Abstract:
Tuberculosis (TB) poses a significant global health threat, with high mortality rates if left untreated.
Current sputum-based TB treatment monitoring methods face numerous challenges, particularly
in relation to sample collection and analysis. This pilot study explores the potential of TB status
assessment using DNA methylation (DNAm) signatures, which are gaining recognition as diagnostic
and predictive tools for various diseases. We collected buccal swab samples from pulmonary TB
patients at the commencement of TB treatment (n=10), and at one, two, and six-month follow-up
intervals. We also collected samples from healthy controls (n=10) and individuals exposed to TB
(n=10). DNAm patterns were mapped using the Illumina Infinium Methylation EPIC 850 K platform.
A DNAm profile distinct from controls was discovered in the oral mucosa of TB patients at the start
of treatment, and this profile changed throughout the course of TB treatment. These findings were
corroborated in a separate validation cohort of TB patients (n=41), monitored at two and six months
into their TB treatment. We developed a machine learning model to predict symptom scores using
the identified DNAm TB profile. The model was trained and evaluated on the pilot, validation, and
two additional independent cohorts, achieving an R2 of 0.80, Pearson correlation of 0.90, and mean
absolute error of 0.13. While validation is needed in larger cohorts, the result opens the possibility of
employing DNAm-based diagnostic and prognostic tools for TB in future clinical practice.
