ungulates. Proc. Aust. Soc. Anim. Prod.  17: 25– 41. Al-Shorepy S. A. Notter D. R. 1998. Genetic parameters for lamb birth weight in spring and autumn lambing. Anim. Sci.  67: 327– 332. Google Scholar CrossRef Search ADS   Atkins K. D. 1980. Selection for skin folds and fertility. Proc. Aust. Soc. Anim. Prod.  13: 174– 176. Barwick S. A. Leymaster K. A. Keele J. W. Harvey W. R. 1990. Estimates of genetic parameters for lamb survival to weaning and birth weight in the U.S. Proc. Assoc. Adv. Anim. Breed. Genet.  8: 311– 334. Bradford G. E. 1972. The role of maternal effects in animal breeding: VII. Maternal effects in sheep. J. Anim. Sci.  35: 1324– 1334. Google Scholar CrossRef Search ADS PubMed  Brien F. D. Hebart M. L. Hocking Edwards J. E. Greeff J. C. Hart K. W. Refshauge G. Gaunt G. Behrendt R. Thomson K. Hinch G. N. Geenty K. G. van der Werf J. H. J. 2009 a. Genetics of lamb survival: Preliminary studies of the Information Nucleus flock. Proc. Assoc. Adv. Anim. Breed. Genet.  18: 108– 111. Brien F. D. Hebart M. L. Jaensch K. S. Smith D. H. Grimson R. J. 2009 b. Genetics of lamb survival: A study of Merino resource flocks in South Australia. Proc. Assoc. Adv. Anim. Breed. Genet.  18: 492– 495. Burfening P. J. 1993. Direct and maternal genetic effects on lamb survival. Small Rumin. Res.  11: 267– 274. Google Scholar CrossRef Search ADS   Ch'ang T. S. Rae A. L. 1972. The genetic basis of growth, reproduction, and maternal environment in Romney ewes. II. Genetic covariation between hogget characters, fertility, and maternal environment of the ewe. Aust. J. Agric. Res.  23: 149– 165. Google Scholar CrossRef Search ADS   Cloete S. W. P. Gilmour A. R. Olivier J. J. Wyk J. B. v. 2004. Genetic and phenotypic trends and parameters in reproduction, greasy fleece weight and liveweight in Merino lines divergently selected for multiple rearing ability. Aust. J. Exp. Agric.  44: 745– 754. Google Scholar CrossRef Search ADS   Cloete S. W. P. Misztal I. Olivier J. J. 2009. Genetic parameters and trends for lamb survival and birth weight in a Merino flock divergently selected for multiple rearing ability. J. Anim. Sci.  87: 2196– 2208. [PubMed] Google Scholar CrossRef Search ADS PubMed  Cloete S. W. P. Scholtz A. J. 1998. Lamb survival in relation to lambing and neonatal behaviour in medium wool Merino lines divergently selected for multiple rearing ability. Aust. J. Exp. Agric.  38: 801– 811. Google Scholar CrossRef Search ADS   Cloete S. W. P. Scholtz A. J. Gilmour A. R. Olivier J. J. 2002. Genetic and environmental effects on lambing and neonatal behaviour of Dormer and SA Mutton Merino lambs. Livest. Prod. Sci.  78: 183– 193. Google Scholar CrossRef Search ADS   Everett-Hincks J. M. Lopez-Villalobos N. Blair H. T. Stafford K. J. 2005. The effect of ewe maternal behaviour score on lamb and litter survival. Livest. Prod. Sci.  93: 51– 61. Google Scholar CrossRef Search ADS   Gama L. T. Dickerson G. E. Young L. D. Leymaster K. A. 1991. Genetic and phenotypic variation in sources of preweaning lamb mortality. J. Anim. Sci.  69: 2744– 2753. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gilmour, A. R., B. J. Gogel, B. R. Cullis, and R. Thompson 2006. ASReml user guide release 2.0.  VSN Int. Ltd., Hemel Hempstead, UK. Hall D. G. Fogarty N. M. Gilmour A. R. 1995. Performance of crossbred progeny of Trangie fertility Merino and Booroola Merino rams and Poll Dorset ewes. 1. Lamb birth weight, survival and growth. Aust. J. Exp. Agric.  35: 1069– 1074. Google Scholar CrossRef Search ADS   Hatcher S. Atkins K. D. Safari E. 2009. Phenotypic aspects of lamb survival in Australian Merino sheep. J. Anim. Sci.  87: 2781– 2790. [PubMed] Google Scholar CrossRef Search ADS PubMed  Haughey K. G. George J. M. McGuirk B. J. 1985. The repeatability of rearing performance of Merino and Dorset Horn ewes and its relationship with mature pelvic size. Aust. J. Exp. Agric.  25: 541– 549. Google Scholar CrossRef Search ADS   Kilgour R. J. 1992. Lambing potential and mortality in Merino sheep as ascertained by ultrasonography. Aust. J. Exp. Agric.  32: 311– 313. Google Scholar CrossRef Search ADS   Kleemann D. O. Walker S. K. 2005. Fertility in South Australian commercial Merino flocks: Sources of reproductive wastage. Theriogenology  63: 2075– 2088. [PubMed] Google Scholar CrossRef Search ADS PubMed  Koch R. M. Clark R. T. 1955. Genetic and environmental relationships among economic characters in beef cattle iii. Evaluating maternal environment. J. Anim. Sci.  14: 979– 996. Google Scholar CrossRef Search ADS   Lindsay, D. R. 1982. The significance of reproductive biology to the genetic improvement of farm animals. Pages 89– 101 in Future developments in the genetic improvement of animals.  J. S. F. Barker, K. Hammond, and A. E. McClintock ed. Academic Press Australia, North Ryde, New South Wales. Lindsay, D. R., R. Nowak, I. Gede Putu, and D. M. McNeill 1990. Behavioural interactions between the ewe and her young at parturition: A vital step for the lamb. Pages 191– 205 in Reproductive Physiology of Merino Sheep: Concepts and Consequences.  C. M. Oldham, G. B. Martin, and I. W. Purvis ed. School of Agriculture (Animal Science), The Univ. Western Australia, Nedlands, Peth, Western Australia. Lopez-Villalobos N. Garrick D. J. 1999. Genetic parameter estimates for lamb survival in Romney sheep. Proc. N.Z. Soc. Anim. Prod.  59: 121– 124. Matos C. A. P. Thomas D. L. Young L. D. Gianola D. 2000. Genetic analyses of lamb survival in Rambouillet and Finnsheep flocks by linear and threshold models. Anim. Sci.  71: 227– 234. Google Scholar CrossRef Search ADS   Maxa J. Sharifi A. R. Pedersen J. Gauly M. Simianer H. Norberg E. 2009. Genetic parameters and factors influencing survival to twenty-four hours after birth in Danish meat sheep breeds. J. Anim. Sci.  87: 1888– 1895. [PubMed] Google Scholar CrossRef Search ADS PubMed  Morley, F. H. W. 1951. Selection for economic characters in Australian Merino sheep. I. Estimates of phenotypic and genetic parameters.  New South Wales Dep. Agric. Sci. Bull. No. 73: 1– 45. Morris C. A. Hickey S. M. Clarke J. N. 2000. Genetic and environmental factors affecting lamb survival at birth and through to weaning. N. Z. J. Agric. Res.  43: 515– 524. Google Scholar CrossRef Search ADS   Mortimer S. I. Atkins K. D. 1989. Genetic evaluation of production traits between and within flocks of Merino sheep. I. Hogget fleece weights, body weight and wool quality. Aust. J. Agric. Res.  40: 433– 443. Google Scholar CrossRef Search ADS   Murphy P. M. Lindsay D. R. Purvis I. W. 1994. The importance of the birth site on the survival of Merino lambs. Proc. Aust. Soc. Anim. Prod.  20: 251– 254. O'Connor C. E. Jay N. P. Nicol A. M. Beatson P. R. 1985. Ewe maternal behaviour score and lamb survival. Proc. N. Z. Soc. Anim. Prod.  45: 159– 162. Piper, L. R., and B. M. Bindon. 1977. The genetics of early viability in Merino sheep.  Pages 10-17– 10-22 in Third Int. Congr. Soc. Adv. Breed. Res. in Asia and Oceania (SABRAO) in association with the Australian Plant Breeding Conference, Australia, February 1977. Animal Breeding Papers. SABRAO, Canberra, Australia. Riggio V. Finocchiaro R. Bishop S. C. 2008. Genetic parameters for early lamb survival and growth in Scottish Blackface sheep. J. Anim. Sci.  86: 1758– 1764. [PubMed] Google Scholar CrossRef Search ADS PubMed  Safari A. Atkins K. D. Fogarty N. M. Gilmour A. R. 2005 a. Analysis of lamb survival in Australian Merino. Proc. Assoc. Adv. Anim. Breed. Genet.  16: 28– 31. Safari E. Fogarty N. M. Gilmour A. R. 2005 b. A review of genetic parameter estimates for wool, growth, meat and reproduction traits in sheep. Livest. Prod. Sci.  92: 271– 289. Google Scholar CrossRef Search ADS   Safari E. Fogarty N. M. Gilmour A. R. Atkins K. D. Mortimer S. I. Swan A. A. Brien F. D. Greeff J. C. van der Werf J. H. J. 2007. Across population genetic parameters for wool, growth, and reproduction traits in Australian Merino sheep. 2. Estimates of heritability and variance components. Aust. J. Agric. Res.  58: 177– 184. Sawalha R. M. Conington J. Brotherstone S. Villanueva B. 2007. Analyses of lamb survival of Scottish Blackface sheep. Animal  1: 151– 157. Google Scholar CrossRef Search ADS PubMed  Smith G. M. 1977. Factors affecting birth weight, dystocia and preweaning survival in sheep. J. Anim. Sci.  44: 745– 753. [PubMed] Google Scholar CrossRef Search ADS PubMed  Snyman M. A. Erasmus G. J. van Wyk J. B. Olivier J. J. 1995. Direct and maternal (co) variance components and heritability estimates for body weight at different ages and fleece traits in Afrino sheep. Livest. Prod. Sci.  44: 229– 235. Google Scholar CrossRef Search ADS   Southey B. R. Rodriguez-Zas S. L. Leymaster K. A. 2001. Survival analysis of lamb mortality in a terminal sire composite population. J. Anim. Sci.  79: 2298– 2306. [PubMed] Google Scholar CrossRef Search ADS PubMed  Vermorel, M., and J. Vernet 1985. Major factors affecting thermogenesis and cold resistance of newborn lambs. Pages 35– 45 in Factors Affecting the Survival of Newborn Lambs.  G. Alexander, J. D. Barker, and J. Slee ed. Commission Eur. Commun., Luxembourg. Footnotes 1 Phenotypic aspects of lamb survival, including birth type, sex, age of dam, flocks, and the nonlinear relationship between birth weight and lamb survival, were reported in Hatcher et al. (2009). 2 The skilled assistance of staff at the Trangie Agricultural Research Centre (Trangie, New South Wales, Australia) in record collection and management is gratefully acknowledged, especially the contributions of A. M. Burns, K. J. Flinn, I. M. Rogan, and S. J. Semple. This research was supported by a grant from the Wool Research Trust Fund on the recommendation of the Australian Wool Corporation. The Cooperative Research Centre for Sheep Industry Innovation provided the funds to allow this analysis to proceed. K. J. Thornberry (Industry and Investment NSW, Orange Agricultural Institute, Orange, New South Wales, Australia) assisted with data preparation before analysis. N. M. Fogarty (Industry and Investment NSW, Orange Agricultural Institute) provided valuable advice during revision of this manuscript. American Society of Animal Science This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial reuse, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]

Sartori, C.;Mantovani, R.

doi: 10.2527/jas.2010-2899pmid: 20581285

ABSTRACT The tendency to fight is a well-known behavior in Valdostana cattle, and noncruel traditional contests are organized yearly by farmers to identify the most dominant cow. Cow battles consist of elimination matches that have important economic implications for both tourism and farmers. The aims of this study are 1) to validate a scoring system to express fighting ability, and 2) to carry out a genetic analysis for this trait using different data sets and models. A data set accounting for 16,509 fighting records of 5,981 cows relevant to contests over 6 yr was retained after editing (data set 1). Data on placements were used to compute a placement score accounting for wins, tournament size, and difficulty, and differentiating the 20 preliminary battles each year from the final match. A second data set was created using only the individual best yearly placement scores, that is, deleting repeats with a smaller placement score for the same animal within each year (data set 2; n = 10,367 records, corresponding to a single datum per year per cow). Compared with the placement or position of each cow, the placement score proved to be less skewed (−1.45 for placement position and 1.25 for placement score, respectively) and exhibited better coefficients for the probability of a normal distribution. An animal model REML method analysis (accounting for 13,456 animals in the pedigree) was carried out, with consideration given to different combinations of fixed and random nongenetic factors other than the random animal and permanent environmental effects. Results indicated that random factors other than additive genetic and permanent environment effects did not improve the model fit; therefore, it was not useful to take them into account. Heritability estimates obtained with the model showing the best fit were 0.078 (data set 1) and 0.098 (data set 2). Results of this study indicate that selection for fighting ability in Valdostana cattle using data on battle performance is possible. INTRODUCTION In all social species, the access to resources is regulated through dominance relationships involving repeated dyadic agonistic interactions generating a within-group hierarchy of social dominance (Drews, 1993). Rigid relationships are typical of confined ungulates living in groups, such as bison, zebus, and cattle (Reinhardt et al., 1986). In cattle herds, firm hierarchies are always established, and the regrouping at pasture or the remixing of unfamiliar animals produces aggression aimed at defining a new social order (Phillips, 1993; Bøe and Færevik, 2003). Dominance shows a heritable component, but few studies in cattle have shed light on this (Dickson et al., 1970; Wieckert, 1971). Fighting ability has been investigated in breeds used for bullfighting and selected for aggressiveness (González Caicedo et al., 1994; Silva et al., 2006). Genetic analyses have also been carried out for Hérens and Valdostana cattle by using data from traditional cow battle contests (Plusquellec and Bouissou, 2001; Mantovani et al., 2007). These competitions between cows represent both an attraction for tourists and a source of income for farmers because of the increased economic value of the most competitive cows and their offspring. Although selection for fighting ability has not yet been formally carried out, farmers pay a great deal of attention to this trait, in addition to selecting for the dual-purpose attitude (i.e., milk and meat production). As part of a project aimed at identifying the possible implementation of selection for fighting ability in the Valdostana breed (Mantovani et al., 2007), this study aims to 1) validate a scoring system as a suitable dependent variable to analyze fighting ability, and 2) investigate different combinations of fixed and random effects in different data sets to identify the model with the best fit. As an outcome, variance components and rank correlations among EBV are analyzed and discussed. MATERIALS AND METHODS Data used in the study were obtained following the guidelines given by the association of farmers responsible for the battle organization. These guidelines are formulated in respect of Italian legislation on animal care. Description of the Subject The Batailles de Reines are yearly traditional contests that have taken place since 1958 in the Valle d'Aosta region (i.e., northwest Italian Alps), in which cows participate in bloodless elimination matches aimed at identifying the most competitive animal (Mantovani et al., 2007). Contests revive the natural behavior to fight that cows exhibit at the beginning of the summer grazing season, when unfamiliar cows meet after regrouping. The fights are carried out in grass arenas, where pairs of cows are left to fight under the supervision of their owners and a judge. Participants are divided into 3 BW categories that battle at the same time but without interactions between BW categories. The escalated fight (Parker, 1974; Clutton-Brock and Albon, 1979) can end quickly if a cow immediately recognizes the superiority of its rival, but it may last more than 1 h, with cows pushing each other until the loser gives way. When a cow recognizes the hierarchical supremacy of the adversary, it is eliminated from the competition, whereas the winner advances in the tournament. Yearly tournaments consist of 20 preliminary battles that begin on the last Sunday of March and a final match that takes place at the end of the summer pasture season (i.e., in about the middle of October). The final match is held every year in a special arena in Aosta, Italy, and the title is disputed for each of 3 BW categories by all animals classified in the elimination tournaments (winner and up to the fourth place) plus the winner from the previous year. The winners of each category gain the title of “queen of the year.” Both the elimination and final battle boards are established within each category and across the tournaments by drawing animal numbers (i.e., without seedings). Only cows belonging to the autochthonous Aosta Chestnut and Aosta Pie Black breeds from farms located within the regional territory are allowed to compete in the tournaments. These breeds have a strict genetic relationship (Del Bo et al., 2001), and they are considered 2 varieties of the same breed managed within the same herdbook. To fight, cows need both ongoing or documented milk production records before fighting and to be diagnosed as pregnant. Last, cows not classified for the final match are allowed to compete in further preliminary tournaments within the same year. It is important to note that the Batailles de Reines tournament does not involve the same ethical problems that can arise from traditional dog, cock, or bullfights because it is a bloodless, noncruel competition. Data Collection and Editing Raw data regarding the results of fights carried out in 6 successive years (2001 to 2006) of the traditional Batailles de Reines contest were collected from the Valle d'Aosta farmer association, which is responsible for organizing the battles and collecting fight data. The original data consisted of the results of 19,665 fighting matches performed by 7,379 cows in 3 BW categories, and accounted for both the preliminary and final tournaments held in each year. The yearly data sets were organized in pairs for participants, reporting the winner and the loser of each match. These original data were edited and rearranged to report, for each cow, the corresponding year-battle for each BW category, the individual BW at the time of the fight, and the final position reached on the battle board. Individual records from each cow were completed with information about the herds, the cow age at the time of the battle (in years), and information on genealogy. The yearly data sets were joined, and data were discarded if they were incomplete or if they belonged to a herd-year class with only 1 cow in the competition. After editing, 16,509 fighting results belonging to 5,891 cows were retained for further analysis (data set 1). This data set could contain several fight results for the same cow within a particular year. Another data set (data set 2) was created from the previous one, keeping only the best yearly performances of each cow within a year (10,367 fighting records) and discarding other performances that led to poorer results. In spite of the nonrandom choice of records, data set 2 aimed to reflect the tendency of breeders to bring animals to more than 1 tournament when they were not satisfied with the placement of an individual cow (mainly because of the absence of seeds). Descriptive statistics concerning the 2 data sets are reported in Table 1. Because there were no changes in the actual number of individual cows included in both data sets, a single relationship matrix was set up containing all available pedigree information. As a result, a total of 13,456 animals were retained in the pedigree file for subsequent analysis. When the maximum number of generations traced for each individual was taken into account, a mean number of 2.3 generations per cow was considered. Moreover, individuals in the pedigree referred to 858 sires drawn from AI and natural insemination programs, for an average half-sib family size of 6.1 daughters per sire. Table 1. Descriptive statistics in data from Batailles de Reines retained in both data sets produced after data editing and relative to 369 levels (123 year-battles × 3 BW categories) and 5,891 cows in all data sets Item  Data set 1  Data set 2  No. of records  16,509  10,367  No. of herd-year classes  2,337  2,182  No. of participants within year-battle × category  44.7 ± 22.9  28.1 ± 14.1  No. of participants within herd-year  7.1 ± 7.0  4.8 ± 3.7  No. of fights/cow  2.8 ± 2.4  1.8 ± 1.0  Age of participant, yr  6.1 ± 1.7  6.0 ± 1.7  BW of participant, kg  548 ± 61  544 ± 60  BW category, kg   1 (heavy)  633 ± 43  629 ± 42   2 (medium)  545 ± 20  543 ± 19   3 (light)  495 ± 22  492 ± 22  Item  Data set 1  Data set 2  No. of records  16,509  10,367  No. of herd-year classes  2,337  2,182  No. of participants within year-battle × category  44.7 ± 22.9  28.1 ± 14.1  No. of participants within herd-year  7.1 ± 7.0  4.8 ± 3.7  No. of fights/cow  2.8 ± 2.4  1.8 ± 1.0  Age of participant, yr  6.1 ± 1.7  6.0 ± 1.7  BW of participant, kg  548 ± 61  544 ± 60  BW category, kg   1 (heavy)  633 ± 43  629 ± 42   2 (medium)  545 ± 20  543 ± 19   3 (light)  495 ± 22  492 ± 22  View Large Table 1. Descriptive statistics in data from Batailles de Reines retained in both data sets produced after data editing and relative to 369 levels (123 year-battles × 3 BW categories) and 5,891 cows in all data sets Item  Data set 1  Data set 2  No. of records  16,509  10,367  No. of herd-year classes  2,337  2,182  No. of participants within year-battle × category  44.7 ± 22.9  28.1 ± 14.1  No. of participants within herd-year  7.1 ± 7.0  4.8 ± 3.7  No. of fights/cow  2.8 ± 2.4  1.8 ± 1.0  Age of participant, yr  6.1 ± 1.7  6.0 ± 1.7  BW of participant, kg  548 ± 61  544 ± 60  BW category, kg   1 (heavy)  633 ± 43  629 ± 42   2 (medium)  545 ± 20  543 ± 19   3 (light)  495 ± 22  492 ± 22  Item  Data set 1  Data set 2  No. of records  16,509  10,367  No. of herd-year classes  2,337  2,182  No. of participants within year-battle × category  44.7 ± 22.9  28.1 ± 14.1  No. of participants within herd-year  7.1 ± 7.0  4.8 ± 3.7  No. of fights/cow  2.8 ± 2.4  1.8 ± 1.0  Age of participant, yr  6.1 ± 1.7  6.0 ± 1.7  BW of participant, kg  548 ± 61  544 ± 60  BW category, kg   1 (heavy)  633 ± 43  629 ± 42   2 (medium)  545 ± 20  543 ± 19   3 (light)  495 ± 22  492 ± 22  View Large Scoring the Position in Each Battle Because the rank on the battle board has a skewed distribution (Mantovani et al., 2007), a position scoring system was developed to obtain an almost normal distribution, to be used in the subsequent analysis. Thus, a dominance index was computed based on the results of dyadic interactions of participants in the tournaments. However, unlike the previous placement score (PS; Mantovani et al., 2007), the present one was formulated by combining the suggestions for scoring a place value, as reported by Langlois (1984) and modified from Dorofejew and Dorofejewa (1976), with a relative place number, as reported by Bruns (1981) and attributed to H. Shertler (unpublished data). Both these methods, previously analyzed by Mantovani et al. (2007), were derived from a scoring system for placement of horses in jumping and dressage competitions. In the present study, the PS accounted for the number of wins obtained by each cow in a specific tournament, correcting for the number of participants in the competition (from 16 to 153) and assigning a different value to the final match as compared with the preliminary battles. The PS can be summarized by the following formula:  ijklijk [1]where PSijkl represents the score of cow l in a given tournament, depending on the type of tournament, tyi (with ty = 0 for i elimination tournaments and ty = 7 for i final tournament in Aosta); on the number of wins (wj) obtained by each animal in the given tournament category (with j = 0, …, 8); and on a tournament difficulty coefficient (dk) related to the number of participants in the tournament category linked to the size of the battle board [5 classes, with k = −2 (>128 participants), −1 (65 to 128 participants), 0 (33 to 64 participants), 1 (17 to 32 participants), and 2 (<17 participants), respectively]. An arbitrary constant value of 20 was added to the final PS to avoid negative values. Table 2 shows all possible values of PS in preliminary tournaments by applying Eq. [1]. Table 2. Possible placement score values, with the number of wins achieved by individuals in parentheses1 No. of participants2  Position achieved by a participant on the battle board  1st  2nd  3rd to 4th  5th to 8th  9th to 16th  17th to 32nd  33rd to 64th  65th to 128th  >129th  0 to 16  30 (4)  28 (3)  26 (2)  24 (1)  22 (0)              17 to 32  31 (5)  29 (4)  27 (3)  25 (2)  23 (1)  21 (0)           33 to 64  32 (6)  30 (5)  28 (4)  26 (3)  24 (2)  22 (1)  20 (0)        65 to 128  33 (7)  31 (6)  29 (5)  27 (4)  25 (3)  23 (2)  21 (1)  19 (0)     >128  34 (8)  32 (7)  30 (6)  28 (5)  26 (4)  24 (3)  22 (2)  20 (1)  18 (0)  No. of participants2  Position achieved by a participant on the battle board  1st  2nd  3rd to 4th  5th to 8th  9th to 16th  17th to 32nd  33rd to 64th  65th to 128th  >129th  0 to 16  30 (4)  28 (3)  26 (2)  24 (1)  22 (0)              17 to 32  31 (5)  29 (4)  27 (3)  25 (2)  23 (1)  21 (0)           33 to 64  32 (6)  30 (5)  28 (4)  26 (3)  24 (2)  22 (1)  20 (0)        65 to 128  33 (7)  31 (6)  29 (5)  27 (4)  25 (3)  23 (2)  21 (1)  19 (0)     >128  34 (8)  32 (7)  30 (6)  28 (5)  26 (4)  24 (3)  22 (2)  20 (1)  18 (0)  1All scores from the final battle in Aosta received 7 points in addition to the depicted values. 2Number of participants = number of contestants on a given battle board within a BW category. View Large Table 2. Possible placement score values, with the number of wins achieved by individuals in parentheses1 No. of participants2  Position achieved by a participant on the battle board  1st  2nd  3rd to 4th  5th to 8th  9th to 16th  17th to 32nd  33rd to 64th  65th to 128th  >129th  0 to 16  30 (4)  28 (3)  26 (2)  24 (1)  22 (0)              17 to 32  31 (5)  29 (4)  27 (3)  25 (2)  23 (1)  21 (0)           33 to 64  32 (6)  30 (5)  28 (4)  26 (3)  24 (2)  22 (1)  20 (0)        65 to 128  33 (7)  31 (6)  29 (5)  27 (4)  25 (3)  23 (2)  21 (1)  19 (0)     >128  34 (8)  32 (7)  30 (6)  28 (5)  26 (4)  24 (3)  22 (2)  20 (1)  18 (0)  No. of participants2  Position achieved by a participant on the battle board  1st  2nd  3rd to 4th  5th to 8th  9th to 16th  17th to 32nd  33rd to 64th  65th to 128th  >129th  0 to 16  30 (4)  28 (3)  26 (2)  24 (1)  22 (0)              17 to 32  31 (5)  29 (4)  27 (3)  25 (2)  23 (1)  21 (0)           33 to 64  32 (6)  30 (5)  28 (4)  26 (3)  24 (2)  22 (1)  20 (0)        65 to 128  33 (7)  31 (6)  29 (5)  27 (4)  25 (3)  23 (2)  21 (1)  19 (0)     >128  34 (8)  32 (7)  30 (6)  28 (5)  26 (4)  24 (3)  22 (2)  20 (1)  18 (0)  1All scores from the final battle in Aosta received 7 points in addition to the depicted values. 2Number of participants = number of contestants on a given battle board within a BW category. View Large Models and Analyses The UNIVARIATE procedure (SAS Inst. Inc., Cary, NC) was applied to data set 1 for a preliminary comparison of the distribution of PS and the simple individual placement (POS). A subsequent ANOVA on nongenetic effects treated as fixed effects was performed on each data set using the GLM procedure of SAS, aimed at identifying the magnitude of each possible source of variation. With the exception of the breed variety (Chestnut or Black Pied), all nongenetic effects taken into account in the ANOVA produced a significant effect on the PS (P < 0.001; data not presented), with a final R2 of 0.45 (data set 1) and 0.50 (data set 2), respectively. Therefore, the nongenetic factors included in the genetic analysis were the effect of the year-battle × BW category (YB×C, 123 different year-battles × 3 categories, for a total of 369 levels), the herd-year effect (HY, with 2,337 different levels in data set 1 and 2,182 in data set 2), the effect of the age class of participants (7 classes: ≤3, 4, 5, 6, 7, 8 and ≥9 yr of age at the time of fighting), and the individual BW as a covariate within each BW category (3 levels). Preliminary ANOVA indicated that all these factors could be retained in the final animal model analysis because no variance overlap among them could be detected. The subsequent analysis, aimed at estimating variance components, was carried out with a single-trait animal model (expectation maximization-REML method) using the appropriate program from the BLUPF90 family (Misztal, 2008). In the genetic analysis, a comparison of data set 1 with data set 2 was also carried out, with consideration given to different combinations of fixed and random nongenetic factors other than the random animal and permanent environmental effects. Model 1 considered YB×C and the HY as fixed effects, whereas models 2 to 4 considered different combinations of YB×C, HY, or both as random effects. Therefore, the most complete matrix notation of the models can be expressed as  y = Xβ + W1q1 + W2q2 + W3p + Zu + e,where y is an N × 1 vector of observations, β is the vector of systematic fixed effects of order p, q1 is the vector of YB×C when considered as a random effect (models 2 and 4), q2 is the vector of HY of order z when considered as a random effect (models 3 and 4), p is the vector of permanent environmental effects of order q, u is the vector of animal effects with order m, and e is the vector of residual effects. Furthermore, X, W1, W2, W3, and Z are the corresponding incidence matrices with the appropriate dimensions. In the model with the greater number of random factors, the assumptions about the structure of (co)variance were as follows:  where is the additive genetic variance, is the permanent environmental variance, is the YB×C variance (in models 2 and 4), is the HY variance (in models 3 and 4), is the random residual variance, A is the numerator relationship matrix, and I are identity matrices. For all data sets and models investigated, the heritability (h2) and repeatability (r) of fighting ability were estimated as follows:  and  where is the total phenotypic variance, given by the sum of all estimated variance components. Because of software limitations, the SE for heritability estimates were approximated by using the following formula (Falconer, 1989):  where t is the intraclass correlation approximated by (h2/4) for paternal half-sib estimates, k is the average number of offspring per sire, and s is the number of sires obtained from the pedigree file. A comparison between Akaike information criterion values (Akaike, 1974) was used to evaluate how well the models fit in all scenarios (models and data sets) as discussed in Ødegard et al. (2003). Rank correlations between EBV in both data sets while using only the model that showed the best fit (model 1) were also obtained separately for animals with fighting records (n = 5,891) and for their sires (n = 858) by using the CORR procedure of SAS. RESULTS The distribution of the POS and PS obtained by applying the above-mentioned formula to the complete data set containing 16,509 records retained for analysis is presented in Figure 1. The PS was closer to the normal distribution than the POS, as indicated by smaller Kolmogorov-Smirnov and Anderson-Darling values (data not shown). Both distributions proved to be skewed, but PS showed a smaller absolute skewness coefficient compared with POS (1.25 vs. −1.45 for PS and POS, respectively) while still indicating that PS was closer to a normal distribution than POS, as shown in Figure 1. Indeed, the distribution of POS was almost totally asymmetric (Figure 1). This is because each subsequent position after the winner and the animals classified as second (which had the same frequency) presented almost double the frequency compared with the next position because of the structure of the battle board used for fighting. Figure 1. View largeDownload slide Frequency distribution of data retained for the study and expressed as placements (i.e., final position on the battle board, indicated by the white bars on the left) or as placement score (dark gray bars on the right, calculated from Eq. [1] in the Materials and Methods section), used to express the fighting ability of each cow that fought in terms of year-battle × BW category. Figure 1. View largeDownload slide Frequency distribution of data retained for the study and expressed as placements (i.e., final position on the battle board, indicated by the white bars on the left) or as placement score (dark gray bars on the right, calculated from Eq. [1] in the Materials and Methods section), used to express the fighting ability of each cow that fought in terms of year-battle × BW category. Table 3 reports REML estimates obtained with different models and data sets. The data set containing only one yearly individual performance (data set 2) and the model that accounted for both YB×C and HY as fixed factors (model 1) gave the best fit, as revealed by the smaller Akaike information criterion value (Table 3). Heritability estimates ranged from 0.068 (model 2, data set 1) to 0.148 (model 3, data set 2). In all cases, the data set including only the best yearly performance of a cow (data set 2) produced greater heritability estimates. In the analysis that produced the best fit (i.e., model 1, data set 2), h2 was 0.098. The SE of heritability estimates proved to be 0.043 on average, with a reduced range of variation among data sets and models (from 0.042 to 0.044; data not shown). The repeatability was on average 0.24 and ranged from 0.21, in the model including both YB×C and HY as random factors, to 0.28, when only HY was considered random. The ratio between the permanent component and the total variance was on average 0.138 (data sets 1 and 2), revealing a slightly greater magnitude of the permanent component than the additive genetic component. The rank correlation among breeding values estimated in the 2 data sets for model 1 was 0.915. This correlation, when limited to the 858 sires, reached a similar value of 0.924. Table 3. Variance components, Akaike information criterion (AIC) estimates, heritability (h2), and repeatability estimates (r) obtained with the different models and data sets used Item  Variance component1  AIC      r                      Model 12                           Data set 13  —  —  0.591  1.295  5.731  77,615  0.078  0.248   Data set 24  —  —  0.752  1.216  5.735  48,380  0.098  0.255  Model 25                           Data set 1  1.236  —  0.609  1.387  5.667  78,818  0.068  0.224   Data set 2  1.917  —  0.796  1.343  5.605  49,774  0.080  0.221  Model 36                           Data set 1  —  0.156  0.869  1.014  5.751  85,570  0.112  0.242   Data set 2  —  0.228  1.190  1.029  5.614  56,431  0.148  0.275  Model 47                           Data set 1  1.489  0.184  0.857  1.085  5.695  86,817  0.092  0.209   Data set 2  1.995  0.244  1.210  1.113  5.528  57,806  0.120  0.230  Item  Variance component1  AIC      r                      Model 12                           Data set 13  —  —  0.591  1.295  5.731  77,615  0.078  0.248   Data set 24  —  —  0.752  1.216  5.735  48,380  0.098  0.255  Model 25                           Data set 1  1.236  —  0.609  1.387  5.667  78,818  0.068  0.224   Data set 2  1.917  —  0.796  1.343  5.605  49,774  0.080  0.221  Model 36                           Data set 1  —  0.156  0.869  1.014  5.751  85,570  0.112  0.242   Data set 2  —  0.228  1.190  1.029  5.614  56,431  0.148  0.275  Model 47                           Data set 1  1.489  0.184  0.857  1.085  5.695  86,817  0.092  0.209   Data set 2  1.995  0.244  1.210  1.113  5.528  57,806  0.120  0.230  1Variance components: = year-battle × category (YB×C); = herd-year (HY); = additive genetic; = permanent environmental; = random residual. 2Model 1: YB×C and HY both treated as fixed effects. 3Data set with all performances for each cows, 16,509 records. 4Data set with the only year best performance for each cow, 10,367 records. 5Model 2: YB×C treated as a random effect and HY treated as a fixed effect. 6Model 3: YB×C treated as fixed effect and HY treated as a random effect. 7Model 4: YB×C and HY both treated as random effects. View Large Table 3. Variance components, Akaike information criterion (AIC) estimates, heritability (h2), and repeatability estimates (r) obtained with the different models and data sets used Item  Variance component1  AIC      r                      Model 12                           Data set 13  —  —  0.591  1.295  5.731  77,615  0.078  0.248   Data set 24  —  —  0.752  1.216  5.735  48,380  0.098  0.255  Model 25                           Data set 1  1.236  —  0.609  1.387  5.667  78,818  0.068  0.224   Data set 2  1.917  —  0.796  1.343  5.605  49,774  0.080  0.221  Model 36                           Data set 1  —  0.156  0.869  1.014  5.751  85,570  0.112  0.242   Data set 2  —  0.228  1.190  1.029  5.614  56,431  0.148  0.275  Model 47                           Data set 1  1.489  0.184  0.857  1.085  5.695  86,817  0.092  0.209   Data set 2  1.995  0.244  1.210  1.113  5.528  57,806  0.120  0.230  Item  Variance component1  AIC      r                      Model 12                           Data set 13  —  —  0.591  1.295  5.731  77,615  0.078  0.248   Data set 24  —  —  0.752  1.216  5.735  48,380  0.098  0.255  Model 25                           Data set 1  1.236  —  0.609  1.387  5.667  78,818  0.068  0.224   Data set 2  1.917  —  0.796  1.343  5.605  49,774  0.080  0.221  Model 36                           Data set 1  —  0.156  0.869  1.014  5.751  85,570  0.112  0.242   Data set 2  —  0.228  1.190  1.029  5.614  56,431  0.148  0.275  Model 47                           Data set 1  1.489  0.184  0.857  1.085  5.695  86,817  0.092  0.209   Data set 2  1.995  0.244  1.210  1.113  5.528  57,806  0.120  0.230  1Variance components: = year-battle × category (YB×C); = herd-year (HY); = additive genetic; = permanent environmental; = random residual. 2Model 1: YB×C and HY both treated as fixed effects. 3Data set with all performances for each cows, 16,509 records. 4Data set with the only year best performance for each cow, 10,367 records. 5Model 2: YB×C treated as a random effect and HY treated as a fixed effect. 6Model 3: YB×C treated as fixed effect and HY treated as a random effect. 7Model 4: YB×C and HY both treated as random effects. View Large DISCUSSION In comparison with our study, other research on social dominance has been based on registering the results of dyadic encounters between members within a social group, used for assigning competitive values to individuals (De Vries, 1998; Langbein and Puppe, 2004; Val-Laillet et al., 2008). Agonistic interactions occurring in a group of cows were generally recorded within a herd during a given period and were plotted to obtain an almost linear hierarchy (e.g., Beilharz and Zeeb, 1982; Reinhardt et al., 1986) and a consequent “aggressive order” (McGlone, 1986). Dominance relationships among individuals have also been investigated by forcing animals to engage in dyadic agonistic interactions in standardized environments. A “competitive order” has thus been obtained (Syme, 1974). The Batailles de Reines represents an ideal scenario for assessing dominance relationships through a competitive order because of its peculiar structure of dyadic agonistic interactions between large numbers of animals under the same conditions. However, interactions between all the members of a group cannot be investigated by using data from the battles because after a defeat, an animal is obliged to withdraw from the contest. Beginning from this point and by considering the importance of a symmetric distribution of fighting results, this study has attempted to identify a suitable scoring system for the fights. From this perspective, the PS was designed to take into account the number of wins each animal achieved within a tournament (i.e., more victories, greater score) corrected according to the number of participants in the tournament (with a large number of participants presenting a greater opportunity to win). Moreover, by assigning a different weight to preliminary tournaments as compared with the final match, a possible overlapping of scores was avoided. In addition, the difficulty coefficient allowed a greater score to be assigned to matches disputed in small tournaments, where the probability of fighting against the final winner or another well-classified cow at the same competitive level was greater because of the reduced number of opponents. In general, the PS led to a fairly normal distribution, mitigating possible statistical analytical problems caused by the asymmetric distribution of simple placement on the battle board. However, further steps in modeling the dyadic data of Batailles could be considered in the future, particularly those aimed at correcting the PS for the strength of the competitor or using a model better suited to analyze rank, such as the Thurstonian model (Gianola and Simianer, 2006). Alternatively, the competitor effect may be accounted for in the genetic model, as has been done in studies on genetic effects in social behavior (Moore et al., 1997; Bijma et al., 2007). Possible comparisons of PS with the literature are not easy because of the absence of population studies on fighting ability based on tournaments. In a pilot study on social dominance in cattle, Schein and Fohrman (1955) observed that age and BW are important factors associated with fighting performance and dominance. Our results confirm that both age and BW can influence the PS, and consequently the fighting ability, of Valdostana cows. However, in our study, we also attempted to identify possible factors related to the specific contest of each fight (YB×C) and the behavioral background of individuals belonging to different herds that could be mirrored by the HY effect. The YB×C effect tells us exactly in which challenge an individual took part, allowing the performance of an individual cow to be adjusted based on the performance of the other participants in the same battle contest. Moreover, the HY effect is aimed at reflecting the within-herd social hierarchy and fighting background, which can change over the years because of variations in herd composition and which can influence the perception of an individual of its own fighting ability, and thus its fighting performance. In the present study, the data structure and the reduced size of half-sib families (6.1 daughters/sire) could have biased the genetic parameter estimates, although it is not easy to quantify the exact amount of such biases. Other possible biases could be due to the nonrandom choice of records in data set 2 (i.e., retaining only the best yearly performance of each cow). This could inflate the heritability estimates. However, the generally low heritability estimate in our study is in agreement with literature values obtained for other behavioral traits (Hohenboken, 1986; Mousseau and Roff, 1987), reflecting a strong behavioral plasticity that allows individuals to adapt to varying environments. Heritability estimates for social dominance ranged from 0.07 (Dickson et al., 1970) to 0.40 (Beilharz et al., 1966) in Holstein dairy cattle. Genetic evaluations carried out on agonistic performances of fighting bulls (i.e., Lidia cattle breed) revealed a heritability of 0.19 for fighting bulls in a Colombian herd (González Caicedo et al., 1994), and a heritability of approximately 0.30 for Spanish fighting bulls (Silva et al., 2006). A preliminary estimation of heritability for fighting ability was also obtained by analyzing traditional tournaments for Hérens cows in Switzerland (Plusquellec, 2001). The scoring method applied to evaluate fighting performance came from the ranking applied in horse competitions (Tavernier, 1991), and the heritability estimate was 0.045. Behaviors typically include several different environmental factors (e.g., learning, social interactions) as well as the genetic component. In this study, the repeatability values obtained indicate that environmental effects were predominant with respect to additive components. This could be due to the kind of phenotype measured, which is modeled by previous experiences with other counterparts and is mainly recorded at an adult age. The correlation between ranks of EBV derived from an analysis of the 2 different data sets showed only small changes in animal ranking, indicating substantial uniformity among data sets in spite of possible overestimates of heritability caused by the nonrandom choice of records in data set 2. Therefore, genetic indexes derived from this study may lead to possible application for selection. The knowledge of genetic components in behavioral traits could be important for developing strategies to modulate behavioral expression genetically, as has already been done for docility in Limousine cattle (Phocas et al., 2006). Future studies accounting for alternative scoring methods or different models as eventual genetic correlations between behavior and productive traits could be useful for better understanding fighting ability and its possible role in animal welfare and management. This study indicates that genetic evaluation and selection for fighting behavior are possible, although the additive genetic component of the trait is small. However, this result is in agreement with the heritability estimates for other behavioral traits, especially those related to social dominance and fighting. The proposed PS obtained from the battle board seems a possible way to express phenotypic values useful for genetic evaluation and to address selection for fighting ability. LITERATURE CITED Akaike H. 1974. A new look at the statistical model identification. IEEE Trans. Automat. Contr.  AC-19: 716– 723. Google Scholar CrossRef Search ADS   Beilharz R. G. Butcher D. F. Freeman A. E. 1966. Social dominance and milk production in Holsteins. J. Dairy Sci.  49: 887– 892. Google Scholar CrossRef Search ADS   Beilharz R. G. Zeeb K. 1982. Social dominance in dairy cattle. Appl. Anim. Ethol.  8: 79– 97. Google Scholar CrossRef Search ADS   Bijma P. Muir W. M. van Arendonk J. A. M. 2007. Multilevel selection 1: Quantitative genetics of inheritance and response to selection. Genetics  175: 277– 288. [PubMed] Google Scholar CrossRef Search ADS PubMed  Bøe K. E. Færevik G. 2003. Grouping and social preference in calves, heifers, and cows. Appl. Anim. Behav. Sci.  80: 175– 190. Google Scholar CrossRef Search ADS   Bruns E. 1981. Estimation of the breeding value of stallions from the tournament performance of their offspring. Livest. Prod. Sci.  8: 465– 473. Google Scholar CrossRef Search ADS   Clutton-Brock T. H. Albon S. D. 1979. The roaring of red deer and the evolution of honest advertisement. Behaviour  69: 146– 170. Google Scholar CrossRef Search ADS   Del Bo L. Polli M. Longeri M. Ceriotti G. Looft C. Barre-Dirie A. Dolf G. Zanotti M. 2001. Genetic diversity among some cattle breeds in the Alpine area. J. Anim. Breed. Genet.  118: 317– 325. Google Scholar CrossRef Search ADS   De Vries H. 1998. Finding a dominance order most consistent with a linear hierarchy: A new procedure and review. Anim. Behav.  55: 827– 843. [PubMed] Google Scholar CrossRef Search ADS PubMed  Dickson D. P. Barr G. R. Johnson L. P. Wieckert D. A. 1970. Social dominance and temperament of Holstein cows. J. Dairy Sci.  53: 904– 907. Google Scholar CrossRef Search ADS   Dorofejew, W. N., and N. W. Dorofejewa 1976. Bewertung von sportpferden nach ihrer leistungsfäheigkeit.  Pages 206– 212 in Proc. 2nd Int. Wiss. Symp., Leipzig, July 13–14, 1976. Drews C. 1993. The concept and definition of dominance in animal behaviour. Behaviour  125: 283– 313. Google Scholar CrossRef Search ADS   Falconer, D. S. 1989. Quantitative Genetics.  3rd ed. Longman Scientific and Technical, Harlow, UK. Gianola D. Simianer H. 2006. A Thurstonian model for quantitative genetic analysis of ranks: A Bayesian approach. Genetics  174: 1613– 1624. [PubMed] Google Scholar CrossRef Search ADS PubMed  González Caicedo G. E. Durán Castro C. V. Dominguez Cadavid J. F. 1994. Heritability and repeatability of the ‘nota de tienta’ and ‘nota de lidia’ traits in a herd of fighting bull cattle. Arch. Zootech.  43: 225– 237. Hohenboken W. D. 1986. Inheritance of behavioural characteristics in livestock. A review. Anim. Breed. Abstr.  54: 623– 639. Langbein J. Puppe B. 2004. Analysing dominance relationships by sociometric methods—A plea for a more standardised and precise approach in farm animals. Appl. Anim. Behav. Sci.  87: 293– 315. Google Scholar CrossRef Search ADS   Langlois, B. 1984. Cheval de loisir et de sport: Aptitudes et sélection. Pages 423– 435 in Le Cheval: Reproduction, Selection, Alimentation Exploitation.  R. Jarrige and W. Martin-Rosset ed. INRA, Paris, France. Mantovani R. Contiero B. Vevey M. 2007. Genetic evaluation of cow fighting ability in the Valdostana breed. Ital. J. Anim. Sci.  6( Suppl. 1): S156– S158. McGlone J. J. 1986. Influence of resources on pig aggression and dominance. Behav. Processes  12: 135– 144. Google Scholar CrossRef Search ADS PubMed  Misztal I. 2008. Reliable computing in estimation of variance components. J. Anim. Breed. Genet.  125: 363– 370. [PubMed] Google Scholar CrossRef Search ADS PubMed  Moore A. J. Brodie E. D.III Wolf J. B. 1997. Interacting phenotypes and the evolutionary process: I. Direct and indirect genetic effects of social interactions. Evolution  51: 1352– 1362. Google Scholar CrossRef Search ADS PubMed  Mousseau T. A. Roff D. A. 1987. Natural selection and the heritability of fitness components. Heredity  59: 181– 197. [PubMed] Google Scholar CrossRef Search ADS PubMed  Ødegard J. Jensen J. Klemetsdal G. Madsen P. Heringstad B. 2003. Genetic analysis of somatic cell score in Norwegian cattle using random regression test-day models. J. Dairy Sci.  86: 4103– 4114. [PubMed] Google Scholar CrossRef Search ADS PubMed  Parker G. A. 1974. Assessment strategy and the evolution of fighting behaviour. J. Theor. Biol.  47: 223– 243. [PubMed] Google Scholar CrossRef Search ADS PubMed  Phillips, C. J. C. 1993. Cattle Behaviour.  Farming Press Books, Ipswich, UK. Phocas F. Boivin X. Sapa J. Trillat G. Boissy A. Le Neindre P. 2006. Genetic correlations between temperament and breeding traits in Limousin heifers. Anim. Sci.  82: 805– 811. Google Scholar CrossRef Search ADS   Plusquellec, P. 2001. Influence d'une sélection pour la combativité et l'aptitude à la dominance sur le comportement social et la réactivité émotionnelle des bovins domestiques femelles de la race d'Hérens. PhD Diss.  Université Paris XIII, Paris, France. Plusquellec P. Bouissou M. F. 2001. Behavioural characteristics of two dairy breeds of cows selected (Hérens) or not (Brune des Alpes) for fighting and dominance ability. Appl. Anim. Behav. Sci.  72: 1– 21. [PubMed] Google Scholar CrossRef Search ADS PubMed  Reinhardt C. Reinhardt A. Reinhardt V. 1986. Social behaviour and reproductive performance in semi-wild Scottish Highland cattle. Appl. Anim. Behav. Sci.  15: 125– 136. Google Scholar CrossRef Search ADS   Schein M. W. Fohrman M. H. 1955. Social dominance relationships in a herd of dairy cattle. Br. J. Anim. Behav.  3: 45– 55. Google Scholar CrossRef Search ADS   Silva B. Gonzalo A. Cañón J. 2006. Genetic parameters of aggressiveness, ferocity and mobility in the fighting bull breed. Anim. Res.  55: 65– 70. Google Scholar CrossRef Search ADS   Syme G. J. 1974. Competitive orders as measures of social dominance. Anim. Behav.  22: 931– 940. Google Scholar CrossRef Search ADS   Tavernier A. 1991. Genetic evaluations of horses based on ranks in competitions. Genet. Sel. Evol.  23: 159– 173. Google Scholar CrossRef Search ADS   Val-Laillet D. de Passille A. M. Rushen J. von Keyserlingk M. 2008. The concept of social dominance and the social distribution of feeding-related displacements between cows. Appl. Anim. Behav. Sci.  111: 158– 172. Google Scholar CrossRef Search ADS   Wieckert D. A. 1971. Social behavior in farm animals. J. Anim. Sci.  32: 1274– 1277. [PubMed] Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Kelly, A. K.;McGee, M.;Jr., D. H. Crews,;Sweeney, T.;Boland, T. M.;Kenny, D. A.

doi: 10.2527/jas.2009-2700pmid: 20525931

ABSTRACT This study examined the relationship between feed efficiency and performance, and feeding behavior, blood metabolic variables, and various ultrasonic measurements in finishing beef heifers. Within-animal repeatability estimates of feed intake and behavior, performance, feed efficiency, ultrasonic body measures, and plasma analytes across the growing and finishing stages of the lifespan of the animal were also calculated. Fifty heifers previously ranked as yearlings on phenotypic residual feed intake (RFI) were used. Animals [initial BW = 418 (SD = 31.5) kg] were offered a TMR diet consisting of 70:30 concentrate and corn silage on a DM basis (ME 10.7 MJ/kg of DM; DM 530 g/kg) for 84 d. Feeding duration (min/d) and feeding frequency (events/d) were calculated for each animal on a daily basis using a computerized feeding system. Ultrasonic kidney fat and lumbar and rump fat and muscle depths were recorded on 3 equally spaced occasions during the experimental period. Blood samples were collected by jugular venipuncture on 4 occasions during the experimental period and analyzed for plasma concentrations of IGF-I, insulin, and various metabolites. Phenotypic RFI was calculated for all animals as the residuals from a regression model regressing DMI on ADG and midtest BW0.75. Repeatability was calculated for several traits both within and between production phase using intraclass correlation and Pearson correlation coefficients as appropriate. Overall ADG, DMI, G:F, and RFI were 1.17 kg/d (SD = 0.19), 10.81 kg/d (SD = 1.02), 0.11 kg of BW gain/kg of DM (SD = 0.02), and 0.00 kg of DM/d (SD 0.59). Daily feeding events and eating rate tended to be positively correlated (P = 0.08) with RFI. Ultrasonic kidney fat depth tended to be related to G:F (r = −0.28; P = 0.07), and kidney fat accretion tended to be related to RFI (r = 0.29; P = 0.08). Plasma urea (r = 0.38; P < 0.01), β-hydroxybutyrate (r = 0.40; P < 0.01), and insulin (r = 0.23; P = 0.07) concentrations were correlated with RFI. Plasma glucose (r = −0.25; P = 0.07), glucose:insulin (r = 0.33; P < 0.05), and insulin (r = −0.30; P < 0.05) were associated with G:F. However, systemic IGF-I was unrelated (P > 0.10) to any measure of feed efficiency. Repeatability estimates within the finishing period for DMI, feeding duration, feeding events, feed intake/feeding event, and eating rate were 0.34, 0.37, 0.60, 0.62, and 0.56, respectively. Repeatability estimates (P < 0.001) between the growing and finishing phases for DMI, G:F, and RFI were r = 0.61, r = 0.37, and r = 0.62, respectively. Moderate to strong repeatability values (ranging from r = 0.40 to 0.76; P < 0.001) were obtained for feeding behavior traits between the yearling and finishing phases. We conclude that RFI and feeding behavior are repeatable traits and that some plasma analytes may be potential indicators of RFI in beef cattle. INTRODUCTION Livestock producers must endeavor to achieve efficiency in production to improve profitability. Given that feed accounts for the single largest input cost in cattle production systems (Basarab et al., 2002; Herd et al., 2003), worldwide interest has focused on the concept of energetic efficiency and the identification of cattle that are more efficient at utilizing feed resources. Residual feed intake (RFI) is increasing in popularity as the measure of choice for estimating energetic efficiency in beef cattle (Moore et al., 2009), given its genetic independence from growth and body size and its moderate heritability (Crews, 2005; Crowley et al., 2010). This, coupled with known interanimal genetic variation for maintenance energy requirements (Johnson et al., 2003), indicates that RFI could be improved in both slaughter and breeding populations through selection. However, the current lack of understanding of the complex biological mechanisms controlling RFI, together with the prohibitive costs of wide-scale measurement, has resulted in little progress in its implementation as an economically relevant trait in genetic selection programs (Herd and Arthur, 2009; Moore et al., 2009). Consequently, there is increasing interest in identifying early-life, easily measured, and cost-effective methodologies for wide-scale identification of feed-efficient animals (Herd et al., 2003). Kelly et al. (2010) reported that feeding behavior traits, carcass composition measures, and circulating blood metabolites in growing heifers contributed to approximately 35% of the variation in RFI. However, there is limited published information on the repeatability of RFI and associated traits between different phases of the production cycle, which is ultimately essential for wide-scale adoption by producers. The objective of the current study was to examine the relationship between feed efficiency, feeding behavior, blood metabolic variables, and various ultrasonic measurements in finishing beef heifers ranked on RFI. Additionally, using data from an associated study (Kelly et al., 2010), within-animal repeatability estimates for RFI and related traits between the growing and finishing stages of the lifespan of an animal were calculated. MATERIALS AND METHODS All procedures involving animals were approved by the University College Dublin, Animal Research Ethics Committee and licensed by the Irish Department of Health and Children in accordance with the European Community Directive 86/609/EC. Animals and Management Fifty Limousin × Friesian heifers were selected from a previous experiment (Kelly et al., 2010) in which RFI was determined for a group of 86 heifers between 8 and 11 mo of age. The 25 highest [mean 0.47 kg/d (SD = 0.38 kg/d)] and 25 lowest [mean −0.53 kg/d (SD = 0.31 kg/d)] ranking animals on RFI were selected for use in the current study. After ranking, heifers were turned out to pasture and rotationally grazed together as a single group on a predominantly perennial ryegrass sward until housing 5 mo later [mean ADG of 0.81 kg/d (SD = 0.13 kg/d)]. At pasture, they were treated with ivermectin (Qualimec, Janssen Animal Health, Janssen-Cilag Ltd., High Wycombe, Bucks, UK) at 3, 8, and 13 wk after turnout and again at rehousing for the control of external and internal parasites. At housing, animals were also treated for liver fluke with Fasinex 5% triclabendazol (Novartis Animal Health Ltd., Roston, UK). During the finishing phase, heifers were penned (21 m long × 17 m wide) as 1 group bedded on peat mulch and had free access to 15 electronic feeding stations (Insentec, Marknesse, the Netherlands). Briefly, similar to the method described by Kelly et al. (2010), animals were allowed an adaptation period of 30 d, during which the concentrate proportion of the diet was gradually increased while, concurrently, the forage (corn silage) proportion was reduced. Ad libitum feed intake was reached at approximately 20 d after the beginning of the acclimatization period; thus, animals were on ad libitum intake for approximately 10 d before the experimental recording period, which lasted for 84 d (i.e., 114-d period in total). The mean age and BW at the beginning of the performance test were 495 d (SD = 13) and 417 kg (SD = 31.4), respectively. The test period diet was a TMR composed of a 70:30 pelleted concentrate:corn silage diet, on a DM basis. The nutritional composition of the diet is outlined in Table 1. The dietary ingredients were mixed and dispensed using a feeding wagon and offered in 1 daily feeding at 0900 h. Each feed station was filled to 110% of the weight of material removed on the previous day to ensure that all animals had constant and unrestricted access to feed. All feed stations were calibrated twice weekly using known weights. Animals also had ad libitum access to fresh drinking water. Table 1. Ingredient composition and chemical analysis of the experimental diet (expressed as g/kg of DM unless otherwise stated)1 Item  Concentrate  TMR2  Ingredient   Corn silage     300   Concentrate     700   Soybean meal  273      Barley  224      Beet pulp  148      Citrus pulp  145      Rapeseed meal  98      Cane molasses  80      Calcium carbonate  7.5      Palm oil  6      Sodium chloride  6      Sodium bicarbonate  5      Mono-dicalcium phosphate  3.5      Trace mineral premix3,4  3      Vitamin E  1  Composition         DM     530 (30.0)   CP     171 (3.2)   ADF     185 (8.0)   NDF     419 (7.8)   Ash     73 (1.0)   Water-soluble carbohydrate     33 (2.7)   Ether extract     24 (0.5)   GE, MJ/kg of DM     18.0 (0.07)   ME,5 MJ/kg of DM     10.7  Item  Concentrate  TMR2  Ingredient   Corn silage     300   Concentrate     700   Soybean meal  273      Barley  224      Beet pulp  148      Citrus pulp  145      Rapeseed meal  98      Cane molasses  80      Calcium carbonate  7.5      Palm oil  6      Sodium chloride  6      Sodium bicarbonate  5      Mono-dicalcium phosphate  3.5      Trace mineral premix3,4  3      Vitamin E  1  Composition         DM     530 (30.0)   CP     171 (3.2)   ADF     185 (8.0)   NDF     419 (7.8)   Ash     73 (1.0)   Water-soluble carbohydrate     33 (2.7)   Ether extract     24 (0.5)   GE, MJ/kg of DM     18.0 (0.07)   ME,5 MJ/kg of DM     10.7  1Values in parentheses represent SE. 2n = 8. 3Premix supplied (per kilogram of supplement) 4,000 IU of vitamin A, 1,200 IU of vitamin D3, and 56 mg of vitamin E as α-tocopherol. 4Premix supplied (per kilogram of supplement) 0.39 mg of selenium as sodium selenite, 6.00 mg of iodine as calcium iodate, and 22.00 mg of copper as cupric sulfate. 5ME = {[GE × DE (NRC, 2000)] × 0.82}, as described by El-Meccawi et al. (2009). View Large Table 1. Ingredient composition and chemical analysis of the experimental diet (expressed as g/kg of DM unless otherwise stated)1 Item  Concentrate  TMR2  Ingredient   Corn silage     300   Concentrate     700   Soybean meal  273      Barley  224      Beet pulp  148      Citrus pulp  145      Rapeseed meal  98      Cane molasses  80      Calcium carbonate  7.5      Palm oil  6      Sodium chloride  6      Sodium bicarbonate  5      Mono-dicalcium phosphate  3.5      Trace mineral premix3,4  3      Vitamin E  1  Composition         DM     530 (30.0)   CP     171 (3.2)   ADF     185 (8.0)   NDF     419 (7.8)   Ash     73 (1.0)   Water-soluble carbohydrate     33 (2.7)   Ether extract     24 (0.5)   GE, MJ/kg of DM     18.0 (0.07)   ME,5 MJ/kg of DM     10.7  Item  Concentrate  TMR2  Ingredient   Corn silage     300   Concentrate     700   Soybean meal  273      Barley  224      Beet pulp  148      Citrus pulp  145      Rapeseed meal  98      Cane molasses  80      Calcium carbonate  7.5      Palm oil  6      Sodium chloride  6      Sodium bicarbonate  5      Mono-dicalcium phosphate  3.5      Trace mineral premix3,4  3      Vitamin E  1  Composition         DM     530 (30.0)   CP     171 (3.2)   ADF     185 (8.0)   NDF     419 (7.8)   Ash     73 (1.0)   Water-soluble carbohydrate     33 (2.7)   Ether extract     24 (0.5)   GE, MJ/kg of DM     18.0 (0.07)   ME,5 MJ/kg of DM     10.7  1Values in parentheses represent SE. 2n = 8. 3Premix supplied (per kilogram of supplement) 4,000 IU of vitamin A, 1,200 IU of vitamin D3, and 56 mg of vitamin E as α-tocopherol. 4Premix supplied (per kilogram of supplement) 0.39 mg of selenium as sodium selenite, 6.00 mg of iodine as calcium iodate, and 22.00 mg of copper as cupric sulfate. 5ME = {[GE × DE (NRC, 2000)] × 0.82}, as described by El-Meccawi et al. (2009). View Large Samples of the TMR diet were taken from 3 locations within each of the 15 feed stations, twice weekly. Samples were composited on a weekly basis and stored at −20°C pending analysis for DM, CP, ADF, NDF, WSC, ether extract, ash, and GE. Samples were milled through a 1-mm screen using a Christy and Norris hammer mill (Christy and Norris Process Engineers Ltd., Chelmsford, UK). Dry matter was determined by oven drying at 104°C for a minimum of 16 h. Ash was determined on all materials after ignition of a known weight of ground material in a muffle furnace (Nabertherm, Bremen, Germany) at 550°C for 4 h. The NDF and ADF concentrations of feed were obtained using an Ankom200 Fiber Analyzer (Ankom Technology, Fairport, NY) according to the method of Van Soest et al. (1991). The CP (total nitrogen × 6.25) was determined as described by Sweeney (1989) using a Leco FP 528 N Analyzer (Leco Instruments, UK Ltd., Stockport, Cheshire, UK). Water-soluble carbohydrate content was established in duplicate using a modification of the of the phenol/H2SO4 method described by Birch and Mwangelwa (1974). Ether extract (petroleum ether extraction, boiling point 40 to 60°C) was determined using a Sortex instrument (Tecator, Höganäs, Sweden), and GE was determined using a Parr 1201 oxygen bomb calorimeter (Parr, Moline, IL). Feed Intake and Growth Data Feed intake was measured for each animal with an Insentec monitoring system, previously validated by Chapinal et al. (2007) and Kelly et al. (2010). Briefly, the system consisted of 15 feed bins (1.0 m wide, 0.75 m high, and 0.84 m deep), a data-logging reader panel connected to each feed node, a personal computer, and Insentec Data Acquisition and Analysis Software. For each visit to the bin, the system recorded the animal number, bin number, initial and final times, and weight of contents and calculated the visit duration and quantity of material removed during a visit. Animals were weighed (unfasted) in the morning before daily feeding at 14-d intervals during the test period. This resulted in 7 records per animal. Animals were weighed on 2 consecutive days at the beginning and again at the end of test. Feeding Behavior Feeding behavior traits were evaluated in this study using feed bin attendance data. The number of daily feeding events was calculated as the number of times an animal entered the feed bin and consumed a minimum of 100 g of feed. Nonfeeding events were calculated as the number of times an animal entered the feed bin without feed consumption occurring or when less than 100 g of feed was consumed. Daily feeding duration was computed as the total daily time taken to consume the recorded intake (min/d). Eating rate was calculated as total DMI per day divided by total daily duration (kg/min). Feed intake/feeding event was calculated as the total DMI per day divided by the number of daily feeding events (kg/event). Ultrasound Measurements Each animal was ultrasonically scanned (Aquila Vet real-time ultrasound scanner, with a 3.5-MHz transducer, Esaote Pie Medical, Pie Medical Equipment B.V., Maastricht, the Netherlands) on 3 separate occasions throughout the experimental period to obtain LM depth and fat depth. Scanning was carried out on the right side of each animal; LM depth was measured at the third lumbar vertebra, where depth of this muscle is greatest, as described by Conroy et al. (2009). Measurement was from the bottom of the backfat layer to the top of the bone. Fat depth was measured at 3 points at the third lumbar vertebra across the width (at either end and in the center) of the muscle and also at the Ausmeat P8 site (rump), as described by Robinson et al. (1992). Lumbar fat depth at each ultrasonic scan was calculated as the mean of the values recorded. Ultrasonic kidney fat depth (an indicator of internal body fat) was measured between the first lumbar vertebra and the 13th rib, as described by Ribeiro et al. (2008). The measurement was taken between the ventral part of the abdominal muscles (iliocostalis, obliquus abdominis interni, and obliquus abdominis externi) and the end of the kidney fat. Blood Collection and Analysis Blood was sampled by jugular venipuncture in the morning, on d 1, 30, 60, and 84 during the experimental period. Samples (10 mL) were collected into heparinized evacuated tubes (170-IU lithium heparin Vacutainer tubes, Becton Dickinson Vacutainer Systems, Plymouth, UK) for plasma concentrations of IGF-I, insulin, glucose, urea, NEFA, and β-hydroxybutyrate (BHB). On collection, samples were immediately stored in ice water and centrifuged at 1,500 × g at 4°C for 15 min. The plasma was then split into borosilicate glass scintillation vials and stored at −20°C until analysis. Total thyroxine (T4) and free triiodothyronine (T3) concentrations were determined by solid phase time-resolved fluoroimmunoassay using AutoDELFIA kits (AutoDELFIA, PerkinElmer Life and Analytical Sciences, Turku, Finland) as described previously by Boland et al. (2007). Intraassay CV for total T4 were 10.7, 5.8, and 10.3% for the low, medium, and high standards, respectively. Corresponding interassay CV were 13.8, 9.6, and 14.2%. For free T3, the intraassay CV were 2.8, 3.9, and 4.6% for the low, medium, and high standards, respectively, and the interassay CV were 13.4, 11.3, and 11.1%. Plasma IGF-I concentrations were determined by RIA, after an acid-ethanol extraction procedure, as described previously by Spicer et al. (1988). Intraassay CV for IGF-I were 12.4, 11.5, and 7.1% for the low, medium, and high standards, respectively; corresponding interassay CV were 12.3, 11.9, and 7.1%. Concentrations of glucose, urea, BHB, and NEFA were analyzed using reagents supplied by Randox Laboratories (Randox Laboratories Ltd., Crumlin, Antrim, N. Ireland; catalog numbers OSR6121, OSR6134, RD1007, and FA115). All plasma metabolite concentrations were quantified by enzymatic colorimetry using an AV400 Clinical Analyzer (Olympus Diagnostics, Tokyo, Japan). Plasma insulin was quantified using fluoroimmunoassay (AutoDELFIA, PerkinElmer Life and Analytical Sciences; catalog number B080-101) and validated for bovine plasma (Ting et al., 2004). Intraassay CV for insulin were 4.5, 3.6, and 3.4% for the low, medium, and high standards, respectively; corresponding interassay CV were 4.2, 3.5, and 3.6%. Traits and Their Derivation Average daily gain during the test period for each animal was computed as the coefficient of the linear regression of BW (kg) on time using REG procedure (SAS Inst. Inc., Cary, NC). Midtest metabolic BW (MBW) was represented as BW0.75 42 d before the end of the test, which was estimated from the intercept and slope of the regression line after fitting a linear regression through all MBW observations. Total daily DMI was calculated as the sum of all the feeding events within the day corrected for DM concentration. Gain:feed of each animal was computed as the ratio of ADG to daily DMI. The additional growth traits of the Kleiber ratio (KR) and relative growth rate (RGR) were examined because they are considered indirect measures of feed efficiency, without the requirement of measuring feed intake (Fitzhugh and St. Taylor, 1971; Bergh et al., 1992). Relative growth rate, growth relative to instantaneous body size, and KR were computed as follows: RGR = 100 × [loge(end BW) − loge(beginning BW)]/days on test, and KR = ADG/MBW. Residual feed intake was computed for each animal and was assumed to represent the residuals from a multiple regression model regressing DMI on ADG and MBW. The base model used was  Yj = β0 + β1MBWj + β2ADGj + ej,where Yj is the standardized DMI of the jth animal, β0 is the regression intercept, β1 is the regression coefficient on MBW, β2 is the regression coefficient on ADG, and ej is the uncontrolled error of the jth animal. Gain in ultrasonic measurement for each individual animal was predicted from regression equations of measurements on time (days). Statistical Analyses Data were checked for normality and homogeneity of variance using histograms, quantile-quantile plots, and formal statistical tests as part of the UNIVARIATE procedure of SAS. Pearson correlation coefficients among traits were determined using the CORR procedure of SAS. Repeated measures means of the blood analyte concentrations and the feeding behavior traits for the entire experimental period were used in the correlation analysis. Correlation coefficients were classified as strong (r > 0.6), moderate (r between 0.4 and 0.6), or weak (r < 0.4), respectively. Data were considered statistically significant when P < 0.05. Repeatability was estimated using 2 procedures: ratio-of-variances estimation (or intraclass correlation) and Pearson correlation coefficient estimation. The ratio-of-variances method was used for the feeding behavior traits over the finishing phase, and the correlation method was adopted for traits between the growing and finishing stages of production (i.e., traits having only 2 measurements per individual). The ratio-of-variances method requires that variance components be estimated for observations both within and among animals. Repeatability was estimated as  where re indicates repeatability, indicates between-animal variance, and indicates within-animal variance. To compute the repeatability, variance components were estimated from a mixed model using the REML procedure (PROC MIXED of SAS). RESULTS Intake, Performance, and Feed Efficiency Descriptive statistics are presented in Table 2. Animals in this study had an overall mean initial BW of 418 kg (SD = 31.5), ADG of 1.17 kg (SD = 0.19), DMI of 10.81 kg/d (SD = 1.02), and G:F of 0.11 kg of BW gain/kg of DMI (SD = 0.02). Residual feed intake averaged 0.00 kg of DM/d (SD = 0.59) and ranged from −1.63 to 1.24 kg of DM/d, representing a difference of 2.87 kg of DM of feed/d between the least and most efficient animals. Table 2. Descriptive statistics [mean, SD, 95% confidence interval (CI), minimum, and maximum] in finishing beef heifers for performance, feed efficiency, feeding behavior, and ultrasonic and metabolic traits Trait  Mean  SD  Low 95% CI  High 95% CI  Minimum  Maximum  Residual feed intake, kg of DM/d  0.00  0.59  −0.171  0.171  −1.63  1.24  G:F, kg of BW gain/kg of DM  0.11  0.02  0.10  0.11  0.07  0.14  DMI, kg/d  10.81  1.02  10.52  11.11  8.69  14.36  Relative growth rate1  0.107  0.018  0.101  0.112  0.073  0.158  Kleiber ratio2  0.012  0.002  0.011  0.012  0.007  0.017  Metabolic BW, kg0.75  99.76  5.28  98.24  101.27  88.51  114.34  ADG, kg/d  1.17  0.19  1.11  1.22  0.74  1.59  Initial BW, kg  417.8  31.49  408.7  426.8  350.0  499.5  Final BW, kg  511.9  36.23  501.5  522.3  433.0  614.0  Feeding duration, min/d  117.9  18.43  110.25  125.47  88.11  158.10  Feeding events, events/d  45.4  10.92  40.9  49.9  21.9  63.6  Feed intake/feeding event, kg/event  0.27  0.07  0.24  0.30  0.16  0.51  Eating rate, kg/min  0.10  0.01  0.09  0.11  0.08  0.16  Nonfeeding events, events/d  3.7  2.20  2.86  4.68  1.9  13.6  Nonfeeding duration, min/d  2.52  0.94  2.12  2.91  1.31  5.58  Initial lumbar fat thickness, mm  3.89  1.39  3.49  4.29  1.03  7.23  Final lumbar fat thickness, mm  6.41  1.62  5.95  6.88  3.27  10.73  Gains in lumbar fat, mm/d  0.03  0.01  0.03  0.04  0.01  0.07  Initial rump fat thickness, mm  2.78  1.64  2.26  3.27  1.00  9.30  Final rump fat thickness, mm  8.02  2.84  7.14  8.85  3.60  18.10  Gains in rump fat, mm/d  0.07  0.02  0.06  0.08  0.02  0.12  Initial kidney fat thickness, mm  16.10  1.84  15.60  16.62  12.02  21.4  Final kidney fat thickness, mm  19.64  1.89  19.32  20.44  15.13  24.15  Gains in kidney fat, mm/d  0.05  0.02  0.04  0.06  0.01  0.09  Initial muscle depth thickness, mm  54.6  3.47  53.6  55.6  46.6  62.7  Final muscle depth thickness, mm  64.9  4.01  63.7  66.0  56.4  72.0  Gains in muscle, mm/d  0.13  0.47  0.12  0.15  0.03  0.25  IGF-I, ng/mL  440.30  84.76  427.92  452.45  229.91  679.73  Insulin, μIU/mL  21.43  4.45  20.15  22.71  13.50  34.77  Glucose, mmol/L  4.39  0.27  4.30  4.46  3.77  5.45  Glucose:insulin  0.21  0.04  0.19  0.22  0.13  0.32  Urea, mmol/L  5.61  0.60  5.44  5.78  4.50  7.08  β-Hydroxybutyrate, mmol/L  0.45  0.06  0.42  0.46  0.34  0.64  NEFA, mmol/L  0.06  0.01  0.05  0.06  0.04  0.12  Trait  Mean  SD  Low 95% CI  High 95% CI  Minimum  Maximum  Residual feed intake, kg of DM/d  0.00  0.59  −0.171  0.171  −1.63  1.24  G:F, kg of BW gain/kg of DM  0.11  0.02  0.10  0.11  0.07  0.14  DMI, kg/d  10.81  1.02  10.52  11.11  8.69  14.36  Relative growth rate1  0.107  0.018  0.101  0.112  0.073  0.158  Kleiber ratio2  0.012  0.002  0.011  0.012  0.007  0.017  Metabolic BW, kg0.75  99.76  5.28  98.24  101.27  88.51  114.34  ADG, kg/d  1.17  0.19  1.11  1.22  0.74  1.59  Initial BW, kg  417.8  31.49  408.7  426.8  350.0  499.5  Final BW, kg  511.9  36.23  501.5  522.3  433.0  614.0  Feeding duration, min/d  117.9  18.43  110.25  125.47  88.11  158.10  Feeding events, events/d  45.4  10.92  40.9  49.9  21.9  63.6  Feed intake/feeding event, kg/event  0.27  0.07  0.24  0.30  0.16  0.51  Eating rate, kg/min  0.10  0.01  0.09  0.11  0.08  0.16  Nonfeeding events, events/d  3.7  2.20  2.86  4.68  1.9  13.6  Nonfeeding duration, min/d  2.52  0.94  2.12  2.91  1.31  5.58  Initial lumbar fat thickness, mm  3.89  1.39  3.49  4.29  1.03  7.23  Final lumbar fat thickness, mm  6.41  1.62  5.95  6.88  3.27  10.73  Gains in lumbar fat, mm/d  0.03  0.01  0.03  0.04  0.01  0.07  Initial rump fat thickness, mm  2.78  1.64  2.26  3.27  1.00  9.30  Final rump fat thickness, mm  8.02  2.84  7.14  8.85  3.60  18.10  Gains in rump fat, mm/d  0.07  0.02  0.06  0.08  0.02  0.12  Initial kidney fat thickness, mm  16.10  1.84  15.60  16.62  12.02  21.4  Final kidney fat thickness, mm  19.64  1.89  19.32  20.44  15.13  24.15  Gains in kidney fat, mm/d  0.05  0.02  0.04  0.06  0.01  0.09  Initial muscle depth thickness, mm  54.6  3.47  53.6  55.6  46.6  62.7  Final muscle depth thickness, mm  64.9  4.01  63.7  66.0  56.4  72.0  Gains in muscle, mm/d  0.13  0.47  0.12  0.15  0.03  0.25  IGF-I, ng/mL  440.30  84.76  427.92  452.45  229.91  679.73  Insulin, μIU/mL  21.43  4.45  20.15  22.71  13.50  34.77  Glucose, mmol/L  4.39  0.27  4.30  4.46  3.77  5.45  Glucose:insulin  0.21  0.04  0.19  0.22  0.13  0.32  Urea, mmol/L  5.61  0.60  5.44  5.78  4.50  7.08  β-Hydroxybutyrate, mmol/L  0.45  0.06  0.42  0.46  0.34  0.64  NEFA, mmol/L  0.06  0.01  0.05  0.06  0.04  0.12  1The relative growth rate was calculated as follows: RGR = 100 × [loge(end BW) − loge(beginning BW)]/days on test. 2The Kleiber ratio was calculated as follows: KR = ADG/metabolic BW. View Large Table 2. Descriptive statistics [mean, SD, 95% confidence interval (CI), minimum, and maximum] in finishing beef heifers for performance, feed efficiency, feeding behavior, and ultrasonic and metabolic traits Trait  Mean  SD  Low 95% CI  High 95% CI  Minimum  Maximum  Residual feed intake, kg of DM/d  0.00  0.59  −0.171  0.171  −1.63  1.24  G:F, kg of BW gain/kg of DM  0.11  0.02  0.10  0.11  0.07  0.14  DMI, kg/d  10.81  1.02  10.52  11.11  8.69  14.36  Relative growth rate1  0.107  0.018  0.101  0.112  0.073  0.158  Kleiber ratio2  0.012  0.002  0.011  0.012  0.007  0.017  Metabolic BW, kg0.75  99.76  5.28  98.24  101.27  88.51  114.34  ADG, kg/d  1.17  0.19  1.11  1.22  0.74  1.59  Initial BW, kg  417.8  31.49  408.7  426.8  350.0  499.5  Final BW, kg  511.9  36.23  501.5  522.3  433.0  614.0  Feeding duration, min/d  117.9  18.43  110.25  125.47  88.11  158.10  Feeding events, events/d  45.4  10.92  40.9  49.9  21.9  63.6  Feed intake/feeding event, kg/event  0.27  0.07  0.24  0.30  0.16  0.51  Eating rate, kg/min  0.10  0.01  0.09  0.11  0.08  0.16  Nonfeeding events, events/d  3.7  2.20  2.86  4.68  1.9  13.6  Nonfeeding duration, min/d  2.52  0.94  2.12  2.91  1.31  5.58  Initial lumbar fat thickness, mm  3.89  1.39  3.49  4.29  1.03  7.23  Final lumbar fat thickness, mm  6.41  1.62  5.95  6.88  3.27  10.73  Gains in lumbar fat, mm/d  0.03  0.01  0.03  0.04  0.01  0.07  Initial rump fat thickness, mm  2.78  1.64  2.26  3.27  1.00  9.30  Final rump fat thickness, mm  8.02  2.84  7.14  8.85  3.60  18.10  Gains in rump fat, mm/d  0.07  0.02  0.06  0.08  0.02  0.12  Initial kidney fat thickness, mm  16.10  1.84  15.60  16.62  12.02  21.4  Final kidney fat thickness, mm  19.64  1.89  19.32  20.44  15.13  24.15  Gains in kidney fat, mm/d  0.05  0.02  0.04  0.06  0.01  0.09  Initial muscle depth thickness, mm  54.6  3.47  53.6  55.6  46.6  62.7  Final muscle depth thickness, mm  64.9  4.01  63.7  66.0  56.4  72.0  Gains in muscle, mm/d  0.13  0.47  0.12  0.15  0.03  0.25  IGF-I, ng/mL  440.30  84.76  427.92  452.45  229.91  679.73  Insulin, μIU/mL  21.43  4.45  20.15  22.71  13.50  34.77  Glucose, mmol/L  4.39  0.27  4.30  4.46  3.77  5.45  Glucose:insulin  0.21  0.04  0.19  0.22  0.13  0.32  Urea, mmol/L  5.61  0.60  5.44  5.78  4.50  7.08  β-Hydroxybutyrate, mmol/L  0.45  0.06  0.42  0.46  0.34  0.64  NEFA, mmol/L  0.06  0.01  0.05  0.06  0.04  0.12  Trait  Mean  SD  Low 95% CI  High 95% CI  Minimum  Maximum  Residual feed intake, kg of DM/d  0.00  0.59  −0.171  0.171  −1.63  1.24  G:F, kg of BW gain/kg of DM  0.11  0.02  0.10  0.11  0.07  0.14  DMI, kg/d  10.81  1.02  10.52  11.11  8.69  14.36  Relative growth rate1  0.107  0.018  0.101  0.112  0.073  0.158  Kleiber ratio2  0.012  0.002  0.011  0.012  0.007  0.017  Metabolic BW, kg0.75  99.76  5.28  98.24  101.27  88.51  114.34  ADG, kg/d  1.17  0.19  1.11  1.22  0.74  1.59  Initial BW, kg  417.8  31.49  408.7  426.8  350.0  499.5  Final BW, kg  511.9  36.23  501.5  522.3  433.0  614.0  Feeding duration, min/d  117.9  18.43  110.25  125.47  88.11  158.10  Feeding events, events/d  45.4  10.92  40.9  49.9  21.9  63.6  Feed intake/feeding event, kg/event  0.27  0.07  0.24  0.30  0.16  0.51  Eating rate, kg/min  0.10  0.01  0.09  0.11  0.08  0.16  Nonfeeding events, events/d  3.7  2.20  2.86  4.68  1.9  13.6  Nonfeeding duration, min/d  2.52  0.94  2.12  2.91  1.31  5.58  Initial lumbar fat thickness, mm  3.89  1.39  3.49  4.29  1.03  7.23  Final lumbar fat thickness, mm  6.41  1.62  5.95  6.88  3.27  10.73  Gains in lumbar fat, mm/d  0.03  0.01  0.03  0.04  0.01  0.07  Initial rump fat thickness, mm  2.78  1.64  2.26  3.27  1.00  9.30  Final rump fat thickness, mm  8.02  2.84  7.14  8.85  3.60  18.10  Gains in rump fat, mm/d  0.07  0.02  0.06  0.08  0.02  0.12  Initial kidney fat thickness, mm  16.10  1.84  15.60  16.62  12.02  21.4  Final kidney fat thickness, mm  19.64  1.89  19.32  20.44  15.13  24.15  Gains in kidney fat, mm/d  0.05  0.02  0.04  0.06  0.01  0.09  Initial muscle depth thickness, mm  54.6  3.47  53.6  55.6  46.6  62.7  Final muscle depth thickness, mm  64.9  4.01  63.7  66.0  56.4  72.0  Gains in muscle, mm/d  0.13  0.47  0.12  0.15  0.03  0.25  IGF-I, ng/mL  440.30  84.76  427.92  452.45  229.91  679.73  Insulin, μIU/mL  21.43  4.45  20.15  22.71  13.50  34.77  Glucose, mmol/L  4.39  0.27  4.30  4.46  3.77  5.45  Glucose:insulin  0.21  0.04  0.19  0.22  0.13  0.32  Urea, mmol/L  5.61  0.60  5.44  5.78  4.50  7.08  β-Hydroxybutyrate, mmol/L  0.45  0.06  0.42  0.46  0.34  0.64  NEFA, mmol/L  0.06  0.01  0.05  0.06  0.04  0.12  1The relative growth rate was calculated as follows: RGR = 100 × [loge(end BW) − loge(beginning BW)]/days on test. 2The Kleiber ratio was calculated as follows: KR = ADG/metabolic BW. View Large Correlation coefficients between intake, performance, and feed efficiency traits are presented in Table 3. Positive associations (P < 0.001) of DMI with MBW (r = 0.76), ADG (r = 0.50), RFI (r = 0.58), initial BW (r = 0.67), and final BW (r = 0.81) were detected. Average daily gain was positively associated (P < 0.001) with G:F (r = 0.81), KR (r = 0.94), and RGR (r = 0.87). Residual feed intake was moderately correlated with G:F (r = −0.36; P < 0.05), but not with MBW, ADG, initial or final BW, KR, or RGR. Table 3. Associations among measures of growth, BW, feed intake, and feed efficiency1 Trait  MBW  ADG  RFI  G:F  KR  RGR  IBW  FBW  DMI  0.76***2  0.50***2  0.58***2  −0.06  0.26  0.21  0.67***2  0.81***2  MBW     0.28  0.00  −0.17  −0.06  −0.16  0.97***2  0.97***2  ADG        0.00  0.81***2  0.94***2  0.87***2  0.05  0.47***2  RFI           −0.36*2  0.01  0.05  0.01  0.03  G:F              0.90***2  0.85***2  −0.37**2  0.01  KR                 0.96***2  −0.29*2  0.15  RGR                    −0.37*2  0.08  IBW                       0.90***2  FBW                          Trait  MBW  ADG  RFI  G:F  KR  RGR  IBW  FBW  DMI  0.76***2  0.50***2  0.58***2  −0.06  0.26  0.21  0.67***2  0.81***2  MBW     0.28  0.00  −0.17  −0.06  −0.16  0.97***2  0.97***2  ADG        0.00  0.81***2  0.94***2  0.87***2  0.05  0.47***2  RFI           −0.36*2  0.01  0.05  0.01  0.03  G:F              0.90***2  0.85***2  −0.37**2  0.01  KR                 0.96***2  −0.29*2  0.15  RGR                    −0.37*2  0.08  IBW                       0.90***2  FBW                          1MBW = metabolic BW; RFI = residual feed intake; KR = Kleiber ratio (calculated as KR = ADG/MBW); RGR = relative growth rate {calculated as RGR = 100 × [loge(end BW) − loge(beginning BW)]/days on test}; IBW = initial BW; FBW = final BW. 2Correlation coefficients are different from zero (P < 0.10). *P < 0.05; **P < 0.01; ***P < 0.001. View Large Table 3. Associations among measures of growth, BW, feed intake, and feed efficiency1 Trait  MBW  ADG  RFI  G:F  KR  RGR  IBW  FBW  DMI  0.76***2  0.50***2  0.58***2  −0.06  0.26  0.21  0.67***2  0.81***2  MBW     0.28  0.00  −0.17  −0.06  −0.16  0.97***2  0.97***2  ADG        0.00  0.81***2  0.94***2  0.87***2  0.05  0.47***2  RFI           −0.36*2  0.01  0.05  0.01  0.03  G:F              0.90***2  0.85***2  −0.37**2  0.01  KR                 0.96***2  −0.29*2  0.15  RGR                    −0.37*2  0.08  IBW                       0.90***2  FBW                          Trait  MBW  ADG  RFI  G:F  KR  RGR  IBW  FBW  DMI  0.76***2  0.50***2  0.58***2  −0.06  0.26  0.21  0.67***2  0.81***2  MBW     0.28  0.00  −0.17  −0.06  −0.16  0.97***2  0.97***2  ADG        0.00  0.81***2  0.94***2  0.87***2  0.05  0.47***2  RFI           −0.36*2  0.01  0.05  0.01  0.03  G:F              0.90***2  0.85***2  −0.37**2  0.01  KR                 0.96***2  −0.29*2  0.15  RGR                    −0.37*2  0.08  IBW                       0.90***2  FBW                          1MBW = metabolic BW; RFI = residual feed intake; KR = Kleiber ratio (calculated as KR = ADG/MBW); RGR = relative growth rate {calculated as RGR = 100 × [loge(end BW) − loge(beginning BW)]/days on test}; IBW = initial BW; FBW = final BW. 2Correlation coefficients are different from zero (P < 0.10). *P < 0.05; **P < 0.01; ***P < 0.001. View Large Feeding Behavior Correlations of intake, performance, and feed efficiency traits with feeding behavior, ultrasound, and metabolic variables are summarized in Table 4. There were positive relationships between feeding events and DMI (r = 0.31; P < 0.05), ADG (r = 0.28; P = 0.07), and RFI (r = 0.24; P = 0.07), but not (P > 0.10) with G:F. Eating rate was positively associated with DMI (r = 0.43; P < 0.01) and RFI (r = 0.25; P = 0.07), but not (P > 0.10) with ADG or G:F. The remaining feeding behavior traits examined (i.e., feeding duration, feed intake/feeding event, nonfeeding events, and duration of nonfeeding) were unrelated (P > 0.10) to intake, performance, or feed efficiency traits. Table 4. Correlation coefficients for association of intake, performance, and feed efficiency traits with feeding behavior or activities, ultrasonic measures, and blood analytes1 Trait  DMI  ADG  G:F  RFI  Feeding duration  0.05  0.10  0.08  0.02  Feeding events  0.31*2  0.28†2  0.12  0.24†2  Feed intake/feeding event  −0.02  −0.14  −0.12  −0.03  Eating rate  0.43**2  0.15  −0.11  0.25†2  Nonfeeding events  −0.14  0.06  0.16  −0.07  Nonfeeding duration  −0.14  0.03  0.15  −0.16  Final muscle depth  0.15  −0.06  −0.17  −0.08  Δ Muscle depth2  0.08  0.16  0.13  −0.08  Final lumbar fat  0.36*2  0.16  −0.08  −0.03  Δ Lumbar fat  −0.01  0.07  0.04  −0.14  Final rump fat  0.32*2  0.06  −0.19  0.02  Δ Rump fat  0.34*2  0.13  −0.15  0.08  Final kidney fat  0.32*2  −0.08  −0.28†2  0.19  Δ Kidney fat  0.1  0.11  0.07  0.29†2  Insulin  0.11  −0.19  −0.30*2  0.23†2  IGF-I  0.03  0.05  0.09  0  Glucose  0.08  0.26†2  0.25†2  −0.09  Glucose:insulin  −0.04  0.25†2  0.33*2  −0.22  Urea  0.26*2  0.07  −0.07  0.38**2  BHB  0.24†2  −0.06  −0.18  0.40**2  NEFA  0.17  −0.12  −0.24  0.19  Trait  DMI  ADG  G:F  RFI  Feeding duration  0.05  0.10  0.08  0.02  Feeding events  0.31*2  0.28†2  0.12  0.24†2  Feed intake/feeding event  −0.02  −0.14  −0.12  −0.03  Eating rate  0.43**2  0.15  −0.11  0.25†2  Nonfeeding events  −0.14  0.06  0.16  −0.07  Nonfeeding duration  −0.14  0.03  0.15  −0.16  Final muscle depth  0.15  −0.06  −0.17  −0.08  Δ Muscle depth2  0.08  0.16  0.13  −0.08  Final lumbar fat  0.36*2  0.16  −0.08  −0.03  Δ Lumbar fat  −0.01  0.07  0.04  −0.14  Final rump fat  0.32*2  0.06  −0.19  0.02  Δ Rump fat  0.34*2  0.13  −0.15  0.08  Final kidney fat  0.32*2  −0.08  −0.28†2  0.19  Δ Kidney fat  0.1  0.11  0.07  0.29†2  Insulin  0.11  −0.19  −0.30*2  0.23†2  IGF-I  0.03  0.05  0.09  0  Glucose  0.08  0.26†2  0.25†2  −0.09  Glucose:insulin  −0.04  0.25†2  0.33*2  −0.22  Urea  0.26*2  0.07  −0.07  0.38**2  BHB  0.24†2  −0.06  −0.18  0.40**2  NEFA  0.17  −0.12  −0.24  0.19  1RFI = residual feed intake; Δ = change over the trial period; BHB = β-hydroxybutyrate. 2Correlation coefficients are different from zero (P < 0.10). †P < 0.10; *P < 0.05; **P < 0.01. View Large Table 4. Correlation coefficients for association of intake, performance, and feed efficiency traits with feeding behavior or activities, ultrasonic measures, and blood analytes1 Trait  DMI  ADG  G:F  RFI  Feeding duration  0.05  0.10  0.08  0.02  Feeding events  0.31*2  0.28†2  0.12  0.24†2  Feed intake/feeding event  −0.02  −0.14  −0.12  −0.03  Eating rate  0.43**2  0.15  −0.11  0.25†2  Nonfeeding events  −0.14  0.06  0.16  −0.07  Nonfeeding duration  −0.14  0.03  0.15  −0.16  Final muscle depth  0.15  −0.06  −0.17  −0.08  Δ Muscle depth2  0.08  0.16  0.13  −0.08  Final lumbar fat  0.36*2  0.16  −0.08  −0.03  Δ Lumbar fat  −0.01  0.07  0.04  −0.14  Final rump fat  0.32*2  0.06  −0.19  0.02  Δ Rump fat  0.34*2  0.13  −0.15  0.08  Final kidney fat  0.32*2  −0.08  −0.28†2  0.19  Δ Kidney fat  0.1  0.11  0.07  0.29†2  Insulin  0.11  −0.19  −0.30*2  0.23†2  IGF-I  0.03  0.05  0.09  0  Glucose  0.08  0.26†2  0.25†2  −0.09  Glucose:insulin  −0.04  0.25†2  0.33*2  −0.22  Urea  0.26*2  0.07  −0.07  0.38**2  BHB  0.24†2  −0.06  −0.18  0.40**2  NEFA  0.17  −0.12  −0.24  0.19  Trait  DMI  ADG  G:F  RFI  Feeding duration  0.05  0.10  0.08  0.02  Feeding events  0.31*2  0.28†2  0.12  0.24†2  Feed intake/feeding event  −0.02  −0.14  −0.12  −0.03  Eating rate  0.43**2  0.15  −0.11  0.25†2  Nonfeeding events  −0.14  0.06  0.16  −0.07  Nonfeeding duration  −0.14  0.03  0.15  −0.16  Final muscle depth  0.15  −0.06  −0.17  −0.08  Δ Muscle depth2  0.08  0.16  0.13  −0.08  Final lumbar fat  0.36*2  0.16  −0.08  −0.03  Δ Lumbar fat  −0.01  0.07  0.04  −0.14  Final rump fat  0.32*2  0.06  −0.19  0.02  Δ Rump fat  0.34*2  0.13  −0.15  0.08  Final kidney fat  0.32*2  −0.08  −0.28†2  0.19  Δ Kidney fat  0.1  0.11  0.07  0.29†2  Insulin  0.11  −0.19  −0.30*2  0.23†2  IGF-I  0.03  0.05  0.09  0  Glucose  0.08  0.26†2  0.25†2  −0.09  Glucose:insulin  −0.04  0.25†2  0.33*2  −0.22  Urea  0.26*2  0.07  −0.07  0.38**2  BHB  0.24†2  −0.06  −0.18  0.40**2  NEFA  0.17  −0.12  −0.24  0.19  1RFI = residual feed intake; Δ = change over the trial period; BHB = β-hydroxybutyrate. 2Correlation coefficients are different from zero (P < 0.10). †P < 0.10; *P < 0.05; **P < 0.01. View Large Ultrasonic Measures Muscle depth or deposition was unrelated (P > 0.10) to ADG, G:F, and RFI (Table 4). Similarly, lumbar and rump fat depths and their estimated gains were not correlated (P > 0.10) with ADG, G:F, or RFI. However, kidney fat depth tended to be associated with G:F (r = −0.28; P = 0.07) and kidney fat accretion tended to be associated with RFI (r = 0.29; P = 0.08). Associations (P < 0.05) between DMI and absolute fat depths (lumbar, rump, and kidney) were generally positive (ranging from r = 0.32 to 0.36). Plasma Analytes Correlations of IGF-I with DMI, ADG, G:F, and RFI were not different from zero (P > 0.10; Table 4). A positive but weak association was observed between insulin and RFI (r = 0.23; P = 0.07). Similarly, a negative association (r = −0.30; P < 0.05) was detected between G:F and plasma insulin, but associations with DMI or ADG were not detected (P > 0.10). Circulating glucose concentration (r = −0.25; P = 0.08) and glucose:insulin were positively associated with G:F (r = 0.33 ; P < 0.05) and ADG (r = 0.25 to 0.26; P = 0.07), but associations with DMI or RFI were not observed (P > 0.10). Plasma urea was positively correlated with DMI (r = 0.26; P < 0.05) and RFI (r = 0.38; P < 0.01), but not with ADG or G:F. Concentrations of BHB were positively associated with DMI (r = 0.24; P = 0.07) and RFI (r = 0.40; P < 0.01), whereas NEFA concentrations were unrelated (P > 0.10) to intake, growth, or feed efficiency traits. Multitrait Equation to Predict RFI After stepwise multiple regression analysis, it was estimated that plasma analyte (insulin, BHB, and urea) concentrations, daily feeding events, and kidney fat accretion concentrations explained 54% of the observed variation in RFI, and there was no advantage from including further variables in the regression model. Therefore, the best multitrait equation (R2 = 0.54), taking collinearity among variables into account, was found to be RFI = −5.41 + (4.23 × BHB) + (0.19 × urea) + (0.06 × insulin) + (0.02 × number of daily feeding events) + (7.25 × mm of kidney fat accretion/d). Within- and Between-Phase Repeatability Analyses Repeatability estimates within the finishing period for DMI, feeding duration, number of feeding events, feed intake/feeding event, and eating rate were 0.34, 0.37, 0.60, 0.62, and 0.56, respectively. Estimates for nonfeeding events and duration were somewhat less (r = 0.25 and 0.04, respectively). Repeatability estimates for traits between the growing and finishing stages are summarized in Table 5. Repeatability estimates (P < 0.001) between the growing and finishing phases for DMI, G:F, and RFI were r = 0.61, r = 0.37, and r = 0.62, respectively. Correspondingly, moderate to strong repeatability values were obtained for feeding behavior traits (ranging from r = 0.40 to 0.76; P < 0.001). Of the metabolic variables examined, IGF-I (r = 0.27), glucose (r = 0.81), glucose:insulin (r = 0.24), urea (r = 0.53), and BHB (r = 0.36) were repeatable between both phases of production. Table 5. Repeatability estimates for traits between the growing1 and finishing stages of the lifespan of an animal Trait2  N  R  P-value  DMI  50  0.613  <0.00013  ADG  50  0.11  0.44  KR  50  0.14  0.34  RGR  50  0.21  0.14  G:F  50  0.373  <0.00013  RFI  50  0.623  <0.00013  Feeding duration  50  0.653  <0.00013  Feeding events  50  0.733  <0.00013  Feed intake/feeding event  50  0.763  <0.00013  Eating rate  50  0.743  <0.00013  Nonfeeding events  50  0.423  0.0033  Nonfeeding duration  50  0.403  0.0043  Δ Muscle depth  50  0.10  0.49  Δ Lumbar fat  50  0.07  0.64  Δ Rump fat  50  0.363  0.013  Insulin  50  0.17  0.23  IGF-I  50  0.273  0.043  Glucose  50  0.813  <0.00013  Glucose:insulin  50  0.243  0.083  Urea  50  0.533  <0.00013  BHB  50  0.363  0.013  NEFA  50  0.00  1.00  Trait2  N  R  P-value  DMI  50  0.613  <0.00013  ADG  50  0.11  0.44  KR  50  0.14  0.34  RGR  50  0.21  0.14  G:F  50  0.373  <0.00013  RFI  50  0.623  <0.00013  Feeding duration  50  0.653  <0.00013  Feeding events  50  0.733  <0.00013  Feed intake/feeding event  50  0.763  <0.00013  Eating rate  50  0.743  <0.00013  Nonfeeding events  50  0.423  0.0033  Nonfeeding duration  50  0.403  0.0043  Δ Muscle depth  50  0.10  0.49  Δ Lumbar fat  50  0.07  0.64  Δ Rump fat  50  0.363  0.013  Insulin  50  0.17  0.23  IGF-I  50  0.273  0.043  Glucose  50  0.813  <0.00013  Glucose:insulin  50  0.243  0.083  Urea  50  0.533  <0.00013  BHB  50  0.363  0.013  NEFA  50  0.00  1.00  1Kelly et al. (2010). 2KR = Kleiber ratio (calculated as KR = ADG/metabolic BW); RGR = relative growth rate {calculated as RGR = 100 × [loge(end BW) − loge(beginning BW)]/days on test}; RFI = residual feed intake; Δ = change over the trial period; BHB = β-hydroxybutyrate. 3Correlation coefficients are different from zero (P < 0.10). View Large Table 5. Repeatability estimates for traits between the growing1 and finishing stages of the lifespan of an animal Trait2  N  R  P-value  DMI  50  0.613  <0.00013  ADG  50  0.11  0.44  KR  50  0.14  0.34  RGR  50  0.21  0.14  G:F  50  0.373  <0.00013  RFI  50  0.623  <0.00013  Feeding duration  50  0.653  <0.00013  Feeding events  50  0.733  <0.00013  Feed intake/feeding event  50  0.763  <0.00013  Eating rate  50  0.743  <0.00013  Nonfeeding events  50  0.423  0.0033  Nonfeeding duration  50  0.403  0.0043  Δ Muscle depth  50  0.10  0.49  Δ Lumbar fat  50  0.07  0.64  Δ Rump fat  50  0.363  0.013  Insulin  50  0.17  0.23  IGF-I  50  0.273  0.043  Glucose  50  0.813  <0.00013  Glucose:insulin  50  0.243  0.083  Urea  50  0.533  <0.00013  BHB  50  0.363  0.013  NEFA  50  0.00  1.00  Trait2  N  R  P-value  DMI  50  0.613  <0.00013  ADG  50  0.11  0.44  KR  50  0.14  0.34  RGR  50  0.21  0.14  G:F  50  0.373  <0.00013  RFI  50  0.623  <0.00013  Feeding duration  50  0.653  <0.00013  Feeding events  50  0.733  <0.00013  Feed intake/feeding event  50  0.763  <0.00013  Eating rate  50  0.743  <0.00013  Nonfeeding events  50  0.423  0.0033  Nonfeeding duration  50  0.403  0.0043  Δ Muscle depth  50  0.10  0.49  Δ Lumbar fat  50  0.07  0.64  Δ Rump fat  50  0.363  0.013  Insulin  50  0.17  0.23  IGF-I  50  0.273  0.043  Glucose  50  0.813  <0.00013  Glucose:insulin  50  0.243  0.083  Urea  50  0.533  <0.00013  BHB  50  0.363  0.013  NEFA  50  0.00  1.00  1Kelly et al. (2010). 2KR = Kleiber ratio (calculated as KR = ADG/metabolic BW); RGR = relative growth rate {calculated as RGR = 100 × [loge(end BW) − loge(beginning BW)]/days on test}; RFI = residual feed intake; Δ = change over the trial period; BHB = β-hydroxybutyrate. 3Correlation coefficients are different from zero (P < 0.10). View Large DISCUSSION The range in intake, performance, and feed efficiency data recorded in the present experiment was consistent with other international studies (Arthur et al., 1999; Archer and Bergh, 2000; Liu et al., 2000; Basarab et al., 2003; Campion et al., 2009), as were the moderate to strong positive correlations between DMI, ADG, and MBW. We observed that 66% of the variation in feed intake was explained by growth rate and MBW, similar to other published estimates (Arthur et al., 2001a,b; Basarab et al., 2003; Nkrumah et al., 2007a). The phenotypic variance for RFI (0.35) in the current study was comparable with that observed by Kelly et al. (2010) and Arthur et al. (2001a,b) and is consistent with known interanimal genetic variation for maintenance energy requirements, as described by Johnson et al. (2003). By design, phenotypic correlations between RFI and ADG and body size were, not surprisingly, near zero. Residual feed intake has been found to be genetically independent of growth and body size in growing bulls (Crowley et al., 2010), heifers (Arthur et al., 2001a), and steers (Nkrumah et al., 2004), although weak genetic correlations were reported in some studies (Herd and Bishop, 2000; Schenkel et al., 2004). Unlike that observed for FCR, there was no relationship between RFI and age or BW at the beginning of the test, indicating that RFI is a robust measure of energetic efficiency. Concurring with our findings, positive phenotypic correlations ranging from 0.60 to 0.72 between RFI and DMI have been reported for various breeds of cattle (Herd and Bishop, 2000; Hoque et al., 2005; Nkrumah et al., 2007a), indicating that selection for more favorable phenotypes would result in significant reductions in feed intake. Several groups (Arthur et al., 2001b; Lancaster et al., 2009; Kelly et al., 2010) have reported negative genetic and phenotypic correlations between G:F and RFI, consistent with our estimate. These correlations imply that selection for either of these measures of feed efficiency (G:F or RFI) should, on average, improve the other (Arthur et al., 2001a; Hennessy and Arthur, 2004). Relationships of KR and RGR with RFI were not different from zero, implying that indirect selection for RFI using either RGR or KR (feed efficiency measures that do not require observations of feed intake) will not be possible. Arthur et al. (2001a,b) and Schenkel et al. (2004) reported strong positive phenotypic and genetic correlations between G:F and ADG. These findings agree with ours, indicating that applying selection pressure for G:F would likely lead to an increase in mature size, and thus an increase in maintenance energy and feed requirements. This has negative ramifications for the efficiency of both the beef cow herd and their progeny; however, it is of much greater significance for the cow component because of the proportionately greater costs associated with it (Arthur et al., 2004). Consequently, the issue of maternal productivity is arguably where RFI will have its greatest potential benefit. Underpinning this, however, will be a requirement for early and accurate identification of potential breeding heifers for retention within the herd. To date, there is a paucity of substantive published information on the repeatability of RFI and associated traits across different phases of animal development. In Charolais-crossbred steers, RFI calculated during the growing phase (roughage-based diet) was genetically related (rg = 0.55) to RFI measured during the subsequent finishing phase (barley grain-based diet; Crews et al., 2003). Archer et al. (2002), offering a roughage-based diet to heifers and later to the same animals as mature cows, reported stronger genetic correlations for RFI (rg = 0.98) than for G:F (r = 0.20) between the 2 stages of production. Similarly, in the current study, in which animals were fed the same diet during the growing (Kelly et al., 2010) and finishing phases, the magnitude of the association between the 2 phases was much greater for RFI than G:F. Indeed, in our study, 54% of the animals reranked by less than 0.5 SD from their original RFI value calculated during the growing phase (Kelly et al., 2010), whereas 24% reranked by more than 1 SD. Collectively, these results indicate that RFI is a consistent trait across different stages of the lifespan of the animal, and this, coupled with its independence from mature size, confirms its value as a measure of energetic efficiency in cattle. Feed-related animal behavioral responses can alter physical activity and thus influence total energy expenditure and feed efficiency (Susenbeth et al., 1998). In a recent review, Herd and Arthur (2009) documented that differences in energy expenditure associated with physical activity and feeding pattern accounted for 10 and 2%, respectively, of the variation in energetic efficiency. Furthermore, studies show that feed-efficient animals typically engage in less daily feeding activity (Nkrumah et al., 2006; Golden et al., 2008; Kelly et al., 2010), which may have evolved as an energy-sparing mechanism. In agreement, the present study observed a trend toward a positive relationship of RFI with both number of daily feeding events and eating rate. Overall, these results may indicate that efficient animals spend more time being sedentary, thus utilizing less energy. The literature documents moderate heritability estimates for feeding behavior traits in sheep (Cammack et al., 2005) and cattle (Robinson and Oddy, 2004). In the present study, strong repeatability estimates were detected for the feeding behavior traits examined. Collectively, the results corroborate earlier findings (Gibb et al., 1998; Nkrumah et al., 2007a) and indicate that feeding behavior of individual cattle is generally consistent within and between the phases of production. There is evidence from several studies that phenotypic ranking (Baker et al., 2006; Castro Bulle et al., 2007; Nkrumah et al., 2007a), but not genetic selection (Hennessy and Arthur, 2004), for energetic efficiency is associated with reduced carcass fatness. Contrary to these studies and to the findings for our animals at an earlier stage of development (Kelly et al., 2010), the association of RFI with measures of peripheral body fat deposition was poor. We did, however, observe trends toward a negative association between ultrasonically measured kidney fat depth and G:F and a positive association with kidney fat accretion with RFI, consistent with the serial slaughter experiments of Basarab et al. (2003) and Mader et al. (2009). Overall, these results are surprising, given that fat is accumulated in different parts of the body with different priorities, with kidney, pelvic, heart, and gastrointestinal tract fat deposited first, followed by intermuscular, subcutaneous, and intramuscular fat depots (Gerrard and Grant, 2003; Jones, 2004). Additionally, it is interesting to note that other authors (Basarab et al., 2003; Crews, 2005) have proposed refining RFI by adjusting for the peripheral fat measure. However, our study implies that when animals consume a high-energy diet in the growing phase, large variation (CV 50 to 66%) in fat accretion potential is expressed; however, the same degree of variation was not sustained in the finishing phase (CV 25 to 32%). This questions the usefulness of including peripheral fat traits in models to calculate RFI, for finishing animals at least. The ambivalence between published reports may potentially be a consequence of differences between the sex, genetic, or environmental backgrounds of the animals used. Generally, published reports indicate a lack of relationship between RFI and LM development in steers (Arthur et al., 2001a), bulls (Basarab et al., 2003), and heifers (Nkrumah et al., 2004), consistent with the findings of the present study. Nonetheless, body composition and physiological maturity are likely factors in defining efficiency, but these account for only approximately 5% or less of the variance in efficiency (Herd and Arthur, 2009). Although influenced by diet and prandial activity, systemic concentrations of key metabolic hormones and metabolites associated with feed intake, growth, fat accumulation, nutrient repartitioning, and utilization have received attention as potential physiological markers of feed efficiency (Richardson et al., 2004; Wood et al., 2004; Nkrumah et al., 2007b). Consistent with the present experiment, previous studies have reported positive associations between insulin and RFI in growing (Richardson et al., 2004) and finishing (Brown, 2005) animals. In agreement with the results of Kolath et al. (2006), our study also shows that blood glucose or the ratio of systemic glucose to insulin concentrations, an indicator of glucose metabolism, did not differ between high- or low-RFI animals. Taken together, these results imply that greater basal insulin concentrations in high-RFI animals may be linked to the greater internal or visceral fat deposition observed because insulin reduces lipolysis and stimulates lipogenesis in adipose tissue (McCann and Reimers, 1986; Brown et al., 2004). Normally, insulin is associated with increased energy requirements by muscle through increasing AA transport and protein synthesis and decreasing muscle degradation (Hocquette et al., 1998). However, some authors (Richardson et al., 2004; Browne, 2005) have hypothesized that energetically inefficient animals may have developed a decrease in insulin sensitivity in muscle tissue, thus diminishing the effect of insulin on muscle proteolysis. Despite the lack of association with RFI, glucose:insulin was positively correlated with G:F. This is similar to the earlier observations of Kelly et al. (2010) for the same animals as yearlings. Based on these studies, more efficient animals, as measured by G:F at least, may have an altered glucose metabolism, which could lead to differences in glucose uptake by tissues, although the magnitude of the relationship is weak. Insulin-like growth factor-I, a known mitogen for cell proliferation, has been genetically (Moore et al., 2005) and phenotypically (Brown et al., 2004) correlated with RFI in growing cattle. Recently, Johnston et al. (2007) reported that as cattle become more physiologically mature, the strength of the genetic relationship between plasma IGF-I concentration and RFI lessens, indicating that the expression of genes responsible for IGF-I concentration may differ as animals mature. In our study, the lack of a relationship of overall IGF-I concentrations with the intake or feed efficiency traits observed is consistent with our previous findings for these animals as yearlings (Kelly et al., 2010). Therefore, because of the inconsistency between studies, systemic concentration of IGF-I is unlikely to be of value in the prediction of RFI. Thyroid hormones play critical roles in the differentiation, growth, metabolism, and physiological functioning of virtually all tissues (Jorritsma et al., 2003). Several studies have demonstrated that thyroid hormones play a major role in thermogenesis in birds (Gabarrou et al., 1997) and mammals (Silva, 2006). In fact, T3 and T4 concentrations have been related to variations of diet-induced thermogenesis among birds selected for high and low RFI (Gabarrou et al., 1997). However, our current findings demonstrate that RFI was not related to either total T4 or free T3 concentrations and agree with the previously published report of Brown et al. (2004). Notwithstanding this, a limited number of studies have examined the relationship between thyroid hormones and section for RFI in cattle; therefore, further research is needed to evaluate the biology of the thyroid gland in reference to RFI differences Contrary to what we found for growing animals (Kelly et al., 2010), increased systemic concentrations of urea during the finishing stage were observed for the less efficient phenotypes, consistent with a previous report from Richardson et al. (2004). This is also in agreement with the finding of an associated study using the same animals and dietary regimen reported here, in which whole-tract CP digestibility was shown to be negatively associated with RFI [K. J. Hart (University College Dublin), T. M. Boland, R. P. McDonnell (University College Dublin), A. K. Kelly, and D. A. Kenny, unpublished data]. The protein concentration of the diet offered (17% CP) in this study most likely exceeded the requirements of the animals. It is possible that greater systemic concentrations of urea in high-RFI animals may be a function of their greater protein intake and potentially greater ruminal passage rate (Hegarty et al., 2007), poorer protein digestibility, greater rate of body protein degradation, or deviation in supply of AA, in part because of variation in efficiency of microbial protein production in the rumen (Lush et al., 1991; Kahn et al., 2000). Beta-hydroxybutyrate is a product of tissue fatty acid catabolism, and systemic concentrations increase in proportion to the degree of fat mobilization. Consistent with this, we found negative associations between BHB and subcutaneous lumbar and rump fat accretion. There are few reports in the literature on the relationship between RFI phenotype and systemic concentrations of BHB, despite the fact that BHB is used more preferentially as an energy substrate by muscle tissue in ruminants compared with nonruminants (Hocquette et al., 1998). Results of the present study demonstrated positive relationships of BHB with RFI and DMI similar to those reported for growing cattle (Richardson et al., 2004; Kelly et al., 2010). Additionally, it should be noted that the strength of the relationship between BHB and RFI was greater than with DMI, indicating that associations are not solely feed intake driven, but reflect an underlying variation in energetic efficiency. Therefore, it is reasonable to assume, based on these metabolite results, that a greater understanding of tissue catabolism (protein turnover or lipolysis) and mitochondrial respiration (BHB is rapidly oxidized to acetyl-CoA in the mitochondria through a series of enzymatic reactions) is essential to improve our knowledge of energetic efficiency. To be a metabolic indicator of RFI, an analyte needs to be independent of growth and be repeatable between successive stages of the lifespan of the animal. When these criteria are taken into account, of the analytes assessed in our studies to date, only circulating BHB was consistently useful as a predictor of energetic efficiency in both the growing and finishing stages. Overall, the multitrait RFI regression analysis identified the importance of feeding activity (daily feeding events), body composition (lumbar fat accretion), tissue anabolism (insulin, kidney fat accretion), and catabolism (urea, BHB) in the explanation of interanimal variation in finishing RFI. The best multivariate model explained 54% of the observed variation in RFI, of which 18% was attributed to BHB, 10% to urea, 10% to insulin, 10% to daily feeding events, and 6% to kidney fat accretion. Interestingly, the relative magnitude of explained variation in RFI was greater during the finishing phase compared with the yearling phase (R2 = 0.35; Kelly et al., 2010) for these animals. The remaining unexplained variation is likely to be associated with other physiological processes such as protein turnover, ion pumping, proton leakage, thermoregulation, digestion, and stress responses (Herd and Arthur, 2009). Hence, a greater understanding of these mechanisms is essential to advance our understanding of the underlying control of energetically efficiency. This study improves our knowledge of the biological control of energetic efficiency in cattle. The data are unique in that they provide novel repeatability estimates of RFI and related traits between 2 important phases of the production cycle. The multifactorial approach taken facilitated the identification of several useful and repeatable predictors of energetic efficiency in finishing cattle. Future studies that merge phenomic and genomic approaches will further aid in elucidating the complex biochemical interplay regulating energetic efficiency, ultimately allowing the early identification of cattle that are more economically and environmentally sustainable to produce. LITERATURE CITED Archer J. A. Bergh L. 2000. Duration of performance tests for growth rate, feed intake and feed efficiency in four biological types of beef cattle. Livest. Prod. Sci.  65: 47– 55. Google Scholar CrossRef Search ADS   Archer, J. A., A. Reverter, R. M. Herd, D. J. Johnston, and P. F. Arthur 2002. Genetic variation in feed intake and efficiency of mature beef cows and relationships with postweaning measurements.  Proc. 7th World Congr. Genet. Appl. Livest. Prod., Montpellier, France. Comm. No. 10-07. Arthur P. F. Archer J. A. Herd R. M. 2004. Feed intake and efficiency in beef cattle: Overview of recent Australian research and challenges for the future. Aust. J. Exp. Agric.  44: 361– 369. Google Scholar CrossRef Search ADS   Arthur P. F. Archer J. A. Herd R. M. Richardson E. C. Exton S. C. Wright J. H. Dibley K. C. P. Burton D. A. 1999. Genetic and phenotypic variation in feed intake, feed efficiency and growth in beef cattle. Proc. Assoc. Adv. Anim. Breed. Genet.  12: 234– 237. Arthur P. F. Archer J. A. Johnson D. J. Herd R. M. Richardson E. C. Parnell P. F. 2001 a. Genetics and phenotypic variance and covariance components for feed intake, feed efficiency and other postweaning traits in Angus cattle. J. Anim. Sci.  79: 2805– 2811. [PubMed] Google Scholar CrossRef Search ADS PubMed  Arthur P. F. Renand G. Krauss D. 2001 b. Genetic and phenotypic relationships among different measures of growth and feed efficiency in young Charolais bulls. Livest. Prod. Sci.  68: 131– 139. Google Scholar CrossRef Search ADS   Baker S. D. Szasz J. I. Klein T. A. Kuber P. S. Hunt C. W. Glaze J. B.Jr. Falk D. Richard R. Miller J. C. Battaglia R. A. Hill R. A. 2006. Residual feed intake of purebred Angus steers: Effects on meat quality and palatability. J. Anim. Sci.  84: 938– 945. [PubMed] Google Scholar CrossRef Search ADS PubMed  Basarab J. A. Price M. A. Aalhus J. L. Okine E. K. Snelling W. M. Lyle K. L. 2003. Residual feed intake and body composition in young growing cattle. Can. J. Anim. Sci.  83: 189– 204. Google Scholar CrossRef Search ADS   Basarab, J. A., M. A. Price, and E. K. Okine 2002. Commercialization of net feed efficiency.  Pages 183– 194 in Proc. 23rd Western Nutr. Conf., Edmonton, Alberta, Canada. Agriculture and Agrifood Canada, Lethbridge Research Centre, Lethbridge, Alberta, Canada. Bergh L. Scholtz M. M. Erasmus G. J. 1992. Identification and assessment of the best animals: The Kleiber ratio (growth/metabolic mass) as a selection criterion for beef cattle. Proc. Aust. Assoc. Anim. Breed. Genet.  10: 338– 340. Birch G. G. Mwangelwa O. M. 1974. Colorimetric determination of sugars in sweetened condensed milk products. J. Sci. Food Agric.  25: 1355– 1362. [PubMed] Google Scholar CrossRef Search ADS PubMed  Brown, E. G. 2005. Sources of biological variation in residual feed intake in growing and finishing steers. PhD Diss.  Texas A&M University, College Station. Brown, E. G., G. E. Carstens, J. T. Fox, M. B. White, K. O. Curley, T. M. Bryan, L. J. Slay, T. H. Welsh Jr., R. D. Randel, J. W. Holloway, and D. H. Keisler 2004. Physiological indicators of performance and feed efficiency traits in growing steers and bulls. Page 13 in Proc. South. Sect.  Am. Soc. Anim. Sci., Tulsa, OK. Cammack K. M. Leymaster K. A. Jenkins T. G. Nielsen M. K. 2005. Estimates of genetic parameters for feed intake, feeding behavior, and daily gain in composite ram lambs. J. Anim. Sci.  83: 777– 785. [PubMed] Google Scholar CrossRef Search ADS PubMed  Campion B. Keane M. G. Kenny D. A. Berry D. P. 2009. Evaluation of estimated genetic merit for carcass weight in beef cattle: Blood metabolites, carcass measurements, carcass composition and selected non-carcass components. Livest. Sci.  126: 100– 111. Google Scholar CrossRef Search ADS   Castro Bulle F. C. P. Paulino P. V. Sanches A. C. Sainz R. D. 2007. Growth, carcass quality, and protein and energy metabolism in beef cattle with different growth potentials and residual feed intakes. J. Anim. Sci.  85: 928– 936. [PubMed] Google Scholar CrossRef Search ADS PubMed  Chapinal N. Veira D. M. Weary D. M. von Keyserlingk M. A. 2007. Technical note: Validation of a system for monitoring individual feeding and drinking behavior and intake in group-housed cattle. J. Dairy Sci.  90: 5732– 5736. [PubMed] Google Scholar CrossRef Search ADS PubMed  Conroy S. Drennan M. Kenny D. McGee M. 2009. The relationship of live animal muscular and skeletal scores, ultrasound measurements and carcass classification scores with carcass composition and value in steers. Animal  3: 1613– 1624. Google Scholar CrossRef Search ADS PubMed  Crews D. H.Jr 2005. Genetics of efficient feed utilization and national cattle evaluation: A review. Genet. Mol. Res.  4: 152– 165. [PubMed] Google Scholar PubMed  Crews D. H.Jr. Shannon N. H. Genswein B. M. A. Crews R. E. Johnson C. M. Kendrick B. A. 2003. Genetic parameters for net feed efficiency of beef cattle measured during postweaning growing versus finishing periods. Proc. West. Sect. Am. Soc. Anim. Sci.  54: 125– 128. Crowley J. J. McGee M. Kenny D. A. Crews D. H.Jr. Evans R. D. Berry D. P. 2010. Phenotypic and genetic parameters for different measures of feed efficiency in Irish performance-tested beef bulls. J. Anim. Sci.  88: 885– 894. https://doi.org/10.2527/jas.2009-1852. [PubMed] Google Scholar CrossRef Search ADS PubMed  El-Meccawi S. Kam M. Brosh A. Degen A. A. 2009. Energy intake, heat production and energy and nitrogen balances of sheep and goats fed wheat straw as a sole diet. Livest. Prod. Sci.  125: 88– 91. Google Scholar CrossRef Search ADS   Fitzhugh H. A.Jr. St. Taylor C. S. 1971. Genetic analysis of degree of maturity. J. Anim. Sci.  33: 717– 725. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gabarrou J. F. Géraert P. A. Picard M. Bordas A. 1997. Diet-induced thermogenesis in cockerels is modulated by genetic selection for high or low residual feed intake. J. Nutr.  127: 2371– 2376. [PubMed] Google Scholar PubMed  Gerrard, E. D., and A. L. Grant 2003. Principles of Animal Growth and Development.  Kendall/Hunt, Dubuque, IA. Gibb D. J. McAllister T. A. Huisma C. Weidmeier R. D. 1998. Bunk attendance of feedlot cattle monitored with radio frequency. Can. J. Anim. Sci.  78: 707– 710. Google Scholar CrossRef Search ADS   Golden J. W. Kerley M. S. Kolath W. H. 2008. The relationship of feeding behavior to residual feed intake in crossbred Angus steers fed traditional and no-roughage diets. J. Anim. Sci.  86: 180– 186. [PubMed] Google Scholar CrossRef Search ADS PubMed  Hegarty R. S. Goopy J. P. Herd R. M. McCorkell B. 2007. Cattle selected for lower residual feed intake have reduced daily methane production. J. Anim. Sci.  85: 1479– 1486. [PubMed] Google Scholar CrossRef Search ADS PubMed  Hennessy D. W. Arthur P. F. 2004. The effect of preweaning growth restriction on the feed intake and efficiency of cattle on a grain-based diet before slaughter. Aust. J. Exp. Agric.  44: 483– 488. Google Scholar CrossRef Search ADS   Herd R. M. Archer J. A. Arthur P. F. 2003. Reducing the cost of beef production through genetic improvement of feed intake: Opportunity and challenges to application. J. Anim. Sci.  81( E-Suppl.): E9– E17. Google Scholar CrossRef Search ADS   Herd R. M. Arthur P. F. 2009. Physiological basis for residual feed intake. J. Anim. Sci.  87( E-Suppl.): E64– E71. [PubMed] Google Scholar CrossRef Search ADS PubMed  Herd R. M. Bishop S. C. 2000. Genetic variation in residual feed intake and its association with other production traits in British Hereford cattle. Livest. Prod. Sci.  63: 111– 119. Google Scholar CrossRef Search ADS   Hocquette J. F. Ortigues-Marty I. Pethick D. Herpin P. Fernandez X. 1998. Nutritional and hormonal regulation of energy metabolism in skeletal muscles of meat-producing animals. Livest. Prod. Sci.  56: 115– 143. Google Scholar CrossRef Search ADS   Hoque M. A. Hiramoto K. Oikawa T. 2005. Genetic relationship of feed efficiency traits of bulls with growth and carcass traits of their progeny for Japanese Black (Wagyu) cattle. Anim. Sci. J.  76: 107– 114. Google Scholar CrossRef Search ADS   Johnson D. E. Ferrell C. L. Jenkins T. G. 2003. The history of energetic efficiency research: Where have we been and where are we going? J. Anim. Sci.  81( E-Suppl.): E27– E38. Johnston, D. J. 2007. Technical Update NFI & IGF-I. Beef Tech. Note March 2007.  Anim. Breed. Genet. Unit, Univ. New England, Armidale, Australia. http://agbu.une.edu.au/cattle/beef9.pdf Accessed Oct. 30, 2008. Jones, S. 2004. Growth of meat animals/growth patterns. Pages 506– 511 in Encyclopedia of Meat Sciences.  Vol. 2. Academic Press, New York, NY. Jorritsma R. Wensing T. Kruip T. Vos P. Noordhuizen J. 2003. Metabolic changes in early lactation and impaired reproductive performance in dairy cows. Vet. Res.  34: 11– 26. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kahn L. P. Leng R. A. Piper L. R. 2000. Rumen microbial yield from sheep genetically different for fleece weight. Asian-australas. J. Anim. Sci.  13C: 137. Kelly A. K. McGee M. Crews D. H.Jr. Fahey A. G. Wylie A. R. Kenny D. A. 2010. Effect of divergence in residual feed intake on feeding behavior, blood metabolic variables, and body composition traits in growing beef heifers. J. Anim. Sci.  88: 109– 123. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kolath W. H. Kerley M. S. Golden J. W. Keisler D. H. 2006. The relationship between mitochondrial function and residual feed intake in Angus steers. J. Anim. Sci.  84: 861– 865. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lancaster P. A. Carstens G. E. Ribeiro F. R. B. Tedeschi L. O. Crews D. H.Jr 2009. Characterization of feed efficiency traits and relationships with feeding behavior and ultrasound carcass traits in growing bulls. J. Anim. Sci.  87: 1528– 1539. [PubMed] Google Scholar CrossRef Search ADS PubMed  Liu M. F. Goonewardene L. A. Bailey D. R. C. Basarab J. A. Kemp R. A. Arthur P. F. Okine E. K. Makarechian M. 2000. A study on the variation of feed efficiency in station tested beef bulls. Can. J. Anim. Sci.  80: 435– 441. Google Scholar CrossRef Search ADS   Lush J. M. Gooden J. M. Annison E. F. 1991. The uptake of nitrogenous compounds from the gut of sheep genetically different in wool production. Proc. Nutr. Soc. Aust.  16: 144. Mader C. J. Montanholi Y. R. Wang Y. J. Miller S. P. Mandell I. B. McBride B. W. Swanson K. C. 2009. Relationships among measures of growth performance and efficiency with carcass traits, visceral organ mass, and pancreatic digestive enzymes in feedlot cattle. J. Anim. Sci.  87: 1548– 1557. [PubMed] Google Scholar CrossRef Search ADS PubMed  McCann J. P. Reimers T. J. 1986. Effects of obesity on insulin and glucose metabolism in cyclic heifers. J. Anim. Sci.  62: 772– 782. [PubMed] Google Scholar CrossRef Search ADS PubMed  Moore K. L. Johnston D. J. Graser H.-U. Herd R. 2005. Genetic and phenotypic relationships between insulin-like growth factor-I (IGF-I) and net feed intake, fat, and growth traits in Angus beef cattle. Aust. J. Agric. Res.  56: 211– 218. Google Scholar CrossRef Search ADS   Moore S. S. Mujibi F. D. Sherman E. L. 2009. Molecular basis for residual feed intake in beef cattle. J. Anim. Sci.  87( E-Suppl.): E41– E47. [PubMed] Google Scholar CrossRef Search ADS PubMed  Nkrumah J. D. Basarab J. A. Price M. A. Okine E. K. Ammoura A. Guercio S. Hansen C. Li C. Murdoch B. Moore S. S. 2004. Different measures of energetic efficiency and their phenotypic relationships with growth, feed intake, and ultrasound and carcass merit in hybrid cattle. J. Anim. Sci.  82: 2451– 2459. [PubMed] Google Scholar CrossRef Search ADS PubMed  Nkrumah J. D. Basarab J. A. Wang Z. Li C. Price M. A. Okine E. K. Crews D. H.Jr. Moore S. S. 2007 a. Genetic and phenotypic relationships of feed intake and different measures of feed efficiency with growth and carcass merit of beef cattle. J. Anim. Sci.  85: 2711– 2720. [PubMed] Google Scholar CrossRef Search ADS PubMed  Nkrumah J. D. Keisler D. H. Crews D. H.Jr. Basarab J. A. Wang Z. Li C. Price M. A. Okine E. K. Moore S. S. 2007 b. Genetic and phenotypic relationships of serum leptin concentration with performance, efficiency of gain, and carcass merit of feedlot cattle. J. Anim. Sci.  85: 2147– 2155. [PubMed] Google Scholar CrossRef Search ADS PubMed  Nkrumah J. D. Okine E. K. Mathison G. W. Schmid K. Li C. Basarab J. A. Price M. A. Wang Z. Moore S. S. 2006. Relationships of feedlot feed efficiency, performance, and feeding behavior with metabolic rate, methane production, and energy partitioning in beef cattle. J. Anim. Sci.  84: 145– 153. [PubMed] Google Scholar CrossRef Search ADS PubMed  NRC 2000. Nutrient Requirements of Beef Cattle.  7th ed. Natl. Acad. Press, Washington, DC. PubMed PubMed  Ribeiro F. R. B. Tedeschi L. O. Stouffer J. R. Carstens G. E. 2008. Technical note: A novel technique to assess internal body fat of cattle by using real-time ultrasound. J. Anim. Sci.  86: 763– 767. [PubMed] Google Scholar CrossRef Search ADS PubMed  Richardson E. C. Herd R. M. Archer J. A. Arthur P. F. 2004. Metabolic differences in Angus steers divergently selected for residual feed intake. Aust. J. Exp. Agric.  44: 441– 452. Google Scholar CrossRef Search ADS   Robinson D. L. McDonald C. A. Hammond K. Turner J. W. 1992. Live animal measurement of carcass traits by ultrasound: Assessment and accuracy of sonographers. J. Anim. Sci.  70: 1667– 1676. Google Scholar CrossRef Search ADS PubMed  Robinson D. L. Oddy V. H. 2004. Genetic parameters for feed efficiency, fatness, muscle area and feeding behavior of feedlot finished beef cattle. Livest. Prod. Sci.  90: 255– 270. Google Scholar CrossRef Search ADS   Schenkel F. S. Miller S. P. Wilton J. W. 2004. Genetic parameters and breed differences for feed efficiency, growth, and body composition traits of young beef bulls. Can. J. Anim. Sci.  84: 177– 185. Google Scholar CrossRef Search ADS   Silva J. E. 2006. Thermogenic mechanisms and their hormonal regulation. Physiol. Rev.  86: 435– 464. [PubMed] Google Scholar CrossRef Search ADS PubMed  Spicer L. J. Echternkamp S. E. Canning S. F. Hammond J. M. 1988. Relationship between concentrations of immunoreactive insulin-like growth factor-I in follicular fluid and various biochemical markers of differentiation in bovine antral follicles. Biol. Reprod.  39: 573– 580. [PubMed] Google Scholar CrossRef Search ADS PubMed  Susenbeth A. Mayer R. Kiehler B. Neumann O. 1998. Energy requirements for eating in cattle. J. Anim. Sci.  76: 2701– 2705. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sweeney R. A. 1989. Generic combustion method for determination of crude protein in feeds: Collaborative study. J. Assoc. Off. Anal. Chem.  72: 770– 774. [PubMed] Google Scholar PubMed  Ting S. T. L. Earley B. Crowe M. A. 2004. Effect of cortisol infusion patterns and castration on metabolic and immunological indices of stress response in cattle. Domest. Anim. Endocrinol.  26: 329– 349. [PubMed] Google Scholar CrossRef Search ADS PubMed  Van Soest P. J. Robertson J. B. Lewis B. A. 1991. Methods for dietary fiber, neutral detergent fiber and no starch polysaccharides in relation to animal nutrition. J. Dairy Sci.  74: 3583– 3597. [PubMed] Google Scholar CrossRef Search ADS PubMed  Wood B. J. Archerb J. A. van der Werf J. H. J. 2004. Response to selection in beef cattle using IGF-I as a selection criterion for residual feed intake under different Australian breeding objectives. Livest. Prod. Sci.  91: 69– 81. Google Scholar CrossRef Search ADS   Footnotes 1 Funding for this research was provided under the National Development Plan, through the Research Stimulus Fund, administered by the Department of Agriculture, Fisheries and Food, Ireland (Dublin; No. RSF 05-214). The skilled technical assistance of P. Quinn (School of Agriculture, Food Science and Veterinary Medicine, University College Dublin), R. McDonnell (School of Agriculture, Food Science and Veterinary Medicine), J. J. Callan (School of Agriculture, Food Science and Veterinary Medicine), P. Furney (School of Agriculture, Food Science and Veterinary Medicine), and J. Larkin (Teagasc, Grange Beef Research Centre, Dunsany, Co. Meath, Ireland) is also appreciated. American Society of Animal Science

Wang, A.;Jiang, H.

doi: 10.2527/jas.2009-2769pmid: 20562358

ABSTRACT It has been known for decades that microbial fermentation within the rumen is critical to postnatal rumen epithelial growth and maturation in ruminants, but the underlying mechanism is largely unknown. In this study, we determined the effect of rumen fluid, which should contain all products from rumen fermentation, on growth of rumen epithelial cells in vitro. Addition of 10% rumen fluid from cows to the culture medium inhibited (P < 0.05), whereas addition of 6.5 mM acetate, 2.5 mM propionate, or 1 mM butyrate had no effect (P > 0.1) on, the proliferation of rumen epithelial cells isolated from newborn calves. Flow cytometric assays showed that 10% rumen fluid inhibited (P < 0.05) the transition of rumen epithelial cells from the G1 phase to the S phase during the cell cycle. Real-time RT-PCR analyses of mRNA for key cell cycle regulators indicated that 10% rumen fluid did not change (P > 0.1) the expression of cyclin D1, D2, D3, E1, or E2 mRNA or that of cyclin-dependent kinase inhibitor 1B or 2B mRNA, but increased (P < 0.05) the expression of cyclin-dependent kinase inhibitors 1A and 2A mRNA in rumen epithelial cells. These mRNA data support the possibility that rumen fluid inhibits proliferation of rumen epithelial cells in vitro by increasing the expression of cyclin-dependent kinase inhibitors 1A and 2A. The result that rumen fluid inhibits proliferation of bovine rumen epithelial cells in culture indicates that rumen fermentation does not stimulate the postnatal rumen epithelial growth in cattle by directly stimulating proliferation of rumen epithelial cells. INTRODUCTION The rumen is the primary site where high-fiber feed is digested through microbial fermentation into short-chain fatty acids (SCFA), or VFA, in ruminants. The VFA are major source of energy for ruminants, and approximately 95% of them are acetic, propionic, and butyric acids, or acetate, propionate, and butyrate, respectively (Siciliano-Jones and Murphy, 1989; Bergman, 1990; Kristensen et al., 1998). The rumen of a calf undergoes significant developmental changes from birth to 4 wk of age (Lyford, 1988). At birth, it has thin walls and short papillae, accounting for less than 30% of the total stomach mass, and is metabolically nonfunctional (Lyford, 1988). By 4 wk of age, the rumen represents 80% of the entire stomach weight, has long and wide dark-colored papillae, and is fully capable of absorbing and metabolizing VFA (Lyford, 1988). These physical and functional changes in rumen epithelium are believed to be caused by VFA, in particular, butyrate (Baldwin and Jesse, 1992; Lane and Jesse, 1997; Lane et al., 2000). The mechanism by which VFA stimulate rumen epithelial growth is unclear. Added to the medium, butyrate inhibited the proliferation of cultured rumen epithelial cells (Gálfi et al., 1981) and other types of cells (Sakata et al., 1980; Sakata and Yajima, 1984; Marsman and McBurney, 1996; Fu et al., 2004; Comalada et al., 2006). These inhibitory effects of VFA on cell growth in vitro have led some to hypothesize that the stimulatory effect of VFA on rumen epithelial growth in vivo is indirect (Sakata and Yajima, 1984; Harmon, 1992; Davie, 2003). In this study, we explored the possibility that rumen fermentation directly stimulates rumen epithelial growth through products other than VFA. We tested this possibility by determining the effects of whole rumen fluid from adult cattle and the individual effects of acetate, propionate, and butyrate on proliferation of rumen epithelial cells from newborn calves in vitro. MATERIALS AND METHODS Procedures involving animals were performed according to the protocols approved by the Virginia Tech Institutional Animal Care and Use Committee. Rumen Fluid Preparation Rumen fluid samples were obtained from lactating Holstein cows via fistula used in an unrelated study (M. Hanigan, Virginia Tech, Blacksburg, unpublished data). The cows were fed corn silage, alfalfa hay, and grain. After collection, the rumen content was filtered through 4 layers of cheese cloth and then centrifuged at 12,000 × g for 1 h at 4°C. The supernatant was collected and filtered through 0.2-μm filters. Isolation and Culturing of Rumen Epithelial Cells Rumen epithelial cells were isolated from newborn calves using the serial tryptic digestion procedure as described previously (Klotz et al., 2001). The Holstein calves were killed with an overdose of pentobarbital for rumen tissue collection. The rumen tissue samples were rinsed with cold running water and transported to the laboratory in PBS on ice. In the laboratory, the epithelial layer of the rumen was separated and minced into small pieces. The minced tissue (~20 g) was digested in 50 mL of digestion solution composed of 5% trypsin (1:250, MP Biomedicals, Solon, OH), 1.08 mM CaCl2, and 25 mM HEPES in Krebs-Ringer buffer for 15 min at 37°C in a slow-shaking incubator. After this digestion, the tissue was filtered through a 300-µm nylon mesh. The filtrate was collected on ice. The tissue remaining on the mesh was rinsed with Krebs-Ringer buffer and digested again as described above. The tissue usually underwent 7 cycles of digestion. The rumen epithelial cells were recovered by centrifuging the combined filtrates at 70 × g for 6 min at 4°C. The cells were cultured at 37°C under 5% CO2 in minimum essential medium (Mediatech, Manassas, VA) supplemented with 10% fetal bovine serum, 100 U/mL of penicillin, 100 μg/mL of streptomycin, and 0.25 μg/mL of amphotericin B. All reagents used in cell culture were purchased from Sigma-Aldrich (St. Louis, MO) unless indicated otherwise. Cell Proliferation Assay After 3 d of initial culturing, the cells were harvested by trypsinization and reseeded into 96-well plates at 4,000 cells per well. The culture medium was the same as described above except with addition of 1 or 10% (vol/vol) rumen fluid, or 6 mM acetate, 2.5 mM propionate, or 1 mM butyrate, or PBS. These concentrations of acetate, propionate, and butyrate approximated their concentrations in 10% rumen fluid (Bergman, 1990; Sutton et al., 2003). Each treatment consisted of 6 wells. The cells were then cultured for 0, 24, 48, or 72 h. The number of viable cells was determined using the Nonradioactive CellTiter 96 Assay kit (Promega, Madison, WI), essentially according to the manufacturer's instructions. Briefly, 15 μL of dye solution was added to each well. After 4 h, 100 μL of solubilization/stop solution was added, and the plate was incubated overnight at 37°C before the absorbance at 570 nm was recorded using a 96-well plate reader. This cell proliferation experiment was repeated 4 times, each time using cells from a different calf. DNA Fragmentation Assay The rumen epithelial cells were cultured in the presence of 10% rumen fluid or PBS for 0, 24, 48, or 72 h before DNA fragmentation assay. This assay was performed as described previously (Kotamraju et al., 2000). Briefly, the cells were lysed with a hypotonic lysis buffer composed of 10 mM Tris-HCl (pH 8.0), 10 mM EDTA, and 0.5% Triton X-100. The lysates were digested with 0.1 mg/mL of RNase A at 37°C for 1 h and then with 100 mg/mL of proteinase K for 2 h at 50°C. The DNA was extracted with phenol, chloroform, and isoamyl alcohol mixture (Fisher Scientific, Pittsburgh, PA) and precipitated by isopropyl alcohol. The DNA was electrophoretically separated on 2% agarose gels containing ethidium bromide. Flow Cytometric Assay The rumen epithelial cells were cultured in the presence of 10% rumen fluid or PBS for 24 h as described above before being collected for flow cytometric assay. The cells were collected by trypsinization, washed with the culture medium twice, and then resuspended in PBS. Aliquots of 0.5-mL cell suspension containing 1 × 106 cells were fixed in 4.5 mL of 70% ethanol on ice for 2 h. After this fixation, the cells were centrifuged at 200 × g at 4°C for 5 min and washed with PBS once. The cells were resuspended in 1 mL of propiodium iodide staining solution consisting of 0.1% Triton X-100, 0.20 mg/mL of DNase-free RNase A, and 0.02 mg/mL of propidium iodide in PBS and incubated at 37°C for 15 min. The DNA content was analyzed using flow cytometry (FACS Aria, BD Biosciences, San Jose, CA). The flow cytometric data were analyzed using FlowJo (Tree Star Inc., Ashland, OR) and ModFit (Verity Software House, Topsham, ME). Quantitative Real-Time PCR The rumen epithelial cells were cultured in the presence of 10% rumen fluid or PBS for 24 h. Total RNA was extracted with TRI Reagent, essentially according to the manufacturer's instructions (Molecular Research Center, Cincinnati OH). Two micrograms of total RNA were reverse-transcribed in a total volume of 20 μL using ImProm-II reverse transcriptase and random primers, under conditions recommended by the manufacturer (Promega). One hundred nanograms of cDNA were amplified in a total volume of 25 μL containing 12.5 μL of SyberGreen PCR Master Mix (Applied Biosystems, Foster City, CA) and 0.2 μM of gene-specific forward and reverse primers (Table 1) under 40 cycles of 95°C for 15 s and 60°C for 1 min. The real-time PCR data were analyzed by the 2−∆∆Ct method (Livak and Schmittgen, 2001), using 18S rRNA as the internal control. Based on the Ct values, expression of 18S rRNA was not different (P > 0.1) between the rumen fluid-treated cells and the control cells. Table 1. The PCR primers used in this study Sequence1  Gene name  GenBank accession No.  Amplicon size, bp  GCACTTCCTCTCCAAGATGC  bCCND1  NM_001046273  204  GTCAGGCGGTGATAGGAGAG        CCAGACCTTCATCGCTCTGT  bCCND2  NM_001076372  163  GATCTTTGCCAGGAGATCCA        TCCAAGCTGCGCGAGACTAC  bCCND3  NM_001034709  178  GAGAGAGCCGGTGCAGAATC        TTGACAGGACTGTGAGAAGC  bCCNE1  XM_612960  187  TTCAGTACAGGCAGTGGCGA        CTGCATTCTGAGTTGGAACC  bCCNE2  NM_001015665  229  CTTGGAGCTTAGGAGCGTAG        GCGGTGGATTATCCTGGACA  bCDKN2B  NM_001075894  210  CATCATCATCACCTGGATCG        CGAACCGTTACGGTCGAAGC  bCDKN2A  XM_868375  208  CAAGCATCGCGCACATCCAG        GCAGACCAGCATGACAGATT  bCDKN1A  NM_001098958  205  GTATGTACAAGAGGAGGCGT        GACCTGCCGCAGATGATTCC  bCDKN1B  NM_001100346  249  CCATTCTTGGAGTCAGCGAT        GTAACCCGTTGAACCCCATT  b18S  DQ222453  150  CCATCCAATCGGTAGTAGCG        Sequence1  Gene name  GenBank accession No.  Amplicon size, bp  GCACTTCCTCTCCAAGATGC  bCCND1  NM_001046273  204  GTCAGGCGGTGATAGGAGAG        CCAGACCTTCATCGCTCTGT  bCCND2  NM_001076372  163  GATCTTTGCCAGGAGATCCA        TCCAAGCTGCGCGAGACTAC  bCCND3  NM_001034709  178  GAGAGAGCCGGTGCAGAATC        TTGACAGGACTGTGAGAAGC  bCCNE1  XM_612960  187  TTCAGTACAGGCAGTGGCGA        CTGCATTCTGAGTTGGAACC  bCCNE2  NM_001015665  229  CTTGGAGCTTAGGAGCGTAG        GCGGTGGATTATCCTGGACA  bCDKN2B  NM_001075894  210  CATCATCATCACCTGGATCG        CGAACCGTTACGGTCGAAGC  bCDKN2A  XM_868375  208  CAAGCATCGCGCACATCCAG        GCAGACCAGCATGACAGATT  bCDKN1A  NM_001098958  205  GTATGTACAAGAGGAGGCGT        GACCTGCCGCAGATGATTCC  bCDKN1B  NM_001100346  249  CCATTCTTGGAGTCAGCGAT        GTAACCCGTTGAACCCCATT  b18S  DQ222453  150  CCATCCAATCGGTAGTAGCG        1All sequences are written from 5′ to 3′. The top sequence of a pair of primers is the forward primer and the bottom sequence the reverse primer. View Large Table 1. The PCR primers used in this study Sequence1  Gene name  GenBank accession No.  Amplicon size, bp  GCACTTCCTCTCCAAGATGC  bCCND1  NM_001046273  204  GTCAGGCGGTGATAGGAGAG        CCAGACCTTCATCGCTCTGT  bCCND2  NM_001076372  163  GATCTTTGCCAGGAGATCCA        TCCAAGCTGCGCGAGACTAC  bCCND3  NM_001034709  178  GAGAGAGCCGGTGCAGAATC        TTGACAGGACTGTGAGAAGC  bCCNE1  XM_612960  187  TTCAGTACAGGCAGTGGCGA        CTGCATTCTGAGTTGGAACC  bCCNE2  NM_001015665  229  CTTGGAGCTTAGGAGCGTAG        GCGGTGGATTATCCTGGACA  bCDKN2B  NM_001075894  210  CATCATCATCACCTGGATCG        CGAACCGTTACGGTCGAAGC  bCDKN2A  XM_868375  208  CAAGCATCGCGCACATCCAG        GCAGACCAGCATGACAGATT  bCDKN1A  NM_001098958  205  GTATGTACAAGAGGAGGCGT        GACCTGCCGCAGATGATTCC  bCDKN1B  NM_001100346  249  CCATTCTTGGAGTCAGCGAT        GTAACCCGTTGAACCCCATT  b18S  DQ222453  150  CCATCCAATCGGTAGTAGCG        Sequence1  Gene name  GenBank accession No.  Amplicon size, bp  GCACTTCCTCTCCAAGATGC  bCCND1  NM_001046273  204  GTCAGGCGGTGATAGGAGAG        CCAGACCTTCATCGCTCTGT  bCCND2  NM_001076372  163  GATCTTTGCCAGGAGATCCA        TCCAAGCTGCGCGAGACTAC  bCCND3  NM_001034709  178  GAGAGAGCCGGTGCAGAATC        TTGACAGGACTGTGAGAAGC  bCCNE1  XM_612960  187  TTCAGTACAGGCAGTGGCGA        CTGCATTCTGAGTTGGAACC  bCCNE2  NM_001015665  229  CTTGGAGCTTAGGAGCGTAG        GCGGTGGATTATCCTGGACA  bCDKN2B  NM_001075894  210  CATCATCATCACCTGGATCG        CGAACCGTTACGGTCGAAGC  bCDKN2A  XM_868375  208  CAAGCATCGCGCACATCCAG        GCAGACCAGCATGACAGATT  bCDKN1A  NM_001098958  205  GTATGTACAAGAGGAGGCGT        GACCTGCCGCAGATGATTCC  bCDKN1B  NM_001100346  249  CCATTCTTGGAGTCAGCGAT        GTAACCCGTTGAACCCCATT  b18S  DQ222453  150  CCATCCAATCGGTAGTAGCG        1All sequences are written from 5′ to 3′. The top sequence of a pair of primers is the forward primer and the bottom sequence the reverse primer. View Large Statistical Analyses All data were analyzed using GLM (SAS Inst. Inc., Cary, NC). Multiple comparisons were done using the Tukey test. A difference was considered statistically significant when P < 0.05 and not significant when P > 0.1. All data were expressed as means ± SEM. RESULTS Rumen Fluid but Not Acetate, Propionate, or Butyrate Inhibited Proliferation of Rumen Epithelial Cells The rumen epithelial cells from newborn calves maintained the ability to proliferate in culture (Figures 1A and 1B). Addition of 10% rumen fluid to the culture medium inhibited the proliferation of these cells (P < 0.05, Figure 1A). This inhibition was more prominent when the cells were cultured for 72 h than for 48 h or 24 h (Figure 1A). Addition of 1% rumen fluid to the culture medium did not inhibit the proliferation of these cells (P = 0.07, Figure 1A). Addition of 6 mM acetate, 2.5 mM propionate, or 1 mM butyrate to the medium did not inhibit the proliferation of these cells either, compared with the PBS control (P > 0.1, Figure 1B). Figure 1. View largeDownload slide Effects of rumen fluid and VFA on proliferation of bovine rumen epithelial cells. Rumen epithelial cells from newborn calves were treated with PBS (as a control), 1%, or 10% rumen fluid (RF) from lactating cows (panel A), or 6 mM acetate, 2.5 mM propionate, or 1 mM butyrate (panel B) for 24, 48, and 72 h, followed by cell proliferation assay. The absorbance at 570 nm on the y-axis represents the number of viable cells. The treatments not labeled with the same letter (a, b) had different effects on proliferation of the cells (P < 0.05, n = 4). Figure 1. View largeDownload slide Effects of rumen fluid and VFA on proliferation of bovine rumen epithelial cells. Rumen epithelial cells from newborn calves were treated with PBS (as a control), 1%, or 10% rumen fluid (RF) from lactating cows (panel A), or 6 mM acetate, 2.5 mM propionate, or 1 mM butyrate (panel B) for 24, 48, and 72 h, followed by cell proliferation assay. The absorbance at 570 nm on the y-axis represents the number of viable cells. The treatments not labeled with the same letter (a, b) had different effects on proliferation of the cells (P < 0.05, n = 4). Rumen Fluid Did Not Induce Detectable Apoptosis in Rumen Epithelial Cells Apoptosis, or programmed cell death, is characterized by the activation of endogenous endonucleases, which in turn results in the cleavage of chromatin DNA, or DNA fragmentation. As shown in Figure 2, the cells cultured in the presence of 10% rumen fluid for 24, 48, or 72 h did not show more DNA fragmentation or any detectable DNA fragmentation by DNA fragmentation assays, compared with control cells. Figure 2. View largeDownload slide The DNA fragmentation assay of bovine rumen epithelial cells. The rumen epithelial cells from newborn calves were treated with PBS or 10% rumen fluid (RF) for indicated times. At the end of these treatments, DNA was extracted and electrophoresed on 2% agarose gels containing ethidium bromide. The assay was repeated 4 times with similar results. A representative photograph of the gels is shown. The M denotes a DNA ladder. Figure 2. View largeDownload slide The DNA fragmentation assay of bovine rumen epithelial cells. The rumen epithelial cells from newborn calves were treated with PBS or 10% rumen fluid (RF) for indicated times. At the end of these treatments, DNA was extracted and electrophoresed on 2% agarose gels containing ethidium bromide. The assay was repeated 4 times with similar results. A representative photograph of the gels is shown. The M denotes a DNA ladder. Rumen Fluid Induced Cell Cycle Arrest at the G1/G0 Phase To understand the mechanism by which the proliferation of rumen epithelial cells was inhibited by rumen fluid, we used flow cytometry to identify the specific phases of cell cycle at which the cells were affected by rumen fluid. As shown in Figure 3, the cells treated with 10% rumen fluid for 24 h contained 20% more cells at the G1 or G0 (G1/G0) phase (P < 0.05) and 10% less at the G2 or M (G2/M) phase (P < 0.05) compared with the control cells. These data indicated that rumen fluid inhibited the proliferation of rumen epithelial cells by blocking their progression from the G1/G0 phase to the S phase during the cell cycle. Figure 3. View largeDownload slide Effect of rumen fluid on cell cycle progression of bovine rumen epithelial cells. The rumen epithelial cells from newborn calves were treated with PBS or 10% rumen fluid (RF) for 24 h before being analyzed by flow cytometry. A) A representative histogram of the flow cytometric analyses. B) Percentages of cells at different phases of the cell cycle. *Indicates P < 0.05, compared with the PBS group (n = 3). Figure 3. View largeDownload slide Effect of rumen fluid on cell cycle progression of bovine rumen epithelial cells. The rumen epithelial cells from newborn calves were treated with PBS or 10% rumen fluid (RF) for 24 h before being analyzed by flow cytometry. A) A representative histogram of the flow cytometric analyses. B) Percentages of cells at different phases of the cell cycle. *Indicates P < 0.05, compared with the PBS group (n = 3). Rumen Fluid Increased mRNA Expression of Cyclin-Dependent Kinase Inhibitors 1A and 2A The transition from the G1/G0 phase to the S phase during the eukaryotic cell cycle is controlled by positive regulators such as cyclins D1, D2, D3, E1, and E2, and negative regulators including cyclin-dependent kinase inhibitors 1A, 2A, 1B, and 2B or p21, p27, p16, and p15, respectively (King and Cidlowski, 1998). As shown in Figure 4, addition of 10% rumen fluid to the culture medium did not affect the expression of cyclins D1, D2, D3, E1, E2 mRNA or cyclin-dependent kinase inhibitors 1B, 2B mRNA (P > 0.1), but caused a nearly 3-fold increase in the expression of cyclin-dependent kinase inhibitors 1A and 2A mRNA (P < 0.05). Figure 4. View largeDownload slide Effect of rumen fluid (RF) on gene expression of cell cycle regulators in bovine rumen epithelial cells. The rumen epithelial cells from newborn calves were treated with 10% RF or PBS for 24 h before total RNA extraction. The mRNA expression of 9 cell cycle regulators and 18S rRNA (internal control) were measured by reverse transcription coupled with real-time PCR. *Indicates P < 0.05, compared with the PBS control (n = 3). bCCND1, bovine cyclin D1; bCCND2, bovine cyclin D2; bCCND3, bovine cyclin D3; bCCNE1, bovine cyclin E1; bCCNE2, bovine cyclin E2; bCDKN2B, bovine cyclin-dependent kinase inhibitor 2B; bCDKN2A, bovine cyclin-dependent kinase inhibitor 2A; bCDKN1A, bovine cyclin-dependent kinase inhibitor 1A; bCDKN1B, bovine cyclin-dependent kinase inhibitor 1B. Figure 4. View largeDownload slide Effect of rumen fluid (RF) on gene expression of cell cycle regulators in bovine rumen epithelial cells. The rumen epithelial cells from newborn calves were treated with 10% RF or PBS for 24 h before total RNA extraction. The mRNA expression of 9 cell cycle regulators and 18S rRNA (internal control) were measured by reverse transcription coupled with real-time PCR. *Indicates P < 0.05, compared with the PBS control (n = 3). bCCND1, bovine cyclin D1; bCCND2, bovine cyclin D2; bCCND3, bovine cyclin D3; bCCNE1, bovine cyclin E1; bCCNE2, bovine cyclin E2; bCDKN2B, bovine cyclin-dependent kinase inhibitor 2B; bCDKN2A, bovine cyclin-dependent kinase inhibitor 2A; bCDKN1A, bovine cyclin-dependent kinase inhibitor 1A; bCDKN1B, bovine cyclin-dependent kinase inhibitor 1B. DISCUSSION Microbial fermentation within the rumen is critical to rumen epithelial growth in young ruminants, but the underlying mechanism is unclear (Zitnan et al., 1999; Baldwin et al., 2004). Previous studies in this area have focused on the role of acetate, butyrate, and propionate in this process because they are the major products of rumen fermentation. Intraruminal administration of acetate, propionate, and butyrate stimulated the growth and functional maturation of the rumen epithelium in young ruminants, with the effect of butyrate being most prominent, followed by propionate (Sakata and Tamate, 1978, 1979; Lane and Jesse, 1997). Limiting rumen production of VFA through feeding only milk inhibited rumen epithelial growth (Warner et al., 1956; Harrison et al., 1960; Tamate et al., 1962). However, VFA, including butyrate, inhibited the growth of many types of cells in vitro (Sakata and Yajima, 1984; Fu et al., 2004; Comalada et al., 2006). These seemingly conflicting effects of VFA between in vivo and in vitro led some investigators to postulate that rumen VFA stimulate rumen development through indirect mechanisms, perhaps through insulin, because VFA stimulate insulin secretion in ruminants (Sakata and Yajima, 1984; Bergman, 1990; Harmon, 1992). In this study, we hypothesized that rumen fermentation generates products that directly stimulate rumen epithelial cell growth and that overtake the growth inhibitory effects of VFA, and we tested this hypothesis by determining the effect of whole rumen fluid on proliferation of bovine rumen epithelial cells in culture. The study showed that 10% rumen fluid from cattle inhibited the proliferation of bovine rumen epithelial cells in culture. This result does not seem to support our original hypothesis and suggests that rumen fermentation does not stimulate rumen epithelial growth in cattle by directly increasing proliferation of rumen epithelial cells. This study has further shown that rumen fluid inhibits the proliferation of rumen epithelial cells by inhibiting their transition from the G1 phase to the S phase during the cell cycle. The progression of the cell cycle from the G1 phase to the S phase is controlled by cyclin-dependent kinases. The activities of these kinases are regulated by cyclins D1, D2, D3, D4, E1, and E2 that bind and activate the cyclin-dependent kinases, and cyclin-dependent kinase inhibitors 1A, 1B, 2A, and 2B that bind and inactivate the cyclin-dependent kinases (King and Cidlowski, 1998). This study has shown that rumen fluid increases mRNA expression of cyclin-dependent kinase inhibitors 1A and 2A in rumen epithelial cells. This result may indicate that rumen fluid inhibits the progression of rumen epithelial cells from the G1 phase to the S phase during the cell cycle by upregulating the expression of cyclin-dependent kinase inhibitors 1A and 2A. What components of rumen fluid are responsible for the inhibitory effect of rumen fluid on proliferation of bovine rumen epithelial cells in vitro? Because VFA are the major components of rumen fluid (except for water), and because VFA, such as butyrate, inhibit proliferation of various types of cells in vitro (Sakata and Yajima, 1984; Fu et al., 2004; Comalada et al., 2006), it is tempting to think that the inhibitory effect of rumen fluid on proliferation of bovine rumen epithelial cells is mediated by VFA. In this study, at its concentration in 10% rumen fluid, neither acetate, nor propionate, nor butyrate inhibited proliferation of bovine rumen epithelial cells. This may indicate that the inhibitory effect of rumen fluid on proliferation of bovine rumen epithelial cells is unlikely due to the effect of a single VFA if it is mediated by VFA. It remains to be determined if acetate, propionate, and butyrate combined have as strong an inhibitory effect on proliferation of rumen epithelial cells as rumen fluid. We cannot exclude the possibility that some of the growth inhibitory effect of rumen fluid on rumen epithelial cells is mediated by non-VFA components of rumen fluid, such as endotoxins and bioactive peptides processed from microbial or diet-derived protein. Butyrate has been shown to arrest cell cycle at the G1/G0 phase (Sakata and Yajima, 1984; Li and Elsasser, 2005; Li and Li, 2006; Hatayama et al., 2007; Wang et al., 2008) and to induce cyclin-dependent kinase inhibitors 1A and 2A expression in various types of cells (Mahyar-Roemer and Roemer, 2001; Hinnebusch et al., 2002; Davie, 2003; Orchel et al., 2003; Shi et al., 2006). This study has shown that rumen fluid also halts the cell cycle of bovine rumen epithelial cells at the G1/G0 phase and increases mRNA expression of cyclin-dependent kinase inhibitors 1A and 2A in these cells. Based on these similar effects of rumen fluid and butyrate on cell cycle and gene expression and the data that butyrate alone does not inhibit proliferation of bovine rumen epithelial cells as much as whole rumen fluid, we speculate that the effects of rumen fluid on cell cycle progression and gene expression in rumen epithelial cells may be mediated in part by butyrate. In summary, this study shows that rumen fluid inhibits proliferation of the bovine rumen epithelial cells in vitro by inhibiting their progression from the G1 phase to the S phase during the cell cycle. This growth inhibitory effect of whole rumen fluid in vitro may indicate that products of rumen fermentation do not stimulate rumen epithelial growth by directly stimulating proliferation of rumen epithelial cells. This study also shows that rumen fluid increases the expression of cyclin-dependent kinase inhibitors 1A and 2A in bovine rumen epithelial cells. This result may indicate that rumen fluid might inhibit proliferation of bovine rumen epithelial cells by increasing the expression of these cell cycle inhibitors. LITERATURE CITED Baldwin R. L.6th Jesse B. W. 1992. Developmental changes in glucose and butyrate metabolism by isolated sheep ruminal cells. J. Nutr.  122: 1149– 1153. [PubMed] Google Scholar CrossRef Search ADS PubMed  Baldwin R. L.6th McLeod K. R. Klotz J. L. 2004. Rumen development, intestinal growth and hepatic metabolism in the pre- and postweaning ruminant. J. Dairy Sci.  87( E. Suppl.): E55– E65. Google Scholar CrossRef Search ADS   Bergman E. N. 1990. Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol. Rev.  70: 567– 590. [PubMed] Google Scholar CrossRef Search ADS PubMed  Comalada M. Bailón E. de Haro O. Lara-Villoslada F. Xaus J. Zarzuelo A. Gálvez J. 2006. The effects of short-chain fatty acids on colon epithelial proliferation and survival depend on the cellular phenotype. J. Cancer Res. Clin. Oncol.  132: 487– 497. [PubMed] Google Scholar CrossRef Search ADS PubMed  Davie J. R. 2003. Inhibition of histone deacetylase activity by butyrate. J. Nutr.  133: 2485S– 2493S. [PubMed] Google Scholar CrossRef Search ADS PubMed  Fu H. Shi Y. Q. Mo S. J. 2004. Effect of short-chain fatty acids on the proliferation and differentiation of the human colonic adenocarcinoma cell line caco-2. Chin. J. Dig. Dis.  5: 115– 117. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gálfi P. Veresegyházy T. Neogrády S. Kutas F. 1981. Effect of sodium n-butyrate on primary ruminal epithelial cell culture. Zentralbl. Veterinarmed. A  28: 259– 261. [PubMed] Google Scholar CrossRef Search ADS PubMed  Harmon D. L. 1992. Impact of nutrition on pancreatic exocrine and endocrine secretion in ruminants: A review. J. Anim. Sci.  70: 1290– 1301. [PubMed] Google Scholar CrossRef Search ADS PubMed  Harrison H. N. Warner R. G. Sander E. G. Loosli J. K. 1960. Changes in the tissue and volume of the stomachs of calves following the removal of dry feed or consumption of inert bulk. J. Dairy Sci.  43: 1301– 1312. Google Scholar CrossRef Search ADS   Hatayama H. Iwashita J. Kuwajima A. Abe T. 2007. The short chain fatty acid, butyrate, stimulates MUC2 mucin production in the human colon cancer cell line, LS174T. Biochem. Biophys. Res. Commun.  356: 599– 603. [PubMed] Google Scholar CrossRef Search ADS PubMed  Hinnebusch B. F. Meng S. Wu J. T. Archer S. Y. Hodin R. A. 2002. The effects of short-chain fatty acids on human colon cancer cell phenotype are associated with histone hyperacetylation. J. Nutr.  132: 1012– 1017. [PubMed] Google Scholar CrossRef Search ADS PubMed  King K. L. Cidlowski J. A. 1998. Cell cycle regulation and apoptosis. Annu. Rev. Physiol.  60: 601– 617. [PubMed] Google Scholar CrossRef Search ADS PubMed  Klotz J. L. Baldwin R. L.6th Gillis R. C. Heitmann R. N. 2001. Refinements in primary rumen epithelial cell incubation techniques. J. Dairy Sci.  84: 183– 193. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kotamraju S. Konorev E. A. Joseph J. Kalyanaraman B. 2000. Doxorubicin-induced apoptosis in endothelial cells and cardiomyocytes is ameliorated by nitrone spin traps and ebselen. Role of reactive oxygen and nitrogen species. J. Biol. Chem.  275: 33585– 33592. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kristensen N. B. Danfaer A. Agergaard N. 1998. Absorption and metabolism of short-chain fatty acids in ruminants. Arch. Tierernahr.  51: 165– 175. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lane M. A. Baldwin R. L.6th Jesse B. W. 2000. Sheep rumen metabolic development in response to age and dietary treatments. J. Anim. Sci.  78: 1990– 1996. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lane M. A. Jesse B. W. 1997. Effect of volatile fatty acid infusion on development of the rumen epithelium in neonatal sheep. J. Dairy Sci.  80: 740– 746. [PubMed] Google Scholar CrossRef Search ADS PubMed  Li C. J. Elsasser T. H. 2005. Butyrate-induced apoptosis and cell cycle arrest in bovine kidney epithelial cells: Involvement of caspase and proteasome pathways. J. Anim. Sci.  83: 89– 97. [PubMed] Google Scholar CrossRef Search ADS PubMed  Li R. W. Li C. 2006. Butyrate induces profound changes in gene expression related to multiple signal pathways in bovine kidney epithelial cells. BMC Genomics  7: 234. [PubMed] Google Scholar CrossRef Search ADS PubMed  Livak K. J. Schmittgen T. D. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta c(t)) method. Methods  25: 402– 408. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lyford, S. J., Jr. 1988. Growth and development of the ruminant digestive system. Pages 44– 63 in The Ruminant Animal: Digestive Physiology and Nutrition.  D. C. Church ed. Waveland Press, Prospect Heights, IL. Mahyar-Roemer M. Roemer K. 2001. P21 waf1/cip1 can protect human colon carcinoma cells against p53-dependent and p53-independent apoptosis induced by natural chemopreventive and therapeutic agents. Oncogene  20: 3387– 3398. [PubMed] Google Scholar CrossRef Search ADS PubMed  Marsman K. E. McBurney M. I. 1996. Dietary fiber and short-chain fatty acids affect cell proliferation and protein synthesis in isolated rat colonocytes. J. Nutr.  126: 1429– 1437. [PubMed] Google Scholar PubMed  Orchel A. Molin I. Dzierzewicz Z. Latocha M. Weglarz L. Wilczok T. 2003. Quantification of p21 gene expression in caco-2 cells treated with sodium butyrate using real-time reverse transcription-PCR (RT-PCR) assay. Acta Pol. Pharm.  60: 103– 105. [PubMed] Google Scholar PubMed  Sakata T. Hikosaka K. Shiomura Y. Tamate H. 1980. Stimulatory effect of insulin on ruminal epithelium cell mitosis in adult sheep. Br. J. Nutr.  44: 325– 331. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sakata T. Tamate H. 1978. Rumen epithelial cell proliferation accelerated by rapid increase in intraruminal butyrate. J. Dairy Sci.  61: 1109– 1113. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sakata T. Tamate H. 1979. Rumen epithelium cell proliferation accelerated by propionate and acetate. J. Dairy Sci.  62: 49– 52. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sakata T. Yajima T. 1984. Influence of short chain fatty acids on the epithelial cell division of digestive tract. Q. J. Exp. Physiol.  69: 639– 648. [PubMed] Google Scholar CrossRef Search ADS PubMed  Shi S. L. Wang Y. Y. Liang Y. Li Q. F. 2006. Effects of tachyplesin and n-sodium butyrate on proliferation and gene expression of human gastric adenocarcinoma cell line bgc-823. World J. Gastroenterol.  12: 1694– 1698. [PubMed] Google Scholar CrossRef Search ADS PubMed  Siciliano-Jones J. Murphy M. R. 1989. Production of volatile fatty acids in the rumen and cecum-colon of steers as affected by forage: Concentrate and forage physical form. J. Dairy Sci.  72: 485– 492. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sutton J. D. Dhanoa M. S. Morant S. V. France J. Napper D. J. Schuller E. 2003. Rates of production of acetate, propionate, and butyrate in the rumen of lactating dairy cows given normal and low-roughage diets. J. Dairy Sci.  86: 3620– 3633. [PubMed] Google Scholar CrossRef Search ADS PubMed  Tamate H. McGilliard A. D. Jacobson N. L. Getty R. 1962. Effect of various dietaries on the anatomical development of the stomach in the calf. J. Dairy Sci.  45: 408– 420. Google Scholar CrossRef Search ADS   Wang Q. Zhou Y. Wang X. Evers B. M. 2008. p27 Kip1 nuclear localization and cyclin-dependent kinase inhibitory activity are regulated by glycogen synthase kinase-3 in human colon cancer cells. Cell Death Differ.  15: 908– 919. [PubMed] Google Scholar CrossRef Search ADS PubMed  Warner R. G. Flatt W. P. Loosli J. K. 1956. Dietary factors influencing the development of the ruminant stomach. J. Agric. Food Chem.  4: 788– 792. Google Scholar CrossRef Search ADS   Zitnan R. Voigt J. Wegner J. Breves G. Schröder B. Winckler C. Levkut M. Kokardová M. Schönhusen U. Kuhla S. Hagemeister H. Sommer A. 1999. Morphological and functional development of the rumen in the calf: Influence of the time of weaning. 1. Morphological development of rumen mucosa. Arch. Tierernahr.  52: 351– 362. [PubMed] Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Yang, C. C.;Deng, S. J.;Hsu, C. C.;Liu, B. H.;Lin, E. C.;Cheng, W. T. K.;Wang, P. H.;Ding, S. T.

doi: 10.2527/jas.2010-2799pmid: 20562354

ABSTRACT Visfatin is a visceral adipose tissue-specific adipocytokine that plays a positive role in attenuating insulin resistance by binding to the insulin receptor. Visfatin has been suggested to play a role in the regulation of lipid metabolism and inflammation; however, the mechanism remains unclear. We investigated the effects of visfatin on the regulation of gene expression in cultured porcine preadipocytes and differentiated adipocytes. In preadipocytes, the mRNA abundance of lipoprotein lipase and PPARγ were significantly increased by visfatin or insulin treatment after 8 d (all P < 0.05). In the presence of insulin, the mRNA abundance of adipocyte fatty acid-binding protein was 24.7-fold greater than in the untreated group (P < 0.05), whereas visfatin alone had no effect on adipocyte fatty acid-binding protein mRNA abundance. Adipocyte differentiation was induced by insulin treatment for 8 d. In differentiated porcine adipocytes, exposure to insulin or visfatin for 24 h increased (P < 0.05) fatty acid synthase mRNA abundance but had no effect on the expression of sterol regulatory element binding-protein 1c mRNA. We also found a 5.8-fold upregulation of IL-6 expression in porcine adipocytes after 24 h of treatment with visfatin (P < 0.05). These results demonstrated that visfatin upregulated lipoprotein lipase expression in preadipocytes, potentially facilitating lipid uptake, and increased the gene expression of fatty acid synthase in differentiated adipocytes to potentially enhance lipogenic activity. Furthermore, visfatin can upregulate IL-6 expression in differentiated porcine adipocytes. The information presented in this study provides insights into the roles of visfatin in lipid metabolism in pigs. INTRODUCTION Adipose tissue is an important endocrine organ that expresses and secretes more than 50 different bioactive peptides (Trayhurn and Wood, 2004), referred to as adipokines. These adipokines appear to be involved in a broad range of physiological or physiopathological processes, including lipid metabolism, insulin sensitivity, blood pressure regulation, energy balance, and angiogenesis (Lago et al., 2009). Visfatin was originally identified as the pre-B-cell colony-enhancing factor 1, and its expression was correlated with obesity. Visfatin participates in the differentiation of pre-B cells (Samal et al., 1994). In mammalian cells, pre-B-cell colony-enhancing factor 1 functions as a nicotinamide phosphoribosyltransferase, a rate-limiting enzyme for the biosynthesis of NAD, and therefore influences cellular differentiation and metabolic responses (Wang et al., 2009). Pre-B-cell colony-enhancing factor 1 was renamed visfatin to distinguish its exclusive expression in visceral adipose tissue (VAT). It plays a positive role in attenuating insulin resistance and facilitates adipogenesis (Sethi and Vidal-Puig, 2005; Chen et al., 2006). Visfatin exerts insulin mimetic properties; however, it does not bind to the same region of the receptor as insulin (Chen et al., 2006). As an autocrine/paracrine signaling factor, visfatin facilitates the differentiation of adipose tissue through its pro-adipogenic and lipogenic actions (Sethi and Vidal-Puig, 2005). The underlying cellular mechanism by which visfatin regulates adipogenic differentiation and lipid accumulation in adipose tissue is not well understood. Accordingly, the aim of the present study was to explore the relationship between visfatin and lipid metabolism-associated genes by the analysis of mRNA profiles after treatment with recombinant porcine visfatin. MATERIALS AND METHODS The procedures for the animal portions of this study were approved by the Institutional Animal Care and Use Committee of National Taiwan University. Construction of Recombinant Visfatin Plasmid Forward (5′-TCCGGGATCCGATGAATGCT-3′) and reverse (5′-ACGCAAGCTTACACACACCC-3′) primers for the visfatin gene were designed according to the published porcine sequence (GenBank accession No. DQ020218). The primers included recognition sites for the restriction enzymes, BamHI and HindIII (restriction sites underlined in primer sequences). We used the AccuPrime Pfx DNA Polymerase Kit (Invitrogen, Carlsbad, CA) for PCR. Conditions for the thermal cycling were as follows: 95°C for 3 min, 30 cycles of 95°C for 30 s, 55°C for 30 s, 68°C for 90 s, and a final extension at 68°C for 10 min. The amplified product was purified and then inserted into the pCR-Blunt II-TOPO (Invitrogen) vector. To create an in-frame fusion protein with a N-terminal 6xHis-tag, the visfatin DNA fragment was digested with BamHI and HindIII restriction enzymes (Takara, Shiga, Japan) and ligated into the pQE31 (Qiagen, Hilden, Germany) vector predigested with the same enzymes using the Quick Ligation Kit (New England Biolabs, Beverly, MA). The recombinant plasmid pQE31-visfatin was transformed into BL21(DE3) competent cells (Novagen, Madison, WI) for expression. Its authenticity was confirmed by sequencing. Expression and Purification of Recombinant Visfatin Protein expression was induced by 1 mM isopropyl-thiogalactopyranoside. Recombinant His-tagged visfatin was purified using the Probond Purification System (Invitrogen) according to the manufacturer's protocol under native conditions. In brief, isopropyl-thiogalactopyranoside-induced cells were harvested, resuspended in a native binding buffer (50 mM NaH2PO4, 500 mM NaCl, pH 7.8), and sonicated for 10 s and repeated 15 times. The sonicated-cell extracts were subjected to centrifugation at 800 × g for 5 min at 37°C, and the supernatant was loaded on a nickel-charged affinity column (Invitrogen). This column was washed twice with the wash buffers (20 mM imidazole, 50 mM NaH2PO4, 500 mM NaCl at pH 7.8, 6.0, and 5.3, respectively). The visfatin-His fusion protein was eluted with an elution buffer (250 mM imidazole, 50 mM NaH2PO4, 500 mM NaCl, pH 4.0). Purity was verified by Coomassie staining of SDS-PAGE gels. The eluate was concentrated using a centrifugal filter device with a 30 kDa cut-off (Millipore, Bedford, MA) and then filtered though an Ultrafree-MC 0.22-µm filter (Millipore) at 4,000 × g for 5 min to eliminate dust, microcrystals, and precipitated protein. Generation of Monoclonal Antibody Mice (BALB/c) were purchased from the Animal Center of the College of Medicine at the National Taiwan University. Immunization of the mice and cell fusion were carried out by a standard procedure which was described in Chen et al. (2002) and modified in the following manner. Briefly, six 4-wk-old BALB/c female mice were immunized intraperitoneally with an emulsion of Freund's complete adjuvant (Sigma-Aldrich, St. Louis, MO) containing 100 µg of purified recombinant visfatin. Four or five booster shots of 50 µg of purified recombinant visfatin in Freund's incomplete adjuvant (Sigma-Aldrich) were given every 2 wk. A final dose was administered 3 d before fusion. Cell fusion was induced by mixing 1.0 × 108 spleen cells from the immunized mice with the myeloma cell line SP2/0-Ag14 (2.0 × 107 cells). The selection of positive hybridomas was carried out using an ELISA procedure, and they were subcloned by the limiting dilution method. For the large-scale production of visfatin monoclonal antibody, the ascites fluid was prepared by injecting 1.0 × 107 hybridoma cells into BALB/c female mice primed with prestane (Sigma-Aldrich). To determine the titer and specificity of the antibody, a Western blot analysis was performed. Purified immunoglobulins were stored at −80°C. Western Blot Analysis of Porcine Visfatin Electrophoresis was performed using protocols according to the method described previously (Laemmli, 1970) with 12% SDS-PAGE gels at 80 V for 90 min. Subsequent immunoblottings were performed using the visfatin monoclonal antibody (1:2,000) prepared from the culture supernate of hybridoma cells. Protein was electro-transferred from the gel onto a Polyscreen PVDF membrane (PerkinElmer, Boston, MA). The membrane was blocked with 5% BSA in TBST (20 mM Tris-HCl, pH 7.5, 150 mM NaCl, 0.1% Tween-20) before incubation with the primary antibody at an appropriate dilution at 4°C overnight. After washing with 0.05% TBST, the membrane was incubated with horseradish peroxidase-conjugated goat anti-mouse IgG (1:10,000; Invitrogen) antibody for 2 h at room temperature. Bound peroxidase-conjugate was detected using the enhanced chemiluminescence system (Millipore) and Hyperfilm ECL photographic film (GE Healthcare, Munich, Germany) following the manufacturer's instructions. Isolation of Porcine Stromal Vascular Cells and Culture System Two-week-old Landrace piglets were obtained from a commercial producer and killed by electrocution combined with exsanguination. Stromal vascular (S/V) cells were isolated and cultured followed the procedure described previously (Wang et al., 2006) and modified by Chen et al. (2008). In the preadipocyte experiment, the medium on confluent S/V cells was replaced with 100 ng/mL of visfatin- or 100 ng/mL of insulin-containing preadipocyte growth medium. For the adipocyte experiment, approximately 50% of the adipocytes were well differentiated; the medium was changed to the 100 ng/mL of visfatin- or 100 ng/mL of insulin-containing Dulbecco's modified Eagle/F12 medium. Cells were used for transcript analysis or stained with Oil Red-O (Sigma-Aldrich) according to a previously described protocol (Ding et al., 2002) to evaluate the degree of adipocyte differentiation. Reverse Transcription Real-Time PCR Analyses Total RNA was extracted from washed preadipocytes and adipocytes using the TRI reagent (Ambion, Austin, TX), according to the manufacturer's protocol. The quantity of extracted RNA was measured at OD260 and OD280. The quality of RNA was determined by examination of the ethidium bromide-stained 18S and 28S ribosomal RNA bands after electrophoresis under denaturing condition. Reverse transcription was performed using 2 μg of total RNA with the High-Capacity cDNA Reverse Transcription kit (Applied Biosystems, Foster City, CA), according to the manufacturer's protocol. Amplification reactions were performed using the FastStart SYBR Green Master Mix kit (Roche, Mannheim, Germany) and a DNA Engine Opticon 2 Real-Time PCR Detection System (Bio-Rad Laboratories, Richmond, CA) according to the instruction manual, but the reactions were scaled down to 10 μL. Each reaction, containing 10 ng of template, was performed in duplicate, and the abundance of β-actin mRNA was used as the internal control. The primer sequences used are described in Table 1. The following thermal cycling conditions for amplification were used: 94°C for 10 min, 40 cycles of 94°C for 30 s, then 60°C for 30 s, and then 72°C for 30 s with a final extension at 72°C for 7 min. Cycle threshold (CT) values were determined with Opticon Monitor 3 software (Bio-Rad Laboratories). Relative differences for a gene under treatment with the recombinant visfatin, insulin, or both were determined using the comparative cycle threshold (ΔΔCT) method (Livak and Schmittgen, 2001). The resulting values were converted to fold-changes compared with the control by 2−ΔΔCT. Table 1. The primer sequences used for real-time PCR Target gene1  Accession No.  Primer sequence  Location  Expected length, bp  β-Actin  AY550069  5′-CAGGTCATCACCATCGGCAA  823–842  93        5′-TTCGTGGATGCCGCAGGA  898–915     Visfatin  DQ020218  5′-TATTGCCTTTGGTTCTGGTG  1131–1150  154        5′-GCCCTTTTTGGACCTTTTGT  1265–1284     SREBP-1c  AY307771  5′-CCTCTGTCTCTCCTGCAACC  1011–1030  229        5′-GACCGGCTCTCCATAGACAA  1220–1239     PPARγ  AB097930  5′-CATGCTGTCATGGGTGAAAC  29–48  188        5′-TCAAAGGAGTGGGAGTGGTC  197–216     LPL  AY559454  5′-TGGACGGTGACAGGAATGTA  378–397  237        5′-AAGGCTGTATCCCAGGAGGT  595–614     aP2  AJ416020  5′-GGTGTCACGGCTACCAGAAT  364–383  156        5′-CAAAATCAGTCTGGGGGAAA  500–519     FAS  EF589048  5′-GGACCTGGTGATGAACGTCT  4510–4529  225        5′-CGGAAGTTGAGGGAGGTGTA  4715–4734     IL-6  AF309651  5′-ATGGCAGAAAAAGACGGATG  328–347  215        5′-GTGGTGGCTTTGTCTGGATT  523–542     Leptin  AF052691  5′-TTCTCTCTCGCTCCGCTAAG  1440–1459  239        5′-AGATGGAACCCTGCTTGATG  1659–1678     Adiponectin  AY135647  5′-TGAAGGATGTGAAGGTCAGC  589–608  221        5′-AGGAAGCCTGTGAAGATGGA  780–799     Target gene1  Accession No.  Primer sequence  Location  Expected length, bp  β-Actin  AY550069  5′-CAGGTCATCACCATCGGCAA  823–842  93        5′-TTCGTGGATGCCGCAGGA  898–915     Visfatin  DQ020218  5′-TATTGCCTTTGGTTCTGGTG  1131–1150  154        5′-GCCCTTTTTGGACCTTTTGT  1265–1284     SREBP-1c  AY307771  5′-CCTCTGTCTCTCCTGCAACC  1011–1030  229        5′-GACCGGCTCTCCATAGACAA  1220–1239     PPARγ  AB097930  5′-CATGCTGTCATGGGTGAAAC  29–48  188        5′-TCAAAGGAGTGGGAGTGGTC  197–216     LPL  AY559454  5′-TGGACGGTGACAGGAATGTA  378–397  237        5′-AAGGCTGTATCCCAGGAGGT  595–614     aP2  AJ416020  5′-GGTGTCACGGCTACCAGAAT  364–383  156        5′-CAAAATCAGTCTGGGGGAAA  500–519     FAS  EF589048  5′-GGACCTGGTGATGAACGTCT  4510–4529  225        5′-CGGAAGTTGAGGGAGGTGTA  4715–4734     IL-6  AF309651  5′-ATGGCAGAAAAAGACGGATG  328–347  215        5′-GTGGTGGCTTTGTCTGGATT  523–542     Leptin  AF052691  5′-TTCTCTCTCGCTCCGCTAAG  1440–1459  239        5′-AGATGGAACCCTGCTTGATG  1659–1678     Adiponectin  AY135647  5′-TGAAGGATGTGAAGGTCAGC  589–608  221        5′-AGGAAGCCTGTGAAGATGGA  780–799     1SREBP-1c, sterol regulatory binding transcription factor 1c; LPL, lipoprotein lipase; aP2, adipocyte fatty acid-binding protein; FAS, fatty acid synthase. View Large Table 1. The primer sequences used for real-time PCR Target gene1  Accession No.  Primer sequence  Location  Expected length, bp  β-Actin  AY550069  5′-CAGGTCATCACCATCGGCAA  823–842  93        5′-TTCGTGGATGCCGCAGGA  898–915     Visfatin  DQ020218  5′-TATTGCCTTTGGTTCTGGTG  1131–1150  154        5′-GCCCTTTTTGGACCTTTTGT  1265–1284     SREBP-1c  AY307771  5′-CCTCTGTCTCTCCTGCAACC  1011–1030  229        5′-GACCGGCTCTCCATAGACAA  1220–1239     PPARγ  AB097930  5′-CATGCTGTCATGGGTGAAAC  29–48  188        5′-TCAAAGGAGTGGGAGTGGTC  197–216     LPL  AY559454  5′-TGGACGGTGACAGGAATGTA  378–397  237        5′-AAGGCTGTATCCCAGGAGGT  595–614     aP2  AJ416020  5′-GGTGTCACGGCTACCAGAAT  364–383  156        5′-CAAAATCAGTCTGGGGGAAA  500–519     FAS  EF589048  5′-GGACCTGGTGATGAACGTCT  4510–4529  225        5′-CGGAAGTTGAGGGAGGTGTA  4715–4734     IL-6  AF309651  5′-ATGGCAGAAAAAGACGGATG  328–347  215        5′-GTGGTGGCTTTGTCTGGATT  523–542     Leptin  AF052691  5′-TTCTCTCTCGCTCCGCTAAG  1440–1459  239        5′-AGATGGAACCCTGCTTGATG  1659–1678     Adiponectin  AY135647  5′-TGAAGGATGTGAAGGTCAGC  589–608  221        5′-AGGAAGCCTGTGAAGATGGA  780–799     Target gene1  Accession No.  Primer sequence  Location  Expected length, bp  β-Actin  AY550069  5′-CAGGTCATCACCATCGGCAA  823–842  93        5′-TTCGTGGATGCCGCAGGA  898–915     Visfatin  DQ020218  5′-TATTGCCTTTGGTTCTGGTG  1131–1150  154        5′-GCCCTTTTTGGACCTTTTGT  1265–1284     SREBP-1c  AY307771  5′-CCTCTGTCTCTCCTGCAACC  1011–1030  229        5′-GACCGGCTCTCCATAGACAA  1220–1239     PPARγ  AB097930  5′-CATGCTGTCATGGGTGAAAC  29–48  188        5′-TCAAAGGAGTGGGAGTGGTC  197–216     LPL  AY559454  5′-TGGACGGTGACAGGAATGTA  378–397  237        5′-AAGGCTGTATCCCAGGAGGT  595–614     aP2  AJ416020  5′-GGTGTCACGGCTACCAGAAT  364–383  156        5′-CAAAATCAGTCTGGGGGAAA  500–519     FAS  EF589048  5′-GGACCTGGTGATGAACGTCT  4510–4529  225        5′-CGGAAGTTGAGGGAGGTGTA  4715–4734     IL-6  AF309651  5′-ATGGCAGAAAAAGACGGATG  328–347  215        5′-GTGGTGGCTTTGTCTGGATT  523–542     Leptin  AF052691  5′-TTCTCTCTCGCTCCGCTAAG  1440–1459  239        5′-AGATGGAACCCTGCTTGATG  1659–1678     Adiponectin  AY135647  5′-TGAAGGATGTGAAGGTCAGC  589–608  221        5′-AGGAAGCCTGTGAAGATGGA  780–799     1SREBP-1c, sterol regulatory binding transcription factor 1c; LPL, lipoprotein lipase; aP2, adipocyte fatty acid-binding protein; FAS, fatty acid synthase. View Large Statistical Analyses Data were presented as the mean ± SEM. Data represent 3 replicates, each using cells isolated from a different pig. For each replicate, the control value for a variable was set to 1.0. A factorial ANOVA (SAS Inst. Inc., Cary, NC) was performed to determine the effect of recombinant visfatin or insulin stimulation. Duncan's multiple range test was used to evaluate the different response values between treatment groups. Differences were considered statistically significant when the P-value was ≤0.05. RESULTS Expression and Purification of Recombinant Visfatin Protein extracts from bacteria expressing recombinant visfatin were separated by SDS-PAGE gel, and the purity of the recombinant protein carrying an N-terminal His tag was confirmed as a single band of 52 kDa after Coomassie brilliant blue staining (Figure 1). The purified recombinant visfatin protein was used to immunize mice and to investigate function in adipocytes. Figure 1. View largeDownload slide Expression and purification of the recombinant porcine visfatin protein. Transformed cell culture was induced by isopropyl-thiogalactopyranoside to produce the His-tagged visfatin fusion protein. The CP lysate was purified by nickel-charged affinity chromatography under native conditions. Protein samples were separated by a 12% SDS-PAGE gel and visualized by Coomassie blue staining. M: protein ladder; CL: cell lysate, FT: flow-through fraction, W: final wash fraction; E: eluted visfatin. The arrowhead indicates visfatin expression, 52 kDa. MW = molecular weight. Color version available in the online PDF. Figure 1. View largeDownload slide Expression and purification of the recombinant porcine visfatin protein. Transformed cell culture was induced by isopropyl-thiogalactopyranoside to produce the His-tagged visfatin fusion protein. The CP lysate was purified by nickel-charged affinity chromatography under native conditions. Protein samples were separated by a 12% SDS-PAGE gel and visualized by Coomassie blue staining. M: protein ladder; CL: cell lysate, FT: flow-through fraction, W: final wash fraction; E: eluted visfatin. The arrowhead indicates visfatin expression, 52 kDa. MW = molecular weight. Color version available in the online PDF. Characterization of Porcine Visfatin in Gene Expression Patterns The endogenous visfatin protein was harvested from porcine blood plasma and detected by western blot analyses using the visfatin monoclonal antibody. Three visfatin protein variants of approximately 52, 27, and 14 kDa were detected (Figure 2A). Tissue distribution of visfatin gene expression was examined (Figure 2B). The mRNA for visfatin was expressed in all tissues examined. In these tissues, the relative mRNA expression of visfatin was greatest (P < 0.05) in the liver and muscle. Figure 2. View largeDownload slide Characterization of porcine endogenous visfatin mRNA and protein. (A) Detection of porcine visfatin isoforms. Total cellular protein (5 to 100 μg) extracted from porcine blood plasma was separated in 12% SDS-PAGE gels and electroblotted onto PVDF (PerkinElmer, Boston, MA) membranes. Visfatin was detected using a mouse anti-pig visfatin monoclonal antibody that we established. Three visfatin protein variants of approximately 52, 27, and 14 kDa were detected. MW = molecular weight. (B) Tissue distribution of the visfatin mRNA. Total RNA was extracted from 10 different porcine tissues [spleen, lung, uterus, kidney, ovary, heart, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), muscle, and liver] collected from three 2-wk-old pigs and used for cDNA synthesis. Real-time PCR was used to determine the endogenous visfatin gene expression pattern. The β-actin expression in each sample was the internal control for normalizing expression, and spleen gene expression was set to 1.0. Data presented are means ± SEM from 3 independent experiments. Statistical analyses were performed using ANOVA and Duncan's new multiple range test. Column means labeled with different letters (a, b) were significantly different (P ≤ 0.05). Color version available in the online PDF. Figure 2. View largeDownload slide Characterization of porcine endogenous visfatin mRNA and protein. (A) Detection of porcine visfatin isoforms. Total cellular protein (5 to 100 μg) extracted from porcine blood plasma was separated in 12% SDS-PAGE gels and electroblotted onto PVDF (PerkinElmer, Boston, MA) membranes. Visfatin was detected using a mouse anti-pig visfatin monoclonal antibody that we established. Three visfatin protein variants of approximately 52, 27, and 14 kDa were detected. MW = molecular weight. (B) Tissue distribution of the visfatin mRNA. Total RNA was extracted from 10 different porcine tissues [spleen, lung, uterus, kidney, ovary, heart, visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), muscle, and liver] collected from three 2-wk-old pigs and used for cDNA synthesis. Real-time PCR was used to determine the endogenous visfatin gene expression pattern. The β-actin expression in each sample was the internal control for normalizing expression, and spleen gene expression was set to 1.0. Data presented are means ± SEM from 3 independent experiments. Statistical analyses were performed using ANOVA and Duncan's new multiple range test. Column means labeled with different letters (a, b) were significantly different (P ≤ 0.05). Color version available in the online PDF. Effects of Visfatin on Porcine Preadipocytes Preadipocytes were treated with visfatin or insulin for 4 or 8 d, and the mRNA abundance of sterol regulatory element binding protein 1c (SREBP-1c), PPARγ, lipoprotein lipase (LPL), and adipocyte fatty acid-binding protein (aP2) were determined by real-time RT-PCR analyses. Visfatin did not increase any of the mRNA abundance at 4 d (Figure 3A). Visfatin increased the mRNA abundance of LPL and PPARγ after 8 d of differentiation by approximately 3.5- and 2.1-fold (P < 0.05), respectively. Insulin had a similar effect on LPL and PPARγ mRNA abundance, but also increased the mRNA at 4 d. The increase at 8 d was approximately 5.0- and 10.7-fold (P < 0.05), respectively. In contrast, neither visfatin nor insulin had an effect on the gene expression of SREBP-1c (P > 0.05). Treatment of preadipocytes with insulin induced a 24.7-fold increase (P < 0.05) in the mRNA abundance of aP2; visfatin alone had no effect. After 8 d, cells were fixed and stained with Oil Red-O. Compared with visfatin-treated preadipocytes, insulin-treated preadipocytes accumulated lipid at a more rapid rate during differentiation/adipogenesis; fully differentiated cells contained greater amounts of lipid with larger cytoplasmic lipid droplets (Figure 3B). Figure 3. View largeDownload slide Effect of visfatin and insulin on the expression of adipogenesis-related genes in porcine preadipocytes. Postconfluent undifferentiated stromal/vascular (S/V) cells were treated with serum-free growth medium containing 100 ng/mL of visfatin or insulin, respectively, for 4 and 8 d. (A) Total RNA was extracted and used for cDNA synthesis and real-time PCR. The expression of each gene was normalized against β-actin and presented as fold changes compared with untreated control cells. The control value was set as 1.0. Error bars represent the mean ± SEM from 3 independent experiments, each with preadipocytes isolated from a different pig. Analyses were performed in duplicate. White bar: control (no treatment), black bar: visfatin treatment, hatched bar: insulin treatment. Different letters (a, b) indicate column means were significantly different, P ≤ 0.05 using Duncan's new multiple range test. SREBP-1c = sterol regulatory element binding protein 1c; LPL = lipoprotein lipase; aP2 = adipocyte fatty acid-binding protein. (B) Oil Red-O staining of S/V cells incubated with visfatin (left panel) or insulin (right panel) for 8 d. The lipid droplets turned red (light spheres as pointed at by arrows) with Oil Red-O staining, whereas the cytoplasms showed no color. The images were photographed at 400× magnification. Color version available in the online PDF. Figure 3. View largeDownload slide Effect of visfatin and insulin on the expression of adipogenesis-related genes in porcine preadipocytes. Postconfluent undifferentiated stromal/vascular (S/V) cells were treated with serum-free growth medium containing 100 ng/mL of visfatin or insulin, respectively, for 4 and 8 d. (A) Total RNA was extracted and used for cDNA synthesis and real-time PCR. The expression of each gene was normalized against β-actin and presented as fold changes compared with untreated control cells. The control value was set as 1.0. Error bars represent the mean ± SEM from 3 independent experiments, each with preadipocytes isolated from a different pig. Analyses were performed in duplicate. White bar: control (no treatment), black bar: visfatin treatment, hatched bar: insulin treatment. Different letters (a, b) indicate column means were significantly different, P ≤ 0.05 using Duncan's new multiple range test. SREBP-1c = sterol regulatory element binding protein 1c; LPL = lipoprotein lipase; aP2 = adipocyte fatty acid-binding protein. (B) Oil Red-O staining of S/V cells incubated with visfatin (left panel) or insulin (right panel) for 8 d. The lipid droplets turned red (light spheres as pointed at by arrows) with Oil Red-O staining, whereas the cytoplasms showed no color. The images were photographed at 400× magnification. Color version available in the online PDF. Effects of Visfatin on the Differentiation of Porcine Preadipocytes After 5 d of differentiation, adipocytes were treated for 24 h with insulin or visfatin and the expression of SREBP-1c, fatty acid synthase (FAS), IL-6, adiponectin, and leptin was determined (Figure 4). Expression of FAS mRNA was increased by 3.2- and 2.9-fold (all P < 0.05) in visfatin-treated and insulin-treated adipocytes, respectively. Treatment of adipocytes with visfatin also induced a 5.8-fold (P < 0.05) increase in IL-6 mRNA expression, but there was no change in SREBP-1c, leptin, or adiponectin mRNA abundance (all P > 0.05). Figure 4. View largeDownload slide Effect of visfatin and insulin on the expression of adipogenesis-related genes in porcine adipocytes. Preadipocytes were differentiated from 8 d and then treated with Dulbecco's modified Eagle/F12 medium containing 100 ng/mL of visfatin or insulin, respectively, for 24 h. Total RNA was extracted from the cells and used for cDNA synthesis and real-time PCR. (A) Sterol regulatory element binding protein 1c (SREBP-1c) and fatty acid synthase (FAS), and (B) IL-6, adiponectin (ADN), and leptin. The expression of each gene was normalized using β-actin expression in the same sample. Normalized data are presented as fold changes compared with untreated control cells. The control value was set as 1.0. Data presented are means ± SEM from 3 independent experiments, each using cells from a different pig. White bar: control (no treatment), black bar: visfatin treatment, hatched bar: insulin treatment. Different letters (a,b) indicate column means were significantly different, P ≤ 0.05 using Duncan's new multiple range test. Figure 4. View largeDownload slide Effect of visfatin and insulin on the expression of adipogenesis-related genes in porcine adipocytes. Preadipocytes were differentiated from 8 d and then treated with Dulbecco's modified Eagle/F12 medium containing 100 ng/mL of visfatin or insulin, respectively, for 24 h. Total RNA was extracted from the cells and used for cDNA synthesis and real-time PCR. (A) Sterol regulatory element binding protein 1c (SREBP-1c) and fatty acid synthase (FAS), and (B) IL-6, adiponectin (ADN), and leptin. The expression of each gene was normalized using β-actin expression in the same sample. Normalized data are presented as fold changes compared with untreated control cells. The control value was set as 1.0. Data presented are means ± SEM from 3 independent experiments, each using cells from a different pig. White bar: control (no treatment), black bar: visfatin treatment, hatched bar: insulin treatment. Different letters (a,b) indicate column means were significantly different, P ≤ 0.05 using Duncan's new multiple range test. DISCUSSION Multiple Alternative Splicing Events of Porcine Visfatin The porcine visfatin gene, localized on chromosome SSC9, is composed of at least 11 exons and has exactly the same exon-intron structure as the human ortholog (Karnuah et al., 2001; Chen et al., 2007). Accumulating evidence indicates that there are at least 8 alternatively spliced transcripts of porcine visfatin that are expressed in a tissue-specific manner (Chen et al., 2007; Palin et al., 2008). Six visfatin transcript variants are the products of alternative splicing or polyadenylation (Palin et al., 2008). Among them, 4 protein isoforms, 46, 54, 60, and 120 kDa, have been identified with Western blot analyses using antibodies against human visfatin. Variant 1 is the predominant form observed in every species and contains an open reading frame of 1,473 bp encoding a 491-AA protein with a molecular mass of 52 kDa (Chen et al., 2007). In this study, we produced a specific monoclonal antibody against porcine visfatin and identified 3 isoforms of visfatin. The 52-kDa protein had the greatest expression. The 14-kDa protein could be detected, but 100 µg of total protein was needed. In support of our findings in the current study, these isoforms corresponded to the primary calculated molecular weight for the open reading frames published on GenBank (National Center for Biotechnology Information, Bethesda, MD) under the accession numbers DQ001974 (2.2 kb, approximately 52 kDa), DQ231167 (1.9 kb, approximately 27 kDa), and DQ231168 (1.2 kb, approximately 14 kDa; Chen et al., 2007). It is suggested that the isoforms result from multiple alternative splicing events and tissue-specific expression. The results also confirmed that the monoclonal antibody against porcine visfatin was successfully prepared with the desired specificity and immunological reactivity. Our data from the current study are in agreement with the report of Chen et al. (2007), but we could not confirm the results of Palin et al. (2008) that revealed the presence of 4 distinct porcine visfatin bands (46, 54, 60, and 120 kDa). This discrepancy may also due to the different specificity of the monoclonal antibody developed by the current study to the human antibody used by the Palin et al. (2008). Therefore, related data should be interpreted with caution before more detailed characterization is confirmed. Pagano et al. (2006) suggested that the amplification of different visfatin transcripts in adipose tissue may be an explanation for the contradictory results obtained in various studies. In humans, genetic variation in visfatin was found to be associated with lipid metabolism (Jian et al., 2006). It is not clear whether these porcine visfatin mRNA variants are all translated into protein with physiological properties. The functional significance of the splice variants in pigs remains unknown. Tissue Distribution of Porcine Visfatin mRNA Expression In Sprague-Dawley rats, visfatin is expressed to a greater extent in VAT than in muscle; the greater VAT expression may result from the extensive adipogenesis occurring in adipocytes in this physiological model (Lv et al., 2009). However, in our studies with 2-wk-old pig tissues, we failed to confirm that there was a difference in visfatin mRNA abundance between VAT and subcutaneous adipose tissue (SAT). Our results in the current study showed that the visfatin gene was transcribed in many porcine tissues; its quantities were greater in the liver and muscle than in the adipose tissue. These observations were in agreement with previous studies using human (Samal et al., 1994), dog (McGlothlin et al., 2005), chicken (Krzysik-Walker et al., 2008), and pig (Palin et al., 2008) tissues. The exact physiological functions of visfatin, however, remain controversial. Such conflicting observations regarding the source of visfatin and the proposed role of visfatin in each insulin-sensitive tissue still need clarification. Regulation of Visfatin in S/V Cell Differentiation Visfatin exhibits insulin-like activity that are dose-dependent and has been shown to activate the insulin receptor in various insulin-sensitive cell lines in vitro, resulting in the enhancement of glucose uptake, suppression of glucose release, accumulation of triglycerides, and induction of gene markers for adipocyte differentiation (Chan et al., 2006; Chen et al., 2006). Glucose treatments cause a time- and dose-dependent visfatin release from subcutaneous adipocytes (Haider et al., 2006). The binding of visfatin for the insulin receptor shows similar affinities to that of insulin, but visfatin inserts its function through a distinct binding site (different from insulin binding site) on the insulin receptor (Kim et al., 2006). At a concentration of 2 nM, visfatin and insulin stimulate nearly identical increase in glucose uptake in partially differentiated 3T3-L1 adipocytes (Morgan et al., 2008). At a concentration of 100 nM, insulin and visfatin also significantly upregulate glucose uptake in adipocytes (Moschen et al., 2007). Increasing visfatin concentrations causes increasing uptake of glucose, and the maximal effect was reached at 2 μM visfatin (or 100 ng/mL) in cultured mesangial cells and osteoblast cells (Xie et al., 2007; Song et al., 2008). To further examine the insulin-mimetic effect of visfatin on lipid metabolism and to determine whether visfatin and insulin share a common signaling pathway, we studied the mRNA abundance of several key genes involved in the insulin-signaling cascade in the presence of exogenous visfatin. Insulin is implicated in the regulation of LPL, a key enzyme in lipid metabolism, in adipose tissue via the activation of PPARγ (Hanyu et al., 2004). Porcine PPARγ is the master regulator that governs the differentiation of adipocytes (Yu et al., 2006, 2008). The active form of PPARγ binds to the consensus peroxisome proliferator response element (PPRE) sequence in the LPL promoter to trigger its expression (Hanyu et al., 2004). Therefore, an increase in the expression of PPARγ may lead to an increase in the expression of LPL. We observed similar adipogenesis-inducing effects for visfatin, an increase in PPARγ and LPL mRNA expressions, thus suggesting visfatin may play a role in adipogenesis. Expression of aP2 in preadipocytes is well-correlated with cell differentiation and regarded as a molecular marker for terminal differentiation. Neither the aP2 protein nor mRNA transcripts are detected in undifferentiated adipocytes (Ding et al., 1999). Activation of PPARγ by ligands is sufficient to stimulate aP2 expression and adipocyte, as well as macrophage, differentiation (Takahashi et al., 2003; Thompson et al., 2004). In contrast, we observed no significant changes in aP2 in S/V cells (preadipocytes) treated with visfatin. Visfatin-induced cells had less lipid accumulation than insulin-induced cells after 8 d of differentiation. This observation is in agreement with the fact that aP2 expression is closely associated with lipid droplet accumulation in many types of cell lines (Ding et al., 1999; El-Jack et al., 1999; Sun et al., 2003; Liu and Nambi, 2004; Kazemi et al., 2005; Makowski et al., 2005). Adipocyte fatty acid-binding protein acts as a cytosolic fatty acid chaperone and facilitates the utilization of lipids in metabolic pathways (Maeda et al., 2005), but mechanisms for the regulation of aP2 expression in different cell types are not well-characterized (Sun et al., 2003; Shum et al., 2006). Collectively, data in the current study indicated that visfatin possesses the potential to induce mRNA of adipose differentiation markers during the differentiation process, but did not result in accumulation of large amounts of lipid or formation of large lipid droplets. The function of visfatin may be attributed to another insulin-like effect. Regulation of Visfatin in Differentiated Adipocytes The crucial transcription factor, SREBP-1c, has been implicated in the insulin-mediated induction of genes associated with glucose and fatty acid metabolism in hepatocytes, adipocytes, and myocytes (Chakravarty et al., 2001; Ducluzeau et al., 2001). Fatty acid synthase is a marker for the lipogenic pathway, and some studies suggest that there is a positive correlation between mRNA abundance of FAS and SREBP-1c for the synthesis of fatty acids stimulated by insulin (Gondret et al., 2001; Li and Yang, 2008). The current data, however, showed contradictory results in which the expression of SREBP-1c mRNA only slightly increased, but mRNA abundance of FAS increased during the visfatin- or insulin-stimulated differentiation of porcine S/V cells. These results imply that there is a potential limit to the regulation of lipogenesis in adipocytes through the induction of SREBP-1c to activate the transcription of FAS. The result is further supported by recent reports claiming that the mRNA abundance of SREBP-1c do not coincide with the changes in adipose lipogenic gene expression (Bertile and Raclot, 2004; Sekiya et al., 2007). In the present study we demonstrated that in adipocytes, visfatin and insulin activated the expression of FAS mRNA, but SREBP-1c mRNA did not change, implying the involvement of other transcription factors. Effect of Visfatin on the Induction of Inflammatory Responses In the 3T3-L1 adipocyte cell line, IL-6 is a strong inhibitor of adipogenesis (Ohsumi et al., 1994) and directly influences the metabolism of human adipocytes by decreasing the activity of LPL (Greenberg et al., 1992). Treatment of adipocytes with visfatin resulted in upregulation of IL-6 in the present study; thus, visfatin may play a role in triggering and coordinating immune responses (Ognjanovic and Bryant-Greenwood, 2002). Lagathu et al. (2003) demonstrated that IL-6 produced by adipocytes is a pro- and anti-inflammatory cytokine and is capable of inducing insulin resistance in differentiating and differentiated adipocytes. Acutely, IL-6 mimics the action of insulin on ERK1/2 and Akt activation and chronically induces insulin resistance via the induction of cytokine signaling-3 proteins (Lagathu et al., 2003; Senn et al., 2003; Gabler and Spurlock, 2008). Berndt et al. (2005) indicated that IL-6 expression, induced by visfatin, might be involved in the pathogenesis of insulin resistance associated with visceral obesity. We found that the abundance of 2 major adipocytokine mRNA, leptin and adiponectin, were not affected by treatments with visfatin. Although adiponectin and leptin are adipocyte cytokines, the biological functions and regulation of their secretion are obviously different and even, to a certain extent, opposite of IL-6 (Zhang et al., 2006). The increase of IL-6 by visfatin treatment may inhibit the expression of adiponectin (Fasshauer et al., 2003); however, we did not observe such an effect. The lack of an adiponectin response may result from the balance of an inhibition effect of abundantly expressed IL-6 and other inducing regulators increased by visfatin. In conclusion, we have presented results of the effects of visfatin on the regulation of genes related to lipid metabolism in porcine adipocytes. Transcriptional changes in visfatin- and lipid metabolism-related genes revealed insulin-mimetic properties of visfatin; however, there are differences in the effects of insulin and visfatin on preadipocyte differentiation and lipid accumulation. Taken together, these results provide further evidence for the molecular mechanisms underlying the effects of visfatin on lipid metabolism and also suggest there are important differences between insulin and visfatin effects. LITERATURE CITED Berndt J. Klöting N. Kralisch S. Kovacs P. Fasshauer M. Schön M. R. Stumvoll M. Blüher M. 2005. Plasma visfatin concentrations and fat depot-specific mRNA expression in humans. Diabetes  54: 2911– 2916. [PubMed] Google Scholar CrossRef Search ADS PubMed  Bertile F. Raclot T. 2004. mRNA levels of SREBP-1c do not coincide with the changes in adipose lipogenic gene expression. Biochem. Biophys. Res. Commun.  325: 827– 834. [PubMed] Google Scholar CrossRef Search ADS PubMed  Chakravarty K. Leahy P. Becard D. Hakimi P. Foretz M. Ferre P. Foufelle F. Hanson R. W. 2001. Sterol regulatory element-binding protein-1c mimics the negative effect of insulin on phosphoenolpyruvate carboxykinase (GTP) gene transcription. J. Biol. Chem.  276: 34816– 34823. [PubMed] Google Scholar CrossRef Search ADS PubMed  Chan T. F. Chen Y. L. Lee C. H. Chou F. H. Wu L. C. Jong S. B. Tsai E. M. 2006. Decreased plasma visfatin concentrations in women with gestational diabetes mellitus. J. Soc. Gynecol. Investig.  13: 364– 367. [PubMed] Google Scholar CrossRef Search ADS PubMed  Chen C. H. Wang P. H. Liu B. H. Hsu H. H. Mersmann H. J. Ding S. T. 2008. Serum amyloid A protein regulates the expression of porcine genes related to lipid metabolism. J. Nutr.  138: 674– 679. [PubMed] Google Scholar CrossRef Search ADS PubMed  Chen H. Xia T. Zhou L. Chen X. Gan L. Yao W. Peng Y. Yang Z. 2007. Gene organization, alternate splicing and expression pattern of porcine visfatin gene. Domest. Anim. Endocrinol.  32: 235– 245. [PubMed] Google Scholar CrossRef Search ADS PubMed  Chen H. M. Chang S. C. Wu C. C. Cuo T. S. Wu J. S. Juang R. H. 2002. Regulation of the catalytic behaviour of l-form starch phosphorylase from sweet potato roots by proteolysis. Physiol. Plant.  114: 506– 515. [PubMed] Google Scholar CrossRef Search ADS PubMed  Chen M. P. Chung F. M. Chang D. M. Tsai J. C. Huang H. F. Shin S. J. Lee Y. J. 2006. Elevated plasma level of visfatin/pre-B cell colony-enhancing factor in patients with type 2 diabetes mellitus. J. Clin. Endocrinol. Metab.  91: 295– 299. [PubMed] Google Scholar CrossRef Search ADS PubMed  Ding S. T. McNeel R. L. Mersmann H. J. 1999. Expression of porcine adipocyte transcripts: Tissue distribution and differentiation in vitro and in vivo. Comp. Biochem. Physiol. B Biochem. Mol. Biol.  123: 307– 318. [PubMed] Google Scholar CrossRef Search ADS PubMed  Ding S. T. McNeel R. L. Mersmann H. J. 2002. Modulation of adipocyte determination and differentiation-dependent factor 1 by selected polyunsaturated fatty acids. In Vitro Cell. Dev. Biol. Anim.  38: 352– 357. [PubMed] Google Scholar CrossRef Search ADS PubMed  Ducluzeau P. H. Perretti N. Laville M. Andreelli F. Vega N. Riou J. P. Vidal H. 2001. Regulation by insulin of gene expression in human skeletal muscle and adipose tissue. Evidence for specific defects in type 2 diabetes. Diabetes  50: 1134– 1142. [PubMed] Google Scholar CrossRef Search ADS PubMed  El-Jack A. K. Hamm J. K. Pilch P. F. Farmer S. R. 1999. Reconstitution of insulin-sensitive glucose transport in fibroblasts requires expression of both PPARgamma and C/EBPalpha. J. Biol. Chem.  274: 7946– 7951. [PubMed] Google Scholar CrossRef Search ADS PubMed  Fasshauer M. Kralisch S. Klier M. Lossner U. Bluher M. Klein J. Paschke R. 2003. Adiponectin gene expression and secretion is inhibited by interleukin-6 in 3T3–L1 adipocytes. Biochem. Biophys. Res. Commun.  301: 1045– 1050. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gabler N. K. Spurlock M. E. 2008. Integrating the immune system with the regulation of growth and efficiency. J. Anim. Sci.  86: E64– E74. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gondret F. Ferré P. Dugail I. 2001. ADD-1/SREBP-1 is a major determinant of tissue differential lipogenic capacity in mammalian and avian species. J. Lipid Res.  42: 106– 113. [PubMed] Google Scholar PubMed  Greenberg A. S. Nordan R. P. McIntosh J. Calvo J. C. Scow R. O. Jablons D. 1992. Interleukin 6 reduces lipoprotein lipase activity in adipose tissue of mice in vivo and in 3T3–L1 adipocytes: A possible role for interleukin 6 in cancer cachexia. Cancer Res.  52: 4113– 4116. [PubMed] Google Scholar PubMed  Haider D. G. Schaller G. Kapiotis S. Maier C. Luger A. Wolzt M. 2006. The release of the adipocytokine visfatin is regulated by glucose and insulin. Diabetologia  49: 1909– 1914. [PubMed] Google Scholar CrossRef Search ADS PubMed  Hanyu O. Miida T. Obayashi K. Ikarashi T. Soda S. Kaneko S. Hirayama S. Suzuki K. Nakamura Y. Yamatani K. Aizawa Y. 2004. Lipoprotein lipase (LPL) mass in preheparin serum reflects insulin sensitivity. Atherosclerosis  174: 385– 390. [PubMed] Google Scholar CrossRef Search ADS PubMed  Jian W. X. Luo T. H. Gu Y. Y. Zhang H. L. Zheng S. Dai M. Han J. F. Zhao Y. Li G. Luo M. 2006. The visfatin gene is associated with glucose and lipid metabolism in a Chinese population. Diabet. Med.  23: 967– 973. [PubMed] Google Scholar CrossRef Search ADS PubMed  Karnuah A. B. Uenishi H. Kiuchi S. Kojima M. Onishi A. Yasue H. Mitsuhashi T. 2001. Assignment of 64 genes expressed in 28-day-old pig embryo to radiation hybrid map. Mamm. Genome  12: 518– 523. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kazemi M. R. McDonald C. M. Shigenaga J. K. Grunfeld C. Feingold K. R. 2005. Adipocyte fatty acid-binding protein expression and lipid accumulation are increased during activation of murine macrophages by Toll-like receptor agonists. Arterioscler. Thromb. Vasc. Biol.  25: 1220– 1224. [PubMed] Google Scholar CrossRef Search ADS PubMed  Kim M. K. Lee J. H. Kim H. Park S. J. Kim S. H. Kang G. B. Lee Y. S. Kim J. B. Kim K. K. Suh S. W. Eom S. H. 2006. Crystal structure of visfatin/pre-B cell colony-enhancing factor 1/nicotinamide phosphoribosyltransferase, free and in complex with the anti-cancer agent FK-866. J. Mol. Biol.  362: 66– 77. [PubMed] Google Scholar CrossRef Search ADS PubMed  Krzysik-Walker S. M. Ocón-Grove O. M. Maddineni S. R. Hendricks G. L.III Ramachandran R. 2008. Is visfatin an adipokine or myokine? Evidence for greater visfatin expression in skeletal muscle than visceral fat in chickens. Endocrinology  149: 1543– 1550. [PubMed] Google Scholar CrossRef Search ADS PubMed  Laemmli U. K. 1970. Cleavage of structural proteins during the assembly of the head of bacteriophage T4. Nature  227: 680– 685. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lagathu C. Bastard J. P. Auclair M. Maachi M. Capeau J. Caron M. 2003. Chronic interleukin-6 (IL-6) treatment increased IL-6 secretion and induced insulin resistance in adipocyte: Prevention by rosiglitazone. Biochem. Biophys. Res. Commun.  311: 372– 379. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lago F. Gómez R. Gómez-Reino J. J. Dieguez C. Gualillo O. 2009. Adipokines as novel modulators of lipid metabolism. Trends Biochem. Sci.  34: 500– 510. [PubMed] Google Scholar CrossRef Search ADS PubMed  Li Y. Yang G. S. 2008. Insulin induced lipogenesis and enhanced mRNA level of sterol regulatory element binding protein-1c in primary porcine adipocyte. Can. J. Anim. Sci.  88: 419– 424. Google Scholar CrossRef Search ADS   Liu Q. Y. Nambi P. 2004. Sirolimus upregulates aP2 expression in human monocytes and macrophages. Transplant. Proc.  36: 3229– 3231. [PubMed] Google Scholar CrossRef Search ADS PubMed  Livak K. J. Schmittgen T. D. 2001. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-delta delta C(T)). Methods  25: 402– 408. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lv Q. Wang Y. Wang W. Wang L. Zhou X. 2009. Effect of pioglitazone on visfatin expression in 3T3–L1 adipocytes and SD rats. Endocr. Res.  34: 130– 141. [PubMed] Google Scholar CrossRef Search ADS PubMed  Maeda K. Cao H. Kono K. Gorgun C. Z. Furuhashi M. Uysal K. T. Cao Q. Atsumi G. Malone H. Krishnan B. Minokoshi Y. Kahn B. B. Parker R. A. Hotamisligil G. S. 2005. Adipocyte/macrophage fatty acid binding proteins control integrated metabolic responses in obesity and diabetes. Cell Metab.  1: 107– 119. [PubMed] Google Scholar CrossRef Search ADS PubMed  Makowski L. Brittingham K. C. Reynolds J. M. Suttles J. Hotamisligil G. S. 2005. The fatty acid-binding protein, aP2, coordinates macrophage cholesterol trafficking and inflammatory activity. Macrophage expression of aP2 impacts peroxisome proliferator-activated receptor gamma and IkappaB kinase activities. J. Biol. Chem.  280: 12888– 12895. [PubMed] Google Scholar CrossRef Search ADS PubMed  McGlothlin J. R. Gao L. Lavoie T. Simon B. A. Easley R. B. Ma S. F. Rumala B. B. Garcia J. G. Ye S. Q. 2005. Molecular cloning and characterization of canine pre-B-cell colony-enhancing factor. Biochem. Genet.  43: 127– 141. [PubMed] Google Scholar CrossRef Search ADS PubMed  Morgan S. A. Bringolf J. B. Seidel E. R. 2008. Visfatin expression is elevated in normal human pregnancy. Peptides  29: 1382– 1389. [PubMed] Google Scholar CrossRef Search ADS PubMed  Moschen A. R. Kaser A. Enrich B. Mosheimer B. Theurl M. Niederegger H. Tilg H. 2007. Visfatin, an adipocytokine with proinflammatory and immunomodulating properties. J. Immunol.  178: 1748– 1758. [PubMed] Google Scholar CrossRef Search ADS PubMed  Ognjanovic S. Bryant-Greenwood G. D. 2002. Pre-B-cell colony-enhancing factor, a novel cytokine of human fetal membranes. Am. J. Obstet. Gynecol.  187: 1051– 1058. [PubMed] Google Scholar CrossRef Search ADS PubMed  Ohsumi J. Sakakibara S. Yamaguchi J. Miyadai K. Yoshioka S. Fujiwara T. Horikoshi H. Serizawa N. 1994. Troglitazone prevents the inhibitory effects of inflammatory cytokines on insulin-induced adipocyte differentiation in 3T3–L1 cells. Endocrinology  135: 2279– 2282. [PubMed] Google Scholar CrossRef Search ADS PubMed  Pagano C. Pilon C. Olivieri M. Mason P. Fabris R. Serra R. Milan G. Rossato M. Federspil G. Vettor R. 2006. Reduced plasma visfatin/pre-B cell colony-enhancing factor in obesity is not related to insulin resistance in humans. J. Clin. Endocrinol. Metab.  91: 3165– 3170. [PubMed] Google Scholar CrossRef Search ADS PubMed  Palin M. F. Labrecque B. Beaudry D. Mayhue M. Bordignon V. Murphy D. 2008. Visfatin expression is not associated with adipose tissue abundance in the porcine model. Domest. Anim. Endocrinol.  35: 58– 73. [PubMed] Google Scholar CrossRef Search ADS PubMed  Samal B. Sun Y. Stearns G. Xie C. Suggs S. McNiece I. 1994. Cloning and characterization of the cDNA encoding a novel human pre-B-cell colony-enhancing factor. Mol. Cell. Biol.  14: 1431– 1437. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sekiya M. Yahagi N. Matsuzaka T. Takeuchi Y. Nakagawa Y. Takahashi H. Okazaki H. Iizuka Y. Ohashi K. Gotoda T. Ishibashi S. Nagai R. Yamazaki T. Kadowaki T. Yamada N. Osuga J. Shimano H. 2007. SREBP-1-independent regulation of lipogenic gene expression in adipocytes. J. Lipid Res.  48: 1581– 1591. [PubMed] Google Scholar CrossRef Search ADS PubMed  Senn J. J. Klover P. J. Nowak I. A. Zimmers T. A. Koniaris L. G. Furlanetto R. W. Mooney R. A. 2003. Suppressor of cytokine signaling-3 (SOCS-3), a potential mediator of interleukin-6-dependent insulin resistance in hepatocytes. J. Biol. Chem.  278: 13740– 13746. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sethi J. K. Vidal-Puig A. 2005. Visfatin: The missing link between intra-abdominal obesity and diabetes? Trends Mol. Med.  11: 344– 347. [PubMed] Google Scholar CrossRef Search ADS PubMed  Shum B. O. V. Mackay C. R. Gorgun C. Z. Frost M. J. Kumar R. K. Hotamisligil G. S. Rolph M. S. 2006. The adipocyte fatty acid-binding protein aP2 is required in allergic airway inflammation. J. Clin. Invest.  116: 2183– 2192. [PubMed] Google Scholar CrossRef Search ADS PubMed  Song H. K. Lee M. H. Kim B. K. Park Y. G. Ko G. J. Kang Y. S. Han J. Y. Han S. Y. Han K. H. Kim H. K. Cha D. R. 2008. Visfatin: A new player in mesangial cell physiology and diabetic nephropathy. Am. J. Physiol. Renal Physiol.  295: F1485– F1494. [PubMed] Google Scholar CrossRef Search ADS PubMed  Sun L. Nicholson A. C. Hajjar D. P. Gotto A. M.Jr. Han J. 2003. Adipogenic differentiating agents regulate expression of fatty acid binding protein and CD36 in the J744 macrophage cell line. J. Lipid Res.  44: 1877– 1886. [PubMed] Google Scholar CrossRef Search ADS PubMed  Takahashi N. Kawada T. Goto T. Kim C. S. Taimatsu A. Egawa K. Yamamoto T. Jisaka M. Nishimura K. Yokota K. Yu R. Fushiki T. 2003. Abietic acid activates peroxisome proliferator-activated receptor-gamma (PPARgamma) in RAW264.7 macrophages and 3T3–L1 adipocytes to regulate gene expression involved in inflammation and lipid metabolism. FEBS Lett.  550: 190– 194. [PubMed] Google Scholar CrossRef Search ADS PubMed  Thompson G. M. Trainor D. Biswas C. LaCerte C. Berger J. P. Kelly L. J. 2004. A high-capacity assay for PPARgamma ligand regulation of endogenous aP2 expression in 3T3–L1 cells. Anal. Biochem.  330: 21– 28. [PubMed] Google Scholar CrossRef Search ADS PubMed  Trayhurn P. Wood I. S. 2004. Adipokines: Inflammation and the pleiotropic role of white adipose tissue. Br. J. Nutr.  92: 347– 355. [PubMed] Google Scholar CrossRef Search ADS PubMed  Wang H. C. Ko Y. H. Mersmann H. J. Chen C. L. Ding S. T. 2006. The expression of genes related to adipocytes in pigs. J. Anim. Sci.  84: 1059– 1066. [PubMed] Google Scholar CrossRef Search ADS PubMed  Wang P. Xu T. Y. Guan Y. F. Su D. F. Fan G. R. Miao C. Y. 2009. Perivascular adipose tissue-derived visfatin is a vascular smooth muscle cell growth factor: Role of nicotinamide mononucleotide. Cardiovasc. Res.  81: 370– 380. [PubMed] Google Scholar CrossRef Search ADS PubMed  Xie H. Tang S. Y. Luo X. H. Huang J. Cui R. R. Yuan L. Q. Zhou H. D. Wu X. P. Liao E. Y. 2007. Insulin-like effects of visfatin on human osteoblasts. Calcif. Tissue Int.  80: 201– 210. [PubMed] Google Scholar CrossRef Search ADS PubMed  Yu Y. H. Lin E. C. Wu S. C. Cheng W. T. K. Mersmann H. J. Wang P. H. Ding S. T. 2008. DHA regulates adipogenic genes in myoblasts via porcine PPARγ. J. Anim. Sci.  86: 3385– 3392. [PubMed] Google Scholar CrossRef Search ADS PubMed  Yu Y. H. Liu B. H. Mersmann H. J. Ding S. T. 2006. Porcine peroxisome- proliferator-activated receptor γ induces transdifferentiation of myoblasts into adipocytes. J. Anim. Sci.  84: 2655– 2665. [PubMed] Google Scholar CrossRef Search ADS PubMed  Zhang X. L. Yin K. S. Wang H. Su S. 2006. Serum adiponectin levels in adult male patients with obstructive sleep apnea hypopnea syndrome. Respiration  73: 73– 77. [PubMed] Google Scholar CrossRef Search ADS PubMed  American Society of Animal Science

Bérard, J.;Pardo, C. E.;Béthaz, S.;Kreuzer, M.;Bee, G.

doi: 10.2527/jas.2010-2867pmid: 20562364

ABSTRACT High prolificacy of sows and increased fetal survival lead to greater incidence of intrauterine crowding (IUC), which may then affect pre- and postnatal development of the progeny. The aim of the study was to assess the impact of IUC, using unilaterally hysterectomized-ovariectomized gilts (UHO), on organ and muscle development of their progeny at birth. In the study, 7 UHO and 7 intact control (Con) Swiss Large White gilts were used. At farrowing, if available, 3 male and 3 female progeny with a low (>0.8 and <1.2 kg), medium (>1.2 and <1.4 kg), and high (>1.6 kg) birth weight (BtW) were killed. Internal organs and brain were weighed, and semitendinosus (STN), psoas major (PM), and rhomboideus (RH) muscles were collected. Histological analyses were performed in PM, RH, and STN (dark and light portion) using myofibrillar ATPase staining after preincubation at pH 10.3. Myosin heavy chain (MyHC) polymorphism was determined in the PM using SDS-PAGE gel electrophoresis. Despite that only one-half of the uterine space was available, litter size was smaller (P < 0.01) only by 35% in UHO compared with Con gilts. However, UHO progeny tended (P = 0.06) to be lighter than Con progeny. The average BtW of the selected piglets did not differ (P = 0.17) between the 2 sow groups, whereas PM and kidneys tended to be lighter (P < 0.07) in UHO than in Con progeny. Compared with Con progeny, the PM and the STNdark of UHO progeny had fewer (P ≤ 0.05) secondary and total myofibers as well as fewer (P = 0.10) primary myofibers in the PM. In the RH, the secondary-to-primary myofiber ratio was smaller (P < 0.01) in UHO than in Con progeny, whereas the total number of myofibers did not (P = 0.96) differ. The relative abundance of fetal MyHC was less (P = 0.02) and that of type I MyHC tended (P = 0.09) to be greater in UHO than in Con offspring. With increasing BtW, organ and brain weights increased (P < 0.01). Muscle cross-sectional area and total number of myofibers in the light portion of the STN were greater (P < 0.05) in high and medium than in low piglets. In conclusion, IUC reduced hyperplasia of secondary and total myofibers in the STNdark and PM. These effects were independent of the BtW and sex. INTRODUCTION Because of high prolificacy, intrauterine growth retardation is a common feature in pigs, inducing a reduction in average birth weight (BtW) and an increase in its variability (Quiniou et al., 2002). Père and Etienne (2000) reported that when litter size increases, the uterine blood flow increases as well, but to a lesser extent than the number of fetuses, resulting in a reduced uterine blood flow and thus a reduced nutrient supply per fetus. There is evidence that intrauterine crowding (IUC) is linked to intrauterine growth retardation. One possible outcome is a greater percentage of lighter BtW pigs within a litter and a concomitant impaired hyperplasia of muscles from lighter BtW pigs (Rehfeldt and Kuhn, 2006). Results of recent studies suggest that because of less myofiber hyperplasia, lighter BtW pigs grow slower, have fatter carcasses, and have impaired meat quality compared with their heavier siblings (Gondret et al., 2006). Bérard et al. (2008) confirmed that litter size affects average BtW of lighter and medium BtW barrows, but its impact on growth performance and carcass quality was minor. A possible reason for the variable effects of litter size on the variables examined is that litter size only partially reflects IUC (especially for small litters) because it does not account for fetal losses during gestation. Unilaterally hysterectomized-ovariectomized (UHO) gilts have been previously used to investigate uterine capacity and embryo development (Knight et al., 1977; Huang et al., 1987), but to our knowledge never to study myogenesis. It has been shown that the remaining ovary of UHO gilts compensates for the missing ovary by a greater ovulation rate. Thus, the fetuses will develop in the remaining uterine horn and, consequently, in a crowded environment (Martin et al., 1986). Therefore, this model offered the possibility to assess the effect of IUC on myogenesis in pigs. MATERIALS AND METHODS All procedures involving animals were approved by the Swiss Cantonal Committee for Animal Care and Use. Animals and Treatments The study used 14 primiparous Swiss Large White gilts (7 pairs of siblings). At approximately 30 kg of BW, 1 of each pair was randomly selected and subjected to the unilateral-hysterectomy-ovariectomy surgery, where the left uterine matrix and the correspondent ovary were removed under general anesthesia. No postsurgical problems occurred and the wounds healed completely. The correspondent sister was kept intact as a control (Con). Subsequently, the gilts were conventionally reared in groups with other breeding gilts until they reached the mating BW of approximately 160 kg. For estrus stimulation, 5 mL/d of Regumate Porcine (Intervet International B.V., Boxmeer, the Netherlands) were injected subcutaneously for 18 d. At d 18, 5 mL of Folligon (Intervet International B.V.) and, 4 d later, 1.5 mL of Chorulon 1500 (Intervet International B.V.) were injected subcutaneously. During estrus, the 14 gilts were artificially inseminated 3 times with unfrozen semen from Swiss Large White boars (Suisag, Sempach, Switzerland). From mating to farrowing, gestating sows were reared with other primiparous gilts in group pens equipped with an automatic feeder (Compident, model 2000, Schauer, Prambachkirchen, Austria). They were offered daily 2.5 kg of a standard gestation diet (Table 1) and had free access to water. Table 1. Ingredient and nutrient composition1 of the gestation diet, as-fed basis Ingredient, g/kg  Gestation diet  Barley  242.0  Oat  200.0  Dried whole corn plant  200.0  Wheat bran  100.0  Soybean meal  100.0  Potato protein  10.0  Rapeseed meal  50.0  Animal fat (50% lard and 50% tallow)  30.2  Molasses  50.0  NaCl  4.8  Calcium carbonate  11.7  Dicalcium phosphate  0.8  Lysine HCl  0.9  l-Threonine (98%)  0.6  Pellan2  4.0  Vitamin-mineral premix3  4.0  Ingredient, g/kg  Gestation diet  Barley  242.0  Oat  200.0  Dried whole corn plant  200.0  Wheat bran  100.0  Soybean meal  100.0  Potato protein  10.0  Rapeseed meal  50.0  Animal fat (50% lard and 50% tallow)  30.2  Molasses  50.0  NaCl  4.8  Calcium carbonate  11.7  Dicalcium phosphate  0.8  Lysine HCl  0.9  l-Threonine (98%)  0.6  Pellan2  4.0  Vitamin-mineral premix3  4.0  1The energy, nutrient, and AA contents per kilogram of DM of the gestation diet were DE, 12.1 MJ; total ash, 62.2 g; ether extract, 68.4 g; crude fiber, 94.0 g; proline, 10.3 g; cysteine, 3.1 g; aspartate, 13.5 g, serine, 7.0 g; glutamate, 30.2 g, histidine, 4.0 g; glycine, 7.4 g; threonine, 6.2 g; arginine, 9.6 g; alanine, 7.2 g, tyrosine, 5.1 g, tryptophan, 2.1 g; methionine, 2.5 g; valine, 8.0 g; isoleucine, 6.6 g; leucine, 11.8 g; lysine, 7.9 g. 2Binder that aids in pellet formation (Mikro-Technik, GmbH & Co. KG, Bürgstadt, Germany). 3Supplied the following nutrients per kilogram of diet: all-trans retinol, 1.2 mg; cholecalciferol, 0.006 mg; vitamin E, 9.9 mg; riboflavin, 2.8 mg; vitamin B6, 1.3 mg; vitamin B12, 0.015 mg; vitamin K3, 0.2 mg; pantothenic acid, 102 mg; niacin, 10 mg; folic acid, 0.48 mg; Fe as Fe-sulfate, 84 mg; I as Ca(IO)3, 0.56 mg; Se as Na2Se, 0.2 mg; Cu as CuSO4, 9.2 mg; Zn as ZnO2, 81 mg; Mn as MnO2, 2.5 mg; choline, 196 g; biotin, 0.99 mg. View Large Table 1. Ingredient and nutrient composition1 of the gestation diet, as-fed basis Ingredient, g/kg  Gestation diet  Barley  242.0  Oat  200.0  Dried whole corn plant  200.0  Wheat bran  100.0  Soybean meal  100.0  Potato protein  10.0  Rapeseed meal  50.0  Animal fat (50% lard and 50% tallow)  30.2  Molasses  50.0  NaCl  4.8  Calcium carbonate  11.7  Dicalcium phosphate  0.8  Lysine HCl  0.9  l-Threonine (98%)  0.6  Pellan2  4.0  Vitamin-mineral premix3  4.0  Ingredient, g/kg  Gestation diet  Barley  242.0  Oat  200.0  Dried whole corn plant  200.0  Wheat bran  100.0  Soybean meal  100.0  Potato protein  10.0  Rapeseed meal  50.0  Animal fat (50% lard and 50% tallow)  30.2  Molasses  50.0  NaCl  4.8  Calcium carbonate  11.7  Dicalcium phosphate  0.8  Lysine HCl  0.9  l-Threonine (98%)  0.6  Pellan2  4.0  Vitamin-mineral premix3  4.0  1The energy, nutrient, and AA contents per kilogram of DM of the gestation diet were DE, 12.1 MJ; total ash, 62.2 g; ether extract, 68.4 g; crude fiber, 94.0 g; proline, 10.3 g; cysteine, 3.1 g; aspartate, 13.5 g, serine, 7.0 g; glutamate, 30.2 g, histidine, 4.0 g; glycine, 7.4 g; threonine, 6.2 g; arginine, 9.6 g; alanine, 7.2 g, tyrosine, 5.1 g, tryptophan, 2.1 g; methionine, 2.5 g; valine, 8.0 g; isoleucine, 6.6 g; leucine, 11.8 g; lysine, 7.9 g. 2Binder that aids in pellet formation (Mikro-Technik, GmbH & Co. KG, Bürgstadt, Germany). 3Supplied the following nutrients per kilogram of diet: all-trans retinol, 1.2 mg; cholecalciferol, 0.006 mg; vitamin E, 9.9 mg; riboflavin, 2.8 mg; vitamin B6, 1.3 mg; vitamin B12, 0.015 mg; vitamin K3, 0.2 mg; pantothenic acid, 102 mg; niacin, 10 mg; folic acid, 0.48 mg; Fe as Fe-sulfate, 84 mg; I as Ca(IO)3, 0.56 mg; Se as Na2Se, 0.2 mg; Cu as CuSO4, 9.2 mg; Zn as ZnO2, 81 mg; Mn as MnO2, 2.5 mg; choline, 196 g; biotin, 0.99 mg. View Large Blood Sample Collection and Analyses At d 24 of gestation and after 10 h feed withdrawal, gilts were individually weighed and blood samples were collected via puncture of a jugular vein into 9-mL heparinized tubes (Vacuette, Greiner Bio-one GmbH, St. Gallen, Switzerland). Samples were then centrifuged at 1,600 × g for 15 min at room temperature. Subsequently, plasma was transferred to 2-mL microtubes (Treff AG, Degersheim, Switzerland) and stored at –20°C until analysis. Estrone sulfate (E1S) concentration in the plasma samples was determined with a commercial RIA kit (Estrone-sulfate DSL-5400, Diagnostic System Laboratories, Webster, TX) modified for the use with swine plasma (Gaustad-Aas et al., 2002). Interassay CV in samples containing 0.62, 2.60, and 5.22 ng/mL were 10.8, 5.2, and 8.9%, respectively. Minimum detection limit in the assay was 0.01 ng/mL. Data and Tissue Sample Collection at Farrowing At the end of farrowing, litter size, BW, and sex of each piglet, as well as the number of the mummified fetuses, were recorded for each sow. To select the piglets for tissue collection, 3 BtW classes were defined: low = <1.2 kg, medium (Med) ≥1.2 to ≤1.6 kg, and high >1.6 kg. Piglets with a BtW less than 0.8 kg were considered runts and were not taken into account for sample collection. It was planned to euthanize 1 male and 1 female piglet per BtW class from each gilt. Instead of the planned 84 (14 gilts × 6 piglets/litter), only 70 piglets were killed because, in several litters, especially in UHO gilts, 14 piglets were not available or their BtW did not meet the aforementioned BtW criteria. The selected piglets were individually stunned using an isofluran-oxygen mixture [4% (vol/vol)] for 2 min and then exsanguinated. Subsequently, the weights of the hearts, livers, kidneys, spleens, lungs, and brains were determined. Brain-to-liver weight ratio was calculated to estimate the fetal growth retardation (Town et al., 2005). In addition, from the left carcass side, 3 muscles were completely excised: the semitendinosus (STN), consisting of a light (STNlight) and dark (STNdark) portion, the psoas major (PM), and the rhomboideus (RH) muscles. These muscles were selected because it is well known that in slaughter pigs they differ in their metabolic and contractile properties: the STNlight will be predominately composed of fast-twitch glycolytic myofibers and the STNdark, RH, and PM of slow- and fast-twitch oxido-glycolytic myofibers. The muscles were immediately frozen in isopentane cooled in liquid nitrogen and stored at −80°C for later histological analysis. Histological Analyses Histological analyses were performed on the STNdark of all selected newborn piglets (n = 70). In contrast, histological analyses of the STNlight, the PM, and the RH muscles were performed in 38 newborn piglets. The latter were selected to highlight the effect of IUC because the piglets originated from 4 pairs of gilts (Con and UHO), where the difference in litter size was less than 35% (14 vs. 9, 11 vs. 8, 10 vs. 10, and 11 vs. 8 piglets/litter, respectively). Frozen muscle samples were equilibrated for 30 min at −20°C, cut from the stick, and trimmed to facilitate transverse sectioning. Samples were then mounted on a cryostat chuck with a few drops of embedding resin (Shandon Cryomatrix, Shandon Inc., Pittsburgh, PA), and 10-μm-thick serial sections were cut using a Cryotome (Shandon Inc.). Two sections were mounted on glass microscopic slides, air-dried for 60 min, and stored at 4°C for approximately 6 h. The same day, immunocytochemical detection of type I myosin heavy-chain (MyHC) isoform (NCL-MHCs diluted: 1:20, Novocastra, Newcastle. UK) was carried out as described previously (Lefaucheur et al., 2002). The sections were also stained for the determination of myofibrillar ATPase (mATPase) activity after alkaline (pH 10.3) or acid (pH 4.5) preincubation. In the STNdark, PM and RH primary (Prim) myofibers stain light and secondary (Sec) myofibers dark using the basic preincubation condition, whereas the opposite occurs after acid preincubation. In addition, Prim myofibers can be segregated by size from the Sec myofibers. In the STNlight neither the MyHC nor the mATPase staining procedure allowed the differentiation between Prim and Sec myofibers. The stained sections were observed at 8 magnifications (MyHC and mATPase) and 20 (mATPase) magnifications using a BX50 microscope in transmitted light mode (Olympus Optical Co., Hamburg, Germany) equipped with a high-resolution digital camera (ColorView12, Soft Imaging System GmbH, Münster, Germany). The sections of all muscle samples at the 2 magnifications were captured as TIFF files, and the cross-sectional area (CSA) as well as the numbers of Prim and Sec myofibers were determined with the analySIS 3.0 image analysis software (Soft Imaging System GmbH) as follows: 1) Cross-sectional area. The CSA of the STN, PM, and RH was determined in the mATPase images (8× magnification). Because the MyHC images permitted a clear differentiation of the STNdark and STNlight portion, they were used to assess the CSA of the STNdark and to calculate the area of the STNlight = CSA of STN − STNdark. 2) Primary myofiber number. The number of Prim myofibers was assessed in the mATPase images where approximately 750 Prim myofibers were counted in an area of 0.89 mm2. The number of Prim myofibers, counted in the selected area, and the CSA of the STNdark, PM, and RH were used to estimate the total number of Prim myofibers. 3) Secondary myofiber number. The number of Sec myofibers was determined in mATPase images (preincubation = pH 10.3; 20× magnification). Four images containing at least 33 Prim myofibers were selected, and all the surrounding Sec myofibers were counted (approximately 1,000 Sec myofibers). This permitted calculating the Sec/Prim ratio as well as estimating the total Sec myofiber number. The images obtained from mATPase stained sections after acid preincubation were used to cross-check the correct assignment of Prim and Sec myofibers. 4) Total number of myofibers (TNF). The TNF in the STNdark, PM, and RH was calculated from the respective estimated total number of Prim and Sec myofibers. In the STNlight, the number of myofibers was determined in mATPase-stained sections after acid preincubation where approximately 3,000 myofibers were counted in an area of 1.47 mm2. The number of counted myofibers, the according measurement area, and the CSA of the STNlight were used to estimate the TNF. SDS-PAGE Gel Electrophoresis The MyHC distribution in the PM of the 38 selected piglets was determined using SDS-PAGE gel electrophoresis. One 20-μm-thick transverse serial section of the muscle, corresponding to approximately 500 μg of tissue, was vortexed 3 × 30 s at room temperature in 500 μL of Laemmli buffer [126 mM Tris-HCl pH 6.8, 20% (wt/vol) glycerin, 4% (wt/vol) SDS, 0.02% (wt/vol) bromphenol blue, and 2% (vol/vol) 2-mercaptoethanol]. Fifty microliters of total dissolved extract was loaded on a 7% polyacrylamide separating gel [acrylamide: N,N′-bis-methylene acrylamide = 50:1 (wt/wt), 30% (wt/vol) glycerol, 100 mM glycine, 0.1% (wt/vol) SDS, 0.05% (vol/vol) N′N′N′N′-tetramethylethylenediamine, 0.1% (wt/vol) ammonium persulfate, and 500 mM Tris-HCl, pH 8.8] and run at 100 V for 22 h on a SE 600 Ruby Hoefer unit, 15 × 12 cm (Amersham Biosciences). A 5% polyacrylamide gel [acrylamide: N,N′-bis-methylene acrylamide = 37.5:1 (wt/wt), 30% (wt/vol) glycerol, 100 mM EDTA, 0.1% (wt/vol) SDS, 0.125% (vol/vol) N′N′N′N′-tetramethylethylenediamine, 0.075% (wt/vol) ammonium persulfate, and 125 mM Tris-HCl, pH 6.8] was used for the stacking gel. The running buffer contained 25 mM Tris, 192 mM glycine, and 0.1% (wt/vol) SDS and 0.1% (vol/vol) 2-mercaptoethanol. After electrophoresis, gels were fixed for 1 h [40% (vol/vol) methanol, 10% (vol/vol) glacial acetic acid] and subsequently stained for 5 h with 0.12% (wt/vol) Coomassie brilliant blue G-250 [20% (vol/vol) methanol, 10% (vol/vol) phosphoric acid 80%, 10% (wt/vol) ammonium sulfate]. Gels were destained using distilled water for 20 min. Densitometry scans of the gels were performed using the GS-710 densitometer (BioRad Laboratories, Reinach, Switzerland) and the Quantity One software (BioRad Laboratories). The relative proportions of the different MyHC isoforms identified according to Lefaucheur et al. (1995) were expressed as a percentage of the sum of the detected individual MyHC isoforms. Figure 1 shows an example of a polyacrylamide gel depicting embryonic, fetal, type II (sum of IIa, IIx, IIb), and type I MyHC isoforms. Running and gel conditions did not allow differentiation between the fast adult MyHC isoforms IIa, IIb, and IIx. However, the separation between fetal, type II, and type I MyHC isoforms was unambiguous. Figure 1. View largeDownload slide Polyacrylamide gel depicting myosin heavy-chain (MyHC) isoforms from whole-muscle extracts of the psoas major from individual piglets selected from control (CON) and unilateral hysterectomized-ovariectomized (UHO) gilts and from low, medium (Med), and high birth weight (BtW) piglets and that of a fetus at d 75 of gestation (fetus). All lanes were loaded with 50 μL of extract. Denomination of fetal, fast (sum of IIa, IIb, and IIx isoforms), and slow (I) MyHC isoforms is reported on the right side of the figure. Figure 1. View largeDownload slide Polyacrylamide gel depicting myosin heavy-chain (MyHC) isoforms from whole-muscle extracts of the psoas major from individual piglets selected from control (CON) and unilateral hysterectomized-ovariectomized (UHO) gilts and from low, medium (Med), and high birth weight (BtW) piglets and that of a fetus at d 75 of gestation (fetus). All lanes were loaded with 50 μL of extract. Denomination of fetal, fast (sum of IIa, IIb, and IIx isoforms), and slow (I) MyHC isoforms is reported on the right side of the figure. Statistical Analyses Data were analyzed using MIXED procedure (SAS Inst. Inc., Cary, NC). The statistical model for the data on reproductive performance and the E1S concentration in the plasma obtained from the sows included treatment as a fixed effect and pedigree of gilts as a random effect. Data obtained from the selected newborn piglets were analyzed using treatment, BtW class, sex, and the 2- and 3-way interactions as fixed effects and pedigree of gilts as a random effect. The piglet was the experimental unit. Least squares means were compared using the PDIFF option with probability levels of P < 0.05 being considered significant, and levels of P < 0.10 were referred to as tendencies. In the tables, data are reported as least squares means and pooled SEM. RESULTS Sow Performance The BW at insemination and at d 110 of pregnancy did not (P ≥ 0.14) differ between the 2 experimental groups of gilts (Table 2). However, the BW gain during gestation was less (P < 0.01) in UHO compared with Con gilts. This is partly due to the decreased (P < 0.01) total litter weight in UHO gilts. Although uterine space was reduced by one-half, litter size was only 35% smaller (P < 0.01) in UHO compared with Con gilts, likely resulting in greater IUC for UHO progeny. Expressed as a percentage of the litter size, the proportion of low BtW (<1.2 kg; 49 vs. 29%) offspring was greater (P < 0.05), whereas that of Med (>1.2 to <1.6 kg; 37 vs. 48%) and high (>1.6 kg; 14 vs. 23%) BtW progeny was less (P < 0.05) in UHO compared with Con gilts. The number of mummies did not (P = 0.37) differ between the 2 sow groups. Overall, average litter BtW tended (P = 0.06) to be less in UHO compared with Con gilts. At d 24, plasma E1S concentration did not (P = 0.31) differ among the 2 groups of gilts. Table 2. Effect of unilateral hysterectomy-ovariectomy (UHO) on BW change during pregnancy, litter characteristics, and plasma estrone sulfate level at d 24 of gestation of gilts1 Trait  Surgical treatment  SEM  P-value  Control (n = 7)  UHO (n = 7)  BW, kg               At insemination  160  170  4.2  0.14   At d 110 of gestation  236  235  3.4  0.79  Total BW gain, kg  76  66  2.7  <0.01  Litter size, n  12.8  8.4  0.65  <0.01  Total litter weight, kg  17.79  10.04  1.056  <0.01  Average birth weight, kg  1.39  1.17  0.080  0.06  Average birth weight of females, kg  1.37  1.15  0.101  0.08  Average birth weight of males, kg  1.41  1.21  0.091  0.15  Mummified fetuses, n  0.86  1.57  0.54  0.37  Plasma estrone sulfate, ng/mL  1.46  0.97  0.322  0.31  Trait  Surgical treatment  SEM  P-value  Control (n = 7)  UHO (n = 7)  BW, kg               At insemination  160  170  4.2  0.14   At d 110 of gestation  236  235  3.4  0.79  Total BW gain, kg  76  66  2.7  <0.01  Litter size, n  12.8  8.4  0.65  <0.01  Total litter weight, kg  17.79  10.04  1.056  <0.01  Average birth weight, kg  1.39  1.17  0.080  0.06  Average birth weight of females, kg  1.37  1.15  0.101  0.08  Average birth weight of males, kg  1.41  1.21  0.091  0.15  Mummified fetuses, n  0.86  1.57  0.54  0.37  Plasma estrone sulfate, ng/mL  1.46  0.97  0.322  0.31  1Results are presented as least squares means and pooled SEM. View Large Table 2. Effect of unilateral hysterectomy-ovariectomy (UHO) on BW change during pregnancy, litter characteristics, and plasma estrone sulfate level at d 24 of gestation of gilts1 Trait  Surgical treatment  SEM  P-value  Control (n = 7)  UHO (n = 7)  BW, kg               At insemination  160  170  4.2  0.14   At d 110 of gestation  236  235  3.4  0.79  Total BW gain, kg  76  66  2.7  <0.01  Litter size, n  12.8  8.4  0.65  <0.01  Total litter weight, kg  17.79  10.04  1.056  <0.01  Average birth weight, kg  1.39  1.17  0.080  0.06  Average birth weight of females, kg  1.37  1.15  0.101  0.08  Average birth weight of males, kg  1.41  1.21  0.091  0.15  Mummified fetuses, n  0.86  1.57  0.54  0.37  Plasma estrone sulfate, ng/mL  1.46  0.97  0.322  0.31  Trait  Surgical treatment  SEM  P-value  Control (n = 7)  UHO (n = 7)  BW, kg               At insemination  160  170  4.2  0.14   At d 110 of gestation  236  235  3.4  0.79  Total BW gain, kg  76  66  2.7  <0.01  Litter size, n  12.8  8.4  0.65  <0.01  Total litter weight, kg  17.79  10.04  1.056  <0.01  Average birth weight, kg  1.39  1.17  0.080  0.06  Average birth weight of females, kg  1.37  1.15  0.101  0.08  Average birth weight of males, kg  1.41  1.21  0.091  0.15  Mummified fetuses, n  0.86  1.57  0.54  0.37  Plasma estrone sulfate, ng/mL  1.46  0.97  0.322  0.31  1Results are presented as least squares means and pooled SEM. View Large Morphometric Measurements in the Selected Newborn Piglets According to our intent, the average BtW of the progeny selected for tissue collection did not differ (P = 0.18) between the 2 treatment groups (Table 3). The PM muscles and kidneys tended (P ≤ 0.07) to be lighter in piglets from UHO compared with Con gilts, whereas the weights of the STN muscle, liver, brain, lung, heart, spleen, as well as the brain-to-liver weight ratio, were not (P ≥ 0.13) affected by treatment. Regardless of the surgical treatment, the weights of all STN, PM, internal organs, and the brain were lighter (P < 0.05), and the brain-to-liver weight ratio was greater (P < 0.05), in low compared with high piglets, with intermediate values for Med piglets. Except for the lungs, which tended (P = 0.10) to be lighter in male compared with female newborn piglets, the other traits were not (P ≥ 0.37) affected by sex and none of the 2- and 3-way interactions was significant (P ≥ 0.10). Table 3. Effect of unilateral hysterectomy-ovariectomy (UHO), birth weight groups, and sex on the birth weight (BtW), the weights of the organs and muscles as well as the brain-to-liver weight ratio1 Trait  Surgical treatment  BtW  Sex  SEM  P-value2  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  39  31  23  35  12  31  39              BtW, kg  1.39  1.32  1.05a  1.42b  1.76c  1.39  1.34  0.034  0.18  <0.01  0.97  Liver, g  34.7  36.7  26.5a  35.2b  45.3c  35.2  36.0  2.04  0.26  <0.01  0.67  Lung, g  18.7  18.9  14.8a  18.9b  22.6c  19.4  18.2  0.89  0.81  <0.01  0.10  Kidney, g  12.2  10.8  8.3a  11.4b  14.8c  11.4  11.6  0.63  0.01  <0.01  0.78  Spleen, g  1.2  1.1  0.8a  1.2b  1.5c  1.2  1.2  0.08  0.31  <0.01  0.64  Brain, g  32.7  33.5  31.8a  32.8b  34.7c  33.2  33.1  0.61  0.13  <0.01  0.86  Heart, g  9.7  9.4  7.6a  9.8b  11.3c  9.6  9.5  0.39  0.28  <0.01  0.68  Semitendinosus muscle, g  3.0  2.8  2.2a  2.9b  3.7c  2.9  3.0  0.13  0.17  <0.01  0.37  Psoas major muscle, g  3.3  3.1  2.4a  3.2b  3.9c  3.2  3.1  0.13  0.07  <0.01  0.67  Brain-to-liver weight ratio  1.03  0.97  1.23a  0.98b  0.80c  1.01  0.99  0.060  0.29  <0.01  0.69  Trait  Surgical treatment  BtW  Sex  SEM  P-value2  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  39  31  23  35  12  31  39              BtW, kg  1.39  1.32  1.05a  1.42b  1.76c  1.39  1.34  0.034  0.18  <0.01  0.97  Liver, g  34.7  36.7  26.5a  35.2b  45.3c  35.2  36.0  2.04  0.26  <0.01  0.67  Lung, g  18.7  18.9  14.8a  18.9b  22.6c  19.4  18.2  0.89  0.81  <0.01  0.10  Kidney, g  12.2  10.8  8.3a  11.4b  14.8c  11.4  11.6  0.63  0.01  <0.01  0.78  Spleen, g  1.2  1.1  0.8a  1.2b  1.5c  1.2  1.2  0.08  0.31  <0.01  0.64  Brain, g  32.7  33.5  31.8a  32.8b  34.7c  33.2  33.1  0.61  0.13  <0.01  0.86  Heart, g  9.7  9.4  7.6a  9.8b  11.3c  9.6  9.5  0.39  0.28  <0.01  0.68  Semitendinosus muscle, g  3.0  2.8  2.2a  2.9b  3.7c  2.9  3.0  0.13  0.17  <0.01  0.37  Psoas major muscle, g  3.3  3.1  2.4a  3.2b  3.9c  3.2  3.1  0.13  0.07  <0.01  0.67  Brain-to-liver weight ratio  1.03  0.97  1.23a  0.98b  0.80c  1.01  0.99  0.060  0.29  <0.01  0.69  a–cWithin a row for the main factor BtW, least squares means without a common superscript differ (P < 0.05). 1Results are presented as least squares means and pooled SEM. 2Probability values for the effects of surgical treatment (Trt), BtW, and sex. None of the 2- and 3-way interactions was significant (P > 0.05). View Large Table 3. Effect of unilateral hysterectomy-ovariectomy (UHO), birth weight groups, and sex on the birth weight (BtW), the weights of the organs and muscles as well as the brain-to-liver weight ratio1 Trait  Surgical treatment  BtW  Sex  SEM  P-value2  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  39  31  23  35  12  31  39              BtW, kg  1.39  1.32  1.05a  1.42b  1.76c  1.39  1.34  0.034  0.18  <0.01  0.97  Liver, g  34.7  36.7  26.5a  35.2b  45.3c  35.2  36.0  2.04  0.26  <0.01  0.67  Lung, g  18.7  18.9  14.8a  18.9b  22.6c  19.4  18.2  0.89  0.81  <0.01  0.10  Kidney, g  12.2  10.8  8.3a  11.4b  14.8c  11.4  11.6  0.63  0.01  <0.01  0.78  Spleen, g  1.2  1.1  0.8a  1.2b  1.5c  1.2  1.2  0.08  0.31  <0.01  0.64  Brain, g  32.7  33.5  31.8a  32.8b  34.7c  33.2  33.1  0.61  0.13  <0.01  0.86  Heart, g  9.7  9.4  7.6a  9.8b  11.3c  9.6  9.5  0.39  0.28  <0.01  0.68  Semitendinosus muscle, g  3.0  2.8  2.2a  2.9b  3.7c  2.9  3.0  0.13  0.17  <0.01  0.37  Psoas major muscle, g  3.3  3.1  2.4a  3.2b  3.9c  3.2  3.1  0.13  0.07  <0.01  0.67  Brain-to-liver weight ratio  1.03  0.97  1.23a  0.98b  0.80c  1.01  0.99  0.060  0.29  <0.01  0.69  Trait  Surgical treatment  BtW  Sex  SEM  P-value2  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  39  31  23  35  12  31  39              BtW, kg  1.39  1.32  1.05a  1.42b  1.76c  1.39  1.34  0.034  0.18  <0.01  0.97  Liver, g  34.7  36.7  26.5a  35.2b  45.3c  35.2  36.0  2.04  0.26  <0.01  0.67  Lung, g  18.7  18.9  14.8a  18.9b  22.6c  19.4  18.2  0.89  0.81  <0.01  0.10  Kidney, g  12.2  10.8  8.3a  11.4b  14.8c  11.4  11.6  0.63  0.01  <0.01  0.78  Spleen, g  1.2  1.1  0.8a  1.2b  1.5c  1.2  1.2  0.08  0.31  <0.01  0.64  Brain, g  32.7  33.5  31.8a  32.8b  34.7c  33.2  33.1  0.61  0.13  <0.01  0.86  Heart, g  9.7  9.4  7.6a  9.8b  11.3c  9.6  9.5  0.39  0.28  <0.01  0.68  Semitendinosus muscle, g  3.0  2.8  2.2a  2.9b  3.7c  2.9  3.0  0.13  0.17  <0.01  0.37  Psoas major muscle, g  3.3  3.1  2.4a  3.2b  3.9c  3.2  3.1  0.13  0.07  <0.01  0.67  Brain-to-liver weight ratio  1.03  0.97  1.23a  0.98b  0.80c  1.01  0.99  0.060  0.29  <0.01  0.69  a–cWithin a row for the main factor BtW, least squares means without a common superscript differ (P < 0.05). 1Results are presented as least squares means and pooled SEM. 2Probability values for the effects of surgical treatment (Trt), BtW, and sex. None of the 2- and 3-way interactions was significant (P > 0.05). View Large Muscle Size and Myofiber Characteristics The IUC environment had no (P ≥ 0.53) effect on muscle CSA of the 3 muscles analyzed (Table 4). Compared with Con progeny, the number of Prim myofiber tended to be smaller (P = 0.10) in the PM of UHO progeny, whereas no (P ≥ 0.15) differences were observed in the STNdark and RH. The Sec myofiber number and, consequently, TNF were less (P ≤ 0.05) in the STNdark and PM muscles of UHO than Con offspring. By contrast, the TNF of the STNlight as well as the STN did not differ (P ≥ 0.80) in UHO and Con offspring. The Sec/Prim ratio was smaller (P < 0.01) in the RH of UHO progeny. Regardless of the surgical treatment, the muscle CSA of the STN (STNdark and STNlight) and PM muscles were smaller (P < 0.05) in low compared with high and Med offspring, whereas the number of Prim and Sec myofibers, the Sec/Prim myofiber ratio, and TNF did not (P > 0.10) differ. The RH muscle was smaller (P < 0.05) in low and Med compared with high progeny. In this muscle, only the Sec/Prim myofiber ratio was affected (P < 0.05) by BtW being smaller in low compared with Med offspring with intermediate values for high piglets. The STN and STNlight CSA of low offspring was smaller (P < 0.05) and this portion of the muscle had fewer (P < 0.05) myofibers than the STNlight of Med and high offspring. Muscle CSA was smaller (P = 0.03) in female than in male piglets in the RH, but was not affected (P ≥ 0.15) by sex in the STN (STNdark and STNlight) and PM. Compared with males, the STNdark and RH of females had fewer (P ≤ 0.08) Prim and Sec myofibers and, consequently, TNF was less (P ≤ 0.08). The muscle size and myofiber characteristics were not (P > 0.10) affected by the 2- and 3-way interactions. Table 4. Effect of unilateral hysterectomy-ovariectomy (UHO), birth weight (BtW), and sex on various muscle-fiber related traits in semitendinosus, psoas major, and rhomboideus muscle1 Item2  Surgical treatment  BtW  Sex  SEM  P-value3  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  21  17  11  18  9  17  21              Cross-sectional area, mm2   STN  69.3  67.5    59.5a  70.9b  74.8b    67.1  69.7  3.75  0.56  <0.01  0.15   STNdark4  24.6  24.4    21.6a  24.6b  27.3b    23.9  25.1  1.69  0.88  <0.01  0.38   STNlight  44.7  43.1    37.9a  46.2b  47.5b    43.1  44.6  3.13  0.53  <0.01  0.56   PM  65.7  63.1    51.2a  65.6b  76.4b    65.2  63.6  3.32  0.56  <0.01  0.74   RH  9.0  9.1    7.3a  8.7a  11.1b    8.2  9.8  0.65  0.91  <0.01  0.03  Primary myofiber number, total per muscle                           STNdark4  5,769  5,278    5,187  5,430  5,954    5,064  5,984  420.6  0.15  0.26  0.01   PM  13,422  11,709    11,903  12,663  13,130    12,244  12,887  927.3  0.10  0.66  0.53   RH  2,038  2,325    2,109  2,063  2,373    1,953  2,409  171.0  0.14  0.38  0.02  Secondary myofiber number, total per muscle                           STNdark4  169,307  147,014    143,957  162,747  167,778    145,394  170,927  13,804  0.05  0.17  0.02   PM  427,963  356,605    347,474  393,591  435,786    376,028  408,539  27,649  0.03  0.12  0.30   RH  63,348  62,765    56,380  64,294  68,494    57,916  68,196  5,110  0.91  0.28  0.08  Secondary/primary myofiber ratio                           STNdark4  29.1  28.3    27.9  30.4  27.9    28.9  28.5  1.68  0.52  0.14  0.76   PM  32.0  30.2    28.9  31.3  33.2    30.5  31.7  1.47  0.28  0.18  0.48   RH  31.4  27.2    27.0a  31.6b  29.7ab    30.1  28.5  1.21  <0.01  0.02  0.25  Total number of myofibers                           STN entire  626,595  611,178    523,599a  652,435b  680,625b    584,420  653,353  30,857  0.88  <0.01  0.10   STNdark4  175,077  152,293    149,145  168,177  173,733    150,459  176,911  14,133  0.05  0.17  0.02   STNlight  451,518  458,885    374,454a  484,258b  506,892b    433,961  476,442  25,434  0.80  <0.01  0.15   PM  441,385  368,313    359,377  406,254  448,916    388,272  421,426  27,887  0.03  0.12  0.30   RH  65,386  65,090    58,488  66,358  70,868    59,870  70,606  5,256  0.96  0.29  0.08  Item2  Surgical treatment  BtW  Sex  SEM  P-value3  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  21  17  11  18  9  17  21              Cross-sectional area, mm2   STN  69.3  67.5    59.5a  70.9b  74.8b    67.1  69.7  3.75  0.56  <0.01  0.15   STNdark4  24.6  24.4    21.6a  24.6b  27.3b    23.9  25.1  1.69  0.88  <0.01  0.38   STNlight  44.7  43.1    37.9a  46.2b  47.5b    43.1  44.6  3.13  0.53  <0.01  0.56   PM  65.7  63.1    51.2a  65.6b  76.4b    65.2  63.6  3.32  0.56  <0.01  0.74   RH  9.0  9.1    7.3a  8.7a  11.1b    8.2  9.8  0.65  0.91  <0.01  0.03  Primary myofiber number, total per muscle                           STNdark4  5,769  5,278    5,187  5,430  5,954    5,064  5,984  420.6  0.15  0.26  0.01   PM  13,422  11,709    11,903  12,663  13,130    12,244  12,887  927.3  0.10  0.66  0.53   RH  2,038  2,325    2,109  2,063  2,373    1,953  2,409  171.0  0.14  0.38  0.02  Secondary myofiber number, total per muscle                           STNdark4  169,307  147,014    143,957  162,747  167,778    145,394  170,927  13,804  0.05  0.17  0.02   PM  427,963  356,605    347,474  393,591  435,786    376,028  408,539  27,649  0.03  0.12  0.30   RH  63,348  62,765    56,380  64,294  68,494    57,916  68,196  5,110  0.91  0.28  0.08  Secondary/primary myofiber ratio                           STNdark4  29.1  28.3    27.9  30.4  27.9    28.9  28.5  1.68  0.52  0.14  0.76   PM  32.0  30.2    28.9  31.3  33.2    30.5  31.7  1.47  0.28  0.18  0.48   RH  31.4  27.2    27.0a  31.6b  29.7ab    30.1  28.5  1.21  <0.01  0.02  0.25  Total number of myofibers                           STN entire  626,595  611,178    523,599a  652,435b  680,625b    584,420  653,353  30,857  0.88  <0.01  0.10   STNdark4  175,077  152,293    149,145  168,177  173,733    150,459  176,911  14,133  0.05  0.17  0.02   STNlight  451,518  458,885    374,454a  484,258b  506,892b    433,961  476,442  25,434  0.80  <0.01  0.15   PM  441,385  368,313    359,377  406,254  448,916    388,272  421,426  27,887  0.03  0.12  0.30   RH  65,386  65,090    58,488  66,358  70,868    59,870  70,606  5,256  0.96  0.29  0.08  a,bWithin a row for the main factor birth weight, least squares means without a common superscript differ (P < 0.05). 1Results are presented as least squares means and pooled SEM. 2n = number of piglets; STN = semitendinosus; STNdark = dark portion of the STN; STNlight = light portion of the STN; PM = psoas major; RH = rhomboideus. 3Probability values for the effects of treatment (Trt), BtW, and sex. None of the 2- and 3-way interactions was significant (P > 0.05). 4n = 39 for control; n = 31 for UHO; n = 23 for low, n = 31 for medium, n = 12 for high BtW, respectively; n = 31 for females, n = 39 for males. View Large Table 4. Effect of unilateral hysterectomy-ovariectomy (UHO), birth weight (BtW), and sex on various muscle-fiber related traits in semitendinosus, psoas major, and rhomboideus muscle1 Item2  Surgical treatment  BtW  Sex  SEM  P-value3  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  21  17  11  18  9  17  21              Cross-sectional area, mm2   STN  69.3  67.5    59.5a  70.9b  74.8b    67.1  69.7  3.75  0.56  <0.01  0.15   STNdark4  24.6  24.4    21.6a  24.6b  27.3b    23.9  25.1  1.69  0.88  <0.01  0.38   STNlight  44.7  43.1    37.9a  46.2b  47.5b    43.1  44.6  3.13  0.53  <0.01  0.56   PM  65.7  63.1    51.2a  65.6b  76.4b    65.2  63.6  3.32  0.56  <0.01  0.74   RH  9.0  9.1    7.3a  8.7a  11.1b    8.2  9.8  0.65  0.91  <0.01  0.03  Primary myofiber number, total per muscle                           STNdark4  5,769  5,278    5,187  5,430  5,954    5,064  5,984  420.6  0.15  0.26  0.01   PM  13,422  11,709    11,903  12,663  13,130    12,244  12,887  927.3  0.10  0.66  0.53   RH  2,038  2,325    2,109  2,063  2,373    1,953  2,409  171.0  0.14  0.38  0.02  Secondary myofiber number, total per muscle                           STNdark4  169,307  147,014    143,957  162,747  167,778    145,394  170,927  13,804  0.05  0.17  0.02   PM  427,963  356,605    347,474  393,591  435,786    376,028  408,539  27,649  0.03  0.12  0.30   RH  63,348  62,765    56,380  64,294  68,494    57,916  68,196  5,110  0.91  0.28  0.08  Secondary/primary myofiber ratio                           STNdark4  29.1  28.3    27.9  30.4  27.9    28.9  28.5  1.68  0.52  0.14  0.76   PM  32.0  30.2    28.9  31.3  33.2    30.5  31.7  1.47  0.28  0.18  0.48   RH  31.4  27.2    27.0a  31.6b  29.7ab    30.1  28.5  1.21  <0.01  0.02  0.25  Total number of myofibers                           STN entire  626,595  611,178    523,599a  652,435b  680,625b    584,420  653,353  30,857  0.88  <0.01  0.10   STNdark4  175,077  152,293    149,145  168,177  173,733    150,459  176,911  14,133  0.05  0.17  0.02   STNlight  451,518  458,885    374,454a  484,258b  506,892b    433,961  476,442  25,434  0.80  <0.01  0.15   PM  441,385  368,313    359,377  406,254  448,916    388,272  421,426  27,887  0.03  0.12  0.30   RH  65,386  65,090    58,488  66,358  70,868    59,870  70,606  5,256  0.96  0.29  0.08  Item2  Surgical treatment  BtW  Sex  SEM  P-value3  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  21  17  11  18  9  17  21              Cross-sectional area, mm2   STN  69.3  67.5    59.5a  70.9b  74.8b    67.1  69.7  3.75  0.56  <0.01  0.15   STNdark4  24.6  24.4    21.6a  24.6b  27.3b    23.9  25.1  1.69  0.88  <0.01  0.38   STNlight  44.7  43.1    37.9a  46.2b  47.5b    43.1  44.6  3.13  0.53  <0.01  0.56   PM  65.7  63.1    51.2a  65.6b  76.4b    65.2  63.6  3.32  0.56  <0.01  0.74   RH  9.0  9.1    7.3a  8.7a  11.1b    8.2  9.8  0.65  0.91  <0.01  0.03  Primary myofiber number, total per muscle                           STNdark4  5,769  5,278    5,187  5,430  5,954    5,064  5,984  420.6  0.15  0.26  0.01   PM  13,422  11,709    11,903  12,663  13,130    12,244  12,887  927.3  0.10  0.66  0.53   RH  2,038  2,325    2,109  2,063  2,373    1,953  2,409  171.0  0.14  0.38  0.02  Secondary myofiber number, total per muscle                           STNdark4  169,307  147,014    143,957  162,747  167,778    145,394  170,927  13,804  0.05  0.17  0.02   PM  427,963  356,605    347,474  393,591  435,786    376,028  408,539  27,649  0.03  0.12  0.30   RH  63,348  62,765    56,380  64,294  68,494    57,916  68,196  5,110  0.91  0.28  0.08  Secondary/primary myofiber ratio                           STNdark4  29.1  28.3    27.9  30.4  27.9    28.9  28.5  1.68  0.52  0.14  0.76   PM  32.0  30.2    28.9  31.3  33.2    30.5  31.7  1.47  0.28  0.18  0.48   RH  31.4  27.2    27.0a  31.6b  29.7ab    30.1  28.5  1.21  <0.01  0.02  0.25  Total number of myofibers                           STN entire  626,595  611,178    523,599a  652,435b  680,625b    584,420  653,353  30,857  0.88  <0.01  0.10   STNdark4  175,077  152,293    149,145  168,177  173,733    150,459  176,911  14,133  0.05  0.17  0.02   STNlight  451,518  458,885    374,454a  484,258b  506,892b    433,961  476,442  25,434  0.80  <0.01  0.15   PM  441,385  368,313    359,377  406,254  448,916    388,272  421,426  27,887  0.03  0.12  0.30   RH  65,386  65,090    58,488  66,358  70,868    59,870  70,606  5,256  0.96  0.29  0.08  a,bWithin a row for the main factor birth weight, least squares means without a common superscript differ (P < 0.05). 1Results are presented as least squares means and pooled SEM. 2n = number of piglets; STN = semitendinosus; STNdark = dark portion of the STN; STNlight = light portion of the STN; PM = psoas major; RH = rhomboideus. 3Probability values for the effects of treatment (Trt), BtW, and sex. None of the 2- and 3-way interactions was significant (P > 0.05). 4n = 39 for control; n = 31 for UHO; n = 23 for low, n = 31 for medium, n = 12 for high BtW, respectively; n = 31 for females, n = 39 for males. View Large MyHC Polymorphism Fetal MyHC was less (P = 0.03) abundant and type I MyHC tended (P = 0.09) to be more abundant in the PM of UHO compared with Con progeny (Table 5). By contrast, the abundance of the fast adult MyHC isoforms (expressed as the sum of IIa, IIb, and IIx MyHC isoforms) was not (P = 0.20) affected by the IUC environment. Regardless of surgical treatment, only the relative abundance of the fetal MyHC isoform differed (P = 0.09) between BtW groups, tending to be greater in low compared with Med and high progeny. The MyHC isoform distribution was not (P ≥ 0.36) affected by sex or by the 2- and 3-way interactions. In the newborn piglets, the embryonic MyHC isoform could not be detected (Figure 1). Table 5. Effect of unilateral hysterectomy-ovariectomy (UHO), birth weight (BtW), and sex on myosin heavy-chain isoforms distribution in psoas major1 Item2  Surgical treatment  BtW  Sex  SEM  P-value3  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  21  17  11  18  9  17  21              Myosin heavy-chain   Fetal, %  36.9  31.3    38.2b  32.9a  31.2a    33.3  34.9  2.99  0.03  0.09  0.52   I/slow, %  5.5  7.9    5.0  6.9  8.2    6.3  7.1  1.64  0.09  0.22  0.60   II/fast, %  57.5  60.8    56.8  60.1  60.6    60.3  58.0  3.67  0.20  0.44  0.36  Item2  Surgical treatment  BtW  Sex  SEM  P-value3  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  21  17  11  18  9  17  21              Myosin heavy-chain   Fetal, %  36.9  31.3    38.2b  32.9a  31.2a    33.3  34.9  2.99  0.03  0.09  0.52   I/slow, %  5.5  7.9    5.0  6.9  8.2    6.3  7.1  1.64  0.09  0.22  0.60   II/fast, %  57.5  60.8    56.8  60.1  60.6    60.3  58.0  3.67  0.20  0.44  0.36  a,bWithin a row for the main factor birth weight, least squares means without a common superscript differ (P < 0.05). 1Results are presented as least squares means and pooled SEM. 2The abundance of the individual isoforms was expressed as the percentage of the individual isoform abundance to the total abundance of all isoforms. 3Probability values for the effects of treatment (Trt), BtW, and sex. View Large Table 5. Effect of unilateral hysterectomy-ovariectomy (UHO), birth weight (BtW), and sex on myosin heavy-chain isoforms distribution in psoas major1 Item2  Surgical treatment  BtW  Sex  SEM  P-value3  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  21  17  11  18  9  17  21              Myosin heavy-chain   Fetal, %  36.9  31.3    38.2b  32.9a  31.2a    33.3  34.9  2.99  0.03  0.09  0.52   I/slow, %  5.5  7.9    5.0  6.9  8.2    6.3  7.1  1.64  0.09  0.22  0.60   II/fast, %  57.5  60.8    56.8  60.1  60.6    60.3  58.0  3.67  0.20  0.44  0.36  Item2  Surgical treatment  BtW  Sex  SEM  P-value3  Control  UHO  Low  Medium  High  Female  Male  Trt  BtW  Sex  n  21  17  11  18  9  17  21              Myosin heavy-chain   Fetal, %  36.9  31.3    38.2b  32.9a  31.2a    33.3  34.9  2.99  0.03  0.09  0.52   I/slow, %  5.5  7.9    5.0  6.9  8.2    6.3  7.1  1.64  0.09  0.22  0.60   II/fast, %  57.5  60.8    56.8  60.1  60.6    60.3  58.0  3.67  0.20  0.44  0.36  a,bWithin a row for the main factor birth weight, least squares means without a common superscript differ (P < 0.05). 1Results are presented as least squares means and pooled SEM. 2The abundance of the individual isoforms was expressed as the percentage of the individual isoform abundance to the total abundance of all isoforms. 3Probability values for the effects of treatment (Trt), BtW, and sex. View Large DISCUSSION Increasing litter size has become a requirement in modern pig breeding programs to reduce production costs and to enhance the profitability of pig production. The long-term consequences of such a genetic selection strategy are not well known because the investigation is complex and the interpretation of the results is sometimes confounded by factors such as the extent of embryonic and fetal losses at different stages during gestation as well as impaired BtW (Foxcroft et al., 2006; Bérard et al., 2008). Unilateral hysterectomy-ovariectomy, ovary ligation, or hyperprolificacy have been described as suitable sow models for examining uterine capacity (Christenson et al., 1987) and its effect on fetal development (Knight et al., 1977; Huang et al., 1987; Foxcroft et al., 2006). Gilts subjected to unilateral hysterectomy-ovariectomy have been previously exploited to induce IUC (Christenson et al., 1987). Results of several studies showed that in response to the removal of the contralateral ovary the remaining ovary undergoes a compensatory hypertrophy, thereby producing double the amount of ovules (Staigmiller et al., 1972; Martin et al., 1986). Therefore, compared with intact gilts, in hysterectomized-ovariectomized gilts after fecundation a comparable amount of embryos will develop in one-half of the endometrial surface area. In gestating sows, each embryonic unit produces estrone, which is conjugated with sulfate groups within the endometrium. Because the greatest embryonic losses occur in the implantation phase (Gadsby et al., 1980; van der Lende and van Rens, 2003; Foxcroft et al., 2006), the postimplantation E1S concentration (d 24) could be a good indicator of the numbers of viable embryos (Gaustad-Aas et al., 2002). In accordance, the same authors found a positive correlation (r = 0.26) between the E1S plasma concentration at d 24 and the number of newborn piglets at farrowing. Accordingly, the lack of differences in plasma E1S concentration observed in the present study could suggest that the number of viable embryos did not substantially differ between UHO and Con gilts. Similarly, Knight et al. (1977) found that before d 35 of gestation the number of live and dead embryos, the percentage of fetal survival, and the weight and length of the fetuses was similar in UHO and Con gilts. In line with these observations, it has been proposed that only after d 35 of gestation, uterine capacity becomes a limiting factor for fetal survival and development (Fenton et al., 1970; Foxcroft et al., 2006). Nonetheless, Knight et al. (1977) showed that placenta surface area, total number of areolae per placenta, and areolae surface were smaller in UHO compared with Con gilts at all stages of gestation (20 to 100 d). The same authors concluded that placental insufficiency had to be the primary cause for the increased fetal loss and decreased fetal growth observed in UHO gilts during mid and late gestation. In the present study, unilateral hysterectomy surgery decreased uterine space by 50%. It might be expected that a 50% reduction would yield a 50% reduction in number of piglets born. However, number of pigs born alive was reduced only 35%. Furthermore, litters from UHO sows were lighter, and consequently, average piglet BtW was lighter. Although the number of mummies did not differ among the 2 groups of gilts, when expressed as a percentage of the litter size, the number was greater in UHO than in Con gilts. Because the uterus is not able to reabsorb the dead embryos after d 40 of gestation, one could conclude that embryonic losses in UHO gilts occurred not during implantation but during mid or late gestation. The shift toward greater embryonic losses in this period might have consequences for myogenesis because mid and late gestation are determinant for the Prim and Sec myofiber formation. Regardless of the BtW, which in the present study was similar for the selected offspring in both sow groups, IUC had only a small direct impact on morphometric measurements. Only the weight of the kidneys and PM muscles were lighter in UHO than in C offspring, whereas among BtW groups, organ and muscle weights decreased with decreasing BtW. As mentioned previously, when all progeny of the litters were considered, the average BtW was markedly less and the percentage of low BtW offspring was greater in UHO compared with Con gilts. Thus, one can hypothesize that overall organ and muscle weight might be less because of IUC. Furthermore, it is well established that low BtW is associated with impaired postnatal growth (Quiniou et al., 2002; Bérard et al., 2008; Rehfeldt et al., 2008) and increased carcass fatness (Bee, 2004; Gondret et al., 2006). Considering that in UHO gilts the percentage of offspring with a low BtW was greater than in Con gilts, IUC will result in a greater percentage of offspring with reduced growth potential and decreased carcass quality. Conversely to the organ weights, IUC markedly affected myofiber hyperplasia in the selected piglets. Myogenesis is a biphasic phenomenon, as Prim and Sec myofibers develop from d 35 to 55 and d 55 to 90 of gestation, respectively. The initial population of myofibers is used as a template for the attachment and fusion of myoblasts to form Sec myofibers (Picard et al., 2002). Dwyer et al. (1993) suggested that Prim myofiber number is a fixed genetic component and its development is unaffected by conditions occurring in utero. This statement is, to some extent, in contradiction with the present findings because in UHO progeny the PM had fewer Prim myofibers than the PM of Con progeny. The IUC effect on Prim myofiber formation was less distinct as might have been anticipated, which could be explained by the fact that uterine capacity at the early stage of gestation is less limiting than it is afterward (Foxcroft et al., 2006). Accordingly, greater effects of IUC were observed in the number of Sec myofibers; on the one hand, fewer Prim fibers were available for the development of the Sec myofibers; on the other hand, the effect of crowding is much more determinant in the second half of gestation when competition among fetuses for maternal nutrient supply is pronounced (Foxcroft et al., 2006). Thus, the fewer Prim and Sec myofibers together with the similar Sec/Prim ratio resulted in fewer TNF in the STNdark and PM muscles of UHO compared with Con progeny. Surprisingly, as a result of the greater number of Prim myofibers and the smaller Sec/Prim ratio, UHO progeny exhibited the same TNF in the RH as Con progeny. This discrepancy of the IUC effect between the RH and the STN and PM might be explained by the developmental time gradient, which runs cephalocaudally and proximodistally. Therefore, Prim myofibers in the less distal and caudal muscles like the RH would be developing when fetuses are small, less demanding, and intrauterine space is not yet a limiting factor. However, this does not explain why UHO progeny developed more Prim myofibers at this stage in the RH muscle. The complete absence of any surgical treatment × BtW interactions indicates that IUC effects on muscle development were similar for the selected BtW classes. However, BtW per se had a distinct effect on the CSA of the muscles being larger in Med and high compared with low BtW piglets. Except for the STNlight, these differences were not paralleled by differences in the TNF. Wigmore and Stickland (1983) found larger Prim myofibers in the STN of larger compared with smaller fetuses up to 70 d of gestation, but during late gestation the difference in Prim myofiber size declined as a result of the disappearance of the larger central myofibril-free region in heavier offspring. In contrast, Sec myofibers were larger throughout gestation in larger compared with smaller piglets. Thus, although the myofiber diameter was not determined in the present study, one can conclude that the larger CSA resulted from larger Sec myofibers in piglets with a greater BtW. In contrast to the STNdark, the smaller CSA of the STNlight in low compared with Med and high BtW offspring matched the reduced TNF. Lefaucheur et al. (1995) found that the majority of the Sec myofibers in the STNlight mature to fast type II myofibers at birth. Compared with slow-twitch myofibers, fast-twitch glycolytic myofibers appear to be more sensitive to atrophic action caused by restricted feeding and resulting in decreased BW (Lefaucheur, 2001). This could explain why the effect of BtW on TNF within the STNdark and STNlight differed. Myofiber hyperplasia in porcine muscles is thought to cease by d 90 of gestation. Therefore, postnatal growth is considered primarily to result from myofiber hypertrophy and increase in myofiber length (Wigmore and Stickland, 1983; Lefaucheur, 2001; Rehfeldt and Kuhn, 2006). However, Mascarello et al. (1992) found very small diameter myofibers containing developmental (embryonic and fetal) isoforms of MyHC scattered between the larger diameter myofibers derived from primary and secondary myotubes. Mascarello et al. (1992) considered this to represent a different population of myotubes, designated tertiary myotubes. Lefaucheur et al. (1995) also reported the presence of very small diameter myofibers containing a fetal MyHC, which were formed early postnatally, confirming the existence of a third generation of myofibers. Recently, Lösel et al. (2009) reported a marked increase in TNF at 4 wk of age, compared with the amount of TNF determined at birth in previous studies, and attributed this effect to the presence of tertiary myofibers. The existence of this population of myofibers is one possible explanation for the noted smaller percentage of fetal and greater percentage of type I/slow MyHC isoforms in UHO compared with Con progeny. These findings could mean that in the PM of UHO offspring, these tertiary myofibers are formed to a lesser extent than in Con offspring, and that this could affect muscle hyperplasia. Whether the development of this tertiary myofiber was related to the overall impaired myogenesis in this muscle cannot be established from the current data. Further studies are needed to determine the origin, extent, and growth potential of these tertiary fibers to develop postnatally. Regardless of the sow treatment and BtW, myofiber hyperplasia in the STNdark and RH as well as in the STNlight differed between sexes, being less in female compared with male offspring. It has been suggested that due to reduced TNF, postnatal growth is impaired (Dwyer et al., 1993; Rehfeldt and Kuhn, 2006). Thus, the observed sex differences in myofiber hyperplasia are in line with the often reported slower growth during the grower and finisher period of females compared with males (Bee, 2004; Rehfeldt et al., 2008). In conclusion, UHO gilts were confirmed to be a predictable model for IUC, inducing a reduction in litter size, but to a much lesser extent than the uterine space is reduced. The expected reduction in average BtW occurred. The surgery of the gilts had a substantial impact on muscle hyperplasia of the offspring, especially on the formation of Sec myofibers. This coincides with the time frame when IUC becomes more determining. The IUC decreases TNF and consequently affects the postnatal growth potential, which can result in economic losses due to reduced lean proportion in carcasses of slaughtered pigs. LITERATURE CITED Bee G. 2004. Effect of early gestation feeding, birth weight, and gender of progeny on muscle fiber characteristics of pigs at slaughter. J. Anim. Sci.  82: 826– 836. [PubMed] Google Scholar CrossRef Search ADS PubMed  Bérard J. Kreuzer M. Bee G. 2008. Effect of litter size and birth weight on growth, carcass and pork quality, and their relationship to postmortem proteolysis. J. Anim. Sci.  86: 2357– 2368. [PubMed] Google Scholar CrossRef Search ADS PubMed  Christenson R. K. Leymaster K. A. Young L. D. 1987. Justification of unilateral hysterectomy-ovariectomy as a model to evaluate uterine capacity in swine. J. Anim. Sci.  65: 738– 744. [PubMed] Google Scholar CrossRef Search ADS PubMed  Dwyer C. M. Fletcher J. M. Stickland N. C. 1993. Muscle cellularity and postnatal growth in the pig. J. Anim. Sci.  71: 3339– 3343. [PubMed] Google Scholar CrossRef Search ADS PubMed  Fenton F. R. Bazer F. W. Robison O. W. Ulberg L. C. 1970. Effect of quantity of uterus on uterine capacity in gilts. J. Anim. Sci.  31: 104– 106. [PubMed] Google Scholar CrossRef Search ADS PubMed  Foxcroft G. R. Dixon W. T. Novak S. Putman C. T. Town S. C. Vinsky M. D. A. 2006. The biological basis for prenatal programming of postnatal performance in pigs. J. Anim. Sci.  84: E105– E112. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gadsby J. E. Heap R. B. Burton R. D. 1980. Oestrogen production by blastocyst and early embryonic tissue of various species. J. Reprod. Fertil.  60: 409– 417. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gaustad-Aas A. H. Ropstad E. Karlberg K. Hofmo P. O. Dahl E. 2002. Oestrone sulphate measurements for the prediction of small or large litters in pigs. Acta Vet. Scand.  43: 157– 164. [PubMed] Google Scholar CrossRef Search ADS PubMed  Gondret F. Lefaucheur L. Juin H. Louveau I. Lebret B. 2006. Low birth weight is associated with enlarged muscle fiber area and impaired meat tenderness of the longissimus muscle in pigs. J. Anim. Sci.  84: 93– 103. [PubMed] Google Scholar CrossRef Search ADS PubMed  Huang Y. T. Johnson R. K. Eckardt G. R. 1987. Effect of unilateral hysterectomy and ovariectomy on puberty, uterine size and embryo development in swine. J. Anim. Sci.  65: 1298– 1305. [PubMed] Google Scholar CrossRef Search ADS PubMed  Knight J. W. Bazer F. W. Thatcher W. W. Franke D. E. Wallace H. D. 1977. Conceptus development in intact and unilaterally hysterectomized-ovariectomized gilts: Interrelations among hormonal status, placental development, fetal fluids and fetal growth. J. Anim. Sci.  44: 620– 637. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lefaucheur L. 2001. Myofiber typing and pig meat production. Slov. Vet. Res.  38: 5– 28. Lefaucheur L. Ecolan P. Plantard L. Gueguen N. 2002. New insights into muscle fiber types in the pig. J. Histochem. Cytochem.  50: 719– 730. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lefaucheur L. Edom F. Ecolan P. Butlerbrowne G. S. 1995. Pattern of muscle-fiber type formation in the pig. Dev. Dyn.  203: 27– 41. [PubMed] Google Scholar CrossRef Search ADS PubMed  Lösel D. Kalbe C. Rehfeldt C. 2009. l-Carnitine supplementation during suckling intensifies the early postnatal skeletal myofiber formation in piglets of low birth weight. J. Anim. Sci.  87: 2216– 2226. [PubMed] Google Scholar CrossRef Search ADS PubMed  Martin M. J. Redmer D. A. Ford J. J. Christenson R. K. Day B. N. 1986. Ovarian compensatory hypertrophy following unilateral ovariectomy in the suckled sow. J. Anim. Sci.  63: 572– 578. [PubMed] Google Scholar CrossRef Search ADS PubMed  Mascarello F. Stecchini M. L. Rowlerson A. Ballocchi E. 1992. Tertiary myotubes in postnatal growing pig muscle detected by their myosin isoform composition. J. Anim. Sci.  70: 1806– 1813. [PubMed] Google Scholar CrossRef Search ADS PubMed  Père M. C. Etienne M. 2000. Uterine blood flow in sows: Effects of pregnancy stage and litter size. Reprod. Nutr. Dev.  40: 369– 382. [PubMed] Google Scholar CrossRef Search ADS PubMed  Picard B. Lefaucheur L. Berri C. Duclos M. J. 2002. Muscle fibre ontogenesis in farm animal species. Reprod. Nutr. Dev.  42: 415– 431. [PubMed] Google Scholar CrossRef Search ADS PubMed  Quiniou N. Dagorn J. Gaudre D. 2002. Variation of piglets' birth weight and consequences on subsequent performance. Livest. Prod. Sci.  78: 63– 70. Google Scholar CrossRef Search ADS   Rehfeldt C. Kuhn G. 2006. Consequences of birth weight for postnatal growth performance and carcass quality in pigs as related to myogenesis. J. Anim. Sci.  84( E-Suppl.): E113– E123. [PubMed] Google Scholar CrossRef Search ADS PubMed  Rehfeldt C. Tuchscherer A. Hartung M. Kuhn G. 2008. A second look at the influence of birth weight on carcass and meat quality in pigs. Meat Sci.  78: 170– 175. Google Scholar CrossRef Search ADS PubMed  Staigmiller R. B. First N. L. Casida L. E. 1972. Ovarian compensatory hypertrophy following unilateral ovariectomy in hysterectomized and early pregnant gilts. J. Anim. Sci.  35: 809– 813. [PubMed] Google Scholar CrossRef Search ADS PubMed  Town S. C. Patterson J. L. Pereira C. Z. Gourley G. Foxcroft G. R. 2005. Embryonic and fetal development in a commercial dam-line genotype. Anim. Reprod. Sci.  85: 301– 316. [PubMed] Google Scholar CrossRef Search ADS PubMed  van der Lende T. van Rens B. T. T. M. 2003. Critical periods for foetal mortality in gilts identified by analysing the length distribution of mummified foetuses and frequency of non-fresh stillborn piglets. Anim. Reprod. Sci.  75: 141– 150. [PubMed] Google Scholar CrossRef Search ADS PubMed  Wigmore P. M. C. Stickland N. C. 1983. Muscle development in large and small pig fetuses. J. Anat.  137: 235– 245. [PubMed] Google Scholar PubMed  American Society of Animal Science