Integrating whole genome sequencing, methylation, gene expression and topological associated domain information in regulatory mutation prediction: a study of follicular lymphoma.

Amna Farooq ¹, Gunhild Trøen ¹, Jan Delabie ³ and Junbai Wang ^2,4*

1. Department of Pathology, Oslo University Hospital - Norwegian Radium Hospital, Oslo, Norway

2. Institute for Clinical Medicine, University of Oslo, Norway.

3. Laboratory Medicine Program, University Health Network and University of Toronto, Toronto, Ontario, Canada.

4. Department of clinical molecular biology (EpiGen), Akershus University Hospital, Lørenskog, Norway.

*To whom correspondence should be addressed.Email: junbai.wang@medisin.uio.no

A major challenge in human genetics is of the analysis of the interplay between genetic and epigenetic factors in a multifactorial disease like cancer. To understand this interplay, it is important to comprehensively analyze genetic and epigenetic features. Here, a novel methodology is proposed to investigate genome-wide regulatory mechanisms in cancer, as studied with the example of follicular Lymphoma (FL). In the first phase, a new machine-learning method is designed to identify Differentially Methylated Regions (DMRs) by computing six attributes. In the second phase, an integrative data analysis method is developed to study regulatory mutations in FL, by considering differential methylation information together with DNA sequence variation, differential gene expression, 3D organization of genome (e.g., topologically associated domains - TADs), and enriched biological pathways. Resulting mutation block-gene pairs are further ranked to find out the significant ones. By this approach, ~159 predicted mutation block-gene pairs with possible relevance to FL were identified. Notably, BCL2 and BCL6 were identified as top-ranking FL-related genes with several mutation blocks and DMRs acting on their regulatory regions. Two additional genes, CDCA and CTSO4, were also found in top rank with significant DNA sequence variation and differential methylation in neighboring areas, pointing towards their potential use as biomarkers for FL. This work provides a novel method for combining both genomic and epigenomic information to investigate genome-wide gene regulatory mechanisms in cancer and contribute to devising novel treatment strategies.

SUPPLEMENTARY MATERIAL:

Following are the additional supplemtary files containing detailed results data. Data can be used to replicate the results reported in main study or can be used a resource to conduct futher research of Follicular lymphoma.

DMR_0.7.bed : File containing whole genome list of DMRs identified by our method . DMRs were filtered for rank score 0.7. File has following coloumns:

chr start end mr_info(chr:mrID:mr_sites:Hyper_hypo_mix_dmr:type_of_DMR) score(from 0 to 1)

mutation_blocks_summary.tsv : Detailed list of all mutation blocks identified by current study. File has following coloumns:

block_id chrom start_pos end_pos number_of_mutations number_of_patients mutation_distribution

DMR_mutation.tsv : List of all DMRs overlapping to mutation blocks. File has following coloumns:

chrs start_pos end_pos block_id mr_chrs mr_start_pos mr_end_pos mr_sites mr_logReg_proba

differentially_expressed_genes_1.25Fd.txt : List of differentially expressed genes in FL (14 tutor vs 4 normal) filtered on 1.25 fd. Quantile normalized RPKM levels for each gene in corresponding sample are given. The RPKM values are calculated by gene length based on the method in Zhao et al. The file has following coloumns:

#gene differential_expression_T_test_p_value 4170686/tumor 4177987_tumor 4160468_tumor 4121361_tumor 4159170_tumor 4134005_tumor 4139696_tumor 4177601_tumor 4105105_tumor 4175837_tumor 4158726_tumor 4189200_tumor 4174905_tumor 4188900_tumor normal_gcb_cells_SRR834985 normal_gcb_cells_SRR834984 normal_gcb_cells_SRR834986 normal_gcb_cells_SRR834983

TAD_summary.tsv : List of all TADs overlapping to genes, mutation blocks and enhancers. TAD2gene = if gene exists in TAD, Boundary2gene = if gene exsists in TAD boundary, TAD2block= if mutation block exists in the TAD, Boundary2block = if the mutation block exists in the TAD boundary. isTAD = will be one if the mutation block and gene exsist in same TAD . File has following coloumns:

gene_name patients gene_type block_id new_mr_sites patient_id enhancers TAD2gene Boundary2gene TAD2block Boundary2block isTAD

gene_summary.csv : Final 159 gene list after applying DAVID/GO pathway enrichment analysis (p-values <0.05) on the unique genes for mutation blocks with overlapping to either DMR or DEG regions and with feature score>=0.5. File has following coloumns:

gene_name gene_type block_id new_mr_sites patients isTAD enhancers patient_id geneType_meanScore num_of_patients diffExp_pval dmr_pval median_scores mean_scores

GO_term_gene.csv : Final 159 gene list after applying DAVID/GO pathway enrichment analysis (p-values <0.05) on the unique genes for mutation blocks with overlapping to either DMR or DEG regions and with feature score>=0.5. File has following coloumns:

Category Term Count % PValue Genes List Total Pop Hits Pop Total Fold Enrichment Bonferroni Benjamini FDR total_patients

s_blocks_summary.tsv : mutation blocks predicted by BayesPI-BAR2 tool in the DMRs overlapping with enhancer regions.(reference supplementary method and results). File has following coloumns:

block_id chrom start_pos end_pos number_of_mutations number_of_patients mutation_distribution region_names

significant_blocks4mr_enhancer_regions.txt : Significant mutation blocks predicted by BayesPI-BAR2 tool in the DMRs overlapping with enhancer regions.(reference supplementary method and results). This file can be used to extract significant blocks from s_blocks_summary.tsv. File has one coloumn only contiating mutation block IDs.

enh_target_gene.zip : Enhancer target genes for the enhnacers around four selected genes (BCL2,BCL6,CDCA4,CTSO).

differentially_expressed_genes_exon.txt : Differentially expressed genes in FL (14 tutor vs 4 normal) with RPKM values normalized on exon length. File has following coloumns:

#gene differential_expression_T_test_p_value 4170686 4121361 4160468 4177987 4139696 4134005 4159170 4177601 4105105 4189200 4158726 4175837 4188900 4174905 SRR834983 SRR834986 SRR834984 SRR834985

Results of three tests (327,758, and 959 genes) on 7 chromatin states : Folder contains results for robustness analysis of mutation block-gene associations by evaluating seven chromatin states with chromatin state data gethered from Hoffman etal. files contain absolute log10 of expected P-values for mutation block-gene testing in 10000 times random sampling.

Batmanov, K., J. Delabie, and J. Wang, BayesPI-BAR2: A New Python Package for Predicting Functional Non-coding Mutations in Cancer Patient Cohorts. Front Genet, 2019. 10: p. 282.
Batmanov, K., et al., Integrative whole-genome sequence analysis reveals roles of regulatory mutations in BCL6 and BCL2 in follicular lymphoma. Scientific Reports, 2017.
Farooq, A., et al., HMST-Seq-Analyzer: A new python tool for differential methylation and hydroxymethylation analysis in various DNA methylation sequencing data. Computational and structural biotechnology journal, 2020. 18: p. 2877-2889.
Hoffman, M.M., et al., Integrative annotation of chromatin elements from ENCODE data. Nucleic Acids Res, 2013. 41(2): p. 827-41.
Kretzmer, H., et al., DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control. Nat Genet, 2015. 47(11): p. 1316-25.
Zhao, Y., Li, M. C., Konaté, M. M., Chen, L., Das, B., Karlovich, C., ... & McShane, L. M. (2021). TPM, FPKM, or normalized counts? A comparative study of quantification measures for the analysis of RNA-seq data from the NCI patient-derived models repository. Journal of translational medicine, 19(1), 1-15.

Integrating whole genome sequencing, methylation, gene expression and topological associated domain information in regulatory mutation prediction: a study of follicular lymphoma.

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