Unbiased metagenomic sequencing for pediatric meningitis in Bangladesh reveals neuroinvasive Chikungunya virus outbreak and other unrealized pathogens

Abstract

The disease burden due to meningitis in low and middle-income countries remains significant and failure to determine an etiology impedes appropriate treatment for patients and evidencebased policy decisions for populations. Broad-range pathogen surveillance using metagenomic next-generation sequencing (mNGS) of RNA isolated from cerebral spinal fluid (CSF) provides an unbiased assessment for possible infectious etiologies. In this study, our objective was to use mNGS to identify etiologies of pediatric meningitis in Bangladesh.We conducted a retrospective case-control mNGS study on CSF from patients with known neurologic infections (n=36), idiopathic meningitis (n=25), without infection (n=30) and six environmental samples collected between 2012-2018. Using an open-access, cloud-based bioinformatics pipeline (IDseq) and machine learning, we identified potential pathogens which were confirmed through qPCR and Sanger sequencing. These cases were followed-up through phone/home-visits. The CSF samples were collected from children with WHO-defined meningeal signs during prospective meningitis surveillance at the largest pediatric referral hospital in Bangladesh.The 91 participants (42% female) ranged in age from 0-160 months (median: 9 months). In samples with known infectious causes of meningitis and without infections (n=66), there was 83% concordance between mNGS and conventional testing. In idiopathic cases (n=25), mNGS identified a potential etiology in 40% (n=10), including bacterial and viral pathogens. There were three instances of neuroinvasive Chikungunya virus (CHIKV). The CHIKV genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause systemic illness in 2017. CHIKV qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital (n=472) revealed 17 additional CHIKV meningitis cases. Orthogonal molecular confirmation of each mNGS-identified infection, casebased clinical data, and follow-up of patients substantiated the key findings.Using mNGS, we obtained a microbiological diagnosis for 40% of idiopathic meningitis cases and identified a previous unappreciated pediatric CHIKV meningitis outbreak. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis and facilitate informed policy decisions. Introduction:Globally there are 10.6 million cases of meningitis and 288,000 deaths every year. 1,2 In addition, at least a quarter of survivors suffer from long-term neurological sequelae. 3 The vast majority of meningitis cases occur in low-and middle-income countries (LMICs) . 4 In a World Health Organization (WHO)-supported meningitis surveillance study in Dhaka, Bangladesh, 5 we collected 23,140 cerebrospinal fluid (CSF) samples from patients with suspected meningitis between 2004 and 2016, 8,125 of which contained ≥10 WBC/µl. We were able to detect a bacterial etiology in only 1,585 (20%) of these cases despite the use of multiple diagnostic tools...

Use of unbiased metagenomic and transcriptomic analyses to investigate the association between feline calicivirus and feline chronic gingivostomatitis in domestic cats

“…That platform is a rapid computational pipeline developed to analyze mNGS reads for potential pathogens by the DeRisi Laboratory and Chan-Zuckerberg Biohub, both of San Francisco. 43,52 Briefly, reads were filtered for the host genome; reagent sequences; quality, complexity, and redundance; and a diverse set of metazoan genomes, then assembled and filtered again and aligned to the NCBI nonredundant genomic (nt) and transcriptomic (nr) databases. The process was repeated without the host genome filter at the beginning and aligned at the end instead of to the host transcriptome.…”

“…[33][34][35] Metagenomic nextgeneration RNA sequencing has been used to identify putatively causal pathogens from every domain of life (ie, prokaryote, virus, and eukaryote) in vertebrate hosts such as snakes, birds, and humans. 31,32,[36][37][38][39][40][41][42][43][44][45][46][47][48][49][50] By means of de novo assembly of raw reads, mNGS can also identify novel microbes with distant similarities to known genomes and enumerate genetic variants.…”

A Metagenomics – Based Diagnostic Approach for Central Nervous System Infections in Hospital Acute Care Setting

“…However, use of the MiSeq platform has not been standardized for metagenomic pathogen detection in clinical samples. In this study, we have determined that the analytical sensitivity of a MiSeq-based, shotgun metagenomic sequencing approach to detect bacterial and viral pathogens in cerebrospinal fluid (CSF) is comparable to PCR assays and then optimized a bioinformatic approach for high specificity based on a publicly accessible software platform for metagenomic detection of pathogens (https://idseq.net)(25, 26). The optimized approach was then applied to a set of retrospectively collected and previously tested CSF specimens (n=74) with an aim to describe the diagnostic accuracy, sensitivity and specificity of mNGS approach with reference to the standard methods.…”

Current Status of Chikungunya in India

“…Moderate thrombocytopenia, leukopenia and lymphopenia are also common ( Staples et al, 2009 ; Staples and Fischer, 2014 ; Madariaga et al, 2016 ). Severe symptoms like bleeding due to thrombocytopenia, bulbous skin lesions, hepatitis, meningoencephalitis, meningitis have been reported in a few patients ( Markoff, 2015 ; Weaver and Lecuit, 2015 ; Abeyratne et al, 2018 ; Saha et al, 2019 ). Ocular complications like retinitis or uveitis, myocarditis, nephritis, cranial nerve palsies and Guillain-Barre Syndrome have been observed for the first time during the 2006 Indian Ocean outbreak ( Wielanek et al, 2007 ; Lebrun et al, 2009 ) as referred in Table 1 .…”