The Journal of infection · 2025
A comparative study of DNA- and RNA-metagenomic next-generation sequencing for pathogen detection in lower respiratory tract infections.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhu TianYuan, Guo JiaYu, Zhang Dong, Weng Li, Peng JinMin, Yang QiWen, Du Bin
Аннотация
OBJECTIVES: To compare the clinical utility of DNA- and RNA-metagenomic next-generation sequencing (mNGS) for pathogen detection in lower respiratory tract infections (LRTIs), and evaluate strategies to optimize RNA-mNGS performance.
METHODS: We retrospectively analyzed 82 patients with suspected LRTI undergoing simultaneous DNA-mNGS and RNA-mNGS testing. The concordance of two methods in detecting microorganisms was assessed. Performance in detecting causative pathogens was compared using multi-label classification metrics. Impacts of RNA-mNGS workflow adjustments were evaluated using mock samples.
RESULTS: In a total of 196 microbial detections, DNA-mNGS and RNA-mNGS showed poor overall agreement (Cohen's κ=0.166, p<0.01). In identifying causative pathogens, RNA-mNGS demonstrated significantly higher precision (1.00 vs. 0.50, p<0.05) and F1 scores (0.80 vs. 0.67, p<0.05) compared to DNA-mNGS. DNA-mNGS possessed higher sensitivity for bacteria, fungi, and atypical pathogens, while RNA-mNGS excelled in detecting RNA viruses. Improved RNA-mNGS sensitivity and significant DNA-RNA read correlations were observed in causative pathogens at high abundance. Neither homogenization nor increased sequencing depth improved RNA-mNGS testing.
CONCLUSIONS: DNA-mNGS and RNA-mNGS exhibited low overall consistency. However, RNA-mNGS showed superior precision in identifying causative pathogens in LRTI and additional capacity for RNA virus detections, while DNA-mNGS possessed essential sensitivity for low abundance pathogens.
METHODS: We retrospectively analyzed 82 patients with suspected LRTI undergoing simultaneous DNA-mNGS and RNA-mNGS testing. The concordance of two methods in detecting microorganisms was assessed. Performance in detecting causative pathogens was compared using multi-label classification metrics. Impacts of RNA-mNGS workflow adjustments were evaluated using mock samples.
RESULTS: In a total of 196 microbial detections, DNA-mNGS and RNA-mNGS showed poor overall agreement (Cohen's κ=0.166, p<0.01). In identifying causative pathogens, RNA-mNGS demonstrated significantly higher precision (1.00 vs. 0.50, p<0.05) and F1 scores (0.80 vs. 0.67, p<0.05) compared to DNA-mNGS. DNA-mNGS possessed higher sensitivity for bacteria, fungi, and atypical pathogens, while RNA-mNGS excelled in detecting RNA viruses. Improved RNA-mNGS sensitivity and significant DNA-RNA read correlations were observed in causative pathogens at high abundance. Neither homogenization nor increased sequencing depth improved RNA-mNGS testing.
CONCLUSIONS: DNA-mNGS and RNA-mNGS exhibited low overall consistency. However, RNA-mNGS showed superior precision in identifying causative pathogens in LRTI and additional capacity for RNA virus detections, while DNA-mNGS possessed essential sensitivity for low abundance pathogens.