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ABSTRACT Extracellular vesicles (EVs) are small particles that are released by cells and mediate cell–cell communication by transferring bioactive molecules such as RNA. RNA cargo of EVs, including coding and non‐coding RNAs, can change the behaviour of recipient cells, affecting processes including gene expression, proliferation, and Fapoptosis. CircRNAs are stable and resistant to degradation and have been shown to be enriched in EVs. They play key roles in gene regulation and are also emerging as promising biomarkers for disease diagnosis due to their stability and disease‐specific expression. Although microRNAs (miRNAs) are the most well studied RNA cargo of EVs, very little is known about the mechanisms of enrichment of circular RNAs (circRNAs) as well as long linear RNAs. Here, we take a comprehensive genome‐wide approach to investigate the role of structuredness and shape along with GC%, size, exon count and coding potential, in the sorting and enrichment of circular and long linear RNAs into EVs. We developed a model using these parameters to predict the likelihood of EV packaging of RNA and it was validated by using single molecule RNA imaging of EV bound RNAs. Furthermore, we found that structuredness could explain the relative enrichment of circRNAs over their linear counterparts. These results were validated on existing public databases of circular and linear RNAs in EVs. By identifying and analysing these factors, we aim to better understand the complex mechanisms behind EV‐mediated RNA transfer and its impact on cell communication in both health and disease. This mechanistic understanding of RNA enrichment in EVs is crucial for engineering EVs with selective RNA cargo.
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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 athttps://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.
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- Award ID(s):
- 2244127
- PAR ID:
- 10673645
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Posted Content:
- https://doi.org/10.64898/2025.12.08.693008
