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Volume 73, Issue 3

Performance of metagenomic next-generation sequencing in cerebrospinal fluid for diagnosis of tuberculous meningitis No Access

Abstract

Metagenomic next-generation sequencing (mNGS) has been widely used in the diagnosis of infectious diseases, while its performance in diagnosis of tuberculous meningitis (TBM) is incompletely characterized. The aim of this study was to assess the performance of mNGS in the diagnosis of TBM, and illustrate the sensitivity and specificity of different methods.

We retrospectively recruited TBM patients between January 2021 and March 2023 to evaluate the performance of mNGS on cerebrospinal fluid (CSF) samples, in comparison with conventional microbiological testing, including culturing of (MTB), acid-fast bacillus (AFB) stain, reverse transcription PCR and Xpert MTB/RIF.

Of the 40 enrolled, 34 participants were diagnosed with TBM, including 15(44.12 %) definite and 19(55.88 %) clinical diagnosis based upon clinical manifestations, CSF parameters, brain imaging, pathogen evidence and treatment response. The mNGS method identified sequences of complex (MTBC) in 11 CSF samples. In patients with definite TBM, the sensitivity, specificity, positive predictive value, negative predictive value and accuracy of mNGS were 78.57, 100, 100, 66.67 and 85 %, respectively. Compared to conventional diagnostic methods, the sensitivity of mNGS (78.57 %) was higher than AFB (0 %), culturing (0 %), RT-PCR (60 %) and Xpert MTB/RIF (14.29 %).

Our study indicates that mNGS of CSF exhibited an overall improved sensitivity over conventional diagnostic methods for TBM and can be considered a front-line CSF test.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): cerebrospinal fluid , metagenomic next-generation sequencing , Mycobacterium tuberculosis , pathogen detection and tuberculous meningitis
Funding
This study was supported by the:
  • National Natural Science Foundation of China (Award 82171841)
    • Principle Award Recipient: Xiang-PingYao
© 2024 The Authors
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/content/journal/jmm/10.1099/jmm.0.001818
2024-03-20
2024-04-27
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References

  1. World Health O. Global Tuberculosis Report 2021 Geneva: World Health Organization; 2021
     [Google Scholar]
  2. Wilkinson RJ, Rohlwink U, Misra UK, van Crevel R, Mai NTH et al. Tuberculous meningitis. Nat Rev Neurol 2017; 13:581–598 [View Article] [PubMed]
     [Google Scholar]
  3. Rock RB, Olin M, Baker CA, Molitor TW, Peterson PK. Central nervous system tuberculosis: pathogenesis and clinical aspects. Clin Microbiol Rev 2008; 21:243–261 [View Article] [PubMed]
     [Google Scholar]
  4. Kent SJ, Crowe SM, Yung A, Lucas CR, Mijch AM. Tuberculous meningitis: a 30-year review. Clin Infect Dis 1993; 17:987–994 [View Article] [PubMed]
     [Google Scholar]
  5. Miller MB, Popowitch EB, Backlund MG, Ager EPC. Performance of Xpert MTB/RIF RUO assay and IS6110 real-time PCR for Mycobacterium tuberculosis detection in clinical samples. J Clin Microbiol 2011; 49:3458–3462 [View Article] [PubMed]
     [Google Scholar]
  6. Davis AG, Wilkinson RJ. Diagnostic tests for tuberculous meningitis. Lancet Infect Dis 2020; 20:262–263 [View Article] [PubMed]
     [Google Scholar]
  7. World Health O. Automated Real-Time Nucleic Acid Amplification Technology for Rapid and Simultaneous Detection of Tuberculosis and Rifampicin Resistance: Xpert MTB/RIF Assay for the Diagnosis of Pulmonary and Extrapulmonary TB in Adults and Children: Policy Update Geneva: World Health Organization; 2013
     [Google Scholar]
  8. Bahr NC, Nuwagira E, Evans EE, Cresswell FV, Bystrom PV et al. Diagnostic accuracy of Xpert MTB/RIF Ultra for tuberculous meningitis in HIV-infected adults: a prospective cohort study. Lancet Infect Dis 2018; 18:68–75 [View Article] [PubMed]
     [Google Scholar]
  9. Bahr NC, Marais S, Caws M, van Crevel R, Wilkinson RJ et al. GeneXpert MTB/Rif to diagnose tuberculous meningitis: perhaps the first test but not the last. Clin Infect Dis 2016; 62:1133–1135 [View Article] [PubMed]
     [Google Scholar]
  10. Chiu CY, Miller SA. Clinical metagenomics. Nat Rev Genet 2019; 20:341–355 [View Article] [PubMed]
     [Google Scholar]
  11. Yan L, Sun W, Lu Z, Fan L. Metagenomic next-generation sequencing (mNGS) in cerebrospinal fluid for rapid diagnosis of Tuberculosis meningitis in HIV-negative population. Int J Infect Dis 2020; 96:270–275 [View Article] [PubMed]
     [Google Scholar]
  12. Wang S, Chen Y, Wang D, Wu Y, Zhao D et al. The feasibility of metagenomic next-generation sequencing to identify pathogens causing tuberculous meningitis in cerebrospinal fluid. Front Microbiol 2019; 10:1993 [View Article] [PubMed]
     [Google Scholar]
  13. Marais S, Thwaites G, Schoeman JF, Török ME, Misra UK et al. Tuberculous meningitis: a uniform case definition for use in clinical research. Lancet Infect Dis 2010; 10:803–812 [View Article] [PubMed]
     [Google Scholar]
  14. Matei Zaharia WJB, Curtis K, Fox A, Patterson D, Shenker S et al. Faster and more accurate sequence alignment with SNAP. arXiv 2011; 23:5572v1
     [Google Scholar]
  15. Li H, Durbin RJB. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 2009; 25:1754–1760 [View Article]
     [Google Scholar]
  16. Miao Q, Ma Y, Wang Q, Pan J, Zhang Y et al. Microbiological diagnostic performance of metagenomic next-generation sequencing when applied to clinical practice. Clin Infect Dis 2018; 67:S231–S240 [View Article] [PubMed]
     [Google Scholar]
  17. Nhu NTQ, Heemskerk D, Thu DDA, Chau TTH, Mai NTH et al. Evaluation of GeneXpert MTB/RIF for diagnosis of tuberculous meningitis. J Clin Microbiol 2014; 52:226–233 [View Article] [PubMed]
     [Google Scholar]
  18. Rufai SB, Singh A, Singh J, Kumar P, Sankar MM et al. Diagnostic usefulness of Xpert MTB/RIF assay for detection of tuberculous meningitis using cerebrospinal fluid. J Infect 2017; 75:125–131 [View Article] [PubMed]
     [Google Scholar]
  19. Ai J-W, Li Y, Cheng Q, Cui P, Wu H-L et al. Diagnosis of local hepatic tuberculosis through next-generation sequencing: smarter, faster and better. Clin Res Hepatol Gastroenterol 2018; 42:178–181 [View Article] [PubMed]
     [Google Scholar]
  20. Kohli M, Schiller I, Dendukuri N, Dheda K, Denkinger CM et al. Xpert® MTB/RIF assay for extrapulmonary tuberculosis and rifampicin resistance. Cochrane Database Syst Rev 2018; 8:CD012768 [View Article] [PubMed]
     [Google Scholar]
  21. Parize P, Muth E, Richaud C, Gratigny M, Pilmis B et al. Untargeted next-generation sequencing-based first-line diagnosis of infection in immunocompromised adults: a multicentre, blinded, prospective study. Clin Microbiol Infect 2017; 23:574 [View Article] [PubMed]
     [Google Scholar]
  22. Nongrum S, Singh VA, Paul R, Karnik S, Mehta S et al. A study about co-infection of fungal pathogens in active tuberculosis patients. Mymensingh Med J 2019; 28:920–924 [PubMed]
     [Google Scholar]
  23. Sawai T, Nakao T, Koga S, Ide S, Yoshioka S et al. Miliary tuberculosis with co-existing pulmonary cryptococcosis in non-HIV patient without underlying diseases: a case report. BMC Pulm Med 2018; 18:6 [View Article] [PubMed]
     [Google Scholar]
  24. Fang W, Zhang L, Liu J, Denning DW, Hagen F et al. Tuberculosis/cryptococcosis co-infection in China between 1965 and 2016. Emerg Microbes Infect 2017; 6:e73 [View Article] [PubMed]
     [Google Scholar]
  25. Lozano R, Naghavi M, Foreman K, Lim S, Shibuya K et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380:2095–2128 [View Article] [PubMed]
     [Google Scholar]
  26. Sun W-W, Sun Q, Yan L-P, Zhang Q. The application of IS6110-baced loop-mediated isothermal amplification (LAMP) in the early diagnosis of tuberculous meningitis. Oncotarget 2017; 8:57537–57542 [View Article] [PubMed]
     [Google Scholar]
  27. Allahyartorkaman M, Mirsaeidi M, Hamzehloo G, Amini S, Zakiloo M et al. Low diagnostic accuracy of Xpert MTB/RIF assay for extrapulmonary tuberculosis: a multicenter surveillance. Sci Rep 2019; 9:18515 [View Article] [PubMed]
     [Google Scholar]
  28. Salter SJ, Cox MJ, Turek EM, Calus ST, Cookson WO et al. Reagent and laboratory contamination can critically impact sequence-based microbiome analyses. BMC Biol 2014; 12:87 [View Article] [PubMed]
     [Google Scholar]
  29. Strong MJ, Xu G, Morici L, Splinter Bon-Durant S, Baddoo M et al. Microbial contamination in next generation sequencing: implications for sequence-based analysis of clinical samples. PLoS Pathog 2014; 10:e1004437 [View Article] [PubMed]
     [Google Scholar]
  30. Burd EM. Validation of laboratory-developed molecular assays for infectious diseases. Clin Microbiol Rev 2010; 23:550–576 [View Article] [PubMed]
     [Google Scholar]
  31. Schlaberg R, Chiu CY, Miller S, Procop GW, Weinstock G et al. Validation of metagenomic next-generation sequencing tests for universal pathogen detection. Arch Pathol Lab Med 2017; 141:776–786 [View Article] [PubMed]
     [Google Scholar]
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Performance of metagenomic next-generation sequencing in cerebrospinal fluid for diagnosis of tuberculous meningitis
J. Med. Microbiol. 73, 001818 (2024); https://doi.org/10.1099/jmm.0.001818
/content/journal/jmm/10.1099/jmm.0.001818
/content/journal/jmm/10.1099/jmm.0.001818
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