Reconstructing bovine disease trajectories through integrative multi-omics: molecular decision nodes, predictive biomarkers and precision intervention.

Bovine diseases arise from dynamic interactions among genetic susceptibility, regulatory responses, immune activity, metabolism, microbial ecology and tissue function. Although individual omics studies have identified numerous disease-associated molecular signatures, many remain context-dependent and poorly reproducible across animals, breeds, disease stages and biological matrices. Integrative multi-omics extends beyond parallel profiling of individual molecular layers by connecting genomic variation with epigenetic regulation, transcriptional activity, protein and metabolite states, and microbial ecology to reconstruct coordinated mechanisms underlying disease development and recovery. This review synthesizes integrative multi-omics data across a trajectory from pre-disease vulnerability through active disease to persistence or functional recovery, while examining ecological destabilization as a process that may arise at different points along this continuum. Across diseases, integration of multiple molecular layers identifies recurrent associations among genetic regulation of disease-response pathways, inflammatory activation, immune-metabolic and redox imbalance, tissue-barrier dysfunction and microbiome-metabolite feedback. These interacting processes have the potential to provide greater biological and predictive information than isolated molecular alterations. We therefore propose that robust biomarker development should prioritize reproducible cross-omics signals and compact panels integrating three complementary components: disease burden or causal trigger, host-response state and functional consequence. Such biomarkers require validation across independent populations, breeds, disease stages and field conditions before clinical deployment. By linking molecular layers rather than cataloguing individual signatures, multi-omics can support more reliable disease prediction, mechanistically informed diagnostics, targeted intervention and selection for improved disease resistance and resilience in cattle.