1
|
Fan E, Liu C, Wang Z, Wang S, Ma W, Lu N, Liu Y, Fu P, Wang R, Lv S, Qu G, Wang J. Genome-Wide Identification and Expression Analysis of the SQUAMOSA Promoter-Binding Protein-like ( SPL) Transcription Factor Family in Catalpabungei. Int J Mol Sci 2023; 25:97. [PMID: 38203267 PMCID: PMC10779025 DOI: 10.3390/ijms25010097] [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: 10/31/2023] [Revised: 11/27/2023] [Accepted: 11/28/2023] [Indexed: 01/12/2024] Open
Abstract
As a plant-specific transcription factor, the SPL gene family plays a critical role in plant growth and development. Although the SPL gene family has been identified in diverse plant species, there have been no genome-wide identification or systematic study reports on the SPL gene family in Catalpa bungei. In this study, we identified 19 putative SPL gene family members in the C. bungei genome. According to the phylogenetic relationship, they can be divided into eight groups, and the genes in the same group have a similar gene structure and conserved motifs. Synteny analysis showed that fragment duplication played an important role in the expansion of the CbuSPL gene family. At the same time, CbuSPL genes have cis-acting elements and functions related to light response, hormone response, growth and development, and stress response. Tissue-specific expression and developmental period-specific expression analysis showed that CbuSPL may be involved in flowering initiation and development, flowering transition, and leaf development. In addition, the ectopic expression of CbuSPL4 in Arabidopsis confirmed that it can promote early flowering and induce the expression of related flowering genes. These systematic research results will lay a foundation for further study on the functional analysis of SPL genes in C. bungei.
Collapse
Affiliation(s)
- Erqin Fan
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; (E.F.); (C.L.); (S.W.); (Y.L.); (P.F.); (R.W.); (S.L.); (G.Q.)
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, National Innovation Alliance of Catalpa bungei, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (Z.W.); (W.M.); (N.L.)
| | - Caixia Liu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; (E.F.); (C.L.); (S.W.); (Y.L.); (P.F.); (R.W.); (S.L.); (G.Q.)
- College of Life Science, Northeast Forestry University, Harbin 150040, China
| | - Zhi Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, National Innovation Alliance of Catalpa bungei, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (Z.W.); (W.M.); (N.L.)
| | - Shanshan Wang
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; (E.F.); (C.L.); (S.W.); (Y.L.); (P.F.); (R.W.); (S.L.); (G.Q.)
| | - Wenjun Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, National Innovation Alliance of Catalpa bungei, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (Z.W.); (W.M.); (N.L.)
| | - Nan Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, National Innovation Alliance of Catalpa bungei, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (Z.W.); (W.M.); (N.L.)
| | - Yuhang Liu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; (E.F.); (C.L.); (S.W.); (Y.L.); (P.F.); (R.W.); (S.L.); (G.Q.)
| | - Pengyue Fu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; (E.F.); (C.L.); (S.W.); (Y.L.); (P.F.); (R.W.); (S.L.); (G.Q.)
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, National Innovation Alliance of Catalpa bungei, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (Z.W.); (W.M.); (N.L.)
| | - Rui Wang
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; (E.F.); (C.L.); (S.W.); (Y.L.); (P.F.); (R.W.); (S.L.); (G.Q.)
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, National Innovation Alliance of Catalpa bungei, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (Z.W.); (W.M.); (N.L.)
| | - Siyu Lv
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; (E.F.); (C.L.); (S.W.); (Y.L.); (P.F.); (R.W.); (S.L.); (G.Q.)
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, National Innovation Alliance of Catalpa bungei, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (Z.W.); (W.M.); (N.L.)
| | - Guanzheng Qu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin 150040, China; (E.F.); (C.L.); (S.W.); (Y.L.); (P.F.); (R.W.); (S.L.); (G.Q.)
| | - Junhui Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, National Innovation Alliance of Catalpa bungei, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China; (Z.W.); (W.M.); (N.L.)
| |
Collapse
|
2
|
Xia H, Hao Z, Shen Y, Tu Z, Yang L, Zong Y, Li H. Genome-wide association study of multiyear dynamic growth traits in hybrid Liriodendron identifies robust genetic loci associated with growth trajectories. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 115:1544-1563. [PMID: 37272730 DOI: 10.1111/tpj.16337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/30/2023] [Accepted: 05/29/2023] [Indexed: 06/06/2023]
Abstract
The genetic factors underlying growth traits differ over time points or stages. However, most current studies of phenotypes at single time points do not capture all loci or explain the genetic differences underlying growth trajectories. Hybrid Liriodendron exhibits obvious heterosis and is widely cultivated, although its complex genetic mechanism underlying growth traits remains unknown. A genome-wide association study (GWAS) is an effective method for elucidating the genetic architecture by identifying genetic loci underlying complex quantitative traits. In the present study, using a GWAS, we identified robust loci associated with growth trajectories in hybrid Liriodendron populations. We selected 233 hybrid progenies derived from 25 crosses for resequencing, and measured their tree height (H) and diameter at breast height (DBH) for 11 consecutive years; 192 972 high-quality single nucleotide polymorphisms (SNPs) were obtained. The dynamics of the multiyear single-trait GWAS showed that year-specific SNPs predominated, and only five robust SNPs for DBH were identified in at least three different years. Multitrait GWAS analysis with model parameters as latent variables also revealed 62 SNPs for H and 52 for DBH associated with the growth trajectory, displaying different biomass accumulation patterns, among which four SNPs exerted pleiotropic effects. All identified SNPs also exhibited temporal variations in effect sizes and inheritance patterns potentially related to different growth and developmental stages. The haplotypes resulting from these significant SNPs might pyramid favorable loci, benefitting the selection of superior genotypes. The present study provides insights into the genetic architecture of dynamic growth traits and lays a basis for future molecular-assisted breeding.
Collapse
Affiliation(s)
- Hui Xia
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Ziyuan Hao
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Yufang Shen
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Zhonghua Tu
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Lichun Yang
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Yaxian Zong
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| | - Huogen Li
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China
| |
Collapse
|
3
|
Wang Y, Zhang H, Zhu S, Shen T, Pan H, Xu M. Association Mapping and Expression Analysis of the Genes Involved in the Wood Formation of Poplar. Int J Mol Sci 2023; 24:12662. [PMID: 37628843 PMCID: PMC10454019 DOI: 10.3390/ijms241612662] [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: 07/06/2023] [Revised: 08/04/2023] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Xylogenesis is a complex and sequential biosynthetic process controlled by polygenes. Deciphering the genetic architecture of this complex quantitative trait could provide valuable information for increasing wood biomass and improving its properties. Here, we performed genomic resequencing of 64 24-year-old trees (64 hybrids of section Aigeiros and their parents) grown in the same field and conducted full-sib family-based association analyses of two growth and six woody traits using GEMMA as a choice of association model selection. We identified 1342 significantly associated single nucleotide polymorphisms (SNPs), 673 located in the region upstream and downstream of 565 protein-encoding genes. The transcriptional regulation network of secondary cell wall (SCW) biosynthesis was further constructed based on the published data of poplar miRNA, transcriptome, and degradome. These provided a certain scientific basis for the in-depth understanding of the mechanism of poplar timber formation and the molecular-assisted breeding in the future.
Collapse
Affiliation(s)
| | | | | | | | | | - Meng Xu
- Co-Innovation Center for Sustainable Forestry in Southern China, Satae Key Laboratory of Tree Genetics and Breeding, Nanjing Forestry University, Nanjing 210037, China; (Y.W.); (H.Z.); (S.Z.); (T.S.); (H.P.)
| |
Collapse
|
4
|
Srivastav M, Radadiya N, Ramachandra S, Jayaswal PK, Singh N, Singh S, Mahato AK, Tandon G, Gupta A, Devi R, Subrayagowda SH, Kumar G, Prakash P, Singh S, Sharma N, Nagaraja A, Kar A, Rudra SG, Sethi S, Jaiswal S, Iquebal MA, Singh R, Singh SK, Singh NK. High resolution mapping of QTLs for fruit color and firmness in Amrapali/Sensation mango hybrids. FRONTIERS IN PLANT SCIENCE 2023; 14:1135285. [PMID: 37351213 PMCID: PMC10282835 DOI: 10.3389/fpls.2023.1135285] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 05/08/2023] [Indexed: 06/24/2023]
Abstract
Introduction Mango (Mangifera indica L.), acclaimed as the 'king of fruits' in the tropical world, has historical, religious, and economic values. It is grown commercially in more than 100 countries, and fresh mango world trade accounts for ~3,200 million US dollars for the year 2020. Mango is widely cultivated in sub-tropical and tropical regions of the world, with India, China, and Thailand being the top three producers. Mango fruit is adored for its taste, color, flavor, and aroma. Fruit color and firmness are important fruit quality traits for consumer acceptance, but their genetics is poorly understood. Methods For mapping of fruit color and firmness, mango varieties Amrapali and Sensation, having contrasting fruit quality traits, were crossed for the development of a mapping population. Ninety-two bi-parental progenies obtained from this cross were used for the construction of a high-density linkage map and identification of QTLs. Genotyping was carried out using an 80K SNP chip array. Results and discussion Initially, we constructed two high-density linkage maps based on the segregation of female and male parents. A female map with 3,213 SNPs and male map with 1,781 SNPs were distributed on 20 linkages groups covering map lengths of 2,844.39 and 2,684.22cM, respectively. Finally, the integrated map was constructed comprised of 4,361 SNP markers distributed on 20 linkage groups, which consisted of the chromosome haploid number in Mangifera indica (n =20). The integrated genetic map covered the entire genome of Mangifera indica cv. Dashehari, with a total genetic distance of 2,982.75 cM and an average distance between markers of 0.68 cM. The length of LGs varied from 85.78 to 218.28 cM, with a mean size of 149.14 cM. Phenotyping for fruit color and firmness traits was done for two consecutive seasons. We identified important consistent QTLs for 12 out of 20 traits, with integrated genetic linkages having significant LOD scores in at least one season. Important consistent QTLs for fruit peel color are located at Chr 3 and 18, and firmness on Chr 11 and 20. The QTLs mapped in this study would be useful in the marker-assisted breeding of mango for improved efficiency.
Collapse
Affiliation(s)
- Manish Srivastav
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Nidhi Radadiya
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Sridhar Ramachandra
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Pawan Kumar Jayaswal
- Genomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India
| | - Nisha Singh
- Genomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India
| | - Sangeeta Singh
- Genomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India
| | - Ajay Kumar Mahato
- Genomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India
| | - Gitanjali Tandon
- Division of Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Ankit Gupta
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Rajni Devi
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Sreekanth Halli Subrayagowda
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Gulshan Kumar
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Pragya Prakash
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Shivani Singh
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Nimisha Sharma
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - A. Nagaraja
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Abhijit Kar
- Division of Food Science and Postharvest Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Shalini Gaur Rudra
- Division of Food Science and Postharvest Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Shruti Sethi
- Division of Food Science and Postharvest Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Sarika Jaiswal
- Division of Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Mir Asif Iquebal
- Division of Agricultural Bioinformatics, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Rakesh Singh
- Division of Genomic Resources, Indian Council of Agricultural Research (ICAR)- National Bureau of Plant Genetic Resources, New Delhi, India
| | - Sanjay Kumar Singh
- Division of Fruits and Horticultural Technology, Indian Council of Agricultural Research (ICAR)- Indian Agricultural Research Institute, New Delhi, India
| | - Nagendra Kumar Singh
- Genomics Laboratory, Indian Council of Agricultural Research (ICAR)- National Institute for Plant Biotechnology, New Delhi, India
| |
Collapse
|
5
|
Zhang M, Liu B, Fei Y, Yang X, Zhao L, Shi C, Zhang Y, Lu N, Wu C, Ma W, Wang J. Genetic architecture of leaf morphology revealed by integrated trait module in Catalpa bungei. HORTICULTURE RESEARCH 2023; 10:uhad032. [PMID: 37090097 PMCID: PMC10120837 DOI: 10.1093/hr/uhad032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 02/14/2023] [Indexed: 05/03/2023]
Abstract
Leaves are crucial for maintaining plant growth and development via photosynthesis, and their function is simultaneously regulated by a suite of phenotypic traits. Although much is known about the genetic architecture of individual leaf traits, unraveling the genetic basis of complex leaf morphology remains a challenge. Based on the functional correlation and coordination of multi-traits, we divided 15 leaf morphological traits into three modules, comprising size (area, length, width, and perimeter), shape (leaf lobes, aspect ratio, circularity, rectangularity, and the relevant ratios), and color (red, green, and blue) for an ornamental tree species, Catalpa bungei. A total of 189 significant single-nucleotide polymorphisms were identified in the leaves of C. bungei: 35, 82, and 76 in the size, shape, and color modules, respectively. Four quantitative trait loci were common between the size and shape modules, which were closely related according to phenotype correlation, genetic mapping, and mRNA analysis. The color module was independent of them. Synergistic changes in the aspect ratio, leaf lobe, and circularity suggest that these traits could be the core indicators of the leaf shape module. The LAS and SRK genes, associated with leaf lobe and circularity, were found to function in plant defense mechanisms and the growth of leaves. The associations between the SRK and CRK2 genes and the leaf lobe and circularity traits were further verified by RT-qPCR. Our findings demonstrate the importance of integrating multi-trait modules to characterize leaf morphology and facilitate a holistic understanding of the genetic architecture of intraspecific leaf morphology diversity.
Collapse
Affiliation(s)
| | | | - Yue Fei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Xiaowei Yang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Linjiao Zhao
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Chaozhong Shi
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Yueying Zhang
- Academy of Forest and Grassland Inventory and Planning, National Forestry and Grassland Administration, Beijing 100714, China
| | - Nan Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Chuangye Wu
- Wenxian Forestry Science Research Institute, Jiaozuo 454850, China
| | - Wenjun Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | | |
Collapse
|
6
|
Zhang M, Lu N, Jiang L, Liu B, Fei Y, Ma W, Shi C, Wang J. Multiple dynamic models reveal the genetic architecture for growth in height of Catalpa bungei in the field. TREE PHYSIOLOGY 2022; 42:1239-1255. [PMID: 34940852 DOI: 10.1093/treephys/tpab171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 12/19/2021] [Indexed: 06/14/2023]
Abstract
Growth in height (GH) is a critical determinant for tree survival and development in forests and can be depicted using logistic growth curves. Our understanding of the genetic mechanism underlying dynamic GH, however, is limited, particularly under field conditions. We applied two mapping models (Funmap and FVTmap) to find quantitative trait loci responsible for dynamic GH and two epistatic models (2HiGWAS and 1HiGWAS) to detect epistasis in Catalpa bungei grown in the field. We identified 13 co-located quantitative trait loci influencing the growth curve by Funmap and three heterochronic parameters (the timing of the inflection point, maximum acceleration and maximum deceleration) by FVTmap. The combined use of FVTmap and Funmap reduced the number of candidate genes by >70%. We detected 76 significant epistatic interactions, amongst which a key gene, COMT14, co-located by three models (but not 1HiGWAS) interacted with three other genes, implying that a novel network of protein interaction centered on COMT14 may control the dynamic GH of C. bungei. These findings provide new insights into the genetic mechanisms underlying the dynamic growth in tree height in natural environments and emphasize the necessity of incorporating multiple dynamic models for screening more reliable candidate genes.
Collapse
Affiliation(s)
- Miaomiao Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Nan Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Libo Jiang
- School of Life Sciences and Medicine, Shandong University of Technology, Zibo 255049, China
| | - Bingyang Liu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Yue Fei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Wenjun Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Chaozhong Shi
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Junhui Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| |
Collapse
|
7
|
Cui Y, Fan B, Xu X, Sheng S, Xu Y, Wang X. A High-Density Genetic Map Enables Genome Synteny and QTL Mapping of Vegetative Growth and Leaf Traits in Gardenia. Front Genet 2022; 12:802738. [PMID: 35132310 PMCID: PMC8817757 DOI: 10.3389/fgene.2021.802738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 12/13/2021] [Indexed: 11/13/2022] Open
Abstract
The gardenia is a traditional medicinal horticultural plant in China, but its molecular genetic research has been largely hysteretic. Here, we constructed an F1 population with 200 true hybrid individuals. Using the genotyping-by-sequencing method, a high-density sex-average genetic map was generated that contained 4,249 SNPs with a total length of 1956.28 cM and an average genetic distance of 0.46 cM. We developed 17 SNP-based Kompetitive Allele-Specific PCR markers and found that 15 SNPs were successfully genotyped, of which 13 single-nucleotide polymorphism genotypings of 96 F1 individuals showed genotypes consistent with GBS-mined genotypes. A genomic collinearity analysis between gardenia and the Rubiaceae species Coffea arabica, Coffea canephora and Ophiorrhiza pumila showed the relativity strong conservation of LG11 with NC_039,919.1, HG974438.1 and Bliw01000011.1, respectively. Lastly, a quantitative trait loci analysis at three phenotyping time points (2019, 2020, and 2021) yielded 18 QTLs for growth-related traits and 31 QTLs for leaf-related traits, of which qBSBN7-1, qCD8 and qLNP2-1 could be repeatably detected. Five QTL regions (qCD8 and qSBD8, qBSBN7 and qSI7, qCD4-1 and qLLLS4, qLNP10 and qSLWS10-2, qSBD10 and qLLLS10) with potential pleiotropic effects were also observed. This study provides novel insight into molecular genetic research and could be helpful for further gene cloning and marker-assisted selection for early growth and development traits in the gardenia.
Collapse
Affiliation(s)
- Yang Cui
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Baolian Fan
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Xu Xu
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Shasha Sheng
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| | - Yuhui Xu
- Adsen Biotechnology Co., Ltd., Urumchi, China
| | - Xiaoyun Wang
- Research Center for Traditional Chinese Medicine Resources and Ethnic Minority Medicine, Jiangxi University of Chinese Medicine, Nanchang, China
| |
Collapse
|
8
|
Lv F, Wang P, Zhang E, Ma L, Gao L, Yang R, Wang Q, Li Y. Efficient Transformation of Catalpa bungei Shows Crystal Genes Conferring Resistance to the Shoot Borer Omphisa plagialis. FRONTIERS IN PLANT SCIENCE 2021; 12:777411. [PMID: 35003162 PMCID: PMC8739885 DOI: 10.3389/fpls.2021.777411] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/26/2021] [Indexed: 06/14/2023]
Abstract
Although Catalpa bungei is a forest plant with considerable economic and ornamental value in China, its wood and decorative qualities are constrained by insect pests such as the shoot borer Omphisa plagialis (Lepidoptera). Overexpressing insect resistance genes such as crystal genes to develop an insect-resistant variety of C. bungei is an environmental and ecological approach. However, genotype limitations and low regeneration rates of embryogenic calli (EC) inhibit the development of transformation and the insect-resistant gene expression system in C. bungei. Here, we first established embryogenic callus induction and regeneration systems of five genotypes using mature seed and stem segment explants; the highest induction and regeneration rates of EC were 39.89 and 100%, respectively. Next, an efficient and stable Agrobacterium-mediated genetic transformation system was developed from EC and its positive frequency was up to 92.31%. Finally, using the transformation system, 15 and 22 transgenic C. bungei lines that expressed Cry2A and Cry9Aa-like were generated, respectively. These transgenic lines that exhibited significantly higher resistance to O. plagialis in the laboratory and field have great promise for meeting the challenge of future pest management under changing climatic conditions. Additionally, this efficient, fast, and stable transformation system could be a potential tool for gene function analysis and forest tree genetic improvement.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Ya Li
- Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province & Chinese Academy of Sciences, Nanjing, China
| |
Collapse
|
9
|
Zhang M, Lu N, Zhu T, Yang G, Qu G, Shi C, Fei Y, Liu B, Ma W, Wang J. A Bivariate Mapping Model Identifies Major Covariation QTLs for Biomass Allocation Between Leaf and Stem Growth of Catalpa bungei. Front Genet 2021; 12:758209. [PMID: 34868235 PMCID: PMC8637733 DOI: 10.3389/fgene.2021.758209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/21/2021] [Indexed: 11/13/2022] Open
Abstract
Biomass allocation plays a critical role in plant morphological formation and phenotypic plasticity, which greatly impact plant adaptability and competitiveness. While empirical studies on plant biomass allocation have focused on molecular biology and ecology approaches, detailed insight into the genetic basis of biomass allocation between leaf and stem growth is still lacking. Herein, we constructed a bivariate mapping model to identify covariation QTLs governing carbon (C) allocation between the leaves and stem as well as the covariation of traits within and between organs in a full-sib mapping population of C. bungei. A total of 123 covQTLs were detected for 23 trait pairs, including six leaf traits (leaf length, width, area, perimeter, length/width ratio and petiole length) and five stem traits (height, diameter at breast height, wood density, stemwood volume and stemwood biomass). The candidate genes were further identified in tissue-specific gene expression data, which provided insights into the genetic architecture underlying C allocation for traits or organs. The key QTLs related to growth and biomass allocation, which included UVH1, CLPT2, GAD/SPL, COG1 and MTERF4, were characterised and verified via gene function annotation and expression profiling. The integration of a bivariate Quantitative trait locus mapping model and gene expression profiling will enable the elucidation of genetic architecture underlying biomass allocation and covariation growth, in turn providing a theoretical basis for forest molecular marker-assisted breeding with specific C allocation strategies for adaptation to heterogeneous environments.
Collapse
Affiliation(s)
- Miaomiao Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Nan Lu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Tianqing Zhu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Guijuan Yang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Guanzheng Qu
- State Key Laboratory of Tree Genetics and Breeding, Northeast Forestry University, Harbin, China
| | - Chaozhong Shi
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Yue Fei
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Bingyang Liu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Wenjun Ma
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| | - Junhui Wang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China
| |
Collapse
|
10
|
Secondary Metabolites from Stem Barks of Catalpa bungei. Chem Nat Compd 2021. [DOI: 10.1007/s10600-021-03561-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
11
|
Ye W, Yang Y, Wang P, Zhang Y, Zhang L, Tian D, Zhang L, Zhang L, Zhou B. InDel marker development and QTL analysis of agronomic traits in mung bean [ Vigna radiate (L.) Wilczek]. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2021; 41:66. [PMID: 37309317 PMCID: PMC10236061 DOI: 10.1007/s11032-021-01233-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/11/2021] [Indexed: 06/14/2023]
Abstract
The stem color of young mung bean is a very useful tool in germplasm identification. Flowering time and plant height (PH) are known to be strongly correlated with crop adaption and yield. However, few studies have focused on elucidating the genetic mechanisms that regulate these five particular traits: young stem color (YSC), days to first flowering (DFF), days to maturity (DM), PH, and nodes on the main stem (NMS). In this study, a genetic linkage map for the F2 population was constructed using 129 InDel markers that were developed based on the sequence variations between parents. A total of 14 QTLs related to YSC, DFF, DM, PH, and NMS were detected. These QTLs were distributed on six chromosomes (1, 3, 4, 6, 7, and 10), which individually accounted for 1.32 to 90.07% of the total phenotypic variation. Using a short and high-density linkage map for the F3 population, six of the seven QTLs which clustered at two intervals on chromosomes 3 and 10 were detected again. Further analysis found that four QTLs between InDel markers R3-15 and R3-19 controlled DFF, DM, PH, and NMS, and each QTL accounted for a large percent of the total phenotypic variation. Analysis of two separated F2:3 lines also found that the phenotype was highly corresponded to its genotype which was between R3-15 and R3-19. Phenotype and genotype analysis for 30 mung bean accessions showed that the major effect QTL qDFF3 was a key regulator for DFF. Using a map-based cloning method, the major effect QTL qYSC4 for YSC was mapped in a 347 Kb interval on chromosome 4. Candidate gene analysis showed that sequence variations and expression level differences existed in the predicted candidate gene between the parents. These results provide a theoretical basis for cloning these QTLs and marker-assisted selection. Supplementary Information The online version contains supplementary material available at 10.1007/s11032-021-01233-0.
Collapse
Affiliation(s)
- Weijun Ye
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
- Anhui Province Key Laboratory of Crop Quality Improvement, Hefei, 230000 Anhui China
| | - Yong Yang
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
| | - Peiran Wang
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
| | - Yin Zhang
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
| | - Liya Zhang
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
| | - Dongfeng Tian
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
| | - Lei Zhang
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
| | - Lingling Zhang
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
| | - Bin Zhou
- Crop Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230000 Anhui China
| |
Collapse
|
12
|
Zhang S, Yu Z, Qi X, Wang Z, Zheng Y, Ren H, Liang S, Zheng X. Construction of a High-Density Genetic Map and Identification of Leaf Trait-Related QTLs in Chinese Bayberry ( Myrica rubra). FRONTIERS IN PLANT SCIENCE 2021; 12:675855. [PMID: 34194452 PMCID: PMC8238045 DOI: 10.3389/fpls.2021.675855] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 05/18/2021] [Indexed: 06/13/2023]
Abstract
Chinese bayberry (Myrica rubra) is an economically important fruit tree that is grown in southern China. Owing to its over 10-year seedling period, the crossbreeding of bayberry is challenging. The characteristics of plant leaves are among the primary factors that control plant architecture and potential yields, making the analysis of leaf trait-related genetic factors crucial to the hybrid breeding of any plant. In the present study, molecular markers associated with leaf traits were identified via a whole-genome re-sequencing approach, and a genetic map was thereby constructed. In total, this effort yielded 902.11 Gb of raw data that led to the identification of 2,242,353 single nucleotide polymorphisms (SNPs) in 140 F1 individuals and parents (Myrica rubra cv. Biqizhong × Myrica rubra cv. 2012LXRM). The final genetic map ultimately incorporated 31,431 SNPs in eight linkage groups, spanning 1,351.85 cM. This map was then used to assemble and update previous scaffold genomic data at the chromosomal level. The genome size of M. rubra was thereby established to be 275.37 Mb, with 94.98% of sequences being assembled into eight pseudo-chromosomes. Additionally, 18 quantitative trait loci (QTLs) associated with nine leaf and growth-related traits were identified. Two QTL clusters were detected (the LG3 and LG5 clusters). Functional annotations further suggested two chlorophyll content-related candidate genes being identified in the LG5 cluster. Overall, this is the first study on the QTL mapping and identification of loci responsible for the regulation of leaf traits in M. rubra, offering an invaluable scientific for future marker-assisted selection breeding and candidate gene analyses.
Collapse
Affiliation(s)
| | | | - Xingjiang Qi
- Institute of Horticulture, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | | | | | | | | | | |
Collapse
|