Expression of Ski induces apoptosis and represses autophagy in cardiac myofibroblasts (868.8)
Bibliographic record
Abstract
Following myocardial infarction (MI), relatively quiestent cardiac fibroblasts undergo phenoconversion to active, hyper‐synthetic myofibroblasts. During the late stages of wound healing, myofibroblasts are removed from the infracted region through apoptosis thus decreasing scar cellularity. Apoptosis and autophagy regulate cell fate. The role of apoptosis within the remodeling heart has been well described however the role that autophagy plays following an MI is relatively unknown. Ski is a negative regulator of TGF‐β signaling and has been implicated to play a role in the post‐MI heart. Herein we investigated the regulatory role that Ski has on apoptosis and autophagy using first passage primary rat cardiac myofibroblasts. Using an adenoviral approach for gene delivery, both MTT and Live/Dead viability/cytoxicity assays demonstrated a reduction in the number of viable cells and an increase in the number of dying cells due to the over‐expression of Ski. Additionally, over‐expression of Ski led to distinct morphological changes characteristic of apoptosis. These findings were confirmed via Western blot analysis, and caspase GLO analysis showing induction of executioner caspases‐3 and ‐7, as well as the mitochondrial regulator Bax. Furthermore, evidence of caspase‐9 cleavage, but not caspase‐8, indicated that this was primarily an intrinsic apoptotic response. Markers for autophagosome formation including LC3‐B and ATG‐7 were found to be significantly reduced following Ski over‐expression. In summary, we found that over‐expression of Ski in primary cardiac myofibroblasts leads to an induction of the intrinsic apoptotic pathway and reduction in autophagy leading to cell death. Grant Funding Source : Canadian Institutes of Health Research
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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) | 0.001 | 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".