Application of next generation sequencing-based rapid detection platform for microbiological diagnosis and drug resistance prediction in acute lower respiratory infection.

Background: Acute lower respiratory infections (ALRIs) have a high mortality rate. We aimed to apply a platform that rapidly detects 36 microorganisms and 49 antibiotic resistance markers in the clinical diagnosis of ALRI and drug resistance prediction.

Methods: Multicenter collection of clinical samples from patients with ALRIs was carried out from 2017 to 2018. Sputum culture (SC) was performed, which provided two outcomes: the detected pathogens and the resistance to different antibiotics. Additionally, each sputum sample was used to extract deoxyribonucleic acids (DNAs) followed by high-throughput sequencing.

Results: Eleven commonly observed pathogens were surveyed, and for all samples with positive SC results (137 cases), the overall coverage was 95.62% according to the sequencing results. The receiver operating characteristic (ROC) curve was drawn, and cutoff reads of the most frequently detected pathogens were acquired. Overall, sequencing exhibited significantly higher sensitivity in the detection of pathogens compared with the traditional SC method, with a generally satisfactory specificity. Furthermore, we investigated the correlation between antibiotic resistance gene phenotypes and the actual outcomes of the drug sensitivity test, and some significant correlations were found, especially for the resistance to Amikacin in the presence of .

Conclusions: Sequencing-based sputum metagenomics can reveal a profile of the lung pathogen microbiome. The sequencing method offers both sufficient accuracy and significantly higher sensitivity in the detection of pathogens, and can be at least a complementary approach to traditional SC reporting. The sequencing technique also revealed some novel potential correlations between the presence of different pathogens, as well as new antimicrobial-resistant genes.