Evidence map›Paper›PMID 31126584›Full record

ArticleBiophysical journal2019

Computational Tool to Study Perturbations in Muscle Regulation and Its Application to Heart Disease.

Samantha K Barrick, Sarah R Clippinger, Lina Greenberg, Michael J Greenberg

Abstract read
In one paragraph

Article in Biophysical journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
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

12 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Hypertrophic cardiomyopathy β-cardiac myosin mutation (P710R) leads to hypercontractility by disrupting super relaxed state.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
  8. Article
  9. Article
  10. Article
  11. Variant R94C inJournal of the American Heart Association · 2020
    Article
  12. Disrupted mechanobiology links the molecular and cellular phenotypes in familial dilated cardiomyopathy.Proceedings of the National Academy of Sciences of the United States of America · 2019
    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

4 authors.

Samantha K BarrickDepartment of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri.
Sarah R ClippingerDepartment of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri.
Lina GreenbergDepartment of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri.
Michael J GreenbergDepartment of Biochemistry and Molecular Biophysics, Washington University School of Medicine, St. Louis, Missouri. Electronic address: greenberg@wustl.edu.

Funding

Regulation of cardiac power output in health and diseaseR01HL141086 · NHLBI · WASHINGTON UNIVERSITY · PI GREENBERG, MICHAEL J · 2018 to 2022
$2.7M
INTERDISCIPLINARY TRAINING IN MECHANOBIOLOGY FROM NM TO CMT32EB018266 · NIBIB · WASHINGTON UNIVERSITY · PI BAYLY, PHILIP V · 2014 to 2018
$987k
Regulation of cardiac power output in health and diseaseR00HL123623 · NHLBI · WASHINGTON UNIVERSITY · PI GREENBERG, MICHAEL J · 2015 to 2017
$733k
NHLBI NIH HHS R00 HL123623NHLBI NIH HHS R01 HL141086NIBIB NIH HHS T32 EB018266
6 · The paper itself

Abstract

Striated muscle contraction occurs when myosin thick filaments bind to thin filaments in the sarcomere and generate pulling forces. This process is regulated by calcium, and it can be perturbed by pathological conditions (e.g., myopathies), physiological adaptations (e.g., β-adrenergic stimulation), and pharmacological interventions. Therefore, it is important to have a methodology to robustly determine the impact of these perturbations and statistically evaluate their effects. Here, we present an approach to measure the equilibrium constants that govern muscle activation, estimate uncertainty in these parameters, and statistically test the effects of perturbations. We provide a MATLAB-based computational tool for these analyses, along with easy-to-follow tutorials that make this approach accessible. The hypothesis testing and error estimation approaches described here are broadly applicable, and the provided tools work with other types of data, including cellular measurements. To demonstrate the utility of the approach, we apply it to elucidate the biophysical mechanism of a mutation that causes familial hypertrophic cardiomyopathy. This approach is generally useful for studying muscle diseases and therapeutic interventions that target muscle contraction.

Indexed as

Computational BiologyCardiomyopathy, HypertrophicHeart DiseasesModels, CardiovascularMusclesMutationMyosin SubfragmentsUncertaintyMyosin Subfragments

Identifiers

PMID31126584
PMCPMC6588827

What OpenQuestion holds

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Registered trials

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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.