DOI: 10.5061/dryad.2fqz612ww
title: Data from: Genetic mechanism of non-targeted-site resistance to diquat in Spirodela polyrhiza

description: 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.
description: # Data from: Genetic mechanism of non-targeted-site resistance to diquat
 in Spirodela polyrhiza 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.