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Volume 72, Issue 11

Performance of targeted next-generation sequencing in the detection of respiratory pathogens and antimicrobial resistance genes for children No Access

Abstract

Respiratory tract infection, which is associated with high morbidity and mortality, occurs frequently in children. At present, the main diagnostic method is culture. However, the low pathogen detection rate of the culture approach prevents timely and accurate diagnosis. Fortunately, next-generation sequencing (NGS) can compensate for the deficiency of culture, and its application in clinical diagnostics has become increasingly available.

Targeted NGS (tNGS) is a platform that can select and enrich specific regions before data enter the NGS pipeline. However, the performance of tNGS in the detection of respiratory pathogens and antimicrobial resistance genes (ARGs) in infections in children is unclear.

In this study, we estimated the performance of tNGS in the detection of respiratory pathogens and ARGs in 47 bronchoalveolar lavage fluid (BALF) specimens from children using conventional culture and antimicrobial susceptibility testing (AST) as the gold standard.

RPIP (Respiratory Pathogen ID/AMR enrichment) sequencing generated almost 500 000 reads for each specimen. In the detection of pathogens, RPIP sequencing showed targeted superiority in detecting difficult-to-culture bacteria, including . Compared with the results of culture, the sensitivity and specificity of RPIP were 84.4 % (confidence interval 70.5–93.5 %) and 97.7 % (95.9 –98.8%), respectively. Moreover, RPIP results showed that a single infection was detected in 10 of the 47 BALF specimens, and multiple infections were detected in 34, with the largest number of bacterial/viral coinfections. Nevertheless, there were also three specimens where no pathogen was detected. Furthermore, we analysed the drug resistance genes of specimens containing , which was detected in 25 out of 47 specimens in the study. A total of 58 ARGs associated with tetracycline, macrolide-lincosamide-streptogramin, beta-lactams, sulfonamide and aminoglycosides were identified by RPIP in 19 of 25 patients. Using the results of AST as a standard, the coincidence rates of erythromycin, tetracycline, penicillin and sulfonamides were 89.5, 79.0, 36.8 and 42.1 %, respectively.

These results demonstrated the superiority of RPIP in pathogen detection, particularly for multiple and difficult-to-culture pathogens, as well as in predicting resistance to erythromycin and tetracycline, which has significance for the accurate diagnosis of pathogenic infection and in the guidance of clinical treatment.

  • Received:
  • Accepted:
  • Published Online:
Keyword(s): antimicrobial resistance gene , bronchoalveolar lavage fluid , children , respiratory pathogens and targeted next-generation sequencing
Funding
This study was supported by the:
  • Guangdong Basic and Applied Basic Research Foundation (Award 2020A1515010246)
    • Principle Award Recipient: XiaorongLIU
  • Shenzhen Science and Technology Programme (Award JCYJ20210324135414039)
    • Principle Award Recipient: XiaorongLIU
  • Health and Family Planning Commission of Shenzhen Municipality Grant (Award SZLY2017016)
    • Principle Award Recipient: DongliMA
  • Guangdong High-level Hospital Construction Fund (Award ynkt2021-zz25 and ESY-GSP-YXPT-A02)
    • Principle Award Recipient: ZhihaoXING
  • Development and Reform Commission of Shenzhen Municipality Grant (Award 2019 [986])
    • Principle Award Recipient: DongliMA
© 2023 The Authors
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/content/journal/jmm/10.1099/jmm.0.001771
2023-11-01
2024-04-27
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References

  1. Collaborators GBDLRI. Estimates of the global, regional, and national morbidity, mortality, and Aetiologies of lower respiratory infections in 195 countries, 1990-2016: a systematic analysis for the global burden of disease study 2016. Lancet Infect Dis 2018; 18:1191–1210
     [Google Scholar]
  2. Koch A, Mizrahi V. Mycobacterium tuberculosis. Trends Microbiol 2018; 26:555–556 [View Article] [PubMed]
     [Google Scholar]
  3. Decker MD, Edwards KM. Pertussis (Whooping Cough). J Infect Dis 2021; 224:S310–S320 [View Article] [PubMed]
     [Google Scholar]
  4. Li Z-J, Zhang H-Y, Ren L-L, Lu Q-B, Ren X et al. Etiological and epidemiological features of acute respiratory infections in China. Nat Commun 2021; 12:5026 [View Article] [PubMed]
     [Google Scholar]
  5. Shi T, McLean K, Campbell H, Nair H. Aetiological role of common respiratory viruses in acute lower respiratory infections in children under five years: a systematic review and meta-analysis. J Glob Health 2015; 5:010408 [View Article] [PubMed]
     [Google Scholar]
  6. Zhu G, Xu D, Zhang Y, Wang T, Zhang L et al. Epidemiological characteristics of four common respiratory viral infections in children. Virol J 2021; 18:10 [View Article] [PubMed]
     [Google Scholar]
  7. de Benedictis FM, Kerem E, Chang AB, Colin AA, Zar HJ et al. Complicated pneumonia in children. Lancet 2020; 396:786–798 [View Article] [PubMed]
     [Google Scholar]
  8. Medernach RL, Logan LK. The growing threat of antibiotic resistance in children. Infect Dis Clin North Am 2018; 32:1–17 [View Article] [PubMed]
     [Google Scholar]
  9. Huemer M, Mairpady Shambat S, Brugger SD, Zinkernagel AS. Antibiotic resistance and persistence-implications for human health and treatment perspectives. EMBO Rep 2020; 21:e51034 [View Article] [PubMed]
     [Google Scholar]
  10. Lin C-Y, Hwang D, Chiu N-C, Weng L-C, Liu H-F et al. Increased detection of viruses in children with respiratory tract infection using PCR. Int J Environ Res Public Health 2020; 17:564 [View Article] [PubMed]
     [Google Scholar]
  11. Zhang Y, Chen Y, He Y, Li Y, Zhang X et al. Development of receptor binding domain-based double-antigen sandwich lateral flow immunoassay for the detection and evaluation of SARS-CoV-2 neutralizing antibody in clinical sera samples compared with the conventional virus neutralization test. Talanta 2023; 255:124200 [View Article] [PubMed]
     [Google Scholar]
  12. Idelevich EA, Becker K. How to accelerate antimicrobial susceptibility testing. Clin Microbiol Infect 2019; 25:1347–1355 [View Article] [PubMed]
     [Google Scholar]
  13. van Belkum A, Burnham C-A, Rossen JWA, Mallard F, Rochas O et al. Innovative and rapid antimicrobial susceptibility testing systems. Nat Rev Microbiol 2020; 18:299–311 [View Article] [PubMed]
     [Google Scholar]
  14. van Dijk EL, Auger H, Jaszczyszyn Y, Thermes C. Ten years of next-generation sequencing technology. Trends Genet 2014; 30:418–426 [View Article] [PubMed]
     [Google Scholar]
  15. Yohe S, Thyagarajan B. Review of clinical next-generation sequencing. Arch Pathol Lab Med 2017; 141:1544–1557 [View Article] [PubMed]
     [Google Scholar]
  16. Takeuchi S, Kawada J-I, Horiba K, Okuno Y, Okumura T et al. Metagenomic analysis using next-generation sequencing of pathogens in bronchoalveolar lavage fluid from pediatric patients with respiratory failure. Sci Rep 2019; 9:12909 [View Article] [PubMed]
     [Google Scholar]
  17. Simner PJ, Miller HB, Breitwieser FP, Pinilla Monsalve G, Pardo CA et al. Development and optimization of metagenomic next-generation sequencing methods for cerebrospinal fluid diagnostics. J Clin Microbiol 2018; 56:e00472-18 [View Article] [PubMed]
     [Google Scholar]
  18. Kohl C, Brinkmann A, Dabrowski PW, Radonić A, Nitsche A et al. Protocol for metagenomic virus detection in clinical specimens. Emerg Infect Dis 2015; 21:48–57 [View Article] [PubMed]
     [Google Scholar]
  19. Gu W, Miller S, Chiu CY. Clinical metagenomic next-generation sequencing for pathogen detection. Annu Rev Pathol 2019; 14:319–338 [View Article] [PubMed]
     [Google Scholar]
  20. LaDuca H, Farwell KD, Vuong H, Lu H-M, Mu W et al. Exome sequencing covers >98% of mutations identified on targeted next generation sequencing panels. PLoS One 2017; 12:e0170843 [View Article] [PubMed]
     [Google Scholar]
  21. Cho WCS, Tan KT, Ma VWS, Li JYC, Ngan RKC et al. Targeted next-generation sequencing reveals recurrence-associated genomic alterations in early-stage non-small cell lung cancer. Oncotarget 2018; 9:36344–36357 [View Article] [PubMed]
     [Google Scholar]
  22. Gaston DC, Miller HB, Fissel JA, Jacobs E, Gough E et al. Evaluation of metagenomic and targeted next-generation sequencing workflows for detection of respiratory pathogens from bronchoalveolar lavage fluid specimens. J Clin Microbiol 2022; 60:e0052622 [View Article] [PubMed]
     [Google Scholar]
  23. Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016; 32:3047–3048 [View Article] [PubMed]
     [Google Scholar]
  24. Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol 2019; 20:257 [View Article] [PubMed]
     [Google Scholar]
  25. Yin X, Jiang X-T, Chai B, Li L, Yang Y et al. ARGs-OAP v2.0 with an expanded SARG database and hidden markov models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. Bioinformatics 2018; 34:2263–2270 [View Article] [PubMed]
     [Google Scholar]
  26. Yang Y, Li B, Ju F, Zhang T. Exploring variation of antibiotic resistance genes in activated sludge over a four-year period through a metagenomic approach. Environ Sci Technol 2013; 47:10197–10205 [View Article] [PubMed]
     [Google Scholar]
  27. van Boheemen S, van Rijn AL, Pappas N, Carbo EC, Vorderman RHP et al. Retrospective validation of a metagenomic sequencing protocol for combined detection of RNA and DNA viruses using respiratory samples from pediatric patients. J Mol Diagn 2020; 22:196–207 [View Article] [PubMed]
     [Google Scholar]
  28. Weiser JN, Ferreira DM, Paton JC. Streptococcus pneumoniae: transmission, colonization and invasion. Nat Rev Microbiol 2018; 16:355–367 [View Article] [PubMed]
     [Google Scholar]
  29. Besser J, Carleton HA, Gerner-Smidt P, Lindsey RL, Trees E. Next-generation sequencing technologies and their application to the study and control of bacterial infections. Clin Microbiol Infect 2018; 24:335–341 [View Article] [PubMed]
     [Google Scholar]
  30. Zhao N, Cao J, Xu J, Liu B, Liu B et al. Targeting RNA with next- and third-generation sequencing improves pathogen identification in clinical samples. Adv Sci 2021; 8:e2102593 [View Article] [PubMed]
     [Google Scholar]
  31. Man WH, de Steenhuijsen Piters WAA, Bogaert D. The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat Rev Microbiol 2017; 15:259–270 [View Article] [PubMed]
     [Google Scholar]
  32. Claassen-Weitz S, Lim KYL, Mullally C, Zar HJ, Nicol MP. The association between bacteria colonizing the upper respiratory tract and lower respiratory tract infection in young children: a systematic review and meta-analysis. Clin Microbiol Infect 2021; 27:1262–1270 [View Article] [PubMed]
     [Google Scholar]
  33. Bakaletz LO. Viral-bacterial co-infections in the respiratory tract. Curr Opin Microbiol 2017; 35:30–35 [View Article] [PubMed]
     [Google Scholar]
  34. Oliva J, Terrier O. Viral and bacterial co-infections in the lungs: dangerous liaisons. Viruses 2021; 13:1725 [View Article] [PubMed]
     [Google Scholar]
  35. Bello S, Mincholé E, Fandos S, Lasierra AB, Ruiz MA et al. Inflammatory response in mixed viral-bacterial community-acquired pneumonia. BMC Pulm Med 2014; 14:123 [View Article] [PubMed]
     [Google Scholar]
  36. Ruuskanen O, Lahti E, Jennings LC, Murdoch DR. Viral pneumonia. Lancet 2011; 377:1264–1275 [View Article] [PubMed]
     [Google Scholar]
  37. Hakenbeck R, Brückner R, Denapaite D, Maurer P. Molecular mechanisms of β-lactam resistance in Streptococcus pneumoniae. Future Microbiol 2012; 7:395–410 [View Article] [PubMed]
     [Google Scholar]
  38. Chewapreecha C, Marttinen P, Croucher NJ, Salter SJ, Harris SR et al. Comprehensive identification of single nucleotide polymorphisms associated with beta-lactam resistance within pneumococcal mosaic genes. PLoS Genet 2014; 10:e1004547 [View Article] [PubMed]
     [Google Scholar]
  39. Zhou Y, Fang J, Davood Z, Han J, Qu D. Fitness cost and compensation mechanism of sulfonamide resistance genes (sul1, sul2, and sul3) in Escherichia coli. Environ Microbiol 2021; 23:7538–7549 [View Article] [PubMed]
     [Google Scholar]
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Performance of targeted next-generation sequencing in the detection of respiratory pathogens and antimicrobial resistance genes for children
J. Med. Microbiol. 72, 001771 (2023); https://doi.org/10.1099/jmm.0.001771
/content/journal/jmm/10.1099/jmm.0.001771
/content/journal/jmm/10.1099/jmm.0.001771
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