Integrated omics reveals actionable drivers of bioactive variation in US milk
Despite milk being a global dietary staple, the molecular basis of its health-relevant bioactivity and the factors governing fine-scale compositional variation remain poorly understood. Here, we present an integrated seven-layer omics characterization of 60 US retail milk samples from ten geographic regions, combining genomics, transcriptomics, peptidomics, proteomics, lipidomics, metabolomics, and glycomics on the same samples. We also organized the identified and quantified compounds into the Dairy Molecule Database (DMD), a web-accessible resource for linking milk compound concentrations with SNPs, miRNAs, and product-level factors to support future milk quality optimization. In total, we identified 6,714 compounds and quantified 5,288 with absolute concentrations, including 5,220 compounds not previously available with absolute concentration estimates in existing milk compound databases. These profiles enabled bioactivity efficacy estimation and association analyses of factors linked to milk compound variation. We tested 54 computationally predicted antimicrobial candidate peptides; 35 showed activity and 6 had IC50 values below 256 μg/mL against A. baumannii, an ESKAPE pathogen. We further identified associations between downstream bioactive compound concentrations or estimated bioactivity efficacy and product-level factors, including purchase region, purchase ambient temperature, and packaging opacity, as well as SNPs, estimated Jersey breed proportion, and miRNA abundance. Overall, bioactivity efficacy profiles were associated with purchase region, selected SNPs, Jersey breed proportion, and selected miRNAs. These findings suggest that selective breeding and supply-chain optimization could help improve the bioactive quality of commercial milk.