Background: Physiological processes aiding the conversion of muscle to meat involve many genes associated with muscle structure and metabolic processes. MicroRNAs regulate networks of genes to orchestrate cellular functions, in turn regulating phenotypes.Results: We applied weighted gene co-expression network analysis to identify co-expression modules that correlated to meat quality phenotypes and were highly enriched for genes involved in glucose metabolism, response to wounding, mitochondrial ribosome, mitochondrion, and extracellular matrix. Negative correlation of miRNA with mRNA and target prediction were used to select transcripts out of the modules of trait-associated mRNAs to further identify those genes that are correlated with post mortem traits.Conclusions: Porcine muscle co-expression transcript networks that correlated topost mortemtraits were identified. The integration of miRNA and mRNA expression analyses, as well as network analysis, enabled us to interpret the differentially-regulated genes from a systems perspective. Linking co-expression networks of transcripts and hierarchically organized pairs of miRNAs and mRNAs to meat properties yields new insight into several biological pathways underlying phenotype differences. These pathways may also be diagnostic for many myopathies, which are accompanied by deficient nutrient and oxygen supply of muscle fibers.

Background: Expanding noninvasive prenatal testing (NIPT) to include the detection of fetal subchromosomal copy number variations (CNVs) significantly decreased the sensitivity and specificity. Developing analytic pipeline to achieve high performance in the noninvasive detection of CNVs will largely contribute to the application of CNVs screening in clinical practice.Methods: We developed the Noninvasively Prenatal Subchromosomal Copy number variation Detection (NIPSCCD) method based on low‐pass whole‐genome sequencing, and evaluated its efficacy in detecting fetal CNVs and chromosomal aneuploidies with 20,003 pregnant women.Results: Totally, NIPSCCD identified 36 CNVs, including 29 CNVs consistent and 7 CNVs inconsistent with amniocytes tests. Additionally, seven fetal CNVs identified by amniocytes testing were undetected by NIPSCCD. The sensitivities for detecting CNVs > 10 Mb, 5 Mb–10 Mb, and CNVs < 5 Mb were 91.67%, 100.00%, and 68.42%, respectively. Moreover, NIPSCCD identified 103/ true positive trisomy 21/18/13 cases and 21 false positives, producing an overall 100.00% sensitivity and 99.89% specificity.Conclusion: NIPSCCD showed a good performance in detecting fetal subchromosomal CNVs, especially for CNVs >10 Mb, and can be incorporated into the routine NIPT chromosomal aneuploidies screening with high sensitivity and specificity.

Skeletal muscle is a highly metabolically active tissue that both stores and consumes energy. Important biological pathways that affect energy metabolism and metabolic fiber type in muscle cells may be identified through transcriptomic profiling of the muscle, especially ante mortem. Here, gene expression was investigated in malignant hyperthermia syndrome (MHS)-negative Duroc and Pietrian (PiNN) pigs significantly differing for the muscle fiber types slow-twitch-oxidative fiber (STO) and fast-twitch-oxidative fiber (FTO) as well as mitochondrial activity (succinate-dependent state 3 respiration rate). Longissimus muscle samples were obtained 24 h before slaughter and profiled using cDNA microarrays. Differential gene expression between Duroc and PiNN muscle samples were associated with protein ubiquitination, stem cell pluripotency, amyloid processing, and 3-phosphoinositide biosynthesis and degradation pathways. In addition, weighted gene co-expression network analysis within both breeds identified several co-expression modules that were associated with the proportion of different fiber types, mitochondrial respiratory activity, and ATP metabolism. In particular, Duroc results revealed strong correlations between mitochondrion-associated co-expression modules and STO (r = 0.78), fast-twitch glycolytic fiber (r = -0.98), complex I (r=0.72) and COX activity (r = 0.86). Other pathways in the protein-kinase-activity enriched module were positively correlated with STO (r=0.93), while negatively correlated with FTO (r = -0.72). In contrast to PiNN, co-expression modules enriched in macromolecule catabolic process, actin cytoskeleton, and transcription activator activity were associated with fiber types, mitochondrial respiratory activity, and metabolic enzyme activities. Our results highlight the importance of mitochondria for the oxidative capacity of porcine muscle and for breed-dependent molecular pathways in muscle cell fibers.

Understanding the genetic contributions behind skeletal muscle composition and metabolism is of great interest in medicine and agriculture. Attempts to dissect these complex traits combine genome-wide genotyping, expression data analyses and network analyses. Weighted gene co-expression network analysis (WGCNA) groups genes into modules based on patterns of co-expression, which can be linked to phenotypes by correlation analysis of trait values and the module eigengenes, i.e. the first principal component of a given module. Network hub genes and regulators of the genes in the modules are likely to play an important role in the emergence of respective traits. In order to detect common regulators of genes in modules showing association with meat quality traits, we identified eQTL for each of these genes, including the highly connected hub genes. Additionally, the module eigengene values were used for association analyses in order to derive a joint eQTL for the respective module. Thereby major sites of orchestrated regulation of genes within trait-associated modules were detected as hotspots of eQTL of many genes of a module and of its eigengene. These sites harbor likely common regulators of genes in the modules. We exemplarily showed the consistent impact of candidate common regulators on the expression of members of respective modules by RNAi knockdown experiments. In fact, Cxcr7 was identified and validated as a regulator of genes in a module, which is involved in the function of defense response in muscle cells. Zfp36l2 was confirmed as a regulator of genes of a module related to cell death or apoptosis pathways. The integration of eQTL in module networks enabled to interpret the differentially-regulated genes from a systems perspective. By integrating genome-wide genomic and transcriptomic data, employing co-expression and eQTL analyses, the study revealed likely regulators that are involved in the fine-tuning and synchronization of genes with trait-associated expression.

Cortisol is a steroid hormone with important roles in regulating immune and metabolic functions and organismal responses to external stimuli are mediated by the glucocorticoid system. Dysregulation of the afferent and efferent axis of glucocorticoid signaling have adverse effects on growth, health status, and well-being. Glucocorticoid secretion and signaling show large interindividual variation that has a considerable genetic component; however, little is known about the underlying genetic variants. Here, we used trait-correlated expression analysis, screening for expression quantitative trait loci (eQTL), genome-wide association (GWA) studies, and causality modeling to identify candidate genes in porcine liver and muscle that affect or respond to plasma cortisol levels. Through trait-correlated expression, we characterized transcript activities in many biological functions in liver and muscle. Candidates from the list of trait-correlated expressed genes were narrowed using only those genes with an eQTL, and these were further prioritized by determining whether their expression was predicted to be related to variation in plasma cortisol levels. Using network edge orienting (NEO), a causality modeling algorithm, 26 of 990 candidates in liver were predicted to affect and 70 to respond to plasma cortisol levels. Of 593 candidates in muscle that were correlated with cortisol levels and were regulated by eQTL, 2 and 25 were predicted as effective and responsive, respectively, to plasma cortisol levels. Comprehensive data integration has helped to elucidate the complex molecular networks contributing to cortisol levels and thus its subsequent metabolic effects. The discrimination of up- and downstream effects of transcripts affecting or responding to plasma cortisol concentrations improves the understanding of the biology of complex traits related to growth, health, and well-being.