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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. 

ABSTRACT Viral infection exerts selection pressure on marine microbes, as virus-induced cell lysis causes 20 to 50% of cell mortality, resulting in fluxes of biomass into oceanic dissolved organic matter. Archaeal and bacterial populations can defend against viral infection using the clustered regularly interspaced short palindromic repeat (CRISPR)-associated (Cas) system, which relies on specific matching between a spacer sequence and a viral gene. If a CRISPR spacer match to any gene within a viral genome is equally effective in preventing lysis, no viral genes should be preferentially matched by CRISPR spacers. However, if there are differences in effectiveness, certain viral genes may demonstrate a greater frequency of CRISPR spacer matches. Indeed, homology search analyses of bacterioplankton CRISPR spacer sequences against virioplankton sequences revealed preferential matching of replication proteins, nucleic acid binding proteins, and viral structural proteins. Positive selection pressure for effective viral defense is one parsimonious explanation for these observations. CRISPR spacers from virioplankton metagenomes preferentially matched methyltransferase and phage integrase genes within virioplankton sequences. These virioplankton CRISPR spacers may assist infected host cells in defending against competing phage. Analyses also revealed that half of the spacer-matched viral genes were unknown, some genes matched several spacers, and some spacers matched multiple genes, a many-to-many relationship. Thus, CRISPR spacer matching may be an evolutionary algorithm, agnostically identifying those genes under stringent selection pressure for sustaining viral infection and lysis. Investigating this subset of viral genes could reveal those genetic mechanisms essential to virus-host interactions and provide new technologies for optimizing CRISPR defense in beneficial microbes. IMPORTANCE The CRISPR-Cas system is one means by which bacterial and archaeal populations defend against viral infection which causes 20 to 50% of cell mortality in the ocean. We tested the hypothesis that certain viral genes are preferentially targeted for the initial attack of the CRISPR-Cas system on a viral genome. Using CASC, a pipeline for CRISPR spacer discovery, and metagenome data from oceanic microbes and viruses, we found a clear subset of viral genes with high match frequencies to CRISPR spacers. Moreover, we observed a many-to-many relationship of spacers and viral genes. These high-match viral genes were involved in nucleotide metabolism, DNA methylation, and viral structure. It is possible that CRISPR spacer matching is an evolutionary algorithm pointing to those viral genes most important to sustaining infection and lysis. Studying these genes may advance the understanding of virus-host interactions in nature and provide new technologies for leveraging CRISPR-Cas systems in beneficial microbes.