Signaling dynamics and cellular context as drivers of the heterogeneity of macrophage infection by M. tuberculosis

Dana-Farber Cancer Inst, Boston MA

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Abstract

Project Summary Tuberculosis (TB), caused by infection with Mycobacterium tuberculosis (Mtb), is a devastating disease that kills 1.4 million and infects 10.4 million people per year. Cases of antibiotic resistance are on the rise, creating a need for new treatment options. Most individuals can contain the infection without medical intervention, but what are the differences in the immune response between those that succeed and those that fail to fight TB? Interestingly, there is significant variability in disease progression and outcome within individuals and among single host macrophage cells. The goal of this project is to study the impact of differences in NF-?B activation dynamics, a critical innate immune signaling pathway, as well as activation of additional innate immune signaling pathways on infection outcome at the single cell level to better understand sources of variability. Variability in the NF-?B signaling pathway leads to different transcriptional responses and cell fate decisions in every cell type where NF-?B has been studied at the single-cell level. In this project, we will test the hypothesis that differences in NF-?B activation determine the ability of individual macrophages to control Mtb infection. Live imaging of macrophages expressing fluorescent protein-NF-?B fusion proteins during Mtb infection will be used to create a dataset of NF-?B activation dynamics and infection outcome of individual cells. From this dataset, the dynamics of activation that are most beneficial for decreasing bacteria burden and activating bacteria killing mechanisms will be determined. A possible source of variability of NF-?B activation is differences in the initial state of the signaling pathway, such as relative abundance of inhibitor and activator proteins. Sensitivity analysis will be performed using a mass-action kinetics model of the NF-?B pathway, adapted to macrophage infection data, to determine features of the initial state most likely to explain variability in the NF-?B response. Key findings will then be tested experimentally by perturbing NF-?B signaling and observing if changes in bacteria burden and killing match model predictions. However, NF-?B alone is unlikely to explain all differences in macrophage outcome as additional innate immune pathways also have important roles during Mtb infection. To investigate the contribution of additional pathways, a highly multiplexed immunofluorescence imaging technique, Cyclic IF (CycIF), will be used to quantify activation of many key innate immune pathways as well as multiple markers of bacteria killing activity from within the same cell. Regression analysis will be performed on data collected at 7 time points during the first 72 hours of infection to determine the degree to which each pathway explains variability in outcome, identify potential instances of cross-talk between pathways, and build a predictive model of individual cell Mtb infection outcome. In sum, by adapting single-cell and mathematical modeling approaches to Mtb infection of macrophages, this project will provide add to our understanding of regulatory mechanisms driving infection susceptibility in macrophages and yield fundamental information to aid future development of new TB therapeutic strategies.