文章基本信息

标题：A metagenomics-based diagnostic approach for central nervous system infections in hospital acute care setting
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作者：Mohammad Rubayet Hasan ; Sathyavathi Sundararaju ; Patrick Tang 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2020
卷号：10
期号：1
页码：1-11
DOI：10.1038/s41598-020-68159-z
出版社：Springer Nature
摘要：The etiology of central nervous system (CNS) infections such as meningitis and encephalitis remains unknown in a large proportion of cases partly because the diversity of pathogens that may cause CNS infections greatly outnumber available test methods. We developed a metagenomic next generation sequencing (mNGS)-based approach for broad-range detection of pathogens associated with CNS infections suitable for application in the acute care hospital setting. The analytical sensitivity of mNGS performed on an Illumina MiSeq was assessed using simulated cerebrospinal fluid (CSF) specimens (n = 9). mNGS data were then used as a training dataset to optimize a bioinformatics workflow based on the IDseq pipeline. For clinical validation, residual CSF specimens (n = 74) from patients with suspected CNS infections previously tested by culture and/or PCR, were analyzed by mNGS. In simulated specimens, the NGS reads aligned to pathogen genomes in IDseq were correlated to qPCR CT values for the respective pathogens (R = 0.96; p < 0.0001), and the results were highly specific for the spiked pathogens. In clinical samples, the diagnostic accuracy, sensitivity and specificity of the mNGS with reference to conventional methods were 100%, 95% and 96%, respectively. The clinical application of mNGS holds promise to benefit patients with CNS infections of unknown etiology.