Transcriptomic and network analyses combine to identify genes that drive the red blood cell cycle of Plasmodium falciparum. (2017)

• Record Type: Journal Article
• Authors: Yu, Xinran; Zhang, Hao; Lilburn, Timothy G.; Cai, Hong; Gu, Jianying; Korkmaz, Turgay; Wang, Yufeng
• Is Part Of: International journal of data mining and bioinformatics. Volume 18:Number 3(2017)
• Page Start: 179
• Page End: 195
• Publication Date: 2017
• Subjects: malaria -- development cycle -- RNA-Seq -- PageRank -- systems biology -- Plasmodium falciparum; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

Despite coordinated attempts to control or eliminate it, malaria remains a widespread public health problem, with half the world's population (3.2 billion people) at risk. While the annual death toll attributed to malaria has declined in recent years, the mortality is still very high. In 2015 the World Health Organisation estimates that between 236, 000 and 635, 000 people died, and the disease cost the continent of Africa, where 91% of cases occur, about USD 12 billion. The contribution of genomics to the defeat of malaria has been relatively small until recently. Although genomic data is available, much of it is difficult to interpret, as this parasite has no well-studied close relatives. This has led to a need for computationally-driven tools that will help us understand the dynamic cellular networks in the malaria parasite. This understanding, in turn, will help us identify new antimalarial targets in the parasite. Here, we coupled RNA-Seq analysis and network mining using a PageRank-based algorithm, and examined the temporal-specific expression of parasite genes during the 48-hour red blood cycle. We identified genes that appear to influence parasite development and red blood cell invasion. The just-in-time mechanism for gene expression may contribute to a dynamic and effective adaptive strategy of the malaria parasite.