Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens

ABSTRACT The burden of meningitis in low-and-middle-income countries remains significant, but the infectious causes remain largely unknown, impeding institution of evidence-based treatment and prevention decisions. We conducted a validation and application study of unbiased metagenomic next-generati...

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Autores principales:	Senjuti Saha, Akshaya Ramesh, Katrina Kalantar, Roly Malaker, Md Hasanuzzaman, Lillian M. Khan, Madeline Y. Mayday, M. S. I. Sajib, Lucy M. Li, Charles Langelier, Hafizur Rahman, Emily D. Crawford, Cristina M. Tato, Maksuda Islam, Yun-Fang Juan, Charles de Bourcy, Boris Dimitrov, James Wang, Jennifer Tang, Jonathan Sheu, Rebecca Egger, Tiago Rodrigues De Carvalho, Michael R. Wilson, Samir K. Saha, Joseph L. DeRisi
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2019
Materias:	idiopathic meningitis metagenomics etiology Bangladesh cerebrospinal fluid Chikungunya virus Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/64da9386f3734a7d9f3ab1be922c9027
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id	oai:doaj.org-article:64da9386f3734a7d9f3ab1be922c9027
record_format	dspace
institution	DOAJ
collection	DOAJ
language	EN
topic	idiopathic meningitis metagenomics etiology Bangladesh cerebrospinal fluid Chikungunya virus Microbiology QR1-502
spellingShingle	idiopathic meningitis metagenomics etiology Bangladesh cerebrospinal fluid Chikungunya virus Microbiology QR1-502 Senjuti Saha Akshaya Ramesh Katrina Kalantar Roly Malaker Md Hasanuzzaman Lillian M. Khan Madeline Y. Mayday M. S. I. Sajib Lucy M. Li Charles Langelier Hafizur Rahman Emily D. Crawford Cristina M. Tato Maksuda Islam Yun-Fang Juan Charles de Bourcy Boris Dimitrov James Wang Jennifer Tang Jonathan Sheu Rebecca Egger Tiago Rodrigues De Carvalho Michael R. Wilson Samir K. Saha Joseph L. DeRisi Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens
description	ABSTRACT The burden of meningitis in low-and-middle-income countries remains significant, but the infectious causes remain largely unknown, impeding institution of evidence-based treatment and prevention decisions. We conducted a validation and application study of unbiased metagenomic next-generation sequencing (mNGS) to elucidate etiologies of meningitis in Bangladesh. This RNA mNGS study was performed on cerebrospinal fluid (CSF) specimens from patients admitted in the largest pediatric hospital, a World Health Organization sentinel site, with known neurologic infections (n = 36), with idiopathic meningitis (n = 25), and with no infection (n = 30), and six environmental samples, collected between 2012 and 2018. We used the IDseq bioinformatics pipeline and machine learning to identify potentially pathogenic microbes, which we then confirmed orthogonally and followed up through phone/home visits. In samples with known etiology and without infections, there was 83% concordance between mNGS and conventional testing. In idiopathic cases, mNGS identified a potential bacterial or viral etiology in 40%. There were three instances of neuroinvasive Chikungunya virus (CHIKV), whose genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause febrile illness in 2017. CHIKV-specific qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 (n = 472) revealed 17 additional CHIKV meningitis cases, exposing an unrecognized meningitis outbreak. Orthogonal molecular confirmation, case-based clinical data, and patient follow-up substantiated the findings. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis, conduct surveillance, and predict outbreaks. The improved patient- and population-level data can inform evidence-based policy decisions. IMPORTANCE Globally, there are an estimated 10.6 million cases of meningitis and 288,000 deaths every year, with the vast majority occurring in low- and middle-income countries. In addition, many survivors suffer from long-term neurological sequelae. Most laboratories assay only for common bacterial etiologies using culture and directed PCR, and the majority of meningitis cases lack microbiological diagnoses, impeding institution of evidence-based treatment and prevention strategies. We report here the results of a validation and application study of using unbiased metagenomic sequencing to determine etiologies of idiopathic (of unknown cause) cases. This included CSF from patients with known neurologic infections, with idiopathic meningitis, and without infection admitted in the largest children’s hospital of Bangladesh and environmental samples. Using mNGS and machine learning, we identified and confirmed an etiology (viral or bacterial) in 40% of idiopathic cases. We detected three instances of Chikungunya virus (CHIKV) that were >99% identical to each other and to a strain previously recognized to cause systemic illness only in 2017. CHIKV qPCR of all remaining stored 472 CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital uncovered an unrecognized CHIKV meningitis outbreak. CSF mNGS can complement conventional diagnostic methods to identify etiologies of meningitis, and the improved patient- and population-level data can inform better policy decisions.
format	article
author	Senjuti Saha Akshaya Ramesh Katrina Kalantar Roly Malaker Md Hasanuzzaman Lillian M. Khan Madeline Y. Mayday M. S. I. Sajib Lucy M. Li Charles Langelier Hafizur Rahman Emily D. Crawford Cristina M. Tato Maksuda Islam Yun-Fang Juan Charles de Bourcy Boris Dimitrov James Wang Jennifer Tang Jonathan Sheu Rebecca Egger Tiago Rodrigues De Carvalho Michael R. Wilson Samir K. Saha Joseph L. DeRisi
author_facet	Senjuti Saha Akshaya Ramesh Katrina Kalantar Roly Malaker Md Hasanuzzaman Lillian M. Khan Madeline Y. Mayday M. S. I. Sajib Lucy M. Li Charles Langelier Hafizur Rahman Emily D. Crawford Cristina M. Tato Maksuda Islam Yun-Fang Juan Charles de Bourcy Boris Dimitrov James Wang Jennifer Tang Jonathan Sheu Rebecca Egger Tiago Rodrigues De Carvalho Michael R. Wilson Samir K. Saha Joseph L. DeRisi
author_sort	Senjuti Saha
title	Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens
title_short	Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens
title_full	Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens
title_fullStr	Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens
title_full_unstemmed	Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens
title_sort	unbiased metagenomic sequencing for pediatric meningitis in bangladesh reveals neuroinvasive chikungunya virus outbreak and other unrealized pathogens
publisher	American Society for Microbiology
publishDate	2019
url	https://doaj.org/article/64da9386f3734a7d9f3ab1be922c9027
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spelling	oai:doaj.org-article:64da9386f3734a7d9f3ab1be922c90272021-11-15T15:54:46ZUnbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens10.1128/mBio.02877-192150-7511https://doaj.org/article/64da9386f3734a7d9f3ab1be922c90272019-12-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mBio.02877-19https://doaj.org/toc/2150-7511ABSTRACT The burden of meningitis in low-and-middle-income countries remains significant, but the infectious causes remain largely unknown, impeding institution of evidence-based treatment and prevention decisions. We conducted a validation and application study of unbiased metagenomic next-generation sequencing (mNGS) to elucidate etiologies of meningitis in Bangladesh. This RNA mNGS study was performed on cerebrospinal fluid (CSF) specimens from patients admitted in the largest pediatric hospital, a World Health Organization sentinel site, with known neurologic infections (n = 36), with idiopathic meningitis (n = 25), and with no infection (n = 30), and six environmental samples, collected between 2012 and 2018. We used the IDseq bioinformatics pipeline and machine learning to identify potentially pathogenic microbes, which we then confirmed orthogonally and followed up through phone/home visits. In samples with known etiology and without infections, there was 83% concordance between mNGS and conventional testing. In idiopathic cases, mNGS identified a potential bacterial or viral etiology in 40%. There were three instances of neuroinvasive Chikungunya virus (CHIKV), whose genomes were >99% identical to each other and to a Bangladeshi strain only previously recognized to cause febrile illness in 2017. CHIKV-specific qPCR of all remaining stored CSF samples from children who presented with idiopathic meningitis in 2017 (n = 472) revealed 17 additional CHIKV meningitis cases, exposing an unrecognized meningitis outbreak. Orthogonal molecular confirmation, case-based clinical data, and patient follow-up substantiated the findings. Case-control CSF mNGS surveys can complement conventional diagnostic methods to identify etiologies of meningitis, conduct surveillance, and predict outbreaks. The improved patient- and population-level data can inform evidence-based policy decisions. IMPORTANCE Globally, there are an estimated 10.6 million cases of meningitis and 288,000 deaths every year, with the vast majority occurring in low- and middle-income countries. In addition, many survivors suffer from long-term neurological sequelae. Most laboratories assay only for common bacterial etiologies using culture and directed PCR, and the majority of meningitis cases lack microbiological diagnoses, impeding institution of evidence-based treatment and prevention strategies. We report here the results of a validation and application study of using unbiased metagenomic sequencing to determine etiologies of idiopathic (of unknown cause) cases. This included CSF from patients with known neurologic infections, with idiopathic meningitis, and without infection admitted in the largest children’s hospital of Bangladesh and environmental samples. Using mNGS and machine learning, we identified and confirmed an etiology (viral or bacterial) in 40% of idiopathic cases. We detected three instances of Chikungunya virus (CHIKV) that were >99% identical to each other and to a strain previously recognized to cause systemic illness only in 2017. CHIKV qPCR of all remaining stored 472 CSF samples from children who presented with idiopathic meningitis in 2017 at the same hospital uncovered an unrecognized CHIKV meningitis outbreak. CSF mNGS can complement conventional diagnostic methods to identify etiologies of meningitis, and the improved patient- and population-level data can inform better policy decisions.Senjuti SahaAkshaya RameshKatrina KalantarRoly MalakerMd HasanuzzamanLillian M. KhanMadeline Y. MaydayM. S. I. SajibLucy M. LiCharles LangelierHafizur RahmanEmily D. CrawfordCristina M. TatoMaksuda IslamYun-Fang JuanCharles de BourcyBoris DimitrovJames WangJennifer TangJonathan SheuRebecca EggerTiago Rodrigues De CarvalhoMichael R. WilsonSamir K. SahaJoseph L. DeRisiAmerican Society for Microbiologyarticleidiopathic meningitismetagenomicsetiologyBangladeshcerebrospinal fluidChikungunya virusMicrobiologyQR1-502ENmBio, Vol 10, Iss 6 (2019)

Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens

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