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Review
. 2018 Jul;34(7):617-632.
doi: 10.1016/j.pt.2018.04.003. Epub 2018 May 18.

Systems Biology-Based Investigation of Host-Plasmodium Interactions

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Review

Systems Biology-Based Investigation of Host-Plasmodium Interactions

Maren L Smith et al. Trends Parasitol. 2018 Jul.

Abstract

Malaria is a serious, complex disease caused by parasites of the genus Plasmodium. Plasmodium parasites affect multiple tissues as they evade immune responses, replicate, sexually reproduce, and transmit between vertebrate and invertebrate hosts. The explosion of omics technologies has enabled large-scale collection of Plasmodium infection data, revealing systems-scale patterns, mechanisms of pathogenesis, and the ways that host and pathogen affect each other. Here, we provide an overview of recent efforts using systems biology approaches to study host-Plasmodium interactions and the biological themes that have emerged from these efforts. We discuss some of the challenges in using systems biology for this goal, key research efforts needed to address those issues, and promising future malaria applications of systems biology.

Keywords: Plasmodium; host–pathogen interaction; malaria; omics; systems biology.

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Figures

Figure 1:
Figure 1:
Life cycle of Plasmodium parasites including developmental stages in vertebrate and invertebrate hosts and transmission between hosts. Asterisks indicate the omic studies (see Table 3 for specific references) that have been performed for the different life cycle stages: black asterisks indicate transcriptomics studies; blue asterisks indicate proteomics studies; and red asterisks indicate metabolomics and/or lipidomics studies. The crescent-shaped gametocytes depicted represent the morphology in Plasmodium falciparum; other Plasmodium species have rounded/brick-shaped gametocytes.
Figure 2:
Figure 2:
Overview of omics data types, methods for generation and analysis of the data, and strategies for integration across data types and the expected information to be learned from such analyses. Solid lines represent direct integration of data types; dotted lines represent inference of one data type from another, with potential validation using experimental measurements of the inferred values.

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