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Published on: March 29, 2024
Accurate Diagnosis of Lower Respiratory Infections Using Host Response and Respiratory Microbiome from a Single
Xiaohui Zou1, Mengwei Yan1, Yeming Wang1
1National Center for Respiratory Medicine, State Key Laboratory of Respiratory Health and Multimorbidity, National Clinical Research Center for Respiratory Diseases, Institute of Respiratory Medicine, Chinese Academy of Medical Sciences, Department of Pulmonary and Critical Care Medicine, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, 100029, China.
Abstract:
Lower respiratory tract infections (LRTIs) diagnosis is challenging because noninfectious diseases mimic its clinical features. The altered host response and respiratory microbiome following LRTIs have the potential to differentiate LRTIs from noninfectious respiratory diseases (non-LRTIs). Patients suspected of having LRTIs are retrospectively enrolled and a clinical metatranscriptome test is performed on bronchoalveolar lavage fluid (BALF). Transcriptomic and metagenomic analysis profiled the host response and respiratory microbiome in patients with confirmed LRTI (n = 126) or non-LRTIs (n = 75). Patients with evidenced LRTIs exhibited enhanced pathways on chemokine and cytokine response, neutrophile recruitment and activation, along with specific gene modules linked to LRTIs status and key blood markers. Moreover, LRTIs patients exhibited reduced diversity and evenness in the lower respiratory microbiome, likely driven by an increased abundance of bacterial pathogens. Host marker genes are selected, and classifiers are developed to distinguish patients with LRTIs, non-LRTIs, and indeterminate status, achieving an area under the receiver operating characteristic curve of 0.80 to 0.86 and validated in a subsequently enrolled cohort. Incorporating respiratory microbiome features further enhanced the classifier's performance. In summary, a single metatranscriptome test of BALF proved detailed profiles of host response and respiratory microbiome, enabling accurate LRTIs diagnosis.

