1
|
Sabag I, Pnini S, Morota G, Peleg Z. Refining flowering date enhances sesame yield independently of day-length. BMC PLANT BIOLOGY 2024; 24:711. [PMID: 39060970 PMCID: PMC11282604 DOI: 10.1186/s12870-024-05431-8] [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: 06/19/2024] [Accepted: 07/17/2024] [Indexed: 07/28/2024]
Abstract
BACKGROUND The transition from vegetative to reproductive growth is a key factor in yield maximization. Sesame (Sesamum indicum), an indeterminate short-day oilseed crop, is rapidly being introduced into new cultivation areas. Thus, decoding its flowering mechanism is necessary to facilitate adaptation to environmental conditions. In the current study, we uncover the effect of day-length on flowering and yield components using F2 populations segregating for previously identified quantitative trait loci (Si_DTF QTL) confirming these traits. RESULTS Generally, day-length affected all phenotypic traits, with short-day preceding days to flowering and reducing yield components. Interestingly, the average days to flowering required for yield maximization was 50 to 55 days, regardless of day-length. In addition, we found that Si_DTF QTL is more associated with seed-yield and yield components than with days to flowering. A bulk-segregation analysis was applied to identify additional QTL differing in allele frequencies between early and late flowering under both day-length conditions. Candidate genes mining within the identified major QTL intervals revealed two flowering-related genes with different expression levels between the parental lines, indicating their contribution to sesame flowering regulation. CONCLUSIONS Our findings demonstrate the essential role of flowering date on yield components and will serve as a basis for future sesame breeding.
Collapse
Affiliation(s)
- Idan Sabag
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot, 7610001, Israel
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Shaked Pnini
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot, 7610001, Israel
| | - Gota Morota
- School of Animal Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA, 24061, USA
| | - Zvi Peleg
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot, 7610001, Israel.
| |
Collapse
|
2
|
Ontoy JC, Ham JH. Mapping and Omics Integration: Towards Precise Rice Disease Resistance Breeding. PLANTS (BASEL, SWITZERLAND) 2024; 13:1205. [PMID: 38732420 PMCID: PMC11085595 DOI: 10.3390/plants13091205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2024] [Revised: 04/18/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
Rice (Oryza sativa), as a staple crop feeding a significant portion of the global population, particularly in Asian countries, faces constant threats from various diseases jeopardizing global food security. A precise understanding of disease resistance mechanisms is crucial for developing resilient rice varieties. Traditional genetic mapping methods, such as QTL mapping, provide valuable insights into the genetic basis of diseases. However, the complex nature of rice diseases demands a holistic approach to gain an accurate knowledge of it. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, enable a comprehensive analysis of biological molecules, uncovering intricate molecular interactions within the rice plant. The integration of various mapping techniques using multi-omics data has revolutionized our understanding of rice disease resistance. By overlaying genetic maps with high-throughput omics datasets, researchers can pinpoint specific genes, proteins, or metabolites associated with disease resistance. This integration enhances the precision of disease-related biomarkers with a better understanding of their functional roles in disease resistance. The improvement of rice breeding for disease resistance through this integration represents a significant stride in agricultural science because a better understanding of the molecular intricacies and interactions underlying disease resistance architecture leads to a more precise and efficient development of resilient and productive rice varieties. In this review, we explore how the integration of mapping and omics data can result in a transformative impact on rice breeding for enhancing disease resistance.
Collapse
Affiliation(s)
- John Christian Ontoy
- Department of Plant Pathology and Crop Physiology, LSU AgCenter, Baton Rouge, LA 70803, USA;
- Department of Plant Pathology and Crop Physiology, College of Agriculture, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Jong Hyun Ham
- Department of Plant Pathology and Crop Physiology, LSU AgCenter, Baton Rouge, LA 70803, USA;
- Department of Plant Pathology and Crop Physiology, College of Agriculture, Louisiana State University, Baton Rouge, LA 70803, USA
| |
Collapse
|
3
|
Zhang L, Duan Y, Zhang Z, Zhang L, Chen S, Cai C, Duan S, Zhang K, Li G, Cheng F. OcBSA: An NGS-based bulk segregant analysis tool for outcross populations. MOLECULAR PLANT 2024; 17:648-657. [PMID: 38369755 DOI: 10.1016/j.molp.2024.02.011] [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: 01/28/2024] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 02/20/2024]
Abstract
Constructing inbred lines for self-incompatible species and species with long generation times is challenging, making the use of F1 outcross/segregating populations the main strategy for genetic studies of such species. However, there is a lack of dedicated algorithms/tools for rapid quantitative trait locus (QTL) mapping using the F1 populations. To this end, we have designed and developed an algorithm/tool called OcBSA specifically for QTL mapping of F1 populations. OcBSA transforms the four-haplotype inheritance problem from the two heterozygous diploid parents of the F1 population into the two-haplotype inheritance problem common in current genetic studies by removing the two haplotypes from the heterozygous parent that do not contribute to phenotype segregation in the F1 population. Testing of OcBSA on 1800 simulated F1 populations demonstrated its advantages over other currently available tools in terms of sensitivity and accuracy. In addition, the broad applicability of OcBSA was validated by QTL mapping using seven reported F1 populations of apple, pear, peach, citrus, grape, tea, and rice. We also used OcBSA to map the QTL for flower color in a newly constructed F1 population of potato generated in this study. The OcBSA mapping result was verified by the insertion or deletion markers to be consistent with a previously reported locus harboring the ANTHOCYANIN 2 gene, which regulates potato flower color. Taken together, these results highlight the power and broad utility of OcBSA for QTL mapping using F1 populations and thus a great potential for functional gene mining in outcrossing species. For ease of use, we have developed both Windows and Linux versions of OcBSA, which are freely available at: https://gitee.com/Bioinformaticslab/OcBSA.
Collapse
Affiliation(s)
- Lingkui Zhang
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Yanfeng Duan
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Zewei Zhang
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lei Zhang
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shumin Chen
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Chengcheng Cai
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Shaoguang Duan
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Kang Zhang
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Guangcun Li
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| | - Feng Cheng
- State Key Laboratory of Vegetable Biobreeding, Key Laboratory of Biology and Genetic Improvement of Horticultural Crops of the Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology and Genetic Improvement of Tuber and Root Crop of Ministry of Agriculture and Rural Affairs, Sino-Dutch Joint Laboratory of Horticultural Genomics, Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
| |
Collapse
|
4
|
Applications of Artificial Intelligence in Climate-Resilient Smart-Crop Breeding. Int J Mol Sci 2022; 23:ijms231911156. [PMID: 36232455 PMCID: PMC9570104 DOI: 10.3390/ijms231911156] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/18/2022] [Accepted: 09/19/2022] [Indexed: 11/21/2022] Open
Abstract
Recently, Artificial intelligence (AI) has emerged as a revolutionary field, providing a great opportunity in shaping modern crop breeding, and is extensively used indoors for plant science. Advances in crop phenomics, enviromics, together with the other “omics” approaches are paving ways for elucidating the detailed complex biological mechanisms that motivate crop functions in response to environmental trepidations. These “omics” approaches have provided plant researchers with precise tools to evaluate the important agronomic traits for larger-sized germplasm at a reduced time interval in the early growth stages. However, the big data and the complex relationships within impede the understanding of the complex mechanisms behind genes driving the agronomic-trait formations. AI brings huge computational power and many new tools and strategies for future breeding. The present review will encompass how applications of AI technology, utilized for current breeding practice, assist to solve the problem in high-throughput phenotyping and gene functional analysis, and how advances in AI technologies bring new opportunities for future breeding, to make envirotyping data widely utilized in breeding. Furthermore, in the current breeding methods, linking genotype to phenotype remains a massive challenge and impedes the optimal application of high-throughput field phenotyping, genomics, and enviromics. In this review, we elaborate on how AI will be the preferred tool to increase the accuracy in high-throughput crop phenotyping, genotyping, and envirotyping data; moreover, we explore the developing approaches and challenges for multiomics big computing data integration. Therefore, the integration of AI with “omics” tools can allow rapid gene identification and eventually accelerate crop-improvement programs.
Collapse
|