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

总结 下呼吸道感染（LRTI）每年导致儿童死亡的人数超过任何其他传染病 疾病类别。尽管如此，潜在的微生物病原体很少被确定，由于限制， 现有的微生物测试，导致不适当的抗菌药物使用和其他不良后果。病毒- 细菌合并感染和类似LRTI的非感染性炎症综合征，常见于重症患者 患者，使诊断更加复杂。为了满足改善呼吸诊断的需求，我们将利用 整合宿主/微生物宏基因组下一代测序（iHM-mNGS）方法，最近 我们的团队开发的，同时描绘了LRTI的三个核心要素：病原体，微生物组 和宿主的反应。 我们将通过对455例已建立的前瞻性、多中心队列研究来实现我们的三个目标。 需要机械通气的急性呼吸衰竭患儿。Aim 1将开发和测试iHM-mNGS 分类器设计用于：a）准确诊断和区分LRTI与非传染性急性呼吸道感染 条件，和B）以高度确定性排除细菌LRTI，以允许明智的抗微生物剂使用。目标2将 开发和测试mNGS模型，用于检测和区分LRTI病原体与气道炎症 微生物，然后确定模型识别患者中新的、以前遗漏的病原体的能力。 临床判定为下呼吸道感染但标准临床检测结果为阴性。Aim 3将利用CRISPR/Cas9靶向 我们小组开发的富集方法检测病原体抗菌素耐药基因， 更快地通知适当的抗菌治疗。我们将开发并测试一个模型， 细菌的抗菌素耐药性，而不需要培养，然后确定这种方法的效用， 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，培养的， 整合宿主反应和无偏病原体检测的独立方法，以实现准确的LRTI 在一个大型多中心队列中进行诊断和排除。我们的方法旨在改变肺的范式 通过同时分析来自单个样品的宿主转录物和微生物序列， 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.