Particulate allergens potentiate allergic asthma in mice through sustained IgE-mediated mast cell activation.

Allergic asthma is characterized by airway hyperresponsiveness, inflammation, and a cellular infiltrate dominated by eosinophils. Numerous epidemiological studies have related the exacerbation of allergic asthma with an increase in ambient inhalable particulate matter from air pollutants. This is because inhalable particles efficiently deliver airborne allergens deep into the airways, where they can aggravate allergic asthma symptoms. However, the cellular mechanisms by which inhalable particulate allergens (pAgs) potentiate asthmatic symptoms remain unknown, in part because most in vivo and in vitro studies exploring the pathogenesis of allergic asthma use soluble allergens (sAgs). Using a mouse model of allergic asthma, we found that, compared with their sAg counterparts, pAgs triggered markedly heightened airway hyperresponsiveness and pulmonary eosinophilia in allergen-sensitized mice. Mast cells (MCs) were implicated in this divergent response, as the differences in airway inflammatory responses provoked by the physical nature of the allergens were attenuated in MC-deficient mice. The pAgs were found to mediate MC-dependent responses by enhancing retention of pAg/IgE/FcεRI complexes within lipid raft–enriched, CD63(+) endocytic compartments, which prolonged IgE/FcεRI-initiated signaling and resulted in heightened cytokine responses. These results reveal how the physical attributes of allergens can co-opt MC endocytic circuitry and signaling responses to aggravate pathological responses of allergic asthma in mice.

A Dynamic Directional Model for Effective Brain Connectivity using Electrocorticographic (ECoG) Time Series.

We introduce a dynamic directional model (DDM) for studying brain effective connectivity based on intracranial electrocorticographic (ECoG) time series. The DDM consists of two parts: a set of differential equations describing neuronal activity of brain components (state equations), and observation equations linking the underlying neuronal states to observed data. When applied to functional MRI or EEG data, DDMs usually have complex formulations and thus can accommodate only a few regions, due to limitations in spatial resolution and/or temporal resolution of these imaging modalities. In contrast, we formulate our model in the context of ECoG data. The combined high temporal and spatial resolution of ECoG data result in a much simpler DDM, allowing investigation of complex connections between many regions. To identify functionally segregated sub-networks, a form of biologically economical brain networks, we propose the Potts model for the DDM parameters. The neuronal states of brain components are represented by cubic spline bases and the parameters are estimated by minimizing a log-likelihood criterion that combines the state and observation equations. The Potts model is converted to the Potts penalty in the penalized regression approach to achieve sparsity in parameter estimation, for which a fast iterative algorithm is developed. The methods are applied to an auditory ECoG dataset.