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dc.contributor.authorSrikakulam, Sanjay K
dc.contributor.authorBastys, Tomas
dc.contributor.authorKalinina, Olga V
dc.date.accessioned2020-07-29T12:05:30Z
dc.date.available2020-07-29T12:05:30Z
dc.date.issued2020-06-12
dc.identifier.citationProteins. 2020;10.1002/prot.25963. doi:10.1002/prot.25963.en_US
dc.identifier.pmid32530065
dc.identifier.doi10.1002/prot.25963
dc.identifier.urihttp://hdl.handle.net/10033/622372
dc.description.abstractIntegrative bioinformatics is an emerging field in the big data era, offering a steadily increasing number of algorithms and analysis tools. However, for researchers in experimental life sciences it is often difficult to follow and properly apply the bioinformatical methods in order to unravel the complexity and systemic effects of omics data. Here, we present an integrative bioinformatics pipeline to decipher crucial biological insights from global transcriptome profiling data to validate innovative therapeutics. It is available as a web application for an interactive and simplified analysis without the need for programming skills or deep bioinformatics background. The approach was applied to an ex vivo cardiac model treated with natural anti-fibrotic compounds and we obtained new mechanistic insights into their anti-fibrotic action and molecular interplay with miRNAs in cardiac fibrosis. Several gene pathways associated with proliferation, extracellular matrix processes and wound healing were altered, and we could identify micro (mi) RNA-21-5p and miRNA-223-3p as key molecular components related to the anti-fibrotic treatment. Importantly, our pipeline is not restricted to a specific cell type or disease and can be broadly applied to better understand the unprecedented level of complexity in big data research.en_US
dc.language.isoenen_US
dc.publisherWileyen_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectKIT tyrosine kinaseen_US
dc.subjectcanceren_US
dc.subjectmolecular dynamics simulationsen_US
dc.subjectmutation Y823Den_US
dc.subjectsecondary resistanceen_US
dc.subjectsignal transductionen_US
dc.titleA shift of dynamic equilibrium between the KIT active and inactive states causes drug resistance.en_US
dc.typeArticleen_US
dc.identifier.eissn1097-0134
dc.contributor.departmentHIPS, Helmholtz-Institut für Pharmazeutische Forschung Saarland, Universitätscampus E8.1 66123 Saarbrücken, Germany.en_US
dc.identifier.journalProteinsen_US
refterms.dateFOA2020-07-29T12:05:32Z
dc.source.journaltitleProteins
dc.source.countryUnited States


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Attribution-NonCommercial-ShareAlike 4.0 International
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