Saliva antibody profiles are associated with reaction threshold and severity of peanut allergic reactions.

BACKGROUND: Reaction threshold and severity in food allergy are difficult to predict, and noninvasive predictors are lacking. OBJECTIVE: We sought to determine the relationships between pre-challenge levels of peanut (PN)-specific antibodies in saliva and reaction threshold, severity, and organ-specific symptoms during PN allergic reactions. METHODS: We measured PN-specific antibody levels in saliva collected from 127 children with suspected PN allergy before double-blind, placebo-controlled PN challenges in which reaction threshold, severity, and symptoms were rigorously characterized. Low threshold (LT) PN allergy was defined as reaction to <300 mg of PN protein cumulatively consumed. A consensus severity grading system was used to grade severity. We analyzed associations between antibody levels and reaction threshold, severity, and organ-specific symptoms. RESULTS: Among the 127 children, those with high pre-challenge saliva PN IgE had higher odds of LT PN allergy (odds ratio [OR] 3.9, 95% CI 1.6-9.5), while those with high saliva PN IgA:PN IgE ratio or PN IgG4:PN IgE ratio had lower odds of LT PN allergy (OR 0.3, 95% CI 0.1-0.8; OR 0.4, 95% CI 0.2-0.9). Children with high pre-challenge saliva PN IgG4 had lower odds of severe PN reactions (OR 0.4, 95% CI 0.2-0.9). Children with high saliva PN IgE had higher odds of respiratory symptoms (OR 8.0, 95% CI 2.2-26.8). Saliva PN IgE modestly correlated with serum PN IgE levels (Pearson r = 0.31, P = .0004). High and low saliva PN IgE levels further distinguished reaction threshold and severity in participants stratified by serum PN IgE, suggesting endotypes. CONCLUSIONS: Saliva PN antibodies could aid in noninvasive risk stratification of PN allergy threshold, severity, and organ-specific symptoms.

Joint transcriptomic and cytometric study of children with peanut allergy reveals molecular and cellular cross talk in reaction thresholds.

BACKGROUND: Reaction thresholds in peanut allergy are highly variable. Elucidating causal relationships between molecular and cellular processes associated with variable thresholds could point to therapeutic pathways for raising thresholds. OBJECTIVE: The aim of this study was to characterize molecular and cellular systemic processes associated with reaction threshold in peanut allergy and causal relationships between them. METHODS: A total of 105 children aged 4 to 14 years with suspected peanut allergy underwent double-blind, placebo-controlled food challenge to peanut. The cumulative peanut protein quantity eliciting allergic symptoms was considered the reaction threshold for each child. Peripheral blood samples collected at 0, 2, and 4 hours after challenge start were used for RNA sequencing, whole blood staining, and cytometry. Statistical and network analyses were performed to identify associations and causal mediation between the molecular and cellular profiles and peanut reaction threshold. RESULTS: Within the cohort (N = 105), 81 children (77%) experienced allergic reactions after ingesting varying quantities of peanut, ranging from 43 to 9043 mg of cumulative peanut protein. Peripheral blood expression of transcripts (eg, IGF1R [false discovery rate (FDR) = 5.4e-5] and PADI4 [FDR = 5.4e-5]) and neutrophil abundance (FDR = 9.5e-4) were associated with peanut threshold. Coexpression network analyses revealed that the threshold-associated transcripts were enriched in modules for FcγR-mediated phagocytosis (FDR = 3.2e-3) and Toll-like receptor (FDR = 1.4e-3) signaling. Bayesian network, key driver, and causal mediation analyses identified key drivers (AP5B1, KLHL21, VASP, TPD52L2, and IGF2R) within these modules that are involved in bidirectional causal mediation relationships with neutrophil abundance. CONCLUSION: Key driver transcripts in FcγR-mediated phagocytosis and Toll-like receptor signaling interact bidirectionally with neutrophils in peripheral blood and are associated with reaction threshold in peanut allergy.

Multi-omic integration reveals alterations in nasal mucosal biology that mediate air pollutant effects on allergic rhinitis.

BACKGROUND: Allergic rhinitis is a common inflammatory condition of the nasal mucosa that imposes a considerable health burden. Air pollution has been observed to increase the risk of developing allergic rhinitis. We addressed the hypotheses that early life exposure to air toxics is associated with developing allergic rhinitis, and that these effects are mediated by DNA methylation and gene expression in the nasal mucosa. METHODS: In a case-control cohort of 505 participants, we geocoded participants' early life exposure to air toxics using data from the US Environmental Protection Agency, assessed physician diagnosis of allergic rhinitis by questionnaire, and collected nasal brushings for whole-genome DNA methylation and transcriptome profiling. We then performed a series of analyses including differential expression, Mendelian randomization, and causal mediation analyses to characterize relationships between early life air toxics, nasal DNA methylation, nasal gene expression, and allergic rhinitis. RESULTS: Among the 505 participants, 275 had allergic rhinitis. The mean age of the participants was 16.4 years (standard deviation = 9.5 years). Early life exposure to air toxics such as acrylic acid, phosphine, antimony compounds, and benzyl chloride was associated with developing allergic rhinitis. These air toxics exerted their effects by altering the nasal DNA methylation and nasal gene expression levels of genes involved in respiratory ciliary function, mast cell activation, pro-inflammatory TGF-ß1 signaling, and the regulation of myeloid immune cell function. CONCLUSIONS: Our results expand the range of air pollutants implicated in allergic rhinitis and shed light on their underlying biological mechanisms in nasal mucosa.

Longitudinal dynamics of the gut microbiome and metabolome in peanut allergy development.

BACKGROUND: Rising rates of peanut allergy (PA) motivate investigations of its development to inform prevention and therapy. Microbiota and the metabolites they produce shape food allergy risk. OBJECTIVE: We sought to gain insight into gut microbiome and metabolome dynamics in the development of PA. METHODS: We performed a longitudinal, integrative study of the gut microbiome and metabolome of infants with allergy risk factors but no PA from a multicenter cohort followed through mid-childhood. We performed 16S rRNA sequencing, short chain fatty acid measurements, and global metabolome profiling of fecal samples at infancy and at mid-childhood. RESULTS: In this longitudinal, multicenter sample (n = 122), 28.7% of infants developed PA by mid-childhood (mean age 9 years). Lower infant gut microbiome diversity was associated with PA development (P = .014). Temporal changes in the relative abundance of specific microbiota and gut metabolite levels significantly differed in children who developed PA. PA-bound children had different abundance trajectories of Clostridium sensu stricto 1 sp (false discovery rate (FDR) = 0.015) and Bifidobacterium sp (FDR = 0.033), with butyrate (FDR = 0.045) and isovalerate (FDR = 0.036) decreasing over time. Metabolites associated with PA development clustered within the histidine metabolism pathway. Positive correlations between microbiota, butyrate, and isovalerate and negative correlations with histamine marked the PA-free network. CONCLUSION: The temporal dynamics of the gut microbiome and metabolome in early childhood are distinct for children who develop PA. These findings inform our thinking on the mechanisms underlying and strategies for potentially preventing PA.

Examination of host genetic effects on nasal microbiome composition.

BACKGROUND: Genetic predisposition increases risk for asthma, and distinct nasal microbial compositions are associated with asthma. Host genetics might shape nasal microbiome composition. OBJECTIVE: We examined associations between host genetics and nasal microbiome composition. METHODS: Nasal samples were collected from 584 participants from the Mount Sinai Health System, New York. Seventy-seven follow-up samples were collected from a subset of 40 participants. 16S rRNA sequencing and RNA sequencing were performed on nasal samples. Beta diversity was calculated, variant calling on RNA sequencing data was performed, and genetic relatedness between individuals was determined. Using linear regression models, we tested for associations between genetic relatedness and nasal microbiome composition. RESULTS: The median age of the cohort was 14.6 (interquartile range 11.2-19.5) years, with participants representing diverse ancestries and 52.7% of the cohort being female. For participants who provided follow-up samples, the median time between samples was 5.1 (interquartile range 1.4-7.2) months. Nasal microbiome composition similarity as reflected by beta diversity was significantly higher within subjects over time versus between subjects (coefficient = 0.091, P = 2.84-7). There was no significant association between genetic relatedness and beta diversity (coefficient = -0.05, P = .29). Additional analyses exploring the relationship between beta diversity and genetic variance yielded similar results. CONCLUSION: Host genetics has little influence on nasal microbiome composition.

Multiscale study of the oral and gut environments in children with high- and low-threshold peanut allergy.

BACKGROUND: The oral and gut microbiomes have each been associated with food allergy status. Within food allergy, they may also influence reaction thresholds. OBJECTIVE: Our aim was to identify oral and gut microbiota associated with reaction thresholds in peanut allergy. METHODS: A total of 59 children aged 4 to 14 years with suspected peanut allergy underwent double-blind, placebo-controlled food challenge to peanut. Those children who reacted at the 300-mg or higher dose of peanut were classified as high-threshold (HT), those who reacted to lower doses were classified as low-threshold (LT), and those children who did not react were classified as not peanut allergic (NPA). Saliva and stool samples collected before challenge underwent DNA isolation followed by 16S rRNA sequencing and short-chain fatty acid measurement. RESULTS: The 59 participants included 38 HT children and 13 LT children. Saliva microbiome α-diversity (Shannon index) was higher in LT children (P = .017). We identified saliva and stool microbiota that distinguished HT children from LT children, including oral Veillonella nakazawae (amplicon sequence variant 1979), which was more abundant in the HT group than in the LT group (false discovery rate [FDR] = 0.025), and gut Bacteroides thetaiotaomicron (amplicon sequence variant 6829), which was less abundant in HT children than in LT children (FDR = 0.039). Comparison with NPA children revealed consistent ordinal trends between these discriminating species and reaction thresholds. Importantly, many of these threshold-associated species were also correlated with short-chain fatty acid levels at the respective body sites, including between oral V nakazawae and oral butyrate (r = 0.57; FDR = 0.049). CONCLUSION: Findings from this multiscale study raise the possibility of microbial therapeutics to increase reaction thresholds in children with food allergy.

The nasal microbiome, nasal transcriptome, and pet sensitization.

BACKGROUND: Pet allergies are common in children with asthma. Microbiota and host responses may mediate allergen sensitization. OBJECTIVE: We sought to uncover host-microbe relationships in pet allergen sensitization via joint examination of the nasal microbiome and nasal transcriptome. METHODS: We collected nasal samples from 132 children with asthma for parallel 16S rRNA and RNA sequencing. Specific IgE levels for cat and dog dander were measured. Analyses of the nasal microbiome, nasal transcriptome, and their correlations were performed with respect to pet sensitization status. RESULTS: Among the 132 children, 91 (68.9%) were cat sensitized and 96 (72.7%) were dog sensitized. Cat sensitization was associated with lower nasal microbial diversity by Shannon index (P = .021) and differential nasal bacterial composition by weighted UniFrac distance (permutational multivariate ANOVA P = .035). Corynebacterium sp and Staphylococcus epidermidis were significantly less abundant, and the metabolic process "fatty acid elongation in mitochondria" was lower in pet-sensitized versus unsensitized children. Correlation networks revealed that the nasal expression levels of 47 genes representing inflammatory processes were negatively correlated with the relative abundances of Corynebacterium sp and S epidermidis. Thus, these species were directly associated not only with the absence of pet sensitization but also with the underexpression of host gene expression of inflammatory processes that contribute to allergen sensitization. Causal mediation analyses revealed that the associations between these nasal species and pet sensitization were mediated by nasal gene expression. CONCLUSIONS: Higher abundances of nasal Corynebacterium sp and S epidermidis are associated with absence of pet sensitization and correlate with lower expression of inflammatory genes.

Integrated study of systemic and local airway transcriptomes in asthma reveals causal mediation of systemic effects by airway key drivers.

BACKGROUND: Systemic and local profiles have each been associated with asthma, but parsing causal relationships between system-wide and airway-specific processes can be challenging. We sought to investigate systemic and airway processes in asthma and their causal relationships. METHODS: Three hundred forty-one participants with persistent asthma and non-asthmatic controls were recruited and underwent peripheral blood mononuclear cell (PBMC) collection and nasal brushing. Transcriptome-wide RNA sequencing of the PBMC and nasal samples and a series of analyses were then performed using a discovery and independent test set approach at each step to ensure rigor. Analytic steps included differential expression analyses, coexpression and probabilistic causal (Bayesian) network constructions, key driver analyses, and causal mediation models. RESULTS: Among the 341 participants, the median age was 13 years (IQR = 10-16), 164 (48%) were female, and 200 (58.7%) had persistent asthma with mean Asthma Control Test (ACT) score 16.6 (SD = 4.2). PBMC genes associated with asthma were enriched in co-expression modules for NK cell-mediated cytotoxicity (fold enrichment = 4.5, FDR = 6.47 × 10-32) and interleukin production (fold enrichment = 2.0, FDR = 1.01 × 10-15). Probabilistic causal network and key driver analyses identified NK cell granule protein (NKG7, fold change = 22.7, FDR = 1.02 × 10-31) and perforin (PRF1, fold change = 14.9, FDR = 1.31 × 10-22) as key drivers predicted to causally regulate PBMC asthma modules. Nasal genes associated with asthma were enriched in the tricarboxylic acid (TCA) cycle module (fold enrichment = 7.5 FDR = 5.09 × 10-107), with network analyses identifying G3BP stress granule assembly factor 1 (G3BP1, fold change = 9.1 FDR = 2.77 × 10-5) and InaD-like protein (INADL, fold change = 5.3 FDR = 2.98 × 10-9) as nasal key drivers. Causal mediation analyses revealed that associations between PBMC key drivers and asthma are causally mediated by nasal key drivers (FDR = 0.0076 to 0.015). CONCLUSIONS: Integrated study of the systemic and airway transcriptomes in a well-phenotyped asthma cohort identified causal key drivers of asthma among PBMC and nasal transcripts. Associations between PBMC key drivers and asthma are causally mediated by nasal key drivers.

Machine learning-driven identification of early-life air toxic combinations associated with childhood asthma outcomes.

Air pollution is a well-known contributor to asthma. Air toxics are hazardous air pollutants that cause or may cause serious health effects. Although individual air toxics have been associated with asthma, only a limited number of studies have specifically examined combinations of air toxics associated with the disease. We geocoded air toxic levels from the US National Air Toxics Assessment (NATA) to residential locations for participants of our AiRway in Asthma (ARIA) study. We then applied Data-driven ExposurE Profile extraction (DEEP), a machine learning-based method, to discover combinations of early-life air toxics associated with current use of daily asthma controller medication, lifetime emergency department visit for asthma, and lifetime overnight hospitalization for asthma. We discovered 20 multi-air toxic combinations and 18 single air toxics associated with at least 1 outcome. The multi-air toxic combinations included those containing acrylic acid, ethylidene dichloride, and hydroquinone, and they were significantly associated with asthma outcomes. Several air toxic members of the combinations would not have been identified by single air toxic analyses, supporting the use of machine learning-based methods designed to detect combinatorial effects. Our findings provide knowledge about air toxic combinations associated with childhood asthma.