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

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

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.

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

Selection of competent recipients before embryo transfer (ET) is indispensable for improving pregnancy and birth rates in cattle. However, pregnancy prediction can fail when the competence of the embryo is ignored. We hypothesized that the pregnancy potential of biomarkers could improve with information on embryonic competence. In vitro-produced embryos cultured singly for 24 h (from d 6 to 7) were transferred to d 7 synchronized recipients as fresh or after freezing and thawing. Recipient blood was collected on d 0 (estrus; n = 108) and d 7 (4-6 h before ET; n = 107) and plasma was analyzed by nuclear magnetic resonance (1H+NMR). Spent embryo culture medium (CM) was collected and analyzed by ultra-high-performance liquid chromatography tandem mass spectrometry in a subset of n = 70 samples. Concentrations of metabolites quantified in plasma (n = 35) were statistically analyzed as a function of pregnancy diagnosed on d 40, d 62 and birth. Univariate analysis with plasma metabolites consisted of a block study with controllable fixed factors (i.e., embryo cryopreservation, recipient breed, and day of blood collection; Wilcoxon test and t-test). Metabolite concentrations in recipients and embryos were independently analyzed by iterations that reclassified embryos or recipients using the support vector machine. Iterations identified some competent embryos, but mostly competent recipients that had a pregnancy incompetent partner embryo. Misclassified recipients that could be classified as competent were reanalyzed in a new iteration to improve the predictive model. After subsequent iterations, the predictive potential of recipient biomarkers was recalculated. On d 0, creatine, acetone and l-phenylalanine were the most relevant biomarkers at d 40, d 62, and birth, and on d 7, l-glutamine, l-lysine, and ornithine. Creatine was the most representative biomarker within blocks (n = 20), with a uniform distribution over pregnancy endpoints and type of embryos. Biomarkers showed higher abundance on d 7 than d 0, were more predictive for d 40 and d 62 than at birth, and the pregnancy predictive ability was lower with frozen-thawed (F-T) embryos. Six metabolic pathways differed between d 40 pregnant recipients for fresh and F-T embryos. Within F-T embryos, more recipients were misclassified, probably due to pregnancy losses, but were accurately identified when combined with embryonic metabolite signals. After recalculation, 12 biomarkers increased receiver operator characteristic-area under the curve (>0.65) at birth, highlighting creatine (receiver operator characteristic-area under the curve = 0.851), and 5 new biomarkers were identified. Combining metabolic information of recipient and embryos improves the confidence and accuracy of single biomarkers.

mRNA Signatures in Peripheral White Blood Cells Predict Reproductive Potential in Beef Heifers at Weaning

Reproductive failure is a major contributor to inefficiency within the cow-calf industry. Particularly problematic is the inability to diagnose heifer reproductive issues prior to pregnancy diagnosis following their first breeding season. Therefore, we hypothesized that gene expression from the peripheral white blood cells at weaning could predict the future reproductive potential of beef heifers. To investigate this, the gene expression was measured using RNA-Seq in Angus-Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 8) or subfertile (SFH, n = 7) after pregnancy diagnosis. We identified 92 differentially expressed genes between the groups. Network co-expression analysis identified 14 and 52 hub targets. ENSBTAG00000052659, OLR1, TFF2, and NAIP were exclusive hubs to the FH group, while 42 hubs were exclusive to the SFH group. The differential connectivity between the networks of each group revealed a gain in connectivity due to the rewiring of major regulators in the SFH group. The exclusive hub targets from FH were over-represented for the CXCR chemokine receptor pathway and inflammasome complex, while for the SFH, they were over-represented for immune response and cytokine production pathways. These multiple interactions revealed novel targets and pathways predicting reproductive potential at an early stage of heifer development.

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

Simple Summary

Female reproductive failure is still a challenge for the beef industry. Several biological processes that underlie fertility-related traits, such as the establishment of pregnancy and embryo survival, are still unclear. Increased availability of transcriptomic data has allowed a deep investigation of the potential mechanisms involved in fertility. This study investigated candidate gene biomarkers predictive of pregnancy status and underlying fertility-related networks. To this end, we integrated gene expression profiles through supervised machine learning and gene network modeling. We identified nine biologically relevant endometrial gene biomarkers that could discriminate against pregnancy status in cows. These biomarkers were co-expressed with genes critical for uterine receptivity, including endometrial tissue remodeling, focal adhesion, and embryo development. This study outlined key pathways involved with pregnancy success and provided predictive candidate biomarkers for pregnancy outcome in cows.

Abstract

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.