Mutations detection and classification in ADPKD

描述(申请人提供)：常染色体显性遗传性多囊肾病(ADPKD)是一种常见的遗传性肾病，以逐渐增大的囊性肾脏为特征，导致终末期肾脏疾病。ADPKD疾病谱与心血管异常和其他器官的囊性病变有关，特别是肝脏。ADPKD是由PKD1和PKD2两个基因的突变引起的。在我们以前对ADPKD的研究中，我们已经开发了用于PKD1和PKD2基因突变分析的分子工具，描述了ADPKD队列中的基因型-表型相关性(突变位置与肾脏疾病严重程度和血管表型发展的关系)，并开发了一个评分矩阵来对这两个基因的错义突变进行分类。用Sanger方法对PKD1和PKD2基因进行测序需要大量的扩增片段，而且主要集中在编码外显子和接近内含子的区域。这种方法不探索深层内含子，而且由于使用了大量的引物，可能容易导致等位基因丢失。我们现在建议利用Illumina Genome Analyzer II下一代测序平台作为全面分析PKD基因突变的工具(特定目标1)。我们建议开发覆盖PKD1和PKD2基因的整个基因组结构的长程PCR扩增产物。我们将利用这些长程PCR扩增和下一代测序技术，对30例ADPKD患者的PKD1和PKD2基因的所有深层内含子区域进行研究，这些患者经Sanger方法测序后均为突变阴性。这将使我们能够确定这组突变阴性的ADPKD患者是否携带影响剪接的深层内含子突变，或者在Sanger分析中这些患者是否发生了等位基因丢失。我们建议评估在特定目标1中发现的所有内含子变化，方法是在计算机和体外方法(特定目标2)。在这个队列中发现的深层内含子变异将通过使用群体数据(NCBI数据库SNP、正常内含子变异模式和分离分析)来评估致病性，以筛选出私人变异中的常见多态；使用电子预测工具(4个预测工具)来预测剪接是否可能受到这些变异的影响；以及体外功能分析(cDNA和微基因分析)，以验证剪接是否实际上受到通过前两个步骤选择的内含子变异的影响。对这一突变阴性队列的进一步鉴定将使我们能够研究ADPKD中深层内含子突变的作用，研究PKD基因中剪接调控的新机制，并分析基因与表型的相关性。 公共卫生相关性：常染色体显性遗传性多囊肾病是一种常见的遗传性肾病，可导致中年终末期肾病。这种疾病是由PKD1和PKD2两个基因的突变引起的。ADPKD 1型患者通常比ADPKD 2型患者早20年达到终末期疾病(54岁比74岁)。这两个基因中的ADPKD都有数百种不同的突变，而且它们大多是单个家族特有的。PKD1和PKD2基因是大而复杂的基因。要完成对这些基因的分析，需要用聚合酶链式反应进行扩增，并用桑格方法对大量片段进行测序。然而，这样的分析主要集中在编码外显子和侧翼内含子，而不是深入的内含子。由于靶序列中存在SNPs，在使用大量的聚合酶链式反应引物时也可能发生等位基因丢失。用Sanger方法对大量队列进行测序显示，约10%的ADPKD患者保持突变阴性。这些患者可能会获得非典型突变的丰富，比如导致异常剪接的深层内含子突变。由于目前还没有发现深层内含子，这种突变类型在ADPKD中还没有被描述。在这里，我们建议利用Illumina Genome Analyzer II下一代平台对30名经过广泛分子鉴定后突变阴性的ADPKD患者进行深度测序。我们将利用远程扩增来覆盖PKD1和PKD2基因的整个基因组结构，并进行下一代测序，以获得这些突变阴性的ADPKD患者这两个基因的完整分子签名。在这些患者中发现的内含子变异将使用群体数据(NCBI数据库SNP和正常内含子变异的模式)、电子工具(预测隐蔽剪接位点的激活)和体外分析(从功能上验证强烈预测会影响正常剪接的序列变异)进行评估。总之，我们建议利用深基因组测序和内含子变异的多步骤验证来确定这些突变阴性的ADPKD患者是否携带深内含子变异。这样的发现将验证这一检测方法作为ADPKD的全面基因分型分析，特别是在突变阴性患者中，并允许新的有趣的基因-表型相关性。影响剪接的深层内含子突变的发现可能会导致对如何调控适当的基因剪接的新的理解。

DESCRIPTION (provided by applicant): Autosomal Dominant Polycystic Kidney Disease (ADPKD) is a common inherited nephropathy, characterized by progressively enlarged cystic kidneys and leading to end stage renal disease. The ADPKD disease spectrum is associated with cardiovascular abnormalities and cysts in other organs, particularly the liver. ADPKD is caused by mutations in two genes, PKD1 and PKD2. In our previous studies in ADPKD, we have developed molecular tools for the mutation analysis of the PKD1 and PKD2 genes, described genotype- phenotype correlations in ADPKD cohorts (association of mutation position with severity of renal disease and with development of a vascular phenotype), and developed a scoring matrix to classify missense mutations in these two genes. Sequencing by Sanger method of the PKD1 and PKD2 genes uses a large number of amplicons, and it is focused around the coding exons and close intronic regions. Such approach does not explore deep introns and may be prone to allele drop-out due to the high number of primers used. We now propose to utilize the Illumina Genome Analyzer II next-generation sequencing platform as a tool for comprehensive mutation analysis of the PKD genes (Specific Aim 1). We propose to develop long-range PCR amplicons that cover the whole genomic structure of both the PKD1 and PKD2 genes. We will utilize these long-range PCR amplicons and next-generation sequencing for exploring all the deep intronic regions of the PKD1 and PKD2 genes in a group of 30 ADPKD patients, who are mutation-negative after sequencing by Sanger method. This will allow determining whether this group of mutation-negative ADPKD patients, likely enriched for atypical mutations, does carry deep intronic mutations affecting splicing, or whether allele drop-out occurred in these patients during Sanger analysis. We propose to evaluate all the intronic changes found in Specific Aim 1 by in silico and in vitro approaches (Specific Aim 2). Deep intronic variants found in this cohort will be evaluated for pathogencity using population data (the NCBI dbSNP, the pattern of normal intronic variation, and segregation analysis), to filter out common polymorphisms from private variants; in silico predictive tools (4 predictive tools), to predict whether splicing is likely affected by these variants; and in vitro functional assays (cDNA and minigene analysis), to verify if splicing is actually affected by the intronic variants selected through the two previous steps. Further characterization of this mutation-negative cohort will allow to investigate the role of deep intronic mutations in ADPKD, investigate new mechanisms by which splicing is regulated in the PKD genes, and analyze genotype-phenotype correlations. PUBLIC HEALTH RELEVANCE: Autosomal Dominant Polycystic Kidney Disease is a frequent inherited nephropathy that leads to end stage renal disease by mid-life. The disease is caused by mutations at two genes, PKD1 and PKD2. ADPKD type1 patients reach ESRD typically 20 years earlier than ADPKD type2 patients (54 years of age versus 74 years of age). Several hundreds different mutations account for ADPKD in both genes, and they are mostly specific to a single family. The PKD1 and PKD2 genes are large and complex genes. Complete analysis of these genes requires the amplification by PCR and sequencing by Sanger methods of a large number of fragments. However, such analysis is focused around coding exons and flanking introns, leaving deep introns unexplored. Allele drop-out may also occur when using a large number of PCR primers, due to the presence of SNPs in the target sequence. Sequencing by Sanger methods of large cohorts reveals that ~10% of ADPKD patients remain mutation-negative. These patients are likely to be enriched for atypical mutations, like deep intronic mutations that lead to abnormal splicing. Since deep introns are not currently explored, such mutation type has not yet been descried in ADPKD. Here we propose to utilize the Illumina Genome Analyzer II next-generation platform for deep sequencing a group of 30 ADPKD patients who are mutation-negative after extensive molecular characterization. We will utilize long-range amplicons to cover the whole genomic structure of both the PKD1 and PKD2 genes, and next-generation sequencing to obtain a complete molecular signature of both genes in these mutation-negative ADPKD patients. The intronic variants found in these patients will be evaluated using population data (the NCBI dbSNP and the pattern of normal intronic variation), in silico tools (to predict the activation of cryptic splicing sites) and in vitro assays (to functionally validate sequence variants strongly predicted to affect normal splicing). In conclusion, we propose to utilize deep genomic sequencing and multi-step validation of intronic variants in order to determine whether these mutation-negative ADPKD patients do carry a deep intronic variant. Such finding will validate this assay as a comprehensive genotyping assay for ADPKD, particularly in mutation- negative patients, and allow novel interesting genotype-phenotype correlations. The finding of deep intronic mutations affecting splicing may lead to new understanding on how proper gene splicing is regulated.