bioRxivBiochemistry
Surveying the landscape of tRNA modifications by combining tRNA sequencing and RNA mass spectrometry
Abstract
Chemical modification of the nucleosides that comprise tRNAs are diverse1-3. Such modifications impact tRNA structure, stability, and mRNA decoding3,4. Although tRNA modifications are present in all kingdoms of life1, the structure, location, and extent of modifications have been systematically charted in very few organisms, in part because mapping modifications to tRNA sequences has been technically challenging. Here, we describe a new approach in which rapid prediction of modified sites through reverse transcription-derived signatures in high-throughput tRNA-sequencing (tRNA-seq) data is coupled with chemical analysis and identification of tRNA modifications through RNA mass spectrometry (tRNA-SMS). As proof of concept, we applied this method to study tRNA modification profiles in two phylogenetically close bacteria, E. coli and Vibrio cholerae. Comparative tRNA-seq enabled prediction of several V. cholerae modifications that are absent from E. coli and showed the effects of various environmental conditions on V. cholerae tRNA modification profiles. Through RNA mass spectrometric analyses, we showed that two of the V. cholerae-specific reverse transcription signatures reflected the presence of a new modification (acetylated acp3U (acacp3U)), while another results from C-to-U RNA editing, a process not described before in bacteria. By combining comparative genomics with mass spectrometry, we identified a putative N- acetyltransferase required for acacp3U acetylation. These findings demonstrate the utility of the tRNA-SMS approach for rapid characterization of tRNA modification profiles and environmental control of tRNA modification. Moreover, our identification of a new modified nucleoside and RNA editing process suggests that there are many tRNA modifications awaiting discovery.
- organism: E. coli
- organism: virus
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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