Computation Identification of Signalling and Metabolic Pathways of Plasmodium Falciparum
Abstract:
Malaria is one of the world’s most common and serious diseases causing death up to about three million people each year. Its most severe occurrence is caused by the protozoan Plasmodium falciparum. Reports have shown that the resistance of the parasite to existing drugs is increasing. Therefore, there is a huge and urgent need to discover and validate new drug or vaccine targets to enable the development of new treatments for malaria. The ability to discover these drug or vaccine targets can only be enhanced from our deep understanding of the detailed biology of the parasite, for example, how cells function and how proteins organize into modules such as metabolic, regulatory and signal transduction pathways. The formally effective and popular anti-malaria drug chloroquine inhibits multiple sites in metabolic pathways, leading to neutrophil superoxide release. It has therefore been noted that the knowledge of metabolic pathways and recently signalling transduction pathways in Plasmodium are fundamental to aid the design of new strategies against malaria. In the first part of this work, a linear-time algorithm for finding paths in a protein-protein interactions network under modified biologically motivated constraints was used. Several important signalling transduction pathways in Plasmodium falciparum were predicted. A viable signalling pathway characterized in terms of the genes responsible that may be the PfPKB pathway recently elucidated in Plasmodium falciparum was predicted. We obtained from the FIKK family, a signal transduction pathway that ends upon a chloroquine resistance marker protein, which indicates that interference with FIKK proteins might reverse Plasmodium falciparum from resistant to sensitive phenotype. We also propose a hypothesis that showed the FIKK proteins in this pathway as enabling the resistance parasite to have a mechanism for releasing chloroquine(via an efflux process). Furthermore, a signalling pathway that may have been responsible for signalling the start of the invasion process of Red Blood Cell(RBC) by the merozoites was also predicted. It has been noted that the understanding of this pathway will give insight into the parasite virulence and will facilitate rational vaccine design against merozoites invasion. And we have a host of other predicted pathways, some of which have been used in this work to predict the functionality of some proteins. In another work, we adapted and extended a method (used in the first work for extracting signalling pathways) to extract linear metabolic pathways from the malaria parasite, Plasmodium falciparum metabolic weighted graphs (networks). The weights are calculated using the metabolite degrees. Adopting the representation of the biochemical metabolic network as we have in Koenig et al., 2006, we are able to make our algorithm tenable to accept metabolic network from other source apart from KEGG. This gives us opportunity for the first time, to compare the metabolic pathways extracted from different metabolic networks. We run our algorithm (for four selected pathways: Pyruvate, Glutamate, Glycolysis and Mitochondrial TCA) on graph from KEGG and compare our results with the results obtained from recent algorithms: ReTrace and xvi atommetanet. Our results compare favourably with these two algorithms. Considering the results with genes classified into these pathways from Plasmodb, resulted into a lot of false positiveness. Furthermore, we compared the runs of our algorithm on graphs from KEGG and PlasmoCyc (from BioCyc). The results are remarkably different and the results from PlasmoCyc produced less false positiveness when compared to the results from Plasmodb. We identify 2, 1, 2, 4 gene(s) in addition to belong to these pathways respectively. Some of the genes have not been classified earlier to any known metabolic pathwaysORDER COMPLETE MATERIAL (CHAPTER 1-5)
