Improving Pulmonary Infection Diagnosis with Metagenomic Next Generation Sequencing

Yi-Yi Qian, Hong-Yu Wang, Yang Zhou, Hao-Cheng Zhang, Yi-Min Zhu, Xian Zhou, Yue Ying, Peng Cui, Hong-Long Wu, Wen-Hong Zhang, Jia-Lin Jin, Jing-Wen Ai, Yi-Yi Qian, Hong-Yu Wang, Yang Zhou, Hao-Cheng Zhang, Yi-Min Zhu, Xian Zhou, Yue Ying, Peng Cui, Hong-Long Wu, Wen-Hong Zhang, Jia-Lin Jin, Jing-Wen Ai

Abstract

Pulmonary infections are among the most common and important infectious diseases due to their high morbidity and mortality, especially in older and immunocompromised individuals. However, due to the limitations in sensitivity and the long turn-around time (TAT) of conventional diagnostic methods, pathogen detection and identification methods for pulmonary infection with greater diagnostic efficiency are urgently needed. In recent years, unbiased metagenomic next generation sequencing (mNGS) has been widely used to detect different types of infectious pathogens, and is especially useful for the detection of rare and newly emergent pathogens, showing better diagnostic performance than traditional methods. There has been limited research exploring the application of mNGS for the diagnosis of pulmonary infections. In this study we evaluated the diagnostic efficiency and clinical impact of mNGS on pulmonary infections. A total of 100 respiratory samples were collected from patients diagnosed with pulmonary infection in Shanghai, China. Conventional methods, including culture and standard polymerase chain reaction (PCR) panel analysis for respiratory tract viruses, and mNGS were used for the pathogen detection in respiratory samples. The difference in the diagnostic yield between conventional methods and mNGS demonstrated that mNGS had higher sensitivity than traditional culture for the detection of pathogenic bacteria and fungi (95% vs 54%; p<0.001). Although mNGS had lower sensitivity than PCR for diagnosing viral infections, it identified 14 viral species that were not detected using conventional methods, including multiple subtypes of human herpesvirus. mNGS detected viruses with a genome coverage >95% and a sequencing depth >100× and provided reliable phylogenetic and epidemiological information. mNGS offered extra benefits, including a shorter TAT. As a complementary approach to conventional methods, mNGS could help improving the identification of respiratory infection agents. We recommend the timely use of mNGS when infection of mixed or rare pathogens is suspected, especially in immunocompromised individuals and or individuals with severe conditions that require urgent treatment.

Keywords: diagnosis; metagenomic; next generation sequencing; pulmonary infection; respiration tract infection.

Conflict of interest statement

H-LW was employed by BGI PathoGenesis Pharmaceutical Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2021 Qian, Wang, Zhou, Zhang, Zhu, Zhou, Ying, Cui, Wu, Zhang, Jin and Ai.

Figures

Figure 1
Figure 1
Overview of patient enrollment workflow.
Figure 2
Figure 2
Distribution and diagnostic performance of identified pathogens in mNGS and traditional pathogen detection. Tra, Traditional Pathogen Detection Methods, culture for Bacteria and Fungi, FilmArray for Virus; NTM, Nontuberculous Mycobacteria. TB, Tuberculosis. RSV, Respiratory Syncytial Virus. hMPV, human Metapneumovirus; Sen, Sensitivity; Spe, Specificity; PPV, Positive Prediction Value; NPV, Negative Prediction Value.
Figure 3
Figure 3
Phylogenetic analysis of the representative adenovirus B1 genomes. This analysis involved 2 newly assembled adenovirus B1 genomes, 36 published human adenovirus B genomes, and 13 human adenoviruses from NCBI Reference Sequence database. The two adenovirus B1 genomes (ADV-17S0835897 and ADV-17S0836382) were located in the same branch, and had high genetic similarity with strains identified in China.

References

    1. Ai J. W., Zhang H. C., Cui P., Xu B., Gao Y., Cheng Q., et al. (2018). Dynamic and direct pathogen load surveillance to monitor disease progression and therapeutic efficacy in central nervous system infection using a novel semi-quantitive sequencing platform. J. Infect. 76, 307–310. 10.1016/j.jinf.2017.11.002
    1. Edgar R. C. (2004). MUSCLE: a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics 5, 113. 10.1186/1471-2105-5-113
    1. Guindon S., Dufayard J. F., Lefort V., Anisimova M., Hordijk W., Gascuel O. (2010). New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Syst. Biol. 59 (3), 307–321. 10.1093/sysbio/syq010
    1. Hardak E., Avivi I., Berkun L., Raz-Pasteur A., Lavi N., Geffen Y., et al. (2016). Polymicrobial pulmonary infection in patients with hematological malignancies: prevalence, co-pathogens, course and outcome. Infection 44, 491–497. 10.1007/s15010-016-0873-3
    1. Huffnagle G. B., Dickson R. P., Lukacs N. W. (2017). The respiratory tract microbiome and lung inflammation: a two-way street. Mucosal Immunol. 10, 299–306. 10.1038/mi.2016.108
    1. Jeon Y. J., Zhou Y., Li Y., Guo Q., Chen J., Quan S., et al. (2014). The feasibility study of non-invasive fetal trisomy 18 and 21 detection with semiconductor sequencing platform. PloS One 9, e110240. 10.1371/journal.pone.0110240
    1. Kumar S., Stecher G., Li M., Knyaz C., Tamura K. (2018). MEGA X: Molecular Evolutionary Genetics Analysis across Computing Platforms. Mol. Biol. Evol. 35, 1547–1549. 10.1093/molbev/msy096
    1. Labelle A. J., Arnold H., Reichley R. M., Micek S. T., Kollef M. H. (2010). A comparison of culture-positive and culture-negative health-care-associated pneumonia. Chest 137, 1130–1137. 10.1378/chest.09-1652
    1. Li H., Durbin R. (2009). Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. 10.1093/bioinformatics/btp324
    1. Li H., Gao H., Meng H., Wang Q., Li S., Chen H., et al. (2018). Detection of Pulmonary Infectious Pathogens From Lung Biopsy Tissues by Metagenomic Next-Generation Sequencing. Front. Cell Infect. Microbiol. 8, 205. 10.3389/fcimb.2018.00205
    1. Loeffelholz M., Chonmaitree T. (2010). Advances in diagnosis of respiratory virus infections. Int. J. Microbiol. 2010, 126049. 10.1155/2010/126049
    1. Lu R., Zhao X., Li J., Niu P. H., Yang B., Wu H. L., et al. (2020). Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 395, 565–574. 10.1016/S0140-6736(20)30251-8
    1. Magill S. S., Edwards J. R., Bamberg W., Beldavs Z. G., Dumyati G., Kainer M. A., et al. (2014). Multistate point-prevalence survey of health care-associated infections. N Engl. J. Med. 370, 1198–1208. 10.1056/NEJMoa1306801
    1. Miao Q., Ma Y., Wang Q., Pan J., Zhang Y., Jin W., et al. (2018). Microbiological Diagnostic Performance of Metagenomic Next-generation Sequencing When Applied to Clinical Practice. Clin. Infect. Dis. 67, S231–S240. 10.1093/cid/ciy693
    1. Pan T., Tan R., Qu H., Weng X., Liu Z., Li M., et al. (2019). Next-generation sequencing of the BALF in the diagnosis of community-acquired pneumonia in immunocompromised patients. J. Infect. 79, 61–74. 10.1016/j.jinf.2018.11.005
    1. Parize P., Muth E., Richaud C., Gratigny M., Pilmis B., Lamamy A., et al. (2017). Untargeted next-generation sequencing-based first-line diagnosis of infection in immunocompromised adults: a multicentre, blinded, prospective study. Clin. Microbiol. Infect. 23, 574 e571–574 e576. 10.1016/j.cmi.2017.02.006
    1. Prachayangprecha S., Schapendonk C. M., Koopmans M. P., Osterhaus A. D., Schürch A. C., Pas S. D., et al. (2014). Exploring the potential of next-generation sequencing in detection of respiratory viruses. J. Clin. Microbiol 52, 3722–3730. 10.1128/JCM.01641-14
    1. Thorburn F., Bennett S., Modha S., Murdoch D., Gunson R., Murcia P. R. (2015). The use of next generation sequencing in the diagnosis and typing of respiratory infections. J. Clin. Virol 69, 96–100. 10.1016/j.jcv.2015.06.082
    1. Wang S., Ai J., Cui P., Zhu Y., Wu H., Zhang W., et al. (2020). Diagnostic value and clinical application of next-generation sequencing for infections in immunosuppressed patients with corticosteroid therapy. Ann. Transl. Med. 8, 227. 10.21037/atm.2020.01.30
    1. Wilson M. R., Sample H. A., Zorn K. C., Arevalo S., Yu G., Neuhaus J., et al. (2019). Clinical Metagenomic Sequencing for Diagnosis of Meningitis and Encephalitis. N Engl. J. Med. 380, 2327–2340. 10.1056/NEJMoa1803396
    1. Wypych T. P., Wickramasinghe L. C., Marsland B. J. (2019). The influence of the microbiome on respiratory health. Nat. Immunol. 20, 1279–1290. 10.1038/s41590-019-0451-9
    1. Xing X. W., Zhang J. T., Ma Y. B., He M. W., Yao G. E., Wang W., et al. (2020). Metagenomic Next-Generation Sequencing for Diagnosis of Infectious Encephalitis and Meningitis: A Large, Prospective Case Series of 213 Patients. Front. Cell Infect. Microbiol. 10 88, 88. 10.3389/fcimb.2020.00088
    1. Zhang H. C., Ai J. W., Cui P., Zhu Y. M., Wu H. L., Li Y. J., et al. (2019). Incremental value of metagenomic next generation sequencing for the diagnosis of suspected focal infection in adults. J. Infect. 79, 419–425. 10.1016/j.jinf.2019.08.012
    1. Zhang H. C., Zhang Q. R., Ai J. W., Cui P., Wu H. L., Zhang W. H., et al. (2020). The role of bone marrow metagenomics next-generation sequencing to differential diagnosis among visceral leishmaniasis, histoplasmosis, and talaromycosis marneffei. Int. J. Lab. Hematol. 42, e52–e54. 10.1111/ijlh.13103
    1. Zhang Y., Cui P., Zhang H. C., Wu H. L., Ye M. Z., Zhu Y. M., et al. (2020). Clinical application and evaluation of metagenomic next-generation sequencing in suspected adult central nervous system infection. J. Transl. Med. 18, 199. 10.1186/s12967-020-02360-6
    1. Zhou X., Wu H., Ruan Q., Jiang N., Chen X., Shen Y., et al. (2019). Clinical Evaluation of Diagnosis Efficacy of Active Mycobacterium tuberculosis Complex Infection via Metagenomic Next-Generation Sequencing of Direct Clinical Samples. Front. Cell Infect. Microbiol 9, 351. 10.3389/fcimb.2019.00351
    1. Zhu Y. M., Ai J. W., Xu B., Cui P., Cheng Q., Wu H., et al. (2018). Rapid and precise diagnosis of disseminated T.marneffei infection assisted by high-throughput sequencing of multifarious specimens in a HIV-negative patient: a case report. BMC Infect. Dis. 18, 379. 10.1186/s12879-018-3276-5
    1. Zhu N., Zhang D., Wang W., Li X., Yang B., Song J., et al. (2020). A Novel Coronavirus from Patients with Pneumonia in China, 2019. N Engl. J. Med. 382, 727–733. 10.1056/NEJMoa2001017

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