Chronic Meningitis Investigated via Metagenomic Next-Generation Sequencing

JAMA Neurol. 2018 Aug 1;75(8):947-955. doi: 10.1001/jamaneurol.2018.0463.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Animals
  • Aspergillus oryzae / genetics
  • Candida / genetics
  • Candidiasis / cerebrospinal fluid
  • Candidiasis / diagnosis
  • Child
  • Chronic Disease
  • Cryptococcus neoformans / genetics
  • Female
  • HIV Infections / cerebrospinal fluid
  • HIV Infections / diagnosis
  • HIV-1 / genetics
  • High-Throughput Nucleotide Sequencing / methods
  • Histoplasma / genetics
  • Histoplasmosis / cerebrospinal fluid
  • Histoplasmosis / diagnosis
  • Humans
  • Male
  • Meningitis / cerebrospinal fluid
  • Meningitis / diagnosis*
  • Meningitis / microbiology
  • Meningitis, Cryptococcal / cerebrospinal fluid
  • Meningitis, Cryptococcal / diagnosis
  • Metagenome / genetics*
  • Metagenomics
  • Middle Aged
  • Neuroaspergillosis / cerebrospinal fluid
  • Neuroaspergillosis / diagnosis
  • Neurocysticercosis / cerebrospinal fluid
  • Neurocysticercosis / diagnosis
  • Sequence Analysis, RNA / methods
  • Taenia solium / genetics
  • Young Adult