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Shi Y, Guo Z, Bao S, Xu J, Li K, Rong S, Li Q, Xu A, Zhandui D, Huang Z, Chu M. Population Genomics analysis of Leptosphaeria biglobosa Associated with Brassica napus in China Reveals That Geographical Distribution Influences Its Genetic Polymorphism. Microorganisms 2024; 12:1347. [PMID: 39065115 PMCID: PMC11278786 DOI: 10.3390/microorganisms12071347] [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: 04/18/2024] [Revised: 06/20/2024] [Accepted: 06/21/2024] [Indexed: 07/28/2024] Open
Abstract
Blackleg disease, a major threat to Brassica crops worldwide, is primarily caused by the pathogen Leptosphaeria biglobosa. Investigating the genetic variation of L. biglobosa is crucial for managing and preventing the disease in Brassica napus. To date, there is scarce genomic variation information available for populations of L. biglobosa in China. In this study, 73 L. biglobosa strains of canola stalks were collected from 12 provinces in China and subjected to re-sequencing. The 73 assemblies averaged 1340 contigs, 72,123 bp N50, and 30.17 Mb in size. In total, 9409 core orthogroups and 867 accessory orthogroups were identified. A total of 727,724 mutation loci were identified, including 695,230 SNPs and 32,494 indels. Principal component analysis (PCA) and population structure analysis showed that these strains could be divided into seven subgroups. The strains in most provinces were clustered into a single subgroup, suggesting a strong influence of the geographic environment on strain variation. The average nucleotide diversity (θπ) of all strains was 1.03 × 10-3, indicating important genetic diversity among strains from different regions of China. This study provides valuable resources for future comparative genomics, gives new insights into the population evolution of L. biglobosa, and supports the development of strategies for managing blackleg disease in canola.
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Affiliation(s)
- Yiji Shi
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230001, China; (Y.S.); (S.R.); (Q.L.)
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China; (Z.G.); (S.B.); (J.X.); (K.L.); (A.X.); (D.Z.)
| | - Zhiting Guo
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China; (Z.G.); (S.B.); (J.X.); (K.L.); (A.X.); (D.Z.)
| | - Shunjun Bao
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China; (Z.G.); (S.B.); (J.X.); (K.L.); (A.X.); (D.Z.)
| | - Jiali Xu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China; (Z.G.); (S.B.); (J.X.); (K.L.); (A.X.); (D.Z.)
| | - Keqi Li
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China; (Z.G.); (S.B.); (J.X.); (K.L.); (A.X.); (D.Z.)
| | - Songbai Rong
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230001, China; (Y.S.); (S.R.); (Q.L.)
- Anhui Provincial Key Laboratory of Crop Quality Improvement, Hefei 230001, China
| | - Qiangsheng Li
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230001, China; (Y.S.); (S.R.); (Q.L.)
- Anhui Provincial Key Laboratory of Crop Quality Improvement, Hefei 230001, China
| | - Aixia Xu
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China; (Z.G.); (S.B.); (J.X.); (K.L.); (A.X.); (D.Z.)
| | - Duojie Zhandui
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China; (Z.G.); (S.B.); (J.X.); (K.L.); (A.X.); (D.Z.)
| | - Zhen Huang
- State Key Laboratory of Crop Stress Biology for Arid Areas, College of Agronomy, Northwest A&F University, Xianyang 712100, China; (Z.G.); (S.B.); (J.X.); (K.L.); (A.X.); (D.Z.)
| | - Mingguang Chu
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei 230001, China; (Y.S.); (S.R.); (Q.L.)
- Anhui Provincial Key Laboratory of Crop Quality Improvement, Hefei 230001, China
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Zhu J, Dai W, Chen B, Cai G, Wu X, Yan G. Research Progress on the Effect of Nitrogen on Rapeseed between Seed Yield and Oil Content and Its Regulation Mechanism. Int J Mol Sci 2023; 24:14504. [PMID: 37833952 PMCID: PMC10572985 DOI: 10.3390/ijms241914504] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 09/10/2023] [Accepted: 09/19/2023] [Indexed: 10/15/2023] Open
Abstract
Rapeseed (Brassica napus L.) is one of the most important oil crops in China. Improving the oil production of rapeseed is an important way to ensure the safety of edible oil in China. Oil production is an important index that reflects the quality of rapeseed and is determined by the oil content and yield. Applying nitrogen is an important way to ensure a strong and stable yield. However, the seed oil content has been shown to be reduced in most rapeseed varieties after nitrogen application. Thus, it is critical to screen elite germplasm resources with stable or improved oil content under high levels of nitrogen, and to investigate the molecular mechanisms of the regulation by nitrogen of oil accumulation. However, few studies on these aspects have been published. In this review, we analyze the effect of nitrogen on the growth and development of rapeseed, including photosynthetic assimilation, substance distribution, and the synthesis of lipids and proteins. In this process, the expression levels of genes related to nitrogen absorption, assimilation, and transport changed after nitrogen application, which enhanced the ability of carbon and nitrogen assimilation and increased biomass, thus leading to a higher yield. After a crop enters the reproductive growth phase, photosynthates in the body are transported to the developing seed for protein and lipid synthesis. However, protein synthesis precedes lipid synthesis, and a large number of photosynthates are consumed during protein synthesis, which weakens lipid synthesis. Moreover, we suggest several research directions, especially for exploring genes involved in lipid and protein accumulation under nitrogen regulation. In this study, we summarize the effects of nitrogen at both the physiological and molecular levels, aiming to reveal the mechanisms of nitrogen regulation in oil accumulation and, thereby, provide a theoretical basis for breeding varieties with a high oil content.
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Affiliation(s)
| | | | | | | | | | - Guixin Yan
- The Key Laboratory of Biology and Genetic Improvement of Oil Crops, The Ministry of Agriculture and Rural Affairs of the PRC, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Wuhan 430062, China; (J.Z.)
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Bhinder G, Sharma S, Kaur H, Akhatar J, Mittal M, Sandhu S. Genomic Regions Associated With Seed Meal Quality Traits in Brassica napus Germplasm. FRONTIERS IN PLANT SCIENCE 2022; 13:882766. [PMID: 35909769 PMCID: PMC9333065 DOI: 10.3389/fpls.2022.882766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/20/2022] [Indexed: 06/15/2023]
Abstract
The defatted Brassica napus (rapeseed) meal can be high-protein feed for livestock as the protein value of rapeseed meal is higher than that of the majority of other vegetable proteins. Extensive work has already been carried out on developing canola rapeseed where the focus was on reducing erucic acid and glucosinolate content, with less consideration to other antinutritional factors such as tannin, phytate, sinapine, crude fiber, etc. The presence of these antinutrients limits the use and marketing of rapeseed meals and a significant amount of it goes unused and ends up as waste. We investigated the genetic architecture of crude protein, methionine, tryptophan, total phenols, β-carotene, glucosinolates (GLSs), phytate, tannins, sinapine, and crude fiber content of defatted seed meal samples by conducting a genome-wide association study (GWAS), using a diversity panel comprising 96 B. napus genotypes. Genotyping by sequencing was used to identify 77,889 SNPs, spread over 19 chromosomes. Genetic diversity and phenotypic variations were generally high for the studied traits. A total of eleven genotypes were identified which showed high-quality protein, high antioxidants, and lower amount of antinutrients. A significant negative correlation between protein and limiting amino acids and a significant positive correlation between GLS and phytic acid were observed. General and mixed linear models were used to estimate the association between the SNP markers and the seed quality traits and quantile-quantile (QQ) plots were generated to allow the best-fit algorithm. Annotation of genomic regions around associated SNPs helped to predict various trait-related candidates such as ASP2 and EMB1027 (amino acid biosynthesis); HEMA2, GLU1, and PGM (tryptophan biosynthesis); MS3, CYSD1, and MTO1 (methionine biosynthesis); LYC (β-carotene biosynthesis); HDR and ISPF (MEP pathway); COS1 (riboflavin synthesis); UGT (phenolics biosynthesis); NAC073 (cellulose and hemicellulose biosynthesis); CYT1 (cellulose biosynthesis); BGLU45 and BGLU46 (lignin biosynthesis); SOT12 and UGT88A1 (flavonoid pathway); and CYP79A2, DIN2, and GSTT2 (GLS metabolism), etc. The functional validation of these candidate genes could confirm key seed meal quality genes for germplasm enhancement programs directed at improving protein quality and reducing the antinutritional components in B. napus.
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Affiliation(s)
| | - Sanjula Sharma
- Oilseeds Section, Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
| | | | - Javed Akhatar
- Oilseeds Section, Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, India
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Tandayu E, Borpatragohain P, Mauleon R, Kretzschmar T. Genome-Wide Association Reveals Trait Loci for Seed Glucosinolate Accumulation in Indian Mustard ( Brassica juncea L.). PLANTS (BASEL, SWITZERLAND) 2022; 11:plants11030364. [PMID: 35161346 PMCID: PMC8838242 DOI: 10.3390/plants11030364] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 01/18/2022] [Accepted: 01/26/2022] [Indexed: 05/05/2023]
Abstract
Glucosinolates (GSLs) are sulphur- and nitrogen-containing secondary metabolites implicated in the fitness of Brassicaceae and appreciated for their pungency and health-conferring properties. In Indian mustard (Brassica juncea L.), GSL content and composition are seed-quality-determining traits affecting its economic value. Depending on the end use, i.e., condiment or oil, different GSL levels constitute breeding targets. The genetic control of GSL accumulation in Indian mustard, however, is poorly understood, and current knowledge of GSL biosynthesis and regulation is largely based on Arabidopsis thaliana. A genome-wide association study was carried out to dissect the genetic architecture of total GSL content and the content of two major GSLs, sinigrin and gluconapin, in a diverse panel of 158 Indian mustard lines, which broadly grouped into a South Asia cluster and outside-South-Asia cluster. Using 14,125 single-nucleotide polymorphisms (SNPs) as genotyping input, seven distinct significant associations were discovered for total GSL content, eight associations for sinigrin content and 19 for gluconapin. Close homologues of known GSL structural and regulatory genes were identified as candidate genes in proximity to peak SNPs. Our results provide a comprehensive map of the genetic control of GLS biosynthesis in Indian mustard, including priority targets for further investigation and molecular marker development.
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Yang H, Mohd Saad NS, Ibrahim MI, Bayer PE, Neik TX, Severn-Ellis AA, Pradhan A, Tirnaz S, Edwards D, Batley J. Candidate Rlm6 resistance genes against Leptosphaeria. maculans identified through a genome-wide association study in Brassica juncea (L.) Czern. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2021; 134:2035-2050. [PMID: 33768283 DOI: 10.1007/s00122-021-03803-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 02/23/2021] [Indexed: 06/12/2023]
Abstract
One hundred and sixty-seven B. juncea varieties were genotyped on the 90K Brassica assay (42,914 SNPs), which led to the identification of sixteen candidate genes for Rlm6. Brassica species are at high risk of severe crop loss due to pathogens, especially Leptosphaeria maculans (the causal agent of blackleg). Brassica juncea (L.) Czern is an important germplasm resource for canola improvement, due to its good agronomic traits, such as heat and drought tolerance and high blackleg resistance. The present study is the first using genome-wide association studies to identify candidate genes for blackleg resistance in B. juncea based on genome-wide SNPs obtained from the Illumina Infinium 90 K Brassica SNP array. The verification of Rlm6 in B. juncea was performed through a cotyledon infection test. Genotyping 42,914 single nucleotide polymorphisms (SNPs) in a panel of 167 B. juncea lines revealed a total of seven SNPs significantly associated with Rlm6 on chromosomes A07 and B04 in B. juncea. Furthermore, 16 candidate Rlm6 genes were found in these regions, defined as nucleotide binding site leucine-rich-repeat (NLR), leucine-rich repeat RLK (LRR-RLK) and LRR-RLP genes. This study will give insights into the blackleg resistance in B. juncea and facilitate identification of functional blackleg resistance genes which can be used in Brassica breeding.
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Affiliation(s)
- Hua Yang
- School of Agriculture and Food Sciences, University of Queensland, Brisbane, QLD, Australia
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | | | | | - Philipp E Bayer
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Ting Xiang Neik
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Anita A Severn-Ellis
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Aneeta Pradhan
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Soodeh Tirnaz
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - David Edwards
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia
| | - Jacqueline Batley
- School of Biological Sciences, University of Western Australia, Perth, WA, Australia.
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Gohain B, Kumar P, Malhotra B, Augustine R, Pradhan AK, Bisht NC. A comprehensive Vis-NIRS equation for rapid quantification of seed glucosinolate content and composition across diverse Brassica oilseed chemotypes. Food Chem 2021; 354:129527. [PMID: 33756325 DOI: 10.1016/j.foodchem.2021.129527] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 02/03/2021] [Accepted: 03/02/2021] [Indexed: 01/28/2023]
Abstract
The globally cultivated Brassica crops contain high deliverable concentrations of health-promoting glucosinolates. Development of a Visible-Near InfraRed Spectroscopy (Vis-NIRS) calibration to profile different glucosinolate components from 641 diverse Brassica juncea chemotypes was attempted in this study. Principal component analysis of HPLC-determined glucosinolates established the distinctiveness of four B. juncea populations used. Subsequently, modified partial least square regression based population-specific and combined Vis-NIRS models were developed, wherein the combined model exhibited higher coefficient of determination (R2; 0.81-0.97) for eight glucosinolates and higher ratio of prediction determination (RPD; 2.42-5.35) for seven glucosinolates in B. juncea populations. Furthermore, range error ratio (RER > 4) for twelve and RER > 10 for eight glucosinolates make the combined model acceptable for screening and quality control. The model also provided excellent prediction for aliphatic glucosinolates in four oilseed Brassica species. Overall, our work highlights the potential of Vis-NIR spectroscopy in estimating glucosinolate content in the economically important Brassica oilseeds.
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Affiliation(s)
- Bornali Gohain
- National Institute for Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
| | - Pawan Kumar
- National Institute for Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
| | - Bhanu Malhotra
- National Institute for Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
| | - Rehna Augustine
- Centre for Plant Biotechnology & Molecular Biology, Kerala Agricultural University, 680656, India.
| | - Akshay K Pradhan
- Department of Genetics, University of Delhi South Campus, New Delhi 110021, India.
| | - Naveen C Bisht
- National Institute for Plant Genome Research, Aruna Asaf Ali Marg, New Delhi 110067, India.
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