Cross-matrix multi-omics profiling identifies host-microbe interactions and diagnostic signatures in bovine...

Subclinical mastitis (SCM) is a widespread but frequently undetected condition in dairy cows, leading to reduced milk quality and compromised animal health. This study utilizes an integrated multi-omics strategy encompassing metabolomics and microbiome analyses to investigate the systemic effects of SCM across four biological matrices: blood, milk, feces, and rumen fluid. Our findings reveal significant alterations in hematological and biochemical parameters, with key biomarkers such as digalacturonic acid and N-ε-methyl-L-lysine indicating systemic metabolic and immune dysregulation. Metabolomic profiling uncovered distinct disease-related metabolic patterns, while 16S rRNA sequencing revealed substantial microbial shifts, particularly involving Succinivibrio and Methanobrevibacter, which are implicated in carbohydrate fermentation and methanogenesis. Noteworthy correlations between specific metabolites (e.g., ropinirole, arachidonic acid) and microbial genera (e.g., Succinivibrionaceae UCG-001, Alistipes) highlight the complex host-microbiome-metabolite interplay associated with SCM. These findings provide new insights into the pathophysiology of SCM and identify candidate biomarkers for early detection. The integrative multi-omics approach adopted in this study offers a valuable framework for developing innovative diagnostic and therapeutic strategies to enhance dairy cow health and productivity.