Genotyping-by-sequencing-based identification of Arabidopsis pattern recognition receptor RLP32 recognizing proteobacterial translation initiation factor IF1
Fan, Li et al. (2022), Genotyping-by-sequencing-based identification of Arabidopsis pattern recognition receptor RLP32 recognizing proteobacterial translation initiation factor IF1, Dryad, Dataset, https://doi.org/10.5061/dryad.h70rxwdkx
Activation of plant pattern-triggered immunity (PTI) relies on the recognition of microbe-derived structures, termed patterns, through plant-encoded surface-resident pattern recognition receptors (PRRs). We show that proteobacterial translation initiation factor 1 (IF1) triggers PTI in Arabidopsis thaliana and related Brassicaceae species. Unlike for most other immunogenic patterns, IF1 elicitor activity cannot be assigned to a small peptide epitope, suggesting that tertiary fold features are required for IF1 receptor activation. We have deployed natural variation in IF1 sensitivity to identify Arabidopsis leucine-rich repeat (LRR) receptor-like protein 32 (RLP32) as IF1 receptor using a restriction site-associated DNA sequencing approach. RLP32 confers IF1 sensitivity to rlp32 mutants, IF1-insensitive Arabidopsis accessions and IF1-insensitive Nicotiana benthamiana, binds IF1 specifically and forms complexes with LRR receptor kinases SOBIR1 and BAK1 to mediate signaling. Similar to other PRRs, RLP32 confers resistance to Pseudomonas syringae, highlighting an unexpectedly complex array of bacterial pattern sensors within a single plant species.
Restriction site–associated DNA sequencing (RAD-seq) and quantitative trait locus mapping using package R/qtl were conducted using the F2 mapping population of a Arabidopsis ICE153 x ICE73 cross as previously described. Details are found in:
Fan, L., Chae, E., Gust, A. A. & Nürnberger, T. Isolation of novel MAMP-like activities and identification of cognate pattern recognition receptors in Arabidopsis thaliana using next-generation sequencing (NGS)-based mapping. Curr. Protoc. Plant Biol. 2, 173-189 (2017).
1. Reference and variant calling data for each sample of RAD-seq. with file name: R-V-data.zip
Information of files named with “qvqr.f2.XXXX.info” in ‘R-V-data.zip’
Each file represents individual sample run by RAD-seq.
Column 1: chromosome number * 100,000,000 + position on the chromosome (chromosome is based on Arabidopsis thaliana reference genome (TAIR10)
Column 2: reference nucleotide call
Column 3: alternative nucleotide call (sample’s call)
Column 4: Quality of a predicted feature (ranging from 0 to 40) as in ‘SHORE’ program
Column 5: depth
Column 6: Ratio of reads supporting a predicted feature to total coverage (excluding quality masked bases). Heterozygous SNPs and SNPs from pooled samples are divided into ’major concordance’ and ’minor concordance’ as in ‘SHORE’ program
Column 7: Number of repetitive positions in the range of the prediction
2. QTL analysis genotype matrix
Column and row information of the file; 'ICE153ICE73.0.8p.3.7.S5.GT.headQTLtrim2.csv'
Column 1: phenotype trait 1: quantitative measurements
Column 2: phenotype trait 2: binary trait
Column 3 and on: genotype call on the indicated position
Row 1: marker name (chromosome*100,000,000 + position on the chromosome)
Row 2: chromosome number
Row 3: position on the respective chromosome
Row 4 and on: genotype call of each sample
Ministry of Education, Singapore, Award: Academic Research Fund (MOE2019-T2-1-134)
Deutsche Forschungsgemeinschaft, Award: Nu70/9-1
Deutsche Forschungsgemeinschaft, Award: Nu70/9-2
Deutsche Forschungsgemeinschaft, Award: Nu 70/15-1
Deutsche Forschungsgemeinschaft, Award: Nu70/16-1
Deutsche Forschungsgemeinschaft, Award: Nu70/17-1
Deutsche Forschungsgemeinschaft, Award: SFB1101