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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Digital imaging allows the identification of genes controlling novel lesion traits. 16Abstract 19 Plant resistance to generalist pathogens with broad host ranges, such as Botrytis cinerea, is 20 typically quantitative and highly polygenic. Recent studies have begun to elucidate the 21 molecular genetic basis underpinning plant-pathogen interactions using commonly measured 22 traits including lesion size and/or pathogen biomass. Yet with the advent of digital imaging and 23 phenomics, there are a large number of additional resistance traits available to study 24 quantitative resistance. In this study, we used high-throughput digital imaging analysis to 25 investigate previously uncharacterized visual traits of plant-pathogen interactions related 26 disease resistance using the Arabidopsis thaliana/Botrytis cinerea pathosystem. Using a large 27 collection of 75 visual traits collected from every lesion, we focused on lesion color, lesion 28 shape, and lesion size, to test how these aspects of the interaction are genetically related. Using 29 genome wide association (GWA) mapping in A. thaliana, we show that lesion color and shape 30 are genetically separable traits associated with plant-disease resistance. Using defined mutants 31 in 23 candidate genes from the GWA mapping, we could identify and show that novel loci 32 associated with each different plant-pathogen interaction trait, which expands our 33 understanding of the functional mechanisms driving plant disease resistance. 34 35 36 99, US NSF grants IOS 1339125, MCB 1330337 and IOS1021861, and the USDA National 595 Institute of Food and Agriculture, Hatch project number CA-D-PLS-7033-H. 596 597 598

computer simulations, these moduli can be determined by fitting power spectra of fluctuating fields to the predictions of those continuum theories. This data analysis involves numerous choices, such as how to define a membrane surface or how to determine the fitting range, which significantly affect the observables of interest. Here, we examine in detail the systematic trends resulting from these choices, on the basis of atomistic simulation trajectories of 13 different lipid model membranes created by Venable et al. [CPL 192, 60 (2015)]. We in particular discuss systematic effects connected with: (1) interpolation of height and directional fields; (2) normalization and averaging of lipid directors; (3) determining small-scale cutoffs. Additionally, we discuss statistical aspects such as correcting for time correlations in the power spectra, getting uncertainties on the parameters, and simultaneously fitting different spectra. The systematic shifts in the moduli arising from equally plausible choices are often larger than the statistical uncertainties. We propose a tentative set of criteria based on which the relative merits of such choices could be evaluated.