Two-dimensional electrophoresis was used to compare Longissimus sarcoplasmic protein abundance between two groups (tough meat and tender meat), defined on the basis of extreme Warner-Bratzler shear force values measured on cooked pork. Fourteen protein spots differed in quantity (P<0.05) between the two groups and were identified. Adypocyte fatty acid binding protein and acyl-CoA binding protein involved in lipid traffic and in the control of gene expression regulating cell proliferation and differentiation, and Enoyl-CoA hydratase, aldose reductase and triosephosphate isomerase indirectly related to lipid metabolism were overrepresented in the tender group. The tender group was further characterized by increased levels of proteins involved in protein folding and polymerization (initiation factor elf-3beta, chaperonin subunit 2, profilin II). The results suggest that the lower post-cooking shear force could at least in part be related to muscle adipogenetic and/or myogenetic status of which the possible underlying mechanisms are discussed.
Muscle tenderness is an important complex trait for meat quality and thus for genetic improvement through animal breeding. However, the physiological or genetic control of tenderness development in muscle is still poorly understood. In this work, using transcriptome analysis, we found a relationship between gene expression variability and tenderness. Muscle (longissimus dorsi) samples from 30 F(2) pigs were characterized by Warner-Bratzler Shear Force (WBSF) on cooked meat as a measurement of muscle tenderness. Gene expression levels were measured using microarrays for 17 muscle samples selected to represent a range of WBSF values. Using a linear regression model, we determined that samples with WBSF values above 30 N could be effectively analysed for genes exhibiting a significant association of their expression level on shear force (false discovery rate <0.05). These genes were shown to be involved in three functional networks: cell cycle, energy metabolism and muscle development. Twenty-two genes were mapped on the pig genome and 12 were found to be located in regions previously reported to contain quantitative trait loci (QTL) affecting pig meat tenderness (chromosomes 2, 6 and 13). Some genes appear therefore as positional candidate genes for QTL.
BackgroundThe genetics of transcript-level variation is an exciting field that has recently given rise to many studies. Genetical genomics studies have mainly focused on cell lines, blood cells or adipose tissues, from human clinical samples or mice inbred lines. Few eQTL studies have focused on animal tissues sampled from outbred populations to reflect natural genetic variation of gene expression levels in animals. In this work, we analyzed gene expression in a whole tissue, pig skeletal muscle sampled from individuals from a half sib F2 family shortly after slaughtering.ResultsQTL detection on transcriptome measurements was performed on a family structured population. The analysis identified 335 eQTLs affecting the expression of 272 transcripts. The ontologic annotation of these eQTLs revealed an over-representation of genes encoding proteins involved in processes that are expected to be induced during muscle development and metabolism, cell morphology, assembly and organization and also in stress response and apoptosis. A gene functional network approach was used to evidence existing biological relationships between all the genes whose expression levels are influenced by eQTLs. eQTLs localization revealed a significant clustered organization of about half the genes located on segments of chromosome 1, 2, 10, 13, 16, and 18. Finally, the combined expression and genetic approaches pointed to putative cis-drivers of gene expression programs in skeletal muscle as COQ4 (SSC1), LOC100513192 (SSC18) where both the gene transcription unit and the eQTL affecting its expression level were shown to be localized in the same genomic region. This suggests cis-causing genetic polymorphims affecting gene expression levels, with (e.g. COQ4) or without (e.g. LOC100513192) potential pleiotropic effects that affect the expression of other genes (cluster of trans-eQTLs).ConclusionGenetic analysis of transcription levels revealed dependence among molecular phenotypes as being affected by variation at the same loci. We observed the genetic variation of molecular phenotypes in a specific situation of cellular stress thus contributing to a better description of muscle physiologic response. In turn, this suggests that large amounts of genetic variation, mediated through transcriptional networks, can drive transient cell response phenotypes and contribute to organismal adaptative potential.
Bidimensional electrophoresis was used to compare sarcoplasmic protein profiles of early post-mortem pig semimembranosus muscles, sampled from pigs of different HAL genotypes (RYR1 mutation 1841T/C): 6 NN, 6 Nn, 6 nn. ANOVA showed that 55 (18%) of the total of 300 matched protein spots were influenced by genotype, and hierarchical clustering analysis identified 31 (10% of the matched proteins) additional proteins coregulated with these proteins. Fold-changes of differentially expressed proteins were between 1.3 and 21.8. Peptide mass fingerprinting identification of 78 of these 86 proteins indicates that faster pH decline of nn pigs was not explained by higher abundance of glycolytic enzymes. Results indicate further that nn muscles contained fewer proteins of the oxidative metabolic pathway, fewer antioxidants, and more protein fragments. Lower abundance of small heat shock proteins and myofibrillar proteins in nn muscles may at least partly be explained by the effect of pH on their extractability. Possible consequences of lower levels of antioxidants and repair capacities, increased protein fragmentation, and lower extractability of certain proteins in nn muscles on meat quality are discussed.
BackgroundDetection of quantitative trait loci (QTLs) affecting meat quality traits in pigs is crucial for the design of efficient marker-assisted selection programs and to initiate efforts toward the identification of underlying polymorphisms. The RYR1 and PRKAG3 causative mutations, originally identified from major effects on meat characteristics, can be used both as controls for an overall QTL detection strategy for diversely affected traits and as a scale for detected QTL effects. We report on a microsatellite-based QTL detection scan including all autosomes for pig meat quality and carcass composition traits in an F2 population of 1,000 females and barrows resulting from an intercross between a Pietrain and a Large White-Hampshire-Duroc synthetic sire line. Our QTL detection design allowed side-by-side comparison of the RYR1 and PRKAG3 mutation effects seen as QTLs when segregating at low frequencies (0.03-0.08), with independent QTL effects detected from most of the same population, excluding any carrier of these mutations.ResultsLarge QTL effects were detected in the absence of the RYR1 and PRKGA3 mutations, accounting for 12.7% of phenotypic variation in loin colour redness CIE-a* on SSC6 and 15% of phenotypic variation in glycolytic potential on SSC1. We detected 8 significant QTLs with effects on meat quality traits and 20 significant QTLs for carcass composition and growth traits under these conditions. In control analyses including mutation carriers, RYR1 and PRKAG3 mutations were detected as QTLs, from highly significant to suggestive, and explained 53% to 5% of the phenotypic variance according to the trait.ConclusionsOur results suggest that part of muscle development and backfat thickness effects commonly attributed to the RYR1 mutation may be a consequence of linkage with independent QTLs affecting those traits. The proportion of variation explained by the most significant QTLs detected in this work is close to the influence of major-effect mutations on the least affected traits, but is one order of magnitude lower than effect on variance of traits primarily affected by these causative mutations. This suggests that uncovering physiological traits directly affected by genetic polymorphisms would be an appropriate approach for further characterization of QTLs.
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