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Willems P, Sterck L, Dard A, Huang J, De Smet I, Gevaert K, Van Breusegem F. The Plant PTM Viewer 2.0: in-depth exploration of plant protein modification landscapes. JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:4611-4624. [PMID: 38872385 DOI: 10.1093/jxb/erae270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Accepted: 06/13/2024] [Indexed: 06/15/2024]
Abstract
Post-translational modifications (PTMs) greatly increase protein diversity and functionality. To help the plant research community interpret the ever-increasing number of reported PTMs, the Plant PTM Viewer (https://www.psb.ugent.be/PlantPTMViewer) provides an intuitive overview of plant protein PTMs and the tools to assess it. This update includes 62 novel PTM profiling studies, adding a total of 112 000 modified peptides reporting plant PTMs, including 14 additional PTM types and three species (moss, tomato, and soybean). Furthermore, an open modification re-analysis of a large-scale Arabidopsis thaliana mass spectrometry tissue atlas identified previously uncharted landscapes of lysine acylations predominant in seed and flower tissues and 3-phosphoglycerylation on glycolytic enzymes in plants. An extra 'Protein list analysis' tool was developed for retrieval and assessing the enrichment of PTMs in a protein list of interest. We conducted a protein list analysis on nuclear proteins, revealing a substantial number of redox modifications in the nucleus, confirming previous assumptions regarding the redox regulation of transcription. We encourage the plant research community to use PTM Viewer 2.0 for hypothesis testing and new target discovery, and also to submit new data to expand the coverage of conditions, plant species, and PTM types, thereby enriching our understanding of plant biology.
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Affiliation(s)
- Patrick Willems
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
- VIB Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
| | - Lieven Sterck
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium
| | - Avilien Dard
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium
| | - Jingjing Huang
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium
| | - Ive De Smet
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium
| | - Kris Gevaert
- Department of Biomolecular Medicine, Ghent University, 9052 Ghent, Belgium
- VIB Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
| | - Frank Van Breusegem
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium
- Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium
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Zhang X, Luo Z, Marand AP, Yan H, Jang H, Bang S, Mendieta JP, Minow MA, Schmitz RJ. A spatially resolved multiomic single-cell atlas of soybean development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.03.601616. [PMID: 39005400 PMCID: PMC11244997 DOI: 10.1101/2024.07.03.601616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Cis-regulatory elements (CREs) precisely control spatiotemporal gene expression in cells. Using a spatially resolved single-cell atlas of gene expression with chromatin accessibility across ten soybean tissues, we identified 103 distinct cell types and 303,199 accessible chromatin regions (ACRs). Nearly 40% of the ACRs showed cell-type-specific patterns and were enriched for transcription factor (TF) motifs defining diverse cell identities. We identified de novo enriched TF motifs and explored conservation of gene regulatory networks underpinning legume symbiotic nitrogen fixation. With comprehensive developmental trajectories for endosperm and embryo, we uncovered the functional transition of the three sub-cell types of endosperm, identified 13 sucrose transporters sharing the DOF11 motif that were co-up-regulated in late peripheral endosperm and identified key embryo cell-type specification regulators during embryogenesis, including a homeobox TF that promotes cotyledon parenchyma identity. This resource provides a valuable foundation for analyzing gene regulatory programs in soybean cell types across tissues and life stages.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Ziliang Luo
- Department of Genetics, University of Georgia, Athens, GA, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Alexandre P. Marand
- Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
- These authors contributed equally: Xuan Zhang, Ziliang Luo, Alexandre P. Marand
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: College of Grassland Science and Technology, Sichuan Agricultural University, Chengdu, China
| | - Hosung Jang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Sohyun Bang
- Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | | | - Mark A.A. Minow
- Department of Genetics, University of Georgia, Athens, GA, USA
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Clevinger EM, Biyashev R, Schmidt C, Song Q, Batnini A, Bolaños-Carriel C, Robertson AE, Dorrance AE, Saghai Maroof MA. Comparison of Rps loci toward isolates, singly and combined inocula, of Phytophthora sojae in soybean PI 407985, PI 408029, PI 408097, and PI424477. FRONTIERS IN PLANT SCIENCE 2024; 15:1394676. [PMID: 39011302 PMCID: PMC11246922 DOI: 10.3389/fpls.2024.1394676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Accepted: 06/05/2024] [Indexed: 07/17/2024]
Abstract
For soybean, novel single dominant Resistance to Phytophthora sojae (Rps) genes are sought to manage Phytophthora root and stem rot. In this study, resistance to P. sojae was mapped individually in four recombinant inbred line (RIL) populations derived from crosses of the susceptible cultivar Williams with PI 407985, PI 408029, PI 408097, and PI424477 previously identified as putative novel sources of disease resistance. Each population was screened for resistance with five to seven isolates of P. sojae separately over multiple F7-F10 generations. Additionally, three of the populations were screened with inoculum from the combination of three P. sojae isolates (PPR), which comprised virulence to 14 Rps genes. Over 2,300 single-nucleotide polymorphism markers were used to construct genetic maps in each population to identify chromosomal regions associated with resistance to P. sojae. Resistance segregated as one or two genes to the individual isolates and one gene toward PPR in each population and mapped to chromosomes 3, 13, or 18 in one or more of the four RIL populations. Resistance to five isolates mapped to the same chromosome 3 region are as follows: OH7 (PI 424477 and PI408029), OH12168, OH7/8, PPR (PI 407985), and 1.S.1.1 (PI408029). The resistance regions on chromosome 13 also overlapped for OH1, OH25, OH-MIA (PI424477), PPR (PI 424477, PI 407985, and PI 408097), PPR and OH0217 (PI 408097), and OH4 (PI 408029), but were distinct for each population suggesting multiple genes confer resistance. Two regions were identified on chromosome 18 but all appear to map to known loci; notably, resistance to the combined inoculum (PPR) did not map at this locus. However, there are putative new alleles in three of four populations, three on chromosome 3 and two on chromosome 13 based on mapping location but also known virulence in the isolate used. This characterization of all the Rps genes segregating in these populations to these isolates will be informative for breeding, but the combined inoculum was able to map a novel loci. Furthermore, within each of these P. sojae isolates, there was virulence to more than the described Rps genes, and the effectiveness of the novel genes requires testing in larger populations.
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Affiliation(s)
- Elizabeth M Clevinger
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Ruslan Biyashev
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
| | - Clarice Schmidt
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, United States
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, Agricultural Research Service, Department of Agriculture, Beltsville, MD, United States
| | - Amine Batnini
- Department of Plant Pathology, The Ohio State University, Wooster, OH, United States
| | | | - Alison E Robertson
- Department of Plant Pathology, Entomology and Microbiology, Iowa State University, Ames, IA, United States
| | - Anne E Dorrance
- Department of Plant Pathology, The Ohio State University, Wooster, OH, United States
| | - M A Saghai Maroof
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA, United States
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Xiong T, Zhang Z, Fan T, Ye F, Ye Z. Origin, evolution, and diversification of inositol 1,4,5-trisphosphate 3-kinases in plants and animals. BMC Genomics 2024; 25:350. [PMID: 38589807 PMCID: PMC11000326 DOI: 10.1186/s12864-024-10257-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Accepted: 03/26/2024] [Indexed: 04/10/2024] Open
Abstract
BACKGROUND In Eukaryotes, inositol polyphosphates (InsPs) represent a large family of secondary messengers and play crucial roes in various cellular processes. InsPs are synthesized through a series of pohophorylation reactions catalyzed by various InsP kinases in a sequential manner. Inositol 1,4,5-trisphosphate 3-kinase (IP3 3-kinase/IP3K), one member of InsP kinase, plays important regulation roles in InsPs metabolism by specifically phosphorylating inositol 1,4,5-trisphosphate (IP3) to inositol 1,3,4,5-tetrakisphosphate (IP4) in animal cells. IP3Ks were widespread in fungi, plants and animals. However, its evolutionary history and patterns have not been examined systematically. RESULTS A total of 104 and 31 IP3K orthologues were identified across 57 plant genomes and 13 animal genomes, respectively. Phylogenetic analyses indicate that IP3K originated in the common ancestor before the divergence of fungi, plants and animals. In most plants and animals, IP3K maintained low-copy numbers suggesting functional conservation during plant and animal evolution. In Brassicaceae and vertebrate, IP3K underwent one and two duplication events, respectively, resulting in multiple gene copies. Whole-genome duplication (WGD) was the main mechanism for IP3K duplications, and the IP3K duplicates have experienced functional divergence. Finally, a hypothetical evolutionary model for the IP3K proteins is proposed based on phylogenetic theory. CONCLUSION Our study reveals the evolutionary history of IP3K proteins and guides the future functions of animal, plant, and fungal IP3K proteins.
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Affiliation(s)
- Tao Xiong
- School of Life and Health Science, Huzhou College, Huzhou, Zhejiang, China
- College of Life Science, Xinyang Normal University, Xinyang, Henan, China
| | - Zaibao Zhang
- School of Life and Health Science, Huzhou College, Huzhou, Zhejiang, China.
- College of Life Science, Xinyang Normal University, Xinyang, Henan, China.
| | - Tianyu Fan
- School of Life and Health Science, Huzhou College, Huzhou, Zhejiang, China
| | - Fan Ye
- College of Forestry and Biotechnology, Zhejiang A & F University, Hangzhou, Zhejiang, China
| | - Ziyi Ye
- School of Life and Health Science, Huzhou College, Huzhou, Zhejiang, China
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Gilbert C, Martin N. Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties. FRONTIERS IN PLANT SCIENCE 2024; 15:1310461. [PMID: 38590744 PMCID: PMC10999551 DOI: 10.3389/fpls.2024.1310461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 02/27/2024] [Indexed: 04/10/2024]
Abstract
This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data and records of soybean varietal trials, this research generates a zonal environmental characterization which captures major components of the growing environment which affect the range of adaptation of soybean varieties. These findings have immediate applications for optimizing multi-environment soybean trials. This characterization allows breeders to assess the environmental representation of a multi-environmental trial of soybean varieties, and to strategize the distribution of testing and the placement of test sites accordingly. This application is demonstrated with a historical scenario of a soybean multi-environment trial, using two resource allocation models: one targeted towards improving the general adaptation of soybean varieties, which focuses on widely cultivated areas, and one targeted towards specific adaptation, which captures diverse environmental conditions. Ultimately, the study aims to improve the efficiency and impact of soybean breeding programs, leading to the development of cultivars resilient to variable and changing climates.
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Affiliation(s)
- Catherine Gilbert
- University of Illinois at Urbana-Champaign, Department of Crop Sciences, Urbana, IL, United States
| | - Nicolas Martin
- University of Illinois at Urbana-Champaign, Department of Crop Sciences, Urbana, IL, United States
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Kwon H, Kim MY, Yang X, Lee SH. Unveiling synergistic QTLs associated with slow wilting in soybean (Glycine max [L.] Merr.). TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2024; 137:85. [PMID: 38502238 PMCID: PMC10951030 DOI: 10.1007/s00122-024-04585-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/17/2024] [Indexed: 03/21/2024]
Abstract
KEY MESSAGE A stable QTL qSW_Gm10 works with a novel locus, qSW_Gm01, in a synergistic manner for controlling slow-wilting traits at the early vegetative stage under drought stress in soybean. Drought is one of the major environmental factors which limits soybean yield. Slow wilting is a promising trait that can enhance drought resilience in soybean without additional production costs. Recently, a Korean soybean cultivar SS2-2 was reported to exhibit slow wilting at the early vegetative stages. To find genetic loci responsible for slow wilting, in this study, quantitative trait loci (QTL) analysis was conducted using a recombinant inbred line (RIL) population derived from crossing between Taekwangkong (fast-wilting) and SS2-2 (slow-wilting). Wilting score and leaf moisture content were evaluated at the early vegetative stages for three years. Using the ICIM-MET module, a novel QTL on Chr01, qSW_Gm01 was identified, together with a previously known QTL, qSW_Gm10. These two QTLs were found to work synergistically for slow wilting of the RILs under the water-restricted condition. Furthermore, the SNP markers from the SoySNP50K dataset, located within these QTLs, were associated with the wilting phenotype in 30 diverse soybean accessions. Two genes encoding protein kinase 1b and multidrug resistance-associated protein 4 were proposed as candidate genes for qSW_Gm01 and qSW_Gm10, respectively, based on a comprehensive examination of sequence variation and gene expression differences in the parental lines under drought conditions. These genes may play a role in slow wilting by optimally regulating stomatal aperture. Our findings provide promising genetic resources for improving drought resilience in soybean and give valuable insights into the genetic mechanisms governing slow wilting.
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Affiliation(s)
- Hakyung Kwon
- Department of Agriculture, Forestry and Bioresources and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
| | - Moon Young Kim
- Department of Agriculture, Forestry and Bioresources and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea
- Plant Genomics and Breeding Institute, Seoul National University, Seoul, Republic of Korea
| | - Xuefei Yang
- Key Laboratory of Herbage and Endemic Crop Biology of Ministry of Education, School of Life Sciences, Inner Mongolia University, Hohhot, 010030, China
| | - Suk-Ha Lee
- Department of Agriculture, Forestry and Bioresources and Research Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of Korea.
- Plant Genomics and Breeding Institute, Seoul National University, Seoul, Republic of Korea.
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Chiteri KO, Rairdin A, Sandhu K, Redsun S, Farmer A, O'Rourke JA, Cannon SB, Singh A. Combining GWAS and comparative genomics to fine map candidate genes for days to flowering in mung bean. BMC Genomics 2024; 25:270. [PMID: 38475739 DOI: 10.1186/s12864-024-10156-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2023] [Accepted: 02/22/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Mung bean (Vigna radiata (L.) Wilczek), is an important pulse crop in the global south. Early flowering and maturation are advantageous traits for adaptation to northern and southern latitudes. This study investigates the genetic basis of the Days-to-Flowering trait (DTF) in mung bean, combining genome-wide association studies (GWAS) in mung bean and comparisons with orthologous genes involved with control of DTF responses in soybean (Glycine max (L) Merr) and Arabidopsis (Arabidopsis thaliana). RESULTS The most significant associations for DTF were on mung bean chromosomes 1, 2, and 4. Only the SNPs on chromosomes 1 and 4 were heavily investigated using downstream analysis. The chromosome 1 DTF association is tightly linked with a cluster of locally duplicated FERONIA (FER) receptor-like protein kinase genes, and the SNP occurs within one of the FERONIA genes. In Arabidopsis, an orthologous FERONIA gene (AT3G51550), has been reported to regulate the expression of the FLOWERING LOCUS C (FLC). For the chromosome 4 DTF locus, the strongest candidates are Vradi04g00002773 and Vradi04g00002778, orthologous to the Arabidopsis PhyA and PIF3 genes, encoding phytochrome A (a photoreceptor protein sensitive to red to far-red light) and phytochrome-interacting factor 3, respectively. The soybean PhyA orthologs include the classical loci E3 and E4 (genes GmPhyA3, Glyma.19G224200, and GmPhyA2, Glyma.20G090000). The mung bean PhyA ortholog has been previously reported as a candidate for DTF in studies conducted in South Korea. CONCLUSION The top two identified SNPs accounted for a significant proportion (~ 65%) of the phenotypic variability in mung bean DTF by the six significant SNPs (39.61%), with a broad-sense heritability of 0.93. The strong associations of DTF with genes that have orthologs with analogous functions in soybean and Arabidopsis provide strong circumstantial evidence that these genes are causal for this trait. The three reported loci and candidate genes provide useful targets for marker-assisted breeding in mung beans.
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Affiliation(s)
- Kevin O Chiteri
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Ashlyn Rairdin
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | | | - Sven Redsun
- National Center for Genome Resources, Santa Fe, NM, 87505, United States
| | - Andrew Farmer
- National Center for Genome Resources, Santa Fe, NM, 87505, United States
| | - Jamie A O'Rourke
- Department of Agronomy, Iowa State University, Ames, IA, United States
- USDA - Agricultural Research Service, Corn Insects, and Crop Genetics Research Unit, Ames, IA, United States
| | - Steven B Cannon
- Department of Agronomy, Iowa State University, Ames, IA, United States.
- USDA - Agricultural Research Service, Corn Insects, and Crop Genetics Research Unit, Ames, IA, United States.
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States.
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Jamison DR, Chen P, Hettiarachchy NS, Miller DM, Shakiba E. Identification of Quantitative Trait Loci (QTL) for Sucrose and Protein Content in Soybean Seed. PLANTS (BASEL, SWITZERLAND) 2024; 13:650. [PMID: 38475496 DOI: 10.3390/plants13050650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 02/11/2024] [Accepted: 02/16/2024] [Indexed: 03/14/2024]
Abstract
Protein and sugar content are important seed quality traits in soybean because they improve the value and sustainability of soy food and feed products. Thus, identifying Quantitative Trait Loci (QTL) for soybean seed protein and sugar content can benefit plant breeders and the soybean market by accelerating the breeding process via marker-assisted selection. For this study, a population of recombinant inbred lines (RILs) was developed from a cross between R08-3221 (high protein and low sucrose) and R07-2000 (high sucrose and low protein). Phenotypic data for protein content were taken from the F2:4 and F2:5 generations. The DA7250 NIR analyzer and HPLC instruments were used to analyze total seed protein and sucrose content. Genotypic data were generated using analysis via the SoySNP6k chip. A total of four QTLs were identified in this study. Two QTLs for protein content were located on chromosomes 11 and 20, and two QTLs associated with sucrose content were located on chromosomes 14 and. 11, the latter of which co-localized with detected QTLs for protein, explaining 10% of the phenotypic variation for protein and sucrose content in soybean seed within the study population. Soybean breeding programs can use the results to improve soybean seed quality.
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Affiliation(s)
- Daniel R Jamison
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Pengyin Chen
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | | | - David M Miller
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
| | - Ehsan Shakiba
- Department of Crop, Soil, and Environmental Sciences, University of Arkansas, Fayetteville, AR 72701, USA
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Cooper L, Elser J, Laporte MA, Arnaud E, Jaiswal P. Planteome 2024 Update: Reference Ontologies and Knowledgebase for Plant Biology. Nucleic Acids Res 2024; 52:D1548-D1555. [PMID: 38055832 PMCID: PMC10767901 DOI: 10.1093/nar/gkad1028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/14/2023] [Accepted: 10/23/2023] [Indexed: 12/08/2023] Open
Abstract
The Planteome project (https://planteome.org/) provides a suite of reference and crop-specific ontologies and an integrated knowledgebase of plant genomics data. The plant genomics data in the Planteome has been obtained through manual and automated curation and sourced from more than 40 partner databases and resources. Here, we report on updates to the Planteome reference ontologies, namely, the Plant Ontology (PO), Trait Ontology (TO), the Plant Experimental Conditions Ontology (PECO), and integration of species/crop-specific vocabularies from our partners, the Crop Ontology (CO) into the TO ontology graph. Currently, 11 CO vocabularies are integrated into the Planteome with the addition of yam, sorghum, and potato since 2018. In addition, the size of the annotation database has increased by 34%, and the number of bioentities (genes, proteins, etc.) from 125 plant taxa has increased by 72%. We developed new tools to facilitate user requests and improvements to the CO vocabularies, and to allow fast searching and browsing of PO terms and definitions. These enhancements and future changes to automate the TO-CO mappings and knowledge discovery tools ensure that the Planteome will continue to be a valuable resource for plant biology.
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Affiliation(s)
- Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Justin Elser
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | | | - Elizabeth Arnaud
- Digital Inclusion, Biodiversity International, 34397 Montpellier, France
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
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Deng CH, Naithani S, Kumari S, Cobo-Simón I, Quezada-Rodríguez EH, Skrabisova M, Gladman N, Correll MJ, Sikiru AB, Afuwape OO, Marrano A, Rebollo I, Zhang W, Jung S. Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences. Database (Oxford) 2023; 2023:baad088. [PMID: 38079567 PMCID: PMC10712715 DOI: 10.1093/database/baad088] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 10/17/2023] [Accepted: 11/28/2023] [Indexed: 12/18/2023]
Abstract
Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021-22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.
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Affiliation(s)
- Cecilia H Deng
- Molecular and Digital Breeding, New Cultivar Innovation, The New Zealand Institute for Plant and Food Research Limited, 120 Mt Albert Road, Auckland 1025, New Zealand
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR 97331, USA
| | - Sunita Kumari
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
| | - Irene Cobo-Simón
- Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
- Institute of Forest Science (ICIFOR-INIA, CSIC), Madrid, Spain
| | - Elsa H Quezada-Rodríguez
- Departamento de Producción Agrícola y Animal, Universidad Autónoma Metropolitana-Xochimilco, Ciudad de México, México
- Centro de Ciencias de la Complejidad, Universidad Nacional Autónoma de México, Ciudad de México, México
| | - Maria Skrabisova
- Department of Biochemistry, Faculty of Science, Palacky University, Olomouc, Czech Republic
| | - Nick Gladman
- Cold Spring Harbor Laboratory, 1 Bungtown Rd, Cold Spring Harbor, New York, NY 11724, USA
- U.S. Department of Agriculture-Agricultural Research Service, NEA Robert W. Holley Center for Agriculture and Health, Cornell University, Ithaca, NY 14853, USA
| | - Melanie J Correll
- Agricultural and Biological Engineering Department, University of Florida, 1741 Museum Rd, Gainesville, FL 32611, USA
| | | | | | - Annarita Marrano
- Phoenix Bioinformatics, 39899 Balentine Drive, Suite 200, Newark, CA 94560, USA
| | | | - Wentao Zhang
- National Research Council Canada, 110 Gymnasium Pl, Saskatoon, Saskatchewan S7N 0W9, Canada
| | - Sook Jung
- Department of Horticulture, Washington State University, 303c Plant Sciences Building, Pullman, WA 99164-6414, USA
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Bellaloui N, Knizia D, Yuan J, Song Q, Betts F, Register T, Williams E, Lakhssassi N, Mazouz H, Nguyen HT, Meksem K, Mengistu A, Kassem MA. Genetic Mapping for QTL Associated with Seed Nickel and Molybdenum Accumulation in the Soybean 'Forrest' by 'Williams 82' RIL Population. PLANTS (BASEL, SWITZERLAND) 2023; 12:3709. [PMID: 37960065 PMCID: PMC10649706 DOI: 10.3390/plants12213709] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/01/2023] [Accepted: 10/23/2023] [Indexed: 11/15/2023]
Abstract
Understanding the genetic basis of seed Ni and Mo is essential. Since soybean is a major crop in the world and a major source for nutrients, including Ni and Mo, the objective of the current research was to map genetic regions (quantitative trait loci, QTL) linked to Ni and Mo concentrations in soybean seed. A recombinant inbred line (RIL) population was derived from a cross between 'Forrest' and 'Williams 82' (F × W82). A total of 306 lines was used for genotyping using 5405 single nucleotides polymorphism (SNP) markers using Infinium SNP6K BeadChips. A two-year experiment was conducted and included the parents and the RIL population. One experiment was conducted in 2018 in North Carolina (NC), and the second experiment was conducted in Illinois in 2020 (IL). Logarithm of the odds (LOD) of ≥2.5 was set as a threshold to report identified QTL using the composite interval mapping (CIM) method. A wide range of Ni and Mo concentrations among RILs was observed. A total of four QTL (qNi-01, qNi-02, and qNi-03 on Chr 2, 8, and 9, respectively, in 2018, and qNi-01 on Chr 20 in 2020) was identified for seed Ni. All these QTL were significantly (LOD threshold > 2.5) associated with seed Ni, with LOD scores ranging between 2.71-3.44, and with phenotypic variance ranging from 4.48-6.97%. A total of three QTL for Mo (qMo-01, qMo-02, and qMo-03 on Chr 1, 3, 17, respectively) was identified in 2018, and four QTL (qMo-01, qMo-02, qMo-03, and qMo-04, on Chr 5, 11, 14, and 16, respectively) were identified in 2020. Some of the current QTL had high LOD and significantly contributed to the phenotypic variance for the trait. For example, in 2018, Mo QTL qMo-01 on Chr 1 had LOD of 7.8, explaining a phenotypic variance of 41.17%, and qMo-03 on Chr 17 had LOD of 5.33, with phenotypic variance explained of 41.49%. In addition, one Mo QTL (qMo-03 on Chr 14) had LOD of 9.77, explaining 51.57% of phenotypic variance related to the trait, and another Mo QTL (qMo-04 on Chr 16) had LOD of 7.62 and explained 49.95% of phenotypic variance. None of the QTL identified here were identified twice across locations/years. Based on a search of the available literature and of SoyBase, the four QTL for Ni, identified on Chr 2, 8, 9, and 20, and the five QTL associated with Mo, identified on Chr 1, 17, 11, 14, and 16, are novel and not previously reported. This research contributes new insights into the genetic mapping of Ni and Mo, and provides valuable QTL and molecular markers that can potentially assist in selecting Ni and Mo levels in soybean seeds.
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Affiliation(s)
- Nacer Bellaloui
- Crop Genetics Research Unit, USDA, Agriculture Research Service, 141 Experiment Station Road, Stoneville, MS 38776, USA
| | - Dounya Knizia
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (K.M.)
- Laboratoire de Biotechnologies & Valorisation des Bio-Ressources (BioVar), Département de Biologie, Faculté des Sciences, Université Moulay Ismail, Meknès 50000, Morocco;
| | - Jiazheng Yuan
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.); (M.A.K.)
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD 20705, USA;
| | - Frances Betts
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.); (M.A.K.)
| | - Teresa Register
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.); (M.A.K.)
| | - Earl Williams
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.); (M.A.K.)
| | - Naoufal Lakhssassi
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (K.M.)
| | - Hamid Mazouz
- Laboratoire de Biotechnologies & Valorisation des Bio-Ressources (BioVar), Département de Biologie, Faculté des Sciences, Université Moulay Ismail, Meknès 50000, Morocco;
| | - Henry T. Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA;
| | - Khalid Meksem
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (K.M.)
| | - Alemu Mengistu
- Crop Genetics Research Unit, USDA, Agricultural Research Service, Jackson, TN 38301, USA;
| | - My Abdelmajid Kassem
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.); (M.A.K.)
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12
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Zhang K, Qin Y, Sun W, Shi H, Zhao S, He L, Li C, Zhao J, Pan J, Wang G, Han Z, Zhao C, Yang X. Phylogenomic Analysis of Cytochrome P450 Gene Superfamily and Their Association with Flavonoids Biosynthesis in Peanut ( Arachis hypogaea L.). Genes (Basel) 2023; 14:1944. [PMID: 37895293 PMCID: PMC10606413 DOI: 10.3390/genes14101944] [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: 09/22/2023] [Revised: 10/10/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Cytochrome P450s (CYPs) constitute extensive enzyme superfamilies in the plants, playing pivotal roles in a multitude of biosynthetic and detoxification pathways essential for growth and development, such as the flavonoid biosynthesis pathway. However, CYPs have not yet been systematically studied in the cultivated peanuts (Arachis hypogaea L.), a globally significant cash crop. This study addresses this knowledge deficit through a comprehensive genome-wide analysis, leading to the identification of 589 AhCYP genes in peanuts. Through phylogenetic analysis, all AhCYPs were systematically classified into 9 clans, 43 gene families. The variability in the number of gene family members suggests specialization in biological functions. Intriguingly, both tandem duplication and fragment duplication events have emerged as pivotal drivers in the evolutionary expansion of the AhCYP superfamily. Ka/Ks analysis underscored the substantial influence of strong purifying selection on the evolution of AhCYPs. Furthermore, we selected 21 genes encoding 8 enzymes associated with the flavonoid pathway. The results of quantitative real-time PCR (qRT-PCR) experiments unveiled stage-specific expression patterns during the development of peanut testa, with discernible variations between pink and red testa. Importantly, we identified a direct correlation between gene expression levels and the accumulation of metabolites. These findings offer valuable insights into elucidating the comprehensive functions of AhCYPs and the underlying mechanisms governing the divergent accumulation of flavonoids in testa of different colors.
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Affiliation(s)
- Kun Zhang
- College of Agricultural Science and Technology, Shandong Agriculture and Engineering University, Jinan 250100, China; (K.Z.); (Y.Q.); (J.Z.)
- Institute of Crop Germplasm Resources (Institute of Biotechnology), Shandong Academy of Agricultural Sciences, Shandong Provincial Key Laboratory of Crop Genetic Improvement, Ecology and Physiology, Jinan 250100, China; (S.Z.); (C.L.); (J.P.); (G.W.); (C.Z.)
| | - Yongmei Qin
- College of Agricultural Science and Technology, Shandong Agriculture and Engineering University, Jinan 250100, China; (K.Z.); (Y.Q.); (J.Z.)
| | - Wei Sun
- Linyi Academy of Agricultural Sciences, Linyi 276003, China;
| | - Hourui Shi
- Shandong Seed Management Station, Jinan 250100, China;
| | - Shuzhen Zhao
- Institute of Crop Germplasm Resources (Institute of Biotechnology), Shandong Academy of Agricultural Sciences, Shandong Provincial Key Laboratory of Crop Genetic Improvement, Ecology and Physiology, Jinan 250100, China; (S.Z.); (C.L.); (J.P.); (G.W.); (C.Z.)
| | - Liangqiong He
- Cash Crop Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, China; (L.H.); (Z.H.)
| | - Changsheng Li
- Institute of Crop Germplasm Resources (Institute of Biotechnology), Shandong Academy of Agricultural Sciences, Shandong Provincial Key Laboratory of Crop Genetic Improvement, Ecology and Physiology, Jinan 250100, China; (S.Z.); (C.L.); (J.P.); (G.W.); (C.Z.)
| | - Jin Zhao
- College of Agricultural Science and Technology, Shandong Agriculture and Engineering University, Jinan 250100, China; (K.Z.); (Y.Q.); (J.Z.)
| | - Jiaowen Pan
- Institute of Crop Germplasm Resources (Institute of Biotechnology), Shandong Academy of Agricultural Sciences, Shandong Provincial Key Laboratory of Crop Genetic Improvement, Ecology and Physiology, Jinan 250100, China; (S.Z.); (C.L.); (J.P.); (G.W.); (C.Z.)
| | - Guanghao Wang
- Institute of Crop Germplasm Resources (Institute of Biotechnology), Shandong Academy of Agricultural Sciences, Shandong Provincial Key Laboratory of Crop Genetic Improvement, Ecology and Physiology, Jinan 250100, China; (S.Z.); (C.L.); (J.P.); (G.W.); (C.Z.)
| | - Zhuqiang Han
- Cash Crop Research Institute, Guangxi Academy of Agricultural Sciences, Nanning 530007, China; (L.H.); (Z.H.)
| | - Chuanzhi Zhao
- Institute of Crop Germplasm Resources (Institute of Biotechnology), Shandong Academy of Agricultural Sciences, Shandong Provincial Key Laboratory of Crop Genetic Improvement, Ecology and Physiology, Jinan 250100, China; (S.Z.); (C.L.); (J.P.); (G.W.); (C.Z.)
| | - Xiangli Yang
- College of Agricultural Science and Technology, Shandong Agriculture and Engineering University, Jinan 250100, China; (K.Z.); (Y.Q.); (J.Z.)
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13
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Knizia D, Bellaloui N, Yuan J, Lakhssasi N, Anil E, Vuong T, Embaby M, Nguyen HT, Mengistu A, Meksem K, Kassem MA. Quantitative Trait Loci and Candidate Genes That Control Seed Sugars Contents in the Soybean 'Forrest' by 'Williams 82' Recombinant Inbred Line Population. PLANTS (BASEL, SWITZERLAND) 2023; 12:3498. [PMID: 37836238 PMCID: PMC10575016 DOI: 10.3390/plants12193498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/03/2023] [Accepted: 10/06/2023] [Indexed: 10/15/2023]
Abstract
Soybean seed sugars are among the most abundant beneficial compounds for human and animal consumption in soybean seeds. Higher seed sugars such as sucrose are desirable as they contribute to taste and flavor in soy-based food. Therefore, the objectives of this study were to use the 'Forrest' by 'Williams 82' (F × W82) recombinant inbred line (RIL) soybean population (n = 309) to identify quantitative trait loci (QTLs) and candidate genes that control seed sugar (sucrose, stachyose, and raffinose) contents in two environments (North Carolina and Illinois) over two years (2018 and 2020). A total of 26 QTLs that control seed sugar contents were identified and mapped on 16 soybean chromosomes (chrs.). Interestingly, five QTL regions were identified in both locations, Illinois and North Carolina, in this study on chrs. 2, 5, 13, 17, and 20. Amongst 57 candidate genes identified in this study, 16 were located within 10 Megabase (MB) of the identified QTLs. Amongst them, a cluster of four genes involved in the sugars' pathway was collocated within 6 MB of two QTLs that were detected in this study on chr. 17. Further functional validation of the identified genes could be beneficial in breeding programs to produce soybean lines with high beneficial sucrose and low raffinose family oligosaccharides.
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Affiliation(s)
- Dounya Knizia
- School of Agricultural Sciences, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (E.A.); (M.E.); (K.M.)
| | - Nacer Bellaloui
- USDA, Agriculture Research Service, Crop Genetics Research Unit, 141 Experiment Station Road, Stoneville, MS 38776, USA;
| | - Jiazheng Yuan
- Plant Genomics and Biotechnology Lab, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA;
| | - Naoufal Lakhssasi
- School of Agricultural Sciences, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (E.A.); (M.E.); (K.M.)
| | - Erdem Anil
- School of Agricultural Sciences, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (E.A.); (M.E.); (K.M.)
| | - Tri Vuong
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (T.V.); (H.T.N.)
| | - Mohamed Embaby
- School of Agricultural Sciences, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (E.A.); (M.E.); (K.M.)
| | - Henry T. Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (T.V.); (H.T.N.)
| | - Alemu Mengistu
- USDA, Agriculture Research Service, Crop Genetics Research Unit, 605 Airways Blvd, Jackson, TN 38301, USA;
| | - Khalid Meksem
- School of Agricultural Sciences, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (E.A.); (M.E.); (K.M.)
| | - My Abdelmajid Kassem
- Plant Genomics and Biotechnology Lab, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA;
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14
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Li H, Chen T, Jia L, Wang Z, Li J, Wang Y, Fu M, Chen M, Wang Y, Huang F, Jiang Y, Li T, Zhou Z, Li Y, Yao W, Wang Y. SoybeanGDB: A comprehensive genomic and bioinformatic platform for soybean genetics and genomics. Comput Struct Biotechnol J 2023; 21:3327-3338. [PMID: 38213885 PMCID: PMC10781885 DOI: 10.1016/j.csbj.2023.06.012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 06/09/2023] [Accepted: 06/10/2023] [Indexed: 01/13/2024] Open
Abstract
Soybean (Glycine max (L.) Merr.) is a globally significant crop, widely cultivated for oilseed production and animal feeds. In recent years, the rapid growth of multi-omics data from thousands of soybean accessions has provided unprecedented opportunities for researchers to explore genomes, genetic variations, and gene functions. To facilitate the utilization of these abundant data for soybean breeding and genetic improvement, the SoybeanGDB database (https://venyao.xyz/SoybeanGDB/) was developed as a comprehensive platform. SoybeanGDB integrates high-quality de novo assemblies of 39 soybean genomes and genomic variations among thousands of soybean accessions. Genomic information and variations in user-specified genomic regions can be searched and downloaded from SoybeanGDB, in a user-friendly manner. To facilitate research on genetic resources and elucidate the biological significance of genes, SoybeanGDB also incorporates a variety of bioinformatics analysis modules with graphical interfaces, such as linkage disequilibrium analysis, nucleotide diversity analysis, allele frequency analysis, gene expression analysis, primer design, gene set enrichment analysis, etc. In summary, SoybeanGDB is an essential and valuable resource that provides an open and free platform to accelerate global soybean research.
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Affiliation(s)
- Haoran Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Tiantian Chen
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Lihua Jia
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Zhizhan Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Jiaming Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yazhou Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Mengjia Fu
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Mingming Chen
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yuping Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Fangfang Huang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yingru Jiang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Tao Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Zhengfu Zhou
- Henan Academy of Crop Molecular Breeding, Henan Academy of Agricultural Sciences, Zhengzhou 450002, China
| | - Yang Li
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Wen Yao
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
| | - Yihan Wang
- National Key Laboratory of Wheat and Maize Crop Science, College of Life Sciences, Henan Agricultural University, Zhengzhou 450002, China
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15
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Jin H, Yang X, Zhao H, Song X, Tsvetkov YD, Wu Y, Gao Q, Zhang R, Zhang J. Genetic analysis of protein content and oil content in soybean by genome-wide association study. FRONTIERS IN PLANT SCIENCE 2023; 14:1182771. [PMID: 37346139 PMCID: PMC10281628 DOI: 10.3389/fpls.2023.1182771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 05/09/2023] [Indexed: 06/23/2023]
Abstract
Soybean seed protein content (PC) and oil content (OC) have important economic value. Detecting the loci/gene related to PC and OC is important for the marker-assisted selection (MAS) breeding of soybean. To detect the stable and new loci for PC and OC, a total of 320 soybean accessions collected from the major soybean-growing countries were used to conduct a genome-wide association study (GWAS) by resequencing. The PC ranged from 37.8% to 46.5% with an average of 41.1% and the OC ranged from 16.7% to 22.6% with an average of 21.0%. In total, 23 and 29 loci were identified, explaining 3.4%-15.4% and 5.1%-16.3% of the phenotypic variations for PC and OC, respectively. Of these, eight and five loci for PC and OC, respectively, overlapped previously reported loci and the other 15 and 24 loci were newly identified. In addition, nine candidate genes were identified, which are known to be involved in protein and oil biosynthesis/metabolism, including lipid transport and metabolism, signal transduction, and plant development pathway. These results uncover the genetic basis of soybean protein and oil biosynthesis and could be used to accelerate the progress in enhancing soybean PC and OC.
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Affiliation(s)
- Hui Jin
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Xue Yang
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Haibin Zhao
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Xizhang Song
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Yordan Dimitrov Tsvetkov
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - YuE Wu
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Qiang Gao
- Horticultural Branch of Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Rui Zhang
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
| | - Jumei Zhang
- Institute of Forage and Grassland Sciences, Heilongjiang Academy of Agricultural Sciences, Harbin, China
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16
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Nidumolu LCM, Lorilla KM, Chakravarty I, Uhde-Stone C. Soybean Root Transcriptomics: Insights into Sucrose Signaling at the Crossroads of Nutrient Deficiency and Biotic Stress Responses. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12112117. [PMID: 37299096 DOI: 10.3390/plants12112117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/22/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
Soybean (Glycine max) is an important agricultural crop, but nutrient deficiencies frequently limit soybean production. While research has advanced our understanding of plant responses to long-term nutrient deficiencies, less is known about the signaling pathways and immediate responses to certain nutrient deficiencies, such as Pi and Fe deficiencies. Recent studies have shown that sucrose acts as a long-distance signal that is sent in increased concentrations from the shoot to the root in response to various nutrient deficiencies. Here, we mimicked nutrient deficiency-induced sucrose signaling by adding sucrose directly to the roots. To unravel transcriptomic responses to sucrose acting as a signal, we performed Illumina RNA-sequencing of soybean roots treated with sucrose for 20 min and 40 min, compared to non-sucrose-treated controls. We obtained a total of 260 million paired-end reads, mapping to 61,675 soybean genes, some of which are novel (not yet annotated) transcripts. Of these, 358 genes were upregulated after 20 min, and 2416 were upregulated after 40 min of sucrose exposure. GO (gene ontology) analysis revealed a high proportion of sucrose-induced genes involved in signal transduction, particularly hormone, ROS (reactive oxygen species), and calcium signaling, in addition to regulation of transcription. In addition, GO enrichment analysis indicates that sucrose triggers crosstalk between biotic and abiotic stress responses.
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Affiliation(s)
| | - Kristina Mae Lorilla
- Department of Biological Sciences, California State University, East Bay, Hayward, CA 94542, USA
| | - Indrani Chakravarty
- Department of Biological Sciences, California State University, East Bay, Hayward, CA 94542, USA
| | - Claudia Uhde-Stone
- Department of Biological Sciences, California State University, East Bay, Hayward, CA 94542, USA
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17
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Cleary AM, Farmer AD. Genome Context Viewer (GCV) version 2: enhanced visual exploration of multiple annotated genomes. Nucleic Acids Res 2023:7173788. [PMID: 37207325 DOI: 10.1093/nar/gkad391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/21/2023] [Accepted: 05/08/2023] [Indexed: 05/21/2023] Open
Abstract
The Genome Context Viewer is a web application for identifying, aligning, and visualizing genomic regions based on their micro and macrosyntenic structures. By using functional elements such as gene annotations as the unit of search and comparison, the Genome Context Viewer can compute and display relationships between regions across many assemblies from federated data sources in real-time, enabling users to rapidly explore multiple annotated genomes and identify divergence and structural events that can help provide insight into evolutionary mechanisms associated with functional consequences. In this work, we introduce version 2 of the Genome Context Viewer and highlight new features that enhance usability, performance, and ease of deployment.
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Affiliation(s)
- Alan M Cleary
- National Center for Genome Resources, 2935 Rodeo Park Dr E, Santa Fe, NM 87505, USA
| | - Andrew D Farmer
- National Center for Genome Resources, 2935 Rodeo Park Dr E, Santa Fe, NM 87505, USA
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18
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Discovering and prioritizing candidate resistance genes against soybean pests by integrating GWAS and gene coexpression networks. Gene 2023; 860:147231. [PMID: 36731618 DOI: 10.1016/j.gene.2023.147231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 01/16/2023] [Accepted: 01/25/2023] [Indexed: 02/02/2023]
Abstract
Soybean is one of the most important legume crops worldwide. Soybean pests have a considerable impact on crop yield. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate resistance genes against the insects Aphis glycines and Spodoptera litura, and the nematode Heterodera glycines. We identified 171, 7, and 228 high-confidence candidate resistance genes against A. glycines, S. litura, and H. glycines, respectively. We found some overlap of candidate genes between insect species, but not between insects and H. glycines. Although 15% of the prioritized candidate genes encode proteins of unknown function, the vast majority of the candidates are related to plant immunity processes, such as transcriptional regulation, signaling, oxidative stress, recognition, and physical defense. Based on the number of resistance alleles, we selected the ten most promising accessions against each pest species in the soybean USDA germplasm. The most resistant accessions do not reach the maximum theoretical resistance potential, indicating that they might be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression networks we inferred in this work are available in a user-friendly web application (https://soypestgcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyPestGCN) that serve as a public resource to explore soybean-pest interactions at the transcriptional level.
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19
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Cai Z, Xian P, Cheng Y, Zhong Y, Yang Y, Zhou Q, Lian T, Ma Q, Nian H, Ge L. MOTHER-OF-FT-AND-TFL1 regulates the seed oil and protein content in soybean. THE NEW PHYTOLOGIST 2023. [PMID: 36740575 DOI: 10.1111/nph.18792] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Accepted: 01/27/2023] [Indexed: 06/18/2023]
Abstract
Soybean is a major crop that produces valuable seed oil and protein for global consumption. Seed oil and protein are regulated by complex quantitative trait loci (QTLs) and have undergone intensive selections during the domestication of soybean. It is essential to identify the major genetic components and understand their mechanism behind seed oil and protein in soybean. We report that MOTHER-OF-FT-AND-TFL1 (GmMFT) is the gene of a classical QTL that has been reported to regulate seed oil and protein content in many studies. Mutation of MFT decreased seeds oil content and weight in both Arabidopsis and soybean, whereas increased expression of GmMFT enhanced seeds oil content and weight. Haplotype analysis showed that GmMFT has undergone selection, which resulted in the extended haplotype homozygosity in the cultivated soybean and the enriching of the oil-favorable allele in modern soybean cultivars. This work unraveled the GmMFT-mediated mechanism regulating seed oil and protein content and seed weight, and revealed a previously unknown function of MFT that provides new insights into targeted soybean improvement and breeding.
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Affiliation(s)
- Zhandong Cai
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- Department of Grassland Science, College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, Guangdong, China
| | - Peiqi Xian
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Yanbo Cheng
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Yiwang Zhong
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Yuan Yang
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Qianghua Zhou
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Tengxiang Lian
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Qibin Ma
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
| | - Hai Nian
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- The Key Laboratory of Plant Molecular Breeding of Guangdong Province, College of Agriculture, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
| | - Liangfa Ge
- The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, South China Agricultural University, Guangzhou, Guangdong, 510642, China
- Department of Grassland Science, College of Forestry and Landscape Architecture, South China Agricultural University, Guangzhou, 510642, Guangdong, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, 510642, Guangdong, China
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20
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Yang Y, Wang R, Wang L, Cui R, Zhang H, Che Z, Hu D, Chu S, Jiao Y, Yu D, Zhang D. GmEIL4 enhances soybean (Glycine max) phosphorus efficiency by improving root system development. PLANT, CELL & ENVIRONMENT 2023; 46:592-606. [PMID: 36419232 DOI: 10.1111/pce.14497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 11/01/2022] [Accepted: 11/22/2022] [Indexed: 06/16/2023]
Abstract
Phosphorus (P) deficiency seriously affects plant growth and development and ultimately limits the quality and yield of crops. Here, a new P efficiency-related major quantitative trait locus gene, GmEIL4 (encoding an ethylene-insensitive 3-like 1 protein), was cloned at qP2, which was identified by linkage analysis and genome-wide association study across four environments. Overexpressing GmEIL4 significantly improved the P uptake efficiency by increasing the number, length and surface area of lateral roots of hairy roots in transgenic soybeans, while interfering with GmEIL4 resulted in poor root phenotypic characteristics compared with the control plants under low P conditions. Interestingly, we found that GmEIL4 interacted with EIN3-binding F box protein 1 (GmEBF1), which may regulate the root response to low P stress. We conclude that the expression of GmEIL4 was induced by low-P stress and that overexpressing GmEIL4 improved P accumulation by regulating root elongation and architecture. Analysis of allele variation of GmEIL4 in 894 soybean accessions suggested that GmEIL4 is undergoing artificial selection during soybean evolution, which will benefit soybean production. Together, this study further elucidates how plants respond to low P stress by modifying root structure and provides insight into the great potential of GmEIL4 in crop P-efficient breeding.
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Affiliation(s)
- Yuming Yang
- Department of Agriculture, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Ruiyang Wang
- Department of Agriculture, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Li Wang
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China
| | - Ruifan Cui
- Department of Agriculture, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Hengyou Zhang
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin, China
| | | | - Dandan Hu
- Department of Agriculture, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Shanshan Chu
- Department of Agriculture, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Yongqing Jiao
- Department of Agriculture, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China
| | - Deyue Yu
- National Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing, China
| | - Dan Zhang
- Department of Agriculture, Collaborative Innovation Center of Henan Grain Crops, College of Agronomy, Henan Agricultural University, Zhengzhou, China
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21
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Xiao Z, Wang Q, Li MW, Huang M, Wang Z, Xie M, Varshney RK, Nguyen HT, Chan TF, Lam HM. Wildsoydb DataHub: a platform for accessing soybean multiomic datasets across multiple reference genomes. PLANT PHYSIOLOGY 2022; 190:2099-2102. [PMID: 36063461 PMCID: PMC9706425 DOI: 10.1093/plphys/kiac419] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 08/15/2022] [Indexed: 05/31/2023]
Abstract
The Wildsoydb DataHub is an integrated interface for biologists and breeders to access soybean genomic resources easily, allowing them to fully utilize the results of genomic research.
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Affiliation(s)
- Zhixia Xiao
- School of Life Sciences, Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Qianwen Wang
- School of Life Sciences, Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Man-Wah Li
- School of Life Sciences, Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Mingkun Huang
- School of Life Sciences, Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
- Lushan Botanical Garden, Chinese Academy of Sciences, Jiujiang 332900, China
| | - Zhili Wang
- School of Life Sciences, Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Min Xie
- School of Life Sciences, Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
- Guangdong Engineering Research Center of Plant and Animal Genomics, BGI Genomics, BGI-Shenzhen, Shenzhen 518083, China
| | - Rajeev K Varshney
- State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Western Australia 6150, Australia
| | - Henry T Nguyen
- Division of Plant Sciences, National Center for Soybean Biotechnology, University of Missouri, Columbia, Missouri 65211, USA
| | - Ting-Fung Chan
- School of Life Sciences, Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - Hon-Ming Lam
- School of Life Sciences, Center for Soybean Research of the State Key Laboratory of Agrobiotechnology, The Chinese University of Hong Kong, Shatin, Hong Kong
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22
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Abdullah-Zawawi MR, Govender N, Harun S, Muhammad NAN, Zainal Z, Mohamed-Hussein ZA. Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom. PLANTS (BASEL, SWITZERLAND) 2022; 11:2614. [PMID: 36235479 PMCID: PMC9573505 DOI: 10.3390/plants11192614] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 06/16/2023]
Abstract
In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.
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Affiliation(s)
- Muhammad-Redha Abdullah-Zawawi
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nisha Govender
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Sarahani Harun
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Nor Azlan Nor Muhammad
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zamri Zainal
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
| | - Zeti-Azura Mohamed-Hussein
- Institute of System Biology (INBIOSIS), Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
- Faculty of Science and Technology, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Malaysia
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23
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Zhu X, Zhang B, Gao F, Huang F, Zhang H, Huang J. A soybean non-coding RNA mining and co-expression resource based on 1,596 RNA-seq and small RNA-seq libraries. PLANT PHYSIOLOGY 2022; 189:1911-1915. [PMID: 35552462 PMCID: PMC9342965 DOI: 10.1093/plphys/kiac222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 04/21/2022] [Indexed: 05/15/2023]
Abstract
The SoyNcRNAExp soybean non-coding RNA expression/co-expression resource can be used for ncRNA expression, mining, and co-expression analysis.
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Affiliation(s)
- Xueai Zhu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Province Engineering Research Center of Seed Industry Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Baoyi Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Province Engineering Research Center of Seed Industry Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Fanqi Gao
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
| | - Fang Huang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- National Center for Soybean Improvement, Nanjing Agricultural University, Nanjing 210095, China
| | - Hongsheng Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Province Engineering Research Center of Seed Industry Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
| | - Ji Huang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Province Engineering Research Center of Seed Industry Science and Technology, Nanjing Agricultural University, Nanjing 210095, China
- Jiangsu Key Laboratory for Information Agriculture, Nanjing Agricultural University, Nanjing 210095, China
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24
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LSAP: A Machine Learning Method for Leaf-Senescence-Associated Genes Prediction. LIFE (BASEL, SWITZERLAND) 2022; 12:life12071095. [PMID: 35888183 PMCID: PMC9316258 DOI: 10.3390/life12071095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/16/2022] [Accepted: 07/17/2022] [Indexed: 11/16/2022]
Abstract
Plant leaves, which convert light energy into chemical energy, serve as a major food source on Earth. The decrease in crop yield and quality is caused by plant leaf premature senescence. It is important to detect senescence-associated genes. In this study, we collected 5853 genes from a leaf senescence database and developed a leaf-senescence-associated genes (SAGs) prediction model using the support vector machine (SVM) and XGBoost algorithms. This is the first computational approach for predicting SAGs with the sequence dataset. The SVM-PCA-Kmer-PC-PseAAC model achieved the best performance (F1score = 0.866, accuracy = 0.862 and receiver operating characteristic = 0.922), and based on this model, we developed a SAGs prediction tool called “SAGs_Anno”. We identified a total of 1,398,277 SAGs from 3,165,746 gene sequences from 83 species, including 12 lower plants and 71 higher plants. Interestingly, leafy species showed a higher percentage of SAGs, while leafless species showed a lower percentage of SAGs. Finally, we constructed the Leaf SAGs Annotation Platform using these available datasets and the SAGs_Anno tool, which helps users to easily predict, download, and search for plant leaf SAGs of all species. Our study will provide rich resources for plant leaf-senescence-associated genes research.
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25
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Yao E, Blake VC, Cooper L, Wight CP, Michel S, Cagirici HB, Lazo GR, Birkett CL, Waring DJ, Jannink JL, Holmes I, Waters AJ, Eickholt DP, Sen TZ. GrainGenes: a data-rich repository for small grains genetics and genomics. Database (Oxford) 2022; 2022:6591224. [PMID: 35616118 PMCID: PMC9216595 DOI: 10.1093/database/baac034] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 04/01/2022] [Accepted: 04/26/2022] [Indexed: 05/16/2023]
Abstract
As one of the US Department of Agriculture-Agricultural Research Service flagship databases, GrainGenes (https://wheat.pw.usda.gov) serves the data and community needs of globally distributed small grains researchers for the genetic improvement of the Triticeae family and Avena species that include wheat, barley, rye and oat. GrainGenes accomplishes its mission by continually enriching its cross-linked data content following the findable, accessible, interoperable and reusable principles, enhancing and maintaining an intuitive web interface, creating tools to enable easy data access and establishing data connections within and between GrainGenes and other biological databases to facilitate knowledge discovery. GrainGenes operates within the biological database community, collaborates with curators and genome sequencing groups and contributes to the AgBioData Consortium and the International Wheat Initiative through the Wheat Information System (WheatIS). Interactive and linked content is paramount for successful biological databases and GrainGenes now has 2917 manually curated gene records, including 289 genes and 254 alleles from the Wheat Gene Catalogue (WGC). There are >4.8 million gene models in 51 genome browser assemblies, 6273 quantitative trait loci and >1.4 million genetic loci on 4756 genetic and physical maps contained within 443 mapping sets, complete with standardized metadata. Most notably, 50 new genome browsers that include outputs from the Wheat and Barley PanGenome projects have been created. We provide an example of an expression quantitative trait loci track on the International Wheat Genome Sequencing Consortium Chinese Spring wheat browser to demonstrate how genome browser tracks can be adapted for different data types. To help users benefit more from its data, GrainGenes created four tutorials available on YouTube. GrainGenes is executing its vision of service by continuously responding to the needs of the global small grains community by creating a centralized, long-term, interconnected data repository. Database URL:https://wheat.pw.usda.gov.
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Affiliation(s)
- Eric Yao
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
- Department of Bioengineering, University of California, Stanley Hall, Berkeley, CA 94720-1762, USA
| | - Victoria C Blake
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
- Department of Plant Sciences and Plant Pathology, Montana State University, 119 Plant Biosciences Building, Bozeman, MT 59717, USA
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, 1500 SW Jefferson Way, Corvallis, OR 97331, USA
| | - Charlene P Wight
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, 960 Carling Ave., Ottawa, ON K1A 0C6, Canada
| | - Steve Michel
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - H Busra Cagirici
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - Gerard R Lazo
- United States Department of Agriculture—Agricultural Research Service, Western Regional Research Center, Crop Improvement and Genetics Research Unit, 800 Buchanan St., Albany, CA 94710, USA
| | - Clay L Birkett
- United States Department of Agriculture—Agricultural Research Service, Robert Holley Center, 538 Tower Rd., Ithaca, NY 14853, USA
| | - David J Waring
- Section of Plant Breeding and Genetics, Cornell University, Bradfield Hall, 306 Tower Rd, Ithaca, NY 14853, USA
| | - Jean-Luc Jannink
- United States Department of Agriculture—Agricultural Research Service, Robert Holley Center, 538 Tower Rd., Ithaca, NY 14853, USA
- Section of Plant Breeding and Genetics, Cornell University, Bradfield Hall, 306 Tower Rd, Ithaca, NY 14853, USA
| | - Ian Holmes
- Department of Bioengineering, University of California, Stanley Hall, Berkeley, CA 94720-1762, USA
| | - Amanda J Waters
- PepsiCo R&D, 1991 Upper Buford Circle, 210 Borlaug Hall, St. Paul, MN 55108, USA
| | - David P Eickholt
- PepsiCo R&D, 1991 Upper Buford Circle, 210 Borlaug Hall, St. Paul, MN 55108, USA
| | - Taner Z Sen
- *Corresponding author: Tel: +1 (510) 559-5982; Fax: + 1 (510) 559-5963;
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26
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Yu Y, Zhang H, Long Y, Shu Y, Zhai J. Plant Public RNA-seq Database: a comprehensive online database for expression analysis of ~45 000 plant public RNA-Seq libraries. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:806-808. [PMID: 35218297 PMCID: PMC9055819 DOI: 10.1111/pbi.13798] [Citation(s) in RCA: 45] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 02/17/2022] [Indexed: 05/13/2023]
Affiliation(s)
- Yiming Yu
- Harbin Institute of TechnologyHarbinChina
- Department of BiologySchool of Life SciencesSouthern University of Science and TechnologyShenzhenChina
- Institute of Plant and Food ScienceSouthern University of Science and TechnologyShenzhenChina
- Key Laboratory of Molecular Design for Plant CellFactory of Guangdong Higher Education InstitutesSouthern University of Science and TechnologyShenzhenChina
| | - Hong Zhang
- Department of BiologySchool of Life SciencesSouthern University of Science and TechnologyShenzhenChina
- Institute of Plant and Food ScienceSouthern University of Science and TechnologyShenzhenChina
- Key Laboratory of Molecular Design for Plant CellFactory of Guangdong Higher Education InstitutesSouthern University of Science and TechnologyShenzhenChina
| | - Yanping Long
- Department of BiologySchool of Life SciencesSouthern University of Science and TechnologyShenzhenChina
- Institute of Plant and Food ScienceSouthern University of Science and TechnologyShenzhenChina
- Key Laboratory of Molecular Design for Plant CellFactory of Guangdong Higher Education InstitutesSouthern University of Science and TechnologyShenzhenChina
| | - Yi Shu
- Department of BiologySchool of Life SciencesSouthern University of Science and TechnologyShenzhenChina
- Institute of Plant and Food ScienceSouthern University of Science and TechnologyShenzhenChina
- Key Laboratory of Molecular Design for Plant CellFactory of Guangdong Higher Education InstitutesSouthern University of Science and TechnologyShenzhenChina
| | - Jixian Zhai
- Department of BiologySchool of Life SciencesSouthern University of Science and TechnologyShenzhenChina
- Institute of Plant and Food ScienceSouthern University of Science and TechnologyShenzhenChina
- Key Laboratory of Molecular Design for Plant CellFactory of Guangdong Higher Education InstitutesSouthern University of Science and TechnologyShenzhenChina
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27
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Genetic and Genomic Resources for Soybean Breeding Research. PLANTS 2022; 11:plants11091181. [PMID: 35567182 PMCID: PMC9101001 DOI: 10.3390/plants11091181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 04/21/2022] [Accepted: 04/22/2022] [Indexed: 11/17/2022]
Abstract
Soybean (Glycine max) is a legume species of significant economic and nutritional value. The yield of soybean continues to increase with the breeding of improved varieties, and this is likely to continue with the application of advanced genetic and genomic approaches for breeding. Genome technologies continue to advance rapidly, with an increasing number of high-quality genome assemblies becoming available. With accumulating data from marker arrays and whole-genome resequencing, studying variations between individuals and populations is becoming increasingly accessible. Furthermore, the recent development of soybean pangenomes has highlighted the significant structural variation between individuals, together with knowledge of what has been selected for or lost during domestication and breeding, information that can be applied for the breeding of improved cultivars. Because of this, resources such as genome assemblies, SNP datasets, pangenomes and associated databases are becoming increasingly important for research underlying soybean crop improvement.
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28
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Bwalya J, Alazem M, Kim K. Photosynthesis-related genes induce resistance against soybean mosaic virus: Evidence for involvement of the RNA silencing pathway. MOLECULAR PLANT PATHOLOGY 2022; 23:543-560. [PMID: 34962034 PMCID: PMC8916206 DOI: 10.1111/mpp.13177] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 11/19/2021] [Accepted: 12/09/2021] [Indexed: 05/17/2023]
Abstract
Increasing lines of evidence indicate that chloroplast-related genes are involved in plant-virus interactions. However, the involvement of photosynthesis-related genes in plant immunity is largely unexplored. Analysis of RNA-Seq data from the soybean cultivar L29, which carries the Rsv3 resistance gene, showed that several chloroplast-related genes were strongly induced in response to infection with an avirulent strain of soybean mosaic virus (SMV), G5H, but were weakly induced in response to a virulent strain, G7H. For further analysis, we selected the PSaC gene from the photosystem I and the ATP-synthase α-subunit (ATPsyn-α) gene whose encoded protein is part of the ATP-synthase complex. Overexpression of either gene within the G7H genome reduced virus levels in the susceptible cultivar Lee74 (rsv3-null). This result was confirmed by transiently expressing both genes in Nicotiana benthamiana followed by G7H infection. Both proteins localized in the chloroplast envelope as well as in the nucleus and cytoplasm. Because the chloroplast is the initial biosynthesis site of defence-related hormones, we determined whether hormone-related genes are involved in the ATPsyn-α- and PSaC-mediated defence. Interestingly, genes involved in the biosynthesis of several hormones were up-regulated in plants infected with SMV-G7H expressing ATPsyn-α. However, only jasmonic and salicylic acid biosynthesis genes were up-regulated following infection with the SMV-G7H expressing PSaC. Both chimeras induced the expression of several antiviral RNA silencing genes, which indicate that such resistance may be partially achieved through the RNA silencing pathway. These findings highlight the role of photosynthesis-related genes in regulating resistance to viruses.
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Affiliation(s)
- John Bwalya
- Department of Agriculture BiotechnologyCollege of Agriculture and Life SciencesSeoul National UniversitySeoulRepublic of Korea
| | - Mazen Alazem
- Plant Genomics and Breeding InstituteSeoul National UniversitySeoulRepublic of Korea
| | - Kook‐Hyung Kim
- Department of Agriculture BiotechnologyCollege of Agriculture and Life SciencesSeoul National UniversitySeoulRepublic of Korea
- Plant Genomics and Breeding InstituteSeoul National UniversitySeoulRepublic of Korea
- Research of Institute Agriculture and Life SciencesSeoul National UniversitySeoulRepublic of Korea
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29
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Su L, Xu C, Zeng S, Su L, Joshi T, Stacey G, Xu D. Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model. FRONTIERS IN PLANT SCIENCE 2022; 13:831204. [PMID: 35310659 PMCID: PMC8927983 DOI: 10.3389/fpls.2022.831204] [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: 12/08/2021] [Accepted: 02/09/2022] [Indexed: 06/14/2023]
Abstract
Plant tissues are distinguished by their gene expression patterns, which can help identify tissue-specific highly expressed genes and their differential functional modules. For this purpose, large-scale soybean transcriptome samples were collected and processed starting from raw sequencing reads in a uniform analysis pipeline. To address the gene expression heterogeneity in different tissues, we utilized an adversarial deconfounding autoencoder (AD-AE) model to map gene expressions into a latent space and adapted a standard unsupervised autoencoder (AE) model to help effectively extract meaningful biological signals from the noisy data. As a result, four groups of 1,743, 914, 2,107, and 1,451 genes were found highly expressed specifically in leaf, root, seed and nodule tissues, respectively. To obtain key transcription factors (TFs), hub genes and their functional modules in each tissue, we constructed tissue-specific gene regulatory networks (GRNs), and differential correlation networks by using corrected and compressed gene expression data. We validated our results from the literature and gene enrichment analysis, which confirmed many identified tissue-specific genes. Our study represents the largest gene expression analysis in soybean tissues to date. It provides valuable targets for tissue-specific research and helps uncover broader biological patterns. Code is publicly available with open source at https://github.com/LingtaoSu/SoyMeta.
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Affiliation(s)
- Lingtao Su
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Chunhui Xu
- Institute for Data Science and Informatics, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Shuai Zeng
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Li Su
- Institute for Data Science and Informatics, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Trupti Joshi
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
- Institute for Data Science and Informatics, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
- Department of Health Management and Informatics and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Gary Stacey
- Division of Plant Sciences and Technology and Biochemistry Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
| | - Dong Xu
- Department of Electrical Engineering and Computer Science and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
- Institute for Data Science and Informatics, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, United States
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Zhang M, Liu S, Wang Z, Yuan Y, Zhang Z, Liang Q, Yang X, Duan Z, Liu Y, Kong F, Liu B, Ren B, Tian Z. Progress in soybean functional genomics over the past decade. PLANT BIOTECHNOLOGY JOURNAL 2022; 20:256-282. [PMID: 34388296 PMCID: PMC8753368 DOI: 10.1111/pbi.13682] [Citation(s) in RCA: 58] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 08/04/2021] [Accepted: 08/09/2021] [Indexed: 05/24/2023]
Abstract
Soybean is one of the most important oilseed and fodder crops. Benefiting from the efforts of soybean breeders and the development of breeding technology, large number of germplasm has been generated over the last 100 years. Nevertheless, soybean breeding needs to be accelerated to meet the needs of a growing world population, to promote sustainable agriculture and to address future environmental changes. The acceleration is highly reliant on the discoveries in gene functional studies. The release of the reference soybean genome in 2010 has significantly facilitated the advance in soybean functional genomics. Here, we review the research progress in soybean omics (genomics, transcriptomics, epigenomics and proteomics), germplasm development (germplasm resources and databases), gene discovery (genes that are responsible for important soybean traits including yield, flowering and maturity, seed quality, stress resistance, nodulation and domestication) and transformation technology during the past decade. At the end, we also briefly discuss current challenges and future directions.
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Affiliation(s)
- Min Zhang
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
| | - Shulin Liu
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
| | - Zhao Wang
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Yaqin Yuan
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Zhifang Zhang
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Qianjin Liang
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Xia Yang
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Zongbiao Duan
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Yucheng Liu
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
| | - Fanjiang Kong
- Innovative Center of Molecular Genetics and EvolutionSchool of Life SciencesGuangzhou UniversityGuangzhouChina
| | - Baohui Liu
- Innovative Center of Molecular Genetics and EvolutionSchool of Life SciencesGuangzhou UniversityGuangzhouChina
| | - Bo Ren
- State Key Laboratory of Plant GenomicsInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
| | - Zhixi Tian
- State Key Laboratory of Plant Cell and Chromosome EngineeringInstitute of Genetics and Developmental BiologyInnovative Academy for Seed DesignChinese Academy of SciencesBeijingChina
- University of Chinese Academy of SciencesBeijingChina
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Gupta P, Naithani S, Preece J, Kim S, Cheng T, D'Eustachio P, Elser J, Bolton EE, Jaiswal P. Plant Reactome and PubChem: The Plant Pathway and (Bio)Chemical Entity Knowledgebases. Methods Mol Biol 2022; 2443:511-525. [PMID: 35037224 DOI: 10.1007/978-1-0716-2067-0_27] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Plant Reactome (https://plantreactome.gramene.org) and PubChem ( https://pubchem.ncbi.nlm.nih.gov ) are two reference data portals and resources for curated plant pathways, small molecules, metabolites, gene products, and macromolecular interactions. Plant Reactome knowledgebase, a conceptual plant pathway network, is built by biocuration and integrating (bio)chemical entities, gene products, and macromolecular interactions. It provides manually curated pathways for the reference species Oryza sativa (rice) and gene orthology-based projections that extend pathway knowledge to 106 plant species. Currently, it hosts 320 reference pathways for plant metabolism, hormone signaling, transport, genetic regulation, plant organ development and differentiation, and biotic and abiotic stress responses. In addition to the pathway browsing and search functions, the Plant Reactome provides the analysis tools for pathway comparison between reference and projected species, pathway enrichment in gene expression data, and overlay of gene-gene interaction data on pathways. PubChem, a popular reference database of (bio)chemical entities, provides information on small molecules and other types of chemical entities, such as siRNAs, miRNAs, lipids, carbohydrates, and chemically modified nucleotides. The data in PubChem is collected from hundreds of data sources, including Plant Reactome. This chapter provides a brief overview of the Plant Reactome and the PubChem knowledgebases, their association to other public resources providing accessory information, and how users can readily access the contents.
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Affiliation(s)
- Parul Gupta
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Justin Preece
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Sunghwan Kim
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | | | - Justin Elser
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA.
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Ferreira EGC, Marcelino-Guimarães FC. Mapping Major Disease Resistance Genes in Soybean by Genome-Wide Association Studies. Methods Mol Biol 2022; 2481:313-340. [PMID: 35641772 DOI: 10.1007/978-1-0716-2237-7_18] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Soybean is one of the most valuable agricultural crops in the world. Besides, this legume is constantly attacked by a wide range of pathogens (fungi, bacteria, viruses, and nematodes) compromising yield and increasing production costs. One of the major disease management strategies is the genetic resistance provided by single genes and quantitative trait loci (QTL). Identifying the genomic regions underlying the resistance against these pathogens on soybean is one of the first steps performed by molecular breeders. In the past, genetic mapping studies have been widely used to discover these genomic regions. However, over the last decade, advances in next-generation sequencing technologies and their subsequent cost decreasing led to the development of cost-effective approaches to high-throughput genotyping. Thus, genome-wide association studies applying thousands of SNPs in large sets composed of diverse soybean accessions have been successfully done. In this chapter, a comprehensive review of the majority of GWAS for soybean diseases published since this approach was developed is provided. Important diseases caused by Heterodera glycines, Phytophthora sojae, and Sclerotinia sclerotiorum have been the focus of the several GWAS. However, other bacterial and fungi diseases also have been targets of GWAS. As such, this GWAS summary can serve as a guide for future studies of these diseases. The protocol begins by describing several considerations about the pathogens and bringing different procedures of molecular characterization of them. Advice to choose the best isolate/race to maximize the discovery of multiple R genes or to directly map an effective R gene is provided. A summary of protocols, methods, and tools to phenotyping the soybean panel is given to several diseases. We also give details of options of DNA extraction protocols and genotyping methods, and we describe parameters of SNP quality to soybean data. Websites and their online tools to obtain genotypic and phenotypic data for thousands of soybean accessions are highlighted. Finally, we report several tricks and tips in Subheading 4, especially related to composing the soybean panel as well as generating and analyzing the phenotype data. We hope this protocol will be helpful to achieve GWAS success in identifying resistance genes on soybean.
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Almeida-Silva F, Venancio TM. Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens. Sci Rep 2021; 11:24453. [PMID: 34961779 PMCID: PMC8712514 DOI: 10.1038/s41598-021-03864-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Accepted: 12/03/2021] [Indexed: 12/15/2022] Open
Abstract
Soybean is one of the most important legume crops worldwide. However, soybean yield is dramatically affected by fungal diseases, leading to economic losses of billions of dollars yearly. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate genes associated with resistance to Cadophora gregata, Fusarium graminearum, Fusarium virguliforme, Macrophomina phaseolina, and Phakopsora pachyrhizi. We identified 188, 56, 11, 8, and 3 high-confidence candidates for resistance to F. virguliforme, F. graminearum, C. gregata, M. phaseolina and P. pachyrhizi, respectively. The prioritized candidate genes are highly conserved in the pangenome of cultivated soybeans and are heavily biased towards fungal species-specific defense responses. The vast majority of the prioritized candidate resistance genes are related to plant immunity processes, such as recognition, signaling, oxidative stress, systemic acquired resistance, and physical defense. Based on the number of resistance alleles, we selected the five most resistant accessions against each fungal species in the soybean USDA germplasm. Interestingly, the most resistant accessions do not reach the maximum theoretical resistance potential. Hence, they can be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression network generated here is available in a user-friendly web application ( https://soyfungigcn.venanciogroup.uenf.br/ ) and an R/Shiny package ( https://github.com/almeidasilvaf/SoyFungiGCN ) that serve as a public resource to explore soybean-pathogenic fungi interactions at the transcriptional level.
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Affiliation(s)
- Fabricio Almeida-Silva
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Lamego 2000, P5, sala 217, Campos dos Goytacazes, RJ, Brazil.
| | - Thiago M Venancio
- Laboratório de Química e Função de Proteínas e Peptídeos, Centro de Biociências e Biotecnologia, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Av. Alberto Lamego 2000, P5, sala 217, Campos dos Goytacazes, RJ, Brazil.
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Yoosefzadeh-Najafabadi M, Torabi S, Tulpan D, Rajcan I, Eskandari M. Genome-Wide Association Studies of Soybean Yield-Related Hyperspectral Reflectance Bands Using Machine Learning-Mediated Data Integration Methods. FRONTIERS IN PLANT SCIENCE 2021; 12:777028. [PMID: 34880894 PMCID: PMC8647880 DOI: 10.3389/fpls.2021.777028] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 10/18/2021] [Indexed: 05/12/2023]
Abstract
In conjunction with big data analysis methods, plant omics technologies have provided scientists with cost-effective and promising tools for discovering genetic architectures of complex agronomic traits using large breeding populations. In recent years, there has been significant progress in plant phenomics and genomics approaches for generating reliable large datasets. However, selecting an appropriate data integration and analysis method to improve the efficiency of phenome-phenome and phenome-genome association studies is still a bottleneck. This study proposes a hyperspectral wide association study (HypWAS) approach as a phenome-phenome association analysis through a hierarchical data integration strategy to estimate the prediction power of hyperspectral reflectance bands in predicting soybean seed yield. Using HypWAS, five important hyperspectral reflectance bands in visible, red-edge, and near-infrared regions were identified significantly associated with seed yield. The phenome-genome association analysis of each tested hyperspectral reflectance band was performed using two conventional genome-wide association studies (GWAS) methods and a machine learning mediated GWAS based on the support vector regression (SVR) method. Using SVR-mediated GWAS, more relevant QTL with the physiological background of the tested hyperspectral reflectance bands were detected, supported by the functional annotation of candidate gene analyses. The results of this study have indicated the advantages of using hierarchical data integration strategy and advanced mathematical methods coupled with phenome-phenome and phenome-genome association analyses for a better understanding of the biology and genetic backgrounds of hyperspectral reflectance bands affecting soybean yield formation. The identified yield-related hyperspectral reflectance bands using HypWAS can be used as indirect selection criteria for selecting superior genotypes with improved yield genetic gains in large breeding populations.
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Affiliation(s)
| | - Sepideh Torabi
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Dan Tulpan
- Department of Animal Biosciences, University of Guelph, Guelph, ON, Canada
| | - Istvan Rajcan
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
| | - Milad Eskandari
- Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada
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Fang Y, Cao D, Yang H, Guo W, Ouyang W, Chen H, Shan Z, Yang Z, Chen S, Li X, Chen L, Zhou X. Genome-Wide Identification and Characterization of Soybean GmLOR Gene Family and Expression Analysis in Response to Abiotic Stresses. Int J Mol Sci 2021; 22:12515. [PMID: 34830397 PMCID: PMC8624885 DOI: 10.3390/ijms222212515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 11/11/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022] Open
Abstract
The LOR (LURP-one related) family genes encode proteins containing a conserved LOR domain. Several members of the LOR family genes are required for defense against Hyaloperonospora parasitica (Hpa) in Arabidopsis. However, there are few reports of LOR genes in response to abiotic stresses in plants. In this study, a genome-wide survey and expression levels in response to abiotic stresses of 36 LOR genes from Glycine max were conducted. The results indicated that the GmLOR gene family was divided into eight subgroups, distributed on 14 chromosomes. A majority of members contained three extremely conservative motifs. There were four pairs of tandem duplicated GmLORs and nineteen pairs of segmental duplicated genes identified, which led to the expansion of the number of GmLOR genes. The expansion patterns of the GmLOR family were mainly segmental duplication. A heatmap of soybean LOR family genes showed that 36 GmLOR genes exhibited various expression patterns in different tissues. The cis-acting elements in promoter regions of GmLORs include abiotic stress-responsive elements, such as dehydration-responsive elements and drought-inducible elements. Real-time quantitative PCR was used to detect the expression level of GmLOR genes, and most of them were expressed in the leaf or root except that GmLOR6 was induced by osmotic and salt stresses. Moreover, GmLOR4/10/14/19 were significantly upregulated after PEG and salt treatments, indicating important roles in the improvement of plant tolerance to abiotic stress. Overall, our study provides a foundation for future investigations of GmLOR gene functions in soybean.
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Affiliation(s)
- Yisheng Fang
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, College of Plant Science and Technology, Huazhong Agricultural University, No. 1 Shizishan Road, Hongshan District, Wuhan 430070, China;
| | - Dong Cao
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Hongli Yang
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Wei Guo
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Wenqi Ouyang
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Haifeng Chen
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Zhihui Shan
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Zhonglu Yang
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Shuilian Chen
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Xia Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, College of Plant Science and Technology, Huazhong Agricultural University, No. 1 Shizishan Road, Hongshan District, Wuhan 430070, China;
| | - Limiao Chen
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
| | - Xinan Zhou
- Key laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs, Oil Crops Research Institute of Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (Y.F.); (H.Y.); (W.G.); (W.O.); (H.C.); (Z.S.); (Z.Y.); (S.C.); (D.C.)
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Brown AV, Grant D, Nelson RT. Using Crop Databases to Explore Phenotypes: From QTL to Candidate Genes. PLANTS 2021; 10:plants10112494. [PMID: 34834856 PMCID: PMC8626016 DOI: 10.3390/plants10112494] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 11/16/2022]
Abstract
Seeds, especially those of certain grasses and legumes, provide the majority of the protein and carbohydrates for much of the world’s population. Therefore, improvements in seed quality and yield are important drivers for the development of new crop varieties to feed a growing population. Quantitative Trait Loci (QTL) have been identified for many biologically interesting and agronomically important traits, including many seed quality traits. QTL can help explain the genetic architecture of the traits and can also be used to incorporate traits into new crop cultivars during breeding. Despite the important contributions that QTL have made to basic studies and plant breeding, knowing the exact gene(s) conditioning each QTL would greatly improve our ability to study the underlying genetics, biochemistry and regulatory networks. The data sets needed for identifying these genes are increasingly available and often housed in species- or clade-specific genetics and genomics databases. In this demonstration, we present a generalized walkthrough of how such databases can be used in these studies using SoyBase, the USDA soybean Genetics and Genomics Database, as an example.
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Affiliation(s)
- Anne V. Brown
- United States Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA; (A.V.B.); (D.G.)
| | - David Grant
- United States Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA; (A.V.B.); (D.G.)
- Department of Agronomy, Iowa State University, Ames, IA 50011, USA
| | - Rex T. Nelson
- United States Department of Agriculture-Agricultural Research Service, Corn Insects and Crop Genetics Research Unit, Ames, IA 50011, USA; (A.V.B.); (D.G.)
- Correspondence: ; Tel.: +1-515-294-1297
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Knizia D, Yuan J, Bellaloui N, Vuong T, Usovsky M, Song Q, Betts F, Register T, Williams E, Lakhssassi N, Mazouz H, Nguyen HT, Meksem K, Mengistu A, Kassem MA. The Soybean High Density 'Forrest' by 'Williams 82' SNP-Based Genetic Linkage Map Identifies QTL and Candidate Genes for Seed Isoflavone Content. PLANTS 2021; 10:plants10102029. [PMID: 34685837 PMCID: PMC8541105 DOI: 10.3390/plants10102029] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Revised: 09/13/2021] [Accepted: 09/21/2021] [Indexed: 11/26/2022]
Abstract
Isoflavones are secondary metabolites that are abundant in soybean and other legume seeds providing health and nutrition benefits for both humans and animals. The objectives of this study were to construct a single nucleotide polymorphism (SNP)-based genetic linkage map using the ‘Forrest’ by ‘Williams 82’ (F×W82) recombinant inbred line (RIL) population (n = 306); map quantitative trait loci (QTL) for seed daidzein, genistein, glycitein, and total isoflavone contents in two environments over two years (NC-2018 and IL-2020); identify candidate genes for seed isoflavone. The FXW82 SNP-based map was composed of 2075 SNPs and covered 4029.9 cM. A total of 27 QTL that control various seed isoflavone traits have been identified and mapped on chromosomes (Chrs.) 2, 4, 5, 6, 10, 12, 15, 19, and 20 in both NC-2018 (13 QTL) and IL-2020 (14 QTL). The six QTL regions on Chrs. 2, 4, 5, 12, 15, and 19 are novel regions while the other 21 QTL have been identified by other studies using different biparental mapping populations or genome-wide association studies (GWAS). A total of 130 candidate genes involved in isoflavone biosynthetic pathways have been identified on all 20 Chrs. And among them 16 have been identified and located within or close to the QTL identified in this study. Moreover, transcripts from four genes (Glyma.10G058200, Glyma.06G143000, Glyma.06G137100, and Glyma.06G137300) were highly abundant in Forrest and Williams 82 seeds. The identified QTL and four candidate genes will be useful in breeding programs to develop soybean cultivars with high beneficial isoflavone contents.
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Affiliation(s)
- Dounya Knizia
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (K.M.)
- Laboratoire de Biotechnologies & Valorisation des Bio-Ressources (BioVar), Department de Biology, Faculté des Sciences, Université Moulay Ismail, Meknès 50000, Morocco;
| | - Jiazheng Yuan
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.)
| | - Nacer Bellaloui
- Crop Genetics Research Unit, USDA, Agriculture Research Service, 141 Experiment Station Road, Stoneville, MS 38776, USA;
| | - Tri Vuong
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (T.V.); (M.U.); (H.T.N.)
| | - Mariola Usovsky
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (T.V.); (M.U.); (H.T.N.)
| | - Qijian Song
- Soybean Genomics and Improvement Laboratory, USDA-ARS, Beltsville, MD 20705, USA;
| | - Frances Betts
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.)
| | - Teresa Register
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.)
| | - Earl Williams
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.)
| | - Naoufal Lakhssassi
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (K.M.)
| | - Hamid Mazouz
- Laboratoire de Biotechnologies & Valorisation des Bio-Ressources (BioVar), Department de Biology, Faculté des Sciences, Université Moulay Ismail, Meknès 50000, Morocco;
| | - Henry T. Nguyen
- Division of Plant Science and Technology, University of Missouri, Columbia, MO 65211, USA; (T.V.); (M.U.); (H.T.N.)
| | - Khalid Meksem
- Department of Plant, Soil, and Agricultural Systems, Southern Illinois University, Carbondale, IL 62901, USA; (D.K.); (N.L.); (K.M.)
| | - Alemu Mengistu
- Crop Genetics Research Unit, USDA, Agricultural Research Service, Jackson, TN 38301, USA;
| | - My Abdelmajid Kassem
- Plant Genomics and Biotechnology Laboratory, Department of Biological and Forensic Sciences, Fayetteville State University, Fayetteville, NC 28301, USA; (J.Y.); (F.B.); (T.R.); (E.W.)
- Correspondence:
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Woodhouse MR, Cannon EK, Portwood JL, Harper LC, Gardiner JM, Schaeffer ML, Andorf CM. A pan-genomic approach to genome databases using maize as a model system. BMC PLANT BIOLOGY 2021; 21:385. [PMID: 34416864 PMCID: PMC8377966 DOI: 10.1186/s12870-021-03173-5] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/11/2021] [Indexed: 05/21/2023]
Abstract
Research in the past decade has demonstrated that a single reference genome is not representative of a species' diversity. MaizeGDB introduces a pan-genomic approach to hosting genomic data, leveraging the large number of diverse maize genomes and their associated datasets to quickly and efficiently connect genomes, gene models, expression, epigenome, sequence variation, structural variation, transposable elements, and diversity data across genomes so that researchers can easily track the structural and functional differences of a locus and its orthologs across maize. We believe our framework is unique and provides a template for any genomic database poised to host large-scale pan-genomic data.
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Affiliation(s)
| | - Ethalinda K Cannon
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, 50011, USA
| | - John L Portwood
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, 50011, USA
| | - Lisa C Harper
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, 50011, USA
| | - Jack M Gardiner
- Division of Animal Sciences, University of Missouri, 65211, Columbia, MO, USA
| | - Mary L Schaeffer
- Division of Plant Sciences, University of Missouri, 65211, Columbia, MO, USA
| | - Carson M Andorf
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, 50011, USA
- Department of Computer Science, Iowa State University, Ames, IA, 50011, USA
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Rigden DJ, Fernández XM. The 2021 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res 2021; 49:D1-D9. [PMID: 33396976 PMCID: PMC7778882 DOI: 10.1093/nar/gkaa1216] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The 2021 Nucleic Acids Research database Issue contains 189 papers spanning a wide range of biological fields and investigation. It includes 89 papers reporting on new databases and 90 covering recent changes to resources previously published in the Issue. A further ten are updates on databases most recently published elsewhere. Seven new databases focus on COVID-19 and SARS-CoV-2 and many others offer resources for studying the virus. Major returning nucleic acid databases include NONCODE, Rfam and RNAcentral. Protein family and domain databases include COG, Pfam, SMART and Panther. Protein structures are covered by RCSB PDB and dispersed proteins by PED and MobiDB. In metabolism and signalling, STRING, KEGG and WikiPathways are featured, along with returning KLIFS and new DKK and KinaseMD, all focused on kinases. IMG/M and IMG/VR update in the microbial and viral genome resources section, while human and model organism genomics resources include Flybase, Ensembl and UCSC Genome Browser. Cancer studies are covered by updates from canSAR and PINA, as well as newcomers CNCdatabase and Oncovar for cancer drivers. Plant comparative genomics is catered for by updates from Gramene and GreenPhylDB. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). The NAR online Molecular Biology Database Collection has been substantially updated, revisiting nearly 1000 entries, adding 90 new resources and eliminating 86 obsolete databases, bringing the current total to 1641 databases. It is available at https://www.oxfordjournals.org/nar/database/c/.
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Affiliation(s)
- Daniel J Rigden
- Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK
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