Food allergy is a potentially fatal disease affecting 8% of children and has become increasingly common in the past two decades. Despite the prevalence and severe nature of the disease, the mechanisms underlying sensitization remain to be further elucidated. The Collaborative Cross is a genetically diverse panel of inbred mice that were specifically developed to study the influence of genetics on complex diseases. Using this panel of mouse strains, we previously demonstrated CC027/GeniUnc mice, but not C3H/HeJ mice, develop peanut allergy after oral exposure to peanut in the absence of a Th2-skewing adjuvant. Here, we investigated factors associated with sensitization in CC027/GeniUnc mice following oral exposure to peanut, walnut, milk, or egg. CC027/GeniUnc mice mounted antigen-specific IgE responses to peanut, walnut and egg, but not milk, while C3H/HeJ mice were not sensitized to any antigen. Naïve CC027/GeniUnc mice had markedly lower total fecal IgA compared to C3H/HeJ, which was accompanied by stark differences in gut microbiome composition. Sensitized CC027/GeniUnc mice had significantly fewer CD3+ T cells but higher numbers of CXCR5+ B cells and T follicular helper cells in the mesenteric lymph nodes compared to C3H/HeJ mice, which is consistent with their relative immunoglobulin production. After oral challenge to the corresponding food, peanut- and walnut-sensitized CC027/GeniUnc mice experienced anaphylaxis, whereas mice exposed to milk and egg did not. Ara h 2 was detected in serum collected post-challenge from peanut-sensitized mice, indicating increased absorption of this allergen, while Bos d 5 and Gal d 2 were not detected in mice exposed to milk and egg, respectively. Machine learning on the change in gut microbiome composition as a result of food protein exposure identified a unique signature in CC027/GeniUnc mice that experienced anaphylaxis, including the depletion of Akkermansia. Overall, these results demonstrate several factors associated with enteral sensitization in CC027/GeniUnc mice, including diminished total fecal IgA, increased allergen absorption and altered gut microbiome composition. Furthermore, peanuts and tree nuts may have inherent properties distinct from milk and eggs that contribute to allergy.

Background: Numerous metagenomic studies aim to discover associations between the microbial composition of an environment (e.g. Gut, Skin, Oral) and a phenotype of interest. Multivariate analysis (MVA) is often performed in these studies without critical a priori knowledge of which taxa are associated with the phenotype being studied. Consequently, non-parametric MVA methods are applied directly to all taxa surveyed independent of noise. This approach typically reduces statistical power in settings where true associations among only a few taxa are obscured by high dimensionality (i.e. sparse association signals). At the same time, the inclusion of all taxa can confound the extraction of key biological insights. Further, low sample size and compositional sample space constraints exist in these data whereby beyond-study generalizability may be reduced if not properly accounted for. More powerful association tests that are interpretable and directly account for compositional constraints while detecting sparse association signals are needed.Methods: We developed Selection-Energy-Permutation (SelEnergyPerm), a non-parametric group association test with embedded feature selection. SelEnergyPerm directly accounts for compositional constraints by selecting parsimonious log ratio signatures from the set of all pairwise log ratios (PLR) between features (OTUs, taxa, etc.). To do this, network methods are used to rank, select, and maximize the between-group association of a candidate log ratio subset. This process is then repeated with an appropriate permutation testing design to simultaneously determine the significance of the selected signatures and association.Results: Simulation results show SelEnergyPerm selects small independent sets of log ratios that capture strong associations in a range of scenarios with small and large dimensional feature spaces. Additionally, our simulation results demonstrate SelEnergyPerm consistently detects/rejects associations in synthetic data with sparse, dense, or no association signals. We demonstrate the novel benefits of our method in four case studies utilizing publicly available 16S rRNA and whole-genome sequencing datasets.Conclusions: Tools to analyze complex high-dimensional metagenomic datasets with sparse association signals using robust PLR have not been sufficiently developed previously. We propose SelEnergyPerm, a novel framework for the discovery of phenotype-associated, metagenomic log ratio signatures for characterizing and understanding alterations in microbial community structure. SelEnergyPerm is implemented in R, available at https://github.com/andrew84830813/selEnergyPermR.

E-cigarettes are often perceived as safer than cigarettes, but previous research suggests that e-cigarettes can alter respiratory innate immune function. The respiratory microbiome plays a key role in respiratory host defense, but the effect of e-cigarettes on the respiratory microbiome has not been studied. Using 16S rRNA gene sequencing on nasal epithelial lining fluid samples from adult e-cigarette users, smokers, and nonsmokers, followed by novel computational analysis of pairwise log ratios, we determined that e-cigarette use and smoking causes differential respiratory microbiome dysbiosis, which was further affected by sex. We also collected nasal lavage fluid for analysis of immune mediators associated with host-microbiota interactions. Our analysis identified disruption of the relationships between host-microbiota mediators in the nose of e-cigarette users and smokers, which is indicative of disrupted respiratory mucosal immune responses. Our approach provides a novel platform that robustly identifies host immune dysfunction caused by e-cigarette use or smoking.

Background E-cigarettes are often perceived as safer than cigarettes, but previous research suggests that e-cigarettes can alter respiratory innate immune function. The respiratory microbiome plays a key role in respiratory host defense, but the effect of e-cigarettes on the respiratory microbiome has not been studied. Results Using 16S rRNA gene sequencing on nasal epithelial lining fluid samples from adult e-cigarette users, smokers, and nonsmokers, followed by novel computational analysis of pairwise log ratios, we determined that e-cigarette use and smoking causes differential respiratory microbiome dysbiosis, which was further affected by sex. We also collected nasal lavage fluid for analysis of immune mediators associated with host-microbiota interactions. Our analysis identified disruption of the relationships between host-microbiota mediators in the nose of e-cigarette users and smokers, which is indicative of disrupted respiratory mucosal immune responses. Conclusions Our data indicate that e-cigarette use, cigarette use, and sex modify the nasal microbiome and nasal host-microbiota interactions. Our approach also provides a novel platform that robustly identifies host immune dysfunction caused by e-cigarette use or smoking.

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