Improved mouse models of the small intestine microbiota using region-specific sampling from humans.

Our understanding of region-specific microbial function within the gut is limited due to reliance on stool. Using a recently developed capsule device, we exploit regional sampling from the human intestines to develop models for interrogating small intestine (SI) microbiota composition and function. In vitro culturing of human intestinal contents produced stable, representative communities that robustly colonize the SI of germ-free mice. During mouse colonization, the combination of SI and stool microbes altered gut microbiota composition, functional capacity, and response to diet, resulting in increased diversity and reproducibility of SI colonization relative to stool microbes alone. Using a diverse strain library representative of the human SI microbiota, we constructed defined communities with taxa that largely exhibited the expected regional preferences. Response to a fiber-deficient diet was region-specific and reflected strain-specific fiber-processing and host mucus-degrading capabilities, suggesting that dietary fiber is critical for maintaining SI microbiota homeostasis. These tools should advance mechanistic modeling of the human SI microbiota and its role in disease and dietary responses.

Combining avidin with CD63 improves basophil activation test accuracy in classifying peanut allergy.

BACKGROUND: Conventional basophil activation tests (BATs) measure basophil activation by the increased expression of CD63. Previously, fluorophore-labeled avidin, a positively-charged molecule, was found to bind to activated basophils, which tend to expose negatively charged granule constituents during degranulation. This study further compares avidin versus CD63 as basophil activation biomarkers in classifying peanut allergy. METHODS: Seventy subjects with either a peanut allergy (N = 47), a food allergy other than peanut (N = 6), or no food allergy (N = 17) were evaluated. We conducted BATs in response to seven peanut extract (PE) concentrations (0.01-10,000 ng/mL) and four control conditions (no stimulant, anti-IgE, fMLP (N-formylmethionine-leucyl-phenylalanine), and anti-FcεRI). We measured avidin binding and CD63 expression on basophils with flow cytometry. We evaluated logistic regression and XGBoost models for peanut allergy classification and feature identification. RESULTS: Avidin binding was correlated with CD63 expression. Both markers discriminated between subjects with and without a peanut allergy. Although small by percentage, an avidin+ /CD63- cell subset was found in all allergic subjects tested, indicating that the combination of avidin and CD63 could allow a more comprehensive identification of activated basophils. Indeed, we obtained the best classification accuracy (97.8% sensitivity, 96.7% specificity) by combining avidin and CD63 across seven PE doses. Similar accuracy was obtained by combining PE dose of 10,000 ng/mL for avidin and PE doses of 10 and 100 ng/mL for CD63. CONCLUSIONS: Avidin and CD63 are reliable BAT activation markers associated with degranulation. Their combination enhances the identification of activated basophils and improves the classification accuracy of peanut allergy.