Quality-controlled R-loop meta-analysis reveals the characteristics of R-loop consensus regions

Methods for constructing and evaluating consensus genomic interval sets

The amount of genomic region data continues to increase. Integrating across diverse genomic region sets requires consensus regions, which enable comparing regions across experiments, but also by necessity lose precision in region definitions. We require methods to assess this loss of precision and build optimal consensus region sets. Here, we introduce the concept of flexible intervals and propose three novel methods for building consensus region sets, or universes: a coverage cutoff method, a likelihood method, and a Hidden Markov Model. We then propose three novel measures for evaluating how well a proposed universe fits a collection of region sets: a base-level overlap score, a region boundary distance score, and a likelihood score. We apply our methods and evaluation approaches to several collections of region sets and show how these methods can be used to evaluate fit of universes and build optimal universes. We describe scenarios where the common approach of merging regions to create consensus leads to undesirable outcomes and provide principled alternatives that provide interoperability of interval data while minimizing loss of resolution.

Deep learning-enhanced R-loop prediction provides mechanistic implications for repeat expansion diseases

R-loops play diverse functional roles, but controversial genomic localization of R-loops have emerged from experimental approaches, posing significant challenges for R-loop research. The development and application of an accurate computational tool for studying human R-loops remains an unmet need. Here, we introduce DeepER, a deep learning-enhanced R-loop prediction tool. DeepER showcases outstanding performance compared to existing tools, facilitating accurate genome-wide annotation of R-loops and a deeper understanding of the position- and context-dependent effects of nucleotide composition on R-loop formation. DeepER also unveils a strong association between certain tandem repeats and R-loop formation, opening a new avenue for understanding the mechanisms underlying some repeat expansion diseases. To facilitate broader utilization, we have developed a user-friendly web server as an integral component of R-loopBase. We anticipate that DeepER will find extensive applications in the field of R-loop research.

TDP1 mutation causing SCAN1 neurodegenerative syndrome hampers the repair of transcriptional DNA double-strand breaks

TDP1 removes transcription-blocking topoisomerase I cleavage complexes (TOP1ccs), and its inactivating H493R mutation causes the neurodegenerative syndrome SCAN1. However, the molecular mechanism underlying the SCAN1 phenotype is unclear. Here, we generate human SCAN1 cell models using CRISPR-Cas9 and show that they accumulate TOP1ccs along with changes in gene expression and genomic distribution of R-loops. SCAN1 cells also accumulate transcriptional DNA double-strand breaks (DSBs) specifically in the G1 cell population due to increased DSB formation and lack of repair, both resulting from abortive removal of transcription-blocking TOP1ccs. Deficient TDP1 activity causes increased DSB production, and the presence of mutated TDP1 protein hampers DSB repair by a TDP2-dependent backup pathway. This study provides powerful models to study TDP1 functions under physiological and pathological conditions and unravels that a gain of function of the mutated TDP1 protein, which prevents DSB repair, rather than a loss of TDP1 activity itself, could contribute to SCAN1 pathogenesis.

Approaches for Mapping and Analysis of R-loops

R-loops are nucleic acid structures composed of a DNA:RNA hybrid with a displaced non-template single-stranded DNA. Current approaches to identify and map R-loop formation across the genome employ either an antibody targeted against R-loops (S9.6) or a catalytically inactivated form of RNase H1 (dRNH1), a nuclease that can bind and resolve DNA:RNA hybrids via RNA exonuclease activity. This overview article outlines several ways to map R-loops using either methodology, explaining the differences and similarities among the approaches. Bioinformatic analysis of R-loops involves several layers of quality control and processing before visualizing the data. This article provides resources and tools that can be used to accurately process R-loop mapping data and explains the advantages and disadvantages of the resources as compared to one another. © 2024 Wiley Periodicals LLC.

RLSuite: An Integrative R-Loop Bioinformatics Framework

We recently described the development of a database of 810 R-loop mapping datasets and used this data to conduct a meta-analysis of R-loops. R-loops are three-stranded nucleic acid structures containing RNA:DNA hybrids and we were able to verify that 30% of expressed genes have an associated R-loop in a location conserved manner.. Moreover, intergenic R-loops map to enhancers, super enhancers and with TAD domain boundaries. This work demonstrated that R-loop mapping via high-throughput sequencing can reveal novel insight into R-loop biology, however the analysis and quality control of these data is a non-trivial task for which few bioinformatic tools exist. Herein we describe RLSuite, an integrative R-loop bioinformatics framework for pre-processing, quality control, and downstream analysis of R-loop mapping data. RLSuite enables users to compare their data to hundreds of public datasets and generate a user-friendly analysis report for sharing with non-bioinformatician colleagues. Taken together, RLSuite is a novel analysis framework that should greatly benefit the emerging R-loop bioinformatics community in a rapidly expanding aspect of epigenetic control that is still poorly understood.

Binding of the nuclear ribonucleoprotein family member FUS to RNA prevents R-loop RNA:DNA hybrid structures

The protein FUS (FUSed in sarcoma) is a metazoan RNA-binding protein that influences RNA production by all three nuclear polymerases. FUS also binds nascent transcripts, RNA processing factors, RNA polymerases, and transcription machinery. Here, we explored the role of FUS binding interactions for activity during transcription. In vitro run-off transcription assays revealed FUS-enhanced RNA produced by a non-eukaryote polymerase. The activity also reduced the formation of R-loops between RNA products and their DNA template. Analysis by domain mutation and deletion indicated RNA-binding was required for activity. We interpret that FUS binds and sequesters nascent transcripts to prevent R-loops from forming with nearby DNA. DRIP-seq analysis showed that a knockdown of FUS increased R-loop enrichment near expressed genes. Prevention of R-loops by FUS binding to nascent transcripts has the potential to affect transcription by any RNA polymerase, highlighting the broad impact FUS can have on RNA metabolism in cells and disease.

Integrative genomic analyses of promoter G-quadruplexes reveal their selective constraint and association with gene activation

G-quadruplexes (G4s) regulate DNA replication and gene transcription, and are enriched in promoters without fully appreciated functional relevance. Here we show high selection pressure on putative G4 (pG4) forming sequences in promoters through investigating genetic and genomic data. Analyses of 76,156 whole-genome sequences reveal that G-tracts and connecting loops in promoter pG4s display lower or higher allele frequencies, respectively, than pG4-flanking regions, and central guanines (Gs) in G-tracts show higher selection pressure than other Gs. Additionally, pG4-promoters produce over 72.4% of transcripts, and promoter G4-containing genes are expressed at relatively high levels. Most genes repressed by TMPyP4, a G4-ligand, regulate epigenetic processes, and promoter G4s are enriched with gene activation histone marks, chromatin remodeler and transcription factor binding sites. Consistently, cis-expression quantitative trait loci (cis-eQTLs) are enriched in promoter pG4s and their G-tracts. Overall, our study demonstrates selective constraint of promoter G4s and reinforces their stimulative role in gene expression.

Stable bulged G-quadruplexes in the human genome: identification, experimental validation and functionalization

DNA sequence composition determines the topology and stability of G-quadruplexes (G4s). Bulged G-quadruplex structures (G4-Bs) are a subset of G4s characterized by 3D conformations with bulges. Current search algorithms fail to capture stable G4-B, making their genome-wide study infeasible. Here, we introduced a large family of computationally defined and experimentally verified potential G4-B forming sequences (pG4-BS). We found 478 263 pG4-BS regions that do not overlap 'canonical' G4-forming sequences in the human genome and are preferentially localized in transcription regulatory regions including R-loops and open chromatin. Over 90% of protein-coding genes contain pG4-BS in their promoter or gene body. We observed generally higher pG4-BS content in R-loops and their flanks, longer genes that are associated with brain tissue, immune and developmental processes. Also, the presence of pG4-BS on both template and non-template strands in promoters is associated with oncogenesis, cardiovascular disease and stemness. Our G4-BS models predicted G4-forming ability in vitro with 91.5% accuracy. Analysis of G4-seq and CUT&Tag data strongly supports the existence of G4-BS conformations genome-wide. We reconstructed a novel G4-B 3D structure located in the E2F8 promoter. This study defines a large family of G4-like sequences, offering new insights into the essential biological functions and potential future therapeutic uses of G4-B.

Exploration and analysis of R-loop mapping data with RLBase

R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA. In 2012, Ginno et al. introduced the first R-loop mapping method. Since that time, dozens of R-loop mapping studies have been conducted, yielding hundreds of publicly available datasets. Current R-loop databases provide only limited access to these data. Moreover, no web tools for analyzing user-supplied R-loop datasets have yet been described. In our recent work, we reprocessed 810 R-loop mapping samples, building the largest R-loop data resource to date. We also defined R-loop consensus regions and developed a framework for R-loop data analysis. Now, we introduce RLBase, a user-friendly database that provides the capability to (i) explore hundreds of public R-loop mapping datasets, (ii) explore R-loop consensus regions, (iii) analyze user-supplied data and (iv) download standardized and reprocessed datasets. RLBase is directly accessible via the following URL: https://gccri.bishop-lab.uthscsa.edu/shiny/rlbase/.