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Record W3008154817 · doi:10.1093/jcag/gwz047.013

A14 GUT-RESIDING BACTERIA CAN SHAPE HOST DRUG METABOLISM IN THE SMALL INTESTINE THROUGH AN INNATE LYMPHOID CELL-IL-22 DRIVEN AXIS

2020· article· en· W3008154817 on OpenAlexaff
Kyle L. Flannigan, Michael J. Johnston, Sarah L. Erickson, Kimberly Nieves, Humberto Jijon, Marco Gallo, Kathy D. McCoy, Simon A. Hirota

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiologyDrug metabolismMetabolismCYP3A4Cytochrome P450Innate immune systemEnzymeCell biologyMicrobiologyImmune systemImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background The ability of the intestinal microbiota to influence drug metabolism has been recognized, however the mechanisms through which this occurs remain unexplored. Recent work in germ-free mice showed that conventionalization with specific pathogen free (SPF) microbiota influences the expression of cytochrome P450 (CYP) enzymes in the liver and small intestine (SI), two important sites of drug metabolism. Given that CYP enzymes, including CYP3A11 in mice, account for roughly 70% of total drug metabolism, we hypothesized that commensal gut bacteria can shape the CYP landscape to influence drug metabolism and therapeutic outcomes. Aims To investigate the role of specific gut-residing microbes in shaping the expression and activity of host drug metabolism enzymes. Methods Segmented filamentous bacteria (SFB)-free mice were obtained from Jackson Lab (Jax) and colonized with feces from SFB-mono-associated mice via oral gavage. 14 days later, expression of drug metabolism enzymes in the SI were probed using PCR arrays, and lamina propria cells isolated for flow cytometry. A monoclonal antibody for Thy1.2 was used to deplete innate lymphoid cells (ILCs) in RAG1-/- mice (lacking T- and B-cells). CYP3A11 activity was determined through the colorimetric breakdown of the CYP3A11-specific substrate 7-benzyloxyresorufin. SI organoids were generated from mice and humans, and treated with IL-22 to further assess the dynamics of CYP3A11/CYP3A4 expression and activity. Results Colonization of Jax mice with immunomodulatory SFB altered the expression of various CYP enzymes in the SI (but not liver), with Cyp3a11 being the most downregulated gene. Further analysis showed that SFB-induced IL-22 production by type 3 ILCs (ILC3) correlated with reduced SI Cyp3a11 expression. Additionally, SFB colonization had no effect on the expression of Cyp3a11 in the SI of mice in which ILCs were depleted. Both SFB colonization and administration of IL-23, to induce IL-22 from ILC3, increased the ability of the CYP3A11-metabolized drug glyburide to decrease blood glucose levels when given orally. In mouse SI enteroid cultures, IL-22 dose-dependently reduced the expression of Cyp3a11 and decreased the ability of enteroids to metabolize CYP3A11-specific substrates. Finally, IL-22 induced wide changes in the transcriptome of human SI enteroids, with substantial effects on a drug metabolism pathway centred around CYP3A. Conclusions Our data suggest that a gut-resident microbe (SFB) can influence the expression and activity of the drug metabolising enzyme CYP3A11 in the SI through an ILC3-IL-22 dependent mechanism. These findings provide an understanding of how the intestinal microbiota may modulate host drug metabolism to influence the efficacy and toxicity of various pharmaceutical compounds. Funding Agencies CAG, CIHRAbbvie, Lloyd Sutherland Investigatorship

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.222
Teacher spread0.211 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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