The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion
Rationale: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment. Objective: Identifying and validating a whole blood-based RNA signature mode...
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| Language: | English |
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Case Western Reserve University
2025-01-01
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| Series: | Pathogens and Immunity |
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| Online Access: | https://www.paijournal.com/index.php/paijournal/article/view/770 |
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| author | Maja Reimann Korkut Avsar Andrew DiNardo Torsten Goldmann Gunar Günther Michael Hoelscher Elmira Ibraim Barbara Kalsdorf Stefan Kaufmann Niklas Köhler Anna Mandalakas Florian Maurer Marius Müller Dörte Nitschkowski Ioana Olaru Cristina Popa Andrea Rachow Thierry Rolling Helmut Salzer Patricia Sanchez-Carballo Maren Schuhmann Dagmar Schaub Victor Spinu Elena Terhalle Markus Unnewehr Nika Zielinski Jan Heyckendorf Christoph Lange |
| author_facet | Maja Reimann Korkut Avsar Andrew DiNardo Torsten Goldmann Gunar Günther Michael Hoelscher Elmira Ibraim Barbara Kalsdorf Stefan Kaufmann Niklas Köhler Anna Mandalakas Florian Maurer Marius Müller Dörte Nitschkowski Ioana Olaru Cristina Popa Andrea Rachow Thierry Rolling Helmut Salzer Patricia Sanchez-Carballo Maren Schuhmann Dagmar Schaub Victor Spinu Elena Terhalle Markus Unnewehr Nika Zielinski Jan Heyckendorf Christoph Lange |
| author_sort | Maja Reimann |
| collection | DOAJ |
| description |
Rationale: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment.
Objective: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy.
Methods: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy.
Results: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98.
Conclusion: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice.
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| format | Article |
| id | doaj-art-3382df10301c4c1f84392118aa6c8d3b |
| institution | Kabale University |
| issn | 2469-2964 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Case Western Reserve University |
| record_format | Article |
| series | Pathogens and Immunity |
| spelling | doaj-art-3382df10301c4c1f84392118aa6c8d3b2025-01-29T20:32:42ZengCase Western Reserve UniversityPathogens and Immunity2469-29642025-01-0110110.20411/pai.v10i1.770The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture ConversionMaja Reimann0Korkut Avsar1Andrew DiNardo2Torsten Goldmann3Gunar Günther4Michael Hoelscher5Elmira Ibraim6Barbara Kalsdorf7Stefan Kaufmann8Niklas Köhler9Anna Mandalakas10Florian Maurer11Marius Müller12Dörte Nitschkowski13Ioana Olaru14Cristina Popa15Andrea Rachow16Thierry Rolling17Helmut Salzer18Patricia Sanchez-Carballo19Maren Schuhmann20Dagmar Schaub21Victor Spinu22Elena Terhalle23Markus Unnewehr24Nika Zielinski25Jan Heyckendorf26Christoph Lange27Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany; Respiratory Medicine & International Health, University of Lübeck, Lübeck, GermanyAsklepios Fachkliniken München-Gauting, Munich, GermanyThe Global Tuberculosis Program, Texas Children’s Hospital, Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, Texas; Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The NetherlandsDivision of Histopathology, Research Center Borstel, Leibniz Lung Center, Borstel, Germany; German Center for Lung Research (DZL), Airway Research Center North, Borstel, GermanyDepartment of Medicine, University of Namibia School of Medicine, Windhoek, Namibia; Inselspital Bern, Department of Pulmonology, Bern, SwitzerlandDivision of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany; German Center for Infection Research (DZIF), partner site Munich, Germany; Fraunhofer Institute for Translational Medicine and Pharmacology ITMP; Immunology, Infection and Pandemic Research, Munich, Germany; Unit Global Health, Helmholtz Zentrum München, German Research Center for Environmental Health (HMGU), Neuherberg, GermanyInstitutul de Pneumoftiziologie “Marius Nasta”, MDR-TB Research Department, Bucharest, RomaniaClinical Infectious Diseases, Research Center Borstel, Borstel, Germany German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany; Respiratory Medicine & International Health, University of Lübeck, Lübeck, GermanyMax Planck Institute for Infection Biology, Berlin, Germany; Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany; Hagler Institute for Advanced Study, Texas A&M University, College Station, TX; Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, GermanyClinical Infectious Diseases, Research Center Borstel, Borstel, Germany German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany; Respiratory Medicine & International Health, University of Lübeck, Lübeck, Germany; Division of Infectious Diseases, I. Department of Internal Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, GermanyClinical Infectious Diseases, Research Center Borstel, Borstel, Germany German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany; The Global Tuberculosis Program, Texas Children’s Hospital, Immigrant and Global Health, Department of Pediatrics, Baylor College of Medicine, Houston, TexasNational and WHO Supranational Reference Laboratory for Mycobacteria, Research Center Borstel, Borstel, Germany; Institute of Medical Microbiology, Virology and Hygiene, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; new affiliation: Roche Diagnostics, Zurich, SwitzerlandSankt Katharinen-Krankenhaus, Frankfurt, Germany; Infektiologikum, Frankfurt, GermanyDivision of Histopathology, Research Center Borstel, Leibniz Lung Center, Borstel, Germany; German Center for Lung Research (DZL), Airway Research Center North, Borstel, GermanyClinical Infectious Diseases, Research Center Borstel, Borstel, Germany; London School of Hygiene and Tropical Medicine, London, United Kingdom; new affiliation: Medical Microbiology, University of Münster, GermanyInstitutul de Pneumoftiziologie “Marius Nasta”, MDR-TB Research Department, Bucharest, RomaniaDivision of Infectious Diseases and Tropical Medicine, University Hospital, LMU Munich, Munich, Germany; German Center for Infection Research (DZIF), partner site Munich, GermanyCharité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; new affiliation: Biontech SE, Mainz, GermanyClinical Infectious Diseases, Research Center Borstel, Borstel, Germany; Division of Infectious Diseases and Tropical Medicine, Department of Internal Medicine 4-Pneumology, Kepler University Hospital, Linz, Austria; Medical Faculty, Johannes Kepler University Linz, Linz, Austria; Ignaz-Semmelweis-Institute, Interuniversity Institute for Infection Research, Vienna, Austria Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany; Respiratory Medicine & International Health, University of Lübeck, Lübeck, GermanyClinical Infectious Diseases, Research Center Borstel, Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany; Respiratory Medicine & International Health, University of Lübeck, Lübeck, GermanyClinical Infectious Diseases, Research Center Borstel, Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, GermanyInstitutul de Pneumoftiziologie “Marius Nasta”, MDR-TB Research Department, Bucharest, RomaniaClinical Infectious Diseases, Research Center Borstel, Borstel, Germany; new affiliation: LungenClinic Großhansdorf, Großhansdorf, GermanyDepartment of Respiratory Medicine and Infectious Diseases, St. Barbara-Klinik, Hamm, Germany; Department of Medicine, Faculty of Health, Witten/Herdecke University, Witten, Germany Clinical Infectious Diseases, Research Center Borstel, Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany; Respiratory Medicine & International Health, University of Lübeck, Lübeck, GermanyClinical Infectious Diseases, Research Center Borstel, Borstel, Germany; new affiliation: Internal Medicine II, Leibniz LungClinic, University Hospital Schleswig-Holstein (UKSH) Campus Kiel, Germany; new affiliation: Pulmonology and Inflammation Medicine, Christian-Albrechts-University Kiel, GermanyClinical Infectious Diseases, Research Center Borstel, Borstel, Germany; German Center for Infection Research (DZIF), Partner Site Hamburg-Lübeck-Borstel-Riems, Germany; Respiratory Medicine & International Health, University of Lübeck, Lübeck, Germany Rationale: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of Mycobacterium tuberculosis. Recently, host RNA signatures have been used to monitor the response to tuberculosis treatment. Objective: Identifying and validating a whole blood-based RNA signature model to predict microbiological treatment responses in patients on tuberculosis therapy. Methods: Using a multi-step machine learning algorithm to identify an RNA-based algorithm to predict the remaining time to culture conversion at flexible time points during anti-tuberculosis therapy. Results: The identification cohort included 149 patients split into a training and a test cohort, to develop a multistep algorithm consisting of 27 genes (TB27) for predicting the remaining time to culture conversion (TCC) at any given time. In the test dataset, predicted TCC and observed TCC achieved a correlation coefficient of r=0.98. An external validation cohort of 34 patients shows a correlation between predicted and observed days to TCC also of r=0.98. Conclusion: We identified and validated a whole blood-based RNA signature (TB27) that demonstrates an excellent agreement between predicted and observed times to M. tuberculosis culture conversion during tuberculosis therapy. TB27 is a potential useful biomarker for anti-tuberculosis drug development and for prediction of treatment responses in clinical practice. https://www.paijournal.com/index.php/paijournal/article/view/770biomarkertherapy responsetuberculosis treatmentprecision medicinesystems biology |
| spellingShingle | Maja Reimann Korkut Avsar Andrew DiNardo Torsten Goldmann Gunar Günther Michael Hoelscher Elmira Ibraim Barbara Kalsdorf Stefan Kaufmann Niklas Köhler Anna Mandalakas Florian Maurer Marius Müller Dörte Nitschkowski Ioana Olaru Cristina Popa Andrea Rachow Thierry Rolling Helmut Salzer Patricia Sanchez-Carballo Maren Schuhmann Dagmar Schaub Victor Spinu Elena Terhalle Markus Unnewehr Nika Zielinski Jan Heyckendorf Christoph Lange The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion Pathogens and Immunity biomarker therapy response tuberculosis treatment precision medicine systems biology |
| title | The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion |
| title_full | The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion |
| title_fullStr | The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion |
| title_full_unstemmed | The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion |
| title_short | The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion |
| title_sort | tb27 transcriptomic model for predicting mycobacterium tuberculosis culture conversion |
| topic | biomarker therapy response tuberculosis treatment precision medicine systems biology |
| url | https://www.paijournal.com/index.php/paijournal/article/view/770 |
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