DOI: 10.5281/zenodo.10499917
title: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)

description: Supplemental datasets associated with publication: Pathogen lifestyle determines host genetic signature of quantitative disease resistance loci in oilseed rape (Brassica napus)

Abstract



Crops are affected by several pathogens, but these are rarely studied in parallel to identify common and unique genetic factors controlling diseases. Broad-spectrum quantitative disease resistance (QDR) is desirable for crop breeding as it confers resistance to several pathogen species.

Here, we use associative transcriptomics (AT) to identify candidate gene loci associated with Brassica napus constitutive QDR to four contrasting fungal pathogens: Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum. We did not identify any loci associated with broad-spectrum QDR to fungal pathogens with contrasting lifestyles. Instead, we observed QDR dependent on the lifestyle of the pathogen—hemibiotrophic and necrotrophic pathogens had distinct QDR responses and associated loci, including some loci associated with early immunity. Furthermore, we identify a genomic deletion associated with resistance to V. longisporum and potentially broad-spectrum QDR.

This is the first time AT has been used for several pathosystems simultaneously to identify host genetic loci involved in broad-spectrum QDR. We highlight constitutively expressed candidate loci for broad-spectrum QDR with no antagonistic effects on susceptibility to the other pathogens studies as candidates for crop breeding. In conclusion, this study represents and advancement in our understanding if broad-spectrum QDR in B. napus and is a significant resource for the scientific community.  


Description of data files

Full dataset for input into AT analysis  Full datasets (infection phenotypes for A. brassicicola, B. cinerea, or V.longisporum, ROS measurements for chitin, flg22, or elf18) and link to original P. brassicae dataset. These datasets were used for input into the Associative Transcriptomics pipeline (Nichols, 2022, https://github.com/bsnichols/GAGA. https://zenodo.org/badge/latestdoi/512807075). 

Table S1 Mean, normalized phenotype data for resistance to pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum) and ROS response induced by PAMPS (chitin, flg22, and elf18). These data were used for association transcriptomic analysis. 

Table S2 Full list of single nucleotide polymorphism (SNP) markers and significance levels from genome-wide association (GWA) analyses for resistance to pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. The best fit model for GWA analysis is indicated in the tab title. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates SNP location along the chromosome; the y-axis indicates the -log10(p) (P value). Qqplots are included to demonstrate model fit.

Table S3 Full list of gene expression markers (GEMs) and significance levels from GEM analyses for resistance to pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum) and ROS response induced by PAMPS (chitin, flg22, and elf18). Each excel tab contains the analyses for a single trait. Manhattan plots showing marker-trait association are included for data visualization; x-axis indicates GEM location along the chromosome; the y-axis indicates the -log10(p) (P value). 

Table S4 184 gene expression markers (GEMs) associated with chitin-induced ROS compared with GEMs associated with resistance to pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum) and ROS response induced by flg22, and elf18. Lists correspond to Venn diagrams in Fig. 2. The first tab includes all 184 GEMs associated with chitin-induced ROS. The subsequent tabs include lists of shared GEMs associated with chitin-induced ROS response and each additional trait (quantitative disease resistance (QDR) to each fungal pathogen or additional PAMP-induced ROS responses). The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted Arabidopsis thaliana orthologs and corresponding descriptions are shown where possible. 

Table S5 Enrichment analyses to determine if the number of gene expression markers (GEMs) shared between different lists is greater than the number of GEMs that would be expected by chance (e.g., lists of quantitative disease resistance (QDR) GEMs for two fungal pathogens). The representation factor is the number of overlapping GEMs divided by the expected number of overlapping GEMs drawn from two independent groups (traits), considering the total number of GEMs sequenced (53884). A representation factor > 1 indicates more overlap than expected of two groups, a representation factor < 1 indicates less overlap than expected, and a representation factor of 1 indicates that the two groups by the number of genes expected for independent groups of genes. 

Table S6 Results from Weighted Co-expression Gene Network Analysis (WGCNA). The first tab indicates significant modules from WGCNA analysis. Black and magenta modules are associated with antagonistic effects on resistance/susceptibility to all four pathogens. The second tab includes a full list of the GEM markers (Table S3), which are in significant WGCNA modules. The third, fourth and, fifth tabs indicate all significant GEMs in the black module,  GO terms associated with GEMs in the black module, and all GO terms associated with the black module, respectively.  The sixth, seventh and, eighth tabs indicate all significant GEMs in the magenta module,  GO terms associated with GEMs in the magenta module, and all GO terms associated with the magenta module, respectively.

Table S7 Shared gene expression markers (GEMs) associated with resistance to different pathogens (Alternaria brassicicola, Botrytis cinerea, Pyrenopeziza brassicae and Verticillium longisporum). Lists correspond to matrices and Venn diagrams in Fig. 3. The first tab includes all GEMs associated quantitative disease resistance (QDR) to the fungal pathogens. The subsequent tabs include lists of shared GEMs associated with QDR to two or more fungal pathogens. The title of each tab indicates the data included in each comparison and the number of shared GEMs. Predicted Arabidopsis thaliana orthologs and corresponding descriptions are shown where possible. 

Table S8 List of genes in linkage disequilibrium with the top marker for Verticillium longisporum resistance from genome-wide association (GWA) analysis on chromosome A09 (107 genes)(Tab 1) and the homoeologous region on C08 (Tab 2). Their percentage identity and query coverage in Brassica napus reference genotypes Quinta, Tapidor, Westar and Zhongshuang 11 compared to the B. napus pantranscriptome is indicated. Predicted Arabidopsis thaliana orthologs and corresponding descriptions are shown where possible.  

 
description: VKM and AKK were supported by the National Center for Research and Development, Poland (grant no. ERA-CAPS II/1/2015). XZ and AvT were supported by the German Research Foundation (grant no. ERA-CAPS DFG; TI170/13–1). HJS, RW, RB, CJR, HF, GKM, HUS and BDLF were funded by the Biotechnology and Biological Sciences Research Council (BBSRC) grants BB/N005007/1 and BB/N005112/1 (as part of the ERA-CAPS consortium "MAQBAT"). HJS, RW, CNJ, RB and CJR were also funded by the John Innes Institute strategic program grants Plant Health BB/P012574/1 (Response BBS/E/J/000PR9796) and Molecules from Nature BB/PO12523/1, Brassica Rapeseed and Vegetable Optimisation (BRAVO) BB/P003095/1.