Chronic Meningitis Investigated via Metagenomic Next-Generation Sequencing

Abstract

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 contr...

Clinicopathology conference: 41‐year‐old woman with chronic relapsing meningitis

“…24 The diagnosis of NCC was confirmed with a clinical CSF cestode antigen assay and serology, which previously had not been performed. After computational filtering, there were 67,334 pairs of high-quality, nonhuman, and nonredundant sequences.…”

“…Many of these conditions can cause chronic meningitis with a relapsing component, and broad diagnostic tests, such as cultures, may detect some (but not all) of these pathogens. 15,22,24,25 However, this patient was not known to be immunocompromised or to have engaged in intravenous drug use, making fungal meningitis less likely. Anatomically related cranial and radicular nerve roots become edematous and hyperemic before being entrapped and clumped together 164 Volume 85, No.…”

“…24,[46][47][48][49][50][51][52] Total RNA is extracted from a patient's CSF, and complementary DNA (cDNA) is generated by reverse transcription with random hexamer primers. The use of mNGS has gained momentum over the past several years, with several notable case reports and case series showcasing mNGS's ability to capture a broad range of infections with a single assay.…”

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“…Traditional testing methods such as PCR, serological typing, bacterial culture and antibody detection, are regarded as the "gold standard" and widely used in ARI diagnosis 6,7 . However, despite an ongoing effort to include multiple pathogens in a single assay 8,9 , it remains difficult to simultaneously identify all potential ARI pathogens and capture new or uncommon respiratory pathogens 10 .Metagenomic next-generation sequencing (mNGS) is an unbiased way of discovering a broad range of infectious agents [11][12][13] , and has been recently introduced into clinical research to investigate the microbial cause of unusual disease cases 14 , perform broad-scale surveys for pathogens in undiagnosed diseases 15,16 , and understand the role of opportunistic infections 17,18 . For example, a study of severe pneumonia revealed that mNGS is both efficient and reliable 19,20 .…”