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Yin Z, Huang W, Li K, Fernie AR, Yan S. Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 38990529 DOI: 10.1111/tpj.16924] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/24/2024] [Accepted: 07/01/2024] [Indexed: 07/12/2024]
Abstract
Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.
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Affiliation(s)
- Zhibin Yin
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
- Institute of Advanced Science Facilities, Shenzhen, 518107, Guangdong, China
| | - Wenjie Huang
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| | - Kun Li
- Guangdong Key Laboratory of Crop Genetic Improvement, Crop Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476, Potsdam-Golm, Germany
| | - Shijuan Yan
- Guangdong Key Laboratory for Crop Germplasm Resources Preservation and Utilization, Agro-biological Gene Research Center, Guangdong Academy of Agricultural Sciences, Guangzhou, 510640, Guangdong, China
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2
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Huo Q, Song R, Ma Z. Recent advances in exploring transcriptional regulatory landscape of crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1421503. [PMID: 38903438 PMCID: PMC11188431 DOI: 10.3389/fpls.2024.1421503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.
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Affiliation(s)
| | | | - Zeyang Ma
- State Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Joint International Research Laboratory of Crop Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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3
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Guerrero-Méndez C, Abraham-Juárez MJ. Factors specifying sex determination in maize. PLANT REPRODUCTION 2024; 37:171-178. [PMID: 37966579 PMCID: PMC11180155 DOI: 10.1007/s00497-023-00485-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 10/21/2023] [Indexed: 11/16/2023]
Abstract
Plant architecture is an important feature for agronomic performance in crops. In maize, which is a monoecious plant, separation of floral organs to produce specific gametes has been studied from different perspectives including genetic, biochemical and physiological. Maize mutants affected in floral organ development have been key to identifying genes, hormones and other factors like miRNAs important for sex determination. In this review, we describe floral organ formation in maize, representative mutants and genes identified with a function in establishing sexual identity either classified as feminizing or masculinizing, and its relationship with hormones associated with sexual organ identity as jasmonic acid, brassinosteroid and gibberellin. Finally, we discuss the challenges and scopes of future research in maize sex determination.
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Affiliation(s)
- Cristina Guerrero-Méndez
- Laboratorio Nacional de Genómica para la Biodiversidad (LANGEBIO), Unidad de Genómica Avanzada, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), 36821, Irapuato, Mexico
| | - María Jazmín Abraham-Juárez
- Laboratorio Nacional de Genómica para la Biodiversidad (LANGEBIO), Unidad de Genómica Avanzada, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (CINVESTAV), 36821, Irapuato, Mexico.
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Liu Y, Li H, Liu J, Wang Y, Jiang C, Zhou Z, Zhuo L, Li W, Fernie AR, Jackson D, Yan J, Luo Y. The additive function of YIGE2 and YIGE1 in regulating maize ear length. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 38804053 DOI: 10.1111/tpj.16851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 05/06/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024]
Abstract
Ear length (EL) is a key trait that greatly contributes to yield in maize. Although dozens of EL quantitative trait loci have been mapped, very few causal genes have been cloned, and the molecular mechanisms remain largely unknown. Our previous study showed that YIGE1 is involved in sugar and auxin pathways to regulate ear inflorescence meristem (IM) development and thus affects EL in maize. Here, we reveal that YIGE2, the paralog of YIGE1, regulates maize ear development and EL through auxin pathway. Knockout of YIGE2 causes a significant decrease of auxin level, IM length, floret number, EL, and grain yield. yige1 yige2 double mutants had even shorter IM and ears implying that these two genes redundantly regulate IM development and EL. The genes controlling auxin levels are differential expressed in yige1 yige2 double mutants, leading to lower auxin level. These results elucidated the critical role of YIGE2 and the redundancy between YIGE2 and YIGE1 in maize ear development, providing a new genetic resource for maize yield improvement.
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Affiliation(s)
- Yu Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Huinan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Jie Liu
- Yazhouwan National Laboratory, Sanya, 572024, China
| | - Yuebin Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Chenglin Jiang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Ziqi Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Lin Zhuo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Wenqiang Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Alisdair R Fernie
- Department of Molecular Physiology, Max-Planck-Institute of Molecular Plant Physiology, Potsdam-Golm, 14476, Germany
| | - David Jackson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, 11724, USA
| | - Jianbing Yan
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
- Yazhouwan National Laboratory, Sanya, 572024, China
| | - Yun Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
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Ai G, He C, Bi S, Zhou Z, Liu A, Hu X, Liu Y, Jin L, Zhou J, Zhang H, Du D, Chen H, Gong X, Saeed S, Su H, Lan C, Chen W, Li Q, Mao H, Li L, Liu H, Chen D, Kaufmann K, Alazab KF, Yan W. Dissecting the molecular basis of spike traits by integrating gene regulatory networks and genetic variation in wheat. PLANT COMMUNICATIONS 2024; 5:100879. [PMID: 38486454 PMCID: PMC11121755 DOI: 10.1016/j.xplc.2024.100879] [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: 01/31/2024] [Revised: 02/25/2024] [Accepted: 03/11/2024] [Indexed: 04/30/2024]
Abstract
Spike architecture influences both grain weight and grain number per spike, which are the two major components of grain yield in bread wheat (Triticum aestivum L.). However, the complex wheat genome and the influence of various environmental factors pose challenges in mapping the causal genes that affect spike traits. Here, we systematically identified genes involved in spike trait formation by integrating information on genomic variation and gene regulatory networks controlling young spike development in wheat. We identified 170 loci that are responsible for variations in spike length, spikelet number per spike, and grain number per spike through genome-wide association study and meta-QTL analyses. We constructed gene regulatory networks for young inflorescences at the double ridge stage and the floret primordium stage, in which the spikelet meristem and the floret meristem are predominant, respectively, by integrating transcriptome, histone modification, chromatin accessibility, eQTL, and protein-protein interactome data. From these networks, we identified 169 hub genes located in 76 of the 170 QTL regions whose polymorphisms are significantly associated with variation in spike traits. The functions of TaZF-B1, VRT-B2, and TaSPL15-A/D in establishment of wheat spike architecture were verified. This study provides valuable molecular resources for understanding spike traits and demonstrates that combining genetic analysis and developmental regulatory networks is a robust approach for dissection of complex traits.
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Affiliation(s)
- Guo Ai
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Chao He
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Siteng Bi
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ziru Zhou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Ankui Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xin Hu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Yanyan Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Liujie Jin
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - JiaCheng Zhou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Heping Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Dengxiang Du
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Hao Chen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Xin Gong
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Sulaiman Saeed
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Handong Su
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Caixia Lan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Wei Chen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Qiang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Hailiang Mao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome, Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China
| | - Hao Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
| | - Dijun Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Kerstin Kaufmann
- Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität Zu Berlin, 10115 Berlin, Germany
| | - Khaled F Alazab
- Plant Research Department, Nuclear Research Center, Egyptian Atomic Energy Authority, Cairo 13759, Egypt
| | - Wenhao Yan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
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Poretsky E, Cagirici HB, Andorf CM, Sen TZ. Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits. G3 (BETHESDA, MD.) 2024; 14:jkae059. [PMID: 38492232 PMCID: PMC11075552 DOI: 10.1093/g3journal/jkae059] [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: 10/20/2023] [Revised: 10/20/2023] [Accepted: 03/08/2024] [Indexed: 03/18/2024]
Abstract
The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein-protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.
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Affiliation(s)
- Elly Poretsky
- Crop Improvement and Genetics Research Unit, U.S. Department of Agriculture, Agricultural Research Service, 800 Buchanan St., Albany, CA 94710, USA
| | - Halise Busra Cagirici
- Crop Improvement and Genetics Research Unit, U.S. Department of Agriculture, Agricultural Research Service, 800 Buchanan St., Albany, CA 94710, USA
| | - Carson M Andorf
- Corn Insects and Crop Genetics Research, U.S. Department of Agriculture, Agricultural Research Service, Ames, IA 50011, USA
- Department of Computer Science, Iowa State University, Ames, IA 50011, USA
| | - Taner Z Sen
- Crop Improvement and Genetics Research Unit, U.S. Department of Agriculture, Agricultural Research Service, 800 Buchanan St., Albany, CA 94710, USA
- Department of Bioengineering, University of California, 306 Stanley Hall, Berkeley, CA 94720, USA
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Rajput P, Urfan M, Sharma S, Hakla HR, Nandan B, Das R, Roychowdhury R, Chowdhary SP. Natural variation in root traits identifies significant SNPs and candidate genes for phosphate deficiency tolerance in Zea mays L. PHYSIOLOGIA PLANTARUM 2024; 176:e14396. [PMID: 38887929 DOI: 10.1111/ppl.14396] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 05/08/2024] [Accepted: 05/30/2024] [Indexed: 06/20/2024]
Abstract
Phosphorus (P) is a crucial macronutrient required for normal plant growth. Its effective uptake from the soil is a trait of agronomic importance. Natural variation in maize (339 accessions) root traits, namely root length and number of primary, seminal, and crown roots, root and shoot phosphate (Pi) contents, and root-to-shoot Pi translocation (root: shoot Pi) under normal (control, 40 ppm) and low phosphate (LP, 1 ppm) conditions, were used for genome-wide association studies (GWAS). The Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK) model of GWAS provided 23 single nucleotide polymorphisms (SNPs) and 12 relevant candidate genes putatively linked with root Pi, root: shoot Pi, and crown root number (CRN) under LP. The DNA-protein interaction analysis of Zm00001d002842, Zm00001d002837, Zm00001d002843 for root Pi, and Zm00001d044312, Zm00001d045550, Zm00001d025915, Zm00001d044313, Zm00001d051842 for root: shoot Pi, and Zm00001d031561, Zm00001d001803, and Zm00001d001804 for CRN showed the presence of potential binding sites of key transcription factors like MYB62, bZIP11, ARF4, ARF7, ARF10 and ARF16 known for induction/suppression of phosphate starvation response (PHR). The in-silico RNA-seq analysis revealed up or down-regulation of candidate genes along with key transcription factors of PHR, while Uniprot analysis provided genetic relatedness. Candidate genes that may play a role in P uptake and root-to-shoot Pi translocation under LP are proposed using common PHR signaling components like MYB62, ARF4, ARF7, ARF10, ARF16, and bZIP11 to induce changes in root growth in maize. Candidate genes may be used to improve low P tolerance in maize using the CRISPR strategy.
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Affiliation(s)
- Prakriti Rajput
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, India
| | - Mohammad Urfan
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, India
| | - Shubham Sharma
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, India
| | - Haroon Rashid Hakla
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu, India
| | - Brij Nandan
- Agronomy Division, SKUAST-JAMMU, Union Territory of Jammu & Kashmir, India
| | - Ranjan Das
- Department of Crop Physiology, Assam Agricultural University, Jorhat, Assam, India
| | - Rajib Roychowdhury
- Department of Plant Pathology and Weed Research, Institute of Plant Protection, Agricultural Research Organization (ARO) - Volcani Institute, Rishon Lezion, Israel
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Yang H, Zhang Z, Zhang N, Li T, Wang J, Zhang Q, Xue J, Zhu W, Xu S. QTL mapping for plant height and ear height using bi-parental immortalized heterozygous populations in maize. FRONTIERS IN PLANT SCIENCE 2024; 15:1371394. [PMID: 38590752 PMCID: PMC10999566 DOI: 10.3389/fpls.2024.1371394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Accepted: 03/06/2024] [Indexed: 04/10/2024]
Abstract
Introduction Plant height (PH) and ear height (EH) are key plant architectural traits in maize, which will affect the photosynthetic efficiency, high plant density tolerance, suitability for mechanical harvesting. Methods QTL mapping were conducted for PH and EH using a recombinant inbred line (RIL) population and two corresponding immortalized backcross (IB) populations obtained from crosses between the RIL population and the two parental lines. Results A total of 17 and 15 QTL were detected in the RIL and IB populations, respectively. Two QTL, qPH1-1 (qEH1-1) and qPH1-2 (qEH1-4) in the RIL, were simultaneously identified for PH and EH. Combing reported genome-wide association and cloned PH-related genes, co-expression network analyses were constructed, then five candidate genes with high confidence in major QTL were identified including Zm00001d011117 and Zm00001d011108, whose homologs have been confirmed to play a role in determining PH in maize and soybean. Discussion QTL mapping used a immortalized backcross population is a new strategy. These identified genes in this study can provide new insights for improving the plant architecture in maize.
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Affiliation(s)
| | | | | | | | | | | | | | - Wanchao Zhu
- College of Agronomy, Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi, China
| | - Shutu Xu
- College of Agronomy, Key Laboratory of Maize Biology and Genetic Breeding in Arid Area of Northwest Region, Yangling, Shaanxi, China
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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] [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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Lu G, Liu P, Wu Q, Zhang S, Zhao P, Zhang Y, Que Y. Sugarcane breeding: a fantastic past and promising future driven by technology and methods. FRONTIERS IN PLANT SCIENCE 2024; 15:1375934. [PMID: 38525140 PMCID: PMC10957636 DOI: 10.3389/fpls.2024.1375934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Accepted: 02/21/2024] [Indexed: 03/26/2024]
Abstract
Sugarcane is the most important sugar and energy crop in the world. During sugarcane breeding, technology is the requirement and methods are the means. As we know, seed is the cornerstone of the development of the sugarcane industry. Over the past century, with the advancement of technology and the expansion of methods, sugarcane breeding has continued to improve, and sugarcane production has realized a leaping growth, providing a large amount of essential sugar and clean energy for the long-term mankind development, especially in the face of the future threats of world population explosion, reduction of available arable land, and various biotic and abiotic stresses. Moreover, due to narrow genetic foundation, serious varietal degradation, lack of breakthrough varieties, as well as long breeding cycle and low probability of gene polymerization, it is particularly important to realize the leapfrog development of sugarcane breeding by seizing the opportunity for the emerging Breeding 4.0, and making full use of modern biotechnology including but not limited to whole genome selection, transgene, gene editing, and synthetic biology, combined with information technology such as remote sensing and deep learning. In view of this, we focus on sugarcane breeding from the perspective of technology and methods, reviewing the main history, pointing out the current status and challenges, and providing a reasonable outlook on the prospects of smart breeding.
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Affiliation(s)
- Guilong Lu
- National Key Laboratory of Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences/Yunan Academy of Agricultural Sciences, Sanya/Kaiyuan, China
- College of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang, China
| | - Purui Liu
- College of Horticulture and Landscape Architecture, Henan Institute of Science and Technology, Xinxiang, China
| | - Qibin Wu
- National Key Laboratory of Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences/Yunan Academy of Agricultural Sciences, Sanya/Kaiyuan, China
- Key Laboratory of Sugarcane Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, National Engineering Research Center for Sugarcane, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Shuzhen Zhang
- National Key Laboratory of Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences/Yunan Academy of Agricultural Sciences, Sanya/Kaiyuan, China
| | - Peifang Zhao
- National Key Laboratory of Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences/Yunan Academy of Agricultural Sciences, Sanya/Kaiyuan, China
| | - Yuebin Zhang
- National Key Laboratory of Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences/Yunan Academy of Agricultural Sciences, Sanya/Kaiyuan, China
| | - Youxiong Que
- National Key Laboratory of Tropical Crop Breeding, Institute of Tropical Bioscience and Biotechnology, Chinese Academy of Tropical Agricultural Sciences/Yunan Academy of Agricultural Sciences, Sanya/Kaiyuan, China
- Key Laboratory of Sugarcane Biology and Genetic Breeding, Ministry of Agriculture and Rural Affairs, National Engineering Research Center for Sugarcane, College of Agriculture, Fujian Agriculture and Forestry University, Fuzhou, China
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11
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Xie S, Luo H, Huang W, Jin W, Dong Z. Striking a growth-defense balance: Stress regulators that function in maize development. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2024; 66:424-442. [PMID: 37787439 DOI: 10.1111/jipb.13570] [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: 08/27/2023] [Accepted: 10/01/2023] [Indexed: 10/04/2023]
Abstract
Maize (Zea mays) cultivation is strongly affected by both abiotic and biotic stress, leading to reduced growth and productivity. It has recently become clear that regulators of plant stress responses, including the phytohormones abscisic acid (ABA), ethylene (ET), and jasmonic acid (JA), together with reactive oxygen species (ROS), shape plant growth and development. Beyond their well established functions in stress responses, these molecules play crucial roles in balancing growth and defense, which must be finely tuned to achieve high yields in crops while maintaining some level of defense. In this review, we provide an in-depth analysis of recent research on the developmental functions of stress regulators, focusing specifically on maize. By unraveling the contributions of these regulators to maize development, we present new avenues for enhancing maize cultivation and growth while highlighting the potential risks associated with manipulating stress regulators to enhance grain yields in the face of environmental challenges.
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Affiliation(s)
- Shiyi Xie
- Maize Engineering and Technology Research Center of Hunan Province, College of Agronomy, Hunan Agricultural University, Changsha, 410128, China
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
| | - Hongbing Luo
- Maize Engineering and Technology Research Center of Hunan Province, College of Agronomy, Hunan Agricultural University, Changsha, 410128, China
| | - Wei Huang
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
| | - Weiwei Jin
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
- Tianjin Key Laboratory of Intelligent Breeding of Major Crops, Fresh Corn Research Center of BTH, College of Agronomy & Resources and Environment, Tianjin Agricultural University, Tianjin, 300384, China
| | - Zhaobin Dong
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing, 100193, China
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Gomez-Cano F, Rodriguez J, Zhou P, Chu YH, Magnusson E, Gomez-Cano L, Krishnan A, Springer NM, de Leon N, Grotewold E. Prioritizing Metabolic Gene Regulators through Multi-Omic Network Integration in Maize. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.26.582075. [PMID: 38464086 PMCID: PMC10925184 DOI: 10.1101/2024.02.26.582075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Elucidating gene regulatory networks (GRNs) is a major area of study within plant systems biology. Phenotypic traits are intricately linked to specific gene expression profiles. These expression patterns arise primarily from regulatory connections between sets of transcription factors (TFs) and their target genes. In this study, we integrated publicly available co-expression networks derived from more than 6,000 RNA-seq samples, 283 protein-DNA interaction assays, and 16 million of SNPs used to identify expression quantitative loci (eQTL), to construct TF-target networks. In total, we analyzed ~4.6M interactions to generate four distinct types of TF-target networks: co-expression, protein-DNA interaction (PDI), trans-expression quantitative loci (trans-eQTL), and cis-eQTL combined with PDIs. To improve the functional annotation of TFs based on its target genes, we implemented three different strategies to integrate these four types of networks. We subsequently evaluated the effectiveness of our method through loss-of function mutant and random networks. The multi-network integration allowed us to identify transcriptional regulators of hormone-, metabolic- and development-related processes. Finally, using the topological properties of the fully integrated network, we identified potentially functional redundant TF paralogs. Our findings retrieved functions previously documented for numerous TFs and revealed novel functions that are crucial for informing the design of future experiments. The approach here-described lays the foundation for the integration of multi-omic datasets in maize and other plant systems.
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Affiliation(s)
- Fabio Gomez-Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824-6473, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Jonas Rodriguez
- Department of Plant and Agroecosystem Sciences, University of Wisconsin Madison, Madison, WI 53706, USA
| | - Peng Zhou
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
| | - Yi-Hsuan Chu
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824-6473, USA
| | - Erika Magnusson
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
| | - Lina Gomez-Cano
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824-6473, USA
| | - Arjun Krishnan
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Nathan M Springer
- Department of Plant and Microbial Biology, University of Minnesota, Saint Paul, MN 55108
- Current address: Global Breeding, Bayer Crop Sciences, Chesterfield MO 63017, USA
| | - Natalia de Leon
- Department of Plant and Agroecosystem Sciences, University of Wisconsin Madison, Madison, WI 53706, USA
| | - Erich Grotewold
- Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824-6473, USA
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13
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Zhu W, Han R, Shang X, Zhou T, Liang C, Qin X, Chen H, Feng Z, Zhang H, Fan X, Li W, Li L. The CropGPT project: Call for a global, coordinated effort in precision design breeding driven by AI using biological big data. MOLECULAR PLANT 2024; 17:215-218. [PMID: 38140725 DOI: 10.1016/j.molp.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/24/2023] [Accepted: 12/20/2023] [Indexed: 12/24/2023]
Affiliation(s)
- Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Key Laboratory of Biology and Genetic Improvement of Maize in Arid Area of Northwest Region, College of Agronomy, Northwest A&F University, Yangling, Shaanxi 712100, China
| | - Rui Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Tao Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Chengyong Liang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Xiaomeng Qin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hong Chen
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Zaiwen Feng
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Hongwei Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Xingming Fan
- Institute of Food Crops Sciences, Yunnan Academy of Agricultural Sciences, 2238 Beijing Road, Kunming, Yunnan 650200, China
| | - Weifu Li
- College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China; Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Wuhan 430070, China; Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518120, China.
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Lindsay P, Swentowsky KW, Jackson D. Cultivating potential: Harnessing plant stem cells for agricultural crop improvement. MOLECULAR PLANT 2024; 17:50-74. [PMID: 38130059 DOI: 10.1016/j.molp.2023.12.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 12/23/2023]
Abstract
Meristems are stem cell-containing structures that produce all plant organs and are therefore important targets for crop improvement. Developmental regulators control the balance and rate of cell divisions within the meristem. Altering these regulators impacts meristem architecture and, as a consequence, plant form. In this review, we discuss genes involved in regulating the shoot apical meristem, inflorescence meristem, axillary meristem, root apical meristem, and vascular cambium in plants. We highlight several examples showing how crop breeders have manipulated developmental regulators to modify meristem growth and alter crop traits such as inflorescence size and branching patterns. Plant transformation techniques are another innovation related to plant meristem research because they make crop genome engineering possible. We discuss recent advances on plant transformation made possible by studying genes controlling meristem development. Finally, we conclude with discussions about how meristem research can contribute to crop improvement in the coming decades.
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Affiliation(s)
- Penelope Lindsay
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA
| | | | - David Jackson
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, USA; National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, Hubei, 430070, China.
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15
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Song YC, Das D, Zhang Y, Chen MX, Fernie AR, Zhu FY, Han J. Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants. Trends Biotechnol 2023; 41:1532-1548. [PMID: 37365082 DOI: 10.1016/j.tibtech.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023]
Abstract
Proteogenomics (PG) integrates the proteome with the genome and transcriptome to refine gene models and annotation. Coupled with single-cell (SC) assays, PG effectively distinguishes heterogeneity among cell groups. Affiliating spatial information to PG reveals the high-resolution circuitry within SC atlases. Additionally, PG can investigate dynamic changes in protein-coding genes in plants across growth and development as well as stress and external stimulation, significantly contributing to the functional genome. Here we summarize existing PG research in plants and introduce the technical features of various methods. Combining PG with other omics, such as metabolomics and peptidomics, can offer even deeper insights into gene functions. We argue that the application of PG will represent an important font of foundational knowledge for plants.
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Affiliation(s)
- Yu-Chen Song
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China
| | - Debatosh Das
- College of Agriculture, Food and Natural Resources (CAFNR), Division of Plant Sciences and Technology, 52 Agricultural Building, University of Missouri-Columbia, MO 65201, USA
| | - Youjun Zhang
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Mo-Xian Chen
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria.
| | - Fu-Yuan Zhu
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
| | - Jiangang Han
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
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Tao K, Li Y, Hu Y, Li Y, Zhang D, Li C, He G, Song Y, Shi Y, Li Y, Wang T, Lu Y, Liu X. Overexpression of ZmEXPA5 reduces anthesis-silking interval and increases grain yield under drought and well-watered conditions in maize. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:84. [PMID: 38009100 PMCID: PMC10667192 DOI: 10.1007/s11032-023-01432-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 11/10/2023] [Indexed: 11/28/2023]
Abstract
Drought is one of the major abiotic stresses affecting the maize production worldwide. As a cross-pollination crop, maize is sensitive to water stress at flowering stage. Drought at this stage leads to asynchronous development of male and female flower organ and increased interval between anthesis and silking, which finally causes failure of pollination and grain yield loss. In the present study, the expansin gene ZmEXPA5 was cloned and its function in drought tolerance was characterized. An indel variant in promoter of ZmEXPA5 is significantly associated with natural variation in drought-induced anthesis-silking interval. The drought susceptible haplotypes showed lower expression level of ZmEXPA5 than tolerant haplotypes and lost the cis-regulatory activity of ZmDOF29. Increasing ZmEXPA5 expression in transgenic maize decreases anthesis-silking interval and improves grain yield under both drought and well-watered environments. In addition, the expression pattern of ZmEXPA5 was analyzed. These findings provide insights into the genetic basis of drought tolerance and a promising gene for drought improvement in maize breeding. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-023-01432-x.
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Affiliation(s)
- Keyu Tao
- College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, 150080 China
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yan Li
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
- College of Agriculture, Yangtze University, Jingzhou, 434000 China
| | - Yue Hu
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yongxiang Li
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Dengfeng Zhang
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Chunhui Li
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Guanhua He
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yanchun Song
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yunsu Shi
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yu Li
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Tianyu Wang
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Yuncai Lu
- College of Advanced Agriculture and Ecological Environment, Heilongjiang University, Harbin, 150080 China
| | - Xuyang Liu
- State Key Lab of Crop Gene Resource and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
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17
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Ma Y, Yang W, Zhang H, Wang P, Liu Q, Li F, Du W. Genetic analysis of phenotypic plasticity identifies BBX6 as the candidate gene for maize adaptation to temperate regions. FRONTIERS IN PLANT SCIENCE 2023; 14:1280331. [PMID: 37964997 PMCID: PMC10642939 DOI: 10.3389/fpls.2023.1280331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 10/12/2023] [Indexed: 11/16/2023]
Abstract
Introduction Climate changes pose a significant threat to crop adaptation and production. Dissecting the genetic basis of phenotypic plasticity and uncovering the responsiveness of regulatory genes to environmental factors can significantly contribute to the improvement of climate- resilience in crops. Methods We established a BC1F3:4 population using the elite inbred lines Zheng58 and PH4CV and evaluated plant height (PH) across four environments characterized by substantial variations in environmental factors. Then, we quantified the correlation between the environmental mean of PH (the mean performance in each environment) and the environmental parameters within a specific growth window. Furthermore, we performed GWAS analysis of phenotypic plasticity, and identified QTLs and candidate gene that respond to key environment index. After that, we constructed the coexpression network involving the candidate gene, and performed selective sweep analysis of the candidate gene. Results We found that the environmental parameters demonstrated substantial variation across the environments, and genotype by environment interaction contributed to the variations of PH. Then, we identified PTT(35-48) (PTT is the abbreviation for photothermal units), the mean PTT from 35 to 48 days after planting, as the pivotal environmental index that closely correlated with environmental mean of PH. Leveraging the slopes of the response of PH to both the environmental mean and PTT(35-48), we successfully pinpointed QTLs for phenotypic plasticity on chromosomes 1 and 2. Notably, the PH4CV genotypes at these two QTLs exhibited positive contributions to phenotypic plasticity. Furthermore, our analysis demonstrated a direct correlation between the additive effects of each QTL and PTT(35-48). By analyzing transcriptome data of the parental lines in two environments, we found that the 1009 genes responding to PTT(35-48) were enriched in the biological processes related to environmental sensitivity. BBX6 was the prime candidate gene among the 13 genes in the two QTL regions. The coexpression network of BBX6 contained other genes related to flowering time and photoperiod sensitivity. Our investigation, including selective sweep analysis and genetic differentiation analysis, suggested that BBX6 underwent selection during maize domestication. Discussion Th is research substantially advances our understanding of critical environmental factors influencing maize adaptation while simultaneously provides an invaluable gene resource for the development of climate-resilient maize hybrid varieties.
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Affiliation(s)
- Yuting Ma
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Wenyan Yang
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Hongwei Zhang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Pingxi Wang
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Qian Liu
- State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, China
| | - Fenghai Li
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
| | - Wanli Du
- College of Agronomy, Shenyang Agricultural University, Shenyang, Liaoning, China
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Tian R, Xie S, Zhang J, Liu H, Li Y, Hu Y, Huang Y, Liu Y. Identification of Morphogenesis-Related NDR Kinase Signaling Network and Its Regulation on Cold Tolerance in Maize. PLANTS (BASEL, SWITZERLAND) 2023; 12:3639. [PMID: 37896102 PMCID: PMC10610150 DOI: 10.3390/plants12203639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2023] [Revised: 10/13/2023] [Accepted: 10/19/2023] [Indexed: 10/29/2023]
Abstract
The MOR (Morphogenesis-related NDR kinase) signaling network, initially identified in yeast, exhibits evolutionary conservation across eukaryotes and plays indispensable roles in the normal growth and development of these organisms. However, the functional role of this network and its associated genes in maize (Zea mays) has remained elusive until now. In this study, we identified a total of 19 maize MOR signaling network genes, and subsequent co-expression analysis revealed that 12 of these genes exhibited stronger associations with each other, suggesting their potential collective regulation of maize growth and development. Further analysis revealed significant co-expression between genes involved in the MOR signaling network and several genes related to cold tolerance. All MOR signaling network genes exhibited significant co-expression with COLD1 (Chilling tolerance divergence1), a pivotal gene involved in the perception of cold stimuli, suggesting that COLD1 may directly transmit cold stress signals to MOR signaling network genes subsequent to the detection of a cold stimulus. The findings indicated that the MOR signaling network may play a crucial role in modulating cold tolerance in maize by establishing an intricate relationship with key cold tolerance genes, such as COLD1. Under low-temperature stress, the expression levels of certain MOR signaling network genes were influenced, with a significant up-regulation observed in Zm00001d010720 and a notable down-regulation observed in Zm00001d049496, indicating that cold stress regulated the MOR signaling network. We identified and analyzed a mutant of Zm00001d010720, which showed a higher sensitivity to cold stress, thereby implicating its involvement in the regulation of cold stress in maize. These findings suggested that the relevant components of the MOR signaling network are also conserved in maize and this signaling network plays a vital role in modulating the cold tolerance of maize. This study offered valuable genetic resources for enhancing the cold tolerance of maize.
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Affiliation(s)
- Ran Tian
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China; (R.T.); (S.X.); (Y.L.); (Y.H.)
| | - Sidi Xie
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China; (R.T.); (S.X.); (Y.L.); (Y.H.)
| | - Junjie Zhang
- College of Life Science, Sichuan Agricultural University, Ya’an 625014, China; (J.Z.); (H.L.)
| | - Hanmei Liu
- College of Life Science, Sichuan Agricultural University, Ya’an 625014, China; (J.Z.); (H.L.)
| | - Yangping Li
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China; (R.T.); (S.X.); (Y.L.); (Y.H.)
| | - Yufeng Hu
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China; (R.T.); (S.X.); (Y.L.); (Y.H.)
| | - Yubi Huang
- State Key Laboratory of Crop Gene Exploration and Utilization in Southwest China, Sichuan Agricultural University, Chengdu 611130, China; (R.T.); (S.X.); (Y.L.); (Y.H.)
| | - Yinghong Liu
- Maize Research Institute, Sichuan Agricultural University, Chengdu 611130, China
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Wang X, Han L, Li J, Shang X, Liu Q, Li L, Zhang H. Next-generation bulked segregant analysis for Breeding 4.0. Cell Rep 2023; 42:113039. [PMID: 37651230 DOI: 10.1016/j.celrep.2023.113039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 07/11/2023] [Accepted: 08/10/2023] [Indexed: 09/02/2023] Open
Abstract
Functional cloning and manipulation of genes controlling various agronomic traits are important for boosting crop production. Although bulked segregant analysis (BSA) is an efficient method for functional cloning, its low throughput cannot satisfy the current need for crop breeding and food security. Here, we review the rationale and development of conventional BSA and discuss its strengths and drawbacks. We then propose next-generation BSA (NG-BSA) integrating multiple cutting-edge technologies, including high-throughput phenotyping, biological big data, and the use of machine learning. NG-BSA increases the resolution of genetic mapping and throughput for cloning quantitative trait genes (QTGs) and optimizes candidate gene selection while providing a means to elucidate the interaction network of QTGs. The ability of NG-BSA to efficiently batch-clone QTGs makes it an important tool for dissecting molecular mechanisms underlying various traits, as well as for the improvement of Breeding 4.0 strategy, especially in targeted improvement and population improvement of crops.
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Affiliation(s)
- Xi Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Juan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China
| | - Qian Liu
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
| | - Hongwei Zhang
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
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Wu Y, Liu C, Koganitsky A, Gong FL, Li S. Discovering Dynamic Plant Enzyme Complexes in Yeast for Kratom Alkaloid Pathway Identification. Angew Chem Int Ed Engl 2023; 62:e202307995. [PMID: 37549372 PMCID: PMC10530425 DOI: 10.1002/anie.202307995] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 07/24/2023] [Accepted: 08/07/2023] [Indexed: 08/09/2023]
Abstract
Discovering natural product biosynthetic pathways of medicinal plants is challenging and laborious. Capturing the coregulation patterns of pathway enzymes, particularly transcriptomic regulation, has proven an effective method to accelerate pathway identification. In this study, we developed a yeast-based screening method to capture the protein-protein interactions (PPI) between plant enzymes, which is another useful pattern to complement the prevalent approach. Combining this method with plant multiomics analysis, we discovered four enzyme complexes and their organized pathways from kratom, an alkaloid-producing plant. The four pathway branches involved six enzymes, including a strictosidine synthase, a strictosidine β-D-glucosidase (MsSGD), and four medium-chain dehydrogenase/reductases (MsMDRs). PPI screening selected six MsMDRs interacting with MsSGD from 20 candidates predicted by multiomics analysis. Four of the six MsMDRs were then characterized as functional, indicating the high selectivity of the PPI screening method. This study highlights the opportunity of leveraging post-translational regulation features to discover novel plant natural product biosynthetic pathways.
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Affiliation(s)
- Yinan Wu
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, 14853, Ithaca, NY, USA
| | - Chang Liu
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, 14853, Ithaca, NY, USA
| | - Anna Koganitsky
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, 14853, Ithaca, NY, USA
| | - Franklin L Gong
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, 14853, Ithaca, NY, USA
| | - Sijin Li
- Robert F. Smith School of Chemical and Biomolecular Engineering, Cornell University, 14853, Ithaca, NY, USA
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21
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Wang X, Li J, Han L, Liang C, Li J, Shang X, Miao X, Luo Z, Zhu W, Li Z, Li T, Qi Y, Li H, Lu X, Li L. QTG-Miner aids rapid dissection of the genetic base of tassel branch number in maize. Nat Commun 2023; 14:5232. [PMID: 37633966 PMCID: PMC10460418 DOI: 10.1038/s41467-023-41022-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 08/21/2023] [Indexed: 08/28/2023] Open
Abstract
Genetic dissection of agronomic traits is important for crop improvement and global food security. Phenotypic variation of tassel branch number (TBN), a major breeding target, is controlled by many quantitative trait loci (QTLs). The lack of large-scale QTL cloning methodology constrains the systematic dissection of TBN, which hinders modern maize breeding. Here, we devise QTG-Miner, a multi-omics data-based technique for large-scale and rapid cloning of quantitative trait genes (QTGs) in maize. Using QTG-Miner, we clone and verify seven genes underlying seven TBN QTLs. Compared to conventional methods, QTG-Miner performs well for both major- and minor-effect TBN QTLs. Selection analysis indicates that a substantial number of genes and network modules have been subjected to selection during maize improvement. Selection signatures are significantly enriched in multiple biological pathways between female heterotic groups and male heterotic groups. In summary, QTG-Miner provides a large-scale approach for rapid cloning of QTGs in crops and dissects the genetic base of TBN for further maize breeding.
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Affiliation(s)
- Xi Wang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Juan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Chengyong Liang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Jiaxin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Xinxin Miao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Zi Luo
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Zhao Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Tianhuan Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- Hubei Hongshan Laboratory, Wuhan, 430070, China
| | - Yongwen Qi
- College of Agriculture and Biology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510325, Guangdong, China
| | - Huihui Li
- Institute of Crop Science, Chinese Academy of Agricultural Sciences, 100081, Beijing, China
| | - Xiaoduo Lu
- Institute of Molecular Breeding for Maize, Qilu Normal University, Jinan, 250200, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- Hubei Hongshan Laboratory, Wuhan, 430070, China.
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22
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Feng JW, Han L, Liu H, Xie WZ, Liu H, Li L, Chen LL. MaizeNetome: A multi-omics network database for functional genomics in maize. MOLECULAR PLANT 2023; 16:1229-1231. [PMID: 37553832 DOI: 10.1016/j.molp.2023.08.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/17/2023] [Accepted: 08/04/2023] [Indexed: 08/10/2023]
Affiliation(s)
- Jia-Wu Feng
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hao Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Wen-Zhao Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Hanmingzi Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan 430070, China.
| | - Ling-Ling Chen
- State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, College of Life Science and Technology, Guangxi University, Nanning 530004, China.
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23
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Zheng J, Yang X, Huang Y, Yang S, Wuchty S, Zhang Z. Deep learning-assisted prediction of protein-protein interactions in Arabidopsis thaliana. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 114:984-994. [PMID: 36919205 DOI: 10.1111/tpj.16188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 02/20/2023] [Accepted: 03/09/2023] [Indexed: 05/27/2023]
Abstract
Currently, the experimentally identified interactome of Arabidopsis (Arabidopsis thaliana) is still far from complete, suggesting that computational prediction methods can complement experimental techniques. Motivated by the prosperity and success of deep learning algorithms and natural language processing techniques, we introduce an integrative deep learning framework, DeepAraPPI, allowing us to predict protein-protein interactions (PPIs) of Arabidopsis utilizing sequence, domain and Gene Ontology (GO) information. Our current DeepAraPPI comprises: (i) a word2vec encoding-based Siamese recurrent convolutional neural network (RCNN) model; (ii) a Domain2vec encoding-based multiple-layer perceptron (MLP) model; and (iii) a GO2vec encoding-based MLP model. Finally, DeepAraPPI combines the prediction results of the three individual predictors through a logistic regression model. Compiling high-quality positive and negative training and test samples by applying strict filtering strategies, DeepAraPPI shows superior performance compared with existing state-of-the-art Arabidopsis PPI prediction methods. DeepAraPPI also provides better cross-species predictive ability in rice (Oryza sativa) than traditional machine learning methods, although the overall performance in cross-species prediction remains to be improved. DeepAraPPI is freely accessible at http://zzdlab.com/deeparappi/. In the meantime, we have also made the source code and data sets of DeepAraPPI available at https://github.com/zjy1125/DeepAraPPI.
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Affiliation(s)
- Jingyan Zheng
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, 100034, China
| | - Yan Huang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami, FL, 33146, USA
- Department of Biology, University of Miami, Miami, FL, 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, 33136, USA
- Institute of Data Science and Computing, University of Miami, Miami, FL, 33146, USA
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
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24
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Li D, Zhang Z, Gao X, Zhang H, Bai D, Wang Q, Zheng T, Li YH, Qiu LJ. The elite variations in germplasms for soybean breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:37. [PMID: 37312749 PMCID: PMC10248635 DOI: 10.1007/s11032-023-01378-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/03/2023] [Indexed: 06/15/2023]
Abstract
The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.
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Affiliation(s)
- Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Zhengwei Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Xinyue Gao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Hao Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Dong Bai
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
- College of Agriculture, Northeast Agricultural University, Harbin, 150030 China
| | - Tianqing Zheng
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
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25
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Li Q, Liu N, Wu C. Novel insights into maize (Zea mays) development and organogenesis for agricultural optimization. PLANTA 2023; 257:94. [PMID: 37031436 DOI: 10.1007/s00425-023-04126-y] [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: 08/04/2022] [Accepted: 03/22/2023] [Indexed: 06/19/2023]
Abstract
In maize, intrinsic hormone activities and sap fluxes facilitate organogenesis patterning and plant holistic development; these hormone movements should be a primary focus of developmental biology and agricultural optimization strategies. Maize (Zea mays) is an important crop plant with distinctive life history characteristics and structural features. Genetic studies have extended our knowledge of maize developmental processes, genetics, and molecular ecophysiology. In this review, the classical life cycle and life history strategies of maize are analyzed to identify spatiotemporal organogenesis properties and develop a definitive understanding of maize development. The actions of genes and hormones involved in maize organogenesis and sex determination, along with potential molecular mechanisms, are investigated, with findings suggesting central roles of auxin and cytokinins in regulating maize holistic development. Furthermore, investigation of morphological and structural characteristics of maize, particularly node ubiquity and the alternate attachment pattern of lateral organs, yields a novel regulatory model suggesting that maize organ initiation and subsequent development are derived from the stimulation and interaction of auxin and cytokinin fluxes. Propositions that hormone activities and sap flow pathways control organogenesis are thoroughly explored, and initiation and development processes of distinctive maize organs are discussed. Analysis of physiological factors driving hormone and sap movement implicates cues of whole-plant activity for hormone and sap fluxes to stimulate maize inflorescence initiation and organ identity determination. The physiological origins and biogenetic mechanisms underlying maize floral sex determination occurring at the tassel and ear spikelet are thoroughly investigated. The comprehensive outline of maize development and morphogenetic physiology developed in this review will enable farmers to optimize field management and will provide a reference for de novo crop domestication and germplasm improvement using genome editing biotechnologies, promoting agricultural optimization.
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Affiliation(s)
- Qinglin Li
- Crop Genesis and Novel Agronomy Center, Yangling, 712100, Shaanxi, China.
| | - Ning Liu
- Shandong ZhongnongTiantai Seed Co., Ltd, Pingyi, 273300, Shandong, China
| | - Chenglai Wu
- State Key Laboratory of Crop Biology, Shandong Agricultural University, Tai'an, 271018, Shandong, China.
- College of Agronomy, Shandong Agricultural University, Tai'an, 271018, Shandong, China.
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26
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Zhu W, Miao X, Qian J, Chen S, Jin Q, Li M, Han L, Zhong W, Xie D, Shang X, Li L. A translatome-transcriptome multi-omics gene regulatory network reveals the complicated functional landscape of maize. Genome Biol 2023; 24:60. [PMID: 36991439 PMCID: PMC10053466 DOI: 10.1186/s13059-023-02890-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 03/04/2023] [Indexed: 03/31/2023] Open
Abstract
BACKGROUND Maize (Zea mays L.) is one of the most important crops worldwide. Although sophisticated maize gene regulatory networks (GRNs) have been constructed for functional genomics and phenotypic dissection, a multi-omics GRN connecting the translatome and transcriptome is lacking, hampering our understanding and exploration of the maize regulatome. RESULTS We collect spatio-temporal translatome and transcriptome data and systematically explore the landscape of gene transcription and translation across 33 tissues or developmental stages of maize. Using this comprehensive transcriptome and translatome atlas, we construct a multi-omics GRN integrating mRNAs and translated mRNAs, demonstrating that translatome-related GRNs outperform GRNs solely using transcriptomic data and inter-omics GRNs outperform intra-omics GRNs in most cases. With the aid of the multi-omics GRN, we reconcile some known regulatory networks. We identify a novel transcription factor, ZmGRF6, which is associated with growth. Furthermore, we characterize a function related to drought response for the classic transcription factor ZmMYB31. CONCLUSIONS Our findings provide insights into spatio-temporal changes across maize development at both the transcriptome and translatome levels. Multi-omics GRNs represent a useful resource for dissection of the regulatory mechanisms underlying phenotypic variation.
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Affiliation(s)
- Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Xinxin Miao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Jia Qian
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Sijia Chen
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
| | - Qixiao Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Mingzhu Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Linqian Han
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Wanshun Zhong
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Dan Xie
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Xiaoyang Shang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China
- HuBei HongShan Laboratory, Wuhan, 430070, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, 430070, China.
- HuBei HongShan Laboratory, Wuhan, 430070, China.
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27
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Miao X, Zhu W, Jin Q, Song Z, Li L. ZmHOX32 is related to photosynthesis and likely functions in plant architecture of maize. FRONTIERS IN PLANT SCIENCE 2023; 14:1119678. [PMID: 37035059 PMCID: PMC10073575 DOI: 10.3389/fpls.2023.1119678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 03/09/2023] [Indexed: 06/19/2023]
Abstract
HOX32, a member of the HD-ZIP III family, functions in the leaf morphogenesis and plant photosynthesis. However, the regulatory mechanism of HOX32 in maize has not been studied and the regulatory relationship in photosynthesis is unclear. We conducted a comprehensive study, including phylogenetic analysis, expression profiling at both transcriptome and translatome levels, subcellular localization, tsCUT&Tag, co-expression analysis, and association analysis with agronomic traits on HOX32 for the dissection of the functional roles of HOX32. ZmHOX32 shows conservation in plants. As expected, maize HOX32 protein is specifically expressed in the nucleus. ZmHOX32 showed constitutively expression at both transcriptome and translatome levels. We uncovered the downstream target genes of ZmHOX32 by tsCUT&Tag and constructed a cascaded regulatory network combining the co-expression networks. Both direct and indirect targets of ZmHOX32 showed significant gene ontology enrichment in terms of photosynthesis in maize. The association study suggested that ZmHOX32 plays an important role in regulation of plant architecture. Our results illustrate a complex regulatory network of HOX32 involving in photosynthesis and plant architecture, which deepens our understanding of the phenotypic variation in plants.
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Affiliation(s)
- Xinxin Miao
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hongshan Laboratory, Wuhan, China
| | - Wanchao Zhu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hongshan Laboratory, Wuhan, China
| | - Qixiao Jin
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hongshan Laboratory, Wuhan, China
| | - Zemeng Song
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hongshan Laboratory, Wuhan, China
| | - Lin Li
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Hongshan Laboratory, Wuhan, China
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28
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Advances of Apetala2/Ethylene Response Factors in Regulating Development and Stress Response in Maize. Int J Mol Sci 2023; 24:ijms24065416. [PMID: 36982510 PMCID: PMC10049130 DOI: 10.3390/ijms24065416] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 02/28/2023] [Accepted: 03/10/2023] [Indexed: 03/18/2023] Open
Abstract
Apetala2/ethylene response factor (AP2/ERF) is one of the largest families of transcription factors, regulating growth, development, and stress response in plants. Several studies have been conducted to clarify their roles in Arabidopsis and rice. However, less research has been carried out on maize. In this review, we systematically identified the AP2/ERFs in the maize genome and summarized the research progress related to AP2/ERF genes. The potential roles were predicted from rice homologs based on phylogenetic and collinear analysis. The putative regulatory interactions mediated by maize AP2/ERFs were discovered according to integrated data sources, implying that they involved complex networks in biological activities. This will facilitate the functional assignment of AP2/ERFs and their applications in breeding strategy.
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