Local and systemic transcriptomic responses from acute exercise induced muscle damage of the human knee extensors
Bibliographic record
Abstract
Skeletal muscle is adaptable to a direct stimulus of exercise-induced muscle damage (EIMD). Local muscle gene networks and systemic circulatory factors respond to EIMD within days, mediating anti-inflammation and cellular proliferation. Here we show in humans that local EIMD of one muscle group is associated with a systemic response of gene networks that regulate muscle structure and cellular development in nonlocal homologous muscle not directly altered by EIMD. In the nondominant knee extensors of seven males, EIMD was induced through voluntary contractions against an electric motor that lengthened muscles. Neuromuscular assessments, vastus lateralis muscle biopsies, and blood draws occurred 2 days prior and 1 and 2 days after the EIMD intervention. From the muscle and blood plasma samples, RNA-Seq measured transcriptome changes of differential expression using bioinformatic analyses. Relative to the time of the EIMD intervention, local muscle that was mechanically damaged had 475 genes differentially expressed, as compared with 33 genes in the nonlocal homologous muscle. Gene and network analysis showed that activity of the local muscle was related to structural maintenance, repair, and energetic processes, whereas gene and network activities of the nonlocal muscle (that was not directly modified by the EIMD) were related to muscle cell development, stress response, and structural maintenance. Altered expression of two novel miRNAs related to the EIMD response supported that systemic factors were active. Together, these results indicate that the expression of genes and gene networks that control muscle contractile structure can be modified in response to nonlocal EIMD in humans.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".