Enhanced virus detection and metagenomic sequencing in patients with meningitis and encephalitis

Abstract

AbstractMeningitis and encephalitis are leading causes of central nervous system (CNS) disease and often result in severe neurological compromise or death. Traditional diagnostic workflows largely rely on pathogen-specific diagnostic tests, sometimes over days to weeks. Metagenomic next-generation sequencing (mNGS) is a high-throughput platform that profiles all nucleic acid in a sample. We prospectively enrolled 68 patients from New England with known or suspected CNS infection and performed mNGS from both RNA and DNA to identify potential pathogens. Using a computational metagenomic classification pipeline based on KrakenUniq and BLAST, we detected pathogen nucleic acid in cerebrospinal fluid (CSF) from 22 subjects. This included some pathogens traditionally diagnosed by serology or not typically identified in CSF, including three transmitted by Ixodes scapularis ticks (Powassan virus, Borrelia burgdorferi, Anaplasma phagocytophilum). Among 24 subjects with no clinical diagnosis, we detected enterovirus in two subjects and Epstein Barr virus in one subject. We also evaluated two methods to enhance detection of viral nucleic acid, hybrid capture and methylated DNA depletion. Hybrid capture nearly universally increased viral read recovery. Although results for methylated DNA depletion were mixed, it allowed detection of varicella zoster virus DNA in two samples that were negative by standard mNGS. Overall, mNGS is a promising approach that can test for multiple pathogens simultaneously, with similar efficacy to pathogen-specific tests, and can uncover geographically relevant infectious CNS disease, such as tick-borne infections in New England. With further laboratory and computational enhancements, mNGS may become a mainstay of workup for encephalitis and meningitis.ImportanceMeningitis and encephalitis are leading global causes of central nervous system (CNS) disability and mortality. Current diagnostic workflows remain inefficient, requiring costly pathogen-specific assays and sometimes invasive surgical procedures. Despite intensive diagnostic efforts, 40-60% of people with meningitis or encephalitis have no clear cause of their CNS disease identified. As diagnostic uncertainty often leads to costly inappropriate therapies, the need for novel pathogen detection methods is paramount. Metagenomic next-generation sequencing (mNGS) offers the unique opportunity to circumvent these challenges using unbiased laboratory and computational methods. Here, we performed comprehensive mNGS from 68 patients with suspected CNS infection, and define enhanced methods to improve the detection of CNS pathogens, including those not traditionally identified in the CNS by nucleic acid detection. Overall, our work helps elucidate how mNGS can become a mainstay in the diagnostic toolkit for CNS infections.