Low baseline ribosome‐related gene expression and resistance training‐induced declines in ribosome‐related gene expression are associated with skeletal muscle hypertrophy in young men and women
Bibliographic record
Abstract
Ribosomes are essential cellular machinery for protein synthesis. It is hypothesised that ribosome content supports muscle growth and that individuals with more ribosomes have greater increases in muscle size following resistance training (RT). Aerobic conditioning (AC) also elicits distinct physiological adaptations; however, no measures of ribosome content following AC have been conducted. We used ribosome-related gene expression as a proxy measure for ribosome content and hypothesised that AC and RT would increase ribosome-related gene expression. Fourteen young men and women performed 6 weeks of single-legged AC followed by 10 weeks of double-legged RT. Muscle biopsies were taken following AC and following RT in the aerobically conditioned (AC+RT) and unconditioned (RT) legs. No differences in regulatory genes (Ubf, Cyclin D1, Tif-1a and Polr-1b) involved in ribosomal biogenesis or ribosomal RNA (45S, 5.8S, 18S and 28S rRNAs) expression were observed following AC and RT, except for c-Myc (RT > AC+RT) and 5S rRNA (RT < AC+RT at pre-RT) with 18S external transcribed spacer and 5.8S internal transcribed spacer expression decreasing from pre-RT to post-RT in the RT leg only. When divided for change in leg-lean soft tissue mass (ΔLLSTM) following RT, legs with the greatest ΔLLSTM had lower expression in 11/13 measured ribosome-related genes before RT and decreased expression in 9/13 genes following RT. These results indicate that AC and RT did not increase ribosome-related gene expression. Contrary to previous research, the greatest increase in muscle mass was associated with lower changes in ribosome-related gene expression over the course of the 10-week training programme. This may point to the importance of translational efficiency rather than translational capacity (i.e. ribosome content) in mediating long-term exercise-induced adaptations in skeletal muscle.
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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) | 0.002 | 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".