Evidence map›Paper›PMID 38643471›Full record

ArticleExperimental physiology2024

Network model of skeletal muscle cell signalling predicts differential responses to endurance and resistance exercise training.

Annabelle Fowler, Katherine R Knaus, Stephanie Khuu, Ali Khalilimeybodi, Simon Schenk, Samuel R Ward, Andrew C Fry, Padmini Rangamani, Andrew D McCulloch

Open access · diamondAbstract read
In one paragraph

Article in Experimental physiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
3.9field-weighted citation impact, top 6% 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

9 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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  6. Review
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  8. The multifaceted impact of physical exercise on FoxO signaling pathways.Frontiers in cell and developmental biology · 2025
    Review
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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

9 authors at 2 institutions in 1 country.

Annabelle FowlerDepartment of Bioengineering, University of California San, Diego, La Jolla, California, USA.
Katherine R KnausDepartment of Bioengineering, University of California San, Diego, La Jolla, California, USA.
Stephanie KhuuDepartment of Bioengineering, University of California San, Diego, La Jolla, California, USA.
Ali KhalilimeybodiDepartment of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA.
Simon SchenkDepartment of Orthopaedic Surgery, University of California San Diego, La Jolla, California, USA.
Samuel R WardDepartment of Orthopaedic Surgery, University of California San Diego, La Jolla, California, USA.
Andrew C FryDepartment of Health, Sport and Exercise Sciences, University of Kansas, Lawrence, Kansas, USA.
Padmini RangamaniDepartment of Mechanical and Aerospace Engineering, University of California San Diego, La Jolla, California, USA.ORCID 0000-0001-5953-4347
Andrew D McCullochDepartment of Bioengineering, University of California San, Diego, La Jolla, California, USA.ORCID 0000-0002-1708-5675
University of California San Diego · USUniversity of Kansas · US

Funding

The Cardiac Atlas ProjectR01HL121754 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MCCULLOCH, ANDREW D., OMENS, JEFFREY H. · 2014 to 2023
$5.0M
CRCNS: Biophysical modeling of axonal morphology and functionR01MH139350 · NIMH · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Padmini Rangamani · 2024 to 2026
$1.1M
Joe and Clara Tsai FoundationNHLBI NIH HHS R01 HL121754NIMH NIH HHS R01 MH139350Wu Tsai Human Performance Alliance
6 · The paper itself

Abstract

Exercise-induced muscle adaptations vary based on exercise modality and intensity. We constructed a signalling network model from 87 published studies of human or rodent skeletal muscle cell responses to endurance or resistance exercise in vivo or simulated exercise in vitro. The network comprises 259 signalling interactions between 120 nodes, representing eight membrane receptors and eight canonical signalling pathways regulating 14 transcriptional regulators, 28 target genes and 12 exercise-induced phenotypes. Using this network, we formulated a logic-based ordinary differential equation model predicting time-dependent molecular and phenotypic alterations following acute endurance and resistance exercises. Compared with nine independent studies, the model accurately predicted 18/21 (85%) acute responses to resistance exercise and 12/16 (75%) acute responses to endurance exercise. Detailed sensitivity analysis of differential phenotypic responses to resistance and endurance training showed that, in the model, exercise regulates cell growth and protein synthesis primarily by signalling via mechanistic target of rapamycin, which is activated by Akt and inhibited in endurance exercise by AMP-activated protein kinase. Endurance exercise preferentially activates inflammation via reactive oxygen species and nuclear factor κB signalling. Furthermore, the expected preferential activation of mitochondrial biogenesis by endurance exercise was counterbalanced in the model by protein kinase C in response to resistance training. This model provides a new tool for investigating cross-talk between skeletal muscle signalling pathways activated by endurance and resistance exercise, and the mechanisms of interactions such as the interference effects of endurance training on resistance exercise outcomes.

Indexed as

Muscle, SkeletalPhysical EnduranceResistance TrainingSignal TransductionAdaptation, PhysiologicalAnimalsExerciseHumansModels, Biologicalcomputational modelendurance exerciseexerciseresistance exercisesignalling networkskeletal muscle

Identifiers

PMID38643471
PMCPMC11140181
OpenAlexW4394984473

What Socratic 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 Socratic graph.