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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).