RNA molecules, such as long noncoding RNAs (lncRNAs), have critical roles in regulating gene expression, chromosome architecture, and the modification states of chromatin. Recent developments suggest that RNA also influences gene expression and chromatin patterns through the interaction of nascent transcripts with their DNA template via the formation of co-transcriptional R-loop structures. R-loop formation over specific, conserved, hotspots occurs at thousands of genes in mammalian genomes and represents an important and dynamic feature of mammalian chromatin. Here, focusing primarily on mammalian systems, I describe the accumulating connections and possible mechanisms linking R-loop formation and chromatin patterning. The possible contribution of aberrant R-loops to pathological conditions is also discussed.

R-loops are prevalent three-stranded non-B DNA structures composed of an RNA-DNA hybrid and a single strand of DNA. R-loops are implicated in various basic nuclear processes, such as class-switch recombination, transcription termination and chromatin patterning. Perturbations in R-loop metabolism have been linked to genomic instability and have been implicated in human disorders, including cancer. As a consequence, the accurate mapping of these structures has been of increasing interest in recent years. Here, we describe two related immunoprecipitation-based methods for mapping R-loop structures: basic DRIP-seq (DNA-RNA immunoprecipitation followed by high-throughput DNA sequencing), an easy, robust, but resolution-limited technique; and DRIPc-seq (DNA-RNA immunoprecipitation followed by cDNA conversion coupled to high-throughput sequencing), a high-resolution and strand-specific iteration of the method that permits accurate R-loop mapping genome wide. Briefly, after gentle DNA extraction and restriction digestion with a cocktail of enzymes, R-loop structures are immunoprecipitated with the anti-RNA-DNA hybrid S9.6 antibody. Compared with DRIP-seq, in which the immunoprecipitated DNA is directly sequenced, DRIPc-seq permits the recovery of the RNA moiety of R-loops, and these RNA strands are subjected to strand-specific RNA sequencing (RNA-seq) analysis. DRIPc-seq can be performed in 5 d and can be applied to any cell type, provided sufficient starting material can be collected. Accurately mapping R-loop distribution in various cell lines and under varied conditions is essential to understanding the formation, roles and dynamic resolution of these important structures.

R-loops are three-stranded structures that form during transcription when the nascent RNA hybridizes with the template DNA resulting in a DNA:RNA hybrid and a looped-out single-stranded DNA (ssDNA) strand. These structures are important for normal cellular processes and aberrant R-loop formation has been implicated in a number of pathological outcomes, including certain cancers and neurodegenerative diseases. Mapping R-loops has primarily been performed using DRIP (DNA:RNA immunoprecipitation) based methods that are dependent on the anti-DNA:RNA hybrid S9.6 antibody and short-read sequencing. While DRIP-based methods are robust and report R-loop formation genome-wide, they only do so at the population average level; interrogating R-loop formation at the single molecule level is not feasible with such approaches. Here we present single molecule R-loop footprinting (SMRF-seq), a method that relies on the chemical reactivity of the displaced ssDNA strand to non-denaturing sodium bisulfite and single molecule long-read sequencing as a readout, to characterize R-loops. SMRF-seq can be used independently of S9.6 to generate high resolution, strand-specific, maps of individual R-loops at ultra-deep coverage on kilobases-length DNA fragments.

R-loop structures are a prevalent class of alternative non-B DNA structures that form during transcription upon invasion of the DNA template by the nascent RNA. R-loops form universally in the genomes of organisms ranging from bacteriophages, bacteria, and yeasts to plants and animals, including mammals. A growing body of work has linked these structures to both physiological and pathological processes, in particular to genome instability. The rising interest in R-loops is placing new emphasis on understanding the fundamental physicochemical forces driving their formation and stability. Pioneering work in Escherichia coli revealed that DNA topology, in particular negative DNA superhelicity, plays a key role in driving R-loops. A clear role for DNA sequence was later uncovered. Here, we review and synthesize available evidence on the roles of DNA sequence and DNA topology in controlling R-loop formation and stability. Factoring in recent developments in R-loop modeling and single-molecule profiling, we propose a coherent model accounting for the interplay between DNA sequence and DNA topology in driving R-loop structure formation. This model reveals R-loops in a new light as powerful and reversible topological stress relievers, an insight that significantly expands the repertoire of R-loops' potential biological roles under both normal and aberrant conditions.

R-loops are non-B DNA structures that form during transcription when the nascent RNA anneals to the template DNA strand forming a RNA:DNA hybrid. Understanding the genomic distribution and function of R-loops is an important goal, since R-loops have been implicated in a number of adaptive and maladaptive processes under physiological and pathological conditions. Based on R-loop mapping datasets, we propose the existence of two main classes of R-loops, each associated with unique characteristics. Promoter-paused R-loops (Class I) are short R-loops that form at high frequency during promoter-proximal pausing by RNA polymerase II. Elongation-associated R-loops (Class II) are long structures that occur throughout gene bodies at modest frequencies. We further discuss the relationships between each R-loop class with instances of genome instability and suggest that increased class I R-loops, resulting from enhanced promoter-proximal pausing, represent the main culprits for R-loop mediated genome instability under pathological conditions.

R-loops constitute a prevalent class of transcription-driven non-B DNA structures that occur in all genomes depending on both DNA sequence and topological favorability. In recent years, R-loops have been implicated in a variety of adaptive and maladaptive roles and have been linked to genomic instability in the context of human disorders. As a consequence, the accurate mapping of these structures in genomes is of high interest to many investigators. DRIP-seq (DNA:RNA Immunoprecipitation followed by high throughput sequencing) is described here. It is a robust and reproducible technique that permits accurate and semi-quantitative mapping of R-loops. A recent iteration of the method is also described in which fragmentation is accomplished using sonication (sDRIP-seq), which allows strand-specific and high-resolution mapping of R-loops. sDRIP-seq thus addresses some of the common limitations of the DRIP-seq method in terms of resolution and strandedness, making it a method of choice for R-loop mapping.

The S9.6 antibody is broadly used to detect RNA:DNA hybrids but has significant affinity for double-stranded RNA. The impact of this off-target RNA binding activity has not been thoroughly investigated, especially in the context of immunofluorescence microscopy. We report that S9.6 immunofluorescence signal observed in fixed human cells arises predominantly from ribosomal RNA, not RNA:DNA hybrids. S9.6 staining was unchanged by pretreatment with the RNA:DNA hybrid-specific nuclease RNase H1, despite verification in situ that S9.6 recognized RNA:DNA hybrids and that RNase H1 was active. S9.6 staining was, however, significantly sensitive to RNase T1, which specifically degrades RNA. Additional imaging and biochemical data indicate that the prominent cytoplasmic and nucleolar S9.6 signal primarily derives from ribosomal RNA. Importantly, genome-wide maps obtained by DNA sequencing after S9.6-mediated DNA:RNA immunoprecipitation (DRIP) are RNase H1 sensitive and RNase T1 insensitive. Altogether, these data demonstrate that imaging using S9.6 is subject to pervasive artifacts without pretreatments and controls that mitigate its promiscuous recognition of cellular RNAs.

Inactive DNA methyltransferase (DNMT) 3B splice isoforms are associated with changes in DNA methylation, yet the mechanisms by which they act remain largely unknown. Using biochemical and cell culture assays, we show here that the inactive DNMT3B3 and DNMT3B4 isoforms bind to and regulate the activity of catalytically competent DNMT3A or DNMT3B molecules. DNMT3B3 modestly stimulated the de novo methylation activity of DNMT3A and also counteracted the stimulatory effects of DNMT3L, therefore leading to subtle and contrasting effects on activity. DNMT3B4, by contrast, significantly inhibited de novo DNA methylation by active DNMT3 molecules, most likely due to its ability to reduce the DNA binding affinity of co-complexes, thereby sequestering them away from their substrate. Immunocytochemistry experiments revealed that in addition to their effects on the intrinsic catalytic function of active DNMT3 enzymes, DNMT3B3 and DNMT34 drive distinct types of chromatin compaction and patterns of histone 3 lysine 9 tri-methylation (H3K9me3) deposition. Our findings suggest that regulation of active DNMT3 members through the formation of co-complexes with inactive DNMT3 variants is a general mechanism by which DNMT3 variants function. This may account for some of the changes in DNA methylation patterns observed during development and disease.

R-loops represent an abundant class of large non-B DNA structures in genomes. Even though they form transiently and at modest frequencies, interfering with R-loop formation or dissolution has significant impacts on genome stability. Addressing the mechanism(s) of R-loop-mediated genome destabilization requires a precise characterization of their distribution in genomes. A number of independent methods have been developed to visualize and map R-loops, but their results are at times discordant, leading to confusion. Here, we review the main existing methodologies for R-loop mapping and assess their limitations as well as the robustness of existing datasets. We offer a set of best practices to improve the reproducibility of maps, hoping that such guidelines could be useful for authors and referees alike. Finally, we propose a possible resolution for the apparent contradictions in R-loop mapping outcomes between antibody-based and RNase H1-based mapping approaches.