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Record W2125160356 · doi:10.1126/sageke.2002.22.nw76

Timing Is Everything: Enzyme inhibitor first encourages, then blocks cells from becoming muscle (Muscle; Chromatin)

2002· article· en· W2125160356 on OpenAlexaboutno aff
R. John Davenport

Bibliographic record

VenueScience of Aging Knowledge Environment · 2002
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMuscle Physiology and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMyocyteMyoDCell biologyChemistryBiologyBiochemistryMyogenesis

Abstract

fetched live from OpenAlex

Yoda wields a mean light saber, but even a Jedi gets weak with age. Scientists hope to rekindle the force in aging muscle by sending more cells to its aid. A new study supports the existence of a regulatory toggle that directs precursor cells down a muscle-bound path and suggests a target for drugs that promote the process. Muscle tissue develops from cells called myoblasts, which can either divide to make more myoblasts or convert into full-blown muscle cells. Previous work suggested that some enzymes that add acetyl groups to proteins encourage specialization, whereas several that remove acetyl groups thwart it. But the effects of the acetyltransferases and deacetylases implicated in muscle formation aren't straightforward: Deacetylase inhibitors (DIs) would be expected to instigate muscle cell formation, but instead they inhibit it. Last year, Sartorelli and colleagues showed that a deacetylase called HDAC1 binds to a protein called MyoD when precursor cells are dividing; the duo shuts off muscle-specific genes, which prevents myoblasts from specializing. When other cues cause the muscle-formation phase to begin, HDAC1 drops MyoD and picks up a different partner, RB. The swap allows the HDAC1-RB team to quell cell-division genes. In the new study, the group used cultured human and mouse myoblasts to test whether HDAC1's fickle behavior explained the effect of DIs. The researchers exposed cells to DIs, either while they were dividing or after they had been put in a medium that spurs myoblasts to become muscle cells. Compared with untreated cells, those exposed to DIs while dividing were more likely to express muscle genes and to assume shapes characteristic of muscle cells. Cells exposed to inhibitors for the first time in the medium, on the other hand, were less likely to show signs of becoming muscle cells. The results suggest that when added to dividing cells, DIs relieve the HDAC1-MyoD block on muscle genes that keeps myoblasts unspecialized. When cells have already embarked on the muscle cell pathway, DIs instead thwart deacetylase pairs that are active at that stage--such as the HDAC1-RB couple that normally dampens cell-division genes--and thus inhibit cells from becoming muscle. The results suggest that under the right circumstances, blocking deacetylases might nudge cells to specialize, says stem cell biologist Michael Rudnicki of the Ottawa Health Research Institute in Canada, although the inhibitors used in this study are likely too toxic for therapeutic purposes in humans. The team is planning to test whether cells induced to form muscle can restore muscle function when transplanted into a mouse model of muscular dystrophy. If that work pans out, it could help keep us as powerful during advancing years as a mythological knight. --R. John Davenport S. Iezzi, G. Cossu, C. Nervi, V. Sartorelli, P. L. Puri, Stage-specific modulation of skeletal myogenesis by inhibitors of nuclear deacetylases. Proc. Natl. Acad. Sci. U.S.A. 99 , 7757-7762 (2002). [Abstract] [Full Text]

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0250.011

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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2002
Admission routes1
Has abstractyes

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