Toward a systems level view of the ECM and related proteins: A framework for the systematic definition and analysis of biological systems
Identifieur interne : 001532 ( Istex/Corpus ); précédent : 001531; suivant : 001533Toward a systems level view of the ECM and related proteins: A framework for the systematic definition and analysis of biological systems
Auteurs : Graham L. Cromar ; Xuejian Xiong ; Emilie Chautard ; Sylvie Ricard-Blum ; John ParkinsonSource :
- Proteins: Structure, Function, and Bioinformatics [ 0887-3585 ] ; 2012-06.
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- KwdEn :
Abstract
Advances in high throughput 'omic technologies are starting to provide unprecedented insights into how components of biological systems are organized and interact. Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems‐level view of the human extracellular matrix (ECM)—a three‐dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene‐disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue‐specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM. Proteins 2012;. © 2012 Wiley Periodicals, Inc.
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DOI: 10.1002/prot.24036
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Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems‐level view of the human extracellular matrix (ECM)—a three‐dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene‐disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue‐specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM. Proteins 2012;. © 2012 Wiley Periodicals, Inc.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>Graham L. 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Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems‐level view of the human extracellular matrix (ECM)—a three‐dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene‐disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue‐specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM. 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Research, Toronto, Ontario M5G0A3, Canada</unparsedAffiliation></affiliation><affiliation xml:id="af4" countryCode="FR" type="organization"><unparsedAffiliation>UMR 5086 CNRS—Université Lyon 1, Institut de Biologie et Chimie des Protéines, 7 passage du Vercors, 69367 Lyon Cedex 07, France</unparsedAffiliation></affiliation><affiliation xml:id="af5" countryCode="CA" type="organization"><unparsedAffiliation>Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en" type="author"><keyword xml:id="kwd1">extracellular matrix</keyword><keyword xml:id="kwd2">protein–protein interactions</keyword><keyword xml:id="kwd3">systems biology</keyword><keyword xml:id="kwd4">functional modules</keyword><keyword xml:id="kwd5">human disease</keyword></keywordGroup><supportingInformation><p> Additional Supporting Information may be found in the online version of this article and at the author's website: <url href="http://compsysbio.org/projects/ecm/"> http://compsysbio.org/projects/ecm/ </url></p><supportingInfoItem><mediaResource alt="supporting information" href="urn-x:wiley:08873585:media:prot24036:PROT_24036_sm_SuppInfoList"></mediaResource><caption>Supporting Information</caption></supportingInfoItem></supportingInformation><abstractGroup><abstract type="main" xml:lang="en"><title type="main">Abstract</title><p>Advances in high throughput 'omic technologies are starting to provide unprecedented insights into how components of biological systems are organized and interact. Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems‐level view of the human extracellular matrix (ECM)—a three‐dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene‐disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue‐specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM. Proteins 2012;. © 2012 Wiley Periodicals, Inc.</p></abstract></abstractGroup></contentMeta></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Toward a systems level view of the ECM and related proteins: A framework for the systematic definition and analysis of biological systems</title></titleInfo><titleInfo type="abbreviated" lang="en"><title>Surveying the Extracellular Matrix</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>Toward a systems level view of the ECM and related proteins: A framework for the systematic definition and analysis of biological systems</title></titleInfo><name type="personal"><namePart type="given">Graham L.</namePart><namePart type="family">Cromar</namePart><affiliation>Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada</affiliation><affiliation>Program in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario M4K 1X8, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Xuejian</namePart><namePart type="family">Xiong</namePart><affiliation>Program in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario M4K 1X8, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Emilie</namePart><namePart type="family">Chautard</namePart><affiliation>Informatics and Bio‐computing platform, Ontario Institute for Cancer Research, Toronto, Ontario M5G0A3, Canada</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sylvie</namePart><namePart type="family">Ricard‐Blum</namePart><affiliation>UMR 5086 CNRS—Université Lyon 1, Institut de Biologie et Chimie des Protéines, 7 passage du Vercors, 69367 Lyon Cedex 07, France</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">John</namePart><namePart type="family">Parkinson</namePart><affiliation>Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada</affiliation><affiliation>Program in Molecular Structure and Function, Hospital for Sick Children, Toronto, Ontario M4K 1X8, Canada</affiliation><affiliation>Department of Biochemistry, University of Toronto, Toronto, Ontario M5S 1A8, Canada</affiliation><affiliation>Molecular Structure and Function, Hospital for Sick Children, Toronto Medical Discovery Tower (East), Suite 15‐704, 101 College Street, Toronto, ON, Canada M5G 1L7===</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Wiley Subscription Services, Inc., A Wiley Company</publisher><place><placeTerm type="text">Hoboken</placeTerm></place><dateIssued encoding="w3cdtf">2012-06</dateIssued><dateCaptured encoding="w3cdtf">2011-10-24</dateCaptured><dateValid encoding="w3cdtf">2011-12-29</dateValid><copyrightDate encoding="w3cdtf">2012</copyrightDate></originInfo><language><languageTerm type="code" authority="rfc3066">en</languageTerm><languageTerm type="code" authority="iso639-2b">eng</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType><extent unit="figures">6</extent><extent unit="references">122</extent><extent unit="words">14504</extent></physicalDescription><abstract lang="en">Advances in high throughput 'omic technologies are starting to provide unprecedented insights into how components of biological systems are organized and interact. Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems‐level view of the human extracellular matrix (ECM)—a three‐dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene‐disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue‐specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM. Proteins 2012;. © 2012 Wiley Periodicals, Inc.</abstract><subject lang="en"><genre>keywords</genre><topic>extracellular matrix</topic><topic>protein–protein interactions</topic><topic>systems biology</topic><topic>functional modules</topic><topic>human disease</topic></subject><relatedItem type="host"><titleInfo><title>Proteins: Structure, Function, and Bioinformatics</title></titleInfo><titleInfo type="abbreviated"><title>Proteins</title></titleInfo><genre type="journal">journal</genre><note type="content"> Additional Supporting Information may be found in the online version of this article and at the author's website: http://compsysbio.org/projects/ecm/Supporting Info Item: Supporting Information - </note><subject><genre>article-category</genre><topic>Article</topic></subject><identifier type="ISSN">0887-3585</identifier><identifier type="eISSN">1097-0134</identifier><identifier type="DOI">10.1002/(ISSN)1097-0134</identifier><identifier type="PublisherID">PROT</identifier><part><date>2012</date><detail type="volume"><caption>vol.</caption><number>80</number></detail><detail type="issue"><caption>no.</caption><number>6</number></detail><extent unit="pages"><start>1522</start><end>1544</end><total>23</total></extent></part></relatedItem><identifier type="istex">9801F938C245982757C63AC40230D88B740CEACE</identifier><identifier type="DOI">10.1002/prot.24036</identifier><identifier type="ArticleID">PROT24036</identifier><accessCondition type="use and reproduction" contentType="copyright">Copyright © 2012 Wiley Periodicals, Inc.</accessCondition><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Wiley Subscription Services, Inc., A Wiley Company</recordOrigin></recordInfo></mods></metadata><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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