Data from: Genetic mechanism of non-targeted-site resistance to diquat in Spirodela polyrhiza
Data files
Feb 08, 2024 version files 2.24 GB
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Appendix_figures.zip
1.66 GB
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Figure1.zip
83.10 MB
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Figure2.zip
197.97 MB
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Figure3.zip
67.75 MB
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Figure4.zip
14.88 MB
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Figure5.zip
3.31 MB
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raw_data_toxicity_assays_GWAS.zip
218.70 MB
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README.md
20.33 KB
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TableS1.xlsx
24.75 KB
Abstract
Understanding non-target-site resistance (NTSR) to herbicides represents a pressing challenge as NTSR is widespread in many weeds. Using the giant duckweed (Spirodela polyrhiza) as a model, we systematically investigated genetic and molecular mechanisms of diquat resistance, which can only be achieved via NTSR. Screening the diquat resistance among 138 genotypes suggested more than 8.5-fold resistance differences. Further experiments suggested that diquat uptake and antioxidant-related processes jointly contributed to diquat resistance in S. polyrhiza. Using a genome-wide association approach, we identified candidate genes that are associated with diquat resistance in S. polyrhiza, which includes a homolog of dienelactone hydrolase.
README: Data from: Genetic mechanism of non-targeted-site resistance to diquat in Spirodela polyrhiza
https://doi.org/10.5061/dryad.2fqz612ww
The dataset contains analyses on diquat toxicity for different Spirodela polyrhiza genotypes. This dataset includes scripts and raw data files of toxicity test, metabolite analyses and enzymatic assays. Additionally the dataset provides pdf versions of all figures associated with the manuscript plants-2849712. The code and raw data that were used to generate the main figures are stored in the folders "Figure1" - "Figure5". The names of the folders correspond to the figure names in the manuscript. Code and data used to generate the Appendix figures were deposited in the folder "Appendix_figures". We named all data collections of appendix figures "Figure Ax", where x corresponds to the number of the appendix figure in the manuscript.The visual documentation (e.g. pictures of cultures) and analysis of the Spirodela genotype screening procedure can be found at raw_data_toxicity_assays_GWAS. Each folder contains a script and a comprehensive data file that were used to create the corresponding figure. For most of our analysis we used R-scripts, which contain the statistical analysis as well.
Description of the data and file structure
folder: Figure1.zip
description:The folder Figure1.zip contains pictures from toxicity assays of S. polyrhiza on diquat and raw data from chlorophyll fluorescence measurements.
subfolder: chlorophyll_fluorescence.zip
description: This folder contains the R script analysis_of_chlorophyll_fluorescence.R which was used to calculate and plot the mean chlorophyll fluorescence values Fv/Fm and Fv'/Fm' for different Spirodela polyrhiza genotypes over time. The table data_table_chlorophyll contains the measured parameters for each timepoint and and phenotype.
subsubfolder: raw_data.zip
description: The raw measurements data are stored in this folder. The names of the subfolders correspond to the timepoints of each chlorophyll fluorescence measurement. The measurements of each plate were documented as screenshots, that can be found as .jpg files in the corresponding folders. The measurements of each plate were summarized in a .csv file.
subfolder: dose_response_curve_resistance.zip
description: The R-script comprehensive_dose_response_curve.R was used to calculate and plot dose-response estimates of different S. polyrhiza genotypes for diquat. The growth of S.polyrhiza cultures was estimated based on their relative growth rates (RGR) per min. The inhibition through the herbicide was quantified as inhibitory effect (IE) relative to an untreated control for growth rates measured on frond area and number.
subsubfolder: raw_data.zip
description: We conducted two toxicity test on different genotypes, whose results were stored in two different folders. Each of these folders contains an image analysis folder and two folders documenting start and endpoint of each toxicity test with .pdf pictures. The growth rates and biomass data derived from these raw data are documented in in data tables termed fresh_Weight_table, image/image_analysis and toxicity_assay_diquat. The raw data in this folder were also used to create Table A4 and FigureA4.
folder: Figure2.zip
description: Figure2.zip contains raw data about the quantification of diquat in dry weigth (DW) plant tissue. We expressed the diquat tissue concentration always in pmol per mg DW or per ml.
subfolder: correlation_culture_diquat_genotypes.zip
description: A correlation between resistance level and diquat tissue concentration was calculated and plotted using the script evaluation_Diquat_plant_conc.R. The diquat concentration was documented in the file comprehensive_data_table_diquat_All_Genotypes.txt, all_Genotypes_data_analysis.txt contains raw toxicity data.
subsubfolder: raw_data.zip
description: Diquat concentration measurements were stored as .lcd files for each plate. The folder diquat_Extraction.zip specifies the location of each sample within the plate. DQ_v7.lcm is the method file used for the quantification of diquat in plant tissue for all analyses belonging to this figure.
subfolder: diquat_plant_concentration_14_12_21.zip
description: The script time_course_diquat_plant.R was used to evaluate the accumulation of diquat in plant tissue over time and to plot the diquat uptake kinetics. The raw data used for this analysis are summarized in the file diquat_uptake_additional_timepoints_13_06_22.txt.
subsubfolder: raw_data.zip
description: For each plate raw data from LC-MS measurements were stored as .lcd files.
subfolder: root_frond_translocation.zip
description: The script root_frond_tanslocation.R was used to quantify diquat in root and frond tissue of S. polyrhiza and to create barplots depicting root and frond concentrations of diquat for several genotypes.
subsubfolder: raw_data.zip
description: For each plate raw data from LC-MS measurements were stored as .lcd files.
folder: Figure3.zip
description: In Figure3.zip contains raw data and scripts used for the analysis of antioxidant metabolites and enzymatic functions in response to diquat. The metabolites Ascorbic acid (AsA), Dehydroascorbate (DHA) and Glutathione (reduced-GSH, oxidized-GSSG) were quantified in µmol per mg of freshweight (FW). The enzymatic function of the superoxide dismutase (SOD) is expressed as percental inhibition of the Xanthinoxidase (XO) per mg protein.
subfolder: antioxidant_concentrations.zip
description: The script quantification_antioxidants.R was used to calculate mean antioxidant concentrations over time and matrix effects of these metabolites. Furthermore the script was used to plot GSH/GSSH and AsA/DHA ratios over time for two genotypes. The data used for this analysis can be found in the files standard_curve_all_data.txt, samplex_analysis.txt and matrix_effect.txt.
subsubfolder: raw_data.zip
description: The file GSH_v4.lcm was used to quantify the antioxidant concentrations. The raw data were stored as .lcm files in subfolders corresponding to the conducted analysis. Details about the cultivation procedure of the plant material and the sample preparation can be found in aliquotation_table.xlsx and antioxidant_measurement.xlsx. The raw data deposited here were also used to create the Appendix figures FigureA6 and FigureA7
subfolder: enzymatic_assays.zip
description: We used the script comp_activity_SOD.R for calculating and plotting SOD activity for several genotypes. The data used for this analysis are stored in the files comprehensive_table_SOD_activity_all_genotypes.txt and comprehensive_table_SOD_activity.txt.
subsubfolder: raw_data.zip
description: The file cultivation_SOD_BCA-assay.xlsx contains metadata about the cultivaiton of plant material used for this assay. Data regarding the quantification of protein content were stored platewise in the folder raw_BCA. The scripts BCA_plate_1.R and BCA_plate_2.R were used to calculcate protein concentration for the respective plate. The absorption values were documented as .png files. Raw data used for the quantification of SOD activity can be retrieved from the folder raw_SOD. The scripts SOD_plate1.R, SOD_plate2.R, SOD_plate3.R and SOD_plate4.R were used to calculate SOD activity from different measurements. The raw data were documented as .png files for each measurement. The raw data deposited here were also used to create the Appendix figure FigureA8.
folder: Figure4.zip
description: Figure4.zip contains scripts and genotype files used for GWAS using structure variations (SVs) as input genetic marker. The file code_vcf2gwas_SVs.docx contains the code used to create all GWAS output data. The Excel file raw_data_GWAS contains all raw fitness data from our herbicide screening experiment.
subfolder: GWAS_output.zip
description: The subfolders SVs_fresh_weight_23, SVs_frond_area_RGR_23 and SVs_frond_number_RGR_23 contain all output data of our GWAS analyses.
subfolder: raw_data.zip
description: Here we stored the input files for the GWAS analysis. The file diquat_fitness_parameters_clonal_families.csv was used as phenotype input file, whereas SVs_reformatted_new.vcf was used as input genotype file. SVs_reformatted_new.vcf was created from merge_MGDLA.graphT.vcf.gz.ff.vcf.filter_0.0Ns.500k.50.SVTYPE.vcf.gz.final.filter.recode.vcf using the code documented at code_recoding_SV_file.docx.
subfolder: SV_effect.zip
description: This folder contains files and scripts analyzing the effect of certain alleles on the diquat resistance of S. polyrhiza genotypes.
subsubfolder: ChrS05_7048033.zip
description: The R-script SV_effect_ChrS05_7048033.R was used to calculate and plot the SV effect for the marker ChrS05_7048033. The file ChrS05_7048033.txt contains the raw data for this analysis. The data and plot of this analysis were also used to create the Appendix figure Figure A12.
subsubfolder: ChrS08_5199142.zip
description: The R-script SV_effect_ChrS08_5199142_DG was used to calculate and plot the SV effect for the marker ChrS08_5199142. The file SV_effect_Chr_08_5199142.txt contains the raw data for this analysis.
subfolder: reformatted_manhattan_plot_SVs.zip
description: The script reformatting_plots.R was used to create manhattan plots for visualizing the GWAS results.
folder: Figure5.zip
description: In Figure5.zip we store raw data obtained from our gene expression analyses. Gene expression was quantified via qPCR using the Delta-Delta Ct (Cycle-threshold) method.
subfolder: primer_efficicency.zip
description: Primer efficiencies were calculated using the scripts primer_efficiencies_ethe1.R and primer_efficiencies_new_candidates.R. The input data for primer efficiency calculation are listed in the files primer_efficiency_new_candidates.txt and primer_efficiency.txt.
subsubfolder: raw_data.zip
description: Reaction files (.rex) used for determining gene expression rate and melting points can be found in two subfolders. The results of each measurement were documented in .csv files. Raw data from this file were used to create FigureA14. The primers used for the qPCR are found in Table A2 in our manuscript.
subfolder: frond_expression_SpETHE1.zip
description: The script frond_expression_pattern_new.R was used to analyse gene expression data and to plot target gene expression for two genotypes. The files root_frond_Expression_new.txt and comp_dat.csv contain the input data for this analysis. Metadata about the cultivation process are documented in the Excel table cultivation_table_root_frond_new.
subsubfolder: raw_data.zip
description: The Ct values measured in each analysis are listed in the respective csv-files. The rex-files contain all raw data about the melting curve analysis and determination of the gene expression rate.
subfolder: alternative_candidates_frond_tissue.zip
description: The script new_candidate_genes_frond.R was used for data evaluation and plotting of gene expression values for four candidate genes. The files analysis_alternative_candidates.txt and comp_dat.csv contain the source data of this analysis.
subsubfolder: raw_data.zip
description: The Ct values measured in each analysis are listed in the respective csv-files. The rex-files contain all raw data about the melting curve analysis and determination of the gene expression rate.
Table: TableS1.xlsx
description: This table lists all genotypes together with their sampling site that were used in our toxicity tests.
folder: raw_data_toxicity_assays_GWAS.zip
description:This folder contains the raw phenotypic data used for the GWAS analyses. The genotypes used for the GWAS were screened in five batches, that we refer to as toxicity assay 1-5. The toxicity data were analyzed for each batch separately. The names of the subfolders correspond to the numbers of the toxicity test. Each subfolder contains pictures documenting the toxicity assays. Furthermore each subfolder contains a documentation of the quantification of frond area. The data analysis of each batch was conducted using R-scripts, which can be found in each subfolder. Raw data from this folder were used for the data analysis in FigureA3, FigureA5, FigureA11 and Table A7 in our manuscript.
folder: Appendix_figures.zip
description: The folder "Appendix figures" contains the data analysis of all Appendix figures in the subfolders Figure A1-14.
subfolder: Diquat_medium_concentration.zip
description: The script diquat_medium_concentration.R was used to calculate diquat concentration in cultivation medium and to create TableA5. The raw data for this analysis are found in the file diquat_medium_comprehensive.txt.
subsubfolder: raw_data.zip
description: The raw data file contains pictures of the cultivation procedure and lcd-files documenting the diquat quantification of diquat in medium samples.
subfolder: FigureA1.zip
description: This folder contains raw toxicity data referring to a dose-response and screening measurements similar to the analysis conducted in Figure1. The analysis was conducted using the script concentration_Estimate_1.R.The file list_herbicide_toxicity_assay_23.04.20-03.05.20.txt contains the input toxicity data for this analysis.
subsubfolder: raw_data.zip
description: This folder contains pictures documenting the growth of S. polyrhiza genotypes under diquat exposure.The data underlying the analysis in FigureA1 were used to create Table A3.
subfolder: FigureA2.zip
description: The data of this folder were used to quantify the phenotypic variation at two different diquat concentrations. The analysis was done using the script 19_genotypes_variation.R. The file 19_genotypes_table.txt contains data on the inhibition of fitness parameters for 19 S. polyrhiza genotypes.
subsubfolder: raw_data.zip
description: This folder contains pictures and R-scripts documenting the growth of diquat treated S. polyrhiza genotypes. The raw data of this file were used for the analysis in FigureA4.
subfolder: heritability_19_genotypes.zip
description: The script heritability_19_genotypes.R was used to estimate the broad-sense heritability of diquat resistance from our raw data. The file heritability_table.txt contains toxicity data of several genotypes used as input for our analysis.
subfolder: FigureA3.zip
description: The script kernel_density.R was used to calculate and plot a density-function of all quantified diquat resistance phenotypes. The raw data of this analysis are found in the text document diquat_fitness_parameters_clonal_families.txt.
subfolder: FigureA4.zip
description: The script correlation_IE_values.R was used to correlate resistance values measured in the screening procedure with those measured in dose response experiments to check the reproducibility of our resistance measurements. Results were plotted and exported as pdf-files. The file correlation_IE_Diquat_curve_Screening.txt contains inhibition values from the screening and dose-response experiments of 16 genotypes.
subfolder: FigureA5.zip
description: We used the script population_resistance.R to compare and plot resistance levels between four geographical populations of S.polyrhiza. Raw data for this analysis are documented in the file population_resistance.txt.
subfolder: FigureA6.zip
description: We used the script plotting_antioxidant_concentrations.R to plot antioxidnat concentrations over time. The file means_sd_table_lab.csv contains mean concentrations of antioxidant metabolites for several genotypes.
subfolder: FigureA7.zip
description: With the script correlation_antioxidants_resistance.R was used to establish and plot multiple correlations between antioxidant levels, ED50 (Effective dose) levels and diquat tissue concentration for eight S.polyrhiza genotypes. The raw data for this analysis can be found in the file means_sd_table_ED50_lab.csv.
subfolder: FigureA8.zip
description: We used the script correlation_SOD_diquat_resistance.R to correlate ED50 values with SOD activity for eight S.polyrhiza genotypes. Measured ED50 values and SOD concentration used for this analysis are documented in the file means_sd_table_ED50_lab.csv.
subfolder: FigureA9.zip
description: FigureA9.zip contains genotype and phenotypic data used for GWAS analyses using SNPs as input genetic markers. The file code_vcf2gwas_SNPs.docx contains the code used to create all GWAS output data.
subsubfolder: GWAS_output.zip
description: The subfolders norm_IE_fresh_weight_di, norm_IE_RGR_area_di and norm_IE_RGR_frond_di contain all output data of our GWAS analyses.
subsubfolder: raw_data.zip
description: The file restructuring_genotype_data_SNPs.R was used to filter out all genotypes not relevant for our analysis from the genotype data file SP_228.basic_set.snp.recode.rm_cluster3-10.vcf.PASS.hmp.txt to reduce computation time. The file SNPs_98_clonal_families.hmp.txt was generated by this analysis. We used the code documented in code_transforming_genotype_data to convert SNPs_98_clonal_families.hmp.txt to vcf-format named SNPs_98_clonal_families.vcf.
subsubfolder: reformatted_manhattan_plot_SNPs.zip
description: The script reformatting_plots.R was used to create manhattan plots to visualize GWAS results.
subsubfolder: SNP_effect.zip
description: The script SNP_01_10117635.R was used to quantify and plot the effect of alleles on diquat resistance. The raw data for the analysis are documented in the file S01_10117635.txt.
subfolder: FigureA10.zip
description: QQ-plots (Quantile-Quantile) of output GWAS data for SVs were generated using the script reformatting_qqPlots.R.
subfolder: FigureA11.zip
description: QQ-plots (Quantile-Quantile) of output GWAS data for SNPs were generated using the script reformatting_qqPlots.R.
subfolder: FigureA12.zip
description: The script SV_05_7048033.R was used to plot the allelic effect on resistance levels for the genetic marker ChrS05_7048033. The file ChrS05_7048033_allele_structure.txt contains the data for this analysis.
subfolder: FigureA13.zip
description: This folder contains gel images from genotyping experiments and splicing analysis.
subfolder: FigureA14.zip
description: Data and scripts used for analysing gene expression in root tissue can be found in this folder.
subsubfolder: alternative_candidates_root_tissue.zip
description: The script new_candidate_genes_root.R was used for analysis and plotting expression levels of candidate genes in root tissue. The analysed data are deposited in the file comp_dat.csv.
subsubfolder: root_expression_SpETHE1
description: The script root_expression_pattern_new.R was used to analyse expression of the gene SpETHE1 in root tissue. The analysed data are deposited in the file comp_dat.csv.
Software/Code
For all analyses we used R-version 4.0.0. The GWAS was conducted using the vcf2gwas platform version 0.8.7. We analyzed the frond area of toxicity assay pictures using the Fiji-platform, applying a customized script, that can be found at "Figure1\dose_response_curve_resistance\raw_data\diquat_toxicity_assay_04_10-12_10_21\image_analysis\script_image_analysis\Spira-v2-2.ijm". Further specifications of R-packages can be found in the methods part of the manuscript.
Usage notes
Plots were exported as .pdf files and can be opened with a pdf-reader. Processed data files are saved as .txt files and can be opened with any text-editor. Images are stored as .png and .jpg formats and can be opened with any graphics editor. Measurements of metabolites and xenobiotics were stored as .lcd files and were analyzed with corresponding method files with the suffix .lcm. Both files can be opened using Lab-Solutions software. The raw data of our chlorophyll fluorescence measurements were stored as .pim files that can be opened with the ImagingWinGigE software. Genotypic information were stored in .vcf files, which can be opened with text editors as well. Reaction and analysis files of qPCR data are stored as .rex files and can be opened using Q-Rex software.