| Summary: | Reproductive failure remains a significant challenge to the beef industry. The omics technologies have provided opportunities to improve reproductive efficiency. We used a multistaged analysis from blood profiles to integrate metabolome (plasma) and transcriptome (peripheral white blood cells) in beef heifers. We used untargeted metabolomics and RNA-Seq paired data from six AI-pregnant (AI-P) and six nonpregnant (NP) Angus-Simmental crossbred heifers at artificial insemination (AI). Based on network co-expression analysis, we identified 17 and 37 hub genes in the AI-P and NP groups, respectively. Further, we identified <i>TGM2</i>, <i>TMEM51</i>, <i>TAC3</i>, <i>NDRG4</i>, and <i>PDGFB</i> as more connected in the NP heifers’ network. The NP gene network showed a connectivity gain due to the rewiring of major regulators. The metabolomic analysis identified 18 and 15 hub metabolites in the AI-P and NP networks. Tryptophan and allantoic acid exhibited a connectivity gain in the NP and AI-P networks, respectively. The gene–metabolite integration identified tocopherol-a as positively correlated with ENSBTAG00000009943 in the AI-P group. Conversely, tocopherol-a was negatively correlated in the NP group with <i>EXOSC2</i>, <i>TRNAUIAP</i>, and <i>SNX12</i>. In the NP group, α-ketoglutarate-<i>SMG8</i> and putrescine-<i>HSD17B13</i> were positively correlated, whereas a-ketoglutarate-<i>ALAS2</i> and tryptophan-<i>MTMR1</i> were negatively correlated. These multiple interactions identified novel targets and pathways underlying fertility in bovines.
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