Kin Katherine, Bhogale Shounak, Zhu Lisha, Thomas Derrick, Bertol Jessica, Zheng W Jim, Sinha Saurabh, Fakhouri Walid D
Department of Diagnostic and Biomedical Sciences, Center for Craniofacial Research, School of Dentistry, University of Texas Health Science Center at Houston.
University of Illinois Urbana-Champaign.
Res Sq. 2023 Jul 12:rs.3.rs-3037310. doi: 10.21203/rs.3.rs-3037310/v1.
Disease risk prediction based on DNA sequence and transcriptional profile can improve disease screening, prevention, and potential therapeutic approaches by revealing contributing genetic factors and altered regulatory networks. Despite identifying many disease-associated DNA variants through genome-wide association studies, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. We previously reported that non-coding variations disrupting cis-overlapping motifs (CisOMs) of opposing transcription factors significantly affect enhancer activity. We designed experiments to uncover the significance of the co-occupancy and competitive binding and inhibition between P53 and cMYC on common target gene expression.
Analyzing publicly available ChIP-seq data for P53 and cMYC in human embryonic stem cells and mouse embryonic cells showed that ~ 344-366 genomic regions are co-occupied by P53 and cMYC. We identified, on average, two CisOMs per region, suggesting that co-occupancy is evolutionarily conserved in vertebrates. Our data showed that treating U2OS cells with doxorubicin increased P53 protein level while reducing cMYC level. In contrast, no change in protein levels was observed in Raji cells. ChIP-seq analysis illustrated that 16-922 genomic regions were co-occupied by P53 and cMYC before and after treatment, and substitutions of cMYC signals by P53 were detected after doxorubicin treatment in U2OS. Around 187 expressed genes near co-occupied regions were altered at mRNA level according to RNA-seq data. We utilized a computational motif-matching approach to determine that changes in predicted P53 binding affinity by DNA variations in CisOMs of co-occupied elements significantly correlate with alterations in reporter gene expression. We performed a similar analysis using SNPs mapped in CisOMs for P53 and cMYC from ChIP-seq data in U2OS and Raji, and expression of target genes from the GTEx portal.
We found a significant correlation between change in motif-predicted cMYC binding affinity by SNPs in CisOMs and altered gene expression. Our study brings us closer to developing a generally applicable approach to filter etiological non-coding variations associated with P53 and cMYC-dependent diseases.
基于DNA序列和转录谱的疾病风险预测，可通过揭示相关遗传因素和改变的调控网络，改善疾病筛查、预防及潜在治疗方法。尽管通过全基因组关联研究已鉴定出许多与疾病相关的DNA变异，但对于大多数常见疾病而言，区分有害的非编码DNA变异仍存在困难。我们之前报道过，破坏相反转录因子的顺式重叠基序（CisOMs）的非编码变异会显著影响增强子活性。我们设计了实验，以揭示P53和cMYC在共同靶基因表达上的共占据、竞争性结合及抑制作用的重要性。
分析人类胚胎干细胞和小鼠胚胎细胞中P53和cMYC的公开可用ChIP-seq数据表明，约344 - 366个基因组区域被P53和cMYC共同占据。我们平均每个区域鉴定出两个CisOMs，这表明共占据在脊椎动物中是进化保守的。我们的数据显示，用阿霉素处理U2OS细胞会增加P53蛋白水平，同时降低cMYC水平。相反，在Raji细胞中未观察到蛋白水平的变化。ChIP-seq分析表明，处理前后有16 - 922个基因组区域被P53和cMYC共同占据，并且在U2OS细胞中阿霉素处理后检测到P53取代了cMYC信号。根据RNA-seq数据，共占据区域附近约187个表达基因在mRNA水平上发生了改变。我们利用一种计算基序匹配方法来确定，共占据元件的CisOMs中DNA变异导致的预测P53结合亲和力变化与报告基因表达改变显著相关。我们使用从U2OS和Raji的ChIP-seq数据中映射到P53和cMYC的CisOMs中的单核苷酸多态性（SNPs）以及GTEx数据库中靶基因的表达，进行了类似分析。
我们发现CisOMs中SNPs导致的基序预测cMYC结合亲和力变化与基因表达改变之间存在显著相关性。我们的研究使我们更接近开发一种普遍适用的方法，以筛选与P53和cMYC依赖性疾病相关的病因性非编码变异。
