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Predicting regulatory mutations and their target genes by new computational integrative analysis: A study of
Junbai Wang1, Mingyi Yang2, Omer Ali3
1Department of Clinical Molecular Biology (EpiGen), Akershus University Hospital and University of Oslo, Lørenskog, Norway; Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Campus AHUS/Oslo, Norway.
Abstract:
Mutations in DNA regulatory regions are increasingly being recognized as important drivers of cancer and other complex diseases. These mutations can regulate gene expression by affecting DNA-protein binding and epigenetic profiles, such as DNA methylation in genome regulatory elements. However, identifying mutation hotspots associated with expression regulation and disease progression in non-coding DNA remains a challenge. Unlike most existing approaches that assign a mutation score to individual single nucleotide polymorphisms (SNP), a mutation block (MB)-based approach was introduced in this study to assess the collective impact of a cluster of SNPs on transcription factor-DNA binding affinity, differential gene expression (DEG), and nearby DNA methylation. Moreover, the long-distance target genes of functional MBs were identified using a new permutation-based algorithm that assessed the significance of correlations between DNA methylation at regulatory regions and target gene expression. Two new Python packages were developed. The Differential Methylation Region (DMR-analysis) analysis tool was used to detect DMR and map them to regulatory elements. The second tool, an integrated DMR, DEG, and SNP analysis tool (DDS-analysis), was used to combine the omics data to identify functional MBs and long-distance target genes. Both tools were validated in follicular lymphoma (FL) cohorts, where not only known functional MBs and their target genes (BCL2 and BCL6) were recovered, but also novel genes were found, including CDCA4 and JAG2, which may be associated with FL development. These genes are linked to target gene expression and are significantly correlated with the methylation of nearby DNA sequences in FL. The proposed computational integrative analysis of multiomics data holds promise for identifying regulatory mutations in cancer and other complex diseases.
Insights
Identifying cancer-driving mutations in non-coding DNA is challenging. This study introduces a mutation block (MB) approach and new Python tools to analyze clusters of SNPs, revealing novel disease-associated genes in follicular lymphoma.
Area of Science:
- Genomics
- Cancer Biology
- Bioinformatics
Background:
- Mutations in DNA regulatory regions are key drivers of cancer and complex diseases.
- These mutations impact gene expression via DNA-protein binding and epigenetic modifications like DNA methylation.
- Identifying mutation hotspots in non-coding DNA linked to gene regulation and disease remains difficult.
Purpose of the Study:
- To develop and validate a novel mutation block (MB)-based approach for assessing the collective impact of SNP clusters on gene expression and DNA methylation.
- To identify long-distance target genes of functional MBs using a new permutation-based algorithm.
- To introduce two Python packages, DMR-analysis and DDS-analysis, for integrated multi-omics data analysis.
Main Methods:
- Developed a mutation block (MB) approach to evaluate the combined effect of single nucleotide polymorphisms (SNPs) on transcription factor-DNA binding, differential gene expression (DEG), and DNA methylation.
- Utilized a novel permutation-based algorithm to identify long-distance target genes by correlating DNA methylation in regulatory regions with target gene expression.
- Created two Python packages: DMR-analysis for detecting differential methylation regions (DMRs) and DDS-analysis for integrating DMR, DEG, and SNP data.
Main Results:
- Validated the MB-based approach and Python tools in follicular lymphoma (FL) cohorts.
- Successfully identified known functional MBs and their target genes (BCL2, BCL6) in FL.
- Discovered novel FL-associated genes, CDCA4 and JAG2, significantly correlated with target gene expression and nearby DNA methylation.
Conclusions:
- The proposed computational integrative analysis of multi-omics data effectively identifies functional mutation blocks and their target genes.
- The novel MB-based approach and associated Python tools offer a promising strategy for discovering regulatory mutations in cancer and other complex diseases.
- The identified novel genes (CDCA4, JAG2) may play roles in FL development, warranting further investigation.
