Related Experiment Video
Updated: May 29, 2025
An Automated Culture System for Use in Preclinical Testing of Host-Directed Therapies for Tuberculosis
Published on: August 16, 2021
The TB27 Transcriptomic Model for Predicting Mycobacterium tuberculosis Culture Conversion
Maja Reimann1,2,3, Korkut Avsar4, Andrew R DiNardo5,6
1Clinical Infectious Diseases, Research Center Borstel, Borstel, 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.
