PuSH - Publikationsserver des Helmholtz Zentrums München

Reimann, M.* ; Avsar, K.* ; DiNardo, A.R.* ; Goldmann, T.* ; Günther, G.* ; Hoelscher, M. ; Ibraim, E.* ; Kalsdorf, B.* ; Kaufmann, S.H.E.* ; Köhler, N.* ; Mandalakas, A.M.* ; Maurer, F.P.* ; Müller, M.* ; Nitschkowski, D.* ; Olaru, I.D.* ; Popa, C.* ; Rachow, A.* ; Rolling, T.* ; Salzer, H.J.F.* ; Sanchez-Carballo, P.* ; Schuhmann, M.* ; Schaub, D.* ; Spinu, V.* ; Terhalle, E.* ; Unnewehr, M.* ; Zielinski, N.J.* ; Heyckendorf, J.* ; Lange, C.*

The TB27 transcriptomic model for predicting Mycobacterium tuberculosis culture conversion.

Pathog. Immun. 10, 120-139 (2025)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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.
Altmetric
Weitere Metriken?
[➜Einloggen]
Zusatzinfos bearbeiten [➜Einloggen]
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Biomarker ; Precision Medicine ; Systems Biology ; Therapy Response ; Tuberculosis Treatment
ISSN (print) / ISBN 2469-2964
e-ISSN 2469-2964
Zeitschrift Pathogens and Immunity
Quellenangaben Band: 10, Heft: 1, Seiten: 120-139 Artikelnummer: , Supplement: ,
Verlag Case Western Reserve University
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Research Unit Global Health (UGH)