Integrated Host/Microbe Metagenomics to Improve Lower Respiratory Tract Infection Diagnosis in Critically Ill Children

摘要 下呼吸道感染(LRTI)每年导致儿童死亡的人数比任何其他传染病都要多 疾病类别。尽管如此，潜在的微生物病原体由于受 现有的微生物学测试，导致不适当的抗菌剂使用和其他不良后果。病毒式的- 类似LRTI的细菌混合感染和非感染性炎症综合征，常见于危重病人 患者，使诊断进一步复杂化。为了满足改善呼吸道诊断的需求，我们将利用 一种整合的宿主/微生物元基因组下一代测序(IHM-MNGS)方法，最近 由我们团队开发，它同时描述了LRTI的三个核心要素：病原体、微生物组 和宿主的反应，从单一的呼吸液样本。 我们将通过批判性地研究455名已建立的预期多中心队列来实现我们的三个目标 患有急性呼吸衰竭需要机械通气的患病儿童。AIM 1将开发和测试IHM-MNGS 设计用于：a)准确诊断和区分LRTI和非传染性急性呼吸道感染的分类器 (B)高度确定地排除细菌LRTI，以允许明智地使用抗菌剂。目标2将 建立和测试用于检测和区分下呼吸道感染病原体和呼吸道共生菌的MNGS模型 微生物，然后确定模型在患者中识别以前遗漏的新病原体的能力 有临床判定的下呼吸道感染，但临床标准试验阴性。AIM 3将利用CRISPR/Cas9目标 本课题组开发的检测病原菌耐药基因的浓缩方法，可以 更快地通知适当的抗菌治疗。我们将开发和测试一个模型来准确预测 细菌抗菌素耐药性，而不需要培养，然后确定这种方法作为一种 使用实时纳米孔测序进行快速诊断。 这项研究将通过开发和测试先进的、文化的 集成宿主反应和无偏病原体检测的独立方法，以实现准确的LRTI 大型多中心队列中的诊断和排除。我们的方法旨在改变肺源性心脏病的治疗模式 通过同时分析宿主转录本和来自单个样本的微生物序列进行诊断 呼吸液。

SUMMARY Lower respiratory tract infections (LRTI) lead to more deaths each year in children than any other infectious disease category. Despite this, the underlying microbial pathogens are rarely identified due to the limitations of existing microbiologic tests, resulting in inappropriate antimicrobial use and other adverse outcomes. Viral- bacterial co-infections and non-infectious inflammatory syndromes resembling LRTI, common in critically ill patients, further complicate diagnosis. To address the need for improved respiratory diagnostics, we will leverage an integrated host/microbe metagenomic next-generation sequencing (iHM-mNGS) approach, recently developed by our group, that simultaneously profiles three central elements of LRTI: the pathogen, microbiome and host response, from a single sample of respiratory fluid. We will accomplish our three aims by studying an established prospective, multicenter cohort of 455 critically ill children with acute respiratory failure requiring mechanical ventilation. Aim 1 will develop and test iHM-mNGS classifiers designed to: a) accurately diagnose and differentiate LRTI from non-infectious acute respiratory conditions, and b) rule-out bacterial LRTI with high certainty to permit judicious antimicrobial use. Aim 2 will develop and test a mNGS model for detecting and differentiating LRTI pathogens from airway commensal microbes, and then determine the capacity of the model to identify new, previously missed pathogens, in patients with clinically adjudicated LRTI but negative standard clinical testing. Aim 3 will leverage CRISPR/Cas9 targeted enrichment methods developed by our group to detect pathogen antimicrobial resistance genes, which could more quickly inform appropriate antimicrobial therapy. We will develop and test a model to accurately predict bacterial antimicrobial resistance without a need for culture, and then determine the utility of this approach as a rapid diagnostic using real-time Nanopore sequencing. This study will address the need for better LRTI diagnostics by developing and testing advanced, culture- independent methods that integrate host response and unbiased pathogen detection to achieve accurate LRTI diagnosis and rule-out in a large multicenter cohort. Our methods aim to change the paradigm of pulmonary diagnostics by simultaneously profiling host transcripts and microbial sequences from a single sample of respiratory fluid.