Optimization of cerebrospinal fluid microbial DNA metagenomic sequencing diagnostics

Abstract

Infection in the central nervous system is a severe condition associated with high morbidity and mortality. Despite ample testing, the majority of encephalitis and meningitis cases remain undiagnosed. Metagenomic sequencing of cerebrospinal fluid has emerged as an unbiased approach to identify rare microbes and novel pathogens. However, several major hurdles remain, including establishment of individual limits of detection, removal of false positives and implementation of universal controls. Twenty-one cerebrospinal fluid samples, in which a known pathogen had been positively identified by available clinical techniques, were subjected to metagenomic DNA sequencing. Fourteen samples contained minute levels of Epstein-Barr virus. The detection threshold for each sample was calculated by using the total leukocyte content in the sample and environmental contaminants found in the bioinformatic classifiers. Virus sequences were detected in all ten samples, in which more than one read was expected according to the calculations. Conversely, no viral reads were detected in seven out of eight samples, in which less than one read was expected according to the calculations. False positive pathogens of computational or environmental origin were readily identified, by using a commonly available cell control. For bacteria, additional filters including a comparison between classifiers removed the remaining false positives and alleviated pathogen identification. Here we show a generalizable method for identification of pathogen species using DNA metagenomic sequencing. The choice of bioinformatic method mainly affected the efficiency of pathogen identification, but not the sensitivity of detection. Identification of pathogens requires multiple filtering steps including read distribution, sequence diversity and complementary verification of pathogen reads.

Emergence of Japanese encephalitis in Australia: a diagnostic perspective

“…74,75 The agnostic nature of this approach enables unexpected pathogens to be revealed including emerging viruses or divergent genotypes of known pathogens that evade directed assays. 76,77 Appropriate sample types include brain tissue or CSF (or blood or urine in particular circumstances) in the context of a suggestive clinical history. While expense and technical constraints dictate that metagenomics still largely functions as a research tool, future advances in the field including dedicated funding streams should see this approach adopted into mainstream diagnostics for cases of unexplained encephalitis.…”

Metagenomic Direct Diagnosis of Polymicrobial Community-Acquired Meningoencephalitis in Niger: A Case Report

“…Despite its previous detection in CSF samples [17,18], no cases of WU polyomavirus meningoencephalitis have been reported at present. It is far more likely that WU polyomavirus is an incidental finding related to virus reactivation or chronic shedding rather than being the etiologic cause of the patient's meningitis [19]. WU polyomavirus is a persisting virus that is reactivated by an inflammatory process, or a predisposing or aggravating factor [20].…”

Pegivirus Detection in Cerebrospinal Fluid from Patients with Central Nervous System Infections of Unknown Etiology in Brazil by Viral Metagenomics

“…In this study, the mNGS method was utilized to determine the etiological cause of undiagnosed CNS disorders from a laboratory surveillance program conducted in São Paulo State, Brazil, between 2017 and 2021. The application of mNGS in clinical diagnostics, particularly for undiagnosed CNS infections, has gained momentum in recent years due to its high sensitivity and broad range of pathogen detection [ 14 , 29 , 30 ].…”