The Quantitative Basis of the Arabidopsis Innate Immune System to Endemic Pathogens Depends on Pathogen Genetics
Indoles
Genotype
Non-P.H.S.
Arabidopsis
Plant Biology
QH426-470
Research Support
Genes, Plant
03 medical and health sciences
Gene Expression Regulation, Plant
Journal Article
Genetics
2.1 Biological and endogenous factors
Innate
Gene Regulatory Networks
Aetiology
Non-U.S. Gov't
0303 health sciences
Research Support, Non-U.S. Gov't
Immunity
Chromosome Mapping
Reproducibility of Results
Plant
Biological Sciences
Immunity, Innate
3. Good health
Thiazoles
Infectious Diseases
Gene Ontology
Phenotype
Gene Expression Regulation
Genes
Host-Pathogen Interactions
Linear Models
U.S. Gov't
Botrytis
Infection
Research Support, U.S. Gov't, Non-P.H.S.
Developmental Biology
Research Article
Genome-Wide Association Study
DOI:
10.1371/journal.pgen.1005789
Publication Date:
2016-02-11T18:44:28Z
AUTHORS (8)
ABSTRACT
The most established model of the eukaryotic innate immune system is derived from examples of large effect monogenic quantitative resistance to pathogens. However, many host-pathogen interactions involve many genes of small to medium effect and exhibit quantitative resistance. We used the Arabidopsis-Botrytis pathosystem to explore the quantitative genetic architecture underlying host innate immune system in a population of Arabidopsis thaliana. By infecting a diverse panel of Arabidopsis accessions with four phenotypically and genotypically distinct isolates of the fungal necrotroph B. cinerea, we identified a total of 2,982 genes associated with quantitative resistance using lesion area and 3,354 genes associated with camalexin production as measures of the interaction. Most genes were associated with resistance to a specific Botrytis isolate, which demonstrates the influence of pathogen genetic variation in analyzing host quantitative resistance. While known resistance genes, such as receptor-like kinases (RLKs) and nucleotide-binding site leucine-rich repeat proteins (NLRs), were found to be enriched among associated genes, they only account for a small fraction of the total genes associated with quantitative resistance. Using publically available co-expression data, we condensed the quantitative resistance associated genes into co-expressed gene networks. GO analysis of these networks implicated several biological processes commonly connected to disease resistance, including defense hormone signaling and ROS production, as well as novel processes, such as leaf development. Validation of single gene T-DNA knockouts in a Col-0 background demonstrate a high success rate (60%) when accounting for differences in environmental and Botrytis genetic variation. This study shows that the genetic architecture underlying host innate immune system is extremely complex and is likely able to sense and respond to differential virulence among pathogen genotypes.
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