PDF(Pubmed)
Abstract：
UNASSIGNED: Modeling disease variants in animals is useful for drug discovery, understanding disease pathology, and classifying variants of uncertain significance (VUS) as pathogenic or benign.
UNASSIGNED: Using Clustered Regularly Interspaced Short Palindromic Repeats, we performed a Whole-gene Humanized Animal Model procedure to replace the coding sequence of the animal model\'s unc-18 ortholog with the coding sequence for the human STXBP1 gene. Next, we used Clustered Regularly Interspaced Short Palindromic Repeats to introduce precise point variants in the Whole-gene Humanized Animal Model-humanized STXBP1 locus from 3 clinical categories (benign, pathogenic, and VUS). Twenty-six phenotypic features extracted from video recordings were used to train machine learning classifiers on 25 pathogenic and 32 benign variants.
UNASSIGNED: Using multiple models, we were able to obtain a diagnostic sensitivity near 0.9. Twenty-three VUS were also interrogated and 8 of 23 (34.8%) were observed to be functionally abnormal. Interestingly, unsupervised clustering identified 2 distinct subsets of known pathogenic variants with distinct phenotypic features; both p.Tyr75Cys and p.Arg406Cys cluster away from other variants and show an increase in swim speed compared with hSTXBP1 worms. This leads to the hypothesis that the mechanism of disease for these 2 variants may differ from most STXBP1-mutated patients and may account for some of the clinical heterogeneity observed in the patient population.
UNASSIGNED: We have demonstrated that automated analysis of a small animal system is an effective, scalable, and fast way to understand functional consequences of variants in STXBP1 and identify variant-specific intensities of aberrant activity suggesting a genotype-to-phenotype correlation is likely to occur in human clinical variations of STXBP1.
UNASSIGNED: Using Clustered Regularly Interspaced Short Palindromic Repeats, we performed a Whole-gene Humanized Animal Model procedure to replace the coding sequence of the animal model\'s unc-18 ortholog with the coding sequence for the human STXBP1 gene. Next, we used Clustered Regularly Interspaced Short Palindromic Repeats to introduce precise point variants in the Whole-gene Humanized Animal Model-humanized STXBP1 locus from 3 clinical categories (benign, pathogenic, and VUS). Twenty-six phenotypic features extracted from video recordings were used to train machine learning classifiers on 25 pathogenic and 32 benign variants.
UNASSIGNED: Using multiple models, we were able to obtain a diagnostic sensitivity near 0.9. Twenty-three VUS were also interrogated and 8 of 23 (34.8%) were observed to be functionally abnormal. Interestingly, unsupervised clustering identified 2 distinct subsets of known pathogenic variants with distinct phenotypic features; both p.Tyr75Cys and p.Arg406Cys cluster away from other variants and show an increase in swim speed compared with hSTXBP1 worms. This leads to the hypothesis that the mechanism of disease for these 2 variants may differ from most STXBP1-mutated patients and may account for some of the clinical heterogeneity observed in the patient population.
UNASSIGNED: We have demonstrated that automated analysis of a small animal system is an effective, scalable, and fast way to understand functional consequences of variants in STXBP1 and identify variant-specific intensities of aberrant activity suggesting a genotype-to-phenotype correlation is likely to occur in human clinical variations of STXBP1.
摘要：
■模拟动物的疾病变异对于药物发现很有用，了解疾病病理学，并将不确定显著性变异(VUS)分类为致病性或良性。
■使用簇状规则间隔的短回文重复，我们进行了全基因人源化动物模型程序，将动物模型的unc-18直系同源物的编码序列替换为人STXBP1基因的编码序列。接下来,我们使用成簇的定期间隔短回文重复序列在全基因人源化动物模型-人源化STXBP1基因座中从3个临床类别（良性，致病性,和VUS）。从视频记录中提取的26个表型特征用于训练25个致病性和32个良性变异的机器学习分类器。
■使用多个模型，我们能够获得接近0.9的诊断灵敏度.还询问了23个VUS，观察到23个中的8个（34.8％）功能异常。有趣的是,无监督聚类确定了具有不同表型特征的已知致病性变体的2个不同子集；与hSTXBP1蠕虫相比，p.Tyr75Cys和p.Arg406Cys均远离其他变体，并显示游泳速度增加。这导致以下假设:这2种变体的疾病机制可能不同于大多数STXBP1突变的患者,并且可以解释在患者群体中观察到的一些临床异质性。
■我们已经证明，自动分析小动物系统是一种有效的，可扩展，以及快速了解STXBP1中变体的功能后果并确定异常活性的变体特异性强度的方法，这表明在STXBP1的人类临床变异中可能发生基因型与表型的相关性。
■使用簇状规则间隔的短回文重复，我们进行了全基因人源化动物模型程序，将动物模型的unc-18直系同源物的编码序列替换为人STXBP1基因的编码序列。接下来,我们使用成簇的定期间隔短回文重复序列在全基因人源化动物模型-人源化STXBP1基因座中从3个临床类别（良性，致病性,和VUS）。从视频记录中提取的26个表型特征用于训练25个致病性和32个良性变异的机器学习分类器。
■使用多个模型，我们能够获得接近0.9的诊断灵敏度.还询问了23个VUS，观察到23个中的8个（34.8％）功能异常。有趣的是,无监督聚类确定了具有不同表型特征的已知致病性变体的2个不同子集；与hSTXBP1蠕虫相比，p.Tyr75Cys和p.Arg406Cys均远离其他变体，并显示游泳速度增加。这导致以下假设:这2种变体的疾病机制可能不同于大多数STXBP1突变的患者,并且可以解释在患者群体中观察到的一些临床异质性。
■我们已经证明，自动分析小动物系统是一种有效的，可扩展，以及快速了解STXBP1中变体的功能后果并确定异常活性的变体特异性强度的方法，这表明在STXBP1的人类临床变异中可能发生基因型与表型的相关性。
