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首页> 外文期刊>MBio >Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens
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Unbiased Metagenomic Sequencing for Pediatric Meningitis in Bangladesh Reveals Neuroinvasive Chikungunya Virus Outbreak and Other Unrealized Pathogens

机译:孟加拉国小儿脑膜炎的无偏基因组测序揭示了神经侵入性基孔肯雅病毒暴发和其他未实现的病原体

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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.
机译:中低收入国家脑膜炎的负担仍然很重,但感染原因仍然未知,这阻碍了建立循证治疗和预防决策的制度。我们进行了无偏见的宏基因组下一代测序(mNGS)的验证和应用研究,以阐明孟加拉国的脑膜炎病因。这项RNA mNGS研究是对来自最大儿科医院(世界卫生组织前哨站点)收治的患者的脑脊液(CSF)标本进行的,该患者患有已知的神经系统感染(n = 36)和特发性脑膜炎(n = 25)。 ),并且没有感染(n == 30),并且在2012年至2018年之间收集了六个环境样本。我们使用IDseq生物信息学管道和机器学习来识别潜在的病原微生物,然后对其进行正交确认并通过电话进行跟进/ home访问。在病因已知且无感染的样本中,mNGS与常规检测之间的一致性为83%。在特发性病例中,mNGS在40%的人群中发现了潜在的细菌或病毒病因。神经侵染性基孔肯雅病毒(CHIKV)的三个实例,其基因组彼此之间和与孟加拉国毒株的基因组彼此之间> 99%相同,该毒株此前仅在2017年才被确认会引起高热疾病。CHIKV特异性qPCR来自儿童于2017年出现特发性脑膜炎(n = 472)的病例显示,另有17例CHIKV脑膜炎病例,暴露出无法识别的脑膜炎暴发。正交分子确认,基于病例的临床数据以及患者的随访证实了这一发现。病例对照脑脊液mNGS调查可以补充常规诊断方法,以识别脑膜炎的病因,进行监测并预测疫情。改进的患者和人群水平数据可以为基于证据的政策决策提供依据。

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