DOI: 10.5061/dryad.np5hqc009
title: Organellar tRNAs in parasitic plant species

description: Eukaryotic nuclear genomes often encode distinct sets of protein
 translation machinery for function in the cytosol vs. organelles
 (mitochondria and plastids). This phenomenon raises questions about why
 multiple translation systems are maintained even though they are capable
 of comparable functions, and whether they evolve differently depending on
 the compartment where they operate. These questions are particularly
 interesting in land plants because translation machinery, including
 aminoacyl-tRNA synthetases (aaRS), is often dual-targeted to both the
 plastids and mitochondria. These two organelles have quite different
 metabolisms, with much higher rates of translation in plastids to supply
 the abundant, rapid-turnover proteins required for photosynthesis.
 Previous studies have indicated that plant organellar aaRS evolve more
 slowly compared to mitochondrial aaRS in other eukaryotes that lack
 plastids. Thus, we investigated the evolution of nuclear-encoded
 organellar and cytosolic translation machinery across a broad sampling of
 angiosperms, including non-photosynthetic (heterotrophic) plant species
 with reduced rates of plastid gene expression to test the hypothesis that
 translational demands associated with photosynthesis constrain the
 evolution of bacterial-like enzymes involved in organellar tRNA
 metabolism. Remarkably, heterotrophic plants exhibited wholesale loss of
 many organelle-targeted aaRS and other enzymes, even though translation
 still occurs in their mitochondria and plastids. These losses were often
 accompanied by apparent retargeting of cytosolic enzymes and tRNAs to the
 organelles, sometimes preserving aaRS-tRNA charging relationships but
 other times creating surprising mismatches between cytosolic aaRS and
 mitochondrial tRNA substrates. Our findings indicate that the presence of
 a photosynthetic plastid drives the retention of specialized systems for
 organellar tRNA metabolism.
description: Input genebank (.gbk) files for various mitochondrial and plastid
 genomes, and accompanying scripts for presence/absence analysis of
 tRNAs.
description: Jupyter notebooks/Python3 is required to open s and run .ipynb
 notebooks. Bioperl is required to run perl script. 
description: # Organellar tRNAs in Parasitic Plant Species --- Here are input genebank
 (.gbk) files for various mitochondrial and plastid genomes, and
 accompanying scripts for generating figures plotting presence/absence
 analysis of tRNAs in relation to aminoacy tRNA-synthetases. ## Description
 of the data and file structure * Zipped data directory gbk/ contains
 genebank annotation files which are input for plotting absence/presence of
 organellar tRNAs, aminoacyl tRNA-synthetases (Fig. 4) * trna_parse2.pl is
 a perl script used for extracting tRNAs from gbk files, originally written
 by Dan Sloan. * jupyter notebook 20240820_parsetRNA.ipynb was used to
 generate plot (Fig. 4) * 20240820_absencepresence_retarget.csv is a
 metadata file containing a data matrix detailing for each aaRS whether the
 organellar enzyme is absent/present, and whether the cytosolic enzyme is
 retargeted/not retargeted/unknown. This data was used by
 20240820_parsetRNA.ipynb to overlay tRNA data. More information about
 ortholog presence/absence data from
 [https://doi.org/10.5061/dryad.0cfxpnw7p](https://doi.org/10.5061/dryad.0cfxpnw7p), targeting from [https://doi.org/10.5061/dryad.6hdr7sr5x](https://doi.org/10.5061/dryad.6hdr7sr5x). Also see manuscript for more details. * AA_codes.txt is a metadatafile containing the 1 letter and 3 letter codes for each amino acid, used by 20240820_parsetRNA.ipynb for axis labels. ## Sharing/Access information .gbk files were downloaded from NCBI. See manuscript for more details. ## Code/Software Requires Bioperl, python3, Jupyter notebooks extract tRNAs from Genebank files (.gbk): ``` for file in *.gbk; do trna_parse2.pl $file &gt; ${file%.gbk}_tRNA.tab; done ``` Then open 20240820_absencepresence_retarget.ipynb to plot. Ensure output .csv file from targeting analysis (i.e.20240820_absencepresence_retarget_both.csv) is included with info *python packages required include:* pandas numpy seaborn matplotlib string re os ## Version Changes **October 2024**: Use of updated metadata file (20240820_absencepresence_retarget.csv) reflecting reanalysis with updated gene models, see publication and associated datasets. Update to 20240820_parsetRNA.ipynb- plot plastid targeting predictions for Rafflesiaceae species. Previously these were excluded.