Evaluating regimen efficacy using transcriptomic patterns of Mycobacterium tuberculosis injury and adaptation: high dimensional pharmacodynamics as a steppingstone to rational regimen design. - PROJECT SUMMARY/ABSTRACT Tuberculosis (TB) is the leading cause of death from infection globally. Standard TB therapies require at least 4- 6 months of treatment with a combination regimen of 3-4 antibiotics. There is urgent need for regimens capable of curing TB more quickly; however, determining the most efficacious combination regimens is stymied by the large number of potential antibiotic combinations, which cannot all be fully evaluated. A key impediment to identifying the most promising 3-4 drug regimens for more in depth testing is that crude culture-based measures of bacterial burden are used to evaluate drug effect during in vitro and murine preclinical testing, and these measures of burden often fail to distinguish between regimens with different time to cure. To meet the need for more reliable preclinical measures of efficacy, we propose to evaluate the effect of regimens on bacterial physiology rather than burden. We recently developed SEARCH-TB, a Mycobacterium tuberculosis targeted RNA-seq platform uniquely capable of quantifying drug-induced injury and compensatory physiologic adaptations during prolonged treatment of mice. Preliminary SEARCH-TB results suggest effective regimens cause a shared bacterial “injury pattern” that may be associated with the time a regimen requires to achieve non- relapsing cure, leading to our central hypothesis that regimens that more rapidly and profoundly induce a common injury cascade shorten time to non-relapsing cure. To evaluate this hypothesis, we will harness RNA- preserved murine and in vitro samples from 12 unique regimens already collected by co-investigators in the Consortium for Applied Microbial Metrics. These regimens were selected to span a broad range of time to cure 95% mice (T95 ranging from 39 to >180 days), enabling linkage of transcriptional injury patterns with treatment- shortening activity. In Aim 1, we will perform SEARCH-TB transcriptional profiling on preserved murine tissue samples for these regimens at 1 and 2 weeks after treatment initiation. Differential expression, correlation, and network analyses will be used to determine the shared transcriptional injury pattern induced by effective regimens and to test whether transcriptional injury predicts time required for cure in the BALB/c mouse. In Aim 2, we will perform SEARCH-TB profiling on in vitro samples from the same regimens to evaluate the correspondence between in vivo and in vitro transcriptional injury patterns and determine whether in vitro transcriptional injury can also be used to identify regimens with shorter time to cure in the BALB/c mouse. This knowledge will help guide rational selection of drug combinations to build more effective regimens by (1) providing a basis for predicting treatment shortening from in vitro results and (2) vastly expanding the information yield of in vivo evaluation while enabling much shorter, smaller mouse studies.
