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Record W4282966985 · doi:10.1158/1538-7445.am2022-3046

Abstract 3046: A common gut commensal utilizes tryptophan to promote cancer via the aryl hydrocarbon receptor

2022· article· en· W4282966985 on OpenAlexaff
Vrishketan Sethi, Junyi Tao, Saba Kurtom, Utpreksha Vaish, Farrukh Afaq, Shuan Zao, Xian Luo, Tejeshwar Jain, Prateek Sharma, Ejas Palathingal Bava, Sundaram Ramakrishnan, Liang Li, Ashok K. Saluja, Vikas Dudeja

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGut floraAryl hydrocarbon receptorCancerBiologyAntibioticsRuminococcusFecesMetabolitePhysiologyMicrobiologyPharmacologyImmunologyEndocrinologyBiochemistryGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Background: We have previously showed that gut microbiota-depleting oral antibiotics decrease tumor growth in C57BL/6J mice via the immune system (Sethi et al, Gastroenterology, 2018). The gut microbiota, however, includes thousands of species producing numerous metabolites. The search for individual bacterial species and metabolites that can promote cancer continues. Methods: We studied the effects of common microbiota modulation techniques in C57BL/6 mice procured from various commercial vendors and injected subcutaneously with cancer cells. 16S rRNA gene sequencing and metabolomics analyses were used to identify the putative cancer-promoting species and metabolites. Results: Mice procured from The Jackson Laboratory (JAX mice) form larger tumors at baseline compared to mice procured from the Charles River Laboratories (CR mice). JAX mice have tumor promoting microbiota that can be targeted by oral antibiotics unlike CR mice. This tumor-promoting microbiota gets transferred from JAX to CR mice via cohousing or fecal microbiota transplant. In a preclinical trial, oral vancomycin slowed the growth of cancer in avatar mice gavaged with stools of some pancreatic cancer patients but not of others. Statistical analyses reveal that the gut of JAX mice hosts a common human gram-positive gut commensal Ruminococcus gnavus that is associated with increased cancer growth in those mice. Monocolonization of germ-free mice with R gnavus directly increases tumor growth. Similarly, feeding R gnavus, but not a control bacterial species, brings the baseline tumor kinetics of CR mice up to that of JAX mice. Our experiments reveal that R gnavus needs sufficient dietary tryptophan (Trp) to promote tumor growth and in turn, catabolizes Trp into various metabolites including the Aryl Hydrocarbon Receptor (AHR) ligand Tryptamine, which we find, is also tumor-promoting per se. The tumor-modulating effects of oral vancomycin, tryptophan catabolites and R gnavus are abolished when murine AHR is stimulated by an exogenous ligand or when it is knocked out. Finally, we find that Ly6G+ cells are essential in effecting these bacteria-cancer interactions. Conclusions: We present initial evidence on how a common gut commensal hijacks an essential dietary amino acid to promote cancer via the AHR. We also demonstrate the essential but underappreciated role that the source of animal procurement plays in studies involving cancer models. Citation Format: Vrishketan Sethi, Junyi Tao, Saba Kurtom, Utpreksha Vaish, Farrukh Afaq, Shuan Zao, Xian Luo, Tejeshwar Jain, Prateek Sharma, Ejas Bava, Sundaram Ramakrishnan, Liang Li, Ashok Saluja, Vikas Dudeja. A common gut commensal utilizes tryptophan to promote cancer via the aryl hydrocarbon receptor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3046.

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 score0.005
Threshold uncertainty score0.018

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.056
GPT teacher head0.404
Teacher spread0.348 · 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
Published2022
Admission routes1
Has abstractyes

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