A single bout of heat stress treatment increases Nrf2 and its target genes in mouse skeletal muscle
Bibliographic record
Abstract
In recent years, we have reported that heat stress induces various skeletal muscle adaptations (e.g. mitochondria, autophagy, unfolded protein response). In this study, we explored further possibilities of muscle adaptations by heat stress treatment using transcriptome approach and subsequent bioinformatics analysis. Immediately after treatment and three hours after a single bout of heat stress treatment (exposing mouse into a hot environment chamber; 40°C, 30 min), gastrocnemius muscles were collected and then examined changes in over 39,000 genes expression using Affimetrix microarray GeneChip. Consequently, we analyzed with bioinformatics algorithms TFactS and BioCarta to predict activated transcriptional factors and activated pathways, respectively. These computational analyses based on transcriptome data indicated that heat stress activates Nrf2 (Nfe2l2), a master transcriptional factor of antioxidant response, in skeletal muscle (TFactS: P<0.05; BioCarta: Fold Enrichment=7.2, P<0.05). We further confirmed by qPCR that heat stress increased Nrf2 (+39.4%, P<0.05) and its target genes such as Cat (+30.9%, P<0.05), Hmox1 (+182.6%, P<0.05), Gclc (+53.8%, P<0.05), Gclm (+32.1%, P<0.05), Gpx1 (+23.7%, P=0.07), Mt1a (+251.6%, P<0.05) and Sod1 (+27.8%, P<0.05) at 3h after treatment. Notably, there were non‐detectable changes in oxidative stress marker (4HNE‐conjugated protein) and gene expression of Nrf2‐independently inducible antioxidant enzyme (Sod2) at both immediately post‐treatment and 3h after treatment. Our observations suggest that the physiological significance of Nrf2 activation by heat stress may be other than defending oxidative stress. Support or Funding Information This study was supported by Grant‐in‐Aid for JSPS Research Fellow.
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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 | 0.001 |
| 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) | 0.002 | 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".