Supplemental Information
Computational modeling of the Plasmodium falciparum interactome
reveals protein function on a genome-wide scale
Shailesh V. Date and Christian J. Stoeckert, Jr
Center for Bioinformatics, Department of Genetics
University of Pennsylvania School of Medicine,
Philadelphia, PA 19104
Filtering of in silico data sets
Phylogenetic profiles (Pellegrini et al. 1999) for 5,334 P. falciparum proteins were
constructed by comparing their amino acid sequences with 163 genomes. These data,
including data from one additional genome (Aeropyrum pernix K1; 164 genomes in all),
were also examined for presence of Rosetta stone (RS) fusion proteins (Marcotte et al.
1999). Phylogenetic profiles of proteins present in P. falciparum and at least one other
organism were retained. This filtering reduced our data set to 2,813 protein profiles.
Linkages for which evidence of a fusion protein was available only in P. falciparum were
excluded from our analysis, leaving us with 5,176 RS linkages between 993 proteins.
Creating gold standards using KEGG and GO annotations
Gold standards were created using pathway annotations available from the KEGG
database (Kanehisa et al. 2004) and gene ontology process (GO Process) (The Gene
Ontology Consortium, 2004) annotations as supplied by the sequencing centers (the
1
Wellcome Trust Sanger Institute, The Institute for Genomic Research (TIGR) and the
Stanford Genome Technology Center), downloaded from PlasmoDB
(http://www.plasmodb.org). KEGG annotations have proven reliable benchmarks, as have
GO annotations (Lehner and Fraser, 2004), as well as combinations of both KEGG and
GO (Lee et al. 2004; Ramani et al. 2005).
Given the non-redundant nature of KEGG and GO annotations (Bork et al. 2004; Lee et
al. 2004), we used GO annotations to filter KEGG negatives, which lead to improvement
in their quality. When using GO, references to 3 broad GO categories (GO:0000004 –
biological_process unknown, 0008151 – metabolism, 0008152 – cell growth and/or
maintenance) were excluded, as were 4 evidence lines (NR – not recorded, ND – no
biological data available, NAS – non-traceable author statement, RCA – reviewed
computational analysis). The most prominent evidence type observed after removing the
above-mentioned categories was the ISS type (Inferred from Sequence or Structural
Similarity). A minimum GO term depth of 5 was maintained, and the term at the lowest
level was retained for proteins with 2 or more associated terms.
We confirmed the utility of including annotation up to 7 levels by measuring the gain in
the number of true positives in our predicted set. These levels were chosen dynamically,
i.e., for each term, we ascended local branches of the hierarchy, rather than using a fixed
level based on the overall GO hierarchy, thereby accounting for non-uniformities and
differences in GO hierarchies. In this way, we could search the neighborhood of each
protein for other proteins with similar assignments, while still maintaining specificity. In
2
all, 1,146,970 pairs with GO information satisfied our criteria. Negative pairs that
overlapped with this set of GO pairs were excluded. Finally, our gold standard data sets
contained 10,267 positive pairs (GP), and 44,812 negative pairs (GN).
The use of GO annotations for filtering the reference standards resulted in a boost in
model performance over all likelihood score thresholds (Figure S1), and allowed more
number of links to be included in the result set at lower thresholds, as evidenced by a
comparison of values obtained by 7-fold cross-validation. In addition, we independently
tested the effect of including annotations from different GO levels, to ascertain the most
useful level of the hierarchy for filtering negatives. As seen in Figure S1, successive
increases in GO levels improve model performance till a level of 7 is reached, after which
gains are minimal. We also assured the homogeneity of the gold standards by randomly
withholding a subset during model training and using this subset for testing the predicted
linkages (Figure S2). This reveals that a careful selection of annotation data for creating
reference standards is feasible, and the resulting integration is of a sufficiently high
quality to permit reconstruction of accurate functional interaction maps.
Choice of reference priors based on an assumed number of linkages.
Based on Bayesian inferences (Perl 1998; Ewens and Grant, 2005), calculation of
likelihood ratios is independent of any requirement for prior knowledge. However,
choosing a likelihood score threshold for accepting functional linkages generated by the
model is necessary to isolate a result set, where the prior odds of finding a pair of linked
3
genes are greater than 1. Given the absence of functional interaction data for the
Plasmodium falciparum genome, we adopted a strategy that lets us utilize arbitrary sets
of links as our reference or non-informative priors.
For each step, links for each uniform reference prior set R were chosen such that |R| > 0
and R  N, N being the total number possible links given all proteins in the P. falciparum
genome. For each change in our number of probable linkages, we measured the global
impact of addition of new evidence by calculating the ratio of true positives to false
positives at the corresponding likelihood score threshold. For instance, a belief that only
40% of all proteins are functionally linked with each other, leads to a likelihood score
threshold of ~6, yielding posterior odds of finding a true pair of functionally linked
proteins that are greater than 1. Similarly, a belief that involves linkages between 50% of
all proteins leads to a corresponding score threshold of ~4, and so on.
As evidenced by the quality of the network produced (Figure 2), this strategy proves
useful when dealing with genomes with little or no useful data on protein-protein
interactions. For the P. falciparum genome, assumptions regarding the number of
possible functional links also corresponded closely with the actual number of links found
in the result sets above the derived thresholds. This correspondence, however, is not
maintained when dealing with beliefs that assume very large or very small numbers of
proteins as likely interactors. As seen in Figure S3, genome coverage does not exceed
80% for a set of links derived at any likelihood threshold. Conversely, the prior beliefs
suggest an increase in genome coverage in sets generated at a much higher likelihood
4
threshold. Thus, for both very high and very low number of possible functional
interactions, this strategy overestimates the degree of coverage afforded by the model,
given a set of reference priors. This inability to effectively reflect true coverage is a result
of the limitations of the individual functional genomics methods, our filtering methods
and our gold standards.
Testing linkage quality and network robustness
Initial quality of the predicted linkages was tested using sets of shuffled linkages. More
rigorous tests involved cross-validation and comparison with shuffled sets of input data.
For cross-validation, gold standards were divided in to seven subsets, each of which was
withheld once for testing, while the remaining 6 were used for training the data. Results
from the 7 individual runs were then summed for each likelihood score threshold (see
Figure 1).
For the test using shuffled input data, values in phylogenetic and expression profiles were
maintained, while shuffling the identifying labels attached to each profile. Mutual
information and Pearson correlation was used to measure profile similarity for the
phylogenetic and expression profile sets, respectively. Rosetta stone protein linkages
present input data in the form of ‘A-B-RS’, where ‘A’ & ‘B’ are individual proteins, while
‘RS’ represents a Rosetta stone fusion protein. For this set, we maintained the ‘B-RS’
relationship in the triplet, while randomizing all ‘A’ proteins. For all sets, the
randomization procedure was repeated 1000 times. Functional linkages within each
5
shuffled set were also subject to the same filters that were applied to the normal sets.
Table S1 shows a comparison of likelihood scores for normal and shuffled sets of
functional genomics data.
Robustness of the network to changing benchmarks was tested using functional genomics
data not used in training the network. This included expression measurements of genes in
all life-cycle stages (Le Roch et al. 2003), and high-throughput mass spectrometric
measurements of the proteome (Florens et al. 2002, Lasonder et al. 2002; referred to as
sets M1 and M2 respectively).
For each data set, proteins in the same life-cycle stage are more likely to be functionally
linked, as they are more likely to take part in stage-specific pathways or be a part of
protein complexes, than are proteins in different life-cycle stages. We therefore paired
proteins from the same life cycle stage to create positive sets, and different life-cycle
stages to create negative sets. While trends in each data set are accurately captured by the
model, we observed some differences when dealing with the two mass spectrometry data
sets- for data set M1, we found that the ratio of positives to negatives remains below 1 for
all sampled thresholds as opposed to M2, where the ratio of positives to negatives is
greater than 1 for all likelihood score thresholds. While these differences cannot be
readily explained in our analysis, we do introduce a moderate degree of error in these
tests, as we use only simple combinations to generate protein pairs that were detected in
the same stage, without regard to spatial or temporal constraints. Further, it is likely that
at least some of our predicted interactions are missed when compared to such proteomic
6
data, given the inclusion of gene expression data in our model and the known delay
between mRNA expression and protein accumulation (Le Roch et al. 2004).
Visualizing spatial separation of predicted linkages based on life-cycle stages
In addition to functional properties, we were also interested in observing spatial
clustering based on nodal properties shared by individual pair-wise linkages. We
expected reasonable spatial separation of linkages based on participation in a particular
life-cycle stage, since candidates sharing a life-cycle stage are more likely to be
functionally linked than candidates from different life-cycle stages. To test this
hypothesis, we measured the distribution of candidates and their shared linkages using
assembled protein pairs from different life-cycle stages, by superimposing them on the P.
falciparum functional linkage map.
For ascertaining nodal properties, we chose the largest life-cycle specific positive
reference set from these three individual data sets. Pairs within this set that appeared as a
positive pair in any other life-cycle stage were discarded. Data from studies of Florens et
al. (2002) for the sporozoite stage, Lasonder et al. (2002) for the gamete/gametocyte
stages and Le Roch et al. (2003) for the erythrocyte-associated stages were found to be
the largest sets for each stage.
Individual proteins modeled as nodes, and functional linkages modeled as edges were
graphed in two-dimensional (2-D) space using the minimal spanning tree approach as
7
implemented in the large graph layout (LGL) algorithm (Adai et al. 2004, see ‘Using the
LGL package for visualizing networks’). When visualized with LGLView, functional
linkages from gamete/gametocytic and erythrocyte-associated life-cycle stages are seen
to possess distinct spatial profiles, and mostly occupy different areas of the map, albeit
with a degree of overlap (Figure S4). Linkages between proteins restricted to the
sporozoite stage are fewer in number as compared to linkages between the other two
stages, and appear scattered throughout the network. An examination of overlapping
linkages from the gamete/gametocytic stages and erythrocyte-associated stages revealed
the presence of mostly hypothetical proteins, majority of which are functionally linked
with proteins that interact with DNA, such as Helicases and proteins containing the zincfinger motif. While the observed overlap is primarily due to the limitations of our
assembled query set and 2-D graphing, it can be speculated that at least some of these
components may be shared between different life-cycle stages of the parasite, and
therefore appear to share nodal properties.
Exploring the interactome for functional associations
The interactome map provides several interesting functional associations. The
hypothetical protein PF10_0214 is seen linked with members of the mRNA-processing
machinery, while PF14_0729 is likely involved in protein transport. The hypothetical
protein PFI1180w is seen linked with members of the ADP-ribosylation factor (Arf)family of small GTP-binding proteins, suggesting a role for this protein in membrane
trafficking and actin remodeling (Nie et al. 2003).
8
Functional information from a subset of high confidence linkages
Phase transitions in our data were prominent at two points- the first was seen when the
threshold was decreased from 14 to 13, representing a five-fold increase in interaction
number from ~12,000 to ~60,000, and a two-fold increase in the number of proteins,
from ~1,400 to ~2,900; a second less abrupt transition was noted between threshold
change from 3 to 2, where the number of links increased from ~126,000 to include the
entire set of 388,969 links, and the number of proteins involved increased from ~3,200 to
3,667.
We have taken advantage of these transitions to delineate a set of high confidence links,
above a likelihood score threshold of 14 or greater. This subset also provides biological
insights into the functions of several proteins. A distinct sub-network composed entirely
of hypothetical proteins- PF11_0122, MAL8P1.151, PF13_0285, PF07_0024 is worth
noting. All of these proteins are either similar to proteins that contain the SacI domain, or
contain the Sac1 domain themselves, based on information available from PlasmoDB.
Sac1 containing proteins are thought to play a variety of roles in yeast, including
exocytosis and endocytosis and, are also known to interact with the actin cytoskeleton
(Hughes et al. 2000). Additionally, PF07_0024 has been recently annotated as the
enzyme Inositol-polyphosphate 5-phosphatase. Along with experimental evidence from
yeast, these hypothetical proteins are therefore likely to play a role in protein trafficking
and secretion (Schorr et al. 2001).
9
Retention of linkages in other apicomplexa
We also superimposed the P. falciparum interaction map on the Plasmodium yoelii,
Toxoplasma gondii and Cryptosporidium parvum genomes, to examine retention of
linkages across the species. Such analyses reveal interactions that are a part of the
apicomplexan signature, and are the first step towards understanding interactions and
identifying components unique to the P. falciparum genome. Given the current quality of
some genomes, for instance missing genes in C. parvum, or low coverage, it is possible
that these comparisons are incomplete, or contain some error. This includes 409 C.
parvum genes that are not a part of comparative analyses between P. falciparum and C.
parvum. These genes are not assigned any sequence in the currently available version of
the genome (Abrahamsen, et al. 2004), due to undetermined start/stop or intron positions.
However, it should be possible to refine these observations as sequence quality improves.
Observed links that are common among the genomes, or exclusive to P. falciparum are
outlined in Table 2. One group of retained linkages includes the Phosphatidylinositol
signaling system, with members of which include GPI-anchored moieties. Plasmodium
and Toxoplasma GPIs are known to induce proinflammatory responses (DebierreGrockiego et al. 2003; Zhu et al. 2005), and antibodies against these have been suggested
as a possible anti-glycolipid vaccine against malaria (Schofield and Hackett, 1993); it
may be possible to further extend versions of this strategy as a generalized way of
10
combating apicomplexan pathogenesis, specifically by using knowledge of shared steps
obtained from this analysis.
Examination of the network for linkages exclusive to P. falciparum, based on comparison
with the apicomplexan genomes, reveals 87,798 linkages represented by 3,307 proteins
(see Table 2). We identified 593 interactions between members of the rif/stevor families
and other proteins, which may prove pharmaceutically useful on further experimental
study. Additionally, links between hypothetical proteins such as PF10_0128 and
MAL8P1.145, which are orthologous to G-protein subunit proteins in other organisms,
and PFL0080c, a putative serine/threonine-protein kinase, point to interesting and unique
signaling cascades.
Accuracy and coverage of individual and combined functional genomics data
We also measured ratio of true positives to false positives in terms of the percentage of
genome covered by individual functional genomics data sets, compared to the integrated
data. The results of this test are illustrated in Figure S5. As expected, based on the
properties of our integration schema, the integrated data set shows higher accuracy in
terms of the ratio of true positives to false positives over all portions of the genome
covered, as opposed to the accuracy of individual functional genomics data sets. Besides
using Bayesian logic, simpler methods for integrating data based on unions or voting can
also be imagined. However, such methods suffer from several drawbacks, such as
requiring a user-defined cutoff to be chosen for each set. Further, given their dependence
11
on the size of each set, such methods are extremely restricted in their coverage, and may
be biased towards a set. The ability to weigh evidences, and combine them to yield
probabilistic scenarios makes a Bayesian model more useful, its utility only increased
given the fact that the included data represents different aspects of the life-cycle of the
parasite.
Using the LGL package for visualizing networks
The networks were laid out and visualized using the LGL package (Adai et al. 2004).
LGL uses a mass-spring algorithm, where edges are treated as springs, which pull
together nodes acting as masses. Coordinates are first generated for the vertices for each
independent set of interconnected links, using a guide tree generated by the minimum
spanning tree of the network (the layout phase). Each independent set is then connected
to reconstruct the complete network (see also the ‘Links of interest’ section for website
information and links to other graphing software).
For visualizing P. falciparum networks, we supplied LGL with a set of unique node
identifiers without any weights. LGL was allowed to arbitrarily designate a root vertex,
and was run with default parameters. The edge and coordinate files generated were
visualized with LGLView, a companion program that plots the coordinates in 2dimensional space.
12
For visualizing areas of the network specific to a life-cycle stage, we chose to color
vertices that belonged to a particular stage. Edges between two vertices that belonged to
the same stage are also assigned the same color.
Gene associations
Associations between individual proteins, regarding function and possible interactions in
various organisms were deduced from literature (NCBI PubMed; iHOP: see Hoffman and
Valencia, 2004).
Data for P. falciparum 3D7 and Dd2 strains
Functional linkages for 3D7 and Dd2 strains were generated using computational data,
and averaged expression profiles for all probes that mapped to a particular gene. Total
likelihood scores were calculated for all pairs, as described for the HB3 strain, and the
accuracy of individual score thresholds measured against the gold standards (see Figure
S6). Based on these comparisons, we excluded all links above score thresholds of 3 and
4, for the 3D7 and Dd2 strains respectively.
Links of interest

Companion website to this paper: http://cbil.upenn.edu/plasmoMAP/
13

The PlasmoDB website: http://www.plasmodb.org

DeRisi Lab (expression data): http://derisilab.ucsf.edu/

Winzeler Lab (expression data): http://www.scripps.edu/cb/winzeler/

Network graphing and visualization tools
o The Large Graph Layout (LGL) Package:
http://bioinformatics.icmb.utexas.edu/lgl/
o Cytoscape: http://www.cytoscape.org/
o Pajek: http://vlado.fmf.uni-lj.si/pub/networks/pajek/
14
Figure S1. Combining KEGG and GO annotation enhances model performance.
Positive and negative sets of gold standards were created using KEGG annotation data.
Additionally, pairs from the negative gold standard set that shared a GO term in 7
consecutively higher levels of the GO hierarchy were further removed. This step
effectively boosts model performance, when compared against results from 7-fold crossvalidation of data that only includes KEGG positives and unfiltered KEGG negatives.
Including keywords from up to 7 levels of the GO hierarchy leads to an increase in
performance, but gains are minimal when keywords from higher levels are included. ‘*’
indicates results of a test where we limited the use of GO annotations to a global level of
5 or below, when ascending a local hierarchy.
Figure S2. Testing homogeneity of our gold standards
A test where we randomly withheld a subset of the gold standards during model training
(equal to 1/7th of the total), reveals uniformity and the unbiased nature of the standards.
The test was repeated 100 times. Individual likelihood score thresholds for each test are
connected for ease of observation.
Figure S3. A comparison of assumed genome coverage based on the prior set and actual
genome coverage based on the predicted linkages
Prior beliefs that hold very large numbers or very small numbers of proteins as possible
interactors overestimate the degree of genome coverage. For instance, maximum genome
coverage achieved by the predicted set of linkages does not exceed 80%, and at the same
time, the likelihood score thresholds are much lower, as opposed to coverage and
15
accuracy based on the prior set. Similarly, actual likelihood scores are much lower than
those assumed when genome coverage decreases below 20%. These differences arise
mainly due to limitations in the resolution of the individual methods, as well as our
manipulation of the input data. The beliefs here correspond to assumptions that 1%, 25%,
33%, 50%, 66%, 75% and 99% of the genes in the genome functionally interact with
each other.
Figure S4. Spatial profiles of proteins from different life-cycle stages of the parasite
Proteins from different life-cycle stages occupy different areas of the map, with some
degree of overlap. Here, vertices represent proteins, while edges represent functional
linkages. Edges are colored based on the properties of two connected nodes; two nodes
that share the same life-cycle stage share the same color, as does the connecting edge.
Blue edges represent proteins expressed only the sporozoite stage (388 nodes), green
edges represent proteins expressed in the gamete or gametocytic stages (607 nodes),
while red edges represent proteins expressed mainly in the erythrocyte associated forms
of the parasite (463 nodes). Life-cycle membership information for individual proteins
was culled from gene expression and mass spectrometry data not used as input or in
testing and training the model. For clarity, functional linkages that cannot be currently
assigned to a specific life cycle stage are omitted.
Figure S5. Estimated accuracy and coverage of individual and integrated functional
genomics data sets
16
For each of our input data sets, we measured the percentage of the genome covered as a
function of the ratio of observed number of true positives to false positives when
compared against our gold standards. As seen from the graph, integration of functional
genomics data leads to a gain in genome coverage, as well as accuracy, for all parts of the
P. falciparum genome measured. The final integrated data set exhibits higher genome
coverage than any of the individual methods.
Figure S6. Accuracy of likelihood score thresholds for 3D7 and Dd2 strain data
Likelihood scores were calculated for all possible pairs in each of the two strains, using
computational data and averaged expression profiles for each gene based on matching
probes. Likelihood score thresholds of 3 and 4 were observed to correspond to a true
positive to false positive ratio of 1 or greater, for the 3D7 and Dd2 strains respectively.
Linkages above these thresholds represent the interaction network for the individual
strains, available through the companion website.
17
18
19
100
100
90
80
70
10
60
50
40
1
30
20
10
0.1
0.01
0.1
1
10
100
1000
Likelihood score thresholds
20
10000
0
100000
Genome covered (%)
Overlap with gold standards (ratio of
true positives to false positives)
TP/FP
Actual coverage
Assumed coverage
21
Ratio of true positives to false positives
100
Microarray expression data linkages
Rosetta stone protein linkages
Phylogenetic profile linkages
Integrated data
10
1
0.1
0.01
100
80
60
40
Genome covered (%)
22
20
0
Overlap with gold standards (ratio of true
positives to false positives)
Overlap with gold standards (ratio of true
positives to false positives)
100
3D7
10
1
0.1
0.01
0.1
0.01
0.1
1
0.1
10
B
23
100
1
Likelihood score thresholds
1000
10
10000
Likelihood score thesholds
A
100
Dd2
10
1
100
Table S1. Likelihood scores calculated using normal and shuffled input data sets. A score
of 0 indicates that likelihood scores could not be calculated due to absence of overlap
between the predicted linkages and the gold standard sets. A hyphen indicates no
predicted pairs for the particular bin.
Bins
Phylogenetic profile linkages
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
LR (Normal data)
LR (Shuffled data)
0.478
0.743
1.010
1.351
1.408
1.980
2.850
6.479
8.959
10.184
28.370
0.000
0.000
0.000
-
0.187
1.182
1.049
1.122
1.309
1.170
0.960
1.420
1.455
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
4.365
0.000
0.000
1.247
7.638
2.182
0.000
0.000
0.000
0.000
24.442
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.128
0.527
0.809
0.755
0.827
0.653
0.863
0.612
0.633
0.725
0.975
0.797
0.906
0.788
0.733
0.775
0.705
0.801
1.086
2.498
13.607
1.532
1.384
1.233
1.008
1.126
1.251
1.317
1.412
1.331
1.182
1.181
1.212
1.274
1.099
1.377
1.267
1.239
1.150
1.136
1.229
0.792
Rosetta stone protein linkages
1e-1
1e-2
1e-3
1e-4
1e-5
1e-6
1e-7
≈0
Expression profile linkages
-1.0
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
24
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27