Abstract
INTRODUCTION: Lactococcus lactis is a crucial lactic acid bacterium of great economically significance for cheese product. The species exhibits wildly distribution and significant genetic diversity, yet the underlying drivers of its differentiation remain elusive. OBJECTIVES: Lactococcus lactis, exhibits complex genetic diversity, yet the mechanisms driving its differentiation and niche adaptation remain poorly understood. METHODS: This study assembled a genome dataset of 1008 isolates of Lactococcus lactis from six major habitats across five continents. And combined with public database data, used population genomics and function genomics to analysis the population structure and adaptation. RESULTS: To elucidate its population structure and domestication history, 1008 genomes from six diverse habitats across five continents were analyzed, revealing two major genetic branches subdivided into ten distinct lineages. Phylogenomic and ancestral analyses support a multiple domestication model, with the ancestral plant-associated lineage (L6) diversified into dairy-adapted lineages (L8-L10) through extensive horizontal gene transfer, primarily facilitated by mobile genetic elements. Notably, intergenic regions (IGRs) critically influence phenotypic diversity and genetic structure, underscoring the functional significance of non-coding sequences in microbial adaptation. Pan-genome analysis highlights extensive accessory gene and IGR diversity, with habitat-specific enrichments: dairy lineages are enriched in mobile genetic elements and carbohydrate-active enzymes, while plant isolates show reduced genetic exchange. A machine learning framework integrating single nucleotide polymorphisms, genes, and IGRs accurately predicts isolate-specific fermentation traits, enabling efficient industrial strain selection. CONCLUSION: These findings redefine non-coding regions as key drivers of microbial domestication and provide a genomic framework to optimize Lactococcus lactis for dairy fermentation and biotechnology, bridging ecological adaptation with applied innovation.