Myocardial Infarction Induces Cardiac Fibroblast Transformation within Injured and Noninjured Regions of the Mouse Heart
Bibliographic record
Abstract
Heart failure (HF) is associated with pathological remodeling of the myocardium, including the initiation of fibrosis and scar formation by activated cardiac fibroblasts (CFs). Although early CF-dependent scar formation helps prevent cardiac rupture by maintaining the heart’s structural integrity, ongoing deposition of the extracellular matrix in the remote and infarct regions can reduce tissue compliance, impair cardiac function, and accelerate progression to HF. In our study, we conducted mass spectrometry (MS) analysis to identify differentially altered proteins and signaling pathways between CFs isolated from 7 day sham and infarcted murine hearts. Surprisingly, CFs from both the remote and infarct regions of injured hearts had a wide number of similarly altered proteins and signaling pathways that were consistent with fibrosis and activation into pathological myofibroblasts. Specifically, proteins enriched in CFs isolated from MI hearts were involved in pathways pertaining to cell–cell and cell–matrix adhesion, chaperone-mediated protein folding, and collagen fibril organization. These results, together with principal component analyses, provided evidence of global CF activation postinjury. Interestingly, however, direct comparisons between CFs from the remote and infarct regions of injured hearts identified 15 differentially expressed proteins between MI remote and MI infarct CFs. Eleven of these proteins ( Gpc1, Cthrc1, Vmac, Nexn, Znf185, Sprr1a, Specc1, Emb, Limd2, Pawr, and Mcam ) were higher in MI infarct CFs, whereas four proteins ( Gstt1, Gstm1, Tceal3, and Inmt ) were higher in MI remote CFs. Collectively, our study shows that MI injury induced global changes to the CF proteome, with the magnitude of change reflecting their relative proximity to the site of 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".