Transforming bacterial disease surveillance and investigation using whole-genome sequence to probe the trace

doi:10.1007/s11684-017-0607-7

Front. Med.

2018, Vol. 12

Issue (1) : 23-33 https://doi.org/10.1007/s11684-017-0607-7

REVIEW

Transforming bacterial disease surveillance and investigation using whole-genome sequence to probe the trace

Biao Kan^1,²(

), Haijian Zhou^1,², Pengcheng Du³, Wen Zhang^1,², Xin Lu^1,², Tian Qin^1,², Jianguo Xu^1,²(

)

¹. State Key Laboratory of Infectious Disease Prevention and Control, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
². Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, Hangzhou 310003, China
³. Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing 100015, China

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Abstract

Two decades have passed since the first bacterial whole-genome sequencing, which provides new opportunity for microbial genome. Consequently, considerable genetic diversity encoded by bacterial genomes and among the strains in the same species has been revealed. In recent years, genome sequencing techniques and bioinformatics have developed rapidly, which has resulted in transformation and expedited the application of strategy and methodology for bacterial genome comparison used in dissection of infectious disease epidemics. Bacterial whole-genome sequencing and bioinformatic computing allow genotyping to satisfy the requirements of epidemiological study in disease control. In this review, we outline the significance and summarize the roles of bacterial genome sequencing in the context of bacterial disease control and prevention. We discuss the applications of bacterial genome sequencing in outbreak detection, source tracing, transmission mode discovery, and new epidemic clone identification. Wide applications of genome sequencing and data sharing in infectious disease surveillance networks will considerably promote outbreak detection and early warning to prevent the dissemination of bacterial diseases.

Keywords genome sequencing genomic epidemiology bacteria surveillance infectious diseases

Corresponding Author(s): Biao Kan,Jianguo Xu

Just Accepted Date: 22 November 2017 Online First Date: 09 January 2018 Issue Date: 06 February 2018

Cite this article:

Biao Kan,Haijian Zhou,Pengcheng Du, et al. Transforming bacterial disease surveillance and investigation using whole-genome sequence to probe the trace[J]. Front. Med., 2018, 12(1): 23-33.

URL:

https://academic.hep.com.cn/fmd/EN/10.1007/s11684-017-0607-7
https://academic.hep.com.cn/fmd/EN/Y2018/V12/I1/23

Fig.1 General workflow of genomic epidemiological study for outbreak investigation and surveillance analysis. On the basis of classical microbiological work, isolates are collected. Information obtained from traditional typing, such as biochemical identification, serotyping, and pulsed-field gel electrophoresis (PFGE), and nongenetic typing methods (including mass spectrometry), remain valuable. The genomic relationship among isolates can be determined by genome sequencing and bioinformatic analysis and combined with additional information, including other detected results and natural and social factors. Transmission characteristics can be identified by epidemiological analysis. Results can also be used to guide further investigation when gaps exist in the primary work. Finally, disease control measures and social actions can be established and conducted on the basis of this information.

Fig.2 Overview of how whole-genome sequencing can be used to identify suspected clusters and outbreaks.

Fig.3 Investigations of an outbreak among humans infected by S. suis that occurred in Sichuan, China in 2005. All the isolates showed the same serotype, ribotype, pulsed-field gel electrophoresis and multilocus sequence typing types by classical epidemiological methods. On the basis of the genomic and epidemiological data, the isolates were classified into six clades, and the relationships of isolates and patients were inferred. Transmission patterns were identified by combining other information, such as the breeding mode and geographic, traffic, and economic factors. This figure is derived from Figures 1, 4 and 5 of reference 60, Figures 1 and 2 of reference 61, and Figures 1, 2, 3, 4 and technical appendix 2 Figure 1 of reference 62 with permission.

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