AUTHOR=Arcila-Galvis Juliana Estefanía , Loria-Kohen Viviana , Ramírez de Molina Ana , Carrillo de Santa Pau Enrique , Marcos-Zambrano Laura Judith TITLE=A comprehensive map of microbial biomarkers along the gastrointestinal tract for celiac disease patients JOURNAL=Frontiers in Microbiology VOLUME=13 YEAR=2022 URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.956119 DOI=10.3389/fmicb.2022.956119 ISSN=1664-302X ABSTRACT=

Dysbiosis of the microbiome has been related to Celiac disease (CeD) progress, an autoimmune disease characterized by gluten intolerance developed in genetically susceptible individuals under certain environmental factors. The microbiome contributes to CeD pathophysiology, modulating the immune response by the action of short-chain fatty acids (SCFA), affecting gut barrier integrity allowing the entrance of gluten-derived proteins, and degrading immunogenic peptides of gluten through endoprolyl peptidase enzymes. Despite the evidence suggesting the implication of gut microbiome over CeD pathogenesis, there is no consensus about the specific microbial changes observed in this pathology. Here, we compiled the largest dataset of 16S prokaryotic ribosomal RNA gene high-throughput sequencing for consensus profiling. We present for the first time an integrative analysis of metataxonomic data from patients with CeD, including samples from different body sites (saliva, pharynx, duodenum, and stool). We found the presence of coordinated changes through the gastrointestinal tract (GIT) characterized by an increase in Actinobacteria species in the upper GIT (pharynx and duodenum) and an increase in Proteobacteria in the lower GIT (duodenum and stool), as well as site-specific changes evidencing a dysbiosis in patients with CeD’ microbiota. Moreover, we described the effect of adherence to a gluten-free diet (GFD) evidenced by an increase in beneficial bacteria and a decrease in some Betaproteobacteriales but not fully restoring CeD-related dysbiosis. Finally, we built a Random Forest model to classify patients based on the lower GIT composition achieving good performance.