New Approaches for in Silico Identification of Cytokine‐Modified β Cell Gene Networks
Identifieur interne : 000C78 ( Istex/Corpus ); précédent : 000C77; suivant : 000C79New Approaches for in Silico Identification of Cytokine‐Modified β Cell Gene Networks
Auteurs : Burak Kutlu ; Najib Naamane ; Laurence Berthou ; Decio L. EizirikSource :
- Annals of the New York Academy of Sciences [ 0077-8923 ] ; 2004-12.
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Abstract
Abstract: Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine‐induced apoptosis depends on the expression of pro‐ and anti‐apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and “effector” gene networks regulated by interleukin‐1β and/or interferon‐γ in β cells. This suggests that β cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF‐κB (previously shown to be pivotal for β cell apoptosis) in 15 temporally related gene clusters, identified by time‐course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer‐based findings must be validated by use of positive and negative controls, and by “ChIP on chip” analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a “proof of principle” for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine‐regulated β cell gene networks leading to β cell apoptosis in T1DM.
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DOI: 10.1196/annals.1337.007
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Cytokine‐induced apoptosis depends on the expression of pro‐ and anti‐apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and “effector” gene networks regulated by interleukin‐1β and/or interferon‐γ in β cells. This suggests that β cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF‐κB (previously shown to be pivotal for β cell apoptosis) in 15 temporally related gene clusters, identified by time‐course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer‐based findings must be validated by use of positive and negative controls, and by “ChIP on chip” analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a “proof of principle” for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine‐regulated β cell gene networks leading to β cell apoptosis in T1DM.</div></front></TEI><istex><corpusName>wiley</corpusName><author><json:item><name>BURAK KUTLU</name><affiliations><json:string>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</json:string></affiliations></json:item><json:item><name>NAJIB NAAMANE</name><affiliations><json:string>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</json:string></affiliations></json:item><json:item><name>LAURENCE BERTHOU</name><affiliations><json:string>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</json:string></affiliations></json:item><json:item><name>DECIO L. 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Cytokine‐induced apoptosis depends on the expression of pro‐ and anti‐apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and “effector” gene networks regulated by interleukin‐1β and/or interferon‐γ in β cells. This suggests that β cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF‐κB (previously shown to be pivotal for β cell apoptosis) in 15 temporally related gene clusters, identified by time‐course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer‐based findings must be validated by use of positive and negative controls, and by “ChIP on chip” analysis. Moreover, new statistical approaches are required to decrease false positive findings. 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Eizirik, Laboratory of Experimental Medicine, ULB, 808 route de Lennik, B‐1070 Brussels, Belgium. Voice: +32‐2‐555‐62‐42; fax: +32‐2‐555‐62‐39.</p></note><affiliation>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</affiliation></author></analytic><monogr><title level="j">Annals of the New York Academy of Sciences</title><idno type="pISSN">0077-8923</idno><idno type="eISSN">1749-6632</idno><idno type="DOI">10.1111/(ISSN)1749-6632</idno><imprint><publisher>Blackwell Publishing Ltd</publisher><pubPlace>Oxford, UK</pubPlace><date type="published" when="2004-12"></date><biblScope unit="volume">1037</biblScope><biblScope unit="issue">1</biblScope><biblScope unit="page" from="41">41</biblScope><biblScope unit="page" to="58">58</biblScope></imprint></monogr><idno type="istex">A9CD582673D7D371BDBB66EA66B9E1294A81C21D</idno><idno type="DOI">10.1196/annals.1337.007</idno><idno type="ArticleID">NYAS41</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2004</date></creation><langUsage><language ident="en">en</language></langUsage><abstract><p>Abstract: Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine‐induced apoptosis depends on the expression of pro‐ and anti‐apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and “effector” gene networks regulated by interleukin‐1β and/or interferon‐γ in β cells. This suggests that β cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF‐κB (previously shown to be pivotal for β cell apoptosis) in 15 temporally related gene clusters, identified by time‐course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer‐based findings must be validated by use of positive and negative controls, and by “ChIP on chip” analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a “proof of principle” for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine‐regulated β cell gene networks leading to β cell apoptosis in T1DM.</p></abstract><textClass xml:lang="en"><keywords scheme="keyword"><list><head>keywords</head><item><term>pancreatic beta cells</term></item><item><term>in silico analysis</term></item><item><term>diabetes mellitus</term></item><item><term>microarray analysis</term></item><item><term>cytokines</term></item><item><term>interferon‐γ</term></item><item><term>interleukin‐1</term></item><item><term>NF‐κB</term></item><item><term>apoptosis</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2004-12">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><original>false</original><mimetype>text/plain</mimetype><extension>txt</extension><uri>https://api.istex.fr/document/A9CD582673D7D371BDBB66EA66B9E1294A81C21D/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Wiley, elements deleted: body"><istex:xmlDeclaration>version="1.0" encoding="UTF-8" standalone="yes"</istex:xmlDeclaration><istex:document><component version="2.0" type="serialArticle" xml:lang="en"><header><publicationMeta level="product"><publisherInfo><publisherName>Blackwell Publishing Ltd</publisherName><publisherLoc>Oxford, UK</publisherLoc></publisherInfo><doi origin="wiley" registered="yes">10.1111/(ISSN)1749-6632</doi><issn type="print">0077-8923</issn><issn type="electronic">1749-6632</issn><idGroup><id type="product" value="NYAS"></id><id type="publisherDivision" value="ST"></id></idGroup><titleGroup><title type="main" sort="ANNALS OF NEW YORK ACADEMY SCIENCES">Annals of the New York Academy of Sciences</title></titleGroup></publicationMeta><publicationMeta level="part" position="12001"><doi origin="wiley">10.1111/nyas.2004.1037.issue-1</doi><titleGroup><title type="main">Immunology of Diabetes III</title></titleGroup><numberingGroup><numbering type="journalVolume" number="1037">1037</numbering><numbering type="journalIssue">1</numbering></numberingGroup><coverDate startDate="2004-12">December 2004</coverDate></publicationMeta><publicationMeta level="unit" type="article" position="8" status="forIssue"><doi origin="wiley">10.1196/annals.1337.007</doi><idGroup><id type="unit" value="NYAS41"></id></idGroup><countGroup><count type="pageTotal" number="18"></count></countGroup><titleGroup><title type="tocHeading1">Immunology of Diabetes III: Part III. Immunology of Type 1 Diabetes</title></titleGroup><eventGroup><event type="firstOnline" date="2006-01-12"></event><event type="publishedOnlineFinalForm" date="2006-01-12"></event><event type="xmlConverted" agent="Converter:BPG_TO_WML3G version:2.3.5 mode:FullText source:FullText result:FullText" date="2010-04-09"></event><event type="xmlConverted" agent="Converter:WILEY_ML3G_TO_WILEY_ML3GV2 version:4.0.1" date="2014-03-19"></event><event type="xmlConverted" agent="Converter:WML3G_To_WML3G version:4.1.7 mode:FullText,remove_FC" date="2014-10-14"></event></eventGroup><numberingGroup><numbering type="pageFirst" number="41">41</numbering><numbering type="pageLast" number="58">58</numbering></numberingGroup><correspondenceTo>Address for correspondence: D.L. Eizirik, Laboratory of Experimental Medicine, ULB, 808 route de Lennik, B‐1070 Brussels, Belgium. Voice: +32‐2‐555‐62‐42; fax: +32‐2‐555‐62‐39. <email>deizirik@ulb.ac.be</email></correspondenceTo><linkGroup><link type="toTypesetVersion" href="file:NYAS.NYAS41.pdf"></link></linkGroup></publicationMeta><contentMeta><countGroup><count type="figureTotal" number="6"></count><count type="tableTotal" number="3"></count><count type="formulaTotal" number="0"></count><count type="referenceTotal" number="64"></count><count type="linksCrossRef" number="105"></count></countGroup><titleGroup><title type="main">New Approaches for <i>in Silico</i> Identification of Cytokine‐Modified β Cell Gene Networks</title></titleGroup><creators><creator creatorRole="author" xml:id="cr1" affiliationRef="#a1" noteRef="#fn1"><personName><givenNames>BURAK</givenNames><familyName>KUTLU</familyName></personName></creator><creator creatorRole="author" xml:id="cr2" affiliationRef="#a1"><personName><givenNames>NAJIB</givenNames><familyName>NAAMANE</familyName></personName></creator><creator creatorRole="author" xml:id="cr3" affiliationRef="#a1"><personName><givenNames>LAURENCE</givenNames><familyName>BERTHOU</familyName></personName></creator><creator creatorRole="author" xml:id="cr4" affiliationRef="#a1" corresponding="yes"><personName><givenNames>DECIO L.</givenNames><familyName>EIZIRIK</familyName></personName></creator></creators><affiliationGroup><affiliation xml:id="a1" countryCode="BE"><unparsedAffiliation>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</unparsedAffiliation></affiliation></affiliationGroup><keywordGroup xml:lang="en"><keyword xml:id="k1">pancreatic beta cells</keyword><keyword xml:id="k2"><i>in silico</i> analysis</keyword><keyword xml:id="k3">diabetes mellitus</keyword><keyword xml:id="k4">microarray analysis</keyword><keyword xml:id="k5">cytokines</keyword><keyword xml:id="k6">interferon‐γ</keyword><keyword xml:id="k7">interleukin‐1</keyword><keyword xml:id="k8">NF‐κB</keyword><keyword xml:id="k9">apoptosis</keyword></keywordGroup><abstractGroup><abstract type="main" xml:lang="en"><p><b>A<sc>bstract</sc>: </b> Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine‐induced apoptosis depends on the expression of pro‐ and anti‐apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and “effector” gene networks regulated by interleukin‐1β and/or interferon‐γ in β cells. This suggests that β cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing <i>in silico</i> analysis for the localization of binding sites for the transcription factor NF‐κB (previously shown to be pivotal for β cell apoptosis) in 15 temporally related gene clusters, identified by time‐course microarray analysis. <i>In silico</i> analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer‐based findings must be validated by use of positive and negative controls, and by “ChIP on chip” analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a “proof of principle” for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine‐regulated β cell gene networks leading to β cell apoptosis in T1DM.</p></abstract></abstractGroup></contentMeta><noteGroup><note xml:id="fn1"><label>a</label><p>Current address: Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103‐8904.</p></note></noteGroup></header></component></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>New Approaches for in Silico Identification of Cytokine‐Modified β Cell Gene Networks</title></titleInfo><titleInfo type="alternative" contentType="CDATA" lang="en"><title>New Approaches for in Silico Identification of Cytokine‐Modified β Cell Gene Networks</title></titleInfo><name type="personal"><namePart type="given">BURAK</namePart><namePart type="family">KUTLU</namePart><affiliation>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</affiliation><description>Current address: Institute for Systems Biology, 1441 North 34th Street, Seattle, WA 98103‐8904.</description><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">NAJIB</namePart><namePart type="family">NAAMANE</namePart><affiliation>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">LAURENCE</namePart><namePart type="family">BERTHOU</namePart><affiliation>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">DECIO L.</namePart><namePart type="family">EIZIRIK</namePart><affiliation>Laboratory of Experimental Medicine, Université Libre de Bruxelles, B‐1070 Brussels, Belgium</affiliation><description>Correspondence: Address for correspondence: D.L. Eizirik, Laboratory of Experimental Medicine, ULB, 808 route de Lennik, B‐1070 Brussels, Belgium. Voice: +32‐2‐555‐62‐42; fax: +32‐2‐555‐62‐39. </description><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="article" displayLabel="article"></genre><originInfo><publisher>Blackwell Publishing Ltd</publisher><place><placeTerm type="text">Oxford, UK</placeTerm></place><dateIssued encoding="w3cdtf">2004-12</dateIssued><copyrightDate encoding="w3cdtf">2004</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="tables">3</extent><extent unit="references">64</extent></physicalDescription><abstract>Abstract: Beta cell dysfunction and death in type 1 diabetes mellitus (T1DM) is caused by direct contact with activated macrophages and T lymphocytes and by exposure to soluble mediators secreted by these cells, such as cytokines and nitric oxide. Cytokine‐induced apoptosis depends on the expression of pro‐ and anti‐apoptotic genes that remain to be characterized. Using microarray analyses, we identified several transcription factor and “effector” gene networks regulated by interleukin‐1β and/or interferon‐γ in β cells. This suggests that β cell fate following exposure to cytokines is a complex and highly regulated process, depending on the duration and severity of perturbation of key gene networks. In order to draw correct conclusions from these massive amounts of data, we need to utilize novel bioinformatics and statistical tools. Thus, we are presently performing in silico analysis for the localization of binding sites for the transcription factor NF‐κB (previously shown to be pivotal for β cell apoptosis) in 15 temporally related gene clusters, identified by time‐course microarray analysis. In silico analysis is based on a broad range of computational techniques used to detect motifs in a DNA sequence corresponding to the binding site of a transcription factor. These computer‐based findings must be validated by use of positive and negative controls, and by “ChIP on chip” analysis. Moreover, new statistical approaches are required to decrease false positive findings. These novel approaches will constitute a “proof of principle” for the integrated use of bioinformatics and functional genomics in the characterization of relevant cytokine‐regulated β cell gene networks leading to β cell apoptosis in T1DM.</abstract><subject lang="en"><genre>keywords</genre><topic>pancreatic beta cells</topic><topic>in silico analysis</topic><topic>diabetes mellitus</topic><topic>microarray analysis</topic><topic>cytokines</topic><topic>interferon‐γ</topic><topic>interleukin‐1</topic><topic>NF‐κB</topic><topic>apoptosis</topic></subject><relatedItem type="host"><titleInfo><title>Annals of the New York Academy of Sciences</title></titleInfo><genre type="journal">journal</genre><identifier type="ISSN">0077-8923</identifier><identifier type="eISSN">1749-6632</identifier><identifier type="DOI">10.1111/(ISSN)1749-6632</identifier><identifier type="PublisherID">NYAS</identifier><part><date>2004</date><detail type="title"><title>Immunology of Diabetes III</title></detail><detail type="volume"><caption>vol.</caption><number>1037</number></detail><detail type="issue"><caption>no.</caption><number>1</number></detail><extent unit="pages"><start>41</start><end>58</end><total>18</total></extent></part></relatedItem><identifier type="istex">A9CD582673D7D371BDBB66EA66B9E1294A81C21D</identifier><identifier type="DOI">10.1196/annals.1337.007</identifier><identifier type="ArticleID">NYAS41</identifier><recordInfo><recordContentSource>WILEY</recordContentSource><recordOrigin>Blackwell Publishing Ltd</recordOrigin></recordInfo></mods></metadata><enrichments><json:item><type>multicat</type><uri>https://api.istex.fr/document/A9CD582673D7D371BDBB66EA66B9E1294A81C21D/enrichments/multicat</uri></json:item></enrichments><serie></serie></istex></record>Pour manipuler ce document sous Unix (Dilib)
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