<scp>ERK3</scp> is involved in regulating cardiac fibroblast function
Bibliographic record
Abstract
Abstract ERK3/MAPK6 activates MAP kinase‐activated protein kinase (MK)‐5 in selected cell types. Male MK5 haplodeficient mice show reduced hypertrophy and attenuated increase in Col1a1 mRNA in response to increased cardiac afterload. In addition, MK5 deficiency impairs cardiac fibroblast function. This study determined the effect of reduced ERK3 on cardiac hypertrophy following transverse aortic constriction (TAC) and fibroblast biology in male mice. Three weeks post‐surgery, ERK3, but not ERK4 or p38α, co‐immunoprecipitated with MK5 from both sham and TAC heart lysates. The increase in left ventricular mass and myocyte diameter was lower in TAC‐ERK3 +/− than TAC‐ERK3 +/+ hearts, whereas ERK3 haploinsufficiency did not alter systolic or diastolic function. Furthermore, the TAC‐induced increase in Col1a1 mRNA abundance was diminished in ERK3 +/− hearts. ERK3 immunoreactivity was detected in atrial and ventricular fibroblasts but not myocytes. In both quiescent fibroblasts and “activated” myofibroblasts isolated from adult mouse heart, siRNA‐mediated knockdown of ERK3 reduced the TGF‐β‐induced increase in Col1a1 mRNA. In addition, intracellular type 1 collagen immunoreactivity was reduced following ERK3 depletion in quiescent fibroblasts but not myofibroblasts. Finally, knocking down ERK3 impaired motility in both atrial and ventricular myofibroblasts. These results suggest that ERK3 plays an important role in multiple aspects of cardiac fibroblast biology.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".