Tuberculosis survivors face a six-fold higher risk of death than the general population, partly because up to half sustain permanent lung damage from their own immune response. The problem is twofold. First, TB requires six months of treatment because some bacteria become drug-tolerant, and patients often stop early, leading to recurrence and drug resistance. Second, the body's own defences—the same ones that drive drug tolerance—can scar the lungs irreversibly, a condition called post-tuberculosis lung disease (PTLD) for which no treatments exist. Hundreds of genes are involved, but researchers don't know which combinations to target, and most evidence comes from animal models, not human patients. This project will profile the gene activity of individual airway cells from TB patients, then use machine learning to simulate which genes—or combinations—should be switched on or off to clear bacteria faster and prevent lung damage. The most promising compounds will be tested in a human cell infection model. If successful, this could shorten TB treatment from six months to weeks, reduce drug resistance, and give survivors healthy lungs for life. The work is fundamental science: it identifies which human molecular pathways actually matter in natural infection, a necessary step before any therapy can be designed.
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Tuberculosis (TB) remains one of the leading infectious causes of death. At present, the World Health Organization (WHO) recommends treating drug susceptible pulmonary TB (pTB) with a two-month course of Rifampicin, Isoniazid, Pyrazinamide and Ethambutol (RHZE), followed by a four-month course of Rifampicin and Isoniazid (RH). Some patients are unable to adhere to this protracted treatment and discontinue treatment prematurely. This often results in TB recurrence and development of drug resistance. TB requires protracted treatment because Mycobacterium tuberculosis (Mtb), the causative agents for TB, exists as a heterogenous population of bacilli, a fraction of which are tolerant to anti-TB agents. New TB treatment regimens that are more effective against drug-tolerant Mtb would therefore help improve outcomes of TB treatment. While more than 800,000 people are successfully cured of TB every year, all-cause mortality rates are 6 times higher in TB survivors than in the general population. This is in-part because up to half of pTB survivors sustain severe lung damage and develop post-tuberculosis lung disease (PTLD) . At present, there are no interventions for preventing or managing PTLD. PTLD is largely caused by host responses to Mtb. Anti-Mtb host responses also promote Mtb drug tolerance. Host-directed therapies could therefore help alleviate both PTLD and Mtb drug tolerance. More than a hundred host molecular pathways, and even more genes, have been implicated in the evolution of PTLD and Mtb drug tolerance. It remains unclear which of these genes, or combinations of genes, should be targeted to reduce Mtb drug tolerance and/or PTLD. While single-gene-knockout experiments can be performed relatively easily, it is difficult to simultaneously knockout multiple genes to identify the ideal combination of genes to target to reduce Mtb drug tolerance or PTLD, given the myriad possibilities. Further, as most of the genes and pathways have been identified from animal models and in-vitro experiments, their relevance in natural human Mtb infections remains unclear. We, therefore, propose to leverage 1) single-cell transcriptomics of lung airway cells from pTB patients, 2) functional assessment of lung injury, and 3) sputum microbiologic assessment to identify the host cell types and molecular pathways associated with Mtb drug tolerance and PTLD. We will then leverage computational biology and machine learning to perform in-silico knock-up and knock-down experiments to hasten identification of single or combination host-directed therapeutics for reversing host transcriptomic perturbations associated with Mtb drug tolerance and PTLD. Finally, we will test the predicted compounds in an ex-vivo Mtb-human alveolar macrophage infection model.
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