Gene expression profiling describes the genetic regulation of Meloidogyne arenaria resistance in Arachis hypogaea and reveals a candidate gene for resistance

Abstract

Resistance to root-knot nematode was introgressed into cultivated peanut Arachis hypogaea from a wild peanut relative, A. cardenasii and previously mapped to chromosome A09. The highly resistant recombinant inbred RIL 46 and moderately resistant RIL 48 were selected from a population with cv. Gregory (susceptible) and Tifguard (resistant) as female and male parents, respectively. RNA-seq analysis was performed on these four genotypes using root tissue harvested from root-knot nematode infected plants at 0, 3, 7 days after inoculation. Differential gene expression analysis provides evidence that root-knot nematodes modulate biological pathways involved in plant hormone, defense, cell signaling, cytoskeleton and cell wall metabolism in a susceptible reaction. Corresponding to resistance reaction, an effector-induced-immune response mediated by an R-gene was identified in Tifguard. Mapping of the introgressed region indicated that 92% of linkage group A09 was of A. cardenasii origin in Tifguard. RIL46 and RIL 48 possessed 3.6% and 83.5% of the introgression on A09, respectively. Within the small introgressed region carried by RIL 46, a constitutively expressed TIR-NBS-LRR gene was identified as the candidate for nematode resistance. Potential defense responsive pathways include effector endocytosis through clathrin-coated vesicle trafficking, defense signaling through membrane lipid metabolism and mucilage production.

Comparison of Arachis monticola with Diploid and Cultivated Tetraploid Genomes Reveals Asymmetric Subgenome Evolution and Improvement of Peanut

“…Resistance alleles to RKN in chromosome A09 of a wild peanut relative ( A. cardenasii ) were introgressed into cultivated peanut . Scanning the transcriptome data of M. arenaria resistance experiments with root‐knot nematode infected plants at 0, 3, and 7 d after inoculation for susceptible and resistance groups showed that fold change (FC) values of RGAs are significantly higher in resistance group (Wilcoxon signed‐rank test, P <= 0.01) (Figure c) . We found that 43 RGAs showed induction after inoculation in high resistance group, while displayed no expression changes or repression in susceptible group (Figure d).…”

“…The genomic location cluster of resistance genes was defined by more than five genes in 500 kb nonoverlapping window for NBS‐coding, RLK, TM‐CC, and RLP class, respectively. A total of 28 transcriptome accessions of M. arenaria infected experiments from root‐knot nematode infected plants at 0, 3, and 7 d after inoculation were obtained from previous publication . The gene expression level (RPKM) and different expression genes were analyzed as described in the Transcriptome Analysis section.…”

Tomato locule number and fruit size controlled by natural alleles of lc and fas

“…Gene expression levels were normalized using the RPM value (Reads Per Million). The significance of differentially expressed genes (DEGs) was determined by linear factorial modeling in DEseq2, of which likelihood ratio test was applied (Clevenger et al., ). To identify genes with significant genotype effects using DEseq2 in R, the full model (Genotype + Time) and reduced model (Time) were used to test whether the observed differences in read counts of a given gene between genotypes were significantly larger than the variations between developmental stages and replicates.…”

Comparison of SNP Calling Pipelines and NGS Platforms to Predict the Genomic Regions Harboring Candidate Genes for Nodulation in Cultivated Peanut

“…Alternatively, several other methods have been developed that could use overall mapped reads for SNP calling and filter out homoeologous SNPs afterward. For example, SWEEP , which utilizes homoeologous SNPs as an anchor to differentiate allelic SNPs, had been successfully applied in peanut (Clevenger et al, 2017;Pandey et al, 2017) with a validation rate of 85% through Sanger sequencing and above 95% through simulation data . In addition, a machinelearning tool called SNP-ML was also developed to predict allelic SNPs with a validation rate of 75-98% (Korani et al, 2019).…”