A cluster of cases of pneumocystis pneumonia identified by shotgun metagenomics approach

doi:10.1016/j.jinf.2018.08.013

Journal of Infection

Volume 78, Issue 2, February 2019, Pages 158-169

https://doi.org/10.1016/j.jinf.2018.08.013 Get rights and content

Highlights

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Metagenomics sequencing performed well in diagnosing PCP compared to conventional methods.
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Metagenomics exhibited the ability to detect co-infections in PCP patients.
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Blood samples metagenomics might suit patients who couldn't tolerate invasive procedures.
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Pathogen reads ranking and proportion rate may be the promising cutoff value.

Section snippets

Declaration

We confirm that each individual named as an author meets the journal's criteria for authorship and neither the entire paper nor any part of its content has been published or accepted elsewhere. It is not being submitted to any other journal.

Funding

This study was supported by the New and Advanced Technology Project of Shanghai Municipal Hospital: Application of next generation sequencing technique in precise diagnosis of infectious diseases (SHDC12017104).

A Conflict of Interest

All authors report no potential conflict of interest.

Acknowledgments

We thank the patients for cooperating with our investigation and acknowledge the professionalism and compassion demonstrated by all the healthcare workers involved in patients’ care.

References (5)

JR Brown et al.
Encephalitis diagnosis using metagenomics: application of next generation sequencing for undiagnosed cases
J Infect
(Mar 2018)
MG de Boer et al.
beta-d-glucan and S-adenosylmethionine serum levels for the diagnosis of Pneumocystis pneumonia in HIV-negative patients: a prospective study
J Infect
(Jan 2011)

There are more references available in the full text version of this article.

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Aim of Review: In this review, we summarize the potential applications and challenges of mNGS in the diagnosis of LRIs to promote the integration of mNGS into the management of patients with respiratory tract infections in a clinical setting.
Key Scientific Concepts of Review: Once its analytical validation, clinical validation and clinical utility been demonstrated, mNGS will become an important tool in the field of infectious disease diagnosis.
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From sample processing to result reporting, testing based on a second-generation sequencing platform (such as illumina sequencing) can be completed within 24–48 h, while a third-generation sequencing platform (such as Nanopore sequencing) takes only 6 h [10]. Respiratory tract infection-based cohort studies have shown that the detection rate of mNGS (>60%) for respiratory tract samples is significantly higher than that of traditional detection methods (30%–50%) [11–13]. In particular, mNGS shows excellent detection performance in identifying unexpected, atypical and slow-growing pathogens within a clinically actionable time frame [9].
Metagenomic next-generation sequencing (mNGS) assay for detecting infectious agents is now in the stage of being translated into clinical practice. With no approved approaches or guidelines available, laboratories adopt customized mNGS assays to detect clinical samples. However, the accuracy, reliability, and problems of these routinely implemented assays are not clear.
To evaluate the performance of 90 mNGS laboratories under routine testing conditions through analyzing identical samples.
Eleven microbial communities were generated using 15 quantitative microbial suspensions. They were used as reference materials to evaluate the false negatives and false positives of participating mNGS protocols, as well as the ability to distinguish genetically similar organisms and to identify true pathogens from other microbes based on fictitious case reports.
High interlaboratory variability was found in the identification and the quantitative reads per million reads (RPM) values of each microbe in the samples, especially when testing microbes present at low concentrations (1 × 10³ cell/ml or less). 42.2% (38/90) of the laboratories reported unexpected microbes (i.e. false positive problem). Only 56.7% (51/90) to 83.3% (75/90) of the laboratories showed a sufficient ability to obtain clear etiological diagnoses for three simulated cases combined with patient information. The analysis of the performance of mNGS in distinguishing genetically similar organisms in three samples revealed that only 56.6% to 63.0% of the laboratories recovered RPM ratios (RPM_{S. aureus}/RPM_{S. epidermidis}) within the range of a 2-fold change of the initial input ratios (indicating a relatively low level of bias).
The high interlaboratory variability found in both identifying microbes and distinguishing true pathogens emphasizes the urgent need for improving the accuracy and comparability of the results generated across different mNGS laboratories, especially in the detection of low-microbial-biomass samples.
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¹: Yi Zhang and Jing-Wen Ai contributed equally to this manuscript.

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Letter to the EditorA cluster of cases of pneumocystis pneumonia identified by shotgun metagenomics approach