Abstract:
In temperate and sub-tropical regions of Asia and Latin America, complicated malaria manifested as hepatic dysfunction or renal dysfunction is seen in all age groups. There has been a concerted focus on understanding the patho-physiological and molecular basis of complicated malaria in children, much less is known about it in adults. We report here, the analysis of data from a custom, cross strain microarray (Agilent Platform) using material from adult patient samples, showing hepatic dysfunction or renal failure. These are the most common manifestations seen in adults along with cerebral malaria. The data has been analyzed with reference to variant surface antigens, encoded by the var, rifin and stevor gene families. The differential regulation profiles of key genes (comparison between Plasmodium falciparum complicated and uncomplicated isolates) have been observed. The exportome has been analyzed using similar parameters. Gene ontology term based functional enrichment of differentially regulated genes identified, up-regulated genes statistically enriched (P < 0.05) to critical biological processes like generation of precursor metabolite and energy, chromosome organization and electron transport chain. Systems network based functional enrichment of overall differentially regulated genes yielded a similar result. We are reporting here, up-regulation of var group B and C genes whose proteins are predicted to interact with CD36 receptor in the host, the up-regulation of domain cassette 13 (DC13) containing var group A, as also the up-regulation of group A rifins and many of the stevors. This is contrary to most other reports from pediatric patients, with cerebral malaria where the up-regulation of mostly var A group genes have been seen. A protein–protein interaction based network has been created and analysis performed. This co-expression and text mining based network has shown overall connectivity between the variant surface antigens (VSA) and the exportome. The up-regulation of var group B and C genes encoding PfEMP1 with different domain architecture would be important for deciding strategies for disease prevention.