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Chronic Meningitis Investigated via Metagenomic Next-Generation Sequencing.

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; Chow, Eric D; 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.
JAMA Neurol; 75(8): 947-955, 2018 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-29710329
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.
Selo DaSilva