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

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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 2015
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[1][2], 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.[3] 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[4], astrovirus[5], and bornavirus.[6] 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.[4] 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.[7]

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.

While identification of causative microorganisms of disease is the chief responsibility of clinical microbiology laboratories, conducting antimicrobial resistance testing to guide therapy is among the most important tests conducted in the laboratory. NGS has the potential to suggest antimicrobial resistance through identification of known resistance genes.[14] Although WGS is in the early stages of development, studies have suggested that WGS can be used to predict antibiotic resistance with performance characteristics approaching those of traditional phenotypic testing. Stoesser et al.[15] demonstrated sensitivity and specificity of 96% and 97%, respectively, when using WGS to predict antibiotic resistance for clinical isolates of E. coli and K. pneumoniae. Comparative genomic sequencing has also been used to identify daptomycin resistance due to point mutations in metabolic genes that occurred during therapy for two cases of vancomycin-resistant enterococcal bacteremia which were ultimately fatal.[16][17] Earlier application of WGS might have detected these point mutations and guided therapy. However, it has not been established if WGS can be broadly applied to the full spectrum of pathogenic bacteria, particularly those with a diverse armamentarium of resistance mechanisms. WGS analysis of antimicrobial resistance genes could be particularly beneficial for slow-growing or difficult-to-culture organisms and organisms that elude phenotypic testing altogether. The use of WGS is not limited to the detection of bacterial resistance genes. WGS has also been applied for the following purposes: detection of low-level drug resistance among human immunodeficiency virus (HIV) “escape” variant populations (e.g., protease inhibitor [PI] and reverse transcriptase minor sequence variants) and coreceptor tropism (CCR5 and CXCR4) and analyses that are not possible using current genotypic and phenotypic HIV assays.[18] These successes and potential applications of WGS analysis have been made possible by the advance of NGS technology, which provides the tools to produce useful WGS data.

Metagenomic shotgun sequencing (mWGS) (Table 1) is often used to study microbial communities in human disease in order to identify correlative or causative relations. Such communities comprise hundreds of different taxa of bacteria, viruses, and fungi and other eukaryotic microbes. Many of these organisms are difficult to culture, and culture-independent methods of performing comprehensive sampling of these complex communities have been the major obstacle to analysis. NGS is currently the best available analytical approach to profile microbiomes (Table 1) for this purpose. To date, NGS has helped elucidate the role of the lung and gut microbiomes in both general health and various diseases, including obesity, inflammatory bowel disease, cystic fibrosis (CF), metabolic syndrome, type II diabetes, and cardiovascular disease.[19][20][21][22][23] As NGS continues to expand our knowledge base on metagenomics, microbiome analysis may produce diagnostic or prognostic biomarkers to guide therapeutic decisions.

There is a long-standing precedent in clinical microbiology laboratories to adopt new technology that complements or supplants existing “gold standard” testing. The viral culture bench is all but extinct in most clinical laboratories, with PCR-based molecular assays currently dominating viral diagnostics. Nonviral microbial diagnostics have been relatively slow to adopt molecular technology, but this is rapidly changing with a new generation of molecular diagnostics that utilize specific PCR primers for different bacterial, parasitic, and fungal targets. Several of these multiplex assays have already received U.S. Food and Drug Administration (FDA) clearance and offer laboratories an attractive and easy way to detect clinically relevant microbial pathogens. Similarly to the impact of PCR technology, microbial NGS diagnostics offer another step forward in the quality and quantity of information that could potentially be provided to clinicians and patients. In April 2015, the American Academy of Microbiology (AAM) conducted a colloquium to critically evaluate the trends in the use of NGS for infectious disease diagnostics. Below, we describe some of the concepts that evolved from the AAM’s NGS colloquium (report to be published).

Next-generation sequencing

DNA sequencing technology was previously dominated by procedures using the dideoxynucleotide chain termination method (also referred to as Sanger sequencing) (Table 1) for close to 30 years. In the mid-2000s, a number of new methods began to appear, commonly referred to as the “next-generation sequencing methods.” The first method that was widely commercialized was the “pyrosequencing” method, a method that formed the basis for the 454 Life Sciences instruments for about 10 years until the company closed in 2015. These instruments had a major impact on microbial genomics and became the leading platform for producing whole-genome sequences from individual bacteria as well as the platform of choice for sequencing 16S rRNA (Table 1) genes in microbiome analyses.

Since the advent of NGS, there has been an ongoing expansion of sequencing methods and instruments as well as continual improvements in the quality, quantity, and cost of sequences that are produced. The spectrum of common and available instruments current as of July 2015 is shown in Table 2, which indicates the considerable range in characteristics of NGS options.

Table 2. Current spectrum of popular, available NGS instruments (as of July 2015). Abbreviations: ILMN, Illumina; LT, Life Technologies; PB, Pacific Biosciences; ONT, Oxford Nanopore Technologies. TBD, to be determined. ILMN is from Illumina. LT is from http://allseq.com/knowledgebank/sequencing-platforms/life-technologies-ion-torrent. Genomes/run, 100× coverage of a 3-Mb genome. Metagenomes/run, 5 million reads (pairs)/metagenome.
Instrument Read length (accuracy) No. of reads/runs Run time Run cost ($) No. of genomes / metagenomes per run Comment
ILMN MiSeq ≤300 bases (high up to 250 bases) ≤20 million ≤2.5 days ≤1,500 40/4 Read pairs
ILMN NextSeq ≤150 bases (high) ≤400 million ≤1.2 days ≤5,000 400/60 Read pairs
ILMN HiSeq 2500 ≤250 bases (high) ≤300 million 2.5 days ≤8,000 300/800 Read pairs, rapid run mode
PB RSII ~10 kb (low for single pass) 70,000 ≤4 h ≤1,200 3/TBD Shorter (e.g. 2-kb) reads have higher quality; no paired ends
LT Ion PGM ≤400 bases ≤5 million ≤7 h ≤750 7/1 PGM318 chip; no paired ends
LT Ion Proton ≤200 bases ≤80 million ≤4 h 1,000 30/16 PI chip; no paired ends
ONT MinION 10 kb–100 kb (low) Variable Variable 1,000 TBD No paired ends; beta testing

An understanding of how these characteristics match the various NGS applications is useful to appreciate the strengths or limitations of different sequencing approaches. To sequence a single microbial genome, the genome is fragmented into manageable-size pieces, which are then used in WGS (Fig. 1).


Fig1 Goldberg mBio2015 6-6.jpg

Figure 1. NGS genome analysis. The general process of using NGS for analysis of a single genome is depicted in this figure. Note that there are many variations on this approach. Purified DNA (i.e., input genome) is fragmented and run through the DNA sequencing process. The sequencing instrument produces either short (e.g., for Illumina) or long (e.g., for Pacific Biosciences) sequence reads depending on the platform used. When the goal is to produce a complete genome (e.g., for identifying virulence or antibiotic resistance genes or for comparative genomic studies), the reads are assembled into genomes using specialized software. Some gaps in the assembly may occur, leading to a draft genome sequence composed of many contigs. When the genome is assembled without gaps, it is said to be closed. When the goal is to identify variants (e.g., SNPs) with respect to a reference genome, the reads can be aligned directly with the reference genome and sequence variants can be identified using specialized programs. These variants serve to define the organism at the subspecies and strain level and are useful in epidemiological tracking as well as to identify mutations that occur.


Depending on the sequencing platform, this can produce sequences (i.e., reads) (Table 1) that range from hundreds of bases to thousands or tens of thousands of bases in length. When the goal is to produce a microbial genome, the reads are assembled into a single genome sequence using a variety of computational strategies. Often, the assembly (Table 1) leaves gaps, and the individual chunks of genomic sequence that result are called contigs (Table 1). When identifying variants in the genome (e.g., for typing or tracking), the reads are directly aligned with a reference genome (Table 1) where the sequence variants are determined. The smaller the length of sequence read, the more contigs and gaps that are produced during the assembly process. Hence, there is a preference for longer sequence reads. To obtain a complete sequence without gaps, one often uses both short and long reads to combine the strengths of these various sequencing approaches.[24] However, the platforms producing shorter reads are often less expensive and can yield large amounts of data. Moreover, the accuracy of base calls comprising the sequence read is also important. The lower the accuracy of the read, the more reads (i.e., coverage) that are required to obtain good contigs, ultimately driving up the sequencing costs. In addition, when reads have errors, it is harder to identify the true sequence variants. At present, platforms that produce longer reads tend to have lower accuracy and users have to consider the tradeoffs between the various platforms when designing a project.

Another major application for NGS is metagenomic sequencing. In this case, one starts with a particular specimen type such as stool, saliva, a nasal swab, etc., with the goal of determining the bacteria, viruses, fungi, and other microbes that are present. Following DNA extraction, a series of processing and bioinformatics steps are performed to produce NGS data for analysis (Fig. 2), analogous to the whole-genome sequence of an individual microbe described above; however, in this case the method is applied to a mixture/community of microbes. While the genomic sequences produced can be assembled, because of the various abundances of organisms, there is often not enough sequence from rarer organisms to produce a complete genome, and assembly results in very short contigs for these genomes in the mixture. More commonly, the sequence reads are aligned against databases of many (typically thousands) genome sequences to identify the likely source organism for the sequence. The abundance of such source organisms is estimated from the number of reads that match their genomes. In this way, one can identify and estimate the abundance of organisms that are present in the original patient sample.


Fig2 Goldberg mBio2015 6-6.jpg

Figure 2. NGS workflow. A high-level overview of the steps taken in the NGS data production process and some of the equipment and software used in this process. The specific instruments and programs may vary as there are multiple solutions (e.g., Table 2). DB, database; QA, quality assurance; seq’ing, sequencing; conc, concentration; LIMS, laboratory information management system.

The high throughput of NGS allows data to be produced from many individual bacteria/samples in a single sequencing run of hours to days. This is a vast improvement over the early days of bacterial sequencing when a single bacterial genome project could take years. Likewise, because a metagenomic sample is complex and contains hundreds of taxa at abundances ranging over many orders of magnitude, previously it was not possible to accurately assess the community structure by culture-based methods or traditional Sanger sequencing, which could handle only cultivable organisms or hundreds of sequences at best. With the availability of NGS, it is now possible to obtain millions or more of sequences per run, thus enabling deep sampling and more descriptive analysis of the microbial community on a truly useful level.

Clinical applications

The potential clinical applications of NGS are vast and engage clinical microbiology at every level of the development process, such as outbreak tracking across the country, hospital infection control surveillance, pathogen discovery, mutation detection in a specific isolate, identification of multiple pathogens in a single sample, identification of viral quasispecies, and individual host response to infection (Fig. 3). These tests are poised to revolutionize infectious disease diagnostics and the practice of clinical microbiology. Initial reports of NGS applications for clinical diagnostics first appeared more than five years ago; however, the technology has not yet entered routine use within clinical microbiology laboratories. Reports continue to appear in the medical literature of individual case studies or small-scale applications of NGS to clinical microbiology, but experience remains limited. The technology itself is still hobbled by the enormous amount of basic research and resources needed to transform this technology into a robust clinical application for infectious disease diagnostics. To date, the medical community is still awaiting large-scale clinical trials to establish the utility of NGS in different clinical settings; however, comprehensive funding and incentives for these studies are limited.


Fig3 Goldberg mBio2015 6-6.jpg

Figure 3. Potential clinical applications for metagenomics sequencing. There are numerous potential applications of NGS technology to the clinical microbiology laboratory. Each entry in the chart represents a potential area for the utilization of NGS diagnostics and/or future research.

Although NGS has the potential to provide a huge volume of information to health care practitioners with respect to both microbial characteristics and the host response to disease, it will be necessary to determine strategies to pragmatically deal with this data barrage. Many of the potential applications to clinical medicine are not known. In the field of bacteriology, NGS has particular limitations in determining which organisms are merely “innocent bystanders” or are colonizers rather than active pathogens. For instance, a potentially pathogenic microorganism identified from one specimen type, such as cerebrospinal fluid (CSF) or synovial fluids, which are typically thought to be sterile, may be normal in the skin, gut, or mouth.

The potential implications for physicians/practitioners, and particularly antimicrobial stewardship, are staggering. One important question: if currently a certain percentage of practitioners treat patients with antibiotics even when lacking evidence of a bacterial pathogen, what will they do when presented with a pathogen that is likely (but not absolutely) a contaminant?

More importantly, a paradigm shift in the understanding of molecular diagnostics will be necessary for clinicians and professionals in the clinical microbiology community. Both molecular diagnostics and traditional culture-based technology have their strengths and weaknesses. One of the strengths of NGS assays is the ability to detect many microorganisms directly from a patient sample without needing additional testing or a priori knowledge of the type of pathogen. NGS can potentially detect viruses, bacteria, yeast, fungi, and parasites without the need for additional individual testing. However, both traditional technologies and NGS assays have limitations in detection that can be clearly defined. But while culture absolutely indicates the presence of living organisms, the presence of organismal DNA is less definitive.

Ultimately, clinical studies and eventually clinical trials examining patient outcomes will be necessary to reinforce the cost savings and benefit of using NGS-based clinical tests for the diagnosis of infectious disease. A prominent issue with such studies is the scarcity of funding. Because of how expensive these analyses are to conduct, they can be unattractive to industry and require a certain amount of patience and determination from the clinical community to perform correctly and in an economically wise manner. Moreover, such studies require establishing fundamentals such as standard operating procedures for NGS diagnostics. At present, there is no standardization between studies, much less between different institutions. All aspects of the NGS diagnostic pipeline remain variable, including clinical specimen collection, sequencing parameters, data analysis/interpretation, and data reporting. Such details must be established before clinical outcomes can be systematically examined.

Determining the optimal approach to advance the use of NGS poses serious challenges. For example, how would a clinical laboratory determine the analytical validation of a device that could potentially pick up any known or novel pathogens? While the majority of clinical infections are typically attributed to a finite number of bacterial, viral, and fungal pathogens, a main attraction of NGS for clinical diagnosis is the ability to capture rare or unsuspected, yet potentially actionable, pathogens. It is tremendously appealing to identify both pathogen and patient genomic sequence information for everything from virulence genes to strain-level sequence variants to an assessment of the patient response to infection or effectiveness of antibiotic therapy. The advancement of NGS technology into pathogen genomics could potentially provide supplemental descriptive or predictive information regarding the potential antimicrobial resistance gene profile of the microorganism and allow for more rapid selection of appropriate antimicrobial therapy. Although early studies have reported promising results for antimicrobial resistance detection, the most valuable piece of antimicrobial resistance information is the antimicrobials to which the organism is susceptible[15][25], and it is unclear what the strength of NGS for predicting susceptibility as opposed to resistance will be. Additionally, as bacteria and viruses are constantly evolving new resistance mechanisms, reference databases would have to be continuously updated to include novel resistance genes and mutations in order for NGS to be an effective predictor. Thus, it seems unlikely that the clinical laboratory will be moving away from phenotypic confirmation in the near future. Additional clinical studies are needed to explore the capabilities of NGS assays for these applications and the potential partnership between NGS and culture-based assays.

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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.