Multi Omics Reveals Biomarkers of Metabolic and Microbial Alterations in Subclinical Mastitis
Subclinical mastitis (SCM) in dairy cows has a significant impact on both milk quality and overall animal health, often going undetected in the early stages. This study employs a multi-omics approach, integrating metabolomics and microbiome analyses across blood, milk, fecal, and rumen fluid samples to investigate the complex pathophysiology of SCM. Key hematological and biochemical changes were identified, with biomarkers such as digalacturonic acid and N-methyl-L-lysine emerging as indicators of systemic metabolic and immune disturbances. Metabolomic profiling revealed distinct patterns in metabolite distribution, highlighting the intricate metabolic interactions associated with SCM. Additionally, 16S rRNA gene sequencing revealed significant shifts in microbial communities in both rumen fluid and feces, particularly in genera such as Succinivibrio and Methanobrevibacter, which are involved in carbohydrate fermentation and methanogenesis, respectively. These shifts in microbial composition may reflect alterations in the metabolic activities of the microbiota, potentially contributing to the inflammatory and metabolic processes underlying SCM. Positive correlations were observed between key metabolites, including ropinirole and arachidonic acid, and specific microbial genera such as Succinivibrionaceae_UCG-001, while inverse relationships were found with Alistipes. These findings underscore the complex interactions between metabolites and the microbiome, suggesting the potential of these biomarkers for early detection and targeted interventions in SCM. Our integrative multi-omics approach provides deeper insights into the mechanisms of SCM, laying the groundwork for novel strategies aimed at enhancing dairy cow health and productivity.