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Dis Model Mech. 2015 Sep;8(9):1141-53. doi: 10.1242/dmm.020867. Epub 2015 Jul 23.

Lung necrosis and neutrophils reflect common pathways of susceptibility to Mycobacterium tuberculosis in genetically diverse, immune-competent mice.

Author information

1
Department of Biomedical Informatics, The Ohio State University, Columbus, 43210 OH, USA.
2
Department of Computer Science and Department of Electrical, Computer and Systems Engineering, Rensselaer Polytechnic Institute, Troy, 12810 NY, USA.
3
Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, Grafton, 01536 MA, USA.
4
The Jackson Laboratory, Bar Harbor, 04662 ME, USA.
5
Department of Medicine, Boston University School of Medicine, Boston, 02215 MA, USA.
6
Department of Infectious Disease and Global Health, Cummings School of Veterinary Medicine, Tufts University, Grafton, 01536 MA, USA gillian.beamer@tufts.edu.

Abstract

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. By 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 molecules from the lung: TNF, CXCL1, CXCL2, CXCL5, interferon-γ (IFN-γ), interleukin 12 (IL-12); and two molecules from blood - IL-2 and TNF - were identified as being important by applying both statistical and machine learning methods. Using molecular features to generate tree classifiers, CXCL1, CXCL2 and CXCL5 distinguished four classes (supersusceptible, susceptible, resistant and non-infected) from each other with approximately 77% accuracy using completely independent experimental data. By 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 levels of immunological cytokines best indicate a lack of exposure to M. tuberculosis but cannot distinguish infection from disease.

KEYWORDS:

CXCL1; CXCL2; CXCL5; DO; Diversity outbred; Machine learning; Mycobacterium tuberculosis; Necrosis; Neutrophils; Tuberculosis

PMID:
26204894
PMCID:
PMC4582107
DOI:
10.1242/dmm.020867
[Indexed for MEDLINE]
Free PMC Article

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