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

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 1.5-2.7 million deaths annually, the

majority in sub-saharan Africa

http://www.sanger.ac.uk/Projects/P_falciparum/

Life Cycle of Malaria

http://www.emro.who.int/rbm/Images/MalariaLifeCycle-1.gif

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

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

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

Yeast Two-Hybrid Assay

Made libraries that

are targeted to genes

expressed in the intra-

erythrocytic-stage of

parasites

Stage responsible for

pathogenesis in humans

Yeast-Two Hybrid Assay

Yeast-Two Hybrid Assay

Positive results were:

PCR

Sequence PCR

products

BlastN

Results of Yeast-Two Hybrid Assay

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

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

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

Results of K-means Clustering Analysis

2,846 unique pair-wise interactions, containing

1,267 proteins core data

Core Data

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

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

Bioinformatic Analysis I

Connectivity coefficient

# interaction / # proteins

Bioinformatic Analysis I

Found regions of highest connectivity in the data set by

MCODE

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

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

This group seems analogous to Ccr4-Not, which also integrates

transcription regulation, chromatin modification, ubiquitination and RNA

stability

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

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

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

Identified 103 interactions among 89 proteins

Deduce possible biological interactions that are involved in the invasion

of red blood cells

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

Bioinformatic Analysis III

Searched the P. falciparum interaction network for

primary subnetworks associated with protein

domains or GO annotations

Identified several subnetworks with an enrichment of RNA

recognition motifs (RRM)

Given that proteins containing SNF2 domains are involved in chromatin

remodeling, PFI1715w might provide a link between gene expression

and splicing

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.

References

Douglas J. LaCount Powerpoint Presentation

LaCount, DJ. et al A protein interaction network of

the malaria parasite Plasmodium falciparum.

Nature 438, 103-107 (2005).

Le Roch, K. G. et al Discovery of gene function by

expression profiling of the malaria parasite life

cycle. Science 301, 503-508 (2003).