Code-Assisted Discovery of TAL Effector Targets in Bacterial Leaf Streak of Rice Reveals Contrast with Bacterial Blight and a Novel Susceptibility Gene

Bacterial leaf streak of rice, caused by Xanthomonas oryzae pv. oryzicola (Xoc) is an increasingly important yield constraint in this staple crop. A mesophyll colonizer, Xoc differs from X. oryzae pv. oryzae (Xoo), which invades xylem to cause bacterial blight of rice. Both produce multiple distinct TAL effectors, type III-delivered proteins that transactivate effector-specific host genes. A TAL effector finds its target(s) via a partially degenerate code whereby the modular effector amino acid sequence identifies nucleotide sequences to which the protein binds. Virulence contributions of some Xoo TAL effectors have been shown, and their relevant targets, susceptibility (S) genes, identified, but the role of TAL effectors in leaf streak is uncharacterized. We used host transcript profiling to compare leaf streak to blight and to probe functions of Xoc TAL effectors. We found that Xoc and Xoo induce almost completely different host transcriptional changes. Roughly one in three genes upregulated by the pathogens is preceded by a candidate TAL effector binding element. Experimental analysis of the 44 such genes predicted to be Xoc TAL effector targets verified nearly half, and identified most others as false predictions. None of the Xoc targets is a known bacterial blight S gene. Mutational analysis revealed that Tal2g, which activates two genes, contributes to lesion expansion and bacterial exudation. Use of designer TAL effectors discriminated a sulfate transporter gene as the S gene. Across all targets, basal expression tended to be higher than genome-average, and induction moderate. Finally, machine learning applied to real vs. falsely predicted targets yielded a classifier that recalled 92% of the real targets with 88% precision, providing a tool for better target prediction in the future. Our study expands the number of known TAL effector targets, identifies a new class of S gene, and improves our ability to predict functional targeting. Author Summary Many crop and ornamental plants suffer losses due to bacterial pathogens in the genus Xanthomonas. Pathogen manipulation of host gene expression by injected proteins called TAL effectors is important in many of these diseases. A TAL effector finds its gene target(s) by virtue of structural repeats in the protein that differ one from another at two amino acids that together identify one DNA base. The number of repeats and those amino acids thereby code for the DNA sequence the protein binds. This code allows target prediction and engineering TAL effectors for custom gene activation. By combining genome-wide analysis of gene expression with TAL effector binding site prediction and verification using designer TAL effectors, we identified 19 targets of TAL effectors in bacterial leaf streak of rice, a disease of growing importance worldwide caused by X. oryzae pv. oryzicola. Among these was a sulfate transport gene that plays a major role. Comparison of true vs. false predictions using machine learning yielded a classifier that will streamline TAL effector target identification in the future. Probing the diversity and functions of such plant genes is critical to expand our knowledge of disease and defense mechanisms, and open new avenues for effective disease control.