Plant pathogenic bacteria cause high crop and economic losses to human societies1,2,3. Infections by such pathogens are challenging to control as they often arise through complex interactions between plants, pathogens and the plant microbiome4,5. Experimental studies of this natural ecosystem at the microbiome-wide scale are rare, and consequently we have a poor understanding of how the taxonomic and functional microbiome composition and the resulting ecological interactions affect pathogen growth and disease outbreak. Here, we combine DNA-based soil microbiome analysis with in vitro and in planta bioassays to show that competition for iron via secreted siderophore molecules is a good predictor of microbe–pathogen interactions and plant protection. We examined the ability of 2,150 individual bacterial members of 80 rhizosphere microbiomes, covering all major phylogenetic lineages, to suppress the bacterium Ralstonia solanacearum, a global phytopathogen capable of infecting various crops6,7. We found that secreted siderophores altered microbiome–pathogen interactions from complete pathogen suppression to strong facilitation. Rhizosphere microbiome members with growth-inhibitory siderophores could often suppress the pathogen in vitro as well as in natural and greenhouse soils, and protect tomato plants from infection. Conversely, rhizosphere microbiome members with growth-promotive siderophores were often inferior in competition and facilitated plant infection by the pathogen. Because siderophores are a chemically diverse group of molecules, with each siderophore type relying on a compatible receptor for iron uptake8,9,10,11,12, our results suggest that pathogen-suppressive microbiome members produce siderophores that the pathogen cannot use. Our study establishes a causal mechanistic link between microbiome-level competition for iron and plant protection and opens promising avenues to use siderophore-mediated interactions as a tool for microbiome engineering and pathogen control.
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Raw data of the high-throughput sequences of the 80 soil samples (accession numbers SRR8949365–SRR8949444) and sequencing data of the 2,150 strains (accession numbers MK823189–MK825338) can be found in the NCBI database. All source data has been deposited to the Dryad Digital Repository with the following digital identifier: https://doi.org/10.5061/dryad.p8cz8w9mb.
All code used in this study are available from the corresponding author on request.
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We thank B. Schmid for insightful comments and suggestions for the manuscript and the students in the class of Re131 who graduated in 2017 from Nanjing Agricultural University for their contributions to this work. This research was financially supported by the National Natural Science Foundation of China (grant nos. 41922053 to Z.W., 41807045 to T.Y. and 31972504 to Y.X.) and the Natural Science Foundation of Jiangsu Province (grant nos. BK20180527 to T.Y. and BK20170085 to Z.W.). V.-P.F. is supported by the Wellcome Trust (grant no. 105624) through the Centre for Chronic Diseases and Disorders (C2D2) and Royal Society Research Grants (grant nos. RSG\R1\180213 and CHL\R1\180031) at the University of York. A.J. is supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (grant no. ALW.870.15.050) and the Koninklijke Nederlandse Akademie van Wetenschappen (grant no. 530-5CDP18). R.K. is supported by the Swiss National Science Foundation (grant no. 31003A-182499) and the European Research Council under the grant agreement no. 681295. J.K. is supported by the German Science Foundation (DFG; grant no. KR 5017/2-1).
The authors declare no competing interests.
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Extended Data Fig. 1 The diversity and taxonomic classification of rhizosphere microbiomes and bacterial isolates.
a, A total of 80 rhizosphere microbiomes were identified by amplifying the V4 hypervariable regions of the bacterial 16S rRNA gene. Eight bacterial groups with highest relative abundances at the phylum, class, order, family, and genus levels are shown in the figure, while groups with relatively low abundances were merged and are presented as one group ‘Others’. b, A total of 2150 rhizosphere isolates were identified by 16s rRNA sequencing and their closest relatives were determined using the NCBI database. Seven bacterial groups with highest relative abundances at the phylum, class, order, family, and genus levels are shown in the figure, while groups with relatively low abundances were merged and are presented as one group ‘Others’. In all panels, percentage (%) values in brackets represent the proportion of each bacterial group of the total OTUs (11929 OTUs; 2150 bacterial isolates).
Extended Data Fig. 2 Siderophore production of defined siderophore producers (WT), their isogenic non-producers (deletion mutants) and the 2,150 rhizosphere isolates.
a, CAS values of the two defined wild-type laboratory strains (producer) and the corresponding siderophore-deficient mutants (non-producer) under iron-limited (yellow) and iron-rich (purple) conditions. We used the background CAS values of the siderophore-deficient mutants as a cut-off value to distinguish background CAS activity from siderophore production. Data represent the mean±s.d. of the siderophore production, n=4 independent biological replicates (shown as black dots over the bars). b, Mean siderophore production of the 2150 rhizosphere isolates is significantly higher under iron-limited compared to iron-rich conditions. Box plots encompass the 25–75th percentiles, the whiskers extend to the minimum and maximum points and the midline indicates the median (n=2150 biologically independent rhizobacterial isolates. P values were determined based on analysis of variance (ANOVA) followed by paired two-sided Student’s t test. P<2.2×10-16 (*P < 0.05, **P < 0.01, ***P < 0.001)). c, Iron limitation induces siderophore production in up to 99% of all siderophore producers (values fall below the solid black line). Data points that fall on the solid black line, ie, the diagonal of the square, denotes that equal amount of siderophores were produced under iron-limited and iron-rich conditions. The black dashed lines represent the background CAS values of the two siderophore non-producers under iron-limited and iron-rich conditions, respectively.
Extended Data Fig. 3 The growth effects of siderophores by rhizosphere bacteria on the plant pathogenic R. solanacearum bacterium.
a, The histogram shows how the growth of the pathogen R. solanacearum was affected by cell-free rhizobacterial supernatants collected from iron-limited (yellow fit, GEli) and iron-rich (purple fit, GEri) media, and when iron-limited supernatants were replenished with iron (blue fit, GEre). b, The panel contrasts GEli (effects mediated by siderophores and other metabolites) with GEli - GEre (effects mediated by siderophores alone). The strong positive correlation demonstrates that growth effects were mainly driven by siderophores and not by other secreted metabolites and residual nutrients. The black line and grey shaded area depict the best-fit trendline and the 95 % confidence interval of the linear regression (adjusted coefficient of determination R2=0.755, n=2150 biologically independent rhizobacterial isolates, F1,1248=6610 and two-side P<2.2×10-16 based on Student’s t-test). c, The siderophore-mediated effects of the wild-type strains (producers) on the pathogen were representative of strong (Pseudomonas aeruginosa) and mild (Burkholderia cepacia) inhibition of the pathogen, while the siderophore-deficient isogenic mutants of both species completely lost their inhibitory effect under iron-limited conditions. Data represent the mean±s.d. of n=3 independent biological replicates (shown as black dots over the bars). d, The growth effect of purified pyoverdines of four Pseudomonas strains on pathogen under iron-limited condition were very similar to the effects of the raw supernatants (shown in brackets on X-axis). Data represent the mean±s.d. of n=4 independent biological replicates (shown as black dots over the bars).
a, Iron deficiency constrains pathogen growth (after 48h growth measured as OD600) in MKB medium (iron-limited) relative to MKB medium supplemented with 50 µM FeCl3 (MKB+iron). Pathogen growth was even further reduced in MKB medium supplemented with 200 μM of the chelator 2,2'-Dipyridyl (MKB+chelator), which mimics the situation where the pathogen is exposed to a heterologous siderophore it cannot use. b, siderophore production of the pathogen R. solanacearum QL-Rs1115 under iron-limited and iron-rich conditions. c, The siderophores produced by the pathogen also promote its own growth under iron-limited conditions. The net effect caused by siderophores alone (right column, black symbols) was obtained by subtracting the growth effect of the iron-replenished supernatant (blue) from the growth effect of the iron-limited supernatant (yellow). Values indicate percentage fold-change in growth.In all panels, bars show the mean±s.d. based on 5 independent biological replicates (shown as black dots over the bars) and different lowercase letters above each bar represent significant differences based on analysis of variance (ANOVA) followed by Duncan’s multiple range test (P < 0.05).
Extended Data Fig. 5 The level of siderophore production scales negatively with siderophore-mediated effects on pathogen growth.
a, The (phylogenetically uncorrected) values of siderophore production and siderophore-mediated effects on pathogen growth are negatively correlated. b, This correlation holds even after applying phylogenetically independent contrasts to both variables, showing that isolates producing high amounts of siderophores are generally more likely to inhibit R. solanacearum growth than isolates producing low amounts of siderophores. In both panels, black dots show values for each rhizosphere isolate (n=2150 biologically independent bacterial isolates). The correlation coefficients and p-values were obtained from Spearman rank correlations that account for the non-normal distribution of the phylogenetically corrected values. The red line and the green shaded area depict the best-fit trendline and the 95 % confidence interval of a linear regression, respectively.
Extended Data Fig. 6 Effect of siderophore-producing rhizosphere bacterial taxa on tomato plant disease incidence and pathogen density.
a, The red dashed line shows the baseline level of disease incidence when soils were not pre-inoculated with rhizobacterial isolates and black dashed line represents results from the negative control treatment, where tomato plants were neither treated with rhizosphere bacteria nor with the pathogen. b, The red dashed line shows the baseline level of pathogen density when soils were not pre-inoculated with rhizobacterial isolates. In (a) and (b), box plots encompass the 25–75th percentiles, the whiskers extend to the minimum and maximum points and the midline indicates the median(n=360 biologically independent rhizobacterial isolates and P values were determined based on analysis of variance (ANOVA) followed by paired two-sided Student’s t test). Significances for pairwise comparisons are (from left to right in) in (a): P=3.92×10-5, P=4.87×10-5 and P=0.933, and in (b): P=0.004, P=0.0001 and P=0.116. NS represents non-significant difference (*P < 0.05, **P < 0.01, ***P < 0.001).
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Gu, S., Wei, Z., Shao, Z. et al. Competition for iron drives phytopathogen control by natural rhizosphere microbiomes. Nat Microbiol 5, 1002–1010 (2020). https://doi.org/10.1038/s41564-020-0719-8
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