EVRCEPT: EV RNA Cargo Enrichment Prediction Tool to predict enrichment of RNA into Extracellular Vesicles

Abstract

Extracellular vesicles (EVs) are small membrane-bound vesicles that are released by most cells. EVs have been shown to transport molecules including proteins and various types of RNAs between cells of even different types. Furthermore, EV RNAs are shown to modulate gene expression in physiological and pathological conditions in recipient cells which can be utilized in therapeutics by engineering cells to enrich RNA of interest in EVs. However, how specific RNA species are enriched in EVs is a long-standing question in the field. Here, we used sequence features of RNAs to predict its enrichment in EVs. These features include length, nucleotide and dinucleotide frequencies, secondary structure information, number of exons, coding probability for non-coding RNAs as well as RNA binding protein (RBP) motifs. The model achieved a performance (AU-ROC: 90%, 77%) for circRNAs and mRNAs, respectively. Here, we present a web tool called, EV RNA Cargo Enrichment Prediction Tool (EVRCEPT), that allows users to predict likelihood of input RNA to be enriched into EVs. This tool will also provide the list of RBPs that are likely to interact with the input RNA and works with both linear and circular RNAs. This webtool, which is freely accessible at https://euler.dbi.udel.edu/evrcept, will help understand extracellular RNA transport and guide the design of therapeutic RNAs to maximize their incorporation in EVs towards targeted personalized medicine.