Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithm
Abstract Mycobacterial infections pose a significant global health concern, requiring precise identification for effective treatment. However, diagnosing them is challenging due to inaccurate identifications and prolonged times. In this study, we aimed to develop a novel peptidome-based method using...
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| Language: | English |
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Springer Nature
2025-03-01
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| Series: | EMBO Molecular Medicine |
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| Online Access: | https://doi.org/10.1038/s44321-025-00207-5 |
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| author | Duran Bao Sudipa Maity Lingpeng Zhan Seungyeon Seo Qingbo Shu Christopher J Lyon Bo Ning Adrian Zelazny Tony Y Hu Jia Fan |
| author_facet | Duran Bao Sudipa Maity Lingpeng Zhan Seungyeon Seo Qingbo Shu Christopher J Lyon Bo Ning Adrian Zelazny Tony Y Hu Jia Fan |
| author_sort | Duran Bao |
| collection | DOAJ |
| description | Abstract Mycobacterial infections pose a significant global health concern, requiring precise identification for effective treatment. However, diagnosing them is challenging due to inaccurate identifications and prolonged times. In this study, we aimed to develop a novel peptidome-based method using mycobacterial growth indicator tube (MGIT) cultures for faster and more accurate identification. We created the PEPtide Taxonomy/ORganism CHecking (PEP-TORCH), an algorithm that analyzes tryptic peptides identified by mass spectrometry to diagnose species and subspecies with predominance scores. PEP-TORCH demonstrated 100% accuracy in identifying mycobacterial species, subspecies, and co-infections in 81 individuals suspected of mycobacterial infections, eliminating the need for a sub-solid culture procedure, the gold standard in clinical practice. A notable strength of PEP-TORCH is its ability to provide information on species and subspecies simultaneously, a process conventionally achieved sequentially. This capability significantly expedites pathogen identification. Furthermore, a targeted proteomics method was validated in 63 clinical samples using the taxa-specific peptides selected by PEP-TORCH, making them suitable as biomarkers in more clinically friendly settings. This comprehensive identification approach holds promise for streamlining treatment strategies in clinical practice. |
| format | Article |
| id | doaj-art-6d58fab9e73249259d6ceab3eae2a1be |
| institution | OA Journals |
| issn | 1757-4684 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | EMBO Molecular Medicine |
| spelling | doaj-art-6d58fab9e73249259d6ceab3eae2a1be2025-08-20T02:11:42ZengSpringer NatureEMBO Molecular Medicine1757-46842025-03-0117484186110.1038/s44321-025-00207-5Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithmDuran Bao0Sudipa Maity1Lingpeng Zhan2Seungyeon Seo3Qingbo Shu4Christopher J Lyon5Bo Ning6Adrian Zelazny7Tony Y Hu8Jia Fan9Center for Cellular and Molecular Diagnostics, Tulane University School of MedicineCenter for Cellular and Molecular Diagnostics, Tulane University School of MedicineCenter for Cellular and Molecular Diagnostics, Tulane University School of MedicineDepartment of Laboratory Medicine, NIH Clinical Center, NIHCenter for Cellular and Molecular Diagnostics, Tulane University School of MedicineCenter for Cellular and Molecular Diagnostics, Tulane University School of MedicineCenter for Cellular and Molecular Diagnostics, Tulane University School of MedicineDepartment of Laboratory Medicine, NIH Clinical Center, NIHCenter for Cellular and Molecular Diagnostics, Tulane University School of MedicineCenter for Cellular and Molecular Diagnostics, Tulane University School of MedicineAbstract Mycobacterial infections pose a significant global health concern, requiring precise identification for effective treatment. However, diagnosing them is challenging due to inaccurate identifications and prolonged times. In this study, we aimed to develop a novel peptidome-based method using mycobacterial growth indicator tube (MGIT) cultures for faster and more accurate identification. We created the PEPtide Taxonomy/ORganism CHecking (PEP-TORCH), an algorithm that analyzes tryptic peptides identified by mass spectrometry to diagnose species and subspecies with predominance scores. PEP-TORCH demonstrated 100% accuracy in identifying mycobacterial species, subspecies, and co-infections in 81 individuals suspected of mycobacterial infections, eliminating the need for a sub-solid culture procedure, the gold standard in clinical practice. A notable strength of PEP-TORCH is its ability to provide information on species and subspecies simultaneously, a process conventionally achieved sequentially. This capability significantly expedites pathogen identification. Furthermore, a targeted proteomics method was validated in 63 clinical samples using the taxa-specific peptides selected by PEP-TORCH, making them suitable as biomarkers in more clinically friendly settings. This comprehensive identification approach holds promise for streamlining treatment strategies in clinical practice.https://doi.org/10.1038/s44321-025-00207-5AlgorithmLC-MS/MSNontuberculosis MycobacteriumPeptidomeSubspecies |
| spellingShingle | Duran Bao Sudipa Maity Lingpeng Zhan Seungyeon Seo Qingbo Shu Christopher J Lyon Bo Ning Adrian Zelazny Tony Y Hu Jia Fan Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithm EMBO Molecular Medicine Algorithm LC-MS/MS Nontuberculosis Mycobacterium Peptidome Subspecies |
| title | Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithm |
| title_full | Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithm |
| title_fullStr | Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithm |
| title_full_unstemmed | Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithm |
| title_short | Precise mycobacterial species and subspecies identification using the PEP-TORCH peptidome algorithm |
| title_sort | precise mycobacterial species and subspecies identification using the pep torch peptidome algorithm |
| topic | Algorithm LC-MS/MS Nontuberculosis Mycobacterium Peptidome Subspecies |
| url | https://doi.org/10.1038/s44321-025-00207-5 |
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