Large‐scale metabolite quantitative trait locus analysis provides new insights for high‐quality maize improvement

Summary It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a...

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Veröffentlicht in:The Plant journal : for cell and molecular biology 2019-07, Vol.99 (2), p.216-230
Hauptverfasser: Li, Kun, Wen, Weiwei, Alseekh, Saleh, Yang, Xiaohong, Guo, Huan, Li, Wenqiang, Wang, Luxi, Pan, Qingchun, Zhan, Wei, Liu, Jie, Li, Yanhua, Wu, Xiao, Brotman, Yariv, Willmitzer, Lothar, Li, Jiansheng, Fernie, Alisdair R., Yan, Jianbing
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container_issue 2
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container_title The Plant journal : for cell and molecular biology
container_volume 99
creator Li, Kun
Wen, Weiwei
Alseekh, Saleh
Yang, Xiaohong
Guo, Huan
Li, Wenqiang
Wang, Luxi
Pan, Qingchun
Zhan, Wei
Liu, Jie
Li, Yanhua
Wu, Xiao
Brotman, Yariv
Willmitzer, Lothar
Li, Jiansheng
Fernie, Alisdair R.
Yan, Jianbing
description Summary It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC2F7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling‐stage leaf, grouting‐stage leaf, young kernel and mature kernel) with clear tissue‐specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi‐tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs. Significance Statement A large scale QTL mapping of primary metabolite traits in a teosinte‐maize population reveals robust advantages existing in teosinte primary metabolism.
doi_str_mv 10.1111/tpj.14317
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To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC2F7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling‐stage leaf, grouting‐stage leaf, young kernel and mature kernel) with clear tissue‐specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi‐tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs. Significance Statement A large scale QTL mapping of primary metabolite traits in a teosinte‐maize population reveals robust advantages existing in teosinte primary metabolism.</description><identifier>ISSN: 0960-7412</identifier><identifier>EISSN: 1365-313X</identifier><identifier>DOI: 10.1111/tpj.14317</identifier><identifier>PMID: 30888713</identifier><language>eng</language><publisher>England: Blackwell Publishing Ltd</publisher><subject>Cluster Analysis ; Corn ; Crop yield ; Crosses, Genetic ; Domestication ; Gene expression ; Gene Expression Profiling ; Gene mapping ; Genes ; Genes, Plant ; genetic basis ; Genomes ; Grouting ; Inbreeding ; Kernels ; Leaves ; maize ; Metabolites ; Metabolomics ; Nutrient deficiency ; Nutritive value ; Nutritive Value - genetics ; Plant breeding ; primary metabolism ; Quantitative Trait Loci ; quantitative trait locus ; Seedlings ; teosinte ; Zea luxurians ; Zea mays - genetics ; Zea mays - growth &amp; development ; Zea mays - metabolism</subject><ispartof>The Plant journal : for cell and molecular biology, 2019-07, Vol.99 (2), p.216-230</ispartof><rights>2019 The Authors The Plant Journal © 2019 John Wiley &amp; Sons Ltd</rights><rights>2019 The Authors The Plant Journal © 2019 John Wiley &amp; Sons Ltd.</rights><rights>Copyright © 2019 John Wiley &amp; Sons Ltd and the Society for Experimental Biology</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4547-a1bf4597449d7fc6b821c117ac4544f6e14edcb83431c970ded4b1f7ca9ccc333</citedby><cites>FETCH-LOGICAL-c4547-a1bf4597449d7fc6b821c117ac4544f6e14edcb83431c970ded4b1f7ca9ccc333</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Ftpj.14317$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Ftpj.14317$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,1427,27903,27904,45553,45554,46387,46811</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30888713$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Kun</creatorcontrib><creatorcontrib>Wen, Weiwei</creatorcontrib><creatorcontrib>Alseekh, Saleh</creatorcontrib><creatorcontrib>Yang, Xiaohong</creatorcontrib><creatorcontrib>Guo, Huan</creatorcontrib><creatorcontrib>Li, Wenqiang</creatorcontrib><creatorcontrib>Wang, Luxi</creatorcontrib><creatorcontrib>Pan, Qingchun</creatorcontrib><creatorcontrib>Zhan, Wei</creatorcontrib><creatorcontrib>Liu, Jie</creatorcontrib><creatorcontrib>Li, Yanhua</creatorcontrib><creatorcontrib>Wu, Xiao</creatorcontrib><creatorcontrib>Brotman, Yariv</creatorcontrib><creatorcontrib>Willmitzer, Lothar</creatorcontrib><creatorcontrib>Li, Jiansheng</creatorcontrib><creatorcontrib>Fernie, Alisdair R.</creatorcontrib><creatorcontrib>Yan, Jianbing</creatorcontrib><title>Large‐scale metabolite quantitative trait locus analysis provides new insights for high‐quality maize improvement</title><title>The Plant journal : for cell and molecular biology</title><addtitle>Plant J</addtitle><description>Summary It is generally recognized that many favorable genes which were lost during domestication, including those related to both nutritional value and stress resistance, remain hidden in wild relatives. To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC2F7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling‐stage leaf, grouting‐stage leaf, young kernel and mature kernel) with clear tissue‐specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi‐tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs. Significance Statement A large scale QTL mapping of primary metabolite traits in a teosinte‐maize population reveals robust advantages existing in teosinte primary metabolism.</description><subject>Cluster Analysis</subject><subject>Corn</subject><subject>Crop yield</subject><subject>Crosses, Genetic</subject><subject>Domestication</subject><subject>Gene expression</subject><subject>Gene Expression Profiling</subject><subject>Gene mapping</subject><subject>Genes</subject><subject>Genes, Plant</subject><subject>genetic basis</subject><subject>Genomes</subject><subject>Grouting</subject><subject>Inbreeding</subject><subject>Kernels</subject><subject>Leaves</subject><subject>maize</subject><subject>Metabolites</subject><subject>Metabolomics</subject><subject>Nutrient deficiency</subject><subject>Nutritive value</subject><subject>Nutritive Value - genetics</subject><subject>Plant breeding</subject><subject>primary metabolism</subject><subject>Quantitative Trait Loci</subject><subject>quantitative trait locus</subject><subject>Seedlings</subject><subject>teosinte</subject><subject>Zea luxurians</subject><subject>Zea mays - genetics</subject><subject>Zea mays - growth &amp; development</subject><subject>Zea mays - metabolism</subject><issn>0960-7412</issn><issn>1365-313X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kUtOwzAQhi0EglJYcAFkiQ0sApnYiZMlqniqEixAYhc5zqR1lUexnaKy4gickZPgUmCBhDe2NJ-_0cxPyAGEp-DPmZvPToEzEBtkACyJAwbsaZMMwiwJA8Eh2iG71s7CEARL-DbZYWGapgLYgPRjaSb48fZulayRNuhk0dXaIX3uZeu0k04vkDojtaN1p3pLZSvrpdWWzk230CVa2uIL1a3Vk6mztOoMnfqnd3qFVy1pI_UrUt2sPmCDrdsjW5WsLe5_30PyeHnxMLoOxndXN6PzcaB4zEUgoah4nAnOs1JUKinSCBSAkKsyrxIEjqUqUuZnV5kISyx5AZVQMlNKMcaG5Hjt9Z2fe7Qub7RVWNeyxa63eQQZhziKIfbo0R901vXGj-qpKBZZyjK_vSE5WVPKdNYarPK50Y00yxzCfJVF7rPIv7Lw7OG3sS8aLH_Jn-V74GwNvOgal_-b8of727XyE3X3l8s</recordid><startdate>201907</startdate><enddate>201907</enddate><creator>Li, Kun</creator><creator>Wen, Weiwei</creator><creator>Alseekh, Saleh</creator><creator>Yang, Xiaohong</creator><creator>Guo, Huan</creator><creator>Li, Wenqiang</creator><creator>Wang, Luxi</creator><creator>Pan, Qingchun</creator><creator>Zhan, Wei</creator><creator>Liu, Jie</creator><creator>Li, Yanhua</creator><creator>Wu, Xiao</creator><creator>Brotman, Yariv</creator><creator>Willmitzer, Lothar</creator><creator>Li, Jiansheng</creator><creator>Fernie, Alisdair R.</creator><creator>Yan, Jianbing</creator><general>Blackwell Publishing Ltd</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7QP</scope><scope>7QR</scope><scope>7TM</scope><scope>8FD</scope><scope>FR3</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>201907</creationdate><title>Large‐scale metabolite quantitative trait locus analysis provides new insights for high‐quality maize improvement</title><author>Li, Kun ; Wen, Weiwei ; Alseekh, Saleh ; Yang, Xiaohong ; Guo, Huan ; Li, Wenqiang ; Wang, Luxi ; Pan, Qingchun ; Zhan, Wei ; Liu, Jie ; Li, Yanhua ; Wu, Xiao ; Brotman, Yariv ; Willmitzer, Lothar ; Li, Jiansheng ; Fernie, Alisdair R. ; Yan, Jianbing</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4547-a1bf4597449d7fc6b821c117ac4544f6e14edcb83431c970ded4b1f7ca9ccc333</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Cluster Analysis</topic><topic>Corn</topic><topic>Crop yield</topic><topic>Crosses, Genetic</topic><topic>Domestication</topic><topic>Gene expression</topic><topic>Gene Expression Profiling</topic><topic>Gene mapping</topic><topic>Genes</topic><topic>Genes, Plant</topic><topic>genetic basis</topic><topic>Genomes</topic><topic>Grouting</topic><topic>Inbreeding</topic><topic>Kernels</topic><topic>Leaves</topic><topic>maize</topic><topic>Metabolites</topic><topic>Metabolomics</topic><topic>Nutrient deficiency</topic><topic>Nutritive value</topic><topic>Nutritive Value - genetics</topic><topic>Plant breeding</topic><topic>primary metabolism</topic><topic>Quantitative Trait Loci</topic><topic>quantitative trait locus</topic><topic>Seedlings</topic><topic>teosinte</topic><topic>Zea luxurians</topic><topic>Zea mays - genetics</topic><topic>Zea mays - growth &amp; 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To uncover such genes in teosinte, an ancestor of maize, we conducted metabolite profiling in a BC2F7 population generated from a cross between the maize wild relative (Zea mays ssp. mexicana) and maize inbred line Mo17. In total, 65 primary metabolites were quantified in four tissues (seedling‐stage leaf, grouting‐stage leaf, young kernel and mature kernel) with clear tissue‐specific patterns emerging. Three hundred and fifty quantitative trait loci (QTLs) for these metabolites were obtained, which were distributed unevenly across the genome and included two QTL hotspots. Metabolite concentrations frequently increased in the presence of alleles from the teosinte genome while the opposite was observed for grain yield and shape trait QTLs. Combination of the multi‐tissue transcriptome and metabolome data provided considerable insight into the metabolic variations between maize and its wild relatives. This study thus identifies favorable genes hidden in the wild relative which should allow us to balance high yield and quality in future modern crop breeding programs. Significance Statement A large scale QTL mapping of primary metabolite traits in a teosinte‐maize population reveals robust advantages existing in teosinte primary metabolism.</abstract><cop>England</cop><pub>Blackwell Publishing Ltd</pub><pmid>30888713</pmid><doi>10.1111/tpj.14317</doi><tpages>15</tpages><oa>free_for_read</oa></addata></record>
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subjects Cluster Analysis
Corn
Crop yield
Crosses, Genetic
Domestication
Gene expression
Gene Expression Profiling
Gene mapping
Genes
Genes, Plant
genetic basis
Genomes
Grouting
Inbreeding
Kernels
Leaves
maize
Metabolites
Metabolomics
Nutrient deficiency
Nutritive value
Nutritive Value - genetics
Plant breeding
primary metabolism
Quantitative Trait Loci
quantitative trait locus
Seedlings
teosinte
Zea luxurians
Zea mays - genetics
Zea mays - growth & development
Zea mays - metabolism
title Large‐scale metabolite quantitative trait locus analysis provides new insights for high‐quality maize improvement
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