Co-Expression Network and Integrative Analysis of Metabolome and Transcriptome Uncovers Biological Pathways for Fertility in Beef Heifers

Banerjee, Priyanka; Rodning, Soren P.; Diniz, Wellison J. S.; Dyce, Paul W.

doi:10.3390/metabo12080708

Cited by 4 publications

(1 citation statement)

References 86 publications

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“…Although many gene connections were identified in the P network, we found that SERPINE3 , FNDC1 , MRTFA , MEF2B , and ENSBTAG00000019474 were more connected in the NP network. Similar findings were reported by Banerjee et al [ 60 ]. Their study identified a rewiring of major gene regulators in the blood transcriptome network of NP crossbred heifers.…”

Section: Discussionsupporting

confidence: 92%

Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows

Diniz

Banerjee²,

Rodning³

et al. 2022

Animals

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Reproductive failure is still a challenge for beef producers and a significant cause of economic loss. The increased availability of transcriptomic data has shed light on the mechanisms modulating pregnancy success. Furthermore, new analytical tools, such as machine learning (ML), provide opportunities for data mining and uncovering new biological events that explain or predict reproductive outcomes. Herein, we identified potential biomarkers underlying pregnancy status and fertility-related networks by integrating gene expression profiles through ML and gene network modeling. We used public transcriptomic data from uterine luminal epithelial cells of cows retrospectively classified as pregnant (P, n = 25) and non-pregnant (NP, n = 18). First, we used a feature selection function from BioDiscML and identified SERPINE3, PDCD1, FNDC1, MRTFA, ARHGEF7, MEF2B, NAA16, ENSBTAG00000019474, and ENSBTAG00000054585 as candidate biomarker predictors of pregnancy status. Then, based on co-expression networks, we identified seven genes significantly rewired (gaining or losing connections) between the P and NP networks. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. We provided insights into the regulatory networks of fertility-related processes and demonstrated the potential of combining different analytical tools to prioritize candidate genes.

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Section: Discussionsupporting

confidence: 92%

Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows

Diniz

Banerjee²,

Rodning³

et al. 2022

Animals

Self Cite

View full text Add to dashboard Cite

show abstract

Biomarker metabolite mating of viable frozen-thawed in vitro-produced bovine embryos with pregnancy-competent recipients leads to improved birth rates

Gimeno

Salvetti²,

Carrocera

et al. 2023

Journal of Dairy Science

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Harnessing Genomics and Transcriptomics Approaches to Improve Female Fertility in Beef Cattle—A Review

Kertz,

Banerjee,

Dyce

et al. 2023

Animals

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Female fertility is the foundation of the cow–calf industry, impacting both efficiency and profitability. Reproductive failure is the primary reason why beef cows are sold in the U.S. and the cause of an estimated annual gross loss of USD 2.8 billion. In this review, we discuss the status of the genomics, transcriptomics, and systems genomics approaches currently applied to female fertility and the tools available to cow–calf producers to maximize genetic progress. We highlight the opportunities and limitations associated with using genomic and transcriptomic approaches to discover genes and regulatory mechanisms related to beef fertility. Considering the complex nature of fertility, significant advances in precision breeding will rely on holistic, multidisciplinary approaches to further advance our ability to understand, predict, and improve reproductive performance. While these technologies have advanced our knowledge, the next step is to translate research findings from bench to on-farm applications.

show abstract

Co-Expression Network and Integrative Analysis of Metabolome and Transcriptome Uncovers Biological Pathways for Fertility in Beef Heifers

Cited by 4 publications

References 86 publications

Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows

Machine Learning-Based Co-Expression Network Analysis Unravels Potential Fertility-Related Genes in Beef Cows

Biomarker metabolite mating of viable frozen-thawed in vitro-produced bovine embryos with pregnancy-competent recipients leads to improved birth rates

Harnessing Genomics and Transcriptomics Approaches to Improve Female Fertility in Beef Cattle—A Review

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