Role of Clinicogenomics in Infectious Disease Diagnostics and PublicHealth Microbiology

Lars F. Westblade,a Alex van Belkum,b Adam Grundhoff,c,d George M. Weinstock,e Eric G. Pamer,f Mark 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; TheJackson 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 tothis 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) presentedat 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.

The explosion of microbiome research is driven by high-throughput DNA sequencing, so-called next-generation se-

quencing (NGS), technologies that allow the genomic content ofentire microbial communities (bacterial, viral, and eukaryotic or-ganisms) to be described. Although much of this work is aimed atdescribing 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 detectionof microbes is independent of culture and is not limited to targetsused for PCR assays. Rather, it is a process of generating large-scalesequence 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, orother features.

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

The sensitivity and specificity of this method are determined inlarge part by the NGS technology. Before NGS, the full-length 16SrRNA gene was sequenced with high-quality, 700-base-long readsof Sanger, or chain termination, sequencing (sometimes referredto as “first-generation” sequencing technology). This was labori-ous and expensive, and deep sampling was not possible. WhenNGS became available, most work was done on the FLX sequenc-ing instrument (a second-generation sequencing technology)from 454 Life Sciences (Roche Diagnostics, Indianapolis, IN,USA). This only permitted 400-base-long sequencing reads, andonly a portion of the 16S rRNA gene was sequenced. The 16SrRNA gene has nine hypervariable regions that provide much ofthe 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 frompatients 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 MiSeqand HiSeq sequencing instruments from Illumina (San Diego,CA, USA). This is in part due to the closing of 454 Life Sciencesand to the higher data production and lower cost of the Illuminainstruments. These instruments produce shorter reads (100 to 300bases) and thus further limit the amount of the 16S rRNA genethat can be sampled and are often limited to a single hypervariableregion. However, organism identification is possible as a result ofthe shotgun sequencing of several hypervariable regions.

A new alternative to Illumina has been developed using thePacific 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 longersequence reads are possible, and full-length 16S rRNA gene se-quencing can now be accomplished at higher data output, lowercost, and much greater convenience than was possible with Sangersequencing. This methodology is still more expensive than Illumi-na’s platform but bodes well for continued improvement in theuse 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 allparts of the genome are sequenced. Whereas the 16S rRNA gene isfound only in bacteria, shotgun sequencing is agnostic, and ar-

Accepted manuscript posted online 24 February 2016

Citation Westblade LF, van Belkum A, Grundhoff A, Weinstock GM, Pamer EG,Pallen MJ, Dunne WM, Jr. 2016. Role of clinicogenomics in infectious diseasediagnostics 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.

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chaebacterial, viruses, and eukaryotic microbes are also sampled.This is often referred to as metagenomic shotgun sequencing sinceall genomes (the metagenome) are sequenced. This approach re-quires many more sequencing reads than 16S rRNA gene sequenc-ing to adequately sample the genomes, and thus only the sequenc-ing platforms that produce the most data are used (Illumina HiSeqand NextSeq instruments). This methodology is significantlymore expensive than 16S rRNA gene sequencing, and this has alsolimited its use. However, metagenomic shotgun sequencing alsoallows 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 nonpathogenic members of aspecies. Shotgun sequencing is also used for analysis of RNA,either to identify RNA viruses or for transcriptional analysis. Inthis case, cDNA is generated and then NGS is performed. Met-agenomic transcription analysis is particularly noteworthy, asthis method determines which organisms are actively growingand/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 limitedby 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 NanoporeTechnologies (Oxford, United Kingdom) is a handheld sequenc-ing instrument, and although these instruments are still in thedevelopment phase, they have been used to sequence bacterial andviral samples (1, 2). Thus, one can expect continued developmentin this area and more routine use of these methods in the future forroutine 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 infectiousdisease diagnostics. However, while generally cost-effective androbust, these methods share a limitation: they represent targeteddetection approaches and require an accurate initial hypothesis asto the type of pathogen(s) that may be present in the sample ofinterest. 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-basedapproaches are less biased but, in most cases, still require cultureof the infectious agent, thus precluding identification of viruses orother pathogens that are difficult to grow in culture. In contrast,with the advent of NGS technologies, it is now possible to performdirect 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 byallowing broad-range detection of bacterial, viral, fungal, or par-asitic agents in a single assay (Fig. 1) (6–10). Moreover, it extendsthe exciting possibility to detect pathogen sequences with onlydistant homology to existing database entries or even to identifyentirely 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 mostfundamental 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 asignificant degree of bioinformatic expertise that is typically notavailable in clinical laboratories. To facilitate clinically actionablediagnostics, appropriate software solutions must also strike a rea-sonable balance between analytical depth and processing time anddeliver 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 acrossall taxa, the majority of existing pipelines are designed with anemphasis on either viral or bacterial sequences. Currently avail-able commercial software solutions are likewise limited to theanalysis of amplicon sequencing of conserved bacterial genes (e.g.,16S rRNA gene) and, therefore, are generally unable to detectviral, fungal, or parasitic agents. One of the few publicly availablepipelines that has been specifically designed for use in clinicaldiagnostics is SURPI, a platform for the unbiased detection ofinfectious agents in shotgun sequencing data, that has been usedto 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 implementation of NGS in infectious diseasediagnostics.

At present, there is also a profound lack of harmonization anduniversally 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 proventhe technique’s ability to identify diverse pathogens directly fromclinical material and, in some instances, in a clinically actionabletime frame (11–16), substantially more empirical data will have tobe collected to address a number of open questions. For example,given that shotgun sequencing usually only recovers snippets ofgenomic information rather than whole genomes, what are therequirements to call the presence of a specific infectious agent to agiven 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 totalabundance values, how should one arrive at a reasonably mean-ingful abundance estimation for individual infectious agents?How should one deal with potential contaminants, especiallythose 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 assample matrix composition can have a dramatic impact on theability to recover infectious agent sequences, what are the readdepths 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 andother issues will not only take time, but also require a significantnumber of systematic multicenter studies with large sample co-horts. The establishment of novel databases that are rigorouslyannotated and provide either primary read or assembled contigsequences 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 anyrecognizable homology to taxonomically classified infectiousagents.

Given the number of issues that still need to be addressed,conventional methods for routine diagnostics are unlikely to becompletely 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 rapidtechnological developments, including the steadily increasingthroughput of second-generation sequencers and the availabilityof the first third-generation sequencing units that are smallenough to be taken into the field (1), it is clear that unbiased NGSwill become an essential instrument in the toolbox of clinical in-fectious disease diagnostics.

ANTIMICROBIAL SUSCEPTIBILITY TESTING USINGNEXT-GENERATION METHODS

Over the past century, antimicrobial susceptibility testing (AST)has been dominated by phenotypic approaches. Assays are largelybased 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 minimumbactericidal 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, suchas 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 1 Next-generation sequencing for clinical infectious disease diagnostics. (A) Schematic depiction of diagnostic NGS workflows. Nucleic acids isolated fromprimary diagnostic material are directly queried by either shotgun or amplicon sequencing. Amplicon sequencing uses PCR amplification with primers that targetconserved regions (e.g., the bacterial 16S rRNA gene). Clustered amplicon sequences are then compared to appropriate databases (e.g., Greengenes or SILVA) toidentify clusters of so-called operational taxonomic units (OTUs) on different taxonomic levels. Amplicon sequencing is sensitive, fast, and cost-effective, but dueto the use of specific PCR primers, it is also strongly biased compared to random shotgun sequencing. Shotgun sequencing reads are usually first aligned to thehuman (or an appropriate animal host) genome to eliminate reads of host origin (digital subtraction). The remaining reads are then either directly mapped tosequence databases or first assembled into longer contiguous sequences (contigs) that are subsequently aligned to the database. De novo assembly considerablyincreases computational overhead and analysis time but at the same time also significantly decreases classification bias by facilitating the identification ofpathogens that exhibit little or no sequence homology to known infectious agents. (B) Whereas the term “metagenomics” in its literal sense suggests the analysisof full genome sequences, the throughput of current NGS technologies usually only allows partial recovery of individual infectious agent genomes, especially incomplex 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-basedAST technology, and most deal with innovative means of distin-guishing growing from inhibited/dead microorganisms. These in-clude the use of microfluidics (NanoDrop BMD), mass spectrom-etry (including matrix-assisted laser desorption ionization–timeof flight), cantilever technology, microcalorimetrics, nuclear mag-netic resonance and magnetic bead rotation, real-time micros-copy, and intrinsic fluorescence to name a few (for a recent review,see reference 18). All of these approaches are promising and arebeyond the proof of principle stage, but none have entered thecurrent in vitro diagnostic market.

Whether nucleic acid-based methods can serve as a proxy forgrowth-based AST methods has yet to be thoroughly vetted formany clinically relevant species (19). These methods excel in re-sistance gene detection, but equating a resistance gene to an actualMIC 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 stressmarker expression (e.g., the SOS response) potentially offering anopportunity to relate molecular AST with phenotypic susceptibil-ity data (20).

To better understand the potential value of NGS for AST, re-cent studies have shown that associations between phenotypic re-sistance profiles (antibiograms) and genotypic resistance pre-dicted from whole-genome sequencing (WGS) data can beaccurately defined. Using genome sequence information, an in-ventory of all known antibiotic resistance determinants, includingmutations within protein-coding and noncoding regions (e.g.,regulatory elements), can be obtained (21). This generates a globalview of the bacterial resistome that can be used to assess the pres-ence/absence of such genes and mutations in de novo microbialgenome sequences. When comparing the Staphylococcus aureusresistome to a comprehensive reference antibiogram for a devel-opment set of ~500 strains and an equally sized validation set, thedocumented percentages of major errors (MEs; predicted to beresistant but phenotypically susceptible) and very major errors(VMEs; predicted to be susceptible but phenotypically resistant)associated with genotypic antibiotic resistance prediction were0.7% and 0.5%, respectively (59). This is in the same range, orbetter, 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 geneticallymore heterogeneous than S. aureus, the levels of MEs and VMEswere higher (22). At present, from a routine laboratory workflowand regulatory standpoint, automated AST systems are bettersuited for clinical diagnostics; however, with ever decreasing over-heads and further maturation of resistome databases, WGS ASTmay become increasingly more competitive and invasive in theclinical management of patients (23). In addition, these ap-proaches may promote the discovery and characterization of newand emerging antibiotic resistance mechanisms, which willbroaden 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 (thesedatabases will need continuous update and perhaps supplemen-tation with phenotypic, metabolomic, clinical, and outcome datato accommodate the emergence of antimicrobial resistance), (ii)

implementation of robust, reproducible testing methodologiesthat 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 ofsuch testing compared to phenotypic AST. Clearly, there must beextensive 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 alsoprovide the identity of the offending microorganism, its virulencepotential, and epidemiological typing.

HUMAN MICROBIOME AS A DIAGNOSTIC AND PROGNOSTICMARKER OF DISEASE

With the advent of benchtop high-throughput DNA sequencingplatforms and accessible computational tools, definition of thecomposition and abundance of microbes (i.e., the microbiome) ina 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 diagnosticfor a given disorder and, possibly, prognostic of the outcome.However, to account for the extensive microbial variation withinand between individuals, it is essential these data are controlled bycomparison with microbiome data obtained from healthy anddiseased persons spanning a wide geographic and ethnic range.

The mammalian gastrointestinal microbiota elicits a numberof key functions, not least of which are the development of theimmune 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 ofintestinal infection with Clostridium difficile (33). Microbes thatundergo marked expansion in the intestine as a result of antibioticexposure 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 obtainedfrom subjects undergoing allogeneic hematopoietic stem celltransplantation (allo-HSCT) (28). Enterococci, streptococci, andvarious Proteobacteria, which include members of the familyEnterobacteriaceae, were found to undergo expansion in the gut.Enterococcal intestinal domination was associated with priormetronidazole administration and increased the risk of vancomy-cin-resistant Enterococcus bacteremia 9-fold. Similarly, proteo-bacterial domination resulted in a 5-fold increase in the risk ofGram-negative bacteremia, while dominance was reduced 10-foldby fluoroquinolone treatment.

In an extension of this work, the diversity of the intestinalmicrobiota 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 3years after allo-HSCT was 36%, 60%, and 67% for the low, inter-

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mediate, and high diversity groups, respectively, implying thathigh intestinal microbial diversity is prognostic of favorable clin-ical outcomes. Additionally, commensal members of the familiesLachnospiraceae and Actinomycetaceae were associated with sur-vival, while Gram-negative bacteria from the phylum Proteobac-teria were positively correlated with mortality.

Exposure to antibiotics is related to C. difficile infection (33,36), which is a major cause of infectious diarrhea in hospitalizedpatients (37). To combat this threat, high-throughput DNA se-quencing of the fecal microbiota of mice and hospitalized patientstreated with antibiotics was utilized to identify bacterial speciesassociated with resistance to C. difficile infection (36). The specieswith the strongest resistance correlation was Clostridium scindens,which dramatically reduced C. difficile infection and attendantweight loss and mortality in an animal model when transferredalone or as part of a microbial consortium postantibiotic expo-sure. The mechanism of C. difficile inhibition centers on the C.scindens-dependent conversion of primary into secondary bile ac-ids in the cecum and colon. These data suggest C. scindens offerspromise as an alternative treatment option for C. difficile-medi-ated intestinal disease.

In addition to its capacity as a marker for intestinal disease, thegut microbiome has potential as a diagnostic and prognosticmarker for systemic diseases, such as rheumatoid arthritis (38). Toidentify 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 withrheumatoid arthritis and controls was performed (39). In the set-ting of untreated new-onset rheumatoid arthritis, Prevotella copriwas considerably more abundant than in healthy individuals, sig-nifying that P. copri may play a role in the pathogenesis of rheu-matoid arthritis. The increase in Prevotella correlated with a re-duction in Bacteroides and the loss of reportedly beneficialmicrobes. Similarly, the gut microbiotas of patients with psoriaticarthritis and skin psoriasis were observed to be less diverse com-pared to healthy controls (40). Whereas some genera were lessabundant in the two conditions, psoriatic arthritis patients had alower abundance of allegedly favorable microbes. Taken together,these data suggest that interrogation of the gut microbiome maybe of diagnostic and prognostic utility for arthritis and other sys-temic ailments.

THE ROLE OF CLINICOGENOMICS IN PUBLIC HEALTHMICROBIOLOGY

Over the past 50� years, public health microbiology (“publichealth microbiology version 1.0”) was constrained with complexand 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 sequencing, has brought manynew opportunities to this field (42–45) and has allowed bacterialgenomics to be integrated into what might be called “public healthmicrobiology 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, arecent case study describes how WGS was applied to a protractedhospital outbreak of multidrug-resistant Acinetobacter baumanniiin 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 genotypicclusters within the outbreak. WGS also allowed the investigativeteam to rule 17 initially suspicious isolates as unrelated to theoutbreak strain. Sequence analysis of multiple strains isolatedfrom the same patient documented strains with various degrees ofgenomic diversity within the patient, including strains with only afew differences at the genomic level and strains that differedgreatly. Using WGS data and conventional epidemiology, thestudy team was able to reconstruct potential transmission eventsthat linked all but seven of the patients and could also associatepatient isolates to those recovered from the environment. WGSfocused attention on a contaminated bed and on a burns unit asthe source and site of transmission, catalyzing improvements indecontamination protocols. This approach has also been adoptedfor Mycobacterium tuberculosis isolates (47).

To fast forward into the near future (public health microbiol-ogy v2.1), it is plausible that culture of bacterial isolates might insome settings be replaced by shotgun metagenomic sequencing ofclinical 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 manydifferent 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 tofinding only what you expected to find. A second case study high-lights this approach, in which metagenomics was applied to fecalsamples that were obtained from patients with diarrhea during the2011 outbreak of Shiga toxin-producing Escherichia coli (STEC)O104:H4 in Germany (16). The investigative team obtained thegenome of the STEC outbreak strain from 10 samples at greaterthan 10-fold coverage and from over two dozen samples at greaterthan 1-fold coverage. In several samples, they found an increasedcoverage of the Shiga toxin bacteriophage genome relative to thoseof other STEC sequences. From some samples, they recoveredsequences from Clostridium difficile, Campylobacter jejuni, andSalmonella enterica, and from one, they recovered sequences fromthe emerging human pathogen Campylobacter concisus, illustrat-ing the ability of metagenomics to deliver unexpected results.

Metagenomic analysis has also be applied to the recovery of M.tuberculosis genomes from historical and contemporary humansamples, and the results have shown that mixed infections werecommon in 18th century Europe. Further, in a proof-of-principlestudy, the same process was used to identify and characterizepathogenic mycobacteria in modern sputum samples (48–50).There have been several other recent proof-of-principle studiesthat demonstrate the utility of this diagnostic approach (13, 15,51, 52).

We can envisage an even more ambitious vision for publichealth microbiology v3.0, in which long-read single-moleculenanopore sequencing will enable an integrated approach to mac-romolecular monitoring, combining analysis of DNA, RNA, andproteins shed in urine and feces together with characterization ofinformational macromolecules circulating in the bloodstream toprovide 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 ResearchCouncil (http://www.climb.ac.uk).

CONCLUSION

Based on the discussions above, next-generation sequencing willsteadily work its way into routine diagnostic use within clinicaland 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 forall manner of microorganisms, be they viral, bacterial, fungal, orparasitic. Furthermore, it will allow for the ability to monitorchanges in the human (or animal) microbiome that forecast thepotential risk for, or the existence of, other noninfectious diseaseprocesses, thus allowing earlier intervention or avoidance andperhaps even alternative treatment modalities. While most of thisreview centers on the use of NGS and all of the analytical permu-tations that have been developed in conjunction with it, we canlikely expect more user-friendly distillations of these studies (i.e.,multiplex PCR assays) to appear in clinical laboratories in the nearfuture.

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 AI95706and from the Tow Foundation. M.J.P. was funded by the United King-dom’s Medical Research Council, Biotechnology and Biological SciencesResearch Council, and National Institute for Health Research togetherwith funding from Warwick Medical School and collaborative input fromIllumina. W.M.D., L.F.W., and A.V.B. did not receive external funding forthis project. W.M.D. and A.V.B. are employees of bioMérieux, Inc.

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Lars F. Westblade, Ph.D., is an Assistant Pro-fessor in Pathology and Laboratory Medicine atWeill Cornell Medical College and the AssociateDirector for Microbiology at New York-Presby-terian Hospital (Weill Cornell Campus). Priorto joining Weill Cornell Medical College, he wasan Assistant Professor at Emory University. Hecompleted his training in Medical and PublicHealth Microbiology under the direction of Dr.Michael Dunne and Dr. Carey-Ann Burnhamat Washington University School of Medicinein St. Louis. Dr. Westblade is a Diplomate of the American Board of MedicalMicrobiology.

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