Revelation of genes associated with energy generating metabolic pathways in the fighter type Aseel chicken of India through skeletal muscle transcriptome sequencing.

In this study, comparative analysis of skeletal muscle transcriptome was carried out for four biological replicates of Aseel, a fighter type breed and Punjab Brown, a meat type breed of India. The profusely expressed genes in both breeds were related to muscle contraction and motor activity. Differential expression analysis identified 961 up-regulated and 979 down-regulated genes in Aseel at a threshold of log2 fold change ≥ ±2.0 (padj<0.05). Significantly enriched KEGG pathways in Aseel included metabolic pathways and oxidative phosphorylation, with higher expression of genes associated with fatty acid beta-oxidation, formation of ATP by chemiosmotic coupling, response to oxidative stress, and muscle contraction. The highly connected hub genes identified through gene network analysis in the Aseel gamecocks were HNF4A, APOA2, APOB, APOC3, AMBP, and ACOT13, which are primarily associated with energy generating metabolic pathways. The up-regulated genes in Punjab Brown chicken were found to be related to muscle growth and differentiation. There was enrichment of pathways such as focal adhesion, insulin signaling pathway and ECM receptor interaction in these birds. The results presented in this study help to improve our understanding of the molecular mechanisms associated with fighting ability and muscle growth in Aseel and Punjab Brown chicken, respectively.

[1]  D. Mishra,et al.  Study on the muscle transcriptome of two diverse Indian backyard poultry breeds acclimatized to different agro-ecological conditions , 2023, Molecular Biology Reports.

[2]  Jingbo Liu,et al.  Transcriptome analysis of breast muscle and liver in full-sibling hybrid broilers at different ages. , 2022, Gene.

[3]  Guiping Zhao,et al.  Differential regulation of intramuscular fat and abdominal fat deposition in chickens , 2022, BMC genomics.

[4]  Rekha Sharma,et al.  An attempt to valorize the only black meat chicken breed of India by delineating superior functional attributes of its meat , 2022, Scientific Reports.

[5]  T. K. Bhattacharya,et al.  Signature of Indian native chicken breeds: a perspective , 2022, World's Poultry Science Journal.

[6]  S. Sudarshan,et al.  Muscle transcriptome provides the first insight into the dynamics of gene expression with progression of age in sheep , 2021, Scientific Reports.

[7]  X. Bustelo,et al.  Rho GTPases in Skeletal Muscle Development and Homeostasis , 2021, Cells.

[8]  Kyriakos E Kypreos,et al.  HDL and type 2 diabetes: the chicken or the egg? , 2021, Diabetologia.

[9]  Manoj Kumar Singh,et al.  Transcriptomic diversity in longissimus thoracis muscles of Barbari and Changthangi goat breeds of India. , 2021, Genomics.

[10]  L. Yang,et al.  Comprehensive Proteomic Characterization of the Pectoralis Major at Three Chronological Ages in Beijing-You Chicken , 2021, Frontiers in Physiology.

[11]  Yuuki Imai,et al.  DNA maintenance methylation enzyme Dnmt1 in satellite cells is essential for muscle regeneration. , 2020, Biochemical and biophysical research communications.

[12]  Jong-Eun Park,et al.  RNA seq analyses of chicken reveals biological pathways involved in acclimation into different geographical locations , 2020, Scientific Reports.

[13]  Rekha Sharma,et al.  Comparative gene expression profiling of milk somatic cells of Sahiwal cattle and Murrah buffaloes. , 2020, Gene.

[14]  Xinchao Zhang,et al.  Transcriptome for the breast muscle of Jinghai yellow chicken at early growth stages , 2020, PeerJ.

[15]  S. Lamont,et al.  Novel Combined Tissue Transcriptome Analysis After Lentogenic Newcastle Disease Virus Challenge in Inbred Chicken Lines of Differential Resistance , 2020, Frontiers in Genetics.

[16]  Wei Liu,et al.  Dynamic Transcriptomic Analysis of Breast Muscle Development From the Embryonic to Post-hatching Periods in Chickens , 2020, Frontiers in Genetics.

[17]  X. Kang,et al.  Identification of differentially expressed genes and pathways between intramuscular and abdominal fat-derived preadipocyte differentiation of chickens in vitro , 2019, BMC Genomics.

[18]  Kaiyu Qian,et al.  ACAT1 and Metabolism-Related Pathways Are Essential for the Progression of Clear Cell Renal Cell Carcinoma (ccRCC), as Determined by Co-expression Network Analysis , 2019, Front. Oncol..

[19]  N. Burd,et al.  The Role of the IGF-1 Signaling Cascade in Muscle Protein Synthesis and Anabolic Resistance in Aging Skeletal Muscle , 2019, Front. Nutr..

[20]  Peter F Surai,et al.  Antioxidant Defence Systems and Oxidative Stress in Poultry Biology: An Update , 2019, Antioxidants.

[21]  Zhuanjian Li,et al.  Analyses of MicroRNA and mRNA Expression Profiles Reveal the Crucial Interaction Networks and Pathways for Regulation of Chicken Breast Muscle Development , 2019, Front. Genet..

[22]  M. Verzi,et al.  A reinforcing HNF4-SMAD4 feed-forward module stabilizes enterocyte identity , 2019, Nature Genetics.

[23]  Chaowu Yang,et al.  Comparative transcriptome analysis reveals regulators mediating breast muscle growth and development in three chicken breeds , 2019, Animal biotechnology.

[24]  Feng-bin Yan,et al.  Characterization of miRNA transcriptome profiles related to breast muscle development and intramuscular fat deposition in chickens , 2018, Journal of cellular biochemistry.

[25]  E. Brzuszkiewicz,et al.  Comparative genome and phenotypic analysis of three Clostridioides difficile strains isolated from a single patient provide insight into multiple infection of C. difficile , 2018, BMC Genomics.

[26]  Hongyu Lei,et al.  Ci-AMBP: a highly conserved member of the microglobulin superfamily of proteinase inhibitors in grass carp, Ctenopharyngodon idellus. , 2017, Genes & genetic systems.

[27]  Genxi Zhang,et al.  Transcriptomic profile of leg muscle during early growth in chicken , 2017, PloS one.

[28]  G. Pazour,et al.  Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness , 2017, Scientific Reports.

[29]  Damian Szklarczyk,et al.  The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible , 2016, Nucleic Acids Res..

[30]  S. Lutsenko,et al.  The Role of Copper Chaperone Atox1 in Coupling Redox Homeostasis to Intracellular Copper Distribution , 2016, Antioxidants.

[31]  K. Sakaguchi,et al.  Tetramer formation of tumor suppressor protein p53: Structure, function, and applications , 2016, Biopolymers.

[32]  Bin Wei,et al.  TNNT1, TNNT2, and TNNT3: Isoform genes, regulation, and structure-function relationships. , 2016, Gene.

[33]  R. Wanders,et al.  The Biochemistry and Physiology of Mitochondrial Fatty Acid β-Oxidation and Its Genetic Disorders. , 2016, Annual review of physiology.

[34]  M. Trujillo,et al.  Interplay between oxidant species and energy metabolism , 2015, Redox biology.

[35]  A. Engler,et al.  Extracellular matrix regulation in the muscle satellite cell niche , 2015, Connective tissue research.

[36]  C. T. Pappas,et al.  Deleting titin’s I-band/A-band junction reveals critical roles for titin in biomechanical sensing and cardiac function , 2014, Proceedings of the National Academy of Sciences.

[37]  S. Burgess,et al.  Protein expression in pectoral skeletal muscle of chickens as influenced by dietary methionine. , 2012, Poultry science.

[38]  Ralf Herwig,et al.  ConsensusPathDB—a database for integrating human functional interaction networks , 2008, Nucleic Acids Res..

[39]  S. Kanginakudru,et al.  Genetic evidence from Indian red jungle fowl corroborates multiple domestication of modern day chicken , 2008, BMC Evolutionary Biology.

[40]  Lars Hofmann,et al.  p53 family members in myogenic differentiation and rhabdomyosarcoma development. , 2006, Cancer cell.

[41]  P. Shannon,et al.  Cytoscape: a software environment for integrated models of biomolecular interaction networks. , 2003, Genome research.

[42]  Thomas D. Schmittgen,et al.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.

[43]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[44]  F. Gonzalez Regulation of Hepatocyte Nuclear Factor 4α-mediated Transcription , 2008 .