PSXIII-B-12 Identifying Gene and Metabolite Signature Patterns by Mining co-Expression Networks for Fertility in Beef Heifers. (21st September 2022)

• Record Type: Journal Article
• Authors: Banerjee, Priyanka; Rodning, Soren P P; Diniz, Wellison; Dyce, Paul W W
• Is Part Of: Journal of animal science. Volume 100(2022)Supplement 3
• Page Start: 326
• Page End: 326
• Publication Date: 2022-09-21
• Subjects: fertility -- metabolome -- transcriptome; Livestock -- Periodicals; Livestock; Electronic journals; Periodicals; 636.005
• DOI: 10.1093/jas/skac247.595
• Languages: English
• ISSNs: 0021-8812

Abstract

Abstract: Fertility is a multifactorial trait partially regulated by genetics. Despite advances in reproductive biotechnologies, reproductive failure remains a challenge to the beef industry. Thus, we hypothesized that blood profiles would differ at artificial insemination (AI) between heifers that become pregnant or remained open following AI and three cycles of natural service. We applied untargeted-metabolomics (blood-plasma) and RNA-Seq approaches (GEO-GSE103628, peripheral white blood cells) on six AI-pregnant (AI-P) and six non-pregnant (NP) Angus-Simmental crossbred heifers. After quality control, differential expression analyses of genes (DEGs) and metabolites were performed. We identified 38 DEGs and 15 metabolites at different levels between the AI-P and NP groups. Co-expression network and differential connectivity (DK) analyses were performed using the PCIT and Cytoscape software platforms. The significantly correlated gene pairs (|r| > 0.99) were filtered with DEGs. The gene co-expression network analysis identified 17 and 37 hub genes in AI-P and NP, respectively. Genes that gained connectivity in NP included TGM2, TMEM51, TAC3, NDRG4, and PDGFB. Likewise, ENSBTAG00000027962, ENSBTAG00000034871, ENSBTAG00000047816 and CYTH3 were less connected. The DEGs, co-expressed and DK genes were over-represented for oocyte meiosis, Wnt and glucagon signaling pathways, propanoate metabolism and N-glycan biosynthesis in the AI-P. MAPK signaling pathway and ubiquitin-mediated proteolysis were over-represented in NP. The metabolomic analysis identified 18 and 15 metabolite pairs significantly correlated (|r| > 0.9) in AI-P and NP. While ornithine and cysteine were identified as hubs for both AI-P and NP metabolites, allantoic acid, methionine, putrescine, phenylethylamine, kynurenine and xylitol were unique to AI-P. Tryptophan and glutamine were unique to the NP. Tryptophan exhibited a gain in connectivity in NP while allantoic acid had more connectivity in the AI-P. Our findings provide new avenues for fertility research. Further validation of genes and metabolites in a cohort with more animals would establish a framework for early fertility prediction.