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Chapter 3 : Microbial Whole-Genome Sequencing: Applications in Clinical Microbiology and Public Health

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Microbial Whole-Genome Sequencing: Applications in Clinical Microbiology and Public Health, Page 1 of 2

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Abstract:

The genomic era of bacteriology began in 1995 when the first bacterial genome () was sequenced using the Sanger sequencing method (1). A decade later, the introduction of high-throughput or next-generation sequencing (NGS) technologies allowed sequencing of bacterial genomes to be performed in days rather than months or years (2). These newer technologies use different processes but rely on a combination of template preparation, sequencing and imaging, and genome alignment and assembly methods (3). The major advantages of NGS over Sanger sequencing are speed of sequencing combined with lower costs (4). The ability to readily sequence the whole genome of microorganisms has enabled the performance of large-scale comparative and evolutionary studies that were unimaginable even a few years ago (5–9). Furthermore, the development of rapid benchtop sequencing platforms (10) that are able to sequence a microbial genome in a day makes them increasingly appropriate for introduction into the diagnostic microbiology laboratory environment (11). This chapter will briefly review the currently available NGS technologies and platforms, followed by an in-depth review of the potential clinical applications of whole-genome sequencing (WGS) in the microbiology laboratory. We will also present the challenges for implementation of WGS in the clinical setting and consider some future directions.

Citation: Török M, Peacock S. 2016. Microbial Whole-Genome Sequencing: Applications in Clinical Microbiology and Public Health, p 32-48. 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.ch3
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FIGURE 1

The current diagnostic microbiology processes for bacterial pathogens. A schematic representation of the current workflow for processing samples for bacterial pathogens is presented, showing high complexity and a typical timescale of a few weeks to a few months. The schematic is an approximation that highlights the principal steps in the workflow; it is not intended to be a comprehensive or precise description. Samples that are likely to be normally sterile are often cultured on a rich medium that will support the growth of any culturable organism. Samples that are contaminated with colonizing flora present a challenge for growing the infecting pathogen. Many types of culture media (referred to as selective media) are used to favor the growth of the suspected pathogen; this approach is particularly important for culturing pathogens from feces. Boxes A to H arbitrarily represent the many different media for culture. Medium H represents a medium designed for growing mycobacteria that have specific growth requirements. When an organism is growing, the morphological appearance and density of growth are properties that need specialist knowledge for deciding whether it is likely to be pathogenic. The likely pathogens are then processed through a complex pathway that has many contingencies to determine species and antimicrobial susceptibility. Broadly, there are two approaches. One approach uses matrix-assisted laser desorption ionization–time of flight mass spectrometry for species identification before setting up susceptibility testing. The other uses Gram staining followed by biochemical testing to determine species; susceptibility testing is often set up simultaneously with biochemical tests. Categorization of pathogens into groups of species is needed to choose the appropriate susceptibility-testing panel. Finally, depending on the species and perceived likelihood of an outbreak, a small subset of isolates may be chosen for further investigation using a wide range of typing tests that are often only provided by reference laboratories. The dashed lines and question marks are positioned arbitrarily to indicate that the further investigation is varied and happens in only a small number of cases. (Reprinted from reference with permission.)

Citation: Török M, Peacock S. 2016. Microbial Whole-Genome Sequencing: Applications in Clinical Microbiology and Public Health, p 32-48. 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.ch3
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Image of FIGURE 2
FIGURE 2

NGS platforms. The schematic shows the main high-throughput sequencing platforms available to microbiologists today and the associated sample preparation and template amplification procedures. PGM, personal genome machine. (Reprinted from reference with permission.)

Citation: Török M, Peacock S. 2016. Microbial Whole-Genome Sequencing: Applications in Clinical Microbiology and Public Health, p 32-48. 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.ch3
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Image of FIGURE 3
FIGURE 3

Global spread of the seventh cholera pandemic. Transmission events inferred for the seventh-pandemic phylogenetic tree, drawn on a global map. The date ranges shown for transmission events are taken from the BEAST analysis and represent the median values for the most recent common ancestor of the transmitted strains (later bound) and the most recent common ancestor of the transmitted strains and their closest relative from the source location (earlier bound). (Reprinted from reference with permission.)

Citation: Török M, Peacock S. 2016. Microbial Whole-Genome Sequencing: Applications in Clinical Microbiology and Public Health, p 32-48. 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.ch3
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Image of FIGURE 4
FIGURE 4

Epidemiology and phylogeny of a neonatal MRSA ST2371 outbreak. (a) Epidemiological map of 14 infants (patients 1 to 14) on the special care baby unit (SCBU). Boxes shown for infants in the SCBU in panel a represent duration of hospital stay (black boxes show infants included by the infection-control investigation and white boxes show infants excluded by the infection-control team). Gray vertical blocks in panels a, c, and e show the time periods on the SCBU when there were no known carriers of MRSA. (b) Phylogenetic tree based on WGS of MRSA isolates from patients 1 to 14. (c) Epidemiological map of patients 1 to 14 and 10 other patients (patients 16, 17, and 19 to 26) with linked MRSA infection detected in the community. The colored lines link members of the same family. (d) Phylogenetic tree based on WGS of MRSA isolates from patients 1 to14 and patients 16, 17, and 19 to 26. (e) Epidemiological map of all cases of MRSA identified by WGS and one patient (patient 18) suspected of being linked to the outbreak but for whom no MRSA colonization was detected. (f) Phylogenetic tree of all cases of MRSA in the outbreak. Twenty individual MRSA colonies from a staff member are shown in red boxes, with multiple colonies from the staff member shown in parentheses. MRSA, methicillin-resistant ; SCBU, special care baby unit; SNP, single-nucleotide polymorphism; P, patient. Note: Out-group was the sequence type 22 reference genome. (Reprinted from reference with permission.)

Citation: Török M, Peacock S. 2016. Microbial Whole-Genome Sequencing: Applications in Clinical Microbiology and Public Health, p 32-48. 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.ch3
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Tables

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TABLE 1

Comparison of NGS platforms

Citation: Török M, Peacock S. 2016. Microbial Whole-Genome Sequencing: Applications in Clinical Microbiology and Public Health, p 32-48. 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.ch3

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