Pathogenic profiles and lower respiratory tract microbiota in severe pneumonia patients using metagenomic...

The homeostatic balance of the lung microbiota is important for the maintenance of normal physiological function of the lung, but its role in pathological processes such as severe pneumonia is poorly understood.

We screened 34 patients with community-acquired pneumonia (CAP) and 12 patients with hospital-acquired pneumonia (HAP), all of whom were admitted to the respiratory intensive care unit. Clinical samples, including bronchoalveolar lavage fluid (BALF), sputum, peripheral blood, and tissue specimens, were collected along with traditional microbiological test results, routine clinical test data, and clinical treatment information. The pathogenic spectrum of lower respiratory tract pathogens in critically ill respiratory patients was characterized through metagenomic next-generation sequencing (mNGS). Additionally, we analyzed the composition of the commensal microbiota and its correlation with clinical characteristics.

The sensitivity of the mNGS test for pathogens was 92.2% and the specificity 71.4% compared with the clinical diagnosis of the patients. Using mNGS, we detected more fungi and viruses in the lower respiratory tract of CAP-onset severe pneumonia patients, whereas bacterial species were predominant in HAP-onset patients. On the other hand, using mNGS data, commensal microorganisms such as Fusobacterium yohimbe were observed in the lower respiratory tract of patients with HAP rather than those with CAP, and most of these commensal microorganisms were associated with hospitalization or the staying time in ICU, and were significantly and positively correlated with the total length of stay.

mNGS can be used to effectively identify pathogenic pathogens or lower respiratory microbiome associated with pulmonary infectious diseases, playing a crucial role in the early and accurate diagnosis of these conditions. Based on the findings of this study, it is possible that a novel set of biomarkers and predictive models could be developed in the future to efficiently identify the cause and prognosis of patients with severe pneumonia.