TuMV resistance in A. thaliana

Code to accompany the following publication:

Butković, A., Ellis, T. J., González, R., Jaegle, B., Nordborg, M., & Elena, S. F. (2022). Genetic basis of Arabidopsis thaliana responses to infection by naïve and adapted isolates of turnip mosaic virus. eLife DOI: 10.7554/eLife.89749.3

Table of contents

1. Experimental set up
2. Data files
3. Analysis
4. Author information

Experimental set up

Screening

• Experiments were performed by Santiago Elena's lab at Valencia. Accessions were screened for infection symptoms when infected with (1) and ‘ancestral’ virus isolated from Nicotiana benthiama, and (2) an ‘evolved’ virus which had been through multiple generations on A. thaliana.
• In the initial main phenotyping effect, 1050 accessions were screened with 8 plants per accession. Owing to growth room requirements, accessions were split into four cohorts, with all plants of the same replicate included in the same cohort.
• Since neither accessions or individual plants were randomised over cohorts in the initial phenotyping effort, Santiago's group repeated the screen using 51 accessions that initial showed necrosis and 67 that did not, chosen from across the cohorts.

Phenotypes

Plants were monitored for 21 days after inocculation and phenotyped for the following symptoms:

1. AUDUPS: The area under the disease progression stairs (a measure of both the speed and severity of infection)
2. Infectivity: proportion of infected plants per number of inoculated plants
3. Necrosis binary trait; 0 meant no necrosis and 1 necrosis. For the initial phenotyping screen 1 indicates necrosis in any individual of an accession; in the replicate experiment they scored necrosis on each plant individually.
4. Symptomatology a semi-quantitative scale ranging from 0 - 5: 0. no symptoms or healthy plant
   1. mild symptoms without chlorosis
   2. chlorosis is visible
   3. advanced chlorosis
   4. strong chlorotic symptoms and beginning of necrosis
   5. clear necrosis and death of the plant.

Data files

SNP data

For genome-wide associations we use the SNP matrix and kinship matrix associated with the 1001 Genomes project. Download and extract them to 01_data/1001_SNP_MATRIX by running the following command from the folder root:

wget -c https://1001genomes.org/data/GMI-MPI/releases/v3.1/SNP_matrix_imputed_hdf5/1001_SNP_MATRIX.tar.gz -O - | tar -xz -C 01_data/

PyGWAS requires specific file names for the input files which don't match the default names from this link. Rename the SNP matrix with:

mv 01_data/1001_SNP_MATRIX/imputed_snps_binary.hdf5 01_data/1001_SNP_MATRIX/all_chromosomes_binary.hdf5

TAIR 10 genome

Download and the TAIR10 reference genome from:

https://www.arabidopsis.org/download/index-auto.jsp?dir=%2Fdownload_files%2FGenes%2FTAIR10_genome_release%2FTAIR10_chromosome_files

Unpack it to 01_data. The specific file needed is TAIR10_chr_all.fas

Araport annotation

Download the Araport 11 annotation file an unzip it to 01_data: https://www.arabidopsis.org/download_files/Genes/Araport11_genome_release/Araport11_GFF3_genes_transposons.current.gff.gz

Note! The Araport file will likely have the date of download as part of the file (e.g. Araport11_GFF3_genes_transposons.May2023.gff), which makes it difficult to keep scripts reproducible. As a workaround, delete the date manually, so the filename looks something like Araport11_GFF3_genes_transposons.gff.

Phenotypes

Aside from genotype files, there are nine additional data files in 01_data:

1. phenotypes_1050_accessions.csv: Disease symptoms in 1050 accessions from the first screen
2. replicate_experiment.csv: Disease symptoms in 118 accessions in a replicate screen.
3. the1001genomes_accessions.csv: Accession information from the 1001 Genomes database.
4. cohort_as_dummy.txt: Text file indicating experimental cohort as four columns of dummy variables.
5. cohort_as_factor.csv: Cohort information, but as a single column.
6. chr2_5923326.csv: SNP genotype of each accession at the SNP showing the strongest association with necrosis
7. GWAS_clean_symptoms_mutants.csv: Necrosis phenotypes of 10 replicates each for Col-0 controls, at2G14080 mutants and at2g14120 mutants in response to ancestral and evolved viruses.
8. lines_with_TE_insert.txt: Which accessions were identified to have a TE insertion in AT2G14080 by Benjamin Jaegle.
9. phenotypes_genotypes.csv: A summary (to plot figure 3) of whether each line showed necrosis in response to either virus, and its genotype at the most strongly associated SNP.

Analysis

Code to create the main and supplementary figures are given in the folder 04_main_figures and 05_supplementary_figures. Each analysis has its own README file giving more information.

Dependencies

Analysis were run on the GMI high-performance cluster. As such, dependencies may be somewhat idiosyncratic, but I have done my best to make the results reproducible on other machines.

Genome-wide associations

GWA is done using the Python package Limix. A conda environment file limix.yml is provided to recapitulate dependencies. Assuming conda is installed on your machine, install the environment with

conda env create -f limix.yml

Activate it before running analyses with

conda activate limix

R

This project uses R 4.0.3 with the following packages:

• The tidyverse bundle of packages, including ggplot2
• ggtext
• grid
• magick. On Ubuntu at least, this depends on the Linux library librsvg2-dev, which is best installed outside of R via apt.
• cowplot
• ggpubr
• reshape2
• maps and mapsdata
• geosphere
• patchwork

Full package versions are given in session_info.txt (this is the output of devtools::session_info()).

BLAST software

Download ncbi-blast-2.7.1+ from the NCBI website, and unpack it to 02_library:

https://blast.ncbi.nlm.nih.gov/doc/blast-help/downloadblastdata.html

Author information

• Principle investigators: Santiago Elena (University of Valencia), Magnus Nordborg (Gregor Mendel Institute, Vienna)
• Data collection performed by Anamarija Butković Rubén González and others in Valencia
• Analyses with PacBio genomes were performed by Benjamin Jaegle; all other analyses were by Tom Ellis.