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Cited by 136Gut microbiota and healthDrug Transport and Resistance MechanismsNutrition, Genetics, and Disease

Genetic determinants of gut microbiota composition and bile acid profiles in mice

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Kemis, Julia H., Linke, Vanessa, Barrett, Kelsey L., Boehm, Frederick J., Traeger, Lindsay L., Keller, Mark P., Rabaglia, Mary E., Schueler, Kathryn L. +10 more·2019·PLOS Genetics
DOI 10.1371/journal.pgen.1008073PMC PMC6715156

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Abstract

Abstract The microbial communities that inhabit the distal gut of humans and other mammals exhibit large inter-individual variation. While host genetics is a known factor that influences gut microbiota composition, the mechanisms underlying this variation remain largely unknown. Bile acids (BAs) are hormones that are produced by the host and chemically modified by gut bacteria. BAs serve as environmental cues and nutrients to microbes, but they can also have antibacterial effects. We hypothesized that host genetic variation in BA metabolism and homeostasis influence gut microbiota composition. To address this, we used the Diversity Outbred (DO) stock, a population of genetically distinct mice derived from eight founder strains. We characterized the fecal microbiota composition and plasma and cecal BA profiles from 400 DO mice maintained on a high-fat high-sucrose diet for ∼22 weeks. Using quantitative trait locus (QTL) analysis, we identified several genomic regions associated with variations in both bacterial and BA profiles. Notably, we found overlapping QTL for Turicibacter sp. and plasma cholic acid, which mapped to a locus containing the gene for the ileal bile acid transporter, Slc10a2 . Mediation analysis and subsequent follow-up validation experiments suggest that differences in Slc10a2 gene expression associated with the different strains influences levels of both traits and revealed novel interactions between Turicibacter and BAs. This work illustrates how systems genetics can be utilized to generate testable hypotheses and provide insight into host-microbe interactions. Author summary Inter-individual variation in the composition of the intestinal microbiota can in part be attributed to host genetics. However, the specific genes and genetic variants underlying differences in the microbiota remain largely unknown. To address this, we profiled the fecal microbiota composition of 400 genetically distinct mice, for which genotypic data is available. We identified many loci of the mouse genome associated with changes in abundance of bacterial taxa. One of these loci is also associated with changes in the abundance of plasma bile acids—metabolites generated by the host that influence both microbiota composition and host physiology. Follow up validation experiments provide mechanistic insights linking host genetic differences, with changes in ileum gene expression, bile acid-bacteria interactions and bile acid homeostasis. Together, this work demonstrates how genetic approaches can be used to generate testable hypothesis to yield novel insight into how host genetics shape gut microbiota composition.

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  • The paper's data suggest that coding variants in Ahr are responsible for the differences in AHR protein abundance.

    “Although p.K432R is not located in a known protein domain [26] nor is c.-100G > A located in a predicted 5' UTR motif, our data do suggest that coding variants in Ahr are responsible for the differences in AHR protein abundance.”

    From Identification of Genetic Variants Regulating the Abundance of Clinically Relevant Plasma Proteins using the Diversity Outbred Mouse Model (2021) · cites this paper as [26]

    In the citing sentence, citation [26] is attached to the statement about p.K432R and a known protein domain; the conclusion about Ahr coding variants and AHR protein abundance is presented as the citing authors’ own data. The indexed paper’s Results and discussion passages concern genetic associations with gut microbes and bile acids, and searches for Ahr, p.K432R, and AHR protein abundance found no passage addressing the quoted conclusion. (LLM, conf=0.77)

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Authors
Kemis, J. H., Linke, V., Barrett, K. L., Boehm, F. J., Traeger, L. L., Keller, M. P., Rabaglia, M. E., Schueler, K. L., Stapleton, D. S., Gatti, D. M., Churchill, G. A., Amador-Noguez, D., Russell, J. D., Yandell, B. S., Broman, K. W., Coon, J. J., Attie, A. D., & Rey, F. E.
Affiliations
  • University of Wisconsin–Madison (US)
  • Jackson Laboratory (US)
Corresponding author
Rey, Federico E.
ORCID
Venue
PLOS Genetics
Publisher
Public Library of Science (PLoS)
Published
2019-08-29
Volume
15
Issue
8
Pages
e1008073
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1008073
PMC
PMC6715156
OpenAlex
W2970423471
Cited by
136
References
17
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FWCI
5.95
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journal article
Language
en
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  • PLoS Genetics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion, cc-by)
Funders
NIH (DK108259, DK101573)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gut microbiota and health
  • Health Sciences › Medicine › Oncology › Drug Transport and Resistance Mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
Keywords
gut microbiota composition, bile acids, host genetics, Diversity Outbred mice, quantitative trait loci, SLC10A2, ileal bile acid transporter, Turicibacter, cholic acid, host-microbe interactions, high-fat high-sugar diet, fecal microbiota
MeSH terms
Firmicutes, Bacillota, Gastrointestinal Microbiome, Biological Variation, Population, Collaborative Cross Mice, Akkermansia, Animals, Bile Acids and Salts, Female, Male, Models, Animal, Symporters, Organic Anion Transporters, Sodium-Dependent, Quantitative Trait Loci, Mice, Metabolic Networks and Pathways, Verrucomicrobia

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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