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:

Outbreak dynamics of foodborne pathogen Vibrio parahaemolyticus over a seventeen year period implies hidden reservoirs

Nature Microbiology volume 7, pages 1221–1229 (2022)Cite this article

Subjects

Abstract

Controlling foodborne diseases requires robust outbreak detection and a comprehensive understanding of outbreak dynamics. Here, by integrating large-scale phylogenomic analysis of 3,642 isolates and epidemiological data, we performed ‘data-driven’ outbreak detection and described the long-term outbreak dynamics of the leading seafood-associated pathogen, Vibrio parahaemolyticus, in Shenzhen, China, over a 17-year period. Contradictory to the widely accepted notion that sporadic patients and independent point-source outbreaks dominated foodborne infections, we found that 71% of isolates from patients grouped into within-1-month clusters that differed by ≤6 single nucleotide polymorphisms, indicating putative outbreaks. Furthermore, we showed that despite the long time spans between clusters, 70% of them were genomically closely related and were inferred to arise from a small number of common sources, which provides evidence that hidden persistent reservoirs generated most of the outbreaks rather than independent point-sources. Phylogeographical analysis further revealed the geographical heterogeneity of outbreaks and identified a coastal district as the potential hotspot of outbreaks and as the hub and major source of cross-district spread events. Our findings provide a comprehensive picture of the long-term spatiotemporal dynamics of foodborne outbreaks and present a different perspective on the major source of foodborne infections, which will inform the design of future disease control strategies.

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: Study design.
Fig. 2: Population structure and spatiotemporal distribution of VP lineages in Shenzhen, China, 2002–2018.
Fig. 3: P-clusters and Ob-clusters.
Fig. 4: Spatiotemporal dynamics of Ob-clusters.

Similar content being viewed by others

Data availability

The sequencing data have been deposited in the NCBI Sequence Read Archive under accession number PRJNA745505. Background information of sequenced isolate is listed in Supplementary Table 1. Source data are provided with this paper.

References

  1. WHO estimates of the global burden of foodborne diseases: foodborne disease burden epidemiology reference group 2007–2015 (WHO, 2015).

  2. Deng, X., den Bakker, H. C. & Hendriksen, R. S. Genomic epidemiology: whole-genome-sequencing–powered surveillance and outbreak investigation of foodborne bacterial pathogens. Annu. Rev. Food Sci. Technol. 7, 353–374 (2016).

    Article  Google Scholar 

  3. Ronholm, J., Nasheri, N., Petronella, N. & Pagotto, F. Navigating microbiological food safety in the era of whole-genome sequencing. Clin. Microbiol. Rev. 29, 837–857 (2016).

    Article  CAS  Google Scholar 

  4. Waldram, A., Dolan, G., Ashton, P. M., Jenkins, C. & Dallman, T. J. Epidemiological analysis of Salmonella clusters identified by whole genome sequencing, England and Wales 2014. Food Microbiol. 71, 39–45 (2018).

    Article  Google Scholar 

  5. Hassoun-Kheir, N. et al. Concordance between epidemiological evaluation of probability of transmission and whole genome sequence relatedness among hospitalized patients acquiring Klebsiella pneumoniae carbapenemase-producing Klebsiella pneumoniae. Clin. Microbiol. Infect. 27, 468.e1–468.e7 (2021).

  6. Jackson, B. R. et al. Implementation of nationwide real-time whole-genome sequencing to enhance listeriosis outbreak detection and investigation. Clin. Infect. Dis. 63, 380–386 (2016).

    Article  Google Scholar 

  7. Moura, A. et al. Real-time whole-genome sequencing for surveillance of Listeria monocytogenes, France. Emerg. Infect. Dis. 23, 1462–1470 (2017).

    Article  CAS  Google Scholar 

  8. Pijnacker, R. et al. An international outbreak of Salmonella enterica serotype Enteritidis linked to eggs from Poland: a microbiological and epidemiological study. Lancet Infect. Dis. 19, 778–786 (2019).

    Article  Google Scholar 

  9. Pettengill, J. B. et al. A multinational listeriosis outbreak and the importance of sharing genomic data. Lancet Microbe 1, e233–e234 (2020).

    Article  CAS  Google Scholar 

  10. Baker-Austin, C. et al. Vibrio spp. infections. Nat. Rev. Dis. Primers 4, 1–19 (2018).

    Article  Google Scholar 

  11. Haendiges, J. et al. Pandemic Vibrio parahaemolyticus, Maryland, USA, 2012. Emerg. Infect. Dis. 20, 718–720 (2014).

    Article  Google Scholar 

  12. Haendiges, J. et al. A nonautochthonous U.S. strain of Vibrio parahaemolyticus isolated from Chesapeake Bay oysters caused the outbreak in Maryland in 2010. Appl. Environ. Microbiol. 82, 3208–3216 (2016).

    Article  CAS  Google Scholar 

  13. Haendiges, J. et al. Characterization of Vibrio parahaemolyticus clinical strains from Maryland (2012–2013) and comparisons to a locally and globally diverse V. parahaemolyticus strains by whole-genome sequence analysis. Front. Microbiol. 6, 125 (2015).

    Article  Google Scholar 

  14. Gonzalez-Escalona, N., Gavilan, R. G., Toro, M., Zamudio, M. L. & Martinez-Urtaza, J. Outbreak of Vibrio parahaemolyticus sequence type 120, Peru, 2009. Emerg. Infect. Dis. 22, 1235–1237 (2016).

    Article  CAS  Google Scholar 

  15. Gonzalez-Escalona, N., Jolley, K. A., Reed, E. & Martinez-Urtaza, J. Defining a core genome multilocus sequence typing scheme for the global epidemiology of Vibrio parahaemolyticus. J. Clin. Microbiol. 55, 1682–1697 (2017).

    Article  CAS  Google Scholar 

  16. Martinez-Urtaza, J. et al. Epidemic dynamics of Vibrio parahaemolyticus illness in a hotspot of disease emergence, Galicia, Spain. Emerg. Infect. Dis. 24, 852–859 (2018).

    Article  Google Scholar 

  17. Baker-Austin, C. et al. Genomic epidemiology of domestic and travel-associated Vibrio parahaemolyticus infections in the UK, 2008–2018. Food Control 115, 107244 (2020).

    Article  CAS  Google Scholar 

  18. Li, Y. et al. Vibrio parahaemolyticus, southern coastal region of China, 2007–2012. Emerg. Infect. Dis. 20, 685–688 (2014).

    Article  Google Scholar 

  19. Azarian, T. et al. Phylodynamic analysis of clinical and environmental Vibrio cholerae isolates from Haiti reveals diversification driven by positive selection. mBio 5, e01824-14 (2014).

    Article  Google Scholar 

  20. Chen, X. et al. A new emerging serotype of Vibrio parahaemolyticus in China is rapidly becoming the main epidemic strain. Clin. Microbiol. Infect. 26, 644.e1–644.e7 (2019).

    Article  Google Scholar 

  21. Marshall, K. E. H. et al. Protracted outbreak of Salmonella newport infections linked to ground beef: possible role of dairy cows — 21 States, 2016–2017. MMWR Morb. Mortal. Wkly Rep. 67, 443–446 (2018).

    Article  Google Scholar 

  22. Martinez-Urtaza, J. et al. Pandemic Vibrio parahaemolyticus O3:K6, Europe. Emerg. Infect. Dis. 11, 1319–1320 (2005).

    Article  Google Scholar 

  23. Zhang, R. et al. Impact of water quality variations on the microbial metagenome across coastal waters in Shenzhen, south China. Ocean Coast. Manage. 208, 105612 (2021).

    Article  Google Scholar 

  24. Advances in Science and Risk Assessment Tools for Vibrio Parahaemolyticus and V. Vulnificus Associated with Seafood: Meeting Report (WHO, FAO, 2021).

  25. Li, Y. et al. Application of digital PCR and next generation sequencing in the etiology investigation of a foodborne disease outbreak caused by Vibrio parahaemolyticus. Food Microbiol. 84, 103233 (2019).

    Article  CAS  Google Scholar 

  26. Benson, G. Tandem repeats finder: a program to analyze DNA sequences. Nucleic Acids Res. 27, 573–580 (1999).

    Article  CAS  Google Scholar 

  27. Croucher, N. J. et al. Rapid phylogenetic analysis of large samples of recombinant bacterial whole genome sequences using Gubbins. Nucleic Acids Res. 43, e15 (2015).

    Article  Google Scholar 

  28. Price, M. N., Dehal, P. S. & Arkin, A. P. FastTree 2 – approximately maximum-likelihood trees for large alignments. PLoS ONE 5, e9490 (2010).

    Article  Google Scholar 

  29. Minh, B. Q. et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol. Biol. Evol. 37, 1530–1534 (2020).

    Article  CAS  Google Scholar 

  30. Zhou, Z. et al. GrapeTree: visualization of core genomic relationships among 100,000 bacterial pathogens. Genome Res. 28, 1395–1404 (2018).

    Article  CAS  Google Scholar 

  31. Yang, C. et al. Recent mixing of Vibrio parahaemolyticus populations. ISME J. 13, 2578–2588 (2019).

    Article  Google Scholar 

  32. Yang, C. et al. Genetic diversity, virulence factors and farm-to-table spread pattern of Vibrio parahaemolyticus food-associated isolates. Food Microbiol. 84, 103270 (2019).

    Article  Google Scholar 

  33. Payne, M. et al. Enhancing genomics-based outbreak detection of endemic Salmonella enterica serovar Typhimurium using dynamic thresholds. Microb. Genom. 7, 000310 (2019).

    PubMed Central  Google Scholar 

  34. Rambaut, A., Lam, T. T., Carvalho, L. M. & Pybus, O. G. Exploring the temporal structure of heterochronous sequences using TempEst (formerly Path-O-Gen). Virus Evol. 2, vew007 (2016).

    Article  Google Scholar 

  35. Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).

    Article  Google Scholar 

  36. Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).

    Article  CAS  Google Scholar 

  37. Bollback, J. P. SIMMAP: stochastic character mapping of discrete traits on phylogenies. BMC Bioinform. 7, 88 (2006).

    Article  Google Scholar 

  38. Revell, L. J. Phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).

    Article  Google Scholar 

Download references

Acknowledgements

We thank D. Falush, B. Kan, B. Pang and E. Tourrette for valuable comments, and the personnel from 16 sentinel hospitals and 10 district Centers for Disease Control and Prevention in Shenzhen for their participation in and contribution to our surveillance work. This study was funded by the National Key Research and Development Program of China (No. 2018YFC1603902 and 2017YFC1601500, Y.C.), the Sanming Project of Medicine in Shenzhen (No. SZSM201811071, Q.H.), the China National Science and Technology Major Projects Foundation (No. 2017ZX10303406, Q.H.), the National Natural Science Foundation of China (No. 32000008 to C.Y. and No. 31770001 to Y.C.), the China Postdoctoral Science Foundation (No. 2020M672836, C.Y.), the Natural Science Foundation of Guangdong Province (No. 2019A1515011523, Y.L.), the Youth Innovation Promotion Association, CAS (No. 2022278, C.Y.), the Shenzhen Key Medical Discipline Construction Fund (No. SZXK064, Q.H.), the Key scientific and technological project of Shenzhen Science and Technology Innovation Committee (No. KCXFZ202002011006190, Q.H.), and the Non-profit Central Research Institute Fund of the Chinese Academy of Medical Sciences (No. 2020-PT330-006, Q.H.).

Author information

Author notes

  1. These authors contributed equally: Chao Yang, Yinghui Li.

Authors and Affiliations

  1. Shenzhen Center for Disease Control and Prevention, Shenzhen, Guangdong Province, China

    Chao Yang, Yinghui Li, Min Jiang, Lei Wang, Yixiang Jiang, Lulu Hu, Xiaolu Shi, Lianhua He, Rui Cai, Shuang Wu, Yaqun Qiu, Linying Lu, Le Zuo, Qiongcheng Chen & Qinghua Hu

  2. State Key Laboratory of Pathogen and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, China

    Chao Yang, Yarong Wu, Ruifu Yang & Yujun Cui

  3. Department of Microbiology, School of Public Health, Southern Medical University, Guangzhou, China

    Chao Yang, Chengsong Wan & Qinghua Hu

  4. The Center for Microbes, Development and Health, CAS Key Laboratory of Molecular Virology and Immunology, Institut Pasteur of Shanghai, Chinese Academy of Sciences, Shanghai, China

    Chao Yang

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

    Jaime Martinez-Urtaza

  6. School of Public Health, Shanxi Medical University, Taiyuan, China

    Qinghua Hu

Authors
  1. Chao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yinghui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Min Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yixiang Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lulu Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xiaolu Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lianhua He
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Rui Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shuang Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yaqun Qiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Linying Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Le Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Qiongcheng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Yarong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jaime Martinez-Urtaza
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Chengsong Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Ruifu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Yujun Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Qinghua Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.C., Q.H., R.Y. and C.Y. designed, initiated and coordinated the study. Y.L., L.L., L.Z., L.W., Y.J., Q.C., L.H., M.J., X.S., L.H., R.C. S.W., C.W. and Y.Q. contributed to data collection and management. C.Y., Y.L. and Y.W. analysed the data. All authors contributed to interpretation of the data. C.Y. wrote the first draft of the paper and Y.L., J.M.-U., R.Y., Y.C. and Q.H. reviewed and revised the paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Ruifu Yang, Yujun Cui or Qinghua Hu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Microbiology thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1

Pairwise SNP distance distribution between all the 3,642 isolates.

Source data

Extended Data Fig. 2 Temporal dynamics of the number of isolates from patients.

Years (2002–2005) with <50 patient isolates were merged. PCG, pathogenic clonal group.

Source data

Extended Data Fig. 3 Spatiotemporal distribution of the number of patient isolates.

(A) Temporal distribution of the number of patient isolates from PCG-others. Point sizes of panel are scaled with the number of patient isolates in a PCG. (B) Geographical distribution of the number of patient isolates from all the PCGs. The numbers in the heatmap indicate the number of patients.

Source data

Extended Data Fig. 4 Pairwise SNP distance distribution between isolates from outbreaks.

(A) Pairwise SNP distance distribution (bar, left Y-axis) between Foodborne Disease Outbreak Surveillance (FDOS)-outbreak isolates of different pathogenic clonal group (PCGs), and the proportion of clustered isolates under different SNP cutoffs (lines and points, right Y-axis). Blue and red indicate all the SNPs and non-recombined SNPs, respectively. (B) Pairwise SNP distance distribution between 34 isolates from four outbreaks of external independent datasets.

Source data

Extended Data Fig. 5

Number and size distribution of Foodborne Disease Outbreak Surveillance (FDOS) outbreaks, P-clusters, and Ob-clusters under different SNP cutoffs (3, 6, and 10 SNPs) and time intervals (1 week and 1 month).

Source data

Extended Data Fig. 6

Size distribution of Ob-clusters detected/not detected by the Foodborne Disease Outbreak Surveillance (FDOS).

Source data

Extended Data Fig. 7 BEAST maximum clade credibility (MCC) trees of two representative P-clusters, PC052 (A) and PC176 (B).

The colors of circles in the tips indicate different Ob-clusters or non-Ob-clusters within a P-cluster.

Source data

Extended Data Fig. 8

Inferred source district distribution (posterior probability >0.7) of cross-district Ob-clusters before and after subsampling.

Source data

Extended Data Fig. 9 SNP distance distribution over different geographical distance to hotspot district between isolates from Ob-clusters sourced from the hotspot.

The bold black horizontal line indicates the mean SNP distance.

Source data

Extended Data Fig. 10

Genome quality assessment flowchart (A) and characteristics of the high-quality genomes (B).

Source data

Supplementary information

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

BEAST maximum clade credibility (MCC) trees (nexus format).

Source Data Extended Data Fig. 8

Statistical source data.

Source Data Extended Data Fig. 9

Statistical source data.

Source Data Extended Data Fig. 10

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, C., Li, Y., Jiang, M. et al. Outbreak dynamics of foodborne pathogen Vibrio parahaemolyticus over a seventeen year period implies hidden reservoirs. Nat Microbiol 7, 1221–1229 (2022). https://doi.org/10.1038/s41564-022-01182-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-022-01182-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing Microbiology

Sign up for the Nature Briefing: Microbiology newsletter — what matters in microbiology research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: Microbiology