Aleksenko Maxim, Vlasova Elena, Kieva Amina, Abasov Ruslan, Rodina Yulia, Maschan Michael, Shcherbina Anna, Raykina Elena
Dmitry Rogachev National Medical Center of Pediatric Hematology, Oncology and Immunology, 117198 Moscow, Russia.
Regional Children's Clinical Hospital, 620102 Yekaterinburg, Russia.
Genes (Basel). 2024 Dec 26;16(1):18. doi: 10.3390/genes16010018.
The advent of next-generation sequencing (NGS) has revolutionized the analysis of genetic data, enabling rapid identification of pathogenic variants in patients with inborn errors of immunity (IEI). Sometimes, the use of NGS-based technologies is associated with challenges in the evaluation of the clinical significance of novel genetic variants. In silico prediction tools, such as SpliceAI neural network, are often used as a first-tier approach for the primary examination of genetic variants of uncertain clinical significance. Such tools allow us to parse through genetic data and emphasize potential splice-altering variants. Further variant assessment requires precise RNA assessment by agarose gel electrophoresis and/or cDNA Sanger sequencing. We found two novel heterozygous variants in the coding region of the gene (c.10104G>T, c.10894A>G) in an individual with a typical clinical presentation of Chediak-Higashi syndrome (CHS). The SpliceAI neural network predicted both variants as probably splice-altering. cDNA assessment by agarose gel electrophoresis revealed the presence of abnormally shortened splicing products in each variant's case, and cDNA Sanger sequencing demonstrated that c.10104G>T and c.10894A>G substitutions resulted in a shortening of the 44 and 49 exons by 41 and 47 bp, respectively. Both mutations probably lead to a frameshift and the formation of a premature termination codon. This, in turn, may disrupt the structure and/or function of the LYST protein. We identified two novel variants in the gene, predicted to be deleterious by the SpliceAI neural network. Agarose gel cDNA electrophoresis and cDNA Sanger sequencing allowed us to verify inappropriate splicing patterns and establish these variants as disease-causing.
下一代测序(NGS)的出现彻底改变了遗传数据分析,能够快速识别免疫缺陷病(IEI)患者的致病变异。有时,基于NGS的技术在评估新遗传变异的临床意义时会面临挑战。诸如SpliceAI神经网络等计算机预测工具通常被用作对临床意义不确定的遗传变异进行初步检查的一线方法。此类工具使我们能够剖析遗传数据,并突出潜在的剪接改变变异。进一步的变异评估需要通过琼脂糖凝胶电泳和/或cDNA Sanger测序进行精确的RNA评估。我们在一名具有典型切-东综合征(CHS)临床表现的个体的该基因编码区发现了两个新的杂合变异(c.10104G>T,c.10894A>G)。SpliceAI神经网络预测这两个变异可能会改变剪接。通过琼脂糖凝胶电泳进行的cDNA评估显示,在每个变异的情况下都存在异常缩短的剪接产物,cDNA Sanger测序表明c.10104G>T和c.10894A>G替换分别导致第44和49外显子缩短41和47 bp。这两个突变可能都会导致移码并形成提前终止密码子。反过来,这可能会破坏LYST蛋白的结构和/或功能。我们在该基因中鉴定出两个新变异,SpliceAI神经网络预测它们具有有害性。琼脂糖凝胶cDNA电泳和cDNA Sanger测序使我们能够验证不适当的剪接模式,并确定这些变异为致病因素。