Metagenomics for chronic meningitis: clarifying interpretation and diagnosis
Authors: Wilson Michael R. , O’Donovan Brian D. , Gelfand Jeffrey M. , Sample Hannah A. , Chow Felicia C. , Betjemann John P. , Shah Maulik P. , Richie Megan B. , Gorman Mark P. , Hajj-Ali Rula A. , Calabrese Leonard H. , Zorn Kelsey C. , Greenlee John E. , Blum Jonathan H. , Green Gary , Khan Lillian M. , Banerji Debarko , Langelier Charles , Bryson-Cahn Chloe , Harrington Whitney , Lingappa Jairam R. , Shanbhag Niraj M. , Green Ari J. , Brew Bruce J. , Soldatos Ariane , Strnad Luke , Doernberg Sarah B. , Jay Cheryl A. , Douglas Vanja , Josephson S. Andrew , DeRisi Joseph L. 
Published: November 2017

AbstractImportanceIdentifying infectious causes of subacute and chronic meningitis can be challenging. Enhanced, unbiased diagnostic approaches are needed.ObjectiveTo present a case series of patients with diagnostically challenging subacute and chronic meningitis in whom metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF), supported by a statistical framework generated from mNGS sequencing of non-infectious patients and environmental controls, identified a pathogen.DesignCase series. Using mNGS data from the CSF of 94 non-infectious neuroinflammatory cases and 24 water and reagent controls, we developed and implemented a weighted scoring metric based on z-scores at the species and genus level for both nucleotide and protein databases to prioritize and rank mNGS results. We performed mNGS on total RNA extracted from CSF of patients with subacute or chronic meningitis and highlight seven cases representing a diverse array of pathogens.SettingA multi-center study of mNGS pathogen discovery in patients with suspected neuroinflammatory conditions.ParticipantsPatients with diagnostically challenging subacute or chronic meningitis enrolled in a research study of mNGS performed on CSF.InterventionmNGS was performed on total RNA extracted from CSF (0.25-0.5 mL). A weighted z-score was used to filter out environmental contaminants and facilitate efficient data triage and analysis.Main Outcomes1) Pathogens identified by mNGS and 2) ability of a statistical model to prioritize, rank, and simplify mNGS results.ResultsmNGS identified parasitic worms, fungi and viruses in seven subjects: Taenia solium (n=2), Cryptococcus neoformans, human immunodeficiency virus-1, Aspergillus oryzae, Histoplasma capsulatum, and Candida dubliniensis. Evaluating mNGS data with a weighted z-score based scoring algorithm effectively separated bona fide pathogen sequences from spurious environmental sequences.Conclusions and RelevancemNGS of CSF identified a diversity of microbial pathogens in patients with diagnostically challenging subacute or chronic meningitis, including a case of subarachnoid neurocysticercosis that defied diagnosis for one year, the first case of CNS vasculitis caused by Aspergillus oryzae, and the fourth reported case of Candida dubliniensis meningitis. Filtering metagenomic data with a scoring algorithm greatly clarified data interpretation and highlights the difficulties attributing biological significance to organisms that may be present in control samples used for metagenomic sequencing studies.Key PointsQuestion: How can metagenomic next-generation sequencing of cerebrospinal fluid be leveraged to aid in the diagnosis of patients with subacute or chronic meningitis?Findings: Metagenomic next-generation sequencing identified parasitic worms, fungi and viruses in a case series of seven subjects. A database of water-only and healthy patient controls enabled application of a z-score based scoring algorithm to effectively separate bona fide pathogen sequences from spurious environmental sequences.Meaning: Our scoring algorithm greatly simplified data interpretation in a series of patients with a wide range of challenging infectious causes of subacute or chronic meningitis identified by metagenomic next-generation sequencing.