PuSH - Publikationsserver des Helmholtz Zentrums München

Export: TXT Text RIS Endnote (RIS) BIB BibTeX
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
Free journal
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.
Impact Factor
Scopus SNIP
Altmetric
0.000
0.954
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Biomarker ; Precision Medicine ; Systems Biology ; Therapy Response ; Tuberculosis Treatment
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
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
Begutachtungsstatus Peer reviewed
Institut(e) Research Unit Global Health (UGH)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540001-003
DOI 10.20411/pai.v10i1.770
Scopus ID 85219259541
PubMed ID 39911144
Erfassungsdatum 2025-04-02
[➜Korrektur melden]