在单基因糖尿病中评估GCK、HNF1A和HNF4A变异体对剪接的影响

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2023-08-31 DOI:10.1155/2023/6661013
D. Bouvet, A. Blondel, Jean-Madeleine de Sainte Agathe, G. Leroy, C. Saint-Martin, C. Bellanné-Chantelot
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引用次数: 0

摘要

GCK、HNF1A和HNF4A基因的变异是单基因糖尿病的三个主要原因。确定分子病因对于单基因糖尿病患者从最合适的治疗中获益至关重要。未知意义变异(VUS)数量的增加是遗传诊断中的一个主要问题,评估变异对RNA剪接的影响具有挑战性,特别是对于在单基因糖尿病中不易获得的组织中表达的基因。基于minigene结构的体外功能剪接试验是一种合适的方法。在这里,我们使用SpliceAI和SPiP进行了硅分析,并对GCK、HNF1A和HNF4A中的36个剪接变异体进行了优先排序。其次,将预测结果与Pangolin和AbSplice-DNA生物信息学工具(包括组织特异性注释)进行比较。我们使用迷你基因试验评估了选定的变异对RNA剪接的影响。这些分析验证了36个剪接变异中的33个剪接缺陷,包括外显子跳变(15%)、外显子缺失(18%)、内含子保留(24%)和复杂剪接模式(42%)。这为将31个VUS中的23个(74%)重新分类提供了额外的证据,包括错义、同义和内含子非典型剪接位点变异作为可能的致病变异。硅分析与minigene结果的比较表明,生物信息学工具在确定剪接变异的优先级方面具有稳稳性,但揭示了在隐剪接位点位置上的不一致,这表明了用功能剪接试验确认预测剪接改变的重要性。我们的研究强调了在先前深入的硅分析后,在单基因糖尿病基因检测中实施微小基因剪接分析的可行性和益处。
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Evaluation in Monogenic Diabetes of the Impact of GCK, HNF1A, and HNF4A Variants on Splicing through the Combined Use of In Silico Tools and Minigene Assays
Variants in GCK, HNF1A, and HNF4A genes are the three main causes of monogenic diabetes. Determining the molecular etiology is essential for patients with monogenic diabetes to benefit from the most appropriate treatment. The increasing number of variants of unknown significance (VUS) is a major issue in genetic diagnosis, and assessing the impact of variants on RNA splicing is challenging, particularly for genes expressed in tissues not easily accessible as in monogenic diabetes. The in vitro functional splicing assay based on a minigene construct is an appropriate approach. Here, we performed in silico analysis using SpliceAI and SPiP and prioritized 36 spliceogenic variants in GCK, HNF1A, and HNF4A. Predictions were secondarily compared with Pangolin and AbSplice-DNA bioinformatics tools which include tissue-specific annotations. We assessed the effect of selected variants on RNA splicing using minigene assays. These assays validated splicing defects for 33 out of 36 spliceogenic variants consisting of exon skipping (15%), exonic deletions (18%), intronic retentions (24%), and complex splicing patterns (42%). This provided additional evidence to reclassify 23 out of 31 (74%) VUS including missense, synonymous, and intronic noncanonical splice site variants as likely pathogenic variants. Comparison of in silico analysis with minigene results showed the robustness of bioinformatics tools to prioritize spliceogenic variants, but revealed inconsistencies in the location of cryptic splice sites underlying the importance of confirming predicted splicing alterations with functional splicing assays. Our study underlines the feasibility and the benefits of implementing minigene-splicing assays in the genetic testing of monogenic diabetes after a prior in-depth in silico analysis.
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