Transcriptome-Wide Profiling of Nascent RNA in Neurons with Enriched H3K27ac Signal Elevates eRNA Identification Efficiency

Growing evidence suggests that activity-dependent gene expression is crucial for neuronal plasticity and behavioral experience. Enhancer RNAs (eRNAs), a class of long noncoding RNAs, play a key role in these processes. However, eRNAs are highly dynamic and are often present at lower levels than their corresponding mRNAs, making them difficult to detect using total RNA-seq techniques. Nascent RNA sequencing, which separates nascent RNAs from the steady-state RNA population, has been shown to increase the ability to detect activity-induced eRNAs with a higher signal-to-noise ratio. However, there is a lack of bioinformatic tools or pipelines for detecting eRNAs utilizing nascent RNA-seq and other multiomics data sets. In this study, we addressed this gap by developing a novel bioinformatic framework, e-finder, for finding eRNAs and have made it available to the scientific community. Additionally, we reanalyzed our previous nascent RNA sequencing data and compared them with total RNA-seq data to identify activity-regulated RNAs in neuronal cell populations. Using H3K27 acetylome data, we characterized activity-dependent eRNAs that drive the transcriptional activity of the target genes. Our analysis identified a subset of eRNAs involved in mediating synapse organization, which showed increased activity-dependent transcription after the potassium chloride stimulation. Notably, our data suggest that nascent RNA-seq with an enriched H3K27ac signal exhibits high resolution to identify potential eRNAs in response to membrane depolarization. Our findings uncover the role of the eRNA-mediated gene activation network in neuronal systems, providing new insights into the molecular processes characterizing neurological diseases.