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Related Concept Videos

Pulmonary Tuberculosis IV01:26

Pulmonary Tuberculosis IV

132
Tuberculosis, more commonly referred to as TB, is an infectious disease stemming from Mycobacterium tuberculosis. While it primarily impacts the lungs, TB can also affect other body areas. Given its severity and global impact, timely and accurate diagnosis is crucial for controlling its spread and improving patient outcomes.
Several diagnostic approaches are used to detect TB. The conventional method is the Tuberculin Skin Test (TST), also known as the Mantoux test. However, this method has...
132

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Updated: May 29, 2025

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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.

Pathogens & Immunity
|February 6, 2025
PubMed
Summary
This summary is machine-generated.

A novel 27-gene RNA signature (TB27) accurately predicts the time to tuberculosis culture conversion in patients undergoing treatment. This biomarker aids in monitoring treatment response and developing new anti-tuberculosis drugs.

Keywords:
biomarkerprecision medicinesystems biologytherapy responsetuberculosis treatment

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Area of Science:

  • Microbiology
  • Genomics
  • Machine Learning

Background:

  • Monitoring tuberculosis (TB) treatment is challenging due to the slow growth of Mycobacterium tuberculosis.
  • Host RNA signatures offer a promising approach for tracking treatment response in TB patients.

Purpose of the Study:

  • To identify and validate a whole blood-based RNA signature for predicting microbiological treatment responses in tuberculosis patients.
  • To develop a machine learning algorithm for predicting time to culture conversion during anti-tuberculosis therapy.

Main Methods:

  • A multi-step machine learning algorithm was employed to identify an RNA signature.
  • The algorithm was developed using a 149-patient training and testing cohort, resulting in a 27-gene signature (TB27).
  • External validation was performed on a separate cohort of 34 patients.

Main Results:

  • The TB27 signature demonstrated high accuracy in predicting the time to culture conversion (TCC).
  • In the test dataset, predicted TCC and observed TCC showed a correlation coefficient of r=0.98.
  • The external validation cohort also showed a strong correlation (r=0.98) between predicted and observed TCC.

Conclusions:

  • A validated whole blood-based RNA signature (TB27) shows excellent agreement for predicting Mycobacterium tuberculosis culture conversion times.
  • TB27 is a potential biomarker for advancing anti-tuberculosis drug development.
  • This signature may improve the prediction of treatment responses in clinical practice.