Maintenance of IRF1 by MAPK inhibition: A mechanism by which DUSP1 reduces glucocorticoid inhibition of CXCL10
Bibliographic record
Abstract
Introduction: The mitogen-activated protein kinase (MAPK) phosphatase, DUSP1, mediates glucocorticoid repression of MAPKs and inflammatory gene expression. However, MAPK inhibition enhances expression of the transcription factor, IRF1. Hypothesis: Inhibition of MAPKs is a mechanism by which anti-inflammatory glucocorticoids maintain IRF1 and downstream gene expression. Methods: Pulmonary A549 and primary human bronchial epithelial (HBE) cells were treated with IL1B (1ng/ml) and dexamethasone (1μM). Gene expression was measured by qPCR, western blotting and ELISA. Results: In A549 cells, DUSP1 over-expression reduced IL1B-induced expression of many inflammatory genes, but enhanced expression of IRF1 and the IRF1-dependent gene, CXCL10. IL1B rapidly, but transiently, induced IRF1 mRNA and protein expression. MAPK inhibition had little effect on this induction, but markedly reduced the loss of IRF1 following peak expression. This involved a failure to reduce IRF1 transcription as well as stabilisation of IRF1 mRNA and protein. Silencing of IL1B-induced DUSP1 increased MAPK activation and reduced IRF1 expression. With IL1B+dexamethasone, DUSP1 silencing not only reduced IRF1 expression, but reduced CXCL10 expression. Thus limited repression by dexamethasone of IL1B-induced IRF1 and CXCL10 expression in A549 and on IRF1 in primary HBE cells is in part explained by: 1) induction of DUSP1, 2) inhibition of MAPKs, and 3) subsequently elevated IRF1 expression. Conclusions: These data confirm a regulatory network, whereby DUSP1 switches off MAPKs to maintain IRF1 expression. In the presence of dexamethasone, this maintains IRF1 and CXCL10 expression.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.003 | 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".