Chan Zuckerberg Biohub, San Francisco, California, USA.
Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, Department of Medicine, and.
J Clin Invest. 2023 Apr 3;133(7):e165904. doi: 10.1172/JCI165904.
BACKGROUNDLower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging because noninfectious respiratory illnesses appear clinically similar and because existing microbiologic tests are often falsely negative or detect incidentally carried microbes, resulting in antimicrobial overuse and adverse outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and treatment remains unclear.METHODSWe used tracheal aspirate RNA-Seq to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n = 117) or of noninfectious respiratory failure (n = 50). We then developed a classifier that integrates the host LRTI probability, abundance of respiratory viruses, and dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm.RESULTSThe host classifier achieved a median AUC of 0.967 by cross-validation, driven by activation markers of T cells, alveolar macrophages, and the interferon response. The integrated classifier achieved a median AUC of 0.986 and increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n = 94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those.CONCLUSIONLower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.FUNDINGSupport for this study was provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and the National Heart, Lung, and Blood Institute (UG1HD083171, 1R01HL124103, UG1HD049983, UG01HD049934, UG1HD083170, UG1HD050096, UG1HD63108, UG1HD083116, UG1HD083166, UG1HD049981, K23HL138461, and 5R01HL155418) as well as by the Chan Zuckerberg Biohub.
下呼吸道感染（LRTI）是全球儿童死亡的主要原因。LRTI 的诊断具有挑战性，因为非传染性呼吸道疾病在临床上表现相似，而且现有的微生物检测方法常常出现假阴性或偶然检测到携带的微生物，导致过度使用抗菌药物和不良后果。下呼吸道宏基因组学有可能检测到 LRTI 的宿主和微生物特征。它是否可以大规模应用于儿科人群，以改善诊断和治疗效果尚不清楚。
我们使用气管抽吸 RNA-Seq 对 261 例急性呼吸衰竭患儿的宿主基因表达和呼吸道微生物群进行了分析。我们通过对已确诊为 LRTI（n=117）或非传染性呼吸衰竭（n=50）的患者进行训练，开发了一种用于 LRTI 的基因表达分类器。然后，我们开发了一种分类器，该分类器将宿主 LRTI 概率、呼吸道病毒丰度以及基于规则算法认为是致病性的细菌/真菌在肺部微生物组中的优势整合在一起。
宿主分类器通过 T 细胞、肺泡巨噬细胞和干扰素反应的激活标志物，在交叉验证中实现了中位数 AUC 为 0.967。整合分类器的中位数 AUC 为 0.986，并提高了患者分类的置信度。当应用于诊断不确定的患者（n=94）时，整合分类器在 52%的病例中提示 LRTI，并在 98%的病例中确定了可能的病原体。
通过整合宿主、病原体和微生物组特征，下呼吸道宏基因组学可在危重患儿的异质队列中实现准确的 LRTI 诊断和病原体鉴定。
本研究得到了美国国立卫生研究院儿童健康与人类发育研究所和美国国立心肺血液研究所的支持（UG1HD083171、1R01HL124103、UG1HD049983、UG1HD049934、UG1HD083170、UG1HD050096、UG1HD63108、UG1HD083116、UG1HD083166、UG1HD049981、K23HL138461 和 5R01HL155418）以及 Chan Zuckerberg Biohub。
