Evidence map›Paper›PMID 31740673›Full record

ArticleNature communications2019

A network-based approach to identify deregulated pathways and drug effects in metabolic syndrome.

Karla Misselbeck, Silvia Parolo, Francesca Lorenzini, Valeria Savoca, Lorena Leonardelli, Pranami Bora, Melissa J Morine, Maria Caterina Mione, Enrico Domenici, Corrado Priami

Abstract read
In one paragraph

Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
41citing papers in PubMed, 2 pooled it
–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

41 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  19. Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks.International journal of molecular sciences · 2023
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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

10 authors.

Karla MisselbeckFondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Silvia ParoloFondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy. parolo@cosbi.eu.ORCID http://orcid.org/0000-0002-3671-5353
Francesca LorenziniDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
Valeria SavocaDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.
Lorena LeonardelliFondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Pranami BoraFondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Melissa J MorineFondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy.
Maria Caterina MioneDepartment of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, Italy.ORCID http://orcid.org/0000-0002-9040-3705
Enrico DomeniciFondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy. enrico.domenici@unitn.it.ORCID http://orcid.org/0000-0001-7436-6919
Corrado PriamiFondazione The Microsoft Research University of Trento, Centre for Computational and Systems Biology (COSBI), Rovereto, Italy. priami@cosbi.eu.ORCID http://orcid.org/0000-0002-3261-6235

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic syndrome is a pathological condition characterized by obesity, hyperglycemia, hypertension, elevated levels of triglycerides and low levels of high-density lipoprotein cholesterol that increase cardiovascular disease risk and type 2 diabetes. Although numerous predisposing genetic risk factors have been identified, the biological mechanisms underlying this complex phenotype are not fully elucidated. Here we introduce a systems biology approach based on network analysis to investigate deregulated biological processes and subsequently identify drug repurposing candidates. A proximity score describing the interaction between drugs and pathways is defined by combining topological and functional similarities. The results of this computational framework highlight a prominent role of the immune system in metabolic syndrome and suggest a potential use of the BTK inhibitor ibrutinib as a novel pharmacological treatment. An experimental validation using a high fat diet-induced obesity model in zebrafish larvae shows the effectiveness of ibrutinib in lowering the inflammatory load due to macrophage accumulation.

Indexed as

Gene Regulatory NetworksAdenineAnimalsDiet, High-FatDrug RepositioningHumansLipid MetabolismMacrophagesMetabolic SyndromeOrgan SpecificityPharmaceutical PreparationsPiperidinesPyrazolesPyrimidinesReproducibility of ResultsSignal TransductionAdenineibrutinibPharmaceutical PreparationsPiperidinesPyrazolesPyrimidines

Identifiers

PMID31740673
PMCPMC6861239

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.