The TAB1-p38α complex aggravates myocardial injury and can be targeted by small molecules
Bibliographic record
Abstract
Inhibiting MAPK14 (p38α) diminishes cardiac damage in myocardial ischemia. During myocardial ischemia, p38α interacts with TAB1, a scaffold protein, which promotes p38α autoactivation; active p38α (pp38α) then transphosphorylates TAB1. Previously, we solved the X-ray structure of the p38α-TAB1 (residues 384-412) complex. Here, we further characterize the interaction by solving the structure of the pp38α-TAB1 (residues 1-438) complex in the active state. Based on this information, we created a global knock-in (KI) mouse with substitution of 4 residues on TAB1 that we show are required for docking onto p38α. Whereas ablating p38α or TAB1 resulted in early embryonal lethality, the TAB1-KI mice were viable and had no appreciable alteration in their lymphocyte repertoire or myocardial transcriptional profile; nonetheless, following in vivo regional myocardial ischemia, infarction volume was significantly reduced and the transphosphorylation of TAB1 was disabled. Unexpectedly, the activation of myocardial p38α during ischemia was only mildly attenuated in TAB1-KI hearts. We also identified a group of fragments able to disrupt the interaction between p38α and TAB1. We conclude that the interaction between the 2 proteins can be targeted with small molecules. The data reveal that it is possible to selectively inhibit signaling downstream of p38α to attenuate ischemic injury.
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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) | 0.001 | 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) | 0.002 | 0.000 |
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".