The recently-emerged field of chemical genomics is centered on the use of small molecules to perturb biological systems as a means of investigating their function. In order to employ this approach for the study of plant-pathogen interactions, I established an assay in which Arabidopsis thaliana seedlings are grown in liquid media in 96-well plates. Inoculation of these seedlings with a virulent strain of the bacterial phytopathogen Pseudomonas syringae resulted in macroscopic bleaching of the cotyledons of these seedlings. This symptom was used as the basis for high-throughput chemical genomic screens aimed at identifying small molecules that protect Arabidopsis seedlings from infection. One of the first chemicals identified through this screen was the sulfanilamide compound sulfamethoxazole (Smex). This compound was later shown to also reduce the susceptibility of both Arabidopsis and wheat to infection by the fungal pathogen Fusarium graminearum, suggesting a broad spectrum of activity. More detailed investigations of Smex indicated that the protective activity of this compound did not derive from antimicrobial effects, and that this activity was not executed through common defence-related signalling pathways. The folate biosynthetic pathway enzyme dihydropteroate synthase is a known target of sulfanilamides, and it does appear to contribute to Smex-induced disease resistance, albeit in a folate-independent manner. In order to identify downstream mediators of Smex activity, I initiated two forward genetic screens intended to recover mutants with altered sensitivity to Smex in a seedling growth assay. Interestingly, while these screens yielded mutants with striking Smex sensitivity phenotypes, disease resistance phenotypes were not altered. Gene expression profiling of Arabidopsis tissues treated with Smex prior to bacterial inoculation suggested that this compound generally affects lipid signalling. Altogether, it is evident that Smex elicits a complex set of responses in Arabidopsis with apparently non-overlapping phenotypic outputs.

Chemical genomics is an interdisciplinary field that combines small molecule perturbation with genomics to understand gene function and to study the mode(s) of drug action. Existing methods for correlating chemical genomic profiles are not ideal as they often require one to define the disrupting effects, commonly known as batch effects. These effects are not always known, and they can mask true biological differences. I present a method, Bucket Evaluations (BE), which surmounts these problems. This method is a non-parametric correlation approach, which is suitable for locating correlations in somewhat perturbed datasets such as chemical genomic profiles. BE can be used on other datasets such as those obtained via gene expression profiling and performs well on both array-based and sequence based readouts. Using BE, along with various correlation methods, on a collection of datasets, showed it to be highly accurate for locating similarity between experiments.

Chemical genomics is an interdisciplinary field that combines small molecule perturbation with genomics to understand gene function and to study the mode(s) of drug action. Existing methods for correlating chemical genomic profiles are not ideal as they often require one to define the disrupting effects, commonly known as batch effects. These effects are not always known, and they can mask true biological differences. I present a method, Bucket Evaluations (BE), which surmounts these problems. This method is a non-parametric correlation approach, which is suitable for locating correlations in somewhat perturbed datasets such as chemical genomic profiles. BE can be used on other datasets such as those obtained via gene expression profiling and performs well on both array-based and sequence based readouts. Using BE, along with various correlation methods, on a collection of datasets, showed it to be highly accurate for locating similarity between experiments.