The Quantitative Basis of the Arabidopsis Innate Immune System to Endemic Pathogens Depends on Pathogen Genetics.

Jason A Corwin,Julin Maloof,Daniel Copeland,Anushriya Subedy,Robert Eshbaugh,Julie Feusier,Christine Palmer,Daniel J Kliebenstein,Daniel Koenig

doi:10.1371/journal.pgen.1005789

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.

Highlights

Our understanding of host/pathogen interactions is largely driven by examples of large-effect, qualitative resistance involving a small subset of host genes focused on detection and signal responses to the pathogen
We use genome wide association (GWA) mapping in a natural population of the Arabidopsis thaliana host against four phenotypically and genetically distinct isolates of the fungal pathogen Botrytis cinerea to describe and validate components of the plant innate immune system. Using both the induced production of a known defense compound, camalexin, and lesion area as quantified outputs of the plant innate immune system, we found approximately 2,982 and 3,354 genes associated with quantitative resistance respectively
Arabidopsis accessions have a wide range of flowering times that can affect the ontogenic status of the plant and influence plant/pathogen interactions [42,44]

Summary

Introduction

Our understanding of host/pathogen interactions is largely driven by examples of large-effect, qualitative resistance involving a small subset of host genes focused on detection and signal responses to the pathogen. Quantitative resistance is characteristic of host resistance to endemic pathogens that are ubiquitous within a host’s environment and create a persistent selective pressure on the innate immunity of the host [1]. This constant selective pressure from endemic pathogens prevents the development of highly susceptible host alleles and may canalize the genetic variability of many genes involved in resistance. The continuous nature of quantitative resistance suggests that there may be alleles at a number of genes that can provide long-term, durable resistance to these pathogens This lack of large effect natural alleles and the large number of genes that are thought to be involved in quantitative resistance complicates our understanding of involved biological functions and pathways

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Discussion

Conclusion