Data from: Leaf abaxial and adaxial surfaces differentially affect the interaction of Botrytis cinerea across several eudicots
Data files
Oct 08, 2024 version files 21.56 MB
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Github_Surface.zip
21.55 MB
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README.md
6.84 KB
Abstract
Eudicot plant species have leaves with two surfaces: the lower abaxial and the upper adaxial surface. Each surface varies in a diversity of components and molecular signals, resulting in potentially different degrees of resistance to pathogens. We tested how Botrytis cinerea, a necrotroph fungal pathogen, interacts with the two different leaf surfaces across 16 crop species and 20 Arabidopsis genotypes. This showed that the abaxial surface is generally more susceptible to the pathogen than the adaxial surface. In Arabidopsis, the differential lesion area between leaf surfaces was associated with jasmonic acid (JA) and salicylic acid (SA) signaling and differential induction of defense chemistry across the two surfaces. When infecting the adaxial surface, leaves mounted stronger defenses by producing more glucosinolates and camalexin defense compounds, partially explaining the differential susceptibility across surfaces. Testing a collection of 96 B. cinerea strains showed genetic heterogeneity of growth patterns, with a few strains preferring the adaxial surface while most are more virulent on the abaxial surface. Overall, we show that leaf-Botrytis interactions are complex with host-specific, surface-specific, and strain-specific patterns.
https://doi.org/10.5061/dryad.r4xgxd2p6
Description of the data and file structure
Check the Rnotebook_surface to find more information on the files and R codes.
This notebook aims to compile and annotate the R scripts used for the analysis of the Leaf surface-Botrytis dataset.
This notebook charges R scripts that contain all the code. The main result figures are plotted directly in the notebook.
Experimental design:
Sixteen Eudicot species and 20 A.thaliana genotypes were analyzed. Detached leaves were inoculated with Botrytis in ‘experimental trays’, that constitutes a micro-environment for a randomized collection of isolates. After 72h, pictures of all trays were taken. Image analysis for calculation of lesion area (and many other parameters) was conducted in R.
For image analysis R codes, see the Image_analysis_pipeline_Final R notebook.
General name usage:
Genotype = plant genotype
Species = different eudicot plant species
Isolate or Iso = Botrytis strains
Surface:
U = Up= leaf adaxial surface
D = Down = leaf Abaxial surface
Treatment: treatment = infection with Botrytis. Control = uninfected leaves.
Image / experimental tray = tray with phytoagar in which the infected detached leaves where pictured to measure the lesion area
Plant tray = tray in the growth chamber in which each individual plant was located
1. Testing the leaf surface effect across 16 eudicot species
Associated files:
Eudi16_72h_all1.txt
Raw data
Columns M:FE units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
Column FG: Units = cm2
Column FH: units = mm2
Eudicot16_UD_clean.txt
Data after removal of failed lesions
Columns M:FE units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
Column FG: Units = cm2
Columns FH-FM: units = mm2
Columns FN:FS: Parameters for the removal of failed lesions
Stomata_forPlot.txt
Average count of stomata on the abaxial and adaxial surfaces [stomata/mm2].
Eudicot_Stomata_counts_col.txt
Raw data : Count of stomata on the abaxial and adaxial surfaces [stomata/mm2].
R Code:
Eudi_UD_analysis_final.R
2. Testing the leaf surface effect within Arabidopsis thaliana
Associated files:
G24I10_72H.txt
Columns K-FC units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
G24I10_96h.txt
Columns F-EX units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
G24I10_UD_48h.txt
Columns F-EX units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
Genotype_Emmeans_Cat
Least-square (emmeans) corrected means, standard error (SE), degree of freedom (DF), lower and upper confidence levels (CL).
Data_Plot_Lesion_GT.txt
Mean lesion area in mm2
R Code:
Ara20_72h_all.R
3. Testing the effect of leaf surface on glucosinolate and camalexin content
Associated files:
GSL_Leaf_Data1.txt
Columns P-AU: HPLC peak area data. Columns starting with: X are raw data peak area; Q are data normalized by absorbance coefficient; QL are data normalized both to absorbance and leaf area.
Columns BD-BT: units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
Columns Bu-CA: Units = cm2
Cam_Leaf_Data1.txt
Columns Q-S : raw data
Columns AB-AR: units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
Columns AS-AW: Units = cm2
Column AX-BB: Normalized data
Chemotype_LM_model_pie.txt
Results from linear modeling. Df= degree of freedom, SumSq= sum of squares, Perc=Percentatage of variance calculated from the sum of squares, MeanSQ = mean of squares. Pr(F) = p-value.
R Code:
Glucosinolates_Camalexin.R
4. Testing how the diverse Botrytis strains interact with the leaf surfaces
Associated files:
Lesion_C0l0_48h.txt
columns J: FB: units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
Col0_UpDown72h.txt
columns K-FC: units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
UpDown_Exp1_Results96.txt
columns I-FA: units = pixels [See Fordyce et al 2018 www.plantphysiol.org/cgi/doi/10.1104/pp.18.00851]
R Code:
Bcin96.R
geom_split_violin
5. Supplemental Material
Supplemental_material.pdf
Supplemental figures
Table S1: Information on the 16 eudicot species used to test how the leaf surfaces influence host-Botrytis interactions across species. In addition to the information on the seeds’ origin, the average leaf thickness, stomata density and lesion area on each leaf surface are provided.Table S2: Information on the 20 *A. thaliana genotypes used to test how leaf surfaces change the interaction with Botrytis within a species. Mutants are deficient in SA and JA defense signaling, aliphatic and indolic glucosinolates, putative cyanogenic glycosides, and camalexin biosynthesis.Table S3: Information on the 96 B.cinerea strains used in this study. For each strain the geographical origin, the host it was isolated from (not equal to host specialization or pathovars) are provided. Values for general virulence and host specificity (Caseys et al. 2021) and camalexin sensitivity (Zhang et al. 2017) were used to subset the 10 strains associated to the Eudicot and Arabidopsis experiments. Which strain was used in which experiment (Eudicots, Arabidopsis, and/or Botrytis96) is also provided.
Files and variables
File: Github_Surface.zip
Description: All input files and R codes for all 4 datasets and analyses
Code/software
R stat
Warning:
This was coded for and functional with R version:
R version 3.6.0 (2019-04-26) – “Planting of a Tree”
Platform: x86_64-apple-darwin15.6.0 (64-bit)
To assess how developmental patterns between leaf surfaces influence Botrytis interactions, we first tested 16 species from eight different plant families with diverse natural histories. These eight families are sampled from the caryophyllales in the basal core eudicots to asterids and rosids. As physical and chemical defenses and also defense signaling were shaped by the environment, herbivore, and pathogen pressures across the evolutionary timescale, those species constitute a sampling of defensive strategies existing in the eudicots while focusing on crops of economical value. To test how the effect of the leaf surfaces varies across genotypes within a host species, we infected 20 A. thaliana genotypes. Those genotypes included SA and JA signaling mutants that control over the chemical defense variation in addition to TFs and enzymes along the pathways. Finally, to assess how the diversity in the pathogen interacts with developmental patterns between leaf surfaces, we infected 96 Botrytis strains on a single host genotype. This provided an analysis of how abaxial/adaxial leaf surface variation influences the host-Botrytis interaction across diverse host species, host genotypes, and pathogen genotypes providing an initial investigation into the conditionality of this phenomena.