ABSTRACT
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 is often performed in these studies without critical a priori knowledge of which taxa are associated with the phenotype being studied. This approach typically reduces statistical power in settings where the true associations among only a few taxa are obscured by high dimensionality (i.e., sparse association signals). At the same time, low sample size and compositional sample space constraints may reduce beyond-study generalizability if not properly accounted for. To address these difficulties, we developed the Selection-Energy-Permutation (SelEnergyPerm) method, a nonparametric group association test with embedded feature selection that directly accounts for compositional constraints using parsimonious logratio signatures between taxonomic features, for characterizing and understanding alterations in microbial community structure. Simulation results show SelEnergyPerm selects small independent sets of logratios that capture strong associations in a range of scenarios. 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 amplicon and whole-genome sequencing datasets. Our R implementation of Selection-Energy-Permutation, including an example demonstration and the code to generate all of the scenarios used here, is available at https://www.github.com/andrew84830813/selEnergyPermR.
ABSTRACT
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.
