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A computational framework to explore cellular response mechanisms from multi-omics datasets

James C. Pino, View ORCID ProfileAlexander L. R. Lubbock, View ORCID ProfileLeonard A. Harris, View ORCID ProfileDanielle B. Gutierrez, Melissa A. Farrow, Nicole Muszynski, Tina Tsui, View ORCID ProfileJeremy L. Norris, Richard M. Caprioli, View ORCID ProfileJohn P. Wikswo, View ORCID ProfileCarlos F. Lopez
doi: https://doi.org/10.1101/2020.03.02.974121
James C. Pino
1Chemical and Physical Biology Graduate Program, Vanderbilt University, Nashville, TN, USA
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Alexander L. R. Lubbock
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
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  • ORCID record for Alexander L. R. Lubbock
Leonard A. Harris
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
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Danielle B. Gutierrez
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
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Melissa A. Farrow
3Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, TN, USA
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Nicole Muszynski
4Department of Surgery, Vanderbilt University School of Medicine, Nashville, TN, USA
6Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, USA
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Tina Tsui
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
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Jeremy L. Norris
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
5Department of Chemistry, Vanderbilt University, Nashville, TN, USA
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Richard M. Caprioli
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
5Department of Chemistry, Vanderbilt University, Nashville, TN, USA
10Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
11Department of Medicine, Vanderbilt University, Nashville, TN, USA
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John P. Wikswo
6Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, USA
7Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, USA
8Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
12Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
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Carlos F. Lopez
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
9Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN, USA
10Department of Pharmacology, Vanderbilt University School of Medicine, Nashville, TN, USA
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  • For correspondence: c.lopez@vanderbilt.edu
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Abstract

Recent technological advances have made it feasible to collect multi-condition transcriptome and proteome time-courses of cellular response to perturbation. The increasing size and complexity of these datasets impedes mechanism of action discovery due to challenges in data management, analysis, visualization, and interpretation. Here, we introduce MAGINE, a software framework to explore complex time-course multi-omics datasets and build mechanistic hypotheses of dynamic cellular response. MAGINE combines data management, enrichment, and network analysis and visualization within an interactive, Jupyter notebook-based environment to enable human-in-the-loop inquiry of complex datasets. We demonstrate how measurements from HL-60 cellular response to bendamustine treatment can be used to build a mechanistic, multi-resolution description of cellular commitment to fate. We present a systems-level description of signal execution from cellular DNA-damage response, to cell cycle arrest, and eventual commitment to apoptosis, mediated by over 2000 biochemical species. We further show that MAGINE can reveal unexpected, non-canonical effects of bendamustine treatment, including disruption of cellular pathways relevant to HIV infection response. MAGINE is available from https://github.com/lolab-vu/magine.

Footnotes

  • https://github.com/lolab-vu/magine

  • https://github.com/LoLab-VU/MAGINE_Supplement_notebooks

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted March 03, 2020.
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A computational framework to explore cellular response mechanisms from multi-omics datasets
James C. Pino, Alexander L. R. Lubbock, Leonard A. Harris, Danielle B. Gutierrez, Melissa A. Farrow, Nicole Muszynski, Tina Tsui, Jeremy L. Norris, Richard M. Caprioli, John P. Wikswo, Carlos F. Lopez
bioRxiv 2020.03.02.974121; doi: https://doi.org/10.1101/2020.03.02.974121
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A computational framework to explore cellular response mechanisms from multi-omics datasets
James C. Pino, Alexander L. R. Lubbock, Leonard A. Harris, Danielle B. Gutierrez, Melissa A. Farrow, Nicole Muszynski, Tina Tsui, Jeremy L. Norris, Richard M. Caprioli, John P. Wikswo, Carlos F. Lopez
bioRxiv 2020.03.02.974121; doi: https://doi.org/10.1101/2020.03.02.974121

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