Chronic Meningitis Investigated via Metagenomic Next-Generation Sequencing

Abstract

Importance: Identifying infectious causes of subacute or chronic meningitis can be challenging. Enhanced, unbiased diagnostic approaches are needed.

Objective: To present a case series of patients with diagnostically challenging subacute or chronic meningitis using metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) supported by a statistical framework generated from mNGS of control samples from the environment and from patients who were noninfectious.

Design, setting, and participants: In this case series, mNGS data obtained from the CSF of 94 patients with noninfectious neuroinflammatory disorders and from 24 water and reagent control samples were used to develop and implement a weighted scoring metric based on z scores at the species and genus levels for both nucleotide and protein alignments to prioritize and rank the mNGS results. 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. The neurologic infections identified by mNGS in these 7 participants represented a diverse array of pathogens. The patients were referred from the University of California, San Francisco Medical Center (n = 2), Zuckerberg San Francisco General Hospital and Trauma Center (n = 2), Cleveland Clinic (n = 1), University of Washington (n = 1), and Kaiser Permanente (n = 1). A weighted z score was used to filter out environmental contaminants and facilitate efficient data triage and analysis.

Main outcomes and measures: Pathogens identified by mNGS and the ability of a statistical model to prioritize, rank, and simplify mNGS results.

Results: The 7 participants ranged in age from 10 to 55 years, and 3 (43%) were female. 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 identified among the 7 participants by using mNGS. Evaluating mNGS data with a weighted z score-based scoring algorithm reduced the reported microbial taxa by a mean of 87% (range, 41%-99%) when taxa with a combined score of 0 or less were removed, effectively separating bona fide pathogen sequences from spurious environmental sequences so that, in each case, the causative pathogen was found within the top 2 scoring microbes identified using the algorithm.

Conclusions and relevance: Diverse microbial pathogens were identified by 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. Prioritizing metagenomic data with a scoring algorithm greatly clarified data interpretation and highlighted the problem of attributing biological significance to organisms present in control samples used for metagenomic sequencing studies.

Figure 1.. Computational Pathogen Detection Pipeline

A rapid computational pipeline was implemented as diagrammed for detection of potential pathogen-derived sequences. Values for participant 1 are shown as an example. The cerebrospinal fluid sample obtained from participant 1 yielded 13 141 550 million read-pairs, which were then subjected to removal of human reads by spliced transcripts alignment to a reference (STAR, version 2.4.2), quality control filtering (PriceSeqFilter, version 1.1.2), compression of duplicate reads (CD-HIT-DUP, version 4.6.4-2015), removal of low-complexity sequences by filtering for high Lempel-Ziv-Welch (LZW) compression ratios, a second round of removal of human reads (Bowtie 2, version 2.2.4), alignment to the National Center for Biotechnology Information (NCBI) nucleotide (nt) database (GMAP/GSNAP, version 2015-12), alignment to the NCBI nonredundant (nr) protein database (RAPSearch2, version 2.23), and statistical calculation and taxonomy reporting using PHP/MySQL, version 5.5.53. The entire computational pipeline was completed in 19.6 min using a single high-end server (32 core, Intel Xeon E5-2667 v3 with a 3.2-GHz processor and 768 Gb of RAM). NA indicates not applicable.

Figure 2.. Ranked Results of Statistical Scoring

Strip plot of normalized species significance scores for microbial taxa (colored circles) in each participant sample (row). In 6 of 7 samples, the neurologic infection (orange circles) is ranked as the most significant by our approach. In participant number 5, Aspergillus oryzae is ranked second behind GB virus C, a likely concurrent infection unassociated with the clinical presentation (blue circle). Microbes likely representing environmental contaminants are also shown (gray circles).

Figure 3.. Selected Neuroimaging

A, Axial T1-weighted brain magnetic resonance image (MRI) with contrast enhancement demonstrating basilar meningitis (arrowheads) in a 28-year-old man (participant 1) with neurocysticercosis identified by metagenomic next-generation sequencing (mNGS). B, Axial T2-weighted brain MRI demonstrating right anterior temporal lobe and prepontine cysts (arrowheads) in a 34-year-old woman with neurocysticercosis (participant 2) identified by mNGS. C-E, Axial T1-weighted MRI with contrast enhancement showing basilar meningitis (C [arrowhead]), and a sagittal T1-weighted lumbar spine MRI showing a loculated rim-enhancing collection extending from the top of the lumbar spinal cord anteriorly and compressing the conus medullaris against the posterior wall without (D) and with (E, arrowheads) contrast in a 26-year-old woman with Candida dubliniensis meningitis (participant 7) identified by mNGS.