Transcriptional evidence for failed reinnervation in aging muscle
Bibliographic record
Abstract
Muscle displays a marked accumulation of denervated myofibers at ages associated with an acceleration of atrophy and weakness. As muscle undergoes repeating cycles of denervation and reinnervation throughout adulthood, we hypothesize that the accumulation of denervated myofibers in advanced age is due to failed reinnervation. To address this issue, we quantified neuromuscular junction (NMJ) associated genes including five AChR subunits, MuSK, Rapsyn, Agrin, Lrp4 and APC in three different model systems. Firstly, we examined vastus lateralis (VL) muscle from young adult (YA) and very old (VO) Fisher 344xBrown Norway F1‐hybrid rats. Secondly, we examined biopsies from human VL in a young physically active group (23.7±2.7), an old active group (71.2±4.9), an old inactive group (64.8±3.1) and a very old inactive group (82.5±4.8). Lastly, we examined Extensor digitorum longus(EDL) & Tibialis anterior (TA) muscle from wild type and neurotrypsin over‐expressing (Sarco) mice as a model of unstable NMJs secondary to reduced MuSK activation. Interestingly, transcripts of MuSK (21 fold), Rapsyn, Agrin and AChR subunits α (68 fold), β, and the fetal isoform γ (47 fold) were upregulated in very old muscle of rat. In contrast, only Musk, Agrin and Rapsyn were increased in aging human muscle of very old inactive group compared with a young active group, consistent with the milder denervation phenotype by morphology. In contrast, in Sarco mice these NMJ transcripts were largely preserved despite a muscle atrophy phenotype and small angular fibers characteristic of long‐term denervation, which were frequent in VO rat and very old inactive human muscle, were rare in Sarco mice suggesting a high fidelity of reinnervation. We conclude that the dramatic changes in the NMJ transcriptional profile in aging muscle is indicative of a failed reinnervation response.
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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".