This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology
Module-based subnetwork alignments reveal novel transcriptional regulators in malaria parasite Plasmodium falciparum
- Equal contributors
1 Department of Biology, University of Texas at San Antonio, San Antonio, TX 78249, USA
2 Department of Computer Science and Engineering, University of Minnesota Twin Cities, Minneapolis, MN 55455, USA
3 Department of Biology, College of Staten Island, City University of New York, Staten Island, NY 10314, USA
4 Department of Bacteriology, American Type Culture Collection, Manassas, VA 20110, USA
5 South Texas Center for Emerging Infectious Diseases, University of Texas at San Antonio, San Antonio, TX 78249, USA
BMC Systems Biology 2012, 6(Suppl 3):S5 doi:10.1186/1752-0509-6-S3-S5Published: 17 December 2012
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.
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.
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.