Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet

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-anal...

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Published inCell host & microbe Vol. 26; no. 2; pp. 265 - 272.e4
Main Authors Bisanz, Jordan E., Upadhyay, Vaibhav, Turnbaugh, Jessie A., Ly, Kimberly, Turnbaugh, Peter J.
Format Journal Article
LanguageEnglish
Published United States Elsevier Inc 14.08.2019
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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. [Display omitted] •A generalizable approach is presented for meta-analysis of microbiome datasets•High-fat diets induce reproducible shifts in the mouse gut microbiome•Nonviable Lactococcus contamination is widespread in experimental diets•Phylogenetic and gene signatures translate to human microbiomes Bisanz and Upadhyay et al. execute a meta-analysis of previous studies evaluating the effect of a high-fat diet on the gut microbiome. They define reproducible features across studies for mechanistic experimentation and uncover that residual DNA contamination in experimental diets should be measured and accounted for in study design.
AbstractList 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. [Display omitted] •A generalizable approach is presented for meta-analysis of microbiome datasets•High-fat diets induce reproducible shifts in the mouse gut microbiome•Nonviable Lactococcus contamination is widespread in experimental diets•Phylogenetic and gene signatures translate to human microbiomes Bisanz and Upadhyay et al. execute a meta-analysis of previous studies evaluating the effect of a high-fat diet on the gut microbiome. They define reproducible features across studies for mechanistic experimentation and uncover that residual DNA contamination in experimental diets should be measured and accounted for in study design.
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.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.
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 1101 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 signal of a HFD are Lactococcus species, which we experimentally demonstrate are 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.
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.
Author Ly, Kimberly
Bisanz, Jordan E.
Turnbaugh, Jessie A.
Upadhyay, Vaibhav
Turnbaugh, Peter J.
AuthorAffiliation 1 Department of Microbiology and Immunology, University of California San Francisco, CA 94143, USA
4 Lead Contact
3 Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
2 Department of Internal Medicine, University of California San Francisco, CA 94143, USA
AuthorAffiliation_xml – name: 4 Lead Contact
– name: 3 Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
– name: 2 Department of Internal Medicine, University of California San Francisco, CA 94143, USA
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  surname: Upadhyay
  fullname: Upadhyay, Vaibhav
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– sequence: 3
  givenname: Jessie A.
  surname: Turnbaugh
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  fullname: Turnbaugh, Peter J.
  email: peter.turnbaugh@ucsf.edu
  organization: Department of Microbiology and Immunology, University of California San Francisco, San Francisco, CA 94143, USA
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31324413$$D View this record in MEDLINE/PubMed
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Thu Apr 24 23:03:54 EDT 2025
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Issue 2
Keywords murine
meta-analysis
machine learning
Lactococcus
microbiome
high-fat diet
Language English
License Copyright © 2019 Elsevier Inc. All rights reserved.
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Conceptualization, JEB, VU, and PJT; Methodology, JEB, JAT; Investigation, JEB, VU, KL, JAT, and PJT; Writing – Original Draft, VU and JEB; Writing - Review and Editing, JEB, VU, and PJT; Funding Acquisition, PJT; Supervision, PJT.
Author Contributions
These authors contributed equally to this manuscript.
OpenAccessLink http://www.cell.com/article/S1931312819303026/pdf
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Snippet Multiple research groups have shown that diet impacts the gut microbiome; however, variability in experimental design and quantitative assessment have made it...
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SubjectTerms Animals
Bacteria - classification
Bacteria - genetics
Databases, Factual
Diet
Diet, High-Fat
Gastrointestinal Microbiome - genetics
Gastrointestinal Microbiome - physiology
high-fat diet
Humans
Lactococcus
Lactococcus - classification
Lactococcus - genetics
Machine Learning
meta-analysis
Mice
microbiome
murine
Phylogeny
Title Meta-Analysis Reveals Reproducible Gut Microbiome Alterations in Response to a High-Fat Diet
URI https://dx.doi.org/10.1016/j.chom.2019.06.013
https://www.ncbi.nlm.nih.gov/pubmed/31324413
https://www.proquest.com/docview/2265780672
https://pubmed.ncbi.nlm.nih.gov/PMC6708278
Volume 26
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