Abstract
Lower respiratory infection (LRI) is the most fatal communicable disease, with only a few pathogens identified. Metagenomic next-generation sequencing (mNGS), as an unbiased, hypothesis-free, and culture-independent method, theoretically enables the detection of all pathogens in a single test. In this study, we developed and validated a DNA-based mNGS method for the diagnosis of LRIs from bronchoalveolar lavage fluid (BALF). We prepared simulated in silico data sets and published raw data sets from patients to evaluate the performance of our in-house bioinformatics pipeline and compared it with the popular metagenomics pipeline Kraken2-Bracken. In addition, a series of biological microbial communities were used to comprehensively validate the performance of our mNGS assay. Sixty-nine clinical BALF samples were used for clinical validation to determine the accuracy. The in-house bioinformatics pipeline validation showed a recall of 88.03%, precision of 99.14%, and F1 score of 92.26% via single-genome simulated data. Mock in silico microbial community and clinical metagenomic data showed that the in-house pipeline has a stricter cutoff value than Kraken2-Bracken, which could prevent false-positive detection by the bioinformatics pipeline. The validation for the whole mNGS pipeline revealed that overwhelming human DNA, long-term storage at 4°C, and repeated freezing-thawing reduced the analytical sensitivity of the assay. The mNGS assay showed a sensitivity of 95.18% and specificity of 91.30% for pathogen detection from BALF samples. This study comprehensively demonstrated the analytical performance of this laboratory-developed mNGS assay for pathogen detection from BALF, which contributed to the standardization of this technology. IMPORTANCE To our knowledge, this study is the first to comprehensively validate the mNGS assay for the diagnosis of LRIs from BALF. This study exhibited a ready-made example for clinical laboratories to prepare reference materials and develop comprehensive validation schemes for their in-house mNGS assays, which would accelerate the standardization of mNGS testing.