Evidence map›Paper›PMID 33338038›Full record

ArticlePLoS computational biology2020

Context-specific network modeling identifies new crosstalk in β-adrenergic cardiac hypertrophy.

Ali Khalilimeybodi, Alexander M Paap, Steven L M Christiansen, Jeffrey J Saucerman

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
0.9field-weighted citation impact, top 26% 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

18 citing papers in PubMed, 27 citations in OpenAlex.

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  8. Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
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  17. Emerging therapeutic targets for cardiac hypertrophy.Expert opinion on therapeutic targets · 2022
    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

4 authors at 1 institution in 1 country.

Ali KhalilimeybodiDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.ORCID 0000-0001-9318-8433
Alexander M PaapDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.ORCID 0000-0001-8092-8105
Steven L M ChristiansenDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.ORCID 0000-0003-0168-4881
Jeffrey J SaucermanDepartment of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, United States of America.ORCID 0000-0001-9464-8374
University of Virginia · US

Funding

Data Coordination and Integration Center for LINCS-BD2KU54HL127624 · NHLBI · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI MA'AYAN, AVI, SCHURER, STEPHAN C · 2014 to 2019
$23.5M
Systems Biology of Hypertrophic Heart Disease from Molecular Pathways to Organ SystemR01HL137100 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MCCULLOCH, ANDREW D. · 2017 to 2020
$2.0M
Systems Pharmacology Model for Spatial Control of Cardiac FibrosisR01HL137755 · NHLBI · UNIVERSITY OF VIRGINIA · PI HOLMES, JEFFREY W, SAUCERMAN, JEFFREY J. · 2017 to 2020
$1.9M
NHLBI NIH HHS R01 HL137100NHLBI NIH HHS R01 HL137755NHLBI NIH HHS U54 HL127624
6 · The paper itself

Abstract

Cardiac hypertrophy is a context-dependent phenomenon wherein a myriad of biochemical and biomechanical factors regulate myocardial growth through a complex large-scale signaling network. Although numerous studies have investigated hypertrophic signaling pathways, less is known about hypertrophy signaling as a whole network and how this network acts in a context-dependent manner. Here, we developed a systematic approach, CLASSED (Context-specific Logic-bASed Signaling nEtwork Development), to revise a large-scale signaling model based on context-specific data and identify main reactions and new crosstalks regulating context-specific response. CLASSED involves four sequential stages with an automated validation module as a core which builds a logic-based ODE model from the interaction graph and outputs the model validation percent. The context-specific model is developed by estimation of default parameters, classified qualitative validation, hybrid Morris-Sobol global sensitivity analysis, and discovery of missing context-dependent crosstalks. Applying this pipeline to our prior-knowledge hypertrophy network with context-specific data revealed key signaling reactions which distinctly regulate cell response to isoproterenol, phenylephrine, angiotensin II and stretch. Furthermore, with CLASSED we developed a context-specific model of β-adrenergic cardiac hypertrophy. The model predicted new crosstalks between calcium/calmodulin-dependent pathways and upstream signaling of Ras in the ISO-specific context. Experiments in cardiomyocytes validated the model's predictions on the role of CaMKII-Gβγ and CaN-Gβγ interactions in mediating hypertrophic signals in ISO-specific context and revealed a difference in the phosphorylation magnitude and translocation of ERK1/2 between cardiac myocytes and fibroblasts. CLASSED is a systematic approach for developing context-specific large-scale signaling networks, yielding insights into new-found crosstalks in β-adrenergic cardiac hypertrophy.

Indexed as

Computer SimulationAnimalsCardiomegalyCells, CulturedMyocytes, CardiacPhosphorylationRatsRats, Sprague-DawleyReceptors, Adrenergic, betaSignal TransductionReceptors, Adrenergic, beta

Identifiers

PMID33338038
PMCPMC7781532
OpenAlexW3110963325

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.