Network-based multi-omics approaches to identify molecular signatures associated with pregnancy status in beef heifers

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. Herein, by combining transcriptomic and proteomic data from peripheral white blood cells (PWBCs) collected before the time of artificial insemination (AI), we investigated molecular differences between fertile and subfertile beef heifers (n = 6 per group) 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. Over-representation analyses revealed that these molecules were associated with 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. Network analyses revealed distinct 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, including MBD1, MBD2, and SMARCE1, were also rewired, suggesting an interplay between hormone signaling and chromatin regulation that modulates transcript expression and consequently fertility outcomes. Our results 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 integrating multi-omics to identify key pathways and molecular targets to improve reproductive efficiency in beef production systems.