Evaluation of novel TB drug regimens by targeting resuscitation promoting factor-dependent persistent Mycobacterium tuberculosis in the Cornell model

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Tuberculosis, although curable, still remains one of the biggest killers in the world. It kills nearly 2 million people worldwide every year. 98% of tuberculosis deaths are in the developing world affecting mostly young adults in their productive years. A quarter of a million tuberculosis deaths are also infected with HIV with weakened immune systems, most of these people are in Africa. Tuberculosis especially affects the most vulnerable populations including children, the poorest and malnourished. Tuberculosis is caused by the bacterium called Mycobacterium tuberculosis. One important characteristic of this disease is that the bacterium has an unusual ability to grow and survive for extended periods of time in human body. Therefore, it has been estimated that 2 billion people, equal to one-third of the world's total population, are infected with the bacterium in whom it causes unnoticeable latent infections that lead to a 5-10% lifetime risk of active disease. These persistent bacteria cannot be cultured using standard microbiological methods and are not killed by the current tuberculosis drugs. Therefore, tuberculosis treatment needs a long period of time with four drugs to cure the patients. This long-term treatment is extremely difficult to implement especially in developing countries because of lack of affordability and limited healthcare services and infrastructure. The proposed research aims to identify and quantify those persistent bacteria in tuberculosis-infected mice before, during and after treatment to predict the outcome of human tuberculosis treatment. The persistent bacteria will be "woken" by the addition of resuscitation promoting factors (RPF) which are proteins produced by M. tuberculosis to restart growth. We have shown in our recent study in mice that high-dose rifampicin or bedaquiline which was added in the current treatment drug regimen was able to kill persistent RPF resuscitated bacteria. This meant that the treatment duration could be shortened with a reduced relapse rate. We also modify the Cornell model to better mimic human infection by enriching persistent bacteria and determining their clearance. In this proposal, we will apply the same principals and techniques which we have learned from studying mice using a set of novel drug regimens to predict the outcome of human tuberculosis treatment, especially disease relapse. We will establish a model system in mice as a testbed to evaluate the potencies of new drug regimens prior to their application in more expensive and time-consuming human testing.