Wilson MR, et al. - Using metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF), authors sought to display a case series of patients with diagnostically challenging subacute or chronic meningitis supported by a statistical framework generated from mNGS of control samples from the environment and from patients who were noninfectious. Data revealed diverse microbial pathogens via mNGS in the CSF of patients with diagnostically challenging subacute or chronic meningitis, including a case of subarachnoid neurocysticercosis that defied diagnosis for 1 year, the first reported case of CNS vasculitis caused by Aspergillus oryzae, and the fourth reported case of C dubliniensis meningitis. Data interpretation was clarified by prioritizing metagenomic data with a scoring algorithm. Furthermore, the issue of attributing biological significance to organisms present in control samples used for metagenomic sequencing studies was underscored.

Methods

• Using mNGS data obtained from the CSF of 94 patients with noninfectious neuroinflammatory disorders and from 24 water and reagent control samples, a weighted scoring metric was formulated and implemented based on z scores at the species and genus levels for both nucleotide and protein alignments to prioritize and rank the mNGS results.
• During this study, total RNA was extracted for mNGS from the CSF of 7 participants with subacute or chronic meningitis who were recruited between September 2013 and March 2017 as part of a multicenter study of mNGS pathogen discovery among patients with suspected neuroinflammatory conditions.
• A weighted z score assisted in filtering out environmental contaminants and enabled efficient data triage and analysis.
• The main outcome included pathogens identified by mNGS and the ability of a statistical model to prioritize, rank, and simplify mNGS results.

Results

• Enrollees were aged 10 to 55 years, and 3 (43%) were female.
• Using mNGSA, a parasitic worm (Taenia solium, in 2 participants), a virus (HIV-1), and 4 fungi (Cryptococcus neoformans, Aspergillus oryzae, Histoplasma capsulatum, and Candida dubliniensis) were detected among them.
• An analysis of mNGS data with a weighted z score-based scoring algorithm led to a reduction in the reported microbial taxa by a mean of 87% (range, 41%-99%) when taxa with a combined score of 0 or less were removed.
• This, in turn, effectively separated bona fide pathogen sequences from spurious environmental sequences so the causative pathogen was discovered within the top 2 scoring microbes identified using the algorithm in each case.