NRT1.1B Is Associated with Root Microbiota Composition and Nitrogen Use in Field-Grown Rice
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ARTICLES https://doi.org/10.1038/s41587-019-0104-4 NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice Jingying Zhang 1,2,10, Yong-Xin Liu 1,2,10, Na Zhang1,2,3,10, Bin Hu 1,10, Tao Jin 4,5,6,10, Haoran Xu1,2,3, Yuan Qin1,2,3, Pengxu Yan4,5,6, Xiaoning Zhang 1,2,3, Xiaoxuan Guo1,2, Jing Hui7, Shouyun Cao1, Xin Wang1,2, Chao Wang1,2, Hui Wang1,2,3, Baoyuan Qu 1,2, Guangyi Fan4,5,6, Lixing Yuan 7, Ruben Garrido-Oter8,9, Chengcai Chu 1,3* and Yang Bai 1,2,3* Nitrogen-use efficiency of indica varieties of rice is superior to that of japonica varieties. We apply 16S ribosomal RNA gene profiling to characterize root microbiota of 68 indica and 27 japonica varieties grown in the field. We find that indica and japonica recruit distinct root microbiota. Notably, indica-enriched bacterial taxa are more diverse, and contain more genera with nitrogen metabolism functions, than japonica-enriched taxa. Using genetic approaches, we provide evidence that NRT1.1B, a rice nitrate transporter and sensor, is associated with the recruitment of a large proportion of indica-enriched bacteria. Metagenomic sequencing reveals that the ammonification process is less abundant in the root microbiome of the nrt1.1b mutant. We isolated 1,079 pure bacterial isolates from indica and japonica roots and derived synthetic communities (SynComs). Inoculation of IR24, an indica variety, with an indica-enriched SynCom improved rice growth in organic nitrogen conditions compared with a japon- ica-enriched SynCom. The links between plant genotype and root microbiota membership established in this study will inform breeding strategies to improve nitrogen use in crops. he evolution of multiple plant traits is linked to the diverse plant subspecies, including indica and japonica rice. Moreover, rice microbe-rich soil environment1,2. Numerous microorganisms roots in flooded soil recruit a specific root microbiota29. The roles of participate in the biogeochemical transformation of soil nutri- the rice genotype in shaping the root microbiota have been shown T 7,30 ents, including nitrogen and phosphorus cycling, which are essen- using a limited set of two to four rice varieties . However, little tial for plant productivity3. Plant roots selectively recruit defined is known about the pattern of variation and functional divergence root microbiota from soil4,5 in both model and crop plants4–10. It in the root microbiota in rice populations. The availability of mul- has been reported that root microbiota are determined by the rhi- tiple indica and japonica varieties makes it possible to investigate the zocompartment, soil type, geographical location, nutrient status, association of root microbiota variation with specific plant traits in developmental stage and host genotype11–17. However, variation in rice subspecies and to identify key genes that contribute to the root the root microbiota within a plant population and the linked plant microbiota variation during rice domestication. genetic basis are not yet clear. In this study, we examined the composition and variation in the Rice (Oryza sativa L.) is an important staple cereal that is grown root microbiota of a landrace population of 68 indica and 27 japon- worldwide. Two main types of Asian cultivated rice, indica and ica varieties grown under field conditions to identify links between japonica, have been recognized18,19. Differences between indica and the root microbiota, nutrient cycles in soil, host genetic regulation japonica rice are well documented at the genomic and phenotypic and NUE in the field. levels20–22. One striking phenotypic difference is the better nitrogen- use efficiency (NUE) of indica varieties compared with japonica vari- Results eties in the field23,24. Variation in NUE has been attributed, in part, to Indica and japonica have distinct root microbiota. To explore a natural variation in NRT1.1B, encoding a nitrate transporter and variation in the root microbiota of cultivated rice, we grew 68 indica sensor (amino acid 327 is methionine in indica versus threonine in and 27 japonica varieties in two fields in separated locations on japonica)25,26. Nitrogen is mainly present as nitrate, ammonium and Lingshui Farm, Hainan Province, China (Supplementary Table 1). organic nitrogen in soil. Different forms of nitrogen can be metabo- These varieties were collected from 44 countries as representa- lized by soil-borne bacteria3,27 and this might affect the efficiency of tives of the rice mini-core collection from the US Department of nitrogen absorption by plant roots, as plants have a preference for Agriculture rice germplasm seed bank (Fig. 1a and Supplementary – + inorganic nitrogen (nitrate (NO3 ) and ammonium (NH4 )) rather Table 2). Rarefaction analysis revealed that our population cap- than organic nitrogen28, resulting in the hypothesis that root-associ- tured most root microbiota members from each rice subspecies ated bacteria might be involved in NUE variation between different (Supplementary Fig. 1). To avoid disturbance from seed-associated 1State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China. 2CAS-JIC Centre of Excellence for Plant and Microbial Science, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China. 3College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China. 4BGI- Shenzhen, Shenzhen, China. 5BGI-Qingdao, BGI-Shenzhen, Qingdao, China. 6China National Genebank-Shenzhen, BGI-Shenzhen, Shenzhen, China. 7Key Lab of Plant–Soil Interactions, MOE, College of Resources and Environmental Sciences, China Agricultural University, Beijing, China. 8Department of Plant Microbe Interactions, Max Planck Institute for Plant Breeding Research, Cologne, Germany. 9Cluster of Excellence on Plant Sciences, Dusseldorf, Germany. 10These authors contributed equally: Jingying Zhang, Yong-Xin Liu, Na Zhang, Bin Hu, Tao Jin. *e-mail: [email protected]; [email protected] 676 NATURE BIotEchnology | VOL 37 | JUNE 2019 | 676–684 | www.nature.com/naturebiotechnology NATURE BIOTECHNOLOGY ARTICLES a b Field IField II Germination indica japonica indica japonica c Field I d Field II indica indica japonica japonica 0.2 0.2 ) 0.0 0.0 PCo2 (12.09% PCo2 (10.61%) –0.2 −0.2 –0.20.0 0.20.4 –0.20.0 0.2 PCo1 (25.96%) PCo1 (19.77%) e f Field I Field II Field I Field II 100 Acidobacteria Actinobacteria a a 6 b Alphaproteobacteria c 75 c ) Bacteroidetes 5 Betaproteobacteria d 50 Chloroflexi Deltaproteobacteria 4 Percentage (% Shannon index indica 25 Firmicutes japonica Gammaproteobacteria 3 Soil Low abundance 0 Soil Soil Soil Soil indica indica indica indica japonica japonica japonica japonica Fig. 1 | Root microbiota of indica and japonica. a, Diagram of original collection sites (44 countries) of indica (red) and japonica (blue) rice. b, Diagram of the experimental design for rice field trials. The indica and japonica varieties were arranged randomly. Harvested samples for each variety were surrounded by protection plants that separated the different varieties. c, Unconstrained PCoA (for principal coordinates PCo1 and PCo2) with Bray–Curtis distance showing that the root microbiota of indica separate from those of japonica in field I in the first axis (P < 0.001, permutational multivariate analysis of variance (PERMANOVA) by Adonis). Ellipses cover 68% of the data for each rice subspecies. d, Unconstrained PCoA with Bray–Curtis distance showing that the root microbiota of indica separate from those of japonica in field II in the first axis (P < 0.001, PERMANOVA by Adonis). e, Shannon index of the microbiota of roots from indica, japonica and the corresponding bulk soils in two fields. The horizontal bars within boxes represent medians. The tops and bottoms of boxes represent the 75th and 25th percentiles, respectively. The upper and lower whiskers extend to data no more than 1.5× the interquartile range from the upper edge and lower edge of the box, respectively. f, Phylum-level distribution of the indica and japonica root microbiota in two fields. Proteobacteria are shown at the class level. The numbers of replicated samples in this figure are as follows: in field I, indica (n = 201), japonica (n = 80), soil (n = 12); in field II, indica (n = 201), japonica (n = 81), soil (n = 12). microbes, we germinated sterilized, dehulled rice seeds on MS in 2014–2016 (Supplementary Table 1 and Methods), which agar medium and transferred 15-day-old seedlings to the fields in enabled us to examine stable patterns of root microbiota member- a random arrangement (Fig. 1b). The two fields had been used to ship between indica and japonica rice in representative agricultural grow rice for the past 10 years, with differential cultivation practices soil. The root-associated microbiota were sampled 8 weeks after the NATURE BIotEchnology | VOL 37 | JUNE 2019 | 676–684 | www.nature.com/naturebiotechnology 677 ARTICLES NATURE BIOTECHNOLOGY rice seedlings were transplanted, when the root microbiota were Supplementary Table 5). The model also successfully classified IR24 well established and stable13,17. For each variety, we harvested three (indica) and ZH11 (japonica), which were not included within the root samples from three representative plants (Fig. 1b). We collected training varieties and were grown on two farms in Beijing, 2,482 km 12 unplanted soil samples from each field. We generated a bacterial away from Lingshui Farm (Fig. 2d and Supplementary Table 5). As community profile for each sample via PCR amplification of the 16S the root microbiota are significantly affected by the geographical ribosomal RNA (rRNA) gene targeting regions V5–V7 using prim- location4,5 and our model was based on six fields over 2 years, data ers 799F and 1193R, followed by Illumina sequencing. We obtained from more rice varieties grown in more different geographical loca- 19,976,393 high-quality sequences from 563 samples (average, tions are likely to further improve model accuracy.