TY - JOUR
T1 - A DNA methylation signature identified in the buccal mucosa reflecting active tuberculosis is changing during tuberculosis treatment
AU - Öhrnberg, Isabelle
AU - Karlsson, Lovisa
AU - Sayyab, Shumaila
AU - Paues, Jakob
AU - Martínez-Enguita, David
AU - Gustafsson, Mika
AU - Espinoza-Lopez, Patricia
AU - Méndez-Aranda, Melissa
AU - Meza, Ericka
AU - Ugarte-Gil, Cesar
AU - Kiprotich, Nicholas
AU - Diero, Lameck
AU - Tonui, Ronald
AU - Lerm, Maria
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Biosignatures
KW - Buccal mucosa
KW - DNA methylation
KW - Oral swabs
KW - Treatment monitoring
KW - Tuberculosis
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U2 - 10.1038/s41598-024-80570-4
DO - 10.1038/s41598-024-80570-4
M3 - Article
C2 - 39609478
AN - SCOPUS:85210571910
SN - 2045-2322
VL - 14
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 29552
ER -