Evidence map›Paper›PMID 32755574›Full record

Trial reportCell reports2020

Molecular Transducers of Human Skeletal Muscle Remodeling under Different Loading States.

Tanner Stokes, James A Timmons, Hannah Crossland, Thomas R Tripp, Kevin Murphy, Chris McGlory, Cameron J Mitchell, Sara Y Oikawa, Robert W Morton, Bethan E Phillips and 4 more

Open access · goldAbstract readClinical Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Cell reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
29citing papers in PubMed, 2 pooled it
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

29 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
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  7. Review
  8. Article
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  16. Metabolomic and proteomic applications to exercise biomedicine.Translational exercise biomedicine · 2024
    Review
  17. Review
  18. Review
  19. Exercise metabolism and adaptation in skeletal muscle.Nature reviews. Molecular cell biology · 2023
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 6 institutions in 3 countries.

Tanner StokesDepartment of Kinesiology, McMaster University, Hamilton, ON, Canada.
James A TimmonsCenter for Therapeutic Innovation, University of Miami Miller School of Medicine, Miami, FL, USA.
Hannah CrosslandSchool of Medicine, Royal Derby Hospital, University of Nottingham, Derby, UK.
Thomas R TrippFaculty of Kinesiology, University of Calgary, Calgary, AB, Canada.
Kevin MurphyDepartment of Kinesiology, McMaster University, Hamilton, ON, Canada.
Chris McGlorySchool of Kinesiology and Health Studies, Queens University, Kingston, ON, Canada.
Cameron J MitchellSchool of Kinesiology, University of British Columbia, BC, Canada.
Sara Y OikawaDepartment of Kinesiology, McMaster University, Hamilton, ON, Canada.
Robert W MortonDepartment of Kinesiology, McMaster University, Hamilton, ON, Canada.
Bethan E PhillipsSchool of Medicine, Royal Derby Hospital, University of Nottingham, Derby, UK.
Steven K BakerPhysical Medicine and Rehabilitation, Department of Medicine, McMaster University, Hamilton, Canada.
Phillip J AthertonSchool of Medicine, Royal Derby Hospital, University of Nottingham, Derby, UK.
Claes WahlestedtCenter for Therapeutic Innovation, University of Miami Miller School of Medicine, Miami, FL, USA.
Stuart M PhillipsDepartment of Kinesiology, McMaster University, Hamilton, ON, Canada. Electronic address: phillis@mcmaster.ca.
McMaster University · CAUniversity of Nottingham · GBUniversity of Miami · USQueen's University · CAUniversity of British Columbia · CAUniversity of Calgary · CA

Funding

Medical Research Council G1100015Medical Research Council MC_PC_12019
6 · The paper itself

Abstract

Loading of skeletal muscle changes the tissue phenotype reflecting altered metabolic and functional demands. In humans, heterogeneous adaptation to loading complicates the identification of the underpinning molecular regulators. A within-person differential loading and analysis strategy reduces heterogeneity for changes in muscle mass by ∼40% and uses a genome-wide transcriptome method that models each mRNA from coding exons and 3' and 5' untranslated regions (UTRs). Our strategy detects ∼3-4 times more regulated genes than similarly sized studies, including substantial UTR-selective regulation undetected by other methods. We discover a core of 141 genes correlated to muscle growth, which we validate from newly analyzed independent samples (n = 100). Further validating these identified genes via RNAi in primary muscle cells, we demonstrate that members of the core genes were regulators of protein synthesis. Using proteome-constrained networks and pathway analysis reveals notable relationships with the molecular characteristics of human muscle aging and insulin sensitivity, as well as potential drug therapies.

Indexed as

AdolescentAdultExerciseGene Expression RegulationGene Regulatory NetworksHumansMuscle Fibers, SkeletalMuscle, SkeletalOrgan SizeProtein BiosynthesisProteomeRNASignal TransductionWeight-BearingYoung AdultProteomeRNAatrophygrowthhumanhypertrophyprotein synthesisprotein turnoverresistance exerciseskeletal muscletranscriptomeunloadinguntranslated region

Identifiers

PMID32755574
PMCPMC7408494
OpenAlexW2987710012

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.