Back

The landscape of Arabidopsis tRNA aminoacylation

Ceriotti, L. F.; Warren, J. M.; Sanchez-Puerta, M. V.; Sloan, D. B.

2024-09-09 molecular biology
10.1101/2024.09.09.612099 bioRxiv
Show abstract

The function of tRNAs depends on enzymes that cleave primary transcript ends, add a 3' CCA tail, introduce post-transcriptional base modifications, and charge (aminoacylate) mature tRNAs with the correct amino acid. Maintaining an available pool of the resulting aminoacylated tRNAs is essential for protein synthesis. High-throughput sequencing techniques have recently been developed to provide a comprehensive view of aminoacylation state in a tRNA-specific fashion. However, these methods have never been applied to plants. Here, we treated Arabidopsis thaliana RNA samples with periodate and then performed tRNA-seq to distinguish between aminoacylated and uncharged tRNAs. This approach successfully captured every tRNA isodecoder family and detected expression of additional tRNA-like transcripts. We found that estimated aminoacylation rates and CCA tail integrity were significantly higher on average for organellar (mitochondrial and plastid) tRNAs than for nuclear/cytosolic tRNAs. Reanalysis of previously published human cell line data showed a similar pattern. Base modifications result in nucleotide misincorporations and truncations during reverse transcription, which we quantified and used to test for relationships with aminoacylation levels. We also determined that the Arabidopsis tRNA-like sequences (t-elements) that are cleaved from the ends of some mitochondrial mRNAs have post-transcriptionally modified bases and CCA-tail addition. However, these t-elements are not aminoacylated, indicating that they are only recognized by a subset of tRNA-interacting enzymes and do not play a role in translation. Overall, this work provides a characterization of the baseline landscape of plant tRNA aminoacylation rates and demonstrates an approach for investigating environmental and genetic perturbations to plant translation machinery.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.