Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Phage combination therapies for bacterial wilt disease in tomato

Nature Biotechnology volume 37, pages 1513–1520 (2019)Cite this article

Subjects

Abstract

Bacteriophages have been proposed as an alternative to pesticides to kill bacterial pathogens of crops. However, the efficacy of phage biocontrol is variable and poorly understood in natural rhizosphere microbiomes. We studied biocontrol efficacy of different phage combinations on Ralstonia solanacearum infection in tomato. Increasing the number of phages in combinations decreased the incidence of disease by up to 80% in greenhouse and field experiments during a single crop season. The decreased incidence of disease was explained by a reduction in pathogen density and the selection for phage-resistant but slow-growing pathogen strains, together with enrichment for bacterial species that were antagonistic toward R. solanacearum. Phage treatment did not affect the existing rhizosphere microbiota. Specific phage combinations have potential as precision tools to control plant pathogenic bacteria.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Phage combinations and incidence of disease.
Fig. 2: Resistance evolution to ancestral and coevolved phages.
Fig. 3: Effects of phages on rhizosphere communities.
Fig. 4: Phage specificity and effects on the suppression of rhizosphere microbiota.
Fig. 5: Mechanisms underlying phage-mediated effects on bacterial wilt disease.

Similar content being viewed by others

Data availability

All sequence data generated in this study has been deposited in NCBI SRA database and the accession numbers are reported in the Methods. Phage sequence accession numbers are: SRR8402465 (NJ-P3), SRR8403229 (NB-P21), SRR8403928 (NC-P34) and SRR8410130 (NN-P42). The rhizosphere microbiome data determined at the end of the greenhouse experiment have accession numbers from SRR8417955 to SRR8417999 (45 samples, paired end sequencing). The rhizosphere microbiome data obtained in a separate laboratory experiment have accession numbers running from SRR8470488 to SRR8501098 (30 samples, paired end reads). All other data has been deposited to Dryad Digital Repository with the following digital identifier: https://doi.org/10.5061/dryad.02v6wwpzq.

Code availability

All code or algorithms used in this study are published and referenced in the Methods.

References

  1. Raaijmakers, J. M., Paulitz, T. C., Steinberg, C., Alabouvette, C. & Moenne-Loccoz, Y. The rhizosphere: a playground and battlefield for soilborne pathogens and beneficial microorganisms. Plant Soil 321, 341–361 (2009).

    CAS  Google Scholar 

  2. Strange, R. N. & Scott, P. R. Plant disease: a threat to global food security. Annu. Rev. Phytopathol. 43, 83–116 (2005).

    CAS  PubMed  Google Scholar 

  3. Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).

    CAS  PubMed  Google Scholar 

  4. Hu, J. et al. Probiotic diversity enhances rhizosphere microbiome function and plant disease suppression. Mbio 7, e01790–16 (2016).

  5. Wei, Z. et al. Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health. Nat. Commun. 6, 8413 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Brodeur, J. Host specificity in biological control: insights from opportunistic pathogens. Evol. Appl. 5, 470–480 (2012).

    PubMed  PubMed Central  Google Scholar 

  7. Kyselková, M. & Moënne-Loccoz, Y. Pseudomonas and other microbes in disease-suppressive soils. in Organic Fertilisation, Soil Quality and Human Health 93–140 (Springer, 2012).

  8. Alvarez, B. & Biosca, E. G. Bacteriophage-based bacterial wilt biocontrol for an environmentally sustainable agriculture. Front. Plant Sci. 8, 1218 (2017).

    PubMed  PubMed Central  Google Scholar 

  9. Buttimer, C. et al. Bacteriophages and bacterial plant diseases. Front. Microbiol. 8, 34 (2017).

    PubMed  PubMed Central  Google Scholar 

  10. Rutherford, S. T. & Bassler, B. L. Bacterial quorum sensing: its role in virulence and possibilities for its control. Cold Spring Harb. Perspect. Med. 2, a012427 (2012).

  11. Levin, B. R. & Bull, J. J. Population and evolutionary dynamics of phage therapy. Nat. Rev. Microbiol. 2, 166–173 (2004).

    CAS  PubMed  Google Scholar 

  12. Hoyland-Kroghsbo, N. M., Maerkedahl, R. B. & Svenningsen, S. L. A quorum-sensing-induced bacteriophage defense mechanism. Mbio 4, e00362–00312 (2013).

    PubMed  PubMed Central  Google Scholar 

  13. Tan, D., Svenningsen, S. L. & Middelboe, M. Quorum sensing determines the choice of antiphage defense strategy in Vibrio anguillarum. Mbio 6, e00627 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. Lenski, R. E. Coevolution of bacteria and phage: are there endless cycles of bacterial defenses and phage counterdefenses? J. Theor. Biol. 108, 319–325 (1984).

    CAS  PubMed  Google Scholar 

  15. Bohannan, B. J. M. & Lenski, R. E. Linking genetic change to community evolution: insights from studies of bacteria and bacteriophage. Ecol. Lett. 3, 362–377 (2000).

    Google Scholar 

  16. Wang, X. F. et al. Parasites and competitors suppress bacterial pathogen synergistically due to evolutionary trade-offs. Evolution 71, 733–746 (2017).

    PubMed  Google Scholar 

  17. Bohannan, B. J., Kerr, B., Jessup, C. M., Hughes, J. B. & Sandvik, G. Trade-offs and coexistence in microbial microcosms. Antonie Van Leeuwenhoek 81, 107–115 (2002).

    CAS  PubMed  Google Scholar 

  18. Peyraud, R., Cottret, L., Marmiesse, L. & Genin, S. Control of primary metabolism by a virulence regulatory network promotes robustness in a plant pathogen. Nat. Commun. 9, 418 (2018).

    PubMed  PubMed Central  Google Scholar 

  19. Perrier, A. et al. Enhanced in planta fitness through adaptive mutations in EfpR, a dual regulator of virulence and metabolic functions in the plant pathogen Ralstonia solanacearum. PLoS Pathog. 12, e1006044 (2016).

    PubMed  PubMed Central  Google Scholar 

  20. Peyraud, R., Cottret, L., Marmiesse, L., Gouzy, J. & Genin, S. A resource allocation trade-Off between virulence and proliferation drives metabolic versatility in the plant pathogen Ralstonia solanacearum. PLoS Pathog. 12, e1005939 (2016).

    PubMed  PubMed Central  Google Scholar 

  21. Hayward, A. C. Biology and epidemiology of bacterial wilt caused by Pseudomonas Solanacearum. Annu. Rev. Phytopathol. 29, 65–87 (1991).

    CAS  PubMed  Google Scholar 

  22. Jiang, G. F. et al. Bacterial wilt in china: History, current status, and future perspectives. Front. Plant Sci. 8, 1549 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. Addy, H. S., Askora, A., Kawasaki, T., Fujie, M. & Yamada, T. Loss of virulence of the phytopathogen Ralstonia solanacearum through infection by phi RSM filamentous phages. Phytopathology 102, 469–477 (2012).

    CAS  PubMed  Google Scholar 

  24. Chapelle, E., Mendes, R., Bakker, P. A. H. M. & Raaijmakers, J. M. Fungal invasion of the rhizosphere microbiome. ISME J.10, 265–268 (2016).

    CAS  PubMed  Google Scholar 

  25. Wei, Z. et al. Ralstonia solanacearum pathogen disrupts bacterial rhizosphere microbiome during an invasion. Soil Biol. Biochem. 118, 8–17 (2018).

    CAS  Google Scholar 

  26. Wright, R. C. T., Friman, V. P., Smith, M. C. M. & Brockhurst, M. A. Cross-resistance is modular in bacteria-phage interactions. PLoS Biol. 16, e2006057 (2018).

    PubMed  PubMed Central  Google Scholar 

  27. Hall, A. R., De Vos, D., Friman, V. P., Pirnay, J. P. & Buckling, A. Effects of sequential and simultaneous applications of bacteriophages on populations of Pseudomonas aeruginosa in vitro and in wax moth larvae. Appl. Environ. Microbiol. 78, 5646–5652 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Kuntal, B. K., Chandrakar, P., Sadhu, S. & Mande, S. S. ‘NetShift’: a methodology for understanding ‘driver microbes’ from healthy and disease microbiome datasets. ISME J. 13, 442–454 (2019).

    PubMed  Google Scholar 

  29. Yu, L. et al. A guard-killer phage cocktail effectively lyses the host and inhibits the development of phage-resistant strains of Escherichia coli. Appl. Microbiol. Biotechnol. 102, 971–983 (2017).

    PubMed  Google Scholar 

  30. Wei, C. et al. Developing a bacteriophage cocktail for biocontrol of potato bacterial wilt. Virologica Sinica 32, 476–484 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Yen, M. M., Cairns, L. S. & Camilli, A. A cocktail of three virulent bacteriophages prevents Vibrio cholerae infection in animal models. Nat. Commun. 8, 14187 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Betts, A., Gray, C., Zelek, M., MacLean, R. C. & King, K. C. High parasite diversity accelerates host adaptation and diversification. Science 360, 907–911 (2018).

    CAS  PubMed  Google Scholar 

  33. Betts, A., Gifford, D. R., MacLean, R. C. & King, K. C. Parasite diversity drives rapid host dynamics and evolution of resistance in a bacteria-phage system. Evolution 70, 969–978 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Hall, A. R., Scanlan, P. D., Morgan, A. D. & Buckling, A. Host-parasite coevolutionary arms races give way to fluctuating selection. Ecol. Lett. 14, 635–642 (2011).

    PubMed  Google Scholar 

  35. Gomez, P. & Buckling, A. Bacteria-phage antagonistic coevolution in soil. Science 332, 106–109 (2011).

    CAS  PubMed  Google Scholar 

  36. Yamada, T. et al. New bacteriophages that infect the phytopathogen Ralstonia solanacearum. Microbiology 153, 2630–2639 (2007).

    CAS  PubMed  Google Scholar 

  37. Taylor, V. L., Fitzpatrick, A. D., Islam, Z. & Maxwell, K. L. The diverse impacts of phage morons on bacterial fitness and virulence. Adv. Virus Res. 103, 1–31 (2019).

    PubMed  Google Scholar 

  38. Khokhani, D., Lowe-Power, T. M., Tran, T. M. & Allen, C. A single regulator mediates strategic switching between attachment/spread and growth/virulence in the plant pathogen Ralstonia solanacearum. Mbio 8, e00895–00817 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Friman, V. P., Dupont, A., Bass, D., Murrell, D. J. & Bell, T. Relative importance of evolutionary dynamics depends on the composition of microbial predator-prey community. ISME J. 10, 1352–1362 (2016).

    PubMed  Google Scholar 

  40. Hiltunen, T. & Becks, L. Consumer co-evolution as an important component of the eco-evolutionary feedback. Nat. Commun. 5, 5226 (2014).

    CAS  PubMed  Google Scholar 

  41. Brunner, F. S., Anaya-Rojas, J. M., Matthews, B. & Eizaguirre, C. Experimental evidence that parasites drive eco-evolutionary feedbacks. Proc. Natl Acad. Sci. USA 114, 3678–3683 (2017).

    CAS  PubMed  Google Scholar 

  42. Gu, Y. et al. Pathogen invasion indirectly changes the composition of soil microbiome via shifts in root exudation profile. Biol. Fertil. Soils 52, 997–1005 (2016).

    CAS  Google Scholar 

  43. Li, M. et al. Facilitation promotes invasions in plant-associated microbial communities. Ecol. Lett. 22, 149–158 (2018).

    PubMed  Google Scholar 

  44. Balogh, B., Jones, J. B., Iriarte, F. B. & Momol, M. T. Phage therapy for plant disease control. Current Pharm. Biotechnol. 11, 48–57 (2010).

    CAS  Google Scholar 

  45. Wei, Z. et al. Efficacy of Bacillus-fortified organic fertiliser in controlling bacterial wilt of tomato in the field. Appl. Soil Ecol. 48, 152–159 (2011).

    Google Scholar 

  46. Schonfeld, J., Heuer, H., van Elsas, J. D. & Smalla, K. Specific and sensitive detection of Ralstonia solanacearum in soil on the basis of PCR amplification of fliC fragments. Appl. Environ. Microbiol. 69, 7248–7256 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Elphinstone, J., Hennessy, J., Wilson, J. & Stead, D. Sensitivity of different methods for the detection of Ralstonia solanacearum in potato tuber extracts. EPPO Bull. 26, 663–678 (1996).

    Google Scholar 

  48. Ji, P. & Wilson, M. Assessment of the importance of similarity in carbon source utilization profiles between the biological control agent and the pathogen in biological control of bacterial speck of tomato. Appl. Environ. Microbiol. 68, 4383–4389 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Cardenas, E. et al. Significant association between sulfate-reducing bacteria and uranium-reducing microbial communities as revealed by a combined massively parallel sequencing-indicator species approach. Appl. Environ. Microbiol. 76, 6778–6786 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. Macdonald, C. A. et al. Long-term impacts of zinc and copper enriched sewage sludge additions on bacterial, archaeal and fungal communities in arable and grassland soils. Soil Biol. Biochem. 43, 932–941 (2011).

    CAS  Google Scholar 

  52. Margesin, R., Płaza, G. A. & Kasenbacher, S. Characterization of bacterial communities at heavy-metal-contaminated sites. Chemosphere 82, 1583–1588 (2011).

    CAS  PubMed  Google Scholar 

  53. Fierer, N., Jackson, J. A., Vilgalys, R. & Jackson, R. B. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl. Environ. Microbiol. 71, 4117–4120 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Eden, P. A., Schmidt, T. M., Blakemore, R. P. & Pace, N. R. Phylogenetic analysis of Aquaspirillum magnetotacticum using polymerase chain reaction-amplified 16S rRNA-specific DNA. Int. J. Syst. Evol. Microbiol. 41, 324–325 (1991).

    CAS  Google Scholar 

  55. Friman, V. P. & Buckling, A. Phages can constrain protist predation-driven attenuation of Pseudomonas aeruginosa virulence in multienemy communities. ISME J. 8, 1820–1830 (2014).

    PubMed  PubMed Central  Google Scholar 

  56. Yang, T. et al. Resource availability modulates biodiversity‐invasion relationships by altering competitive interactions. Environ. Microbiol. 19, 2984–2991 (2017).

    PubMed  Google Scholar 

  57. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Google Scholar 

  58. Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. in Third International AAAI Conference on Weblogs and Social Media (2009).

  59. R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2015).

  60. Hair, J. F., Ringle, C. M. & Sarstedt, M. Partial least squares structural equation modeling: rigorous applications, better results and higher acceptance. Long Range Plann. 46, 1–12 (2013).

    Google Scholar 

Download references

Acknowledgements

This research was financially supported by the National Natural Science Foundation of China (41922053, 41671248; Z.W.), the National Key Basic Research Program of China (2015CB150503; Q.S.), the National Key Research and Development Program of China (2018YFD1000800; Z.W.) and the 111 project (B12009; Q.S.). A.J. is supported by the Netherlands Organization for Scientific Research (NWO) project ALW.870.15.050. V.P.F. is supported by the Wellcome Trust (ref. 105624) through the Center for Chronic Diseases and Disorders (C2D2) and Royal Society Research Grants (RSG\R1\180213 and CHL\R1\180031) at the University of York.

Author information

Author notes

  1. These authors contributed equally: Xiaofang Wang, Zhong Wei.

Authors and Affiliations

  1. Jiangsu Provincial Key Laboratory for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, National Engineering Research Center for Organic-Based Fertilizers, Nanjing Agricultural University, Weigang, Nanjing, China

    Xiaofang Wang, Zhong Wei, Keming Yang, Jianing Wang, Alexandre Jousset, Yangchun Xu, Qirong Shen & Ville-Petri Friman

  2. Institute of Environmental Biology, Ecology and Biodiversity, Utrecht University, Utrecht, the Netherlands

    Alexandre Jousset

  3. Department of Biology, University of York, York, UK

    Ville-Petri Friman

Authors
  1. Xiaofang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhong Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Keming Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianing Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexandre Jousset
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yangchun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Qirong Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ville-Petri Friman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

V.P.F., X.F.W. and Z.W. developed the ideas and designed the experimental plans. X.F.W., J.N.W. and Z.W. performed the experiments. X.F.W., K.E.Y., A.J., Z.W. and V.P.F. analyzed the data. All the authors wrote the manuscript.

Corresponding authors

Correspondence to Zhong Wei, Qirong Shen or Ville-Petri Friman.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrated supplementary information

Supplementary Figure 1 A neighbor-joining phylogenetic tree showing the relatedness of the four phage types used in the experiments with similar phages publicly available in NCBI.

A neighbor-joining phylogenetic tree based on phage head-tail connector protein similarity between the phages used in this experiment (NJ-P3, NB-P21, NC-P34, NN-P42; red colour) and other similar phages that are publicly available at NCBI (black colour). All four phages belonged to order Caudovirales and the family Podoviridae, were highly similar with each other and a Ralstonia phage DU_RP_II. The scale bar represents 0.2 amino acid substitutions per site and values next to the nodes show bootstrap values based on 500 samples.

Supplementary Figure 2 Annotated genome maps for the four phage types used in the experiments.

In all circular genome maps, the outermost black circle represents the full length of the genome, the second outermost multicolored circle represents the forward and reverse reading frames, the third outermost green circle represents GC content and the innermost purple circle represents GC skew.

Supplementary Figure 3 Genetic distance between the four phage types used in our experiment and similar phages publicly available in NCBI with reported information on lytic and lysogenic activity.

Phylogenetic tree showing the relatedness of lytic and lysogenic phages based on genomic similarity. Each genome was first compared to another and to themselves to account and normalize for differences in phage genomes sizes and a neighbor joining tree was then constructed based on the complete distance matrix based on 500 bootstrap sample values as described previously (Mizuno, C.M., Rodriguez-Valera, F., Kimes, N.E. & Ghai, R. Expanding the Marine Virosphere Using Metagenomics. PLoS Genetics. 9 (2013)). Branch colours show different phage families and known lytic and lysogenic phages are shown in black and red, respectively. The phage types used in the experiments (NJ-P3, NB-P21, NC-P34, NN-P42) were most closely related to a lytic phage DU_RP_II.

Supplementary Figure 4 The mean infectivity of phage types to Ralstonia solanacearum clones isolated from China.

The average infectivity of four phage types against 96 R. solanacearum bacterial isolates originating from the tomato rhizosphere of four independent tomato fields in China (24 independent R. solanacearum isolates from each field: Nanjing, Nanchang, Ningbo and Nanning). Bars represent standard deviation between four different fields.

Supplementary Figure 5 Correlation between phage and pathogen densities in tomato rhizosphere.

The R2 and P value refer to the most parsimonious model fitted based on linear regression analysis (n=3 for ancestral and control treatment, n=12 for one- and three-phage treatments and n=18 for two-phage treatment; all n are biologically independent samples).

Supplementary Figure 6 Relationships between pathogen growth rate, phage combination treatment and phage resistance at the end of the greenhouse experiment.

Panel A shows the growth rate for evolved pathogen clones isolated from different phage combination treatments at the end of the greenhouse experiment. Lowercase letters above boxplots denote for significant differences between phage combination treatments (multiple comparisons were conducted by Tukey test, FDR adjusted P<0.05) and R2 and red dashed line represents the maximum growth rate of ancestral pathogen strain. Box-plots show interquartile range (25 to 75% of the data), the median as lines and outliers as dots. Panel B shows the correlation between pathogen maximum growth rate and mean phage resistance to all ancestral phages at the end of the greenhouse experiment. The R2 and P values refer to the most parsimonious model, black line shows the mean regression based on all the data points and the small inset in top right corner shows the change in regression coefficient between phage combination treatments (n=3 for ancestral and control treatment, n=12 for one- and three-phage treatments and n=18 for two-phage treatment; all n are biologically independent samples). N0 to N3 denote for no-phage, single-phage, two-phage and three-phage treatments, respectively.

Supplementary Figure 7 Relationship between pathogen competitiveness and phage combination treatment at the end of the greenhouse experiment.

Pathogen competitive ability was measured as the relative growth against the phage-susceptible ancestral pathogen in terms of reduction in the ancestral pathogen abundance. All box-plots show interquartile range (25 to 75% of the data) and median as line. Lowercase letters above boxplots denote for significant differences between phage combination treatments (multiple comparisons were conducted by Tukey test, FDR adjusted P<0.05, n=3 for ancestral and control treatment, n=12 for one- and three-phage treatments and n=18 for two-phage treatment; all n are biologically independent samples).

Supplementary Figure 8 Effects of phage combination treatments on microbial OTU richness at the end of the greenhouse experiment.

Microbial OTU richness correlated positively with the number of phages present in the phage combination treatment. The R2 and P value refer to the most parsimonious model fitted based on linear regression analysis and red dashed line shows the OTU richness of no-phage control treatment (n=3 for control treatment, n=12 for one- and three-phage treatments and n=18 for two-phage treatment; all n are biologically independent samples).

Supplementary Figure 9 Effects of phage combination treatments on relative abundance of different bacterial phyla at the end of the greenhouse experiment.

Correlations between relative abundances of different bacterial phyla and the number of phages present in the phage combinations at the end of the greenhouse experiment. Panels A-H show the correlation for Ralstonia, Proteobacteria, Bacteroidetes, Chloroflexi, Acidobacteria, Planctomycetes, Firmicutes and Actinobacteria, respectively. Red dashed lines in all panels show the no-phage control treatment. The R2 and P value refer to the most parsimonious model fitted based on linear regression analysis (n=3 for control treatment, n=12 for one- and three-phage treatments and n=18 for two-phage treatment; all n are biologically independent samples).

Supplementary Figure 10 Effects of phages on bacterial co-occurrence networks at the end of the greenhouse experiment.

The bacterial co-occurrence networks associated with single-phage and three-phage combinations treatments. Each node represents a bacterial OTU, and each edge represents a Spearman correlation with negative (blue) and positive correlations (red). The node colours represent taxa classification at the phylum level (n=12 biologically independent samples for one- and three-phage treatments).

Supplementary Figure 11 The resistance of pathogenic and non-pathogenic bacterial isolates to phage types used in the experiments.

The average infectivity of four phage types against 96 R. solanacearum bacterial isolates originating from the tomato rhizosphere of four independent tomato fields in China (24 independent R. solanacearum isolates from each field: Nanjing, Nanchang, Ningbo and Nanning) and 400 non-pathogenic bacterial isolates from the soil used in the greenhouse experiment. Bars represent standard deviation between four different field for pathogen isolates.

Supplementary Figure 12 Effects of phage on the abundance of bacterial genera.

Panel A shows significant positive (red) and negative (blue) correlation coefficients between bacterial genera abundances and number of phages present in the phage combinations. Correlations were determined using Spearman method and all correlations were FDR adjusted P<0.05. Panel B shows to which phyla positively (top pie chart) and negatively (bottom pie chart) correlated bacteria belonged to.

Supplementary Figure 13 Simplified structural equation models explaining pathogen density, microbiome diversity and disease incidence.

Structural equation model path diagrams showing the phage combination-mediated effects on the pathogen density (a), microbiome diversity (b) and the disease incidence (c). Red, blue and grey arrows denote for positive, negative and non-significant pathways, respectively, and the numbers beside arrows denote for the magnitude of these effects. Numbers within the circles show how much of the variance of each variable was explained by the other variables and χ2 and NFI values denote for the fit of the models.

Supplementary Figure 14 Phylogenetic relationship between Ralstonia solanacearum clones isolated from four different tomato fields.

Phylogenetic neighbour-joining tree based on partial endoglucanase (egl) gene sequences of Chinese Ralstonia solanacearum strains and most closely related reference strains (red circles), that were used to test the phage type infectivity ranges (Supplementary Fig. 4). The tips of the tree show 96 Chinese R. solanacearum isolates with sample location abbreviation and sample ID. Chinese isolates are assigned to specific sequevar types based on their closest relative reference strains and sequevar type numbers are shown in boxes over each clade.

Supplementary information

Supplementary Information

Supplementary Figs. 1–14 and Tables 1–8.

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Wei, Z., Yang, K. et al. Phage combination therapies for bacterial wilt disease in tomato. Nat Biotechnol 37, 1513–1520 (2019). https://doi.org/10.1038/s41587-019-0328-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-019-0328-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research