qiita_sid_12906
Allergic microbial signature in IgE-mediated food allergies
Multiple studies suggest a key pathogenetic role for gut microbiota modifications in food allergy (FA) development, but to date, no specific bacterial taxa have been consistently associated with FA. Here we characterized the gut microbiota composition and SCFA profile associated with an IgE-mediated FA state. We analyzed blood and fecal samples from 233 IgE-mediated FA patients, and from an age-matched control group (n=58). Our results demonstrate a link between IgE-mediated FA and the gut microbiota composition. We found the microbiome composition of allergic patients to be significantly different compared to age-matched controls, with distinct microbial signatures that differentiate FA patients from healthy non-allergic controls, and distinct microbial signatures for FA to different foods. We used these microbial differences to distinguish between FA patients and non- allergic healthy controls with 85% accuracy, and for the classification of FA patients according to their FA types using a supervised learning algorithm.
Multiple studies suggest a key pathogenetic role for gut microbiota modifications in food allergy (FA) development, but to date, no specific bacterial taxa have been consistently associated with FA. Here we characterized the gut microbiota composition and SCFA profile associated with an IgE-mediated FA state. We analyzed blood and fecal samples from 233 IgE-mediated FA patients, and from an age-matched control group (n=58). Our results demonstrate a link between IgE-mediated FA and the gut microbiota composition. We found the microbiome composition of allergic patients to be significantly different compared to age-matched controls, with distinct microbial signatures that differentiate FA patients from healthy non-allergic controls, and distinct microbial signatures for FA to different foods. We used these microbial differences to distinguish between FA patients and non- allergic healthy controls with 85% accuracy, and for the classification of FA patients according to their FA types using a supervised learning algorithm.