Role of Clinicogenomics in Infectious Disease Diagnostics and Public Health Microbiology

Abstract

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 disease 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 collection of the most salient and promising aspects from each presentation at the symposium.

INTRODUCTION

The explosion of microbiome research is driven by high-throughput DNA sequencing, so-called next-generation sequencing (NGS), technologies that allow the genomic content of entire microbial communities (bacterial, viral, and eukaryotic organisms) to be described. Although much of this work is aimed at describing the structure of “commensal” communities, the methodology works equally well to identify pathogens in clinical samples. 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 microbial content and then of applying computational methods to resolve the sequences into individual species, genes, pathways, or other features.

Most microbiome analyses have focused on describing bacterial 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 different organisms in the community is then sequenced, and the abundance of each species is determined by the number of sequences 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 laborious and expensive, and deep sampling was not possible. When NGS became available, most work was done on the FLX sequencing instrument (a second-generation sequencing 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, typically only three of these regions can be sequenced. Nevertheless, this allowed detection to the genus level of most taxa. This methodology can correctly identify pathogens in stool samples from patients with diarrhea compared to culture results (G. M. Weinstock, unpublished data). In addition, when using this NGS approach, an additional pathogen that was not reported by the diagnostic 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 referred 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 sequencing 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 Illumina'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 microbiome 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 archaebacterial, 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 requires many more sequencing reads than 16S rRNA gene sequencing to adequately sample the genomes, and thus only the sequencing 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 virulence factors and other features that can help distinguish a pathogen at the strain level from other nonpathogenic 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. Metagenomic transcription analysis is particularly noteworthy, as this method determines which organisms are actively growing and/or whether a gene of interest (antibiotic-resistant determinant) is expressed and thus contributes to the organism's phenotype.

Although use of metagenomic shotgun sequencing is limited by the output and cost required, trends in DNA sequencing technology continue to emphasize instruments that are smaller, faster, and lower cost. The MinION instrument from Oxford Nanopore Technologies (Oxford, United Kingdom) is a handheld sequencing 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 microbial culture, have been validated and standardized over decades 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 diagnostic tests fail to detect a causative agent in a significant number 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 diagnostic material. Hence, metagenomic shotgun sequencing has the potential to fundamentally improve infectious disease diagnostics by allowing broad-range detection of bacterial, viral, fungal, or parasitic 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.

FIG 1.

Next-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 novo assembly 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.

In recent years, the steadily decreasing cost of NGS infrastructure and reagents as well as the development of increasingly simplified library preparation workflows have made the establishment 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 software 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 reasonable balance between analytical depth and processing time and deliver results within hours rather than days (or even weeks). Furthermore, 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 available 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 interpretation by noninformatics personnel, will be a pivotal requirement for the future implementation of NGS in infectious disease diagnostics.

At present, there is also a profound lack of harmonization and universally recognized standards for NGS-based microbial diagnostics, a fact that is not surprising given that NGS is still a relatively 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 unequivocally assign fragments to a single species and since current second-generation high-throughput DNA sequencers utilize PCR amplification and thus can only deliver relative rather than total abundance values, how should one arrive at a reasonably meaningful abundance estimation for individual infectious agents? How should one deal with potential contaminants, especially those nucleic acids that are frequently introduced via library preparation kits (17)? Considering that not only the choice of the sequencing 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 sequenced, and what are the limits of detection that should be expected 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 cohorts. 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 invaluable resource, as they would greatly facilitate the identification of “unusual” sequence signatures that may indicate the presence of 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 investigation of challenging clinical cases or outbreak samples, however, it has already become an invaluable complement to conventional 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 infectious 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 utilize solid or liquid culture media, where the concentration of antimicrobial agent is adjusted to permit definition of minimum bactericidal or bacteriostatic (collectively, inhibitory) concentrations. Formats for such measurements include agar dilution, broth microdilution (BMD), antibiotic gradient diffusion, selective 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).

Recently, new approaches have been adapted to growth-based AST technology, and most deal with innovative means of distinguishing growing from inhibited/dead microorganisms. These include the use of microfluidics (NanoDrop BMD), mass spectrometry (including matrix-assisted laser desorption ionization–time of flight), cantilever technology, microcalorimetrics, nuclear magnetic resonance and magnetic bead rotation, real-time microscopy, and intrinsic fluorescence to name a few (for a recent review, see reference 18). All of these approaches are promising and are beyond the proof of principle stage, but none have entered the current in vitro diagnostic 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 resistance gene detection, but equating a resistance gene to an actual MIC value is still a work in progress. This may change as high-throughput genomics, including NGS and transcriptomics, become increasingly accessible, with transcriptomic analysis of stress marker expression (e.g., the SOS response) potentially offering an opportunity to relate molecular AST with phenotypic susceptibility data (20).

To better understand the potential value of NGS for AST, recent studies have shown that associations between phenotypic resistance profiles (antibiograms) and genotypic resistance predicted from whole-genome sequencing (WGS) data can be accurately defined. Using genome sequence information, an inventory 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 presence/absence of such genes and mutations in de novo microbial genome sequences. When comparing the Staphylococcus aureus resistome to a comprehensive reference antibiogram for a development 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. Additional studies have demonstrated the applicability of this approach for other organisms, but for species that are genetically more heterogeneous than S. 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 overheads 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 approaches 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 discovery of novel antibiotics.

Despite the obvious optimism surrounding NGS AST platforms, prior to their routine implementation in the clinical setting, there are several important aspects that must be addressed: (i) establishment of tightly regulated genomic databases (these databases will need continuous update and perhaps supplementation 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) development 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 regulatory bodies to ensure NGS-based AST moves into practice to combat the frightening frequency at which multi- and pan-drug-resistant 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. Utilizing these high-throughput DNA sequencing platforms, numerous studies have linked the structure of the microbiome, in particular 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 antibiotic-resistant microorganisms (33). Administration of antibiotics can perturb this fragile ecological niche (34), resulting in colonization with antibiotic-resistant organisms or enhanced risk of intestinal infection with Clostridium difficile (33). Microbes that undergo marked expansion in the intestine as a result of antibiotic exposure have been associated with invasive bloodstream infection.

To explore a possible relationship between dense intestinal colonization and bloodstream invasion in humans, investigators performed 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 vancomycin-resistant Enterococcus bacteremia 9-fold. Similarly, proteobacterial 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 specimens collected from 80 subjects at the time of stem cell engraftment, 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, intermediate, and high diversity groups, respectively, implying that high intestinal microbial diversity is prognostic of favorable clinical outcomes. Additionally, commensal members of the families Lachnospiraceae and Actinomycetaceae were associated with survival, while Gram-negative bacteria from the phylum Proteobacteria 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 hospitalized patients (37). To combat this threat, high-throughput DNA sequencing of the fecal microbiota of mice and hospitalized patients treated with antibiotics was utilized to identify bacterial species associated with resistance to C. difficile infection (36). The species with the strongest resistance correlation was Clostridium scindens, which dramatically reduced C. difficile infection and attendant weight loss and mortality in an animal model when transferred alone or as part of a microbial consortium postantibiotic exposure. The mechanism of C. difficile inhibition centers on the C. scindens-dependent conversion of primary into secondary bile acids in the cecum and colon. These data suggest C. scindens offers promise as an alternative treatment option for C. difficile-mediated 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 arthritis, high-throughput 16S rRNA gene sequencing of DNA extracted from 114 stool specimens obtained from patients with rheumatoid arthritis and controls was performed (39). In the setting of untreated new-onset rheumatoid arthritis, Prevotella copri was considerably more abundant than in healthy individuals, signifying that P. copri may play a role in the pathogenesis of rheumatoid arthritis. The increase in Prevotella correlated with a reduction 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 compared 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 systemic 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 culture, identification, growth-based phenotypic susceptibility testing, and strain typing (41). Recently, high-throughput DNA sequencing, particularly bench-top sequencing, 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 isolates to provide simultaneous information on organism identity, epidemiology, and antimicrobial therapy (Fig. 2).

FIG 2.

Progressive 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.

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-resistant Acinetobacter baumannii in Birmingham, England (46). The results showed that the outbreak 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 for Mycobacterium tuberculosis isolates (47).

To fast forward into the near future (public health microbiology 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 diagnostic metagenomics (10). It represents a one-size-fits-all approach to all bacteria that contrasts with the need for so many different laboratory media and atmospheric conditions in conventional bacteriology, it avoids the onerous optimization of target-specific assays needed for amplification- or probe-based diagnosis, and it is unbiased and open-ended, i.e., not restricted to finding only what you expected to find. A second case study highlights this approach, in which metagenomics was applied to fecal samples that were obtained from patients with diarrhea during the 2011 outbreak of Shiga toxin-producing Escherichia 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 pathogen Campylobacter concisus, illustrating 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 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 macromolecular 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 example, cancer and the health of the fetus or of organ transplants (53–57).

However, there will be a need for a new computational infrastructure to cope with the demands of big data in clinical microbiology, including the role of cloud computing (58), illustrated by 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 prediction, albeit not entirely in the near future, is based on the universality of the science, i.e., its applicability to the diagnosis of infectious 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 permutations 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.

Biography

Lars F. Westblade, Ph.D., is an Assistant Professor in Pathology and Laboratory Medicine at Weill Cornell Medical College and the Associate Director for Microbiology at New York-Presbyterian 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.