SEGN: Inferring real-time gene networks mediating phenotypic plasticity.
Identifieur interne : 000138 ( Main/Exploration ); précédent : 000137; suivant : 000139SEGN: Inferring real-time gene networks mediating phenotypic plasticity.
Auteurs : Libo Jiang [République populaire de Chine] ; Christopher H. Griffin [États-Unis] ; Rongling Wu [République populaire de Chine, États-Unis]Source :
- Computational and structural biotechnology journal [ 2001-0370 ] ; 2020.
Abstract
The capacity of an organism to alter its phenotype in response to environmental perturbations changes over developmental time and is a process determined by multiple genes that are co-expressed in intricate but organized networks. Characterizing the spatiotemporal change of such gene networks can offer insight into the genomic signatures underlying organismic adaptation, but it represents a major methodological challenge. Here, we integrate the holistic view of systems biology and the interactive notion of evolutionary game theory to reconstruct so-called systems evolutionary game networks (SEGN) that can autonomously detect, track, and visualize environment-induced gene networks along the time axis. The SEGN overcomes the limitations of traditional approaches by inferring context-specific networks, encapsulating bidirectional, signed, and weighted gene-gene interactions into fully informative networks, and monitoring the process of how networks topologically alter across environmental and developmental cues. Based on the design principle of SEGN, we perform a transcriptional plasticity study by culturing Euphrates poplar, a tree that can grow in the saline desert, in saline-free and saline-stress conditions. SEGN characterize previously unknown gene co-regulation that modulates the time trajectories of the trees' response to salt stress. As a marriage of multiple disciplines, SEGN shows its potential to interpret gene interdependence, predict how transcriptional co-regulation responds to various regimes, and provides a hint for exploring the mass, energetic, or signal basis that drives various types of gene interactions.
DOI: 10.1016/j.csbj.2020.08.029
PubMed: 33005313
PubMed Central: PMC7516210
Affiliations:
- République populaire de Chine, États-Unis
- Pennsylvanie
- Pékin, University Park (Pennsylvanie)
- Université d'État de Pennsylvanie
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Griffin</name><affiliation wicri:level="4"><nlm:affiliation>Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">University Park (Pennsylvanie)</settlement></placeName><orgName type="university">Université d'État de Pennsylvanie</orgName></affiliation></author><author><name sortKey="Wu, Rongling" sort="Wu, Rongling" uniqKey="Wu R" first="Rongling" last="Wu">Rongling Wu</name><affiliation wicri:level="1"><nlm:affiliation>Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation><affiliation wicri:level="1"><nlm:affiliation>Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation><affiliation wicri:level="4"><nlm:affiliation>Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033</wicri:regionArea><placeName><region type="state">Pennsylvanie</region><settlement type="city">University Park (Pennsylvanie)</settlement></placeName><orgName type="university">Université d'État de Pennsylvanie</orgName></affiliation></author></analytic><series><title level="j">Computational and structural biotechnology journal</title><idno type="ISSN">2001-0370</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The capacity of an organism to alter its phenotype in response to environmental perturbations changes over developmental time and is a process determined by multiple genes that are co-expressed in intricate but organized networks. Characterizing the spatiotemporal change of such gene networks can offer insight into the genomic signatures underlying organismic adaptation, but it represents a major methodological challenge. Here, we integrate the holistic view of systems biology and the interactive notion of evolutionary game theory to reconstruct so-called systems evolutionary game networks (SEGN) that can autonomously detect, track, and visualize environment-induced gene networks along the time axis. The SEGN overcomes the limitations of traditional approaches by inferring context-specific networks, encapsulating bidirectional, signed, and weighted gene-gene interactions into fully informative networks, and monitoring the process of how networks topologically alter across environmental and developmental cues. Based on the design principle of SEGN, we perform a transcriptional plasticity study by culturing Euphrates poplar, a tree that can grow in the saline desert, in saline-free and saline-stress conditions. SEGN characterize previously unknown gene co-regulation that modulates the time trajectories of the trees' response to salt stress. As a marriage of multiple disciplines, SEGN shows its potential to interpret gene interdependence, predict how transcriptional co-regulation responds to various regimes, and provides a hint for exploring the mass, energetic, or signal basis that drives various types of gene interactions.</div></front></TEI><pubmed><MedlineCitation Status="PubMed-not-MEDLINE" Owner="NLM"><PMID Version="1">33005313</PMID><DateRevised><Year>2020</Year><Month>10</Month><Day>03</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Print">2001-0370</ISSN><JournalIssue CitedMedium="Print"><Volume>18</Volume><PubDate><Year>2020</Year></PubDate></JournalIssue><Title>Computational and structural biotechnology journal</Title><ISOAbbreviation>Comput Struct Biotechnol J</ISOAbbreviation></Journal><ArticleTitle>SEGN: Inferring real-time gene networks mediating phenotypic plasticity.</ArticleTitle><Pagination><MedlinePgn>2510-2521</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1016/j.csbj.2020.08.029</ELocationID><Abstract><AbstractText>The capacity of an organism to alter its phenotype in response to environmental perturbations changes over developmental time and is a process determined by multiple genes that are co-expressed in intricate but organized networks. Characterizing the spatiotemporal change of such gene networks can offer insight into the genomic signatures underlying organismic adaptation, but it represents a major methodological challenge. Here, we integrate the holistic view of systems biology and the interactive notion of evolutionary game theory to reconstruct so-called systems evolutionary game networks (SEGN) that can autonomously detect, track, and visualize environment-induced gene networks along the time axis. The SEGN overcomes the limitations of traditional approaches by inferring context-specific networks, encapsulating bidirectional, signed, and weighted gene-gene interactions into fully informative networks, and monitoring the process of how networks topologically alter across environmental and developmental cues. Based on the design principle of SEGN, we perform a transcriptional plasticity study by culturing Euphrates poplar, a tree that can grow in the saline desert, in saline-free and saline-stress conditions. SEGN characterize previously unknown gene co-regulation that modulates the time trajectories of the trees' response to salt stress. As a marriage of multiple disciplines, SEGN shows its potential to interpret gene interdependence, predict how transcriptional co-regulation responds to various regimes, and provides a hint for exploring the mass, energetic, or signal basis that drives various types of gene interactions.</AbstractText><CopyrightInformation>© 2020 The Author(s).</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Jiang</LastName><ForeName>Libo</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Griffin</LastName><ForeName>Christopher H</ForeName><Initials>CH</Initials><AffiliationInfo><Affiliation>Applied Research Laboratory, The Pennsylvania State University, University Park, PA 16802, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Wu</LastName><ForeName>Rongling</ForeName><Initials>R</Initials><AffiliationInfo><Affiliation>Beijing Advanced Innovation Center for Tree Breeding by Molecular Design, Beijing Forestry University, Beijing 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Computational Biology, College of Biological Sciences and Technology, Beijing Forestry University, Beijing 100083, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Center for Statistical Genetics, Departments of Public Health Sciences and Statistics, The Pennsylvania State University, Hershey, PA 17033, USA.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2020</Year><Month>09</Month><Day>05</Day></ArticleDate></Article><MedlineJournalInfo><Country>Netherlands</Country><MedlineTA>Comput Struct Biotechnol J</MedlineTA><NlmUniqueID>101585369</NlmUniqueID><ISSNLinking>2001-0370</ISSNLinking></MedlineJournalInfo><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="N">Dynamic transcriptional plasticity</Keyword><Keyword MajorTopicYN="N">Euphrates poplar</Keyword><Keyword MajorTopicYN="N">Gene regulatory networks</Keyword><Keyword MajorTopicYN="N">Phenotypic plasticity</Keyword><Keyword MajorTopicYN="N">Salt stress</Keyword></KeywordList><CoiStatement>The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.</CoiStatement></MedlineCitation><PubmedData><History><PubMedPubDate 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Griffin</name></region><name sortKey="Wu, Rongling" sort="Wu, Rongling" uniqKey="Wu R" first="Rongling" last="Wu">Rongling Wu</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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