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.

Glutamate is the most abundant neurotransmitter found in the brain, controlling fast signalling throughout all sections and being especially involved in memory recollection and learning. Long-Term Potentiation (LTP) is the strengthening of neural connections through receptor synthesis over consistent usage, first triggered by synapse activation by a small amount of glutamate. However, in heavy (prolonged instance of exposure) and habitual users of cannabis, the effects of LTP are exacerbated by N-methyl-D-Aspartic Acid (NMDA) Receptor Hypofunction (NRHypo) which in turn affects memory, learning, reasoning and other aspects of one's function. Emerging evidence has associated the inhibition of long-term potentiation by Delta 9-Tetrahydrocannabinol (D9-THC) activating presynaptic Cannabinoid Receptor Type 1 (CB1) receptors to the inhibition of the ability to stop production of glutamate (GLU). An excess of glutamate will overstimulate the postsynaptic NMDA and α-Amino-3-Hydroxy-5-Methyl-4-Isoxazolepropionic Acid (AMPA) receptors in the neurons commonly in the hippocampus, basal ganglia, and prefrontal cortex, which allow excessive influx of calcium Ca 2+ ions, causing neurotoxic conditions. Glutamate Decarboxylase 67 molecule has been shown bind in high concentrations with GLU and lower the harmful effects of D9-THC on the brain by converting GLU to Gamma-Aminobutyric Acid (GABA), an inhibitory neurotransmitter. GAD67 will be distributed to mice in this proposed experiment and the behaviour of the mice will be monitored. D9-THC affected, D9-THC and GAD67 affected, and normal mice will be subjected to behavioral interaction and maze tests which will show differences in their learning, spatial awareness and orientation, and reasoning abilities. Chemical analysis of cerebral fluid and brain slices will determine chemical concentrations of GAD67 and D9-THC in the brain. Using direct injections into the cerebrospinal fluid (CSF) and bloodstream in mouse models, our aim is to determine the selectivity of the blood brain barrier (BBB) to enzymes such as GAD67 via both channels as well as assess the interaction GAD67 has with cascading neurological effects caused by NRHypo and LTP.