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Wave succession in the pandemic clone of Vibrio parahaemolyticus driven by gene loss

Nature Ecology & Evolution (2025)Cite this article

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Abstract

While spontaneous mutation and gene acquisition are well-established drivers of pathogen adaptation, the role of gene loss remains underexplored. Here we investigated the emergence and diversification of the pandemic clone of Vibrio parahaemolyticus through large-scale phylogenomic analysis of 8,684 global isolates. The pandemic clone rapidly acquired multiple marker genes and genomic islands, subsequently diverging into successive sublineages mediating independent waves of cross-country transmission, as also observed in Vibrio cholerae. Wave succession in the last two decades was driven by loss of putrescine utilization (Puu) genes, conferring phenotypic advantages for environmental adaptation (enhanced biofilm formation) and human transmission (increased cell adhesion and intestinal colonization and reduced virulence), consistent with the virulence trade-off hypothesis. We identified Puu-gene loss in several bacterial genera, with effects on biofilm and adhesion replicated in V. cholerae and Escherichia coli, suggesting convergent evolution and universal phenotypic effects. Our results highlight the indispensable role of gene loss in bacterial pathogen adaptation.

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Fig. 1: From pre-PC to PC.
Fig. 2: Fine-scale population structure and spatiotemporal and population dynamics of the PC.
Fig. 3: Phenotypic effects of gene loss in the Puu-pathway.
Fig. 4: Phenotypes and clinical symptoms of waves-1–3 and wave-4 strains.
Fig. 5: Puu-genes in 36 bacterial species.

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Data availability

The sequencing data have been deposited in the NCBI SRA or GenBank database under accession numbers PRJNA1117214 and PRJNA1062747. Background information of sequenced isolate is listed in Supplementary Tables 1 and 2. Source data are provided with this paper.

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Acknowledgements

This work was supported by National Key Research and Development Program of China (no. 2022YFC2304700 to D.F. and no. 2022YFD2101500 to H. Wang), National Natural Science Foundation of China (no. 32270003 and no. 32000008 to C.Y., no. 32170640 and no. 32211550014 to D.F., no. 32250610209 to S.L.S. and no. 82030099 to H. Wang), Youth Innovation Promotion Association, Chinese Academy of Sciences (no. 2022278 to C.Y.), Ministerio de Ciencia e Innovación (Spain) grant PID2021-127107NB-I00 (to J.M.-U.), Generalitat de Catalunya Grant 2021 SGR 00526 (to J.M.-U.), Shanghai Rising-Star Program (no. 23QA1410500 to C.Y.), Shanghai Public Health System Construction Three-Year Action Plan (no. GWVI-11.1-43 to H. Wang), Wuxi Science and Technology Development Fund’s ‘Light of Taihu Lake’ Science and Technology Research Program (Basic Research) (no. K20231033 to C.N. and no. K20231045 to S.G.) and Innovative research team of high-level local universities in Shanghai. We are grateful to the laboratories of Q. Wang, K. Orth and J. He for providing strains and plasmids. We thank L. Pan for valuable comments.

Author information

Author notes

  1. These authors contributed equally: Chao Yang, Hongling Qiu, Sarah L. Svensson, Chengpei Ni.

Authors and Affiliations

  1. The Center for Microbes, Development and Health, CAS Key Laboratory of Molecular Virology and Immunology, Shanghai Institute of Immunity and Infection, Chinese Academy of Sciences, Shanghai, China

    Chao Yang, Sarah L. Svensson, Zhizhou Jia, Huiqi Wen, Li Xie, Wenxuan Xu, Yujiao Qin, Shuzhu Lin, Jiancheng Wang, Yanjie Chao & Daniel Falush

  2. Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

    Chao Yang, Sarah L. Svensson, Zhizhou Jia, Huiqi Wen, Li Xie, Wenxuan Xu, Yujiao Qin, Shuzhu Lin, Jiancheng Wang, Yanjie Chao & Daniel Falush

  3. State Key Laboratory of Systems Medicine for Cancer, Center for Single–Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Hongling Qiu & Hui Wang

  4. The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China

    Chengpei Ni & Song Gao

  5. Department of Clinical Laboratory, Nantong Third People’s Hospital, Affiliated Nantong Hospital 3 of Nantong University, Nantong, China

    Yiquan Zhang

  6. Shenzhen Center for Disease Control and Prevention, Shenzhen, China

    Yinghui Li, Min Jiang, Xiaolu Shi & Qinghua Hu

  7. Key Laboratory of Alkene-Carbon Fibers-Based Technology & Application for Detection of Major Infectious Diseases, Cancer Institute, Suzhou Medical College, Soochow University, Suzhou, China

    Zhemin Zhou

  8. National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, Beijing, China

    Zhemin Zhou

  9. State Key Laboratory of Pathogen and Biosecurity, Academy of Military Medical Sciences, Beijing, China

    Ruifu Yang & Yujun Cui

  10. Department of Genetics and Microbiology, Facultat de Biociències, Universitat Autònoma de Barcelona, Barcelona, Spain

    Jaime Martinez-Urtaza

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  1. Chao Yang
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  2. Hongling Qiu
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Contributions

C.Y., J.M.-U., H. Wang and D.F. designed, initiated and coordinated the study. J.M.-U., L.X., Y.L., M.J., X.S. and Q.H. contributed to data collection. C.Y., Z.J., J.W. and W.X. performed bioinformatics analysis. H.Q., S.L.S., C.N., S.G., Z.J., H. Wen, Y.Q. and S.L. performed experiments. All authors contributed to interpretation of the data. C.Y. wrote the first draft of the paper and H.Q., S.L.S., Y.Z., Z.Z., Y. Chao, J.M.-U., H. Wang, R.Y., Y. Cui and D.F. reviewed and revised the paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Chao Yang, Jaime Martinez-Urtaza, Hui Wang or Daniel Falush.

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The authors declare no competing interests.

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Nature Ecology & Evolution thanks Yann Boucher and the other, anonymous, reviewer(s) 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 Phylogenetic tree of pre-pandemic clone (pre-PC) strains and sequence coverage of virulence-associated loci and PC marker genes and genomic islands.

Heatmap colors to the right indicate BLASTN-based sequence coverage of each loci, gene or island.

Extended Data Fig. 2 Phylogenetic distribution of virulence-associated loci and pandemic clone (PC) marker genes and genomic islands among PC, pre-PC, and non-PC strains.

Virulence-associated loci: tdh, T3SS1, T6SS1, and T6SS2. PC marker genes/genomic islands: toxRS/new, orf8, f237, and VPaI-1 to VPaI-7. Bar colors on the right indicate strain classification (PC, pre-PC, or non-PC).

Extended Data Fig. 3 Tempo-geographical distribution of pandemic clone (PC) strains and putative hypermutators.

a) Temporal distribution of PC strains. b) Geographical distribution of PC strains. Colors in panels a and b indicate strain classifications as shown in the legend at the top. c) Root-to-tip distances (left) and phylogenetic branch lengths (right) of putative hypermutator strains. Putative hypermutators are highlighted in red.

Extended Data Fig. 4 Distribution of homologous recombination events and homoplastic SNP in pandemic clone (PC) strains.

Phylogenetic trees based on non-recombined and non-homoplastic SNPs are shown on the left. In panel a, the top box summarizes the number and classification of recombination events (see legend at the top), while the bottom box displays their distribution across strains/nodes, represented by red bars. In panel b, bar colors indicate the status of SNPs or gaps, as defined in the legend at the bottom.

Extended Data Fig. 5 Maximum likelihood (ML) phylogeny and fastBAPS hierarchical clustering of representative pandemic clone (PC) strains.

The branch colors of the ML tree on the left indicate the four waves defined in this study. The blue bars on the right display the fastBAPS hierarchical clustering results of PC strains.

Extended Data Fig. 6 Phylogenetic distribution of pandemic clone (PC) marker genes, virulence-associated loci, antimicrobial resistance genes, and other accessory genes.

Branch colors in the ML tree (panel a) indicate the four waves defined in this study. Blue and white bars on the right denote the presence or absence of each locus/gene/island. In panel b, the bar colors on the right represent strain classification (four waves). Wave-specific variations are highlighted.

Extended Data Fig. 7 Global transmission events of the pandemic clone.

a) Summary of global transmission events. Dashed lines represent transient transmission not leading to local colonization. Solid lines represent transmissions resulting in local colonization (TC). The colors indicate different waves. Pie charts indicate the composition of waves of countries. The size of circles scale with the number of representative strains. b) Inferred transmission events (T1-T67) of different waves. The branch colors indicate the geographical regions of strains. Specific transmission events are labeled on the dated phylogenetic tree. Map in a adapted from OpenStreetMap under a Creative Commons license CC BY-SA 2.0.

Extended Data Fig. 8 Phylogenetic trees based on core-genome SNPs and distribution of Puu-genes in 10 bacterial species.

The bar colors on the right side of the tree indicate the presence, absence, or pseudogenization of Puu-genes, as shown in the legend. Double slashes indicate artificially shortened branches.

Extended Data Table 1 Wave-4-specific genomic variations
Full size table
Extended Data Table 2 Homolog genes of Puu-genes in 36 bacterial species
Full size table

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Statistical source data.

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Yang, C., Qiu, H., Svensson, S.L. et al. Wave succession in the pandemic clone of Vibrio parahaemolyticus driven by gene loss. Nat Ecol Evol (2025). https://doi.org/10.1038/s41559-025-02827-z

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