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Record W3122601017 · doi:10.3390/ijms22031074

The MHC Class II Transactivator CIITA: Not (Quite) the Odd-One-Out Anymore among NLR Proteins

2021· review· en· W3122601017 on OpenAlexafffund
Jorge Alfonso León Machado, Viktor Steimle

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2021
Typereview
Languageen
FieldImmunology and Microbiology
TopicImmunotherapy and Immune Responses
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCIITABiologyGeneticsRegulatorMHC class ITranscriptional regulationMajor histocompatibility complexTransactivationGeneCell biologyRegulation of gene expressionMHC class IITranscription factor

Abstract

fetched live from OpenAlex

In this review, we discuss the major histocompatibility complex (MHC) class II transactivator (CIITA), which is the master regulator of MHC class II gene expression. CIITA is the founding member of the mammalian nucleotide-binding and leucine-rich-repeat (NLR) protein family but stood apart for a long time as the only transcriptional regulator. More recently, it was found that its closest homolog, NLRC5 (NLR protein caspase activation and recruitment domain (CARD)-containing 5), is a regulator of MHC-I gene expression. Both act as non-DNA-binding activators through multiple protein-protein interactions with an MHC enhanceosome complex that binds cooperatively to a highly conserved combinatorial cis-acting module. Thus, the regulation of MHC-II expression is regulated largely through the differential expression of CIITA. In addition to the well-defined role of CIITA in MHC-II GENE regulation, we will discuss several other aspects of CIITA functions, such as its role in cancer, its role as a viral restriction element contributing to intrinsic immunity, and lastly, its very recently discovered role as an inhibitor of Ebola and SARS-Cov-2 virus replication. We will briefly touch upon the recently discovered role of NLRP3 as a transcriptional regulator, which suggests that transcriptional regulation is, after all, not such an unusual feature for NLR proteins.

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)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.981
Threshold uncertainty score0.774

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
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 head0.037
GPT teacher head0.324
Teacher spread0.287 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations84
Published2021
Admission routes2
Has abstractyes

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