Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease

Abstract

Thousands of pathogens are known to infect humans, but only a fraction are readily identifiable using current diagnostic methods. Microbial cell-free DNA sequencing offers the potential to non-invasively identify a wide range of infections throughout the body, but the challenges of clinical-grade metagenomic testing must be addressed. Here we describe the analytical and clinical validation of a next-generation sequencing test that identifies and quantifies microbial cell-free DNA in plasma from 1,250 clinically relevant bacteria, DNA viruses, fungi and eukaryotic parasites. Test accuracy, precision, bias and robustness to a number of metagenomics-specific challenges were determined using a panel of 13 microorganisms that model key determinants of performance in 358 contrived plasma samples, as well as 2,625 infections simulated in silico and 580 clinical study samples. The test showed 93.7% agreement with blood culture in a cohort of 350 patients with a sepsis alert and identified an independently adjudicated cause of the sepsis alert more often than all of the microbiological testing combined (169 aetiological determinations versus 132). Among the 166 samples adjudicated to have no sepsis aetiology identified by any of the tested methods, sequencing identified microbial cell-free DNA in 62, likely derived from commensal organisms and incidental findings unrelated to the sepsis alert. Analysis of the first 2,000 patient samples tested in the CLIA laboratory showed that more than 85% of results were delivered the day after sample receipt, with 53.7% of reports identifying one or more microorganisms.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: The Karius test workflow.
Fig. 2: A reference panel of 13 microorganisms was designed to reflect the diversity of the 1,250 microorganisms tested by the assay.
Fig. 3: Analytical sensitivity.
Fig. 4: Analytical specificity.
Fig. 5: Clinical validity.

Code availability

The core software used as part of the Karius test is described in the Clinical-grade microbial cfDNA sequencing for infectious disease section in Methods, under the sub-sections Sequence data processing and alignment, Microorganism abundance estimation and Pathogen detection. The open source software includes the following external tools: bcl2fastq v2.17.1.14, Trimmomatic v0.32, Bowtie v2.2.4 and BLAST v2.2.30. A description of all open source code is included in Methods and further details are available on request. The proprietary portions of the code are not available.

Data availability

The data that support the findings of this study are available from the corresponding author on request. Sequencing data that support the finding of this study (with human reads removed) have been deposited in NCBI SRA and can be accessed with the BioProject identifier PRJNA507824.

References

  1. 1.

    Christensen, K. L. et al. Infectious disease hospitalizations in the United States. Clin. Infect. Dis. 49, 1025–1035 (2009).

    Article  Google Scholar 

  2. 2.

    Barlam, T. F. et al. Implementing an antibiotic stewardship program: guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. Clin. Infect. Dis. 62, e51–e77 (2016).

    Article  Google Scholar 

  3. 3.

    Liesenfeld, O., Lehman, L., Hunfeld, K. P. & Kost, G. Molecular diagnosis of sepsis: new aspects and recent developments. Eur. J. Microbiol. Immunol. 4, 1–25 (2014).

    CAS  Article  Google Scholar 

  4. 4.

    Kumar, A. et al. Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock. Chest 136, 1237–1248 (2009).

    Article  Google Scholar 

  5. 5.

    Fenollar, F. & Raoult, D. Molecular diagnosis of bloodstream infections caused by non-cultivable bacteria. Int. J. Antimicrob. Agents 30, S7–S15 (2007).

    CAS  Article  Google Scholar 

  6. 6.

    Mancini, N. et al. The era of molecular and other non-culture-based methods in diagnosis of sepsis. Clin. Microbiol. Rev. 23, 235–251 (2010).

    CAS  Article  Google Scholar 

  7. 7.

    Fishman, J. A. Infection in solid-organ transplant recipients. N. Engl. J. Med. 357, 2601–2614 (2007).

    CAS  Article  Google Scholar 

  8. 8.

    Tomblyn, M. et al. Guidelines for preventing infectious complications among hematopoietic cell transplantation recipients: a global perspective. Biol. Blood Marrow Transplant. 15, 1143–1238 (2009).

    CAS  Article  Google Scholar 

  9. 9.

    Paul, M. et al. Systematic review and meta-analysis of the efficacy of appropriate empiric antibiotic therapy for sepsis. Antimicrob. Agents Chemother. 54, 4851–4863 (2010).

    CAS  Article  Google Scholar 

  10. 10.

    Kumar, A. An alternate pathophysiologic paradigm of sepsis and septic shock: implications for optimizing antimicrobial therapy. Virulence 5, 80–97 (2014).

    Article  Google Scholar 

  11. 11.

    Ramanan, P., Bryson, A. L., Binnicker, M. J., Pritt, B. S. & Patel, R. Syndromic panel-based testing in clinical microbiology. Clin. Microbiol. Rev. 31, 1–28 (2018).

    Google Scholar 

  12. 12.

    Schreckenberger, P. C. & McAdam, A. J. Point–counterpoint: large multiplex PCR panels should be first-line tests for detection of respiratory and intestinal pathogens. J. Clin. Microbiol. 53, 3110–3115 (2015).

    Article  Google Scholar 

  13. 13.

    Kothari, A., Morgan, M. & Haake, D. A. Emerging technologies for rapid identification of bloodstream pathogens. Clin. Infect. Dis. 59, 272–278 (2014).

    Article  Google Scholar 

  14. 14.

    Simner, P. J., Miller, S. & Carroll, K. C. Understanding the promises and hurdles of metagenomic next-generation sequencing as a diagnostic tool for infectious diseases. Clin. Infect. Dis. 66, 778–788 (2018).

    CAS  Article  Google Scholar 

  15. 15.

    Greninger, A. L. et al. Clinical metagenomic identification of Balamuthia mandrillaris encephalitis and assembly of the draft genome: the continuing case for reference genome sequencing. Genome Med. 7, 113 (2015).

    Article  Google Scholar 

  16. 16.

    Wilson, M. R. et al. Actionable diagnosis of neuroleptospirosis by next-generation sequencing. N. Engl. J. Med. 370, 2408–2417 (2014).

    Article  Google Scholar 

  17. 17.

    Schlaberg, R. et al. Validation of metagenomic next-generation sequencing tests for universal pathogen detection. Arch. Pathol. Lab. Med. 141, 776–786 (2017).

    CAS  Article  Google Scholar 

  18. 18.

    Naccache, S. N. et al. Diagnosis of neuroinvasive astrovirus infection in an immunocompromised adult with encephalitis by unbiased next-generation sequencing. Clin. Infect. Dis. 60, 919–923 (2015).

    Article  Google Scholar 

  19. 19.

    Stokowski, R. et al. Clinical performance of non-invasive prenatal testing (NIPT) using targeted cell-free DNA analysis in maternal plasma with microarrays or next generation sequencing (NGS) is consistent across multiple controlled clinical studies. Prenat. Diagn. 35, 1243–1246 (2015).

    CAS  Article  Google Scholar 

  20. 20.

    Song, K., Musci, T. J. & Caughey, A. B. Clinical utility and cost of non-invasive prenatal testing with cfDNA analysis in high-risk women based on a US population. J. Matern. Fetal Neonat. Med. 26, 1180–1185 (2013).

    Article  Google Scholar 

  21. 21.

    Fan, H. C., Blumenfeld, Y. J., Chitkara, U., Hudgins, L. & Quake, S. R. Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc. Natl Acad. Sci. USA 105, 16266–16271 (2008).

    CAS  Article  Google Scholar 

  22. 22.

    Schutz, E. et al. Graft-derived cell-free DNA, a noninvasive early rejection and graft damage marker in liver transplantation: a prospective, observational, multicenter cohort study. PLoS Med. 14, e1002286 (2017).

    Article  Google Scholar 

  23. 23.

    Bloom, R. D. et al. Cell-free DNA and active rejection in kidney allografts. J. Am. Soc. Nephrol. 28, 2221–2232 (2017).

    CAS  Article  Google Scholar 

  24. 24.

    De Vlaminck, I. et al. Noninvasive monitoring of infection and rejection after lung transplantation. Proc. Natl Acad. Sci. USA 112, 13336–13341 (2015).

    Article  Google Scholar 

  25. 25.

    De Vlaminck, I. et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci. Transl. Med. 6, 241ra277 (2014).

    Article  Google Scholar 

  26. 26.

    Snyder, T. M., Khush, K. K., Valantine, H. A. & Quake, S. R. Universal noninvasive detection of solid organ transplant rejection. Proc. Natl Acad. Sci. USA 108, 6229–6234 (2011).

    CAS  Article  Google Scholar 

  27. 27.

    Aravanis, A. M., Lee, M. & Klausner, R. D. Next-generation sequencing of circulating tumor DNA for early cancer detection. Cell 168, 571–574 (2017).

    CAS  Article  Google Scholar 

  28. 28.

    Lanman, R. B. et al. Analytical and clinical validation of a digital sequencing panel for quantitative, highly accurate evaluation of cell-free circulating tumor DNA. PLoS ONE 10, e0140712 (2015).

    Article  Google Scholar 

  29. 29.

    Bettegowda, C. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci. Transl. Med. 6, 224ra224 (2014).

    Article  Google Scholar 

  30. 30.

    Dawson, S. J. et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N. Engl. J. Med. 368, 1199–1209 (2013).

    CAS  Article  Google Scholar 

  31. 31.

    Abril, M. K. et al. Diagnosis of Capnocytophaga canimorsus sepsis by whole-genome next-generation sequencing. Open Forum Infect. Dis. 3, ofw144 (2016).

    Article  Google Scholar 

  32. 32.

    Hong, D. K. et al. Liquid biopsy for infectious diseases: sequencing of cell-free plasma to detect pathogen DNA in patients with invasive fungal disease. Diagn. Microbiol. Infect. Dis. 92, 210–213 (2018).

    CAS  Article  Google Scholar 

  33. 33.

    Lefterova, M. I., Suarez, C. J., Banaei, N. & Pinsky, B. A. Next-generation sequencing for infectious disease diagnosis and management: a report of the association for molecular pathology. J. Mol. Diagn. 17, 623–634 (2015).

    CAS  Article  Google Scholar 

  34. 34.

    Dunne, W. M. Jr, Westblade, L. F. & Ford, B. Next-generation and whole-genome sequencing in the diagnostic clinical microbiology laboratory. Eur. J. Clin. Microbiol. Infect. Dis. 31, 1719–1726 (2012).

    CAS  Article  Google Scholar 

  35. 35.

    Kim, D. et al. Optimizing methods and dodging pitfalls in microbiome research. Microbiome 5, 52 (2017).

    Article  Google Scholar 

  36. 36.

    Salter, S. J. et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol. 12, 87 (2014).

    Article  Google Scholar 

  37. 37.

    Weiss, S. et al. Tracking down the sources of experimental contamination in microbiome studies. Genome Biol. 15, 564 (2014).

    Article  Google Scholar 

  38. 38.

    Naccache, S. N. et al. The perils of pathogen discovery: origin of a novel parvovirus-like hybrid genome traced to nucleic acid extraction spin columns. J. Virol. 87, 11966–11977 (2013).

    CAS  Article  Google Scholar 

  39. 39.

    Infectious Disease Next Generation Sequencing Based Diagnostic Devices: Microbial Identification and Detection of Antimicrobial Resistance and Virulence Markers. Draft Guidance for Industry and Food and Drug Administration Staff (Food and Drug Administration, 2016).

  40. 40.

    Chang, C. P. et al. Elevated cell-free serum DNA detected in patients with myocardial infarction. Clin. Chim. Acta 327, 95–101 (2003).

    CAS  Article  Google Scholar 

  41. 41.

    Lo, Y. M., Rainer, T. H., Chan, L. Y., Hjelm, N. M. & Cocks, R. A. Plasma DNA as a prognostic marker in trauma patients. Clin. Chem. 46, 319–323 (2000).

    CAS  PubMed  Google Scholar 

  42. 42.

    Vincent, J. L., Martinez, E. O. & Silva, E. Evolving concepts in sepsis definitions. Crit. Care Nurs. Clin. North. Am. 23, 29–39 (2011).

    Article  Google Scholar 

  43. 43.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS  Article  Google Scholar 

  44. 44.

    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    CAS  Article  Google Scholar 

  45. 45.

    Camacho, C. et al. BLAST+: architecture and applications. BMC Bioinform. 10, 421 (2009).

    Article  Google Scholar 

  46. 46.

    Xia, L. C., Cram, J. A., Chen, T., Fuhrman, J. A. & Sun, F. Accurate genome relative abundance estimation based on shotgun metagenomic reads. PLoS ONE 6, e27992 (2011).

    CAS  Article  Google Scholar 

  47. 47.

    Bennett J. E., Dolin, R. & Blaser, M. J. Mandell, Douglas, and Bennett’s Principles and Practice of Infectious Diseases 8th edn (Saunders, Philadelphia, 2015).

  48. 48.

    Burnham, P. et al. Single-stranded DNA library preparation uncovers the origin and diversity of ultrashort cell-free DNA in plasma. Sci. Rep. 6, 27859 (2016).

    CAS  Article  Google Scholar 

  49. 49.

    Kalia, V. C. et al. Analysis of the unexplored features of rrs (16S rDNA) of the Genus Clostridium. BMC Genomics 12, 18 (2011).

    CAS  Article  Google Scholar 

  50. 50.

    Evaluation of Precision Performance of Quantitative Measurement Methods; Approved Guideline—Second Edition. NCCLS document EP5-A2 (NCCLS, 2004).

  51. 51.

    Dellinger, R. P. et al. Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock, 2012. Intensive Care Med. 39, 165–228 (2013).

    CAS  Article  Google Scholar 

  52. 52.

    Bagdasarian, N., Rao, K. & Malani, P. N. Diagnosis and treatment of Clostridium difficile in adults: a systematic review. JAMA 313, 398–408 (2015).

    Article  Google Scholar 

  53. 53.

    Focosi, D., Antonelli, G., Pistello, M. & Maggi, F. Torquetenovirus: the human virome from bench to bedside. Clin. Microbiol. Infect. 22, 589–593 (2016).

    CAS  Article  Google Scholar 

  54. 54.

    Karius, Inc. Pathogen List https://www.kariusdx.com/pathogen-list/3.1.1 (2018).

Download references

Acknowledgements

The authors would like to thank S. Sinha for assistance with the preparation of the manuscript, as well as H. Quach, R. Davila, S. Madan, V. Baichwal, C. Ho, H. Seng, R. Aquino, A. Parham, R. Mann, I. Brown, P. Callagy, A. Visweswaran, C. Keller, C. Bucsit and A. Araya for their contributions to these validation studies.

Author information

Affiliations

Authors

Contributions

S.T., M.J.R., L.B., M.S.L., I.D.V., T.K., F.C.C., S.V., G.D.W., A.C., Z.N.R., G.M.-S., L.H., S.Balakrishnan, J.V.Q., D.H., D.K.H. and M.L.V. designed and carried out experiments, analysed data and summarized the results. T.A.B., S.T., M.L.V., M.K., S.Bercovici, J.C.W. and S.Y. analysed data and supervised the work. T.A.B., S.Bercovici, S.T., D.H. and D.K.H. wrote the paper.

Corresponding author

Correspondence to Timothy A. Blauwkamp.

Ethics declarations

Competing interests

This study was funded by Karius, Inc. and describes the validation of a product developed by Karius, Inc. All authors (excepting S.T., J.V.Q. and S.Y.) are current or former employees and/or share -holders of Karius, Inc. This does not alter our adherence to Nature Microbiology policies on sharing data and materials.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figures 1–9, Supplementary Tables 1–4 and Supplementary Table 8.

Reporting Summary

Supplementary Table 5

Subjects with definite and probable infections identified by NGS.

Supplementary Table 6

Aetiology of infection for patients with positive NGS results adjudicated as possible.

Supplementary Table 7

Subjects with ‘unlikely’ infections identified by NGS.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Blauwkamp, T.A., Thair, S., Rosen, M.J. et al. Analytical and clinical validation of a microbial cell-free DNA sequencing test for infectious disease. Nat Microbiol 4, 663–674 (2019). https://doi.org/10.1038/s41564-018-0349-6

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing