摘要：SummaryHost-microbiota interactions create a unique metabolic milieu that modulates intestinal environments. Integration of 16S ribosomal RNA (rRNA) sequences and mass spectrometry (MS)-based lipidomics has a great potential to reveal the relationship between bacterial composition and the complex metabolic network in the gut. In this study, we conducted untargeted lipidomics followed by a feature-based molecular MS/MS spectral networking to characterize gut bacteria-dependent lipid subclasses in mice. An estimated 24.8% of lipid molecules in feces were microbiota-dependent, as judged by > 10-fold decrease in antibiotic-treated mice. Among these, there was a series of unique and microbiota-related lipid structures, including acyl alpha-hydroxyl fatty acid (AAHFA) that was newly identified in this study.Based on the integrated analysis of 985 lipid profiles and 16S rRNA sequence data providing 2,494 operational taxonomic units, we could successfully predict the bacterial species responsible for the biosynthesis of these unique lipids, including AAHFA.Graphical AbstractDisplay OmittedHighlights•Feature-based molecular networking was explored in untargeted lipidomics analysis•Of all lipids, 24.8% of fecal lipids were decreased >10-fold in antibiotics-treated mice•Acyl alpha-hydroxy fatty acid (AAHFA) was identified as microbiota-specific lipid•With 16S rRNA data, we revealed the relevance of microbiome and lipidome in miceMicrobiome; Omics; Lipidomics
