Project description:16S metagenomic analysis has provided much information about the intestinal microbiome. However, there are no reports that describe their appropriate taxonomic classification rank. Moreover, studies differ on the taxonomic ranking of the same microorganism. Intestinal microbiome exerts a significant degree of influence on the host via the metabolome. In the present study, we searched for their appropriate classification ranks based on the correlation between the metabolome and microbiome. Over 200 metabolites were identified by capillary electrophoresis and time-of-flight mass spectrometry (CE-TOFMS) based metabolomics of faeces from antibiotics-treated and untreated mice. Nine phyla, 15 orders, 22 classes, 40 families, and 59 genera were identified by 16S metagenomic analysis. Principle component analysis (PCA) of the intestinal microbiome at each taxonomic rank revealed differences between the antibiotic-treated and untreated groups on the first principle component in the family and genus-level analyses. These differences were similar to those found in the metabolome PCA. In addition, a strong correlation between principle component score of the metabolome and that of the microbiome was observed in the case of family and genus-level analyses. From these results, we hypothesize that lower taxonomic ranks such as family or genus are preferable while using 16S metagenome analysis for investigating the crosstalk, via metabolites, between intestinal microbiome and the host.
Project description:Interventions: Case series:N/A
Primary outcome(s): Serum immune cytokines;Blood immune cells;SCFAs of bacterial metabolites;Gut microbial genomics;Metabolic function of intestinal microorganism
Study Design: Sequential
Project description:Evaluation of short-read-only, long-read-only, and hybrid assembly approaches on metagenomic samples demonstrating how they affect gene and protein prediction which is relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic, and metaproteomic data to evaluate the metagenomic-based protein predictions.
