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Record W2091801719 · doi:10.1016/j.rinim.2012.09.001

Contrasting effects of IFNα on MHC class II expression in professional vs. nonprofessional APCs: Role of CIITA type IV promoter

2012· article· en· W2091801719 on OpenAlexfundno aff
Laura Pisapia, Giovanna Del Pozzo, Pasquale Barba, Alessandra Citro, Paul E. Harris, Antonella Maffei

Bibliographic record

VenueResults in Immunology · 2012
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsCIITAGene knockdownAntigen-presenting cellMHC class IIMajor histocompatibility complexCell biologyImmunologyAntigen presentationImmune systemGene silencingBiologyMedicineT cellGeneGenetics

Abstract

fetched live from OpenAlex

We previously demonstrated that, in ex vivo cultures, IFNα downregulates the expression of MHC class II (MHCII) genes in human non-professional APCs associated with pancreatic islets. IFNα has an opposing effect on MHCII expression in professional APCs. In this study, we found that the mechanism responsible for the IFNα-mediated MHCII's downregulation in human MHCII-positive non-professional antigen presenting human non-hematopoietic cell lines is the result of the negative feedback system that regulates cytokine signal transduction, which eventually inhibits promoters III and IV of CIITA gene. Because the CIITA-PIV isoform is mostly responsible for the constitutive expression of MHCII genes in non-professional APCs, we pursued and achieved the specific knockdown of CIITA-PIV mRNA in our in vitro system, obtaining a partial silencing of MHCII molecules similar to that obtained by IFNα. We believe that our results offer a new understanding of the potential significance of CIITA-PIV as a therapeutic target for interventional strategies that can manage autoimmune disease and allograft rejection with little interference on the function of professional APCs of the immune system.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.134
Threshold uncertainty score0.747

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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