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Root microbiota drive direct integration of phosphate stress and immunity

Nature volume 543, pages 513–518 (2017)Cite this article

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Abstract

Plants live in biogeochemically diverse soils with diverse microbiota. Plant organs associate intimately with a subset of these microbes, and the structure of the microbial community can be altered by soil nutrient content. Plant-associated microbes can compete with the plant and with each other for nutrients, but may also carry traits that increase the productivity of the plant. It is unknown how the plant immune system coordinates microbial recognition with nutritional cues during microbiome assembly. Here we establish that a genetic network controlling the phosphate stress response influences the structure of the root microbiome community, even under non-stress phosphate conditions. We define a molecular mechanism regulating coordination between nutrition and defence in the presence of a synthetic bacterial community. We further demonstrate that the master transcriptional regulators of phosphate stress response in Arabidopsis thaliana also directly repress defence, consistent with plant prioritization of nutritional stress over defence. Our work will further efforts to define and deploy useful microbes to enhance plant performance.

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Figure 1: PSR mutants assemble an altered root microbiota.
Figure 2: A bacterial SynCom differentially colonizes PSR mutants.
Figure 3: PHR1 mediates interaction of the PSR and plant immune system outputs.
Figure 4: Loss of PHR1 activity results in enhanced activation of plant immunity.

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Acknowledgements

Support by NSF INSPIRE grant IOS-1343020 and DOE-USDA Feedstock Award DE-SC001043 to J.L.D. S.H.P. was supported by NIH Training Grant T32 GM067553-06 and is a Howard Hughes Medical Institute International Student Research Fellow. P.J.P.L.T. was supported by The Pew Latin American Fellows Program in the Biomedical Sciences. J.L.D. is an Investigator of the Howard Hughes Medical Institute, supported by the HHMI and the Gordon and Betty Moore Foundation (GBMF3030). M.E.F. and O.M.F. are supported by NIH NRSA Fellowships F32-GM112345-02 and F32-GM117758-01, respectively. N.W.B. was supported by NIH NRSA Fellowship F32-GM103156. J.P.-A. is funded by the Spanish Ministry of Economy and Competitiveness (MINECO BIO2014-60453-R and EUI2008-03748). We thank S. Barth and E. Getzen for technical assistance, the Dangl laboratory microbiome group for useful discussions and S. Grant, D. Lundberg, F. El Kasmi, P. Schulze-Lefert and his colleagues for critical comments on the manuscript. Supplement contains additional data. Raw sequence data are available at the EBI Sequence Read Archive accession PRJEB15671 for microbiome 16S profiling, and at the Gene Expression Omnibus accessions GSE87339 for transcriptomic experiments. J.L.D. is a co-founder of, and shareholder in, and S.H.P. collaborates with, AgBiome LLC, a corporation whose goal is to use plant-associated microbes to improve plant productivity.

Author information

Author notes

  1. Laura de Lorenzo & Natalie W. Breakfield

    Present address: †Present addresses: NewLeaf Symbiotics, St. Louis, Missouri 63132, USA (N.W.B.); Department of Plant and Soil Sciences, University of Kentucky, Lexington, Kentucky 40546, USA (L.d.L.).,

  2. Gabriel Castrillo, Paulo José Pereira Lima Teixeira and Sur Herrera Paredes: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biology, University of North Carolina, Chapel Hill, North Carolina 27599-3280, USA,

    Gabriel Castrillo, Paulo José Pereira Lima Teixeira, Sur Herrera Paredes, Theresa F. Law, Meghan E. Feltcher, Omri M. Finkel, Natalie W. Breakfield, Corbin D. Jones & Jeffery L. Dangl

  2. Howard Hughes Medical Institute, University of North Carolina, Chapel Hill, 27599-3280, North Carolina, USA

    Gabriel Castrillo, Paulo José Pereira Lima Teixeira, Sur Herrera Paredes, Theresa F. Law, Meghan E. Feltcher, Omri M. Finkel, Natalie W. Breakfield & Jeffery L. Dangl

  3. Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, 27599-3280, North Carolina, USA

    Sur Herrera Paredes, Corbin D. Jones & Jeffery L. Dangl

  4. Department of Plant Molecular Genetics, Centro Nacional de Biotecnología, CNB-CSIC, Darwin 3, Madrid, 28049, Spain

    Laura de Lorenzo & Javier Paz-Ares

  5. Department of Genetics, University of North Carolina, Chapel Hill, North Carolina, USA

    Piotr Mieczkowski & Corbin D. Jones

  6. Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, 27599-3280, North Carolina, USA

    Piotr Mieczkowski & Corbin D. Jones

  7. Carolina Center for Genome Sciences, University of North Carolina, Chapel Hill, 27599-3280, North Carolina, USA

    Piotr Mieczkowski, Corbin D. Jones & Jeffery L. Dangl

  8. Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, 27599-3280, North Carolina, USA

    Corbin D. Jones & Jeffery L. Dangl

  9. Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, 27599-3280, North Carolina, USA

    Jeffery L. Dangl

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  1. Gabriel Castrillo
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Contributions

G.C., P.J.P.L.T., S.H.P. and J.L.D. designed the project, G.C., S.H.P., T.F.L. and M.E.F. set up the experiments, collected samples and organized construction of 16S sequencing libraries. G.C. and T.F.L. performed control experiments related with PSR induced by the SynCom. G.C., N.W.B., M.E.F. and T.F.L. set up the experiments, collected samples and isolated RNA. P.J.P.L.T. organized, performed construction of RNA-seq libraries and analysed RNA-seq data. S.H.P. analysed 16S sequencing data. S.H.P. and P.J.P.L.T. oversaw data deposition. G.C., T.F.L. and P.J.P.L.T. performed pathology experiments. G.C., P.J.P.L.T., S.H.P., T.F.L., O.M.F. and J.L.D. analysed data and created figures. L.d.L. performed the ChIP–seq experiment. C.D.J. and P.M. advised on sequencing process and statistical methods. G.C., P.J.P.L.T., S.H.P. and J.L.D. wrote the manuscript with input from O.M.F., C.D.J. and J.P.-A.

Corresponding author

Correspondence to Jeffery L. Dangl.

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Competing interests

The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks P. Finnegan and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Extended data figures and tables

Extended Data Figure 1 The Arabidopsis PSR alters highly specific bacterial taxa abundances.

a, Alpha diversity of bacterial root microbiome in wild-type Col-0, PSR mutants and bulk soil samples. We used ANOVA methods and no statistical differences were detected between plant genotypes after controlling for experiment. b, Additive beta-diversity curves showing how many OTUs are found in bulk soil samples or root endophytic samples of the same genotype as more samples (pots) are added. The curves show the mean and the 95% confidence interval calculated from 20 permutations. c, Phylum-level distributions of plant root endophytic communities from different plant genotypes and bulk soil samples. d, Principal coordinates analysis based on Bray–Curtis dissimilarity of root and bulk soil bacterial communities showing a large effect of experiment on variation, as expected according to previous studies19. For a–d, the number of biological replicates per genotype and soil are: Col-0 (n = 17), pht1;1 (n = 18), pht1;1;pht1;4 (n = 17), phf1 (n = 13), nla (n = 16), pho2 (n = 16), phr1 (n = 18), spx1;spx2 (n = 14) and soil (n = 17). e, Bacterial taxa that are differentially abundant (DA) between PSR mutants and Col-0. Each row represents a bacterial family (left) or OTU (right) that shows a significant abundance difference between Col-0 and at least one mutant. The heat map grey scale shows the mean abundance of the given taxa in the corresponding genotype, and significant enrichments and depletions with respect to Col-0 are indicated with a red or blue rectangle, respectively. Taxa are organized by phylum shown on the right bar coloured according to f. f, Doughnut plot showing family-level (top) and OTU-level (bottom) differences in endophytic root microbiome compositions between mutants (columns) and Col-0 plants. The number inside each doughnut indicates how many bacterial families are enriched or depleted in each mutant with respect to Col-0, and the colours in the doughnut show the phylum level distribution of those differential abundances. g, Tables of P values from Monte-Carlo pairwise comparisons between mutants. A significant P value (cyan) indicates that two genotypes are more similar than expected by chance. Results of family-level comparison are shown. This plot should be compared with the corresponding OTU-level plot in Fig. 1d. h, Distributions of plant genotypic effects on taxonomic abundances at the family (up) or OTU (down) level. For each genotype, the value of the linear model coefficients for individual OTUs or families is plotted grouped by their sign. Positive values indicate that a given taxon has increased abundance in a mutant with respect to Col-0, whereas a negative value represents the inverse pattern. Only coefficients from significant comparisons are shown. The number of taxa (that is, points) on each box and whisker plot is indicated in the corresponding doughnut plot in f.

Extended Data Figure 2 Plants grown in Mason Farm wild soil or Pi-replete potting soil do not induce PSR and accumulate the same amount of Pi.

a, Plants overexpressing the PSR reporter construct IPS1:GUS grown in Mason Farm wild soil (MF) or in Pi-replete potting soil (GH) (250 p.p.m. of 20-20-20 Peters Professional Fertilizer). b, Expression analysis of the reporter constructs IPS1:GUS (n = 12) shows lack of induction of PSR for both soils analysed. In this construct, the promoter region of IPS1, highly induced by low Pi, drives the expression of GUS. Plants were grown in the conditions described in a. The number of GUS positive plants relative to the total number of plants analysed in each condition is shown in parentheses. c, Pi concentration in shoots (n = 6) of plants grown in both soils analysed shows no differences. Plants were grown in a growth chamber in a 15-h light/9-h dark regime (21 °C day/18 °C night). Images shown here are representative of the 12 plants analysed in each case. Error bars, standard deviation.

Extended Data Figure 3 Phylogenetic composition of the 35-member SynCom.

Left, comparison of taxonomic composition of soil (S), rhizosphere (R) and endophyte (EC) communities from ref. 19, with the taxonomic composition of the isolate collection obtained from the same samples and the SynCom selected from within it and used in this work. Right, maximum likelihood phylogenetic tree of the 35-member SynCom based on a concatenated alignment of 31 single-copy core proteins.

Extended Data Figure 4 Induction of the PSR triggered by the SynCom is mediated by PHR1 activity.

a, Venn diagram with the overlap among genes found upregulated during phosphate starvation in four different gene expression experiments13,43,44,45. The intersection (193 genes) was used as a robust core set of PSR for the analysis of our transcriptional data (Supplementary Table 3). b, Expression profile of the 193 core PSR genes indicating that the SynCom triggers phosphate starvation under low Pi conditions in a manner that depends on PHR1 activity. The RPKM expression values of these genes were z-score transformed and used to generate box and whiskers plots that show the distribution of the expression values of this gene set. Col-0, the single mutant phr1 and the double mutant phr1;phl1 were germinated at 1 mM Pi with sucrose and then transferred to low Pi (50 μM) and high Pi (625 μM Pi) alone or with the SynCom. The figure shows the average measurement of ten biological replicates for Col-0 and phr1 and six for phr1;phl1. c, Percentage of genes per cluster (from Fig. 3) containing the PHR1 binding site (P1BS, GNATATNC) within 1,000 bp of their promoters. The red line indicates the percentage of Arabidopsis genes in the whole genome that contain the analysed feature. Asterisk denotes significant enrichment or depletion (P ≤ 0.05; hypergeometric test).

Extended Data Figure 5 The SynCom induces PSR independently of sucrose in Arabidopsis.

a, Expression analysis of a core of 193 PSR marker genes in an RNA-seq experiment using Col-0 plants. The RPKM expression values of these genes were z-score transformed and used to generate box and whiskers plots that show the distribution of the expression values of this gene set. Plants were grown in Johnson medium containing replete (1 mM Pi; (+Pi)) or stress (5 μM Pi; (−Pi)) Pi concentrations with (+Suc) or without (−Suc) 1% sucrose. b, Expression analysis of the reporter constructs IPS1:GUS (n = 20). In this construct, the promoter region of IPS1, highly induced by low Pi, drives the expression of GUS. Plants were grown in Johnson medium +Pi or −Pi at different percentages of sucrose. These results show that sucrose is required for the induction of the PSR in typical sterile conditions. Images shown are representative of the 20 plants analysed in each case. c, Top, plants grown in sterile conditions at different Pi concentrations (left (no bacteria)) or with a SynCom (right (+SynCom)). Bottom, histochemical analysis of β-glucoronidase (GUS) activity in overexpressing IPS1:GUS plants (n = 20) from top panel. Images shown are representative of the 20 plants analysed in each case. d, Pi concentration in plant shoots from c, in all cases n = 5. Analysis of variance indicated a significant effect of the Pi level in the media (F = 44.12, d.f. = 1, P value = 9.72 × 10−8), the presence of SynCom (F = 32.61, d.f. = 1, P value = 1.69 × 10−6) and a significant interaction between those two terms (F = 4.748, d.f. = 1, P value = 0.036) on Pi accumulation. F, F-value statistic from the ANOVA, d.f. is the degrees of freedom from the same test. e, Top, plants grown in axenic conditions (no bacteria), with a concentration gradient of heat-killed SynCom (2 h at 95 °C, (+heat-killed SynCom)) or with SynCom alive. Bottom, histochemical analysis of GUS activity in overexpressing IPS1:GUS plants (n = 15) from top panel. All plants were grown at 50 μM Pi. Images shown are representative of the 15 plants analysed in each case. f, Quantification of Pi concentration in plant shoots from e, (in all cases n = 5). The SynCom effect on Pi concentration requires live bacteria. Plants were germinated on Johnson medium containing 0.5% sucrose, with 1 mM Pi for 7 days in a vertical position, then transferred to 0, 10, 30, 50, 625 μM Pi media (without sucrose) alone or with the SynCom at 105 c.f.u. ml−1 (only for the conditions 0, 50 and 625 μM Pi), for another 12 days. For the heat-killed SynCom experiments, plants were grown as above. Heat-killed SynComs were obtained by heating different concentrations of bacteria 105 c.f.u. ml−1, 106 c.f.u. ml−1 and 107 c.f.u. ml−1 at 95 °C for 2 h in an oven. The whole content of the heat-killed SynCom solutions were add to the media. In all cases, addition of the SynCom did not change significantly the final Pi concentration or the pH in the media. Letters indicates grouping based on ANOVA and Tukey post hoc test at 95% confidence, conditions with the same letter are statistically indistinguishable.

Extended Data Figure 6 Bacteria induce the PSR using the canonical pathway in Arabidopsis.

a, Pi concentration in the shoot of Col-0 plants germinated in three different conditions, 5 μM Pi (−Pi) (n = 14), 1 mM Pi (+Pi) (n = 15) and 1 mM KH2PO3 (Phi) (n = 15) for 7 days. Phi is a non-metabolizable analogue of Pi; its accumulation delays the response to phosphate stress. b, Expression profile analysis of a core of PSR-marker genes in Col-0, phf1 and phr1;phl1. The RPKM expression values of these genes were z-score transformed and used to generate box and whiskers plots that show the distribution of the expression values of this gene set. Plants were germinated in three different conditions, 5 μM Pi (−Pi), 1 mM Pi (+Pi) and 1 mM KH2PO3 (Phi) and then transferred to low Pi (50 μM Pi) and high Pi (625 μM Pi) alone or with the SynCom for another 12 days. The figure shows the average measurement of four biological replicates. c, Phenotype of plants grown in axenic conditions at 625 μM Pi (top) or at 50 μM Pi (bottom) (left (no bacteria)) or with a SynCom (right (+SynCom)). Images showed here are representative of the total number of plants analysed in each case as noted below. d, Quantification of the main root elongation. e, f, Number of lateral roots per plant (e) and number of lateral roots per cm of main root (f) in plants from c. For d–f, the number of biological replicates are: 625 μM no bacteria (n = 48), 625 μM + SynCom (n = 46), 50 μM no bacteria (n = 73), and 50 μM SynCom (n = 56), distributed across two independent experiments indicated with different shades of colour. Measurements were analysed with ANOVA, controlling for biological replicate. Asterisks denote a significant effect (P value < 1 × 10−16) of treatment with SynCom for the three phenotypes in d–f. In all cases, neither the interaction between Pi and bacteria, nor Pi concentrations alone had a significant effect and were dropped from the ANOVA model. In all cases, residual quantiles from the ANOVA model were compared with residuals from a normal distribution to confirm that the assumptions made by ANOVA hold (see code on GitHub for details, see Methods).

Extended Data Figure 7 Plant genotype and Pi concentration alter SynCom strain abundances.

a, Number of bacterial reads in samples of different types (left) and number of reads after blank normalization (right, see Methods). The number of biological replicates are: inoculum (n = 8), agar + SynCom (n = 41), agar no bacteria (n = 2), root + SynCom (n = 36), root no bacteria (n = 6) and blank (n = 3), across two independent experiments. b, Richness (number of isolates detected) in SynCom samples. No differences were observed between plant genotypes. The number of biological replicates per group is n = 12 except for inoculum (n = 4) and phf1 (n = 11). c, Exemplary SynCom strains that show quantitative abundance differences between genotypes. Genotypes with the same letter are statistically indistinguishable. d, Exemplary SynCom strains that show quantitative abundance differences depending on Pi concentration in the media. Asterisks note statistically significant differences between the two Pi concentrations. e, CAP analysis of agar versus root difference in SynCom communities. These differences explained 9.1% of the variance. The number of biological replicates per fraction is: agar (n = 12) and root (n = 35), distributed across two independent experiments. f, Exemplary SynCom strain that shows a statistically significant differential abundance between root and agar samples. Statistically significant differences are defined as FDR < 0.05. For c, d and f, the number of biological replicates for every combination of genotype and Pi level is always n = 6, evenly distributed across two independent experiments.

Extended Data Figure 8 PHR1 controls the balance between the SA and JA regulons during the PSR induced by a 35-member SynCom.

a, Total number of differentially expressed genes (FDR ≤ 0.01 and minimum of 1.5× fold-change) in Col-0, phr1 and phr1;phl1 with respect to low Pi (50 μM Pi), bacteria presence and the interaction between low Pi and bacteria. In this experiment, plants were grown for 7 days in Johnson medium containing 1 mM Pi, and then transferred for 12 days to low (50 μM Pi) and high Pi (625 μM Pi) conditions alone or with the SynCom. No sucrose was added to the medium. b, Venn diagram showing the overlap between the PSR marker genes (core Pi) and the genes that were upregulated in Col-0 by each of the three variables analysed. The combination of bacteria and low Pi induced the majority (85%) of the marker genes. c, PHR1 negatively regulates the expression of a set of SA-responsive genes during co-cultivation with the SynCom. Venn diagram showing the overlap among PSR-SynCom DEGs, genes upregulated by BTH treatment of Arabidopsis seedlings, and the direct targets of PHR1 identified by ChIP–seq. The red ellipse indicates the 468 BTH/SA-responsive genes that were differentially expressed. A total of 99 of these genes (21%) are likely direct targets of PHR1. The yellow ellipse indicates 272 SA-responsive genes that were bound by PHR1 in a ChIP–seq experiment (see Fig. 3e). Approximately one-third of them (99 out of 272) were differentially expressed in the SynCom experiment. d, Hierarchical clustering analysis showed that nearly half of the BTH/SA-induced genes that were differentially expressed in our experiment are more expressed in phr1 or phr1;phl1 mutants compared to Col-0 (dashed box). The columns on the right indicate those genes that belong to the core PSR marker genes (‘core’ lane) or that contain a PHR1 ChIP–seq peak (‘ChIP–seq’ lane). A subset of the SA marker genes is less expressed in the mutant lines (thin dashed box). This set of genes is also enriched in the core PSR markers and in PHR1 direct targets (P < 0.001; hypergeometric test), indicating that PHR1 can function as a positive activator of a subset of SA-responsive genes. Importantly, these genes are not typical components of the plant immune system but rather encode proteins that play a role in the physiological response to low phosphate availability (for example, phosphatases and transporters). e, Examples of typical SA-responsive genes are shown on the right along with their expression profiles in response to MeJA or BTH/SA treatment compared to Col-0. f, PHR1 activity is required for the activation of JA-responsive genes during co-cultivation with the SynCom. Venn diagram showing the overlap among DEG from this work (PSR-SynCom), genes upregulated by MeJA treatment of Arabidopsis seedlings and the genes bound by PHR1 in a ChIP–seq analysis. Red ellipse indicates 165 JA-responsive genes that were differentially expressed. Thirty-one of these (19%) were defined as direct targets of PHR1. The yellow ellipse indicates 96 JA-responsive genes that were bound by PHR1 in a ChIP–seq experiment. Approximately one-third of them (31 out of 96) were differentially expressed in the SynCom experiment. g, Hierarchical clustering analysis showed that almost 75% of the JA-induced genes that were differentially expressed in our experiment are less expressed in the phr1 mutants (dashed box). The columns on the right indicate those genes that belong to the core PSR marker genes (‘core’ lane) or that contain a PHR1 ChIP–seq peak (‘ChIP–seq’ lane). h, Examples of well-characterized JA-responsive genes are shown on the right along with their expression profiles in response to BTH and MeJA treatments obtained in an independent experiment. i, Heat map showing the expression profile of the 18 genes that were differentially expressed in our experiment and participate in the biosynthesis of glucosinolates21. In general, these genes showed lower expression in the phr1 mutants indicating that PHR1 activity is required for the activation of a sub-set of JA-responsive genes that mediate glucosinolate biosynthesis. The transcriptional response to BTH/SA and MeJA treatments is shown on the right and was determined in an independent experiment in which Arabidopsis seedlings were sprayed with either hormone. MeJA induces the expression of these glucosinolate biosynthetic genes, whereas BTH represses many of them. The gene IDs and the enzymatic activity of the encoded proteins are shown on the right. Results presented in this figure are based on ten biological replicates for Col-0 and phr1 and six for phr1;phl1. The colour key (blue to red) related to d, e, g, h, i represents gene expression as z-scores and the colour key (green to purple) related to e, h, i represents gene expression as log2 fold changes.

Extended Data Figure 9 PHR1 activity effects on flg22- and MeJA-induced transcriptional responses.

a, Total number of differentially expressed genes (FDR ≤ 0.01 and minimum of 1.5× fold-change) in Col-0 and phr1;phl1 with respect to low Pi (50 μM Pi), flg22 treatment (1 μM) and MeJA (10 μM). In this experiment, plants were grown for 7 days in Johnson medium containing 1 mM Pi, and then transferred for 12 days to low (50 μM Pi) and high Pi (625 μM Pi) conditions alone, or in combination with each treatment. Sucrose was added to the medium at a final concentration of 1%. b, Venn diagram showing the overlap among genes that were upregulated by chronic exposure to flg22 in Col-0 and in phr1;phl1 and a literature-based set of genes that were upregulated by acute exposure (between 8 to 180 min) to flg22 (ref. 23). The red ellipse indicates the 251 chronic flg22-responsive genes defined here. c, Venn diagram showing the overlap among genes that were upregulated by chronic exposure to MeJA in Col-0 and in phr1;phl1 in this work and a set of genes that were upregulated by MeJA treatment of Arabidopsis seedlings (between 1 h and 8 h). The red ellipse indicates the intersection of JA-responsive genes identified in both experiments. d, Col-0 and phr1;phl1 exhibit similar transcriptional activation of 426 common JA-marker genes (c) independent of phosphate concentration. As a control we used coi1-16, a mutant impaired in the perception of JA. The gene expression results are based on six biological replicates per condition. e, Growth inhibition of primary roots by MeJA. Root length of wild-type Col-0 (n = 125 (+Pi, −MeJA), 120 (+Pi, +MeJA), 126 (−Pi, −MeJA), 125 (−Pi, +MeJA)), phr1;phl1 (n = 85, 103, 90, 80) and the JA perception mutant coi1-16 (n = 125, 120, 124, 119) was measured after 4 days of growth in the presence or not of MeJA with or without 1 mM Pi. Letters indicate grouping based on multiple comparisons from a Tukey post hoc test at 95% confidence. In agreement with the RNA-seq results, no difference in root length inhibition was observed between Col-0 and phr1;phl1.

Extended Data Figure 10 Number of mapped reads for each RNA-seq library used in this study.

The figure shows the maximum, minimum, average and median number of reads mapping per gene for all RNA-seq libraries generated. The total number of reads mapping to genes is also shown for each library. With the exception of the minimum number of mapped reads, which is zero for all libraries, all values are shown in a log scale.

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Castrillo, G., Teixeira, P., Paredes, S. et al. Root microbiota drive direct integration of phosphate stress and immunity. Nature 543, 513–518 (2017). https://doi.org/10.1038/nature21417

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