Metagenomics for chronic meningitis: clarifying interpretation and diagnosis

Abstract

Question: 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. AbstractImportance: Identifying infectious causes of subacute and chronic meningitis can be challenging. Enhanced, unbiased diagnostic approaches are needed.Objective: To 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.Design: Case 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 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/213561 doi: bioRxiv preprint first posted online Nov. 7, 2017; 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.Setting: A multi-center study of mNGS pathogen discovery in patients with suspected neuroinflammatory conditions. Participants:Patients with diagnostically challenging subacute or chronic meningitis enrolled in a research study of mNGS performed on CSF.Intervention: mNGS 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 Outcomes: 1) Pathogens identified by mNGS and 2) ability of a statistical model to prioritize, rank, and simplify mNGS results.

Metagenomic Next-generation Sequencing of Cerebrospinal Fluid for the Diagnosis of Central Nervous System Infections: A Multicentre Cohort Study

“…Importantly, mNGS can detect unsuspected pathogens that clinicians may fail to consider because of atypical clinical manifestations. Many cases of neurological infections have been unexpectedly diagnosed by mNGS of CSF [11,22,29,30] similar to the present study for the cases of L. monocytogenes, Brucella and T. solium [12][13][14]. In addition, as demonstrated in previous studies [10,20,21] and in the present study for the case of encephalitis caused by Suid herpesvirus 1 [31], mNGS of CSF has the ability to identify novel aetiologies of CNS infections.…”

“…mNGS of CSF is being increasingly utilized in routine clinical settings for the rapid diagnosis of central nervous system infections. However, most published studies are retrospective case reports or case series [11][12][13][14][15][16][17][19][20][21][22][23][24], and thus, large prospective studies are needed to demonstrate the clinical impact and cost-effectiveness of mNGS for the diagnosis of meningitis and encephalitis. We undertook a multicentre prospective study to comprehensively evaluate the performance of mNGS of CSF for the diagnosis of central nervous system (CNS) infections compared to conventional microbiological methodologies.…”

Pulmonary Metagenomic Sequencing Suggests Missed Infections in Immunocompromised Children

“…To avoid spurious alignments and sequencing artifacts, we only included read pairs where both ends aligned to the same taxonomic ID. The prevalence of each microbe was described (1) relative to other microbes in the same sample, wherein we normalized sequencing reads per million total sequencing reads (rpm); and (2) relative to the same microbe in other samples in the cohort, wherein we normalized sequencing reads as the number of standard deviations above or below the mean log10-transformed rpm for the total cohort (Z-score) (23). Outliers were defined as microbes with RNA alignments that were both abundant within a sample (≥10% of all bacterial reads in a sample, or >1 rpm for fungi and viruses) and ≥2 standard deviations above the cohort mean for that particular microbe (Z-score ≥2).…”

Metagenomic sequencing as a clinical diagnostic tool for infectious diseases: a systematic review and meta-analysis

“…37 Five (14%) studies analysed orthopaedic device sonication fluid and bone and joint samples for bacteria and fungi. 6,[38][39][40][41] Three (8%) studies analysed cerebrospinal fluid and two (6%) studies analysed intraocular fluid for multiple pathogens including bacteria, viruses, fungi and parasites, 5,[42][43][44] while one study (3%) analysed cerebrospinal fluid for fungi only. 45 There were single studies of heart All rights reserved.…”