Computational 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

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 pathways.