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...

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Main Authors: Ferry Dwi Kurniawan, Dina Alia, Mamoru Shiraishi, Megumi Higo, Yoshiaki Inoue, Koichi Hagiwara
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-14841-z
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author Ferry Dwi Kurniawan
Dina Alia
Mamoru Shiraishi
Megumi Higo
Yoshiaki Inoue
Koichi Hagiwara
author_facet Ferry Dwi Kurniawan
Dina Alia
Mamoru Shiraishi
Megumi Higo
Yoshiaki Inoue
Koichi Hagiwara
author_sort Ferry Dwi Kurniawan
collection DOAJ
description 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.
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spelling doaj-art-aa73bdf8e6bb480ea1d2a12706b1e23b2025-08-20T03:43:21ZengNature PortfolioScientific Reports2045-23222025-08-011511910.1038/s41598-025-14841-zA systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogensFerry Dwi Kurniawan0Dina Alia1Mamoru Shiraishi2Megumi Higo3Yoshiaki Inoue4Koichi Hagiwara5Comprehensive Medicine I, Saitama Medical Center, Jichi Medical UniversityComprehensive Medicine I, Saitama Medical Center, Jichi Medical UniversityDivision of Pulmonary Medicine, Department of Internal Medicine, Jichi Medical UniversityComprehensive Medicine I, Saitama Medical Center, Jichi Medical UniversityGeneral Thoracic Surgery, Saitama Medical Center, Saitama Medical UniversityComprehensive Medicine I, Saitama Medical Center, Jichi Medical UniversityAbstract 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.https://doi.org/10.1038/s41598-025-14841-zPneumonia16S ribosomal RNANext-generation sequencingStreptococcus pneumoniaeBattlefield hypothesis
spellingShingle Ferry Dwi Kurniawan
Dina Alia
Mamoru Shiraishi
Megumi Higo
Yoshiaki Inoue
Koichi Hagiwara
A systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogens
Scientific Reports
Pneumonia
16S ribosomal RNA
Next-generation sequencing
Streptococcus pneumoniae
Battlefield hypothesis
title A systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogens
title_full A systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogens
title_fullStr A systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogens
title_full_unstemmed A systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogens
title_short A systematic algorithm using 16S ribosomal RNA for accurate diagnosis of pneumonia pathogens
title_sort systematic algorithm using 16s ribosomal rna for accurate diagnosis of pneumonia pathogens
topic Pneumonia
16S ribosomal RNA
Next-generation sequencing
Streptococcus pneumoniae
Battlefield hypothesis
url https://doi.org/10.1038/s41598-025-14841-z
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