Franzoni Alessandra, Baldan Federica, Passon Nadia, Mio Catia, Driul Daniela, Cogo Paola, Fogolari Federico, D'Aurizio Federica, Damante Giuseppe
SOC Istituto di Genetica Medica, Azienda Sanitaria Universitaria Friuli Centrale Udine, Udine, Italy.
Dipartimento di Area Medica, Università di Udine, Udine, Italy.
Endocrine. 2023 Feb;79(2):292-295. doi: 10.1007/s12020-022-03244-z. Epub 2022 Nov 9.
According to the American College of Medical Genetics (ACMG) classification, variants of uncertain significance (VUS) are gene variations whose impact on the disease risk is not yet known. VUS, therefore, represent an unmet need for genetic counselling. Aim of the study is the use the AlphaFold artificial intelligence algorithm to predict the impact of novel mutations of the IGFALS gene, detected in a subject with short stature and initially classified as VUS according to the ACMG classification.
A short-stature girl and her parents have been investigated. IGFALS mutations have been detected through clinical exome and confirmed by Sanger sequencing. The potential presence of co-occurring gene alterations was investigated in the proband by whole exome and CGH array. Structure of the ALS protein (encoded by the IGFALS gene) was evaluated through the AlphaFold artificial intelligence algorithm.
Two IGFALS variants were found in the proband: c.1349T > C (p.Leu450Pro) and c.1363_1365delCTC (p.Leu455del), both classified as VUS, according to ACMG. Parents' analysis highlighted the in trans position of the two variants. AlphaFold showed that the mutated positions were found the concave side a horseshoe structure of the ALS protein, likely interfering with protein-protein interactions. According to a loss of function (LoF) effect of the two variants, reduced levels of the IGF1 and IGFBP-3 proteins, as well as a growth hormone (GH) excess were detected in the proband's serum.
By using the AlphaFold structure we were able to predict two IGFALS gene mutations initially classified as VUS, as potentially pathogenetic. Our proof-of-concept showed a potential application of AlphaFold as tool to a better inform VUS interpretation of genetic tests.
根据美国医学遗传学学会（ACMG）的分类，意义未明的变异（VUS）是指那些对疾病风险影响尚不清楚的基因变异。因此，VUS代表了遗传咨询中尚未满足的需求。本研究的目的是使用AlphaFold人工智能算法来预测在一名身材矮小的受试者中检测到的IGFALS基因新突变的影响，该突变最初根据ACMG分类被归类为VUS。
对一名身材矮小的女孩及其父母进行了调查。通过临床外显子组检测到IGFALS突变，并通过桑格测序进行了确认。通过全外显子组和CGH阵列在先证者中研究了同时发生的基因改变的潜在存在情况。通过AlphaFold人工智能算法评估了ALS蛋白（由IGFALS基因编码）的结构。
在先证者中发现了两个IGFALS变异：c.1349T＞C（p.Leu450Pro）和c.1363_1365delCTC（p.Leu455del），根据ACMG分类，这两个变异均被归类为VUS。对父母的分析突出了这两个变异的反式位置。AlphaFold显示，突变位置位于ALS蛋白马蹄形结构的凹面，可能会干扰蛋白质-蛋白质相互作用。根据这两个变异的功能丧失（LoF）效应，在先证者的血清中检测到IGF1和IGFBP-3蛋白水平降低以及生长激素（GH）过量。
通过使用AlphaFold结构，我们能够预测两个最初被归类为VUS的IGFALS基因突变具有潜在致病性。我们的概念验证表明，AlphaFold作为一种工具，有可能更好地为基因检测的VUS解释提供信息。
