Sequence-to-expression approach to identify etiological non-coding DNA variations in P53 and cMYC-driven diseases

Abstract

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 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. 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. Therefore, we designed in vitro experiments to uncover the significance of the co-occupancy and competitive binding and inhibition between P53 and cMYC on target gene expression. We found 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 showed 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 gene expression. In conclusion, our study suggests a generally applicable approach to filter etiological non-coding variations associated with P53 and cMYC-dependent diseases. Author SummaryMost DNA variants associated with common complex diseases fall outside the protein-coding regions of the genome, making them hard to detect and relate to a function. Although many computational tools are available for prioritizing functional disease risk variants outside the protein-coding regions of the genome, the precision of prediction of these tools is mostly unreliable and hence not close to cancer risk prediction. This study brings to light a novel way to improve prediction accuracy of publicly available tools by integrating the impact of cis-overlapping binding sites of opposing cancer proteins, such as P53 and cMYC, in their analysis to filter out deleterious DNA variants outside the protein-coding regions of the human genome. Using a biology-based statistical approach, DNA variants within cis-overlapping motifs impacting the binding affinity of opposing transcription factors can significantly alter the expression of target genes and regulatory networks. This study brings us closer to developing a generally applicable approach capable of filtering etiological non-coding variations in co-occupied genomic regions of P53 and cMYC family members to improve disease risk assessment.