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Network integration and modelling of dynamic drug responses at multi-omics levels.

  • Selevsek N Functional Genomics Center, ETH Zurich, Switzerland.
  • Caiment F Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands.
  • Nudischer R Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
  • Gmuender H Genedata AG, Basel, Switzerland.
  • Agarkova I Insphero AG, Schlieren, Switzerland.
  • Atkinson FL European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
  • Bachmann I MicroDiscovery GmbH, Berlin, Germany.
  • Baier V Institute of Applied Microbiology, RWTH, Aachen, Germany.
  • Barel G Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Berlin, Germany.
  • Bauer C MicroDiscovery GmbH, Berlin, Germany.
  • Boerno S Max-Planck-Institute for Molecular Genetics, Sequencing Unit, Berlin, Germany.
  • Bosc N European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
  • Clayton O Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
  • Cordes H Institute of Applied Microbiology, RWTH, Aachen, Germany.
  • Deeb S Genedata AG, Basel, Switzerland.
  • Gotta S Genedata AG, Basel, Switzerland.
  • Guye P Insphero AG, Schlieren, Switzerland.
  • Hersey A European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
  • Hunter FMI European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
  • Kunz L Functional Genomics Center, ETH Zurich, Switzerland.
  • Lewalle A Department of Biomedical Engineering, King's College London, London, UK.
  • Lienhard M Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Berlin, Germany.
  • Merken J CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • Minguet J European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
  • Oliveira B Department of Biomedical Engineering, King's College London, London, UK.
  • Pluess C Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
  • Sarkans U European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
  • Schrooders Y Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands.
  • Schuchhardt J MicroDiscovery GmbH, Berlin, Germany.
  • Smit I European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK.
  • Thiel C Institute of Applied Microbiology, RWTH, Aachen, Germany.
  • Timmermann B Max-Planck-Institute for Molecular Genetics, Sequencing Unit, Berlin, Germany.
  • Verheijen M Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands.
  • Wittenberger T Genedata AG, Basel, Switzerland.
  • Wolski W Functional Genomics Center, ETH Zurich, Switzerland.
  • Zerck A MicroDiscovery GmbH, Berlin, Germany.
  • Heymans S CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands.
  • Kuepfer L Institute of Applied Microbiology, RWTH, Aachen, Germany.
  • Roth A Roche Pharma Research and Early Development, Roche Innovation Center Basel, Basel, Switzerland.
  • Schlapbach R Functional Genomics Center, ETH Zurich, Switzerland.
  • Niederer S Department of Biomedical Engineering, King's College London, London, UK.
  • Herwig R Department of Computational Molecular Biology, Max-Planck-Institute for Molecular Genetics, Berlin, Germany. herwig@molgen.mpg.de.
  • Kleinjans J Department of Toxicogenomics, Maastricht University, Maastricht, The Netherlands.
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  • 2020-10-16
Published in:
  • Communications biology. - 2020
English Uncovering cellular responses from heterogeneous genomic data is crucial for molecular medicine in particular for drug safety. This can be realized by integrating the molecular activities in networks of interacting proteins. As proof-of-concept we challenge network modeling with time-resolved proteome, transcriptome and methylome measurements in iPSC-derived human 3D cardiac microtissues to elucidate adverse mechanisms of anthracycline cardiotoxicity measured with four different drugs (doxorubicin, epirubicin, idarubicin and daunorubicin). Dynamic molecular analysis at in vivo drug exposure levels reveal a network of 175 disease-associated proteins and identify common modules of anthracycline cardiotoxicity in vitro, related to mitochondrial and sarcomere function as well as remodeling of extracellular matrix. These in vitro-identified modules are transferable and are evaluated with biopsies of cardiomyopathy patients. This to our knowledge most comprehensive study on anthracycline cardiotoxicity demonstrates a reproducible workflow for molecular medicine and serves as a template for detecting adverse drug responses from complex omics data.
Language
  • English
Open access status
gold
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https://folia.unifr.ch/global/documents/36996
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