Microbial signature in IgE-mediated food allergies

Genome Med. 2020 Oct 27;12(1):92. doi: 10.1186/s13073-020-00789-4.


Background: Multiple studies suggest a key role for gut microbiota in IgE-mediated food allergy (FA) development, but to date, none has studied it in the persistent state.

Methods: To characterize the gut microbiota composition and short-chain fatty acid (SCFAs) profiles associated with major food allergy groups, we recruited 233 patients with FA including milk (N = 66), sesame (N = 38), peanut (N = 71), and tree nuts (N = 58), and non-allergic controls (N = 58). DNA was isolated from fecal samples, and 16S rRNA gene sequences were analyzed. SCFAs in stool were analyzed from patients with a single allergy (N = 84) and controls (N = 31).

Results: The gut microbiota composition of allergic patients was significantly different compared to age-matched controls both in α-diversity and β-diversity. Distinct microbial signatures were noted for FA to different foods. Prevotella copri (P. copri) was the most overrepresented species in non-allergic controls. SCFAs levels were significantly higher in the non-allergic compared to the FA groups, whereas P. copri significantly correlated with all three SCFAs. We used these microbial differences to distinguish between FA patients and non-allergic healthy controls with an area under the curve of 0.90, and for the classification of FA patients according to their FA types using a supervised learning algorithm. Bacteroides and P. copri were identified as taxa potentially contributing to KEGG acetate-related pathways enriched in non-allergic compared to FA. In addition, overall pathway dissimilarities were found among different FAs.

Conclusions: Our results demonstrate a link between IgE-mediated FA and the composition and metabolic activity of the gut microbiota.

Keywords: Food allergy; Microbiota; P. copri; Postbiotics; Prebiotics; SCFA; Supervised learning.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Biomarkers
  • Disease Susceptibility*
  • Fatty Acids, Volatile / metabolism
  • Female
  • Food Hypersensitivity / etiology*
  • Food Hypersensitivity / metabolism
  • Gastrointestinal Microbiome
  • Humans
  • Immunoglobulin E / immunology*
  • Machine Learning
  • Male
  • Microbiota* / immunology
  • Middle Aged
  • Probiotics
  • RNA, Ribosomal, 16S / genetics


  • Biomarkers
  • Fatty Acids, Volatile
  • RNA, Ribosomal, 16S
  • Immunoglobulin E