Extracellular vesicles carry transcriptional ‘dark matter’ revealing tissue‐specific information

• DOI: 10.1002/jev2.12481
• Publication Date: 2024-08-16T05:20:12Z
• Authors: Navneet Dogra; Tzu‐Yi Chen; Edgar Gonzalez‐Ko...; Rebecca Miceli; Carlos Cordon‐Cardo; Ashutosh K. Tewari; Bojan Losic; Gustavo Stolovitzky
• Keywords: Exosome

From eukaryotes to prokaryotes, all cells secrete extracellular vesicles (EVs) as part of their regular homeostasis, intercellular communication, and cargo disposal. Accumulating evidence suggests that small EVs carry functional RNAs, potentially serving messengers liquid-biopsy markers. Yet, the complete transcriptomic landscape EV-associated RNAs during disease progression is poorly delineated due critical limitations including protocols used for sequencing, suboptimal alignment short reads (20-50 nt), uncharacterized genome annotations-often denoted 'dark matter' genome. In this study, we investigate unannotated arise from endogenous genes are genomic matter', which may play a key emerging role in regulating gene expression translational mechanisms. To address this, created distinct RNAseq dataset human prostate cancer & benign tissues, derived blood (pre- post-prostatectomy), urine, carcinoma epithelial cell line. We then developed an unsupervised data-based bioinformatic pipeline recognizes biologically relevant transcriptional signals irrespective annotation. Using approach, discovered EV-RNA patterns un-annotated regions (UGRs) transcriptomes associated with tissue-specific phenotypes. have named these novel 'EV-UGRs' or "EV-dark matter". Here, demonstrate EV-UGR expressions downregulated by ∼100 fold (FDR < 0.05) circulating serum aggressive subjects. Remarkably, EV-UGRs signatures were regained (upregulated) after radical prostatectomy same follow-up patients. Finally, stem-loop RT-qPCR assay validated cancer-specific selective fluid-based diagnostics. Overall, using data driven EV-transcriptome Information significantly alters pre- post-prostatectomy Although further validation randomized clinical trials required, new class EV-RNAs hold promise avoiding highly invasive biopsy procedures cancer.