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

主要研究者（Fakhouri，Walid D.），Co-Chu-I（Qutub，Amina） P53顺式重叠基序中非编码DNA变异的病理学意义的建模 cMyc Layperson的总结 这项研究计划旨在确定位于蛋白质编码区之外的功能性DNA变异 并开发一个计算模型来预测它们对靶基因表达改变的影响。 鉴定致病DNA变异体对于改善癌症和其他遗传疾病的预后至关重要， 高风险个体，以及现有遗传疾病患者的靶向治疗。研究已经 先前针对位于编码序列内的DNA变异，由于它们对功能的影响， 对应的基因/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如何影响蛋白质活性。然而所 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。最近的遗传学研究表明，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变异。 迄今为止，已鉴定出编码DNA变体。因此，非常需要了解 非编码DNA变异改变基因表达的潜在机制， 预测病因变异和靶基因表达的预期变化的计算模型。我们 对癌细胞和胚胎细胞中DNA-蛋白结合信号的生物信息学分析表明， 大量的基因组区域含有肿瘤抑制蛋白P53的重叠结合位点 和癌基因cMYC。这些数据表明了基因调控的一个重要机制， 转录因子P53和cMyc竞争调节元件以调节靶基因的表达 通过竞争性抑制机制。我们的目标是通过P53和cMYC来解读这种机制的影响。 在全基因组水平上对靶基因表达的影响，并预测非编码DNA变体对 正常和癌细胞中的靶基因。该提案的目标在临床上很重要，因为它将 加速癌症和其他遗传疾病中致病突变和相关基因的鉴定 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.