Deciphering the microbial landscape of lower respiratory tract infections: insights from metagenomics and machine learning

• Published in: Frontiers in cellular and infection microbiology Vol. 14; p. 1385562
• Main Authors: Li, Jiahuan, Xiong, Anying, Wang, Junyi, Wu, Xue, Bai, Lingling, Zhang, Lei, He, Xiang, Li, Guoping
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 23.05.2024
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Bacteria - classification; Bacteria - genetics; Bacteria - isolation & purification; Bronchiectasis - microbiology; Bronchoalveolar Lavage Fluid - microbiology; Cellular and Infection Microbiology; feature engineering; Female; High-Throughput Nucleotide Sequencing; Hospitalization; Humans; lower respiratory tract infection; Lung - microbiology; Lung - virology; Machine Learning; Male; Metagenome; Metagenomics - methods; Microbiota - genetics; Middle Aged; respiratory microorganisms; Respiratory Tract Infections - microbiology; Respiratory Tract Infections - virology; Young Adult; feature engineering; metagenome; machine learning; respiratory microorganisms; lower respiratory tract infection
• ISSN: 2235-2988; 2235-2988
• DOI: 10.3389/fcimb.2024.1385562

Lower respiratory tract infections represent prevalent ailments. Nonetheless, current comprehension of the microbial ecosystems within the lower respiratory tract remains incomplete and necessitates further comprehensive assessment. Leveraging the advancements in metagenomic next-generation sequencing (mNGS) technology alongside the emergence of machine learning, it is now viable to compare the attributes of lower respiratory tract microbial communities among patients across diverse age groups, diseases, and infection types. We collected bronchoalveolar lavage fluid samples from 138 patients diagnosed with lower respiratory tract infections and conducted mNGS to characterize the lung microbiota. Employing various machine learning algorithms, we investigated the correlation of key bacteria in patients with concurrent bronchiectasis and developed a predictive model for hospitalization duration based on these identified key bacteria. We observed variations in microbial communities across different age groups, diseases, and infection types. In the elderly group, exhibited the highest relative abundance, followed by and . and emerged as the dominant genera at the genus level in the younger group, while and were prevalent species. Within the bronchiectasis group, dominant bacteria included , , and . Significant differences in the presence of were noted between the bronchiectasis group and the control group. In the group with concomitant fungal infections, the most abundant genera were and , with and as the predominant species. Notable differences were observed in the presence of , , , , and between the group with concomitant fungal infections and the bacterial group. Machine learning algorithms were utilized to select bacteria and clinical indicators associated with hospitalization duration, confirming the excellent performance of bacteria in predicting hospitalization time. Our study provided a comprehensive description of the microbial characteristics among patients with lower respiratory tract infections, offering insights from various perspectives. Additionally, we investigated the advanced predictive capability of microbial community features in determining the hospitalization duration of these patients.