p38γ and p38δ modulate innate immune response by regulating MEF2D activation
Bibliographic record
Abstract
Evidence implicating p38γ and p38δ (p38γ/p38δ) in inflammation are mainly based on experiments using Mapk12/Mapk13 -deficient (p38γ/δKO) mice, which show low levels of TPL2, the kinase upstream of MKK1–ERK1/2 in myeloid cells. This could obscure p38γ/p38δ roles, since TPL2 is essential for regulating inflammation. Here, we generated a Mapk12 D171A/D171A / Mapk13 −/− (p38γ/δKIKO) mouse, expressing kinase-inactive p38γ and lacking p38δ. This mouse exhibited normal TPL2 levels, making it an excellent tool to elucidate specific p38γ/p38δ functions. p38γ/δKIKO mice showed a reduced inflammatory response and less susceptibility to lipopolysaccharide (LPS)-induced septic shock and Candida albicans infection than wild-type (WT) mice. Gene expression analyses in LPS-activated wild-type and p38γ/δKIKO macrophages revealed that p38γ/p38δ-regulated numerous genes implicated in innate immune response. Additionally, phospho-proteomic analyses and in vitro kinase assays showed that the transcription factor myocyte enhancer factor-2D (MEF2D) was phosphorylated at Ser444 via p38γ/p38δ. Mutation of MEF2D Ser444 to the non-phosphorylatable residue Ala increased its transcriptional activity and the expression of Nos2 and Il1b mRNA. These results suggest that p38γ/p38δ govern innate immune responses by regulating MEF2D phosphorylation and transcriptional activity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".