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Wang Y, Luo Y, Guo X, Li Y, Yan J, Shao W, Wei W, Wei X, Yang T, Chen J, Chen L, Ding Q, Bai M, Zhuo L, Li L, Jackson D, Zhang Z, Xu X, Yan J, Liu H, Liu L, Yang N. A spatial transcriptome map of the developing maize ear. Nat Plants 2024:10.1038/s41477-024-01683-2. [PMID: 38745100 DOI: 10.1038/s41477-024-01683-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 04/03/2024] [Indexed: 05/16/2024]
Abstract
A comprehensive understanding of inflorescence development is crucial for crop genetic improvement, as inflorescence meristems give rise to reproductive organs and determine grain yield. However, dissecting inflorescence development at the cellular level has been challenging owing to a lack of specific marker genes to distinguish among cell types, particularly in different types of meristems that are vital for organ formation. In this study, we used spatial enhanced resolution omics-sequencing (Stereo-seq) to construct a precise spatial transcriptome map of the developing maize ear primordium, identifying 12 cell types, including 4 newly defined cell types found mainly in the inflorescence meristem. By extracting the meristem components for detailed clustering, we identified three subtypes of meristem and validated two MADS-box genes that were specifically expressed at the apex of determinate meristems and involved in stem cell determinacy. Furthermore, by integrating single-cell RNA transcriptomes, we identified a series of spatially specific networks and hub genes that may provide new insights into the formation of different tissues. In summary, this study provides a valuable resource for research on cereal inflorescence development, offering new clues for yield improvement.
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Affiliation(s)
- Yuebin Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Yun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xing Guo
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- BGI Research, Wuhan, China
| | - Yunfu Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Jiali Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Wenwen Shao
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- BGI Research, Wuhan, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Wenjie Wei
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Xiaofeng Wei
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- China National GeneBank, Shenzhen, China
| | - Tao Yang
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- China National GeneBank, Shenzhen, China
| | - Jing Chen
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
- China National GeneBank, Shenzhen, China
| | - Lihua Chen
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
| | - Qian Ding
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Minji Bai
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Lin Zhuo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Li Li
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
| | - David Jackson
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Zuxin Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Xun Xu
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hubei Hongshan Laboratory, Wuhan, China
| | - Huan Liu
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen, China.
- Guangdong Laboratory of Lingnan Modern Agriculture, Agricultural Genomics Institute at Shenzhen, Shenzhen, China.
| | - Lei Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
| | - Ning Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China.
- Hubei Hongshan Laboratory, Wuhan, China.
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2
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Mishra S, Srivastava AK, Khan AW, Tran LSP, Nguyen HT. The era of panomics-driven gene discovery in plants. Trends Plant Sci 2024:S1360-1385(24)00063-3. [PMID: 38658292 DOI: 10.1016/j.tplants.2024.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 03/01/2024] [Accepted: 03/08/2024] [Indexed: 04/26/2024]
Abstract
Panomics is an approach to integrate multiple 'omics' datasets, generated using different individuals or natural variations. Considering their diverse phenotypic spectrum, the phenome is inherently associated with panomics-based science, which is further combined with genomics, transcriptomics, metabolomics, and other omics techniques, either independently or collectively. Panomics has been accelerated through recent technological advancements in the field of genomics that enable the detection of population-wide structural variations (SVs) and hence offer unprecedented insights into the genetic variations contributing to important agronomic traits. The present review provides the recent trends of panomics-driven gene discovery toward various traits related to plant development, stress tolerance, accumulation of specialized metabolites, and domestication/dedomestication. In addition, the success stories are highlighted in the broader context of enhancing crop productivity.
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Affiliation(s)
- Shefali Mishra
- Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Centre, Mumbai, Maharashtra 400085, India
| | - Ashish Kumar Srivastava
- Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Centre, Mumbai, Maharashtra 400085, India; Homi Bhabha National Institute, Mumbai 400094, India.
| | - Aamir W Khan
- Division of Plant Science and Technology and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211, USA
| | - Lam-Son Phan Tran
- Institute of Genomics for Crop Abiotic Stress Tolerance, Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, USA
| | - Henry T Nguyen
- Division of Plant Science and Technology and National Center for Soybean Biotechnology, University of Missouri-Columbia, Columbia, MO 65211, USA.
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3
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Chen K, Yang H, Wu D, Peng Y, Lian L, Bai L, Wang L. Weed biology and management in the multi-omics era: Progress and perspectives. Plant Commun 2024; 5:100816. [PMID: 38219012 PMCID: PMC11009161 DOI: 10.1016/j.xplc.2024.100816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/20/2023] [Accepted: 01/08/2024] [Indexed: 01/15/2024]
Abstract
Weeds pose a significant threat to crop production, resulting in substantial yield reduction. In addition, they possess robust weedy traits that enable them to survive in extreme environments and evade human control. In recent years, the application of multi-omics biotechnologies has helped to reveal the molecular mechanisms underlying these weedy traits. In this review, we systematically describe diverse applications of multi-omics platforms for characterizing key aspects of weed biology, including the origins of weed species, weed classification, and the underlying genetic and molecular bases of important weedy traits such as crop-weed interactions, adaptability to different environments, photoperiodic flowering responses, and herbicide resistance. In addition, we discuss limitations to the application of multi-omics techniques in weed science, particularly compared with their extensive use in model plants and crops. In this regard, we provide a forward-looking perspective on the future application of multi-omics technologies to weed science research. These powerful tools hold great promise for comprehensively and efficiently unraveling the intricate molecular genetic mechanisms that underlie weedy traits. The resulting advances will facilitate the development of sustainable and highly effective weed management strategies, promoting greener practices in agriculture.
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Affiliation(s)
- Ke Chen
- Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture and Rural Affairs, Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China; State Key Laboratory of Hybrid Rice, Hunan Academy of Agricultural Sciences, Changsha 410125, China; Longping Branch, College of Biology, Hunan University, Changsha 410125, China; Hunan Weed Science Key Laboratory, Hunan Agricultural Biotechnology Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China
| | - Haona Yang
- State Key Laboratory of Hybrid Rice, Hunan Academy of Agricultural Sciences, Changsha 410125, China; Hunan Weed Science Key Laboratory, Hunan Agricultural Biotechnology Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China
| | - Di Wu
- State Key Laboratory of Hybrid Rice, Hunan Academy of Agricultural Sciences, Changsha 410125, China
| | - Yajun Peng
- State Key Laboratory of Hybrid Rice, Hunan Academy of Agricultural Sciences, Changsha 410125, China; Hunan Weed Science Key Laboratory, Hunan Agricultural Biotechnology Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China
| | - Lei Lian
- Qingdao Kingagroot Compounds Co. Ltd, Qingdao 266000, China
| | - Lianyang Bai
- Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture and Rural Affairs, Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China; State Key Laboratory of Hybrid Rice, Hunan Academy of Agricultural Sciences, Changsha 410125, China; Longping Branch, College of Biology, Hunan University, Changsha 410125, China; Huangpu Research Institute of Longping Agricultural Science and Technology, Guangzhou 510715, China; Hunan Weed Science Key Laboratory, Hunan Agricultural Biotechnology Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China.
| | - Lifeng Wang
- Key Laboratory of Indica Rice Genetics and Breeding in the Middle and Lower Reaches of Yangtze River Valley, Ministry of Agriculture and Rural Affairs, Hunan Rice Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China; State Key Laboratory of Hybrid Rice, Hunan Academy of Agricultural Sciences, Changsha 410125, China; Longping Branch, College of Biology, Hunan University, Changsha 410125, China; Huangpu Research Institute of Longping Agricultural Science and Technology, Guangzhou 510715, China; Hunan Weed Science Key Laboratory, Hunan Agricultural Biotechnology Research Institute, Hunan Academy of Agricultural Sciences, Changsha 410125, China.
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4
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Nie Y, Wang H, Zhang G, Ding H, Han B, Liu L, Shi J, Du J, Li X, Li X, Zhao Y, Zhang X, Liu C, Weng J, Li X, Zhang X, Zhao X, Pan G, Jackson D, Li QB, Stinard PS, Arp J, Sachs MM, Moose S, Hunter CT, Wu Q, Zhang Z. The maize PLASTID TERMINAL OXIDASE (PTOX) locus controls the carotenoid content of kernels. Plant J 2024; 118:457-468. [PMID: 38198228 DOI: 10.1111/tpj.16618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 12/16/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024]
Abstract
Carotenoids perform a broad range of important functions in humans; therefore, carotenoid biofortification of maize (Zea mays L.), one of the most highly produced cereal crops worldwide, would have a global impact on human health. PLASTID TERMINAL OXIDASE (PTOX) genes play an important role in carotenoid metabolism; however, the possible function of PTOX in carotenoid biosynthesis in maize has not yet been explored. In this study, we characterized the maize PTOX locus by forward- and reverse-genetic analyses. While most higher plant species possess a single copy of the PTOX gene, maize carries two tandemly duplicated copies. Characterization of mutants revealed that disruption of either copy resulted in a carotenoid-deficient phenotype. We identified mutations in the PTOX genes as being causal of the classic maize mutant, albescent1. Remarkably, overexpression of ZmPTOX1 significantly improved the content of carotenoids, especially β-carotene (provitamin A), which was increased by ~threefold, in maize kernels. Overall, our study shows that maize PTOX locus plays an important role in carotenoid biosynthesis in maize kernels and suggests that fine-tuning the expression of this gene could improve the nutritional value of cereal grains.
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Affiliation(s)
- Yongxin Nie
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
| | - Hui Wang
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Guan Zhang
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Haiping Ding
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
| | - Beibei Han
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Lei Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jian Shi
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - Jiyuan Du
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
| | - Xiaohu Li
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
| | - Xinzheng Li
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
| | - Yajie Zhao
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
| | - Xiaocong Zhang
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Changlin Liu
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jianfeng Weng
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xinhai Li
- Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xiansheng Zhang
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
| | - Xiangyu Zhao
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
| | - Guangtang Pan
- Maize Research Institute, Sichuan Agricultural University, Chengdu, 611130, China
| | - David Jackson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
| | - Qin-Bao Li
- USDA-ARS, Chemistry Research Unit, Gainesville, Florida, 32608, USA
| | - Philip S Stinard
- USDA-ARS, Maize Genetics Cooperation Stock Center, Urbana, Illinois, 61801, USA
| | - Jennifer Arp
- University of Illinois at Urbana-Champaign, Department of Crop Sciences, Urbana, Illinois, 61801, USA
- Bayer Crop Science 700 Chesterfield Parkway West, Chesterfield, Missouri, 63017, USA
| | - Martin M Sachs
- USDA-ARS, Maize Genetics Cooperation Stock Center, Urbana, Illinois, 61801, USA
- University of Illinois at Urbana-Champaign, Department of Crop Sciences, Urbana, Illinois, 61801, USA
| | - Steven Moose
- University of Illinois at Urbana-Champaign, Department of Crop Sciences, Urbana, Illinois, 61801, USA
| | - Charles T Hunter
- USDA-ARS, Chemistry Research Unit, Gainesville, Florida, 32608, USA
| | - Qingyu Wu
- State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhiming Zhang
- State Key Laboratory of Crop Biology, College of Life Sciences, Shandong Agricultural University, Taian, 271018, China
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5
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Liu L, Zhan J, Yan J. Engineering the future cereal crops with big biological data: toward an intelligence-driven breeding by design. J Genet Genomics 2024:S1673-8527(24)00058-4. [PMID: 38531485 DOI: 10.1016/j.jgg.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 03/17/2024] [Accepted: 03/17/2024] [Indexed: 03/28/2024]
Abstract
How to feed 10 billion human populations is one of the challenges that need to be addressed in the following decades, especially under an unpredicted climate change. Crop breeding, initiating from the phenotype-based selection by local farmers and developing into current biotechnology-based breeding, has played a critical role in securing the global food supply. However, regarding the changing environment and ever-increasing human population, can we breed outstanding crop varieties fast enough to achieve high productivity, good quality, and widespread adaptability? This review outlines the recent achievements in understanding cereal crop breeding, including the current knowledge about crop agronomic traits, newly developed techniques, crop big biological data research, and the possibility of integrating them for intelligence-driven breeding by design, which ushers in a new era of crop breeding practice and shapes the novel architecture of future crops. This review focuses on the major cereal crops, including rice, maize, and wheat, to explain how intelligence-driven breeding by design is becoming a reality.
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Affiliation(s)
- Lei Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China.
| | - Jimin Zhan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, Hubei 430070, China
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Schreiber M, Jayakodi M, Stein N, Mascher M. Plant pangenomes for crop improvement, biodiversity and evolution. Nat Rev Genet 2024:10.1038/s41576-024-00691-4. [PMID: 38378816 DOI: 10.1038/s41576-024-00691-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2023] [Indexed: 02/22/2024]
Abstract
Plant genome sequences catalogue genes and the genetic elements that regulate their expression. Such inventories further research aims as diverse as mapping the molecular basis of trait diversity in domesticated plants or inquiries into the origin of evolutionary innovations in flowering plants millions of years ago. The transformative technological progress of DNA sequencing in the past two decades has enabled researchers to sequence ever more genomes with greater ease. Pangenomes - complete sequences of multiple individuals of a species or higher taxonomic unit - have now entered the geneticists' toolkit. The genomes of crop plants and their wild relatives are being studied with translational applications in breeding in mind. But pangenomes are applicable also in ecological and evolutionary studies, as they help classify and monitor biodiversity across the tree of life, deepen our understanding of how plant species diverged and show how plants adapt to changing environments or new selection pressures exerted by human beings.
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Affiliation(s)
- Mona Schreiber
- Department of Biology, University of Marburg, Marburg, Germany
| | - Murukarthick Jayakodi
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany
- Martin Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Martin Mascher
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben, Seeland, Germany.
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany.
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7
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Zhang X, Chen Y, Wang L, Yuan Y, Fang M, Shi L, Lu R, Comes HP, Ma Y, Chen Y, Huang G, Zhou Y, Zheng Z, Qiu Y. Pangenome of water caltrop reveals structural variations and asymmetric subgenome divergence after allopolyploidization. Hortic Res 2023; 10:uhad203. [PMID: 38046854 PMCID: PMC10689057 DOI: 10.1093/hr/uhad203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/01/2023] [Indexed: 12/05/2023]
Abstract
Water caltrop (Trapa spp., Lythraceae) is a traditional but currently underutilized non-cereal crop. Here, we generated chromosome-level genome assemblies for the two diploid progenitors of allotetraploid Trapa. natans (4x, AABB), i.e., diploid T. natans (2x, AA) and Trapa incisa (2x, BB). In conjunction with four published (sub)genomes of Trapa, we used gene-based and graph-based pangenomic approaches and a pangenomic transposable element (TE) library to develop Trapa genomic resources. The pangenome displayed substantial gene-content variation with dispensable and private gene clusters occupying a large proportion (51.95%) of the total cluster sets in the six (sub)genomes. Genotyping of presence-absence variation (PAVs) identified 40 453 PAVs associated with 2570 genes specific to A- or B-lineages, of which 1428 were differentially expressed, and were enriched in organ development process, organic substance metabolic process and response to stimulus. Comparative genome analyses showed that the allotetraploid T. natans underwent asymmetric subgenome divergence, with the B-subgenome being more dominant than the A-subgenome. Multiple factors, including PAVs, asymmetrical amplification of TEs, homeologous exchanges (HEs), and homeolog expression divergence, together affected genome evolution after polyploidization. Overall, this study sheds lights on the genome architecture and evolution of Trapa, and facilitates its functional genomic studies and breeding program.
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Affiliation(s)
- Xinyi Zhang
- Systematic and Evolutionary Botany and Biodiversity Laboratory, College of Life Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
| | - Yang Chen
- Systematic and Evolutionary Botany and Biodiversity Laboratory, College of Life Sciences, Zhejiang University, Hangzhou, 310058, Zhejiang, China
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
| | - Lingyun Wang
- Provincial Key Laboratory of Characteristic Aquatic Vegetable Breeding and Cultivation, Jinhua Academy of Agricultural Sciences (Zhejiang Institute of Agricultural Machinery), Jinhua, 321000, Zhejiang, China
| | - Ye Yuan
- Jiaxing Academy of Agricultural Sciences, Jiaxing, 314016, Zhejiang, China
| | - Mingya Fang
- Provincial Key Laboratory of Characteristic Aquatic Vegetable Breeding and Cultivation, Jinhua Academy of Agricultural Sciences (Zhejiang Institute of Agricultural Machinery), Jinhua, 321000, Zhejiang, China
| | - Lin Shi
- Provincial Key Laboratory of Characteristic Aquatic Vegetable Breeding and Cultivation, Jinhua Academy of Agricultural Sciences (Zhejiang Institute of Agricultural Machinery), Jinhua, 321000, Zhejiang, China
| | - Ruisen Lu
- Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, Jiangsu, China
| | - Hans Peter Comes
- Department of Environment & Biodiversity, Salzburg University, Salzburg, 5020, Austria
| | - Yazhen Ma
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
| | - Yuanyuan Chen
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
| | - Guizhou Huang
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture; Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, Guangdong, China
| | - Yongfeng Zhou
- State Key Laboratory of Tropical Crop Breeding, Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture; Key Laboratory of Synthetic Biology, Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, Guangdong, China
| | - Zhaisheng Zheng
- Provincial Key Laboratory of Characteristic Aquatic Vegetable Breeding and Cultivation, Jinhua Academy of Agricultural Sciences (Zhejiang Institute of Agricultural Machinery), Jinhua, 321000, Zhejiang, China
| | - Yingxiong Qiu
- CAS Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan, 430074, Hubei, China
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8
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Aylward AJ, Petrus S, Mamerto A, Hartwick NT, Michael TP. PanKmer: k-mer-based and reference-free pangenome analysis. Bioinformatics 2023; 39:btad621. [PMID: 37846049 PMCID: PMC10603592 DOI: 10.1093/bioinformatics/btad621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 08/29/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023] Open
Abstract
SUMMARY Pangenomes are replacing single reference genomes as the definitive representation of DNA sequence within a species or clade. Pangenome analysis predominantly leverages graph-based methods that require computationally intensive multiple genome alignments, do not scale to highly complex eukaryotic genomes, limit their scope to identifying structural variants (SVs), or incur bias by relying on a reference genome. Here, we present PanKmer, a toolkit designed for reference-free analysis of pangenome datasets consisting of dozens to thousands of individual genomes. PanKmer decomposes a set of input genomes into a table of observed k-mers and their presence-absence values in each genome. These are stored in an efficient k-mer index data format that encodes SNPs, INDELs, and SVs. It also includes functions for downstream analysis of the k-mer index, such as calculating sequence similarity statistics between individuals at whole-genome or local scales. For example, k-mers can be "anchored" in any individual genome to quantify sequence variability or conservation at a specific locus. This facilitates workflows with various biological applications, e.g. identifying cases of hybridization between plant species. PanKmer provides researchers with a valuable and convenient means to explore the full scope of genetic variation in a population, without reference bias. AVAILABILITY AND IMPLEMENTATION PanKmer is implemented as a Python package with components written in Rust, released under a BSD license. The source code is available from the Python Package Index (PyPI) at https://pypi.org/project/pankmer/ as well as Gitlab at https://gitlab.com/salk-tm/pankmer. Full documentation is available at https://salk-tm.gitlab.io/pankmer/.
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Affiliation(s)
- Anthony J Aylward
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
| | - Semar Petrus
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
| | - Allen Mamerto
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
| | - Nolan T Hartwick
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
| | - Todd P Michael
- The Plant Molecular and Cellular Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA 92037, United States
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9
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Naithani S, Deng CH, Sahu SK, Jaiswal P. Exploring Pan-Genomes: An Overview of Resources and Tools for Unraveling Structure, Function, and Evolution of Crop Genes and Genomes. Biomolecules 2023; 13:1403. [PMID: 37759803 PMCID: PMC10527062 DOI: 10.3390/biom13091403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/29/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
The availability of multiple sequenced genomes from a single species made it possible to explore intra- and inter-specific genomic comparisons at higher resolution and build clade-specific pan-genomes of several crops. The pan-genomes of crops constructed from various cultivars, accessions, landraces, and wild ancestral species represent a compendium of genes and structural variations and allow researchers to search for the novel genes and alleles that were inadvertently lost in domesticated crops during the historical process of crop domestication or in the process of extensive plant breeding. Fortunately, many valuable genes and alleles associated with desirable traits like disease resistance, abiotic stress tolerance, plant architecture, and nutrition qualities exist in landraces, ancestral species, and crop wild relatives. The novel genes from the wild ancestors and landraces can be introduced back to high-yielding varieties of modern crops by implementing classical plant breeding, genomic selection, and transgenic/gene editing approaches. Thus, pan-genomic represents a great leap in plant research and offers new avenues for targeted breeding to mitigate the impact of global climate change. Here, we summarize the tools used for pan-genome assembly and annotations, web-portals hosting plant pan-genomes, etc. Furthermore, we highlight a few discoveries made in crops using the pan-genomic approach and future potential of this emerging field of study.
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Affiliation(s)
- Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA;
| | - Cecilia H. Deng
- Molecular & Digital Breeing Group, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, Private Bag 92169, Auckland 1142, New Zealand;
| | - Sunil Kumar Sahu
- State Key Laboratory of Agricultural Genomics, Key Laboratory of Genomics, Ministry of Agriculture, BGI Research, Shenzhen 518083, China;
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA;
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10
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Duan H, Xue Z, Ju X, Yang L, Gao J, Sun L, Xu S, Li J, Xiong X, Sun Y, Wang Y, Zhang X, Ding D, Zhang X, Tang J. The genetic architecture of prolificacy in maize revealed by association mapping and bulk segregant analysis. Theor Appl Genet 2023; 136:182. [PMID: 37555969 DOI: 10.1007/s00122-023-04434-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 06/26/2023] [Indexed: 08/10/2023]
Abstract
KEY MESSAGE Here, we revealed maize prolificacy highly correlated with domestication and identified a causal gene ZmEN1 located in one novel QTL qGEN261 that regulating maize prolificacy by using multiple-mapping methods. The development of maize prolificacy (EN) is crucial for enhancing yield and breeding specialty varieties. To achieve this goal, we employed a genome-wide association study (GWAS) to analyze the genetic architecture of EN in maize. Using 492 inbred lines with a wide range of EN variability, our results demonstrated significant differences in genetic, environmental, and interaction effects. The broad-sense heritability (H2) of EN was 0.60. Through GWAS, we identified 527 significant single nucleotide polymorphisms (SNPs), involved 290 quantitative trait loci (QTL) and 806 genes. Of these SNPs, 18 and 509 were classified as major effect loci and minor loci, respectively. In addition, we performed a bulk segregant analysis (BSA) in an F2 population constructed by a few-ears line Zheng58 and a multi-ears line 647. Our BSA results identified one significant QTL, qBEN1. Importantly, combining the GWAS and BSA, four co-located QTL, involving six genes, were identified. Three of them were expressed in vegetative meristem, shoot tip, internode and tip of ear primordium, with ZmEN1, encodes an unknown auxin-like protein, having the highest expression level in these tissues. It suggested that ZmEN1 plays a crucial role in promoting axillary bud and tillering to encourage the formation of prolificacy. Haplotype analysis of ZmEN1 revealed significant differences between different haplotypes, with inbred lines carrying hap6 having more EN. Overall, this is the first report about using GWAS and BSA to dissect the genetic architecture of EN in maize, which can be valuable for breeding specialty maize varieties and improving maize yield.
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Affiliation(s)
- Haiyang Duan
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Zhengjie Xue
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Xiaolong Ju
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Lu Yang
- State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, People's Republic of China
| | - Jionghao Gao
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Li Sun
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Shuhao Xu
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Jianxin Li
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Xuehang Xiong
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Yan Sun
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Yan Wang
- Zhucheng Mingjue Tender Company Limited, Weifang, People's Republic of China
| | - Xuebin Zhang
- State Key Laboratory of Crop Stress Adaptation and Improvement, School of Life Sciences, Henan University, Kaifeng, People's Republic of China
| | - Dong Ding
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China
| | - Xuehai Zhang
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China.
| | - Jihua Tang
- National Key Laboratory of Wheat and Maize Crop Science, Department of Agronomy, College of Agronomy, Henan Agricultural University, No. 218 Ping'an Avenue, Zhengdong New District, Zhengzhou, 450046, People's Republic of China.
- The Shennong Laboratory, Zhengzhou, People's Republic of China.
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11
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Li C, Li Y, Song G, Yang D, Xia Z, Sun C, Zhao Y, Hou M, Zhang M, Qi Z, Wang B, Wang H. Gene expression and expression quantitative trait loci analyses uncover natural variations underlying the improvement of important agronomic traits during modern maize breeding. Plant J 2023; 115:772-787. [PMID: 37186341 DOI: 10.1111/tpj.16260] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 04/15/2023] [Accepted: 04/20/2023] [Indexed: 05/17/2023]
Abstract
Maize (Zea mays L.) is a major staple crop worldwide, and during modern maize breeding, cultivars with increased tolerance to high-density planting and higher yield per plant have contributed significantly to the increased yield per unit land area. Systematically identifying key agronomic traits and their associated genomic changes during modern maize breeding remains a significant challenge because of the complexity of genetic regulation and the interactions of the various agronomic traits, with most of them being controlled by numerous small-effect quantitative trait loci (QTLs). Here, we performed phenotypic and gene expression analyses for a set of 137 elite inbred lines of maize from different breeding eras in China. We found four yield-related traits are significantly improved during modern maize breeding. Through gene-clustering analyses, we identified four groups of expressed genes with distinct trends of expression pattern change across the historical breeding eras. In combination with weighted gene co-expression network analysis, we identified several candidate genes regulating various plant architecture- and yield-related agronomic traits, such as ZmARF16, ZmARF34, ZmTCP40, ZmPIN7, ZmPYL10, ZmJMJ10, ZmARF1, ZmSWEET15b, ZmGLN6 and Zm00001d019150. Further, by combining expression quantitative trait loci (eQTLs) analyses, correlation coefficient analyses and population genetics, we identified a set of candidate genes that might have been under selection and contributed to the genetic improvement of various agronomic traits during modern maize breeding, including a number of known key regulators of plant architecture, flowering time and yield-related traits, such as ZmPIF3.3, ZAG1, ZFL2 and ZmBES1. Lastly, we validated the functional variations in GL15, ZmPHYB2 and ZmPYL10 that influence kernel row number, flowering time, plant height and ear height, respectively. Our results demonstrates the effectiveness of our combined approaches for uncovering key candidate regulatory genes and functional variation underlying the improvement of important agronomic traits during modern maize breeding, and provide a valuable genetic resource for the molecular breeding of maize cultivars with tolerance for high-density planting.
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Affiliation(s)
- Changyu Li
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- Key Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, Inner Mongolia University, Hohhot, 010070, China
| | - Yaoyao Li
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China
| | - Guangshu Song
- Maize Research Institute, Jilin Academy of Agricultural Sciences, Gongzhuling, 136100, China
| | - Di Yang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhanchao Xia
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Changhe Sun
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yuelei Zhao
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Mei Hou
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Mingyue Zhang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China
| | - Zhi Qi
- Key Laboratory of Herbage and Endemic Crop Biology, Ministry of Education, Inner Mongolia University, Hohhot, 010070, China
| | - Baobao Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
- HainanYazhou Bay Seed Lab, Sanya, 572025, China
| | - Haiyang Wang
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-Bioresources, College of Life Sciences, South China Agricultural University, Guangzhou, 510642, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, China
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12
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Sarkar B, Varalaxmi Y, Vanaja M, RaviKumar N, Prabhakar M, Yadav SK, Maheswari M, Singh VK. Mapping of QTLs for morphophysiological and yield traits under water-deficit stress and well-watered conditions in maize. Front Plant Sci 2023; 14:1124619. [PMID: 37223807 PMCID: PMC10200936 DOI: 10.3389/fpls.2023.1124619] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/27/2023] [Indexed: 05/25/2023]
Abstract
Maize productivity is significantly impacted by drought; therefore, improvement of drought tolerance is a critical goal in maize breeding. To achieve this, a better understanding of the genetic basis of drought tolerance is necessary. Our study aimed to identify genomic regions associated with drought tolerance-related traits by phenotyping a mapping population of recombinant inbred lines (RILs) for two seasons under well-watered (WW) and water-deficit (WD) conditions. We also used single nucleotide polymorphism (SNP) genotyping through genotyping-by-sequencing to map these regions and attempted to identify candidate genes responsible for the observed phenotypic variation. Phenotyping of the RILs population revealed significant variability in most of the traits, with normal frequency distributions, indicating their polygenic nature. We generated a linkage map using 1,241 polymorphic SNPs distributed over 10 chromosomes (chrs), covering a total genetic distance of 5,471.55 cM. We identified 27 quantitative trait loci (QTLs) associated with various morphophysiological and yield-related traits, with 13 QTLs identified under WW conditions and 12 under WD conditions. We found one common major QTL (qCW2-1) for cob weight and a minor QTL (qCH1-1) for cob height that were consistently identified under both water regimes. We also detected one major and one minor QTL for the Normalized Difference Vegetation Index (NDVI) trait under WD conditions on chr 2, bin 2.10. Furthermore, we identified one major QTL (qCH1-2) and one minor QTL (qCH1-1) on chr 1 that were located at different genomic positions to those identified in earlier studies. We found co-localized QTLs for stomatal conductance and grain yield on chr 6 (qgs6-2 and qGY6-1), while co-localized QTLs for stomatal conductance and transpiration rate were identified on chr 7 (qgs7-1 and qTR7-1). We also attempted to identify the candidate genes responsible for the observed phenotypic variation; our analysis revealed that the major candidate genes associated with QTLs detected under water deficit conditions were related to growth and development, senescence, abscisic acid (ABA) signaling, signal transduction, and transporter activity in stress tolerance. The QTL regions identified in this study may be useful in designing markers that can be utilized in marker-assisted selection breeding. In addition, the putative candidate genes can be isolated and functionally characterized so that their role in imparting drought tolerance can be more fully understood.
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13
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Gong Y, Li Y, Liu X, Ma Y, Jiang L. A review of the pangenome: how it affects our understanding of genomic variation, selection and breeding in domestic animals? J Anim Sci Biotechnol 2023; 14:73. [PMID: 37143156 PMCID: PMC10161434 DOI: 10.1186/s40104-023-00860-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/01/2023] [Indexed: 05/06/2023] Open
Abstract
As large-scale genomic studies have progressed, it has been revealed that a single reference genome pattern cannot represent genetic diversity at the species level. While domestic animals tend to have complex routes of origin and migration, suggesting a possible omission of some population-specific sequences in the current reference genome. Conversely, the pangenome is a collection of all DNA sequences of a species that contains sequences shared by all individuals (core genome) and is also able to display sequence information unique to each individual (variable genome). The progress of pangenome research in humans, plants and domestic animals has proved that the missing genetic components and the identification of large structural variants (SVs) can be explored through pangenomic studies. Many individual specific sequences have been shown to be related to biological adaptability, phenotype and important economic traits. The maturity of technologies and methods such as third-generation sequencing, Telomere-to-telomere genomes, graphic genomes, and reference-free assembly will further promote the development of pangenome. In the future, pangenome combined with long-read data and multi-omics will help to resolve large SVs and their relationship with the main economic traits of interest in domesticated animals, providing better insights into animal domestication, evolution and breeding. In this review, we mainly discuss how pangenome analysis reveals genetic variations in domestic animals (sheep, cattle, pigs, chickens) and their impacts on phenotypes and how this can contribute to the understanding of species diversity. Additionally, we also go through potential issues and the future perspectives of pangenome research in livestock and poultry.
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Affiliation(s)
- Ying Gong
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
- National Germplasm Center of Domestic Animal Resources, Ministry of Technology, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Yefang Li
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
- National Germplasm Center of Domestic Animal Resources, Ministry of Technology, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
| | - Xuexue Liu
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
- National Germplasm Center of Domestic Animal Resources, Ministry of Technology, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China
- Centre d'Anthropobiologie et de Génomique de Toulouse, Université Paul Sabatier, 37 allées Jules Guesde, Toulouse, 31000, France
| | - Yuehui Ma
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China.
- National Germplasm Center of Domestic Animal Resources, Ministry of Technology, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China.
| | - Lin Jiang
- Laboratory of Animal Genetics, Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China.
- National Germplasm Center of Domestic Animal Resources, Ministry of Technology, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences (CAAS), Beijing, 100193, China.
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14
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Grzybowski MW, Mural RV, Xu G, Turkus J, Yang J, Schnable JC. A common resequencing-based genetic marker data set for global maize diversity. Plant J 2023; 113:1109-1121. [PMID: 36705476 DOI: 10.1111/tpj.16123] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Maize (Zea mays ssp. mays) populations exhibit vast ranges of genetic and phenotypic diversity. As sequencing costs have declined, an increasing number of projects have sought to measure genetic differences between and within maize populations using whole-genome resequencing strategies, identifying millions of segregating single-nucleotide polymorphisms (SNPs) and insertions/deletions (InDels). Unlike older genotyping strategies like microarrays and genotyping by sequencing, resequencing should, in principle, frequently identify and score common genetic variants. However, in practice, different projects frequently employ different analytical pipelines, often employ different reference genome assemblies and consistently filter for minor allele frequency within the study population. This constrains the potential to reuse and remix data on genetic diversity generated from different projects to address new biological questions in new ways. Here, we employ resequencing data from 1276 previously published maize samples and 239 newly resequenced maize samples to generate a single unified marker set of approximately 366 million segregating variants and approximately 46 million high-confidence variants scored across crop wild relatives, landraces as well as tropical and temperate lines from different breeding eras. We demonstrate that the new variant set provides increased power to identify known causal flowering-time genes using previously published trait data sets, as well as the potential to track changes in the frequency of functionally distinct alleles across the global distribution of modern maize.
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Affiliation(s)
- Marcin W Grzybowski
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Plant Molecular Ecophysiology, Institute of Plant Experimental Biology and Biotechnology, Faculty of Biology, University of Warsaw, Warsaw, Poland
| | - Ravi V Mural
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Gen Xu
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Jonathan Turkus
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - Jinliang Yang
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
| | - James C Schnable
- Center for Plant Science Innovation, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
- Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, Nebraska, USA
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15
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Gui S, Martinez-Rivas FJ, Wen W, Meng M, Yan J, Usadel B, Fernie AR. Going broad and deep: sequencing-driven insights into plant physiology, evolution, and crop domestication. Plant J 2023; 113:446-459. [PMID: 36534120 DOI: 10.1111/tpj.16070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 06/17/2023]
Abstract
Deep sequencing is a term that has become embedded in the plant genomic literature in recent years and with good reason. A torrent of (largely) high-quality genomic and transcriptomic data has been collected and most of this has been publicly released. Indeed, almost 1000 plant genomes have been reported (www.plabipd.de) and the 2000 Plant Transcriptomes Project has long been completed. The EarthBioGenome project will dwarf even these milestones. That said, massive progress in understanding plant physiology, evolution, and crop domestication has been made by sequencing broadly (across a species) as well as deeply (within a single individual). We will outline the current state of the art in genome and transcriptome sequencing before we briefly review the most visible of these broad approaches, namely genome-wide association and transcriptome-wide association studies, as well as the compilation of pangenomes. This will include both (i) the most commonly used methods reliant on single nucleotide polymorphisms and short InDels and (ii) more recent examples which consider structural variants. We will subsequently present case studies exemplifying how their application has brought insight into either plant physiology or evolution and crop domestication. Finally, we will provide conclusions and an outlook as to the perspective for the extension of such approaches to different species, tissues, and biological processes.
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Affiliation(s)
- Songtao Gui
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | | | - Weiwei Wen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Minghui Meng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Björn Usadel
- IBG-4 Bioinformatics, Forschungszentrum Jülich, Wilhelm Johnen Str, BioSc, 52428, Jülich, Germany
- Institute for Biological Data Science, CEPLAS, Heinrich Heine University, 40225, Düsseldorf, Germany
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam-Golm, 14476, Germany
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16
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Abstract
Plant genomes are so highly diverse that a substantial proportion of genomic sequences are not shared among individuals. The variable DNA sequences, along with the conserved core sequences, compose the more sophisticated pan-genome that represents the collection of all non-redundant DNA in a species. With rapid progress in genome sequencing technologies, pan-genome research in plants is now accelerating. Here we review recent advances in plant pan-genomics, including major driving forces of structural variations that constitute the variable sequences, methodological innovations for representing the pan-genome, and major successes in constructing plant pan-genomes. We also summarize recent efforts toward decoding the remaining dark matter in telomere-to-telomere or gapless plant genomes. These new genome resources, which have remarkable advantages over numerous previously assembled less-than-perfect genomes, are expected to become new references for genetic studies and plant breeding.
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Affiliation(s)
- Junpeng Shi
- State Key Laboratory of Biocontrol, School of Agriculture, Sun Yat-sen University, Shenzhen 518107, China.
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing 100101, China
| | - Jinsheng Lai
- State Key Laboratory of Plant Physiology and Biochemistry and National Maize Improvement Center, Department of Plant Genetics and Breeding, China Agricultural University, Beijing 100193, China
| | - Xuehui Huang
- Shanghai Key Laboratory of Plant Molecular Sciences, College of Life Sciences, Shanghai Normal University, Shanghai 200234, China.
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17
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Dreni L. The ABC of Flower Development in Monocots: The Model of Rice Spikelet. Methods Mol Biol 2023; 2686:59-82. [PMID: 37540354 DOI: 10.1007/978-1-0716-3299-4_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
The initial seminal studies of flower developmental genetics were made from observations in several eudicot model species, particularly Arabidopsis and Antirrhinum. However, an increasing amount of research in monocot model and crop species is finally giving the credit that monocots deserve for their position in the evolutionary history of Angiosperms, their astonishing diversification and adaptation, their diversified floral structures, their pivotal function in most ecosystems on Earth and, finally, their importance in agriculture and farming, economy, landscaping and feeding mankind. Rice is a staple crop and the major monocot model to study the reproductive phase and flower evolution. Inspired by this, this chapter reviews a story of highly conserved functions related to the ABC model of flower development. Nevertheless, this model is complicated in rice by cases of gene neofunctionalization, like the recruitment of MADS-box genes for the development of the unique organs known as lemma and palea, subfunctionalization, and rewiring of conserved molecular pathways.
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Affiliation(s)
- Ludovico Dreni
- Instituto de Biología Molecular y Celular de Plantas (IBMCP), Consejo Superior de Investigaciones Científicas-Universidad Politécnica de Valencia, Valencia, Spain
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18
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Ali A, Davidson S, Fraenkel E, Gilmore I, Hankemeier T, Kirwan JA, Lane AN, Lanekoff I, Larion M, McCall LI, Murphy M, Sweedler JV, Zhu C. Single cell metabolism: current and future trends. Metabolomics 2022; 18:77. [PMID: 36181583 PMCID: PMC10063251 DOI: 10.1007/s11306-022-01934-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/29/2022]
Abstract
Single cell metabolomics is an emerging and rapidly developing field that complements developments in single cell analysis by genomics and proteomics. Major goals include mapping and quantifying the metabolome in sufficient detail to provide useful information about cellular function in highly heterogeneous systems such as tissue, ultimately with spatial resolution at the individual cell level. The chemical diversity and dynamic range of metabolites poses particular challenges for detection, identification and quantification. In this review we discuss both significant technical issues of measurement and interpretation, and progress toward addressing them, with recent examples from diverse biological systems. We provide a framework for further directions aimed at improving workflow and robustness so that such analyses may become commonly applied, especially in combination with metabolic imaging and single cell transcriptomics and proteomics.
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Affiliation(s)
- Ahmed Ali
- Leiden Academic Centre for Drug Research, University of Leiden, Gorlaeus Building Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Shawn Davidson
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Ernest Fraenkel
- Department of Biological Engineering and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ian Gilmore
- National Physical Laboratory, Teddington, TW11 0LW, Middlesex, UK
| | - Thomas Hankemeier
- Leiden Academic Centre for Drug Research, University of Leiden, Room number GW4.07, Gorlaeus Building, Einsteinweg 55, 2333 CC, Leiden, The Netherlands
| | - Jennifer A Kirwan
- Berlin Institute of Health, Metabolomics Platform, Translational Research Unit of the Charite-Universitätsmedizin Berlin, Anna-Louisa-Karsch-Str 2, 10178, Berlin, Germany
| | - Andrew N Lane
- Department of Toxicology and Cancer Biology, and Center for Environmental and Systems Biochemistry, University of Kentucky, 789 S. Limestone St, Lexington, KY, 40536, USA.
| | - Ingela Lanekoff
- Department of Chemistry-BMC, Uppsala University, Husargatan 3 (576), 751 23, Uppsala, Sweden
| | - Mioara Larion
- Center for Cancer Research, National Cancer Institute, Building 37, Room 1136A, Bethesda, MD, 20892, USA
| | - Laura-Isobel McCall
- Department of Chemistry & Biochemistry, Department of Microbiology and Plant Biology, Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, 101 Stephenson Parkway, room 3750, Norman, OK, 73019-5251, USA
| | - Michael Murphy
- Departments of Biological Engineering, Department of Electrical Engineering, and Computer Science and the Computational and Systems Biology Program, Massachusetts Institute of Technology, Cambridge, USA
| | - Jonathan V Sweedler
- Department of Chemistry, and the Beckman Institute, University of Illinois Urbana-Champaign, 505 South Mathews Avenue, Urbana, IL, 61801, USA
| | - Caigang Zhu
- Department of Biomedical Engineering, University of Kentucky, Lexington, KY, 40536, USA
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