Potential Roles of Biomarkers in Tuberculosis Diagnostics Across Studies

This paper was completed as part of the Young Scholars Research Program. Tuberculosis (TB), caused primarily by Mycobacterium tuberculosis, remains as the leading infectious cause of death worldwide, with 1.25 million deaths and 10.8 million new cases in 2023. Accurate and accessible diagnosis is critical for controlling transmission, yet many existing methods fall short in low-resource settings. This review highlights recent advances in TB diagnostics, focusing on animal models, computational tools, and human biomarker studies. Research using Diversity Outbred (DO) mice identified reproducible “supersusceptible” phenotypes with distinct gene expression profiles, pointing to potential genetic and protein biomarkers such as MMP8 and CXCL1. Deep learning approaches applied to lung tissue histopathology achieved comparable accuracy, suggesting that imaging-based biomarkers could provide scalable diagnostic options. Clinical studies in South Africa further demonstrated the potential of salivary biosignatures to distinguish TB from other respiratory diseases with high sensitivity and specificity. Collectively, these studies show that integrative approaches combining genomics, computational modeling, and biomarker profiling offer promising paths for advancing TB diagnostics.