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Lv S, Tang X, Jiang L, Zhang J, Sun B, Liu Q, Mao X, Yu H, Chen P, Chen W, Fan Z, Li C. OsLSC6 Regulates Leaf Sheath Color and Cold Tolerance in Rice Revealed by Metabolite Genome Wide Association Study. RICE (NEW YORK, N.Y.) 2024; 17:34. [PMID: 38739288 DOI: 10.1186/s12284-024-00713-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/08/2024] [Indexed: 05/14/2024]
Abstract
Plant metabolites including anthocyanins play an important role in the growth of plants, as well as in regulating biotic and abiotic stress responses to the environment. Here we report comprehensive profiling of 3315 metabolites and a further metabolic-based genome-wide association study (mGWAS) based on 292,485 SNPs obtained from 311 rice accessions, including 160 wild and 151 cultivars. We identified hundreds of common variants affecting a large number of secondary metabolites with large effects at high throughput. Finally, we identified a novel gene namely OsLSC6 (Oryza sativa leaf sheath color 6), which encoded a UDP 3-O-glucosyltransferase and involved in the anthocyanin biosynthesis of Cyanidin-3-Galc (sd1825) responsible for leaf sheath color, and resulted in significant different accumulation of sd1825 between wild (purple) and cultivars (green). The results of knockout transgenic experiments showed that OsLSC6 regulated the biosynthesis and accumulation of sd1825, controlled the purple leaf sheath. Our further research revealed that OsLSC6 also confers resistance to cold stress during the seedling stage in rice. And we identified that a SNP in OsLSC6 was responsible for the leaf sheath color and chilling tolerance, supporting the importance of OsLSC6 in plant adaption. Our study could not only demonstrate that OsLSC6 is a vital regulator during anthocyanin biosynthesis and abiotic stress responses, but also provide a powerful complementary tool based on metabolites-to-genes analysis by mGWAS for functional gene identification andpromising candidate in future rice breeding and improvement.
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Affiliation(s)
- Shuwei Lv
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Xuan Tang
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Liqun Jiang
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Jing Zhang
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Bingrui Sun
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Qing Liu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Xingxue Mao
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Hang Yu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Pingli Chen
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Wenfeng Chen
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Zhilan Fan
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China
| | - Chen Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangdong Key Laboratory of New Technology in Rice Breeding, Guangdong Rice Engineering Laboratory, Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Guangzhou, 510640, China.
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Naake T, Zhu F, Alseekh S, Scossa F, Perez de Souza L, Borghi M, Brotman Y, Mori T, Nakabayashi R, Tohge T, Fernie AR. Genome-wide association studies identify loci controlling specialized seed metabolites in Arabidopsis. PLANT PHYSIOLOGY 2024; 194:1705-1721. [PMID: 37758174 PMCID: PMC10904349 DOI: 10.1093/plphys/kiad511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/01/2023] [Accepted: 08/24/2023] [Indexed: 10/03/2023]
Abstract
Plants synthesize specialized metabolites to facilitate environmental and ecological interactions. During evolution, plants diversified in their potential to synthesize these metabolites. Quantitative differences in metabolite levels of natural Arabidopsis (Arabidopsis thaliana) accessions can be employed to unravel the genetic basis for metabolic traits using genome-wide association studies (GWAS). Here, we performed metabolic GWAS on seeds of a panel of 315 A. thaliana natural accessions, including the reference genotypes C24 and Col-0, for polar and semi-polar seed metabolites using untargeted ultra-performance liquid chromatography-mass spectrometry. As a complementary approach, we performed quantitative trait locus (QTL) mapping of near-isogenic introgression lines between C24 and Col-0 for specific seed specialized metabolites. Besides common QTL between seeds and leaves, GWAS revealed seed-specific QTL for specialized metabolites, indicating differences in the genetic architecture of seeds and leaves. In seeds, aliphatic methylsulfinylalkyl and methylthioalkyl glucosinolates associated with the ALKENYL HYDROXYALKYL PRODUCING loci (GS-ALK and GS-OHP) on chromosome 4 containing alkenyl hydroxyalkyl producing 2 (AOP2) and 3 (AOP3) or with the GS-ELONG locus on chromosome 5 containing methylthioalkyl malate synthase (MAM1) and MAM3. We detected two unknown sulfur-containing compounds that were also mapped to these loci. In GWAS, some of the annotated flavonoids (kaempferol 3-O-rhamnoside-7-O-rhamnoside, quercetin 3-O-rhamnoside-7-O-rhamnoside) were mapped to transparent testa 7 (AT5G07990), encoding a cytochrome P450 75B1 monooxygenase. Three additional mass signals corresponding to quercetin-containing flavonols were mapped to UGT78D2 (AT5G17050). The association of the loci and associating metabolic features were functionally verified in knockdown mutant lines. By performing GWAS and QTL mapping, we were able to leverage variation of natural populations and parental lines to study seed specialized metabolism. The GWAS data set generated here is a high-quality resource that can be investigated in further studies.
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Affiliation(s)
- Thomas Naake
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany
| | - Feng Zhu
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany
| | - Saleh Alseekh
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
| | - Federico Scossa
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany
- Research Center for Genomics and Bioinformatics (CREA-GB), Council for Agricultural Research and Economics, Via Ardeatina 546, 00178 Rome, Italy
| | - Leonardo Perez de Souza
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany
| | - Monica Borghi
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany
- Department of Biology, Utah State University, 5305 Old Main Hill, Logan, UT 84321-5305, USA
| | - Yariv Brotman
- Department of Life Sciences, Ben-Gurion University of the Negev, 8410501 Be’er Sheva, Israel
| | - Tetsuya Mori
- RIKEN Center for Sustainable Resource Science, Tsurumi, 1-7-22 Suehiro, Yokohama, Kanagawa 230-0045, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, Tsurumi, 1-7-22 Suehiro, Yokohama, Kanagawa 230-0045, Japan
| | - Takayuki Tohge
- Graduate School of Biological Science, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
| | - Alisdair R Fernie
- Central Metabolism, Max Planck Institute of Molecular Plant Physiology, Am Muehlenberg 1, 14476 Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
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3
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Moroldo M, Blanchet N, Duruflé H, Bernillon S, Berton T, Fernandez O, Gibon Y, Moing A, Langlade NB. Genetic control of abiotic stress-related specialized metabolites in sunflower. BMC Genomics 2024; 25:199. [PMID: 38378469 PMCID: PMC10877922 DOI: 10.1186/s12864-024-10104-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 02/09/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Abiotic stresses in plants include all the environmental conditions that significantly reduce yields, like drought and heat. One of the most significant effects they exert at the cellular level is the accumulation of reactive oxygen species, which cause extensive damage. Plants possess two mechanisms to counter these molecules, i.e. detoxifying enzymes and non-enzymatic antioxidants, which include many classes of specialized metabolites. Sunflower, the fourth global oilseed, is considered moderately drought resistant. Abiotic stress tolerance in this crop has been studied using many approaches, but the control of specialized metabolites in this context remains poorly understood. Here, we performed the first genome-wide association study using abiotic stress-related specialized metabolites as molecular phenotypes in sunflower. After analyzing leaf specialized metabolites of 450 hybrids using liquid chromatography-mass spectrometry, we selected a subset of these compounds based on their association with previously known abiotic stress-related quantitative trait loci. Eventually, we characterized these molecules and their associated genes. RESULTS We putatively annotated 30 compounds which co-localized with abiotic stress-related quantitative trait loci and which were associated to seven most likely candidate genes. A large proportion of these compounds were potential antioxidants, which was in agreement with the role of specialized metabolites in abiotic stresses. The seven associated most likely candidate genes, instead, mainly belonged to cytochromes P450 and glycosyltransferases, two large superfamilies which catalyze greatly diverse reactions and create a wide variety of chemical modifications. This was consistent with the high plasticity of specialized metabolism in plants. CONCLUSIONS This is the first characterization of the genetic control of abiotic stress-related specialized metabolites in sunflower. By providing hints concerning the importance of antioxidant molecules in this biological context, and by highlighting some of the potential molecular mechanisms underlying their biosynthesis, it could pave the way for novel applications in breeding. Although further analyses will be required to better understand this topic, studying how antioxidants contribute to the tolerance to abiotic stresses in sunflower appears as a promising area of research.
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Affiliation(s)
- Marco Moroldo
- UMR LIPME, INRAE, CNRS, Université de Toulouse, 31326, Castanet Tolosan, France.
| | - Nicolas Blanchet
- UMR LIPME, INRAE, CNRS, Université de Toulouse, 31326, Castanet Tolosan, France
| | - Harold Duruflé
- UMR LIPME, INRAE, CNRS, Université de Toulouse, 31326, Castanet Tolosan, France
- UMR BioForA, INRAE, ONF, Orléans, 45075, France
| | - Stéphane Bernillon
- UMR BFP, INRAE, Université de Bordeaux, 33140, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
- UMR MYCSA, INRAE, 33140, Villenave d'Ornon, France
| | - Thierry Berton
- UMR BFP, INRAE, Université de Bordeaux, 33140, Villenave d'Ornon, France
| | - Olivier Fernandez
- UMR BFP, INRAE, Université de Bordeaux, 33140, Villenave d'Ornon, France
- USC RIBP, INRAE, Université de Reims, 51100, Reims, France
| | - Yves Gibon
- UMR BFP, INRAE, Université de Bordeaux, 33140, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Annick Moing
- UMR BFP, INRAE, Université de Bordeaux, 33140, Villenave d'Ornon, France
- Bordeaux Metabolome, MetaboHUB, PHENOME-EMPHASIS, 33140, Villenave d'Ornon, France
| | - Nicolas B Langlade
- UMR LIPME, INRAE, CNRS, Université de Toulouse, 31326, Castanet Tolosan, France
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4
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Shelake RM, Jadhav AM, Bhosale PB, Kim JY. Unlocking secrets of nature's chemists: Potential of CRISPR/Cas-based tools in plant metabolic engineering for customized nutraceutical and medicinal profiles. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2023; 203:108070. [PMID: 37816270 DOI: 10.1016/j.plaphy.2023.108070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Revised: 09/26/2023] [Accepted: 09/28/2023] [Indexed: 10/12/2023]
Abstract
Plant species have evolved diverse metabolic pathways to effectively respond to internal and external signals throughout their life cycle, allowing adaptation to their sessile and phototropic nature. These pathways selectively activate specific metabolic processes, producing plant secondary metabolites (PSMs) governed by genetic and environmental factors. Humans have utilized PSM-enriched plant sources for millennia in medicine and nutraceuticals. Recent technological advances have significantly contributed to discovering metabolic pathways and related genes involved in the biosynthesis of specific PSM in different food crops and medicinal plants. Consequently, there is a growing demand for plant materials rich in nutrients and bioactive compounds, marketed as "superfoods". To meet the industrial demand for superfoods and therapeutic PSMs, modern methods such as system biology, omics, synthetic biology, and genome editing (GE) play a crucial role in identifying the molecular players, limiting steps, and regulatory circuitry involved in PSM production. Among these methods, clustered regularly interspaced short palindromic repeats-CRISPR associated protein (CRISPR/Cas) is the most widely used system for plant GE due to its simple design, flexibility, precision, and multiplexing capabilities. Utilizing the CRISPR-based toolbox for metabolic engineering (ME) offers an ideal solution for developing plants with tailored preventive (nutraceuticals) and curative (therapeutic) metabolic profiles in an ecofriendly way. This review discusses recent advances in understanding the multifactorial regulation of metabolic pathways, the application of CRISPR-based tools for plant ME, and the potential research areas for enhancing plant metabolic profiles.
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Affiliation(s)
- Rahul Mahadev Shelake
- Division of Applied Life Science (BK21 Four Program), Plant Molecular Biology and Biotechnology Research Center, Gyeongsang National University, Jinju, 52828, Republic of Korea.
| | - Amol Maruti Jadhav
- Research Institute of Green Energy Convergence Technology (RIGET), Gyeongsang National University, 501 Jinju-daero, Jinju, 52828, Republic of Korea
| | - Pritam Bhagwan Bhosale
- Department of Veterinary Medicine, Research Institute of Life Science, Gyeongsang National University, 501 Jinju-daero, Jinju, 52828, Republic of Korea
| | - Jae-Yean Kim
- Division of Applied Life Science (BK21 Four Program), Plant Molecular Biology and Biotechnology Research Center, Gyeongsang National University, Jinju, 52828, Republic of Korea; Division of Life Science, Gyeongsang National University, 501 Jinju-daero, Jinju, 52828, Republic of Korea; Nulla Bio Inc, 501 Jinju-daero, Jinju, 52828, Republic of Korea.
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5
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Liu D, Zhang H, Yang Y, Liu T, Guo Z, Fan W, Wang Z, Yang X, Zhang B, Liu H, Tang H, Yu D, Yu S, Gai K, Mou Q, Cao J, Hu J, Tang J, Hou S, Zhou Z. Metabolome-Based Genome-Wide Association Study of Duck Meat Leads to Novel Genetic and Biochemical Insights. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2300148. [PMID: 37013465 PMCID: PMC10288243 DOI: 10.1002/advs.202300148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 02/20/2023] [Indexed: 06/19/2023]
Abstract
Meat is among the most consumed foods worldwide and has a unique flavor and high nutrient density in the human diet. However, the genetic and biochemical bases of meat nutrition and flavor are poorly understood. Here, 3431 metabolites and 702 volatiles in 423 skeletal muscle samples are profiled from a gradient consanguinity segregating population generated by Pekin duck × Liancheng duck crosses using metabolomic approaches. The authors identified 2862 metabolome-based genome-wide association studies (mGWAS) signals and 48 candidate genes potentially modulating metabolite and volatile levels, 79.2% of which are regulated by cis-regulatory elements. The level of plasmalogen is significantly associated with TMEM189 encoding plasmanylethanolamine desaturase 1. The levels of 2-pyrrolidone and glycerophospholipids are regulated by the gene expression of AOX1 and ACBD5, which further affects the levels of volatiles, 2-pyrrolidone and decanal, respectively. Genetic variations in GADL1 and CARNMT2 determine the levels of 49 metabolites including L-carnosine and anserine. This study provides novel insights into the genetic and biochemical basis of skeletal muscle metabolism and constitutes a valuable resource for the precise improvement of meat nutrition and flavor.
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Affiliation(s)
- Dapeng Liu
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - He Zhang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Youyou Yang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Tong Liu
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Zhanbao Guo
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Wenlei Fan
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Zhen Wang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Xinting Yang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Bo Zhang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Hongfei Liu
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Hehe Tang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Daxin Yu
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Simeng Yu
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Kai Gai
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Qiming Mou
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Junting Cao
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Jian Hu
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Jing Tang
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Shuisheng Hou
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
| | - Zhengkui Zhou
- Institute of Animal ScienceChinese Academy of Agricultural SciencesBeijing100193P. R. China
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Vallarino JG, Jun H, Wang S, Wang X, Sade N, Orf I, Zhang D, Shi J, Shen S, Cuadros-Inostroza Á, Xu Q, Luo J, Fernie AR, Brotman Y. Limitations and advantages of using metabolite-based genome-wide association studies: focus on fruit quality traits. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 333:111748. [PMID: 37230189 DOI: 10.1016/j.plantsci.2023.111748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/19/2023] [Accepted: 05/21/2023] [Indexed: 05/27/2023]
Abstract
In the last decades, linkage mapping has help in the location of metabolite quantitative trait loci (QTL) in many species; however, this approach shows some limitations. Recently, thanks to the most recent advanced in high-throughput genotyping technologies like next-generation sequencing, metabolite genome-wide association study (mGWAS) has been proposed a powerful tool to identify the genetic variants in polygenic agrinomic traits. Fruit flavor is a complex interaction of aroma volatiles and taste being sugar and acid ratio key parameter for flavor acceptance. Here, we review recent progress of mGWAS in pinpoint gene polymorphisms related to flavor-related metabolites in fruits. Despite clear successes in discovering novel genes or regions associated with metabolite accumulation affecting sensory attributes in fruits, GWAS incurs in several limitations summarized in this review. In addition, in our own work, we performed mGWAS on 194 Citrus grandis accessions to investigate the genetic control of individual primary and lipid metabolites in ripe fruit. We have identified a total of 667 associations for 14 primary metabolites including amino acids, sugars, and organic acids, and 768 associations corresponding to 47 lipids. Furthermore, candidate genes related to important metabolites related to fruit quality such as sugars, organic acids and lipids were discovered.
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Affiliation(s)
- José G Vallarino
- Instituto de Hortofruticultura Subtropical y Mediterránea "La Mayora", Universidad de Málaga-Consejo Superior de Investigaciones Científicas, Departamento de Biología Molecular y Bioquímica, Campus de Teatinos, 29071 Málaga, Spain
| | - Hong Jun
- Department of Genetics and Developmental Science, Joint International Research Laboratory of Metabolic and Developmental Sciences, State Key Laboratory of Hybrid Rice, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Waite Research Institute, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA, Australia
| | | | - Xia Wang
- Key Laboratory of Horticultural Plant Biology (Ministry of Education), Huazhong Agricultural University, Wuhan, China
| | - Nir Sade
- School of Plant Sciences and Food Security, Tel Aviv University, P.O.B. 39040, 55 Haim Levanon St., Tel Aviv 6139001, Israel
| | - Isabel Orf
- Department of Life Sciences, Ben Gurion University of the Negev, Beersheva, Israel
| | - Dabing Zhang
- Department of Genetics and Developmental Science, Joint International Research Laboratory of Metabolic and Developmental Sciences, State Key Laboratory of Hybrid Rice, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China; Waite Research Institute, School of Agriculture, Food and Wine, University of Adelaide, Urrbrae, SA, Australia
| | - Jianxin Shi
- Department of Genetics and Developmental Science, Joint International Research Laboratory of Metabolic and Developmental Sciences, State Key Laboratory of Hybrid Rice, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Shuangqian Shen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
| | | | - Qiang Xu
- Key Laboratory of Horticultural Plant Biology (Ministry of Education), Huazhong Agricultural University, Wuhan, China
| | - Jie Luo
- College of Tropical Crops, Hainan University, Haikou, China; National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, China
| | - Alisdair R Fernie
- Department of Root Biology and Symbiosis, Max Planck Institute of Molecular Plant Physiology, 1 Am Mühlenberg, Golm, Potsdam 14476, Germany; Department of Plant Metabolomics, Center for Plant Systems Biology and Biotechnology, 139 Ruski Blvd., Plovdiv 4000, Bulgaria.
| | - Yariv Brotman
- Department of Life Sciences, Ben Gurion University of the Negev, Beersheva, Israel.
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7
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Bulut M, Alseekh S, Fernie AR. Natural variation of respiration-related traits in plants. PLANT PHYSIOLOGY 2023; 191:2120-2132. [PMID: 36546766 PMCID: PMC10069898 DOI: 10.1093/plphys/kiac593] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 12/09/2022] [Indexed: 06/17/2023]
Abstract
Plant respiration is one of the greatest global metabolic fluxes, but rates of respiration vary massively both within different cell types as well as between different individuals and different species. Whilst this is well known, few studies have detailed population-level variation of respiration until recently. The last 20 years have seen a renaissance in studies of natural variance. In this review, we describe how experimental breeding populations and collections of large populations of accessions can be used to determine the genetic architecture of plant traits. We further detail how these approaches have been used to study the rate of respiration per se as well as traits that are intimately associated with respiration. The review highlights specific breakthroughs in these areas but also concludes that the approach should be more widely adopted in the study of respiration per se as opposed to the more frequently studied respiration-related traits.
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Affiliation(s)
- Mustafa Bulut
- Department of Root Biology and Symbiosis, Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm 14476, Germany
| | - Saleh Alseekh
- Department of Root Biology and Symbiosis, Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm 14476, Germany
- Center for Plant Systems Biology and Biotechnology, Plovdiv 4000, Bulgaria
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8
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Aharoni A, Goodacre R, Fernie AR. Plant and microbial sciences as key drivers in the development of metabolomics research. Proc Natl Acad Sci U S A 2023; 120:e2217383120. [PMID: 36930598 PMCID: PMC10041103 DOI: 10.1073/pnas.2217383120] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023] Open
Abstract
This year marks the 25th anniversary of the coinage of the term metabolome [S. G. Oliver et al., Trends Biotech. 16, 373-378 (1998)]. As the field rapidly advances, it is important to take stock of the progress which has been made to best inform the disciplines future. While a medical-centric perspective on metabolomics has recently been published [M. Giera et al., Cell Metab. 34, 21-34 (2022)], this largely ignores the pioneering contributions made by the plant and microbial science communities. In this perspective, we provide a contemporary overview of all fields in which metabolomics is employed with particular emphasis on both methodological and application breakthroughs made in plant and microbial sciences that have shaped this evolving research discipline from the very early days of its establishment. This will not cover all types of metabolomics assays currently employed but will focus mainly on those utilizing mass spectrometry-based measurements since they are currently by far the most prominent. Having established the historical context of metabolomics, we will address the key challenges currently facing metabolomics and offer potential approaches by which these can be faced. Most salient among these is the fact that the vast majority of mass features are as yet not annotated with high confidence; what we may refer to as definitive identification. We discuss the potential of both standard compound libraries and artificial intelligence technologies to address this challenge and the use of natural variance-based approaches such as genome-wide association studies in attempt to assign specific functions to the myriad of structurally similar and complex specialized metabolites. We conclude by stating our contention that as these challenges are epic and that they will need far greater cooperative efforts from biologists, chemists, and computer scientists with an interest in all kingdoms of life than have been made to date. Ultimately, a better linkage of metabolome and genome data will likely also be needed particularly considering the Earth BioGenome Project.
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Affiliation(s)
- Asaph Aharoni
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot76100, Israel
| | - Royston Goodacre
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, LiverpoolL69 7BE, UK
| | - Alisdair R. Fernie
- Max-Planck-Institute for Molecular Plant Physiology, Potsdam14476, Germany
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9
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He L, Wang H, Sui Y, Miao Y, Jin C, Luo J. Genome-wide association studies of five free amino acid levels in rice. FRONTIERS IN PLANT SCIENCE 2022; 13:1048860. [PMID: 36420042 PMCID: PMC9676653 DOI: 10.3389/fpls.2022.1048860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 10/18/2022] [Indexed: 06/16/2023]
Abstract
Rice (Oryza sativa L.) is one of the important staple foods for human consumption and livestock use. As a complex quality trait, free amino acid (FAA) content in rice is of nutritional importance. To dissect the genetic mechanism of FAA level, five amino acids' (Val, Leu, Ile, Arg, and Trp) content and 4,325,832 high-quality SNPs of 448 rice accessions were used to conduct genome-wide association studies (GWAS) with nine different methods. Of these methods, one single-locus method (GEMMA), seven multi-locus methods (mrMLM, pLARmEB, FASTmrEMMA, pKWmEB, FASTmrMLM, ISIS EM-BLASSO, and FarmCPU), and the recent released 3VmrMLM were adopted for methodological comparison of quantitative trait nucleotide (QTN) detection and identification of stable quantitative trait nucleotide loci (QTLs). As a result, 987 QTNs were identified by eight multi-locus GWAS methods; FASTmrEMMA detected the most QTNs (245), followed by 3VmrMLM (160), and GEMMA detected the least QTNs (0). Among 88 stable QTLs identified by the above methods, 3VmrMLM has some advantages, such as the most common QTNs, the highest LOD score, and the highest proportion of all detected stable QTLs. Around these stable QTLs, candidate genes were found in the GO classification to be involved in the primary metabolic process, biosynthetic process, and catalytic activity, and shown in KEGG analysis to have participated in metabolic pathways, biosynthesis of amino acids, and tryptophan metabolism. Natural variations of candidate genes resulting in the content alteration of five FAAs were identified in this association panel. In addition, 95 QTN-by-environment interactions (QEIs) of five FAA levels were detected by 3VmrMLM only. GO classification showed that the candidate genes got involved in the primary metabolic process, transport, and catalytic activity. Candidate genes of QEIs played important roles in valine, leucine, and isoleucine degradation (QEI_09_03978551 and candidate gene LOC_Os09g07830 in the Leu dataset), tryptophan metabolism (QEI_01_00617184 and candidate gene LOC_Os01g02020 in the Trp dataset), and glutathione metabolism (QEI_12_09153839 and candidate gene LOC_Os12g16200 in the Arg dataset) pathways through KEGG analysis. As an alternative of the multi-locus GWAS method, these findings suggested that the application of 3VmrMLM may provide new insights into better understanding FAA accumulation and facilitate the molecular breeding of rice with high FAA level.
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Affiliation(s)
- Liqiang He
- College of Tropical Crops, Hainan University, Haikou, China
| | - Huixian Wang
- College of Tropical Crops, Hainan University, Haikou, China
| | - Yao Sui
- College of Tropical Crops, Hainan University, Haikou, China
| | - Yuanyuan Miao
- College of Tropical Crops, Hainan University, Haikou, China
- Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, China
| | - Cheng Jin
- College of Tropical Crops, Hainan University, Haikou, China
- Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, China
| | - Jie Luo
- College of Tropical Crops, Hainan University, Haikou, China
- Sanya Nanfan Research Institute of Hainan University, Hainan Yazhou Bay Seed Laboratory, Sanya, China
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10
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Singh DP, Bisen MS, Shukla R, Prabha R, Maurya S, Reddy YS, Singh PM, Rai N, Chaubey T, Chaturvedi KK, Srivastava S, Farooqi MS, Gupta VK, Sarma BK, Rai A, Behera TK. Metabolomics-Driven Mining of Metabolite Resources: Applications and Prospects for Improving Vegetable Crops. Int J Mol Sci 2022; 23:ijms232012062. [PMID: 36292920 PMCID: PMC9603451 DOI: 10.3390/ijms232012062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/13/2022] [Accepted: 09/23/2022] [Indexed: 11/16/2022] Open
Abstract
Vegetable crops possess a prominent nutri-metabolite pool that not only contributes to the crop performance in the fields, but also offers nutritional security for humans. In the pursuit of identifying, quantifying and functionally characterizing the cellular metabolome pool, biomolecule separation technologies, data acquisition platforms, chemical libraries, bioinformatics tools, databases and visualization techniques have come to play significant role. High-throughput metabolomics unravels structurally diverse nutrition-rich metabolites and their entangled interactions in vegetable plants. It has helped to link identified phytometabolites with unique phenotypic traits, nutri-functional characters, defense mechanisms and crop productivity. In this study, we explore mining diverse metabolites, localizing cellular metabolic pathways, classifying functional biomolecules and establishing linkages between metabolic fluxes and genomic regulations, using comprehensive metabolomics deciphers of the plant’s performance in the environment. We discuss exemplary reports covering the implications of metabolomics, addressing metabolic changes in vegetable plants during crop domestication, stage-dependent growth, fruit development, nutri-metabolic capabilities, climatic impacts, plant-microbe-pest interactions and anthropogenic activities. Efforts leading to identify biomarker metabolites, candidate proteins and the genes responsible for plant health, defense mechanisms and nutri-rich crop produce are documented. With the insights on metabolite-QTL (mQTL) driven genetic architecture, molecular breeding in vegetable crops can be revolutionized for developing better nutritional capabilities, improved tolerance against diseases/pests and enhanced climate resilience in plants.
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Affiliation(s)
- Dhananjaya Pratap Singh
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
- Correspondence:
| | - Mansi Singh Bisen
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Renu Shukla
- Indian Council of Agricultural Research (ICAR), Krishi Bhawan, Dr. Rajendra Prasad Road, New Delhi 110001, India
| | - Ratna Prabha
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Sudarshan Maurya
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Yesaru S. Reddy
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Prabhakar Mohan Singh
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Nagendra Rai
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Tribhuwan Chaubey
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
| | - Krishna Kumar Chaturvedi
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Sudhir Srivastava
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Mohammad Samir Farooqi
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Vijai Kumar Gupta
- Biorefining and Advanced Materials Research Centre, Scotland’s Rural College, Kings Buildings, West Mains Road, Edinburgh EH9 3JG, UK
| | - Birinchi K. Sarma
- Department of Mycology and Plant Pathology, Institute of Agricultural Sciences, Banaras Hindu University, Varanasi 221005, India
| | - Anil Rai
- ICAR-Indian Agricultural Statistics Research Institute, Centre for Agricultural Bioinformatics, Library Avenue, Pusa, New Delhi 110012, India
| | - Tusar Kanti Behera
- ICAR-Indian Institute of Vegetable Research, Jakhini, Shahanshahpur, Varanasi 221305, India
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11
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Li X, Gao J, Song J, Guo K, Hou S, Wang X, He Q, Zhang Y, Zhang Y, Yang Y, Tang J, Wang H, Persson S, Huang M, Xu L, Zhong L, Li D, Liu Y, Wu H, Diao X, Chen P, Wang X, Han Y. Multi-omics analyses of 398 foxtail millet accessions reveal genomic regions associated with domestication, metabolite traits, and anti-inflammatory effects. MOLECULAR PLANT 2022; 15:1367-1383. [PMID: 35808829 DOI: 10.1016/j.molp.2022.07.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 06/22/2022] [Accepted: 07/06/2022] [Indexed: 05/12/2023]
Abstract
Foxtail millet (Setaria italica), which was domesticated from the wild species green foxtail (Setaria viridis), is a rich source of phytonutrients for humans. To evaluate how breeding changed the metabolome of foxtail millet grains, we generated and analyzed the datasets encompassing the genomes, transcriptomes, metabolomes, and anti-inflammatory indices from 398 foxtail millet accessions. We identified hundreds of common variants that influence numerous secondary metabolites. We observed tremendous differences in natural variations of the metabolites and their underlying genetic architectures between distinct sub-groups of foxtail millet. Furthermore, we found that the selection of the gene alleles associated with yellow grains led to altered profiles of metabolites such as carotenoids and endogenous phytohormones. Using CRISPR-mediated genome editing we validated the function of PHYTOENE SYNTHASE 1 (PSY1) gene in affecting millet grain color and quality. Interestingly, our in vitro cell inflammation assays showed that 83 metabolites in millet grains have anti-inflammatory effects. Taken together, our multi-omics study illustrates how the breeding history of foxtail millet has shaped its metabolite profile. The datasets we generated in this study also provide important resources for further understanding how millet grain quality is affected by different metabolites, laying the foundations for future millet genetic research and metabolome-assisted improvement.
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Affiliation(s)
- Xukai Li
- Shanxi Key Laboratory of Minor Crop Germplasm Innovation and Molecular Breeding, College of Life Sciences, Shanxi Agricultural University, Taigu, China
| | - Jianhua Gao
- Shanxi Key Laboratory of Minor Crop Germplasm Innovation and Molecular Breeding, College of Life Sciences, Shanxi Agricultural University, Taigu, China
| | - Jingyi Song
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing, China
| | - Kai Guo
- Department of Neurology, University of Michigan, Ann Arbor, MI, USA
| | - Siyu Hou
- Shanxi Key Laboratory of Minor Crop Germplasm Innovation and Molecular Breeding, College of Agriculture, Shanxi Agricultural University, Taigu, China
| | - Xingchun Wang
- Shanxi Key Laboratory of Minor Crop Germplasm Innovation and Molecular Breeding, College of Life Sciences, Shanxi Agricultural University, Taigu, China
| | - Qiang He
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yanyan Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yakun Zhang
- Shanxi Key Laboratory of Minor Crop Germplasm Innovation and Molecular Breeding, College of Agriculture, Shanxi Agricultural University, Taigu, China
| | - Yulu Yang
- Shanxi Key Laboratory of Minor Crop Germplasm Innovation and Molecular Breeding, College of Agriculture, Shanxi Agricultural University, Taigu, China
| | - Jiaoyan Tang
- Shanxi Key Laboratory of Minor Crop Germplasm Innovation and Molecular Breeding, College of Agriculture, Shanxi Agricultural University, Taigu, China
| | - Hailang Wang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Staffan Persson
- Copenhagen Plant Science Centre, Department of Plant & Environmental Sciences, University of Copenhagen, Frederiksberg C, Denmark; Joint International Research Laboratory of Metabolic and Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Minhang, Shanghai 200240, China
| | - Mingquan Huang
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing, China
| | - Lishuai Xu
- College of Resources and Environment, Shanxi Agricultural University, Taigu, China
| | - Linlin Zhong
- College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, China
| | - Dongqin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Yongming Liu
- Grandomics Biosciences Company Limited, Beijing, China
| | - Hua Wu
- Key Laboratory of Brewing Molecular Engineering of China Light Industry, Beijing Technology and Business University, Beijing, China.
| | - Xianmin Diao
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China.
| | - Peng Chen
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China.
| | - Xiaowen Wang
- College of Food Science and Engineering, Shanxi Agricultural University, Taigu, China.
| | - Yuanhuai Han
- Shanxi Key Laboratory of Minor Crop Germplasm Innovation and Molecular Breeding, College of Agriculture, Shanxi Agricultural University, Taigu, China.
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12
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Cao S, Duan H, Sun Y, Hu R, Wu B, Lin J, Deng W, Li Y, Zheng H. Genome-Wide Association Study With Growth-Related Traits and Secondary Metabolite Contents in Red- and White-Heart Chinese Fir. FRONTIERS IN PLANT SCIENCE 2022; 13:922007. [PMID: 35845628 PMCID: PMC9280351 DOI: 10.3389/fpls.2022.922007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/27/2022] [Indexed: 06/15/2023]
Abstract
Chinese fir [Cunninghamia lanceolata (Lamb.) Hook] is an important evergreen coniferous tree species that is widely distributed in many southern provinces of China and has important economic value. The Chinese fir accounts for 1/4 and 1/3 of the total artificial forest area and stock volume, respectively. Red-heart Chinese fir is popular in the market because of its high density and red heartwood. The long-growth cycle hindered the breeding process of Chinese fir, while molecular marker-assisted breeding could accelerate it. However, Chinese fir, a perennial conifer species, has a large genome, which has not yet been published. In this study, the growth-related traits and secondary metabolite contents of red- and white-heart Chinese fir were measured and found to be different between them. There are extremely significant differences among growth-related traits (p < 0.001), but secondary metabolite contents have different correlations due to differences in chemical structure. Moreover, genotype effect analysis of the substantially correlated single nucleotide polymorphisms (SNPs) revealed that most of the loci related to each growth-related traits were different from each other, indicating a type specificity of the genes regulated different growth-related traits. Furthermore, among the loci related to secondary metabolite contents, nine loci associated with multiple metabolite phenotypes such as Marker21022_4, Marker21022_172, Marker24559_31, Marker27425_37, Marker20748_85, Marker18841_115, Marker18841_198, Marker65846_146, and Marker21486_163, suggesting the presence of pleiotropic genes. This study identified the potential SNP markers associated with secondary metabolites in Chinese fir, thus setting the basis for molecular marker-assisted selection.
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Affiliation(s)
- Sen Cao
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Hongjing Duan
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Experimental School Affiliated to Chinese Academy of Sciences, Beijing, China
| | - Yuhan Sun
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Ruiyang Hu
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
- Experimental Center of Forestry in North China, Chinese Academy of Forestry, Beijing, China
| | - Bo Wu
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Jun Lin
- Longshan State Forest Farm of Lechang, Lechang, China
| | - Wenjian Deng
- Longshan State Forest Farm of Lechang, Lechang, China
| | - Yun Li
- National Engineering Laboratory for Tree Breeding, Key Laboratory of Genetics and Breeding in Forest Trees and Ornamental Plants of Ministry of Education, The Tree and Ornamental Plant Breeding and Biotechnology Laboratory of National Forestry and Grassland Administration, College of Biological Sciences and Technology, Beijing Forestry University, Beijing, China
| | - Huiquan Zheng
- Guangdong Provincial Key Laboratory of Silviculture, Protection and Utilization, Guangdong Academy of Forestry, Guangzhou, China
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13
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Brzozowski LJ, Campbell MT, Hu H, Caffe M, Gutiérrez LA, Smith KP, Sorrells ME, Gore MA, Jannink JL. Generalizable approaches for genomic prediction of metabolites in plants. THE PLANT GENOME 2022; 15:e20205. [PMID: 35470586 DOI: 10.1002/tpg2.20205] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/21/2022] [Indexed: 06/14/2023]
Abstract
Plant metabolites are important traits for plant breeders seeking to improve nutrition and agronomic performance yet integrating selection for metabolomic traits can be limited by phenotyping expense and degree of genetic characterization, especially of uncommon metabolites. As such, developing generalizable genomic selection methods based on biochemical pathway biology for metabolites that are transferable across plant populations would benefit plant breeding programs. We tested genomic prediction accuracy for >600 metabolites measured by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) in oat (Avena sativa L.) seed. Using a discovery germplasm panel, we conducted metabolite genome-wide association study (mGWAS) and selected loci to use in multikernel models that encompassed metabolome-wide mGWAS results or mGWAS from specific metabolite structures or biosynthetic pathways. Metabolite kernels developed from LC-MS metabolites in the discovery panel improved prediction accuracy of LC-MS metabolite traits in the validation panel consisting of more advanced breeding lines. No approach, however, improved prediction accuracy for GC-MS metabolites. We ranked model performance by metabolite and found that metabolites with similar polarity had consistent rankings of models. Overall, testing biological rationales for developing kernels for genomic prediction across populations contributes to developing frameworks for plant breeding for metabolite traits.
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Affiliation(s)
- Lauren J Brzozowski
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Malachy T Campbell
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Haixiao Hu
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Melanie Caffe
- Dep. of Agronomy, Horticulture & Plant Science, South Dakota State Univ., Brookings, SD, 57006, USA
| | - Lucı A Gutiérrez
- Dep. of Agronomy, Univ. of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Kevin P Smith
- Dep. of Agronomy & Plant Genetics, Univ. of Minnesota, St. Paul, MN, 55108, USA
| | - Mark E Sorrells
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Michael A Gore
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell Univ., Ithaca, NY, 14853, USA
- USDA-ARS, Robert W. Holley Center for Agriculture and Health, Ithaca, NY, 14853, USA
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14
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Vasseur F, Cornet D, Beurier G, Messier J, Rouan L, Bresson J, Ecarnot M, Stahl M, Heumos S, Gérard M, Reijnen H, Tillard P, Lacombe B, Emanuel A, Floret J, Estarague A, Przybylska S, Sartori K, Gillespie LM, Baron E, Kazakou E, Vile D, Violle C. A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy. FRONTIERS IN PLANT SCIENCE 2022; 13:836488. [PMID: 35668791 PMCID: PMC9163986 DOI: 10.3389/fpls.2022.836488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 03/09/2022] [Indexed: 05/31/2023]
Abstract
The trait-based approach in plant ecology aims at understanding and classifying the diversity of ecological strategies by comparing plant morphology and physiology across organisms. The major drawback of the approach is that the time and financial cost of measuring the traits on many individuals and environments can be prohibitive. We show that combining near-infrared spectroscopy (NIRS) with deep learning resolves this limitation by quickly, non-destructively, and accurately measuring a suite of traits, including plant morphology, chemistry, and metabolism. Such an approach also allows to position plants within the well-known CSR triangle that depicts the diversity of plant ecological strategies. The processing of NIRS through deep learning identifies the effect of growth conditions on trait values, an issue that plagues traditional statistical approaches. Together, the coupling of NIRS and deep learning is a promising high-throughput approach to capture a range of ecological information on plant diversity and functioning and can accelerate the creation of extensive trait databases.
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Affiliation(s)
| | - Denis Cornet
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Grégory Beurier
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Julie Messier
- Department of Biology, University of Waterloo, Waterloo, ON, Canada
| | - Lauriane Rouan
- CIRAD, UMR AGAP Institut, Montpellier, France
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Justine Bresson
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Martin Ecarnot
- UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Mark Stahl
- Center for Plant Molecular Biology (ZMBP), University of Tübingen, Tübingen, Germany
| | - Simon Heumos
- Quantitative Biology Center (QBiC), University of Tübingen, Quantitative Biology Center (QBiC), University of Tübingen, Germany
- Biomedical Data Science, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Marianne Gérard
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Hans Reijnen
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Pascal Tillard
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Benoît Lacombe
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Amélie Emanuel
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- BPMP, Univ Montpellier, CNRS, INRAE, Montpellier, France
| | - Justine Floret
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | | | | | - Kevin Sartori
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | | | - Etienne Baron
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
| | - Elena Kazakou
- CEFE, Univ Montpellier, CNRS, EPHE, Institut Agro, IRD, Montpellier, France
| | - Denis Vile
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Cyrille Violle
- CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France
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15
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Wang P, Schumacher AM, Shiu SH. Computational prediction of plant metabolic pathways. CURRENT OPINION IN PLANT BIOLOGY 2022; 66:102171. [PMID: 35078130 DOI: 10.1016/j.pbi.2021.102171] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Revised: 12/07/2021] [Accepted: 12/18/2021] [Indexed: 06/14/2023]
Abstract
Uncovering genes encoding enzymes responsible for the biosynthesis of diverse plant metabolites is essential for metabolic engineering and production of plant metabolite-derived medicine. With the availability of multi-omics data for an ever-increasing number of plant species and the development of computational approaches, the metabolic pathways of many important plant compounds can be predicted, complementing a more traditional genetic and/or biochemical approach. Here, we summarize recent progress in predicting plant metabolic pathways using genome, transcriptome, proteome, interactome, and/or metabolome data, and the utility of integrating these data with machine learning to further improve metabolic pathway predictions.
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Affiliation(s)
- Peipei Wang
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA.
| | - Ally M Schumacher
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA
| | - Shin-Han Shiu
- Department of Plant Biology, Michigan State University, East Lansing, MI, 48824, USA; Department of Computational Mathematics, Science, and Engineering, Michigan State University, East Lansing, MI, 48824, USA.
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16
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Vishweswaraiah S, Akyol S, Yilmaz A, Ugur Z, Gordevičius J, Oh KJ, Brundin P, Radhakrishna U, Labrie V, Graham SF. Methylated Cytochrome P450 and the Solute Carrier Family of Genes Correlate With Perturbations in Bile Acid Metabolism in Parkinson’s Disease. Front Neurosci 2022; 16:804261. [PMID: 35431771 PMCID: PMC9009246 DOI: 10.3389/fnins.2022.804261] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 02/18/2022] [Indexed: 12/15/2022] Open
Abstract
Parkinson’s disease (PD) is second most prevalent neurodegenerative disorder following Alzheimer’s disease. Parkinson’s disease is hypothesized to be caused by a multifaceted interplay between genetic and environmental factors. Herein, and for the first time, we describe the integration of metabolomics and epigenetics (genome-wide DNA methylation; epimetabolomics) to profile the frontal lobe from people who died from PD and compared them with age-, and sex-matched controls. We identified 48 metabolites to be at significantly different concentrations (FDR q < 0.05), 4,313 differentially methylated sites [5’-C-phosphate-G-3’ (CpGs)] (FDR q < 0.05) and increased DNA methylation age in the primary motor cortex of people who died from PD. We identified Primary bile acid biosynthesis as the major biochemical pathway to be perturbed in the frontal lobe of PD sufferers, and the metabolite taurine (p-value = 5.91E-06) as being positively correlated with CpG cg14286187 (SLC25A27; CYP39A1) (FDR q = 0.002), highlighting previously unreported biochemical changes associated with PD pathogenesis. In this novel multi-omics study, we identify regulatory mechanisms which we believe warrant future translational investigation and central biomarkers of PD which require further validation in more accessible biomatrices.
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Affiliation(s)
| | | | - Ali Yilmaz
- Beaumont Health, Royal Oak, MI, United States
| | - Zafer Ugur
- Beaumont Health, Royal Oak, MI, United States
| | | | | | | | | | | | - Stewart F. Graham
- Beaumont Health, Royal Oak, MI, United States
- *Correspondence: Stewart F. Graham,
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17
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Metabolite Diversity and Metabolic Genome-Wide Marker Association Studies (Mgwas) for Health Benefiting Nutritional Traits in Pearl Millet Grains. Cells 2021; 10:cells10113076. [PMID: 34831297 PMCID: PMC8621611 DOI: 10.3390/cells10113076] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/01/2021] [Accepted: 11/05/2021] [Indexed: 12/03/2022] Open
Abstract
As efforts are made to increase food security, millets are gaining increasing importance due to their excellent nutritional credentials. Among the millets, pearl millet is the predominant species possessing several health benefiting nutritional traits in its grain that are helpful in mitigating chronic illnesses such as type−2 diabetes and obesity. In this paper, we conducted metabolomic fingerprinting of 197 pearl millet inbred lines drawn randomly from within the world collection of pearl millet germplasm and report the extent of genetic variation for health benefitting metabolites in these genotypes. Metabolites were extracted from seeds and assessed using flow infusion high-resolution mass spectrometry (FIE-HRMS). Metabolite features (m/z), whose levels significantly differed among the germplasm inbred lines, were identified by ANOVA corrected for FDR and subjected to functional pathway analysis. A number of health-benefiting metabolites linked to dietary starch, antioxidants, vitamins, and lipid metabolism-related compounds were identified. Metabolic genome-wide association analysis (mGWAS) performed using the 396 m/z as phenotypic traits and the 76 K SNP as genotypic variants identified a total of 897 SNPs associated with health benefiting nutritional metabolite at the -log p-value ≤ 4.0. From these associations, 738 probable candidate genes were predicted to have an important role in starch, antioxidants, vitamins, and lipid metabolism. The mGWAS analysis focused on genes involved in starch branching (α-amylase, β-amylase), vitamin-K reductase, UDP-glucuronosyl, and UDP-glucosyl transferase (UGTs), L-ascorbate oxidase, and isoflavone 2′-monooxygenase genes, which are known to be linked to increases in human health benefiting metabolites. We demonstrate how metabolomic, genomic, and statistical approaches can be utilized to pinpoint genetic variations and their functions linked to key nutritional properties in pearl millet, which in turn can be bred into millets and other cereals crops using plant breeding methods.
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18
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Genome-wide association studies: assessing trait characteristics in model and crop plants. Cell Mol Life Sci 2021; 78:5743-5754. [PMID: 34196733 PMCID: PMC8316211 DOI: 10.1007/s00018-021-03868-w] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 05/28/2021] [Accepted: 05/29/2021] [Indexed: 01/19/2023]
Abstract
GWAS involves testing genetic variants across the genomes of many individuals of a population to identify genotype–phenotype association. It was initially developed and has proven highly successful in human disease genetics. In plants genome-wide association studies (GWAS) initially focused on single feature polymorphism and recombination and linkage disequilibrium but has now been embraced by a plethora of different disciplines with several thousand studies being published in model and crop species within the last decade or so. Here we will provide a comprehensive review of these studies providing cases studies on biotic resistance, abiotic tolerance, yield associated traits, and metabolic composition. We also detail current strategies of candidate gene validation as well as the functional study of haplotypes. Furthermore, we provide a critical evaluation of the GWAS strategy and its alternatives as well as future perspectives that are emerging with the emergence of pan-genomic datasets.
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19
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Rohde PD, Kristensen TN, Sarup P, Muñoz J, Malmendal A. Prediction of complex phenotypes using the Drosophila melanogaster metabolome. Heredity (Edinb) 2021; 126:717-732. [PMID: 33510469 PMCID: PMC8102504 DOI: 10.1038/s41437-021-00404-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 01/30/2023] Open
Abstract
Understanding the genotype-phenotype map and how variation at different levels of biological organization is associated are central topics in modern biology. Fast developments in sequencing technologies and other molecular omic tools enable researchers to obtain detailed information on variation at DNA level and on intermediate endophenotypes, such as RNA, proteins and metabolites. This can facilitate our understanding of the link between genotypes and molecular and functional organismal phenotypes. Here, we use the Drosophila melanogaster Genetic Reference Panel and nuclear magnetic resonance (NMR) metabolomics to investigate the ability of the metabolome to predict organismal phenotypes. We performed NMR metabolomics on four replicate pools of male flies from each of 170 different isogenic lines. Our results show that metabolite profiles are variable among the investigated lines and that this variation is highly heritable. Second, we identify genes associated with metabolome variation. Third, using the metabolome gave better prediction accuracies than genomic information for four of five quantitative traits analyzed. Our comprehensive characterization of population-scale diversity of metabolomes and its genetic basis illustrates that metabolites have large potential as predictors of organismal phenotypes. This finding is of great importance, e.g., in human medicine, evolutionary biology and animal and plant breeding.
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Affiliation(s)
- Palle Duun Rohde
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark.
| | - Torsten Nygaard Kristensen
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
- Department of Animal Science, Aarhus University, Tjele, Denmark
| | - Pernille Sarup
- Department of Molecular Biology and Genetics, Aarhus University, Tjele, Denmark
- Nordic Seed A/S, Odder, Denmark
| | - Joaquin Muñoz
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Anders Malmendal
- Department of Science and Environment, Roskilde University, Roskilde, Denmark.
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20
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Kumar R, Sharma V, Suresh S, Ramrao DP, Veershetty A, Kumar S, Priscilla K, Hangargi B, Narasanna R, Pandey MK, Naik GR, Thomas S, Kumar A. Understanding Omics Driven Plant Improvement and de novo Crop Domestication: Some Examples. Front Genet 2021; 12:637141. [PMID: 33889179 PMCID: PMC8055929 DOI: 10.3389/fgene.2021.637141] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Accepted: 03/02/2021] [Indexed: 01/07/2023] Open
Abstract
In the current era, one of biggest challenges is to shorten the breeding cycle for rapid generation of a new crop variety having high yield capacity, disease resistance, high nutrient content, etc. Advances in the "-omics" technology have revolutionized the discovery of genes and bio-molecules with remarkable precision, resulting in significant development of plant-focused metabolic databases and resources. Metabolomics has been widely used in several model plants and crop species to examine metabolic drift and changes in metabolic composition during various developmental stages and in response to stimuli. Over the last few decades, these efforts have resulted in a significantly improved understanding of the metabolic pathways of plants through identification of several unknown intermediates. This has assisted in developing several new metabolically engineered important crops with desirable agronomic traits, and has facilitated the de novo domestication of new crops for sustainable agriculture and food security. In this review, we discuss how "omics" technologies, particularly metabolomics, has enhanced our understanding of important traits and allowed speedy domestication of novel crop plants.
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Affiliation(s)
- Rakesh Kumar
- Department of Life Science, Central University of Karnataka, Kalaburagi, India
| | - Vinay Sharma
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Srinivas Suresh
- Department of Life Science, Central University of Karnataka, Kalaburagi, India
| | | | - Akash Veershetty
- Department of Life Science, Central University of Karnataka, Kalaburagi, India
| | - Sharan Kumar
- Department of Life Science, Central University of Karnataka, Kalaburagi, India
| | - Kagolla Priscilla
- Department of Life Science, Central University of Karnataka, Kalaburagi, India
| | | | - Rahul Narasanna
- Department of Life Science, Central University of Karnataka, Kalaburagi, India
| | - Manish Kumar Pandey
- International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | | | - Sherinmol Thomas
- Department of Biosciences & Bioengineering, Indian Institute of Technology Bombay, Mumbai, India
| | - Anirudh Kumar
- Department of Botany, Indira Gandhi National Tribal University, Amarkantak, India
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21
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Brotman Y, Llorente-Wiegand C, Oyong G, Badoni S, Misra G, Anacleto R, Parween S, Pasion E, Tiozon RN, Anonuevo JJ, deGuzman MK, Alseekh S, Mbanjo EGN, Boyd LA, Fernie AR, Sreenivasulu N. The genetics underlying metabolic signatures in a brown rice diversity panel and their vital role in human nutrition. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2021; 106:507-525. [PMID: 33529453 DOI: 10.1111/tpj.15182] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Revised: 01/19/2021] [Accepted: 01/26/2021] [Indexed: 06/12/2023]
Abstract
Brown rice (Oryza sativa) possesses various nutritionally dense bioactive phytochemicals exhibiting a wide range of antioxidant, anti-cancer, and anti-diabetic properties known to promote various human health benefits. However, despite the wide claims made about the importance of brown rice for human nutrition the underlying metabolic diversity has not been systematically explored. Non-targeted metabolite profiling of developing and mature seeds of a diverse genetic panel of 320 rice cultivars allowed quantification of 117 metabolites. The metabolite genome-wide association study (mGWAS) detected genetic variants influencing diverse metabolic targets in developing and mature seeds. We further interlinked genetic variants on chromosome 7 (6.06-6.43 Mb region) with complex epistatic genetic interactions impacting multi-dimensional nutritional targets, including complex carbohydrate starch quality, the glycemic index, antioxidant catechin, and rice grain color. Through this nutrigenomics approach rare gene bank accessions possessing genetic variants in bHLH and IPT5 genes were identified through haplotype enrichment. These variants were associated with a low glycemic index, higher catechin levels, elevated total flavonoid contents, and heightened antioxidant activity in the whole grain with elevated anti-cancer properties being confirmed in cancer cell lines. This multi-disciplinary nutrigenomics approach thus allowed us to discover the genetic basis of human health-conferring diversity in the metabolome of brown rice.
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Affiliation(s)
- Yariv Brotman
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Department of Life Sciences, Ben-Gurion University of the Negev, Beersheba, Israel
| | | | - Glenn Oyong
- Molecular Science Unit Laboratory - Center for Natural Sciences and Environmental Research, De La Salle University, 2401 Taft Avenue, Manila, 1004, Philippines
| | - Saurabh Badoni
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Gopal Misra
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Roslen Anacleto
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Sabiha Parween
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Erstelle Pasion
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Rhowell N Tiozon
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Joanne J Anonuevo
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Maria K deGuzman
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Saleh Alseekh
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Edwige G N Mbanjo
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
| | - Lesley A Boyd
- NIAB, 93 Lawrence Weaver Road, Cambridge, CB3 0LE, UK
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Nese Sreenivasulu
- International Rice Research Institute, DAPO Box 7777, Metro Manila, Philippines
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22
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Campbell MT, Hu H, Yeats TH, Caffe-Treml M, Gutiérrez L, Smith KP, Sorrells ME, Gore MA, Jannink JL. Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.). Genetics 2021; 217:iyaa043. [PMID: 33789350 PMCID: PMC8045723 DOI: 10.1093/genetics/iyaa043] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Accepted: 12/08/2020] [Indexed: 12/13/2022] Open
Abstract
Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional "omics" data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.
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Affiliation(s)
- Malachy T Campbell
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Haixiao Hu
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Trevor H Yeats
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Melanie Caffe-Treml
- Department of Agronomy, Horticulture & Plant Science, South Dakota State University, Brookings, SD 57007, USA
| | - Lucía Gutiérrez
- Department of Agronomy, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Kevin P Smith
- Department of Agronomy & Plant Genetics, University of Minnesota, St. Paul, MN 55108, USA
| | - Mark E Sorrells
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Michael A Gore
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- Plant Breeding & Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
- R.W. Holley Center for Agriculture & Health US Department of Agriculture, Agricultural Research Service, Ithaca, NY 14853, USA
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23
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Gemmer MR, Richter C, Schmutzer T, Raorane ML, Junker B, Pillen K, Maurer A. Genome-wide association study on metabolite accumulation in a wild barley NAM population reveals natural variation in sugar metabolism. PLoS One 2021; 16:e0246510. [PMID: 33592061 PMCID: PMC7886226 DOI: 10.1371/journal.pone.0246510] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 01/20/2021] [Indexed: 12/03/2022] Open
Abstract
Metabolites play a key role in plants as they are routing plant developmental processes and are involved in biotic and abiotic stress responses. Their analysis can offer important information on the underlying processes. Regarding plant breeding, metabolite concentrations can be used as biomarkers instead of or in addition to genetic markers to predict important phenotypic traits (metabolic prediction). In this study, we applied a genome-wide association study (GWAS) in a wild barley nested association mapping (NAM) population to identify metabolic quantitative trait loci (mQTL). A set of approximately 130 metabolites, measured at early and late sampling dates, was analysed. For four metabolites from the early and six metabolites from the late sampling date significant mQTL (grouped as 19 mQTL for the early and 25 mQTL for the late sampling date) were found. Interestingly, all of those metabolites could be classified as sugars. Sugars are known to be involved in signalling, plant growth and plant development. Sugar-related genes, encoding mainly sugar transporters, have been identified as candidate genes for most of the mQTL. Moreover, several of them co-localized with known flowering time genes like Ppd-H1, HvELF3, Vrn-H1, Vrn-H2 and Vrn-H3, hinting on the known role of sugars in flowering. Furthermore, numerous disease resistance-related genes were detected, pointing to the signalling function of sugars in plant resistance. An mQTL on chromosome 1H in the region of 13 Mbp to 20 Mbp stood out, that alone explained up to 65% of the phenotypic variation of a single metabolite. Analysis of family-specific effects within the diverse NAM population showed the available natural genetic variation regarding sugar metabolites due to different wild alleles. The study represents a step towards a better understanding of the genetic components of metabolite accumulation, especially sugars, thereby linking them to biological functions in barley.
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Affiliation(s)
- Mathias Ruben Gemmer
- Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Chris Richter
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Thomas Schmutzer
- Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Manish L. Raorane
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Björn Junker
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Klaus Pillen
- Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Andreas Maurer
- Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
- * E-mail:
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24
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Sun CH, Wang JH, Gu KD, Zhang P, Zhang XY, Zheng CS, Hu DG, Ma F. New insights into the role of MADS-box transcription factor gene CmANR1 on root and shoot development in chrysanthemum (Chrysanthemum morifolium). BMC PLANT BIOLOGY 2021; 21:79. [PMID: 33549046 PMCID: PMC7866475 DOI: 10.1186/s12870-021-02860-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 01/28/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND MADS-box transcription factors (TFs) are the key regulators of multiple developmental processes in plants; among them, a chrysanthemum MADS-box TF CmANR1 has been isolated and described as functioning in root development in response to high nitrate concentration signals. However, how CmANR1 affects root and shoot development remains unclear. RESULTS We report that CmANR1 plays a positive role in root system development in chrysanthemum throughout the developmental stages of in vitro tissue cultures. Metabolomics combined with transcriptomics assays show that CmANR1 promotes robust root system development by facilitating nitrate assimilation, and influencing the metabolic pathways of amino acid, glycolysis, and the tricarboxylic acid cycle (TCA) cycle. Also, we found that the expression levels of TFs associated with the nitrate signaling pathways, such as AGL8, AGL21, and LBD29, are significantly up-regulated in CmANR1-transgenic plants relative to the wild-type (WT) control; by contrast, the expression levels of RHD3-LIKE, LBD37, and GATA23 were significantly down-regulated. These results suggest that these nitrate signaling associated TFs are involved in CmANR1-modulated control of root development. In addition, CmANR1 also acts as a positive regulator to control shoot growth and development. CONCLUSIONS These findings provide potential mechanisms of MADS-box TF CmANR1 modulation of root and shoot development, which occurs by regulating a series of nitrate signaling associated TFs, and influencing the metabolic pathways of amino acid and glycolysis, as well as TCA cycle and nitrate assimilation.
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Affiliation(s)
- Cui-Hui Sun
- National Key Laboratory of Crop Biology, MOA Key Laboratory of Horticultural Crop Biology and Germplasm Innovation, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, 271018, Shandong, China
| | - Jia-Hui Wang
- National Key Laboratory of Crop Biology, MOA Key Laboratory of Horticultural Crop Biology and Germplasm Innovation, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, 271018, Shandong, China
| | - Kai-Di Gu
- National Key Laboratory of Crop Biology, MOA Key Laboratory of Horticultural Crop Biology and Germplasm Innovation, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, 271018, Shandong, China
| | - Peng Zhang
- National Key Laboratory of Crop Biology, MOA Key Laboratory of Horticultural Crop Biology and Germplasm Innovation, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, 271018, Shandong, China
| | - Xin-Yi Zhang
- National Key Laboratory of Crop Biology, MOA Key Laboratory of Horticultural Crop Biology and Germplasm Innovation, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, 271018, Shandong, China
| | - Cheng-Shu Zheng
- National Key Laboratory of Crop Biology, MOA Key Laboratory of Horticultural Crop Biology and Germplasm Innovation, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, 271018, Shandong, China.
| | - Da-Gang Hu
- National Key Laboratory of Crop Biology, MOA Key Laboratory of Horticultural Crop Biology and Germplasm Innovation, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, 271018, Shandong, China.
| | - Fangfang Ma
- National Key Laboratory of Crop Biology, MOA Key Laboratory of Horticultural Crop Biology and Germplasm Innovation, College of Horticulture Science and Engineering, Shandong Agricultural University, Tai'an, 271018, Shandong, China.
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25
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Fujii H, Nonaka K, Minamikawa MF, Endo T, Sugiyama A, Hamazaki K, Iwata H, Omura M, Shimada T. Allelic composition of carotenoid metabolic genes in 13 founders influences carotenoid composition in juice sac tissues of fruits among Japanese citrus breeding population. PLoS One 2021; 16:e0246468. [PMID: 33539435 PMCID: PMC7861536 DOI: 10.1371/journal.pone.0246468] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 01/19/2021] [Indexed: 11/24/2022] Open
Abstract
To enrich carotenoids, especially β-cryptoxanthin, in juice sac tissues of fruits via molecular breeding in citrus, allele mining was utilized to dissect allelic variation of carotenoid metabolic genes and identify an optimum allele on the target loci characterized by expression quantitative trait (eQTL) analysis. SNPs of target carotenoid metabolic genes in 13 founders of the Japanese citrus breeding population were explored using the SureSelect target enrichment method. An independent allele was determined based on the presence or absence of reliable SNPs, using trio analysis to confirm inheritability between parent and offspring. Among the 13 founders, there were 7 PSY alleles, 7 HYb alleles, 11 ZEP alleles, 5 NCED alleles, and 4 alleles for the eQTL that control the transcription levels of PDS and ZDS among the ancestral species, indicating that some founders acquired those alleles from them. The carotenoid composition data of 263 breeding pedigrees in juice sac tissues revealed that the phenotypic variance of carotenoid composition was similar to that in the 13 founders, whereas the mean of total carotenoid content increased. This increase in total carotenoid content correlated with the increase in either or both β-cryptoxanthin and violaxanthin in juice sac tissues. Bayesian statistical analysis between allelic composition of target genes and carotenoid composition in 263 breeding pedigrees indicated that PSY-a and ZEP-e alleles at PSY and ZEP loci had strong positive effects on increasing the total carotenoid content, including β-cryptoxanthin and violaxanthin, in juice sac tissues. Moreover, the pyramiding of these alleles also increased the β-cryptoxanthin content. Interestingly, the offset interaction between the alleles with increasing and decreasing effects on carotenoid content and the epistatic interaction among carotenoid metabolic genes were observed and these interactions complexed carotenoid profiles in breeding population. These results revealed that allele composition would highly influence the carotenoid composition in citrus fruits. The allelic genotype information for the examined carotenoid metabolic genes in major citrus varieties and the trio-tagged SNPs to discriminate the optimum alleles (PSY-a and ZEP-e) from the rest would promise citrus breeders carotenoid enrichment in fruit via molecular breeding.
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Affiliation(s)
- Hiroshi Fujii
- National Agriculture and Food Research Organization Institute of Fruit and Tea Tree Science, Shimizu, Shizuoka, Japan
| | - Keisuke Nonaka
- National Agriculture and Food Research Organization Institute of Fruit and Tea Tree Science, Shimizu, Shizuoka, Japan
| | - Mai F. Minamikawa
- Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Tomoko Endo
- National Agriculture and Food Research Organization Institute of Fruit and Tea Tree Science, Shimizu, Shizuoka, Japan
| | - Aiko Sugiyama
- Faculty of Agriculture, Shizuoka University, Suruga, Shizuoka, Japan
| | - Kosuke Hamazaki
- Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Hiroyoshi Iwata
- Laboratory of Biometry and Bioinformatics, Department of Agricultural and Environmental Biology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo, Tokyo, Japan
| | - Mitsuo Omura
- Faculty of Agriculture, Shizuoka University, Suruga, Shizuoka, Japan
| | - Takehiko Shimada
- National Agriculture and Food Research Organization Institute of Fruit and Tea Tree Science, Shimizu, Shizuoka, Japan
- * E-mail:
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26
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Li J, Akanno EC, Valente TS, Abo-Ismail M, Karisa BK, Wang Z, Plastow GS. Genomic Heritability and Genome-Wide Association Studies of Plasma Metabolites in Crossbred Beef Cattle. Front Genet 2020; 11:538600. [PMID: 33193612 PMCID: PMC7542097 DOI: 10.3389/fgene.2020.538600] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 09/01/2020] [Indexed: 11/16/2022] Open
Abstract
Metabolites, substrates or products of metabolic processes, are involved in many biological functions, such as energy metabolism, signaling, stimulatory and inhibitory effects on enzymes and immunological defense. Metabolomic phenotypes are influenced by combination of genetic and environmental effects allowing for metabolome-genome-wide association studies (mGWAS) as a powerful tool to investigate the relationship between these phenotypes and genetic variants. The objectives of this study were to estimate genomic heritability and perform mGWAS and in silico functional enrichment analyses for a set of plasma metabolites in Canadian crossbred beef cattle. Thirty-three plasma metabolites and 45,266 single nucleotide polymorphisms (SNPs) were available for 475 animals. Genomic heritability for all metabolites was estimated using genomic best linear unbiased prediction (GBLUP) including genomic breed composition as covariates in the model. A single-step GBLUP implemented in BLUPF90 programs was used to determine SNP P values and the proportion of genetic variance explained by SNP windows containing 10 consecutive SNPs. The top 10 SNP windows that explained the largest genetic variation for each metabolite were identified and mapped to detect corresponding candidate genes. Functional enrichment analyses were performed on metabolites and their candidate genes using the Ingenuity Pathway Analysis software. Eleven metabolites showed low to moderate heritability that ranged from 0.09 ± 0.15 to 0.36 ± 0.15, while heritability estimates for 22 metabolites were zero or negligible. This result indicates that while variations in 11 metabolites were due to genetic variants, the majority are largely influenced by environment. Three significant SNP associations were detected for betaine (rs109862186), L-alanine (rs81117935), and L-lactic acid (rs42009425) based on Bonferroni correction for multiple testing (family wise error rate <0.05). The SNP rs81117935 was found to be located within the Catenin Alpha 2 gene (CTNNA2) showing a possible association with the regulation of L-alanine concentration. Other candidate genes were identified based on additive genetic variance explained by SNP windows of 10 consecutive SNPs. The observed heritability estimates and the candidate genes and networks identified in this study will serve as baseline information for research into the utilization of plasma metabolites for genetic improvement of crossbred beef cattle.
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Affiliation(s)
- Jiyuan Li
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - Everestus C Akanno
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - Tiago S Valente
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada.,Department of Animal Science, Ethology and Animal Ecology Research Group, São Paulo State University, Jaboticabal, Brazil
| | - Mohammed Abo-Ismail
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada.,Department of Animal Science, College of Agriculture, Food and Environmental Sciences, California Polytechnic State University, San Luis Obispo, CA, United States
| | - Brian K Karisa
- Ministry of Agriculture and Forestry, Edmonton, AB, Canada
| | - Zhiquan Wang
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
| | - Graham S Plastow
- Livestock Gentec, Department of Agriculture, Food and Nutritional Science, Faculty of Agricultural, Life and Environmental Sciences, University of Alberta, Edmonton, AB, Canada
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Zhang W, Alseekh S, Zhu X, Zhang Q, Fernie AR, Kuang H, Wen W. Dissection of the domestication-shaped genetic architecture of lettuce primary metabolism. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 104:613-630. [PMID: 32772408 DOI: 10.1111/tpj.14950] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Revised: 07/14/2020] [Accepted: 07/21/2020] [Indexed: 05/11/2023]
Abstract
Lettuce (Lactuca sativa L.) is an important vegetable crop species worldwide. The primary metabolism of this species is essential for its growth, development and reproduction as well as providing a considerable direct source of energy and nutrition for humans. Here, through investigating 77 primary metabolites in 189 accessions including all major horticultural types and wild lettuce L. serriola we showed that the metabolites in L. serriola were different from those in cultivated lettuce. The findings were consistent with the demographic model of lettuce and supported a single domestication event for this species. Selection signals among these metabolic traits were detected. Specifically, galactinol, malate, quinate and threonate were significantly affected by the domestication process and cultivar differentiation of lettuce. Galactinol and raffinose might have been selected during stem lettuce cultivation as an adaption to the local environments in China. Furthermore, we identified 154 loci significantly associated with the level of 51 primary metabolites. Three genes (LG8749721, LG8763094 and LG5482522) responsible for the levels of galactinol, raffinose, quinate and chlorogenic acid were further dissected, which may have been the target of domestication and/or affected by local adaptation. Additionally, our findings strongly suggest that human selection resulted in reduced quinate and chlorogenic acid levels in cultivated lettuce. Our study thus provides beneficial genetic resources for lettuce quality improvement and sheds light on the domestication and evolution of this important leafy green.
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Affiliation(s)
- Weiyi Zhang
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Saleh Alseekh
- Max-Planck-Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, 14476, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Xiang Zhu
- Thermo Fisher Scientific, Shanghai, 201206, China
| | - Qinghua Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Muehlenberg 1, Potsdam-Golm, 14476, Germany
- Center of Plant Systems Biology and Biotechnology, Plovdiv, 4000, Bulgaria
| | - Hanhui Kuang
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
| | - Weiwei Wen
- Key Laboratory of Horticultural Plant Biology (MOE), College of Horticulture and Forestry Sciences, Huazhong Agricultural University, Wuhan, 430070, China
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28
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Seyler L, Kujawinski EB, Azua-Bustos A, Lee MD, Marlow J, Perl SM, Cleaves II HJ. Metabolomics as an Emerging Tool in the Search for Astrobiologically Relevant Biomarkers. ASTROBIOLOGY 2020; 20:1251-1261. [PMID: 32551936 PMCID: PMC7116171 DOI: 10.1089/ast.2019.2135] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
It is now routinely possible to sequence and recover microbial genomes from environmental samples. To the degree it is feasible to assign transcriptional and translational functions to these genomes, it should be possible, in principle, to largely understand the complete molecular inputs and outputs of a microbial community. However, gene-based tools alone are presently insufficient to describe the full suite of chemical reactions and small molecules that compose a living cell. Metabolomic tools have developed quickly and now enable rapid detection and identification of small molecules within biological and environmental samples. The convergence of these technologies will soon facilitate the detection of novel enzymatic activities, novel organisms, and potentially extraterrestrial life-forms on solar system bodies. This review explores the methodological problems and scientific opportunities facing researchers who hope to apply metabolomic methods in astrobiology-related fields, and how present challenges might be overcome.
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Affiliation(s)
- Lauren Seyler
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
- Blue Marble Space Institute of Science, Seattle, Washington, USA
- Address correspondence to: Lauren Seyler, Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, 86 Water Street, Woods Hole, MA 02543, USA
| | - Elizabeth B. Kujawinski
- Department of Marine Chemistry and Geochemistry, Woods Hole Oceanographic Institution, Woods Hole, Massachusetts, USA
| | - Armando Azua-Bustos
- Department of Planetology and Habitability, Centro de Astrobiología (CSIC-INTA), Madrid, Spain
- Instituto de Ciencias Biomédicas, Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Santiago, Chile
| | - Michael D. Lee
- Blue Marble Space Institute of Science, Seattle, Washington, USA
- Exobiology Branch, NASA Ames Research Center, Moffett Field, California, USA
| | - Jeffrey Marlow
- Blue Marble Space Institute of Science, Seattle, Washington, USA
- Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, Massachusetts, USA
- Department of Biology, Boston University, Boston, Massachusetts, USA
| | - Scott M. Perl
- Geological and Planetary Sciences, California Institute of Technology/NASA Jet Propulsion Laboratory, Pasadena, California, USA
- Mineral Sciences, Los Angeles Natural History Museum, Los Angeles, California, USA
| | - Henderson James Cleaves II
- Blue Marble Space Institute of Science, Seattle, Washington, USA
- Earth-Life Science Institute, Tokyo Institute of Technology, Tokyo, Japan
- School of Natural Sciences, Institute for Advanced Study, Princeton, New Jersey, USA
- Geographical Research Laboratory, Carnegie Institution of Washington
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29
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Drapal M, Amah D, Schöny H, Brown A, Swennen R, Fraser PD. Assessment of metabolic variability and diversity present in leaf, peel and pulp tissue of diploid and triploid Musa spp. PHYTOCHEMISTRY 2020; 176:112388. [PMID: 32344192 DOI: 10.1016/j.phytochem.2020.112388] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 04/08/2020] [Accepted: 04/10/2020] [Indexed: 05/26/2023]
Abstract
Banana (Musa spp.) plants produce many health promoting compounds in leaf, peel and pulp. For a robust metabolic analysis of these tissues, leaf at five developmental stages were compared to assess suitable sampling practices. Results confirmed that the common sampling practise of leaf 3 is applicable for metabolic comparisons. The developed work flow was applied to analyse the metabolite diversity present in 18 different Musa varieties, providing baseline levels of metabolites in leaf, peel and pulp tissue. Correlation analysis was then used to ascertain whether similar trends can be detected in the three plant tissues of the diversity panel. The genome group displayed a dominant role in the composition of the metabolome in all three tissues. This led to the conclusion that a correlation between tissues was only possible within a genome group as the different parental backgrounds caused too great a variation in the metabolomes. It also suggests the metabolome could be used to monitor the interaction/hybridisation of genomes during breeding programmes.
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Affiliation(s)
- Margit Drapal
- School of Biological Sciences, Royal Holloway, University of London, Egham Hill, Egham, Surrey, TW20 0EX, UK
| | - Delphine Amah
- International Institute of Tropical Agriculture, Ibadan, Nigeria
| | - Harald Schöny
- School of Biological Sciences, Royal Holloway, University of London, Egham Hill, Egham, Surrey, TW20 0EX, UK
| | - Allan Brown
- International Institute of Tropical Agriculture, Arusha, Tanzania
| | - Rony Swennen
- International Institute of Tropical Agriculture, Arusha, Tanzania; Bioversity International, W. De Croylaan 42, 3001, Heverlee, Belgium; Department of Biosystems, KU Leuven University, W. De Croylaan 42, 3001, Leuven, Belgium
| | - Paul D Fraser
- School of Biological Sciences, Royal Holloway, University of London, Egham Hill, Egham, Surrey, TW20 0EX, UK.
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30
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Chen J, Hu X, Shi T, Yin H, Sun D, Hao Y, Xia X, Luo J, Fernie AR, He Z, Chen W. Metabolite-based genome-wide association study enables dissection of the flavonoid decoration pathway of wheat kernels. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:1722-1735. [PMID: 31930656 PMCID: PMC7336285 DOI: 10.1111/pbi.13335] [Citation(s) in RCA: 80] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 12/29/2019] [Indexed: 05/02/2023]
Abstract
The marriage of metabolomic approaches with genetic design has proven a powerful tool in dissecting diversity in the metabolome and has additionally enhanced our understanding of complex traits. That said, such studies have rarely been carried out in wheat. In this study, we detected 805 metabolites from wheat kernels and profiled their relative contents among 182 wheat accessions, conducting a metabolite-based genome-wide association study (mGWAS) utilizing 14 646 previously described polymorphic SNP markers. A total of 1098 mGWAS associations were detected with large effects, within which 26 candidate genes were tentatively designated for 42 loci. Enzymatic assay of two candidates indicated they could catalyse glucosylation and subsequent malonylation of various flavonoids and thereby the major flavonoid decoration pathway of wheat kernel was dissected. Moreover, numerous high-confidence genes associated with metabolite contents have been provided, as well as more subdivided metabolite networks which are yet to be explored within our data. These combined efforts presented the first step towards realizing metabolomics-associated breeding of wheat.
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Affiliation(s)
- Jie Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanChina
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Xin Hu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanChina
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Taotao Shi
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanChina
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Huanran Yin
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanChina
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Dongfa Sun
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanChina
| | - Yuanfeng Hao
- National Wheat Improvement CenterInstitute of Crop SciencesChinese Academy of Agricultural SciencesBeijingChina
| | - Xianchun Xia
- National Wheat Improvement CenterInstitute of Crop SciencesChinese Academy of Agricultural SciencesBeijingChina
| | - Jie Luo
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanChina
| | | | - Zhonghu He
- National Wheat Improvement CenterInstitute of Crop SciencesChinese Academy of Agricultural SciencesBeijingChina
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan)Huazhong Agricultural UniversityWuhanChina
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhanChina
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31
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Wen YJ, Zhang YW, Zhang J, Feng JY, Dunwell JM, Zhang YM. An efficient multi-locus mixed model framework for the detection of small and linked QTLs in F2. Brief Bioinform 2020; 20:1913-1924. [PMID: 30032279 PMCID: PMC6917223 DOI: 10.1093/bib/bby058] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 06/05/2018] [Indexed: 01/03/2023] Open
Abstract
In the genetic system that regulates complex traits, metabolites, gene expression levels, RNA editing levels and DNA methylation, a series of small and linked genes exist. To date, however, little is known about how to design an efficient framework for the detection of these kinds of genes. In this article, we propose a genome-wide composite interval mapping (GCIM) in F2. First, controlling polygenic background via selecting markers in the genome scanning of linkage analysis was replaced by estimating polygenic variance in a genome-wide association study. This can control large, middle and minor polygenic backgrounds in genome scanning. Then, additive and dominant effects for each putative quantitative trait locus (QTL) were separately scanned so that a negative logarithm P-value curve against genome position could be separately obtained for each kind of effect. In each curve, all the peaks were identified as potential QTLs. Thus, almost all the small-effect and linked QTLs are included in a multi-locus model. Finally, adaptive least absolute shrinkage and selection operator (adaptive lasso) was used to estimate all the effects in the multi-locus model, and all the nonzero effects were further identified by likelihood ratio test for true QTL identification. This method was used to reanalyze four rice traits. Among 25 known genes detected in this study, 16 small-effect genes were identified only by GCIM. To further demonstrate GCIM, a series of Monte Carlo simulation experiments was performed. As a result, GCIM is demonstrated to be more powerful than the widely used methods for the detection of closely linked and small-effect QTLs.
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Affiliation(s)
- Yang-Jun Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Ya-Wen Zhang
- College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
| | - Jin Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jian-Ying Feng
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China
| | - Jim M Dunwell
- School of Agriculture, Policy and Development, University of Reading, Reading RG6 6AR, United Kingdom
| | - Yuan-Ming Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing, China.,Crop Information Center, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
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32
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Jin K, Wilson KA, Beck JN, Nelson CS, Brownridge GW, Harrison BR, Djukovic D, Raftery D, Brem RB, Yu S, Drton M, Shojaie A, Kapahi P, Promislow D. Genetic and metabolomic architecture of variation in diet restriction-mediated lifespan extension in Drosophila. PLoS Genet 2020; 16:e1008835. [PMID: 32644988 PMCID: PMC7347105 DOI: 10.1371/journal.pgen.1008835] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Accepted: 05/06/2020] [Indexed: 01/08/2023] Open
Abstract
In most organisms, dietary restriction (DR) increases lifespan. However, several studies have found that genotypes within the same species vary widely in how they respond to DR. To explore the mechanisms underlying this variation, we exposed 178 inbred Drosophila melanogaster lines to a DR or ad libitum (AL) diet, and measured a panel of 105 metabolites under both diets. Twenty four out of 105 metabolites were associated with the magnitude of the lifespan response. These included proteinogenic amino acids and metabolites involved in α-ketoglutarate (α-KG)/glutamine metabolism. We confirm the role of α-KG/glutamine synthesis pathways in the DR response through genetic manipulations. We used covariance network analysis to investigate diet-dependent interactions between metabolites, identifying the essential amino acids threonine and arginine as “hub” metabolites in the DR response. Finally, we employ a novel metabolic and genetic bipartite network analysis to reveal multiple genes that influence DR lifespan response, some of which have not previously been implicated in DR regulation. One of these is CCHa2R, a gene that encodes a neuropeptide receptor that influences satiety response and insulin signaling. Across the lines, variation in an intronic single nucleotide variant of CCHa2R correlated with variation in levels of five metabolites, all of which in turn were correlated with DR lifespan response. Inhibition of adult CCHa2R expression extended DR lifespan of flies, confirming the role of CCHa2R in lifespan response. These results provide support for the power of combined genomic and metabolomic analysis to identify key pathways underlying variation in this complex quantitative trait. Dietary restriction extends lifespan across most organisms in which it has been tested. However, several studies have now demonstrated that this effect can vary dramatically across different genotypes within a population. Within a population, dietary restriction might be beneficial for some, yet detrimental for others. Here, we measure the metabolome of 178 genetically characterized fly strains on fully fed and restricted diets. The fly strains vary widely in their lifespan response to dietary restriction. We then use information about each strain’s genome and metabolome (a measure of small molecules circulating in flies) to pinpoint cellular pathways that govern this variation in response. We identify a novel pathway involving the gene CCHa2R, which encodes a neuropeptide receptor that has not previously been implicated in dietary restriction or age-related signaling pathways. This study demonstrates the power of leveraging systems biology and network biology methods to understand how and why different individuals vary in their response to health and lifespan-extending interventions.
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Affiliation(s)
- Kelly Jin
- Department of Pathology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Kenneth A. Wilson
- Buck Institute for Research on Aging, Novato, California, United States of America
- Davis School of Gerontology, University of Southern California, University Park, Los Angeles, California, United States of America
| | - Jennifer N. Beck
- Buck Institute for Research on Aging, Novato, California, United States of America
| | | | - George W. Brownridge
- Buck Institute for Research on Aging, Novato, California, United States of America
- Dominican University of California, San Rafael, California, United States of America
| | - Benjamin R. Harrison
- Department of Pathology, University of Washington School of Medicine, Seattle, Washington, United States of America
| | - Danijel Djukovic
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington, United States of America
| | - Daniel Raftery
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, Washington, United States of America
| | - Rachel B. Brem
- Buck Institute for Research on Aging, Novato, California, United States of America
- Davis School of Gerontology, University of Southern California, University Park, Los Angeles, California, United States of America
- Department of Plant and Microbial Biology, University of California, Berkeley, Berkeley, California, United States of America
| | - Shiqing Yu
- Department of Statistics, University of Washington, Seattle, Washington, United States of America
| | - Mathias Drton
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Ali Shojaie
- Department of Biostatistics, University of Washington, Seattle, Washington, United States of America
| | - Pankaj Kapahi
- Buck Institute for Research on Aging, Novato, California, United States of America
- Davis School of Gerontology, University of Southern California, University Park, Los Angeles, California, United States of America
| | - Daniel Promislow
- Department of Pathology, University of Washington School of Medicine, Seattle, Washington, United States of America
- Department of Biology, University of Washington, Seattle, Washington, United States of America
- * E-mail:
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33
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Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK. PANOMICS meets germplasm. PLANT BIOTECHNOLOGY JOURNAL 2020; 18:1507-1525. [PMID: 32163658 PMCID: PMC7292548 DOI: 10.1111/pbi.13372] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Revised: 02/17/2020] [Accepted: 02/26/2020] [Indexed: 05/14/2023]
Abstract
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
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Affiliation(s)
- Wolfram Weckwerth
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
- Vienna Metabolomics Center (VIME)University of ViennaViennaAustria
| | - Arindam Ghatak
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Anke Bellaire
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Palak Chaturvedi
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadTelanganaIndia
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34
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Weckwerth W, Ghatak A, Bellaire A, Chaturvedi P, Varshney RK. PANOMICS meets germplasm. PLANT BIOTECHNOLOGY JOURNAL 2020; 18. [PMID: 32163658 PMCID: PMC7292548 DOI: 10.1111/pbi.13372,10.13140/rg.2.1.1233.5760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Genotyping-by-sequencing has enabled approaches for genomic selection to improve yield, stress resistance and nutritional value. More and more resource studies are emerging providing 1000 and more genotypes and millions of SNPs for one species covering a hitherto inaccessible intraspecific genetic variation. The larger the databases are growing, the better statistical approaches for genomic selection will be available. However, there are clear limitations on the statistical but also on the biological part. Intraspecific genetic variation is able to explain a high proportion of the phenotypes, but a large part of phenotypic plasticity also stems from environmentally driven transcriptional, post-transcriptional, translational, post-translational, epigenetic and metabolic regulation. Moreover, regulation of the same gene can have different phenotypic outputs in different environments. Consequently, to explain and understand environment-dependent phenotypic plasticity based on the available genotype variation we have to integrate the analysis of further molecular levels reflecting the complete information flow from the gene to metabolism to phenotype. Interestingly, metabolomics platforms are already more cost-effective than NGS platforms and are decisive for the prediction of nutritional value or stress resistance. Here, we propose three fundamental pillars for future breeding strategies in the framework of Green Systems Biology: (i) combining genome selection with environment-dependent PANOMICS analysis and deep learning to improve prediction accuracy for marker-dependent trait performance; (ii) PANOMICS resolution at subtissue, cellular and subcellular level provides information about fundamental functions of selected markers; (iii) combining PANOMICS with genome editing and speed breeding tools to accelerate and enhance large-scale functional validation of trait-specific precision breeding.
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Affiliation(s)
- Wolfram Weckwerth
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
- Vienna Metabolomics Center (VIME)University of ViennaViennaAustria
| | - Arindam Ghatak
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Anke Bellaire
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Palak Chaturvedi
- Molecular Systems Biology (MOSYS)Department of Functional and Evolutionary EcologyFaculty of Life SciencesUniversity of ViennaViennaAustria
| | - Rajeev K. Varshney
- Center of Excellence in Genomics & Systems BiologyInternational Crops Research Institute for the Semi‐Arid Tropics (ICRISAT)HyderabadTelanganaIndia
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35
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Gemmer MR, Richter C, Jiang Y, Schmutzer T, Raorane ML, Junker B, Pillen K, Maurer A. Can metabolic prediction be an alternative to genomic prediction in barley? PLoS One 2020; 15:e0234052. [PMID: 32502173 PMCID: PMC7274421 DOI: 10.1371/journal.pone.0234052] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Accepted: 05/17/2020] [Indexed: 11/18/2022] Open
Abstract
Like other crop species, barley, the fourth most important crop worldwide, suffers from the genetic bottleneck effect, where further improvements in performance through classical breeding methods become difficult. Therefore, indirect selection methods are of great interest. Here, genomic prediction (GP) based on 33,005 SNP markers and, alternatively, metabolic prediction (MP) based on 128 metabolites with sampling at two different time points in one year, were applied to predict multi-year agronomic traits in the nested association mapping (NAM) population HEB-25. We found prediction abilities of up to 0.93 for plant height with SNP markers and of up to 0.61 for flowering time with metabolites. Interestingly, prediction abilities in GP increased after reducing the number of incorporated SNP markers. The estimated effects of GP and MP were highly concordant, indicating MP as an interesting alternative to GP, being able to reflect a stable genotype-specific metabolite profile. In MP, sampling at an early developmental stage outperformed sampling at a later stage. The results confirm the value of GP for future breeding. With MP, an interesting alternative was also applied successfully. However, based on our results, usage of MP alone cannot be recommended in barley. Nevertheless, MP can assist in unravelling physiological pathways for the expression of agronomically important traits.
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Affiliation(s)
- Mathias Ruben Gemmer
- Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Chris Richter
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Yong Jiang
- Department of Breeding Research, Quantitative Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany
| | - Thomas Schmutzer
- Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Manish L. Raorane
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Björn Junker
- Institute of Pharmacy, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Klaus Pillen
- Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
| | - Andreas Maurer
- Institute of Agricultural and Nutritional Sciences, Chair of Plant Breeding, Martin Luther University Halle-Wittenberg, Halle, Germany
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36
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Soltis NE, Caseys C, Zhang W, Corwin JA, Atwell S, Kliebenstein DJ. Pathogen Genetic Control of Transcriptome Variation in the Arabidopsis thaliana - Botrytis cinerea Pathosystem. Genetics 2020; 215:253-266. [PMID: 32165442 PMCID: PMC7198280 DOI: 10.1534/genetics.120.303070] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 03/11/2020] [Indexed: 01/12/2023] Open
Abstract
In plant-pathogen relations, disease symptoms arise from the interaction of the host and pathogen genomes. Host-pathogen functional gene interactions are well described, whereas little is known about how the pathogen genetic variation modulates both organisms' transcriptomes. To model and generate hypotheses on a generalist pathogen control of gene expression regulation, we used the Arabidopsis thaliana-Botrytis cinerea pathosystem and the genetic diversity of a collection of 96 B. cinerea isolates. We performed expression-based genome-wide association (eGWA) for each of 23,947 measurable transcripts in Arabidopsis (host), and 9267 measurable transcripts in B. cinerea (pathogen). Unlike other eGWA studies, we detected a relative absence of locally acting expression quantitative trait loci (cis-eQTL), partly caused by structural variants and allelic heterogeneity hindering their identification. This study identified several distantly acting trans-eQTL linked to eQTL hotspots dispersed across Botrytis genome that altered only Botrytis transcripts, only Arabidopsis transcripts, or transcripts from both species. Gene membership in the trans-eQTL hotspots suggests links between gene expression regulation and both known and novel virulence mechanisms in this pathosystem. Genes annotated to these hotspots provide potential targets for blocking manipulation of the host response by this ubiquitous generalist necrotrophic pathogen.
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Affiliation(s)
- Nicole E Soltis
- Department of Plant Sciences, University of California, Davis, California 95616
- Plant Biology Graduate Group, University of California, Davis, California 95616
| | - Celine Caseys
- Department of Plant Sciences, University of California, Davis, California 95616
| | - Wei Zhang
- Department of Plant Pathology, Kansas State University, Manhattan, Kansas 66506
| | - Jason A Corwin
- Department of Ecology and Evolution Biology, University of Colorado, Boulder, Colorado 80309-0334
| | - Susanna Atwell
- Plant Biology Graduate Group, University of California, Davis, California 95616
| | - Daniel J Kliebenstein
- Department of Plant Sciences, University of California, Davis, California 95616
- Plant Biology Graduate Group, University of California, Davis, California 95616
- DynaMo Center of Excellence, University of Copenhagen, DK-1871, Frederiksberg C, Denmark
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37
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Identifying genetic variants underlying phenotypic variation in plants without complete genomes. Nat Genet 2020; 52:534-540. [PMID: 32284578 PMCID: PMC7610390 DOI: 10.1038/s41588-020-0612-7] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 03/10/2020] [Indexed: 12/11/2022]
Abstract
Structural variants and presence/absence polymorphisms are common in plant genomes, yet they are routinely overlooked in genome-wide association studies (GWAS). Here, we expand the type of genetic variants detected in GWAS to include major deletions, insertions and rearrangements. We first use raw sequencing data directly to derive short sequences, k-mers, that mark a broad range of polymorphisms independently of a reference genome. We then link k-mers associated with phenotypes to specific genomic regions. Using this approach, we reanalyzed 2,000 traits in Arabidopsis thaliana, tomato and maize populations. Associations identified with k-mers recapitulate those found with SNPs, but with stronger statistical support. Importantly, we discovered new associations with structural variants and with regions missing from reference genomes. Our results demonstrate the power of performing GWAS before linking sequence reads to specific genomic regions, which allows the detection of a wider range of genetic variants responsible for phenotypic variation.
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38
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Kliebenstein DJ. Using networks to identify and interpret natural variation. CURRENT OPINION IN PLANT BIOLOGY 2020; 54:122-126. [PMID: 32413801 DOI: 10.1016/j.pbi.2020.04.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 04/20/2020] [Indexed: 06/11/2023]
Abstract
Studies on natural variation and network biology inherently work to summarize vast amounts of information and data. The combination of these two areas of study while creating datasets of immense complexity is critical to their mutual progress. Networks are necessary as a way to work to reduce the dimensionality inherent in natural variation with 100 s to 1000 s of genotypes. Correspondingly natural variation is essential for testing how networks may or may not be shared across individuals or species. Advances in this area of cross-fertilization including using networks directly as phenotypes and the use of networks to help in prioritizing candidate gene validation efforts. Interesting new observations on frequent presence-absence variation in gene content and adaptation is beginning to highlight the potential for natural variation in network presence-absence. This review attempts to delve into these new insights.
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Affiliation(s)
- Daniel J Kliebenstein
- Department of Plant Sciences, University of California, Davis, One Shields Avenue, Davis, CA, 95616, USA; DynaMo Center of Excellence, University of Copenhagen, Thorvaldsensvej 40, DK-1871, Frederiksberg C, Denmark.
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39
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Price EJ, Drapal M, Perez‐Fons L, Amah D, Bhattacharjee R, Heider B, Rouard M, Swennen R, Becerra Lopez‐Lavalle LA, Fraser PD. Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2020; 101:1258-1268. [PMID: 31845400 PMCID: PMC7383867 DOI: 10.1111/tpj.14649] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 11/09/2019] [Accepted: 11/28/2019] [Indexed: 05/06/2023]
Abstract
Roots, tubers, and bananas (RTB) are vital staples for food security in the world's poorest nations. A major constraint to current RTB breeding programmes is limited knowledge on the available diversity due to lack of efficient germplasm characterization and structure. In recent years large-scale efforts have begun to elucidate the genetic and phenotypic diversity of germplasm collections and populations and, yet, biochemical measurements have often been overlooked despite metabolite composition being directly associated with agronomic and consumer traits. Here we present a compound database and concentration range for metabolites detected in the major RTB crops: banana (Musa spp.), cassava (Manihot esculenta), potato (Solanum tuberosum), sweet potato (Ipomoea batatas), and yam (Dioscorea spp.), following metabolomics-based diversity screening of global collections held within the CGIAR institutes. The dataset including 711 chemical features provides a valuable resource regarding the comparative biochemical composition of each RTB crop and highlights the potential diversity available for incorporation into crop improvement programmes. Particularly, the tropical crops cassava, sweet potato and banana displayed more complex compositional metabolite profiles with representations of up to 22 chemical classes (unknowns excluded) than that of potato, for which only metabolites from 10 chemical classes were detected. Additionally, over 20% of biochemical signatures remained unidentified for every crop analyzed. Integration of metabolomics with the on-going genomic and phenotypic studies will enhance 'omics-wide associations of molecular signatures with agronomic and consumer traits via easily quantifiable biochemical markers to aid gene discovery and functional characterization.
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Affiliation(s)
- Elliott J. Price
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
- Present address:
Masaryk UniversityBrno‐Bohunice625 00Czech Republic
| | - Margit Drapal
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
| | - Laura Perez‐Fons
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
| | - Delphine Amah
- International Institute of Tropical AgriculturePMB 5320IbadanNigeria
| | | | | | - Mathieu Rouard
- Bioversity InternationalParc Scientifique Agropolis II34397MontpellierFrance
| | - Rony Swennen
- Laboratory of Tropical Crop ImprovementDivision of Crop BiotechnicsKU LeuvenB‐3001LeuvenBelgium
- Bioversity InternationalWillem De Croylaan 42B‐3001LeuvenBelgium
- International Institute of Tropical Agriculture. C/0 The Nelson Mandela African Institution of Science and TechnologyP.O. Box 44ArushaTanzania
| | | | - Paul D. Fraser
- Royal Holloway University of London, SurreyTW20 0EXEghamUnited Kingdom
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40
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Zhou S, Morgante F, Geisz MS, Ma J, Anholt RRH, Mackay TFC. Systems genetics of the Drosophila metabolome. Genome Res 2020; 30:392-405. [PMID: 31694867 PMCID: PMC7111526 DOI: 10.1101/gr.243030.118] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 03/11/2019] [Indexed: 02/06/2023]
Abstract
How effects of DNA sequence variants are transmitted through intermediate endophenotypes to modulate organismal traits remains a central question in quantitative genetics. This problem can be addressed through a systems approach in a population in which genetic polymorphisms, gene expression traits, metabolites, and complex phenotypes can be evaluated on the same genotypes. Here, we focused on the metabolome, which represents the most proximal link between genetic variation and organismal phenotype, and quantified metabolite levels in 40 lines of the Drosophila melanogaster Genetic Reference Panel. We identified sex-specific modules of genetically correlated metabolites and constructed networks that integrate DNA sequence variation and variation in gene expression with variation in metabolites and organismal traits, including starvation stress resistance and male aggression. Finally, we asked to what extent SNPs and metabolites can predict trait phenotypes and generated trait- and sex-specific prediction models that provide novel insights about the metabolomic underpinnings of complex phenotypes.
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Affiliation(s)
- Shanshan Zhou
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Fabio Morgante
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Matthew S Geisz
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Junwu Ma
- Key Laboratory for Animal Biotechnology of Jiangxi Province and the Ministry of Agriculture of China, JiangXi Agricultural University, JiangXi, China
| | - Robert R H Anholt
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Trudy F C Mackay
- Program in Genetics, W.M. Keck Center for Behavioral Biology and Department of Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA
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41
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Scossa F, Fernie AR. The evolution of metabolism: How to test evolutionary hypotheses at the genomic level. Comput Struct Biotechnol J 2020; 18:482-500. [PMID: 32180906 PMCID: PMC7063335 DOI: 10.1016/j.csbj.2020.02.009] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2019] [Revised: 02/12/2020] [Accepted: 02/13/2020] [Indexed: 01/21/2023] Open
Abstract
The origin of primordial metabolism and its expansion to form the metabolic networks extant today represent excellent systems to study the impact of natural selection and the potential adaptive role of novel compounds. Here we present the current hypotheses made on the origin of life and ancestral metabolism and present the theories and mechanisms by which the large chemical diversity of plants might have emerged along evolution. In particular, we provide a survey of statistical methods that can be used to detect signatures of selection at the gene and population level, and discuss potential and limits of these methods for investigating patterns of molecular adaptation in plant metabolism.
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Affiliation(s)
- Federico Scossa
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476 Potsdam-Golm, Germany
- Council for Agricultural Research and Economics (CREA), Research Centre for Genomics and Bioinformatics (CREA-GB), Via Ardeatina 546, 00178 Rome, Italy
| | - Alisdair R. Fernie
- Max-Planck-Institut für Molekulare Pflanzenphysiologie, 14476 Potsdam-Golm, Germany
- Center of Plant Systems Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
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42
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Li K, Wang D, Gong L, Lyu Y, Guo H, Chen W, Jin C, Liu X, Fang C, Luo J. Comparative analysis of metabolome of rice seeds at three developmental stages using a recombinant inbred line population. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 100:908-922. [PMID: 31355982 PMCID: PMC6899760 DOI: 10.1111/tpj.14482] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2019] [Revised: 07/13/2019] [Accepted: 07/23/2019] [Indexed: 05/23/2023]
Abstract
Plants are considered an important food and nutrition source for humans. Despite advances in plant seed metabolomics, knowledge about the genetic and molecular bases of rice seed metabolomes at different developmental stages is still limited. Here, using Zhenshan 97 (ZS97) and Minghui 63 (MH63), we performed a widely targeted metabolic profiling in seeds during grain filling, mature seeds and germinating seeds. The diversity between MH63 and ZS97 was characterized in terms of the content of metabolites and the metabolic shifting across developmental stages. Taking advantage of the ultra-high-density genetic map of a population of 210 recombinant inbred lines (RILs) derived from a cross between ZS97 and MH63, we identified 4681 putative metabolic quantitative trait loci (mQTLs) in seeds across the three stages. Further analysis of the mQTLs for the codetected metabolites across the three stages revealed that the genetic regulation of metabolite accumulation was closely related to developmental stage. Using in silico analyses, we characterized 35 candidate genes responsible for 30 structurally identified or annotated compounds, among which LOC_Os07g04970 and LOC_Os06g03990 were identified to be responsible for feruloylserotonin and l-asparagine content variation across populations, respectively. Metabolite-agronomic trait association and colocation between mQTLs and phenotypic quantitative trait loci (pQTLs) revealed the complexity of the metabolite-agronomic trait relationship and the corresponding genetic basis.
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Affiliation(s)
- Kang Li
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant ResearchHuazhong Agricultural UniversityWuhan430070China
| | - Dehong Wang
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant ResearchHuazhong Agricultural UniversityWuhan430070China
| | - Liang Gong
- The Jackson Laboratory for Genomic MedicineFarmingtonCTUSA
| | - Yuanyuan Lyu
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant ResearchHuazhong Agricultural UniversityWuhan430070China
| | - Hao Guo
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant ResearchHuazhong Agricultural UniversityWuhan430070China
| | - Wei Chen
- College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan430070China
| | - Cheng Jin
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant ResearchHuazhong Agricultural UniversityWuhan430070China
| | - Xianqing Liu
- Hainan Key Laboratory for Sustainable Utilization of Tropical BioresourceCollege of Tropical CropsHainan UniversityHaikou570288China
| | - Chuanying Fang
- Hainan Key Laboratory for Sustainable Utilization of Tropical BioresourceCollege of Tropical CropsHainan UniversityHaikou570288China
| | - Jie Luo
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant ResearchHuazhong Agricultural UniversityWuhan430070China
- Hainan Key Laboratory for Sustainable Utilization of Tropical BioresourceCollege of Tropical CropsHainan UniversityHaikou570288China
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43
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Wang S, Alseekh S, Fernie AR, Luo J. The Structure and Function of Major Plant Metabolite Modifications. MOLECULAR PLANT 2019; 12:899-919. [PMID: 31200079 DOI: 10.1016/j.molp.2019.06.001] [Citation(s) in RCA: 191] [Impact Index Per Article: 38.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 05/27/2019] [Accepted: 06/04/2019] [Indexed: 05/23/2023]
Abstract
Plants produce a myriad of structurally and functionally diverse metabolites that play many different roles in plant growth and development and in plant response to continually changing environmental conditions as well as abiotic and biotic stresses. This metabolic diversity is, to a large extent, due to chemical modification of the basic skeletons of metabolites. Here, we review the major known plant metabolite modifications and summarize the progress that has been achieved and the challenges we are facing in the field. We focus on discussing both technical and functional aspects in studying the influences that various modifications have on biosynthesis, degradation, transport, and storage of metabolites, as well as their bioactivity and toxicity. Finally, we discuss some emerging insights into the evolution of metabolic pathways and metabolite functionality.
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Affiliation(s)
- Shouchuang Wang
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresource, College of Tropical Crops, Hainan University, Haikou 572208, China
| | - Saleh Alseekh
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany; Centre of Plant Systems Biology and Biotechnology, Plovdiv 4000, Bulgaria
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm 14476, Germany; Centre of Plant Systems Biology and Biotechnology, Plovdiv 4000, Bulgaria.
| | - Jie Luo
- Hainan Key Laboratory for Sustainable Utilization of Tropical Bioresource, College of Tropical Crops, Hainan University, Haikou 572208, China; National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan 430070, China.
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44
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Chu SH, Huang M, Kelly RS, Benedetti E, Siddiqui JK, Zeleznik OA, Pereira A, Herrington D, Wheelock CE, Krumsiek J, McGeachie M, Moore SC, Kraft P, Mathé E, Lasky-Su J. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites 2019; 9:E117. [PMID: 31216675 PMCID: PMC6630728 DOI: 10.3390/metabo9060117] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/12/2019] [Accepted: 06/14/2019] [Indexed: 12/30/2022] Open
Abstract
It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.
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Affiliation(s)
- Su H Chu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Mengna Huang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Elisa Benedetti
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Jalal K Siddiqui
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Alexandre Pereira
- Department of Genetics and Molecular Medicine, University of Sao Paulo Medical School, Sao Paulo 01246-903, Brazil.
| | - David Herrington
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA.
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, Sweden.
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Michael McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA.
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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45
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Zhou S, Kremling KA, Bandillo N, Richter A, Zhang YK, Ahern KR, Artyukhin AB, Hui JX, Younkin GC, Schroeder FC, Buckler ES, Jander G. Metabolome-Scale Genome-Wide Association Studies Reveal Chemical Diversity and Genetic Control of Maize Specialized Metabolites. THE PLANT CELL 2019; 31:937-955. [PMID: 30923231 PMCID: PMC6533025 DOI: 10.1105/tpc.18.00772] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2018] [Revised: 03/05/2019] [Accepted: 03/27/2019] [Indexed: 05/17/2023]
Abstract
Cultivated maize (Zea mays) has retained much of the genetic diversity of its wild ancestors. Here, we performed nontargeted liquid chromatography-mass spectrometry metabolomics to analyze the metabolomes of the 282 maize inbred lines in the Goodman Diversity Panel. This analysis identified a bimodal distribution of foliar metabolites. Although 15% of the detected mass features were present in >90% of the inbred lines, the majority were found in <50% of the samples. Whereas leaf bases and tips were differentiated by flavonoid abundance, maize varieties (stiff-stalk, nonstiff-stalk, tropical, sweet maize, and popcorn) showed differential accumulation of benzoxazinoid metabolites. Genome-wide association studies (GWAS), performed for 3,991 mass features from the leaf tips and leaf bases, showed that 90% have multiple significantly associated loci scattered across the genome. Several quantitative trait locus hotspots in the maize genome regulate the abundance of multiple, often structurally related mass features. The utility of maize metabolite GWAS was demonstrated by confirming known benzoxazinoid biosynthesis genes, as well as by mapping isomeric variation in the accumulation of phenylpropanoid hydroxycitric acid esters to a single linkage block in a citrate synthase-like gene. Similar to gene expression databases, this metabolomic GWAS data set constitutes an important public resource for linking maize metabolites with biosynthetic and regulatory genes.
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Affiliation(s)
- Shaoqun Zhou
- Boyce Thompson Institute, Ithaca, New York 14853
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | - Karl A Kremling
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | - Nonoy Bandillo
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | | | - Ying K Zhang
- Boyce Thompson Institute, Ithaca, New York 14853
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853
| | - Kevin R Ahern
- Boyce Thompson Institute, Ithaca, New York 14853
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | | | - Joshua X Hui
- Boyce Thompson Institute, Ithaca, New York 14853
| | - Gordon C Younkin
- Boyce Thompson Institute, Ithaca, New York 14853
- Plant Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
| | - Frank C Schroeder
- Boyce Thompson Institute, Ithaca, New York 14853
- Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853
| | - Edward S Buckler
- Plant Breeding and Genetics Section, School of Integrative Plant Science, Cornell University, Ithaca, New York 14853
- U.S. Department of Agriculture-Agricultural Research Service, Robert W. Holley Center for Agriculture and Health, Ithaca, New York 14853
| | - Georg Jander
- Boyce Thompson Institute, Ithaca, New York 14853
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46
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Vasseur F, Fouqueau L, de Vienne D, Nidelet T, Violle C, Weigel D. Nonlinear phenotypic variation uncovers the emergence of heterosis in Arabidopsis thaliana. PLoS Biol 2019; 17:e3000214. [PMID: 31017902 PMCID: PMC6481775 DOI: 10.1371/journal.pbio.3000214] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2018] [Accepted: 03/21/2019] [Indexed: 12/22/2022] Open
Abstract
Heterosis describes the phenotypic superiority of hybrids over their parents in traits related to agronomic performance and fitness. Understanding and predicting nonadditive inheritance such as heterosis is crucial for evolutionary biology as well as for plant and animal breeding. However, the physiological bases of heterosis remain debated. Moreover, empirical data in various species have shown that diverse genetic and molecular mechanisms are likely to explain heterosis, making it difficult to predict its emergence and amplitude from parental genotypes alone. In this study, we examined a model of physiological dominance initially proposed by Sewall Wright to explain the nonadditive inheritance of traits like metabolic fluxes at the cellular level. We evaluated Wright's model for two fitness-related traits at the whole-plant level, growth rate and fruit number, using 450 hybrids derived from crosses among natural accessions of A. thaliana. We found that allometric relationships between traits constrain phenotypic variation in a nonlinear and similar manner in hybrids and accessions. These allometric relationships behave predictably, explaining up to 75% of heterosis amplitude, while genetic distance among parents at best explains 7%. Thus, our findings are consistent with Wright's model of physiological dominance and suggest that the emergence of heterosis on plant performance is an intrinsic property of nonlinear relationships between traits. Furthermore, our study highlights the potential of a geometric approach of phenotypic relationships for predicting heterosis of major components of crop productivity and yield.
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Affiliation(s)
- François Vasseur
- Max Planck Institute for Developmental Biology, Tübingen, Germany
- CEFE, CNRS, Univ Montpellier, Univ Paul Valéry Montpellier, EPHE, IRD, Montpellier, France
- Laboratoire d’Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), INRA, Montpellier SupAgro, UMR759, Montpellier, France
| | - Louise Fouqueau
- CEFE, CNRS, Univ Montpellier, Univ Paul Valéry Montpellier, EPHE, IRD, Montpellier, France
| | - Dominique de Vienne
- GQE–Le Moulon, INRA, Univ Paris-Sud, CNRS, AgroParisTech, Univ Paris-Saclay, Gif-sur-Yvette, France
| | - Thibault Nidelet
- SPO, INRA, Montpellier SupAgro, Univ Montpellier, Montpellier, France
| | - Cyrille Violle
- CEFE, CNRS, Univ Montpellier, Univ Paul Valéry Montpellier, EPHE, IRD, Montpellier, France
| | - Detlef Weigel
- Max Planck Institute for Developmental Biology, Tübingen, Germany
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Nunes-Nesi A, Alseekh S, de Oliveira Silva FM, Omranian N, Lichtenstein G, Mirnezhad M, González RRR, Sabio Y Garcia J, Conte M, Leiss KA, Klinkhamer PGL, Nikoloski Z, Carrari F, Fernie AR. Identification and characterization of metabolite quantitative trait loci in tomato leaves and comparison with those reported for fruits and seeds. Metabolomics 2019; 15:46. [PMID: 30874962 PMCID: PMC6420416 DOI: 10.1007/s11306-019-1503-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Accepted: 01/12/2019] [Indexed: 01/10/2023]
Abstract
INTRODUCTION To date, most studies of natural variation and metabolite quantitative trait loci (mQTL) in tomato have focused on fruit metabolism, leaving aside the identification of genomic regions involved in the regulation of leaf metabolism. OBJECTIVE This study was conducted to identify leaf mQTL in tomato and to assess the association of leaf metabolites and physiological traits with the metabolite levels from other tissues. METHODS The analysis of components of leaf metabolism was performed by phenotypying 76 tomato ILs with chromosome segments of the wild species Solanum pennellii in the genetic background of a cultivated tomato (S. lycopersicum) variety M82. The plants were cultivated in two different environments in independent years and samples were harvested from mature leaves of non-flowering plants at the middle of the light period. The non-targeted metabolite profiling was obtained by gas chromatography time-of-flight mass spectrometry (GC-TOF-MS). With the data set obtained in this study and already published metabolomics data from seed and fruit, we performed QTL mapping, heritability and correlation analyses. RESULTS Changes in metabolite contents were evident in the ILs that are potentially important with respect to stress responses and plant physiology. By analyzing the obtained data, we identified 42 positive and 76 negative mQTL involved in carbon and nitrogen metabolism. CONCLUSIONS Overall, these findings allowed the identification of S. lycopersicum genome regions involved in the regulation of leaf primary carbon and nitrogen metabolism, as well as the association of leaf metabolites with metabolites from seeds and fruits.
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Affiliation(s)
- Adriano Nunes-Nesi
- Departamento de Biologia Vegetal, Universidade Federal de Viçosa, Viçosa, Minas Gerais, 36570-900, Brazil.
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Golm, OT, Germany.
| | - Saleh Alseekh
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Golm, OT, Germany
- Center of Plant System Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | | | - Nooshin Omranian
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Golm, OT, Germany
- Center of Plant System Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
| | - Gabriel Lichtenstein
- Instituto de Biotecnología, Instituto Nacional de Tecnología Agropecuaría, Consejo Nacional de Investigaciones Científicas y Técnicas, B1712WAA, Castelar, Argentina
| | - Mohammad Mirnezhad
- Plant Ecology, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE, Leiden, The Netherlands
| | - Roman R Romero González
- Plant Ecology, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE, Leiden, The Netherlands
| | - Julia Sabio Y Garcia
- Instituto de Biotecnología, Instituto Nacional de Tecnología Agropecuaría, Consejo Nacional de Investigaciones Científicas y Técnicas, B1712WAA, Castelar, Argentina
| | - Mariana Conte
- Instituto de Biotecnología, Instituto Nacional de Tecnología Agropecuaría, Consejo Nacional de Investigaciones Científicas y Técnicas, B1712WAA, Castelar, Argentina
| | - Kirsten A Leiss
- Plant Ecology, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE, Leiden, The Netherlands
- Business Unit Horticulture, Wageningen University & Research, Postbus 20, 2665 ZG, Bleiswijk, The Netherlands
| | - Peter G L Klinkhamer
- Plant Ecology, Institute of Biology, Leiden University, Sylviusweg 72, 2333 BE, Leiden, The Netherlands
| | - Zoran Nikoloski
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Golm, OT, Germany
- Bioinformatics Group, Institute of Biochemistry and Biology, University of Potsdam, Potsdam, Germany
| | - Fernando Carrari
- Instituto de Biotecnología, Instituto Nacional de Tecnología Agropecuaría, Consejo Nacional de Investigaciones Científicas y Técnicas, B1712WAA, Castelar, Argentina
- Instituto de Fisiología, Biología Molecular y Neurociencias (IFIBYNE-UBA-CONICET), Universidad de Buenos Aires, Ciudad Universitaria, C1428EHA Buenos Aires, Argentina
- Facultad de Agronomía, Cátedra de Genética, Universidad de Buenos Aires, Buenos Aires, Argentina
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Golm, OT, Germany
- Center of Plant System Biology and Biotechnology (CPSBB), Plovdiv, Bulgaria
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Luo B, Ma P, Nie Z, Zhang X, He X, Ding X, Feng X, Lu Q, Ren Z, Lin H, Wu Y, Shen Y, Zhang S, Wu L, Liu D, Pan G, Rong T, Gao S. Metabolite profiling and genome-wide association studies reveal response mechanisms of phosphorus deficiency in maize seedling. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:947-969. [PMID: 30472798 PMCID: PMC6850195 DOI: 10.1111/tpj.14160] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Revised: 11/05/2018] [Accepted: 11/06/2018] [Indexed: 05/21/2023]
Abstract
Inorganic phosphorus (Pi) is an essential element in numerous metabolic reactions and signaling pathways, but the molecular details of these pathways remain largely unknown. In this study, metabolite profiles of maize (Zea mays L.) leaves and roots were compared between six low-Pi-sensitive lines and six low-Pi-tolerant lines under Pi-sufficient and Pi-deficient conditions to identify pathways and genes associated with the low-Pi stress response. Results showed that under Pi deprivation the concentrations of nucleic acids, organic acids and sugars were increased, but that the concentrations of phosphorylated metabolites, certain amino acids, lipid metabolites and nitrogenous compounds were decreased. The levels of secondary metabolites involved in plant immune reactions, including benzoxazinoids and flavonoids, were significantly different in plants grown under Pi-deficient conditions. Among them, the 11 most stable metabolites showed significant differences under low- and normal-Pi conditions based on the coefficient of variation (CV). Isoleucine and alanine were the most stable metabolites for the identification of Pi-sensitive and Pi-resistant maize inbred lines. With the significant correlation between morphological traits and metabolites, five low-Pi-responding consensus genes associated with morphological traits and simultaneously involved in metabolic pathways were mined by combining metabolites profiles and genome-wide association study (GWAS). The consensus genes induced by Pi deficiency in maize seedlings were also validated by reverse-transcription quantitative polymerase chain reaction (RT-qPCR). Moreover, these genes were further validated in a recombinant inbred line (RIL) population, in which the glucose-6-phosphate-1-epimerase encoding gene mediated yield and correlated traits to phosphorus availability. Together, our results provide a framework for understanding the metabolic processes underlying Pi-deficient responses and give multiple insights into improving the efficiency of Pi use in maize.
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Affiliation(s)
- Bowen Luo
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Peng Ma
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Zhi Nie
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Xiao Zhang
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Xuan He
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Xin Ding
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Xing Feng
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Quanxiao Lu
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Zhiyong Ren
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Haijian Lin
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Yuanqi Wu
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Yaou Shen
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
- State Key Laboratory of Crop Genetics of Disease Resistance and Disease ControlSichuanChengduChina
| | - Suzhi Zhang
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Ling Wu
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Dan Liu
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Guangtang Pan
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Tingzhao Rong
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
| | - Shibin Gao
- Maize Research InstituteSichuan Agricultural University611130SichuanChengduChina
- State Key Laboratory of Crop Genetics of Disease Resistance and Disease ControlSichuanChengduChina
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49
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De La Torre AR, Puiu D, Crepeau MW, Stevens K, Salzberg SL, Langley CH, Neale DB. Genomic architecture of complex traits in loblolly pine. THE NEW PHYTOLOGIST 2019; 221:1789-1801. [PMID: 30318590 DOI: 10.1111/nph.15535] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 10/06/2018] [Indexed: 05/02/2023]
Abstract
Dissecting the genetic and genomic architecture of complex traits is essential to understand the forces maintaining the variation in phenotypic traits of ecological and economical importance. Whole-genome resequencing data were used to generate high-resolution polymorphic single nucleotide polymorphism (SNP) markers and genotype individuals from common gardens across the loblolly pine (Pinus taeda) natural range. Genome-wide associations were tested with a large phenotypic dataset comprising 409 variables including morphological traits (height, diameter, carbon isotope discrimination, pitch canker resistance), and molecular traits such as metabolites and expression of xylem development genes. Our study identified 2335 new SNP × trait associations for the species, with many SNPs located in physical clusters in the genome of the species; and the genomic location of hotspots for metabolic × genotype associations. We found a highly polygenic basis of quantitative inheritance, with significant differences in number, effects size, genomic location and frequency of alleles contributing to variation in phenotypes in the different traits. While mutation-selection balance might be shaping the genetic variation in metabolic traits, balancing selection is more likely to shape the variation in expression of xylem development genes. Our work contributes to the study of complex traits in nonmodel plant species by identifying associations at a whole-genome level.
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Affiliation(s)
- Amanda R De La Torre
- School of Forestry, Northern Arizona University, 200 E. Pine Knoll Drive, Flagstaff, AZ, 86011, USA
- Department of Plant Sciences, University of California-Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Daniela Puiu
- Center for Computational Biology, Johns Hopkins University, 1900 E. Monument St, Baltimore, MD, 21205, USA
| | - Marc W Crepeau
- Department of Evolution and Ecology, University of California-Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Kristian Stevens
- Department of Evolution and Ecology, University of California-Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - Steven L Salzberg
- Center for Computational Biology, Johns Hopkins University, 1900 E. Monument St, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Computer Science and Biostatistics, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Charles H Langley
- Department of Evolution and Ecology, University of California-Davis, One Shields Avenue, Davis, CA, 95616, USA
| | - David B Neale
- Department of Plant Sciences, University of California-Davis, One Shields Avenue, Davis, CA, 95616, USA
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50
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Fang C, Luo J. Metabolic GWAS-based dissection of genetic bases underlying the diversity of plant metabolism. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2019; 97:91-100. [PMID: 30231195 DOI: 10.1111/tpj.14097] [Citation(s) in RCA: 101] [Impact Index Per Article: 20.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Revised: 09/07/2018] [Accepted: 09/11/2018] [Indexed: 05/21/2023]
Abstract
Plants have served as sources providing humans with metabolites for food and nutrition, biomaterials for living, and treatment for pain and disease. Plants produce a huge array of metabolites, with an immense diversity at both the population and individual levels. Dissection of the genetic bases for metabolic diversity has attracted increasing research attention. The concept of genome-wide association study (GWAS) was extended to studies on the diversity of plant metabolome that benefitted from the development of mass-spectrometry-based analytical systems and genome sequencing technologies. Metabolic genome-wide association study (mGWAS) is one of the most powerful tools for global identification of genetic determinants for diversity of plant metabolism. Recently, mGWAS has been performed for various species with continuous improvements, providing deeper insights into the genetic bases of metabolic diversity. In this review, we discuss fully the achievements to date and remaining challenges that are associated with both mGWAS and mGWAS-based multi-dimensional analysis. We begin with a summary of GWAS and its development based on statistical methods and populations. As variation in targeted traits is essential for GWAS, we review metabolic diversity and its rise at both the population and individual levels. Subsequently, the application of mGWAS for plants and its corresponding achievements are fully discussed. We address the current knowledge on mGWAS-based multi-dimensional analysis and emerging insights into the diversity of metabolism.
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Affiliation(s)
- Chuanying Fang
- Hainan Key Laboratory for Sustainable Utilisation of Tropical Bioresource, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, 470228, China
| | - Jie Luo
- Hainan Key Laboratory for Sustainable Utilisation of Tropical Bioresource, Institute of Tropical Agriculture and Forestry, Hainan University, Haikou, 470228, China
- National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research (Wuhan), Huazhong Agricultural University, Wuhan, 430070, China
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