Introduction: While reliable  in vitro testing for sensitivity to common aeroallergens has been available for decades, if and how asthma might be predictably expressed in people matched for comparable multiple sensitizations is unknown.   Objective: Our aim is to develop an understanding of these relations, which are known as allergic poly-sensitizations (APS) using a machine learning approach. We performed an audit of adult urban patients with moderate to severe asthma who presented to an urban outpatient pulmonary clinic. Methods: We constructed a database of sensitizations to the 25 aeroallergens in the zone 1 ImmunoCAP® assay. We used the Scikit-Learn® machine learning library to perform model-based clustering to identify APS clusters. Subsequently, clusters were compared for differences in clinical markers of allergic asthma.  Results: The database consisted of 509 patients. Mixture modeling identified ten clusters of increasing APS of varying size (n = 1 to 339). There were significant increases in mean serum immunoglobulin E (p<.001), peripheral blood eosinophil count (p<.001), and D LCO (p=.02) with increasing APS. There was a significant decline in mean age at presentation (p<.001), FEV 1/FVC (p=.01), and FEF 25-75 (p=.002), but not FEV 1 (p=.29), nor RV/TLC (p=.14) with increasing APS by simple linear regression. Finally, we identified two apparent divergent paths for the poly-atopic march, one driven by perennial allergens and the other by seasonal allergens.  Conclusion: We provide the framework for a novel machine learning understanding and approach to the classification of APS and its heretofore under-appreciated potential influences on asthma cluster analyses. To our knowledge, this represents the first attempt to identify poly-sensitization patterns that have clinical implications.

Background: Mast cells express multiple metabotropic purinergic receptor (P2YR) subtypes, however, only few studies have evaluated their role in human mast cells (HMC) allergic response as measured by degranulation resulting from FcεRI-activation. We have previously shown that extracellular nucleotides modify the FcεRI-activation-dependent degranulation in HMC derived from human lungs, but the mechanism of this action has not been fully delineated. The present study was undertaken to determine the mechanism of P2YR’s activation on HMC’s degranulation and elucidate the specific post-receptor mechanistic steps/pathways involved. Methods: Sensitized LAD2 cells, a human derived mast cell line, were subjected to a weak allergic stimulation (WAS) using a low concentration of antigen in the absence and presence of the P2Y11R agonist, ATPγS. Results: In the presence of ATPγS, WAS-induced degranulation was enhanced by 7-fold (N = 4, p < 0.01). None of the other P2YR agonists tested, including high concentrations of ATPγS (1000 μM), enhanced WAS-induced intracellular Ca2+ mobilization, which is an important component of degranulation. Both a phosphoinositide 3-kinase (PI3K) inhibitor and the relevant gene knockout decreased the ATPγS-induced enhancement s of degranulation. ATPγS’ effect was associated with enhanced phosphorylation of PI3K type δ (PI3K(δ)) and protein kinase B (Akt), but not the phosphoinositide-dependent kinase-1 (PDK-1). The effects of ATPγS were dose dependently inhibited by NF157, a P2Y11R antagonist. Conclusion: We determined for the first time that at least one subtype of P2YR, i.e., P2Y11R is linked to enhancement of allergic degranulation in HMC independent of [Ca2+]i mobilization.