Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children.
Integrated host/microbe metagenomics enables accurate lower respiratory tract infection diagnosis in critically ill children.
复制标题
整合的宿主/微生物宏基因组学可以准确诊断重症儿童的下呼吸道感染。
DOI：
10.1172/jci165904
复制
发表时间：
2023-04-03
影响因子：
15.9
通讯作者：
Langelier, Charles R.
中科院分区：
文献类型：
--
作者：
Mick, Eran;Tsitsiklis, Alexandra;Kamm, Jack;Kalantar, Katrina L.;Caldera, Saharai;Lyden, Amy;Tan, Michelle;Detweiler, Angela M.;Neff, Norma;Osborne, Christina M.;Williamson, Kayla M.;Soesanto, Victoria;Leroue, Matthew;Maddux, Aline B.;Simoes, Eric A. F.;Carpenter, Todd C.;Wagner, Brandie D.;DeRisi, Joseph L.;Ambroggio, Lilliam;Mourani, Peter M.;Langelier, Charles R.
下呼吸道感染（LRTI）是全球儿童死亡的主要原因。下呼吸道感染的诊断具有挑战性，因为非感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通常呈假阴性或检测到偶然携带的微生物，导致抗菌药物过度使用和不良后果。下呼吸道宏基因组学具有检测LRTI的宿主和微生物特征的潜力。它是否可以大规模应用于儿科人群，以改善诊断和治疗仍不清楚。我们使用气管吸出物RNA-Seq分析了261名急性呼吸衰竭儿童的宿主基因表达和呼吸道微生物群。我们开发了一个LRTI的基因表达分类器，通过对确诊为LRTI（n = 117）或非感染性呼吸衰竭（n = 50）的患者进行训练。然后，我们开发了一种分类器，该分类器集成了宿主LRTI概率、呼吸道病毒丰度以及通过基于规则的算法认为致病的细菌/真菌在肺部微生物组中的优势。通过交叉验证，宿主分类器实现了0.967的中位AUC，由T细胞、肺泡巨噬细胞和干扰素应答的活化标志物驱动。集成分类器实现了0.986的中位AUC，并增加了患者分类的置信度。当应用于诊断不确定的患者（n = 94）时，综合分类器在52%的病例中显示了LRTI，并在98%的病例中指定了可能的致病病原体。下呼吸道宏基因组学通过整合宿主、病原体和微生物组特征，在危重儿童的异质队列中实现了准确的LRTI诊断和病原体鉴定。Eunice Kennedy Shriver国家儿童健康与人类发展研究所和国家心肺血液研究所为这项研究提供了支持（UG1HD083171、1R01HL124103、UG1HD049983、UG01HD049934、UG1HD083170、UG1HD050096、UG1HD63108、UG1HD083116、UG1HD083166、UG1HD049981、K23HL138461、和5R01HL155418）以及Chan Zuckerberg Biohub。
Lower 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. We 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. The 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. Lower 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. Support 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.
影响因子：
3.7
作者：
Lytkin NI;McVoy L;Weitkamp JH;Aliferis CF;Statnikov A
通讯作者：
Statnikov A