Pulmonary tuberculosis (TB) is caused by Mycobacterium tuberculosis in susceptible humans. Here, we infected Diversity Outbred (DO) mice with ∼100 bacilli by aerosol to model responses in a highly heterogeneous population. Following infection, Supersusceptible, Susceptible, and Resistant phenotypes emerged. TB disease (reduced survival, weight loss, high bacterial load) correlated strongly with neutrophils, neutrophil chemokines, Tumor Necrosis Factor (TNF) and cell death. In contrast, immune cytokines were weak correlates of disease. We next applied statistical and machine learning approaches to our dataset of cytokines and chemokines from lungs and blood. Six lung molecules: TNF, CXCL1, CXCL2, CXCL5, Interferon-γ (IFN-γ), Interleukin (IL)-12; and two blood molecules IL-2 and TNF, were identified as important by both statistical and machine learning methods. Using molecular features to generate tree classifiers, CXCL1, CXCL2, and CXCL5 discriminated four classes (Supersusceptible, Susceptible, Resistant, and Non-infected) from each other with approximately 77% accuracy using completely independent experimental data. In contrast, models based on other molecules were less accurate. Low to no IFN-γ, IL-12, IL-2, and IL-10 successfully discriminated Non-infected mice from infected mice, but failed to discriminate disease status amongst Supersusceptible, Susceptible, and Resistant M. tuberculosis infected DO mice. Additional analyses identified CXCL1 as a promising peripheral biomarker of disease and of CXCL1 production in the lungs. From these results, we conclude that: 1) DO mice respond variably to M. tuberculosis infection and will be useful to identify pathways involving necrosis and neutrophils; 2) Data from DO mice is suited for machine learning methods to build, validate, and test models with independent data based solely on molecular biomarkers; 3) Low immunological cytokines best indicate no exposure to M. tuberculosis but cannot distinguish infection from disease.
- Received March 23, 2015.
- Accepted July 16, 2015.
- © 2015. Published by The Company of Biologists Ltd
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