Modeling of pathological significance of non-coding DNA variants in cis-overlapping motifs of p53 and cMyc

首席调查员(Fakhouri，Walid D.D.)，Co--I(Qutub，Amina)。 P53和P53顺式重叠模体中非编码DNA变异的病理意义建模 CMYC 非专业人士摘要 这项研究计划试图确定位于蛋白质编码区之外的功能性DNA变异 并开发了一个计算模型，预测它们对目标基因表达变化的影响。 识别致病DNA变异对于改善癌症和其他遗传性疾病的预后至关重要 用于高危人群，并用于现有遗传病患者的靶向治疗。研究一直在进行 以前针对的是编码序列中的DNA变异，因为它们对功能的影响 相应的基因/蛋白质产物。有几个可用的计算程序可以 在实验研究之前预测突变可能如何影响蛋白质活性。然而，技术上的 预测位于影响表达的蛋白质编码区之外的变异的影响的知识 而不是蛋白质的功能还不是很清楚。最近的遗传学研究报告说，大量的DNA 与癌症和其他常见疾病相关的变异是非编码的，然而，很少有致病的非编码的 到目前为止，已经鉴定出编码DNA的变体。因此，非常有必要了解 非编码DNA变异改变基因表达并发展出强大的 预测病原学变异和预期目标基因表达变化的计算模型。我们的 对癌细胞和胚胎细胞中DNA-蛋白质结合信号的生物信息学分析表明， 大量基因组区域包含肿瘤抑制蛋白P53的重叠结合部位 以及致癌基因cmyc。这一数据表明了一种重要的基因调控机制，其中 转录因子P53和cMyc竞争调控元件以调控靶基因的表达 通过竞争抑制机制。我们的目标是破译p53和cmyc对这一机制的影响。 在全基因组水平上研究靶基因的表达，并预测非编码DNA变异对基因表达的影响 正常细胞和癌细胞中的靶基因。这项提案的目标具有重要的临床意义，因为它将 加速识别癌症和其他基因中的致病突变和相关基因 疾病。

Principal Investigator (Fakhouri, Walid D.), Co-­‐I (Qutub, Amina)  Modeling of pathological significance of non-coding DNA variants in cis-overlapping motifs of P53 and cMYC Layperson's Summary This research proposal seeks to identify functional DNA variations that lie outside the protein-coding regions and develop a computational model that predicts their effect on alterations of target gene expression. Identification of causative DNA variants is critical for better prognosis of cancer and other genetic diseases in high-risk individuals, and for targeted therapies in patients with existing genetic disease. Research has been previously directed towards DNA variations located within coding sequences due to their effect on the function of the corresponding gene/protein product. There are several available computational programs that can predict how mutations may affect protein activity prior to experimental investigation. However, the technical knowledge to predict the effect of variations located outside the protein-coding regions that affect expression rather than protein function are not available yet. Recent genetic studies reported that a large number of DNA variants associated with cancer and other common diseases are non-coding, however, few causative non- coding DNA variants were identified thus far. Therefore, there is a tremendous need to understand the underlying mechanism by which non-coding DNA variations alter gene expression and to develop a powerful computational model that predicts etiologic variants and expected change in target gene expression. Our bioinformatic analysis of DNA-protein binding signals in both cancer and embryonic cells showed that a significant number of genomic regions contain overlapping binding sites for the tumor suppressor protein P53 and the oncogene cMYC. This data suggests an important mechanism of gene regulation where both transcription factors P53 and cMyc compete at regulatory elements to regulate the expression of target genes by a competitive inhibitory mechanism. Our goal is to decipher the impact of this mechanism by P53 and cMYC on target gene expression at the genome-wide level and predict the effect of non-coding DNA variants on target genes in normal and cancer cells. The goal of this proposal is clinically important because it will accelerate the identification of causative mutations and associated genes in cancer and other genetic diseases.