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学術論文

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

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Barel,  Gal
Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Boerno,  Stefan
Sequencing (Head: Bernd Timmermann), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Lienhard,  Matthias
Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Timmermann,  Bernd
Sequencing (Head: Bernd Timmermann), Scientific Service (Head: Christoph Krukenkamp), Max Planck Institute for Molecular Genetics, Max Planck Society;

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Herwig,  Ralf
Bioinformatics (Ralf Herwig), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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フルテキスト (公開)

Selevsek_2020.pdf
(出版社版), 4MB

Selevsek_Suppl.Inf..pdf
(出版社版), 3MB

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引用

Selevsek, N., Caiment, F., Nudischer, R., Gmuender, H., Agarkova, I., Atkinson, F. L., Bachmann, I., Baier, V., Barel, G., Bauer, C., Boerno, S., Bosc, N., Clayton, O., Cordes, H., Deeb, S., Gotta, S., Guye, P., Hersey, A., Hunter, F. M. I., Kunz, L., Lewalle, A., Lienhard, M., Merken, J., Minguet, J., Oliveira, B., Pluess, C., Sarkans, U., Schrooders, Y., Schuchhardt, J., Smit, I., Thiel, C., Timmermann, B., Verheijen, M., Wittenberger, T., Wolski, W., Zerck, A., Heymans, S., Kuepfer, L., Roth, A., Schlapbach, R., Niederer, S., Herwig, R., & Kleinjans, J. (2020). Network integration and modelling of dynamic drug responses at multi-omics levels. Communications Biology, 3:. doi:10.1038/s42003-020-01302-8.


引用: https://hdl.handle.net/21.11116/0000-0007-81FB-C
要旨
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.