Host-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 microbiotarelated 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.

Summary We describe a protocol for identifying bacteria-derived lipid metabolites produced in the guts using antibiotic-treated mice, liquid chromatography tandem mass spectrometry-based lipidomics, and feature-based molecular spectrum networking (FBMN). Untargeted lipidomics using the MS-DIAL 4 program provides information on known and unknown complex lipid molecules. The FBMN technique clusters similar MS2 spectra, facilitating the identification of bacterial lipids. Targeted analysis was used as a complementary method to cover oxylipins. Here, we provide details for targeted and untargeted analyses. For complete details on the use and execution of this protocol, please refer to Yasuda et al. (2020) .

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