MICRO AND MACRO VIEWS OF THE MAIZE-SETOSPHAERIA TURCICA PATHOSYSTEM

Tyr Wiesner-Hanks
2020
i © 2020 Tyr Wiesner-Hanks ii Micro and macro views of the maize-Setosphaeria turcica pathosystem Tyr Wiesner-Hanks, Ph. D. Cornell University 2020 Interactions between host and pathogen can be understood at many different spatial scales, from nanometers to kilometers. In this dissertation, I explored multiple diseases at two very different spatial scales, focusing chiefly on the economically important disease Northern Leaf Blight (NLB) and the components of its pathosystem-the host, maize, and
more » ... the fungal pathogen, Setosphaeria turcica. At the "micro" scale, I reviewed the genetics of multiple disease resistance (MDR), what is known about the biological mechanisms thereof, and the ways in which MDR can be improved in plants. I mapped genetic loci conditioning MDR in order to understand the genetic architecture thereof, finding that the high degree of MDR observed in a maize line derived from recurrent selection was mostly attributable to independent loci for resistance to individual diseases, rather than pleiotropic loci conditioning MDR. I used RNA-seq to explore the transcriptomic aspects of infection, with a focus on the pathogen's transition from biotrophy to necrotrophy and the impacts of pathogen virulence/avirulence in the presence or absence of the host Ht2 R gene. Gene iii expression in both host and pathogen shifted dramatically between biotrophy and necrotrophy, with specific trends demonstrating the different molecular mechanisms of infection and host defense during each phase. Pathogen avirulence, due to R-gene mediated resistance, led to an apparent arrest of the pathogen in the biotrophic phase. The importance of gene-sparse regions of the S. turcica genome for pathogenesis was shown for the first time. At the "macro" scale, I combined crowdsourcing and machine learning to develop a new method for aerial detection of disease symptoms in the field. The task of annotating thousands of disease lesions in order to train a machine learning model was split in two, with experts annotating lesions in low resolution and numerous non-experts performing the more time-consuming task of outlining lesions, using the expert annotations as a base. This method allowed us to generate a large amount of reliable training data very quickly and at low cost. These data were used to train a convolutional neural network (CNN) to high accuracy, and a fully-connected conditional random field (CRF) was used to segment images into lesion and non-lesion areas using the CNN output. The final model was able to delineate lesions in aerial images down to the millimeter level, a finer spatial scale than any previously reported method. It also outperformed human experts by identifying lesions that they had missed. Though the techniques, findings, and impacts involved in work at these two very different scales are accordingly varied, they all contribute to a holistic understanding of the pathosystem and our ability to make practical change. iv BIOGRAPHICAL SKETCH Tyr was raised in Mequon, Wisconsin, where he developed a love for mathematics from an early age and a passion for plants from a slightly older age. He attended Northwestern University and graduated cum laude with a B.A. in Biology, concentrating in Plant Biology as part of a fairly new joint program between Northwestern and the Chicago Botanic Garden. While at Northwestern, he conducted undergraduate research with Nyree Zerega on germplasm characterization and population genetics in breadfruit and related Artocarpus species. In the fall of 2013, he joined Rebecca Nelson's lab at Cornell University as a Ph.D. student, studying the maize-Setosphaeria turcica pathosystem and related topics in plant and fungal genetics. He spent his summers conducting field research in Aurora, NY and the rest of the year in the greenhouse or growth chamber and in front of the computer.
doi:10.7298/5yt0-pd05 fatcat:bi5h4xl7dnfbbgy5ehrlvgfpey