A metagenomics-based diagnostic approach for central nervous system infections in hospital acute care setting

Mohammad Rubayet Hasan,Rusung Tan,Peter Tilley,Patrick Tang,Sathyavathi Sundararaju,Mohammad Janahi,Andres Perez Lopez,Kin-Ming Tsui

doi:10.1038/s41598-020-68159-z

Abstract

The etiology of central nervous system (CNS) infections such as meningitis and encephalitis remains unknown in a large proportion of cases partly because the diversity of pathogens that may cause CNS infections greatly outnumber available test methods. We developed a metagenomic next generation sequencing (mNGS)-based approach for broad-range detection of pathogens associated with CNS infections suitable for application in the acute care hospital setting. The analytical sensitivity of mNGS performed on an Illumina MiSeq was assessed using simulated cerebrospinal fluid (CSF) specimens (n = 9). mNGS data were then used as a training dataset to optimize a bioinformatics workflow based on the IDseq pipeline. For clinical validation, residual CSF specimens (n = 74) from patients with suspected CNS infections previously tested by culture and/or PCR, were analyzed by mNGS. In simulated specimens, the NGS reads aligned to pathogen genomes in IDseq were correlated to qPCR CT values for the respective pathogens (R = 0.96; p < 0.0001), and the results were highly specific for the spiked pathogens. In clinical samples, the diagnostic accuracy, sensitivity and specificity of the mNGS with reference to conventional methods were 100%, 95% and 96%, respectively. The clinical application of mNGS holds promise to benefit patients with CNS infections of unknown etiology.

Highlights

The etiology of central nervous system (CNS) infections such as meningitis and encephalitis remains unknown in a large proportion of cases partly because the diversity of pathogens that may cause CNS infections greatly outnumber available test methods
Negative cerebrospinal fluid (CSF) specimens and nuclease free water (NFW) were assessed by metagenomic next generation sequencing (mNGS) to determine the level of background noise and contamination
While a statistically significant correlation (R = 0.63; p < 0.001) was observed between approximate titers of spiked pathogens with their respective NT reads, the qPCR C T values for different pathogens were more precisely correlated to their mNGS NT reads (R = 0.96; p < 0.0001) (Fig. 1A and B). mNGS assay detected all spiked pathogens that were detectable by qPCR including a bacterium that was spiked at 100 CFU/ml final concentration (Table 1)

Summary

Introduction

The etiology of central nervous system (CNS) infections such as meningitis and encephalitis remains unknown in a large proportion of cases partly because the diversity of pathogens that may cause CNS infections greatly outnumber available test methods. Current diagnostic test methods for CNS infections include CSF Gram staining, CSF cell count, glucose, and protein measurements and biomarkers such as procalcitonin (PCT) and lactate These tests are generally performed to distinguish between bacterial versus viral infections, and they are not specific for any causative pathogens. NGS based metagenomic sequencing, which involves sequencing of all DNA content in the sample, has been applied mostly in research settings for microbiome studies as well as pathogen detection or characterization directly from clinical specimens[10,11,12,13,14,15,16,17] Application of this technology in acute care diagnostic microbiology is limited due to its higher cost compared to conventional microbiological methods, lack of standardized methods, data interpretation challenges and the burden of analyzing and storing large datasets of sequences

Methods

Results

Conclusion