The Promise of Systems Biology Approaches for Revealing Host Pathogen Interactions in Malaria

Abstract

Despite global eradication efforts over the past century, malaria remains a devastating public health burden, causing almost half a million deaths annually (WHO, 2016). A detailed understanding of the mechanisms that control malaria infection has been hindered by technical challenges of studying a complex parasite life cycle in multiple hosts. While many interventions targeting the parasite have been implemented, the complex biology of Plasmodium poses a major challenge, and must be addressed to enable eradication. New approaches for elucidating key host-parasite interactions, and predicting how the parasite will respond in a variety of biological settings, could dramatically enhance the efficacy and longevity of intervention strategies. The field of systems biology has developed methodologies and principles that are well poised to meet these challenges. In this review, we focus our attention on the Liver Stage of the Plasmodium lifecycle and issue a "call to arms" for using systems biology approaches to forge a new era in malaria research. These approaches will reveal insights into the complex interplay between host and pathogen, and could ultimately lead to novel intervention strategies that contribute to malaria eradication.

Keywords: computational modeling; liver; malaria; omics-technologies; plasmodium; systems biology.

Figure 1

Plasmodium life cycle. (A) Each stage of the malaria life cycle is accompanied by unique transcriptional or translational changes, which ultimately allow for successful transition to each stage of the life cycle. Red Blood Cell is abbreviated “RBC.” (B) Liver stage infection of a hepatocyte is a unique microenvironment that allows the parasite to invad and differentiate into several forms to ensure growth, replication, and eventual egress from the hepatocyte. These key transitions occur in specific subcellular locations during liver stage infection.

Figure 2

The processes involved in generating systems biology-informed models. To inform the biological question, quantitative datasets are generated, which are then used in quantitative analysis, data visualization, and modeling to describe how the system behaves. These tools can be used interchangeably and/or in succession before further refining the model. Refinement of the model can then provide new biological insights.