Abstract:
Celiac disease (CeD) is a multifactorial autoimmune enteropathy characterized by the overactivation of the immune system in response to dietary gluten. The molecular etiology of CeD is still not well-understood. Therefore, this study aims to identify potential candidate genes involved in CeD pathogenesis by applying multilayered system biology approaches. Initially, we identified rare coding variants shared between the affected siblings in two rare Arab CeD families by whole-exome sequencing (WES). Then we used the STRING database to construct a protein network of rare variants and genome-wide association study (GWAS) loci to explore their molecular interactions in CeD. Furthermore, the hub genes identified based on network topology parameters were subjected to a series of computational validation analyses like pathway enrichment, gene expression, knockout mouse model, and variant pathogenicity predictions. Our findings have shown the absence of rare variants showing classical Mendelian inheritance in both families. However, interactome analysis of rare WES variants and GWAS loci has identified a total of 11 hub genes. The multidimensional computational analysis of hub genes has prioritized IL1R1 for family A and CD3E for family B as potential genes. These genes were connected to CeD pathogenesis pathways of T-cell selection, cytokine signaling, and adaptive immune response. Future multi-omics studies may uncover the roles of IL1R1 and CD3E in gluten sensitivity. The present investigation lays forth a novel approach integrating next-generation sequencing (NGS) of familial cases, GWAS, and computational analysis for solving the complex genetic architecture of CeD.
摘要:
乳糜泻(CeD)是一种多因素的自身免疫性肠病,其特征是免疫系统对饮食麸质的过度激活。CeD的分子病因学尚不清楚。因此,本研究旨在通过应用多层系统生物学方法鉴定参与CeD发病的潜在候选基因。最初,我们通过全外显子组测序(WES)鉴定了两个罕见的阿拉伯CeD家族中受影响的兄弟姐妹之间共有的罕见编码变异.然后,我们使用STRING数据库构建了一个稀有变体和全基因组关联研究(GWAS)基因座的蛋白质网络,以探索它们在CeD中的分子相互作用。此外,基于网络拓扑参数识别的集线器基因进行了一系列计算验证分析,如途径富集,基因表达,敲除小鼠模型,和变异致病性预测。我们的发现表明,在两个家庭中都没有罕见的变体显示经典的孟德尔遗传。然而,罕见的WES变异体和GWAS基因座的相互作用组分析已经鉴定出总共11个hub基因。hub基因的多维计算分析已经优先考虑家族A的IL1R1和家族B的CD3E作为潜在基因。这些基因与T细胞选择的CeD发病途径有关,细胞因子信号,和适应性免疫反应。未来的多组学研究可能揭示IL1R1和CD3E在谷蛋白敏感性中的作用。本研究提出了一种新的方法,整合家族病例的下一代测序(NGS),GWAS,和计算分析,以解决CeD的复杂遗传结构。