Mutational scanning of seizure-related gene para in fruit flies

项目总结/摘要 基因中的一个突变可能导致疾病。然而，许多突变是良性的。怎样确定 哪些突变是致病的哪些是良性的有数亿个单核苷酸 人类基因组中已经通过测序确定的变异，其中500万种是蛋白质编码的。 DNA.尽管有如此丰富的数据，这些变体中只有约2%具有临床注释，其中一半是“变体 不确定的意义”。因此，需要对数百万的人进行变体的高通量功能筛选。 没有注释的变体。研究这种大型变体库的一种方法是使用一种方法 称为深度突变扫描，其中包含数百万个基因变体的文库被并行筛选。 这种方法已被证明在分类BRCA 1，PTEN和其他变异体中是有用的，通过在细胞中进行筛选， 细胞培养虽然这种方法具有规模的好处，但它不能在一个特定的环境中研究突变。 发育或组织特异性背景。然而，人类遗传疾病，包括癌症，是组织- 特定.因此，希望在整个生物体的背景下研究突变。 我们最近在我们的实验室中表明，我们可以产生许多果蝇基因组 整合的基因变体数量级为1，000或更多变体，DNA递送的成本约为1美元/变体。 利用这种新的能力，产生大量的转基因D。melanogaster，我们建议使用D. 黑腹癫痫病模型表型超过1,000个基因变异的帕拉，一个密切相关的直系同源物 人类基因SCN 1A，最常见的涉及癫痫的基因。目标1a：产生超过1，000 D。 具有不同的帕拉变体的黑腹动物。目的1b：使用癫痫发作对变异果蝇进行平行表型分析 比色法按癫痫发作严重程度汇集苍蝇，对其进行测序，并将结果报告给ClinVar，以改善临床 变体注释。目标2a：扩大D.黑腹素通过 表征用于将DNA基因组整合到基因组中的更高效的DNA重组酶。目的 2b：使用基因枪提高DNA输送到胚胎的速度，最多可达1000个或更多胚胎 每秒 在这项工作中，我们将使用我们最近开发的方法来产生大量的转基因D。 melanogaster进行突变扫描的1,000多个变种的段落。这些结果将提供支持 人类基因SCN 1A的类似变体的变体分类的证据，这是非常接近的 在这项工作中，我们还将增加DNA递送的规模和整合到DNA中的效率。 果蝇基因组，以便将来可以筛选出更多的基因变体。我们相信我们的方法 将被证明是非常有用的分配临床注释的绝大多数变异的疾病相关基因 显示出组织特异性效应。我们还希望这项技术能被发展中国家广泛采用。 生物学社区，因为它将使数以千计的发展重要的变异快速表型 基因在整个生物体中的作用。

PROJECT SUMMARY/ABSTRACT A single mutation in a gene can lead to a disease. Many mutations, however, are benign. How do we determine which mutations are pathogenic and which are benign? There are hundreds of millions of single nucleotide variants in the human genome that have been identified by sequencing, with 5 million of these in protein-coding DNA. Despite this wealth of data, only ~2% of these variants have clinical annotations, half of which are “variants of uncertain significance”. High throughput functional screening of variants is thus needed for the millions of variants that have not been annotated. One approach for studying such large variant libraries is to use a method called deep mutational scanning, in which a library containing millions of gene variants is screened in parallel. This approach has proven useful in classifying variants of BRCA1, PTEN, and others by carrying out screens in cell culture. While this approach has the benefit of scale, it does not enable mutations to be studied in a developmental or tissue-specific context. However, human genetic diseases, including cancer, are tissue- specific. It is therefore desirable to study mutations in the context of whole organisms. We have recently shown in our lab that we can generate many Drosophila melanogaster with genomically integrated gene variants on the order of 1,000 or more variants at a cost of ~$1/variant for DNA delivery. Using this new ability to generate large numbers of transgenic D. melanogaster, we propose use a D. melanogaster seizure disease model to phenotype over 1,000 gene variants of para, a closely related ortholog to the human gene SCN1A, the most commonly implicated gene in epilepsy. Aim 1a: Generate over 1,000 D. melanogaster harboring different para variants. Aim 1b: Phenotype the variant flies in parallel using a seizure assay. Pool flies by their seizure severity, sequence them, and report findings to ClinVar to improve clinical variant annotation. Aim 2a: Increase the scale of variant libraries that can be made in D. melanogaster by characterizing more highly efficiency DNA recombinases for genomic integration of DNA into the genome. Aim 2b: Use a gene gun to increase the rate at which DNA is delivered to embryos, up to a thousand or more embryos per second. In this work, we will use our recently developed approach for generating large numbers of transgenic D. melanogaster to perform mutational scanning of over 1,000 variants of para. These results will provide supporting evidence for variant classification of the analogous variants of the human gene SCN1A, which is very closely related to para. In this work we will also increase the scale of DNA delivery and efficiency of integration into the fly genome so that larger numbers of gene variants can be screened in the future. We believe our approaches will prove very useful for assigning clinical annotations for the vast majority of variants of disease-related genes that show tissue-specific effects. We also expect the technology to be widely adopted by the developmental biology community, as it will enable rapid phenotyping of thousands of variants of developmentally important genes in a whole organism context.