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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

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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.

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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.

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.

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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.).

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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

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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.

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

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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.

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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

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