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Revealing the grammar of small RNA secretion using interpretable machine learning

Zirak, B ; Sharif University of Technology | 2024

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  1. Type of Document: Article
  2. DOI: 10.1016/j.xgen.2024.100522
  3. Publisher: 2024
  4. Abstract:
  5. Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are not well understood. Here, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequences. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins (RBPs), e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications. © 2024 The Authors
  6. Keywords:
  7. ExoCLIP ; ExoGRU ; Extracellular RNA ; Small RNA ; Small RNA secretion ; Heterogeneous nuclear ribonucleoprotein ; Heterogeneous nuclear ribonucleoprotein A2B1 ; RNA binding protein ; RNA binding protein RBM24 ; Small untranslated RNA ; Unclassified drug ; Y box binding protein 1 ; Cell free system ; Controlled study ; Exosome ; Gene knockdown ; Gene overexpression ; HEK293T cell line ; Machine learning ; MDA-MB-231 cell line ; Mutagenesis ; Promoter region ; RNA binding ; RNA isolation ; RNA secretion ; RNA sequence ; RNA synthesis ; Secretion (process) ; Synthetic biology
  8. Source: Cell Genomics ; Volume 4, Issue 4 , 2024 ; 2666979X (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S2666979X24000648