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Phenazines contribute to microbiome dynamics by targeting topoisomerase IV

Nature Microbiology volume 10, pages 2396–2411 (2025)Cite this article

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Abstract

Phenazines are highly prevalent, natural bioactive substances secreted by microbes. However, their mode of action and potential involvement in shaping microbiomes remain elusive. Here we performed a comprehensive analysis of over 1.35 million bacterial genomes to identify phenazine-producing bacteria distributed across 193 species in 34 families. Analysis of rhizosphere microbiome and public rhizosphere metagenomic datasets revealed that phenazines could shape the microbial community by inhibiting Gram-positive bacteria, which was verified by pairwise interaction assays using Phenazine-1-carboxamide (PCN)-producing Pseudomonas chlororaphis. PCN induced DNA damage in Bacillus subtilis, a model Gram-positive target, where it directly bound to the bacterial topoisomerase IV, inhibiting its decatenation activity and leading to cell death. A two-species consortium of phenazine-producing Pseudomonas and resistant B. subtilis exhibited superior synergistic activity in preventing Fusarium crown rot in wheat plants. This work advances our understanding of a prevalent microbial interaction and its potential for biocontrol.

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Fig. 1: Phylogenetic profiling and geographic distribution of phenazine-producing (phz+) bacteria.
Fig. 2: Phenazines exhibit bactericidal activity and impact the population structure of Gram-positive bacteria within microbiomes.
Fig. 3: PCN induces DNA damage in Gram-positive bacteria, leading to cell death.
Fig. 4: DNA topoisomerase IV mediates resistance of B. subtilis to PCN.
Fig. 5: Phenazine compounds target bacterial topoisomerase IV.
Fig. 6: Compatible bacterial two-species consortium exhibits synergistic effects in controlling Fusarium crown rot (FCR) of wheat.

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Data availability

Amplicon sequencing data, genome sequences of PCN-resistant strains, and transcriptomic data of PCN-treated B. subtilis 3610 have been deposited in the Genome Sequence Archive of the Beijing Institute of Genomics (BIG) Data Center with accession numbers CRA028325, CRA028335 and CRA019862. The information on the phz+ phenazine biosynthesis gene cluster is available in the Science Data Bank at https://www.scidb.cn/s/EfmaIb (ref. 71). All data supporting the findings of this study are available in this article and its Supplementary Information. Source data are provided with this paper.

Code availability

The scripts employed in the ‘Identifier of Phenazine-Producing Bacteria (IPPB)’ pipeline are available in GitHub at https://github.com/xzliu919/Identifier-of-Phenazine-Producing-Bacteria-IPPB- (ref. 72).

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Acknowledgements

We thank R. Losick (Harvard University, USA) for his suggestions during the revision of the paper; K. Lewis (Northeastern University, USA) for providing the E. coli MG1655, WO153 and ΔtolC mutant strains; and J. Feng (Chinese Academy of Sciences, China) for providing the S. pneumoniae R6WT and R6M-3 strains. This research was supported by the Natural Science Foundation of Zhejiang Province (LZ23C140004) to Y. Chen, National Natural Science Foundation of China U21A20219 to Z.M., National Key R&D Program of China 2022YFD1400100 to Y. Chen, National Natural Science Foundation of China U24A20808 to H.L., and ‘Pioneer’ and ‘Leading Goose’ R&D Program of Zhejiang 2023C02030 to Y. Chen.

Author information

Authors and Affiliations

  1. State Key Laboratory of Rice Biology and Breeding, Zhejiang Key Laboratory of Biology and Ecological Regulation of Crop Pathogens and Insects, Institute of Biotechnology, Zhejiang University, Hangzhou, China

    Yaqi Zhou, Hongkai Wang, Jiaxin Sun, Chao Liu, Zhonghua Ma & Yun Chen

  2. Institute of Environmental Biotechnology, Graz University of Technology, Graz, Austria

    Wisnu Adi Wicaksono & Gabriele Berg

  3. Department of Biology, Northeastern University, Boston, MA, USA

    Yinghao He, Yuxuan Qin & Yunrong Chai

  4. Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany

    Gabriele Berg

  5. Institute for Biochemistry and Biology, University of Potsdam, Potsdam, Germany

    Gabriele Berg

  6. Marine Drug Integrated Innovation Center, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Lei Li & Houwen Lin

  7. Peking-Tsinghua Center for Life Sciences, College of Life Sciences, Peking University, Beijing, China

    Yang Bai

  8. School of Biological Sciences, Faculty of Environmental and Life Sciences, University of Southampton, Southampton, UK

    Tomislav Cernava

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Contributions

Y. Chen initiated, coordinated and supervised the project. Y.Z. performed and analysed most of the experiments. Y.Z., H.W. and J.S. performed MIC tests. C.L. collected and analysed the phz+ strains data. T.C. and W.A.W. collected and analysed the metagenome and metatranscriptome datasets. Y.H. performed ROS staining experiments. Y.Q. constructed the KD-ParE/C and KD-GyrA/B strains. L.L. and H.L. performed structural determination of phenazine-1-carboxylic acid. Y. Chen and Y.Z. wrote the paper. Y. Chen, Z.M., T.C., Y.B., Y. Chai and G.B. revised the paper.

Corresponding author

Correspondence to Yun Chen.

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Nature Microbiology thanks Wulf Blankenfeldt, Axel Brakhage and Ákos T Kovács for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 IPPB pipeline for identifying phz+ bacteria.

a, Schematic representation of the Identifier of Phenazine-Producing Bacteria (IPPB) pipeline. The pipeline automates the search for homologous proteins of phenazine biosynthetic core proteins (PhzA/B, PhzC, PhzD, PhzE, PhzF, PhzG) within a strain’s genome. Protein identity thresholds are applied as indicated, and strains are classified as phz+ if all core proteins are identified and the corresponding genes are located within 20 kb of each other. Strains meeting these criteria are classified as phz+ bacterium, and included in the phylogenetic tree. Abbreviations: NGDC, National Geoscience Data Centre; IMG/M, Integrated Microbial Genomes & Microbiomes system. b, Schematic representation of 23 reference phenazine biosynthetic gene clusters. Core biosynthetic genes (phzA/B, phzC, phzD, phzE, phzF, phzG) are shown in color, while adjacent, non-core genes are depicted in grey.

Extended Data Fig. 2 Phylogenetic profiling and structural determination of phenazine-1-carboxylic acid produced in Spongiactinospora rosea LHW63015.

a, Maximum-likelihood phylogenetic tree of the phz+ bacterial genera based full-length 16S rRNA gene sequences. Phylum-level classifications are indicated. Bar plots on the right show the number of species within each phz+ genus and the total number of phz+ strains. b, 1H NMR (nuclear magnetic resonance) spectrum of phenazine-1-carboxylic acid (PCA) produced in S. rosea LHW63015. c, 13C NMR spectrum of PCA. d, DEPT135 NMR spectrum of PCA. e, X-ray ORTEP drawing for PCA.

Extended Data Fig. 3 phz+ bacteria drive shifts in soil microbiomes.

a, Number of ASVs in samples treated with phz+ ZJU60 and its phenazine mutant ΔphzA-H. b, Shannon diversity index in samples treated with phz+ ZJU60 and ΔphzA-H. c, Community composition in samples treated with phz+ ZJU60 and ΔphzA-H. d, Dynamics of wheat rhizosphere microbiota following soil irrigation with ZJU60 and ΔphzA–H. Soil samples were collected on days 3 and 7 after irrigation with ZJU60 and ΔphzA–H in wheat field microcosms. 16S rRNA gene fragment amplicon sequencing revealed distinct temporal changes in the composition and structure of the rhizosphere microbiomes. Data are presented as means ± s.d. of n = 5 biological replicates. For graphs shown in a, b and d, letters that are different from one another indicate that their means are statistically different using a two-sided ANOVA and Tukey’s HSD (P < 0.05).

Source data

Extended Data Fig. 4 Correlation analysis between the relative abundances of phz+ Pseudomonas species and other bacterial taxa within rhizosphere microbiomes.

a, Correlation analysis between the relative abundances (RA) of phz+ bacteria and Pseudomonas in rhizosphere microbiomes. b-d, Pairwise Spearman correlation between the abundance of the three most prevalent bacterial taxa at each taxonomic level and the abundance of phz+ Pseudomonas. The relative abundances of the analyzed taxa were obtained from shotgun-sequenced metagenome datasets and subsequently log-transformed. The Spearman correlation coefficient (R) and corresponding P value was calculated using the ggscatter function from the ggpubr package in R. The grey shaded area indicates the 95% confidence interval of the regression line, shown in blue.

Extended Data Fig. 5 Correlation analysis between the phzA/BDEFG gene transcripts and the abundances of bacteria assigned to the class Bacilli and to the genus Pseudomonas.

a, Correlation analysis between phzA/BDEFG gene transcripts (log-transformed counts per million, CPM) and the relative abundances (RA) of bacteria assigned to the class Bacilli. b, Correlation analysis between the phzA/BDEFG gene transcripts and the relative abundance of bacteria assigned to the genus Pseudomonas. Metatranscriptomic datasets were used to determine both gene expression and the relative abundances of the target taxa. The Spearman correlation coefficient (R) and corresponding P value (P < 0.05) were calculated using the ggscatter function from the ggpubr package in R. The grey shaded area indicates the 95% confidence interval of the regression line, shown in blue.

Extended Data Fig. 6 Antimicrobial activity of Pseudomonas chlororaphis ZJU60 against representative strains isolated from the wheat microbiome.

The antimicrobial activity of ZJU60 was assessed on LBGM agar medium against 70 bacterial species. One representative strain from each species was selected for testing. ZJU60 was inoculated on the top of a lawn of the individual test strains, and inhibition zones were indicated by dashed circles after 72 h of incubation. Scale bar = 5 mm. Detailed information on the tested strains can be found in Supplementary Table 1.

Extended Data Fig. 7 PCN is the primary antibacterial compound produced by P. chlororaphis ZJU60.

a, b, Time-resolved interactions between P. chlororaphis ZJU60 or ΔphzA-H and B. subtilis 3610-GFP over 24, 48, and 72 h in LBGM agar medium (left panel). The log(CFU·mL⁻¹·cm⁻²) of ZJU60 or ΔphzA-H and 3610 in the interaction areas at different time points (24, 48, 72 h) is shown in the right panel. Cells from the three sections of the interaction zone were collected, sonicated, fully re-suspended, diluted, and plated to determine the CFU of each species. Scale bar = 8 mm. c, Time-resolved interactions between ΔphzA-H (supplemented with 50 µg/mL PCN) and B. subtilis 3610 over 24, 48, and 72 h in LBGM medium. d, Growth curves of B. subtilis 3610 with ZJU60 supernatant, ΔphzA-H supernatant, and ΔphzA-H supernatant supplemented with 50 µg/mL PCN. Data for graphs in a, b and d are presented as means ± s.d. For a and b, n = 3 biological replicates. For d, n = 6 biological replicates.

Source data

Extended Data Fig. 8 Morphological changes in four PCN-sensitive bacterial strains following PCN treatment.

Differential interference contrast (DIC) images (left) showing cell morphology of four PCN-sensitive bacterial strains in the absence and presence of PCN. Scale bar = 5 μm. Quantification of cell length from images (right). The graphs represent results from one representative experiment. Statistical differences were analyzed by a two-sided Student’s t-test. Data are presented as means ± s.d. of n = 50 individual cells. Experiments were independently repeated three times with similar results.

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Extended Data Fig. 9 ROS is not the primary contributor to DNA damage induced by PCN.

a, Fluorescence staining of reactive oxygen species (ROS) signals following PCN treatment of B. subtilis 3610. Scale bar = 10 μm. b, Quantification of fluorescence intensity from panel a. Data are presented as mean ± s.d. of n = 78 individual cells. Differences between groups were analyzed using a two-sided Student’s t-test. c, Analysis of the expression of ROS response-related mutant genes following PCN treatment. d, Growth curves for B. subtilis 3610 and ROS-scavenging mutants, with or without treatment with 15 μg/mL PCN. Data are presented as means ± s.d. of n = 3 biological replicates.

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Zhou, Y., Wang, H., Sun, J. et al. Phenazines contribute to microbiome dynamics by targeting topoisomerase IV. Nat Microbiol 10, 2396–2411 (2025). https://doi.org/10.1038/s41564-025-02118-0

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