| Literature DB >> 31324413 |
Jordan E Bisanz1, Vaibhav Upadhyay2, Jessie A Turnbaugh1, Kimberly Ly1, Peter J Turnbaugh3.
Abstract
Multiple research groups have shown that diet impacts the gut microbiome; however, variability in experimental design and quantitative assessment have made it challenging to assess the degree to which similar diets have reproducible effects across studies. Through an unbiased subject-level meta-analysis framework, we re-analyzed 27 dietary studies including 1,101 samples from rodents and humans. We demonstrate that a high-fat diet (HFD) reproducibly changes gut microbial community structure. Finer taxonomic analysis revealed that the most reproducible signals of a HFD are Lactococcus species, which we experimentally demonstrate to be common dietary contaminants. Additionally, a machine-learning approach defined a signature that predicts the dietary intake of mice and demonstrated that phylogenetic and gene-centric transformations of this model can be translated to humans. Together, these results demonstrate the utility of microbiome meta-analyses in identifying robust and reproducible features for mechanistic studies in preclinical models.Entities:
Keywords: Lactococcus; high-fat diet; machine learning; meta-analysis; microbiome; murine
Mesh:
Year: 2019 PMID: 31324413 PMCID: PMC6708278 DOI: 10.1016/j.chom.2019.06.013
Source DB: PubMed Journal: Cell Host Microbe ISSN: 1931-3128 Impact factor: 21.023