Transcription factors involved in the regulatory networks governing the Calvin-Benson-Bassham cycle.
Identifieur interne : 000658 ( Main/Exploration ); précédent : 000657; suivant : 000659Transcription factors involved in the regulatory networks governing the Calvin-Benson-Bassham cycle.
Auteurs : Longxin Wang [République populaire de Chine] ; Jianbo Xie [République populaire de Chine] ; Qingzhang Du [République populaire de Chine] ; Fangyuan Song [République populaire de Chine] ; Liang Xiao [République populaire de Chine] ; Mingyang Quan [République populaire de Chine] ; Deqiang Zhang [République populaire de Chine]Source :
- Tree physiology [ 1758-4469 ] ; 2019.
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Abstract
Transcription factors (TFs) play crucial roles in the regulation of photosynthesis; elucidating these roles will facilitate our understanding of photosynthesis and thus accelerate its improvement for enhancing crop yield. Promoter analysis of 52 nuclear-encoded Populus tomentosa Carr. genes involved in the Calvin-Benson-Bassham (CBB) cycle revealed 706 motifs and 326 potentially interacting TFs. A backward elimination random forest (BWERF) algorithm reduced the number of TFs to 40, involved in a three-layer gene regulatory network (GRN) including 46 photosynthesis genes (bottom layer), 25 TFs (second layer) and 15 TFs (top layer). Phenotype-genotype association identified 248 single-nucleotide polymorphisms (SNPs) within 72 genes associated with 11 photosynthesis traits. Of the regulatory pairs identified by the BWERF (202 pairs), 77 TF-target combinations harbored SNPs associated with the same trait, supporting similar mechanisms of phenotype modulation. We used expression quantitative trait nucleotide (eQTN) analysis to identify causal SNPs affecting gene expression, identifying 1851 eQTN signals for 50 eGenes (genes whose expressions are regulated by eQTNs). Distribution patterns identified 14 eQTNs from seven TFs associated with eight expression levels of their downstream targets (defined in the GRN), whereas seven TF-target pairs were also identified by phenotype-genotype associations. To further validate the roles of TFs at the metabolic level, we selected 6764 SNPs from 55 genes (identified by GRN-association or GRN-eQTN pairs or both) for metabolic association, identifying variants within 10 TFs affecting metabolic processes underlying the CBB cycle. Our study provides new insights into the photosynthesis pathway in poplar and may facilitate understanding of processes underlying photosynthesis improvement.
DOI: 10.1093/treephys/tpz025
PubMed: 30941430
Affiliations:
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and Technology, Beijing Forestry University, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing</wicri:regionArea><placeName><settlement 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wicri:level="3"><nlm:affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author><author><name sortKey="Zhang, Deqiang" sort="Zhang, Deqiang" uniqKey="Zhang D" first="Deqiang" last="Zhang">Deqiang Zhang</name><affiliation wicri:level="3"><nlm:affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation><affiliation wicri:level="3"><nlm:affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing</wicri:regionArea><placeName><settlement type="city">Pékin</settlement></placeName></affiliation></author></analytic><series><title level="j">Tree physiology</title><idno type="eISSN">1758-4469</idno><imprint><date when="2019" type="published">2019</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Gene Regulatory Networks (MeSH)</term><term>Photosynthesis (MeSH)</term><term>Populus (MeSH)</term><term>Promoter Regions, Genetic (MeSH)</term><term>Transcription Factors (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Facteurs de transcription (MeSH)</term><term>Photosynthèse (MeSH)</term><term>Populus (MeSH)</term><term>Régions promotrices (génétique) (MeSH)</term><term>Réseaux de régulation génique (MeSH)</term></keywords><keywords scheme="MESH" type="chemical" xml:lang="en"><term>Transcription Factors</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Gene Regulatory Networks</term><term>Photosynthesis</term><term>Populus</term><term>Promoter Regions, Genetic</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Facteurs de transcription</term><term>Photosynthèse</term><term>Populus</term><term>Régions promotrices (génétique)</term><term>Réseaux de régulation génique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Transcription factors (TFs) play crucial roles in the regulation of photosynthesis; elucidating these roles will facilitate our understanding of photosynthesis and thus accelerate its improvement for enhancing crop yield. Promoter analysis of 52 nuclear-encoded Populus tomentosa Carr. genes involved in the Calvin-Benson-Bassham (CBB) cycle revealed 706 motifs and 326 potentially interacting TFs. A backward elimination random forest (BWERF) algorithm reduced the number of TFs to 40, involved in a three-layer gene regulatory network (GRN) including 46 photosynthesis genes (bottom layer), 25 TFs (second layer) and 15 TFs (top layer). Phenotype-genotype association identified 248 single-nucleotide polymorphisms (SNPs) within 72 genes associated with 11 photosynthesis traits. Of the regulatory pairs identified by the BWERF (202 pairs), 77 TF-target combinations harbored SNPs associated with the same trait, supporting similar mechanisms of phenotype modulation. We used expression quantitative trait nucleotide (eQTN) analysis to identify causal SNPs affecting gene expression, identifying 1851 eQTN signals for 50 eGenes (genes whose expressions are regulated by eQTNs). Distribution patterns identified 14 eQTNs from seven TFs associated with eight expression levels of their downstream targets (defined in the GRN), whereas seven TF-target pairs were also identified by phenotype-genotype associations. To further validate the roles of TFs at the metabolic level, we selected 6764 SNPs from 55 genes (identified by GRN-association or GRN-eQTN pairs or both) for metabolic association, identifying variants within 10 TFs affecting metabolic processes underlying the CBB cycle. Our study provides new insights into the photosynthesis pathway in poplar and may facilitate understanding of processes underlying photosynthesis improvement.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Automated" Owner="NLM"><PMID Version="1">30941430</PMID><DateCompleted><Year>2020</Year><Month>05</Month><Day>18</Day></DateCompleted><DateRevised><Year>2020</Year><Month>05</Month><Day>18</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1758-4469</ISSN><JournalIssue CitedMedium="Internet"><Volume>39</Volume><Issue>7</Issue><PubDate><Year>2019</Year><Month>07</Month><Day>18</Day></PubDate></JournalIssue><Title>Tree physiology</Title><ISOAbbreviation>Tree Physiol</ISOAbbreviation></Journal><ArticleTitle>Transcription factors involved in the regulatory networks governing the Calvin-Benson-Bassham cycle.</ArticleTitle><Pagination><MedlinePgn>1159-1172</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/treephys/tpz025</ELocationID><Abstract><AbstractText>Transcription factors (TFs) play crucial roles in the regulation of photosynthesis; elucidating these roles will facilitate our understanding of photosynthesis and thus accelerate its improvement for enhancing crop yield. Promoter analysis of 52 nuclear-encoded Populus tomentosa Carr. genes involved in the Calvin-Benson-Bassham (CBB) cycle revealed 706 motifs and 326 potentially interacting TFs. A backward elimination random forest (BWERF) algorithm reduced the number of TFs to 40, involved in a three-layer gene regulatory network (GRN) including 46 photosynthesis genes (bottom layer), 25 TFs (second layer) and 15 TFs (top layer). Phenotype-genotype association identified 248 single-nucleotide polymorphisms (SNPs) within 72 genes associated with 11 photosynthesis traits. Of the regulatory pairs identified by the BWERF (202 pairs), 77 TF-target combinations harbored SNPs associated with the same trait, supporting similar mechanisms of phenotype modulation. We used expression quantitative trait nucleotide (eQTN) analysis to identify causal SNPs affecting gene expression, identifying 1851 eQTN signals for 50 eGenes (genes whose expressions are regulated by eQTNs). Distribution patterns identified 14 eQTNs from seven TFs associated with eight expression levels of their downstream targets (defined in the GRN), whereas seven TF-target pairs were also identified by phenotype-genotype associations. To further validate the roles of TFs at the metabolic level, we selected 6764 SNPs from 55 genes (identified by GRN-association or GRN-eQTN pairs or both) for metabolic association, identifying variants within 10 TFs affecting metabolic processes underlying the CBB cycle. Our study provides new insights into the photosynthesis pathway in poplar and may facilitate understanding of processes underlying photosynthesis improvement.</AbstractText><CopyrightInformation>© The Author(s) 2019. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</CopyrightInformation></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Longxin</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xie</LastName><ForeName>Jianbo</ForeName><Initials>J</Initials><AffiliationInfo><Affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Du</LastName><ForeName>Qingzhang</ForeName><Initials>Q</Initials><AffiliationInfo><Affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Song</LastName><ForeName>Fangyuan</ForeName><Initials>F</Initials><AffiliationInfo><Affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Xiao</LastName><ForeName>Liang</ForeName><Initials>L</Initials><AffiliationInfo><Affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Quan</LastName><ForeName>Mingyang</ForeName><Initials>M</Initials><AffiliationInfo><Affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhang</LastName><ForeName>Deqiang</ForeName><Initials>D</Initials><AffiliationInfo><Affiliation>National Engineering Laboratory for Tree Breeding, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo><AffiliationInfo><Affiliation>Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants, Ministry of Education, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China.</Affiliation></AffiliationInfo></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>Canada</Country><MedlineTA>Tree Physiol</MedlineTA><NlmUniqueID>100955338</NlmUniqueID><ISSNLinking>0829-318X</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D014157">Transcription Factors</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D053263" MajorTopicYN="N">Gene Regulatory Networks</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D010788" MajorTopicYN="Y">Photosynthesis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="Y">Populus</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011401" MajorTopicYN="N">Promoter Regions, Genetic</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014157" MajorTopicYN="N">Transcription Factors</DescriptorName></MeshHeading></MeshHeadingList><KeywordList Owner="NOTNLM"><Keyword MajorTopicYN="Y">Populus tomentosa
</Keyword><Keyword MajorTopicYN="Y">Calvin–Benson–Bassham (CBB) cycle</Keyword><Keyword MajorTopicYN="Y">association genetics</Keyword><Keyword MajorTopicYN="Y">backward elimination random forest (BWERF) algorithm</Keyword><Keyword MajorTopicYN="Y">eQTN</Keyword><Keyword MajorTopicYN="Y">gene regulatory network (GRN)</Keyword><Keyword MajorTopicYN="Y">metabolite</Keyword><Keyword MajorTopicYN="Y">transcription factor</Keyword></KeywordList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2019</Year><Month>01</Month><Day>24</Day></PubMedPubDate><PubMedPubDate PubStatus="revised"><Year>2019</Year><Month>02</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2019</Year><Month>03</Month><Day>31</Day></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2019</Year><Month>4</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2020</Year><Month>5</Month><Day>19</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2019</Year><Month>4</Month><Day>4</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">30941430</ArticleId><ArticleId IdType="pii">5426493</ArticleId><ArticleId IdType="doi">10.1093/treephys/tpz025</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country><settlement><li>Pékin</li></settlement></list><tree><country name="République populaire de Chine"><noRegion><name sortKey="Wang, Longxin" sort="Wang, Longxin" uniqKey="Wang L" first="Longxin" last="Wang">Longxin Wang</name></noRegion><name sortKey="Du, Qingzhang" sort="Du, Qingzhang" uniqKey="Du Q" first="Qingzhang" last="Du">Qingzhang Du</name><name sortKey="Du, Qingzhang" sort="Du, Qingzhang" uniqKey="Du Q" first="Qingzhang" last="Du">Qingzhang Du</name><name sortKey="Quan, Mingyang" sort="Quan, Mingyang" uniqKey="Quan M" first="Mingyang" last="Quan">Mingyang Quan</name><name sortKey="Quan, Mingyang" sort="Quan, Mingyang" uniqKey="Quan M" first="Mingyang" last="Quan">Mingyang Quan</name><name sortKey="Song, Fangyuan" sort="Song, Fangyuan" uniqKey="Song F" first="Fangyuan" last="Song">Fangyuan Song</name><name sortKey="Song, Fangyuan" sort="Song, Fangyuan" uniqKey="Song F" first="Fangyuan" last="Song">Fangyuan Song</name><name sortKey="Wang, Longxin" sort="Wang, Longxin" uniqKey="Wang L" first="Longxin" last="Wang">Longxin Wang</name><name sortKey="Xiao, Liang" sort="Xiao, Liang" uniqKey="Xiao L" first="Liang" last="Xiao">Liang Xiao</name><name sortKey="Xiao, Liang" sort="Xiao, Liang" uniqKey="Xiao L" first="Liang" last="Xiao">Liang Xiao</name><name sortKey="Xie, Jianbo" sort="Xie, Jianbo" uniqKey="Xie J" first="Jianbo" last="Xie">Jianbo Xie</name><name sortKey="Xie, Jianbo" sort="Xie, Jianbo" uniqKey="Xie J" first="Jianbo" last="Xie">Jianbo Xie</name><name sortKey="Zhang, Deqiang" sort="Zhang, Deqiang" uniqKey="Zhang D" first="Deqiang" last="Zhang">Deqiang Zhang</name><name sortKey="Zhang, Deqiang" sort="Zhang, Deqiang" uniqKey="Zhang D" first="Deqiang" last="Zhang">Deqiang Zhang</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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