Back

Deciphering the RNA Modification Landscape in Arabidopsis Chloroplast tRNAs and rRNAs Reveals a Blend of Ancestral and Acquired Characteristics

Golebiewska, K.; Gregorova, P.; Sarin, P.; Gawronski, P.

2024-06-15 plant biology
10.1101/2024.06.14.598963 bioRxiv
Show abstract

Chloroplasts in plant leaves are essential for protein synthesis, relying on transfer RNAs (tRNAs) and ribosomal RNAs (rRNAs) encoded by the chloroplast genome. Although post-transcriptional modifications of these non-coding RNAs are common in many systems, chloroplast tRNA and rRNA modifications are not well characterised. In this study, we investigated the post-transcriptional modifications in chloroplast tRNAs and rRNAs of Arabidopsis thaliana using tRNA sequencing, liquid chromatography-mass spectrometry, targeted rRNA sequencing, and analysis of public data. Our results revealed similarities between chloroplast non-coding RNAs and bacterial systems (e.g., Escherichia coli), such as modification patterns at the anticodon-adjacent position and the variable loop of tRNAs, along with conserved modifications in the small subunit rRNA. Additionally, we identified features shared with eukaryotic systems that likely contribute to the correct three-dimensional structure of chloroplast tRNAs. Unique modifications were also discovered, including a potential novel modification at wobble position in tRNA-IleCAU, which may be crucial for distinguishing isoleucine codons from methionine codons, and chloroplast-specific rRNA modifications that likely compensate for altered ribosome structure. These findings suggest that the chloroplast translation machinery, through co-evolution with its eukaryotic host, has adopted features beyond those typically found in bacteria, reflecting a blend of ancestral and acquired characteristics.

Matching journals

The top 2 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.