Full-length tRNAs lacking a functional CCA tail are selectively sorted into the lumen of extracellular vesicles
Scheepbouwer, C.; Aparicio-Puerta, E.; Gomez-Martin, C.; van Eijndhoven, M. A. J.; Drees, E. E. E.; Bosch, L.; de Jong, D.; Wurdinger, T.; Zijlstra, J. M.; Hackenberg, M.; Gerber, A.; Pegtel, D. M.
Show abstract
Small extracellular vesicles (sEVs) are heterogenous lipid membrane particles typically less than 200 nm in size and secreted by most cell types either constitutively or upon activation signals. sEVs isolated from biofluids contain RNAs, including small non-coding RNAs (ncRNAs), that can be either encapsulated within the EV lumen or bound to the EV surface. EV-associated microRNAs (miRNAs) are, despite a relatively low abundance, extensively investigated for their selective incorporation and their role in cell-cell communication. In contrast, the sorting of highly-structured ncRNA species is understudied, mainly due to technical limitations of traditional small RNA sequencing protocols. Here, we adapted ALL-tRNAseq to profile the relative abundance of highly structured and potentially methylated small ncRNA species, including transfer RNAs (tRNAs), small nucleolar RNAs (snoRNAs), and Y RNAs in bulk EV preparations. We determined that full-length tRNAs, typically 75 to 90 nucleotides in length, were the dominant small ncRNA species (>60% of all reads in the 18-120 nucleotides size-range) in all cell culture-derived EVs, as well as in human plasma-derived EV samples, vastly outnumbering 21 nucleotides-long miRNAs. Nearly all EV-associated tRNAs were protected from external RNAse treatment, indicating a location within the EV lumen. Strikingly, the vast majority of luminal-sorted, full-length, nucleobase modification-containing EV-tRNA sequences, harbored a dysfunctional 3 CCA tail, 1 to 3 nucleotides truncated, rendering them incompetent for amino acid loading. In contrast, in non-EV associated extracellular particle fractions (NVEPs), tRNAs appeared almost exclusively fragmented or nicked into tRNA-derived small RNAs (tsRNAs) with lengths between 18 to 35 nucleotides. We propose that in mammalian cells, tRNAs that lack a functional 3 CCA tail are selectively sorted into EVs and shuttled out of the producing cell, offering a new perspective into the physiological role of secreted EVs and luminal cargo-selection.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Semi-quantitative detection of pseudouridine modifications and type I/II hypermodifications in human mRNAs using direct and long-read sequencing 94%
- The tumor suppressor microRNA let-7 inhibits human LINE-1 retrotransposition 94%
- Identification of Human Pathways Acting on Nuclear Non-Coding RNAs Using the Mirror Forward Genetic Approach 94%
Similar papers in this journal
- Fragmentation of extracellular ribosomes and tRNAs shapes the extracellular RNAome 97%
- Small molecule inhibitors of hnRNPA2B1-RNA interactions reveal a predictable sorting of RNA subsets into extracellular vesicles 95%
- MoDorado: Enhanced detection of tRNA modifications in nanopore sequencing by off-label use of modification callers 94%
Similar papers in this journal
Similar papers in this journal
- Subcellular relocalization and nuclear redistribution of the RNA methyltransferases TRMT1 and TRMT1L upon neuronal activation 92%
- Coordination of transcriptional and translational regulations in human cells infected by <em>Listeria monocytogenes</em> 91%
- rG4-seeker enables high-confidence identification of novel and non-canonical rG4 motifs from rG4-seq experiments 90%
Similar papers in this journal
- Defining the parameters for sorting of different RNA cargo into Extracellular vesicles 94%
- Distinct non-coding RNA cargo of extracellular vesicles from M1 and M2 human primary macrophages 92%
- Small RNAs in plasma extracellular vesicles define biomarkers of premanifest changes in Huntington's disease 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.