Pathologic expression of MHC class II is driven by mitogen‐activated protein kinases
Bibliographic record
Abstract
The class II transactivator (CIITA) is the master regulator of MHC class II molecules (MHC II). In melanoma, the MHC II are constitutively expressed due to an abnormal transcription of CIITA from its promoter III (pIII), and requires the presence of a 1-kb enhancer located upstream from this latter. Since mitogen-activated protein kinases (MAPK) have been shown to be activated in most melanomas, we sought to analyze their possible involvement in CIITA expression. Using chemical inhibitors and dominant-negative constructs of MAPK-ERK kinase (Mek1) and MAPK-JNK, we evidenced the inhibition of MHC II and CIITA expression in melanoma cell lines displaying activated MAPK. Transcriptional regulation by MAPK is known to involve the AP-1 transcription factor family. Sequence analysis revealed an AP-1-responsive motif in the enhancer of CIITA pIII at -5954/-5947 from the site of transcription initiation. Its mutagenesis reduced CIITA expression four- to fivefold in melanoma cell lines and alleviated the effect of dominant-negative constructs of the MAPK pathway. Together, our findings demonstrate that MAPK-ERK and MAPK-JNK are regulators of CIITA transcription in melanoma, and pinpoint an AP-1-responsive site in the CIITA gene pIII. This should have considerable impact on our understanding of the physio-pathologic expression of MHC II.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".