bioRxivSystems BiologyPreprint
Chemokine Signaling Caused by Mycobacterium avium Biofilms in the Lung Airway Increases Bacterial Loads by Spatially Diverting Macrophages
Catherine WeatherediD, Kelly M. PenningtoniD, Patricio EscalanteiD, Elsje PienaariD
Abstract
Mycobacterium avium Complex (MAC) are ubiquitous environmental biofilm-forming microbes that can colonize and infect patient lungs. Incidence and prevalence of MAC infections are increasing globally, and reinfection is common. Thus, MAC infections present a significant public health challenge. MAC infections are notoriously difficult to treat and there is an urgent need for MAC-targeted therapeutics. To identify potential drug targets, we quantify the impact of MAC biofilms and repeated exposure on infection progression using a computational model of MAC infection in lung airways. MAC biofilms aid epithelial cell invasion, cause premature macrophage apoptosis, and limit antibiotic efficacy. We develop an agent-based model that incorporates the interactions between bacteria, biofilm and immune cells. We perform virtual knockouts to quantify the effects of the sources of biofilm (biofilm simultaneously deposited with bacteria vs. formed in the airway after initial bacterial deposition), and their effects on macrophages (inducing apoptosis and slowing phagocytosis). We also quantify the effects of repeated bacterial exposure to assess the impact of reinfection on infection progression. Our results show that chemokines released by biofilm-induced apoptosis bias macrophage chemotaxis towards pockets of infected and apoptosed macrophages. This bias results in fewer macrophages finding extracellular bacteria, allowing the extracellular planktonic bacteria to replicate freely. These spatial macrophage trends are further exacerbated with repeated deposition of bacteria. Our model indicates that interventions to either abrogate macrophages apoptotic responses to bacterial biofilms and/or reduce frequency of patient exposure to bacteria will lower bacterial load, and likely overall risk of infection.
- research focus: benchmarking
- research focus: software / tool
- research focus: drug response / drug discovery
- approach: statistical model
- approach: simulation
- Citations
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