Network-Based Multi-Omics Approaches to Identify Molecular Signatures Associated with Pregnancy Status in Beef Heifers

Abstract

Fertility is a multifactorial trait and a key determinant of productivity and sustainability in beef cattle production. Identifying molecular mechanisms and biomarkers associated with fertility could improve the prediction of reproductive potential in beef heifers. In this study, we integrated transcriptomic and proteomic data from peripheral white blood cells (PWBCs) collected prior to the time of artificial insemination (AI) to investigate molecular differences between fertile (n = 6) and subfertile beef heifers (n = 6) classified based on their reproductive outcomes. RNA sequencing and untargeted proteomics identified 230 differentially expressed genes (DEGs; P ≤ 0.05 and |log2FC| > 0.5) and 70 differentially abundant proteins (DAPs; P ≤ 0.05) between groups. Functional enrichment analyses revealed that these molecules were involved in cell cycle regulation, metabolism, and immune-related pathways, including chemokine and JAK-STAT signaling (P ≤ 0.01). Data integration revealed limited overlap between DEGs and DAPs (UROS, KIFC3, DHRSX, and NPL). Among these, NPL expression was previously reported to be progesterone-responsive, supporting its potential role in early pregnancy establishment. Co-expression network analyses revealed contrasting regulatory patterns between groups (|r > 0.95| and P < 0.05). At the transcript level, subfertile heifers exhibited increased connectivity, indicating potential compensatory transcriptional rewiring. We identified 92 regulatory impact factor (RIF) genes with potential modulatory roles, including ESR1. Epigenetic transcription factors such as MBD1, MBD2, and SMARCE1 were also rewired, suggesting an interplay between hormone signaling and chromatin regulation influencing transcript expression and consequently fertility outcomes. Functional validation of candidate genes (NPL, DHRSX, ADAMDEC1, NPL and BOLA-DQB) confirmed consistent expression patterns across methods for most targets, although not significantly different (P > 0.05). Collectively, our findings show that PWBCs reflect systemic molecular changes associated with fertility status and represent a promising, non-invasive source for biomarker discovery. This integrative multi-omics approach provided novel insights into the regulatory networks underlying fertility in beef heifers, highlighting the value of combining transcriptomic and proteomic profiling to identify key pathways and molecular targets for improving reproductive efficiency in beef production systems.