Optimization of cerebrospinal fluid microbial metagenomic sequencing diagnostics
2020
Background:
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 remains, including establishment of individual limits of
detection, removal of false positives and implementation of universal controls.
Results:
Twenty-one cerebrospinal fluid samples, in which a known pathogen had been
positively identified by available clinical techniques, were subjected to metagenomic
DNA sequencing using massive parallel sequencing. Fourteen samples contained
minute levels of Epstein-Barr virus. Calculation of the detection threshold for each
sample was made using total leukocyte content in the sample and environmental
contaminants found in bioinformatic classifiers. Virus sequences were detected in all
ten samples, in which more than one read was expected according to calculations.
Conversely, no viral reads were detected in seven out of eight samples, in which less
than one read was expected according to 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.
Conclusions:
Here we show a generalizable method for detection and identification of pathogen
species using metagenomic sequencing. The sensitivity for each sample can be
calculated using the leukocyte count and environmental contamination. The choice of
bioinformatic method mainly affected the efficiency of pathogen identification, but not
the sensitivity of detection. Identification of pathogens require multiple filtering steps
including read distribution, sequence diversity and complementary verification of
pathogen reads.
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