Weighted gene co-expression network analysis identifies important modules and hub genes involved in the regulation of breast muscle yield in broilers
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Objective: Increasing breast meat production is one of the primary goals of the broiler industry. Over the past few decades, tremendous progress has been made in genetic selection and the identification of candidate genes for improving the breast muscle mass. However, the molecular network contributing to muscle production traits in chickens still needs to be further illuminated.Methods: A total of 150 1-day-old male 817 broilers were reared in a floor litter system. At the market age of 50 d, eighteen healthy 817 broilers were slaughtered and the left pectoralis major muscle sample from each bird was collected for RNA-seq sequencing. The birds were then plucked and eviscerated and the whole breast muscle was removed and weighed. Breast muscle yield was calculated as the ratio of the breast muscle weight to the eviscerated weight. To identify the co-expression networks and hub genes contributing to breast muscle yield in chickens, we performed weighted gene co-expression network analysis (WGCNA) based on the 18 transcriptome datasets of pectoralis major muscle from eighteen 817 broilers.Results: The WGCNA analysis classified all co-expressed genes in the pectoral muscle of 817 broilers into 44 modules. Among these modules, the turquoise and skyblue3 modules were found to be most significantly positively (r = 0.78, p = 1e-04) and negatively (r = –0.57, p = 0.01) associated with breast meat yield, respectively. Further analysis identified several hub genes (e.g., DLX3, SH3RF2, TPM1, CAV3, MYF6, and CFL2) that involved in muscle structure and muscle development were identified as potential regulators of breast meat production.Conclusion: The present study has advanced our understanding of the molecular regulatory networks contributing to muscle growth and breast muscle production and will contribute to the molecular breeding of chickens in the future.
Abstract
Objective: Increasing breast meat production is one of the primary goals of the broiler industry. Over the past few decades, tremendous progress has been made in genetic selection and the identification of candidate genes for improving the breast muscle mass. However, the molecular network contributing to muscle production traits in chickens still needs to be further illuminated. Methods: A total of 150 1-day-old male 817 broilers were reared in a floor litter system. At the market age of 50 d, eighteen healthy 817 broilers were slaughtered and the left pectoralis major muscle sample from each bird was collected for RNA-seq sequencing. The birds were then plucked and eviscerated and the whole breast muscle was removed and weighed. Breast muscle yield was calculated as the ratio of the breast muscle weight to the eviscerated weight. To identify the co-expression networks and hub genes contributing to breast muscle yield in chickens, we performed weighted gene co-expression network analysis (WGCNA) based on the 18 transcriptome datasets of pectoralis major muscle from eighteen 817 broilers. Results: The WGCNA analysis classified all co-expressed genes in the pectoral muscle of 817 broilers into 44 modules. Among these modules, the turquoise and skyblue3 modules were found to be most significantly positively (r = 0.78, p = 1e-04) and negatively (r = –0.57, p = 0.01) associated with breast meat yield, respectively. Further analysis identified several hub genes (e.g., DLX3, SH3RF2, TPM1, CAV3, MYF6, and CFL2) that involved in muscle structure and muscle development were identified as potential regulators of breast meat production. Conclusion: The present study has advanced our understanding of the molecular regulatory networks contributing to muscle growth and breast muscle production and will contribute to the molecular breeding of chickens in the future.
참고문헌 (45)
- He Y, "Identification of new genes and genetic variant loci associated with breast muscle development in the mini-cobb F2 chicken population using a genome-wide association study", Genes(Basel), 13, pp.2153~ (2022)[DOI]
- Scheuermann GN, "Breast muscle development in commercial broiler chickens", Poult Sci, 82, pp.1648~1658 (2003)[DOI]
- Xue X, "Ascorbic acid regulates mouse spermatogonial stem cell proliferation in a Wnt", beta-catenin, Theriogenology, pp.2022, 184[DOI]
- Petracci M, "Meat quality in fast-growing broiler chickens", Worlds Poult Sci J, 71, pp.363~374 (2019)[DOI]
- Li Y, "Weighted gene co-expression network indicates that the DYNLL2 is an important regulator of chicken breast muscle development and is regulated by miR-148a-3p", BMC Genomics, 23, pp.258~ (2022)[DOI]
- Zhang Z, "Comparative transcriptome analysis reveals regulators mediating breast muscle growth and development in three chicken breeds", Anim Biotechnol, 30, pp.233~241 (2019)[DOI]
- Li G, "Analysis of gene co-expression networks and function modules at different developmental stages of chicken breast muscle", Biochem Biophys Res Commun, 508, pp.177~183 (2019)[DOI]
- Bailey RA, "The genetic basis of pectoralis major myopathies in modern broiler chicken lines", Poult Sci, 94, pp.2870~2879 (2015)[DOI]
- Chen JL, "Estimation of genetic parameters for contents of intramuscular fat and inosine-5'-monophosphate and carcass traits in Chinese Beijing-You chickens", Poult Sci, 87, pp.1098~1104 (2008)[DOI]
- Zhou H, "Genome-wide linkage analysis to identify chromosomal regions affecting phenotypic traits in the chicken. II. body composition", Poult Sci, 85, pp.1712~1721 (2006)[DOI]
- Allais S, "Mapping of QTL for chicken body weight, carcass composition, and meat quality traits in a slow-growing line", Poult Sci, 98, pp.1960~1967 (2019)[DOI]
- Li YD, "Integration of genome-wide association study and selection signatures reveals genetic determinants for skeletal muscle production traits in an F2 chicken population", J Integr Agric, 21, pp.2065~2075 (2022)[DOI]
- Liu R, "Genome-wide association study identifies Loci and candidate genes for body composition and meat quality traits in Beijing-You chickens", PLoS One, 8, pp.e61172~ (2013)[DOI]
- Tang H, "Variation of meat quality traits among five genotypes of chicken", Poult Sci, 88, pp.2212~2218 (2009)[DOI]
- Weng K, "Fiber characteristics and meat quality of different muscular tissues from slow-and fastgrowing broilers", Poult Sci, 101, pp.101537~ (2022)[DOI]
- Chen Y, "Differences in physicochemical and nutritional properties of breast and thigh meat from crossbred chickens, commercial broilers, and spent hens", Asian-Australas J Anim Sci, 29, pp.855~864 (2016)[DOI]
- Langfelder P, "WGCNA : an R package for weighted correlation network analysis", BMC Bioinformatics, 9, pp.559~ (2008)[DOI]
- Zhai B, "Weighted gene co-expression network analysis identified hub genes critical to fatty acid composition in Gushi chicken breast muscle", BMC Genomics, 24, pp.594~ (2023)[DOI]
- Xing S, "Time course transcriptomic study reveals the gene regulation during liver development and the correlation with abdominal fat weight in chicken", Front Genet, 12, pp.723519~ (2021)[DOI]
- Guo X, "Identification of key modules and hub genes involved in regulating the color of chicken breast meat using WGCNA", Animals(Basel), 13, pp.2356~ (2023)[DOI]
- Kim D, "Graphbased genome alignment and genotyping with HISAT2 and HISAT-genotype", Nat Biotechnol, 37, pp.907~915 (2019)[DOI]
- Li H, "The sequence Alignment", Map format and SAMtools, 2009, pp.25, 2078~2079
- Anders S, "HTSeq—a python framework to work with high-throughput sequencing data", Bioinformatics, 31, pp.166~169 (2015)[DOI]
- Love MI, "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2", Genome Biol, 15, pp.550~ (2014)[DOI]
- Shannon P, "Cytoscape : a software environment for integrated models of biomolecular interaction networks", Genome Res, 13, pp.2498~2504 (2003)[DOI]
- Livak KJ, "Analysis of relative gene expression data using real-time quantitative PCR and the 2-ΔΔCT method", Methods, 25, pp.402~408 (2001)[DOI]
- Reimand J, "g : Profiler—a web server for functional interpretation of gene lists(2016 update)", Nucleic Acids Res, 44, pp.W83~W89 (2016)[DOI]
- Buckingham M, "Gene regulatory networks and cell lineages that underlie the formation of skeletal muscle", Proc Natl Acad Sci USA, 114, pp.5830~5837 (2017)[DOI]
- Sun Y, "Functional and comparative analysis of two subtypes of cofilin family on cattle myoblasts differentiation", Agriculture, 12, pp.1420~ (2022)[DOI]
- Ran J, "Comparative analysis of skeletal muscle DNA methylation and transcriptome of the chicken embryo at different developmental stages", Front Physiol, 12, pp.697121~ (2021)[DOI]
- Qadir AS, "Distal-less homeobox 3, a negative regulator of myogenesis, is downregulated by microRNA-133", J Cell Biochem, 120, pp.2226~2235 (2018)[DOI]
- Rubin CJ, "Whole-genome resequencing reveals loci under selection during chicken domestication", Nature, 464, pp.587~591 (2010)[DOI]
- Jing Z, "Detection of CNV in the SH3RF2 gene and its effects on growth and carcass traits in chickens", BMC Genet, 21, pp.22~ (2020)[DOI]
- Shibata T, "Lens–specific conditional knockout of tropomyosin 1 gene in mice causes abnormal fiber differentiation and lens opacity", Mech Ageing Dev, 196, pp.111492~ (2021)[DOI]
- Moraczewska J, "Thin filament dysfunctions caused by mutations in tropomyosin Tpm3. 12 and Tpm1. 1", J Muscle Res Cell Motil, 41, pp.39~53 (2019)[DOI]
- Wang J, "Ectopic expression and dynamics of TPM1α and TPM1κ in myofibrils of avian myotubes", Cell Motil Cytoskeleton, 64, pp.767~776 (2007)[DOI]
- Matsunobe M, "Caveolin-3 regulates the activity of Ca2+", calmodulindependent protein kinase II in C2C12 cells, 2022, pp.323, C1137~C1148
- Parton RG, "Caveolin-3 associates with developing T-tubules during muscle differentiation", J Cell Biol, 136, pp.137~154 (1997)[DOI]
- Hadj Sassi A, "Overexpression of caveolin-3-enhanced protein synthesis rather than proteolysis inhibition in C2C12 myoblasts : relationship with myostatin activity", J Physiol Biochem, 68, pp.683~690 (2012)[DOI]
- Galbiati F, "Targeted down-regulation of caveolin-3 is sufficient to inhibit myotube formation in differentiating C2C12 myoblasts. : transient activation of p38 mitogen-activated protein kinase is required for induction of caveolin-3 expression and subsequent myotube formation", J Biol Chem, 274, pp.30315~30321 (1999)[DOI]
- Nguyen MT, "CFL2 is an essential mediator for myogenic differentiation in C2C12myoblasts", Biochem Biophys Res Commun, 533, pp.710~716 (2020)[DOI]
- Agrawal PB, "Normal myofibrillar development followed by progressive sarcomeric disruption with actin accumulations in a mouse Cfl2 knockout demonstrates requirement of cofilin-2 for muscle maintenance", Hum Mol Genet, 21, pp.2341~2356 (2012)[DOI]
- Maak S, "Identification and analysis of putative regulatory sequences for the MYF5", MYF6 locus in different vertebrate species, 2006, pp.379, 141~147
- Buckingham M, "Muscle differentiation : which myogenic factors make muscle?", Curr Biol, 4, pp.61~63 (1994)[DOI]
- Lazure F, "Myf6", MRF4is a myogenic niche regulator required for the maintenance of the muscle stem cell pool, 2020, pp.21, e49499~