Natalia Benova

Enhancer RNAs (eRNAs) are best known for their role in transcriptional regulation, where they facilitate enhancer-promoter communication and chromatin remodelling. Yet growing evidence suggests that their function may extend beyond the nucleus. Here, we systematically characterise the decay kinetics of eRNAs across human cell types using time-resolved transcriptomics and kinetic modelling. While most eRNAs undergo canonical exponential decay, a subset displays non-linear dynamics, suggesting context-dependent degradation mechanisms. Perturbation of core decay regulators, including components of the m⁶A and CCR4-NOT pathways, reveals that eRNA stability is modulated by a patchwork of pathways governing mRNA turnover. Integrating transcriptome-wide ribosome profiling, RNA-Seq, and half-life data, we identify eRNAs associated with changes in mRNA stability and translation efficiency of their target protein-coding transcripts. Functional validation of one such eRNA, en4528, shows it regulates CDKN2C mRNA independently of transcription and impacts cell migration. These findings redefine the regulatory scope of eRNAs, positioning them as active participants in post-transcriptional gene control and cellular behaviour. The resulting decay profiles and regulatory annotations have been incorporated into the eRNAkit database, available at https://github.com/AneneLab/eRNAkit, enhancing its capacity for integrative systems-level analysis of eRNA function.

Abstract

Noncoding variants and mutations outnumber their coding counterparts but remain challenging to interpret functionally. We present TranCi, a method that prioritises human genetic variations by integrating enhancer RNA (eRNA) expression with eRNA-mRNA interactome maps. By linking variant-associated changes in eRNA to downstream gene regulation, TranCi captures functional effects missed by sequence-based or chromatin-centric approaches. In esophageal squamous cell cancer, TranCi identifies noncoding mutations with roles in disease initiation and progression. A personalised mode enables analysis at single-patient resolution, uncovering potential individual-specific regulatory variants. TranCi thus provides a mechanistic framework for interpreting noncoding variations and uniquely identifies their downstream targets, where standard methods often fall short. TranCi is available as a module within the eRNAkit R package ( https://github.com/AneneLab/eRNAkit ), leveraging its database of eRNA expression and interactions for functional variant interpretation.

Motivation Recent RNA-centric experimental methods have significantly expanded our knowledge of proteins with known RNA-binding functions. However, the complete regulatory network and pathways for many of these RNA-binding proteins (RBPs) in different cellular contexts remain unknown. Although critical to understanding the role of RBPs in health and disease, experimentally mapping the RBP–RNA interactomes in every single context is an impossible task due the cost and manpower required. Additionally, identifying relevant RNAs bound by RBPs is challenging due to their diverse binding modes and function. Results To address these challenges, we developed RBP interaction mapper RBPInper an integrative framework that discovers global RBP interactome using statistical data fusion. Experiments on splicing factor proline and glutamine rich (SFPQ) datasets revealed cogent global SFPQ interactome. Several biological processes associated with this interactome were previously linked with SFPQ function. Furthermore, we conducted tests using independent dataset to assess the transferability of the SFPQ interactome to another context. The results demonstrated robust utility in generating interactomes that transfers to unseen cellular context. Overall, RBPInper is a fast and user-friendly method that enables a systems-level understanding of RBP functions by integrating multiple molecular datasets. The tool is designed with a focus on simplicity, minimal dependencies, and straightforward input requirements. This intentional design aims to empower everyday biologists, making it easy for them to incorporate the tool into their research.

Abstract

Transcript-resolved models of RNA enable functional interrogation of RNA biology by linking processing, structure, localisation, and regulatory interactions to specific RNA molecules. Across coding and noncoding transcriptomes, such models have been essential for defining RNA-level mechanisms relevant to physiology and disease. Enhancer RNAs (eRNAs), however, remain largely characterised without transcript-level definitions, and no widely adopted transcript-resolved reference exists, limiting investigation of how individual eRNAs are processed, localised, and participate in transcriptional regulation or their emerging post-transcriptional functions. Here, we reconstruct a transcript-resolved catalogue of human eRNAs by pan-transcriptome assembly across diverse tissues, cell types and compartments, defining 36,536 transcripts, including a subset with multi-exonic structure.

We show that eRNA splice junctions are reproducible features that exhibit cell-type specificity, subcellular localisation bias, and sensitivity to spliceosome perturbation. In perturbation experiments, eRNA splice junction usage responded to SF3B1 mutation, nuclear–cytoplasmic partitioning, and pharmacological inhibition of RNA export, demonstrating regulation across multiple layers of RNA biology. In head and neck squamous cell carcinoma, a subset of these junctions showed altered usage between tumour and matched normal tissue, indicating that processing varies in disease contexts. Across three validation contexts, nearly one-fifth of reconstructed junctions were detectable, with some showing regulated usage, supporting biological reproducibility.

Motivated by these observations, we provide both the GTF annotation and junctions BED file, as a framework for studying eRNAs, enabling RNA-centric investigation of their potential functions. The annotations have been incorporated into the eRNAkit database, available at https://github.com/AneneLab/eRNAkit .

Abstract

Abstract

Enhancer RNAs (eRNAs) are a class of non-coding RNAs transcribed from active enhancers that regulate various aspects of transcription. Although traditionally viewed as nuclear-localised and unstable transcripts, large transcriptomic studies reveal they vary widely in localisation and biochemical properties, including detectable accumulation in cytoplasmic compartments. These observations suggest potential non-nuclear functions for eRNAs, yet existing databases remain focused on nuclear, cis-regulatory roles, limiting systematic exploration of their broader regulatory repertoire. Here we present eRNAkit , a comprehensive and accessible resource designed to address this gap by integrating subcellular localisation, RNA-RNA interaction and expression data for annotated eRNAs. Leveraging fractionation-based RNA-Seq datasets, eRNAkit profiles eRNA distribution across nuclear and cytoplasmic compartments. It incorporates gene expression data spanning major human organs and primary cell types, enabling tissue-specific analysis of eRNA function. Crucially, eRNAkit includes experimentally derived RNA-RNA interaction data from RIC-Seq, PARIS, and KARR-Seq, supporting exploration of trans-acting and cytoplasmic roles for eRNAs grounded in physical interaction evidence. eRNAkit expands current eRNA resources beyond the enhancer-promoter paradigm, offering a robust platform for dissecting non-canonical functions of eRNAs and advancing our understanding of their full regulatory potential in human biology. The eRNAkit resource is available to download at https://github.com/AneneLab/eRNAkit .

Abstract