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Zhou X, Xiang X, Zhang M, Cao D, Du C, Zhang L, Hu J. Combining GS-assisted GWAS and transcriptome analysis to mine candidate genes for nitrogen utilization efficiency in Populus cathayana. BMC PLANT BIOLOGY 2023; 23:182. [PMID: 37020197 PMCID: PMC10074878 DOI: 10.1186/s12870-023-04202-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/06/2022] [Accepted: 03/29/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND Forest trees such as poplar, shrub willow, et al. are essential natural resources for sustainable and renewable energy production, and their wood can reduce dependence on fossil fuels and reduce environmental pollution. However, the productivity of forest trees is often limited by the availability of nitrogen (N), improving nitrogen use efficiency (NUE) is an important way to address it. Currently, NUE genetic resources are scarce in forest tree research, and more genetic resources are urgently needed. RESULTS Here, we performed genome-wide association studies (GWAS) using the mixed linear model (MLM) to identify genetic loci regulating growth traits in Populus cathayana at two N levels, and attempted to enhance the signal strength of single nucleotide polymorphism (SNP) detection by performing genome selection (GS) assistance GWAS. The results of the two GWAS analyses identified 55 and 40 SNPs that were respectively associated with plant height (PH) and ground diameter (GD), and 92 and 69 candidate genes, including 30 overlapping genes. The prediction accuracy of the GS model (rrBLUP) for phenotype exceeds 0.9. Transcriptome analysis of 13 genotypes under two N levels showed that genes related to carbon and N metabolism, amino acid metabolism, energy metabolism, and signal transduction were differentially expressed in the xylem of P. cathayana under N treatment. Furthermore, we observed strong regional patterns in gene expression levels of P. cathayana, with significant differences between different regions. Among them, P. cathayana in Longquan region exhibited the highest response to N. Finally, through weighted gene co-expression network analysis (WGCNA), we identified a module closely related to the N metabolic process and eight hub genes. CONCLUSIONS Integrating the GWAS, RNA-seq and WGCNA data, we ultimately identified four key regulatory genes (PtrNAC123, PtrNAC025, Potri.002G233100, and Potri.006G236200) involved in the wood formation process, and they may affect P. cathayana growth and wood formation by regulating nitrogen metabolism. This study will provide strong evidence for N regulation mechanisms, and reliable genetic resources for growth and NUE genetic improvement in poplar.
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Affiliation(s)
- Xinglu Zhou
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Xiaodong Xiang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Min Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Demei Cao
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Changjian Du
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China
| | - Lei Zhang
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, Jiangsu, China.
| | - Jianjun Hu
- State Key Laboratory of Tree Genetics and Breeding, Key Laboratory of Tree Breeding and Cultivation of National Forestry and Grassland Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, 100091, China.
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, Jiangsu, China.
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Genome-Wide Identification of WRKY Family Genes and the Expression Profiles in Response to Nitrogen Deficiency in Poplar. Genes (Basel) 2022; 13:genes13122324. [PMID: 36553591 PMCID: PMC9777946 DOI: 10.3390/genes13122324] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 11/27/2022] [Accepted: 12/08/2022] [Indexed: 12/14/2022] Open
Abstract
The fast-growing arbor poplar is widely distributed across the world and is susceptible to nitrogen availability. The WRKY transcription factor is an important regulatory node of stress tolerance as well as nutrient utilization. However, the potential response mechanism of WRKY genes toward nitrogen is poorly understood. Therefore, the identification of WRKY genes on the Populus trichocarpa genome was performed, and 98 PtWRKYs (i.e., PtWRKY1 to PtWRKY98) were identified. Phylogenetic analysis and the promoter cis-acting element detection revealed that PtWRKYs have multiple functions, including phosphorus and nitrogen homeostasis. By constructing multilayer-hierarchical gene regulatory networks (ML-hGRNs), it was predicted that many WRKY transcription factors were involved in the nitrogen response, such as PtWRKY33 and PtWRKY95. They mainly regulated the expression of primary nitrogen-responsive genes (NRGs), such as PtNRT2.5A, PtNR2 and PtGLT2. The integrative analysis of transcriptome and RT-qPCR results show that the expression levels of 6 and 15 PtWRKYs were regulated by nitrogen availability in roots and leaves, respectively, and those were also found in ML-hGRN. Our study demonstrates that PtWRKYs respond to nitrogen by regulating NRGs, which enriches the nitrate-responsive transcription factor network and helps to uncover the hub of nitrate and its related signaling regulation.
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Zhou J, Yang LY, Jia CL, Shi WG, Deng SR, Luo ZB. Identification and Functional Prediction of Poplar Root circRNAs Involved in Treatment With Different Forms of Nitrogen. FRONTIERS IN PLANT SCIENCE 2022; 13:941380. [PMID: 35874008 PMCID: PMC9305699 DOI: 10.3389/fpls.2022.941380] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
Circular RNAs (circRNAs) are a class of noncoding RNA molecules with ring structures formed by covalent bonds and are commonly present in organisms, playing an important regulatory role in plant growth and development. However, the mechanism of circRNAs in poplar root responses to different forms of nitrogen (N) is still unclear. In this study, high-throughput sequencing was used to identify and predict the function of circRNAs in the roots of poplar exposed to three N forms [1 mM NO3 - (T1), 0.5 mM NH4NO3 (T2, control) and 1 mM NH4 + (T3)]. A total of 2,193 circRNAs were identified, and 37, 24 and 45 differentially expressed circRNAs (DECs) were screened in the T1-T2, T3-T2 and T1-T3 comparisons, respectively. In addition, 30 DECs could act as miRNA sponges, and several of them could bind miRNA family members that play key roles in response to different N forms, indicating their important functions in response to N and plant growth and development. Furthermore, we generated a competing endogenous RNA (ceRNA) regulatory network in poplar roots treated with three N forms. DECs could participate in responses to N in poplar roots through the ceRNA regulatory network, which mainly included N metabolism, amino acid metabolism and synthesis, response to NO3 - or NH4 + and remobilization of N. Together, these results provide new insights into the potential role of circRNAs in poplar root responses to different N forms.
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Chen C, Chu Y, Huang Q, Zhang W, Ding C, Zhang J, Li B, Zhang T, Li Z, Su X. Morphological, physiological, and transcriptional responses to low nitrogen stress in Populus deltoides Marsh. clones with contrasting nitrogen use efficiency. BMC Genomics 2021; 22:697. [PMID: 34579659 PMCID: PMC8474845 DOI: 10.1186/s12864-021-07991-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Accepted: 09/07/2021] [Indexed: 12/02/2022] Open
Abstract
Background Nitrogen (N) is one of the main factors limiting the wood yield in poplar cultivation. Understanding the molecular mechanism of N utilization could play a guiding role in improving the nitrogen use efficiency (NUE) of poplar. Results In this study, three N-efficient genotypes (A1-A3) and three N-inefficient genotypes (C1-C3) of Populus deltoides were cultured under low N stress (5 μM NH4NO3) and normal N supply (750 μM NH4NO3). The dry matter mass, leaf morphology, and chlorophyll content of both genotypes decreased under N starvation. The low nitrogen adaptation coefficients of the leaves and stems biomass of group A were significantly higher than those of group C (p < 0.05). Interestingly, N starvation induced fine root growth in group A, but not in group C. Next, a detailed time-course analysis of enzyme activities and gene expression in leaves identified 2062 specifically differentially expressed genes (DEGs) in group A and 1118 in group C. Moreover, the sensitivity to N starvation of group A was weak, and DEGs related to hormone signal transduction and stimulus response played an important role in the low N response this group. Weighted gene co-expression network analysis identified genes related to membranes, catalytic activity, enzymatic activity, and response to stresses that might be critical for poplar’s adaption to N starvation and these genes participated in the negative regulation of various biological processes. Finally, ten influential hub genes and twelve transcription factors were identified in the response to N starvation. Among them, four hub genes were related to programmed cell death and the defense response, and PodelWRKY18, with high connectivity, was involved in plant signal transduction. The expression of hub genes increased gradually with the extension of low N stress time, and the expression changes in group A were more obvious than those in group C. Conclusions Under N starvation, group A showed stronger adaptability and better NUE than group C in terms of morphology and physiology. The discovery of hub genes and transcription factors might provide new information for the analysis of the molecular mechanism of NUE and its improvement in poplar. Supplementary Information The online version contains supplementary material available at 10.1186/s12864-021-07991-7.
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Affiliation(s)
- Cun Chen
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Yanguang Chu
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Qinjun Huang
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Weixi Zhang
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Changjun Ding
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Jing Zhang
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Bo Li
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Tengqian Zhang
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Zhenghong Li
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China.,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China
| | - Xiaohua Su
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing, China. .,Key Laboratory of Tree Breeding and Cultivation, State Forestry and Grassland Administration, Beijing, China. .,Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, Jiangsu Province, China.
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Lebedev VG, Kovalenko NP, Shestibratov KA. Influence of Nitrogen Availability on Growth of Two Transgenic Birch Species Carrying the Pine GS1a Gene. PLANTS (BASEL, SWITZERLAND) 2017; 6:E4. [PMID: 28067821 PMCID: PMC5371763 DOI: 10.3390/plants6010004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2016] [Revised: 12/22/2016] [Accepted: 12/30/2016] [Indexed: 11/17/2022]
Abstract
An alternative way to increase plant productivity through the use of nitrogen fertilizers is to improve the efficiency of nitrogen utilization via genetic engineering. The effects of overexpression of pine glutamine synthetase (GS) gene and nitrogen availability on growth and leaf pigment levels of two Betula species were studied. Untransformed and transgenic plants of downy birch (B. pubescens) and silver birch (B. pendula) were grown under open-air conditions at three nitrogen regimes (0, 1, or 10 mM) for one growing season. The transfer of the GS1a gene led to a significant increase in the height of only two transgenic lines of nine B. pubescens, but three of five B. pendula transgenic lines were higher than the controls. In general, nitrogen supply reduced the positive effect of the GS gene on the growth of transgenic birch plants. No differences in leaf pigment levels between control and transgenic plants were found. Nitrogen fertilization increased leaf chlorophyll content in untransformed plants but its effect on most of the transgenic lines was insignificant. The results suggest that birch plants carrying the GS gene use nitrogen more efficiently, especially when growing in nitrogen deficient soil. Transgenic lines were less responsive to nitrogen supply in comparison to wild-type plants.
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Affiliation(s)
- Vadim G Lebedev
- Branch of Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Science avenue 6, Pushchino, Moscow Region 142290, Russia.
| | - Nina P Kovalenko
- Branch of Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Science avenue 6, Pushchino, Moscow Region 142290, Russia.
| | - Konstantin A Shestibratov
- Branch of Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Science avenue 6, Pushchino, Moscow Region 142290, Russia.
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