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Zhao J, Sun L, Hu M, Liu Q, Xu J, Mu L, Wang J, Yang J, Wang P, Li Q, Li H, Zhang Y. Pleiotropic Quantitative Trait Loci (QTL) Mining for Regulating Wheat Processing Quality- and Yield-Related Traits. PLANTS (BASEL, SWITZERLAND) 2024; 13:2545. [PMID: 39339520 PMCID: PMC11435383 DOI: 10.3390/plants13182545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2024] [Revised: 08/12/2024] [Accepted: 09/09/2024] [Indexed: 09/30/2024]
Abstract
To investigate the genetic basis of processing quality- and yield-related traits in bread wheat (Triticum aestivum L., AABBDD), a systematic analysis of wheat processing quality- and yield-related traits based on genome-wide association studies (GWASs) of 285 regional test lines of wheat from Hebei province, China, was conducted. A total of 87 quantitative trait loci (QTL), including twenty-one for water absorption (WA), four for wet gluten content, eight for grain protein content, seventeen for dough stability time (DST), thirteen for extension area (EA), twelve for maximum resistance (MR), five for thousand-grain weight (TGW), one for grain length, and six for grain width were identified. These QTL harbored 188 significant single-nucleotide polymorphisms (SNPs). Twenty-five SNPs were simultaneously associated with multiple traits. Notably, the SNP AX-111015470 on chromosome 1A was associated with DST, EA, and MR. SNPs AX-111917292 and AX-109124553 on chromosome 5D were associated with wheat WA and TGW. Most processing quality-related QTL and seven grain yield-related QTL identified in this study were newly discovered. Among the surveyed accessions, 18 rare superior alleles were identified. This study identified significant QTL associated with quality-related and yield-related traits in wheat, and some of them showed pleiotropic effects. This study will facilitate molecular designs that seek to achieve synergistic improvements of wheat quality and yield.
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Affiliation(s)
- Jie Zhao
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Lijing Sun
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Mengyun Hu
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Qian Liu
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Junjie Xu
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Liming Mu
- Dingxi Academy of Agricultural Sciences, Dingxi 743000, China; (L.M.)
| | - Jianbing Wang
- Dingxi Academy of Agricultural Sciences, Dingxi 743000, China; (L.M.)
| | - Jing Yang
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Peinan Wang
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Qianying Li
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Hui Li
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
| | - Yingjun Zhang
- Hebei Key Laboratory of Crop Genetics and Breeding, Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050035, China; (J.Z.); (L.S.); (M.H.); (Q.L.); (Q.L.); (H.L.)
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Chang-Brahim I, Koppensteiner LJ, Beltrame L, Bodner G, Saranti A, Salzinger J, Fanta-Jende P, Sulzbachner C, Bruckmüller F, Trognitz F, Samad-Zamini M, Zechner E, Holzinger A, Molin EM. Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding. FRONTIERS IN PLANT SCIENCE 2024; 15:1319938. [PMID: 38699541 PMCID: PMC11064034 DOI: 10.3389/fpls.2024.1319938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 03/13/2024] [Indexed: 05/05/2024]
Abstract
Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.
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Affiliation(s)
- Ignacio Chang-Brahim
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Lorenzo Beltrame
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Gernot Bodner
- Department of Crop Sciences, Institute of Agronomy, University of Natural Resources and Life Sciences Vienna, Tulln, Austria
| | - Anna Saranti
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Jules Salzinger
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Phillipp Fanta-Jende
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Christoph Sulzbachner
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Felix Bruckmüller
- Unit Assistive and Autonomous Systems, Center for Vision, Automation & Control, AIT Austrian Institute of Technology, Vienna, Austria
| | - Friederike Trognitz
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
| | | | - Elisabeth Zechner
- Verein zur Förderung einer nachhaltigen und regionalen Pflanzenzüchtung, Zwettl, Austria
| | - Andreas Holzinger
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
| | - Eva M. Molin
- Unit Bioresources, Center for Health & Bioresources, AIT Austrian Institute of Technology, Tulln, Austria
- Human-Centered AI Lab, Department of Forest- and Soil Sciences, Institute of Forest Engineering, University of Natural Resources and Life Sciences Vienna, Vienna, Austria
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Dorantes-Palma D, Pérez-Mora S, Azuara-Liceaga E, Pérez-Rueda E, Pérez-Ishiwara DG, Coca-González M, Medel-Flores MO, Gómez-García C. Screening and Structural Characterization of Heat Shock Response Elements (HSEs) in Entamoeba histolytica Promoters. Int J Mol Sci 2024; 25:1319. [PMID: 38279319 PMCID: PMC10815948 DOI: 10.3390/ijms25021319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/28/2024] Open
Abstract
Entamoeba histolytica (E. histolytica) exhibits a remarkable capacity to respond to thermal shock stress through a sophisticated genetic regulation mechanism. This process is carried out via Heat Shock Response Elements (HSEs), which are recognized by Heat Shock Transcription Factors (EhHSTFs), enabling fine and precise control of gene expression. Our study focused on screening for HSEs in the promoters of the E. histolytica genome, specifically analyzing six HSEs, including Ehpgp5, EhrabB1, EhrabB4, EhrabB5, Ehmlbp, and Ehhsp100. We discovered 2578 HSEs, with 1412 in promoters of hypothetical genes and 1166 in coding genes. We observed that a single promoter could contain anywhere from one to five HSEs. Gene ontology analysis revealed the presence of HSEs in essential genes for the amoeba, including cysteine proteinases, ribosomal genes, Myb family DNA-binding proteins, and Rab GTPases, among others. Complementarily, our molecular docking analyses indicate that these HSEs are potentially recognized by EhHSTF5, EhHSTF6, and EhHSTF7 factors in their trimeric conformation. These findings suggest that E. histolytica has the capability to regulate a wide range of critical genes via HSE-EhHSTFs, not only for thermal stress response but also for vital functions of the parasite. This is the first comprehensive study of HSEs in the genome of E. histolytica, significantly contributing to the understanding of its genetic regulation and highlighting the complexity and precision of this mechanism in the parasite's survival.
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Affiliation(s)
- David Dorantes-Palma
- Laboratorio de Biomedicina Molecular 1, ENMyH, Instituto Politécnico Nacional, Mexico City 07320, Mexico; (D.D.-P.); (S.P.-M.); (D.G.P.-I.); (M.C.-G.); (M.O.M.-F.)
| | - Salvador Pérez-Mora
- Laboratorio de Biomedicina Molecular 1, ENMyH, Instituto Politécnico Nacional, Mexico City 07320, Mexico; (D.D.-P.); (S.P.-M.); (D.G.P.-I.); (M.C.-G.); (M.O.M.-F.)
| | - Elisa Azuara-Liceaga
- Posgrado en Ciencias Genómicas, Universidad Autónoma de la Ciudad de México, Mexico City 03100, Mexico;
| | - Ernesto Pérez-Rueda
- Unidad Académica del Estado de Yucatán, Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas, Universidad Nacional Autónoma de México, Mexico City 97302, Mexico;
| | - David Guillermo Pérez-Ishiwara
- Laboratorio de Biomedicina Molecular 1, ENMyH, Instituto Politécnico Nacional, Mexico City 07320, Mexico; (D.D.-P.); (S.P.-M.); (D.G.P.-I.); (M.C.-G.); (M.O.M.-F.)
| | - Misael Coca-González
- Laboratorio de Biomedicina Molecular 1, ENMyH, Instituto Politécnico Nacional, Mexico City 07320, Mexico; (D.D.-P.); (S.P.-M.); (D.G.P.-I.); (M.C.-G.); (M.O.M.-F.)
| | - María Olivia Medel-Flores
- Laboratorio de Biomedicina Molecular 1, ENMyH, Instituto Politécnico Nacional, Mexico City 07320, Mexico; (D.D.-P.); (S.P.-M.); (D.G.P.-I.); (M.C.-G.); (M.O.M.-F.)
| | - Consuelo Gómez-García
- Laboratorio de Biomedicina Molecular 1, ENMyH, Instituto Politécnico Nacional, Mexico City 07320, Mexico; (D.D.-P.); (S.P.-M.); (D.G.P.-I.); (M.C.-G.); (M.O.M.-F.)
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Guo J, Guo J, Li L, Bai X, Huo X, Shi W, Gao L, Dai K, Jing R, Hao C. Combined linkage analysis and association mapping identifies genomic regions associated with yield-related and drought-tolerance traits in wheat (Triticum aestivum L.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2023; 136:250. [PMID: 37982873 DOI: 10.1007/s00122-023-04494-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 10/26/2023] [Indexed: 11/21/2023]
Abstract
KEY MESSAGE Combined linkage analysis and association mapping identified genomic regions associated with yield and drought tolerance, providing information to assist breeding for high yield and drought tolerance in wheat. Wheat (Triticum aestivum L.) is one of the most widely grown food crops and provides adequate amounts of protein to support human health. Drought stress is the most important abiotic stress constraining yield during the flowering and grain development periods. Precise targeting of genomic regions underlying yield- and drought tolerance-responsive traits would assist in breeding programs. In this study, two water treatments (well-watered, WW, and rain-fed water stress, WS) were applied, and five yield-related agronomic traits (plant height, PH; spike length, SL; spikelet number per spike, SNPS; kernel number per spike, KNPS; thousand kernel weight, TKW) and drought response values (DRVs) were used to characterize the drought sensitivity of each accession. Association mapping was performed on an association panel of 304 accessions, and linkage analysis was applied to a doubled haploid (DH) population of 152 lines. Eleven co-localized genomic regions associated with yield traits and DRV were identified in both populations. Many previously cloned key genes were located in these regions. In particular, a TKW-associated region on chromosome 2D was identified using both association mapping and linkage analysis and a key candidate gene, TraesCS2D02G142500, was detected based on gene annotation and differences in expression levels. Exonic SNPs were analyzed by sequencing the full length of TraesCS2D02G142500 in the association panel, and a rare haplotype, Hap-2, which reduced TKW to a lesser extent than Hap-1 under drought stress, and the Hap-2 varieties presented drought-insensitive. Altogether, this study provides fundamental insights into molecular targets for high yield and drought tolerance in wheat.
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Affiliation(s)
- Jie Guo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Jiahui Guo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
- College of Agronomy, Yangzhou University, Yangzhou, 225009, Jiangsu, China
| | - Long Li
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Xionghui Bai
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Xiaoyu Huo
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Weiping Shi
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China
| | - Lifeng Gao
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Keli Dai
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China.
| | - Ruilian Jing
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
| | - Chenyang Hao
- College of Agronomy, Key Laboratory of Sustainable Dryland Agriculture (Co-Construction By Ministry and Province), Ministry of Agriculture and Rural Affairs, Shanxi Agricultural University, Jinzhong, 030801, Shanxi, China.
- State Key Laboratory of Crop Gene Resources and Breeding/Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081, China.
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