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Münzbergová Z, Šurinová M, Biscarini F, Níčová E. Genetic response of a perennial grass to warm and wet environments interacts and is associated with trait means as well as plasticity. J Evol Biol 2024; 37:704-716. [PMID: 38761114 DOI: 10.1093/jeb/voae060] [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: 06/29/2023] [Revised: 04/15/2024] [Accepted: 05/17/2024] [Indexed: 05/20/2024]
Abstract
The potential for rapid evolution is an important mechanism allowing species to adapt to changing climatic conditions. Although such potential has been largely studied in various short-lived organisms, to what extent we can observe similar patterns in long-lived plant species, which often dominate natural systems, is largely unexplored. We explored the potential for rapid evolution in Festuca rubra, a long-lived grass with extensive clonal growth dominating in alpine grasslands. We used a field sowing experiment simulating expected climate change in our model region. Specifically, we exposed seeds from five independent seed sources to novel climatic conditions by shifting them along a natural climatic grid and explored the genetic profiles of established seedlings after 3 years. Data on genetic profiles of plants selected under different novel conditions indicate that different climate shifts select significantly different pools of genotypes from common seed pools. Increasing soil moisture was more important than increasing temperature or the interaction of the two climatic factors in selecting pressure. This can indicate negative genetic interaction in response to the combined effects or that the effects of different climates are interactive rather than additive. The selected alleles were found in genomic regions, likely affecting the function of specific genes or their expression. Many of these were also linked to morphological traits (mainly to trait plasticity), suggesting these changes may have a consequence on plant performance. Overall, these data indicate that even long-lived plant species may experience strong selection by climate, and their populations thus have the potential to rapidly adapt to these novel conditions.
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Affiliation(s)
- Zuzana Münzbergová
- Department of Botany, Faculty of Science, Charles University, Benátská 2, Prague, Czech Republic
- Department of Population Ecology, Institute of Botany, Czech Academy of Sciences, Zámek 1, Průhonice, Czech Republic
| | - Maria Šurinová
- Department of Botany, Faculty of Science, Charles University, Benátská 2, Prague, Czech Republic
- Department of Population Ecology, Institute of Botany, Czech Academy of Sciences, Zámek 1, Průhonice, Czech Republic
| | - Filippo Biscarini
- Institute of Agricultural Biology and Biotechnology, National Research Council (IBBA-CNR), Milan, Italy
| | - Eva Níčová
- Department of Population Ecology, Institute of Botany, Czech Academy of Sciences, Zámek 1, Průhonice, Czech Republic
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2
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Li L, Jiang F, Bi Y, Yin X, Zhang Y, Li S, Zhang X, Liu M, Li J, Shaw RK, Ijaz B, Fan X. Dissection of Common Rust Resistance in Tropical Maize Multiparent Population through GWAS and Linkage Studies. PLANTS (BASEL, SWITZERLAND) 2024; 13:1410. [PMID: 38794480 PMCID: PMC11125173 DOI: 10.3390/plants13101410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2024] [Revised: 05/02/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024]
Abstract
Common rust (CR), caused by Puccina sorghi, is a major foliar disease in maize that leads to quality deterioration and yield losses. To dissect the genetic architecture of CR resistance in maize, this study utilized the susceptible temperate inbred line Ye107 as the male parent crossed with three resistant tropical maize inbred lines (CML312, D39, and Y32) to generate 627 F7 recombinant inbred lines (RILs), with the aim of identifying maize disease-resistant loci and candidate genes for common rust. Phenotypic data showed good segregation between resistance and susceptibility, with varying degrees of resistance observed across different subpopulations. Significant genotype effects and genotype × environment interactions were observed, with heritability ranging from 85.7% to 92.2%. Linkage and genome-wide association analyses across the three environments identified 20 QTLs and 62 significant SNPs. Among these, seven major QTLs explained 66% of the phenotypic variance. Comparison with six SNPs repeatedly identified across different environments revealed overlap between qRUST3-3 and Snp-203,116,453, and Snp-204,202,469. Haplotype analysis indicated two different haplotypes for CR resistance for both the SNPs. Based on LD decay plots, three co-located candidate genes, Zm00001d043536, Zm00001d043566, and Zm00001d043569, were identified within 20 kb upstream and downstream of these two SNPs. Zm00001d043536 regulates hormone regulation, Zm00001d043566 controls stomatal opening and closure, related to trichome, and Zm00001d043569 is associated with plant disease immune responses. Additionally, we performed candidate gene screening for five additional SNPs that were repeatedly detected across different environments, resulting in the identification of five candidate genes. These findings contribute to the development of genetic resources for common rust resistance in maize breeding programs.
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Affiliation(s)
- Linzhuo Li
- Institute of Resource Plants, Yunnan University, Kunming 650500, China; (L.L.); (S.L.); (X.Z.); (M.L.); (J.L.)
| | - Fuyan Jiang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (F.J.); (Y.B.); (X.Y.); (Y.Z.); (R.K.S.); (B.I.)
| | - Yaqi Bi
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (F.J.); (Y.B.); (X.Y.); (Y.Z.); (R.K.S.); (B.I.)
| | - Xingfu Yin
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (F.J.); (Y.B.); (X.Y.); (Y.Z.); (R.K.S.); (B.I.)
| | - Yudong Zhang
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (F.J.); (Y.B.); (X.Y.); (Y.Z.); (R.K.S.); (B.I.)
| | - Shaoxiong Li
- Institute of Resource Plants, Yunnan University, Kunming 650500, China; (L.L.); (S.L.); (X.Z.); (M.L.); (J.L.)
| | - Xingjie Zhang
- Institute of Resource Plants, Yunnan University, Kunming 650500, China; (L.L.); (S.L.); (X.Z.); (M.L.); (J.L.)
| | - Meichen Liu
- Institute of Resource Plants, Yunnan University, Kunming 650500, China; (L.L.); (S.L.); (X.Z.); (M.L.); (J.L.)
| | - Jinfeng Li
- Institute of Resource Plants, Yunnan University, Kunming 650500, China; (L.L.); (S.L.); (X.Z.); (M.L.); (J.L.)
| | - Ranjan K. Shaw
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (F.J.); (Y.B.); (X.Y.); (Y.Z.); (R.K.S.); (B.I.)
| | - Babar Ijaz
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (F.J.); (Y.B.); (X.Y.); (Y.Z.); (R.K.S.); (B.I.)
| | - Xingming Fan
- Institute of Food Crops, Yunnan Academy of Agricultural Sciences, Kunming 650205, China; (F.J.); (Y.B.); (X.Y.); (Y.Z.); (R.K.S.); (B.I.)
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3
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Zhang Y, Gu S, Du J, Huang G, Shi J, Lu X, Wang J, Yang W, Guo X, Zhao C. Plant microphenotype: from innovative imaging to computational analysis. PLANT BIOTECHNOLOGY JOURNAL 2024; 22:802-818. [PMID: 38217351 PMCID: PMC10955502 DOI: 10.1111/pbi.14244] [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: 03/10/2023] [Revised: 11/09/2023] [Accepted: 11/11/2023] [Indexed: 01/15/2024]
Abstract
The microphenotype plays a key role in bridging the gap between the genotype and the complex macro phenotype. In this article, we review the advances in data acquisition and the intelligent analysis of plant microphenotyping and present applications of microphenotyping in plant science over the past two decades. We then point out several challenges in this field and suggest that cross-scale image acquisition strategies, powerful artificial intelligence algorithms, advanced genetic analysis, and computational phenotyping need to be established and performed to better understand interactions among genotype, environment, and management. Microphenotyping has entered the era of Microphenotyping 3.0 and will largely advance functional genomics and plant science.
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Affiliation(s)
- Ying Zhang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Shenghao Gu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jianjun Du
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Guanmin Huang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jiawei Shi
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Jinglu Wang
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xinyu Guo
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
| | - Chunjiang Zhao
- Beijing Key Lab of Digital Plant, Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing, China
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4
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Alemu A, Åstrand J, Montesinos-López OA, Isidro Y Sánchez J, Fernández-Gónzalez J, Tadesse W, Vetukuri RR, Carlsson AS, Ceplitis A, Crossa J, Ortiz R, Chawade A. Genomic selection in plant breeding: Key factors shaping two decades of progress. MOLECULAR PLANT 2024; 17:552-578. [PMID: 38475993 DOI: 10.1016/j.molp.2024.03.007] [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: 11/03/2023] [Revised: 01/22/2024] [Accepted: 03/08/2024] [Indexed: 03/14/2024]
Abstract
Genomic selection, the application of genomic prediction (GP) models to select candidate individuals, has significantly advanced in the past two decades, effectively accelerating genetic gains in plant breeding. This article provides a holistic overview of key factors that have influenced GP in plant breeding during this period. We delved into the pivotal roles of training population size and genetic diversity, and their relationship with the breeding population, in determining GP accuracy. Special emphasis was placed on optimizing training population size. We explored its benefits and the associated diminishing returns beyond an optimum size. This was done while considering the balance between resource allocation and maximizing prediction accuracy through current optimization algorithms. The density and distribution of single-nucleotide polymorphisms, level of linkage disequilibrium, genetic complexity, trait heritability, statistical machine-learning methods, and non-additive effects are the other vital factors. Using wheat, maize, and potato as examples, we summarize the effect of these factors on the accuracy of GP for various traits. The search for high accuracy in GP-theoretically reaching one when using the Pearson's correlation as a metric-is an active research area as yet far from optimal for various traits. We hypothesize that with ultra-high sizes of genotypic and phenotypic datasets, effective training population optimization methods and support from other omics approaches (transcriptomics, metabolomics and proteomics) coupled with deep-learning algorithms could overcome the boundaries of current limitations to achieve the highest possible prediction accuracy, making genomic selection an effective tool in plant breeding.
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Affiliation(s)
- Admas Alemu
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Johanna Åstrand
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden; Lantmännen Lantbruk, Svalöv, Sweden
| | | | - Julio Isidro Y Sánchez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Javier Fernández-Gónzalez
- Centro de Biotecnología y Genómica de Plantas (CBGP, UPM-INIA), Universidad Politécnica de Madrid (UPM) - Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Campus de Montegancedo-UPM, 28223 Madrid, Spain
| | - Wuletaw Tadesse
- International Center for Agricultural Research in the Dry Areas (ICARDA), Rabat, Morocco
| | - Ramesh R Vetukuri
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | - Anders S Carlsson
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Km 45, Carretera México-Veracruz, Texcoco, México 52640, Mexico
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden.
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, Sweden
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5
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Dai T, Ban S, Han L, Li L, Zhang Y, Zhang Y, Zhu W. Effects of exogenous glycine betaine on growth and development of tomato seedlings under cold stress. FRONTIERS IN PLANT SCIENCE 2024; 15:1332583. [PMID: 38584954 PMCID: PMC10995342 DOI: 10.3389/fpls.2024.1332583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 03/11/2024] [Indexed: 04/09/2024]
Abstract
Low temperature is a type of abiotic stress affecting the tomato (Solanum lycopersicum) growth. Understanding the mechanisms and utilization of exogenous substances underlying plant tolerance to cold stress would lay the foundation for improving temperature resilience in this important crop. Our study is aiming to investigate the effect of exogenous glycine betaine (GB) on tomato seedlings to increase tolerance to low temperatures. By treating tomato seedlings with exogenous GB under low temperature stress, we found that 30 mmol/L exogenous GB can significantly improve the cold tolerance of tomato seedlings. Exogenous GB can influence the enzyme activity of antioxidant defense system and ROS levels in tomato leaves. The seedlings with GB treatment presented higher Fv/Fm value and photochemical activity under cold stress compared with the control. Moreover, analysis of high-throughput plant phenotyping of tomato seedlings also supported that exogenous GB can protect the photosynthetic system of tomato seedlings under cold stress. In addition, we proved that exogenous GB significantly increased the content of endogenous abscisic acid (ABA) and decreased endogenous gibberellin (GA) levels, which protected tomatoes from low temperatures. Meanwhile, transcriptional analysis showed that GB regulated the expression of genes involved in antioxidant capacity, calcium signaling, photosynthesis activity, energy metabolism-related and low temperature pathway-related genes in tomato plants. In conclusion, our findings indicated that exogenous GB, as a cryoprotectant, can enhance plant tolerance to low temperature by improving the antioxidant system, photosynthetic system, hormone signaling, and cold response pathway and so on.
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Affiliation(s)
- Taoyu Dai
- Shanghai Key Laboratory of Protected Horticulture Technology, The Protected Horticulture Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Songtao Ban
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Liyuan Han
- Shanghai Key Laboratory of Protected Horticulture Technology, The Protected Horticulture Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Linyi Li
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Yingying Zhang
- Shanghai Key Laboratory of Protected Horticulture Technology, The Protected Horticulture Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
| | - Yuechen Zhang
- State Key Laboratory of North China Crop Improvement and Regulation/Key Laboratory of Crop Growth Regulation of Hebei Province/College of Agronomy, Hebei Agricultural University, Baoding, Hebei, China
| | - Weimin Zhu
- Shanghai Key Laboratory of Protected Horticulture Technology, The Protected Horticulture Institute, Shanghai Academy of Agricultural Sciences, Shanghai, China
- Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs, Agricultural Information Institute of Science and Technology, Shanghai Academy of Agricultural Sciences, Shanghai, China
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6
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McLaughlin CM, Li M, Perryman M, Heymans A, Schneider H, Lasky JR, Sawers RJH. Evidence that variation in root anatomy contributes to local adaptation in Mexican native maize. Evol Appl 2024; 17:e13673. [PMID: 38468714 PMCID: PMC10925829 DOI: 10.1111/eva.13673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/07/2024] [Accepted: 02/22/2024] [Indexed: 03/13/2024] Open
Abstract
Mexican native maize (Zea mays ssp. mays) is adapted to a wide range of climatic and edaphic conditions. Here, we focus specifically on the potential role of root anatomical variation in this adaptation. Given the investment required to characterize root anatomy, we present a machine-learning approach using environmental descriptors to project trait variation from a relatively small training panel onto a larger panel of genotyped and georeferenced Mexican maize accessions. The resulting models defined potential biologically relevant clines across a complex environment that we used subsequently for genotype-environment association. We found evidence of systematic variation in maize root anatomy across Mexico, notably a prevalence of trait combinations favoring a reduction in axial hydraulic conductance in varieties sourced from cooler, drier highland areas. We discuss our results in the context of previously described water-banking strategies and present candidate genes that are associated with both root anatomical and environmental variation. Our strategy is a refinement of standard environmental genome-wide association analysis that is applicable whenever a training set of georeferenced phenotypic data is available.
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Affiliation(s)
- Chloee M. McLaughlin
- Intercollege Graduate Degree Program in Plant BiologyThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Meng Li
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Melanie Perryman
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Adrien Heymans
- Umeå Plant Science Centre, Department of Forest Genetics and Plant PhysiologySwedish University of Agricultural SciencesUmeåSweden
- Earth and Life InstituteUC LouvainLouvain‐la‐NeuveBelgium
| | - Hannah Schneider
- Department of Physiology and Cell BiologyLeibniz Institute for Plant Genetics and Crop Plant Research (IPK)SeelandGermany
| | - Jesse R. Lasky
- Department of BiologyThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
| | - Ruairidh J. H. Sawers
- Department of Plant ScienceThe Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA
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7
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Li ZY, Ma N, Zhang FJ, Li LZ, Li HJ, Wang XF, Zhang Z, You CX. Functions of Phytochrome Interacting Factors (PIFs) in Adapting Plants to Biotic and Abiotic Stresses. Int J Mol Sci 2024; 25:2198. [PMID: 38396875 PMCID: PMC10888771 DOI: 10.3390/ijms25042198] [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: 01/06/2024] [Revised: 02/03/2024] [Accepted: 02/04/2024] [Indexed: 02/25/2024] Open
Abstract
Plants possess the remarkable ability to sense detrimental environmental stimuli and launch sophisticated signal cascades that culminate in tailored responses to facilitate their survival, and transcription factors (TFs) are closely involved in these processes. Phytochrome interacting factors (PIFs) are among these TFs and belong to the basic helix-loop-helix family. PIFs are initially identified and have now been well established as core regulators of phytochrome-associated pathways in response to the light signal in plants. However, a growing body of evidence has unraveled that PIFs also play a crucial role in adapting plants to various biological and environmental pressures. In this review, we summarize and highlight that PIFs function as a signal hub that integrates multiple environmental cues, including abiotic (i.e., drought, temperature, and salinity) and biotic stresses to optimize plant growth and development. PIFs not only function as transcription factors to reprogram the expression of related genes, but also interact with various factors to adapt plants to harsh environments. This review will contribute to understanding the multifaceted functions of PIFs in response to different stress conditions, which will shed light on efforts to further dissect the novel functions of PIFs, especially in adaption to detrimental environments for a better survival of plants.
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Affiliation(s)
- Zhao-Yang Li
- College of Horticulture Science and Engineering, State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271000, China; (Z.-Y.L.); (N.M.); (F.-J.Z.); (L.-Z.L.); (H.-J.L.); (X.-F.W.)
| | - Ning Ma
- College of Horticulture Science and Engineering, State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271000, China; (Z.-Y.L.); (N.M.); (F.-J.Z.); (L.-Z.L.); (H.-J.L.); (X.-F.W.)
| | - Fu-Jun Zhang
- College of Horticulture Science and Engineering, State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271000, China; (Z.-Y.L.); (N.M.); (F.-J.Z.); (L.-Z.L.); (H.-J.L.); (X.-F.W.)
- Department of Horticulture, College of Agriculture, Shihezi University, Shihezi 832003, China
| | - Lian-Zhen Li
- College of Horticulture Science and Engineering, State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271000, China; (Z.-Y.L.); (N.M.); (F.-J.Z.); (L.-Z.L.); (H.-J.L.); (X.-F.W.)
| | - Hao-Jian Li
- College of Horticulture Science and Engineering, State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271000, China; (Z.-Y.L.); (N.M.); (F.-J.Z.); (L.-Z.L.); (H.-J.L.); (X.-F.W.)
| | - Xiao-Fei Wang
- College of Horticulture Science and Engineering, State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271000, China; (Z.-Y.L.); (N.M.); (F.-J.Z.); (L.-Z.L.); (H.-J.L.); (X.-F.W.)
| | - Zhenlu Zhang
- College of Horticulture Science and Engineering, State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271000, China; (Z.-Y.L.); (N.M.); (F.-J.Z.); (L.-Z.L.); (H.-J.L.); (X.-F.W.)
| | - Chun-Xiang You
- College of Horticulture Science and Engineering, State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271000, China; (Z.-Y.L.); (N.M.); (F.-J.Z.); (L.-Z.L.); (H.-J.L.); (X.-F.W.)
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Cao Y, Tian D, Tang Z, Liu X, Hu W, Zhang Z, Song S. OPIA: an open archive of plant images and related phenotypic traits. Nucleic Acids Res 2024; 52:D1530-D1537. [PMID: 37930849 PMCID: PMC10767956 DOI: 10.1093/nar/gkad975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 11/08/2023] Open
Abstract
High-throughput plant phenotype acquisition technologies have been extensively utilized in plant phenomics studies, leading to vast quantities of images and image-based phenotypic traits (i-traits) that are critically essential for accelerating germplasm screening, plant diseases identification and biotic & abiotic stress classification. Here, we present the Open Plant Image Archive (OPIA, https://ngdc.cncb.ac.cn/opia/), an open archive of plant images and i-traits derived from high-throughput phenotyping platforms. Currently, OPIA houses 56 datasets across 11 plants, comprising a total of 566 225 images with 2 417 186 labeled instances. Notably, it incorporates 56 i-traits of 93 rice and 105 wheat cultivars based on 18 644 individual RGB images, and these i-traits are further annotated based on the Plant Phenotype and Trait Ontology (PPTO) and cross-linked with GWAS Atlas. Additionally, each dataset in OPIA is assigned an evaluation score that takes account of image data volume, image resolution, and the number of labeled instances. More importantly, OPIA is equipped with useful tools for online image pre-processing and intelligent prediction. Collectively, OPIA provides open access to valuable datasets, pre-trained models, and phenotypic traits across diverse plants and thus bears great potential to play a crucial role in facilitating artificial intelligence-assisted breeding research.
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Affiliation(s)
- Yongrong Cao
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Dongmei Tian
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
| | - Zhixin Tang
- University of Chinese Academy of Sciences, Beijing 100049, China
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaonan Liu
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weijuan Hu
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Zhang Zhang
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shuhui Song
- National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing 100101, China
- University of Chinese Academy of Sciences, Beijing 100049, China
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9
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Zhang S, Xu J, Zhang Y, Cao Y. Identification and Characterization of ABCG15-A Gene Required for Exocarp Color Differentiation in Pear. Genes (Basel) 2023; 14:1827. [PMID: 37761967 PMCID: PMC10530978 DOI: 10.3390/genes14091827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 09/29/2023] Open
Abstract
Exocarp color is a commercially essential quality for pear which can be divided into two types: green and russet. The occurrence of russet color is associated with deficiencies and defects in the cuticular and epidermal layers, which affect the structure of the cell wall and the deposition of suberin. Until now, the genetic basics triggering this trait have not been well understood, and limited genes have been identified for the trait. To figure out the gene controlling the trait of exocarp color, we perform a comprehensive genome-wide association study, and we describe the candidate genes. One gene encoding the ABCG protein has been verified to be associated with the trait, using an integrative analysis of the metabolomic and transcriptomic data. This review covers a variety of omics resources, which provide a valuable resource for identifying gene-controlled traits of interest. The findings in this study help to elucidate the genetic components responsible for the trait of exocarp color in pear, and the implications of these findings for future pear breeding are evaluated.
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Affiliation(s)
| | | | | | - Yufen Cao
- Research Institute of Pomology, Chinese Academy of Agricultural Sciences, Xinghai South Street 98, Xingcheng 125100, China; (S.Z.); (J.X.); (Y.Z.)
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Zhang Y, Zhang N, Chai X, Sun T. Machine learning for image-based multi-omics analysis of leaf veins. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4928-4941. [PMID: 37410807 DOI: 10.1093/jxb/erad251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/29/2023] [Indexed: 07/08/2023]
Abstract
Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.
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Affiliation(s)
- Yubin Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Ning Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Xiujuan Chai
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Tan Sun
- Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China
- Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
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Fang Y, Liu H, Qin L, Qi F, Sun Z, Wu J, Dong W, Huang B, Zhang X. Identification of QTL for kernel weight and size and analysis of the pentatricopeptide repeat (PPR) gene family in cultivated peanut (Arachis hypogaea L.). BMC Genomics 2023; 24:495. [PMID: 37641021 PMCID: PMC10463326 DOI: 10.1186/s12864-023-09568-y] [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: 06/08/2023] [Accepted: 08/09/2023] [Indexed: 08/31/2023] Open
Abstract
Peanut (Arachis hypogaea L.) is an important oilseed crop worldwide. Improving its yield is crucial for sustainable peanut production to meet increasing food and industrial requirements. Deciphering the genetic control underlying peanut kernel weight and size, which are essential components of peanut yield, would facilitate high-yield breeding. A high-density single nucleotide polymorphism (SNP)-based linkage map was constructed using a recombinant inbred lines (RIL) population derived from a cross between the variety Yuanza9102 and a germplasm accession wt09-0023. Kernel weight and size quantitative trait loci (QTLs) were co-localized to a 0.16 Mb interval on Arahy07 using inclusive composite interval mapping (ICIM). Analysis of SNP, and Insertion or Deletion (INDEL) markers in the QTL interval revealed a gene encoding a pentatricopeptide repeat (PPR) superfamily protein as a candidate closely linked with kernel weight and size in cultivated peanut. Examination of the PPR gene family indicated a high degree of collinearity of PPR genes between A. hypogaea and its diploid progenitors, Arachis duranensis and Arachis ipaensis. The candidate PPR gene, Arahy.JX1V6X, displayed a constitutive expression pattern in developing seeds. These findings lay a foundation for further fine mapping of QTLs related to kernel weight and size, as well as validation of candidate genes in cultivated peanut.
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Affiliation(s)
- Yuanjin Fang
- College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China
- Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, 450002, China
| | - Hua Liu
- Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, 450002, China
| | - Li Qin
- Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, 450002, China
| | - Feiyan Qi
- Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, 450002, China
| | - Ziqi Sun
- Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, 450002, China
| | - Jihua Wu
- Shangqiu Academy of Agriculture and Forestry, Shangqiu, 476002, China
| | - Wenzhao Dong
- Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, 450002, China
| | - Bingyan Huang
- Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, 450002, China.
| | - Xinyou Zhang
- College of Agriculture, Nanjing Agricultural University, Nanjing, 210095, China.
- Henan Academy of Agricultural Sciences/Henan Institute of Crop Molecular Breeding/Shennong Laboratory/Key Laboratory of Oil Crops in Huang-Huai-Hai Planis, Ministry of Agriculture and Rural Affairs/Henan Provincial Key Laboratory for Oil Crops Improvement, Zhengzhou, 450002, China.
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12
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Gautam RK, Singh PK, Venkatesan K, Rakesh B, Sakthivel K, Swain S, Srikumar M, Zamir Ahmed SK, Devakumar K, Rao SS, Vijayan J, Ali S, Langyan S. Harnessing intra-varietal variation for agro-morphological and nutritional traits in a popular rice landrace for sustainable food security in tropical islands. Front Nutr 2023; 10:1088208. [PMID: 36908925 PMCID: PMC9995847 DOI: 10.3389/fnut.2023.1088208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 01/19/2023] [Indexed: 02/25/2023] Open
Abstract
Introduction Rice crop meets the calorie and nutritional requirements of a larger segment of the global population. Here, we report the occurrence of intra-varietal variation in a popular rice landrace C14-8 traditionally grown under the geographical isolation of the Andaman Islands. Methods Based on grain husk color, four groups were formed, wherein the extent of intra-varietal variation was studied by employing 22 agro-morphological and biochemical traits. Results Among the traits studied, flavonoid and anthocyanin contents and grain yield exhibited a wider spectrum of variability due to more coefficients of variation (>25%). The first five principal components (PCs) of principal components analysis explained a significant proportion of the variation (91%) and the first two PCs explained 63.3% of the total variation, with PC1 and PC2 explaining 35.44 and 27.91%, respectively. A total of 50 highly variable SSR (HvSSR) markers spanning over 12 chromosomes produced 314 alleles, which ranged from 1 to 15 alleles per marker, with an average of 6.28. Of the 314 alleles, 64 alleles were found to be rare among the C14-8 selections. While 62% of HvSSR markers exhibited polymorphism among the C14-8 population, chromosomes 2, 7, 9, and 11 harbored the most polymorphic loci. The group clustering of the selections through HvSSR markers conformed to the grouping based on grain husk coloration. Discussion Our studies on the existence and pertinence of intra-varietal variations are expected to be of significance in the realms of evolutionary biology and sustainable food and nutritional security under the changing climate.
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Affiliation(s)
- Raj Kumar Gautam
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India.,ICAR-National Bureau of Plant Genetic Resources, Pusa, New Delhi, India
| | - Pankaj Kumar Singh
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India
| | - Kannan Venkatesan
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India
| | - Bandol Rakesh
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India
| | - Krishnan Sakthivel
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India.,ICAR-Indian Institute of Oilseed Research, Hyderabad, Telangana, India
| | - Sachidananda Swain
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India
| | - Muthulingam Srikumar
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India
| | - S K Zamir Ahmed
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India
| | - Kishnamoorthy Devakumar
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India.,ICAR-Sugarcane Breeding Institute, Coimbatore, Tamil Nadu, India
| | - Shyam Sunder Rao
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India
| | - Joshitha Vijayan
- ICAR-Central Island Agricultural Research Institute, Port Blair, Andaman and Nicobar Islands, India.,ICAR-National Institute of Plant Biotechnology, Pusa, New Delhi, India
| | - Sharik Ali
- ICAR-National Bureau of Plant Genetic Resources, Pusa, New Delhi, India
| | - Sapna Langyan
- ICAR-National Bureau of Plant Genetic Resources, Pusa, New Delhi, India
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