Microglial roles in Alzheimer's disease: An agent-based model to elucidate microglial spatiotemporal response to beta-amyloid.

Alzheimer's disease (AD) is characterized by beta-amyloid (Aß) plaques in the brain and widespread neuronal damage. Because of the high drug attrition rates in AD, there is increased interest in characterizing neuroimmune responses to Aß plaques. In response to AD pathology, microglia are innate phagocytotic immune cells that transition into a neuroprotective state and form barriers around plaques. We seek to understand the role of microglia in modifying Aß dynamics and barrier formation. To quantify the influence of individual microglia behaviors (activation, chemotaxis, phagocytosis, and proliferation) on plaque size and barrier coverage, we developed an agent-based model to characterize the spatiotemporal interactions between microglia and Aß. Our model qualitatively reproduces mouse data trends where the fraction of microglia coverage decreases as plaques become larger. In our model, the time to microglial arrival at the plaque boundary is significantly negatively correlated (p < 0.0001) with plaque size, indicating the importance of the time to microglial activation for regulating plaque size. In addition, in silico behavioral knockout simulations show that phagocytosis knockouts have the strongest impact on plaque size, but modest impacts on microglial coverage and activation. In contrast, the chemotaxis knockouts had a strong impact on microglial coverage with a more modest impact on plaque volume and microglial activation. These simulations suggest that phagocytosis, chemotaxis, and replication of activated microglia have complex impacts on plaque volume and coverage, whereas microglial activation remains fairly robust to perturbations of these functions. Thus, our work provides insights into the potential and limitations of targeting microglial activation as a pharmacological strategy for the treatment of AD.

Reduced macrophage killing of M. avium drives infection risk in post-menopausal patients.

Non-tuberculous mycobacterial (NTM) infections, and Mycobacterium avium Complex (MAC) in particular, affect women at nearly twice the rate of men, and post-menopausal patients are at higher risk than pre-menopausal patients. The reasons for the disproportionate number of cases in women and post-menopausal patients remain unclear. One possibility is that menopause-associated immunological changes contribute to higher MAC prevalence post-menopause compared to pre-menopause. Menopause-associated immune disruption includes increased cytokine and chemokine production, and reduced cytotoxicity and phagocytosis in macrophages. Here we use an agent-based model of bacterial and host immune interactions in the airway to translate the combined impact of menopause-associated cellular immune disruptions to tissue scale outcomes. Our simulations indicate that menopause-associated immune disruptions can result in increased macrophage recruitment. However, this increase in macrophage number is unable to overcome functional deficits in macrophage phagocytosis and killing, since the post-menopausal simulations also show increased bacterial loads. Post-menopausal conditions are also associated with a lower number of cleared infections, and more simulations that have predominantly extracellular bacteria. Taken together, our work quantifies the potential impact of menopause-associated disruptions of innate immune functions on early MAC infection progression. Our findings will support the development of new therapies targeted to this high-risk group of patients.

Agent-based model indicates chemoattractant signaling caused by Mycobacterium avium biofilms in the lung airway increases bacterial loads by spatially diverting macrophages.

Incidence and prevalence of MAC infections are increasing globally, and reinfection is common. Thus, MAC infections present a significant public health challenge. 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. In this computational work we develop an agent-based model that incorporates the interactions between bacteria, biofilm, and immune cells. In this computational model, we perform virtual knockouts to quantify the effects of the biofilm sources (deposited with bacteria vs. formed in the airway), and their impacts on macrophages (inducing apoptosis and slowing phagocytosis). We also quantify the effects of repeated bacterial exposures to assess their impact on infection progression. Our simulations show that chemoattractants 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 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.

The Role of Biofilms, Bacterial Phenotypes, and Innate Immune Response in Mycobacterium avium Colonization to Infection.

Mycobacterium avium complex (MAC), is known for colonizing and infecting humans following inhalation of the bacteria. MAC pulmonary disease is notoriously difficult to treat and prone to recurrence. Both the incidence and prevalence MAC pulmonary disease have been increasing globally. MAC is well known to form biofilms in the environment. In vitro, these biofilms have been shown to aid MAC in epithelial cell invasion, protect MAC from phagocytosis, and cause premature apoptosis in macrophages. In vivo, the system of interactions between MAC, biofilms and host macrophages is complex, difficult to replicate in vitro and in animal models, has not been fully characterized. Here we present a three-dimensional agent-based model of a lung airway to help understand how these interactions evolve in the first 14 days post-bacterial inhalation. We parameterized the model using published data and performed uncertainty analysis to characterize outcomes and parameters' effects on those outcomes. Model results show diverse outcomes, including wide ranges of macrophage recruitment levels, and bacterial loads and phenotype distribution. Though most bacteria are phagocytosed by macrophages and remain intracellular, there are also many simulations in which extracellular bacteria continue to drive the colonization and infection. Initial parameters dictating host immune levels, bacterial loads introduced to the airway, and biofilm conditions have significant and lasting impacts on the course of these results. Additionally, though macrophage recruitment is key for suppressing bacterial loads, there is evidence of significant excess recruitment that fail to impact bacterial numbers. These results highlight a need and identify a path for further exploration into the inhalation events in MAC infection. Early infection dynamics could have lasting impacts on the development of nodular bronchiectatic or fibrocavitary disease as well as inform possible preventative and treatment intervention targeting biofilm-macrophage interactions.