IFN-γ-Induced MHC Class II Expression: Transactivation of Class II Transactivator Promoter IV by IFN Regulatory Factor-1 is Regulated by Protein Kinase C-α
Bibliographic record
Abstract
Previous studies based on pharmacological evidence suggested a requirement for protein kinase C (PKC) activity in the regulation of IFN-gamma-induced MHC class II (MHC-II) expression. In the present study, we investigated the molecular mechanisms by which PKC-alpha modulates IFN-gamma-induced MHC-II expression in the mouse macrophage cell line RAW 264.7. Overexpression of a dominant-negative (DN) mutant of PKC-alpha inhibited the expression of IFN-gamma-induced MHC-II but had no effect on IFN-gamma-induced STAT1 nuclear translocation and DNA binding activity, as well as on the expression of inducible NO synthase, IFN consensus sequence binding protein, MHC class I, IFN regulatory factor (IRF)-1, and IFN-gamma-inducible protein-10. Further analysis showed that IFN-gamma-induced expression of the MHC class II transactivator (CIITA), a transcriptional coactivator essential for MHC-II expression, was inhibited in DN PKC-alpha-overexpressing cells. Studies with reporter constructs containing the promoter IV region of CIITA revealed that overexpression of a constitutively active mutant of PKC-alpha enhanced IRF-1, but not IRF-2, transcriptional activity. Furthermore, characterization of IRF-1 from both normal and DN PKC-alpha-overexpressing cells revealed differences in IRF-1 posttranslational modifications. Collectively, our data suggest a novel regulatory mechanism for IFN-gamma-induced MHC-II expression, whereby PKC regulates CIITA expression by selectively modulating the transcriptional activity of IRF-1.
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 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 | 0.001 |
| 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".