Revealing the grammar of small RNA secretion using interpretable machine learning

Bahar Zirak; Mohsen Naghipourfar; Ali Saberi; Delaram Pouyabahar; Amirhossein Zarezadeh; Lixi Luo; Lisa Fish; Doowon Huh; Albertas Navickas; Ali Sharifi-Zarchi; Hani Goodarzi

doi:10.1016/j.xgen.2024.100522

Revealing the grammar of small RNA secretion using interpretable machine learning

Cell Genom. 2024 Apr 10;4(4):100522. doi: 10.1016/j.xgen.2024.100522. Epub 2024 Mar 8.

Authors

Affiliations

¹ Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, US.
² Department of Computer Engineering, Sharif University of Technology, Tehran, Iran.
³ Department of Electrical and Computer Engineering, McGill University, Montreal, QC H3A 0E9, Canada; McGill Genome Centre, Victor Phillip Dahdaleh Institute of Genomic Medicine, 740 Dr Penfield Avenue, Montreal, QC H3A 0G1, Canada.
⁴ Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada; The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
⁵ Department of Stem Cells and Developmental Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran; Department of Developmental Biology, School of Basic Sciences and Advanced Technologies in Biology, University of Science and Culture, Tehran, Iran.
⁶ Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, US; Department of Surgical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
⁷ Laboratory of Systems Cancer Biology, The Rockefeller University, New York, NY, USA.
⁸ Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, US; Institut Curie, CNRS UMR3348, INSERM U1278, Orsay, France. Electronic address: albertas.navickas@curie.fr.
⁹ Department of Computer Engineering, Sharif University of Technology, Tehran, Iran. Electronic address: asharifiz@gmail.com.
¹⁰ Department of Biochemistry & Biophysics, University of California, San Francisco, San Francisco, CA, USA; Department of Urology, University of California, San Francisco, San Francisco, CA, USA; Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, US. Electronic address: hani.goodarzi@ucsf.edu.

Abstract

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.

Keywords: ExoCLIP; ExoGRU; extracellular RNA; machine learning; small RNA; small RNA secretion.

MeSH terms

Extracellular Vesicles* / metabolism
Machine Learning
Mutagenesis
RNA* / genetics
RNA-Binding Proteins / genetics

Substances

RNA
RNA-Binding Proteins