A quantitative model of transcriptional differentiation driving host-pathogen interactions.
Identifieur interne : 002817 ( Main/Exploration ); précédent : 002816; suivant : 002818A quantitative model of transcriptional differentiation driving host-pathogen interactions.
Auteurs : Sheng Zhu [États-Unis] ; Zhong Wang ; Jianxin Wang ; Yaqun Wang ; Ningtao Wang ; Zuoheng Wang ; Meng Xu ; Xiaohua Su ; Mingxiu Wang ; Shougong Zhang ; Minren Huang ; Rongling WuSource :
- Briefings in bioinformatics [ 1477-4054 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
- microbiologie : Populus.
- physiologie : Ascomycota.
- Fonctions de vraisemblance, Interactions hôte-pathogène, Transcription génétique.
English descriptors
- KwdEn :
- MESH :
- microbiology : Populus.
- physiology : Ascomycota.
- Host-Pathogen Interactions, Likelihood Functions, Transcription, Genetic.
Abstract
Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host-pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host-pathogen interactions. The framework was validated by a pathological study of poplar leaves infected by fungal Marssonina brunnea in which co-evolving and interactive genes that determine poplar-fungus interactions are identified. The new framework should find its wide application to studying host-pathogen interactions for any other interconnected systems.
DOI: 10.1093/bib/bbs047
PubMed: 22962337
Affiliations:
Links toward previous steps (curation, corpus...)
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Tel: +001 717 531 2037; Fax: +001 717 531 0480; E-mail: rwu@phs.psu.edu.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn; Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033</wicri:regionArea><placeName><region type="state">Pennsylvanie</region></placeName></affiliation></author><author><name sortKey="Wang, Zhong" sort="Wang, Zhong" uniqKey="Wang Z" first="Zhong" last="Wang">Zhong Wang</name></author><author><name sortKey="Wang, Jianxin" sort="Wang, Jianxin" uniqKey="Wang J" first="Jianxin" last="Wang">Jianxin Wang</name></author><author><name sortKey="Wang, Yaqun" sort="Wang, Yaqun" uniqKey="Wang Y" first="Yaqun" last="Wang">Yaqun Wang</name></author><author><name sortKey="Wang, Ningtao" sort="Wang, Ningtao" uniqKey="Wang N" first="Ningtao" last="Wang">Ningtao Wang</name></author><author><name sortKey="Wang, Zuoheng" sort="Wang, Zuoheng" uniqKey="Wang Z" first="Zuoheng" last="Wang">Zuoheng Wang</name></author><author><name sortKey="Xu, Meng" sort="Xu, Meng" uniqKey="Xu M" first="Meng" last="Xu">Meng Xu</name></author><author><name sortKey="Su, Xiaohua" sort="Su, Xiaohua" uniqKey="Su X" first="Xiaohua" last="Su">Xiaohua Su</name></author><author><name sortKey="Wang, Mingxiu" sort="Wang, Mingxiu" uniqKey="Wang M" first="Mingxiu" last="Wang">Mingxiu Wang</name></author><author><name sortKey="Zhang, Shougong" sort="Zhang, Shougong" uniqKey="Zhang S" first="Shougong" last="Zhang">Shougong Zhang</name></author><author><name sortKey="Huang, Minren" sort="Huang, Minren" uniqKey="Huang M" first="Minren" last="Huang">Minren Huang</name></author><author><name sortKey="Wu, Rongling" sort="Wu, Rongling" uniqKey="Wu R" first="Rongling" last="Wu">Rongling Wu</name></author></analytic><series><title level="j">Briefings in bioinformatics</title><idno type="eISSN">1477-4054</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Ascomycota (physiology)</term><term>Host-Pathogen Interactions (MeSH)</term><term>Likelihood Functions (MeSH)</term><term>Populus (microbiology)</term><term>Transcription, Genetic (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Ascomycota (physiologie)</term><term>Fonctions de vraisemblance (MeSH)</term><term>Interactions hôte-pathogène (MeSH)</term><term>Populus (microbiologie)</term><term>Transcription génétique (MeSH)</term></keywords><keywords scheme="MESH" qualifier="microbiologie" xml:lang="fr"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="microbiology" xml:lang="en"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="physiologie" xml:lang="fr"><term>Ascomycota</term></keywords><keywords scheme="MESH" qualifier="physiology" xml:lang="en"><term>Ascomycota</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Host-Pathogen Interactions</term><term>Likelihood Functions</term><term>Transcription, Genetic</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Fonctions de vraisemblance</term><term>Interactions hôte-pathogène</term><term>Transcription génétique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host-pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host-pathogen interactions. The framework was validated by a pathological study of poplar leaves infected by fungal Marssonina brunnea in which co-evolving and interactive genes that determine poplar-fungus interactions are identified. The new framework should find its wide application to studying host-pathogen interactions for any other interconnected systems. </div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22962337</PMID><DateCompleted><Year>2014</Year><Month>06</Month><Day>16</Day></DateCompleted><DateRevised><Year>2013</Year><Month>11</Month><Day>11</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1477-4054</ISSN><JournalIssue CitedMedium="Internet"><Volume>14</Volume><Issue>6</Issue><PubDate><Year>2013</Year><Month>Nov</Month></PubDate></JournalIssue><Title>Briefings in bioinformatics</Title><ISOAbbreviation>Brief Bioinform</ISOAbbreviation></Journal><ArticleTitle>A quantitative model of transcriptional differentiation driving host-pathogen interactions.</ArticleTitle><Pagination><MedlinePgn>713-23</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bib/bbs047</ELocationID><Abstract><AbstractText>Despite our expanding knowledge about the biochemistry of gene regulation involved in host-pathogen interactions, a quantitative understanding of this process at a transcriptional level is still limited. We devise and assess a computational framework that can address this question. This framework is founded on a mixture model-based likelihood, equipped with functionality to cluster genes per dynamic and functional changes of gene expression within an interconnected system composed of the host and pathogen. If genes from the host and pathogen are clustered in the same group due to a similar pattern of dynamic profiles, they are likely to be reciprocally co-evolving. If genes from the two organisms are clustered in different groups, this means that they experience strong host-pathogen interactions. The framework can test the rates of change for individual gene clusters during pathogenic infection and quantify their impacts on host-pathogen interactions. The framework was validated by a pathological study of poplar leaves infected by fungal Marssonina brunnea in which co-evolving and interactive genes that determine poplar-fungus interactions are identified. The new framework should find its wide application to studying host-pathogen interactions for any other interconnected systems. </AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Zhu</LastName><ForeName>Sheng</ForeName><Initials>S</Initials><AffiliationInfo><Affiliation>Center for Computational Biology, Beijing Forestry University, Beijing 100083, China. rwu@bjfu.edu.cn; Center for Statistical Genetics, Pennsylvania State University, Hershey, PA 17033, USA. Tel: +001 717 531 2037; Fax: +001 717 531 0480; E-mail: rwu@phs.psu.edu.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Zhong</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Jianxin</ForeName><Initials>J</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Yaqun</ForeName><Initials>Y</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Ningtao</ForeName><Initials>N</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Zuoheng</ForeName><Initials>Z</Initials></Author><Author ValidYN="Y"><LastName>Xu</LastName><ForeName>Meng</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Su</LastName><ForeName>Xiaohua</ForeName><Initials>X</Initials></Author><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Mingxiu</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Shougong</ForeName><Initials>S</Initials></Author><Author ValidYN="Y"><LastName>Huang</LastName><ForeName>Minren</ForeName><Initials>M</Initials></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Rongling</ForeName><Initials>R</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2012</Year><Month>09</Month><Day>08</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Brief Bioinform</MedlineTA><NlmUniqueID>100912837</NlmUniqueID><ISSNLinking>1467-5463</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D001203" MajorTopicYN="N">Ascomycota</DescriptorName><QualifierName UI="Q000502" MajorTopicYN="Y">physiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D054884" MajorTopicYN="Y">Host-Pathogen Interactions</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016013" MajorTopicYN="N">Likelihood Functions</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000382" MajorTopicYN="Y">microbiology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014158" MajorTopicYN="Y">Transcription, Genetic</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Marssonina leaf spot</Keyword><Keyword MajorTopicYN="N">RNA-seq</Keyword><Keyword MajorTopicYN="N">gene expression</Keyword><Keyword MajorTopicYN="N">host–pathogen interaction</Keyword><Keyword MajorTopicYN="N">poplar</Keyword><Keyword MajorTopicYN="N">statistical mode</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>9</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>9</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2014</Year><Month>6</Month><Day>17</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">22962337</ArticleId><ArticleId IdType="pii">bbs047</ArticleId><ArticleId IdType="doi">10.1093/bib/bbs047</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Pennsylvanie</li></region></list><tree><noCountry><name sortKey="Huang, Minren" sort="Huang, Minren" uniqKey="Huang M" first="Minren" last="Huang">Minren Huang</name><name sortKey="Su, Xiaohua" sort="Su, Xiaohua" uniqKey="Su X" first="Xiaohua" last="Su">Xiaohua Su</name><name sortKey="Wang, Jianxin" sort="Wang, Jianxin" uniqKey="Wang J" first="Jianxin" last="Wang">Jianxin Wang</name><name sortKey="Wang, Mingxiu" sort="Wang, Mingxiu" uniqKey="Wang M" first="Mingxiu" last="Wang">Mingxiu Wang</name><name sortKey="Wang, Ningtao" sort="Wang, Ningtao" uniqKey="Wang N" first="Ningtao" last="Wang">Ningtao Wang</name><name sortKey="Wang, Yaqun" sort="Wang, Yaqun" uniqKey="Wang Y" first="Yaqun" last="Wang">Yaqun Wang</name><name sortKey="Wang, Zhong" sort="Wang, Zhong" uniqKey="Wang Z" first="Zhong" last="Wang">Zhong Wang</name><name sortKey="Wang, Zuoheng" sort="Wang, Zuoheng" uniqKey="Wang Z" first="Zuoheng" last="Wang">Zuoheng Wang</name><name sortKey="Wu, Rongling" sort="Wu, Rongling" uniqKey="Wu R" first="Rongling" last="Wu">Rongling Wu</name><name sortKey="Xu, Meng" sort="Xu, Meng" uniqKey="Xu M" first="Meng" last="Xu">Meng Xu</name><name sortKey="Zhang, Shougong" sort="Zhang, Shougong" uniqKey="Zhang S" first="Shougong" last="Zhang">Shougong Zhang</name></noCountry><country name="États-Unis"><region name="Pennsylvanie"><name sortKey="Zhu, Sheng" sort="Zhu, Sheng" uniqKey="Zhu S" first="Sheng" last="Zhu">Sheng Zhu</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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