RESUMEN
BACKGROUND & AIMS: Histologic evaluation of gut biopsies is a cornerstone for diagnosis and management of celiac disease (CeD). Despite its wide use, the method depends on proper biopsy orientation, and it suffers from interobserver variability. Biopsy proteome measurement reporting on the tissue state can be obtained by mass spectrometry analysis of formalin-fixed paraffin-embedded tissue. Here we aimed to transform biopsy proteome data into numerical scores that give observer-independent measures of mucosal remodeling in CeD. METHODS: A pipeline using glass-mounted formalin-fixed paraffin-embedded sections for mass spectrometry-based proteome analysis was established. Proteome data were converted to numerical scores using 2 complementary approaches: a rank-based enrichment score and a score based on machine learning using logistic regression. The 2 scoring approaches were compared with each other and with histology analyzing 18 patients with CeD with biopsies collected before and after treatment with a gluten-free diet as well as biopsies from patients with CeD with varying degree of remission (n = 22). Biopsies from individuals without CeD (n = 32) were also analyzed. RESULTS: The method yielded reliable proteome scoring of both unstained and H&E-stained glass-mounted sections. The scores of the 2 approaches were highly correlated, reflecting that both approaches pick up proteome changes in the same biological pathways. The proteome scores correlated with villus height-to-crypt depth ratio. Thus, the method is able to score biopsies with poor orientation. CONCLUSIONS: Biopsy proteome scores give reliable observer and orientation-independent measures of mucosal remodeling in CeD. The proteomic method can readily be implemented by nonexpert laboratories in parallel to histology assessment and easily scaled for clinical trial settings.
RESUMEN
CD4+ T cells specific for cereal gluten proteins are key players in celiac disease (CeD) pathogenesis. While several CeD-relevant gluten T cell epitopes have been identified, epitopes recognized by a substantial proportion of gluten-reactive T cells remain unknown. The identification of such CeD-driving gluten epitopes is important for the food industry and in clinical settings. Here, we have combined the knowledge of a distinct phenotype of gluten-reactive T cells and key features of known gluten epitopes for the discovery of unknown epitopes. We tested 42 wheat gluten-reactive T cell clones, isolated on the basis of their distinct phenotype and with no reactivity to known epitopes, against a panel of synthetic peptides bioinformatically identified from a wheat gluten protein database. We were able to assign reactivity to 10 T cell clones and identified a 9-nucleotide oligomer core region of five previously uncharacterized gliadin/glutenin epitopes. This work represents an advance in the effort to identify CeD-driving gluten epitopes.
