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Record W2114465073 · doi:10.1113/jphysiol.2009.170738

Sirolimus and mTORC1: centre stage in the story of what makes muscles bigger?

2009· letter· en· W2114465073 on OpenAlexaff
Stuart M. Phillips

Bibliographic record

VenueThe Journal of Physiology · 2009
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsMcMaster University
Fundersnot available
KeywordsmTORC1ConfusionSirolimusChemistryPhosphorylationMedicineInternal medicinePsychologyBiochemistryProtein kinase B

Abstract

fetched live from OpenAlex

Sirolimus, or rapamycin as it commonly known, is a potent immunosuppressant and possesses both antifungal and antineoplastic properties. As such it has clinically important uses in oncology, cardiology and transplantation medicine. In this issue of The Journal of Physiology, Drummond and co-workers used rapamycin to elucidate in greater detail the signalling pathways that are activated by high force contractions in human skeletal muscle (Drummond et al. 2009). We have known for some time that resistance exercise employing high force contractions stimulates muscle protein synthesis (Chesley et al. 1992; Welle et al. 1993). The critical experiments in humans, following some very elegant work in rodents (Bodine et al. 2001; Kubica et al. 2005), showed that phosphorylation of a critical activation site on the mammalian target of rapamycin complex 1 (mTORC1), namely Ser2448 (Nave et al. 1999), was increased following resistance exercise (Dreyer et al. 2006). With excitement, many exercise physiologists no doubt rushed to see whether mTORC1 activation was as critical in humans as it appeared to be in rats. Sadly, however, many other studies have not reported phosphorylation of mTORC1 following resistance exercise (Eliasson et al. 2006; Glover et al. 2008). Issues of the timing of muscle sampling along with differences in nutritional status have no doubt contributed to the confusion. Nonetheless, the intriguing results of Drummond et al. (2009) show us that somewhere along the way mTORC1 is involved in turning on the process of protein synthesis following resistance exercise. Complicating the picture, however, are the findings that proteins downstream of mTORC1 such as ribosomal S6 kinase 1 (S6K1) and ribosomal protein S6 (rpS6) as well mTORC1 do get phosphorylated, but not until some 2 h after exercise. Why then did protein synthesis not rise at that time? Several other recent papers have shown that other proteins such as mammalian vacuole protein sorting 34 (mVsp34) are also probably playing a role in activating high force contraction-induced muscle protein synthesis (MacKenzie et al. 2009). Thus, what is perhaps now much clearer than before is that there is tremendous redundancy in how signalling processes affect muscle protein synthesis. Indeed, recent data even show that changes in signalling protein phosphorylation can be almost completely divorced from protein synthesis with a stimulus such as insulin (Greenhaff et al. 2008). It appears that we are inching closer to an understanding of how high force contractions, a potent anabolic and anti-catabolic stimulus, may be triggering a rise in muscle protein synthesis. The work from Drummond and colleagues (Drummond et al. 2009) sheds new light on the importance of mTOR in the post-exercise phenotype and these workers are to be congratulated for their efforts. At the same time, the results of this study raise important questions. For example, what is the true role of the extracellular regulated kinase (ERK) pathway? This pathway too appears to be affected by rapamycin. We know that protein synthesis is elevated for some time (at least 24–48 h) after resistance exercise; hence, what mechanisms are active at the later times beyond the initial 1–2 h? No doubt mTORC1 will be playing a role at some point.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.001
Science and technology studies0.0030.007
Scholarly communication0.0080.018
Open science0.0020.003
Research integrity0.0120.026
Insufficient payload (model declined to judge)0.0060.003

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.

Opus teacher head0.009
GPT teacher head0.231
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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