Gene coexpression networks reveal key drivers of phenotypic divergence in porcine muscle

Genome-wide association study for primal cut lean traits in Canadian beef cattle

This study identified genomic variants and underlying candidate genes related to the whole carcass and individual primal cut lean content in Canadian commercial crossbred beef cattle. Genotyping information of 1035 crossbred beef cattle were available alongside estimated and actual carcass lean meat yield and individual primal cut lean content in all carcasses. Significant fixed effects and covariates were identified and included in the animal model. Genome-wide association analysis were implemented using the weighted single-step genomic best linear unbiased prediction (WssGBLUP). A number of candidate genes identified linked to lean tissue production were unrelated to estimated lean meat yield and were specific to the actual lean traits. Among these, 41 genes were common for actual lean traits, on specific regions of BTA4, BTA13 and BTA25 indicating potential involvement in lean mass synthesis. Therefore, the results suggested the inclusion of primal cut lean traits as a selection objective in breeding programs with consideration of further functional studies of the identified genes could help in optimizing lean yield for maximal carcass value.

Whole-genome sequence-based association analyses on an eight-breed crossed heterogeneous stock of pigs reveal the genetic basis of skeletal muscle fiber characteristics

Skeletal muscle fiber characteristics (MFCs) have been extensively studied due to their importance to human health and athletic ability, as well as to the quantity and quality of livestock meat production. Hence, we performed a genome-wide association study (GWAS) on nine muscle fiber traits by using whole genome sequence data in an eight-breed crossed heterogeneous stock pig population. This GWAS revealed 67 quantitative trait loci (QTLs) for these traits. The most significant GWAS signal was detected in the region of Sus scrofa chromosome 12 (SSC12) containing the MYH gene family. Notably, we identified a significant SNP rs322008693 (P = 7.52E-09) as the most likely causal mutation for the total number of muscle fibers (TNMF) QTL on SSC1. The results of EMSA and luciferase assays indicated that the rs322008693 SNP resided in a functional element. These findings provide valuable molecular markers for pig meat production selection as well as for deciphering the genetic mechanisms of the muscle fiber physiology.

Genome-Wide Expression Profiling of mRNAs, lncRNAs and circRNAs in Skeletal Muscle of Two Different Pig Breeds

Simple Summary

Variation exists in muscle-related traits, such as muscle growth and meat quality, between obese and lean pigs. In this study, the transcriptome profiles of skeletal muscle between Beijing Blackand Yorkshire pigs were characterized to explore the molecular mechanism underlying skeletal muscle-relatedtraits. Gene Ontology (GO) and KEGG pathway enrichment analyses showed that differentially expressed mRNAs, lncRNAs, and circRNAs involved in skeletal muscle development and fatty acid metabolism played a key role in the determination of muscle-related traits between different pig breeds. These results provide candidate genes responsible for muscle phenotypic variation and are valuable for pig breeding.

Abstract

RNA-Seq technology is widely used to analyze global changes in the transcriptome and investigate the influence on relevant phenotypic traits. Beijing Black pigs show differences in growth rate and meat quality compared to western pig breeds. However, the molecular mechanisms responsible for such phenotypic differences remain unknown. In this study, longissimus dorsi muscles from Beijing Black and Yorkshire pigs were used to construct RNA libraries and perform RNA-seq. Significantly different expressions were observed in 1051 mRNAs, 322 lncRNAs, and 82 circRNAs. GO and KEGG pathway annotation showed that differentially expressed mRNAs participated in skeletal muscle development and fatty acid metabolism, which determined the muscle-related traits. To explore the regulatory role of lncRNAs, the cis and trans-target genes were predicted and these lncRNAswere involved in the biological processes related to skeletal muscle development and fatty acid metabolismvia their target genes. CircRNAs play a ceRNA role by binding to miRNAs. Therefore, the potential miRNAs of differentially expressed circRNAs were predicted and interaction networks among circRNAs, miRNAs, and key regulatory mRNAs were constructed to illustrate the function of circRNAs underlying skeletal muscle development and fatty acid metabolism. This study provides new clues for elucidating muscle phenotypic variation in pigs.

Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model

Measuring conditional relatedness between a pair of genes is a fundamental technique and still a significant challenge in computational biology. Such relatedness can be assessed by gene expression similarities while suffering high false discovery rates. Meanwhile, other types of features, e.g., prior-knowledge based similarities, is only viable for measuring global relatedness. In this paper, we propose a novel machine learning model, named Multi-Features Relatedness (MFR), for accurately measuring conditional relatedness between a pair of genes by incorporating expression similarities with prior-knowledge based similarities in an assessment criterion. MFR is used to predict gene-gene interactions extracted from the COXPRESdb, KEGG, HPRD, and TRRUST databases by the 10-fold cross validation and test verification, and to identify gene-gene interactions collected from the GeneFriends and DIP databases for further verification. The results show that MFR achieves the highest area under curve (AUC) values for identifying gene-gene interactions in the development, test, and DIP datasets. Specifically, it obtains an improvement of 1.1% on average of precision for detecting gene pairs with both high expression similarities and high prior-knowledge based similarities in all datasets, comparing to other linear models and coexpression analysis methods. Regarding cancer gene networks construction and gene function prediction, MFR also obtains the results with more biological significances and higher average prediction accuracy, than other compared models and methods. A website of the MFR model and relevant datasets can be accessed from http://bmbl.sdstate.edu/MFR.

Transcriptomic profiling in muscle and adipose tissue identifies genes related to growth and lipid deposition

Growth performance and meat quality are important traits for the pig industry and consumers. Adipose tissue is the main site at which fat storage and fatty acid synthesis occur. Therefore, we combined high-throughput transcriptomic sequencing in adipose and muscle tissues with the quantification of corresponding phenotypic features using seven Chinese indigenous pig breeds and one Western commercial breed (Yorkshire). We obtained data on 101 phenotypic traits, from which principal component analysis distinguished two groups: one associated with the Chinese breeds and one with Yorkshire. The numbers of differentially expressed genes between all Chinese breeds and Yorkshire were shown to be 673 and 1056 in adipose and muscle tissues, respectively. Functional enrichment analysis revealed that these genes are associated with biological functions and canonical pathways related to oxidoreductase activity, immune response, and metabolic process. Weighted gene coexpression network analysis found more coexpression modules significantly correlated with the measured phenotypic traits in adipose than in muscle, indicating that adipose regulates meat and carcass quality. Using the combination of differential expression, QTL information, gene significance, and module hub genes, we identified a large number of candidate genes potentially related to economically important traits in pig, which should help us improve meat production and quality.

Spatiotemporal Transcriptome Analysis Provides Insights into Bicolor Tepal Development in Lilium "Tiny Padhye"

The bicolor Asiatic hybrid lily cultivar "Tiny Padhye" is an attractive variety because of its unique color pattern. During its bicolor tepal development, the upper tepals undergo a rapid color change from green to white, while the tepal bases change from green to purple. However, the molecular mechanisms underlying these changes remain largely uncharacterized. To systematically investigate the dynamics of the lily bicolor tepal transcriptome during development, we generated 15 RNA-seq libraries from the upper tepals (S2-U) and basal tepals (S1-D, S2-D, S3-D, and S4-D) of Lilium "Tiny Padhye." Utilizing the Illumina platform, a total of 295,787 unigenes were obtained from 713.12 million high-quality paired-end reads. A total of 16,182 unigenes were identified as differentially expressed genes during tepal development. Using Kyoto Encyclopedia of Genes and Genomes pathway analysis, candidate genes involved in the anthocyanin biosynthetic pathway (61 unigenes), and chlorophyll metabolic pathway (106 unigenes) were identified. Further analyses showed that most anthocyanin biosynthesis genes were transcribed coordinately in the tepal bases, but not in the upper tepals, suggesting that the bicolor trait of "Tiny Padhye" tepals is caused by the transcriptional regulation of anthocyanin biosynthetic genes. Meanwhile, the high expression level of chlorophyll degradation genes and low expression level of chlorophyll biosynthetic genes resulted in the absence of chlorophylls from "Tiny Padhye" tepals after flowering. Transcription factors putatively involved in the anthocyanin biosynthetic pathway and chlorophyll metabolism in lilies were identified using a weighted gene co-expression network analysis and their possible roles in lily bicolor tepal development were discussed. In conclusion, these extensive transcriptome data provide a platform for elucidating the molecular mechanisms of bicolor tepals in lilies and provide a basis for similar research in other closely related species.

Integrative Analysis of Metabolomic, Proteomic and Genomic Data to Reveal Functional Pathways and Candidate Genes for Drip Loss in Pigs

The aim of this study was to integrate multi omics data to characterize underlying functional pathways and candidate genes for drip loss in pigs. The consideration of different omics levels allows elucidating the black box of phenotype expression. Metabolite and protein profiling was applied in Musculus longissimus dorsi samples of 97 Duroc × Pietrain pigs. In total, 126 and 35 annotated metabolites and proteins were quantified, respectively. In addition, all animals were genotyped with the porcine 60 k Illumina beadchip. An enrichment analysis resulted in 10 pathways, amongst others, sphingolipid metabolism and glycolysis/gluconeogenesis, with significant influence on drip loss. Drip loss and 22 metabolic components were analyzed as intermediate phenotypes within a genome-wide association study (GWAS). We detected significantly associated genetic markers and candidate genes for drip loss and for most of the metabolic components. On chromosome 18, a region with promising candidate genes was identified based on SNPs associated with drip loss, the protein "phosphoglycerate mutase 2" and the metabolite glycine. We hypothesize that association studies based on intermediate phenotypes are able to provide comprehensive insights in the genetic variation of genes directly involved in the metabolism of performance traits. In this way, the analyses contribute to identify reliable candidate genes.

Genetic association of marbling score with intragenic nucleotide variants at selection signals of the bovine genome

Selection signals of Korean cattle might be attributed largely to artificial selection for meat quality. Rapidly increased intragenic markers of newly annotated genes in the bovine genome would help overcome limited findings of genetic markers associated with meat quality at the selection signals in a previous study. The present study examined genetic associations of marbling score (MS) with intragenic nucleotide variants at selection signals of Korean cattle. A total of 39 092 nucleotide variants of 407 Korean cattle were utilized in the association analysis. A total of 129 variants were selected within newly annotated genes in the bovine genome. Their genetic associations were analyzed using the mixed model with random polygenic effects based on identical-by-state genetic relationships among animals in order to control for spurious associations produced by population structure. Genetic associations of MS were found (P<3.88×10-4) with six intragenic nucleotide variants on bovine autosomes 3 (cache domain containing 1, CACHD1), 5 (like-glycosyltransferase, LARGE), 16 (cell division cycle 42 binding protein kinase alpha, CDC42BPA) and 21 (snurportin 1, SNUPN; protein tyrosine phosphatase, non-receptor type 9, PTPN9; chondroitin sulfate proteoglycan 4, CSPG4). In particular, the genetic associations with CDC42BPA and LARGE were confirmed using an independent data set of Korean cattle. The results implied that allele frequencies of functional variants and their proximity variants have been augmented by directional selection for greater MS and remain selection signals in the bovine genome. Further studies of fine mapping would be useful to incorporate favorable alleles in marker-assisted selection for MS of Korean cattle.