Chronic Meningitis Investigated via Metagenomic Next-Generation Sequencing
Chronic Meningitis Investigated via Metagenomic Next-Generation Sequencing
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DOI：
10.1001/jamaneurol.2018.0463
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发表时间：
2018-08-01
期刊：
影响因子：
29
通讯作者：
DeRisi, Joseph L.
中科院分区：
文献类型：
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作者：
Wilson, Michael R.;O'Donovan, Brian D.;DeRisi, Joseph L.
重要性确定亚急性或慢性脑膜炎的感染原因可能是具有挑战性的增强，目的利用脑脊液（CSF）的宏基因组下一代测序（mNGS）技术，对亚急性或慢性脑膜炎患者进行诊断由来自环境和非感染性患者的对照样品的mNGS产生的统计框架支持。设计，设置，和参与者在该病例系列中，从94例非感染性神经炎性疾病患者的CSF和24份水和试剂对照样本中获得的mNGS数据用于开发和实施基于核苷酸和蛋白质比对的种属水平的z评分的加权评分度量，以优先考虑mNGS结果并对其进行排名。从2013年9月至2017年3月期间招募的7名亚急性或慢性脑膜炎受试者的CSF中提取mNGS的总RNA，作为疑似神经炎性疾病患者中mNGS病原体发现的多中心研究的一部分。在这7名参与者中通过mNGS确定的神经系统感染代表了一系列不同的病原体。患者来自加州大学旧金山弗朗西斯科医学中心（n = 2）、扎克伯格旧金山弗朗西斯科综合医院和创伤中心（n = 2）、克利夫兰诊所（n = 1）、华盛顿大学（n = 1）和Kaiser Permanente（n = 1）。A加权z得分被用来过滤掉环境污染物，并促进有效的数据分流和analysis.Main结果和措施病原体确定mNGS和统计模型的能力，以优先级，排名，并简化mNGS results.RESULTS的7名参与者的年龄范围从10到55岁，3（43%）是女性。使用mNGS在7名参与者中鉴定出一种寄生虫（猪带绦虫，2名参与者）、一种病毒（HIV-1）和4种真菌（新型隐球菌、米曲霉、组织胞浆菌和都柏林念珠菌）。使用基于加权z评分的评分算法评估mNGS数据，将报告的微生物分类群平均减少了87%。（范围，41%-99%），有效地将真正的病原体序列与虚假的环境序列分离，使得在每种情况下，结论和相关性通过mNGS在诊断上具有挑战性的患者的CSF中鉴定出多种微生物病原体。亚急性或慢性脑膜炎，包括1例蛛网膜下神经囊虫病，1年来一直无法诊断，第一例报告的由曲霉菌引起的CNS血管炎，第4例报告的都柏林念珠菌脑膜炎。用评分算法对宏基因组数据进行优先排序极大地澄清了数据解释，并突出了将生物学意义归因于用于宏基因组测序研究的对照样品中存在的生物体的问题。
IMPORTANCE Identifying infectious causes of subacute or chronic meningitis can be challenging Enhanced, unbiased diagnostic approaches are needed.OBJECTIVE To present a case series of patients with diagnostically challenging subacute or chronic meningitis using metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) supported by a statistical framework generated from mNGS of control samples from the environment and from patients who were noninfectious.DESIGN, SETTING, AND PARTICIPANTS In this case series, mNGS data obtained from the CSF of 94 patients with noninfectious neuroinflammatory disorders and from 24 water and reagent control samples were used to develop and implement a weighted scoring metric based on z scores at the species and genus levels for both nucleotide and protein alignments to prioritize and rank the mNGS results. Total RNA was extracted for mNGS from the CSF of 7 participants with subacute or chronic meningitis who were recruited between September 2013 and March 2017 as part of a multicenter study of mNGS pathogen discovery among patients with suspected neu roi nflammatory conditions. The neurologic infections identified by mNGS in these 7 participants represented a diverse array of pathogens. The patients were referred from the University of California, San Francisco Medical Center (n = 2), Zuckerberg San Francisco General Hospital and Trauma Center (n = 2), Cleveland Clinic (n = 1), University of Washington (n = 1), and Kaiser Permanente (n = 1). A weighted z score was used to filter out environmental contaminants and facilitate efficient data triage and analysis.MAIN OUTCOMES AND MEASURES Pathogens identified by mNGS and the ability of a statistical model to prioritize, rank, and simplify mNGS results.RESULTS The 7 participants ranged in age from 10 to 55 years, and 3 (43%) were female. A parasitic worm (Taenia solium, in 2 participants), a virus (HIV-1), and 4 fungi (Cryptococcus neoformans, Aspergillus oryzae, Histoplasma capsulatum, and Candida dubliniensis) were identified among the 7 participants by using mNGS. Evaluating mNGS data with a weighted z score-based scoring algorithm reduced the reported microbial taxa by a mean of 87% (range, 41%-99%) when taxa with a combined score of 0 or less were removed, effectively separating bona fide pathogen sequences from spurious environmental sequences so that, in each case, the causative pathogen was found within the top 2 scoring microbes identified using the algorithm.CONCLUSIONS AND RELEVANCE Diverse microbial pathogens were identified by mNGS in the CSF of patients with diagnostically challenging subacute or chronic meningitis, including a case of subarachnoid neurocysticercosis that defied diagnosis for 1 year, the first reported case of CNS vasculitis caused by Aspergillus oryzae, and the fourth reported case of C dubliniensis meningitis. Prioritizing metagenomic data with a scoring algorithm greatly clarified data interpretation and highl ighted the problem of attributing biological signifies nee to organisms present in control samples used for metagenomic sequencing studies.