Factors influencing age of common allergen introduction in early childhood

ObjectivesWe evaluated factors influencing the timing of allergen introduction in the U.S., including updated peanut introduction guidelines.Study designThe Gastrointestinal Microbiome and Allergic Proctocolitis (GMAP) study is a prospective observational cohort in suburban Massachusetts. Infants' caregivers enrolled between 2014 and 2017, and they reported when they introduced common allergens to their child. Multivariable linear and survival regression analyses were used to examine factors influencing time of introduction of allergens.ResultsBy 9 months, children old enough to be potentially affected by NIAID's 2017 peanut introduction guidelines were more often introduced to peanut than children enrolled well before guidelines publication [54% vs. 42%, OR: 1.63, CI: (1.03, 2.57), P = 0.03]. At any given time, Black children were 73% [HR: 0.27, CI: (0.11, 0.69), P = 0.006] less likely to be introduced to peanut as early as White children. Asian children were, respectively, 36% [HR: 0.64, CI: (0.47, 0.86), P = 0.003] and 26% [HR: 0.74, CI: (0.55, 0.97), P = 0.03] less likely to be introduced to peanut and egg as early as White children. A first child was 27% [HR: 1.27, CI: (1.04, 1.56), P = 0.02] more likely to have been introduced to peanut earlier than a non-first child. There was no association between age of introduction and sex, gestational age, family history of food allergy, or other allergic comorbidities.ConclusionUpdated introduction guidelines, race, and birth order all influenced earlier introduction of peanut. Further studies to evaluate current practices for allergen introduction with a focus on potential disparities are needed

Novel baseline predictors of adverse events during oral immunotherapy in children with peanut allergy

Though peanut oral immunotherapy (OIT) is a promising investigational therapy, its potential is limited by substantial adverse events (AEs), which are relatively understudied

Identification of antigen-specific TCR sequences based on biological and statistical enrichment in unselected individuals

Recent advances in high-throughput T cell receptor (TCR) sequencing have allowed for new insights into the human TCR repertoire. However, methods for capturing antigen-specific repertoires remain an area of development. Here, we describe a potentially novel approach that utilizes both a biological and statistical enrichment to define putatively antigen-specific complementarity-determining region 3 (CDR3) repertoires in unselected individuals. The biological enrichment entailed FACS of in vitro antigen-activated memory CD4+ T cells, followed by TCRβ sequencing. The resulting TCRβ sequences were then filtered by selecting those that are statistically enriched when compared with their frequency in the autologous resting T cell compartment. Applying this method to define putatively peanut protein-specific repertoires in 27 peanut-allergic individuals resulted in a library of 7345 unique CDR3β amino acid sequences that had similar characteristics to other validated antigen-specific repertoires in terms of homology and diversity. In-depth analysis of these CDR3βs revealed 36 public sequences that demonstrated high levels of convergent recombination. In a network analysis, the public CDR3βs were shown to be core sequences with more edges than their private counterparts. This method has the potential to be applied to a wide range of T cell-mediated disorders and to yield new biomarkers and biological insights

Expansion of the CD4+ effector T-cell repertoire characterizes peanut-allergic patients with heightened clinical sensitivity

© 2019 American Academy of Allergy, Asthma & Immunology Background: Individuals with peanut allergy range in clinical sensitivity: some can consume grams of peanut before experiencing any symptoms, whereas others suffer systemic reactions to 10 mg or less. Current diagnostic testing only partially predicts this clinical heterogeneity. Objective: We sought to identify characteristics of the peanut-specific CD4+ T-cell response in peanut-allergic patients that correlate with high clinical sensitivity. Methods: We studied the T-cell receptor β-chain (TCRβ) usage and phenotypes of peanut-activated, CD154+ CD4+ memory T cells using fluorescence-activated cell sorting, TCRβ sequencing, and RNA-Seq, in reactive and hyporeactive patients who were stratified by clinical sensitivity. Results: TCRβ analysis of the CD154+ and CD154− fractions revealed more than 6000 complementarity determining region 3 sequences and motifs that were significantly enriched in the activated cells and 17% of the sequences were shared between peanut-allergic individuals, suggesting strong convergent selection of peanut-specific clones. These clones were more numerous among the reactive patients, and this expansion was identified within effector, but not regulatory T-cell populations. The transcriptional profile of CD154+ T cells in the reactive group skewed toward a polarized TH2 effector phenotype, and expression of TH2 cytokines strongly correlated with peanut-specific IgE levels. There were, however, also non–TH2-related differences in phenotype. Furthermore, the ratio of peanut-specific clones in the effector versus regulatory T-cell compartment, which distinguished the clinical groups, was independent of specific IgE concentration. Conclusions: Expansion of the peanut-specific effector T-cell repertoire is correlated with clinical sensitivity, and this observation may be useful to inform our assessment of disease phenotype and to monitor disease longitudinally

Partial Least Squares Discriminant Analysis and Bayesian Networks for Metabolomic Prediction of Childhood Asthma

To explore novel methods for the analysis of metabolomics data, we compared the ability of Partial Least Squares Discriminant Analysis (PLS-DA) and Bayesian networks (BN) to build predictive plasma metabolite models of age three asthma status in 411 three year olds (n = 59 cases and 352 controls) from the Vitamin D Antenatal Asthma Reduction Trial (VDAART) study. The standard PLS-DA approach had impressive accuracy for the prediction of age three asthma with an Area Under the Curve Convex Hull (AUCCH) of 81%. However, a permutation test indicated the possibility of overfitting. In contrast, a predictive Bayesian network including 42 metabolites had a significantly higher AUCCH of 92.1% (p for difference < 0.001), with no evidence that this accuracy was due to overfitting. Both models provided biologically informative insights into asthma; in particular, a role for dysregulated arginine metabolism and several exogenous metabolites that deserve further investigation as potential causative agents. As the BN model outperformed the PLS-DA model in both accuracy and decreased risk of overfitting, it may therefore represent a viable alternative to typical analytical approaches for the investigation of metabolomics data

Additional file 3 of Longitudinal disease-associated gut microbiome differences in infants with food protein-induced allergic proctocolitis

Additional file 3: Supplemental Table 2. Summary table of all significant MaAsLin results. All MaAsLin results from 16S rRNA gene sequencing with q-values for significant taxa which met our predetermined threshold for significance (q<0.20). Mixed effects linear models using arcsine transform on relative abundances were used to determine significance. p-values were adjusted for multiple comparisons using the Benjamini-Hochberg false discovery rate (FDR) method with FDR set at <0.20. (XLS 224 kb

An Integrative Transcriptomic and Metabolomic Study of Lung Function in Children With Asthma

Background: Single omic analyses have provided some insight into the basis of lung function in children with asthma, but the underlying biologic pathways are still poorly understood. / Methods: Weighted gene coexpression network analysis (WGCNA) was used to identify modules of coregulated gene transcripts and metabolites in blood among 325 children with asthma from the Genetic Epidemiology of Asthma in Costa Rica study. The biology of modules associated with lung function as measured by FEV1, the FEV1/FVC ratio, bronchodilator response, and airway responsiveness to methacholine was explored. Significantly correlated gene-metabolite module pairs were then identified, and their constituent features were analyzed for biologic pathway enrichments. / Results: WGCNA clustered 25,060 gene probes and 8,185 metabolite features into eight gene modules and eight metabolite modules, where four and six, respectively, were associated with lung function (P ≤ .05). The gene modules were enriched for immune, mitotic, and metabolic processes and asthma-associated microRNA targets. The metabolite modules were enriched for lipid and amino acid metabolism. Integration of correlated gene-metabolite modules expanded the single omic findings, linking the FEV1/FVC ratio with ORMDL3 and dysregulated lipid metabolism. This finding was replicated in an independent population. / Conclusions: The results of this hypothesis-generating study suggest a mechanistic basis for multiple asthma genes, including ORMDL3, and a role for lipid metabolism. They demonstrate that integrating multiple omic technologies may provide a more informative picture of asthmatic lung function biology than single omic analyses

Additional file 2 of Longitudinal disease-associated gut microbiome differences in infants with food protein-induced allergic proctocolitis

Additional file 2: Supplemental Table 1. Demographics. Demographics of the infants from the GMAP cohort selected for this nested case-control microbiome study

Additional file 1 of Longitudinal disease-associated gut microbiome differences in infants with food protein-induced allergic proctocolitis

Additional file 1: Supplemental Figure 1. Overall longitudinal microbiome composition. (A) Composition plot of the full first year of life showing the mean relative abundance of the top 15 taxa and their longitudinal taxonomic assemblage over the first year. (B) Longitudinal microbial richness (using the chao1 index) over the first year. The center line denotes the median, the boxes cover the 25th to 75th percentiles. (C) Relative abundance (arcsine transformed, AST) of key taxonomic differences mediated by important environmental factors: Bacteroides by delivery mode, Bifidobacterium by infant diet, and Lactobacillus by probiotic use. FDR-corrected q-values and coefficients are calculated from the multivariate analysis across all samples. Supplemental Figure 2. Community stability analysis calculated by Bray-Curtis beta diversity method for all consecutive sample pairs from the same subject. Each dot represents a sample pair, and is colored by the disease state of the first sample in the pair (p-values were calculated using a two sided t-test). Supplemental Figure 3. Sample subsets. (A) Flow diagram showing sample subsets used for analyses with their corresponding sample sizes and rationale. (B) A sample map showing longitudinal samples used for each subset analyzed, axes and colors as in Fig. 1. The horizontal light gray bars represent the time from diagnosis to resolution of symptoms. The ‘+’ sign represents samples that were not selected in any model. Supplemental Figure 4. Differential trajectory of Lactobacillus across disease states in infants with FPIAP compared to controls, stratified by probiotic use. Box plots of the relative abundance (AST) trajectories of Lactobacillus across disease states (from pre-symptomatic to symptomatic to resolved) compared to controls, stratified by (largely LGG-containing) probiotic use across all samples (p-values calculated using t-test)