Transcriptomic links to muscle mass loss and declines in cumulative muscle protein synthesis during short‐term disuse in healthy younger humans
Bibliographic record
Abstract
Abstract Muscle disuse leads to a rapid decline in muscle mass, with reduced muscle protein synthesis (MPS) considered the primary physiological mechanism. Here, we employed a systems biology approach to uncover molecular networks and key molecular candidates that quantitatively link to the degree of muscle atrophy and/or extent of decline in MPS during short‐term disuse in humans. After consuming a bolus dose of deuterium oxide (D 2 O; 3 mL.kg −1 ), eight healthy males (22 ± 2 years) underwent 4 days of unilateral lower‐limb immobilization. Bilateral muscle biopsies were obtained post‐intervention for RNA sequencing and D 2 O‐derived measurement of MPS, with thigh lean mass quantified using dual‐energy X‐ray absorptiometry. Application of weighted gene co‐expression network analysis identified 15 distinct gene clusters (“modules”) with an expression profile regulated by disuse and/or quantitatively connected to disuse‐induced muscle mass or MPS changes. Module scans for candidate targets established an experimentally tractable set of candidate regulatory molecules (242 hub genes, 31 transcriptional regulators) associated with disuse‐induced maladaptation, many themselves potently tied to disuse‐induced reductions in muscle mass and/or MPS and, therefore, strong physiologically relevant candidates. Notably, we implicate a putative role for muscle protein breakdown‐related molecular networks in impairing MPS during short‐term disuse, and further establish DEPTOR (a potent mTOR inhibitor) as a critical mechanistic candidate of disuse driven MPS suppression in humans. Overall, these findings offer a strong benchmark for accelerating mechanistic understanding of short‐term muscle disuse atrophy that may help expedite development of therapeutic interventions.
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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 | 0.001 | 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".