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Prolonged drought imparts lasting compositional changes to the rice root microbiome

Nature Plants volume 7, pages 1065–1077 (2021)Cite this article

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Abstract

Microbial symbioses can mitigate drought stress in crops but harnessing these beneficial interactions will require an in-depth understanding of root microbiome responses to drought cycles. Here, by detailed temporal characterization of root-associated microbiomes of rice plants during drought stress and recovery, we find that endosphere communities remained compositionally altered after rewatering, with prolonged droughts leading to decreased resilience. Several endospheric Actinobacteria were significantly enriched during drought and for weeks after rewatering. Notably, the most abundant endosphere taxon during this period was a Streptomyces, and a corresponding isolate promoted root growth. Additionally, drought stress disrupted the temporal dynamics of late-colonizing microorganisms, permanently altering the normal successional trends of root microbiota. These findings reveal that severe drought results in enduring impacts on rice root microbiomes, including enrichment of taxonomic groups that could shape the recovery response of the host, and have implications relevant to drought protection strategies using root microbiota.

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Fig. 1: Compositional dynamics of rhizosphere and endosphere communities before, during and after drought.
Fig. 2: Drought-responsive OTUs show distinct longitudinal trends within and between compartments.
Fig. 3: A drought-enriched OTU becomes the most abundant member of the endosphere communities.
Fig. 4: Streptomyces sp. SLBN-177 significantly increases root length under controlled conditions.
Fig. 5: Persistent immaturity of root microbiomes in drought-stressed plants.

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Data availability

Raw reads have been deposited in the Sequene Read Archive under BioProject PRJNA551661. The SLBN-177 sequence has been deposited within the 100K Project BioProject PRJNA743693 with accession number SRR15049341. The Greengenes database (v.13_8) can be downloaded from http://qiime.org/home_static/dataFiles.html. The SILVA database (v.132) can be downloaded from https://www.arb-silva.de/download/archive/qiime/.

Code availability

All scripts and intermediate files are available in GitHub (https://github.com/cmsantosm/RiceDroughtRecovery).

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Acknowledgements

We thank R. Melnyk for helpful advice regarding assembly, annotation and interpretation of the SLBN-177 genome, and A. Bennett for helpful suggestions. This project was supported by grants to V.S. from the National Science Foundation (no. IOS 1444974) and the United States Department of Agriculture (no. NIFA 2021-67013-34607 and Agricultural Experiment Station project no. CA-D-XXX-6973-H). C.S.M. acknowledges support from the University of California Institute for Mexico, Consejo Nacional de Ciencia y Tecnología and Secretaría de Educación Pública (Mexico). C.S.M. and Z.L. also acknowledge partial support from the Elsie Taylor Stocking Memorial Research Fellowship and the Henry A. Jastro Graduate Research Award. J.E. is supported by USDA National Institute of Food and Agriculture Postdoctoral Fellowship (grant no. 2019-67012-2971/project accession no. 1019437). Sequencing was performed by the DNA Technologies and Expression Analysis Cores at the UC Davis Genome Center supported by the National Institutes of Health Shared Instrumentation grant no. 1S10OD010786-01.

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Author notes

  1. Christian Santos-Medellín

    Present address: Department of Plant Pathology, University of California, Davis, Davis, CA, USA

  2. Joseph Edwards

    Present address: Department of Integrative Biology, University of Texas, Austin, TX, USA

  3. Bao Nguyen

    Present address: Microbiology and Environmental Toxicology Department, University of California, Santa Cruz, Santa Cruz, CA, USA

  4. These authors contributed equally: Christian Santos-Medellín, Zachary Liechty.

Authors and Affiliations

  1. Department of Plant Biology, University of California, Davis, Davis, CA, USA

    Christian Santos-Medellín, Zachary Liechty, Joseph Edwards, Bao Nguyen & Venkatesan Sundaresan

  2. Department of Population Health and Reproduction, 100K Pathogen Genome Project, University of California, Davis, Davis, CA, USA

    Bihua Huang & Bart C. Weimer

  3. Department of Plant Sciences, University of California, Davis, Davis, CA, USA

    Venkatesan Sundaresan

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Contributions

C.S.-M., Z.L. and V.S. conceptualized the study. C.S.M., Z.L., J.E. and B.N. performed the experiments. B.H. and B.C.W. generated the SLBN-177 genome sequence and reviewed the paper. C.S.-M. and Z.L. analysed the data. C.S.-M., Z.L., J.E. and V.S. wrote the paper.

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Correspondence to Venkatesan Sundaresan.

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Peer review information Nature Plants thanks Alex Williams, Maggie Wagner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Compartments harbor compositionally distinct microbial communities.

a, Principal coordinates analysis (PCoA) performed on weighted UniFrac distances across the whole dataset. Colours indicate compartment. b, Distribution of rhizosphere and endosphere within-group distances, that is distances between samples within each treatment and time point combination. The large variation displayed by endospheres in panels a and b is, in part, a result of the strong effect that drought treatments had on this compartment. c-f, PCoA performed on the rhizosphere (c & e) and endosphere (d & f) subsets. In panels c and d, colours indicate plant age. In panels e and f, data points are faceted by plant age and coloured by drought treatment.

Extended Data Fig. 2 Complete set of drought-responsive OTUs.

Heatmap displaying the log2 fold-changes between water controls and drought treatments: depletion under drought tends towards green while enrichment under drought tends toward brown. Each row represents a differentially abundant OTU detected as significant (Wald test, FDR < 0.05) in at least one pair-wise comparison. Horizontal facets indicate each of the modules detected through hierarchical clustering in the rhizosphere (RS) and endosphere (ES). Clusters shown in Fig. 2b are highlighted in black. Vertical dotted lines delimit the periods of suspended irrigation for each of the drought treatments.

Extended Data Fig. 3 Modules of drought responsive OTUs exhibit a strong taxonomic signature.

Classification of the differentially abundant OTUs in each of the distinct drought modules detected through hierarchical clustering. Each tile indicates the number of classified OTUs in a particular module, while the colour represents membership to a specific Phylum / Proteobacteria class. Orders with only one representative have been excluded to ease visualization.

Extended Data Fig. 4 OTU 1037355 displays reproducible trends in an independent experiment.

a, Timeline of the watering regimes followed by control (WC) and drought-stressed (DS) plants. Horizontal lines represent the watering status during the experiment: solid segments indicate periods of constant irrigation while dotted segments indicate periods of suspended irrigation. Upside down triangles mark each of 5 collection time points. b, Ranked relative abundances of individual community members throughout time. Each ribbon represents a single OTU in the community: for each time point, width indicates its relative abundance while the position across the y axis indicates its rank within the community. The most abundant member of the semipersistent enrichment module, Streptomyces sp. (OTU ID: 1037355), is highlighted. In all panels, the vertical dotted lines delimit the periods of suspended irrigation in each of the drought treatments. c,d, Beta-diversity patterns in the rhizosphere (c) and endosphere (d) communities. In both cases, the y axis displays the position of each sample across the first principal coordinate (PCo) from a weighted UniFrac PCo analysis and the x axis displays the age of the plant at the moment of sample collection. In all panels, the vertical dotted lines delimit the periods of suspended irrigation for the drought treatment.

Extended Data Fig. 5 OTU 1037355 is highly occurring and displays a significant drought enrichment across compositionally distinct soils.

a, Occupancy-abundance curves for rhizosphere and endosphere communities of rice plants grown in three California soils (Arbuckle, Biggs, and Davis), and one Arkansas field. The x axis displays the log-transformed mean relative abundance of each OTU while the y axis displays the percent of samples in which each OTU was detected. OTU 1037355 is highlighted in orange. b, Relative abundance of OTU 1037355 in rhizosphere and endosphere communities of 49-day-old rice plants grown under well irrigated (WC) or drought-stressed (DS) conditions. Asterisks on top indicate a significant difference (PFDR < 0.001) between WC and DS treatments. Statistical significance was determined by negative binomial generalized linear models and pairwise Wald tests (two-sided) corrected with the Benjamini-Hochberg procedure.

Extended Data Fig. 6 Other phenotypic traits were not impacted by Streptomyces sp. SLBN-177.

a, Distribution of measured plant phenotypes (number of roots, root weight, shoot weight, and shoot length) across microbial treatments and watering regimes. b, Protein alignment of putative iaaM genes from SLBN-177, S. coelicolor, S. scabiei, and S. sp ADI96-02 (refs. 16,17). Black lines indicate an amino acid mismatch with a negative score on the BLOSUM62 matrix, and dark grey bars represent a mismatch with a positive score. The vertical black dashed lines indicate the bounds of the amino oxidase functional domain. c, Relative abundances of inoculated isolates in the endospheres of rice plants. Reads identified as mitochondria or chloroplast (collapsed as organellar reads and represented in black) were not discarded in order to measure the degree of colonization. Reads classified as OTUs other than 1037355 (SLBN-177) and 1108350 (SLBN-111) were collapsed and are represented in gray.

Extended Data Fig. 7 Random forests can identify age discriminant OTUs.

a, Cross validation error as a function of the number of OTUs used in each model. For both compartments, the lowest error was detected when using the 65 most important taxa. b, Overlap between the age-discriminant OTUs and the drought-responsive OTUs detected in each module (Fig. 2b). c, Hierarchical clustering of the relative abundances of the age-discriminant OTUs. The heatmap displays the z-transformed mean relative abundances of each OTU across drought treatments and time points. The colours at the left end of each vertical facet indicate the longitudinal trends exhibited by each taxa. In all panels, the vertical dotted lines delimit the periods of suspended irrigation for the drought treatment.

Extended Data Fig. 8 Taxonomic classification of the age-discriminant OTUs in each of the longitudinal trends.

Each tile indicates the number of classified OTUs in a particular module, while the colour represents membership to a specific Phylum / Proteobacteria class.

Extended Data Fig. 9 Drought stress delayed transition to flowering.

a, Developmental growth stages of well-watered and drought-stressed rice plants through the experiment. Photos of each sample were taken at each time point and defined as either pre-panicle emergence (no portion of the panicle visible), panicle emergence (panicle partially emerged from flag leaf), anthesis (panicle fully emerged and anthers visible), grain filling (panicles bent over instead of standing upright), or maturity (flower colour appears yellow). b, Developmental growth stage and microbiome age predictions of rhizosphere and endosphere communities across drought treatments (D1, D2, and D3). The dashed curve represents the baseline microbiome development under well-irrigated conditions and was calculated by fitting a loess curve between the predicted microbiome age and the chronological plant age in the control (WC) test set.

Extended Data Fig. 10 Calculation of relative microbiome maturity of drought-stressed samples.

a, After applying the sparse random forest models to the validating set of well-watered plants, a loess curve (dashed green line) was fit between host chronological age (x axis) and microbiome predicted age (y axis). b, Using the fitted loess curve as a baseline of microbiome development, relative microbiome maturity was estimated by calculating the difference between the predicted microbiome age of an individual sample and the corresponding baseline microbiome age of well-watered plants. c, Using the approach described in B, relative microbiome maturity was calculated for all drought-stressed samples.

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Santos-Medellín, C., Liechty, Z., Edwards, J. et al. Prolonged drought imparts lasting compositional changes to the rice root microbiome. Nat. Plants 7, 1065–1077 (2021). https://doi.org/10.1038/s41477-021-00967-1

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  • DOI: https://doi.org/10.1038/s41477-021-00967-1

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