Impact of oral immunotherapy on diversity of gut microbiota in food-allergic children

Impact of oral immunotherapy on diversity of gut microbiota in food-allergic children | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Pediatric Allergy and Immunology This is a preprint and has not been peer reviewed. Data may be preliminary. 28 January 2025 V1 Latest version Share on Impact of oral immunotherapy on diversity of gut microbiota in food-allergic children Authors : Thanina Bouabid , Bénédicte L. Tremblay , Marie-Ève Lavoie 0009-0009-9804-9323 , Anne-Marie Boucher-Lafleur , Frédérique Gagnon-Brassard , Philippe Bégin , Sarah Lavoie , … Show All … , Cloé Rochefort-Beaudoin , Claudia Nuncio-Naud , Guy Parizeault , Charles Morin , Catherine Girard , Anne-Marie Madore 0009-0007-5980-3931 , and Catherine Laprise 0000-0001-5526-9945 [email protected] Show Fewer Authors Info & Affiliations https://doi.org/10.22541/au.173809222.25150623/v1 Published Pediatric Allergy and Immunology Version of record Peer review timeline 363 views 215 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Food allergies (FAs) are an increasing public health concern, particularly in children. Oral immunotherapy (OIT) is an emerging treatment strategy under clinical investigation for desensitization of children with FA to food allergens. Dysbiosis of the gut microbiota has been implicated in FAs, and various factors influence its composition; however, the impact of OIT on the gut microbiota remains largely unexplored. Objective: This study aimed to identify the changes in diversity of the gut microbiota following OIT in children with FA. Methods: Thirty children with FA undergoing oral immunotherapy and seven non-allergic controls participated in this study. Fecal samples were collected before and after OIT from children with FA, and once from controls. The gut microbiota was profiled using 16S rRNA sequencing, followed by diversity and differential abundance analyses. Alpha and beta diversities were compared, and differential abundance was assessed. Results: Beta diversity analysis revealed small but significant differences in microbial composition between children with FA before and after OIT, and between controls and children with FA before OIT. Differential abundance analysis showed that OIT induced a reversion of the abundance levels of Bacteroidota and Verrucomicrobiota towards those observed in controls. Conclusion: To our knowledge, this is the first study to investigate the impact of OIT on the gut microbiota in children with different FAs for identifying potential microbial biomarkers, and convincingly demonstrated their interrelation. These findings may help improve and personalize FA treatment. \received DD MMMM YYYY \acceptedDD MMMM YYYY Impact of oral immunotherapy on diversity of gut microbiota in food-allergic children Thanina Bouabid 1, 2 , Bénédicte L. Tremblay 1, 2 , Marie-Ève Lavoie 1,2 , Anne-Marie Boucher-Lafleur 1,2 , Frédérique Gagnon-Brassard 1,2 , Philippe Bégin 3 , Sarah Lavoie 2,4 , Cloé Rochefort-Beaudoin 2,4 , Claudia Nuncio-Naud 2,4 , Guy Parizeault 2,4 , Charles Morin 2,4 , Catherine Girard 1,2 , Anne-Marie Madore 1, 2 , Catherine Laprise 1,2,4 * 1 Département des sciences fondamentales, Université du Québec à Chicoutimi, Saguenay, QC, Canada 2 Centre intersectoriel en santé durable, Université du Québec à Chicoutimi, Saguenay, QC, Canada 3 Division d’immunologie clinique, de rhumatologie et d’allergie, Département de pédiatrie, Centre hospitalier universitaire Sainte-Justine, Montréal, Canada 4 Zéro allergie research clinic of the Université du Québec à Chicoutimi and the Centre intégré universitaire de santé et de services sociaux du Saguenay–Lac-Saint-Jean, Saguenay, QC, Canada *Corresponding author Catherine Laprise, Ph.D. Université du Québec à Chicoutimi 555 Boulevard de l’Université, Saguenay, QC G7H 2B1, Canada Phone: 418 545-5011 ext. 5659 Fax: 418 615-1203 E-mail: [email protected] Running title Oral immunotherapy impact on gut microbiota \received DD MMMM YYYY \acceptedDD MMMM YYYY Word count 234 (abstract), 3 498 (text) Number of tables and figures 1 table, 5 figures Conflict of interest statement The authors declare no conflict of interest in relation to this study. Funding This work was supported by the Canada Research Chair in Genomics of Asthma and Allergic Diseases (award number CRC-2021-00108), Fondation de l’UQAC (https://fuqac.ca/) and Fondation de ma vie (https://www.fondationdemavie.qc.ca/). Abstract Background: Food allergies (FAs) are an increasing public health concern, particularly in children. Oral immunotherapy (OIT) is an emerging treatment strategy under clinical investigation for desensitization of children with FA to food allergens. Dysbiosis of the gut microbiota has been implicated in FAs, and various factors influence its composition; however, the impact of OIT on the gut microbiota remains largely unexplored. Objective: This study aimed to identify the changes in diversity of the gut microbiota following OIT in children with FA. Methods: Thirty children with FA undergoing oral immunotherapy and seven non-allergic controls participated in this study. Fecal samples were collected before and after OIT from children with FA, and once from controls. The gut microbiota was profiled using 16S rRNA sequencing, followed by diversity and differential abundance analyses. Alpha and beta diversities were compared, and differential abundance was assessed. Results: Beta diversity analysis revealed small but significant differences in microbial composition between children with FA before and after OIT, and between controls and children with FA before OIT. Differential abundance analysis showed that OIT induced a reversion of the abundance levels of Bacteroidota and Verrucomicrobiota towards those observed in controls. Conclusion: To our knowledge, this is the first study to investigate the impact of OIT on the gut microbiota in children with different FAs for identifying potential microbial biomarkers, and convincingly demonstrated their interrelation. These findings may help improve and personalize FA treatment. Key words Oral immunotherapy, Children, Food allergy, Gut microbiota, Bacteroidota, Verrucomicrobiota Abbreviations ANOVA - Analysis of variance CIUSSS - Centre intégré universitaire de santé et de services sociaux FA - Food allergy FDR - False discovery rate OIT - Oral immunotherapy PCoA - Principal coordinate analysis PERMANOVA - Permutational multivariate analysis of variance T0 FA - Timepoint 0, children with food allergy (before oral immunotherapy) T1 FA - Timepoint 1, children with food allergy (after oral immunotherapy) ZAC - Zéro allergie cohort Introduction Food allergies (FAs) are a public health concern, which affect both children and adults, and their prevalence has increased in recent decades 1 . Approximately 8% children in Western countries are affected, 40% of whom are polyallergic 2,3 . Approximately 90% allergic reactions are caused by eight foods; cow milk, eggs, soy, peanuts, tree nuts, wheat, fish, and shellfish 2 . The increasing prevalence of FA is owing to a combination of genetic factors and other intrinsic and environmental factors, including age, eczema, introduction of allergens, breastfeeding, exposure to microorganisms, and alteration of the gut microbiota 4 . The symptomatology of FA varies, ranging from pruritus and urticaria to severe manifestations affecting more than one organ, which may potentially lead to respiratory failure, shock, or death 2 . FAs can be managed by avoiding allergenic food 1 . This can put allergic individuals at risk of nutritional deficiencies and significantly affects their quality of life 3 . Several studies are underway to develop new therapies for managing FAs. Oral immunotherapy (OIT) involves the consumption of increasing amounts of food allergens to induce desensitization 5 . The initial dose, generally varying between 0.1–25 mg, is gradually increased every one to two weeks until a target maintenance dose is reached. This target dose usually varies between 300–4000 mg depending on the food and must be ingested daily to maintain protection for an indefinite period of time 6 . Approximately 80% individuals undergoing OIT achieve complete desensitization; therefore, they can eat a full serving of allergen proteins without developing an allergic reaction while in therapy, and 30–50% of them acquire sustained unresponsiveness, indicating that they can eat a full serving following extended avoidance of exposure to food-allergens 5,7,8 . Healthy gut microbiota contributes to protection against FA, whereas dysbiosis confers susceptibility to FA 9,10 . Four microbial phyla, Pseudomonadota, Actinomycetota, Firmicutes, and Bacteroidota, compose 98% of the gut microbiota 11 . The composition changes with age; the microbiota of newborns is dominated by Pseudomonadota (Escherichia, Shigella) and Actinomycetota (Bifidobacterium) , whereas that of adults is dominated by Firmicutes and Bacteroidota 11 . Several factors can affect the gut microbiota in children and lead to dysbiosis 12 , which may contribute to FAs by decreasing the production of short-chain fatty acids 4 . These are produced through fermentation of dietary fibers and influence epigenetics and gene expression by inhibiting histone deacetylase, modulating the immune system, and providing protection against developing FAs 13,14 . A study involving 233 children with FA and 58 controls have revealed differences in alpha and beta diversities in the gut microbiota of children of the same age 15 . A difference in the composition of the gut microbiota has been observed for 128 children with resolved cow milk allergy and 98 other allergic children when compared to controls 16 . To our knowledge, only two studies have evaluated the effects of OIT on gut microbiota. The first one has been conducted on seven adults with peanut allergy, while the second one has focused on 32 school-age children with cow milk allergy, and changes in modules consisting of a combination of microbial taxa and metabolite have been assessed 17,18 . Therefore, this study aimed to identify the changes in diversity of the microbiota diversity following OIT in children with FA for a relatively deep understanding of the effects of this treatment on the gut microbiome, thereby potentially leading to a highly personalized approach for optimizing this new treatment strategy. \received DD MMMM YYYY \acceptedDD MMMM YYYY Study population Thirty children with FA and undergoing OIT were selected from the Zéro allergie cohort (ZAC) 19 . This cohort was built by recruiting children with FA who followed an OIT at the Zéro allergie research clinic of the Université du Québec à Chicoutimi and the Centre intégré universitaire de santé et de services sociaux (CIUSSS) du Saguenay–Lac-Saint-Jean (SLSJ) at Saguenay, Canada 19 . Diagnoses of FA were confirmed by a pediatrician based on clinical symptoms and skin prick test (wheal diameter ≥ 3 mm of the negative control, measured after 10 min) 20 . Seven children without FA were included as controls. They were recruited by word-of-mouth, with first-time participants recommending others who met the eligibility criteria (i.e., in the same age group and ethnic background, and with no asthma or allergies). Information on sex, age, premature birth, mode of delivery, breastfeeding, and personal and family histories of allergic diseases were collected using a questionnaire. Family history included father, mother, and siblings. Informed consent was obtained from the legal guardians of all participants. The experimental protocol was approved by the Ethics Committee of the CIUSSS du SLSJ (project #2022-015). The phenotypic descriptions of children with FA and controls are shown in Table 1 . OIT All children with FA underwent OIT at the Zéro allergie clinical research. The OIT protocol was optimized based on the 2020 Canadian Society of Allergy and Clinical Immunology OIT guidelines under the supervision of an allergist 21 . Details of the protocol, dosages, and duration of OIT can be found as previously described 19 . Microbiome Stool samples of the children with FA were collected in an airtight container at home, before (T0 FA) and after (T1 FA) the updosing phase of OIT; stool samples were collected on sterile toilet paper folded in aluminum foil only once from the controls 22 . Stool samples from children with FAs were collected before the recruitment of controls, explaining the change in the sampling method following protocol optimization. Stool samples were frozen until next visit to the clinic, when they were placed in an insulated bag. All samples were analyzed in replicate for each participant. DNA extraction and sequencing Microbial DNA was extracted using a DNeasy Powersoil Pro Kit (Qiagen, ON, Canada), following the manufacturer’s instructions, and stored at -80 °C. DNA concentration was measured using a Qubit 4.0 fluorimeter with a Broad Range Assay kit (Thermo Fisher Scientific, Waltham, MA, USA), and purity was assessed using a NanoDrop spectrophotometer (Thermo Fisher Scientific). Samples were diluted at 10 ng/uL concentration. The hypervariable region of the 16S ribosomal RNA gene was sequenced at the Institute of Integrative Biology and Systems (Université Laval, Quebec, Canada). To target the V3-V4-specific regions, a two-step amplification technique was used with dedicated primers for the sense strands 341F (5’-CCTACGGGNGGCWGCAG-3’) and antisense strands 805R (5’-GACTACHVGGGTATCTAATCC-3’). Sequencing was performed using a MiSeq equipment (Illumina, San Diego, CA, USA) that produced 300-bp paired-end sequences. Statistical analyses Statistical analysis was performed using R v.4.3.2 23 . The filterAndTrim function of DADA2 package v.1.30.0 24 was used for qualitative sorting, filtering (minimum length of 265 and 225 bp for forward and reverse sequences, respectively), and trimming (removal of 20 and 21 bp for forward and reverse sequences, respectively). Sequences with more than two or five errors (forward and reverse sequences, respectively) and with a quality score lower than two were excluded. Chimeric sequences were removed using removeBimeraDenovo function in DADA2 package 24 . Taxonomy was assigned to nonchimeric sequences using the SILVA reference database v.138.1. All data were integrated into a phyloseq object 25 . To filter taxa with nonsignificant contributions or contaminants, a covariance-based filtering method from the PERFect package v.1.16.0 26 was used. Fig 1 presents the study design, with all the analyses and comparisons performed between the phenotypic groups. The same analyses were conducted for two types of comparisons: between groups of children with FA before (T0 FA) and after (T1 FA) OIT, and between controls and children with FA before OIT (T0 FA). Replicates of all samples were included in this study. Alpha diversity between the T0 FA and T1 FA groups, or between the control and T0 FA groups was calculated using vegan package v.2.6.4 27 based on the richness, Chao1, evenness, and Shannon indices. Graphs were generated using ggplot2 library v.3.5.1 28 . Analysis of variance (ANOVA) was subsequently performed to compare alpha diversity between groups ( p < 0.05). The diversity between communities (beta) was evaluated using vegan package 27 . Normalized data using the relative log expression method implemented in edgeR package v.4.0.16 29 were used to calculate the Bray–Curtis dissimilarities between the samples of the T0 FA and T1 FA groups and between the control and T0 FA groups. Principal coordinate analysis (PCoA) was used to visualize Bray–Curtis dissimilarity matrices. A beta dispersion test (betadisper) and permutations (permutest; 9,999 permutations and p -value cut-off of 0.05) from vegan package 27 were used to check the homogeneity of variances between the groups and sample replicates. Permutation patterns were used to compare paired samples (T0 FA and T1 FA groups) and replicates (T0 FA vs. T1 FA and controls vs. T0 FA). Following this, permutation multivariate analysis of variance (PERMANOVA) was performed using adonis2 function of the same package with 9,999 permutations using the same permutation pattern ( p < 0.05). The variables, including sex, age, number of food allergens, and duration of OIT treatment, were considered for the T0 FA and T1 FA groups. Therefore, sex and age were also considered in the control and T0 FA groups. Differences in bacterial abundance between groups were determined using analysis of microbiome differential abundance and correlation analyses with bias correction (ANCOMBC package v.2.4.0) 30 . The Benjamini–Hochberg method of false discovery rate (FDR) was used to obtain the adjusted p -values considering multiple comparisons (FDR < 0.05). For comparison between the T0 FA and T1 FA groups, paired samples were considered by including them as random variables, whereas the fixed variables for both comparisons were previously mentioned in the adonis2 description above. Genera and phyla were analyzed. Bar plots were generated using ggplot2 library 28 . Results \received DD MMMM YYYY \acceptedDD MMMM YYYY Participant characteristics The characteristics of children with FA and controls are presented in Table 1 . No significant differences were observed between the children with FA and control group regarding age, sex, prematurity, mode of delivery, and breastfeeding status. Personal histories of atopic dermatitis and family histories of asthma, allergy, and eczema were more frequent in children with FA than in the controls. Personal history of asthma was observed only in children with FA; however, the difference was not significant. Fig 2 presents the FA profiles of children with allergies, including the frequency related to the number of food allergens, types of food allergens targeted, and allergens targeted by desensitization. Most children with allergies (86.67%) had one FA; 6.67% had two FAs; and 6.67% had three or more FAs. Legumes (60.00%) were the most common food allergen, whereas fish and shellfish were least common (8.57%). Two children were desensitized to two allergens, while one child was desensitized to three allergens. OIT The mean duration of the updosing phase of OIT was 215.90 ± 68.65 days. Among the 30 allergic children, 14 (46.67%) were no longer allergic and reached complete desensitization, indicating that they could eat any amount of allergenic food without reaction during therapy. Clinical desensitization was achieved in 13 children (43.33 %), which persisted throughout the maintenance phase. These children still avoided consuming the allergen through diet; however, they tolerated traces and were no longer at the risk of severe reactions following accidental exposures 21 . Finally, three children (10.00%) reached complete clinical remission for one of the allergens; therefore, they could consume this allergen ad libitum 31 , and continued the maintenance phase for the other allergens. Alpha diversity Alpha diversity of the microbiota was compared between the T0 FA and T1 FA groups. None of the measures tested were significant (Supplementary Fig 1). Alpha diversity was significantly different between the control and T0 FA groups for Chao1, richness, and Shannon indices, but not for evenness. Notably, although the Shannon index also incorporates evenness, it still showed significant differences between the groups. The evenness index measures the equal abundances of different taxa within a sample 32 (Fig 3). Beta diversity between allergic children before and after OIT PCoA was performed to assess the similarity of the samples with respect to their replicates ( Fig 4A ). To validate the results, a beta dispersion test was used to assess their homogeneity of variance. Homogeneity of variance between replicates was observed for samples from the T0 FA and T1 FA groups ( p = 6.04e-01; Supplementary Fig 2A, 3A ). Therefore, the replicates were included in all analyses. According to the beta dispersion test between samples from the T0 FA and T1 FA groups, these had heterogenous variance ( p = 7.00e-03; Supplementary Fig 2B, 3B ). To quantify the differences in microbial community composition, a PERMANOVA revealed a small but significant difference in community patterns between T0 FA and T1 FA groups ( p = 7.60e-03, R 2 = 0.02622). Based on the dispersion results, this difference in composition of the microbial communities was mostly owing to variance heterogeneity between the samples of the two groups. Beta diversity in controls and allergic children before OIT PCoA was conducted to evaluate the similarity of the samples with respect to their replicates in samples from the control and T0 FA groups, which confirmed their similarity ( Fig 4B ). The beta dispersion test demonstrated replicate homogeneity for these samples ( p = 8.58e-01). Both results justified their inclusion in downstream analyses ( Supplementary Fig 2C, 3C ). Moreover, the beta dispersion test between the control and T0 FA groups revealed heterogeneity between the samples from these groups ( p = 8.10e-03). Significant differences in the composition of microbial communities were noticed between the controls and children with FA before OIT ( p = 1.00e-04, R 2 = 0.17451). Based on the dispersion results, this difference was partly owing to variance heterogeneity between the groups. However, the distance between the centroids of the control and T0 FA groups ( Supplementary Fig 2D, 3D) combined with the high resistance of adonis2 algorithm to non-homogeneity of variance between groups highlights a small difference in composition between the two microbial communities. \received DD MMMM YYYY \acceptedDD MMMM YYYY Differential abundances Three bacterial phyla, Bacteroidota , Pseudomonadota, and Verrucomicrobiota, were identified as being less abundant in the T1 FA group than in the T0 FA group ( Fig 5A ). Two bacterial phyla, Bacteroidota and Verrucomicrobiota, were found to be less abundant in the control group than in the T0 FA group ( Fig 5B ). No bacterial genera were identified as being more or less abundant between the T0 FA and T1 FA groups, considering an FDR < 0.05. However, 19 bacterial genera were more abundant in the T0 FA group, whereas 11 others were more abundant in the control group. The significant results for all genera are shown in Supplementary Table 1 . The bacterial phyla Bacteroidota and Verrucomicrobiota were significantly less abundant in the T1 FA group than in the T0 FA group ( p = 2.41e-02 and 3.42e-02, respectively; Fig 5C, D ), and in the control group than in the T0 FA group ( p = 4.13e-03 and 2.64e-08, respectively; Fig 5C, D ). This suggests that OIT may normalize the abundance of these phyla, bringing their levels close to those observed in control group. Interestingly, although not all results were significant, a tendency towards reversion was observed for all five identified phyla ( Supplementary Table 2 ). Discussion This study aimed to explore the gut microbial composition and diversity before and after OIT in children with FA, and between children with FA and controls. Alterations in the intestinal microbiota are linked to FA development. Several studies have reported differences in intestinal microbial composition and diversity between individuals with FA and controls 10,15,16,33,34 . However, only two studies have examined differences in the gut microbiome after OIT 17,18 . In this study, the analytical design allowed the assessment of changes induced by OIT in the gut microbiome of children with FA, and the observed changes in the microbiome were compared with those of control individuals, thereby providing an important advantage to interpret the changes. Regarding alpha diversity, no significant difference was noted when comparing children with allergies before and after the updosing phase of OIT. Furthermore, the difference in alpha diversity between the control and T0 FA groups was significant, except for the evenness index. Richness, Chao1, and Shannon indices were higher in children with allergies than in controls, indicating a relatively high estimated diversity in the number and abundance of species in children with allergies. However, the results of gut microbial alpha diversity in the literature are contradictory. Several studies have reported higher gut microbial diversity in controls than in children with FA 3536, while others have reported the opposite result 33,36 or no significant difference 10,37 . Beta diversity analysis revealed a small but significant difference between the T0 FA and T1 FA groups, which highlighted a change in the intestinal microbial composition after the updosing phase of OIT. This has also been previously observed in adults with peanut allergy undergoing OIT 17 . However, the results indicated that changes in the intestinal microbial composition following OIT may be significantly influenced by the heterogeneity of the microbial variance in samples of the two groups. A comparison between the controls and children with FA before OIT also revealed a significant difference in beta diversity, indicating a distinct microbial composition between the two groups. This reinforces the findings of previous studies showing that children with FA possess a gut microbiota distinct from that of the controls 33,38 . At the genus level, no significant differences were observed between children with allergies before and after OIT. However, 11 bacterial genera were enriched in the control group compared to those in the allergic children before OIT. Similar results have been previously observed for seven of these genera ( Romboutsia, Megamonas, Clostridium sensu stricto 1, Eggerthella, Escherichia/Shigella, Blautia, Streptococcus ) 35,39-41 . Nineteen bacterial genera were also found to be enriched in children with allergies before OIT compared to those in the control group, with five being associated with FA ( Parasutterella, Lachnospira, Sutterella, [Eubacterium] siraeum group , [Eubacterium] oxidoreducens group), as previously reported 36,42-45 . Changes in phylum abundance may be relatively hard to interpret because of the numerous genera that compose them; however, it can still provide information on the general impact of OIT on diversity of the microbiota. Although all results were not significant, the tendency of all five phyla to revert towards the control levels after OIT was very promising. Some results showed the attainment of the significance threshold. Two phyla, Bacteroidota and Verrucomicrobiota, were less abundant in children following OIT than that before treatment. These bacterial phyla were also less abundant in the controls than in the children before OIT. Verrucomicrobiota is more abundant in children with egg allergy than in nonallergic children 33 . Furthermore, a study on toddlers allergic to cow milk protein has shown an increased abundance of the phylum Bacteroidota compared to that in healthy individuals 46 . Both studies have validated the observed differences between the children with FA and controls in this study; however, our study is the first to document the reversion of their abundance after OIT. The bacterial phylum Pseudomonadota was also found to be more abundant in allergic children before OIT than in children after OIT; however, the comparison between the control group and children with FA was not significant. A previous study has also identified this phylum as being significantly higher in the breast milk of mothers of infants with FA than in the controls 35 , which is consistent with the results obtained in this study. Reversion to normal abundance levels was not observed in genus analysis. This could be partly explained by the use of 16S rRNA gene-targeted sequencing rather than complete metagenomic sequencing. While this approach is useful for bacterial classification, it has limited phylogenetic resolution at the species level and reduced discriminatory potential for some genera 47 . Moreover, a relatively large cohort may have potentially led to the identification of high number of genera or each genus in increased number of samples, thereby increasing the analytical potential at this taxon level. However, this study included samples from children with FAs similar in number with that of other published reports 36,39 . Notably, the two studies that considered the impact of OIT on the gut microbiome were conducted involving seven adults and 32 school-age children, respectively 17 . The main strength of this study is its novel design, as it assessed the differences in composition of the gut microbiota following OIT in children with different FAs and included a control group for further comparison. This design has some limitations, including the small number of controls (n = 7), which reflects the challenge of recruiting nonallergic children, and the difference in methodology between the children with FAs and controls owing to evolving laboratory protocols over time. Additionally, while this analysis does not account for different sensitizations owing to constraints in sample size, future work should address this issue by considering the impact of specific sensitizations on the observed outcomes. However, this design validated previously reported results for children with FAs and controls and assessed an acceptable level of confidence if OIT induces a possible reversion of certain taxa towards the abundance levels observed in controls. This provides a considerable understanding of the effects of OIT and may potentially help improve this new treatment strategy. Incorporating probiotics into therapeutic strategies according to the microbiota profile of patients shows potential for modulating the gut microbiota and improving FA management 48 . In summary, the insights of this study are important to improve and personalize the management of FAs. Acknowledgements The authors are grateful to all the children and their families of the Zéro allergie cohort who participated in this study. Key Messages A shift occurs in the composition and diversity of gut microbiota in children with food allergy undergoing oral immunotherapy. 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Sex, male:female 1:0.76 1:1.33 6.80e-01 Age, years (mean ± standard deviation) 3.93 ± 2.20 2.65 ± 1.86 1.47e-01 Prematurity a , n (%) 3 (10.00) 0 (0.00) 1.00e+00 Cesarean delivery, n (%) 8 (26.67) 3 (42.86) 4.03e-01 Breastfeeding, n (%) 29 (96.67) 5 (71.43) 8.56e-02 Asthma, n (%) 9 (30.00) 0 (0.00) 1.60e-01 Eczema, n (%) 26 (86.67) 0 (0.00) 3.21e-05 Family history of asthma, n (%) 20 (66.67) 1 (14.29) 2.87e-02 Family history of allergy, n (%) 22 (73.33) 2 (28.57) 7.23e-02 Family history of eczema, n (%) 26 (86.67) 2 (28.57) 4.86e-03 Differences between children with FA and controls were assessed using Fisher’s exact test for categorical variables and the Mann–Whitney test for continuous variables. a Prematurity was defined as < 37 weeks of gestation. Figure legends Fig 1. Schematic of the study design. The analysis steps include comparisons of alpha and beta diversities and identification of intestinal microbial taxa with differential abundance (1) between children with FA before (T0 FA) and after (T1 FA) the updosing phase of oral immunotherapy and (2) between controls and the T0 FA group. Finally, (3) comparisons were made between differently abundant taxa identified in the two previous comparisons for assessing if OIT induces a reversion of certain taxa to normal abundance levels. Fig 2. Description of FA profile among allergic children. (A) The frequency related to the number of food allergens leading to allergic reaction for each child. (B) Types of food allergens in allergic children. (C) Food allergens targeted by desensitization in allergic children. (Nuts - cashews, hazelnuts, and pistachios; legumes - lentils, peanuts, and peas; Animal products - egg, milk). Fig 3 . Alpha diversity between controls (C, green) and children with FA before oral immunotherapy (T0 FA, pink) measured by (A) observed index, (B) Chao1 index, (C) evenness index, and (D) Shannon index. Boxes represent quartiles, illustrating the largest distribution of both sample groups, with the line showing the median. The p -values were calculated using ANOVA ( p < 0.05). Fig 4 . Plots showing Bray–Curtis dissimilarity in (PCoA) space for samples of (A) children with FA before and after the updosing phase of OIT and (B) children with FA before OIT and controls. Pink, violet, and green circles represent children with FA before OIT (T0 FA), children with FA after OIT (T1 FA), controls (C), respectively. Triangles of the same colors represent their replicates. Fig 5: Phyla with significant differences in abundance levels. (A) Barplots showing significant log (fold changes) (LFC; y-axis) by bacterial phyla (x-axis) generated using ANCOM-BC test with significance threshold set to false discovery rate (FDR) < 0.05 for the comparison between allergic children before (T0 FA) and after (T1 FA) the updosing phase of the OIT groups. Pink bars represent the bacterial phyla more abundant in the T0 FA group than in the T1 FA group (negative LFC values). (B) Significant LFCs of bacterial phyla between the control and T0 FA groups. Pink bars represent bacterial phyla more abundant in the T0 FA group than in the control group (positive LFC values). (C) Abundance of the phylum Bacteroidota across the three groups: controls (C), T0 FA, and T1 FA. (D) Abundance of the phylum Verrucomicrobiota across the three groups: C, T0 FA, and T1 FA. The FDR values for C and D were calculated using ANCOM-BC analyses. Supplementary Material File (fig 5.tif) Download 1.49 MB Information & Authors Information Version history V1 Version 1 28 January 2025 Peer review timeline Published Pediatric Allergy and Immunology Version of Record 1 Aug 2025 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Pediatric Allergy and Immunology Authors Affiliations Thanina Bouabid Universite du Quebec a Chicoutim departement des sciences fondamentales View all articles by this author Bénédicte L. Tremblay Universite du Quebec a Chicoutim departement des sciences fondamentales View all articles by this author Marie-Ève Lavoie 0009-0009-9804-9323 Universite du Quebec a Chicoutim departement des sciences fondamentales View all articles by this author Anne-Marie Boucher-Lafleur Universite du Quebec a Chicoutim departement des sciences fondamentales View all articles by this author Frédérique Gagnon-Brassard Universite du Quebec a Chicoutim departement des sciences fondamentales View all articles by this author Philippe Bégin Centre Hospitalier Universitaire Sainte-Justine Departement de Pediatrie View all articles by this author Sarah Lavoie Universite du Quebec a Chicoutimi View all articles by this author Cloé Rochefort-Beaudoin Universite du Quebec a Chicoutimi View all articles by this author Claudia Nuncio-Naud Universite du Quebec a Chicoutimi View all articles by this author Guy Parizeault Universite du Quebec a Chicoutimi View all articles by this author Charles Morin Universite du Quebec a Chicoutimi View all articles by this author Catherine Girard Universite du Quebec a Chicoutim departement des sciences fondamentales View all articles by this author Anne-Marie Madore 0009-0007-5980-3931 Universite du Quebec a Chicoutim departement des sciences fondamentales View all articles by this author Catherine Laprise 0000-0001-5526-9945 [email protected] Universite du Quebec a Chicoutim departement des sciences fondamentales View all articles by this author Metrics & Citations Metrics Article Usage 363 views 215 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Thanina Bouabid, Bénédicte L. Tremblay, Marie-Ève Lavoie, et al. Impact of oral immunotherapy on diversity of gut microbiota in food-allergic children. Authorea . 28 January 2025. DOI: https://doi.org/10.22541/au.173809222.25150623/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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