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A gut commensal protist protects against virus-mediated loss of oral tolerance

2023· article· en· W4385686283 on OpenAlexaff
Luzmariel Medina Sanchez, Yanlin Zeng, Magdalena Siller, Pamela H. Brigleb, Kishan Sangani, Terence S. Dermody, Bana Jabrì, Elena F. Verdú, Marlies Meisel, Reinhard Hinterleitner

Bibliographic record

VenueThe Journal of Immunology · 2023
Typearticle
Languageen
FieldMedicine
TopicCeliac Disease Research and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsImmune systemBiologyProinflammatory cytokineVirusVirologyMicrobiologyImmunologyInflammation

Abstract

fetched live from OpenAlex

Abstract Loss of oral tolerance (LOT) to gluten, characterized by a T helper 1 (Th1) gluten-specific immune response, is a hallmark of celiac disease (CeD) and can be triggered by enteric viral infections. We hypothesized that certain gut microbes have the capacity to protect against virus-mediated LOT. By using our previously defined reovirus-mediated LOT CeD model, we discovered that the gut colonizing protist Tritrichomonas promotes oral tolerance and protects against reovirus-mediated LOT by suppressing the reovirus-induced proinflammatory program of dietary-antigen-presenting CD103+ dendritic cells. Importantly, Tritrichomonas-mediated protection against T1L-induced LOT is not attributable to differences in antiviral host responses and is independent of the microbiota. Mechanistically, we show that Tritrichomonas colonization restrains reovirus-induced inflammatory responses in dendritic cells and thus limit their ability to promote Th1 immune responses. Finally, our studies using human stool samples support a role for Tritrichomonas sp. colonization in protecting against development of CeD. Supported by grants from NIH (T32 AI089443)

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 score0.001
Threshold uncertainty score0.005

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)0.0010.000

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 head0.023
GPT teacher head0.306
Teacher spread0.283 · 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
Published2023
Admission routes1
Has abstractyes

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