Department of Diagnostic and Biomedical Sciences, Center for Craniofacial Research, School of Dentistry, University of Texas Health Science Center at Houston, 7500 Cambridge St, Houston, TX 77054, United States.
Center for Biophysics and Quantitative Biology, University of Illinois at Urbana-Champaign, 600 S Mathews Ave, Urbana, IL 61801, United States.
Hum Mol Genet. 2024 Sep 19;33(19):1697-1710. doi: 10.1093/hmg/ddae109.
Disease risk prediction based on genomic sequence and transcriptional profile can improve disease screening and prevention. Despite identifying many disease-associated DNA variants, distinguishing deleterious non-coding DNA variations remains poor for most common diseases. In this study, we designed in vitro experiments to uncover the significance of occupancy and competitive binding between P53 and cMYC on common target genes. Analyzing publicly available ChIP-seq data for P53 and cMYC in embryonic stem cells showed that ~344-366 regions are co-occupied, and on average, two cis-overlapping motifs (CisOMs) per region were identified, suggesting that co-occupancy is evolutionarily conserved. Using U2OS and Raji cells untreated and treated with doxorubicin to increase P53 protein level while potentially reducing cMYC level, ChIP-seq analysis illustrated that around 16 to 922 genomic regions were co-occupied by P53 and cMYC, and substitutions of cMYC signals by P53 were detected post doxorubicin treatment. Around 187 expressed genes near co-occupied regions were altered at mRNA level according to RNA-seq data analysis. We utilized a computational motif-matching approach to illustrate that changes in predicted P53 binding affinity 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, and expression of target genes from GTEx portal. We found significant correlation between change in cMYC-motif binding affinity in CisOMs and altered expression. Our study brings us closer to developing a generally applicable approach to filter etiological non-coding variations associated with common diseases.
基于基因组序列和转录谱的疾病风险预测可以改善疾病筛查和预防。尽管已经鉴定出许多与疾病相关的 DNA 变体，但对于大多数常见疾病来说，区分有害的非编码 DNA 变异仍然很差。在这项研究中，我们设计了体外实验来揭示 P53 和 cMYC 对常见靶基因的占据和竞争结合的意义。分析胚胎干细胞中 P53 和 cMYC 的公开可用 ChIP-seq 数据表明，约有 344-366 个区域被共同占据，并且每个区域平均鉴定出两个顺式重叠基序 (CisOM)，表明共同占据是进化保守的。使用 U2OS 和 Raji 细胞，未经处理和用阿霉素处理以增加 P53 蛋白水平，同时可能降低 cMYC 水平，ChIP-seq 分析表明，约 16 到 922 个基因组区域被 P53 和 cMYC 共同占据，并且在阿霉素处理后检测到 cMYC 信号被 P53 取代。根据 RNA-seq 数据分析，约 187 个位于共同占据区域附近的表达基因在 mRNA 水平上发生了改变。我们利用一种计算基序匹配方法来表明，共同占据元件的 CisOM 中预测的 P53 结合亲和力的变化与报告基因表达的变化显著相关。我们使用从 ChIP-seq 数据映射到 CisOM 的 P53 和 cMYC 的 SNP 以及 GTEx 门户中的靶基因表达数据进行了类似的分析。我们发现 CisOM 中 cMYC 基序结合亲和力的变化与表达的改变之间存在显著相关性。我们的研究使我们更接近于开发一种普遍适用的方法来筛选与常见疾病相关的病因性非编码变异。
