Histone deacetylase regulation of MAP kinase phosphatase-1 acetylation and inflammation (135.36)
Bibliographic record
Abstract
Abstract The mitogen activated protein kinase (MAPK) pathway mediates Toll-like receptor signaling during innate immune responses. Histone deacetylases (HDACs) modulate inflammation by deacetylating histone and non-histone proteins. Previous studies in our lab identified MAP Kinase Phosphatase-1 (MKP-1) as a potential acetylation target. Thus, we hypothesize that HDACs regulate LPS-induced inflammation by deacetylating MKP-1. RAW264.7 mouse macrophages preferentially expressed HDAC1, 2, and 3 (class I HDAC isoforms). HDAC1, 2, and 3 interacted with MKP-1. To support our hypothesis, we adopted both genetic and pharmacologic approaches, using HDAC1, 2, and 3 siRNAs and MS-275, a HDAC1, 2, 3 specific inhibitor. Both silencing and pharmacologic inhibition of HDAC1, 2, and 3 increased MKP-1 acetylation in cells. HDAC1 decreased MKP-1 acetylation in vitro. Furthermore, both silencing and pharmacologic inhibition of HDAC1, 2, and 3 decreased LPS-induced p38 MAPK activation and iNOS expression. Finally, pharmacologic inhibition of HDAC1, 2, and 3 decreased LPS-induced NO and IL-6 production. Taken together, our results show that class I HDACs deacetylate MKP-1, and that this modification can significantly affect MAPK signaling and inflammation. Furthermore, our results suggest that MKP-1 is a potential therapeutic target in inflammatory diseases. Supported by the American Heart Association grant (0815093E).
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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) | 0.003 | 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".