Angiogenesis-Associated Crosstalk Between Collagens,
CXC Chemokines, and Thrombospondin Domain-Containing Proteins
CORBAN G. RIVERA, JOEL S. BADER, and ALEKSANDER S. POPEL
Department of Biomedical Engineering, School of Medicine, Johns Hopkins University, 720 Rutland Avenue, 613 Traylor Bldg,
Baltimore, MD 21205, USA
(Received 9 February 2011; accepted 5 May 2011; published online 18 May 2011)
Associate Editor Sriram Neelamegham oversaw the review of this article.
Abstract—Excessive vascularization is a hallmark of many
diseases including cancer, rheumatoid arthritis, diabetic
nephropathy, pathologic obesity, age-related macular degen-
eration, and asthma. Compounds that inhibit angiogene-
sis represent potential therapeutics for many diseases.
Karagiannis and Popel [Proc. Natl. Acad. Sci. USA 105(37):
13775–13780, 2008] used a bioinformatics approach to
identify more than 100 peptides with sequence homology
to known angiogenesis inhibitors. The peptides could be
grouped into families by the conserved domain of the
proteins they were derived from. The families included type
IV collagen fibrils, CXC chemokine ligands, and type I
thrombospondin domain-containing proteins. The relation-
ships between these families have received relatively little
attention. To investigate these relationships, we approached
the problem by placing the families of proteins in the context
of the human interactome including >120,000 physical
interactions among proteins, genes, and transcripts. We built
on a graph theoretic approach to identify proteins that may
represent conduits of crosstalk between protein families. We
validated these findings by statistical analysis and analysis of
a time series gene expression data set taken during angio-
genesis. We identified six proteins at the center of the
angiogenesis-associated network including three syndecans,
MMP9, CD44, and versican. These findings shed light on
the complex signaling networks that govern angiogenesis
Keywords—CXC chemokine, Type IV collagen, Thrombo-
spondin-1, Angiogenesis, Crosstalk, Interactome, Syndecan.
Excessive vascularization is a hallmark of many
diseases including cancer, rheumatoid arthritis, dia-
betic nephropathy, pathologic obesity, age-related
macular degeneration, and asthma. Compounds that
inhibit angiogenesis represent potential therapeutics
for many diseases. Judah Folkman performed pio-
neering research in the field of angiogenesis7; his work
led to the identification of a number of proteins and
polypeptides with anti-angiogenic activity.8
Karagiannis and Popel14used a bioinformatics
approach to group the peptides with anti-angiogenic
activity into families by the conserved domain of the
proteins they are derived from. The families included
type IV collagens, CXC chemokines, and type I
thrombospondin domain TSP1-containing proteins.
Karagiannis and Popel identified conserved domains
within each family by performing a multiple sequence
alignment. They ran BLAST for each conserved
domain against the proteome to identify other peptides
with sequence homology. Their work revealed more
than 100 peptides derived from over 80 proteins with
sequence homology to known angiogenesis inhibitors.
We will refer to this set of proteins throughout the rest
of the article as angiogenesis-associated proteins. We
extended the series of work from Karagiannis and
Popel14to investigate the collection of interactions
surrounding the angiogenesis-associated proteins. In
this study, we selected three families: type IV collagen,
CXC chemokines, and TSP1-containing proteins, for
which we identified interactions with other proteins,
thus building a protein–protein interaction (PPI)
network. Note that the grouping of these angiogenesis-
associated proteins into families only indicates that
they share one or more conserved domains.
Karagiannis and Popel experimentally validated
in vitro inhibition of endothelial cell (EC) proliferation
and migration by peptides derived from type IV col-
thrombospondin domain-containing pro-
and CXC chemokines.15
showed that a large fraction of the peptides had
Address correspondence to Corban G. Rivera, Department of
Biomedical Engineering, School of Medicine, Johns Hopkins
University, 720 Rutland Avenue, 613 Traylor Bldg, Baltimore,
MD 21205, USA. Electronic mail: firstname.lastname@example.org
Annals of Biomedical Engineering, Vol. 39, No. 8, August 2011 (? 2011) pp. 2213–2222
0090-6964/11/0800-2213/0 ? 2011 Biomedical Engineering Society
anti-angiogenic potential. Using EC proliferation
assays, they also revealed synergy between the peptides
derived fromthe CXC chemokines and TSP1-
containing protein families,14thus suggesting a possi-
ble crosstalk between the signaling networks. A greater
understanding of the signaling pathways associated
with the peptides is an important step in understanding
their mechanisms of action. In vivo experiments with
selected peptides demonstrated anti-angiogenic activity
in tumor xenografts18,19and ocular models.5
While the functional relationships between these
protein families and angiogenesis have been catalogued
by the gene ontology (GO),2the relationships between
pairs of protein families are not well characterized. To
better understand the relationships within and between
type IV collagens, CXC chemokines, and TSP1-
containing proteins, we placed each family of proteins
in the context of the human interactome including
126,763 physical protein–protein, protein–DNA, or
protein–RNA interactions accumulated in the Michi-
gan Molecular Interactions database (MiMI).10We
used graph diffusion (see ‘‘Materials and Methods’’
section) to identify those proteins that are in close
topological proximity with multiple angiogenesis-
associated protein families. The proteins that are
well connected to multiple protein families represent
potential mediators of crosstalk. We verified their
statistical significance by repeatedly rewiring the
human PPI network. We found that many of these
proteins had perturbed gene expression during time
course measurements of VEGF-stimulated angiogene-
sis in ECs.
MATERIALS AND METHODS
The interaction data set was taken from the
Michigan Molecular Interaction database (MiMI)10
(Feb 2009 version). The data set is composed of 13,491
genes, proteins, and RNA connected by 126,763
physical interactions. The interaction types include
protein–protein, protein–DNA, protein–RNA, and
RNA–RNA. As a result, the data set captures diverse
aspects of biomolecular interactions including protein
complexation, transcriptional regulation, and RNA
interference. The data set consists of interactions
curated from reputable online databases such as
Reactome,37BIND, BioGrid,4HPRD.16This network
of physical interactions forms the basis for crosstalk
discovery. GO2annotations were used for verification
(6/2010 version). For additional verification, we used a
time series gene expression data set of VEGF-induced
capillary endothelial tube formation in a 3D collagen
matrix in vitro.23The data set included 8 time points:
15 min, and 1, 3, 6, 9, 12, 18, and 24 h of VEGF
By treating a biomolecular interaction network as a
graph where nodes correspond to biomolecules and
edges represent physical interactions between those
biomolecules, we can efficiently find topological asso-
ciations between protein families. Diffusion kernel
algorithms have proven to be powerful tools for
identifying topological associations between a node
and a seed set of nodes. The method can be thought of
in terms of repeated random walks originating at the
seed nodes. A parameter c controls the length of the
random walks. A lower value for c results in longer
random walks. Nodes are then assigned a diffusion
kernel score (DKS) based on the fraction of random
walks that pass through the node. While many values
of c will suffice, we selected c such that all nodes have
some non-zero DKS.
Figure 1 illustrates the principle of graph diffusion
on a simple network consisting of a chain of 11 nodes
connected by 10 edges. The nodes lie along the x-axis.
Node set 1 (NS1) consists of nodes 1 through 3. The
DKS of all the nodes with respect to NS1 is given by
the blue line. Node set 2 (NS2) consists of only node 10
in green. The DKS with respect to NS2 is given by the
green line. To identify the crosstalk proteins with
Diffusion Kernel Score
Node Set 1 (NS1)
Node Set 2 (NS2)
min [NS1, NS2 diffusion]
maximum dist. cutoff
Graph diffusion on a network consisting of a chain of 11
nodes connected by 10 edges. The nodes lie along the x-axis.
Node set 1 (NS1) consists of nodes 1 through 3. The DKS of all
the nodes with respect to NS1 is given by the blue line. Node
set 2 (NS2) consists of only node 10 in green. The DKS with
respect to NS2 is given by the green line. To identify the
crosstalk proteins with respect to NS1 and NS2, we identify
the intersection of the minimum of NS1 and NS2 diffusion
(shown in red) and a minimum DKS threshold (black line). The
curves intersect before node 4 and after node 6. As a result,
nodes 4, 5, and 6 would be labeled crosstalk nodes with
respect to NS1 and NS2.
Graph diffusion on a simple linear network.
RIVERA et al. 2214
respect to NS1 and NS2, we identify the intersection of
the minimum of NS1 and NS2 diffusion (shown in red)
and a minimum DKS threshold. The curves intersect
before node 4 and after node 6. As a result, nodes 4, 5,
and 6 would be labeled crosstalk nodes with respect to
NS1 and NS2.
through multiple short length paths. Even if a single
path is found to be incorrect, alternate paths through
the network will still support the associations. This
aggregation of evidence from multiple paths leads to a
more stable result from potentially unreliable data. As
the DKS is additive, we normalize the DKS by the
number of query nodes. The software for performing
this operation is provided through our website (sysbio.
For a weighted undirected graph G(V, E) with ver-
tex set V and edge set E, let A be the symmetric
adjacency matrix representing G. Let qibe 1 if node i is
in the query set or zero otherwise. We express the time
derivative _ siof the DKS si(q) for node i 2 V as:
Aijsi? csiþ qi:
Let D be the degree weighted diagonal matrix of A.
In matrix notation, we have
_ s ¼ As ? Ds ? cIs þ q:
Our goal is to identify the values of s at steady-state.
We set _ s ¼ 0Tand solve for s.
s ¼ ½D ? A þ cI??1q:
We define crosstalk proteins as topologically close
to multiple node sets. A protein p is a crosstalk pro-
tein if the normalized DKS is greater than a threshold
for multiple node sets. A maximum distance (mini-
mum DKS) threshold u marks the annotation
boundary for a node set. The parameter u is constant
across all node sets. In this study, the parameter u is
set at 0.018. We use statistics to verify the significance
of the results found using our fixed values of u. We
define a crosstalk protein p relative to node sets q1,
q2,…, qn, if
We computed the normalized DKS for each pro-
tein to each node sets. We normalized by the number
of proteins in the node set. In this way, DKS are
comparable for different node set sizes. DKS results
are given in Table 2. Then for a given protein, we
could identify all potential crosstalk with other node
We computed the statistical significance of crosstalk
proteins by permutation testing. We tested the null
hypothesis that the DKS of a protein is equal to the
DKS of a protein in rewired networks. The alternative
hypothesis is that the DKS of a protein lower in
rewired networks. To test these hypotheses, we gener-
ated 300 randomly edge swapped networks. The prob-
ability of the null hypothesis is given by the fraction of
rewired networks where the DKS of the protein
exceeds the DKS of the protein in the real network to a
fixed set of seed proteins.
The statistical significance calculation controls for
node set size and node degree. Crosstalk proteins can
be compared and ranked based on their statistical
significance. By computing statistical significance, we
eliminate a bias toward hub proteins in the network.
The computation of statistical significance gives a
global measure of the important of the associations
that we identify through crosstalk proteins. We do not
evaluate the statistical significance of the seed nodes
(i.e., the angiogenesis-annotated proteins). Seed nodes
were selected for the study and as such they are
We identified enriched functions for sets of crosstalk
proteins using Ontologizer 2.0.3Results are shown
using default settings with Parent–Child-Union asso-
ciation and the Benjamini–Hochberg method of mul-
tiple hypothesis correction. The background set
consisted of all human proteins in the interactome
according to MiMI.33All network images were pro-
duced using the Cytoscape17network visualization
We aimed to (i) identify proteins that may be
associated protein families and (ii) characterize their
association with angiogenesis. We accomplished the
first aim by application of graph diffusion on the
human molecular interaction network followed by
verification of statistical significance. We accomplished
the second aim using a previously reported time series
gene expression experimental data set taken during
Angiogenesis-Associated Crosstalk 2215
The Search for Angiogenesis-Associated Proteins
We used the human physical interactome as a basis
for the analysis. We used a graph theoretic technique
called graph diffusion to quantify the distance between
proteins in the interactome36(see ‘‘Materials and
Methods’’ section). The graph diffusion method also
known as the diffusion kernel allowed us to quantify
the distance between a single protein and a protein
family. We referred to the distance between a protein
and a protein family as the DKS. A protein with a high
DKS interacts closely with the protein family. For
example, consider the family of type IV collagen fibrils,
a protein that physically interacts with all type IV
collagens would receive a high DKS, while a protein
that only indirectly interacts with type IV collagens
would receive a relatively lower DKS. We use the DKS
to estimate the association between a single protein
and a family of proteins.
To locate those proteins that potentially mediate
crosstalk between families, we define crosstalk proteins
that are highly associated with multiple protein fami-
lies (i.e., the proteins have a DKS which is greater than
a threshold for multiple families). For example, a
crosstalk protein for type IV collagens and CXC che-
mokines would have many direct and indirect inter-
actions with both protein families. We evaluate the
statistical significance of a crosstalk protein by con-
sidering hundreds of rewired networks. We create each
rewired network by repeatedly swapping interactions.
The statistical test that we use for crosstalk proteins
controls for the size of the protein families and the
degree of protein interaction.
Using this approach, we found 126 proteins
that were topologically close to the angiogenesis-
associated protein families. We evaluated the quality of
the protein annotations by their statistical significance
and functional enrichment in angiogenesis. To put this
network in context with the rest of the known human
interactome, these are less than 1% of proteins (i.e.,
0.93%) and interactions (0.25%). The analysis pointed
to many proteins whose role in angiogenesis is well
known, which serves as a validation of the approach.
There are 194 human proteins that have angiogenesis
as part of a GO annotation (as of 6/2010). The likeli-
hood that a protein is annotated with angiogenesis by
chance is 0.014. Excluding 31 seed proteins, our anal-
ysis of the human PPI network identifies four proteins
that have angiogenesis as part of their GO annotation.
The probability that the 95 (i.e., 126 associated—31
seeds) proteins contained four angiogenesis-annotated
proteins by chance is 0.045. We calculated the p value
using Fisher’s exact test. Our analysis suggests new
or understudied modulators of angiogenesis. These
their potential to manipulate multiple protein families.
In Fig. 2, we show a Venn diagram to illustrate the
associations of the 126 proteins. These proteins are
topologically close to the type IV collagens, CXC
chemokines, or TSP1-containing proteins or some
combination of families, as indicated by the figure. The
figure gives the putative crosstalk between three angio-
genesis-associated protein families: type IV collagens
(blue), CXC chemokines (red), and TSP1-containing
proteins (green). The crosstalk proteins are shown for
CXC chemokines and type IV collagens (purple), CXC
chemokines and TSP1-containing proteins (tan), type
IV collagens and TSP1-containing proteins (yellow),
and between all three (orange). The number of angio-
genesis-associated proteins is shown in parentheses. In
the results, we focus on the proteins associated with
multiple families. First, we discuss crosstalk proteins
between type IV collagen and TSP1-containing pro-
teins. Then, we highlight six proteins identified as
crosstalk proteins between all three families. These six
proteins: three syndecans, matrix metalloproteinase 9
(MMP9), CD44, and versican (VCAN) may be impor-
tant mediators of crosstalk for these angiogenesis-
associated protein families.
the associations of 126 proteins that are topologically close to
type IV collagens, CXC chemokines, TSP1-containing pro-
teins, or some combination, as indicated by the Venn diagram.
A Venn diagram between three angiogenesis-associated pro-
tein families: type IV collagens (blue), CXC chemokines (red),
and TSP1-containing proteins (green). The crosstalk proteins
are shown for CXC chemokines and type IV collagens (purple),
CXC chemokines and TSP1-containing proteins (tan), type IV
collagens and TSP1-containing proteins (yellow), and between
all three (orange). While crosstalk proteins may not be directly
part of multiple protein families, they are in close topological
proximity to multiple protein families. The number of angio-
genesis-annotated proteins (i.e., seed proteins) is shown in
Venn diagram of the putative crosstalk. We show
RIVERA et al. 2216
Method for Comparison of Topology-Based Annotation
Crosstalk between pathways is an important con-
cept in biology. There have been both computational21
between pathways. Some of these approaches are not
suitable in this context because they rely on overlap-
ping pathwaysto identify
approaches might consider ‘‘first neighbors’’ or ‘‘sec-
ond neighbors’’ to identify association between path-
ways or modules. These rigid approaches have the
inherent disadvantage of being unable to identify
crosstalk between modules of distance 2 for ‘‘first
neighbors’’ or distance 3 for ‘‘second neighbors.’’
Other studies used shortest paths to help define
crosstalk proteins.21,38These methods borrow from
concepts such as betweenness centrality. Because graph
diffusion considers all paths, our method has inherent
advantages over those that only consider shortest
paths between proteins.
To motivate the use of the graph diffusion method,
we performed a systematic comparison of three alter-
native methods in a head-to-head comparison with
graph diffusion. Then we compared graph diffusion
with methods based on first neighbors, second neigh-
bors, and betweenness centrality. In Table 1, we show
the results of this comparison. We found that graph
diffusion identified more statistically significant pro-
teins at both the 0.01 and 0.05 levels. The graph dif-
fusion method identified a more functionally cohesive
set of proteins as demonstrated by the number of GO
term enrichments at the 0.001 and 0.0001 levels.
Gene Expression Validates Crosstalk Proteins
To further validate the role of the crosstalk proteins
in angiogenesis, we reanalysed a time series gene
expression data set taken during VEGF-induced
angiogenesis.We expectedthatmany crosstalk
proteins would have perturbed gene expression during
angiogenesis. If this proved to be the case, the micro-
array data set would provide additional evidence of the
role of crosstalk proteins in angiogenesis.
A research team led by Claesson-Welsh took mea-
surements from a gene expression time series of
VEGF-induced capillary endothelial tube formation in
a 3D collagen matrix in vitro.23The data set included 8
time points: 15 min, and 1, 3, 6, 9, 12, 18, and 24 h of
VEGF stimulation. We reanalysed these data to iden-
tify the transcription profiles that are significantly
increasing or decreasing during tube formation (that
we refer to as angiogenesis). To accomplish this, we
ranked transcripts by the absolute value of the
covariance between the transcript measurements and
the time points. We tested the null hypothesis that the
crosstalk proteins are uniformly distributed among the
ranked list of genes. We computed the family-wise
error rate (FWER) p value using gene set enrichment
analysis31which is based the Kolmogorov–Smirnov
test followed by permutation testing. We found the
crosstalk proteins significantly enriched at the head of
the ranked list of perturbed genes (p = 3 9 1024). In
Table 2, we give the trajectory of gene expression
during VEGF-induced angiogenesis. We measure the
trajectory of gene expression change by the covariance
between the gene expression and the time points. The
statistical test indicates that many of the crosstalk
proteins have either increasing or decreasing gene
expression during angiogenesis. This analysis helped
confirm the importance of these crosstalk proteins in
VEGF-induced angiogenesis and serves as a validation
of our bioinformatics analysis.
Putative Crosstalk Between Type IV Collagens
and TSP1-Containing Proteins
We studied the association between type IV colla-
gens and TSP1-containing proteins to reveal the
TABLE 1. Head-to-head comparison of topological annotation methods.
Proteins (p<0.05) out of 13491
Proteins (p<0.01) & seed (out of 31)
GO enrichments (p<0.01)
GO enrichments (p<0.001)
GO enrichments (p<0.0001)
We compared four methods including graph diffusion, betweenness centrality, first neighbors, and second neighbors.
The table shows the number of statistically significant crosstalk proteins at the 0.05 and 0.01 levels. We show the
number of significant proteins excluding seed proteins. Seed proteins are excluded because they were selected for
this study. We show the significance of the enrichment in angiogenesis terms for the crosstalk proteins found using
each method. The table also shows the number of GO term enrichments at the 0.01, 0.001, and 0.0001 levels.
Angiogenesis-Associated Crosstalk 2217
TABLE 2.Normalized DKS and statistical significance for
proteins in Fig. 3.
Symbol DKSp valueCov
CXC, COL4, TSP1 crosstalk
COL4, TSP1 crosstalk proteins
CXC, COL4 crosstalk proteins
CXC, TSP1 crosstalk proteins
COL4 associated proteins
CXC associated proteins
Symbol DKSp value Cov
TSP1 associated proteins
RIVERA et al.2218
mediators of crosstalk between these two families. In
Fig. 3, the crosstalk proteins between type IV collagens
and TSP1-containing proteins are highlighted in yel-
low. A significant number of these proteins bind col-
lagen and associate with the vesicle lumen (Table 3).
CD36 is also known to interact with type IV colla-
gens.20The identification of CD36 as a crosstalk pro-
tein for type IV collagens and TSP1-containing
proteins helps confirm our approach. Decorin (DCN)
is another proteoglycan that we identify as a crosstalk
protein. DCN interacts with collagens and extracellu-
lar matrix (ECM) and promotes angiogenesis.29
Fibronectin 1 (FN1) is an important connective mol-
ecule in the extracellular space. FN1 has domains for
collagens, fibulin 1, heparin, and syndecan binding.28
We identify FN1 as a crosstalk protein between type
IV collagens and TSP1-containing proteins. FN1
connects extracellular collagens with membrane-bound
integrins (Fig. 3). As such, FN1 has a central role in
EC adhesion to the ECM. Another important conduit
of information between TSP1-containing proteins and
type IV collagens is through aggrecan (ACAN) and
brevican (BCAN) through fibulin 2 (FBLN2).1,30The
crosstalk between type IV collagens and TSP1-con-
taining proteins through ACAN, BCAN, and FBLN2
has not been reported in the context of angiogenesis,
although it is known that FBLN2 inhibits tumor
angiogenesis.1The crosstalk between type IV collagens
and TSP1-containing proteins may be significantly
influenced by FN1, ACAN, BCAN, and FBLN2. The
amyloid beta (A4) precursor protein (APP) is also
annotated as a crosstalk protein between type IV col-
lagen and TSP1-containing proteins. Figure 3 shows
the direct interaction between APP and COL4A1,
COL4A2, COL4A5, COL4A6, and TSP1-containing
spondin 1 (SPON1). APP is known to be associated
with Alzheimer’s disease.22,32It is also known that
Alzheimer’s disease is related to angiogenesis.35This
study suggests angiogenesis might influence Alzhei-
mer’s disease through the association between APP
and type IV collagens and TSP1-containing proteins.
Putative Crosstalk Between Type IV Collagens,
CXC Chemokines and TSP1-Containing Proteins
We were also interested in identifying the potential
avenues of crosstalk between type IV collagens, CXC
chemokines, and TSP1-containing proteins. We iden-
tified six proteins that are well connected to all three
families of angiogenesis-associated proteins. In Fig. 3,
we show the crosstalk proteins between all three fam-
ilies in orange. A significant number of these proteins
bind collagen and are localized on the cell surface
(Table 3). MMP9 was identified as a crosstalk protein
between the three families of angiogenesis-associated
proteins. MMP9 is known to degrade type IV colla-
gens9,24and CXC chemokines like PF4.27Thrombo-
spondins are known to regulate the amount of
MMP9.25These functions outline the pivotal role of
MMP9 in association with angiogenesis. Although
MMP9 degrades many proteins, the interaction
between MMP9 and the angiogenesis-associated pro-
tein families is highly significant (Table 2, p = 0.004).
Our work highlights syndecan 1 (SDC1), syndecan 2
(SDC2), and syndecan 4 (SDC4) at the centre of
crosstalk between type IV collagens, CXC chemokines,
and TSP1-containing proteins. Syndecans have been
previously implicated in angiogenesis.34Endothelial
CD44 plays an important role in tube formation during
angiogenesis.6Our study suggests that CD44 may
operate as a mediator of crosstalk between type IV
collagens, CXC chemokines, and TSP1-containing
proteins. Note that WISP-1, a TSP1-containing pro-
tein, is connected to the type IV collagen family
through Bone Morphogenetic Protein 3 (BMP-3). An
anti-angiogenic peptide derived from WISP-1 with
relatively low anti-proliferative and anti-migratory
in vitro activity identified in Karagiannis and Popel12
showed a significant in vivo activity in corneal and laser-
induced choroidal neovascularization mouse models.5
Versican (VCAN) is the last protein in the set of
centrally located proteins. VCAN is involved in the
TABLE 2. continued.
SymbolDKSp value Cov
The proteins presented in this study are grouped by category and
sorted by statistical significance. The normalized DKS for each
protein is also given. The table shows the 126 proteins discussed in
this study. The original 31 angiogenesis-associated proteins are
indicated as seed proteins (p value column). The statistical signif-
icance of the seed proteins is not evaluated. The additional 95
proteins are all statistically significant (p<0.05) with respect to
their category (e.g., CXC associated proteins, COL4 associated
proteins, and CXC, COL4 crosstalk proteins). A p value of 0.004
may indicate that a pseudocount was added to avoid a fitted
probability of zero. The fourth column gives the trajectory of gene
expression change during a time series of VEGF-induced angio-
genesis. We measure the trajectory of gene expression change by
the covariance between the gene expression and the time series.
Missing or unreliable measurements were marked not available
Angiogenesis-Associated Crosstalk 2219
attachment of ECs to the ECM. The importance of
VCAN in angiogenesis could easily be missed by other
methods that only consider the direct interactions.
VCAN has only a few physical PPIs, and it has only
one direct interaction with the angiogenesis-associated
proteins (i.e., ADAMTS1). Still, our analysis highlights
association between three angiogenesis-associated protein families: type IV collagens (blue), CXC chemokines (red), and TSP1-
containing proteins (green). The crosstalk proteins are shown for CXC chemokines and type IV collagens (purple), CXC chemo-
kines and TSP1-containing proteins (tan), type IV collagens and TSP1-containing proteins (yellow), and between all three (orange).
Network of association between type IV collagens, CXC chemokines, and TSP1-containing proteins. The network of
TABLE 3.Crosstalk protein functional enrichment.
AnnotationAdj. p valueTypeGenes
(A) TSP1, CXC, and COL4 crosstalk protein enrichment
(B) COL4 and TSP1 crosstalk protein enrichment
Thrombospondin receptor activity
(C) COL4 and CXC crosstalk protein enrichment
G-protein receptor binding
CD44, SDC1, SDC4
APP, FN1, THBS3
BGN, FN1, DCN
FN1, GP6, DCN
CXCL2, CXCR3, IL8, PF4
LDLR, PF4, SRGN
CXCL2, IL8, PF4
Interesting and statistically significant functional enrichments from the sets of crosstalk proteins. (A) Functions of
crosstalk proteins from TSP1-containing proteins, CXC chemokines, and type IV collagens. (B) Functions of
crosstalk proteins from TSP1-containing proteins and type IV collagens. (C) Functions of crosstalk proteins from
CXC chemokines and type IV collagens.
RIVERA et al.2220
VCAN as a potential component of crosstalk between
type IV collagens, CXC chemokines, and TSP1-
containing proteins. Using the quantitative compari-
son shown in Table 1, we confirmed that local
approaches like first neighbors (p = 0.046) and second
neighbors (p = 0.11) would have missed VCAN, while
non-local approaches like graph diffusion (p = 0.008)
and betweenness centrality (p = 0.006) would have
identified the significance of VCAN at the 0.01 level.
We identify six proteins at the center of the type IV
collagen, CXC chemokine, and TSP1-containing pro-
tein network. These proteins, SDC1, SDC2, SDC4,
MMP9, CD44, and VCAN, appear to be important
components of angiogenesis, based on their position
within the angiogenesis-associated network.
Figure 3 reflects the three families of angiogenesis-
associated proteins and the putative crosstalk identified
between each family. We identified proteins that either
directly or indirectly interact with many of proteins
from individual families. The association of proteins to
angiogenesis-associated families was computed using
graph diffusion. By identifying proteins that are well
connected to multiple protein families, we identified
proteins that are likely to represent conduits of cross-
talk between these important angiogenesis-associated
families. Statistical analysis and the incorporation of a
time series gene expression data set helped confirm the
role of these proteins in angiogenesis.
In our study of type IV collagens, CXC chemokines,
and TSP1-containing proteins, we identified many
classes of proteins that are known to be associated with
angiogenesis such as vascular endothelial growth fac-
tor A (VEGFA) as well as other families that receive
less attention such as proteoglycans DCN, ACAN,
BCAN, and VCAN. We also identified six proteins
that appear to be at the center of the network between
type IV collagens, CXC chemokines, and TSP1-
containing proteins. Those proteins are SDC1, SDC2,
SDC4, VCAN, CD44, and MMP9. These proteins may
facilitate crosstalk between type IV collagens, CXC
chemokines, and TSP1-containing proteins.
We examined PPI that are related to three
angiogenesis-associated protein families: type IV collagen
fibrils, CXC chemokine ligands, and TSP-1 domain-
containing proteins. To our knowledge, this work
represents the first integrated network analysis of these
angiogenesis-associated protein families. We identified
several proteins that appear to be important mediators
of crosstalk, and yet they have received relatively little
attention such as the proteoglycans DCN, ACAN,
BCAN, and VCAN. We identified syndecans at the
centre of the network associating type IV collagens,
CXC chemokines, and TSP1-containing proteins.
The work was supported by NIH grants R01
HL101200 and R01 CA138264. The authors would like
to thank Emmanouil Karagiannis for helpful discus-
sions at the initial stage of the project. We would also
like to thank Sofie Mellberg and Lena Claesson-Welsh
for use of their time series gene expression dataset.
CGR implemented the method, performed the analy-
sis, generated the images and wrote the paper. ASP
and JSB designed the study and edited the paper.
CONFLICT OF INTERESTS
The authors declare no competing interests.
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