Epigenetic changes in lung immune cells and buccal mucosa during tuberculosis infection and treatment

Tuberculosis (TB) is the leading infectious disease in terms of global mortality and remains a major contributor to poor health worldwide. Infection with Mycobacterium tuberculosis, the causative agent of TB, can result in a wide range of clinical outcomes – from early clearance by the innate immune system to TB infection, subclinical disease and active TB. This variability highlights TB as a disease with a broad clinical spectrum. Although TB is curable with antibiotics, some patients still experience poor outcomes due to relapse or treatment failure. Drug resistance further complicates care, highlighting the need for better tools to monitor treatment and predict outcomes. Epigenetic mechanisms like DNA methylation have shown promise in disease diagnostics and monitoring. Notably, DNA methylation changes can be detected in buccal cells collected via non-invasive mouth swabs, offering a practical approach for clinical sampling. To address the need for improved TB monitoring tools, this thesis investigates whether DNA methylation can serve as a biomarker for TB diagnosis, treatment response and prognosis. The research is driven by the hypothesis that exposure to M. tuberculosis induces epigenetic changes in host immune cells, potentially reflecting disease status and progression. Paper I and II investigate the DNA methylation landscape of host immune cells in the lung compartment and buccal mucosa in healthy controls, individuals exposed to TB and in patients with active TB at the onset of treatment. Notably, TB exposure, TB infection (as indicated by a positive interferongamma release assay) and active TB were associated with distinct DNA methylation signatures in lung and buccal mucosa, reflecting the clinical spectrum of TB. In Paper II, we developed and validated a DNA methylation-based classifier for active TB, comprising seven CpG sites selected for their ability to distinguish TB patients from TB-exposed individuals and healthy controls. Furthermore, the longitudinal study presented in Paper III followed TB patients from the start of treatment through six months of therapy. During treatment, the DNA methylation signature derived from buccal swab samples showed dynamic changes, indicating treatment-associated epigenetic alterations. Using a machine learning approach, we developed a regression model based on nine CpG sites to predict TB symptom severity across multiple populations. Collectively, these findings support the potential of DNA methylation-based biomarkers to aid in TB diagnosis and prognosis. While the hypothesis was partially confirmed (DNA methylation changes were observed in TB exposure and infection) the specificity to M. tuberculosis remains to be fully established. Further longitudinal studies and mechanistic investigations are needed to validate these epigenetic signatures and their clinical utility.