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Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

Author

Listed:
  • Edik M. Blais

    (University of Virginia)

  • Kristopher D. Rawls

    (University of Virginia)

  • Bonnie V. Dougherty

    (University of Virginia)

  • Zhuo I. Li

    (University of Virginia)

  • Glynis L. Kolling

    (University of Virginia)

  • Ping Ye

    (Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command
    Present address: Department of Molecular and Experimental Medicine, Avera Cancer Institute, Sioux Falls, South Dakota 57105, USA)

  • Anders Wallqvist

    (Telemedicine and Advanced Technology Research Center, US Army Medical Research and Materiel Command)

  • Jason A. Papin

    (University of Virginia)

Abstract

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

Suggested Citation

  • Edik M. Blais & Kristopher D. Rawls & Bonnie V. Dougherty & Zhuo I. Li & Glynis L. Kolling & Ping Ye & Anders Wallqvist & Jason A. Papin, 2017. "Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions," Nature Communications, Nature, vol. 8(1), pages 1-15, April.
  • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms14250
    DOI: 10.1038/ncomms14250
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    Cited by:

    1. Domenic H. Cerri & Daniel L. Albaugh & Lindsay R. Walton & Brittany Katz & Tzu-Wen Wang & Tzu-Hao Harry Chao & Weiting Zhang & Randal J. Nonneman & Jing Jiang & Sung-Ho Lee & Amit Etkin & Catherine N., 2024. "Distinct neurochemical influences on fMRI response polarity in the striatum," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    2. Anne Richelle & Austin W T Chiang & Chih-Chung Kuo & Nathan E Lewis, 2019. "Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions," PLOS Computational Biology, Public Library of Science, vol. 15(4), pages 1-19, April.

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