EVOLUTION OF TAL EFFECTORS, HOST SUSCEPTIBILITY MECHANISMS, AND SENSOR-BASED DISEASE PHENOTYPING IN RICE-XANTHOMONAS INTERACTIONS
Bacterial leaf streak (BLS) and bacterial leaf blight (BLB) are two important diseases of rice, caused by Xanthomonas oryzae pv. oryzicola (Xoc) and X. oryzae pv. oryzae (Xoo) respectively, which are two distinct, yet related pathogens in the Xo species complex. While BLB is common, and severe in Asia and Africa, with established breeding programs focused on developing resistant cultivars, BLS is rising in incidence across both regions, highlighting the urgent need for resistance breeding strategies. This thesis investigates the genetic and physiological mechanisms underlying BLS, that includes effector biology and the evolutionary dynamics of Xoc, and downstream events from induction of the susceptibility (S) gene, OsSULTR3;6. In parallel, it advances non-destructive, imaging pipelines for high-throughput phenotyping of BLB severity. The thesis begins with a review of susceptibility genes of bacterial diseases in plants. The first chapter describes a survey of 32 Xoc genomes, collected from diverse regions within Africa, compared to 3 strains from Asia, revealing regional relationships in transcription activator-like effector (TALE) repertoires, or “TALomes.” By defining a conserved core and an accessory TALome, comprising TALEs shared across regions as well as those unique to specific regions, the thesis highlights TALEs that are under selection, and facilitates the discovery of a novel source of strain-specific resistance to BLS. Chapter 2 examines the underlying mechanism by which OsSULTR3;6 contributes to longer lesions and increased bacterial exudation on the leaf surface. In particular, it explores the role of OsSULTR3;6 in modulating stomatal conductance during BLS. Chapter 3 introduces a multimodal, high-throughput phenotyping platform for quantifying BLB severity using RGB-NIR imaging and 3D structural sensing. By integrating vegetation indices, color metrics, and point cloud traits, the framework achieves accurate and reliable disease predictions, offering a scalable solution for disease phenotyping. Together, these studies provide new insight into pathogen evolution, host susceptibility mechanisms, and practical tools for crop disease assessment, supporting more informed breeding and disease management strategies.