Recently, I co-first authored a paper with several members of my two labs (I am co-mentored by both Dr. Denise Kirschner and Dr. Jennifer Linderman). In this work, which was published in PLoS Computational Biology, we present a computational, multi-scale model of granuloma formation and dissemination within monkey lungs. In Mycobacterium tuberculosis infection, granulomas are spherical structures in the lungs, that form as part of immune response to surround and suppress infection. However, sometimes bacteria escape the granuloma environment and disseminate. Broadly defined, dissemination is the spread of bacteria throughout the lungs, and is associated with much poorer outcomes for the infected individual. At the moment, dissemination is very difficult to address experimentally, and it is not known what causes dissemination. Thus, we built a computational model to probe questions about dissemination.
We named this model MultiGran, as it tracks the evolution of cells, cytokines, and bacterial populations within multiple granulomas throughout a simulated Mycobacterium tuberculosis infection. The model matches multiple experimental datasets across the cellular, granuloma, and whole-lung scales of monkey infection. These datasets were provided by our co-authors from the Flynn Lab in Pittsburgh, world-wide leaders in tuberculosis research. Importantly, these are unique and critical datasets that present new insights into early events occurring during Mtb infection.
After validating our model, we use MultiGran to make two key predictions about dissemination events within monkey lungs during infection.
First, we predict that, in a typical infection, local dissemination is much more likely than non-local dissemination. In fact, we report that the likelihood of local dissemination is approximately two times greater than non-local dissemination. Non-local dissemination is thought to spread through either the lymphatics system or through airways in the lung, thus this type of dissemination can be much worse for the outcome of an individual.
Second, we predict a critical role for multi-functional CD8+ T cells in preventing dissemination. If, within a granuloma, a greater percentage of CD8+ T cells can perform dual functions of cytokine expression and cytotoxicity, then the likelihood of that granuloma disseminating significantly decreases. While the role of CD8+ T cells is being more appreciated in the tuberculosis research literature, this finding suggests a need for an even more increased focus on this specific cell type to evaluate the therapeutic potential that CD8+ multifunctional cells may offer.
Moving forward, we have many more questions that we could address with this model framework, so be on the lookout for coming research papers featuring MultiGran!
Louis R. Joslyn 