Role of Clinicogenomics in Infectious Disease Diagnostics and Public Health Microbiology

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Role of Clinicogenomics in Infectious Disease Diagnostics and Public

Health Microbiology

Lars F. Westblade,aAlex van Belkum,bAdam Grundhoff,c,dGeorge M. Weinstock,eEric G. Pamer,fMark J. Pallen,g W. Michael Dunne, Jr.h

Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, New York, USAa

; bioMérieux, Inc., LaBalme, Franceb

; Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg, Germanyc

; German Center for Infection Research, Partner Site Hamburg-Lübeck-Borstel, Hamburg, Germanyd ; The Jackson Laboratory for Genomic Medicine, Farmington, Connecticut, USAe

; Memorial Sloan Kettering Cancer Center, New York, New York, USAf

; Warwick Medical School, University of Warwick, Coventry, United Kingdomg

; bioMérieux, Inc., Durham, North Carolina, USAh

Clinicogenomics is the exploitation of genome sequence data for diagnostic, therapeutic, and public health purposes. Central to this field is the high-throughput DNA sequencing of genomes and metagenomes. The role of clinicogenomics in infectious dis-ease diagnostics and public health microbiology was the topic of discussion during a recent symposium (session 161) presented at the 115th general meeting of the American Society for Microbiology that was held in New Orleans, LA. What follows is a col-lection of the most salient and promising aspects from each presentation at the symposium.

T

he explosion of microbiome research is driven by high-throughput DNA sequencing, so-called next-generation se-quencing (NGS), technologies that allow the genomic content of entire microbial communities (bacterial, viral, and eukaryotic or-ganisms) to be described. Although much of this work is aimed at describing the structure of “commensal” communities, the meth-odology works equally well to identify pathogens in clinical sam-ples. The key concept in using NGS methodology is that detection of microbes is independent of culture and is not limited to targets used for PCR assays. Rather, it is a process of generating large-scale sequence data sets that adequately sample a specimen for micro-bial content and then of applying computational methods to re-solve the sequences into individual species, genes, pathways, or other features.

Most microbiome analyses have focused on describing bacte-rial content, and this is usually performed by sequencing the 16S rRNA gene. PCR primers with degenerative sequences are used to amplify all or part of the 16S rRNA gene from a broad range of species in the sample. The mix of amplicons generated from dif-ferent organisms in the community is then sequenced, and the abundance of each species is determined by the number of se-quences found for its respective 16S rRNA gene. Although this is useful for defining communities, it also affords the identification of pathogens with unique 16S rRNA sequences.

The sensitivity and specificity of this method are determined in large part by the NGS technology. Before NGS, the full-length 16S rRNA gene was sequenced with high-quality, 700-base-long reads of Sanger, or chain termination, sequencing (sometimes referred to as “first-generation” sequencing technology). This was labori-ous and expensive, and deep sampling was not possible. When NGS became available, most work was done on the FLX sequenc-ing instrument (a second-generation sequencsequenc-ing technology) from 454 Life Sciences (Roche Diagnostics, Indianapolis, IN, USA). This only permitted 400-base-long sequencing reads, and only a portion of the 16S rRNA gene was sequenced. The 16S rRNA gene has nine hypervariable regions that provide much of the specificity in species identification. With 454 sequencing, typ-ically only three of these regions can be sequenced. Nevertheless, this allowed detection to the genus level of most taxa. This

meth-odology can correctly identify pathogens in stool samples from patients with diarrhea compared to culture results (G. M. Wein-stock, unpublished data). In addition, when using this NGS ap-proach, an additional pathogen that was not reported by the diag-nostic laboratory in 15% of the samples was identified.

Recently, 16S rRNA gene sequencing has moved to the MiSeq and HiSeq sequencing instruments from Illumina (San Diego, CA, USA). This is in part due to the closing of 454 Life Sciences and to the higher data production and lower cost of the Illumina instruments. These instruments produce shorter reads (100 to 300 bases) and thus further limit the amount of the 16S rRNA gene that can be sampled and are often limited to a single hypervariable region. However, organism identification is possible as a result of the shotgun sequencing of several hypervariable regions.

A new alternative to Illumina has been developed using the Pacific Biosciences RS II sequencing platform, which is often re-ferred to as a third-generation sequencing technology (PacBio, Menlo Park, CA, USA). With PacBio sequencing, much longer sequence reads are possible, and full-length 16S rRNA gene se-quencing can now be accomplished at higher data output, lower cost, and much greater convenience than was possible with Sanger sequencing. This methodology is still more expensive than Illumi-na’s platform but bodes well for continued improvement in the use of 16S rRNA gene sequencing for microbiome analysis.

The alternative to focusing on the 16S rRNA gene for micro-biome analysis is shotgun sequencing of the sample so that all parts of the genome are sequenced. Whereas the 16S rRNA gene is found only in bacteria, shotgun sequencing is agnostic, and

ar-Accepted manuscript posted online24 February 2016

CitationWestblade LF, van Belkum A, Grundhoff A, Weinstock GM, Pamer EG, Pallen MJ, Dunne WM, Jr. 2016. Role of clinicogenomics in infectious disease diagnostics and public health microbiology. J Clin Microbiol 54:1686 –1693.

doi:10.1128/JCM.02664-15.

Editor:C. S. Kraft

Address correspondence to W. Michael Dunne, Jr., william.dunne@biomerieux.com.

Copyright © 2016, American Society for Microbiology. All Rights Reserved.

MINIREVIEW

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chaebacterial, viruses, and eukaryotic microbes are also sampled. This is often referred to as metagenomic shotgun sequencing since all genomes (the metagenome) are sequenced. This approach re-quires many more sequencing reads than 16S rRNA gene ing to adequately sample the genomes, and thus only the sequenc-ing platforms that produce the most data are used (Illumina HiSeq and NextSeq instruments). This methodology is significantly more expensive than 16S rRNA gene sequencing, and this has also limited its use. However, metagenomic shotgun sequencing also allows for antibiotic resistance genes to be detected as well as vir-ulence factors and other features that can help distinguish a patho-gen at the strain level from other nonpathopatho-genic members of a species. Shotgun sequencing is also used for analysis of RNA, either to identify RNA viruses or for transcriptional analysis. In this case, cDNA is generated and then NGS is performed. Met-agenomic transcription analysis is particularly noteworthy, as this method determines which organisms are actively growing and/or whether a gene of interest (antibiotic-resistant determi-nant) is expressed and thus contributes to the organism’s phe-notype.

Although use of metagenomic shotgun sequencing is limited by the output and cost required, trends in DNA sequencing tech-nology continue to emphasize instruments that are smaller, faster, and lower cost. The MinION instrument from Oxford Nanopore Technologies (Oxford, United Kingdom) is a handheld sequenc-ing instrument, and although these instruments are still in the development phase, they have been used to sequence bacterial and viral samples (1,2). Thus, one can expect continued development in this area and more routine use of these methods in the future for routine diagnostic microbiology.

UNBIASED INFECTIOUS DISEASE DIAGNOSTICS

Conventional diagnostic methods, such as PCR, serology, or mi-crobial culture, have been validated and standardized over de-cades and continue to represent the gold standard for infectious disease diagnostics. However, while generally cost-effective and robust, these methods share a limitation: they represent targeted detection approaches and require an accurate initial hypothesis as to the type of pathogen(s) that may be present in the sample of interest. Their narrow scope, especially for PCR- and serology-based methods, is likely one of the reasons why conventional di-agnostic tests fail to detect a causative agent in a significant num-ber of cases (3–5). Recently established mass spectrometry-based approaches are less biased but, in most cases, still require culture of the infectious agent, thus precluding identification of viruses or other pathogens that are difficult to grow in culture. In contrast, with the advent of NGS technologies, it is now possible to perform direct sequencing of DNA or RNA isolated from primary diagnos-tic material. Hence, metagenomic shotgun sequencing has the po-tential to fundamentally improve infectious disease diagnostics by allowing broad-range detection of bacterial, viral, fungal, or par-asitic agents in a single assay (Fig. 1) (6–10). Moreover, it extends the exciting possibility to detect pathogen sequences with only distant homology to existing database entries or even to identify entirely novel infectious agents.

In recent years, the steadily decreasing cost of NGS infrastruc-ture and reagents as well as the development of increasingly sim-plified library preparation workflows have made the establish-ment of NGS platforms in clinical labs technically feasible. However, a number of challenges still hinder the widespread use

of this technique in infectious disease diagnostics. One of the most fundamental requirements is the development of analysis soft-ware that is streamlined for the needs of diagnostic laboratories. Although a number of open-source analysis pipelines for NGS-based pathogen detection are available, their use often requires a significant degree of bioinformatic expertise that is typically not available in clinical laboratories. To facilitate clinically actionable diagnostics, appropriate software solutions must also strike a rea-sonable balance between analytical depth and processing time and deliver results within hours rather than days (or even weeks). Fur-thermore, whereas samples that are subject to truly hypothesis-free clinical diagnostics will require pathogen identification across all taxa, the majority of existing pipelines are designed with an emphasis on either viral or bacterial sequences. Currently avail-able commercial software solutions are likewise limited to the analysis of amplicon sequencing of conserved bacterial genes (e.g., 16S rRNA gene) and, therefore, are generally unable to detect viral, fungal, or parasitic agents. One of the few publicly available pipelines that has been specifically designed for use in clinical diagnostics is SURPI, a platform for the unbiased detection of infectious agents in shotgun sequencing data, that has been used to identify viral or bacterial agents in primary diagnostic material (11–13). Clearly, further refinement of this and other pipelines, preferentially with a graphical user interface that facilitates inter-pretation by noninformatics personnel, will be a pivotal require-ment for the future implerequire-mentation of NGS in infectious disease diagnostics.

At present, there is also a profound lack of harmonization and universally recognized standards for NGS-based microbial diag-nostics, a fact that is not surprising given that NGS is still a rela-tively young technique. While a number of studies have proven the technique’s ability to identify diverse pathogens directly from clinical material and, in some instances, in a clinically actionable time frame (11–16), substantially more empirical data will have to be collected to address a number of open questions. For example, given that shotgun sequencing usually only recovers snippets of genomic information rather than whole genomes, what are the requirements to call the presence of a specific infectious agent to a given taxonomic level? Since it is often not possible to unequivo-cally assign fragments to a single species and since current second-generation high-throughput DNA sequencers utilize PCR ampli-fication and thus can only deliver relative rather than total abundance values, how should one arrive at a reasonably mean-ingful abundance estimation for individual infectious agents? How should one deal with potential contaminants, especially those nucleic acids that are frequently introduced via library prep-aration kits (17)? Considering that not only the choice of the se-quencing platform, but also library preparation methods as well as sample matrix composition can have a dramatic impact on the ability to recover infectious agent sequences, what are the read depths at which different diagnostic sample entities should be se-quenced, and what are the limits of detection that should be ex-pected for individual pathogens? Resolving these questions and other issues will not only take time, but also require a significant number of systematic multicenter studies with large sample co-horts. The establishment of novel databases that are rigorously annotated and provide either primary read or assembled contig sequences together with clinical metadata would also be an invalu-able resource, as they would greatly facilitate the identification of “unusual” sequence signatures that may indicate the presence of

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putative pathogens, even if such sequences do not exhibit any recognizable homology to taxonomically classified infectious agents.

Given the number of issues that still need to be addressed, conventional methods for routine diagnostics are unlikely to be completely replaced by unbiased NGS anytime soon. For the in-vestigation of challenging clinical cases or outbreak samples, how-ever, it has already become an invaluable complement to conven-tional tests. In view of its tremendous potential and rapid technological developments, including the steadily increasing throughput of second-generation sequencers and the availability of the first third-generation sequencing units that are small enough to be taken into the field (1), it is clear that unbiased NGS will become an essential instrument in the toolbox of clinical in-fectious disease diagnostics.

ANTIMICROBIAL SUSCEPTIBILITY TESTING USING NEXT-GENERATION METHODS

Over the past century, antimicrobial susceptibility testing (AST) has been dominated by phenotypic approaches. Assays are largely based on the detection of microbial growth. These strategies uti-lize solid or liquid culture media, where the concentration of an-timicrobial agent is adjusted to permit definition of minimum bactericidal or bacteriostatic (collectively, inhibitory) concentra-tions. Formats for such measurements include agar dilution, broth microdilution (BMD), antibiotic gradient diffusion, selec-tive chromogenic media, and ultimately automated systems, such as the Beckman Coulter MicroScan Walkaway (Brea, CA, USA), the Becton, Dickinson and Company Phoenix (Sparks, MD, USA), and the bioMérieux Vitek 2 (Marcy I’Etoile, France). FIG 1Next-generation sequencing for clinical infectious disease diagnostics. (A) Schematic depiction of diagnostic NGS workflows. Nucleic acids isolated from primary diagnostic material are directly queried by either shotgun or amplicon sequencing. Amplicon sequencing uses PCR amplification with primers that target conserved regions (e.g., the bacterial 16S rRNA gene). Clustered amplicon sequences are then compared to appropriate databases (e.g., Greengenes or SILVA) to identify clusters of so-called operational taxonomic units (OTUs) on different taxonomic levels. Amplicon sequencing is sensitive, fast, and cost-effective, but due to the use of specific PCR primers, it is also strongly biased compared to random shotgun sequencing. Shotgun sequencing reads are usually first aligned to the human (or an appropriate animal host) genome to eliminate reads of host origin (digital subtraction). The remaining reads are then either directly mapped to sequence databases or first assembled into longer contiguous sequences (contigs) that are subsequently aligned to the database.De novoassembly considerably increases computational overhead and analysis time but at the same time also significantly decreases classification bias by facilitating the identification of pathogens that exhibit little or no sequence homology to known infectious agents. (B) Whereas the term “metagenomics” in its literal sense suggests the analysis of full genome sequences, the throughput of current NGS technologies usually only allows partial recovery of individual infectious agent genomes, especially in complex diagnostic samples (e.g., stool or respiratory samples). Thus, diagnostic NGS requires bioinformatic approaches that sort sequence fragments (or tags) into taxonomic bins to evaluate the composition of clinical samples.

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Recently, new approaches have been adapted to growth-based AST technology, and most deal with innovative means of distguishing growing from inhibited/dead microorganisms. These in-clude the use of microfluidics (NanoDrop BMD), mass spectrom-etry (including matrix-assisted laser desorption ionization–time of flight), cantilever technology, microcalorimetrics, nuclear mag-netic resonance and magmag-netic bead rotation, real-time micros-copy, and intrinsic fluorescence to name a few (for a recent review, see reference18). All of these approaches are promising and are beyond the proof of principle stage, but none have entered the currentin vitrodiagnostic market.

Whether nucleic acid-based methods can serve as a proxy for growth-based AST methods has yet to be thoroughly vetted for many clinically relevant species (19). These methods excel in re-sistance gene detection, but equating a rere-sistance gene to an actual MIC value is still a work in progress. This may change as high-throughput genomics, including NGS and transcriptomics, be-come increasingly accessible, with transcriptomic analysis of stress marker expression (e.g., the SOS response) potentially offering an opportunity to relate molecular AST with phenotypic susceptibil-ity data (20).

To better understand the potential value of NGS for AST, cent studies have shown that associations between phenotypic re-sistance profiles (antibiograms) and genotypic rere-sistance pre-dicted from whole-genome sequencing (WGS) data can be accurately defined. Using genome sequence information, an in-ventory of all known antibiotic resistance determinants, including mutations within protein-coding and noncoding regions (e.g., regulatory elements), can be obtained (21). This generates a global view of the bacterial resistome that can be used to assess the pres-ence/absence of such genes and mutations inde novomicrobial genome sequences. When comparing theStaphylococcus aureus

resistome to a comprehensive reference antibiogram for a devel-opment set of ~500 strains and an equally sized validation set, the documented percentages of major errors (MEs; predicted to be resistant but phenotypically susceptible) and very major errors (VMEs; predicted to be susceptible but phenotypically resistant) associated with genotypic antibiotic resistance prediction were 0.7% and 0.5%, respectively (59). This is in the same range, or better, than that demonstrated for commercial AST systems. Ad-ditional studies have demonstrated the applicability of this ap-proach for other organisms, but for species that are genetically more heterogeneous thanS. aureus, the levels of MEs and VMEs were higher (22). At present, from a routine laboratory workflow and regulatory standpoint, automated AST systems are better suited for clinical diagnostics; however, with ever decreasing over-heads and further maturation of resistome databases, WGS AST may become increasingly more competitive and invasive in the clinical management of patients (23). In addition, these ap-proaches may promote the discovery and characterization of new and emerging antibiotic resistance mechanisms, which will broaden the reliability of WGS AST, and may stimulate the dis-covery of novel antibiotics.

Despite the obvious optimism surrounding NGS AST plat-forms, prior to their routine implementation in the clinical set-ting, there are several important aspects that must be addressed: (i) establishment of tightly regulated genomic databases (these databases will need continuous update and perhaps supplemen-tation with phenotypic, metabolomic, clinical, and outcome data to accommodate the emergence of antimicrobial resistance), (ii)

implementation of robust, reproducible testing methodologies that generate data in a clinically actionable time frame, (iii) devel-opment of interpretative guidelines specific for these data (24), (iv) approval by various regulatory bodies, and (v) the expense of such testing compared to phenotypic AST. Clearly, there must be extensive collaboration between academic, corporate, and regula-tory bodies to ensure NGS-based AST moves into practice to com-bat the frightening frequency at which multi- and pan-drug-resis-tant strains are isolated (25). Importantly, WGS AST will also provide the identity of the offending microorganism, its virulence potential, and epidemiological typing.

HUMAN MICROBIOME AS A DIAGNOSTIC AND PROGNOSTIC MARKER OF DISEASE

With the advent of benchtop high-throughput DNA sequencing platforms and accessible computational tools, definition of the composition and abundance of microbes (i.e., the microbiome) in a given anatomical environment has been greatly facilitated. Uti-lizing these high-throughput DNA sequencing platforms, numer-ous studies have linked the structure of the microbiome, in par-ticular the fecal microbiome, with human diseases/conditions, including obesity (26), type 2 diabetes (27), bacterial infection (28), cancer (29), malnutrition (30) and drug metabolism (31). Consequently, survey of an individual’s microbiome using high-throughput DNA sequencing methodologies may be diagnostic for a given disorder and, possibly, prognostic of the outcome. However, to account for the extensive microbial variation within and between individuals, it is essential these data are controlled by comparison with microbiome data obtained from healthy and diseased persons spanning a wide geographic and ethnic range.

The mammalian gastrointestinal microbiota elicits a number of key functions, not least of which are the development of the immune system (32) and protection against colonization by anti-biotic-resistant microorganisms (33). Administration of antibiot-ics can perturb this fragile ecological niche (34), resulting in col-onization with antibiotic-resistant organisms or enhanced risk of intestinal infection withClostridium difficile(33). Microbes that undergo marked expansion in the intestine as a result of antibiotic exposure have been associated with invasive bloodstream infec-tion.

To explore a possible relationship between dense intestinal col-onization and bloodstream invasion in humans, investigators per-formed NGS of DNA extracted from fecal specimens obtained from subjects undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) (28). Enterococci, streptococci, and various Proteobacteria, which include members of the family

Enterobacteriaceae, were found to undergo expansion in the gut. Enterococcal intestinal domination was associated with prior metronidazole administration and increased the risk of vancomy-cin-resistantEnterococcus bacteremia 9-fold. Similarly, proteo-bacterial domination resulted in a 5-fold increase in the risk of Gram-negative bacteremia, while dominance was reduced 10-fold by fluoroquinolone treatment.

In an extension of this work, the diversity of the intestinal microbiota was demonstrated to be predictive of mortality in allo-HSCT recipients (35). By analyzing the microbiota of fecal speci-mens collected from 80 subjects at the time of stem cell engraft-ment, it was possible to stratify subjects into high, intermediate, and low microbial diversity groups. Strikingly, overall survival 3 years after allo-HSCT was 36%, 60%, and 67% for the low,

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mediate, and high diversity groups, respectively, implying that high intestinal microbial diversity is prognostic of favorable clin-ical outcomes. Additionally, commensal members of the families

LachnospiraceaeandActinomycetaceaewere associated with sur-vival, while Gram-negative bacteria from the phylum Proteobac-teriawere positively correlated with mortality.

Exposure to antibiotics is related toC. difficileinfection (33,

36), which is a major cause of infectious diarrhea in hospitalized patients (37). To combat this threat, high-throughput DNA se-quencing of the fecal microbiota of mice and hospitalized patients treated with antibiotics was utilized to identify bacterial species associated with resistance toC. difficileinfection (36). The species with the strongest resistance correlation wasClostridium scindens, which dramatically reducedC. difficile infection and attendant weight loss and mortality in an animal model when transferred alone or as part of a microbial consortium postantibiotic expo-sure. The mechanism ofC. difficileinhibition centers on theC. scindens-dependent conversion of primary into secondary bile ac-ids in the cecum and colon. These data suggestC. scindensoffers promise as an alternative treatment option forC. difficile -medi-ated intestinal disease.

In addition to its capacity as a marker for intestinal disease, the gut microbiome has potential as a diagnostic and prognostic marker for systemic diseases, such as rheumatoid arthritis (38). To identify and validate microbial species allied with rheumatoid ar-thritis, high-throughput 16S rRNA gene sequencing of DNA ex-tracted from 114 stool specimens obtained from patients with rheumatoid arthritis and controls was performed (39). In the set-ting of untreated new-onset rheumatoid arthritis,Prevotella copri

was considerably more abundant than in healthy individuals, sig-nifying thatP. coprimay play a role in the pathogenesis of rheu-matoid arthritis. The increase inPrevotellacorrelated with a re-duction in Bacteroides and the loss of reportedly beneficial microbes. Similarly, the gut microbiotas of patients with psoriatic arthritis and skin psoriasis were observed to be less diverse com-pared to healthy controls (40). Whereas some genera were less abundant in the two conditions, psoriatic arthritis patients had a lower abundance of allegedly favorable microbes. Taken together, these data suggest that interrogation of the gut microbiome may be of diagnostic and prognostic utility for arthritis and other sys-temic ailments.

THE ROLE OF CLINICOGENOMICS IN PUBLIC HEALTH MICROBIOLOGY

Over the past 50⫹ years, public health microbiology (“public health microbiology version 1.0”) was constrained with complex and labor-intensive workflows and protocols for microbial cul-ture, identification, growth-based phenotypic susceptibility test-ing, and strain typing (41). Recently, high-throughput DNA se-quencing, particularly bench-top sese-quencing, has brought many new opportunities to this field (42–45) and has allowed bacterial genomics to be integrated into what might be called “public health microbiology version 2.0” (v2.0) through WGS of cultured iso-lates to provide simultaneous information on organism identity, epidemiology, and antimicrobial therapy (Fig. 2).

As a practical example of public health microbiology v2.0, a recent case study describes how WGS was applied to a protracted hospital outbreak of multidrug-resistantAcinetobacter baumannii

in Birmingham, England (46). The results showed that the out-break strain was distinct from previously genome-sequenced

strains and enabled the identification of seven major genotypic clusters within the outbreak. WGS also allowed the investigative team to rule 17 initially suspicious isolates as unrelated to the outbreak strain. Sequence analysis of multiple strains isolated from the same patient documented strains with various degrees of genomic diversity within the patient, including strains with only a few differences at the genomic level and strains that differed greatly. Using WGS data and conventional epidemiology, the study team was able to reconstruct potential transmission events that linked all but seven of the patients and could also associate patient isolates to those recovered from the environment. WGS focused attention on a contaminated bed and on a burns unit as the source and site of transmission, catalyzing improvements in decontamination protocols. This approach has also been adopted forMycobacterium tuberculosisisolates (47).

To fast forward into the near future (public health microbiol-ogy v2.1), it is plausible that culture of bacterial isolates might in some settings be replaced by shotgun metagenomic sequencing of clinical samples. There are several potential advantages of diag-nostic metagenomics (10). It represents a one-size-fits-all ap-proach to all bacteria that contrasts with the need for so many different laboratory media and atmospheric conditions in con-ventional bacteriology, it avoids the onerous optimization of tar-get-specific assays needed for amplification- or probe-based diag-nosis, and it is unbiased and open-ended, i.e., not restricted to finding only what you expected to find. A second case study high-lights this approach, in which metagenomics was applied to fecal samples that were obtained from patients with diarrhea during the 2011 outbreak of Shiga toxin-producingEscherichia coli(STEC) O104:H4 in Germany (16). The investigative team obtained the genome of the STEC outbreak strain from 10 samples at greater than 10-fold coverage and from over two dozen samples at greater than 1-fold coverage. In several samples, they found an increased coverage of the Shiga toxin bacteriophage genome relative to those of other STEC sequences. From some samples, they recovered sequences from Clostridium difficile, Campylobacter jejuni, and

Salmonella enterica, and from one, they recovered sequences from the emerging human pathogenCampylobacter concisus, illustrat-ing the ability of metagenomics to deliver unexpected results.

Metagenomic analysis has also be applied to the recovery ofM. tuberculosisgenomes from historical and contemporary human samples, and the results have shown that mixed infections were common in 18th century Europe. Further, in a proof-of-principle study, the same process was used to identify and characterize pathogenic mycobacteria in modern sputum samples (48–50). There have been several other recent proof-of-principle studies that demonstrate the utility of this diagnostic approach (13,15,

51,52).

We can envisage an even more ambitious vision for public health microbiology v3.0, in which long-read single-molecule nanopore sequencing will enable an integrated approach to mac-romolecular monitoring, combining analysis of DNA, RNA, and proteins shed in urine and feces together with characterization of informational macromolecules circulating in the bloodstream to provide information not just on infection but also on, for exam-ple, cancer and the health of the fetus or of organ transplants (53–57).

However, there will be a need for a new computational infra-structure to cope with the demands of big data in clinical micro-biology, including the role of cloud computing (58), illustrated by

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the CLIMB (CLoud Infrastructure for Microbial Bioinformatics) project supported by the United Kingdom’s Medical Research Council (http://www.climb.ac.uk).

CONCLUSION

Based on the discussions above, next-generation sequencing will steadily work its way into routine diagnostic use within clinical and public health laboratories over the coming years. This predic-tion, albeit not entirely in the near future, is based on the univer-sality of the science, i.e., its applicability to the diagnosis of infec-tious processes and resistance markers in an unbiased fashion for all manner of microorganisms, be they viral, bacterial, fungal, or parasitic. Furthermore, it will allow for the ability to monitor changes in the human (or animal) microbiome that forecast the potential risk for, or the existence of, other noninfectious disease processes, thus allowing earlier intervention or avoidance and perhaps even alternative treatment modalities. While most of this review centers on the use of NGS and all of the analytical permu-tations that have been developed in conjunction with it, we can likely expect more user-friendly distillations of these studies (i.e., multiplex PCR assays) to appear in clinical laboratories in the near future.

ACKNOWLEDGMENTS

A.G. receives project funding from the German Centre for Infection Re-search under grant TTU 07.802. E.G.P. receives project funding from the

National Institutes of Health under grants 1RO1 AI42135 and AI95706 and from the Tow Foundation. M.J.P. was funded by the United King-dom’s Medical Research Council, Biotechnology and Biological Sciences Research Council, and National Institute for Health Research together with funding from Warwick Medical School and collaborative input from Illumina. W.M.D., L.F.W., and A.V.B. did not receive external funding for this project. W.M.D. and A.V.B. are employees of bioMérieux, Inc.

REFERENCES

1.Quick J, Ashton P, Calus S, Chatt C, Gossain S, Hawker J, Nair S, Neal K, Nye K, Peters T, De Pinna E, Robinson E, Struthers K, Webber M, Catto A, Dallman TJ, Hawkey P, Loman NJ.2015. Rapid draft sequenc-ing and real-time nanopore sequencsequenc-ing in a hospital outbreak of Salmo-nella. Genome Biol16:114.http://dx.doi.org/10.1186/s13059-015-0677-2. 2.Judge K, Harris SR, Reuter S, Parkhill J, Peacock SJ.2015. Early insights into the potential of the Oxford Nanopore MinION for the detection of antimicrobial resistance genes. J Antimicrob Chemother70:2775–2778.

http://dx.doi.org/10.1093/jac/dkv206.

3.Ambrose HE, Granerod J, Clewley JP, Davies NW, Keir G, Cunning-ham R, Zuckerman M, Mutton KJ, Ward KN, Ijaz S, Crowcroft S, Brown DW, UK Aetiology of Encephalitis Study Group.2011. Diag-nostic strategy used to establish etiologies of encephalitis in a prospective cohort of patients in England. J Clin Microbiol49:3576 –3583.http://dx .doi.org/10.1128/JCM.00862-11.

4.Denno DM, Shaikh N, Stapp JR, Qin X, Hutter CM, Hoffman V, Mooney JC, Wood KM, Stevens HJ, Jones R, Tarr PI, Klein EJ.2012. Diarrhea etiology in a pediatric emergency department: a case control study. Clin Infect Dis55:897–904.http://dx.doi.org/10.1093/cid/cis553. 5.Louie JK, Hacker JK, Gonzales R, Mark J, Maselli JH, Yagi S, Drew WL.

2005. Characterization of viral agents causing acute respiratory infection FIG 2Progressive integration of genomics and metagenomics into public health microbiology. As time progresses, we anticipate that the 19th century techniques of microscopy and culture will give way to sequence-based approaches, which will also lead to closer integration with the rest of laboratory medicine.

on May 16, 2020 by guest

http://jcm.asm.org/

(7)

in a San Francisco University Medical Center clinic during the influenza season. Clin Infect Dis41:822– 828.http://dx.doi.org/10.1086/432800. 6.Barzon L, Lavezzo E, Constanzi G, Franchin E, Toppo S, Palu G.2013.

Next-generation sequencing technologies in diagnostic virology. J Clin Virol58:346 –350.http://dx.doi.org/10.1016/j.jcv.2013.03.003. 7.Chiu CY.2013. Viral pathogen discovery. Curr Opin Microbiol16:468 –

478.http://dx.doi.org/10.1016/j.mib.2013.05.001.

8.Dunne WM, Jr, Westblade LF, Ford B. 2012. Next-generation and whole-genome sequencing in the diagnostic clinical microbiology labora-tory. Eur J Clin Microbiol Infect Dis31:1719 –1726.http://dx.doi.org/10 .1007/s10096-012-1641-7.

9.Miller RR, Montoya V, Gardy JL, Patrick DM, Tang P.2013. Metag-enomics for pathogen detection in public health. Genome Med5:81.http: //dx.doi.org/10.1186/gm485.

10. Pallen MJ.2014. Diagnostic metagenomics: potential applications to bac-terial, viral, and parasitic infections. Parasitology141:1856 –1862.http: //dx.doi.org/10.1017/S0031182014000134.

11. Greninger AL, Naccache SN, Messacar K, Clayton A, Yu G, Somasekar S, Federman S, Stryke D, Anderson C, Yagi S, Messenger S, Wadford D, Xia D, Watt JP, van Haren K, Dominguez SR, Glaser C, Aldrovandi G, Chiu CY.2015. A novel outbreak enterovirus D68 strain associated with acute flaccid myelitis cases in the USA (2012–14): a retrospective cohort study. Lancet Infect Dis15:671– 682.http://dx.doi.org/10.1016/S1473 -3099(15)70093-9.

12. Naccache SN, Federman S, Veeraraghavan N, Zaharia M, Lee D, Sa-mayoa E, Bouquet J, Greninger AL, Luk KC, Enge B, Wadford DA, Messenger SL, Genrich GL, Pellegrino K, Grard G, Leroy E, Schneider BS, Fair JN, Martinez MA, Isa P, Crump JA, DeRisi JL, Sittler T, Hackett J, Jr, Miller S, Chiu CY.2014. A cloud-compatible bioinformat-ics pipeline for ultrarapid pathogen identification from next-generation sequencing of clinical samples. Genome Res24:1180 –1192.http://dx.doi .org/10.1101/gr.171934.113.

13. Wilson MR, Naccache SN, Samayoa E, Biagtan M, Bashir H, Yu G, Salamat SM, Somasekar S, Federman S, Miller S, Sokolic R, Garabedian E, Candotti F, Buckley RH, Reed KD, Meyer TL, Seroogy CM, Galloway R, Henderson SL, Gern JE, DeRisi JL, Chiu CY. 2014. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N Engl J Med370:2408 –2417.http://dx.doi.org/10.1056/NEJMoa1401268. 14. Fischer N, Indenbirken D, Meyer T, Lutgehetmann M, Lellek H, Spohn

M, Aepfelbacher M, Alawi M, Grundoff A.2015. Evaluation of unbiased next-generation sequencing of RNA (RNA-seq) as a diagnostic method in influenza virus-positive respiratory samples. J Clin Microbiol53:2238 – 2250.http://dx.doi.org/10.1128/JCM.02495-14.

15. Fischer N, Rohde H, Indenbirken D, Gunther T, Reumann K, Lutge-hetmann M, Meyer T, Kluge S, Aepfelbacker M, Alawi M, Grundoff A. 2014. Rapid metagenomic diagnostics for suspected outbreak of severe pneumonia. Emerg Infect Dis20:1072–1075.http://dx.doi.org/10.3201 /eid2006.131526.

16. Loman NJ, Constantinidou C, Christner M, Rohde H, Chan JZ, Quick J, Weir JC, Quince C, Smith GP, Betley JR, Aepfelbacher M, Pallen MJ. 2013. A culture-independent sequence-based metagenomics approach to the investigation of an outbreak of Shiga-toxigenicEscherichia coliO104: H4. JAMA309:1502–1510.http://dx.doi.org/10.1001/jama.2013.3231. 17. Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO, Moffatt MF,

Turner P, Parkhill J, Loman NJ, Walker AW.2014. Reagent and labo-ratory contamination can critically impact sequence-based microbiome analyses. BMC Biol12:87.http://dx.doi.org/10.1186/s12915-014-0087-z. 18. van Belkum A, Dunne WM, Jr.2013. Next-generation antimicrobial susceptibility testing. J Clin Microbiol51:2018 –2024.http://dx.doi.org/10 .1128/JCM.00313-13.

19. Cangelosi GA, Meschke JS.2014. Dead or alive: molecular assessment of microbial viability. Appl Environ Microbiol80:5884 –5891.http://dx.doi .org/10.1128/AEM.01763-14.

20. Barczak AK, Gomez JE, Kaufmann BB, Hinson ER, Cosimi L, Borowsky ML, Onderdonk AB, Stanley SA, Kaur D, Bryant KF, Knipe DM, Sloutsky A, Hung DT.2012. RNA signatures allow rapid identification of pathogens and antibiotic susceptibilities. Proc Natl Acad Sci U S A109: 6217– 6222.http://dx.doi.org/10.1073/pnas.1119540109.

21. Walker TM, Kohl TA, Omar SV, Hedge J, Del Ojo Elias C, Bradley P, Iqbal Z, Feuerriegel S, Niehaus KE, Wilson DJ, Clifton DA, Kapatai G, Ip CL, Bowden R, Drobniewski FA, Allix-Béguec C, Gaudin C, Parkhill J, Diel R, Supply P, Crook DW, Smith EG, Walker AS, Ismail N,

Niemann S, Peto TE, Modernizing Medical Microbiology (MMM) Informatics Group.2015. Whole-genome sequencing for prediction of

Mycobacterium tuberculosisdrug susceptibility and resistance: a retrospec-tive cohort study. Lancet Infect Dis15:1193–1202.http://dx.doi.org/10 .1016/S1473-3099(15)00062-6.

22. Holt KE, Wertheim H, Zadoks RN, Baker S, Whitehouse CA, Dance D, Jenney A, Connor TR, Hsu LY, Severin J, Brisse S, Cao H, Wilksch J, Gorrie C, Schultz MB, Edwards DJ, Nguyen KV, Nguyen TV, Dao TT, Mensink M, Minh VL, Nhu NT, Schultsz C, Kuntaman K, Newton PN, Moore CE, Strugnell RA, Thomson NR.2015. Genomic analysis of diversity, population structure, virulence, and antimicrobial resistance in

Klebsiella pneumoniae, an urgent threat to public health. Proc Natl Acad Sci U S A112:E3574 –E3581.http://dx.doi.org/10.1073/pnas.1501049112. 23. Wright GD.2007. The antibiotic resistome: the nexus of chemical and genetic diversity. Nat Rev Microbiol5:175–186.http://dx.doi.org/10.1038 /nrmicro1614.

24. Kahlmeter G.2015. The 2014 Garrod lecture: EUCAST – are we heading towards international agreement? J Antimicrob Chemother70:2427– 2439.http://dx.doi.org/10.1093/jac/dkv145.

25. Nathan C, Cars O.2014. Antibiotic resistance–problems, progress, and prospects. N Engl J Med 371:1761–1763. http://dx.doi.org/10.1056 /NEJMp1408040.

26. Turnbaugh PJ, Hamady M, Yatsunenko T, Cantarel BL, Duncan A, Ley RE, Sogin ML, Jones WJ, Roe BA, Affourtit JP, Egholm M, Henrissat B, Heath AC, Knight R, Gordon JI.2009. A core gut microbiome in obese and lean twins. Nature 457:480 – 484. http://dx.doi.org/10.1038 /nature07540.

27. Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, Peng Y, Zhang D, Jie Z, Wu W, Qin Y, Xue W, Li J, Han L, Lu D, Wu P, Dai Y, Sun X, Li Z, Tang A, Zhong S, Li X, Chen W, Xu R, Wang M, Feng Q, Gong M, Yu J, Zhang Y, Xhang M, Hansen T, Sanchez G, Raes J, Falony G, Okuda S, Almeida M, LeChatelier E, Renault P, Pons N, Batto JM, Zhang Z, Chen H, Yang R, Zheng W, Li S, Yang H, Wang J, Ehrlich SD, Nielsen R, Pedersen O, Kristiansen K, Wang J.2012. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490:55– 60. http://dx.doi.org/10.1038 /nature11450.

28. Taur Y, Xavier JB, Lipuma L, Ubeda C, Goldberg J, Gobourne A, Lee YJ, Dubin KA, Socc ND, Viale A, Perales MA, Jenq RR, van den Brink MR, Pamer EG.2012. Intestinal domination and the risk of bacteremia in patients undergoing allogeneic hematopoietic stem cell transplantation. Clin Infect Dis55:905–914.http://dx.doi.org/10.1093/cid/cis580. 29. Ahn J, Sinha R, Pei Z, Dominianni C, Wu J, Shi J, Goedert JJ, Hayes RB,

Yang L.2013. Human gut microbiome and risk for colorectal cancer. J Natl Cancer Inst18:1907–1911.

30. Smith MI, Yatsunenko T, Manary MJ, Trehan I, Mkakosya R, Cheng J, Kau AL, Rich SS, Concannon P, Mychaleckyj JC, Liu J, Houpt E, Li JV, Holmes E, Nicholson J, Knights D, Ursell LK, Knight R, Gordon JI. 2013. Gut microbiomes of Malawian twin pairs discordant for kwashior-kor. Science339:548 –554.http://dx.doi.org/10.1126/science.1229000. 31. Haiser HJ, Gootenberg DB, Chatman K, Sirasani G, Balskus EP,

Turn-baugh PJ.2013. Predicting and manipulating cardiac drug inactivation by the human gut bacteriumEggerthella lenta. Science341:295–298.http://dx .doi.org/10.1126/science.1235872.

32. Cebra JJ.1999. Influences of microbiota on intestinal immune system development. Am J Clin Nutr69:S1046 –S1051.

33. Buffie CG, Pamer EG.2013. Microbiota-mediated colonization resis-tance against intestinal pathogens. Nat Rev Immunol13:790 – 801.http: //dx.doi.org/10.1038/nri3535.

34. Dethlefsen L, Huse S, Sogin ML, Relman DA.2008. The pervasive effects on an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing. PLoS Biol 6:e280. http://dx.doi.org/10.1371/journal .pbio.0060280.

35. Taur Y, Jenq RR, Perales MA, Littmann ER, Morjaria S, Ling L, No D, Gobourne A, Viale A, Dahi PB, Ponce DM, Barker JN, Giralt S, van den Brink M, Pamer EG.2014. The effects of intestinal tract bacterial diversity on mortality following allogeneic hematopoietic stem cell transplantation. Blood124:1174 –1182.http://dx.doi.org/10.1182/blood-2014-02-554725. 36. Buffie CG, Bucci V, Stein RR, McKenney PT, Ling L, Gobourne A, No D, Liu H, Kinnebrew M, Viale A, Littmann E, van den Brink MR, Jenq RR, Taur Y, Sander C, Cross JR, Toussaint NC, Xavier JB, Pamer EG. 2015. Precision microbiome reconstitution restores bile acid mediated resistance toClostridium difficile. Nature517:205–208.

on May 16, 2020 by guest

http://jcm.asm.org/

(8)

37. Rupnik M, Wilcox MH, Gerding DH.2009.Clostridium difficile infec-tion: new developments in epidemiology and pathogenesis. Nat Rev Mi-crobiol7:526 –536.http://dx.doi.org/10.1038/nrmicro2164.

38. Scher JU, Abramson SB.2011. The microbiome and rheumatoid arthri-tis. Nat Rev Rheumatol7:569 –578.

39. Scher JU, Sczesnak A, Longman RS, Segata N, Ubeda C, Bielski C, Rostron T, Cerundolo V, Pamer EG, Abramson SB, Huttenhower C, Littman DR.2013. Expansion of intestinalPrevotella copricorrelates with enhanced susceptibility to arthritis. eLife2:e01202.

40. Scher JU, Ubeda C, Artacho A, Attur M, Isaac S, Reddy SM, Marmon S, Neimann A, Brusca S, Patel T, Manasson J, Pamer EG, Littman DR, Abramson SB.2015. Decreased bacterial diversity characterizes the al-tered gut microbiota in patients with psosiatic arthritis, resembling dys-biosis in inflammatory bowel disease. Arthritis Rheumatol67:128 –139.

http://dx.doi.org/10.1002/art.38892.

41. Didelot X, Bowden R, Wilson DJ, Peto TE, Crook DW.2012. Trans-forming clinical microbiology with bacterial genome sequencing. Nat Rev Genet13:601– 612.http://dx.doi.org/10.1038/nrg3226.

42. Loman NJ, Constantinidou C, Chan JZ, Halachev M, Sergeant M, Penn CW, Robinson ER, Pallen MJ.2012. High-throughput bacterial genome sequencing: an embarrassment of choice, a world of opportunity. Nat Rev Microbiol10:599 – 606.http://dx.doi.org/10.1038/nrmicro2850. 43. Pallen MJ, Loman NJ.2011. Are diagnostic and public health

bacteriol-ogy ready to become branches of genomic medicine? Genome Med3:53.

http://dx.doi.org/10.1186/gm269.

44. Pallen MJ, Loman NJ, Penn CW.2010. High-throughput sequencing and clinical microbiology: progress, opportunities and challenges. Curr Opin Mi-crobiol13:625– 631.http://dx.doi.org/10.1016/j.mib.2010.08.003. 45. Robinson ER, Walker TM, Pallen MJ. 2013. Genomics and outbreak

investigation: from sequence to consequence. Genome Med5:36. 46. Halachev MR, Chan JZ, Constantinidou CI, Cumley N, Bradley C,

Smith-Banks M, Oppenheim B, Pallen MJ.2014. Genomic epidemiology of a protracted hospital outbreak caused by multidrug-resistant Acineto-bacter baumanniiin Birmingham, England. Genome Med6:70.http://dx .doi.org/10.1186/s13073-014-0070-x.

47. Heart of England NHS Foundation Trust.2014. TB genomics service pilot project. HEFT Pathology, Birmingham, United Kingdom. http: //www.heftpathology.com/item/tb-genomics-pilot-scheme.html. 48. Chan JZ, Sergeant MJ, Lee OY, Minnikin DE, Besra GS, Pap I,

Spigel-man M, Donoghue HD, Pallen MJ. 2013. Metagenomic analysis of tuberculosis in a mummy. N Engl J Med369:289 –290.http://dx.doi.org /10.1056/NEJMc1302295.

49. Doughty EL, Sergeant MJ, Adetifa I, Antonio M, Pallen MJ. 2014. Culture-independent detection and characterisation ofMycobacterium tuberculosisandM. africanumin sputum samples using shotgun meta-genomics on a benchtop sequencer. PeerJ2:e585.http://dx.doi.org/10 .7717/peerj.585.

50. Kay GL, Sergeant MJ, Zhou Z, Chan JZ, Millard A, Quick J, Szikossy I, Pap I, Spigelman M, Loman NJ, Achtman M, Donoghue HD, Pallen MJ.2015. Eighteenth-century genomes show that mixed infections were common at time of peak tuberculosis in Europe. Nat Commun6:6717.

http://dx.doi.org/10.1038/ncomms7717.

51. Andersson P, Klein M, Lilliebridge RA, Giffard PM.2013. Sequences of multiple bacterial genomes and aChlamydia trachomatisgenotype from direct sequencing of DNA derived from a vaginal swab diagnostic speci-men. Clin Microbiol Infect 19:E405–E408. http://dx.doi.org/10.1111 /1469-0691.12237.

52. Hasman H, Saputra D, Sicheritz-Ponten T, Lund O, Svendsen CA, Frimodt-Moller N, Aarestrup FM.2014. Rapid whole-genome sequenc-ing for detection and characterization of microorganisms directly from clinical samples. J Clin Microbiol52:139 –146.http://dx.doi.org/10.1128 /JCM.02452-13.

53. Acharya S, Edwards S, Schmidt J.2015. Research highlights: nanopore protein detection and analysis. Lab Chip15:3424 –3427.http://dx.doi.org /10.1039/C5LC90076J.

54. Ayub M, Stoddart D, Bayley H.2015. Nucleobase recognition by trun-cated alpha hemolysin pores. ACS Nano9:7895–7903.http://dx.doi.org /10.1021/nn5060317.

55. Daly KP.2015. Circulating donor-derived cell-free DNA: a true bio-marker for cardiac allograft rejection? Ann Transl Med3:47.

56. Ignatiadis M, Dawson SJ.2014. Circulating tumor cells and circulating tumor DNA for precision medicine: dream or reality? Ann Oncol25: 2304 –2313.http://dx.doi.org/10.1093/annonc/mdu480.

57. Liao GJ, Gronowski AM, Zhao Z.2014. Non-invasive prenatal testing using cell-free fetal DNA in maternal circulation. Clin Chim Acta428:44 – 50.http://dx.doi.org/10.1016/j.cca.2013.10.007.

58. Drake N.2015. How to catch a cloud. Nature522:115–116.http://dx.doi .org/10.1038/522115a.

59. Gordon NC, Price JR, Cole K, Everitt R, Morgan M, Finney J, Kearns AM, Pichon B, Young B, Wilson DJ, Llewelyn MJ, Paul J, Peto TEA, Crook DW, Walker AS, Golubchik T.2014. Prediction ofStaphylococcus aureusantimicrobial resistance by whole-genome sequencing. J Clin Mi-crobiol52:1182–1191.http://dx.doi.org/10.1128/JCM.03117-13.

Lars F. Westblade,Ph.D., is an Assistant Pro-fessor in Pathology and Laboratory Medicine at Weill Cornell Medical College and the Associate Director for Microbiology at New York-Presby-terian Hospital (Weill Cornell Campus). Prior to joining Weill Cornell Medical College, he was an Assistant Professor at Emory University. He completed his training in Medical and Public Health Microbiology under the direction of Dr. Michael Dunne and Dr. Carey-Ann Burnham at Washington University School of Medicine

in St. Louis. Dr. Westblade is a Diplomate of the American Board of Medical Microbiology.

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Figure

FIG 1 Next-generation sequencing for clinical infectious disease diagnostics. (A) Schematic depiction of diagnostic NGS workflows

FIG 1

Next-generation sequencing for clinical infectious disease diagnostics. (A) Schematic depiction of diagnostic NGS workflows p.3
FIG 2 Progressive integration of genomics and metagenomics into public health microbiology

FIG 2

Progressive integration of genomics and metagenomics into public health microbiology p.6

References