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Record W2982950298 · doi:10.4049/jimmunol.1900781

The Transcription Factor RORα Preserves ILC3 Lineage Identity and Function during Chronic Intestinal Infection

2019· article· en· W2982950298 on OpenAlexafffund
Bernard C. Lo, Diana Canals Hernaez, R. Wilder Scott, Michael R. Hughes, Samuel B. Shin, T. Michael Underhill, Fumio Takei, Kelly M. McNagny

Bibliographic record

VenueThe Journal of Immunology · 2019
Typearticle
Languageen
FieldImmunology and Microbiology
TopicIL-33, ST2, and ILC Pathways
Canadian institutionsTerry Fox Research InstituteBC Cancer AgencyUniversity of British Columbia
FundersCanadian Institutes of Health Research
KeywordsRAR-related orphan receptor gammaInnate lymphoid cellTranscription factorOrphan receptorBiologyImmunologySOX9Cell biologyCancer researchImmunityImmune systemGeneGenetics

Abstract

fetched live from OpenAlex

Abstract Innate lymphoid cells (ILCs) are critical for host defense and tissue repair but can also contribute to chronic inflammatory diseases. The transcription factor RORα is required for ILC2 development but is also highly expressed by other ILC subsets where its function remains poorly defined. We previously reported that Rorasg/sg bone marrow chimeric mice (C57BL/6J) were protected from Salmonella-induced intestinal fibrosis due to defective ILC3 responses. In this study, single-cell RNA analysis of ILCs isolated from inflamed tissues indicates that RORα perturbation led to a reduction in ILC3 lineages. Furthermore, residual Rorasg/sg ILC3s have decreased expression of key signature genes, including Rorc and activating cytokine receptors. Collectively, our data suggest that RORα plays a key role in preserving functional ILC3s by modulating their ability to integrate environmental cues to efficiently produce cytokines.

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 score0.002

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 head0.014
GPT teacher head0.227
Teacher spread0.212 · 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

Citations43
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of ImmunologySame topicIL-33, ST2, and ILC PathwaysFrench-language works237,207