Genome-Wide Association Study-Guided Exome Rare Variant Burden Analysis Identifies IL1R1 and CD3E as Potential Autoimmunity Risk Genes for Celiac Disease

New Insights on Genes, Gluten, and Immunopathogenesis of Celiac Disease

Celiac disease (CeD) is a gluten-induced enteropathy that develops in genetically susceptible individuals upon consumption of cereal gluten proteins. It is a unique and complex immune disorder to study as the driving antigen is known and the tissue targeted by the immune reaction can be interrogated. This review integrates findings gained from genetic, biochemical, and immunologic studies, which together have revealed mechanisms of gluten peptide modification and HLA binding, thereby enabling a maladapted anti-gluten immune response. Observations in human samples combined with experimental mouse models have revealed that the gluten-induced immune response involves CD4+ T cells, cytotoxic CD8+ T cells, and B cells; their cross-talks are critical for the tissue-damaging response. The emergence of high-throughput technologies is increasing our understanding of the phenotype, location, and presumably function of the gluten-specific cells, which are all required to identify novel therapeutic targets and strategies for CeD.

Rare variant burden analysis from exomes of three consanguineous families reveals LILRB1 and PRSS3 as potential key proteins in inflammatory bowel disease pathogenesis

Background

Inflammatory bowel disease (IBD) is a chronic autoimmune disorder characterized by severe inflammation and mucosal destruction of the intestine. The specific, complex molecular processes underlying IBD pathogenesis are not well understood. Therefore, this study is aimed at identifying and uncovering the role of key genetic factors in IBD.

Method

The whole exome sequences (WESs) of three consanguineous Saudi families having many siblings with IBD were analyzed to discover the causal genetic defect. Then, we used a combination of artificial intelligence approaches, such as functional enrichment analysis using immune pathways and a set of computational functional validation tools for gene expression, immune cell expression analyses, phenotype aggregation, and the system biology of innate immunity, to highlight potential IBD genes that play an important role in its pathobiology.

Results

Our findings have shown a causal group of extremely rare variants in the LILRB1 (Q53L, Y99N, W351G, D365A, and Q376H) and PRSS3 (F4L and V25I) genes in IBD-affected siblings. Findings from amino acids in conserved domains, tertiary-level structural deviations, and stability analysis have confirmed that these variants have a negative impact on structural features in the corresponding proteins. Intensive computational structural analysis shows that both genes have very high expression in the gastrointestinal tract and immune organs and are involved in a variety of innate immune system pathways. Since the innate immune system detects microbial infections, any defect in this system could lead to immune functional impairment contributing to IBD.

Conclusion

The present study proposes a novel strategy for unraveling the complex genetic architecture of IBD by integrating WES data of familial cases, with computational analysis.

Malignancies in Patients with Celiac Disease: Diagnostic Challenges and Molecular Advances

Celiac disease (CD) is a multiorgan autoimmune disorder of the chronic intestinal disease group characterized by duodenal inflammation in genetically predisposed individuals, precipitated by gluten ingestion. The pathogenesis of celiac disease is now widely studied, overcoming the limits of the purely autoimmune concept and explaining its hereditability. The genomic profiling of this condition has led to the discovery of numerous genes involved in interleukin signaling and immune-related pathways. The spectrum of disease manifestations is not limited to the gastrointestinal tract, and a significant number of studies have considered the possible association between CD and neoplasms. Patients with CD are found to be at increased risk of developing malignancies, with a particular predisposition of certain types of intestinal cancer, lymphomas, and oropharyngeal cancers. This can be partially explained by common cancer hallmarks present in these patients. The study of gut microbiota, microRNAs, and DNA methylation is evolving to find the any possible missing links between CD and cancer incidence in these patients. However, the literature is extremely mixed and, therefore, our understanding of the biological interplay between CD and cancer remains limited, with significant implications in terms of clinical management and screening protocols. In this review article, we seek to provide a comprehensive overview of the genomics, epigenomics, and transcriptomics data on CD and its relation to the most frequent types of neoplasms that may occur in these patients.

Artificial Intelligence Analysis of Celiac Disease Using an Autoimmune Discovery Transcriptomic Panel Highlighted Pathogenic Genes including BTLA

Celiac disease is a common immune-related inflammatory disease of the small intestine caused by gluten in genetically predisposed individuals. This research is a proof-of-concept exercise focused on using Artificial Intelligence (AI) and an autoimmune discovery gene panel to predict and model celiac disease. Conventional bioinformatics, gene set enrichment analysis (GSEA), and several machine learning and neural network techniques were used on a publicly available dataset (GSE164883). Machine learning and deep learning included C5, logistic regression, Bayesian network, discriminant analysis, KNN algorithm, LSVM, random trees, SVM, Tree-AS, XGBoost linear, XGBoost tree, CHAID, Quest, C&R tree, random forest, and neural network (multilayer perceptron). As a result, the gene panel predicted celiac disease with high accuracy (95–100%). Several pathogenic genes were identified, some of the immune checkpoint and immuno-oncology pathways. They included CASP3, CD86, CTLA4, FASLG, GZMB, IFNG, IL15RA, ITGAX, LAG3, MMP3, MUC1, MYD88, PRDM1, RGS1, etc. Among them, B and T lymphocyte associated (BTLA, CD272) was highlighted and validated at the protein level by immunohistochemistry in an independent series of cases. Celiac disease was characterized by high BTLA, expressed by inflammatory cells of the lamina propria. In conclusion, artificial intelligence predicted celiac disease using an autoimmune discovery gene panel.

Complex Inheritance of Rare Missense Variants in PAK2, TAP2, and PLCL1 Genes in a Consanguineous Arab Family With Multiple Autoimmune Diseases Including Celiac Disease

BACKGROUND

Autoimmune diseases (AIDs) share a common molecular etiology and often present overlapping clinical presentations. Thus, this study aims to explore the complex molecular basis of AID by whole exome sequencing and computational biology analysis.

METHODS

Molecular screening of the consanguineous AID family and the computational biology characterization of the potential variants were performed. The potential variants were searched against the exome data of 100 healthy individuals and 30 celiac disease patients.

RESULT

A complex inheritance pattern of PAK2 (V43A), TAP2 (F468Y), and PLCL1 (V473I) genetic variants was observed in the three probands of the AID family. The PAK2 variant (V43A) is a novel one, but TAP2 (F468Y) and PLCL1 (V473I) variants are extremely rare in local Arab (SGHP and GME) and global (gnomAD) databases. All these variants were localized in functional domains, except for the PAK2 variant (V43A) and were predicted to alter the structural (secondary structure elements, folding, active site confirmation, stability, and solvent accessibility) and functional (gene expression) features. Therefore, it is reasonable to postulate that the dysregulation of PAK2, TAP2, and PLCL1 genes is likely to elicit autoimmune reactions by altering antigen processing and presentation, T cell receptor signaling, and immunodeficiency pathways.

CONCLUSION

Our findings highlight the importance of exploring the alternate inheritance patterns in families presenting complex autoimmune diseases, where classical genetic models often fail to explain their molecular basis. These findings may have potential implications for developing personalized therapies for complex disease patients.