High intensity interval training alters gene expression linked to mitochondrial biogenesis and dynamics in high fat diet fed rats
Bibliographic record
Abstract
A High-Fat Diet (HFD) leads to disruption of mitochondrial biogenesis and dynamics. Exercise training, especially High-Intensity Interval Training (HIIT) increases mitochondrial biogenesis and dynamics. The present study aimed to investigate the effect of a period of HIIT with and without HFD consumption on the expression of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (Pgc1-α), Mitofusins-2 (Mfn2), Optic atrophy-1 (Opa1), Dynamin-related protein-1 (Drp1) and mitochondrial Fission protein-1 (Fis1) genes as indicators of mitochondrial biogenesis and dynamics function in the soleus muscle of male Wistar rats. Twenty-four healthy male Wistar rats were randomly divided into four groups: (1) Control, (2) Control + HIIT, (3) HFD, and (4) HFD + HIIT. The HIIT training protocol lasted for 10 weeks with a frequency of 3 sessions per week. The Real-Time Quantitative Reverse Transcription PCR method was used to investigate the gene expression. One-way ANOVA and Fisher's post-hoc analyses were used to examine group differences. HFD consumption caused an increase in weight (P < 0.05), the expression of Drp1 and Fis1 genes (P < 0.001), and a decreased expression of Pgc1-α, Mfn2, and Opa1 genes (P < 0.001). HIIT training increased the expression of PGC1-α (P = 0.009), Mfn2 (P < 0.004), and Opa1 (P < 0.011) genes, while it decreased the expression of Drp1 (P = 0.003) and Fis1 genes (P = 0.027). These findings suggest that HIIT can counteract the negative effects of HFD on mitochondrial function by modulating gene expression related to mitochondrial biogenesis and dynamics.
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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".