转录组测序在林草植物抗逆性研究中的应用

doi:10.11686/cyxb2019352

摘要/Abstract

摘要： 生物和非生物胁迫对农作物产量和生态环境造成极大的威胁,林草植物对外界环境具有较强的适应能力,因此挖掘林草植物抗逆基因,分析其抗逆机制已经成为当下的研究趋势。随着科学技术的发展,利用生物测序技术研究植物抗逆的分子机制已成为当今植物逆境生理和分子生物学研究的一个重大课题,其中转录组测序(RNA-seq)研究是揭示植物抗逆机理以及筛选抗逆基因的主要研究技术。以Roche/454、Illumina/Solexa和ABI/SOLID为代表的二代测序技术(NGS)发展迅速,相较于传统测序手段具有成本低、测序时间短、高通量等优点,已被广泛应用于全基因组、RNA-seq和miRNA测序等研究。因此,RNA-seq技术可加快林草植物抗逆机制的研究和抗逆基因资源的挖掘,从而为农作物、优良林草植物抗逆性遗传改良和新品种培育奠定基础。本研究详细介绍了近年来NGS技术在林草植物应对病虫害、高温、冷害、盐、干旱以及土地贫瘠等方面的转录组学的研究进展,最后针对转录组测序的发展趋势和应用前景进行了展望。

关键词: RNA-seq, 生物胁迫, 非生物胁迫, 抗逆性

Abstract: Biotic and abiotic stresses pose a great threat to crop yields and ecological environment conservation. As herbs, shrubs and trees are extremely adaptable to the external environment, it has become a current trend to identify the stress-tolerant genes and investigate the adaptive mechanisms of herbs, shrubs and trees. Research on molecular mechanisms of plant resistance to stress, using sequencing technology, has become a major part of the investigation of plant stress physiology. As biotechnology has developed, the transcriptome approach (RNA-seq) has emerged as one of the crucial approaches for identification of stress-tolerant genes. The next-generation sequencing technologies (NGS) are low cost and have greater depth than ever before. These include Roche/454, Illumina/Solexa, and ABI/Solid, and have been widely applied to whole-genome, RNA-seq and miRNA sequencing studies. Therefore, RNA-seq has high potential to accelerate the elucidation of the molecular mechanisms of herb, shrub and tree resistance and thus for identification of a large resource of gene sequence information. Such research will lay a solid foundation for developing stress-tolerant crops, herbs, shrubs and tree species. Research examples of the biotic and abiotic stress-tolerance of herbs, shrubs and trees identified using RNA-seq are introduced in this paper. The emerging trends and prospects for further applications of RNA-seq are also discussed.

Key words: RNA-seq, biotic stress, abiotic stress, plant stress resistance

高慧娟, 吕昕培, 王润娟, 任伟, 程济南, 汪永平, 邵坤仲, 张金林. 转录组测序在林草植物抗逆性研究中的应用[J]. 草业学报, 2019, 28(12): 184-196.

GAO Hui-juan, LÜ Xin-pei, WANG Run-juan, REN Wei, CHENG Ji-nan, WANG Yong-ping, SHAO Kun-zhong, ZHANG Jin-lin. Application of RNA-seq technology in research on herb, shrub and tree stress resistance[J]. Acta Prataculturae Sinica, 2019, 28(12): 184-196.

参考文献

[1] Jia Q, Wu M Y, Liang K J, et al. Advances in applications of genomics in stress resistance studies of crops. Chinese Journal of Eco-Agriculture, 2014, 22(4): 375-385.
贾琪, 吴名耀, 梁康迳, 等. 基因组学在作物抗逆性研究中的新进展. 中国农业学报, 2014, 22(4): 375-385.
[2] Wang Z, Gerstein M, Snyder M.RNA-seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 2009, 10(1): 57-63.
[3] Varshney R K, Nayak S N, May G D.Next-generation sequencing technologies and their implications for crop genetics and breeding. Trends Biotechnology, 2009, 27(9): 522-530.
[4] Mardis E R.Theimpact of next-generation sequencing technology on genetics. Trends in Genetics, 2007, 24(3): 133-141.
[5] Zhou H, Zhang X, Liu T Y, et al. Data processing and gene discovery of high-throughput transcriptome sequencing. Jiangxi Science, 2012, 35(5): 607-611.
周华, 张新, 刘腾云, 等. 高通量转录组测序的数据分析与基因发掘. 江西科学, 2012, 35(5): 607-611.
[6] Li Z Y, Ning W, Chen L P, et al. The next generation sequencing technology and its application in plant transcriptome. Journal of Henan Agricultural Sciences, 2013, 42(12): 1-5.
李智奕, 宁维, 陈利平, 等. 新一代测序技术及其在植物转录组研究中的应用. 河南农业科学, 2013, 42(12): 1-5.
[7] Olena M, Marco A.Applications of next-generation sequencing technologies in functional genomics. Genomics, 2008, 92: 255-264.
[8] Yue G D, Gao Q, Luo L H.The application of high-throughput sequencing technology in plant and animal research. Scientia Sinica Vitae, 2012, 42(2): 107-124.
岳桂东, 高强, 罗龙海. 高通量测序技术在动植物研究领域中的应用. 生命科学, 2012, 42(2): 107-124.
[9] Yan S P, Yang R H, Leng S J.High-fluxed DNA sequencing technology and its application in agricultural science research. Chinese Agricultural Science Bulletin, 2012, 28(30): 171-176.
闫绍彭, 杨瑞华, 冷淑娇. 高通量测序技术及其在农业科学研究中的应用. 中国农学通报, 2012, 28(30): 171-176.
[10] Zhang Q F, Li J, Fan Z X, et al. Application of high-throughput sequencing technology in agricultural research. Shandong Agricultural Sciences, 2013, 45(1): 137-140.
张全芳, 李军, 范仲学, 等. 高通量测序技术在农业研究中的应用. 山东农业科学, 2013, 45(1): 137-140.
[11] Luo C, Zhang Q L, Luo Z R.Application of next-generation sequencing technology in plant genetic research. Guangdong Agricultural Sciences, 2015, 42(3): 186-192.
罗纯, 张青林, 罗正荣. 第二代测序技术在植物遗传研究中的应用. 广东农业科学, 2015, 42(3): 186-192.
[12] Liu H L, Zheng L M, Liu Q Q, et al. Studies on the transcriptomes of non-model organisms. Hereditas, 2013, 35(8): 955-970.
刘红亮, 郑丽明, 刘青青, 等. 非模式生物转录组研究. 遗传, 2013, 35(8): 955-970.
[13] Wang J D, Yu T Y, Zhang C B.Research progress on transcriptomics in maize. Acta Agriculturae Boreali-Sinica, 2014, 29(b12): 10-15.
王继碉, 余庭跃, 张采波. 玉米转录组学研究进展. 华北农学报, 2014, 29(b12): 10-15.
[14] Mutz K O, Heilkenbrinker A, Lönne M.Transcriptome analysis using next-generation sequencing. Current Opinion in Biotechnology, 2013, 24(1): 22-30.
[15] Huang X H, Xu F, Cheng H, et al. Recent advances of transcriptome sequencing in higher plants. Journal of Huanggang Normal University, 2014, 34(6): 28-35.
黄小花, 许锋, 程华, 等. 转录组测序在高等植物中的研究进展. 黄冈师范学院学报, 2014, 34(6): 28-35.
[16] Anderson J T, Mitchell-Olds T.Ecological genetics and genomics of plant defenses: Evidence and approaches. Functional Ecology, 2011, 25(2): 312-324.
[17] Li M W, Qi X P, Ni M.Silicon era of carbon-based life: Application of genomics and bioinformatics in crop stress research. International Journal of Molecular Sciences, 2013, 14(6): 11444-11483.
[18] Barakat A, Diloreto D S, Zhang Y, et al. Comparison of the transcriptomes of American chestnut (Castanea dentata) and Chinese chestnut (Castanea mollissima) in response to the chestnut blight infection. BMC Plant Biology, 2009, 9(1): 51.
[19] Wu J, Zhang Y L, Zhang H Q.Whole genome wide expression profiles of Vitis amurensis grape responding to downy midew by using Solexa sequencing technology. BMC Plant Biology, 2010, 10(1): 234.
[20] Wang Z, Zhang J B, Jia C H, et al. De Novo characterization of the banana root transcriptome and analysis of gene expression under Fusarium oxysporumf. sp. cubense tropical race 4 infection. BMC Genomics, 2012, 13(1): 650.
[21] Li C Y, Deng G M, Yang J, et al. Transcriptome profiling of resistant and susceptible Cavendish banana roots following inoculation with Fusarium oxysporumf. sp. cubense tropical race 4. BMC Genomics, 2012, 13(1): 374.
[22] Wei T, Li J B, Zhao Y, et al. Transcriptional profiling of rice early response to Magnaporthe oryzae identified OsWRKYs as important regulators in rice blast resistance. PLoS One, 2013, 8(3): e59720.
[23] Wang Y, Zhou Z, Gao J, et al. The mechanisms of maize resistance to Fusarium verticillioides by comprehensive analysis of RNA-seq data. Frontiers in Plant Science, 2016, 7: 1654.
[24] Sun F, Fang P, Li J, et al. RNA-seq-based digital gene expression analysis reveals modification of host defense responses by rice stripe virus during disease symptom development in Arabidopsis. Virology Journal, 2016, 13(1): 202.
[25] Jia H, Wei X C, Yang Y, et al. Root RNA-seq analysis reveals a distinct transcriptome landscape between clubroot-susceptible and clubroot-resistant Chinese cabbage lines after Plasmodiophora brassicaeinfection. Plant Soil, 2017, 421(1/2): 93-105.
[26] Martinelli F, Uratsu S L, Albrecht U, et al. Transcriptome profiling of citrus fruit response to huanglongbing disease. PLoS One, 2012, 7(5): e38039.
[27] Zuluaga A P, Solé M, Lu H, et al. Transcriptome responses to Ralstonia solanacearum infection in the roots of the wild potato Solanum commersonii. BMC Genomics, 2015, 16(1): 246.
[28] Van de Mortel J E, de Vos R C H, Dekkers E, et al. Metabolic and transcriptomic changes induced in Arabidopsis by the rhizobacterium Pseudomonas fluorescens SS101. Plant Physiology, 2012, 160(4): 2173-2188.
[29] Xu D L, Long H, Liang J J, et al. De novo assembly and characterization of the root transcriptome of Aegilops variabilis during an interraction with the cereal cyst nematode. BMC Genomics, 2012, 13(1): 133.
[30] Jain S, Chittem K, Brueggeman R, et al. Comparative transcriptome analysis of resistant and susceptible common bean genotypes in response to soybean cyst nematode infection. PLoS One, 2016, 11(7): e0159338.
[31] Li S, Chen Y, Zhu X F, et al. The transcriptomic changes of Huipizhi Heidou (Glycine max), a nematode-resistant black soybean during Heterodera glycines race 3 infection. Journal of Plant Physiology, 2018, 220: 96-104.
[32] Kang W S, Zhu X F, Wang Y Y, et al. Transcriptomic and metabolomic analyses reveal that bacteria promote plant defense during infection of soybean cyst nematode in soybean. BMC Plant Biology, 2018, 18(1): 86.
[33] Rubio M, Rodríguez-Moreno L, Ballester A R, et al. Analysis of gene expression changes in peach leaves in response to Plum pox virus infection using RNA-seq. Molecular Plant Pathology, 2015, 16(2): 164-176.
[34] Goyer A, Hamlin L, Crosslin J M, et al. RNA-seq analysis of resistant and susceptible potato varieties during the early stages of potato virus Y infection. BMC Genomics, 2015, 16(1): 472.
[35] Rodamilans B, Leo’n D S, Mühlberger L.Transcriptomic analysis of Prunus domestica undergoing hypersensitive response to Plum pox virus infection. PLoS One, 2014, 9(6): e100477.
[36] Qiu Q, Ma T, Hu Q J, et al. Genome-scale transcriptome analysis of the desert poplar, Populus euphratica. Tree Physiology, 2011, 31(4): 452-461.
[37] Chen S F, Zhou R C, Huang Y L, et al. Transcriptome sequencing of a highly salt tolerant mangrove species Sonneratia alba using illumine platform. Marine Genomics, 2011, 4(2): 129-136.
[38] Chen S, Jiang J, Li H Y, et al. The salt-responsive transcriptome of Populus simonii×Populus nigra via DGE. Gene, 2012, 504(2): 203-212.
[39] Huang J Z, Lu X, Yan H, et al. Transcriptome characterization and sequencing-based identification of salt-responsive genes in Millettia pinnata, a semi-mangrove plant. Dna Research, 2012, 19(2): 195-207.
[40] Ma T, Wang J Y, Zhou G K, et al. Genomic insights into salt adaptation in a desert poplar. Nature Communications, 2013, 4: 2797.
[41] Chen J B, Zhang F R, Huang D F, et al. Transcriptome analysis of transcription factors in two melon (Cucumis melo L.) cultivars under salt stress. Plant Physiology Journal, 2014, 50(2): 150-158.
陈嘉贝, 张芙蓉, 黄丹枫, 等. 盐胁迫下两个甜瓜品种转录因子的转录组分析. 植物生理学报, 2014, 50(2): 150-158.
[42] Zhao H, Jia F Q, Zhang F C, et al. The transcriptome information analysis of differentially expressed genes of Halostachys Caspica under salt stress. Chinese Journal of Bioinformatics, 2014, 2(2): 90-98.
赵航, 贾富强, 张富春, 等. 盐胁迫下盐穗木差异表达基因的转录组信息分析. 生物信息学, 2014, 2(2): 90-98.
[43] Joann D A, Mark C, Bilquees G, et al. Transcriptome assembly, profiling and differential gene expression analysis of the halophyte Suaeda fruticosa provides insights into salt tolerance. BMC Genomics, 2015, 16(1): 353.
[44] Gao C T, Liu J H, Xu S J, et al. Exploring the relationship of differentially expressed genes and physiological of oats in response to salt stress. Acta Botanica Boreali-Occidentalia Sinica, 2015, 35(7): 1385-1393.
高彩婷, 刘景辉, 徐寿军, 等. 燕麦盐胁迫响应基因的差异表达与生理响应的关系. 西北植物学报, 2015, 35(7): 1385-1393.
[45] Ma Q, Bao A K, Chai W W, et al. Transcriptomic analysis of the succulent xerophyte Zygophyllum xanthoxylum in response to salt treatment and osmotic stress. Plant Soil, 2016, 402(1/2): 343-361.
[46] An Y M, Song L L, Liu Y R, et al. De Novo transcriptional analysis of alfalfa in response to saline-alkaline stress. Frontiers in Plant Science, 2016, 7(153): 931.
[47] Li H, Li D F, Chen A G, et al. RNA-seq for comparative transcript profiling of kenaf under salinity stress. Journal of Plant Research, 2017, 130(2): 365-372.
[48] Zhou Y J, Gao F, Liu R, et al. De novo sequencing and analysis of root transcriptome using 454 pyrosequencing to discover putative genes associated with drought tolerance in Ammopiptanthus mongolicus. BMC Genomics, 2012, 13(1): 266.
[49] Yu S C, Zhang F L, Yu Y J, et al. Transcriptome profiling of dehydration stress in the chinese cabbage (Brassica rapa L. ssp. pekinensis) by tag sequencing. Plant Molecular Biology Reporter, 2012, 30(1): 17.
[50] Chen Y, Liu Z H, Li F, et al. Genome-wide functional analysis of cotton (Gossypium hirsutum) in response to drought. PLoS One, 2013, 8(11): e80879.
[51] Shi Y, Yan X, Zhao P S, et al. Transcriptomic analysis of a tertiary relict plant, extreme xerophyte Reaumuria soongoricato identify genes related to drought adaptation. PLoS One, 2013, 8(5): e63993.
[52] Long Y, Zhang J G, Tian X J, et al. De novo assembly of the desert tree Haloxylon ammodendron (C. A. Mey.) based on RNA-seq data provides insight into drought response, gene discovery and marker identification. BMC Genomics, 2014, 15(1): 1111.
[53] Steven A Y, Martin T S, Matthew J H, et al. De novo assembly of red clover transcriptome based on RNA-Seq data provides insight into drought response, gene discovery and marker identification. BMC Genomics, 2014, 15(1): 453.
[54] Xiao L H, Yang G, Zhang L C, et al. The resurrection genome of Boea hygrometrica: A blueprint for survival of dehydration. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(18): 5833-5837.
[55] Zhu Y, Wang X, Huang L, et al. Transcriptomic identification of drought-related genes and SSR Markers in Sudan grass based on RNA-seq. Frontiers in Plant Science, 2017, 8: 687.
[56] Gao H J, Lü X P, Zhang L, et al. Transcriptomic profiling and physiological analysis of Haloxylon ammodendron in response to osmotic stress. International Journal of Molecular Sciences, 2018, 19(1): 84.
[57] Zhao Z G, Tan L L, Dang C Y, et al. Deep-sequencing transcriptome analysis of chilling tolerance mechanisms of a subnival alpine plant, Chorispora bungeana. BMC Plant Biology, 2012, 12(1): 222.
[58] Tian D Q, Pan X Y, Yu Y M, et al. De novo characterization of the Anthurium transcriptome and analysis of its digital gene expression under cold stress. BMC Genomics, 2013, 14(1): 827.
[59] Xin H, Zhu W, Wang L, et al. Genome wide transcriptional profile analysis of Vitis amurensis and Vitis vinifera in response to cold stress. PLoS One, 8(3): e58740.
[60] Wang X C, Zhao Q Y, Ma C L.Global transcriptome profiles of Camellia sinensis during cold acclimation. BMC Genomics, 2013, 14(1): 415.
[61] Sperotto R A, De Araújo Junior A T, Adamski J M, et al. Deep RNA-seq indicates protective mechanisms of cold-tolerant indica rice plants during early vegetative stage. Plant Cell Reports, 2018, 37(2): 347-375.
[62] Chen S, Li H.Heat stress regulates the expression of genes at transcriptional and post-transcriptional levels, revealed by RNA-seq in Brachypodium distachyon. Frontiers in Plant Science, 2016, 7(273): 2067.
[63] O’Rourke J A, Yang S S, Miller S S, et al. An RNA-seq transcriptome analysis of orthophosphate-deficient white lupin reveals novel insights into phosphorus acclimation in plants. Plant Physiology, 2013, 161(2): 705-724.
[64] Secco D, Shou H X, Whelan J.RNA-seq analysis identifies an intricate regulatory network controlling cluster root development in white lupin. BMC Genomics, 2014, 15(1): 230.
[65] Shao C H, Li Y, Qian Y F, et al. Transcriptional analysis of rice root under nitrogen deficiency. Acta Agriculturae Boreali-Sinica, 2018, 33(1): 168-175.
邵彩虹, 李瑶, 钱银飞, 等. 氮素胁迫对水稻根系影响的转录组分析. 华北农学, 2018, 33(1): 168-175.
[66] Giambartolomei C, Vukcevic D, Schadt E E, et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet, 2014, 10(5): e1004383.
[67] Lappalainen T, Sammeth M, Friedlander M, et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature, 2013, 501: 506-511.
[68] Mcpherson A, Hormozdiari F, Zayed A, et al. deFuse: An algorithm for gene fusion discovery in tumor RNA-seq data. PLoS Computational Biology, 2011, 7(5): e1001138.
[69] Louhimo R, Lepikhova T, Monni O, et al. Comparative analysis of algorithms for integration of copy number and expression data. Nature Methods, 2012, 9(4): 351-355.
[70] Jiao Y, Widschwendter M, Teschendorff A E.A systems-level integrative framework for genome-wide DNA methylation and gene expression data identifies differential gene expression modules under epigenetic control. Bioinformatics, 2014, 30(16): 2360-2366.
[71] Zhang S, Li Q, Liu J, et al. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics, 2011, 27(13): i401-i409.
[72] Le H S, Bar-Joseph Z.Integrating sequence, expression and interaction data to determine condition-specific miRNA regulation. Bioinformatics, 2013, 29(13): i89-i97.
[73] Sass S, Buettner F, Mueller N S, et al. A modular framework for gene set analysis integrating multilevel omics data. Nucleic Acids Research, 2013, 41(21): 9622-9633.
[74] García-Alcalde F, García-López F, Dopazo J, et al. Paintomics: A web based tool for the joint visualization of transcriptomics and metabolomics data. Bioinformatics, 2011, 27(1): 137-139.
[75] Kuo T C, Tian T F, Tseng Y J.Omics: A web-based systems biology tool for analysis, integration and visualization of human transcriptomic, proteomic and metabolomic data. BMC Systems Biology, 2013, 7(1): 64.