Abstract
Bacteria–phage coevolution often results in correlated fitness effects on partner species. Whether coevolutionary changes impact the ecology of the surrounding communities is unclear. Here we link coevolution between the phytopathogenic bacterium Ralstonia pseudosolanacearum and its phage parasites to bacterial wilt disease patterns across four geographically disconnected tomato fields. We find that bacteria and phages are locally adapted between and within fields. Phage infectivity was highest on sympatric bacteria, and bacteria showed greater phage resistance when isolated from healthy than diseased plants. The modularity of phage–bacteria coevolution was associated with field-specific anti-phage defence system patterns and locally adapted phage populations. Moreover, phages selected for field-specific mutations in different phage receptor genes, which were negatively associated with virulence measured in planta, suggesting why phage-resistant but weakly virulent pathogen isolates are associated with healthy tomato plants within fields. Our findings show that bacteria–phage coevolution results in patchy plant disease distribution through phage resistance–virulence trade-offs.
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Data availability
The genome sequence data were uploaded to NCBI under BioProject ID PRJNA1255453 and PRJNA1255458. The metaviromic raw data used in this study were deposited in the NCBI database under Bioproject ID SRP679249. Source data are provided with this paper. These data are also available via Dryad at https://doi.org/10.5061/dryad.3j9kd520g (ref. 59).
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Acknowledgements
This study was supported by the National Natural Science Foundation of China (42325704 and 42377118), the Fundamental Research Funds for the Central Universities (KJYQ2025034), the Natural Science Foundation of Jiangsu Province (BK20240194), Fundamental and Interdisciplinary Disciplines Breakthrough Plan of the Ministry of Education of China (JYB2025XDXM703), Research Council of Finland (number 355505) and jointly by a grant from UKRI, Defra and the Scottish Government, under the Strategic Priorities Fund Plant Bacterial Diseases programme (BB/T010606/1) at the University of York. The Viking cluster was used during this project, which is a high-performance computing facility provided by the University of York. We are grateful for computational support from the University of York, IT Services and the Research IT team.
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Conceptualization: Z.W., Y.X., X.W., K.Y. and V.-P.F. Methodology: X.W., S.W., Y.Z. and S.G. Investigation: X.W. and K.Y. Visualization: X.W., K.Y., S.F.O., E.H., B.F., N.W. and G.J. Funding acquisition: Z.W., X.W., V.-P.F. and Q.S. Writing—original draft: X.W. and V.-P.F. Writing—review and editing: X.W., V.-P.F. and Z.W.
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Extended data
Extended Data Fig. 1 Bacterial community structure and abiotic physico-chemical properties drive pathogen phage resistance across different fields.
a, Bacterial community composition differences between fields and plant health status (n = 12 for each sample field; D: diseased plants; H: healthy plants). b, Differences in abiotic soil properties between fields and healthy and diseased plants. ADONIS test was used to compare differences between fields and plant health status (n = 12 for each sample field). c and d, Random Forest classification showing the relative importance of six measured soil properties on pathogen phage resistance, measured by increase in node purity (c) and mean squared error (d). e, Spearman’s correlation between soil available phosphorus (AP) and pathogen phage resistance across all samples (n = 48). The shaded area represents the 95% confidence interval. f, Variation in AP contents between healthy and diseased plants across four different fields. In box plots, the center line denotes the median; box limits represent the 25th and 75th percentiles; whiskers extend to the minimum and maximum values, and the midline indicates the median. Different lowercase letters represent significant differences (one-way ANOVA with Tukey test, n = 12 for each sample location); ns denotes non-significant differences (two-sided Wilcoxon rank sum test; NJ: nD = 7, nH = 5; other fields: nD = 8, nH = 4). Abbreviations: AP: Available phosphorus content (mg kg−1), AK: Available potassium content (mg kg−1), TC: Total carbon content (mg kg−1), TN: Total nitrogen content (mg kg−1), swc: soil water content (%).
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Extended Data Fig. 2 Differences in phage community diversity and community composition between fields and diseased and healthy plants.
Phage community richness (a), Shannon diversity (b), and community composition (c) across fields and diseased and healthy plants (H, healthy; D, diseased; all data measured at plant level). d, the relative abundances of the top 10 phage families in healthy and diseased plants across different fields. In a and b, box plots show the median (center line), the 25th and 75th percentiles (box limits), and the minimum and maximum values (whiskers). ns denotes non-significant differences between diseased and healthy cohorts (two-sided Wilcoxon rank-sum test). Different lowercase letters represent significant differences between fields (one-way ANOVA with Tukey’s HSD test). For all panels, sample sizes are as follows: NJ: nD = 7, nH = 5; other fields: nD = 8, nH = 4.
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Extended Data Fig. 3 Differences in Ralstonia-specific phage community diversity and community composition between fields and diseased and healthy plants.
Abundances of Ralstonia-specific phages (a), Shannon diversity (b) and community composition (c) between fields and diseased and healthy plants. d, the relative abundances of the top 5 phage families across fields and diseased and healthy plants. In a and b, the center line of each box plot indicates the median; box limits represent the 25th and 75th percentiles; whiskers extend to the minimum and maximum values. ns denotes non-significant differences between diseased and healthy cohorts (two-sided Wilcoxon rank-sum test). Different lowercase letters indicate significant differences between locations (one-way ANOVA with Tukey’s HSD test). Data were measured at the plant level. For all panels, sample sizes: NJ: nD = 7, nH = 5; other fields: nD = 8, nH = 4.
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Extended Data Fig. 4 Differential enrichment of Ralstonia-specific vOTUs in diseased and healthy plant rhizosphere samples.
Significant enrichment of Ralstonia-specific viral operational taxonomic units (vOTUs) in diseased (red) versus healthy (blue) plant rhizosphere samples across NJ, NB, and NN fields. No significantly enriched Ralstonia-specific vOTUs were detected in the NC field. Differential abundances were determined using DESeq2; vOTUs were considered significantly enriched with a |log2 fold change | > 1 and an adjusted p-values < 0.05 (Wald test with Benjamini–Hochberg correction). Detailed information of significantly enriched vOTUs are provided in the Supplementary Table 2. For all panels, sample sizes: NJ: nD = 7, nH = 5; other fields: nD = 8, nH = 4.
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Extended Data Fig. 5 Correlations between focal phage resistance and other non-exposed focal phages resistance for R. pseudosolanacearum isolates derived from the endpoint of in planta experimental evolution.
Spearman’s correlations between R. pseudosolanacearum resistance to the focal phage (the isolate used during selection experiment) and resistance to three non-exposed, focal phages (n = 24 isolates for each phage selection treatment). Each panel represents isolates derived from a specific focal phage selection treatment. R2 values denote Spearman’s rank correlation coefficients; the shaded areas represent the 95% confidence intervals. Resistance was measured for all isolates collected at the conclusion of the in planta experiment.
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Extended Data Fig. 6 Evolutionary trade-offs between phage resistance, pathogen virulence and growth-associated traits for a subset of isolates derived from the endpoint of in planta experimental evolution.
In planta virulence of R. pseudosolanacearum isolates measured in tomato (a) and Arabidopsis systems (b) systems following experimental evolution. c, Spearman’s correlation between phage resistance and in planta virulence in tomato (cyan) and Arabidopsis (red) systems. Variation in pathogen fitness-associated traits across treatments, including maximum growth rate (d), carrying capacity (e), carbon utilization (f), competitive ability (g), swarming motility (h), and antibiotic resistance against Bacillus amyloliquefaciens T-5 (i). In all box plots, the center line denotes the median; box limits represent the 25th and 75th percentiles; whiskers extend to the minimum and maximum values. P values were determined by two-sided Wilcoxon rank-sum tests (a, b, ncontrol = 6, nphage = 24) or one-way ANOVA with Tukey’s HSD test (d–i; n = 6 per treatment). Asterisks and different lowercase letters indicate significant differences. In c, R2 values denote Spearman’s rank correlation coefficients; the shaded areas represent the 95% confidence intervals (n = 30).
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Extended Data Fig. 7 Phylogenetic distribution and prevalence of Type II secretion system (gspD/E/L/G) and effector gene (ripS6) in Ralstonia field isolates.
Maximum-likelihood phylogenetic tree illustrating the genetic relatedness of 80 Ralstonia isolates from four tomato fields, alongside the ancestral R. pseudosolanacearum QL-Rs1115 strain and the outgroup R. pickettii 12J. Phylogenetic inference is based on whole-genome MASH (MinHash) distances. The conservation of gspD/E/L/G and ripS6 across the population is summarized by colored circles (black, present in ≥95% of strains; gray, 15–95%; white, <15%). The presence (yellow) or absence (gray) of specific genes in individual isolates is indicated by the heatmap adjacent to the phylogeny. Scale bar represents 0.1 substitutions per nucleotide site. Sample size: n = 80.
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Extended Data Fig. 8 Phylogenetic relatedness, genomic synteny, and ecological abundance of Ralstonia-specific phages.
a, Neighbor-joining phylogenetic tree of 14 Ralstonia phages isolated from four tomato fields, based on whole-genome MASH distances. The four focal phages used in evolution experiments are highlighted in bold red; 10 additional isolates are shown in black. Tree topology was validated with 999 bootstrap replicates. Scale bar represents 1 substitution per nucleotide site. Genomic maps (right) illustrate functional gene annotations (colored by category) and gene synteny based on tBLASTx similarity. b, Abundances of Ralstonia-specific phages in the metaviromic dataset across plant health statuses and locations. Due to high genetic similarity, the four focal phages were grouped as a single operational taxonomic unit (vOTU). Abundances for two additional isolates, P38 and P44 (both from diseased plants in Nanning), are also shown. For all fields, sample sizes are: NJ: nD = 7, nH = 5; other fields: nD = 8, nH = 4.
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Extended Data Fig. 9 Validation of genetic determinants of phage resistance in R. pseudosolanacearum through site-directed mutagenesis.
Spearman’s correlations between resistance to focal phages (NJ-P3, NB-P21, NC-P34, and NN-P42) and resistance to additional field phage isolates (a), and between focal phage resistance and phage adsorption efficiency (b) across engineered knockout mutants (n = 12). R2 values denote Spearman’s rank correlation coefficients; the shaded areas represent the 95% confidence intervals. c, Relative adsorption of the four focal phages to the R. pseudosolanacearum wild-type (WT) strain QL-Rs1115 and three knockout mutants (ΔripS6, ΔgspG, and ΔgspD). d, Relative replication of focal phages on knockout mutants compared to the ancestral WT strain, measured by Efficiency of Plating (EOP) assays. In c and d, box plots show the median (center line), 25th and 75th percentiles (box limits), and minimum/maximum values (whiskers). Different lowercase letters indicate significant differences between strains (one-way ANOVA with Tukey’s HSD test, n = 3 independent biological replicates per treatment). Sample size: n = 3 for each treatment.
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Extended Data Fig. 10 Relationship between genetic similarity, phage resistance profiles, and anti-phage defense systems.
a, Heatmap of pairwise Average Nucleotide Identity (ANI) for all sequenced R. pseudosolanacearum field isolates. Blue and white colors indicate high genomic similarity and dissimilarity, respectively. Sample sizes: NJ: nD = 7, nH = 5; other fields: nD = 8, nH = 4. b, Spearman’s correlation between bacterial genomic similarity (ANI-based Bray-Curtis distance) and phage resistance dissimilarity across isolates from diseased (red) and healthy (blue) plants. c, Negative correlation between genomic similarity (ANI) and the dissimilarity of anti-phage defense system profiles (Bray-Curtis distance) across all sequenced isolates. In b and c, R2 values represent Spearman’s rank correlation coefficients. All data were measured at the isolate level. For panel b and c, sample size: NJ: nD = 10, nH = 10; NB: nD = 11, nH = 9; NC: nD = 8, nH = 12 and NN: nD = 11, nH = 9.
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Supplementary Table 1 The enriched phage vOTUs in diseased and healthy plant rhizosphere at each field. Supplementary Table 2 The enriched Ralstonia-specific vOTUs in diseased and healthy plant rhizospheres for NJ, NB and NN fields (no Ralstonia-specific vOTUs were enriched in NC). Supplementary Table 3 Random forest results using the first effect of each mutation reported by SnpEff and their impact to predict virulence and phage resistance. Supplementary Table 4 The total number of unique variants in the remaining 29 samples when samples with <60% mapping rate was removed (A12 isolated from the NJ-P3 treatment). Supplementary Table 5 Total number and type of mutations identified in evolved R. pseudosolanacearum colonies in each treatment. Supplementary Table 6 Number and annotation of non-synonymous mutations identified in evolved R. pseudosolanacearum isolates in each treatment. Supplementary Table 7 Information on the 80 sequenced R. pseudosolanacearum isolates originating from four different fields. Supplementary Table 8 Information on the focal and additional phages used in the experiments. Focal phages used in the experimental evolution experiment in planta are shown with grey background.
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Wang, X., Yang, K., Wang, S. et al. Bacteria–phage coevolution drives variation in bacterial wilt disease incidence via resistance–virulence trade-offs.
Nat Microbiol (2026). https://doi.org/10.1038/s41564-026-02373-9
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DOI: https://doi.org/10.1038/s41564-026-02373-9
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