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Assembling the interactome of human extracellular
matrix to understand its role in health and
disease Graham L. Cromar and John
ParkinsonProgram in Molecular Structure and
Function, Hospital for Sick Children, Toronto,
Ontario
Results
Introduction The extracellular matrix (ECM) is
composed of a variety of proteins secreted by the
cell and self-organized into a complex mesh of
fibres and soluble components. These materials
are capable of forming a diverse set of
structures (e.g. bone, blood vessels). A number
of biological processes are influenced by the
surrounding matrices including cell adhesion,
migration, proliferation and differentiation.
Changes in the structure and function of ECM
components are known to be associated with a
number of complex and diverse diseases such as
arthritis, atherosclerosis and cancer
3. Although there has been considerable growth
in databases concerned with metabolic proteins,
kinases or other signaling elements, very little
attention has been devoted to structural proteins
and the role of network connectivity in their
self-organization.

• Conclusions
• We have created the first interaction map of the
  extracellular matrix and estimate that the
  catalogue of human ECM proteins and their
  interactors may exceed 2500 proteins. If current
  estimates for the number of genes in the human
  genome are true (approximately 30,000) this
  implies that approaching 10 of the human genome
  is dedicated to dealing with extracellular
  organization. This level of complexity goes well
  beyond typical conceptual representations of the
  extracellular matrix (e.g. Fig. 4) and justifies
  comprehensive analysis of this system.
• Our efforts demonstrate that the observed lack
  of attention paid to structural proteins in
  databases in general also extends to GO
  annotations. Consequently, additional terms will
  need to be included to capture all of the known
  ECM components including all related biological
  process and molecular function terms. Mapping of
  annotated proteins from mouse and rat will aid
  considerably in addressing the surprisingly
  incomplete human annotations. We found that the
  total number of rat proteins annotated as ECM
  components was 2402 (as compared to 1682 for
  humans).
• Since 40 of the ECM proteins we identified so
  far have no known interactions in BioGRID there
  is considerable opportunity to extend the network
  by examining additional datasets (e.g. MINT,
  Intact, BIND, DIP, HPRD) for which there appear
  to be only minimal overlap 1.
• Future Work
• Enlarge the human ECM map based on orthology
  and an expanded list of GO terms.
• Include interaction data from additional
  databases.
• Provide a detailed functional annotation of the
  resulting network.
• Construct ECM networks of other metazoans as a
  basis for determining adaptation and evolutionary
  conservation.

Our initial network representing human ECM
proteins and their interactors consists of 361
nodes and 547 edges (inset top right). There
remain 61 proteins, identified as matrix
components based on Gene Ontology (GO) for which
no known interactions were present in BioGRID
(40).
Comparing the ECM of several metazoans
(Fig. 1) allows us to explore the evolution of
self-organization and its normal role in the
development and maintenance of multi-cellularity.
Evol-utionary conservation, for instance, can
identify functionally important network
components. A proper understanding of such
functions, will shed light on the ECMs role in
health and disease.
Figure 1. A phylogeny derived primarily from
morphological features (after 2) emphasizing
the common names of some organisms we hope to
include in our study.
Careful examination of sub-graphs such as that
of elastin (ELN) and its nearest neighbours (Fig.
3 main) demonstrates that many of the interactors
identified from the BioGRID dataset are known ECM
components missed in the initial GO search due to
incomplete annotation of these proteins in the
Gene Ontology. It is apparent that the
corresponding orthologues in rat and mouse are
much more completely annotated (data not shown).
A subsequent attempt to pull down proteins
matching all possible cellular component,
biological process and molecular function terms
associated with the extracellular matrix shows
that the ECM graph can be expanded to at least
1682 nodes. The network appears to be rooted to
core structural components, key amongst these are
various collagens which are either adjacent, or
interconnected by short path lengths (Fig. 3
inset left).
Materials and methods
The Gene Ontology (GO) project 6 addresses the
need for a consistent vocabulary in describing
biological processes, cellular components and
molecular functions associated with gene
products. We derived a list of ECM proteins
matching cellular component terms extracellular
matrix part, middle lamella-containing
extracellular matrix and, proteinaceous
extracellular matrix. These proteins were
cross-referenced in BioGRID 5, a database
containing over 116,000 literature-curated
interactions. The network was rendered in
Cytoscape 4.
Figure 3 (Inset top right) A physical
protein-protein interaction map of the human
extracellular matrix based on interactions from
curated literature sources deposited in BioGRID
5. A list of ECM proteins was derived from Gene
Ontology 6 (all nodes shown in blue).
Interactors resulting from the BioGRID search are
shown in yellow. (Main figure) A sub-network
showing elastin (ELN) and its nearest neighbours.
Many of the interactors are known ECM proteins
that should have been picked up in the initial
search of the GO data. (Inset left) The ECM
network appears to be rooted in core structural
components such as various collagens featured in
the sub-network shown here.
Figure 4. Typical representations of the
extracellular matrix, such as this one, include
perhaps a dozen components which grossly
under-represent the true complexity of this
system. Based on our findings we estimate that
approaching 10 of the 30,000 genes in the human
proteome may be involved in extracellular
organization. Image from www.e22.physik.tu-muenc
hen.de/bausch/Oli_ECM.html
Figure 2. ECM interactions were derived by
filtering The Gene Ontology 6 and
cross-referencing to BioGRID 5. Cytoscape 4
was used to render the network.
Acknowledgments Thanks to my supervisory
committee Johanna Rommens, Andrew Emili, Gary
Bader and my supervisor, John Parkinson, for
advice and encouragement. James Wasmuth, David
He and members of the Parkinson Lab for copious
amounts of tea, advice and discussions. Funding
for this project was generously provided by the
Heart and Stroke Foundation.
For further information Please contact
graham.cromar_at_utoronto.ca. A copy of this poster
as well as more information on this and related
projects can be obtained at www.compsysbio.org/lab
/.
Literature cited 1. Cesareni et al. 2005. FEBS
Lett. 579(8)1828-1833. 2. Nielsen 2001. Animal
Evolution. Second ed. Oxford University Press.
3. Online Mendelian Inheritance in Man, OMIM
(TM). Johns Hopkins University, Baltimore, MD.
MIM Number 123700 4/19/2006 . World Wide
Web URL http//www.ncbi.nlm.nih.gov/omim/
4. Shannon et al. 2003. Genome Res 1124982504.
World Wide Web URL http//www.cytoscape.org/ 5.
Stark et al. 2006. Nuc Acids Res 34535-539.
World Wide Web URL http//www.thebiogrid.org/ 6.
The Gene Ontology Consortium. 2000. Gene
Ontology tool for the unification of biology.
Nature Genet. 25 25-29 accessed March 2007.
World Wide Web URL http//wiki.geneontology.org/