7 research outputs found
A Stress-Induced TRNA Depletion Response Mediates Codon-Based Translational Repression and Growth Suppression
Eukaryotic transfer RNAs (tRNAs) can become fragmented upon various cellular stresses, generating tRNA-derived RNA fragments (tRFs). Though this process has been observed for numerous cellular stresses and in many species ranging from plant cells to yeast and human cells, it is still poorly characterized and understood. Such tRNA fragmentation has previously been thought to affect a small fraction of the tRNA pool and was thus presumed to not affect the role of tRNAs in translation. We report that in human cells, oxidative stress can rapidly generate tRFs derived from tyrosyl tRNAGUA—resulting in a significant depletion of the precursor tRNA molecule and mature tRNA while also leading to elevated levels of the tRF. Proteomic and ribosomal profiling of tyrosyl tRNAGUA-depleted cells revealed impaired expression of proteins enriched in its cognate tyrosine codons, comprising growth and metabolic genes. Consistent with these affected pathways, depletion of tyrosyl tRNAGUA or its downstream targets, EPCAM, SCD, or USP3, repressed growth—revealing a tRNA-dependent growth suppressive pathway for oxidative stress response. A synthetic mimetic of the tRF induced upon oxidative stress was used to identify interactions with RNA binding proteins through mass spectrometry. High-throughput sequencing of RNA isolated by crosslinking immunoprecipitation (HITS-CLIP) of hnRNPA1 and SSB confirmed the mass spectrometry results and identified endogenous reciprocal interactions between the protein and tRF. Binding of this tRF to hnRNPA1 inhibits destabilization of endogenous targets of this RNA binding protein, leading to increased mRNA expression of DNA damage response and cell cycle regulatory genes. Thus, tRNA fragmentation can both deplete a precursor tRNA molecule with codon-dependent regulatory consequences and also generate small-RNAs that can interact with and regulate RNA binding proteins
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Revealing the grammar of small RNA secretion using interpretable machine learning.
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