A systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogens
Abstract Over half of community-acquired pneumonia cases are caused by a few dozen bacterial species, and accurate identification of these pathogens is essential for effective treatment. In this study, we developed a reliable diagnostic method using 16S ribosomal RNA (16S rRNA) sequencing, consideri...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-14841-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Over half of community-acquired pneumonia cases are caused by a few dozen bacterial species, and accurate identification of these pathogens is essential for effective treatment. In this study, we developed a reliable diagnostic method using 16S ribosomal RNA (16S rRNA) sequencing, considering intra-species variation, the need to differentiate Streptococcus pneumoniae from oral α-hemolytic streptococci, and applicability to the battlefield hypothesis, which helps distinguish true pathogens from commensal organisms that are not causative pathogens. We designed specific primers and a BLAST wrapper program, Cheryblast + ob, to classify 37 pneumonia-causing bacteria and 4 α-hemolytic streptococci. In simulation experiments involving a total of 20,309 copies of the 16S rRNA from 41 species of bacteria deposited in Genbank, the algorithm achieved a sensitivity greater than 0.996 and a specificity of 1.000. It was robust against sequencing errors and successfully distinguished S. pneumoniae from closely related species. In an experiment using next-generation sequencing on artificial mixtures containing genomic DNA from 10 bacterial species and human DNA at varying two-fold ratios, the species with the highest copy number was correctly identified in 8 out of 11 samples, and the top two species by copy number were identified in all 11 samples. This high-performance method offers a promising tool for accurate pneumonia diagnosis and could also be applied to other infections in which a limited number of bacterial species must be reliably identified. |
|---|---|
| ISSN: | 2045-2322 |