Characteristics of the Gut Microbiota in Regard to Atopic Dermatitis and Food Allergies of Children

Abstract

The gut microbiota plays an important role in maintaining human health, as well as in the development of various pathologies, as indicated by a large amount of research. One of the manifestations of an imbalance in the gut microbiome composition is the appearance of various diseases or immune reactions, in particular, atopic dermatitis (AD) and/or food allergies (FA). In this research, using 16S NGS sequencing, it was found that the gut microbiome of children with food allergies and children with atopic dermatitis can be characterized as having higher inflammatory potential. Both groups exhibited an abundance of representatives from the Pasteurellaceae and Erysipelotrichaceae families, as well as a decrease in the relative number of representatives from the Barnesiellaceae family compared to healthy participants. In the group of participants with food allergies, there was a decrease in the relative number of Desulfovibrionaceae representatives and Bifidobacteriaceae family enrichment in relatively healthy participants. In addition, when comparing this group with patients with atopic dermatitis, it was revealed that a number of representatives of such families as Erysipelotrichaceae, Ruminococcaceae and Sutterellaceae prevailed. This information confirms that AD and FA correlate with changes in the composition of the gut microbiota. Further research is needed to determine the cause-effect connections and the effect of compounds derived from the microbiota on the AD and FA development and progression, as well as to create new probiotic drugs to prevent and modulate immune responses, including at an early age.

Keywords: 16S rRNA; atopic dermatitis; atopy; dysbiosis; food allergy; gut microbiota; microbial diversity; microbiome.

Figure 1

Statistical analysis results for alpha diversity values. The p-value values are represented above each line. (AD—atopic dermatitis, FA—food allergy, control—healthy people).

Figure 2

Results of the β-diversity analysis. (a) Ordinal analysis of groups for RDP data (b) Ordinal analysis of groups for SILVA data (*—significant differences).

Figure 3

Relative abundance of bacteria.

Figure 4

The results of the Random Forest model (a) for RDP data, (b) SILVA data (c), ROC curves for four classification models: LDA, SVM, RF, CART for constructing an optimal binary classification model. The best accuracy estimation result was achieved via the Random Forest model.

Figure 5

Regression tree for IgE as an independent variable (Ruminococcaceae and Sutterellaceae (RDP: bac12 and bac82; SILVA: bac12 and bac100, respectively).