DOI: 10.5061/dryad.6hdr7sr5x
title: In silico subcellular targeting predictions for cytosolic aminoacyl tRNA-synthetases (aaRS) in parasitic plants

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: Subcellular predictions generated from in silico prediction
 programs TargetP2.0 (Armenteros et al 2019) and LOCALIZER (Sperschneider
 et al 2017). Included also are input sequences for aaRS and other enzymes
 used for predictions, which were pulled from publically available sequence
 data. Included are scripts and metadata files for reproducing results if
 desired.
description: MEGA11 to look at all multiple sequence alignments. 
 Jupyter Notebooks, Rstudio are needed to look at
 scripts.  Running scripts/analysis requires
 dependencies described in README.md  
description: # Subcellular targeting of aminoacyl tRNA synthetases (aaRS) and other
 proteins in parasitic plants --- Here is the output of subcellular
 targeting prediction programs for aminoacyl tRNA-synthetases (aaRS) and
 other proteins from a sampling of parasitic and autotrophic plants
 species. Provided are protein models used to generate predictions, and
 script and files for downstream analysis including filtering out sequences
 lacking an N-terminus starting within 100 AA of the orthologous protein
 from model plant Arabidopsis thaliana. Jupyter notebooks used for
 statistics and plotting are also included. ## Description of the data and
 file structure **Zipped data directories:** * input_prot_fasta.zip
 contains fasta files of protein sequences used for the analysis. These
 files contain all transcripts with sufficient similarity to Arabidopsis
 ortholog from heterotrophic species and close relatives. To recapitulate
 my analysis, run all scripts in this directory. -
 Subcellular_Predictions.zip contains the predictions for protein
 sequences, prior to or after tidying. There are two subfolders containing
 predictions for all protein models (alltrans_predictions/), or only those
 models which have a start within 100 AA of the start of the Arabidopsis
 ortholog (first100bpAthal_predictions) **Metadata files needed for various
 steps in the analysis:** * 20240805_aaRS_presenceabsence.csv - contains a
 data matrix with information about presence (2), partial presence (1), or
 absence (0) of aminoacyl tRNA synthetases in parasitic plants and related
 species. For more information, see manuscript or
 [https://doi.org/10.5061/dryad.0cfxpnw7p](https://doi.org/10.5061/dryad.0cfxpnw7p). Used for checking and plotting concordance between loss of organellar enzymes and retargeting of cytosolic enzymes. - targeting_orthogroups.csv - Metadata for each aaRS enzyme, including targeting, description including target amino acid, and orthogroup from Orthfinder analysis (see manuscript for more details). Formatted for use by .ipynb notebooks. * targeting_orthogroups_Rheatmaps - Metadata for each aaRS enzyme, including targeting, description including target amino acid, and orthogroup from Orthfinder analysis (see manuscript for more details). Formatted slightly differently for plotting heatmaps for each enzyme using R script. **Scripts for data processing:** *bash scripts for running steps of analysis:* * step1_predicttargeting.sh - does targeting prediction - step2_mafft.sh - aligns all orthologues using mafft, and generates information about the alignment, including flagging sequences that do not start within 100 AA of the Arabidopsis ortholog N-terminus, and thus are likely truncated due to low quality transcript assembly. * step3_subsetpredictions.sh - sorts out predictions that start within 100 AA of the Arabidopsis ortholog into separate file. 6. Also sorts a separate file of the most retargeted protein model/transcript that starts within 100 AA of Arabidopsis which is used for downstream plotting. I provide these files in .zip directories described above. *Jupyter notebooks for plotting and statistics:* * To make heatmaps, box plots of retargeting ratios (Fig. 2): 20240815_parse_targeting_aaRS_cutoff_0.50_dryad.ipynb * To make Figure S2: 20240815_parse_overlap_presenceabsence_retargetboth.ipynb * To make Presence/Absence-Retargeting matrix for tRNA plot: 20240820_parse_overlap_presenceabsence_retarget_ind.ipynb *R script for plotting all protein models:* * transcript_by_transcript_heatmap.r *Helper scripts and functions called through pipeline:* * mafftparse4.0.py * tidy_predictions_100bpAthal.py * tidy_predictionscmd_alltrans.py ## Sharing/Access information Data generated by running *in silico* programs to predict subcellular localization/targeting of protein. Wrapper scripts for upstream processing of protein sequences via blast, running prediction programs, as well as downstream analysis and visualization are included. Protein sequences (provided as a .fa file) come from Orthogroup finding step, see Dryad dataset [https://doi.org/10.5061/dryad.0cfxpnw7p](https://doi.org/10.5061/dryad.0cfxpnw7p) and publication for details. Programs for subcellular targeting prediction: TargetP2.0: Armenteros JJA, Salvatore M, Emanuelsson O et al., 2019. Detecting sequence signals in targeting peptides using deep learning. Life Science Alliance 2. LOCALIZER Sperschneider J, Catanzariti AM, Deboer K et al., 2017. LOCALIZER: Subcellular localization prediction of both plant and effector proteins in the plant cell. Scientific Reports 7. MAFFT was used for multiple sequence alignment: Katoh K, Misawa K, Kuma K-I, Miyata T, 2002. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Research 30, 3059–3066. Blast+ was used for upstream filtering of protein sequences: Camacho C, Coulouris G, Avagyan V et al., 2009. BLAST+: Architecture and applications. BMC Bioinformatics 10. Seqkit is used for basic fasta manipulation: Shen W, Le S, Li Y, Hu F, 2016. SeqKit: A cross-platform and ultrafast toolkit for FASTA/Q file manipulation. PLoS ONE 11. ## Code/Software Includes python scripts, bash scripts, and one R script used for upstream protein sequence processing and downstream analysis and data visualization. Analysis also requires MAFFT, TargetP2.0 and LOCALIZER to be installed locally. Starting with individual .fa files with sequences from all species for each gene of interest in one folder, navigate to folder (i.e. input_prot_fasta/) and do: ``` bash ../step1_predicttargeting.sh bash ../step2_mafft.sh bash ../step3_subsetpredictions.sh ``` Navigate to PLOTTING/ Use Jupyter notebooks for plotting and statistics: To make heatmaps, box plots of retargeting ratios (Fig. 2): 20240815_parse_targeting_aaRS_cutoff_0.50_dryad.ipynb To make Figure S2: 20240815_parse_overlap_presenceabsence_retargetboth.ipynb Absence/Presence-Retargeting matrix for tRNA plot: 20240820_parse_overlap_presenceabsence_retarget_ind.ipynb Note: To plot targeting prediction for each protein model on a gene-by-gene basis as seen in supplemental dataset 6, use R script: transcript_by_transcript_heatmap.r *python packages required include:* pandas numpy seaborn scipy.stats matplotlib re os datetime math *R packages required include:* tidyverse aplot dplyr ## Version Changes As of October: **20240805:** Use of new input orthogroup sequence files/orthogroup names due to rerun of Orthofinder with new gene models. Addition of plastid-specific targeting control sequences including enzymes involved in fatty acid biosynthesis and the Clp protease complex. Changes are reflected in updated folder input_prot_fasta/ and in metadata files targeting_orthogroups.csv, 20240805_aaRS_presenceabsence.csv. Also, provided protein sequences represent filtered sequences after finding all protein models for a given locus, then using BLASTP against the *A. thaliana *protein model to exclude any models from that locus that may be from a different reading frame and thus are not true orthologous. Originally, the unfiltered files with all proteins models for a given locus were provided along with a bash script used to run blastp. These are excluded from the updated submission for ease of use for the user, but more information about where to find these models is in the publication. **20240820:** Changes to scripts: * 20240815_parse_targeting_aaRS_cutoff_0.50_dryad.ipynb- raised threshold for probability/likelihood of targeting prediction from TargetP or LOCALIZER to 0.5 (formerly 0.25) for protein to be considered "retargeted". * mafftparse4.0.py- updated to require 15 contiguously aligned amino acids (no gaps) within 100 amino acids of the start of Arabidopsis thaliana reference sequence. Formerly (mafftparse3.0.py) just required any number of aligned amino acids within 100 amino acids. * Minor cosmetic changes to 20240820_parse_overlap_presenceabsence_retarget_ind.ipynb, 20240815_parse_overlap_presenceabsence_retargetboth.ipynb.