Molecular composition of skeletal muscle in infants and adults: a comparative proteomic and transcriptomic study
Bibliographic record
Abstract
To gain a deeper understanding of skeletal muscle function in younger age and aging in elderly, identification of molecular signatures regulating these functions under physiological conditions is needed. Although molecular studies of healthy muscle have been conducted on adults and older subjects, there is a lack of research on infant muscle in terms of combined morphological, transcriptomic and proteomic profiles. To address this gap of knowledge, we performed RNA sequencing (RNA-seq), tandem mass spectrometry (LC-MS/MS), morphometric analysis and assays for mitochondrial maintenance in skeletal muscle biopsies from both, infants aged 4-28 months and adults aged 19-65 years. We identified differently expressed genes (DEGs) and differentially expressed proteins (DEPs) in adults compared to infants. The down-regulated genes in adults were associated with functional terms primarily related to sarcomeres, cellular maintenance, and metabolic, immunological and developmental processes. Thus, our study indicates age-related differences in the molecular signatures and associated functions of healthy skeletal muscle. Moreover, the findings assert that processes previously associated solely with aging are indeed part of development and healthy aging. Hence, combined findings of this study also indicate that age-dependent controls are crucial in muscle disease studies, as otherwise the comparative results may not be reliable.
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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".