Large–scale data–driven network analysis of human–plasmodium falciparum interactome: extracting essential targets and processes for malaria drug discovery

Background: Plasmodium falciparum malaria is an infectious disease considered to have great impact on public health due to its associated high mortality rates especially in sub Saharan Africa. Falciparum drugresistant strains, notably, to chloroquine and sulfadoxine-pyrimethamine in Africa is traced mainly to Southeast Asia where artemisinin resistance rate is increasing. Although careful surveillance to monitor the emergence and spread of artemisinin-resistant parasite strains in Africa is on-going, research into new drugs, particularly, for African populations, is critical since there is no replaceable drug for artemisinin combination therapies (ACTs) yet. Objective: The overall objective of this study is to identify potential protein targets through host–pathogen protein–protein functional interaction network analysis to understand the underlying mechanisms of drug failure and identify those essential targets that can play their role in predicting potential drug candidates specific to the African populations through a protein-based approach of both host and Plasmodium falciparum genomic analysis. Methods: We leveraged malaria-specific genome wide association study summary statistics data obtained from Gambia, Kenya and Malawi populations, Plasmodium falciparum selective pressure variants and functional datasets (protein sequences, interologs, host-pathogen intra-organism and host-pathogen inter-organism protein-protein interactions (PPIs)) from various sources (STRING, Reactome, HPID, Uniprot, IntAct and literature) to construct overlapping functional network for both host and pathogen. Developed algorithms and a large-scale data-driven computational framework were used in this study to analyze the datasets and the constructed networks to identify densely connected subnetworks or hubs essential for network stability and integrity. The host-pathogen network was analyzed to elucidate the influence of parasite candidate key proteins within the network and predict possible resistant pathways due to host-pathogen candidate key protein interactions. We performed biological and pathway enrichment analysis on critical proteins identified to elucidate their functions. In order to leverage disease-target-drug relationships to identify potential repurposable already approved drug candidates that could be used to treat malaria, pharmaceutical datasets from drug bank were explored using semantic similarity approach based of target–associated biological processes Results: About 600,000 significant SNPs (p-value< 0.05) from the summary statistics data were mapped to their associated genes, and we identified 79 human-associated malaria genes. The assembled parasite network comprised of 8 clusters containing 799 functional interactions between 155 reviewed proteins of which 5 clusters contained 43 key proteins (selective variants) and 2 clusters contained 2 candidate key proteins(key proteins characterized by high centrality measure), C6KTB7 and C6KTD2. The human network comprised of 32 clusters containing 4,133,136 interactions between 20,329 unique reviewed proteins of which 7 clusters contained 760 key proteins and 2 clusters contained 6 significant human malaria-associated candidate key proteins or genes P22301 (IL10), P05362 (ICAM1), P01375 (TNF), P30480 (HLA-B), P16284 (PECAM1), O00206 (TLR4). The generated host-pathogen network comprised of 31,512 functional interactions between 8,023 host and pathogen proteins. We also explored the association of pfk13 gene within the host-pathogen. We observed that pfk13 cluster with host kelch–like proteins and other regulatory genes but no direct association with our identified host candidate key malaria targets. We implemented semantic similarity based approach complemented by Kappa and Jaccard statistical measure to identify 115 malaria–similar diseases and 26 potential repurposable drug hits that can be 3 appropriated experimentally for malaria treatment. Conclusion: In this study, we reviewed existing antimalarial drugs and resistance–associated variants contributing to the diminished sensitivity of antimalarials, especially chloroquine, sulfadoxine-pyrimethamine and artemisinin combination therapy within the African population. We also described various computational techniques implemented in predicting drug targets and leads in drug research. In our data analysis, we showed that possible mechanisms of resistance to artemisinin in Africa may arise from the combinatorial effects of many resistant genes to chloroquine and sulfadoxine–pyrimethamine. We investigated the role of pfk13 within the host–pathogen network. We predicted key targets that have been proposed to be essential for malaria drug and vaccine development through structural and functional analysis of host and pathogen function networks. Based on our analysis, we propose these targets as essential co-targets for combinatorial malaria drug discovery