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

Area of Science:

• Genomics
• Molecular Biology
• Computational Biology

Background:

• Distinguishing deleterious non-coding DNA variations for common diseases remains a significant challenge in disease risk prediction.
• Understanding the interplay between transcription factors like P53 and cMYC on shared target genes is crucial for deciphering gene regulation.

Purpose of the Study:

• To investigate the significance of P53 and cMYC occupancy and competitive binding on common target genes.
• To develop a computational approach for filtering etiological non-coding variations associated with common diseases.

Main Methods:

• Analysis of publicly available ChIP-seq data for P53 and cMYC in embryonic stem cells.
• ChIP-seq analysis in U2OS and Raji cells (untreated and doxorubicin-treated) to assess dynamic binding changes.
• RNA-seq analysis to evaluate mRNA level alterations near co-occupied regions.
• Computational motif-matching approach to correlate binding affinity changes with reporter gene expression and GTEx data.

Main Results:

• Identified conserved co-occupancy of P53 and cMYC on ~344-366 regions in embryonic stem cells, with an average of two cis-overlapping motifs (CisOMs) per region.
• Observed dynamic changes in co-occupied regions (16 to 922) and P53 substitutions for cMYC upon doxorubicin treatment.
• Found significant correlation between altered P53/cMYC binding affinity in CisOMs and changes in reporter gene expression and GTEx-derived gene expression.
• Reported alterations in mRNA levels for ~187 genes near co-occupied regions.

Conclusions:

• P53 and cMYC exhibit conserved co-occupancy on target genes, with dynamic binding alterations influenced by cellular conditions.
• Changes in transcription factor binding affinity within cis-overlapping motifs significantly impact target gene expression.
• The study provides a foundation for a generalizable method to filter non-coding variations linked to common diseases.