The MHC Class II Transactivator CIITA: Not (Quite) the Odd-One-Out Anymore among NLR Proteins
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".