Journal:Making the leap from research laboratory to clinic: Challenges and opportunities for next-generation sequencing in infectious disease diagnostics

Full article title	Making the leap from research laboratory to clinic: Challenges and opportunities for next-generation sequencing in infectious disease diagnostics
Journal	mBio
Author(s)	Goldberg, B.; Sichtig, H.; Geyer, C.; Ledeboer, N.; Weinstock, G.M.
Author affiliation(s)	Children’s National Medical Center, Food and Drug Administration, American Society for Microbiology, Medical College of Wisconsin, Jackson Laboratory for Genomic Medicine
Primary contact	Email: George dot Weinstock at jax dot org
Year published	2016
Volume and issue	6(6)
Page(s)	e01888-15
DOI	10.1128/mBio.01888-15
ISSN	2150-7511
Distribution license	Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported
Website	http://mbio.asm.org/content/6/6/e01888-15.full
Download	http://mbio.asm.org/content/6/6/e01888-15.full.pdf (PDF)

Abstract

Next-generation DNA sequencing (NGS) has progressed enormously over the past decade, transforming genomic analysis and opening up many new opportunities for applications in clinical microbiology laboratories. The impact of NGS on microbiology has been revolutionary, with new microbial genomic sequences being generated daily, leading to the development of large databases of genomes and gene sequences. The ability to analyze microbial communities without culturing organisms has created the ever-growing field of metagenomics and microbiome analysis and has generated significant new insights into the relation between host and microbe. The medical literature contains many examples of how this new technology can be used for infectious disease diagnostics and pathogen analysis. The implementation of NGS in medical practice has been a slow process due to various challenges such as clinical trials, lack of applicable regulatory guidelines, and the adaptation of the technology to the clinical environment. In April 2015, the American Academy of Microbiology (AAM) convened a colloquium to begin to define these issues, and in this document, we present some of the concepts that were generated from these discussions.

Minireview

Use of next-generation DNA sequencing (NGS) (Table 1) in infectious disease diagnostics has progressed slowly over the past 10 years despite continued advances in sequencing technology. The first commercial NGS platform, the GS20 sequencer from 454 Life Sciences, which was originally released in 2005, resulted in a more than 100-fold increase in the amount of microbial genomic sequence data produced in a day compared to preceding instruments. Despite the growing body of literature and research broadly applying sequencing-based technology to disease pathophysiology, epidemiology, and clinical diagnostics, the clinical microbiology laboratory has yet to widely adopt NGS technology. As microbiology laboratories are faced with a wealth of innovative and often costly molecular technologies, the role of NGS in clinical infectious disease diagnostics needs to be carefully evaluated.

Table 1. Glossary of terms used in DNA sequence analysis Term Abbreviation Definition 16S rRNA gene A slowly evolving gene in bacteria whose sequence is used for definition of taxa. It is a gene that is targeted for sequencing in microbiome analysis, where the goal is enumeration of the taxa present in a community. Alignment The process of comparing the sequence of a single sequencing read or a contig/whole genome following assembly to a reference genome. The goal is often to identify the organism from which a sequencing read came or to identify variants within the sequence. Assembly Reconstructing a genome, in whole or in part, from the fragment sequences produced by WGS (or mWGS). Contig A contiguous stretch of sequence produced when a series of overlapping sequence reads are merged to produce a single longer sequence. Dideoxynucleotide sequencing A “classical” method of DNA sequencing that preceded NGS and is frequently called Sanger sequencing. Metagenomics Analyzing a mixture of microbial genomes, a metagenome, without separating the genomes or culturing the organisms. Metagenomic whole-genome shotgun sequencing mWGS The application of WGS to a metagenomics sample. DNA is extracted from the sample, producing a mixture of genomes, which are then subjected to WGS en masse. Microbiome A community of microbes comprising bacteria, viruses, and fungi and other eukaryotic microbes. Often the target of metagenomic analyses. Next-generation sequencing NGS A collection of DNA sequencing methods that each use different biochemical approaches and instruments to produce data in vastly larger amounts, at greatly lower cost, in shorter time, and with less manual intervention than previous methods. Reference genome A genome sequence of a particular organism that can be used as a standard, e.g., for alignment or comparison of other genomes. Read The basic element produced by DNA sequencing. Sequencing of a DNA fragment produces a series of bases called a sequencing read. Sanger sequencing A “classical” method of DNA sequencing that preceded NGS but was almost exclusively used from the 1970s until the advent of NGS. Compared to NGS, it produced fewer data, was more expensive, and required more manual work. Single nucleotide polymorphism SNP A difference of a single base compared to a reference genome. These can be substitutions of one base for another or insertion/deletion of a base (indel). Variant Any difference in a DNA sequence compared to a reference sequence. This can be a single-base difference (SNP) or insertions, deletions, inversions, or translocations of larger stretches of sequence (structural variants). Whole-genome shotgun sequencing WGS Randomly fragmenting an entire genome and obtaining DNA sequence from the fragments to produce a collection of random DNA sequences. This can be applied to a single bacterium or to a mixture (metagenomc; see mWGS). These data can be used to identify variants following alignment of genes by comparison to sequence databases or to compare genome structures following assembly.

A number of highly publicized case reports and clinical studies have showcased the application of NGS as a single diagnostic tool with the potential to be broadly applicable to infectious disease diagnostics. Metagenomic (Table 1) sequencing has demonstrated its ability to identify microbial pathogens where traditional diagnostics have otherwise failed. For example, it is estimated that 63% of encephalitis cases go undiagnosed despite extensive testing. Several cases in the literature have successfully employed NGS to diagnose rare, novel, or atypical infectious etiologies for encephalitis, including cases of infection by Leptospira, astrovirus, and bornavirus. In one case, 38 different diagnostic tests had been conducted and failed to yield an actionable answer before a single NGS assay was performed, which identified the pathogen. Similarly, the utilization of metagenomic NGS identified divergent astrovirus clades in a pair of patients with encephalitis and demonstrated the unusual zoonotic potential of a group of these viruses.

Another promising application of NGS technology is hospital infection control surveillance programs and community outbreak investigations (8). By conducting whole-genome sequencing (WGS) (Table 1), organisms can be identified at the subspecies/strain level based on the single nucleotide polymorphisms (SNPs) (Table 1) and other variants (Table 1) in their genotype. WGS through NGS technology offers greater precision than do more-traditional typing tools such as multilocus sequence typing and pulsed-field gel electrophoresis, which may assist in refining outbreak investigations and better guide infection control interventions (9). Because WGS analysis requires significant amounts of sequencing data, traditional sequencing methods preclude the use of WGS analysis for outbreak investigations. However, NGS platforms can generate the large volume of data needed for SNP or variant analysis and have led to a rapid expansion in the use of WGS for public health investigations. For example, WGS using NGS technology was applied to investigate an outbreak of hemolytic-uremic syndrome caused by an unusual strain of Escherichia coli in Germany (10), the origins of the 2010 Haitian Vibrio cholerae epidemic (11), a series of methicillin-resistant Staphylococcus aureus infections in a neonatal intensive care unit (12), and the origins of a series of nosocomial carbapenem-resistant Klebsiella pneumoniae infections (13), among many others.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.