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Peng S, Rajjou L. Advancing plant biology through deep learning-powered natural language processing. PLANT CELL REPORTS 2024; 43:208. [PMID: 39102077 DOI: 10.1007/s00299-024-03294-9] [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: 05/07/2024] [Accepted: 07/19/2024] [Indexed: 08/06/2024]
Abstract
The application of deep learning methods, specifically the utilization of Large Language Models (LLMs), in the field of plant biology holds significant promise for generating novel knowledge on plant cell systems. The LLM framework exhibits exceptional potential, particularly with the development of Protein Language Models (PLMs), allowing for in-depth analyses of nucleic acid and protein sequences. This analytical capacity facilitates the discernment of intricate patterns and relationships within biological data, encompassing multi-scale information within DNA or protein sequences. The contribution of PLMs extends beyond mere sequence patterns and structure--function recognition; it also supports advancements in genetic improvements for agriculture. The integration of deep learning approaches into the domain of plant sciences offers opportunities for major breakthroughs in basic research across multi-scale plant traits. Consequently, the strategic application of deep learning methodologies, particularly leveraging the potential of LLMs, will undoubtedly play a pivotal role in advancing plant sciences, plant production, plant uses and propelling the trajectory toward sustainable agroecological and agro-food transitions.
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Affiliation(s)
- Shuang Peng
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin for Plant Sciences (IJPB), 78000, Versailles, France
| | - Loïc Rajjou
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin for Plant Sciences (IJPB), 78000, Versailles, France.
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2
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Ruan M, Zhao H, Wen Y, Chen H, He F, Hou X, Song X, Jiang H, Ruan YL, Wu L. The complex transcriptional regulation of heat stress response in maize. STRESS BIOLOGY 2024; 4:24. [PMID: 38668992 PMCID: PMC11052759 DOI: 10.1007/s44154-024-00165-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 04/01/2024] [Indexed: 04/29/2024]
Abstract
As one of the most important food and feed crops worldwide, maize suffers much more tremendous damages under heat stress compared to other plants, which seriously inhibits plant growth and reduces productivity. To mitigate the heat-induced damages and adapt to high temperature environment, plants have evolved a series of molecular mechanisms to sense, respond and adapt high temperatures and heat stress. In this review, we summarized recent advances in molecular regulations underlying high temperature sensing, heat stress response and memory in maize, especially focusing on several important pathways and signals in high temperature sensing, and the complex transcriptional regulation of ZmHSFs (Heat Shock Factors) in heat stress response. In addition, we highlighted interactions between ZmHSFs and several epigenetic regulation factors in coordinately regulating heat stress response and memory. Finally, we laid out strategies to systematically elucidate the regulatory network of maize heat stress response, and discussed approaches for breeding future heat-tolerance maize.
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Affiliation(s)
- Mingxiu Ruan
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Heng Zhao
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Yujing Wen
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Hao Chen
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Feng He
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Xingbo Hou
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Xiaoqin Song
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Haiyang Jiang
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China
| | - Yong-Ling Ruan
- State Key Laboratory of Crop Stress Biology in Arid Areas and College of Horticulture, Northwest A&F University, Yangling, 712100, China.
- School of Agronomy, Anhui Agricultural University, Hefei, 230036, China.
- Division of Plant Sciences, Research School of Biology, The Australian National University, Canberra, ACT, 2601, Australia.
| | - Leiming Wu
- The National Engineering Laboratory of Crop Resistance Breeding, School of Life Sciences, Anhui Agricultural University, Hefei, 230036, China.
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3
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Chen Y, Wang W, Yang Z, Peng H, Ni Z, Sun Q, Guo W. Innovative computational tools provide new insights into the polyploid wheat genome. ABIOTECH 2024; 5:52-70. [PMID: 38576428 PMCID: PMC10987449 DOI: 10.1007/s42994-023-00131-7] [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: 09/13/2023] [Accepted: 12/14/2023] [Indexed: 04/06/2024]
Abstract
Bread wheat (Triticum aestivum) is an important crop and serves as a significant source of protein and calories for humans, worldwide. Nevertheless, its large and allopolyploid genome poses constraints on genetic improvement. The complex reticulate evolutionary history and the intricacy of genomic resources make the deciphering of the functional genome considerably more challenging. Recently, we have developed a comprehensive list of versatile computational tools with the integration of statistical models for dissecting the polyploid wheat genome. Here, we summarize the methodological innovations and applications of these tools and databases. A series of step-by-step examples illustrates how these tools can be utilized for dissecting wheat germplasm resources and unveiling functional genes associated with important agronomic traits. Furthermore, we outline future perspectives on new advanced tools and databases, taking into consideration the unique features of bread wheat, to accelerate genomic-assisted wheat breeding.
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Affiliation(s)
- Yongming Chen
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Wenxi Wang
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Zhengzhao Yang
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Huiru Peng
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Zhongfu Ni
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Qixin Sun
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
| | - Weilong Guo
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing, 100193 China
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4
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Zhu W, Han R, Shang X, Zhou T, Liang C, Qin X, Chen H, Feng Z, Zhang H, Fan X, Li W, Li L. The CropGPT project: Call for a global, coordinated effort in precision design breeding driven by AI using biological big data. MOLECULAR PLANT 2024; 17:215-218. [PMID: 38140725 DOI: 10.1016/j.molp.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/24/2023] [Accepted: 12/20/2023] [Indexed: 12/24/2023]
Affiliation(s)
- Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Rui Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Tao Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Chengyong Liang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaomeng Qin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hong Chen
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zaiwen Feng
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Hongwei Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xingming Fan
- Institute of Food Crops Sciences, Yunnan Academy of Agricultural Sciences, 2238 Beijing Road, Kunming, Yunnan 650200, China
| | - Weifu Li
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China.
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5
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Zhao T, Wu H, Wang X, Zhao Y, Wang L, Pan J, Mei H, Han J, Wang S, Lu K, Li M, Gao M, Cao Z, Zhang H, Wan K, Li J, Fang L, Zhang T, Guan X. Integration of eQTL and machine learning to dissect causal genes with pleiotropic effects in genetic regulation networks of seed cotton yield. Cell Rep 2023; 42:113111. [PMID: 37676770 DOI: 10.1016/j.celrep.2023.113111] [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: 02/27/2023] [Revised: 06/19/2023] [Accepted: 08/24/2023] [Indexed: 09/09/2023] Open
Abstract
The dissection of a gene regulatory network (GRN) that complements the genome-wide association study (GWAS) locus and the crosstalk underlying multiple agronomical traits remains a major challenge. In this study, we generate 558 transcriptional profiles of lint-bearing ovules at one day post-anthesis from a selective core cotton germplasm, from which 12,207 expression quantitative trait loci (eQTLs) are identified. Sixty-six known phenotypic GWAS loci are colocalized with 1,090 eQTLs, forming 38 functional GRNs associated predominantly with seed yield. Of the eGenes, 34 exhibit pleiotropic effects. Combining the eQTLs within the seed yield GRNs significantly increases the portion of narrow-sense heritability. The extreme gradient boosting (XGBoost) machine learning approach is applied to predict seed cotton yield phenotypes on the basis of gene expression. Top-ranking eGenes (NF-YB3, FLA2, and GRDP1) derived with pleiotropic effects on yield traits are validated, along with their potential roles by correlation analysis, domestication selection analysis, and transgenic plants.
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Affiliation(s)
- Ting Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Hongyu Wu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Xutong Wang
- Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Yongyan Zhao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Luyao Wang
- Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Jiaying Pan
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Huan Mei
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Jin Han
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Siyuan Wang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Kening Lu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Menglin Li
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Mengtao Gao
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Zeyi Cao
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Hailin Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China
| | - Ke Wan
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Li
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cotton Hybrid R & D Engineering Center (the Ministry of Education), College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
| | - Lei Fang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Tianzhen Zhang
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China
| | - Xueying Guan
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, The Advanced Seed Institute, Plant Precision Breeding Academy, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou 300058, China; Hainan Institute of Zhejiang University, Building 11, Yonyou Industrial Park, Yazhou Bay Science and Technology City, Yazhou District, Sanya 572025, China.
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6
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Wang X, Han L, Li J, Shang X, Liu Q, Li L, Zhang H. Next-generation bulked segregant analysis for Breeding 4.0. Cell Rep 2023; 42:113039. [PMID: 37651230 DOI: 10.1016/j.celrep.2023.113039] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/11/2023] [Accepted: 08/10/2023] [Indexed: 09/02/2023] Open
Abstract
Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.
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Affiliation(s)
- Xi Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Juan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Qian Liu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Hongwei Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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Wang X, Li J, Han L, Liang C, Li J, Shang X, Miao X, Luo Z, Zhu W, Li Z, Li T, Qi Y, Li H, Lu X, Li L. QTG-Miner aids rapid dissection of the genetic base of tassel branch number in maize. Nat Commun 2023; 14:5232. [PMID: 37633966 PMCID: PMC10460418 DOI: 10.1038/s41467-023-41022-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/21/2023] [Indexed: 08/28/2023] Open
Abstract
Genetic dissection of agronomic traits is important for crop improvement and global food security. Phenotypic variation of tassel branch number (TBN), a major breeding target, is controlled by many quantitative trait loci (QTLs). The lack of large-scale QTL cloning methodology constrains the systematic dissection of TBN, which hinders modern maize breeding. Here, we devise QTG-Miner, a multi-omics data-based technique for large-scale and rapid cloning of quantitative trait genes (QTGs) in maize. Using QTG-Miner, we clone and verify seven genes underlying seven TBN QTLs. Compared to conventional methods, QTG-Miner performs well for both major- and minor-effect TBN QTLs. Selection analysis indicates that a substantial number of genes and network modules have been subjected to selection during maize improvement. Selection signatures are significantly enriched in multiple biological pathways between female heterotic groups and male heterotic groups. In summary, QTG-Miner provides a large-scale approach for rapid cloning of QTGs in crops and dissects the genetic base of TBN for further maize breeding.
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Affiliation(s)
- Xi Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Juan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Chengyong Liang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Jiaxin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Xinxin Miao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Zi Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Zhao Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Tianhuan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Yongwen Qi
- College of Agriculture and Biology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510325, Guangdong, China
| | - Huihui Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081, Beijing, China
| | - Xiaoduo Lu
- Institute of Molecular Breeding for Maize, Qilu Normal University, Jinan, 250200, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Hongshan Laboratory, Wuhan, 430070, China.
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8
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Feng JW, Han L, Liu H, Xie WZ, Liu H, Li L, Chen LL. MaizeNetome: A multi-omics network database for functional genomics in maize. MOLECULAR PLANT 2023; 16:1229-1231. [PMID: 37553832 DOI: 10.1016/j.molp.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/17/2023] [Accepted: 08/04/2023] [Indexed: 08/10/2023]
Affiliation(s)
- Jia-Wu Feng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Wen-Zhao Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hanmingzi Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
| | - Ling-Ling Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning 530004, China.
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9
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Raudenska M, Vicar T, Gumulec J, Masarik M. Johann Gregor Mendel: the victory of statistics over human imagination. Eur J Hum Genet 2023; 31:744-748. [PMID: 36755104 PMCID: PMC9909140 DOI: 10.1038/s41431-023-01303-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 01/11/2023] [Accepted: 01/24/2023] [Indexed: 02/10/2023] Open
Abstract
In 2022, we celebrated 200 years since the birth of Johann Gregor Mendel. Although his contributions to science went unrecognized during his lifetime, Mendel not only described the principles of monogenic inheritance but also pioneered the modern way of doing science based on precise experimental data acquisition and evaluation. Novel statistical and algorithmic approaches are now at the center of scientific work, showing that work that is considered marginal in one era can become a mainstream research approach in the next era. The onset of data-driven science caused a shift from hypothesis-testing to hypothesis-generating approaches in science. Mendel is remembered here as a promoter of this approach, and the benefits of big data and statistical approaches are discussed.
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Affiliation(s)
- Martina Raudenska
- Department of Physiology, Faculty of Medicine, Masaryk University/Kamenice 5, CZ-625 00, Brno, Czech Republic
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University/Kamenice 5, CZ-625 00, Brno, Czech Republic
| | - Tomas Vicar
- Department of Physiology, Faculty of Medicine, Masaryk University/Kamenice 5, CZ-625 00, Brno, Czech Republic
- Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 3058/10, Brno, Czech Republic
| | - Jaromir Gumulec
- Department of Physiology, Faculty of Medicine, Masaryk University/Kamenice 5, CZ-625 00, Brno, Czech Republic
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University/Kamenice 5, CZ-625 00, Brno, Czech Republic
| | - Michal Masarik
- Department of Physiology, Faculty of Medicine, Masaryk University/Kamenice 5, CZ-625 00, Brno, Czech Republic.
- Department of Pathological Physiology, Faculty of Medicine, Masaryk University/Kamenice 5, CZ-625 00, Brno, Czech Republic.
- BIOCEV, First Faculty of Medicine, Charles University, Prumyslova 595, CZ-252 50, Vestec, Czech Republic.
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10
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Xie YN, Qi QQ, Li WH, Li YL, Zhang Y, Wang HM, Zhang YF, Ye ZH, Guo DP, Qian Q, Zhang ZF, Yan N. Domestication, breeding, omics research, and important genes of Zizania latifolia and Zizania palustris. FRONTIERS IN PLANT SCIENCE 2023; 14:1183739. [PMID: 37324716 PMCID: PMC10266587 DOI: 10.3389/fpls.2023.1183739] [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: 03/10/2023] [Accepted: 05/17/2023] [Indexed: 06/17/2023]
Abstract
Wild rice (Zizania spp.), an aquatic grass belonging to the subfamily Gramineae, has a high economic value. Zizania provides food (such as grains and vegetables), a habitat for wild animals, and paper-making pulps, possesses certain medicinal values, and helps control water eutrophication. Zizania is an ideal resource for expanding and enriching a rice breeding gene bank to naturally preserve valuable characteristics lost during domestication. With the Z. latifolia and Z. palustris genomes completely sequenced, fundamental achievements have been made toward understanding the origin and domestication, as well as the genetic basis of important agronomic traits of this genus, substantially accelerating the domestication of this wild plant. The present review summarizes the research results on the edible history, economic value, domestication, breeding, omics research, and important genes of Z. latifolia and Z. palustris over the past decades. These findings broaden the collective understanding of Zizania domestication and breeding, furthering human domestication, improvement, and long-term sustainability of wild plant cultivation.
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Affiliation(s)
- Yan-Ning Xie
- Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Qian-Qian Qi
- Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Wan-Hong Li
- Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Ya-Li Li
- Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Yu Zhang
- Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Hui-Mei Wang
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Ya-Fen Zhang
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Sciences, China Jiliang University, Hangzhou, China
| | - Zi-Hong Ye
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Sciences, China Jiliang University, Hangzhou, China
| | - De-Ping Guo
- Department of Horticulture, College of Agriculture and Biotechnology, Zhejiang University, Hangzhou, China
| | - Qian Qian
- State Key Laboratory of Rice Biology, China National Rice Research Institute, Hangzhou, China
| | - Zhong-Feng Zhang
- Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
| | - Ning Yan
- Tobacco Research Institute of Chinese Academy of Agricultural Sciences, Qingdao, China
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11
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Usman B, Derakhshani B, Jung KH. Recent Molecular Aspects and Integrated Omics Strategies for Understanding the Abiotic Stress Tolerance of Rice. PLANTS (BASEL, SWITZERLAND) 2023; 12:2019. [PMID: 37653936 PMCID: PMC10221523 DOI: 10.3390/plants12102019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/11/2023] [Accepted: 05/17/2023] [Indexed: 09/02/2023]
Abstract
Rice is an important staple food crop for over half of the world's population. However, abiotic stresses seriously threaten rice yield improvement and sustainable production. Breeding and planting rice varieties with high environmental stress tolerance are the most cost-effective, safe, healthy, and environmentally friendly strategies. In-depth research on the molecular mechanism of rice plants in response to different stresses can provide an important theoretical basis for breeding rice varieties with higher stress resistance. This review presents the molecular mechanisms and the effects of various abiotic stresses on rice growth and development and explains the signal perception mode and transduction pathways. Meanwhile, the regulatory mechanisms of critical transcription factors in regulating gene expression and important downstream factors in coordinating stress tolerance are outlined. Finally, the utilization of omics approaches to retrieve hub genes and an outlook on future research are prospected, focusing on the regulatory mechanisms of multi-signaling network modules and sustainable rice production.
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Affiliation(s)
- Babar Usman
- Graduate School of Green Green-Bio Science and Crop Biotech Institute, Kyung Hee University, Yongin 17104, Republic of Korea; (B.U.)
| | - Behnam Derakhshani
- Graduate School of Green Green-Bio Science and Crop Biotech Institute, Kyung Hee University, Yongin 17104, Republic of Korea; (B.U.)
| | - Ki-Hong Jung
- Graduate School of Green Green-Bio Science and Crop Biotech Institute, Kyung Hee University, Yongin 17104, Republic of Korea; (B.U.)
- Research Center for Plant Plasticity, Kyung Hee University, Yongin 17104, Republic of Korea
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12
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Yang Z, Wang S, Wei L, Huang Y, Liu D, Jia Y, Luo C, Lin Y, Liang C, Hu Y, Dai C, Guo L, Zhou Y, Yang QY. BnIR: A multi-omics database with various tools for Brassica napus research and breeding. MOLECULAR PLANT 2023; 16:775-789. [PMID: 36919242 DOI: 10.1016/j.molp.2023.03.007] [Citation(s) in RCA: 34] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 02/15/2023] [Accepted: 03/09/2023] [Indexed: 06/18/2023]
Abstract
In the post-genome-wide association study era, multi-omics techniques have shown great power and potential for candidate gene mining and functional genomics research. However, due to the lack of effective data integration and multi-omics analysis platforms, such techniques have not still been applied widely in rapeseed, an important oil crop worldwide. Here, we report a rapeseed multi-omics database (BnIR; http://yanglab.hzau.edu.cn/BnIR), which provides datasets of six omics including genomics, transcriptomics, variomics, epigenetics, phenomics, and metabolomics, as well as numerous "variation-gene expression-phenotype" associations by using multiple statistical methods. In addition, a series of multi-omics search and analysis tools are integrated to facilitate the browsing and application of these datasets. BnIR is the most comprehensive multi-omics database for rapeseed so far, and two case studies demonstrated its power to mine candidate genes associated with specific traits and analyze their potential regulatory mechanisms.
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Affiliation(s)
- Zhiquan Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China; Innovative Center of Molecular Genetics and Evolution, School of Life Sciences, Guangzhou University, Guangzhou 510405, China
| | - Shengbo Wang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Lulu Wei
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yiming Huang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Dongxu Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yupeng Jia
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Chengfang Luo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yuchen Lin
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Congyuan Liang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Yue Hu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Cheng Dai
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Yongming Zhou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Qing-Yong Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
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13
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Zheng H, Fu X, Shao J, Tang Y, Yu M, Li L, Huang L, Tang K. Transcriptional regulatory network of high-value active ingredients in medicinal plants. TRENDS IN PLANT SCIENCE 2023; 28:429-446. [PMID: 36621413 DOI: 10.1016/j.tplants.2022.12.007] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2022] [Revised: 11/29/2022] [Accepted: 12/08/2022] [Indexed: 05/14/2023]
Abstract
High-value active ingredients in medicinal plants have attracted research attention because of their benefits for human health, such as the antimalarial artemisinin, anticardiovascular disease tanshinones, and anticancer Taxol and vinblastine. Here, we review how hormones and environmental factors promote the accumulation of active ingredients, thereby providing a strategy to produce high-value drugs at a low cost. Focusing on major hormone signaling events and environmental factors, we review the transcriptional regulatory network mediating biosynthesis of representative active ingredients. In this network, many transcription factors (TFs) simultaneously control multiple synthase genes; thus, understanding the molecular mechanisms affecting transcriptional regulation of active ingredients will be crucial to developing new breeding possibilities.
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Affiliation(s)
- Han Zheng
- Frontiers Science Center for Transformative Molecules, Joint International Research Laboratory of Metabolic and Developmental Sciences, Plant Biotechnology Research Center, Fudan-SJTU-Nottingham Plant Biotechnology R&D Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China; State Key Laboratory of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Xueqing Fu
- School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jin Shao
- Frontiers Science Center for Transformative Molecules, Joint International Research Laboratory of Metabolic and Developmental Sciences, Plant Biotechnology Research Center, Fudan-SJTU-Nottingham Plant Biotechnology R&D Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yueli Tang
- Key Laboratory of Eco-environments in Three Gorges Reservoir Region (Ministry of Education), SWU-TAAHC Medicinal Plant Joint R&D Centre,School of Life Sciences, Southwest University, Chongqing 400715, China
| | - Muyao Yu
- State Key Laboratory of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Ling Li
- Frontiers Science Center for Transformative Molecules, Joint International Research Laboratory of Metabolic and Developmental Sciences, Plant Biotechnology Research Center, Fudan-SJTU-Nottingham Plant Biotechnology R&D Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Luqi Huang
- State Key Laboratory of Dao-di Herbs, National Resource Center for Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Kexuan Tang
- Frontiers Science Center for Transformative Molecules, Joint International Research Laboratory of Metabolic and Developmental Sciences, Plant Biotechnology Research Center, Fudan-SJTU-Nottingham Plant Biotechnology R&D Center, School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai 200240, China; Key Laboratory of Eco-environments in Three Gorges Reservoir Region (Ministry of Education), SWU-TAAHC Medicinal Plant Joint R&D Centre,School of Life Sciences, Southwest University, Chongqing 400715, China.
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14
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Yoosefzadeh Najafabadi M, Hesami M, Eskandari M. Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs. Genes (Basel) 2023; 14:genes14040777. [PMID: 37107535 PMCID: PMC10137951 DOI: 10.3390/genes14040777] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/11/2023] [Accepted: 03/21/2023] [Indexed: 04/29/2023] Open
Abstract
In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvement and develop new varieties with higher yield performance and greater resilience to climate changes, pests, and diseases. With the use of these new advanced technologies, large amounts of data have been generated on the genetic architecture of plants, which can be exploited for manipulating the key characteristics of plants that are important for crop improvement. Therefore, plant breeders have relied on high-performance computing, bioinformatics tools, and artificial intelligence (AI), such as machine-learning (ML) methods, to efficiently analyze this vast amount of complex data. The use of bigdata coupled with ML in plant breeding has the potential to revolutionize the field and increase food security. In this review, some of the challenges of this method along with some of the opportunities it can create will be discussed. In particular, we provide information about the basis of bigdata, AI, ML, and their related sub-groups. In addition, the bases and functions of some learning algorithms that are commonly used in plant breeding, three common data integration strategies for the better integration of different breeding datasets using appropriate learning algorithms, and future prospects for the application of novel algorithms in plant breeding will be discussed. The use of ML algorithms in plant breeding will equip breeders with efficient and effective tools to accelerate the development of new plant varieties and improve the efficiency of the breeding process, which are important for tackling some of the challenges facing agriculture in the era of climate change.
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Affiliation(s)
| | - Mohsen Hesami
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON N1G 2W1, Canada
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15
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Fan R, Zhu C, Qiu D, Mao G, Mueller-Roeber B, Zeng J. Integrated transcriptomic and metabolomic analyses reveal key genes controlling flavonoid biosynthesis in Citrus grandis 'Tomentosa' fruits. PLANT PHYSIOLOGY AND BIOCHEMISTRY : PPB 2023; 196:210-221. [PMID: 36724705 DOI: 10.1016/j.plaphy.2023.01.050] [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: 10/12/2022] [Revised: 12/01/2022] [Accepted: 01/25/2023] [Indexed: 06/18/2023]
Abstract
As a well-recognized traditional Chinese medicine (TCM), immature fruits of Citrus grandis 'Tomentosa' (CGT) serve to cure chronic cough in humans. Specialized metabolites including flavonoids may have contribute to this curing effect. Knowledge about the molecular mechanisms underlying flavonoid biosynthesis in 'Tomentosa' fruits will, therefore, support the breeding of varieties with improved medicinal properties. Hence, we profiled the transcriptomes and metabolites of the fruits of two contrasting C. grandis varieties, namely 'Zheng-Mao' ('ZM') used in TCM production, and a locally cultivated pomelo, namely 'Guang-Qing' ('GQ'), at four developmental stages. A total of 39 flavonoids, including 14 flavanone/flavone, 5 isoflavonoids, 12 flavonols, and 6 anthocyanins, were identified, and 16 of which were quantitatively determined in the fruits of the two varieties. We found that 'ZM' fruits contain more flavonoids than 'GQ'. Specifically, rhoifolin levels were significantly higher in 'ZM' than in 'GQ'. We annotated 31,510 genes, including 1,387 previously unknown ones, via transcriptome sequencing of 'ZM' and 'GQ.' A total of 646 genes were found to be differentially expressed between 'ZM' and 'GQ' throughout at all four fruit developmental stages, indicating that they are robust expression markers for future breeding programs. Weighted gene co-expression network analysis identified 18 modules. Combined transcriptional and metabolic analysis revealed 25 genes related to flavonoid biosynthesis and 16 transcriptional regulators (MYBs, bHLHs, WD40) that may be involved in the flavonoids biosynthesis in C. grandis 'Tomentosa' fruits.
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Affiliation(s)
- Ruiyi Fan
- Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences, Key Laboratory of South Subtropical Fruit Biology and Genetic Resource Utilization, Ministry of Agriculture and Rural Affairs, Guangdong Provincial Key Laboratory of Tropical and Subtropical Fruit Tree Research, Guangzhou, 510640, China.
| | - Congyi Zhu
- Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences, Key Laboratory of South Subtropical Fruit Biology and Genetic Resource Utilization, Ministry of Agriculture and Rural Affairs, Guangdong Provincial Key Laboratory of Tropical and Subtropical Fruit Tree Research, Guangzhou, 510640, China.
| | - Diyang Qiu
- Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences, Key Laboratory of South Subtropical Fruit Biology and Genetic Resource Utilization, Ministry of Agriculture and Rural Affairs, Guangdong Provincial Key Laboratory of Tropical and Subtropical Fruit Tree Research, Guangzhou, 510640, China.
| | - Genlin Mao
- Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences, Key Laboratory of South Subtropical Fruit Biology and Genetic Resource Utilization, Ministry of Agriculture and Rural Affairs, Guangdong Provincial Key Laboratory of Tropical and Subtropical Fruit Tree Research, Guangzhou, 510640, China.
| | - Bernd Mueller-Roeber
- Institute of Biochemistry and Biology, University of Potsdam, 14476, Potsdam, Golm, Germany; Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Golm, Germany; Center of Plant Systems Biology and Biotechnology (CPSBB), 139 Ruski Blvd., 4000, Plovdiv, Bulgaria.
| | - Jiwu Zeng
- Institute of Fruit Tree Research, Guangdong Academy of Agricultural Sciences, Key Laboratory of South Subtropical Fruit Biology and Genetic Resource Utilization, Ministry of Agriculture and Rural Affairs, Guangdong Provincial Key Laboratory of Tropical and Subtropical Fruit Tree Research, Guangzhou, 510640, China.
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16
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Chen Y, Guo Y, Guan P, Wang Y, Wang X, Wang Z, Qin Z, Ma S, Xin M, Hu Z, Yao Y, Ni Z, Sun Q, Guo W, Peng H. A wheat integrative regulatory network from large-scale complementary functional datasets enables trait-associated gene discovery for crop improvement. MOLECULAR PLANT 2023; 16:393-414. [PMID: 36575796 DOI: 10.1016/j.molp.2022.12.019] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 11/28/2022] [Accepted: 12/18/2022] [Indexed: 06/17/2023]
Abstract
Gene regulation is central to all aspects of organism growth, and understanding it using large-scale functional datasets can provide a whole view of biological processes controlling complex phenotypic traits in crops. However, the connection between massive functional datasets and trait-associated gene discovery for crop improvement is still lacking. In this study, we constructed a wheat integrative gene regulatory network (wGRN) by combining an updated genome annotation and diverse complementary functional datasets, including gene expression, sequence motif, transcription factor (TF) binding, chromatin accessibility, and evolutionarily conserved regulation. wGRN contains 7.2 million genome-wide interactions covering 5947 TFs and 127 439 target genes, which were further verified using known regulatory relationships, condition-specific expression, gene functional information, and experiments. We used wGRN to assign genome-wide genes to 3891 specific biological pathways and accurately prioritize candidate genes associated with complex phenotypic traits in genome-wide association studies. In addition, wGRN was used to enhance the interpretation of a spike temporal transcriptome dataset to construct high-resolution networks. We further unveiled novel regulators that enhance the power of spike phenotypic trait prediction using machine learning and contribute to the spike phenotypic differences among modern wheat accessions. Finally, we developed an interactive webserver, wGRN (http://wheat.cau.edu.cn/wGRN), for the community to explore gene regulation and discover trait-associated genes. Collectively, this community resource establishes the foundation for using large-scale functional datasets to guide trait-associated gene discovery for crop improvement.
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Affiliation(s)
- Yongming Chen
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Yiwen Guo
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Panfeng Guan
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Yongfa Wang
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Xiaobo Wang
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Zihao Wang
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Zhen Qin
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Shengwei Ma
- Hainan Yazhou Bay Seed Laboratory, Sanya, Hainan, China; State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China
| | - Mingming Xin
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Zhaorong Hu
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Yingyin Yao
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Zhongfu Ni
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Qixin Sun
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China
| | - Weilong Guo
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China.
| | - Huiru Peng
- Frontiers Science Center for Molecular Design Breeding, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China.
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17
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Han L, Zhong W, Qian J, Jin M, Tian P, Zhu W, Zhang H, Sun Y, Feng JW, Liu X, Chen G, Farid B, Li R, Xiong Z, Tian Z, Li J, Luo Z, Du D, Chen S, Jin Q, Li J, Li Z, Liang Y, Jin X, Peng Y, Zheng C, Ye X, Yin Y, Chen H, Li W, Chen LL, Li Q, Yan J, Yang F, Li L. A multi-omics integrative network map of maize. Nat Genet 2023; 55:144-153. [PMID: 36581701 DOI: 10.1038/s41588-022-01262-1] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 11/03/2022] [Indexed: 12/31/2022]
Abstract
Networks are powerful tools to uncover functional roles of genes in phenotypic variation at a system-wide scale. Here, we constructed a maize network map that contains the genomic, transcriptomic, translatomic and proteomic networks across maize development. This map comprises over 2.8 million edges in more than 1,400 functional subnetworks, demonstrating an extensive network divergence of duplicated genes. We applied this map to identify factors regulating flowering time and identified 2,651 genes enriched in eight subnetworks. We validated the functions of 20 genes, including 18 with previously unknown connections to flowering time in maize. Furthermore, we uncovered a flowering pathway involving histone modification. The multi-omics integrative network map illustrates the principles of how molecular networks connect different types of genes and potential pathways to map a genome-wide functional landscape in maize, which should be applicable in a wide range of species.
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Affiliation(s)
- Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Wanshun Zhong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Jia Qian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Minliang Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Peng Tian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Hongwei Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Yonghao Sun
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Jia-Wu Feng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xiangguo Liu
- Institute of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences, Changchun, China
| | - Guo Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Institute of Nuclear and Biological Technology, Xinjiang Academy of Agricultural Sciences, Urumqi, China
| | - Babar Farid
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Institute of Plant Breeding and Biotechnology, Muhammad Nawaz Shareef University of Agriculture Multan, Multan, Pakistan
| | - Ruonan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Zimo Xiong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Zhihui Tian
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Juan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Zi Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Dengxiang Du
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Sijia Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Qixiao Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Jiaxin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Zhao Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Yan Liang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xiaomeng Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Yong Peng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Chang Zheng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xinnan Ye
- Institute of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences, Changchun, China
| | - Yuejia Yin
- Institute of Agricultural Biotechnology, Jilin Academy of Agricultural Sciences, Changchun, China
| | - Hong Chen
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Weifu Li
- College of Science, Huazhong Agricultural University, Wuhan, China
| | - Ling-Ling Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Qing Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.,Hubei Hongshan Laboratory, Wuhan, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China. .,Hubei Hongshan Laboratory, Wuhan, China.
| | - Fang Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China. .,Hubei Hongshan Laboratory, Wuhan, China.
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China. .,Hubei Hongshan Laboratory, Wuhan, China.
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18
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Wong A, Bi C, Chi W, Hu N, Gehring C. Amino acid motifs for the identification of novel protein interactants. Comput Struct Biotechnol J 2022; 21:326-334. [PMID: 36582434 PMCID: PMC9791077 DOI: 10.1016/j.csbj.2022.12.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/06/2022] [Accepted: 12/06/2022] [Indexed: 12/14/2022] Open
Abstract
Biological systems consist of multiple components of different physical and chemical properties that require complex and dynamic regulatory loops to function efficiently. The discovery of ever more novel interacting sites in complex proteins suggests that we are only beginning to understand how cellular and biological functions are integrated and tuned at the molecular and systems levels. Here we review recently discovered interacting sites which have been identified through rationally designed amino acid motifs diagnostic for specific molecular functions, including enzymatic activities and ligand-binding properties. We specifically discuss the nature of the latter using as examples, novel hormone recognition and gas sensing sites that occur in moonlighting protein complexes. Drawing evidence from the current literature, we discuss the potential implications at the cellular, tissue, and/or organismal levels of such non-catalytic interacting sites and provide several promising avenues for the expansion of amino acid motif searches to discover hitherto unknown protein interactants and interaction networks. We believe this knowledge will unearth unexpected functions in both new and well-characterized proteins, thus filling existing conceptual gaps or opening new avenues for applications either as drug targets or tools in pharmacology, cell biology and bio-catalysis. Beyond this, motif searches may also support the design of novel, effective and sustainable approaches to crop improvements and the development of new therapeutics.
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Affiliation(s)
- Aloysius Wong
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
- Wenzhou Municipal Key Lab for Applied Biomedical and Biopharmaceutical Informatics, Ouhai, Wenzhou, Zhejiang Province 325060, China
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Chuyun Bi
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
- Wenzhou Municipal Key Lab for Applied Biomedical and Biopharmaceutical Informatics, Ouhai, Wenzhou, Zhejiang Province 325060, China
- Zhejiang Bioinformatics International Science and Technology Cooperation Center, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Wei Chi
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Ningxin Hu
- Department of Biology, College of Science and Technology, Wenzhou-Kean University, 88 Daxue Road, Ouhai, Wenzhou, Zhejiang Province 325060, China
| | - Chris Gehring
- Department of Chemistry, Biology & Biotechnology, University of Perugia, Perugia 06121, Italy
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Xu Y, Zhang X, Li H, Zheng H, Zhang J, Olsen MS, Varshney RK, Prasanna BM, Qian Q. Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. MOLECULAR PLANT 2022; 15:1664-1695. [PMID: 36081348 DOI: 10.1016/j.molp.2022.09.001] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 08/20/2022] [Accepted: 09/02/2022] [Indexed: 05/12/2023]
Abstract
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.
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Affiliation(s)
- Yunbi Xu
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; CIMMYT-China Tropical Maize Research Center, School of Food Science and Engineering, Foshan University, Foshan, Guangdong 528231, China; Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China.
| | - Xingping Zhang
- Peking University Institute of Advanced Agricultural Sciences, Weifang, Shandong 261325, China
| | - Huihui Li
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China; National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, Hainan 572024, China
| | - Hongjian Zheng
- CIMMYT-China Specialty Maize Research Center, Shanghai Academy of Agricultural Sciences, Shanghai 201400, China
| | - Jianan Zhang
- MolBreeding Biotechnology Co., Ltd., Shijiazhuang, Hebei 050035, China
| | - Michael S Olsen
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Australia
| | - Boddupalli M Prasanna
- CIMMYT (International Maize and Wheat Improvement Center), ICRAF Campus, United Nations Avenue, Nairobi, Kenya
| | - Qian Qian
- Institute of Crop Sciences, CIMMYT-China, Chinese Academy of Agricultural Sciences, Beijing 100081, China
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20
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Yan M, Nie H, Wang Y, Wang X, Jarret R, Zhao J, Wang H, Yang J. Exploring and exploiting genetics and genomics for sweetpotato improvement: Status and perspectives. PLANT COMMUNICATIONS 2022; 3:100332. [PMID: 35643086 PMCID: PMC9482988 DOI: 10.1016/j.xplc.2022.100332] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 04/17/2022] [Accepted: 05/02/2022] [Indexed: 05/14/2023]
Abstract
Sweetpotato (Ipomoea batatas (L.) Lam.) is one of the most important root crops cultivated worldwide. Because of its adaptability, high yield potential, and nutritional value, sweetpotato has become an important food crop, particularly in developing countries. To ensure adequate crop yields to meet increasing demand, it is essential to enhance the tolerance of sweetpotato to environmental stresses and other yield-limiting factors. The highly heterozygous hexaploid genome of I. batatas complicates genetic studies and limits improvement of sweetpotato through traditional breeding. However, application of next-generation sequencing and high-throughput genotyping and phenotyping technologies to sweetpotato genetics and genomics research has provided new tools and resources for crop improvement. In this review, we discuss the genomics resources that are available for sweetpotato, including the current reference genome, databases, and available bioinformatics tools. We systematically review the current state of knowledge on the polyploid genetics of sweetpotato, including studies of its origin and germplasm diversity and the associated mapping of important agricultural traits. We then outline the conventional and molecular breeding approaches that have been applied to sweetpotato. Finally, we discuss future goals for genetic studies of sweetpotato and crop improvement via breeding in combination with state-of-the-art multi-omics approaches such as genomic selection and gene editing. These approaches will advance and accelerate genetic improvement of this important root crop and facilitate its sustainable global production.
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Affiliation(s)
- Mengxiao Yan
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China
| | - Haozhen Nie
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China
| | - Yunze Wang
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China; College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Xinyi Wang
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China; College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | | | - Jiamin Zhao
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China; College of Life Sciences, Shanghai Normal University, Shanghai 200234, China
| | - Hongxia Wang
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China; National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China.
| | - Jun Yang
- Shanghai Key Laboratory of Plant Functional Genomics and Resources, Shanghai Chenshan Botanical Garden, Shanghai 201602, China; National Key Laboratory of Plant Molecular Genetics, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200032, China.
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21
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Gui S, Wei W, Jiang C, Luo J, Chen L, Wu S, Li W, Wang Y, Li S, Yang N, Li Q, Fernie AR, Yan J. A pan-Zea genome map for enhancing maize improvement. Genome Biol 2022; 23:178. [PMID: 35999561 PMCID: PMC9396798 DOI: 10.1186/s13059-022-02742-7] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 07/27/2022] [Indexed: 12/22/2022] Open
Abstract
Background Maize (Zea mays L.) is at the vanguard facing the upcoming breeding challenges. However, both a super pan-genome for the Zea genus and a comprehensive genetic variation map for maize breeding are still lacking. Results Here, we construct an approximately 6.71-Gb pan-Zea genome that contains around 4.57-Gb non-B73 reference sequences from fragmented de novo assemblies of 721 pan-Zea individuals. We annotate a total of 58,944 pan-Zea genes and find around 44.34% of them are dispensable in the pan-Zea population. Moreover, 255,821 common structural variations are identified and genotyped in a maize association mapping panel. Further analyses reveal gene presence/absence variants and their potential roles during domestication of maize. Combining genetic analyses with multi-omics data, we demonstrate how structural variants are associated with complex agronomic traits. Conclusions Our results highlight the underexplored role of the pan-Zea genome and structural variations to further understand domestication of maize and explore their potential utilization in crop improvement. Supplementary Information The online version contains supplementary material available at 10.1186/s13059-022-02742-7.
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Affiliation(s)
- Songtao Gui
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenjie Wei
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Chenglin Jiang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jingyun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Lu Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shenshen Wu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Wenqiang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Yuebin Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Shuyan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ning Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.,Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Qing Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.,Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Alisdair R Fernie
- Department of Molecular Physiology, Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam, Golm, Germany
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China. .,Hubei Hongshan Laboratory, Wuhan, 430070, China.
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22
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Kumar J, Kumar A, Sen Gupta D, Kumar S, DePauw RM. Reverse genetic approaches for breeding nutrient-rich and climate-resilient cereal and food legume crops. Heredity (Edinb) 2022; 128:473-496. [PMID: 35249099 PMCID: PMC9178024 DOI: 10.1038/s41437-022-00513-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 02/10/2022] [Accepted: 02/10/2022] [Indexed: 12/21/2022] Open
Abstract
In the last decade, advancements in genomics tools and techniques have led to the discovery of many genes. Most of these genes still need to be characterized for their associated function and therefore, such genes remain underutilized for breeding the next generation of improved crop varieties. The recent developments in different reverse genetic approaches have made it possible to identify the function of genes controlling nutritional, biochemical, and metabolic traits imparting drought, heat, cold, salinity tolerance as well as diseases and insect-pests. This article focuses on reviewing the current status and prospects of using reverse genetic approaches to breed nutrient-rich and climate resilient cereal and food legume crops.
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Affiliation(s)
- Jitendra Kumar
- Division of Crop Improvement, ICAR-Indian Institute of Pulses Research, Kanpur, India.
| | - Ajay Kumar
- Department of Plant Sciences, North Dakota State University, Fargo, ND, 58108, USA
| | - Debjyoti Sen Gupta
- Division of Crop Improvement, ICAR-Indian Institute of Pulses Research, Kanpur, India
| | - Sachin Kumar
- Department of Genetics and Plant Breeding, Chaudhary Charan Singh University, Meerut, 250 004, India
| | - Ron M DePauw
- Advancing Wheat Technologies, 118 Strathcona Rd SW, Calgary, AB, T3H 1P3, Canada
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23
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Ma Y, Li D, Xu Z, Gu R, Wang P, Fu J, Wang J, Du W, Zhang H. Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize. Int J Mol Sci 2022; 23:5074. [PMID: 35563470 PMCID: PMC9102962 DOI: 10.3390/ijms23095074] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/22/2022] [Accepted: 04/25/2022] [Indexed: 12/31/2022] Open
Abstract
Dissecting the genetic basis of yield traits in hybrid populations and identifying the candidate genes are important for molecular crop breeding. In this study, a BC1F3:4 population, the line per se (LPS) population, was constructed by using elite inbred lines Zheng58 and PH4CV as the parental lines. The population was genotyped with 55,000 SNPs and testcrossed to Chang7-2 and PH6WC (two testers) to construct two testcross (TC) populations. The three populations were evaluated for hundred kernel weight (HKW) and yield per plant (YPP) in multiple environments. Marker-trait association analysis (MTA) identified 24 to 151 significant SNPs in the three populations. Comparison of the significant SNPs identified common and specific quantitative trait locus/loci (QTL) in the LPS and TC populations. Genetic feature analysis of these significant SNPs proved that these SNPs were associated with the tested traits and could be used to predict trait performance of both LPS and TC populations. RNA-seq analysis was performed using maize hybrid varieties and their parental lines, and differentially expressed genes (DEGs) between hybrid varieties and parental lines were identified. Comparison of the chromosome positions of DEGs with those of significant SNPs detected in the TC population identified potential candidate genes that might be related to hybrid performance. Combining RNA-seq analysis and MTA results identified candidate genes for hybrid performance, providing information that could be useful for maize hybrid breeding.
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Affiliation(s)
- Yuting Ma
- Agronomy College, Shenyang Agricultural University, Shenyang 110866, China;
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
| | - Dongdong Li
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
| | - Zhenxiang Xu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Riliang Gu
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Pingxi Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
| | - Junjie Fu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
| | - Jianhua Wang
- Center for Seed Science and Technology, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, China; (Z.X.); (R.G.); (J.W.)
| | - Wanli Du
- Agronomy College, Shenyang Agricultural University, Shenyang 110866, China;
| | - Hongwei Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China; (D.L.); (P.W.); (J.F.)
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24
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Wang C, Han B. Twenty years of rice genomics research: From sequencing and functional genomics to quantitative genomics. MOLECULAR PLANT 2022; 15:593-619. [PMID: 35331914 DOI: 10.1016/j.molp.2022.03.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/04/2022] [Accepted: 03/18/2022] [Indexed: 06/14/2023]
Abstract
Since the completion of the rice genome sequencing project in 2005, we have entered the era of rice genomics, which is still in its ascendancy. Rice genomics studies can be classified into three stages: structural genomics, functional genomics, and quantitative genomics. Structural genomics refers primarily to genome sequencing for the construction of a complete map of rice genome sequence. This is fundamental for rice genetics and molecular biology research. Functional genomics aims to decode the functions of rice genes. Quantitative genomics is large-scale sequence- and statistics-based research to define the quantitative traits and genetic features of rice populations. Rice genomics has been a transformative influence on rice biological research and contributes significantly to rice breeding, making rice a good model plant for studying crop sciences.
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Affiliation(s)
- Changsheng Wang
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200233, China.
| | - Bin Han
- National Center for Gene Research, State Key Laboratory of Plant Molecular Genetics, Center for Excellence in Molecular Plant Sciences, Institute of Plant Physiology and Ecology, Chinese Academy of Sciences, Shanghai 200233, China.
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25
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Regulation of Calvin-Benson cycle enzymes under high temperature stress. ABIOTECH 2022; 3:65-77. [PMID: 36311539 PMCID: PMC9590453 DOI: 10.1007/s42994-022-00068-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Accepted: 01/01/2022] [Indexed: 10/19/2022]
Abstract
The Calvin-Benson cycle (CBC) consists of three critical processes, including fixation of CO2 by Rubisco, reduction of 3-phosphoglycerate (3PGA) to triose phosphate (triose-P) with NADPH and ATP generated by the light reactions, and regeneration of ribulose 1,5-bisphosphate (RuBP) from triose-P. The activities of photosynthesis-related proteins, mainly from the CBC, were found more significantly affected and regulated in plants challenged with high temperature stress, including Rubisco, Rubisco activase (RCA) and the enzymes involved in RuBP regeneration, such as sedoheptulose-1,7-bisphosphatase (SBPase). Over the past years, the regulatory mechanism of CBC, especially for redox-regulation, has attracted major interest, because balancing flux at the various enzymatic reactions and maintaining metabolite levels in a range are of critical importance for the optimal operation of CBC under high temperature stress, providing insights into the genetic manipulation of photosynthesis. Here, we summarize recent progress regarding the identification of various layers of regulation point to the key enzymes of CBC for acclimation to environmental temperature changes along with open questions are also discussed.
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26
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Wu L, Zhang M, Zhang R, Yu H, Wang H, Li J, Wang Y, Hu Z, Wang Y, Luo Z, Li L, Wang L, Peng L, Xia T. Down-regulation of OsMYB103L distinctively alters beta-1,4-glucan polymerization and cellulose microfibers assembly for enhanced biomass enzymatic saccharification in rice. BIOTECHNOLOGY FOR BIOFUELS 2021; 14:245. [PMID: 34961560 PMCID: PMC8713402 DOI: 10.1186/s13068-021-02093-8] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Accepted: 12/13/2021] [Indexed: 05/12/2023]
Abstract
BACKGROUND As a major component of plant cell walls, cellulose provides the most abundant biomass resource convertible for biofuels. Since cellulose crystallinity and polymerization have been characterized as two major features accounting for lignocellulose recalcitrance against biomass enzymatic saccharification, genetic engineering of cellulose biosynthesis is increasingly considered as a promising solution in bioenergy crops. Although several transcription factors have been identified to regulate cellulose biosynthesis and plant cell wall formation, much remains unknown about its potential roles for genetic improvement of lignocellulose recalcitrance. RESULTS In this study, we identified a novel rice mutant (Osfc9/myb103) encoded a R2R3-MYB transcription factor, and meanwhile generated OsMYB103L-RNAi-silenced transgenic lines. We determined significantly reduced cellulose levels with other major wall polymers (hemicellulose, lignin) slightly altered in mature rice straws of the myb103 mutant and RNAi line, compared to their wild type (NPB). Notably, the rice mutant and RNAi line were of significantly reduced cellulose features (crystalline index/CrI, degree of polymerization/DP) and distinct cellulose nanofibers assembly. These alterations consequently improved lignocellulose recalcitrance for significantly enhanced biomass enzymatic saccharification by 10-28% at p < 0.01 levels (n = 3) after liquid hot water and chemical (1% H2SO4, 1% NaOH) pretreatments with mature rice straws. In addition, integrated RNA sequencing with DNA affinity purification sequencing (DAP-seq) analyses revealed that the OsMYB103L might specifically mediate cellulose biosynthesis and deposition by regulating OsCesAs and other genes associated with microfibril assembly. CONCLUSIONS This study has demonstrated that down-regulation of OsMYB103L could specifically improve cellulose features and cellulose nanofibers assembly to significantly enhance biomass enzymatic saccharification under green-like and mild chemical pretreatments in rice. It has not only indicated a powerful strategy for genetic modification of plant cell walls in bioenergy crops, but also provided insights into transcriptional regulation of cellulose biosynthesis in plants.
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Affiliation(s)
- Leiming Wu
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Laboratory of Biomass Engineering & Nanomaterial Application in Automobiles, College of Food Science & Chemical Engineering, Hubei University of Arts & Science, Xiangyang, China
- College of Life Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Mingliang Zhang
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
| | - Ran Zhang
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- Laboratory of Biomass Engineering & Nanomaterial Application in Automobiles, College of Food Science & Chemical Engineering, Hubei University of Arts & Science, Xiangyang, China
| | - Haizhong Yu
- Laboratory of Biomass Engineering & Nanomaterial Application in Automobiles, College of Food Science & Chemical Engineering, Hubei University of Arts & Science, Xiangyang, China
| | - Hailang Wang
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- Laboratory of Biomass Engineering & Nanomaterial Application in Automobiles, College of Food Science & Chemical Engineering, Hubei University of Arts & Science, Xiangyang, China
| | - Jingyang Li
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- Haikou Experimental Station, Chinese Academy of Tropical Agricultural Sciences, Haikou, 570102, China
| | - Youmei Wang
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- Laboratory of Biomass Engineering & Nanomaterial Application in Automobiles, College of Food Science & Chemical Engineering, Hubei University of Arts & Science, Xiangyang, China
| | - Zhen Hu
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- Laboratory of Biomass Engineering & Nanomaterial Application in Automobiles, College of Food Science & Chemical Engineering, Hubei University of Arts & Science, Xiangyang, China
| | - Yanting Wang
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- Laboratory of Biomass Engineering & Nanomaterial Application in Automobiles, College of Food Science & Chemical Engineering, Hubei University of Arts & Science, Xiangyang, China
| | - Zi Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Lingqiang Wang
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- State Key Laboratory for Conservation & Utilization of Subtropical Agro-Bioresources, College of Agriculture, Guangxi University, Nanning, China
| | - Liangcai Peng
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China
- Laboratory of Biomass Engineering & Nanomaterial Application in Automobiles, College of Food Science & Chemical Engineering, Hubei University of Arts & Science, Xiangyang, China
| | - Tao Xia
- Biomass & Bioenergy Research Centre, College of Plant Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China.
- College of Life Science & Technology, Huazhong Agricultural University, Wuhan, 430070, China.
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27
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Francisco FR, Aono AH, da Silva CC, Gonçalves PS, Scaloppi Junior EJ, Le Guen V, Fritsche-Neto R, Souza LM, de Souza AP. Unravelling Rubber Tree Growth by Integrating GWAS and Biological Network-Based Approaches. FRONTIERS IN PLANT SCIENCE 2021; 12:768589. [PMID: 34992619 PMCID: PMC8724537 DOI: 10.3389/fpls.2021.768589] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 11/02/2021] [Indexed: 06/08/2023]
Abstract
Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.
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Affiliation(s)
- Felipe Roberto Francisco
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | - Alexandre Hild Aono
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | - Carla Cristina da Silva
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
| | - Paulo S. Gonçalves
- Center of Rubber Tree and Agroforestry Systems, Agronomic Institute (IAC), Votuporanga, Brazil
| | | | - Vincent Le Guen
- Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), UMR AGAP, Montpellier, France
- AGAP, Univ Montpellier, CIRAD, INRAE, Institut Agro, Montpellier, France
| | - Roberto Fritsche-Neto
- Department of Genetics, Luiz de Queiroz College of Agriculture (ESALQ), University of São Paulo (USP), Piracicaba, Brazil
| | - Livia Moura Souza
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
- São Francisco University (USF), Itatiba, Brazil
| | - Anete Pereira de Souza
- Molecular Biology and Genetic Engineering Center (CBMEG), University of Campinas (UNICAMP), Campinas, Brazil
- Department of Plant Biology, Biology Institute, University of Campinas (UNICAMP), Campinas, Brazil
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Rice functional genomics: decades' efforts and roads ahead. SCIENCE CHINA. LIFE SCIENCES 2021; 65:33-92. [PMID: 34881420 DOI: 10.1007/s11427-021-2024-0] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 11/01/2021] [Indexed: 12/16/2022]
Abstract
Rice (Oryza sativa L.) is one of the most important crops in the world. Since the completion of rice reference genome sequences, tremendous progress has been achieved in understanding the molecular mechanisms on various rice traits and dissecting the underlying regulatory networks. In this review, we summarize the research progress of rice biology over past decades, including omics, genome-wide association study, phytohormone action, nutrient use, biotic and abiotic responses, photoperiodic flowering, and reproductive development (fertility and sterility). For the roads ahead, cutting-edge technologies such as new genomics methods, high-throughput phenotyping platforms, precise genome-editing tools, environmental microbiome optimization, and synthetic methods will further extend our understanding of unsolved molecular biology questions in rice, and facilitate integrations of the knowledge for agricultural applications.
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Metabolomics for Crop Breeding: General Considerations. Genes (Basel) 2021; 12:genes12101602. [PMID: 34680996 PMCID: PMC8535592 DOI: 10.3390/genes12101602] [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: 08/13/2021] [Revised: 10/05/2021] [Accepted: 10/08/2021] [Indexed: 12/16/2022] Open
Abstract
The development of new, more productive varieties of agricultural crops is becoming an increasingly difficult task. Modern approaches for the identification of beneficial alleles and their use in elite cultivars, such as quantitative trait loci (QTL) mapping and marker-assisted selection (MAS), are effective but insufficient for keeping pace with the improvement of wheat or other crops. Metabolomics is a powerful but underutilized approach that can assist crop breeding. In this review, basic methodological information is summarized, and the current strategies of applications of metabolomics related to crop breeding are explored using recent examples. We briefly describe classes of plant metabolites, cellular localization of metabolic pathways, and the strengths and weaknesses of the main metabolomics technique. Among the commercialized genetically modified crops, about 50 with altered metabolic enzyme activities have been identified in the International Service for the Acquisition of Agri-biotech Applications (ISAAA) database. These plants are reviewed as encouraging examples of the application of knowledge of biochemical pathways. Based on the recent examples of metabolomic studies, we discuss the performance of metabolic markers, the integration of metabolic and genomic data in metabolic QTLs (mQTLs) and metabolic genome-wide association studies (mGWAS). The elucidation of metabolic pathways and involved genes will help in crop breeding and the introgression of alleles of wild relatives in a more targeted manner.
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Zhang H, Lu Y, Ma Y, Fu J, Wang G. Genetic and molecular control of grain yield in maize. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:18. [PMID: 37309425 PMCID: PMC10236077 DOI: 10.1007/s11032-021-01214-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 02/07/2021] [Indexed: 06/14/2023]
Abstract
Understanding the genetic and molecular basis of grain yield is important for maize improvement. Here, we identified 49 consensus quantitative trait loci (cQTL) controlling maize yield-related traits using QTL meta-analysis. Then, we collected yield-related traits associated SNPs detected by association mapping and identified 17 consensus significant loci. Comparing the physical positions of cQTL with those of significant SNPs revealed that 47 significant SNPs were located within 20 cQTL regions. Furthermore, intensive reviews of 31 genes regulating maize yield-related traits found that the functions of many genes were conservative in maize and other plant species. The functional conservation indicated that some of the 575 maize genes (orthologous to 247 genes controlling yield or seed traits in other plant species) might be functionally related to maize yield-related traits, especially the 49 maize orthologous genes in cQTL regions, and 41 orthologous genes close to the physical positions of significant SNPs. In the end, we prospected on the integration of the public sources for exploring the genetic and molecular mechanisms of maize yield-related traits, and on the utilization of genetic and molecular mechanisms for maize improvement. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01214-3.
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Affiliation(s)
- Hongwei Zhang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
| | - Yantian Lu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
| | - Yuting Ma
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
| | - Junjie Fu
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
| | - Guoying Wang
- National Key Facility for Crop Gene Resources and Genetic Improvement, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 The People’s Republic of China
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