An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric
Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosy...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Jingru [verfasserIn] Cui, Guanghong [verfasserIn] Ma, Xiaohui [verfasserIn] Zhan, Zhilai [verfasserIn] Ma, Ying [verfasserIn] Teng, Zhongqiu [verfasserIn] Gao, Wei [verfasserIn] Wang, Yanan [verfasserIn] Chen, Tong [verfasserIn] Lai, Changjiangsheng [verfasserIn] Zhao, Yujun [verfasserIn] Tang, Jinfu [verfasserIn] Lin, Huixin [verfasserIn] Shen, Ye [verfasserIn] Zeng, Wen [verfasserIn] Guo, Juan [verfasserIn] Huang, Luqi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Plant molecular biology - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1981, 101(2019), 3 vom: 15. Juni, Seite 221-234 |
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| Übergeordnetes Werk: |
volume:101 ; year:2019 ; number:3 ; day:15 ; month:06 ; pages:221-234 |
| Links: |
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| DOI / URN: |
10.1007/s11103-019-00892-0 |
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| Katalog-ID: |
SPR01669158X |
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| 245 | 1 | 3 | |a An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric |
| 264 | 1 | |c 2019 | |
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| 337 | |a Computermedien |b c |2 rdamedia | ||
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| 520 | |a Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. | ||
| 650 | 4 | |a Metabolite module |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Gene expression pattern |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a PLS |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Terpene synthase |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Functional prediction |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Cui, Guanghong |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Xiaohui |e verfasserin |4 aut | |
| 700 | 1 | |a Zhan, Zhilai |e verfasserin |4 aut | |
| 700 | 1 | |a Ma, Ying |e verfasserin |4 aut | |
| 700 | 1 | |a Teng, Zhongqiu |e verfasserin |4 aut | |
| 700 | 1 | |a Gao, Wei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yanan |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Tong |e verfasserin |4 aut | |
| 700 | 1 | |a Lai, Changjiangsheng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Yujun |e verfasserin |4 aut | |
| 700 | 1 | |a Tang, Jinfu |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Huixin |e verfasserin |4 aut | |
| 700 | 1 | |a Shen, Ye |e verfasserin |4 aut | |
| 700 | 1 | |a Zeng, Wen |e verfasserin |4 aut | |
| 700 | 1 | |a Guo, Juan |e verfasserin |4 aut | |
| 700 | 1 | |a Huang, Luqi |e verfasserin |4 aut | |
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10.1007/s11103-019-00892-0 doi (DE-627)SPR01669158X (SPR)s11103-019-00892-0-e DE-627 ger DE-627 rakwb eng 580 ASE 42.43 bkl 48.58 bkl Sun, Jingru verfasserin aut An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. Metabolite module (dpeaa)DE-He213 Gene expression pattern (dpeaa)DE-He213 PLS (dpeaa)DE-He213 Terpene synthase (dpeaa)DE-He213 Functional prediction (dpeaa)DE-He213 Cui, Guanghong verfasserin aut Ma, Xiaohui verfasserin aut Zhan, Zhilai verfasserin aut Ma, Ying verfasserin aut Teng, Zhongqiu verfasserin aut Gao, Wei verfasserin aut Wang, Yanan verfasserin aut Chen, Tong verfasserin aut Lai, Changjiangsheng verfasserin aut Zhao, Yujun verfasserin aut Tang, Jinfu verfasserin aut Lin, Huixin verfasserin aut Shen, Ye verfasserin aut Zeng, Wen verfasserin aut Guo, Juan verfasserin aut Huang, Luqi verfasserin aut Enthalten in Plant molecular biology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1981 101(2019), 3 vom: 15. Juni, Seite 221-234 (DE-627)269758658 (DE-600)1475712-6 1573-5028 nnns volume:101 year:2019 number:3 day:15 month:06 pages:221-234 https://dx.doi.org/10.1007/s11103-019-00892-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.43 ASE 48.58 ASE AR 101 2019 3 15 06 221-234 |
| spelling |
10.1007/s11103-019-00892-0 doi (DE-627)SPR01669158X (SPR)s11103-019-00892-0-e DE-627 ger DE-627 rakwb eng 580 ASE 42.43 bkl 48.58 bkl Sun, Jingru verfasserin aut An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. Metabolite module (dpeaa)DE-He213 Gene expression pattern (dpeaa)DE-He213 PLS (dpeaa)DE-He213 Terpene synthase (dpeaa)DE-He213 Functional prediction (dpeaa)DE-He213 Cui, Guanghong verfasserin aut Ma, Xiaohui verfasserin aut Zhan, Zhilai verfasserin aut Ma, Ying verfasserin aut Teng, Zhongqiu verfasserin aut Gao, Wei verfasserin aut Wang, Yanan verfasserin aut Chen, Tong verfasserin aut Lai, Changjiangsheng verfasserin aut Zhao, Yujun verfasserin aut Tang, Jinfu verfasserin aut Lin, Huixin verfasserin aut Shen, Ye verfasserin aut Zeng, Wen verfasserin aut Guo, Juan verfasserin aut Huang, Luqi verfasserin aut Enthalten in Plant molecular biology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1981 101(2019), 3 vom: 15. Juni, Seite 221-234 (DE-627)269758658 (DE-600)1475712-6 1573-5028 nnns volume:101 year:2019 number:3 day:15 month:06 pages:221-234 https://dx.doi.org/10.1007/s11103-019-00892-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.43 ASE 48.58 ASE AR 101 2019 3 15 06 221-234 |
| allfields_unstemmed |
10.1007/s11103-019-00892-0 doi (DE-627)SPR01669158X (SPR)s11103-019-00892-0-e DE-627 ger DE-627 rakwb eng 580 ASE 42.43 bkl 48.58 bkl Sun, Jingru verfasserin aut An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. Metabolite module (dpeaa)DE-He213 Gene expression pattern (dpeaa)DE-He213 PLS (dpeaa)DE-He213 Terpene synthase (dpeaa)DE-He213 Functional prediction (dpeaa)DE-He213 Cui, Guanghong verfasserin aut Ma, Xiaohui verfasserin aut Zhan, Zhilai verfasserin aut Ma, Ying verfasserin aut Teng, Zhongqiu verfasserin aut Gao, Wei verfasserin aut Wang, Yanan verfasserin aut Chen, Tong verfasserin aut Lai, Changjiangsheng verfasserin aut Zhao, Yujun verfasserin aut Tang, Jinfu verfasserin aut Lin, Huixin verfasserin aut Shen, Ye verfasserin aut Zeng, Wen verfasserin aut Guo, Juan verfasserin aut Huang, Luqi verfasserin aut Enthalten in Plant molecular biology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1981 101(2019), 3 vom: 15. Juni, Seite 221-234 (DE-627)269758658 (DE-600)1475712-6 1573-5028 nnns volume:101 year:2019 number:3 day:15 month:06 pages:221-234 https://dx.doi.org/10.1007/s11103-019-00892-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.43 ASE 48.58 ASE AR 101 2019 3 15 06 221-234 |
| allfieldsGer |
10.1007/s11103-019-00892-0 doi (DE-627)SPR01669158X (SPR)s11103-019-00892-0-e DE-627 ger DE-627 rakwb eng 580 ASE 42.43 bkl 48.58 bkl Sun, Jingru verfasserin aut An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. Metabolite module (dpeaa)DE-He213 Gene expression pattern (dpeaa)DE-He213 PLS (dpeaa)DE-He213 Terpene synthase (dpeaa)DE-He213 Functional prediction (dpeaa)DE-He213 Cui, Guanghong verfasserin aut Ma, Xiaohui verfasserin aut Zhan, Zhilai verfasserin aut Ma, Ying verfasserin aut Teng, Zhongqiu verfasserin aut Gao, Wei verfasserin aut Wang, Yanan verfasserin aut Chen, Tong verfasserin aut Lai, Changjiangsheng verfasserin aut Zhao, Yujun verfasserin aut Tang, Jinfu verfasserin aut Lin, Huixin verfasserin aut Shen, Ye verfasserin aut Zeng, Wen verfasserin aut Guo, Juan verfasserin aut Huang, Luqi verfasserin aut Enthalten in Plant molecular biology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1981 101(2019), 3 vom: 15. Juni, Seite 221-234 (DE-627)269758658 (DE-600)1475712-6 1573-5028 nnns volume:101 year:2019 number:3 day:15 month:06 pages:221-234 https://dx.doi.org/10.1007/s11103-019-00892-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.43 ASE 48.58 ASE AR 101 2019 3 15 06 221-234 |
| allfieldsSound |
10.1007/s11103-019-00892-0 doi (DE-627)SPR01669158X (SPR)s11103-019-00892-0-e DE-627 ger DE-627 rakwb eng 580 ASE 42.43 bkl 48.58 bkl Sun, Jingru verfasserin aut An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. Metabolite module (dpeaa)DE-He213 Gene expression pattern (dpeaa)DE-He213 PLS (dpeaa)DE-He213 Terpene synthase (dpeaa)DE-He213 Functional prediction (dpeaa)DE-He213 Cui, Guanghong verfasserin aut Ma, Xiaohui verfasserin aut Zhan, Zhilai verfasserin aut Ma, Ying verfasserin aut Teng, Zhongqiu verfasserin aut Gao, Wei verfasserin aut Wang, Yanan verfasserin aut Chen, Tong verfasserin aut Lai, Changjiangsheng verfasserin aut Zhao, Yujun verfasserin aut Tang, Jinfu verfasserin aut Lin, Huixin verfasserin aut Shen, Ye verfasserin aut Zeng, Wen verfasserin aut Guo, Juan verfasserin aut Huang, Luqi verfasserin aut Enthalten in Plant molecular biology Dordrecht [u.a.] : Springer Science + Business Media B.V, 1981 101(2019), 3 vom: 15. Juni, Seite 221-234 (DE-627)269758658 (DE-600)1475712-6 1573-5028 nnns volume:101 year:2019 number:3 day:15 month:06 pages:221-234 https://dx.doi.org/10.1007/s11103-019-00892-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_211 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_647 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.43 ASE 48.58 ASE AR 101 2019 3 15 06 221-234 |
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Enthalten in Plant molecular biology 101(2019), 3 vom: 15. Juni, Seite 221-234 volume:101 year:2019 number:3 day:15 month:06 pages:221-234 |
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Enthalten in Plant molecular biology 101(2019), 3 vom: 15. Juni, Seite 221-234 volume:101 year:2019 number:3 day:15 month:06 pages:221-234 |
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Metabolite module Gene expression pattern PLS Terpene synthase Functional prediction |
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Sun, Jingru @@aut@@ Cui, Guanghong @@aut@@ Ma, Xiaohui @@aut@@ Zhan, Zhilai @@aut@@ Ma, Ying @@aut@@ Teng, Zhongqiu @@aut@@ Gao, Wei @@aut@@ Wang, Yanan @@aut@@ Chen, Tong @@aut@@ Lai, Changjiangsheng @@aut@@ Zhao, Yujun @@aut@@ Tang, Jinfu @@aut@@ Lin, Huixin @@aut@@ Shen, Ye @@aut@@ Zeng, Wen @@aut@@ Guo, Juan @@aut@@ Huang, Luqi @@aut@@ |
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2019-06-15T00:00:00Z |
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Sun, Jingru |
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Sun, Jingru ddc 580 bkl 42.43 bkl 48.58 misc Metabolite module misc Gene expression pattern misc PLS misc Terpene synthase misc Functional prediction An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric |
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580 ASE 42.43 bkl 48.58 bkl An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric Metabolite module (dpeaa)DE-He213 Gene expression pattern (dpeaa)DE-He213 PLS (dpeaa)DE-He213 Terpene synthase (dpeaa)DE-He213 Functional prediction (dpeaa)DE-He213 |
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An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric |
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Sun, Jingru Cui, Guanghong Ma, Xiaohui Zhan, Zhilai Ma, Ying Teng, Zhongqiu Gao, Wei Wang, Yanan Chen, Tong Lai, Changjiangsheng Zhao, Yujun Tang, Jinfu Lin, Huixin Shen, Ye Zeng, Wen Guo, Juan Huang, Luqi |
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integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric |
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An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric |
| abstract |
Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. |
| abstractGer |
Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. |
| abstract_unstemmed |
Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. This integrated strategy provides a methodology for gene function predictions, which represents a substantial improvement in the elucidation of biosynthetic pathways in nonmodel plants. |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR01669158X</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519141620.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11103-019-00892-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR01669158X</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11103-019-00892-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">580</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.43</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">48.58</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Sun, Jingru</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="3"><subfield code="a">An integrated strategy to identify genes responsible for sesquiterpene biosynthesis in turmeric</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Key message Metabolic module, gene expression pattern and PLS modeling were integrated to precisely identify the terpene synthase responsible for sesquiterpene formation. Functional characterization confirmed the feasibility and sensitivity of this strategy. Abstract Plant secondary metabolite biosynthetic pathway elucidation is crucial for the production of these compounds with metabolic engineering. In this study, an integrated strategy was employed to predict the gene function of sesquiterpene synthase (STS) genes using turmeric as a model. Parallel analysis of gene expression patterns and metabolite modules narrowed the candidates into an STS group in which the STSs showed a similar expression pattern. The projections to latent structures by means of partial least squares model was further employed to establish a clear relationship between the candidate STS genes and metabolites and to predict three STSs (ClTPS16, ClTPS15 and ClTPS14) involved in the biosynthesis of several sesquiterpene skeletons. Functional characterization revealed that zingiberene and β-sesquiphellandrene were the major products of ClTPS16, and β-eudesmol was produced by ClTPS15, both of which indicated the accuracy of the prediction. Functional characterization of a control STS, ClTPS1, produced a small amount of β-sesquiphellandrene, as predicted, which confirmed the sensitivity of metabolite module analysis. 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