Computational and Empirical Studies Predict Mycobacterium tuberculosis-Specific T Cells as a Biomarker for Infection Outcome - Marino, Simeone; Gideon, Hannah P.; Gong, Chang; Mankad, Shawn; McCrone, John T.; Lin, Philana Ling; Linderman, Jennifer J.; Flynn, JoAnne L.; Kirschner, Denise E. (PLOS Computational Biology, 2016)
Author Summary Tuberculosis (TB) is a disease that is caused by infection after inhaling the bacterium Mycobacterium tuberculosis. Not everyone infected with TB bacteria becomes sick. As a result, two TB-related conditions have been categorized: latent TB infection (not sick but still harboring the bacteria) and active TB disease. If not treated properly, active TB disease can be fatal. Almost 1.3 million die of TB worldwide each year, with ~8,6 million new infections in 2013. No effective vaccine is available to protect against TB and treatment of infection with multiple antibiotics is lengthy (6–9 months), with non-compliance being a major factor for the emergence of drug-resistant strains. A key step in developing effective vaccines and possibly shorter treatment regimens is the ability to identify biomarkers that correlate prognosis and progression to infection (similar to how cholesterol levels are a measure of heart health). In this study we show how pairing computer modeling, statistics and mathematics with datasets derived from non-human primate studies can accelerate biomarker discovery, and offer a new approach to identifying correlates of protection that will be useful in clinical practice, particularly in developing countries where TB is most prevalent.