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Author Notes:

Eberhard O. Voit (Eberhard.voit@bme.gatech.edu)

The authors acknowledge members of the MaHPIC Consortium (http://systemsbiology.emory.edu) for insightful discussion.

The authors declare that they have no competing interests.

The authors declare no conflict of interest.

Subjects:

Keywords:

  • Science & Technology
  • Life Sciences & Biomedicine
  • Biochemistry & Molecular Biology
  • Biophysics
  • Cell Biology
  • Biochemical systems theory
  • Dynamic model
  • Generalized mass action system
  • Malaria
  • Metabolic modeling
  • Transcriptomics
  • SET ENRICHMENT ANALYSIS
  • PURINE METABOLISM
  • PLASMODIUM-FALCIPARUM
  • EXPRESSION PROFILES
  • RHESUS MACAQUES
  • INOSINE
  • GENE
  • ERYTHROPOIESIS
  • INFLAMMATION
  • MONOCYTES

Metabolic modeling helps interpret transcriptomic changes during malaria

Tools:

Journal Title:

BIOCHIMICA ET BIOPHYSICA ACTA-MOLECULAR BASIS OF DISEASE

Volume:

Volume 1864, Number 6

Publisher:

, Pages 2329-2340

Type of Work:

Article | Post-print: After Peer Review

Abstract:

Disease represents a specific case of malfunctioning within a complex system. Whereas it is often feasible to observe and possibly treat the symptoms of a disease, it is much more challenging to identify and characterize its molecular root causes. Even in infectious diseases that are caused by a known parasite, it is often impossible to pinpoint exactly which molecular profiles of components or processes are directly or indirectly altered. However, a deep understanding of such profiles is a prerequisite for rational, efficacious treatments. Modern omics methodologies are permitting large-scale scans of some molecular profiles, but these scans often yield results that are not intuitive and difficult to interpret. For instance, the comparison of healthy and diseased transcriptome profiles may point to certain sets of involved genes, but a host of post-transcriptional processes and regulatory mechanisms renders predictions regarding metabolic or physiological consequences of the observed changes in gene expression unreliable. Here we present proof of concept that dynamic models of metabolic pathway systems may offer a tool for interpreting transcriptomic profiles measured during disease. We illustrate this strategy with the interpretation of expression data of genes coding for enzymes associated with purine metabolism. These data were obtained during infections of rhesus macaques (Macaca mulatta) with the malaria parasite Plasmodium cynomolgi or P. coatneyi. The model-based interpretation reveals clear patterns of flux redistribution within the purine pathway that are consistent between the two malaria pathogens and are even reflected in data from humans infected with P. falciparum. This article is part of a Special Issue entitled: Accelerating Precision Medicine through Genetic and Genomic Big Data Analysis edited by Yudong Cai & Tao Huang.

Copyright information:

© 2017 Elsevier B.V.

This is an Open Access work distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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