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Visualizing metabolic network dynamics through time-series metabolomics data

View ORCID ProfileLea F Buchweitz, View ORCID ProfileJames T Yurkovich, Christoph Blessing, Veronika Kohler, Fabian Schwarzkopf, View ORCID ProfileZachary A King, Laurence Yang, Freyr Johannsson, View ORCID ProfileOlafur Sigurjonsson, View ORCID ProfileOttar Rolfsson, View ORCID ProfileJulian Heinrich, View ORCID ProfileAndreas Draeger
doi: https://doi.org/10.1101/426106
Lea F Buchweitz
University of Tuebingen, 72076 Tuebingen, Germany;
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James T Yurkovich
Institute for Systems Biology, Seattle, WA 98109, United States;
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Christoph Blessing
University of Tuebingen, 72076 Tuebingen, Germany;
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Veronika Kohler
University of Tuebingen, 72076 Tuebingen, Germany;
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Fabian Schwarzkopf
yWorks GmbH, Vor dem Kreuzberg 28, 72070 Tuebingen, Germany;
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Zachary A King
Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, United States;
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Laurence Yang
Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, United States;
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Freyr Johannsson
Center for Systems Biology, University of Iceland, Reykjavik, Iceland;
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Olafur Sigurjonsson
The Blood Bank, Landspitali-University Hospital, Reykjavik, Iceland
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Ottar Rolfsson
Center for Systems Biology, University of Iceland, Reykjavik, Iceland;
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Julian Heinrich
University of Tuebingen, 72076 Tuebingen, Germany;
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Andreas Draeger
University of Tuebingen, 72076 Tuebingen, Germany;
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  • For correspondence: dr.andreas.draeger@gmail.com
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Abstract

New technologies have given rise to an abundance of -omics data, particularly metabolomics data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of new computational visualization methodologies. Here, we present a new method for the visualization of time-course metabolomics data within the context of metabolic network maps. We demonstrate the utility of this method by examining previously published data for two cellular systems - the human platelet and erythrocyte under cold storage for use in transfusion medicine. The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation which mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures. In conclusion, this new visualization technique introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types.

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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-ND 4.0 International license.
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Posted September 26, 2018.
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Visualizing metabolic network dynamics through time-series metabolomics data
Lea F Buchweitz, James T Yurkovich, Christoph Blessing, Veronika Kohler, Fabian Schwarzkopf, Zachary A King, Laurence Yang, Freyr Johannsson, Olafur Sigurjonsson, Ottar Rolfsson, Julian Heinrich, Andreas Draeger
bioRxiv 426106; doi: https://doi.org/10.1101/426106
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Visualizing metabolic network dynamics through time-series metabolomics data
Lea F Buchweitz, James T Yurkovich, Christoph Blessing, Veronika Kohler, Fabian Schwarzkopf, Zachary A King, Laurence Yang, Freyr Johannsson, Olafur Sigurjonsson, Ottar Rolfsson, Julian Heinrich, Andreas Draeger
bioRxiv 426106; doi: https://doi.org/10.1101/426106

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