Toward predictive R-loop computational biology: genome-scale prediction of R-loops reveals their association with complex promoter structures, G-quadruplexes and transcriptionally active enhancers

Abstract

R-loops are three-stranded RNA:DNA hybrid structures essential for many normal and pathobiological processes. Previously, we generated a quantitative R-loop forming sequence (RLFS) model, quantitative model of R-loop-forming sequences (QmRLFS) and predicted ∼660 000 RLFSs; most of them located in genes and gene-flanking regions, G-rich regions and disease-associated genomic loci in the human genome. Here, we conducted a comprehensive comparative analysis of these RLFSs using experimental data and demonstrated the high performance of QmRLFS predictions on the nucleotide and genome scales. The preferential co-localization of RLFS with promoters, U1 splice sites, gene ends, enhancers and non-B DNA structures, such as G-quadruplexes, provides evidence for the mechanical linkage between DNA tertiary structures, transcription initiation and R-loops in critical regulatory genome regions. We introduced and characterized an abundant class of reverse-forward RLFS clusters highly enriched in non-B DNA structures, which localized to promoters, gene ends and enhancers. The RLFS co-localization with promoters and transcriptionally active enhancers suggested new models for in cis and in trans regulation by RNA:DNA hybrids of transcription initiation and formation of 3D-chromatin loops. Overall, this study provides a rationale for the discovery and characterization of the non-B DNA regulatory structures involved in the formation of the RNA:DNA interactome as the basis for an emerging quantitative R-loop biology and pathobiology.

MYCN amplification and ATRX mutations are incompatible in neuroblastoma

“…We identified the reproducible peaks in each group (ATRX, MYCN, and WT) and created a reference list of 51,919 H3.3 peaks across our data set. Among those peaks, 18,436 (35%) overlapped with DNA sequences that are predicted to form G4 structures and 70% (12,932) of those are predicted to form R-loops 42 . As expected, there were fewer H3.3 peaks in the ATRX group and nearly a twofold reduction in H3.3 peaks overlapping G4 sequences (Fig.…”

Laboratory Evolution Experiments Help Identify a Predominant Region of Constitutive Stable DNA Replication Initiation

“…Note however that there are many other regions that are highly bisulfite sensitive in the Leela et al Data but not proximal to any potential oriK site. We then used a computational technique that searches for two G-rich patterns on a given DNA sequence to identify loci that have the propensity to form RNA-DNA hybrids (Jenjaroenpun et al 2015;Kuznetsov et al 2018). This method predicted ~30 R-loop favouring sites, showing homology to at least one of the two RNA-DNA hybrid-forming sequence patterns, across the E. coli chromosome.…”

“…To predict RNA-DNA hybrids on the chromosome we used QmRLFs model (Kuznetsov et al 2018;Jenjaroenpun et al 2015) on Escherichia coli K12 MG1655 (NC_000913.3) genome with default parameters. From the output file we considered starting position of a predicted R-loop and plotted a line plot for these positions using custom R scripts for both the models (m1 and m2) separately.…”

Laboratory Evolution Experiments Help Identify a Predominant Region of Constitutive Stable DNA Replication Initiation

“…We asked whether oriK45 is proximal to regions with high propensities to form RNA-DNA hybrids. We used a computational technique that searches for two G-rich patterns on a given DNA sequence to identify loci that have the propensity to form RNA-DNA hybrids (34,35). This method predicted ϳ30 R-loop-favoring sites, showing homology to at least one of the two RNA-DNA hybrid-forming sequence patterns, across the E. coli chromosome (Fig.…”

“…R-loop predictions using QmRLFs finder. To predict RNA-DNA hybrids on the chromosome, we used the QmRLFs model (34,35) on the Escherichia coli K-12 MG1655 (NC_000913.3) genome with default parameters. From the output file, we considered the starting position of a predicted R-loop and plotted a line plot for these positions using custom R scripts for both the models (m1 and m2) separately.…”