Next Article in Journal
Towards Precision Sports Nutrition for Endurance Athletes: A Scoping Review of Application of Omics and Wearables Technologies
Previous Article in Journal
The Impact of the Dietary Inflammatory Index, Fasting Blood Glucose, and Smoking Status on the Incidence and Survival of Pancreatic Cancer: A Retrospective Case–Control Study and a Prospective Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Nutrients
Volume 16
Issue 22
10.3390/nu16223942
nutrients-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Characterization of the Gut and Skin Microbiome over Time in Young Children with IgE-Mediated Food Allergy

by
Michèle S. Roth
1,*,†,
Muriel d’Aujourd’hui
1,†,
Axel Künstner
2,
Misa Hirose
2,
Michael Olbrich
2,3,
Saleh Ibrahim
2,3,
Karin Hartmann
4,5,6,
Caroline Roduit
7,8,
Hauke Busch
2,‡ and
Felicitas Bellutti Enders
1,‡
1
Division of Pediatric Allergy, University Children’s Hospital Basel, 4031 Basel, Switzerland
2
Institute for Experimental Dermatology, University of Lubeck, 23538 Lubeck, Germany
3
College of Medicine and Health Sciences, Khalifa University, Abu Dhabi 127788, United Arab Emirates
4
Division of Allergy, Department of Dermatology, University Hospital Basel and University of Basel, 4031 Basel, Switzerland
5
Department of Biomedicine, University Hospital Basel and University of Basel, 4031 Basel, Switzerland
6
Department of Clinical Research, University Hospital Basel and University of Basel, 4031 Basel, Switzerland
7
Children’s Hospital of Eastern Switzerland, 9000 St. Gallen, Switzerland
8
Division of Pediatric Respiratory Medicine and Allergology, Department of Paediatrics, Inselspital, University of Bern, 3010 Bern, Switzerland
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors contributed equally to this work.
Nutrients 2024, 16(22), 3942; https://doi.org/10.3390/nu16223942
Submission received: 20 September 2024 / Revised: 10 November 2024 / Accepted: 12 November 2024 / Published: 19 November 2024
(This article belongs to the Section Pediatric Nutrition)
Browse Figures
Review Reports Versions Notes

Abstract

:
Background/Objectives: The prevalence of food allergy (FA) in children is increasing. Dysbiosis of the microbiome has been linked to FA but needs to be better understood. We aimed to characterize the gut and skin microbiome of young food-allergic children over time and within different types of immunoglobulin E (IgE)-mediated FA. Methods: We studied 23 patients, as a pilot study of an ongoing prospective multicenter cohort study including children < 2y with newly diagnosed IgE-mediated FA. Samples (feces/skin swabs) were collected at enrollment and at 1-year follow-up and sequenced for the bacterial 16S rRNA gene (hypervariable v1–v2 region). Results: Gut and skin bacterial diversity was significantly higher in patients compared with controls and increased over time (beta test, Shannon diversity, p < 0.01). Within different types of IgE-mediated FA, bacterial diversity was similar. Community composition differed significantly over time and within IgE-mediated FA types (PERMANOVA: p < 0.01). Several significantly different genus abundances were revealed. We observed a positive correlation between high total IgE and a high abundance of the genus Collinsella in patients with a higher number of allergies/sensitizations (≥3), and patients with tree nut and/or peanut allergy. Conclusions: This study revealed an increased bacterial diversity in children with FA compared with non-atopic children. Importantly, the gut and skin microbiome differed in their composition over time and within different types of IgE-mediated FA. These findings contribute to the understanding of microbiome changes in children with FA and indicate the potential of the genus Collinsella as a biomarker for tree nut and/or peanut allergy and possibly for allergy persistence.

Graphical Abstract

1. Introduction

The prevalence of food allergy (FA) is rising, affecting up to 8% of children worldwide [1,2,3]. Children with atopic dermatitis (AD) are at a higher risk of developing FA, especially those with onset in the first year of life [4,5]. Immunoglobulin-E (IgE)-mediated FA shows an individual natural course. Egg and milk allergies resolve in 70–80% of all cases, whereas tree nut and/or peanut allergies tend to persist, only resolving in about 20% [6,7,8,9,10]. Several risk factors are associated with FA persistence, mainly described in egg, milk, and peanut allergies, such as a larger wheal size in a skin prick test (SPT), higher specific IgE (sIgE), or a reaction to small amounts of allergen [6,9,11,12]. The early introduction of complementary feeding including allergenic foods in high-risk infants reduces the risk of developing FA [13,14]. Additionally, oral immunotherapies improve the course of FA by lowering the risk of severe allergic reactions, increasing the reaction threshold, and, in certain cases, achieving sustained unresponsiveness [15,16,17]. Nevertheless, biomarkers for FA persistence to improve prevention recommendations and to personalize treatments are not yet established.
The dual exposure hypothesis highlights the interplay between the gut, skin, and immune system to develop either allergy or tolerance to food allergens [18]. The microbiome plays an important role in educating and shaping the immune system in early life [19]. Early studies indicate that germ-free mice lack the ability to develop immunologically competent cells [20]. The microbial colonization stimulates the expansion of regulatory T-cells, favoring the development of tolerance to food allergens [21]. Dysbiosis is associated with atopic diseases, including FA [22,23,24]. Certain bacterial species, such as Bifidobacterium adolescentis, are considered protective against FA, whereas a lower abundance of short-chain fatty acid (SCFA)-producing bacteria, such as Prevotella, are associated with FA [25]. The transmission of the gut flora from non-allergic infants to allergy-prone mice resulted in reduced sensitization and allergy to food allergens [26]. Studies investigating the skin microbiome in children with AD observe an increased Staphylococcus aureus colonization in both lesional and non-lesional skin [27].
To date, no specific cluster of dysbiosis has been identified in individuals with FA. Few studies investigate the skin microbiome in children with IgE-mediated FA [28,29,30]. Several studies of the skin microbiome focus on children with AD [27,30,31,32]. The gut microbiome in children with FA is investigated in more detail, but mainly in older children and mostly with sampling at a single time point [25,33]. Furthermore, most studies include different types of IgE-mediated FA. Only a few studies focus on a specific type of IgE-mediated FA, e.g., egg, milk, or peanut allergy, and rarely include tree nut allergy [21,25,34,35,36,37].
As the gut, skin, and immune system are closely linked and the microbiome has a crucial impact on the immune system and thus on the development of allergy, it is important to characterize the gut and also the skin microbiome in young children with IgE-mediated FA. Different types of IgE-mediated FA show diverse clinical courses; thus, it is essential to describe their microbiome differences. Furthermore, as the microbiome is still developing in the first years of life, it is important to follow the microbiome changes over time. It is crucial to understand the microbiome’s impact on the course of FA to improve prevention and personalize treatment, possibly by identifying biomarkers.
Therefore, the objectives of our study are as follows: first, to investigate changes in gut and skin microbiome over time in children with IgE-mediated FA; second, to characterize the gut and skin microbiome of children with IgE-mediated tree nut and/or peanut allergy; and finally to identify biomarkers that can help predict the course of FA.

2. Materials and Methods

2.1. Study Design and Population

This study is a pilot study investigating 23 patients as a subset of a larger ongoing prospective multicenter cohort study designed to follow the clinical course of a high-risk group of children with IgE-mediated FA (Supplementary Figure S1). Patients were recruited between July 2021 and 2022. At enrollment (denoted by t0) and 1-year follow-up (denoted by t1), medical history and diagnostic tests were performed. Recruitment took place at the Allergy Division of the University Children’s Hospital Basel (Switzerland) and at the Children’s Hospital of Eastern Switzerland, (Switzerland). Inclusion criteria were age < 2 years and diagnosis of IgE-mediated FA. Criteria for the diagnosis of an IgE-mediated FA were defined as a reliable history of an allergic reaction combined with the evidence of an IgE sensitization to the specific food allergen (positive SPT > 3 mm and/or sIgE ≥ 0.35 kU/l), and/or a positive result in an oral food challenge (OFC). These children are all at the stage of introducing new foods into their diet and families have followed the recommendations to introduce one new food at a time. This increases the accuracy of a reliable history. In the case of an unclear history or a positive IgE sensitization with unknown clinical relevance at baseline screening, further investigation was performed by OFC. A total of 44 OFCs were performed in 23 patients and FA was confirmed in 13 cases. Patients with IgE-mediated egg and/or milk allergy were started on an egg and/or milk ladder adapted from the Canadian protocol [38]. Briefly, a multistep protocol was used, starting with baked egg and/or milk and ending with raw food products. When egg and/or milk could be consumed without restriction, the allergy was considered resolved.
AD was diagnosed according to the Hanifin and Rajka criteria [39] and the severity was quantified using the SCORing Atopic Dermatitis (SCORAD) [40]. Patients were treated with a daily application of emollients. Topical corticosteroids were used intermittently as needed. None of the patients used probiotics or other oral supplements except the nationally recommended daily dose of vitamin D (400 IU/day).
The control group consisted of 8 children described by Reimer-Taschenbrecker et al. [41]. We selected controls that were best matched to the age of our patients. Controls were recruited by the pediatric dermatology clinic of the Department of Dermatology, University of Freiburg (Freiburg im Breisgau, Germany), between June 2018 and April 2020. Controls did not have FA or any atopic disease (AD, allergic rhinoconjunctivitis, or asthma), nor any other inflammatory skin diseases such as psoriasis. Skin swabs were taken from the forearm and feces samples were collected. The samples were processed the same way as in our cohort. All clinical and laboratory data were documented in SecuTrial® (version 6.5.1.5, 2024).

2.2. Sample Collection

To investigate the gut microbiome, native feces were collected by parents at home using a provided feces sample collection kit (Stool Sample Collection and Stabilization Kit SC0011, Canvax Biotech S.L., Cordoba, Spain). Parents received verbal and written instructions. Samples were collected within 3–5 days after visits, kept at room temperature, mailed to the hospital, and then stored at −80 °C. This kit uses a DNA Stabilization Buffer to prevent DNA degradation and ensure nucleic acid stability. It allows storage at room temperature for up to 3 months, ensuring a stable microbiome. Feces samples were frozen within a median time of 6 days (t0 median 6 d (range 1–15), t1 median 7 d (range 1–15)).
For the skin microbiome, skin swabs were collected from two sites: the dorsal side of the forearm (referred to as “forearm”) and the antecubital fossa (“elbow”). These sites are considered to represent different predilection sites for AD. Inflammatory skin lesions are common in the antecubital fossa whereas the dorsal side of the forearm is often unaffected [42]. Skin sampling is described elsewhere [41]. Briefly, premoistened swabs were taken from the two sites and then stored at −80 °C on the same day.

2.3. Sequencing and Data Processing

For sequencing and analysis, samples were sent to the Lübeck Institute for Experimental Dermatology (LIED), Lübeck, Germany. Details about bacterial isolation, library preparation, sequencing, and sequence data processing are provided in the Supplementary Materials.

2.4. Statistical Analysis

Alpha diversity was estimated on total abundances using the Shannon estimates from the DivNet package (v0.4.0) and heterogeneity of total diversity (observed plus unobserved) between conditions was assessed using the betta test (breakaway v4.8.4) with age as a fixed factor in addition to the factor investigated [43,44]. If necessary, patient ID was included as a random factor for repeated measurements. To estimate beta diversity, total abundance data were centered log-ratio (clr) transformed, and distances were calculated using Euclidean distance (Aitchison distance) [45]. Permutational multivariate ANOVA using distance matrices (permutational multivariate ANOVA) was used to analyze differences in beta diversity (adonis2 function, vegan package v2.6-4, with 9999 permutations) with patient ID as strata for constraint permutation. Pairwise adonis tests were performed using the package pairwise Adonis (v0.4). Differential taxa abundance analysis was performed on total abundance data using MaAsLin2 (v1.10.0) on TSS-normalized and arc-sine square root-transformed counts (as suggested in Nearing et al. [46,47]) for genera present in at least 20% of the samples with age as a fixed factor to adjust for age effects in the test. If necessary, patient ID was included as a random factor to adjust for repeated measurements. Correlations were calculated on relative abundances and Spearman’s ρ was used as correlation measurement.
Statistical analysis was performed using R (version 4.3.2). Visualizations of the microbiome data were performed using ggplot2 (version 3.4.4), ggpubr (version 0.6.0), and patchwork (version 1.2.0); the R packages phyloseq (version 1.44.0) and tidyverse (version 2.0.0) were used for data handling [48]; p-value and q-value were considered to be significant if <0.05, or otherwise stated.

3. Results

3.1. Study Population Characteristics

The baseline characteristics are shown in Table 1. The median patient age at enrollment (t0) and 1-year follow-up (t1) was 12 and 24 months, respectively. The ages of patients at their 1-year follow-up and controls were similar (p = 0.8564). The details of atopic history, allergy characteristics, treatment, and clinical course are shown in Table 2. Tree nut and/or peanut allergy was present in fifteen patients (65%), of whom six were also diagnosed with egg and/or milk allergy. The remaining eight patients (35%) only had egg and/or milk allergy. Thirteen of the fourteen patients with egg and/or milk allergy had completed an egg and/or milk ladder, resulting in a tolerance of raw egg and/or milk. The patient who did not conduct an egg ladder remained allergic to egg. In the latest clinical follow-up in September 2024, 11 out of 15 patients (73.3%) with a tree nut and/or peanut allergy had persistent allergy. In four patients, the tree nut and/or peanut allergy resolved spontaneously with a tolerance of unrestricted allergen quantity.

3.2. Higher Diversity and Distinct Community Composition of Gut and Skin Microbiome in Patients with IgE-Mediated Food Allergy Compared with Controls

First, we compared the gut and skin microbiome of patients with controls. To evaluate differences in diversity we estimated sample-wise diversity (alpha diversity).
We observed a higher alpha diversity (Shannon) in patients’ feces samples at t1 and in patients’ forearm samples at t0 and t1 compared with controls (betta test; feces t1 p = 0.0048; forearm t0 and t1 p < 0.0001). The alpha diversity of feces samples at t0 was similar between patients and controls (betta test: t0 p = 0.4113) (Figure 1A, Supplementary Figure S2A). The Aitchinson distance was used to assess differences in community composition (beta diversity). In feces samples, PERMANOVA revealed a significant effect of group (control or patient) and sampling time point (t0 or t1) (p = 0.0001), whereas no significant effect of age on the community composition was detected (p = 0.1520). Community composition was significantly different when comparing feces samples (pairwise adonis test: p < 0.01, R2 = 0.06–0.08) and forearm samples (p = 0.01, R2 = 0.03–0.09) of patients at t0 and t1 with controls (Figure 1B, Supplementary Figure S2B).
The relative abundance analysis of the gut and the microbiome at phylum and genus level revealed a high heterogeneity between patients and controls, and between sampling time points and sampling sources (Figure 1C, Supplementary Figures S2C and S3A,B). Patients and controls showed similar dominant phyla. Firmicutes (also known as Bacillota) and Bacteroidota were the two dominant phyla in the gut microbiome. Actinobacteriota (also known as Actinomycetota) and Firmicutes were the dominant phyla in both patients, controls, and additionally Proteobacteria in the patients’ skin microbiome. At the genus level, Faecalibacterium and Phocaeicola were the two dominant genera in the gut microbiome. Cutibacterium and Streptococcus were the two dominant genera in patients’ forearm samples, and Pseudoclavibacter the was the dominant genus in patients’ elbow samples, whereas Cutibacterium and Staphylococcus were the two dominant genera in the skin microbiome of controls (Supplementary Figure S4A–D).
Comparing clr-transformed abundances at the genus level, we found a significantly increased abundance (q ≤ 0.05) of Bacillus (t1) in the gut microbiome and of Alloprevotella (t0, t1), Arachnia (t1), Capnocytophaga (t1), F0422 (family Veillonellceae) (t0), Fusobacterium (t1), Haemophilus A (t1), Lautropia (t1), Neisseria (t0, t1), Prophyromonas (t0, t1), Prevotella (t0, t1), and Pseudomonas E (t0, t1) in the skin microbiome (forearm) in patients compared with controls. Further, we observed a decreased abundance (q ≤ 0.05) of CAG 1427 (family Eggerthellaceae) (t0) in the gut microbiome and of Gemella (t0) and Staphylococcus (t0, t1) in the skin microbiome (forearm) of patients compared with controls (Figure 1D, Supplementary Figure S2D). Upon further investigation of the genus Staphylococcus, we found Staphylococcus epidermidis to be the dominant species in the skin microbiome (forearm) of patients and in controls. The identified Staphylococcus species all belong to the group of coagulase-negative Staphylococci (CoNS), and abundances were similar between patients at different time points and controls (Figure 1E and Supplementary Figure S5). The species Staphylococcus aureus could not be detected due to its low abundance. However, it is debatable whether this species could not be detected due to its similarity to the other species.

3.3. Increasing Diversity and Changes In Gut and Skin Microbiome over Time Within Patients with IgE-Mediated Food Allergy

Next, we compared patients’ gut and skin microbiome at different sampling time points to assess changes over time.
The differences in alpha diversity and community composition are shown in Figure 2A,B. We found an increasing alpha diversity from t0 to t1 in both feces and the two skin sample sources (betta test: feces p < 0.0001, difference = 0.2509; forearm p < 0.0001, difference = 0.1274; elbow p < 0.0001, difference = 0.9421). The community composition was significantly different at the two sampling time points in feces (p = 0.0001) and forearm samples (p = 0.0028); no differences were detected for elbow samples (p = 0.0798). In elbow samples, we observed an age effect (p = 0.0102), whereas feces and forearm samples showed no effect of age on community composition (p > 0.1).
Comparing the abundances at the genus level, we found, from sampling time point t0 to t1, an increase in abundances (q ≤ 0.05) of Bacillus, Faecalibacterium, Pseudomonas, and Sellimonas in the gut microbiome and of Abiotrophia (elbow and forearm), Actinomyces (forearm), Arachnia (forearm), Kingella A (forearm), Knoellia (forearm), Lautropia (forearm), Neisseria (forearm), and Lactococcus A (elbow) in the skin microbiome. We observed a significant decrease in abundances (q ≤ 0.05) of Pseudoclavibacte, and Veillonella in the gut microbiome, and Pseudoxanthomonas (forearm) in the skin microbiome over time from t0 to t1 (Figure 2C–E).

3.4. Similar Diversity in Gut and Skin Microbiome and Distinct Community Composition in Gut Microbiome of Patients with Different Types of IgE-Mediated Food Allergies

Third, we compared the gut and skin microbiome at enrollment of patients with IgE-mediated tree nut and/or peanut allergy (n = 15) with patients with an IgE-mediated FA other than tree nut and/or peanut (n = 8) to identify differences within different types of IgE-mediated FA.
The alpha diversity of the gut and skin microbiome (forearm and elbow) was similar in patients with different types of IgE-medaited FA (betta test; feces p = 0.970; forearm p = 0.0538; elbow p = 0.7002) (Figure 3A). Community composition significantly differed in the gut microbiome, but not in the skin microbiome of patients with tree nut and/or peanut allergy compared with patients with other types of FA, with an age effect observed in feces and elbow samples (nut vs. no nut: feces p = 0.0114; forearm p = 0.2365; elbow p = 0.9783) (age: feces p = 0.0070; forearm p = 0.1231; elbow p = 0.0039) (Figure 3B).
Comparing the abundances at genus, we found significantly increased abundances (p < 0.05) of Blautia A, Enterocloster, Lawsonella, and Phocaeicola in the gut microbiome and of Cutibacterium (forearm) and Sphinogomonas (elbow) in the skin microbiome of patients with tree nut and/or peanut allergy. We observed significantly decreased abundances (p < 0.05) of Enterococcus D, Neisseria, Pseudomonas E, and Veillonella in the gut microbiome and of Gemella, Granulicatella, Haemophilus D, Lancefieldella, Nanogingivalis, Nanosynbacter, Pauljensenia, and Veillonella in the skin microbiome (forearm) of patients with tree nut and/or peanut allergy (nut) compared with patients with FA other than tree and/or peanut (no nut) (Figure 3C–E).

3.5. Correlation of the Genus Collinsella with High Total IgE in Patients with Increased Numbers of Allergies/Sensitizations

Further, we investigated whether total IgE levels and the number of allergies/sensitizations to food allergens correlated with genera abundances at enrollment using Spearman’s rank correlation.
We observed that patients with high total IgE (>100 kIU/mL) show more allergies/sensitizations (Fisher’s exact test: p = 0.0499). High total IgE positively correlated with a high abundance of the genus Collinsella in the gut and skin microbiome when stratifying according to number of allergies/sensitization (≥3) (feces p = 0.0052; elbow p = 0.0049, forearm p = 0.0432). Interestingly, this was the only genus with a significant correlation in all three sample sources (Figure 4). Stratifying only according to the number of allergies (without the number of sensitization), we found an increased abundance of the genus Collinsella in the skin microbiome, but not in the gut microbiome (feces p > 0.05, forearm p = 0.041, elbow = 0.022). However, stratifying according to the type of FA, we found a significant correlation between high total IgE and the abundance of the genus Collinsella in the feces and elbow samples of patients with tree nut and/or peanut allergy, but not in forearm samples (feces p = 0.001, elbow = 0.0418, forearm = 0.1296) (Supplementary Figure S6).

4. Discussion

As the prevalence of FA increases, the ability to personalize treatment and tailor preventions becomes more important. The microbiome is an important player in the development of FA. To better understand this interaction, we investigated the gut and skin microbiome in a high-risk population of young children with newly diagnosed IgE-mediated FA. We would like to emphasize that we did not only investigate the gut, but also the skin microbiome, and we followed the microbiome’s evolvement over time and differentiated between different types of IgE-mediated FA. We identified higher bacterial diversity in patients with FA and an increase in diversity over time. Furthermore, we determined a distinct community composition in our patient, with shifts in the gut and skin microbiome over time and within different types of IgE-mediated FA. Collinsella emerged as a noteworthy genus, as its abundance in the gut and skin microbiome is correlated with high total IgE in patients with increased numbers of allergies/sensitizations to food allergens and, particularly, is elevated in patients with tree nut and/or peanut allergy.
The alpha diversity of our patients’ gut and skin microbiomes increased over time. Few longitudinal studies have documented these changes in children with FA and AD. Kennedy et al. reported an increase in the skin microbiome diversity (Shannon index) from 2 to 6 months in children with AD [49]. Another study observed, after an initial decrease from birth to 6 months, a continuous increase in the alpha diversity (Sobs, Shannon, and Simpson indices) of the gut microbiome of both children who developed AD at two years and controls [50]. Similarly, Galazzo et al. found an increase in the gut microbiome diversity (Shannon index) after 31 weeks, linking it to atopic diseases [51]. These findings indicate that the microbiome development in children with FA and AD is similar to those without these conditions [52,53,54]. Furthermore, the bacterial diversity of both microbiome sources was similar in patients with different types of IgE-mediated FA. Chun et al. noted shifts in the gut microbiome diversity (Simpson index) in food-allergic children who later developed peanut allergy, with lower diversity in infancy but similar diversity by mid-childhood [55]. Our observations suggest shared underlying pathophysiological mechanisms within FA, despite the individual clinical courses, highlighting the importance of conducting longitudinal studies to reveal similarities and differences. Interestingly, our patients’ gut and skin microbiome showed higher bacterial diversity than the controls. It is well known that the microbiome is influenced by multiple factors. Regarding the gut microbiome, several studies have reported lower diversity in children with FA [25,33,35,56], but other studies have found no effect [29,57,58,59]. Berni Canani et al. and Fazlollahi et al. are in line with our findings, showing higher gut microbiome diversity in children with egg and milk allergies [34,60]. One explanation for the high diversity of our patients’ gut microbiome might be the early introduction of a diverse diet, which was encouraged by dietary counseling. A broad diet is associated with a favorable effect on the microbiome [61,62]. Regarding the skin microbiome, our patients with mild AD regularly applied emollient, which is suggested to improve the skin condition. Previous studies comparing the effects of an emollient on the skin microbiome in children with the risk of developing AD or having a diagnosis of AD showed an increased diversity of the skin microbiome when treated with emollients [63,64,65].
Like previous studies, we observed high heterogeneity between the samples, reflecting the evolvement of the microbiome in infants [49,51,54]. This age period has been hypothesized to be the window for shaping the immune system and allowing the development of tolerance to food allergens, thus making our analysis at this age significant [19]. Despite the heterogeneity, taxonomic abundance analysis revealed the most dominant phyla and a distinct community composition with significantly different genus abundances in our patients. Firmicutes and Bacteroidota were the two dominant phyla in the gut microbiome of both patients and controls, which is related to the introduction of solid food [66].
At the genus level, the shifts in the gut microbiome over time of several genera of the phylum Firmicutes are noteworthy. While Faecalibacterium, Bacillus, and Sellimonas increased, Veillonella decreased over time. This is in line with a study evaluating the gut microbiome of children with food sensitization at 13 months of age [33]. Similarly, Filippis et al., studying children (5 years) with respiratory allergy and FA, and Reddel et al., investigating toddlers (2 years) with AD, observed an increased abundance of Faecalibacterium in the gut microbiome [36,67]. A decreased abundance of Veillonella is observed already at the age of 2 months in the gut microbiome of Japanese children who developed FA within the first two years of life [68]. Fan et al. described a lower abundance of Veillonella in the gut microbiome of children with AD at 2 years of age [50]. Our observations, together with these studies, suggest that we have revealed important changes in the microbiome of young children with FA.
In our study, we correlated the abundance of the genus Collinsella in the gut and skin microbiome with high total IgE in patients with three or more allergies/sensitizations to food allergens. Further, we observed an increased abundance of the genus Collinsella in the gut and skin microbiome in patients with tree nut and/or peanut allergy, which are allergies that tend to persist. Consistent with our findings, the genus Collinsella is described as one of thirteen dominant genera in children with food sensitization [33]. Moreover, Goldberg et al. observe an increased abundance of Collinsella aerofaciens in children with FA [25]. Collinsella (phylum Actinobacteriota) is described to produce short-chain-fatty acids (SCFA), specifically butyric acid, which was shown to have a possible protective effect against allergic diseases [69,70]. Hence, we would expect a low abundance of Collinsella in patients with tree nut and/or peanut allergy, which contradicts our results. However, studies with a bigger sample size of tree nut and peanut allergic patients and investigations of the metabolic function of this genus would be required to evaluate whether Collinsella may serve as a biomarker for tree nut and/or peanut allergy and thus allergy persistence.
Previous studies have focused on Staphylococcus aureus and AD, limiting the interpretation of the skin microbiome results [71]. More recently, the diversity of the skin microbiome has been better recognized, but few studies examine the skin microbiome in children with FA [28,29]. The dominant phyla in the skin microbiome of our patients and controls were Actinobacteriota and Firmicutes, which is in line with previous studies [54,72]. Our findings showed a lower abundance of the genus Staphylococcus compared with controls. These were mainly Staphylococcus species belonging to the coagulase-negative Staphylococci (CoNS) with S. epidermidis as the most dominant one. Our patients had mild AD, which was represented by the low SCORAD, as a basic treatment with emollients was applied regularly. Byrd et al., comparing the skin microbiome composition at different states of pediatric AD, showed, similarly to our results, a predominance of S. epidermidis in less severe disease and at post-flare state, but is not significant in the latter [27]. Furthermore, the genus Staphylococcus is lower in the skin microbiome of infants who were later affected by AD, emphasizing a protective role of Staphylococcus, mainly CoNS, in the development of skin diseases [49]. Following the microbiome over time and investigating different types of IgE-mediated FA, we found no changes in the genus Staphylococcus.

5. Limitations

We recognize that there are limitations to this study. The small sample size limits the generalization of our results. This is a study with young children, and the availability of controls was challenging; therefore, we included only eight controls without any allergies. As this is an ongoing cohort study, we could provide, to date, only data from two sampling time points, and the evaluation of allergy persistence is pending. Furthermore, the analysis of the metabolic function of a specific bacterium, e.g., Collinsella in a murine or an organoid model, would provide further insights into the host–microbiome interaction.

6. Conclusions

This cohort study provides an extension of the few studies investigating the gut and especially the skin microbiome in young children with FA. Additionally, the study analyzes the microbiome longitudinally and includes the description of the microbiome of different types of IgE-mediated FA. By characterizing the gut and skin microbiome, we found an increase in bacterial diversity in young children with FA and over time. Importantly, the gut and skin microbiome differed in community composition over time and within different IgE-mediated FA. This indicates that the microbiome evolves with the host over time and supports the hypothesis that underlying pathomechanisms within different IgE-mediated FA may vary according to their different clinical courses. Further studies with longer observation periods are needed to better understand the dynamics of the gut and skin microbiome over time. In addition, to gain insight into the interaction between the host and the microbiome, the integration of the metabolome is desirable.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/nu16223942/s1, Figure S1. Study design, Figure S2. Diversity and community composition of the gut of patients with IgE-mediated food allergy (FA) compared with controls, Figure S3. Individual Taxonomic abundances at genus level, Figure S4. Average taxonomic abundances of gut and skin microbiome of patients with IgE-mediated food allergy (FA) and controls, Figure S5. Relative abundances of Staphylococcus species of patients with IgE-mediated food allergy (FA) and controls, Figure S6. Correlation of gut and skin microbiome of patients with tree nut and/or peanut allergy and total IgE. References [73,74,75,76,77,78,79,80,81] are cited in the Supplementary Materials.

Author Contributions

F.B.E., K.H. and H.B. directed the Study. F.B.E., M.S.R. and C.R. led the recruitment and the follow-up visits of subjects. M.H., M.O., S.I., H.B. and A.K. performed the laboratory and the statistical analysis. M.S.R., M.d. and A.K. curated the clinical data and wrote the manuscript guided by F.B.E. and H.B. All authors have read and agreed to the published version of the manuscript.

Funding

This study was funded by different private foundations (Freie Akademische Gesellschaft Basel, Switzerland; Bangerter-Rhyner Stiftung, Basel, Switzerland; Thomi-Hopf Stiftung, Basel, Switzerland) and by the Research Fond of the University of Basel, Switzerland.

Institutional Review Board Statement

This study was approved by the local institutional ethics committee (Ethikkommission Nord-West-Zentral-Schweiz and Ethikkommission Ost-Schweiz, BASEC Project ID 2019-01897, 5 December 2019) and conducted according to the Declaration of Helsinki.

Informed Consent Statement

Written informed consent was given by parents or legal representatives, as children included were under the age of 12.

Data Availability Statement

All related data and materials are published in this manuscript. Patient sequencing data (fastq files) have been deposited in the European Nucleotide Archive (ENA) under accession number PRJEB61516. For the controls, data (fastq files) were retrieved from the European Nucleotide Archive accession number PRJEB47047.

Acknowledgments

First, we thank the patients and families for participating in this ongoing cohort study. We thank the clinical nurses and doctors at the division of pediatric allergy at each institution, especially Tina Trachsel, Marius Elkuch, Lara Hess, Pascale Kurt, Camille Boucq, Viviane Steiner-Monard, Roberto Lo Presti, and Mareike Hug. Further, we thank Sandra Marti Pasqual, our study nurse, and Maé Mühlethaler for administrative assistance. AK, MO, and HB acknowledge computational support from the OMICS compute cluster at the University of Lübeck. HB and AK acknowledge support from the BMBF project OUTLIVE-CRC (FKZ 01KD2103A). We thank Fiona Beck (University Children’s Hospital Basel, Basel, Switzerland) for English editing.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References

  1. Warren, C.M.; Jiang, J.; Gupta, R.S. Epidemiology and Burden of Food Allergy. Curr. Allergy Asthma Rep. 2020, 20, 6. [Google Scholar] [CrossRef] [PubMed]
  2. Nwaru, B.I.; Hickstein, L.; Panesar, S.S.; Roberts, G.; Muraro, A.; Sheikh, A. Prevalence of common food allergies in Europe: A systematic review and meta-analysis. Allergy 2014, 69, 992–1007. [Google Scholar] [CrossRef] [PubMed]
  3. Sicherer, S.H.; Sampson, H.A. Food allergy: A review and update on epidemiology, pathogenesis, diagnosis, prevention, and management. J. Allergy Clin. Immunol. 2018, 141, 41–58. [Google Scholar] [CrossRef] [PubMed]
  4. Tsilochristou, O.; du Toit, G.; Sayre, P.H.; Roberts, G.; Lawson, K.; Sever, M.L.; Bahnson, H.T.; Radulovic, S.; Basting, M.; Plaut, M.; et al. Association of Staphylococcus aureus colonization with food allergy occurs independently of eczema severity. J. Allergy Clin. Immunol. 2019, 144, 494–503. [Google Scholar] [CrossRef] [PubMed]
  5. Roduit, C.; Frei, R.; Depner, M.; Karvonen, A.M.; Renz, H.; Braun-Fahrländer, C.; Schmausser-Hechfellner, E.; Pekkanen, J.; Riedler, J.; Dalphin, J.C.; et al. Phenotypes of Atopic Dermatitis Depending on the Timing of Onset and Progression in Childhood. JAMA Pediatr. 2017, 171, 655–662. [Google Scholar] [CrossRef]
  6. Elizur, A.; Rajuan, N.; Goldberg, M.R.; Leshno, M.; Cohen, A.; Katz, Y. Natural course and risk factors for persistence of IgE-mediated cow’s milk allergy. J. Pediatr. 2012, 161, 482–487.e1. [Google Scholar] [CrossRef]
  7. Peters, R.L.; Allen, K.J.; Dharmage, S.C.; Koplin, J.J.; Dang, T.; Tilbrook, K.P.; Lowe, A.; Tang, M.L.; Gurrin, L.C. Natural history of peanut allergy and predictors of resolution in the first 4 years of life: A population-based assessment. J. Allergy Clin. Immunol. 2015, 135, e1251–e1252. [Google Scholar] [CrossRef]
  8. McWilliam, V.L.; Perrett, K.P.; Dang, T.; Peters, R.L. Prevalence and natural history of tree nut allergy. Ann. Allergy Asthma Immunol. Off. Publ. Am. Coll. Allergy Asthma Immunol. 2020, 124, 466–472. [Google Scholar] [CrossRef]
  9. Savage, J.; Sicherer, S.; Wood, R. The Natural History of Food Allergy. J. Allergy Clin. Immunol. Pract. 2016, 4, 196–203, quiz 204. [Google Scholar] [CrossRef]
  10. Savage, J.H.; Matsui, E.C.; Skripak, J.M.; Wood, R.A. The natural history of egg allergy. J. Allergy Clin. Immunol. 2007, 120, 1413–1417. [Google Scholar] [CrossRef]
  11. Ho, M.H.; Wong, W.H.; Heine, R.G.; Hosking, C.S.; Hill, D.J.; Allen, K.J. Early clinical predictors of remission of peanut allergy in children. J. Allergy Clin. Immunol. 2008, 121, 731–736. [Google Scholar] [CrossRef] [PubMed]
  12. Leonard, S.A.; Sampson, H.A.; Sicherer, S.H.; Noone, S.; Moshier, E.L.; Godbold, J.; Nowak-Wegrzyn, A. Dietary baked egg accelerates resolution of egg allergy in children. J. Allergy Clin. Immunol. 2012, 130, 473–480.e471. [Google Scholar] [CrossRef] [PubMed]
  13. Adam, T.; Divaret-Chauveau, A.; Roduit, C.; Adel-Patient, K.; Deschildre, A.; Raherison, C.; Charles, M.A.; Nicklaus, S.; de Lauzon-Guillain, B. Complementary feeding practices are related to the risk of food allergy in the ELFE cohort. Allergy 2023, 78, 2456–2466. [Google Scholar] [CrossRef] [PubMed]
  14. Du Toit, G.; Roberts, G.; Sayre, P.H.; Bahnson, H.T.; Radulovic, S.; Santos, A.F.; Brough, H.A.; Phippard, D.; Basting, M.; Feeney, M.; et al. Randomized trial of peanut consumption in infants at risk for peanut allergy. N. Engl. J. Med. 2015, 372, 803–813. [Google Scholar] [CrossRef] [PubMed]
  15. Jones, S.M.; Kim, E.H.; Nadeau, K.C.; Nowak-Wegrzyn, A.; Wood, R.A.; Sampson, H.A.; Scurlock, A.M.; Chinthrajah, S.; Wang, J.; Pesek, R.D.; et al. Efficacy and safety of oral immunotherapy in children aged 1-3 years with peanut allergy (the Immune Tolerance Network IMPACT trial): A randomised placebo-controlled study. Lancet 2022, 399, 359–371. [Google Scholar] [CrossRef]
  16. Hourihane, J.O.B.; Beyer, K.; Abbas, A.; Fernández-Rivas, M.; Turner, P.J.; Blumchen, K.; Nilsson, C.; Ibáñez, M.D.; Deschildre, A.; Muraro, A.; et al. Efficacy and safety of oral immunotherapy with AR101 in European children with a peanut allergy (ARTEMIS): A multicentre, double-blind, randomised, placebo-controlled phase 3 trial. Lancet Child. Adolesc. Health 2020, 4, 728–739. [Google Scholar] [CrossRef]
  17. Vickery, B.P.; Vereda, A.; Casale, T.B.; Beyer, K.; du Toit, G.; Hourihane, J.O.; Jones, S.M.; Shreffler, W.G.; Marcantonio, A.; Zawadzki, R.; et al. AR101 Oral Immunotherapy for Peanut Allergy. N. Engl. J. Med. 2018, 379, 1991–2001. [Google Scholar] [CrossRef]
  18. Du Toit, G.; Sampson, H.A.; Plaut, M.; Burks, A.W.; Akdis, C.A.; Lack, G. Food allergy: Update on prevention and tolerance. J. Allergy Clin. Immunol. 2018, 141, 30–40. [Google Scholar] [CrossRef]
  19. Gensollen, T.; Iyer, S.S.; Kasper, D.L.; Blumberg, R.S. How colonization by microbiota in early life shapes the immune system. Science 2016, 352, 539–544. [Google Scholar] [CrossRef]
  20. Bauer, H.; Horowitz, R.E.; Levenson, S.M.; Popper, H. The response of the lymphatic tissue to the microbial flora. Studies on germfree mice. Am. J. Pathol. 1963, 42, 471–483. [Google Scholar]
  21. Abdel-Gadir, A.; Stephen-Victor, E.; Gerber, G.K.; Noval Rivas, M.; Wang, S.; Harb, H.; Wang, L.; Li, N.; Crestani, E.; Spielman, S.; et al. Author Correction: Microbiota therapy acts via a regulatory T cell MyD88/RORγt pathway to suppress food allergy. Nat. Med. 2019, 25, 1458. [Google Scholar] [CrossRef] [PubMed]
  22. Björkstén, B.; Naaber, P.; Sepp, E.; Mikelsaar, M. The intestinal microflora in allergic Estonian and Swedish 2-year-old children. Clin. Exp. Allergy 1999, 29, 342–346. [Google Scholar] [CrossRef] [PubMed]
  23. Thompson-Chagoyan, O.C.; Fallani, M.; Maldonado, J.; Vieites, J.M.; Khanna, S.; Edwards, C.; Doré, J.; Gil, A. Faecal microbiota and short-chain fatty acid levels in faeces from infants with cow’s milk protein allergy. Int. Arch. Allergy Immunol. 2011, 156, 325–332. [Google Scholar] [CrossRef] [PubMed]
  24. Azad, M.B.; Konya, T.; Guttman, D.S.; Field, C.J.; Sears, M.R.; HayGlass, K.T.; Mandhane, P.J.; Turvey, S.E.; Subbarao, P.; Becker, A.B.; et al. Infant gut microbiota and food sensitization: Associations in the first year of life. Clin. Exp. Allergy 2015, 45, 632–643. [Google Scholar] [CrossRef] [PubMed]
  25. Goldberg, M.R.; Mor, H.; Magid Neriya, D.; Magzal, F.; Muller, E.; Appel, M.Y.; Nachshon, L.; Borenstein, E.; Tamir, S.; Louzoun, Y.; et al. Microbial signature in IgE-mediated food allergies. Genome Med. 2020, 12, 92. [Google Scholar] [CrossRef]
  26. Rodriguez, B.; Prioult, G.; Hacini-Rachinel, F.; Moine, D.; Bruttin, A.; Ngom-Bru, C.; Labellie, C.; Nicolis, I.; Berger, B.; Mercenier, A.; et al. Infant gut microbiota is protective against cow’s milk allergy in mice despite immature ileal T-cell response. FEMS Microbiol. Ecol. 2012, 79, 192–202. [Google Scholar] [CrossRef]
  27. Byrd, A.L.; Deming, C.; Cassidy, S.K.B.; Harrison, O.J.; Ng, W.I.; Conlan, S.; Belkaid, Y.; Segre, J.A.; Kong, H.H. Staphylococcus aureus and Staphylococcus epidermidis strain diversity underlying pediatric atopic dermatitis. Sci. Transl. Med. 2017, 9, eaal4651. [Google Scholar] [CrossRef]
  28. Leung, D.Y.M.; Calatroni, A.; Zaramela, L.S.; LeBeau, P.K.; Dyjack, N.; Brar, K.; David, G.; Johnson, K.; Leung, S.; Ramirez-Gama, M.; et al. The nonlesional skin surface distinguishes atopic dermatitis with food allergy as a unique endotype. Sci. Transl. Med. 2019, 11, eaav2685. [Google Scholar] [CrossRef]
  29. Łoś-Rycharska, E.; Gołębiewski, M.; Grzybowski, T.; Rogalla-Ładniak, U.; Krogulska, A. The microbiome and its impact on food allergy and atopic dermatitis in children. Postepy Dermatol. Alergol. 2020, 37, 641–650. [Google Scholar] [CrossRef]
  30. Rapin, A.; Rehbinder, E.M.; Macowan, M.; Pattaroni, C.; Lødrup Carlsen, K.C.; Harris, N.L.; Jonassen, C.M.; Landrø, L.; Lossius, A.H.; Nordlund, B.; et al. The skin microbiome in the first year of life and its association with atopic dermatitis. Allergy 2023, 78, 1949–1963. [Google Scholar] [CrossRef]
  31. Kong, H.H.; Oh, J.; Deming, C.; Conlan, S.; Grice, E.A.; Beatson, M.A.; Nomicos, E.; Polley, E.C.; Komarow, H.D.; Murray, P.R.; et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 2012, 22, 850–859. [Google Scholar] [CrossRef] [PubMed]
  32. Chaudhary, P.P.; Myles, I.A.; Zeldin, J.; Dabdoub, S.; Deopujari, V.; Baveja, R.; Baker, R.; Bengtson, S.; Sutton, A.; Levy, S.; et al. Shotgun metagenomic sequencing on skin microbiome indicates dysbiosis exists prior to the onset of atopic dermatitis. Allergy 2023, 78, 2724–2731. [Google Scholar] [CrossRef] [PubMed]
  33. Chen, C.C.; Chen, K.J.; Kong, M.S.; Chang, H.J.; Huang, J.L. Alterations in the gut microbiotas of children with food sensitization in early life. Pediatr Allergy Immunol 2016, 27, 254–262. [Google Scholar] [CrossRef] [PubMed]
  34. Fazlollahi, M.; Chun, Y.; Grishin, A.; Wood, R.A.; Burks, A.W.; Dawson, P.; Jones, S.M.; Leung, D.Y.M.; Sampson, H.A.; Sicherer, S.H.; et al. Early-life gut microbiome and egg allergy. Allergy 2018, 73, 1515–1524. [Google Scholar] [CrossRef] [PubMed]
  35. Bunyavanich, S.; Shen, N.; Grishin, A.; Wood, R.; Burks, W.; Dawson, P.; Jones, S.M.; Leung, D.Y.M.; Sampson, H.; Sicherer, S.; et al. Early-life gut microbiome composition and milk allergy resolution. J. Allergy Clin. Immunol. 2016, 138, 1122–1130. [Google Scholar] [CrossRef]
  36. De Filippis, F.; Paparo, L.; Nocerino, R.; Della Gatta, G.; Carucci, L.; Russo, R.; Pasolli, E.; Ercolini, D.; Berni Canani, R. Specific gut microbiome signatures and the associated pro-inflamatory functions are linked to pediatric allergy and acquisition of immune tolerance. Nat. Commun. 2021, 12, 5958. [Google Scholar] [CrossRef]
  37. Fieten, K.B.; Totte, J.E.E.; Levin, E.; Reyman, M.; Meijer, Y.; Knulst, A.; Schuren, F.; Pasmans, S. Fecal Microbiome and Food Allergy in Pediatric Atopic Dermatitis: A Cross-Sectional Pilot Study. Int. Arch. Allergy Immunol. 2018, 175, 77–84. [Google Scholar] [CrossRef]
  38. Chomyn, A.; Chan, E.S.; Yeung, J.; Vander Leek, T.K.; Williams, B.A.; Soller, L.; Abrams, E.M.; Mak, R.; Wong, T. Canadian food ladders for dietary advancement in children with IgE-mediated allergy to milk and/or egg. Allergy Asthma Clin. Immunol. 2021, 17, 83. [Google Scholar] [CrossRef]
  39. Hanifin, J.M.; Rajka, G. Diagnostic Features of Atopic Dermatitis. Acta Derm.-Venereol. 1980, 60, 44–47. [Google Scholar] [CrossRef]
  40. Kunz, B.; Oranje, A.P.; Labrèze, L.; Stalder, J.F.; Ring, J.; Taïeb, A. Clinical validation and guidelines for the SCORAD index: Consensus report of the European Task Force on Atopic Dermatitis. Dermatology 1997, 195, 10–19. [Google Scholar] [CrossRef]
  41. Reimer-Taschenbrecker, A.; Kunstner, A.; Hirose, M.; Hubner, S.; Gewert, S.; Ibrahim, S.; Busch, H.; Has, C. Predominance of Staphylococcus Correlates with Wound Burden and Disease Activity in Dystrophic Epidermolysis Bullosa: A Prospective Case-Control Study. J. Investig. Dermatol. 2022, 142, 2117–2127.e8. [Google Scholar] [CrossRef] [PubMed]
  42. Chatrath, S.; Silverberg, J.I. Phenotypic differences of atopic dermatitis stratified by age. JAAD Int. 2023, 11, 1–7. [Google Scholar] [CrossRef] [PubMed]
  43. Willis, A.; Bunge, J.; Whitman, T. Improved detection of changes in species richness in high diversity microbial communities. J. R. Stat. Soc. C 2017, 66, 963–977. [Google Scholar] [CrossRef]
  44. Willis, A.D.; Martin, B.D. Estimating diversity in networked ecological communities. Biostatistic. Biostat. 2020, 23, 207–222. [Google Scholar] [CrossRef] [PubMed]
  45. Aitchison, J. The Statistical Analysis of Compositional Data. J. R. Stat. Soc. Ser. B 1982, 44, 139–160. [Google Scholar] [CrossRef]
  46. Mallick, H.; Rahnavard, A.; McIver, L.J.; Ma, S.; Zhang, Y.; Nguyen, L.H.; Tickle, T.L.; Weingart, G.; Ren, B.; Schwager, E.H.; et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput. Biol. 2021, 17, e1009442. [Google Scholar] [CrossRef]
  47. Nearing, J.T.; Douglas, G.M.; Hayes, M.G.; MacDonald, J.; Desai, D.K.; Allward, N.; Jones, C.M.A.; Wright, R.J.; Dhanani, A.S.; Comeau, A.M.; et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat. Commun. 2022, 13, 342. [Google Scholar] [CrossRef]
  48. McMurdie, P.J.; Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 2013, 8, e61217. [Google Scholar] [CrossRef]
  49. Kennedy, E.A.; Connolly, J.; Hourihane, J.O.; Fallon, P.G.; McLean, W.H.I.; Murray, D.; Jo, J.H.; Segre, J.A.; Kong, H.H.; Irvine, A.D. Skin microbiome before development of atopic dermatitis: Early colonization with commensal staphylococci at 2 months is associated with a lower risk of atopic dermatitis at 1 year. J. Allergy Clin. Immunol. 2017, 139, 166–172. [Google Scholar] [CrossRef]
  50. Fan, X.; Zang, T.; Dai, J.; Wu, N.; Hope, C.; Bai, J.; Liu, Y. The associations of maternal and children’s gut microbiota with the development of atopic dermatitis for children aged 2 years. Front. Immunol. 2022, 13, 1038876. [Google Scholar] [CrossRef]
  51. Galazzo, G.; van Best, N.; Bervoets, L.; Dapaah, I.O.; Savelkoul, P.H.; Hornef, M.W.; Lau, S.; Hamelmann, E.; Penders, J. Development of the Microbiota and Associations With Birth Mode, Diet, and Atopic Disorders in a Longitudinal Analysis of Stool Samples, Collected From Infancy Through Early Childhood. Gastroenterology 2020, 158, 1584–1596. [Google Scholar] [CrossRef] [PubMed]
  52. Yatsunenko, T.; Rey, F.E.; Manary, M.J.; Trehan, I.; Dominguez-Bello, M.G.; Contreras, M.; Magris, M.; Hidalgo, G.; Baldassano, R.N.; Anokhin, A.P.; et al. Human gut microbiome viewed across age and geography. Nature 2012, 486, 222–227. [Google Scholar] [CrossRef] [PubMed]
  53. Bäckhed, F.; Roswall, J.; Peng, Y.; Feng, Q.; Jia, H.; Kovatcheva-Datchary, P.; Li, Y.; Xia, Y.; Xie, H.; Zhong, H.; et al. Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. Cell Host Microbe 2015, 17, 690–703. [Google Scholar] [CrossRef] [PubMed]
  54. Capone, K.A.; Dowd, S.E.; Stamatas, G.N.; Nikolovski, J. Diversity of the human skin microbiome early in life. J. Investig. Dermatol. 2011, 131, 2026–2032. [Google Scholar] [CrossRef] [PubMed]
  55. Chun, Y.; Grishin, A.; Rose, R.; Zhao, W.; Arditi, Z.; Zhang, L.; Wood, R.A.; Burks, A.W.; Jones, S.M.; Leung, D.Y.M.; et al. Longitudinal dynamics of the gut microbiome and metabolome in peanut allergy development. J. Allergy Clin. Immunol. 2023, 152, 1569–1580. [Google Scholar] [CrossRef]
  56. Chen, C.C.; Huang, J.L.; Chen, K.J.; Kong, M.S.; Hua, M.C.; Yeh, Y.M.; Chang, H.J. Comparison of 16S rRNA gene sequencing microbiota among children with serological IgE-mediated food hypersensitivity. Pediatr. Res. 2023, 95, 241–250. [Google Scholar] [CrossRef]
  57. Savage, J.H.; Lee-Sarwar, K.A.; Sordillo, J.; Bunyavanich, S.; Zhou, Y.; O’Connor, G.; Sandel, M.; Bacharier, L.B.; Zeiger, R.; Sodergren, E.; et al. A prospective microbiome-wide association study of food sensitization and food allergy in early childhood. Allergy 2018, 73, 145–152. [Google Scholar] [CrossRef]
  58. Ling, Z.; Li, Z.; Liu, X.; Cheng, Y.; Luo, Y.; Tong, X.; Yuan, L.; Wang, Y.; Sun, J.; Li, L.; et al. Altered fecal microbiota composition associated with food allergy in infants. Appl. Environ. Microbiol. 2014, 80, 2546–2554. [Google Scholar] [CrossRef]
  59. Lee, K.H.; Guo, J.; Song, Y.; Ariff, A.; O’Sullivan, M.; Hales, B.; Mullins, B.J.; Zhang, G. Dysfunctional Gut Microbiome Networks in Childhood IgE-Mediated Food Allergy. Int. J. Mol. Sci. 2021, 22, 2079. [Google Scholar] [CrossRef]
  60. Berni Canani, R.; Sangwan, N.; Stefka, A.T.; Nocerino, R.; Paparo, L.; Aitoro, R.; Calignano, A.; Khan, A.A.; Gilbert, J.A.; Nagler, C.R. Lactobacillus rhamnosus GG-supplemented formula expands butyrate-producing bacterial strains in food allergic infants. ISME J 2016, 10, 742–750. [Google Scholar] [CrossRef]
  61. Venter, C. Immunonutrition: Diet Diversity, Gut Microbiome and Prevention of Allergic Diseases. Allergy Asthma Immunol. Res. 2023, 15, 545–561. [Google Scholar] [CrossRef] [PubMed]
  62. Xiao, C.; Wang, J.T.; Su, C.; Miao, Z.; Tang, J.; Ouyang, Y.; Yan, Y.; Jiang, Z.; Fu, Y.; Shuai, M.; et al. Associations of dietary diversity with the gut microbiome, fecal metabolites, and host metabolism: Results from 2 prospective Chinese cohorts. Am. J. Clin. Nutr. 2022, 116, 1049–1058. [Google Scholar] [CrossRef] [PubMed]
  63. Harder, I.; Stölzl, D.; Sander, N.; Hartmann, J.; Rodriguez, E.; Mazur, C.; Kerzel, S.; Kabesch, M.; Küster, D.; Schmitt, J.; et al. Effects of Early Emollient Use in Children at High Risk of Atopic Dermatitis: A German Pilot Study. Acta Derm. Venereol. 2023, 103, adv5671. [Google Scholar] [CrossRef] [PubMed]
  64. Capone, K.; Kirchner, F.; Klein, S.L.; Tierney, N.K. Effects of Colloidal Oatmeal Topical Atopic Dermatitis Cream on Skin Microbiome and Skin Barrier Properties. J. Drugs Dermatol. 2020, 19, 524–531. [Google Scholar] [CrossRef] [PubMed]
  65. Chandra, J.; Retuerto, M.; Seité, S.; Martin, R.; Kus, M.; Ghannoum, M.A.; Baron, E.; Mukherjee, P.K. Effect of an Emollient on the Mycobiome of Atopic Dermatitis Patients. J. Drugs Dermatol. 2018, 17, 1039–1048. [Google Scholar]
  66. Fusco, W.; Lorenzo, M.B.; Cintoni, M.; Porcari, S.; Rinninella, E.; Kaitsas, F.; Lener, E.; Mele, M.C.; Gasbarrini, A.; Collado, M.C.; et al. Short-Chain Fatty-Acid-Producing Bacteria: Key Components of the Human Gut Microbiota. Nutrients 2023, 15, 2211. [Google Scholar] [CrossRef]
  67. Reddel, S.; Del Chierico, F.; Quagliariello, A.; Giancristoforo, S.; Vernocchi, P.; Russo, A.; Fiocchi, A.; Rossi, P.; Putignani, L.; El Hachem, M. Gut microbiota profile in children affected by atopic dermatitis and evaluation of intestinal persistence of a probiotic mixture. Sci. Rep. 2019, 9, 4996. [Google Scholar] [CrossRef]
  68. Tanaka, M.; Korenori, Y.; Washio, M.; Kobayashi, T.; Momoda, R.; Kiyohara, C.; Kuroda, A.; Saito, Y.; Sonomoto, K.; Nakayama, J. Signatures in the gut microbiota of Japanese infants who developed food allergies in early childhood. FEMS Microbiol. Ecol. 2017, 93, fix099. [Google Scholar] [CrossRef]
  69. Qin, P.; Zou, Y.; Dai, Y.; Luo, G.; Zhang, X.; Xiao, L. Characterization a Novel Butyric Acid-Producing Bacterium Collinsella aerofaciens Subsp. Shenzhenensis Subsp. Nov. Microorg. 2019, 7, 78. [Google Scholar] [CrossRef]
  70. Sasaki, M.; Suaini, N.H.A.; Afghani, J.; Heye, K.N.; O’Mahony, L.; Venter, C.; Lauener, R.; Frei, R.; Roduit, C. Systematic review of the association between short-chain fatty acids and allergic diseases. Allergy 2024, 79, 1789–1811. [Google Scholar] [CrossRef]
  71. Byrd, A.L.; Belkaid, Y.; Segre, J.A. The human skin microbiome. Nat. Rev. Microbiol. 2018, 16, 143–155. [Google Scholar] [CrossRef] [PubMed]
  72. Luna, P.C. Skin Microbiome as Years Go By. Am. J. Clin. Dermatol. 2020, 21, 12–17. [Google Scholar] [CrossRef] [PubMed]
  73. Hirose, M.; Kunstner, A.; Schilf, P.; Sunderhauf, A.; Rupp, J.; Johren, O.; Schwaninger, M.; Sina, C.; Baines, J.F.; Ibrahim, S.M. Mitochondrial gene polymorphism is associated with gut microbial communities in mice. Sci. Rep. 2017, 7, 15293. [Google Scholar] [CrossRef] [PubMed]
  74. Hirose, M.; Kunstner, A.; Schilf, P.; Tietjen, A.K.; Johren, O.; Huebbe, P.; Rimbach, G.; Rupp, J.; Schwaninger, M.; Busch, H.; et al. A Natural mtDNA Polymorphism in Complex III Is a Modifier of Healthspan in Mice. Int. J. Mol. Sci. 2019, 20, 2359. [Google Scholar] [CrossRef] [PubMed]
  75. Rognes, T.; Flouri, T.; Nichols, B.; Quince, C.; Mahe, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 2016, 4, e2584. [Google Scholar] [CrossRef]
  76. Edgar, R.C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 2010, 26, 2460–2461. [Google Scholar] [CrossRef]
  77. Montgomery, T.L.; Kunstner, A.; Kennedy, J.J.; Fang, Q.; Asarian, L.; Culp-Hill, R.; D’Alessandro, A.; Teuscher, C.; Busch, H.; Krementsov, D.N. Interactions between host genetics and gut microbiota determine susceptibility to CNS autoimmunity. Proc. Natl. Acad. Sci. USA 2020, 117, 27516–27527. [Google Scholar] [CrossRef]
  78. Edgar, R.C. SINTAX: A simple non-Bayesian taxonomy classifier for 16S and ITS sequences. bioRxiv 2016, 074161. [Google Scholar]
  79. Parks, D.H.; Chuvochina, M.; Chaumeil, P.A.; Rinke, C.; Mussig, A.J.; Hugenholtz, P. A complete domain-to-species taxonomy for Bacteria and Archaea. Nat. Biotechnol. 2020, 38, 1079–1086. [Google Scholar] [CrossRef]
  80. Davis, N.M.; Proctor, D.M.; Holmes, S.P.; Relman, D.A.; Callahan, B.J. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome 2018, 6, 226. [Google Scholar] [CrossRef]
  81. Olbrich, M.; Kunstner, A.; Busch, H. MBECS: Microbiome Batch Effects Correction Suite. BMC Bioinform. 2023, 24, 182. [Google Scholar] [CrossRef] [PubMed]
Nutrients 16 03942 g001
Figure 1. Diversity and community composition of the skin microbiome of patients with IgE-mediated food allergy (FA) compared with controls. (A) Violin plot of alpha diversity (Shannon estimate; betta test) and (B) RDA (redundancy analysis) plot of community composition (beta diversity) of forearm samples of controls and of patients at two different sampling time points (ctrl, sample of control; t0, sample of patients at enrollment; t1, sample of patient at 1-year follow-up; PC, principal coordinate; each dot represents one sample; gray dot, sample of control; yellow dot, sample of patient at enrollment (t0), blue dot, sample of patient at 1-year follow-up (t1); colored ellipses comprise 80% of the data). (C) Panel of relative abundances at phylum level of individual samples of skin of patients and controls. Violin plot of differences in clr-transformed genus abundances of forearm (D). (E) Panel of the relative abundance of the genus Staphylococcus at species level of forearm samples. (forearm/elbow t0 n = 23, t1 n = 21; significance level as indicated in figures).
Figure 1. Diversity and community composition of the skin microbiome of patients with IgE-mediated food allergy (FA) compared with controls. (A) Violin plot of alpha diversity (Shannon estimate; betta test) and (B) RDA (redundancy analysis) plot of community composition (beta diversity) of forearm samples of controls and of patients at two different sampling time points (ctrl, sample of control; t0, sample of patients at enrollment; t1, sample of patient at 1-year follow-up; PC, principal coordinate; each dot represents one sample; gray dot, sample of control; yellow dot, sample of patient at enrollment (t0), blue dot, sample of patient at 1-year follow-up (t1); colored ellipses comprise 80% of the data). (C) Panel of relative abundances at phylum level of individual samples of skin of patients and controls. Violin plot of differences in clr-transformed genus abundances of forearm (D). (E) Panel of the relative abundance of the genus Staphylococcus at species level of forearm samples. (forearm/elbow t0 n = 23, t1 n = 21; significance level as indicated in figures).
Nutrients 16 03942 g001
Nutrients 16 03942 g002
Figure 2. Diversity and community composition over time of the gut and skin microbiome of patients with IgE-mediated food allergy (FA). (A) Violin plot of alpha diversity (Shannon estimate; betta test) and (B) RDA (Redundancy Analysis) plot of community composition (beta diversity) of feces, forearm, and elbow samples at different sampling time points (PC, principal coordinate; each dot represents one sample of a different sampling time point: yellow dot, sample at enrollment (t0); blue dot, sample at 1-year follow-up (t1); colored ellipses comprise 80% of the data). Violin plot of differences in clr-transformed genus abundances at two different sampling time points of feces (C), forearm (D), and elbow samples (E). (feces t0 n = 22, t1 n = 20; forearm/elbow t0 n = 23, t1 n = 21; significance level as indicated in figures).
Figure 2. Diversity and community composition over time of the gut and skin microbiome of patients with IgE-mediated food allergy (FA). (A) Violin plot of alpha diversity (Shannon estimate; betta test) and (B) RDA (Redundancy Analysis) plot of community composition (beta diversity) of feces, forearm, and elbow samples at different sampling time points (PC, principal coordinate; each dot represents one sample of a different sampling time point: yellow dot, sample at enrollment (t0); blue dot, sample at 1-year follow-up (t1); colored ellipses comprise 80% of the data). Violin plot of differences in clr-transformed genus abundances at two different sampling time points of feces (C), forearm (D), and elbow samples (E). (feces t0 n = 22, t1 n = 20; forearm/elbow t0 n = 23, t1 n = 21; significance level as indicated in figures).
Nutrients 16 03942 g002
Nutrients 16 03942 g003
Figure 3. Diversity and community composition of the gut and skin microbiome of patients with different types of IgE-mediated food allergies (FA). (A) Violin plot of alpha diversity (Shannon estimate, betta test) and (B) RDA (redundancy analysis) plot of community composition (beta diversity) of feces, forearm, and elbow samples. (PC, principal coordinate; colored ellipses comprise 80% of data; each dot represents one sample: yellow dot, sample of patient with an IgE-mediated FA other than tree nut/peanut allergy; blue dot, sample of patient with a tree nut and/or peanut allergy). Violin plot of differences in clr-transformed genus abundances of feces (C), forearm (D), and elbow (E) samples. (Significance level as indicated in figures; investigated time point t0; no nut, sample of patient with an IgE-mediated FA other than tree nut and/or peanut, n = 8; nut, sample of patient with a tree nut and/or peanut allergy, n = 15).
Figure 3. Diversity and community composition of the gut and skin microbiome of patients with different types of IgE-mediated food allergies (FA). (A) Violin plot of alpha diversity (Shannon estimate, betta test) and (B) RDA (redundancy analysis) plot of community composition (beta diversity) of feces, forearm, and elbow samples. (PC, principal coordinate; colored ellipses comprise 80% of data; each dot represents one sample: yellow dot, sample of patient with an IgE-mediated FA other than tree nut/peanut allergy; blue dot, sample of patient with a tree nut and/or peanut allergy). Violin plot of differences in clr-transformed genus abundances of feces (C), forearm (D), and elbow (E) samples. (Significance level as indicated in figures; investigated time point t0; no nut, sample of patient with an IgE-mediated FA other than tree nut and/or peanut, n = 8; nut, sample of patient with a tree nut and/or peanut allergy, n = 15).
Nutrients 16 03942 g003
Nutrients 16 03942 g004
Figure 4. Correlation of gut and skin microbiome of patients with IgE-mediated food allergy (FA) with allergy parameters. Spearman’s correlation of genera abundances of feces, forearm, and elbow samples with total IgE, stratified according to number of allergies/sensitizations (x-axis: number of allergies/sensitization; low ≤ 2; high ≥ 3; only significant p-values are shown (p < 0.05); red, positive correlation; blue, negative correlation; investigated time point t0).
Figure 4. Correlation of gut and skin microbiome of patients with IgE-mediated food allergy (FA) with allergy parameters. Spearman’s correlation of genera abundances of feces, forearm, and elbow samples with total IgE, stratified according to number of allergies/sensitizations (x-axis: number of allergies/sensitization; low ≤ 2; high ≥ 3; only significant p-values are shown (p < 0.05); red, positive correlation; blue, negative correlation; investigated time point t0).
Nutrients 16 03942 g004
Table 1. Demographic characteristics of patients grouped according to type of IgE-mediated food allergy (FA) at enrollment and of controls.
Table 1. Demographic characteristics of patients grouped according to type of IgE-mediated food allergy (FA) at enrollment and of controls.
PatientsControls n = 8p-Value
(All vs. Controls)
All
n = 23		Nut
n = 15		No Nut
n = 8
sex (female)7 (30%)434 (50%)0.4055
age (month)12 (3–21)13.7 (3–14)10.5 (7–18)26 (3–46)0.0237
delivery mode 0.3931
 vaginal18 (78%)1455 (63%)
 Cesarian section5 (22%)033 (38%)
birth weight (g)3325
(1845–4850)		3399
(2900–3940)		3437
(1845–4850)		na
gestational age (weeks)40 (34–42)40 (37–42)40 (34–41)na
breast feeding21 (95%) §147na
age of food introduction (month)5 (4–6) ç5(4–6)4.7 (4–5)na
sibling(s)12 (71%) °123na
living condition 0.6417
 urban16 (70%)977 (88%)
 rural7 (30%)641 (13%)
day care3 (23%) $21na
pets3 (13%)301 (13%)1.0000
sampling of microbiome 0.0736
 season (winter/spring/summer/autumn)4/9/5/53/5/4/31/4/1/21/1/0/6
 skin
   forearm231588
   elbow23158na
feces221488
Fisher’s exact test was used for categorial variables and the Wilcoxon test for continuous variables. Data are shown as median (range) or numbers (%). p-value comparing “all patients” with “controls”. “nut”-group includes patients with tree nut and/or peanut allergy; “no nut”-group includes patients with IgE-mediated FA other than tree nut and/or peanut. Deviation of sample size: § n = 22; ° n = 17; ç n = 14; $ n = 13. Abbreviations: IgE, immunoglobulin E; na, not available.
Table 2. Patients’ atopic history, allergy characteristics at enrollment (t0), treatment, and clinical course.
Table 2. Patients’ atopic history, allergy characteristics at enrollment (t0), treatment, and clinical course.
Patientsp-Value
(Nut vs.
No Nut)
All
n = 23 		Nut
n = 15		No Nut
n = 8
Atopic history
 atopic dermatitis21 (91%)147
  SCORAD7.04 (0–24)8 (0–24)5 (0–8.8)0.4711
 allergic rhinoconjunctivitis1 (4%)10
 multiple trigger wheezing1 (4%)01
 atopic family history19 (83%)127
Allergy characteristics
 total IgE (kIU/mL)36.5
(1.48–1230) *		211.96
(9–1230)		69.35
(1.48–356)		0.0569
 number of allergies/sensitizations (sensitizations with and without clinical relevance) 2 (1–8)4 (1–8)1 (1–2)0.0457
  low: ≤2 total sensitizations1248
  high: ≥3 total sensitizations11110
 number of allergies (range)2 (1–4)2 (1–4)1 (1–2)
 allergy to …
  egg11 (48%)
  milk6 (26%)
  tree nut12 (52%)
    cashew nut7
    hazelnut5
    walnut3
  peanut7 (30%)
  allergy distribution
  only egg and/or milk allergy8 (35%)
  only tree nut and/or peanut allergy9 (39%)
  mixed allergy to egg, milk, tree nut and/or peanut6 (26%)
Fisher’s exact test was used for categorical variables and the Wilcoxon test for continuous variables. Data are shown as median (range) or numbers (%). p-value for comparison of “nut” and “no nut”-groups; “nut”-group includes patients with tree nut and/or peanut allergy; “no nut”-group includes patients with IgE-mediated FA other than tree nut and/or peanut. Deviation of sample size: * n = 20. Abbreviations: IgE, immunoglobulin E; SCORAD, SCORing Atopic Dermatitis.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Roth, M.S.; d’Aujourd’hui, M.; Künstner, A.; Hirose, M.; Olbrich, M.; Ibrahim, S.; Hartmann, K.; Roduit, C.; Busch, H.; Bellutti Enders, F. Characterization of the Gut and Skin Microbiome over Time in Young Children with IgE-Mediated Food Allergy. Nutrients 2024, 16, 3942. https://doi.org/10.3390/nu16223942

AMA Style

Roth MS, d’Aujourd’hui M, Künstner A, Hirose M, Olbrich M, Ibrahim S, Hartmann K, Roduit C, Busch H, Bellutti Enders F. Characterization of the Gut and Skin Microbiome over Time in Young Children with IgE-Mediated Food Allergy. Nutrients. 2024; 16(22):3942. https://doi.org/10.3390/nu16223942

Chicago/Turabian Style

Roth, Michèle S., Muriel d’Aujourd’hui, Axel Künstner, Misa Hirose, Michael Olbrich, Saleh Ibrahim, Karin Hartmann, Caroline Roduit, Hauke Busch, and Felicitas Bellutti Enders. 2024. "Characterization of the Gut and Skin Microbiome over Time in Young Children with IgE-Mediated Food Allergy" Nutrients 16, no. 22: 3942. https://doi.org/10.3390/nu16223942

APA Style

Roth, M. S., d’Aujourd’hui, M., Künstner, A., Hirose, M., Olbrich, M., Ibrahim, S., Hartmann, K., Roduit, C., Busch, H., & Bellutti Enders, F. (2024). Characterization of the Gut and Skin Microbiome over Time in Young Children with IgE-Mediated Food Allergy. Nutrients, 16(22), 3942. https://doi.org/10.3390/nu16223942

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop