Short Term Exercise Induces PGC-1α, Ameliorates Inflammation and Increases Mitochondrial Membrane Proteins but Fails to Increase Respiratory Enzymes in Aging Diabetic Hearts
Bibliographic record
Abstract
PGC-1α, a transcriptional coactivator, controls inflammation and mitochondrial gene expression in insulin-sensitive tissues following exercise intervention. However, attributing such effects to PGC-1α is counfounded by exercise-induced fluctuations in blood glucose, insulin or bodyweight in diabetic patients. The goal of this study was to investigate the role of PGC-1α on inflammation and mitochondrial protein expressions in aging db/db mice hearts, independent of changes in glycemic parameters. In 8-month-old db/db mice hearts with diabetes lasting over 22 weeks, short-term, moderate-intensity exercise upregulated PGC-1α without altering body weight or glycemic parameters. Nonetheless, such a regimen lowered both cardiac (macrophage infiltration, iNOS and TNFα) and systemic (circulating chemokines and cytokines) inflammation. Curiously, such an anti-inflammatory effect was also linked to attenuated expression of downstream transcription factors of PGC-1α such as NRF-1 and several respiratory genes. Such mismatch between PGC-1α and its downstream targets was associated with elevated mitochondrial membrane proteins like Tom70 but a concurrent reduction in oxidative phosphorylation protein expressions in exercised db/db hearts. As mitochondrial oxidative stress was predominant in these hearts, in support of our in vivo data, increasing concentrations of H2O2 dose-dependently increased PGC-1α expression while inhibiting expression of inflammatory genes and downstream transcription factors in H9c2 cardiomyocytes in vitro. We conclude that short-term exercise-induced oxidative stress may be key in attenuating cardiac inflammatory genes and impairing PGC-1α mediated gene transcription of downstream transcription factors in type 2 diabetic hearts at an advanced age.
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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) | 0.001 | 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".