•COVER ARTICLE• . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . June 2018 Vol.61 No.6: 613–621. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .https://doi.org/10.1007/s11427-018-9284-4

Root microbiota shift in rice correlates with resident time in thefield and developmental stage

Jingying Zhang1,2†, Na Zhang1,2,3†, Yong-Xin Liu1,2†, Xiaoning Zhang1,2,3, Bin Hu1, Yuan Qin1,2,3,Haoran Xu1,2,3, Hui Wang1,2,3, Xiaoxuan Guo1,2, Jingmei Qian1,2,3, Wei Wang1, Pengfan Zhang4,5,

Tao Jin4,5*, Chengcai Chu1* & Yang Bai1,2*

1State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101,China;

2CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academyof Sciences (CAS), Beijing 100101, China;

3College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100039, China;4China National Genebank-Shenzhen, BGI-Shenzhen, Shenzhen 518083, China;

5BGI-Qingdao, Qingdao 266510, China

Received February 27, 2018; accepted March 6, 2018; published online March 23, 2018

Land plants in natural soil form intimate relationships with the diverse root bacterial microbiota. A growing body of evidenceshows that these microbes are important for plant growth and health. Root microbiota composition has been widely studied inseveral model plants and crops; however, little is known about how root microbiota vary throughout the plant’s life cycle underfield conditions. We performed longitudinal dense sampling in field trials to track the time-series shift of the root microbiotafrom two representative rice cultivars in two separate locations in China. We found that the rice root microbiota varieddramatically during the vegetative stages and stabilized from the beginning of the reproductive stage, after which the rootmicrobiota underwent relatively minor changes until rice ripening. Notably, both rice genotype and geographical locationinfluenced the patterns of root microbiota shift that occurred during plant growth. The relative abundance of Deltaproteobacteriain roots significantly increased overtime throughout the entire life cycle of rice, while that of Betaproteobacteria, Firmicutes, andGammaproteobacteria decreased. By a machine learning approach, we identified biomarker taxa and established a model tocorrelate root microbiota with rice resident time in the field (e.g., Nitrospira accumulated from 5 weeks/tillering in field-grownrice). Our work provides insights into the process of rice root microbiota establishment.

rice, root microbiota, time-series shift, biomarker taxa, residence time in the field, developmental stages, modeling,machine learning

Citation: Zhang, J., Zhang, N., Liu, Y.X., Zhang, X., Hu, B., Qin, Y., Xu, H., Wang, H., Guo, X., Qian, J., et al. (2018). Root microbiota shift in rice correlateswith resident time in the field and developmental stage. Sci China Life Sci 61, 613–621. https://doi.org/10.1007/s11427-018-9284-4

INTRODUCTION

In nature, plant roots associate with a diverse soil-derived

bacterial microbiota, which influences plant development,nutrient uptake, and disease resistance positively or nega-tively (Berendsen et al., 2012). With the development ofhigh-throughput sequencing, the root microbiota has beenwidely studied in several model plants and crops, such asArabidopsis, rice, and maize (Bulgarelli et al., 2012; Lund-

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SCIENCE CHINALife Sciences

†Contributed equally to this work*Corresponding authors (Tao Jin, email: jintao@genomics.cn; Chengcai Chu, email:ccchu@genetics.ac.cn; Yang Bai, email: ybai@genetics.ac.cn)

https://doi.org/10.1007/s11427-018-9284-4https://doi.org/10.1007/s11427-018-9284-4http://life.scichina.comhttp://link.springer.comhttp://crossmark.crossref.org/dialog/?doi=10.1007/s11427-018-9284-4&domain=pdf&date_stamp=2018-03-13

berg et al., 2012; Peiffer et al., 2013; Schlaeppi et al., 2014;Edwards et al., 2015; Santos-Medellín et al., 2017). Inmonocots and dicots, roots mainly associate with Proteo-bacteria, Actinobacteria, Bacteroidetes, and Firmicutes. Theroot microbiota composition is determined by environmentalfactors and plant genotype (Müller et al., 2016). In Arabi-dopsis, rice, and maize, soil types define the composition ofthe root microbiota and the host genotype determines the rootmicrobiota to a limited extend (Bulgarelli et al., 2012;Lundberg et al., 2012; Peiffer et al., 2013; Edwards et al.,2015).The establishment of the root microbiota distinct from that

found in the soil is a dynamic process. The initial bacterialcommunities are similar to their soil origin, becoming moreplant-specific as plants grow (Chaparro et al., 2014; Su-giyama et al., 2014; Edwards et al., 2015; Shi et al., 2015;Breidenbach et al., 2016; Hamonts et al., 2018). Currentevidence supports a two-step selection hypothesis for therecruitment of rhizosphere bacteria from soil by plants(Bulgarelli et al., 2013). First, root exudates and cell wallspromote the growth of a proportion of soil microbes, re-sulting in a shift in the rhizosphere bacterial community.Second, host-microbe interactions finetune the bacterialcommunities thriving inside roots. In annual grasses andsoybeans, the root microbiota varied during the entire growthcycle with the trend toward gradual divergence from the soilmicrobiota and enrichment of specific taxa (Sugiyama et al.,2014; Shi et al., 2015). A study in Arabis alpine, a perennialplant, showed significantly different root microbiota at threetime points across a 28-week greenhouse experiment(Dombrowski et al., 2017). In rice, the root microbiota in theseedling stage are significantly different from those in othergrowth stages (Chaparro et al., 2014); time-series green-house data showed that the root microbiota at 13 days ap-proaches that of 42-day-old plants regarding microbialquantity and diversity (Edwards et al., 2015). In Arabidopsisand rice, little difference was observed between the stages ofinflorescence meristem appearance and fruiting (Lundberg etal., 2012; Chaparro et al., 2014). However, these studieswere carried out either with sparse time points or undergreenhouse conditions. Little is known about how the rootmicrobiota changes at a high resolution during the entire lifecycle of host plants under field conditions.In this work, we focused on rice (Oryza sativa), a globally

important staple crop plant. We performed time-series fieldtrials to track the root microbiota shift in the entire rice lifecycle, with representative Japonica and Indica cultivars intwo separate fields in China. We explored the associationbetween root microbiota composition and rice residence timein the field by using a Random Forests model. Our resultsprovide insight into the process rice root microbiota devel-opment.

RESULTS

The rice root microbiota varies over time during the ricelife cycle in the field

To explore changes in the root microbiota during rice growthunder field conditions, we performed longitudinal densesampling with two representative rice cultivars, Nipponbareand IR24, in two separate fields on Changping Farm (CP)and Shangzhuang Farm (SZ) in Beijing, China. To avoid riceendophytes and seed-associated microbes, we sterilized thedehulled rice seeds (Materials and Methods). After germi-nation on MS agar media, we transferred 2-week-old riceseedlings to the two aforementioned fields, which have beenused to cultivate rice in last several years. The root micro-biota, including microbes at the rhizoplane and in the interiorof roots, were harvested from roots at a depth of 0–10 cm andwashed continuously (Materials and Methods). We collectedroot samples at successive intervals (0, 1, 2, 3, 5, 7, 10, and14 days) in the first 2 weeks after seedling transfer, and everyweek in remaining periods of rice growth. We collected nineroot replicates (six individual Nipponbare and three IR24plants) at each time point, three replicates of unplanted soil atday 0 and three unplanted soil samples after 4 weeks in eachfield at each time point (Figure 1A). The bacterial commu-nity profile for each sample was generated by 16S rRNAgene amplification targeting V5–V7 region using primers799F and 1193R, followed by Illumina sequencing (Mate-rials and Methods). The 799F primer was used to excludeamplicons from plant plastids. The 799F primer does notamplify chloroplast DNA, and it generates PCR bands of ahigher molecular size from the plant mitochondrion thanfrom the bacterial 16S rRNA gene (Chelius and Triplett,2001). We generated a total of 42,871,626 high-quality se-quences from 446 samples (averaging 51,932 and rangingfrom 20,642 to 120,022 reads per sample). We analyzedhigh-quality reads with Unoise, discarded low-abundanceOTUs (

shift of the root microbiota showed consistent trends in thetwo separate fields (Figure 1C).

The rice root microbiota stabilizes after 8–10 weeks ofgrowth in the field

Pairwise correlation analysis revealed that the root micro-biota varied dramatically 24 h after the plants were trans-ferred to the fields and gradually stabilized after 8–10 weekswhen rice plants reach the reproductive stage. This patternwas consistent within the two cultivars in two separate fields(Figure 1B and C; Figure 2A−D). Furthermore, the Bray-Curtis distance between the root microbiota of samplescollected last and those collected at each time point de-creased with increasing rice residence time in the field(Figure 2E). This result indicates that, although the keyfeature of the root microbiota—the variation explained bythe first two axes of PCoA (Figure 1B and C) and highcorrelation efficiency (Figure 2E), stabilized after 8–10weeks, the entire root microbiota underwent relatively minorchanges until rice ripening, probably reflecting the senes-cence process. To determine whether rice plants had in-

creased or decreased ability to regulate root-associatedmicrobiota, we calculated Bray-Curtis distances between theroot microbiota from each cultivar in two separate fields ateach time point. Although the difference in the bulk soilmicrobiota between the two fields was highly consistent, thelargely different root microbiota in two fields at early timepoints gradually approached similarity over time (Figure 2F).This trend was consistent in both Nipponbare and IR24.

Specific taxa of the root microbiota associated with riceresidence time in the field and developmental stage

To further investigate the changes in specific taxa throughoutthe rice life cycle, we compared the relative abundance ofroot microbiota at the phylum level. Firstly, the root micro-biota 1 day after the plants were transferred to the fieldsdiffered significantly from the root microbiota present at day0 (roots were collected 1 min after being transferred to soil;P

(Figure 3A and D), root-associated specific taxa shiftedacross rice residence time and developmental stages of bothcultivars in two separate fields (Figure 3B, C, E and F). Fromrice seedlings to the reproductive stage of Nipponbare andIR24, the relative abundance of Deltaproteobacteria sig-nificantly increased over time throughout the entire life cycleof rice, whereas Betaproteobacteria, Firmicutes, and Gam-maproteobacteria dramatically decreased (Figure 3B, C, Eand F). Moreover, Bacteroidetes from Nipponbare sig-nificantly increased over time only on Shangzhuang Farm(Figure 3E), whereas it remained largely consistent in othertreatments (Figure 3B, C and F). The root microbiota ofNipponbare and IR24 on Shangzhuang Farm showed largedifferences at the phylum level across the entire reproductivestage (P

taxa in the model. The list of the top 23 bacterial taxa at theclass level across rice residence time in the field, in order oftime-discriminatory importance, is shown in Figure 4A andB. The majority of biomarker taxa showed high relativeabundance in the corresponding rice residence time in thefield. For example, Nitrospira, Spirochetes, and Deltapro-teobacteria started to accumulate in rice root at 5–6 weeks(tillering and elongation stages) after being transferred to thefields and remained at high levels during the rice re-productive stage.

DISCUSSION

Root microbiota dynamics during the entire life cycle ofrice grown under field conditions

Previous work showed that plant root microbiota composi-tion varied with rice residence time in the field and devel-opmental stage, but these studies were carried out either withsparse time points or under greenhouse conditions (Chaparroet al., 2014; Sugiyama et al., 2014; Edwards et al., 2015;Dombrowski et al., 2017). We performed longitudinal dense

sampling across the entire life cycle of representative ricecultivars, Nipponbare and IR24, in two separate fields. Ourdata demonstrated that the rice root microbiota under fieldconditions shifted across rice residence time in the field anddevelopmental stages, varied dramatically in vegetativegrowth, and stabilized 8–10 weeks after plants were trans-ferred to the fields (Figure 1B and C; Figure 2A−D). Sub-sequently, the root microbiota underwent relatively minorchanges until rice ripening (Figure 2E; Figure 3B, C, E andF), suggesting that plants may recruit different root microbesduring different developmental stages. For example, Ni-trospira began to accumulate in rice root 5 weeks/tilleringafter the plants were transferred to the fields and remained athigh levels during the reproductive stage (Figure 4B), in-dicating that rice may actively recruit these bacteria tomodulate the nitrogen cycle and facilitate rapid growth andseed production. The trend was robust in two representativerice cultivars in two separate fields, although Nipponbarereached the reproductive stage slightly earlier than IR24. Thedynamic pattern of the root microbiota during the entire ricelife cycle is largely consistent with time-series results of riceroot microbiota changes in American fields (Edwards et al.,

Figure 3 Bacterial phyla of rice root-associated microbiota change over time. Relative abundance of bacterial phyla over the life cycle of rice cultivars intwo separate fields: bulk soil in Changping Farm (A), Nipponbare in Changping Farm (B), IR24 in Changping Farm (C), bulk soil in Shangzhuang Farm (D),Nipponbare in Shangzhuang Farm (E), and IR24 in Shangzhuang Farm (F).

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2018), indicating that our data reflect the general of trend ofrice root microbiota dynamics in the rice life cycle.

The ability of rice plants to modulate the root microbiotaincreases over time

Root samples at day 0 were collected 1 min after the plantswere transferred to soil; thus, the root microbiota at day 0 are

similar to that of the corresponding bulk soil (Figure 1B andC). Interestingly, the root microbiota at 24 h after transfer tothe field were significantly different from the root microbiotaat day 0 (P

rhizocompartments at 24 h after transfer to soil (Edwards etal., 2015). Moreover, we found that in both cultivars, the rootmicrobiota in two separate fields differed greatly at earlytime points but became similar over time, which is in linewith the rice root microbiota changes in two American fields(Edwards et al., 2018). This result could be explained by thehypothesis that the ability of rice plants to modulate the rootmicrobiota increases over time.

Biomarker taxa correlated with rice residence time inthe field

The roots of both cultivars in two fields showed the sameincreasing and decreasing trends in bacterial taxa while thesoil microbiota remained largely consistent: Deltaproteo-bacteria significantly increased, and Betaproteobacteria,Firmicutes, and Gammaproteobacteria dramatically de-creased from the seedling to the ripening stage (Figure 3B, C,E and F). Interestingly, Bacteroidetes from Nipponbare sig-nificantly increased across rice residence time in the fieldonly on Shangzhuang Farm (Figure 3E), while it remainedlargely consistent in other treatments (Figure 3B, C and F),indicating that the shift pattern in the root microbiota duringthe rice life cycle is determined by both the host genotypeand the geographic location. This result is consistent withseveral studies conducted at a single time point (Bulgarelli etal., 2012; Lundberg et al., 2012; Edwards et al., 2015). Wefurther identified biomarker classes correlating with riceresidence time in the field by using a Random Forests model.For example, Nitrospira started to accumulate in rice root 5weeks/tillering after transfer to the fields and remained athigh levels during the rice reproductive stage (Figure 4B).Nitrospira species play pivotal roles in nitrification by oxi-dizing nitrite to nitrate (Kowalchuk and Stephen, 2001). Theenrichment of Nitrospira may be due to root environmentalchanges, such as root exudates or pH, during rice growth. Itis also possible that these microbes were actively recruitedby rice to facilitate nitrate assimilation, which may provideadvantages for rapid growth of rice cultivars during the til-lering and heading stages. Our work provides insight into theprocess of rice root microbiota establishment and may aid infuture biofertilizer applications.

MATERIALS AND METHODS

Plant growth

In the summer of 2017, two rice (Oryza sativa) cultivars,IR24 and Nipponbare, were grown in two separate rice fieldsin China to track the root microbiota establishment proce-dure during the entire plant growth cycle. To avoid seedendophytes and surface-associated microbes, rice seeds weredehulled, surface sterilized in 75% ethanol for 30 s and 2.5%

sodium hypochlorite three times for 15 min, then germinatedon MS agar media. After germination, 2-week-old riceseedlings in MS agar were transferred to fields on Changpingfarm (116.424E, 40.109N) and Shangzhuang farm(116.206E, 40.122N). Both fields had only been used for ricecultivation for several years.

Sample collection

Root samples were collected at successive intervals (0, 1, 2,3, 5, 7, 10, and 14 days) in the first 2 weeks after seedlingtransfer and then every week during the remainder of the ricegrowth cycle. Corresponding bulk soils were collected fromthe unplanted sites as controls. Six individual Nipponbareand three IR24 rice plants were collected at each time pointin each field. Rice roots were collected at a depth of0–10 cm, washed with sterile water to remove loosely at-tached soil particles, and further cleaned three times in25 mL sterile Phosphate Buffered Saline in a 50-mL Falcontube three times with shaking at a speed of 180 r min−1. Afterwashing, roots were dried on sterile filter paper and stored at−80°C.

DNA extraction, PCR amplification, and sequencing

Frozen root and corresponding soil samples were homo-genized twice at 7,200 r for 30 s with Precellys Evolution(Bertin Technologies, France), and DNAwas extracted usingthe FastDNA SPIN Kit (MP Biomedicals) according to themanufacturer’s instructions. DNA concentrations weremeasured with the PicoGreen dsDNA Assay Kit (Lifetechnologies, USA), and subsequently diluted to 3.5 ng µL−1.Variable regions V5–V7 of bacterial 16S rRNA gene wereamplified with degenerate PCR primers, 799F (Chelius andTriplett, 2001) and 1193R (Lundberg et al., 2012). Eachsample was amplified in triplicate (together with a watercontrol) in a 30 μL reaction containing 3 µL template, 0.75 UPrimeSTAR HS DNA Polymerase, 1× PrimeSTAR Buffer(TaKaRa, Japan), 0.2 mmol L−1 dNTPs, and 10 pmol L−1 ofbarcoded forward and reverse primers with linker regions.After an initial denaturation step at 98°C for 30 s, the tar-geted region was amplified by 25 cycles of 98°C for 10 s, 55°Cfor 15 s and 72°C for 60 s, followed by a final elongationstep of 5 min at 72°C. If no amplification was visible fromthe negative control (no template added), triplicate PCRproducts were pooled and purified using an AMPure XP Kit(Beckman Coulter), measured by Nanodrop (NanoDrop2000C, Thermo Scientific), and diluted to 10 ng µL−1 astemplates for the second-step PCR using Illumina-compa-tible primers. All samples were amplified in triplicate foreight cycles under identical conditions to those of the first-step PCR. Technical replicates of each sample were com-bined, separated on a 1.2% (w/v) agarose gel, and the bac-

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terial 16S rRNA gene amplicons were extracted using theQIAquick Gel Extraction Kit (Qiagen, USA) according tothe manufacturer’s instructions. DNA concentration wassubsequently measured using the PicoGreen dsDNA AssayKit (Life technologies), and 200 ng of each sample weremixed. Final amplicon libraries were cleaned twice using theAgencourt AMPure XP Kit (Beckman Coulter GmbH, USA)and subjected to a single sequencing run on the HiSeq 2500platform (Illumina Inc., USA).

Bioinformatics analysis

The 16S rRNA gene sequences were processed using QIIME1.9.1, USEARCH 10.0 and in-house scripts. Paired-end Il-lumina reads were filtered by FastQC, and joined by join_-paired_ends.py script. Barcodes were extracted by theextract_barcodes.py script. Another filter step was per-formed to remove non-bacterial 16S sequences by aligningall OTU representative sequences to the Greengenes data-base using PyNAST (align_seqs.py script). Unaligned 16Ssequences were discarded at a threshold of 75% identityusing the filter_fasta.py script. Based on the high-confidence16S representative sequences, an OTU table was generatedby USEARCH (-usearch_global and uc2otutab.py scripts).The taxonomy of the representative sequences was classifiedwith the RDP classifier.Analysis of differential OTU abundance and taxa were

performed using a negative binomial generalized linearmodel in the edgeR package44. We first obtained normal-ization factors with the calcNormFactors function and thenused the estimateGLMCommonDisp and estima-teGLMTagwiseDisp functions to estimate common and tag-wise dispersions for a Negative Binomial Generalized LinearModel. We fitted a negative binomial generalized log-linearmodel with OTU read counts by the glmFit function to testdifferential OTU abundance; corresponding P values werecorrected for multiple tests using a false discovery rate of0.05. The Pearson correlation coefficient was calculated by acor() function using the mean of 3–6 root microbiota re-plicates from each condition at each time point and visua-lized by using the corrplot package. A comparison ofmicrobiota was performed by an adonis() function in thevegan package.In order to acquire the best discriminant performance of

taxa across rice residence time in the field, we regressed therelative abundances of bacterial taxa at the class level againstrice residence time in the field using default parameters ofthe R implementation of the algorithm (R pack-age‘randomForest’, ntree=1,000, using default mtry of p/3where p is the number of taxa of class).Random Forests approach is one of the most robust en-

semble machine learning methods for classification and re-gression. In order to produce a strong classifier, multiple

weak classifiers were assembled, applying the philosophythat two heads are better than one. Based on the constructionof single classification trees, like a bootstrapping algorithm,Random Forests tries to grow multiple decision (CART)trees with different samples and different initial variables.Each tree gives a classification. Multiple trees choose theclassification with the most votes to perform the final pre-diction (Ref as follows: https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm).Lists of taxa ranked by Random Forests in order of feature

importance were determined over 100 iterations. The numberof marker taxa were identified using 10-fold cross-validationimplemented with the rfcv() function in the R package“randomForest” with five repeats. The minimum cross-va-lidation error was obtained when using 47 important classes;however, the number of classes against cross-validation errorcurve stabilized when using 23 important class, so we chosethe 23 most important classes as marker taxa correlating withrice residence time in the field.

Accession numbers

Bacterial 16S rRNA gene sequencing data, the OTU tableand experimental design were uploaded to the NCBI SRAdatabase, with accession PRJNA435900.

Compliance and ethics The author(s) declare that they have no conflictof interest.

Acknowledgements This work was supported by the “Strategic PriorityResearch Program” of the Chinese Academy of Sciences (XDB11020700),CPSF-CAS Joint Foundation for Excellent Postdoctoral Fellows(2016LH00012), Strategic Priority Research Program of the ChineseAcademy of Sciences (QYZDB-SSW-SMC021) and the National NaturalScience Foundation of China (31772400).

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Root microbiota shift in rice correlates with resident time in the field and developmental stage INTRODUCTIONRESULTSThe rice root microbiota varies over time during the rice life cycle in the fieldThe rice root microbiota stabilizes after 8–10 weeks of growth in the fieldSpecific taxa of the root microbiota associated with rice residence time in the field and developmental stageA model to correlate root bacterial taxonomic biomarkers with rice residence time in the field

DISCUSSIONRoot microbiota dynamics during the entire life cycle of rice grown under field conditionsThe ability of rice plants to modulate the root microbiota increases over timeBiomarker taxa correlated with rice residence time in the field

MATERIALS AND METHODSPlant growthSample collectionDNA extraction, PCR amplification, and sequencingBioinformatics analysisAccession numbers