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Genomic Analysis Highlights Putative Defective Susceptibility Genes in Tomato Germplasm.

Ruiling LiAlex MaioliSergio LanteriAndrea MogliaYuling BaiAlberto Acquadro
Published in: Plants (Basel, Switzerland) (2023)
Tomato ( Solanum lycopersicum L.) is one of the most widely grown vegetables in the world and is impacted by many diseases which cause yield reduction or even crop failure. Breeding for disease resistance is thus a key objective in tomato improvement. Since disease arises from a compatible interaction between a plant and a pathogen, a mutation which alters a plant susceptibility (S) gene facilitating compatibility may induce broad-spectrum and durable plant resistance. Here, we report on a genome-wide analysis of a set of 360 tomato genotypes, with the goal of identifying defective S-gene alleles as a potential source for the breeding of resistance. A set of 125 gene homologs of 10 S-genes ( PMR 4 , PMR5 , PMR6 , MLO , BIK1 , DMR1 , DMR6 , DND1 , CPR5 , and SR1 ) were analyzed. Their genomic sequences were examined and SNPs/indels were annotated using the SNPeff pipeline. A total of 54,000 SNPs/indels were identified, among which 1300 were estimated to have a moderate impact (non-synonymous variants), while 120 were estimated to have a high impact (e.g., missense/nonsense/frameshift variants). The latter were then analyzed for their effect on gene functionality. A total of 103 genotypes showed one high-impact mutation in at least one of the scouted genes, while in 10 genotypes, more than 4 high-impact mutations in as many genes were detected. A set of 10 SNPs were validated through Sanger sequencing. Three genotypes carrying high-impact homozygous SNPs in S-genes were infected with Oidium neolycopersici , and two highlighted a significantly reduced susceptibility to the fungus. The existing mutations fall within the scope of a history of safe use and can be useful to guide risk assessment in evaluating the effect of new genomic techniques.
Keyphrases
  • genome wide
  • copy number
  • dna methylation
  • genome wide identification
  • risk assessment
  • climate change
  • heavy metals
  • single cell
  • bioinformatics analysis
  • transcription factor