Variations in muscle glycogen storage are highly correlated with variations in meat ultimate pH (pHu), a key factor for poultry meat quality. A total of two chicken lines were divergently selected on breast pHu to understand the biological basis for variations in meat quality (i.e., the pHu- and the pHu+ lines that are characterized by a 17% difference in muscle glycogen content). The effects of this selection on bird metabolism were investigated by quantifying muscle metabolites by high-resolution NMR ((1)H and (31)P) and serum metabolites by (1)H NMR. A total of 20 and 26 discriminating metabolites between the two lines were identified by orthogonal partial least-squares discriminant analysis (OPLS-DA) in the serum and muscle, respectively. There was over-representation of carbohydrate metabolites in the serum and muscle of the pHu- line, consistent with its high level of muscle glycogen. However, the pHu+ line was characterized by markers of oxidative stress and muscle catabolism, probably because of its low level of energy substrates. After OPLS-DA multiblock analysis, a metabolic set of 15 high-confidence biomarkers was identified that could be used to predict the quality of poultry meat after validation on an independent population.

BackgroundWhite striping (WS) is an emerging muscular defect occurring on breast and thigh muscles of broiler chickens. It is characterized by the presence of white striations parallel to the muscle fibers and has significant consequences for meat quality. The etiology of WS remains poorly understood, even if previous studies demonstrated that the defect prevalence is related to broiler growth and muscle development. Moreover, recent studies showed moderate to high heritability values of WS, which emphasized the role of genetics in the expression of the muscle defect. The aim of this study was to identify the first quantitative trait loci (QTLs) for WS as well as breast muscle yield (BMY) and meat quality traits using a genome-wide association study (GWAS). We took advantage of two divergent lines of chickens selected for meat quality through Pectoralis major ultimate pH (pHu) and which exhibit the muscular defect. An expression QTL (eQTL) detection was further performed for some candidate genes, either suggested by GWAS analysis or based on their biological function.ResultsForty-two single nucleotide polymorphisms (SNPs) associated with WS and other meat quality traits were identified. They defined 18 QTL regions located on 13 chromosomes. These results supported a polygenic inheritance of the studied traits and highlighted a few pleiotropic regions. A set of 16 positional and/or functional candidate genes was designed for further eQTL detection. A total of 132 SNPs were associated with molecular phenotypes and defined 21 eQTL regions located on 16 chromosomes. Interestingly, several co-localizations between QTL and eQTL regions were observed which could suggest causative genes and gene networks involved in the variability of meat quality traits and BMY.ConclusionsThe QTL mapping carried out in the current study for WS did not support the existence of a major gene, but rather suggested a polygenic inheritance of the defect and of other studied meat quality traits. We identified several candidate genes involved in muscle metabolism and structure and in muscular dystrophies. The eQTL analyses showed that they were part of molecular networks associated with WS and meat quality phenotypes and suggested a few putative causative genes.Electronic supplementary materialThe online version of this article (10.1186/s12864-018-4598-9) contains supplementary material, which is available to authorized users.

The processing ability and sensory quality of chicken breast meat are highly related to its ultimate pH (pHu), which is mainly determined by the amount of glycogen in the muscle at death. To unravel the molecular mechanisms underlying glycogen and meat pHu variations and to identify predictive biomarkers of these traits, a transcriptome profiling analysis was performed using an Agilent custom chicken 8 × 60 K microarray. The breast muscle gene expression patterns were studied in two chicken lines experimentally selected for high (pHu+) and low (pHu−) pHu values of the breast meat. Across the 1,436 differentially expressed (DE) genes found between the two lines, many were involved in biological processes related to muscle development and remodelling and carbohydrate and energy metabolism. The functional analysis showed an intensive use of carbohydrate metabolism to produce energy in the pHu− line, while alternative catabolic pathways were solicited in the muscle of the pHu+ broilers, compromising their muscle development and integrity. After a validation step on a population of 278 broilers using microfluidic RT-qPCR, 20 genes were identified by partial least squares regression as good predictors of the pHu, opening new perspectives of screening broilers likely to present meat quality defects.

This review is aimed at providing an overview of recent advances made in the field of meat quality prediction, particularly in Europe. The different methods used in research labs or by the production sectors for the development of equations and tools based on different types of biological (genomic or phenotypic) or physical (spectroscopy) markers are discussed. Through the various examples, it appears that although biological markers have been identified, quality parameters go through a complex determinism process. This makes the development of generic molecular tests even more difficult. However, in recent years, progress in the development of predictive tools has benefited from technological breakthroughs in genomics, proteomics, and metabolomics. Concerning spectroscopy, the most significant progress was achieved using near-infrared spectroscopy (NIRS) to predict the composition and nutritional value of meats. However, predicting the functional properties of meats using this method—mainly, the sensorial quality—is more difficult. Finally, the example of the MSA (Meat Standards Australia) phenotypic model, which predicts the eating quality of beef based on a combination of upstream and downstream data, is described. Its benefit for the beef industry has been extensively demonstrated in Australia, and its generic performance has already been proven in several countries.