水稻典型品种日本晴和IR24根系微生物组的解析

doi:10.16288/j.yczz.20-070

遗传 ›› 2020, Vol. 42 ›› Issue (5): 506-518.doi: 10.16288/j.yczz.20-070

• 研究报告 • 上一篇

水稻典型品种日本晴和IR24根系微生物组的解析

胡雅丽^1,^2,³, 戴睿^2,^3,^4,⁵, 刘永鑫^2,^3,⁴, 张婧赢^2,^3,⁴, 胡斌², 储成才², 袁怀波¹(), 白洋^2,^3,^4,⁵()

^1. 合肥工业大学,食品与生物工程学院,合肥 230009
^2. 中国科学院遗传与发育生物学研究所,植物基因组学国家重点实验室,北京 100101
^3. 中国科学院大学,生物互作卓越创新中心,北京 100049
^4. 中国科学院遗传与发育生物学研究所,中国科学院-英国约翰英纳斯中心植物和微生物科学联合研究中心,北京 100101
^5. 中国科学院大学现代农学院,北京 100049

收稿日期:2020-03-15 修回日期:2020-04-16 出版日期:2020-05-20 发布日期:2020-04-26
通讯作者: 袁怀波,白洋 E-mail:yuanhuaibo001@163.com;ybai@genetics.ac.cn
作者简介:胡雅丽,在读硕士研究生,专业方向：根系微生物组。E-mail:2017111262@mail.hfut.edu.cn|戴睿,在读博士研究生,专业方向：根系微生物组,生物信息学。E-mail: raydai@genetics.ac.cn; 胡雅丽和戴睿为并列第一作者。
基金资助:
中国科学院前沿科学重点研究项目编号(QYZDB-SSW-SMC021);国家自然科学基金面上项目资助编号(31772400)

Analysis of rice root bacterial microbiota of Nipponbare and IR24

Yali Hu^1,^2,³, Rui Dai^2,^3,^4,⁵, Yongxin Liu^2,^3,⁴, Jingying Zhang^2,^3,⁴, Bin Hu², Chengcai Chu², Huaibo Yuan¹(), Yang Bai^2,^3,^4,⁵()

^1. School of Food and Biological Engineering, Hefei University of Technology, Hefei 230009, China
^2. State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
^3. CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing 100049, China
^4. CAS-JIC Centre of Excellence for Plant and Microbial Science, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
^5. College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing 100101, China

Received:2020-03-15 Revised:2020-04-16 Online:2020-05-20 Published:2020-04-26
Contact: Yuan Huaibo,Bai Yang E-mail:yuanhuaibo001@163.com;ybai@genetics.ac.cn
Supported by:
Supported by the Key Research Program of Frontier Sciences of the Chinese Academy of Science No(QYZDB-SSW-SMC021);the National Natural Science Foundation of China No(31772400)

摘要/Abstract

摘要：

植物的各项生命活动与其根系微生物组密不可分,且根系微生物组的组成易受到植物生长环境和基因型的影响。为进一步探究中国北方地区种植的不同品种水稻根系微生物组的差异及其相互作用机制,本研究以种植于北京昌平和上庄农场的水稻典型品种日本晴(Nipponbare)和IR24为研究对象,基于16S rRNA基因扩增子测序技术获得根系微生物组序列,利用多样性分析、组成型分析、机器学习的随机森林和网络分析等方法,对旺盛生长期的两种不同品种的水稻根系微生物组进行详细比较。研究发现,种植地点和水稻基因型显著影响了水稻根系微生物组的群落结构,不同基因型导致了根系微生物组在物种分类组成上以及细菌间相互关系的差异,而且根系微生物组能作为生物标记跨地点区分宿主的基因型。本研究结果为深入理解我国北方种植的水稻根系微生物组的组成规律以及从根系微生物与植物互作的角度对品种进行改良提供了数据和理论基础。

关键词: 水稻根系生物组, 多样性分析, 组成型分析, 机器学习, 网络分析

Abstract:

The root-associated bacterial microbiota is closely related to life activities of land plants, and its composition is affected by geographic locations and plant genotypes. However, the influence of plant genotypes on root microbiota in rice grown in northern China remains to be explained. In this study, we performed 16S rRNA gene amplicon sequencing to generate bacterial community profiles of two representative rice cultivars, Nipponbare and IR24. They are planted in Changping and Shangzhuang farms in Beijing and have reached the reproductive stage. We compared their root microbiota in details by Random Forest machine learning algorithm and network analysis. We found that the diversity of rice root microbiota was significantly affected by geographic locations and rice genotypes. Nipponbare and IR24 showed distinct taxonomic composition of the root microbiota and the interactions between different bacteria. Moreover, the root bacteria could be used as biomarkers to distinguish Nipponbare from IR24 across regions. Our study provides a theoretical basis for the in-depth understanding of rice root microbiota in Northern China and the improvement of rice breeding from the perspective of the interaction between root microorganisms and plants.

Key words: rice root microbiota, diversity analysis, taxonomic composition, machine learning, network analysis

胡雅丽, 戴睿, 刘永鑫, 张婧赢, 胡斌, 储成才, 袁怀波, 白洋. 水稻典型品种日本晴和IR24根系微生物组的解析[J]. 遗传, 2020, 42(5): 506-518.

Yali Hu, Rui Dai, Yongxin Liu, Jingying Zhang, Bin Hu, Chengcai Chu, Huaibo Yuan, Yang Bai. Analysis of rice root bacterial microbiota of Nipponbare and IR24[J]. Hereditas(Beijing), 2020, 42(5): 506-518.

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参考文献 65

[1]	Lundberg DS, Lebeis SL, Paredes SH, Yourstone S, Gehring J, Malfatti S, Tremblay J, Engelbrektson A, Kunin V, Rio TGd, Edgar RC, Eickhorst T, Ley RE, Hugenholtz P, Tringe SG, Dangl JL . Defining the core Arabidopsis thaliana root microbiome. Nature, 2012,488(7409):86-90.
[2]	Bulgarelli D, Rott M, Schlaeppi K, Ver Loren van Themaat E, Ahmadinejad N, Assenza F, Rauf P, Huettel B, Reinhardt R, Schmelzer E, Peplies J, Gloeckner FO, Amann R, Eickhorst T, Schulze-Lefert P. Revealing structure and assembly cues for Arabidopsis root-inhabiting bacterial microbiota. Nature, 2012,488(7409):91-95.
[3]	Müller DB, Vogel C, Bai Y, Vorholt JA . The plant microbiota: systems-level insights and perspectives. Annu Rev Genet, 2016,50(1):211-234.
[4]	Bai Y, Qian JM, Zhou JM, Qian W . Crop Microbiome: breakthrough technology for agriculture. Bull Chin Acad Sci, 2017,32(3):260-265.
	白洋, 钱景美, 周俭民, 钱韦 . 农作物微生物组:跨越转化临界点的现代生物技术. 中国科学院院刊, 2017,32(3):260-265.
[5]	Durán P, Thiergart T, Garrido-Oter R, Agler M, Kemen E, Schulze-Lefert P, Hacquard S . Microbial interkingdom interactions in roots promote Arabidopsis survival. Cell, 2018,175(4):973-983.
[6]	Hiruma K, Gerlach N, Sacristán S, Nakano RT, Hacquard S, Kracher B, Neumann U, Ramírez D, Bucher M, O’Connell RJ, Schulze-Lefert P,. Root endophyte colletotrichum tofieldiae confers plant fitness benefits that are phosphate status dependent. Cell, 2016,165(2):464-474. doi: 10.1016/j.cell.2016.02.028
[7]	Wang XL, Wang ET . NRT1.1B connects root microbiota and nitrogen use in rice. Bull Bot, 2019,54(3):285-287. doi: 10.11983/CBB19060
	王孝林, 王二涛 . 根际微生物促进水稻氮利用的机制. 植物学报, 2019,54(3):285-287. doi: 10.11983/CBB19060
[8]	Kwak MJ, Kong HG, Choi K, Kwon SK, Song JY, Lee J, Lee PA, Choi SY, Seo M, Lee HJ, Jung EJ, Park H, Roy N, Kim H, Lee MM, Rubin EM, Lee SW, Kim JF . Rhizosphere microbiome structure alters to enable wilt resistance in tomato. Nat Biotechnol, 2018,36:1100-1109.
[9]	Wang XF, Wei Z, Yang KM, Wang JN, Jousset A, Xu YC, Shen QR, Friman VP . Phage combination therapies for bacterial wilt disease in tomato. Nat Biotechnol, 2019,37(12):1513-1520.
[10]	Carrión VJ, Perez-Jaramillo J, Cordovez V, Tracanna V, de Hollander M, Ruiz-Buck D, Mendes LW, van Ijcken WFJ, Gomez-Exposito R, Elsayed SS, Mohanraju P, Arifah A, van der Oost J, Paulson JN, Mendes R, van Wezel GP, Medema MH, Raaijmakers JM. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science, 2019,366(6465):606-612.
[11]	Zhang JY, Liu YX, Zhang N, Hu B, Jin T, Xu HR, Qin Y, Yan PX, Zhang XN, Guo XX, Hui J, Cao SY, Wang X, Wang C, Wang H, Qu BY, Fan GY, Yuan LX, Garrido-Oter R, Chu CC, Bai Y . NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat Biotechnol, 2019,37(6):676-684.
[12]	Castrillo G, Teixeira PJPL, Paredes SH, Law TF, de Lorenzo L, Feltcher ME, Finkel OM, Breakfield NW, Mieczkowski P, Jones CD, Paz-Ares J, Dangl JL. Root microbiota drive direct integration of phosphate stress and immunity. Nature, 2017,543(7646):513-518.
[13]	Van Deynze A, Zamora P, Delaux PM, Heitmann C, Jayaraman D, Rajasekar S, Graham D, Maeda J, Gibson D, Schwartz KD, Berry AM, Bhatnagar S, Jospin G, Darling A, Jeannotte R, Lopez J, Weimer BC, Eisen JA, Shapiro HY, Ané JM, Bennett AB . Nitrogen fixation in a landrace of maize is supported by a mucilage-associated diazotrophic microbiota. PLoS Biol, 2018,16(8):e2006352.
[14]	Wang C, Bai Y . Maize aerial roots fix atmospheric N₂ by interacting with nitrogen fixing bacteria (in Chinese). Sci Sin Vitae, 2019,49(1):89-90. doi: 10.1360/N052018-00215
	王超, 白洋 . 玉米可利用气生根进行高效生物固氮. 中国科学:生命科学, 2019,49(1):89-90. doi: 10.1360/N052018-00215
[15]	Huang AC, Jiang T, Liu YX, Bai YC, Reed J, Qu B, Goossens A, Nützmann HW, Bai Y, Osbourn A. A specialized metabolic network selectively modulates Arabidopsis root microbiota. Science, 2019, 364(6440): eaau6389.
[16]	Yuan J, Zhao J, Wen T, Zhao M, Li R, Goossens P, Huang Q, Bai Y, Vivanco JM, Kowalchuk GA, Berendsen RL, Shen Q . Root exudates drive the soil-borne legacy of aboveground pathogen infection. Microbiome, 2018,6(1):156.
[17]	Bai Y, Müller DB, Srinivas G, Garrido-Oter R, Potthoff E, Rott M, Dombrowski N, Münch PC, Spaepen S, Remus-Emsermann M, Hüttel B, McHardy AC, Vorholt JA, Schulze-Lefert P. Functional overlap of the Arabidopsis leaf and root microbiota. Nature, 2015,528(7582):364-369.
[18]	Chen QW, Jiang T, Liu YX, Liu HL, Zhao T, Liu ZX, Gan XC, Hallab A, Wang XM, He J, Ma YH, Zhang FX, Jin T, Schranz ME, Wang Y, Bai Y, Wang GD . Recently duplicated sesterterpene (C25) gene clusters in Arabidopsis thaliana modulate root microbiota. Science China Life Sciences, 2019,62(7):947-958.
[19]	Wang W, Yang J, Zhang J, Liu YX, Tian CP, Qu BY, Gao CL, Xin PY, Cheng SJ, Zhang WJ, Miao P, Li L, Zhang XJ, Chu JF, Zuo JR, Li JY, Bai Y, Lei XG, Zhou JM . An Arabidopsis secondary metabolite directly targets expression of the bacterial type III secretion system to inhibit bacterial virulence. Cell Host Microbe, 2020,27(4):601-613.
[20]	Chen T, Nomura K, Wang XL, Sohrabi R, Xu J, Yao YL, Paasch BC, Ma L, Kremer J, Cheng YT, Zhang L, Wang N, Wang ET, Xin XF, He SY . A plant genetic network for preventing dysbiosis in the phyllosphere. Nature, 2020,580(7804):653-657.
[21]	Edwards J, Johnson C, Santos-Medellín C, Lurie E, Podishetty NK, Bhatnagar S, Eisen JA, Sundaresan V . Structure, variation, and assembly of the root-associated microbiomes of rice. Proc Natl Acad Sci USA, 2015,112(8):E911-E920.
[22]	Zhang JY, Zhang N, Liu YX, Zhang XN, Hu B, Qin Y, Xu HR, Wang H, Guo XX, Qian JM, Wang W, Zhang PF, Jin T, Chu CC, Bai Y . Root microbiota shift in rice correlates with resident time in the field and developmental stage. Science China Life Sciences, 2018,61(6):613-621.
[23]	Edwards JA, Santos-Medellín CM, Liechty ZS, Nguyen B, Lurie E, Eason S, Phillips G, Sundaresan V . Compositional shifts in root-associated bacterial and archaeal microbiota track the plant life cycle in field-grown rice. PLoS Biol, 2018,16(2):e2003862.
[24]	Chen SM, Waghmode TR, Sun RB, Kuramae EE, Hu CS, Liu BB . Root-associated microbiomes of wheat under the combined effect of plant development and nitrogen fertilization. Microbiome, 2019,7(1):136.
[25]	Shi Y, Li YT, Xiang XJ, Sun RB, Yang T, He D, Zhang KP, Ni YY, Zhu YG, Adams JM, Chu HY . Spatial scale affects the relative role of stochasticity versus determinism in soil bacterial communities in wheat fields across the North China Plain. Microbiome, 2018,6(1):27. doi: 10.1186/s40168-018-0409-4
[26]	Sun RB, Li WY, Dong WX, Tian YP, Hu CS, Liu BB . Tillage changes vertical distribution of soil bacterial and fungal communities. Front Microbiol, 2018,9:699.
[27]	Walters WA, Jin Z, Youngblut N, Wallace JG, Sutter J, Zhang W, González-Peña A, Peiffer J, Koren O, Shi QJ, Knight R, Glavina del Rio T, Tringe SG, Buckler ES, Dangl JL, Ley RE,. Large-scale replicated field study of maize rhizosphere identifies heritable microbes. Proc Natl Acad Sci USA, 2018,115(28):7368-7373.
[28]	Wei Z, Gu Y, Friman VP, Kowalchuk GA, Xu YC, Shen QR , Jousset A. Initial soil microbiome composition and functioning predetermine future plant health. Sci Adv, 2019,5(9): eaaw0759.
[29]	Xiong W, Song YQ, Yang KM, Gu Y, Wei Z, Kowalchuk GA, Xu YC, Jousset A, Shen QR, Geisen S . Rhizosphere protists are key determinants of plant health. Microbiome, 2020,8(1):27.
[30]	Jin T, Wang YY, Huang YY, Xu J, Zhang PF, Wang N, Liu X, Chu HY, Liu G, Jiang HG, Li YZ, Xu J, Kristiansen K, Xiao L, Zhang YZ, Zhang GY, Du GH, Zhang HB, Zou HF, Zhang HF, Jie ZY, Liang SS, Jia HJ, Wan JW, Lin DC, Li JY, Fan GY, Yang HM, Wang J, Bai Y, Xu X . Taxonomic structure and functional association of foxtail millet root microbiome. GigaScience, 2017,6(10):1-12.
[31]	Liu YX, Qin Y, Bai Y . Reductionist synthetic community approaches in root microbiome research. Curr Opin Microbiol, 2019,49:97-102.
[32]	Fan KK, Weisenhorn P, Gilbert JA, Shi Y, Bai Y, Chu HY . Soil pH correlates with the co-occurrence and assemblage process of diazotrophic communities in rhizosphere and bulk soils of wheat fields. Soil Biol Biochem, 2018,121:185-192.
[33]	Mori A, Kirk GJD, Lee JS, Morete MJ, Nanda AK, Johnson-Beebout SE, Wissuwa M . Rice genotype differences in tolerance of zinc-deficient soils: evidence for the importance of root-induced changes in the rhizosphere. Front Plant Sci, 2016,6:1160.
[34]	Fan KK, Delgado-Baquerizo M, Guo XS, Wang DZ, Wu YY, Zhu M, Yu W, Yao HY, Zhu YG, Chu HY . Suppressed N fixation and diazotrophs after four decades of fertilization. Microbiome, 2019,7(1):143.
[35]	Zhao Q, Feng Q, Lu HY, Li Y, Wang AH, Tian QL, Zhan QL, Lu YQ, Zhang L, Huang T, Wang YC, Fan DL, Zhao Y, Wang ZQ, Zhou CC, Chen JY, Zhu CR, Li WJ, Weng QJ, Xu Q, Wang ZX, Wei XH, Han B, Huang XH . Pan-genome analysis highlights the extent of genomic variation in cultivated and wild rice. Nat Genet, 2018,50(2):278-284.
[36]	Wang W, Hu B, Yuan DY, Liu YQ, Che RH, Hu YC, Ou SJ, Liu YX, Zhang ZH, Wang H, Li H, Jiang ZM, Zhang ZL, Gao XK, Qiu YH, Meng XB, Liu YX, Bai Y, Liang Y, Wang YQ, Zhang LH, Li LG, Sodmergen S, Jing HC, Li JY, Chu CC . Expression of the nitrate transporter gene OsNRT1.1A/OsNPF6.3 confers high yield and early maturation in rice. Plant Cell, 2018,30(3):638-651.
[37]	Stein JC, Yu Y, Copetti D, Zwickl DJ, Zhang L, Zhang C, Chougule K, Gao D, Iwata A, Goicoechea JL, Wei S, Wang J, Liao Y, Wang M, Jacquemin J, Becker C, Kudrna D, Zhang J, Londono CEM, Song X, Lee S, Sanchez P, Zuccolo A, Ammiraju JSS, Talag J, Danowitz A, Rivera LF, Gschwend AR, Noutsos C, Wu CC, Kao SM, Zeng JW, Wei FJ, Zhao Q, Feng Q, El Baidouri M, Carpentier MC, Lasserre E, Cooke R, Rosa Farias Dd, da Maia LC, dos Santos RS, Nyberg KG, McNally KL, Mauleon R, Alexandrov N, Schmutz J, Flowers D, Fan C, Weigel D, Jena KK, Wicker T, Chen M, Han B, Henry R, Hsing YC, Kurata N, de Oliveira AC, Panaud O, Jackson SA, Machado CA, Sanderson MJ, Long M, Ware D, Wing RA. Genomes of 13 domesticated and wild rice relatives highlight genetic conservation, turnover and innovation across the genus Oryza. Nat Genet, 2018,50(2):285-296.
[38]	Guo XX, Zhang XN, Qin Y, Liu YX, Zhang JY, Zhang N, Wu K, Qu BY, He ZS, Wang X, Zhang XJ, Hacquard S, Fu XD, Bai Y . Host-associated quantitative abundance profiling reveals the microbial load variation of root microbiome. Plant Commun, 2020,1(1):100003.
[39]	Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods, 2010,7(5):335-336.
[40]	Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, Alexander H, Alm EJ, Arumugam M, Asnicar F, Bai Y, Bisanz JE, Bittinger K, Brejnrod A, Brislawn CJ, Brown CT, Callahan BJ, Caraballo- Rodríguez AM, Chase J, Cope EK, Da Silva R, Diener C, Dorrestein PC, Douglas GM, Durall DM, Duvallet C, Edwardson CF, Ernst M, Estaki M, Fouquier J, Gauglitz JM, Gibbons SM, Gibson DL, Gonzalez A, Gorlick K, Guo J, Hillmann B, Holmes S, Holste H, Huttenhower C, Huttley GA, Janssen S, Jarmusch AK, Jiang L, Kaehler BD, Kang KB, Keefe CR, Keim P, Kelley ST, Knights D, Koester I, Kosciolek T, Kreps J, Langille MGI, Lee J, Ley R, Liu YX, Loftfield E, Lozupone C, Maher M, Marotz C, Martin BD, McDonald D, McIver LJ, Melnik AV, Metcalf JL, Morgan SC, Morton JT, Naimey AT, Navas-Molina JA, Nothias LF, Orchanian SB, Pearson T, Peoples SL, Petras D, Preuss ML, Pruesse E, Rasmussen LB, Rivers A, Robeson MS, Rosenthal P, Segata N, Shaffer M, Shiffer A, Sinha R, Song SJ, Spear JR, Swafford AD, Thompson LR, Torres PJ, Trinh P, Tripathi A, Turnbaugh PJ, Ul-Hasan S, van der Hooft JJJ, Vargas F, Vázquez-Baeza Y, Vogtmann E, von Hippel M, Walters W, Wan Y, Wang M, Warren J, Weber KC, Williamson CHD, Willis AD, Xu ZZ, Zaneveld JR, Zhang Y, Zhu Q, Knight R, Caporaso JG. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol, 2019,37(8):852-857.
[41]	Edgar RC . Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 2010,26(19):2460-2461.
[42]	Liu YX, Qin Y, Guo XX, Bai Y . Methods and applications for microbiome data analysis. Hereditas(Beijing), 2019,41(9):845-826.
	刘永鑫, 秦媛, 郭晓璇, 白洋 . 微生物组数据分析方法与应用. 遗传, 2019,41(9):845-826.
[43]	Zheng MS, Zhou N, Liu SF, Dang CY, Liu YX, He SS, Zhao YJ, Liu W, Wang XK . N₂O and NO emission from a biological aerated filter treating coking wastewater: main source and microbial community. J Clean Prod, 2019,213:365-374.
[44]	Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, Suggests M,. The vegan package. Community Ecology Package, 2007,10:631-637.
[45]	Wickham H . Ggplot2: Elegant Graphics for Data Analysis. Springer, 2016.
[46]	Liaw A, Wiener M . Classification and regression by randomForest. R News, 2002,2(3):18-22.
[47]	Qian XB, Liu YX, Ye XH, Zheng WJ, Lv SX, Mo MJ, Lin JJ, Wang WQ, Wang WH, Zhang XN, Lu MP . Gut microbiota in children with juvenile idiopathic arthritis: characteristics, biomarker identification, and usefulness in clinical prediction. BMC Genomics, 2020,21(1):286.
[48]	Csardi G, Nepusz T . The igraph software package for complex network research. InterJ Complex Systems, 2006,1695(5):1-9.
[49]	Wang JF, Zheng JY, Shi WY, Du N, Xu XM, Zhang YM, Ji PF, Zhang FY, Jia Z, Wang YP, Zheng Z, Zhang HP, Zhao FQ . Dysbiosis of maternal and neonatal microbiota associated with gestational diabetes mellitus. Gut, 2018,67(9):1614-1625.
[50]	Deng Y, Jiang YH, Yang YF, He ZL, Luo F, Zhou JZ . Molecular ecological network analyses. BMC Bioinformatics, 2012,13(1):113.
[51]	National Genomics Data Center Members and Partners. Database resources of the national genomics data center in 2020. Nucleic Acids Res, 2020,48(D1):D24-D33.
[52]	Wang YQ, Song FH, Zhu JW, Zhang SS, Yang YD, Chen TT, Tang BX, Dong LL, Ding N, Zhang Q, Bai ZX, Dong XN, Chen HX, Sun MY, Zhai SA, Sun YB, Yu L, Lan L, Xiao JF, Fang XD, Lei HX, Zhang Z, Zhao WM . GSA: Genome sequence archive. Genomics Proteomics Bioinformatics, 2017,15(1):14-18.
[53]	Zhang CL, Zhang YM, Ding ZJ, Bai Y . Contribution of microbial inter-kingdom balance to plant health. Mol Plant, 2019,12(2):148-149.
[54]	Toju H, Peay KG, Yamamichi M, Narisawa K, Hiruma K, Naito K, Fukuda S, Ushio M, Nakaoka S, Onoda Y, Yoshida K, Schlaeppi K, Bai Y, Sugiura R, Ichihashi Y, Minamisawa K, Kiers ET . Core microbiomes for sustainable agroecosystems. Nat Plants, 2018,4(5):247-257.
[55]	Xie JP, Han YB, Liu G, Bai LQ . Research advances on microbial genetics in China in 2015. Hereditas(Beijing), 2016,38(9):765-790.
	谢建平, 韩玉波, 刘钢, 白林泉 . 2015年中国微生物遗传学研究领域若干重要进展. 遗传, 2016,38(9):765-790.
[56]	Wen CL, Sun CJ, Yang N . The concepts and research progress: from heritability to microbiability. Hereditas (Beijing), 2019,41(11):1023-1040.
	文超良孙从佼, 杨宁 . 从遗传力到肠菌力:概念及研究进展. 遗传, 2019,41(11):1023-1040.
[57]	Liu BB, Zhang XJ, Bakken LR, Snipen L, Frostegård Å . Rapid succession of actively transcribing denitrifier populations in agricultural soil during an anoxic spell. Front Microbiol, 2019,9:3208.
[58]	Zhang KP, Shi Y, Cui XQ, Yue P, Li KH, Liu XJ, Tripathi BM, Chu HY . Salinity is a key determinant for soil microbial communities in a desert ecosystem. mSystems, 2019,4(1):e00225-00218.
[59]	Lebeis SL, Paredes SH, Lundberg DS, Breakfield N, Gehring J, McDonald M, Malfatti S, Glavina del Rio T, Jones CD, Tringe SG, Dangl JL. Salicylic acid modulates colonization of the root microbiome by specific bacterial taxa. Science, 2015,349(6250):860-864. doi: 10.1126/science.aaa8764
[60]	Voges MJEEE, Bai Y, Schulze-Lefert P, Sattely ES . Plant-derived coumarins shape the composition of an Arabidopsis synthetic root microbiome. Proc Natl Acad Sci USA, 2019,116(25):12558-12565.
[61]	Zamioudis C, Korteland J, Van Pelt JA, van Hamersveld M, Dombrowski N, Bai Y, Hanson J, Van Verk MC, Ling HQ, Schulze-Lefert P, Pieterse CMJ,. Rhizobacterial volatiles and photosynthesis-related signals coordinate MYB72 expression in Arabidopsis roots during onset of induced systemic resistance and iron-deficiency responses. Plant J, 2015,84(2):309-322.
[62]	Niu B, Paulson JN, Zheng X, Kolter R . Simplified and representative bacterial community of maize roots. Proc Natl Acad Sci USA, 2017,114(12):E2450-E2459.
[63]	Hu B, Wang W, Ou SJ, Tang JY, Li H, Che RH, Zhang ZH, Chai XY, Wang HR, Wang YQ, Liang CZ, Liu LC, Piao ZZ, Deng QY, Deng K, Xu C, Liang Y, Zhang LH, Li LG, Chu CC . Variation in NRT1.1B contributes to nitrate-use divergence between rice subspecies. Nat Genet, 2015,47(7):834-838.
[64]	Getzke F, Thiergart T, Hacquard S . Contribution of bacterial-fungal balance to plant and animal health. Curr Opin Microbiol, 2019,49:66-72.
[65]	Kim PJ, Price ND . Genetic co-occurrence network across sequenced microbes. PLoS Comput Biol, 2011,7(12):e1002340.

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引物名称	引物序列(5'→3')	扩增区域
799F	AACMGGATTAGATACCCKG	V5~V7
1193R	ACGTCATCCCCACCTTCC	V5~V7

水稻典型品种日本晴和IR24根系微生物组的解析

Analysis of rice root bacterial microbiota of Nipponbare and IR24

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