A PROTEIN INTERACTION NETWORK OF THE MALARIA PARASITE PLASMODIUM FALCIPARUM

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1 A PROTEIN INTERACTION NETWORK OF THE MALARIA PARASITE PLASMODIUM FALCIPARUM DOUGLAS J. LACOUNT, MARISSA VIGNALI, RAKESH CHETTIER, AMIT PHANSALKAR, RUSSELL BELL, JAY R. HESSELBERTH, LORI W. SCHOENFELD, IRENE OTA, SUDHIR SAHASRABUDHE, CORNELIA KURSCHNER, STANLEY FIELDS & ROBERT E. HUGHES Jung Ah (Grace) Lee

2 Why Plasmodium falciparum? Most deadly of the four Plasmodium species that cause human malaria Malaria has a massive impact on human death Around 300 million clinical cases occur each year resulting in between million deaths annually, the majority in sub-saharan Africa

3 Life Cycle of Malaria

4 What can research bring us? Understand the pathways and processes of the parasite Discover new drug and vaccine targets Important because resistance to common drugs are increasing

5 Not so easy! 80% AT content of P. falciparum genome hinders protein expression in heterologous systems Cannot use model organisms Limits both conventional biochemical approaches and comprehensive analyses of this organism s proteins Greater than 60% of the proteins are annotated as hypothetical

6 Then how did the paper do it? Functions of uncharacterized proteins can be inferred from the functions of binding partners Large scale yeast-two hybrid assay To screen for protein-protein interactions BIOINFORMATICS! To characterize the proteins

7 Yeast Two-Hybrid Assay Made libraries that are targeted to genes expressed in the intraerythrocytic-stage of parasites Stage responsible for pathogenesis in humans

8 Yeast-Two Hybrid Assay

9 Yeast-Two Hybrid Assay Positive results were: PCR Sequence PCR products BlastN

10 Results of Yeast-Two Hybrid Assay

11 False Positives! Many proteins are promiscuous Multiple binding partners (up to 207) Then what s real? Ideally, retest all positives and confirm them with an independent method (co-ip and mass spectrometry?) Not ideal for P. falciparum project Expression problems Large scale K-means clustering analysis

belongs to the cluster with the nearest mean In the paper, k=2 (2

12 K-means Clustering Analysis Method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean In the paper, k=2 (2 clusters or 2 populations) Clusters are based on number of interacting partners

13 Results of K-means Clustering Analysis 13 promiscuous prey fragments with more than 31 partners 28 promiscuous bait fragments with more than 25 partners Total removal of 2,155 interactions involving these fragments

14 Results of K-means Clustering Analysis 2,846 unique pair-wise interactions, containing 1,267 proteins core data

15 Core Data

16 Core Data Analysis Core data set includes 23 interactions that were previously identified PFC0255c ubiquitin conjugating enzyme E2 homologous to S. cerevisiae Mms2 PFE1350c ubiquitin-conjugating enzyme homologous to S. cerevisiae Ubc13 Mms2 and Ubc13 interact 82% of the interactions include at least one protein annotated as hypothetical 33% of the interactions include two hypothetical proteins Use bioinformatic analyses to uncover biologically interesting regions of the network

17 Bioinformatic Analysis I Survey regions of the network with higher than expected connectivity High degree of local network interconnectivity can identify sets of functionally related proteins Parsed the network into 1,308 primary subnetworks containing a protein, its direct binding partners and all interactions between them Calculated connectivity coefficient

18 Bioinformatic Analysis I Connectivity coefficient # interaction / # proteins

19 Bioinformatic Analysis I Found regions of highest connectivity in the data set by MCODE

20 Bioinformatic Analysis I Based on these analysis, they identified a group of interacting proteins that may be involved in: chromatin modification transcriptional regulation mrna stability ubiquitination

21 These interactions indicate that chromatin-modifying complexes might be targeted to specific regions in the genome to regulate transcription and are of particular significance given unique features of gene expression of the parasite and the absence of recognizable transcription factors encoded by the genome

22 This group seems analogous to Ccr4-Not, which also integrates transcription regulation, chromatin modification, ubiquitination and RNA stability

23 Bioinformatic Analysis II Examined the relationship between P. falciparum protein interaction data and mrna abundance Interacting proteins are more likely to be co-expressed than non-interacting proteins Microarray data sets available from DeRisi and Winzeler labs (mrna abundace) Grouped into clusters of proteins that are co-expressed

24 Bioinformatic Analysis II For each protein pair, calculated Pearson correlation coefficient (PCCs), which measures the degree of correlation Cluster 15 of microarray: contains proteins implicated in the invasion of host cells, including Merozoite Surface Protein 1 (MSP1, PFI1475w) Essential protein that coats the surface of merozoites and is thought to be required for the invasion of red blood cells Potential vaccine target

25 Bioinformatic Analysis II Contains several conserved blocks of sequence, some of which establish interactions with uncharacterized, co-expressed proteins that might also have a function in the invasion of host cells

26 Identified 103 interactions among 89 proteins Deduce possible biological interactions that are involved in the invasion of red blood cells

27 Bioinformatic Analysis III Because specific protein domains are often associated with discrete biological processes, enrichment of particular domains in subnetworks can implicate proteins relevant to a process Similarly, enrichment of proteins sharing common Gene Ontology (GO) annotations can also implicate proteins from a subnetwork in biological processes Molecular function & biological processes

28 Bioinformatic Analysis III Searched the P. falciparum interaction network for primary subnetworks associated with protein domains or GO annotations

29 Identified several subnetworks with an enrichment of RNA recognition motifs (RRM)

30 Given that proteins containing SNF2 domains are involved in chromatin remodeling, PFI1715w might provide a link between gene expression and splicing

33 Conclusion Speculate possible functions to uncharacterized proteins Identify novel parasite pathways Provide a resource for the malaria research community Some future direction: Identify host factors required for P. falciparum growth Better understand regulation of gene expression in P. falciparum Ultimately lead to better drugs and vaccines.

34 References Douglas J. LaCount Powerpoint Presentation LaCount, DJ. et al A protein interaction network of the malaria parasite Plasmodium falciparum. Nature 438, (2005). Le Roch, K. G. et al Discovery of gene function by expression profiling of the malaria parasite life cycle. Science 301, (2003).

3.1.4 DNA Microarray Technology

3.1.4 DNA Microarray Technology Scientists have discovered that one of the differences between healthy and cancer is which genes are turned on in each. Scientists can compare the gene expression patterns