Dataset Information

Elucidation of Gut Microbiota-Associated Lipids Using LC-MS/MS and 16S rRNA Sequence Analyses.

ABSTRACT: 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 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.

SUBMITTER: Yasuda S

PROVIDER: S-EPMC7721639 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Elucidation of Gut Microbiota-Associated Lipids Using LC-MS/MS and 16S rRNA Sequence Analyses.

Yasuda Shu S Okahashi Nobuyuki N Tsugawa Hiroshi H Ogata Yusuke Y Ikeda Kazutaka K Suda Wataru W Arai Hiroyuki H Hattori Masahira M Arita Makoto M

iScience 20201123 12

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. ...[more]

PMID: 33313490

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Project description:ObjectiveThe goal of this study was to comprehensively investigate the characteristics of gut microbiota dysbiosis and metabolites levels in very low or extremely low birth weight (VLBW/ELBW) infants with white matter injury (WMI).MethodsIn this prospective cohort study, preterm infants with gestational age < 32 weeks and weight < 1.5 kg were investigated. Additionally, fecal samples were collected on days zero, 14d and 28d after admission to the intensive care unit. All subjects underwent brain scan via MRI and DTI at a corrected gestational age of 37 ~ 40 weeks. Based on the results of MRI examination, the VLBW/ELBW infants were divided into two groups: WMI and non-WMI. Finally, based on a multi-omics approach, we performed 16S rRNA gene sequencing, LC-MS/MS, and diffusion tension imaging to identify quantifiable and informative biomarkers for WMI.ResultWe enrolled 23 patients with and 48 patients without WMI. The results of 16S RNA sequencing revealed an increase in the number of Staphylococcus and Acinetobacter species in the fecal samples of infants with WMI, as well as increasing levels of S. caprae and A._johnsonii. LEfSe analysis (LDA ≥ 4) showed that the WMI group carried an abundance of Staphylococcus species including S. caprae, members of the phyla Bacteroidota and Actinobacteriota, and Acinetobacter species. A total of 139 metabolic markers were significantly and differentially expressed between WMI and nWMI. KEGG pathway enrichment analysis revealed that the WMI group showed significant downregulation of 17 metabolic pathways including biosynthesis of arginine and primary bile acids. The WMI group showed delayed brain myelination, especially in the paraventricular white matter and splenium of corpus callosum. Staphylococcus species may affect WMI by downregulating metabolites such as cholic acid, allocholic acid, and 1,3-butadiene. Gut microbiota such as Acinetobacter and Bacteroidetes may alter white matter structurally by upregulating metabolites such as cinobufagin.ConclusionBased on 16S RNA sequencing results, severe gut microbiota dysbiosis was observed in the WMI group. The results might reveal damage to potential signaling pathways of microbiota-gut-brain axis in gut microbiota. The mechanism was mediated via downregulation of the bile acid biosynthetic pathway.

Dataset Information

Elucidation of Gut Microbiota-Associated Lipids Using LC-MS/MS and 16S rRNA Sequence Analyses.

Publications

Elucidation of Gut Microbiota-Associated Lipids Using LC-MS/MS and 16S rRNA Sequence Analyses.

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