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Chapter 19 : The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections

Authors: John Besser1, Heather Carleton2, Richard Goering3, Peter Gerner-Smidt4
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Affiliations: 1: Enteric Disease Laboratory Branch, Centers for Disease Control and Prevention, Atlanta, GA 30333.; 2: Enteric Disease Laboratory Branch, Centers for Disease Control and Prevention, Atlanta, GA 30333.; 3: Department of Medical Microbiology and Immunology, Creighton University School of Medicine, Omaha, NE 68178.; 4: Enteric Disease Laboratory Branch, Centers for Disease Control and Prevention, Atlanta, GA 30333.
Content Type: Reference
Publication Year: 2016

Category: Clinical Microbiology

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Abstract:

The Centers for Disease Control and Prevention (CDC) estimates that foodborne diseases cause illness in one in six Americans (or 48 million people) each year, leading to 128,000 hospitalizations and 3,000 deaths (1, 2). Among the known foodborne pathogens, bacteria proportionally cause the most severe illness, being associated with 64% of hospitalizations and 64% of the deaths. The foodborne bacteria associated with most hospitalizations and deaths are , , Shiga toxin-producing (STEC), and These bacteria are all zoonotic; i.e., they may be found in the normal intestinal biota of animals without causing disease and may spread to the environment and contaminate the food we eat.

Citation: Besser J, Carleton H, Goering R, Gerner-Smidt P. 2016. The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections, p 235-244. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch19
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The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections
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Citation: Besser J, Carleton H, Goering R, Gerner-Smidt P. 2016. The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections, p 235-244. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch19
/content/10.1128/9781555819071.ch19.ch19fig1
FIGURE 1

Schematic analytical principle of three approaches to whole-genome sequence analysis. (A) kmer, (B) hqSNP, (C) wgMLST.

10.1128/9781555819071/9071ch19f01_thmb.gif
10.1128/9781555819071/9071ch19f01.gif
Image of FIGURE 1
FIGURE 1

Schematic analytical principle of three approaches to whole-genome sequence analysis. (A) kmer, (B) hqSNP, (C) wgMLST.

Citation: Besser J, Carleton H, Goering R, Gerner-Smidt P. 2016. The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections, p 235-244. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch19
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/content/10.1128/9781555819071.ch19.ch19fig2
FIGURE 2

hqSNP (A) and wgMLST (B) trees of isolates of that are indistinguishable by PFGE (C). Isolates associated with an outbreak related to consumption of artisan cheese are highlighted with brackets and shown along with contemporary and historical isolates with no relation to the outbreak. The isolates from the patient who did not consume the artisan cheese product or did not have exposure information are denoted. The hqSNP and wgMLST analyses produced equivalent results.The hqSNPs were defined by consensus ≥75% and coverage ≥×10. The wgMLST analysis and UPGMA tree was generated using BioNumerics 7.5 (Applied Maths).

10.1128/9781555819071/9071ch19f02_thmb.gif
10.1128/9781555819071/9071ch19f02.gif
Image of FIGURE 2
FIGURE 2

hqSNP (A) and wgMLST (B) trees of isolates of that are indistinguishable by PFGE (C). Isolates associated with an outbreak related to consumption of artisan cheese are highlighted with brackets and shown along with contemporary and historical isolates with no relation to the outbreak. The isolates from the patient who did not consume the artisan cheese product or did not have exposure information are denoted. The hqSNP and wgMLST analyses produced equivalent results.The hqSNPs were defined by consensus ≥75% and coverage ≥×10. The wgMLST analysis and UPGMA tree was generated using BioNumerics 7.5 (Applied Maths).

Citation: Besser J, Carleton H, Goering R, Gerner-Smidt P. 2016. The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections, p 235-244. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch19
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Tables

The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections
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Citation: Besser J, Carleton H, Goering R, Gerner-Smidt P. 2016. The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections, p 235-244. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch19
/content/10.1128/9781555819071.ch19.ch19tab1
TABLE 1

Key characteristics of different whole-genome sequencing analysis techniques used in outbreak investigations

Generic image for table
TABLE 1

Key characteristics of different whole-genome sequencing analysis techniques used in outbreak investigations

Citation: Besser J, Carleton H, Goering R, Gerner-Smidt P. 2016. The Impact of Molecular Diagnostics on Surveillance of Foodborne Infections, p 235-244. In Persing D, Tenover F, Hayden R, Ieven M, Miller M, Nolte F, Tang Y, van Belkum A (ed), Molecular Microbiology. ASM Press, Washington, DC. doi: 10.1128/9781555819071.ch19

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