Predicting malaria interactome classifications from time-course transcriptomic data along the intraerythrocytic developmental cycle

Reconstruction and Application of Protein-Protein Interaction Network

The protein-protein interaction network (PIN) is a useful tool for systematic investigation of the complex biological activities in the cell. With the increasing interests on the proteome-wide interaction networks, PINs have been reconstructed for many species, including virus, bacteria, plants, animals, and humans. With the development of biological techniques, the reconstruction methods of PIN are further improved. PIN has gradually penetrated many fields in biological research. In this work we systematically reviewed the development of PIN in the past fifteen years, with respect to its reconstruction and application of function annotation, subsystem investigation, evolution analysis, hub protein analysis, and regulation mechanism analysis. Due to the significant role of PIN in the in-depth exploration of biological process mechanisms, PIN will be preferred by more and more researchers for the systematic study of the protein systems in various kinds of organisms.

Module-based subnetwork alignments reveal novel transcriptional regulators in malaria parasite Plasmodium falciparum

Background

Malaria causes over one million deaths annually, posing an enormous health and economic burden in endemic regions. The completion of genome sequencing of the causative agents, a group of parasites in the genus Plasmodium, revealed potential drug and vaccine candidates. However, genomics-driven target discovery has been significantly hampered by our limited knowledge of the cellular networks associated with parasite development and pathogenesis. In this paper, we propose an approach based on aligning neighborhood PPI subnetworks across species to identify network components in the malaria parasite P. falciparum.

Results

Instead of only relying on sequence similarities to detect functional orthologs, our approach measures the conservation between the neighborhood subnetworks in protein-protein interaction (PPI) networks in two species, P. falciparum and E. coli. 1,082 P. falciparum proteins were predicted as functional orthologs of known transcriptional regulators in the E. coli network, including general transcriptional regulators, parasite-specific transcriptional regulators in the ApiAP2 protein family, and other potential regulatory proteins. They are implicated in a variety of cellular processes involving chromatin remodeling, genome integrity, secretion, invasion, protein processing, and metabolism.

Conclusions

In this proof-of-concept study, we demonstrate that a subnetwork alignment approach can reveal previously uncharacterized members of the subnetworks, which opens new opportunities to identify potential therapeutic targets and provide new insights into parasite biology, pathogenesis and virulence. This approach can be extended to other systems, especially those with poor genome annotation and a paucity of knowledge about cellular networks.

Comparative Genomics and Systems Biology of Malaria Parasites Plasmodium

Malaria is a serious infectious disease that causes over one million deaths yearly. It is caused by a group of protozoan parasites in the genus Plasmodium. No effective vaccine is currently available and the elevated levels of resistance to drugs in use underscore the pressing need for novel antimalarial targets. In this review, we survey omics centered developments in Plasmodium biology, which have set the stage for a quantum leap in our understanding of the fundamental processes of the parasite life cycle and mechanisms of drug resistance and immune evasion.

Defining the protein interaction network of human malaria parasite Plasmodium falciparum

Malaria, caused by the protozoan parasite Plasmodium falciparum, affects around 225 million people yearly and a huge international effort is directed towards combating this grave threat to world health and economic development. Considerable advances have been made in malaria research triggered by the sequencing of its genome in 2002, followed by several high-throughput studies defining the malaria transcriptome and proteome. A protein-protein interaction (PPI) network seeks to trace the dynamic interactions between proteins, thereby elucidating their local and global functional relationships. Experimentally derived PPI network from high-throughput methods such as yeast two hybrid (Y2H) screens are inherently noisy, but combining these independent datasets by computational methods tends to give a greater accuracy and coverage. This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from analysis of the PPI network.