The nucleus is a spherical dual‐membrane bound organelle that encapsulates genomic DNA. In eukaryotes, messenger RNAs (mRNA) are transcribed in the nucleus and transported through nuclear pores into the cytoplasm for translation into protein. In certain cell types and pathological conditions, nuclei harbor tubular invaginations of the nuclear envelope known as the “nucleoplasmic reticulum.” Nucleoplasmic reticulum expansion has recently been established as a mediator of neurodegeneration in tauopathies, including Alzheimer's disease. While the presence of pore‐lined, cytoplasm‐filled, nuclear envelope invaginations has been proposed to facilitate the rapid export of RNAs from the nucleus to the cytoplasm, the functional significance of nuclear envelope invaginations in regard to RNA export in any disorder is currently unknown. Here, we report that polyadenylated RNAs accumulate within and adjacent to tau‐induced nuclear envelope invaginations in a Drosophila model of tauopathy. Genetic or pharmacologic inhibition of RNA export machinery reduces accumulation of polyadenylated RNA within and adjacent to nuclear envelope invaginations and reduces tau‐induced neuronal death. These data are the first to point toward a possible role for RNA export through nuclear envelope invaginations in the pathogenesis of a neurodegenerative disorder and suggest that nucleocytoplasmic transport machinery may serve as a possible novel class of therapeutic targets for the treatment of tauopathies.
R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA during transcription. While the pathological consequences of R-loops have been well-studied to date, the locations, classes, and dynamics of physiological R-loops remain poorly understood. R-loop mapping studies provide insight into R-loop dynamics, but their findings are challenging to generalize. This is due to the narrow biological scope of individual studies, the limitations of each mapping modality, and, in some cases, poor data quality. In this study, we reprocessed 693 R-loop mapping datasets from a wide array of biological conditions and mapping modalities. From this data resource, we developed an accurate method for R-loop data quality control, and we reveal the extent of poor-quality data within previously published studies. We then identified a set of high-confidence R-loop mapping samples and used them to define consensus R-loop sites called “R-loop regions” (RL regions). In the process, we revealed the stark divergence between S9.6 and dRNH-based R-loop mapping methods and identified biologically meaningful subtypes of both constitutive and variable R-loops. Taken together, this work provides a much-needed method to assess R-loop data quality and reveals intriguing aspects of R-loop biology.
R-loops are three-stranded nucleic acid structures formed from the hybridization of RNA and DNA. While the pathological consequences of R-loops have been well-studied to date, the locations, classes, and dynamics of physiological R-loops remain poorly understood. R-loop mapping studies provide insight into R-loop dynamics, but their findings are challenging to generalize. This is due to the narrow biological scope of individual studies, the limitations of each mapping modality, and, in some cases, poor data quality. In this study, we reprocessed 810 R-loop mapping datasets from a wide array of biological conditions and mapping modalities. From this data resource, we developed an accurate R-loop data quality control method, and we reveal the extent of poor-quality data within previously published studies. We then identified a set of high-confidence R-loop mapping samples and used them to define consensus R-loop sites called ‘R-loop regions’ (RL regions). In the process, we identified a stark divergence between RL regions detected by S9.6 and dRNH-based mapping methods, particularly with respect to R-loop size, location, and colocalization with RNA binding factors. Taken together, this work provides a much-needed method to assess R-loop data quality and offers novel context regarding the differences between dRNH- and S9.6-based R-loop mapping approaches.
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