MiRNAs with Apoptosis Regulating Potential Are Differentially Expressed in Chronic Exercise-Induced Physiologically Hypertrophied Hearts
Bibliographic record
Abstract
Physiological cardiac hypertrophy is an adaptive mechanism, induced during chronic exercise. As it is reversible and not associated with cardiomyocyte death, it is considered as a natural tactic to prevent cardiac dysfunction and failure. Though, different studies revealed the importance of microRNAs (miRNAs) in pathological hypertrophy, their role during physiological hypertrophy is largely unexplored. Hence, this study is aimed at revealing the global expression profile of miRNAs during physiological cardiac hypertrophy. Chronic swimming protocol continuously for eight weeks resulted in induction of physiological hypertrophy in rats and histopathology revealed the absence of tissue damage, apoptosis or fibrosis. Subsequently, the total RNA was isolated and small RNA sequencing was executed. Analysis of small RNA reads revealed the differential expression of a large set of miRNAs during physiological hypertrophy. The expression profile of the significantly differentially expressed miRNAs was validated by qPCR. In silico prediction of target genes by miRanda, miRdB and TargetScan and subsequent qPCR analysis unraveled that miRNAs including miR-99b, miR-100, miR-19b, miR-10, miR-208a, miR-133, miR-191a, miR-22, miR-30e and miR-181a are targeting the genes that primarily regulate cell proliferation and cell death. Gene ontology and pathway mapping showed that the differentially expressed miRNAs and their target genes were mapped to apoptosis and cell death pathways principally via PI3K/Akt/mTOR and MAPK signaling. In summary, our data indicates that regulation of these miRNAs with apoptosis regulating potential can be one of the major key factors in determining pathological or physiological hypertrophy by controlling fibrosis, apoptosis and cell death mechanisms.
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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".