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Record W3045398072 · doi:10.4049/jimmunol.2000225

IFN-λ Enhances Constitutive Expression of MHC Class I Molecules on Thymic Epithelial Cells

2020· article· en· W3045398072 on OpenAlexafffund
Mohamed Benhammadi, Justine Mathé, Maude Dumont-Lagacé, Koichi S. Kobayashi, Louis Gaboury, Sylvie Brochu, Claude Perreault

Bibliographic record

VenueThe Journal of Immunology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsUniversité de MontréalInstitute for Research in Immunology and Cancer
FundersCanadian Institutes of Health Research
KeywordsCell biologyMHC class IClass (philosophy)MHC class IIMajor histocompatibility complexCD74Expression (computer science)BiologyCancer researchImmunologyImmune systemComputer sciencePhilosophyEpistemology

Abstract

fetched live from OpenAlex

Abstract Regulation of MHC class I (MHC I) expression has been studied almost exclusively in hematolymphoid cells. We report that thymic epithelial cells (TECs), particularly the medullary TECs, constitutively express up to 100-fold more cell surface MHC I proteins than epithelial cells (ECs) from the skin, colon, and lung. Differential abundance of cell surface MHC I in primary ECs is regulated via transcription of MHC I and of genes implicated in the generation of MHC I–binding peptides. Superior MHC I expression in TECs is unaffected by deletion of Ifnar1 or Ifngr1, but is lessened by deletion of Aire, Ifnlr1, Stat1, or Nlrc5, and is driven mainly by type III IFN produced by medullary TECs. Ifnlr1−/− mice show impaired negative selection of CD8 thymocytes and, at 9 mo of age, present autoimmune manifestations. Our study shows unanticipated variation in MHC I expression by ECs from various sites and provides compelling evidence that superior expression of MHC I in TECs is crucial for proper thymocyte education.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.035
Threshold uncertainty score0.962

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.229
Teacher spread0.217 · 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 teacher head, 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

Citations53
Published2020
Admission routes2
Has abstractyes

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