Journal:Making the leap from research laboratory to clinic: Challenges and opportunities for next-generation sequencing in infectious disease diagnostics
Full article title | Making the leap from research laboratory to clinic: Challenges and opportunities for next-generation sequencing in infectious disease diagnostics |
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Journal | mBio |
Author(s) | Goldberg, B.; Sichtig, H.; Geyer, C.; Ledeboer, N.; Weinstock, G.M. |
Author affiliation(s) |
Children’s National Medical Center, Food and Drug Administration, American Society for Microbiology, Medical College of Wisconsin, Jackson Laboratory for Genomic Medicine |
Primary contact | Email: George dot Weinstock at jax dot org |
Year published | 2015 |
Volume and issue | 6(6) |
Page(s) | e01888-15 |
DOI | 10.1128/mBio.01888-15 |
ISSN | 2150-7511 |
Distribution license | Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported |
Website | http://mbio.asm.org/content/6/6/e01888-15.full |
Download | http://mbio.asm.org/content/6/6/e01888-15.full.pdf (PDF) |
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Abstract
Next-generation DNA sequencing (NGS) has progressed enormously over the past decade, transforming genomic analysis and opening up many new opportunities for applications in clinical microbiology laboratories. The impact of NGS on microbiology has been revolutionary, with new microbial genomic sequences being generated daily, leading to the development of large databases of genomes and gene sequences. The ability to analyze microbial communities without culturing organisms has created the ever-growing field of metagenomics and microbiome analysis and has generated significant new insights into the relation between host and microbe. The medical literature contains many examples of how this new technology can be used for infectious disease diagnostics and pathogen analysis. The implementation of NGS in medical practice has been a slow process due to various challenges such as clinical trials, lack of applicable regulatory guidelines, and the adaptation of the technology to the clinical environment. In April 2015, the American Academy of Microbiology (AAM) convened a colloquium to begin to define these issues, and in this document, we present some of the concepts that were generated from these discussions.
Minireview
Use of next-generation DNA sequencing (NGS) (Table 1) in infectious disease diagnostics has progressed slowly over the past 10 years despite continued advances in sequencing technology. The first commercial NGS platform, the GS20 sequencer from 454 Life Sciences, which was originally released in 2005[1][2], resulted in a more than 100-fold increase in the amount of microbial genomic sequence data produced in a day compared to preceding instruments. Despite the growing body of literature and research broadly applying sequencing-based technology to disease pathophysiology, epidemiology, and clinical diagnostics, the clinical microbiology laboratory has yet to widely adopt NGS technology. As microbiology laboratories are faced with a wealth of innovative and often costly molecular technologies, the role of NGS in clinical infectious disease diagnostics needs to be carefully evaluated.
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A number of highly publicized case reports and clinical studies have showcased the application of NGS as a single diagnostic tool with the potential to be broadly applicable to infectious disease diagnostics. Metagenomic (Table 1) sequencing has demonstrated its ability to identify microbial pathogens where traditional diagnostics have otherwise failed. For example, it is estimated that 63% of encephalitis cases go undiagnosed despite extensive testing.[3] Several cases in the literature have successfully employed NGS to diagnose rare, novel, or atypical infectious etiologies for encephalitis, including cases of infection by Leptospira[4], astrovirus[5], and bornavirus.[6] In one case, 38 different diagnostic tests had been conducted and failed to yield an actionable answer before a single NGS assay was performed, which identified the pathogen.[4] Similarly, the utilization of metagenomic NGS identified divergent astrovirus clades in a pair of patients with encephalitis and demonstrated the unusual zoonotic potential of a group of these viruses.[7]
Another promising application of NGS technology is hospital infection control surveillance programs and community outbreak investigations.[8] By conducting whole-genome sequencing (WGS) (Table 1), organisms can be identified at the subspecies/strain level based on the single nucleotide polymorphisms (SNPs) (Table 1) and other variants (Table 1) in their genotype. WGS through NGS technology offers greater precision than do more-traditional typing tools such as multilocus sequence typing and pulsed-field gel electrophoresis, which may assist in refining outbreak investigations and better guide infection control interventions.[9] Because WGS analysis requires significant amounts of sequencing data, traditional sequencing methods preclude the use of WGS analysis for outbreak investigations. However, NGS platforms can generate the large volume of data needed for SNP or variant analysis and have led to a rapid expansion in the use of WGS for public health investigations. For example, WGS using NGS technology was applied to investigate an outbreak of hemolytic-uremic syndrome caused by an unusual strain of Escherichia coli in Germany[10], the origins of the 2010 Haitian Vibrio cholerae epidemic[11], a series of methicillin-resistant Staphylococcus aureus infections in a neonatal intensive care unit[12], and the origins of a series of nosocomial carbapenem-resistant Klebsiella pneumoniae infections[13], among many others.
While identification of causative microorganisms of disease is the chief responsibility of clinical microbiology laboratories, conducting antimicrobial resistance testing to guide therapy is among the most important tests conducted in the laboratory. NGS has the potential to suggest antimicrobial resistance through identification of known resistance genes.[14] Although WGS is in the early stages of development, studies have suggested that WGS can be used to predict antibiotic resistance with performance characteristics approaching those of traditional phenotypic testing. Stoesser et al.[15] demonstrated sensitivity and specificity of 96% and 97%, respectively, when using WGS to predict antibiotic resistance for clinical isolates of E. coli and K. pneumoniae. Comparative genomic sequencing has also been used to identify daptomycin resistance due to point mutations in metabolic genes that occurred during therapy for two cases of vancomycin-resistant enterococcal bacteremia which were ultimately fatal.[16][17] Earlier application of WGS might have detected these point mutations and guided therapy. However, it has not been established if WGS can be broadly applied to the full spectrum of pathogenic bacteria, particularly those with a diverse armamentarium of resistance mechanisms. WGS analysis of antimicrobial resistance genes could be particularly beneficial for slow-growing or difficult-to-culture organisms and organisms that elude phenotypic testing altogether. The use of WGS is not limited to the detection of bacterial resistance genes. WGS has also been applied for the following purposes: detection of low-level drug resistance among human immunodeficiency virus (HIV) “escape” variant populations (e.g., protease inhibitor [PI] and reverse transcriptase minor sequence variants) and coreceptor tropism (CCR5 and CXCR4) and analyses that are not possible using current genotypic and phenotypic HIV assays.[18] These successes and potential applications of WGS analysis have been made possible by the advance of NGS technology, which provides the tools to produce useful WGS data.
Metagenomic shotgun sequencing (mWGS) (Table 1) is often used to study microbial communities in human disease in order to identify correlative or causative relations. Such communities comprise hundreds of different taxa of bacteria, viruses, and fungi and other eukaryotic microbes. Many of these organisms are difficult to culture, and culture-independent methods of performing comprehensive sampling of these complex communities have been the major obstacle to analysis. NGS is currently the best available analytical approach to profile microbiomes (Table 1) for this purpose. To date, NGS has helped elucidate the role of the lung and gut microbiomes in both general health and various diseases, including obesity, inflammatory bowel disease, cystic fibrosis (CF), metabolic syndrome, type II diabetes, and cardiovascular disease.[19][20][21][22][23] As NGS continues to expand our knowledge base on metagenomics, microbiome analysis may produce diagnostic or prognostic biomarkers to guide therapeutic decisions.
References
- ↑ Margulies, M.; Egholm, M.; Altman, W.E. et al. (2005). "Genome sequencing in microfabricated high-density picolitre reactors". Nature 437 (7057): 376–80. doi:10.1038/nature03959. PMC PMC1464427. PMID 16056220. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1464427.
- ↑ Liu, L.; Li, Y.; Li, S. et al. (2012). "Comparison of next-generation sequencing systems". Journal of Biomedicine and Biotechnology 2012: 251364. doi:10.1155/2012/251364. PMC PMC3398667. PMID 22829749. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3398667.
- ↑ Brown, J.R.; Morfopoulou, S.; Hubb, J. et al. (2015). "Astrovirus VA1/HMO-C: An increasingly recognized neurotropic pathogen in immunocompromised patients". Clinical Infectious Diseases 60 (6): 881-8. doi:10.1093/cid/ciu940. PMC PMC4345817. PMID 25572899. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4345817.
- ↑ 4.0 4.1 Wilson, M.R.; Naccache, S.N.; Samayoa, E. et al. (2014). "Actionable diagnosis of neuroleptospirosis by next-generation sequencing". New England Journal of Medicine 37 (25): 2408-17. doi:10.1056/NEJMoa1401268. PMC PMC4134948. PMID 24896819. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4134948.
- ↑ Naccache, S.N.; Peggs, K.S.; Mattes, F.M. et al. (2015). "Diagnosis of neuroinvasive astrovirus infection in an immunocompromised adult with encephalitis by unbiased next-generation sequencing". Clinical Infectious Diseases 60 (6): 919-23. doi:10.1093/cid/ciu912. PMC PMC4345816. PMID 25572898. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4345816.
- ↑ Hoffmann, B.; Tappe, D.; Höper, D. et al. (2015). "A Variegated Squirrel Bornavirus Associated with Fatal Human Encephalitis". New England Journal of Medicine 372 (2): 154-62. doi:10.1056/NEJMoa1415627. PMID 26154788.
- ↑ Quan, P.L.; Wagner, T.A.; Briese, T. et al. (2010). "Astrovirus encephalitis in boy with X-linked agammaglobulinemia". Emerging Infectious Diseases 16 (6): 918-25. doi:10.3201/eid1606.091536. PMC PMC4102142. PMID 20507741. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4102142.
- ↑ GenomeWeb staff reporter (7 August 2015). "CDC Earmarks $2.3M for NGS, Bioinformatic Approaches to Combat Infectious Disease". GenomeWeb. Genomeweb LLC. https://www.genomeweb.com/research-funding/cdc-earmarks-23m-ngs-bioinformatic-approaches-combat-infectious-disease. Retrieved 19 September 2016.
- ↑ Turabelidze, G.; Lawrence, S.J.; Gao, H. et al. (2013). "Precise dissection of an Escherichia coli O157:H7 outbreak by single nucleotide polymorphism analysis". Journal of Clinical Microbiology 51 (12): 3950-4. doi:10.1128/JCM.01930-13. PMC PMC3838074. PMID 24048526. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3838074.
- ↑ Rasko, D.A.; Webster, D.R.; Sahl, J.W. et al. (2011). "Origins of the E. coli strain causing an outbreak of hemolytic-uremic syndrome in Germany". New England Journal of Medicine 365 (8): 709-17. doi:10.1056/NEJMoa1106920. PMC PMC3168948. PMID 21793740. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168948.
- ↑ Chin, C.S.; Sorenson, J.; Harris, J.B. et al. (2011). "The origin of the Haitian cholera outbreak strain". New England Journal of Medicine 364 (1): 33–42. doi:10.1056/NEJMoa1012928. PMC PMC3030187. PMID 21142692. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3030187.
- ↑ Azarian, T.; Cook, R.L.; Johnson, J.A. et al. (2015). "Whole-genome sequencing for outbreak investigations of methicillin-resistant Staphylococcus aureus in the neonatal intensive care unit: Time for routine practice?". Infection Control and Hospital Epidemiology 36 (7): 777–785. doi:10.1017/ice.2015.73. PMC PMC4507300. PMID 25998499. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4507300.
- ↑ Snitkin, E.S.; Zelazny, A.M.; Thomas, P.J. et al. (2012). "Tracking a hospital outbreak of carbapenem-resistant Klebsiella pneumoniae with whole-genome sequencing". Science Translational Medicine 4 (148): 148ra116. doi:10.1126/scitranslmed.3004129. PMC PMC3521604. PMID 22914622. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521604.
- ↑ Dunne Jr., W.M.; Westblade, L.F.; Ford, B. (2012). "Next-generation and whole-genome sequencing in the diagnostic clinical microbiology laboratory". European Journal of Clinical Microbiology & Infectious Diseases 31 (8): 1719-26. doi:10.1007/s10096-012-1641-7. PMID 22678348.
- ↑ Stoesser, N.; Batty, E.M.; Eyre, D.W. et al. (2013). "Predicting antimicrobial susceptibilities for Escherichia coli and Klebsiella pneumoniae isolates using whole genomic sequence data". Journal of Antimicrobial Chemotherapy 68 (10): 2234-44. doi:10.1093/jac/dkt180. PMC PMC3772739. PMID 23722448. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3772739.
- ↑ Arias, C.A.; Panesso, D.; McGrath, D.M. et al. (2011). "Genetic basis for in vivo daptomycin resistance in enterococci". New England Journal of Medicine 365 (10): 892-900. doi:10.1056/NEJMoa1011138. PMC PMC3205971. PMID 21899450. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3205971.
- ↑ Tran, T.T.; Panesso, D.; Gao, H. et al. (2013). "Whole-genome analysis of a daptomycin-susceptible enterococcus faecium strain and its daptomycin-resistant variant arising during therapy". Antimicrobial Agents and Chemotherapy 57 (1): 261-8. doi:10.1128/AAC.01454-12. PMC PMC3535923. PMID 23114757. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3535923.
- ↑ Fisher, R.; van Zy, G.U.; Travers, S.A. et al. (2012). "Deep sequencing reveals minor protease resistance mutations in patients failing a protease inhibitor regimen". Journal of Virology 86 (11): 6231-7. doi:10.1128/JVI.06541-11. PMC PMC3372173. PMID 22457522. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3372173.
- ↑ Ley, R.E.; Bäckhed, F.; Turnbaugh, P. et al. (2005). "Obesity alters gut microbial ecology". Proceedings of the National Academy of Sciences of the United States of America 102 (31): 11070-5. doi:10.1073/pnas.0504978102. PMC PMC1176910. PMID 16033867. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1176910.
- ↑ Qin, J.; Li, R.; Raes, J. et al. (2010). "A human gut microbial gene catalogue established by metagenomic sequencing". Nature 464 (7285): 59-65. doi:10.1038/nature08821. PMC PMC3779803. PMID 20203603. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3779803.
- ↑ Graessler, J.; Qin, Y.; Zhong, H. et al. (2013). "Metagenomic sequencing of the human gut microbiome before and after bariatric surgery in obese patients with type 2 diabetes: Correlation with inflammatory and metabolic parameters". Pharmacogenomics Journal 13 (6): 514-22. doi:10.1038/tpj.2012.43. PMID 23032991.
- ↑ Wang, Z.; Klipfell, E.; Bennett, B.J. et al. (2011). "Gut flora metabolism of phosphatidylcholine promotes cardiovascular disease". Nature 472 (7341): 57-63. doi:10.1038/nature09922. PMC PMC3086762. PMID 21475195. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3086762.
- ↑ Price, K.E.; Hampton, T.H.; Gifford, A.H. et al. (2013). "Unique microbial communities persist in individual cystic fibrosis patients throughout a clinical exacerbation". Microbiome 1 (1): 27. doi:10.1186/2049-2618-1-27. PMC PMC3971630. PMID 24451123. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3971630.
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In some cases important information was missing from the references, and that information was added.