Progress 07/01/24 to 06/30/25

Outputs
Target Audience:University and USDA-ARS researchers & extension personnel Graduate & undergraduate students at UCR and UCD Changes/Problems:1. We experienced some delays in sequencing new Lso genomes for inclusion in computational analyses due to a slowdown in being able to process projects at one of our core facilities. The sequencing is now completed, and we are proceeding with genome assembly. 2. TurboID proximity labeling was problematic for the single effector we have tried so far (addition of the tag affected the plant phenotype). We still plan to continue this approach for a few more effectors to determine if this is a common problem. In the meantime, we have expanded our analyses for objective 2 to cover more pathogens. We also have promising targets based on yeast two-hybrid data. What opportunities for training and professional development has the project provided?Across the Coaker and Mauck labs, we trained one PhD level researcher, three research technicians (postbacs), one PhD student, and two undergraduate students. One of the undergraduates attended a workshop on Vector-Borne Disease in June 2025 at the University of Idaho. One postbac technician presented their research at an international conference, one postbac presented their research at an internal UC Davis Host-Microbe meeting, and one undergrad presented their research at the UC Davis Undergraduate Research Symposium. The PhD student graduated in June, with a postdoctoral position at Oxford (UK). How have the results been disseminated to communities of interest?So far, results have been disseminated through presentations at regional and national meetings. Please see "other products" section of this report. What do you plan to do during the next reporting period to accomplish the goals?Complete assembly of new Lso genomes and incorporate these into the computational effector analysis. Use data to select informative time points for more detailed RNAseq and phytohormone profiling, focusing on jasmonic acid, salicylic acid, auxins, and cytokines, and complete these experiments. Focus on screening new effectors for plant phenotypes, their ability to alter vector attractiveness/performance (and plant traits driving responses), and identifying their plant targets.

Impacts
What was accomplished under these goals? What was accomplished under these goals? Project impact This project focuses on bacteria called Candidatus Liberibacter that are spread by small insects called psyllids and cause serious plant diseases. These bacteria are responsible for billions of dollars in crop damage each year, and new harmful types are appearing quickly, posing a growing threat to U.S. agriculture. Right now, the main way to control them is through insecticides, but better, more sustainable solutions are needed. The bacteria infect plants by releasing proteins called effectors that interfere with the plant's natural defenses. Learning more about these effectors and how they work could help scientists detect diseases earlier and develop plants that are resistant to infection. To help with this, our project uses computer tools to study the variety and structure of effectors across all Ca. Liberibacter types. When we find effectors, we will use molecular biology tools to study how they affect plants and psyllids. Our project focuses on a specific disease-causing type of bacteria, Ca. Liberibacter solanacearum (Lso), which infects crops like potatoes and carrots. Using this pathogen as a model, we are studying 1. how Lso changes plant traits to help it infect and spread, 2. which effectors are involved in these changes, and 3. which parts of the plant the effectors target. During the project period, we completed experiments to understand how Lso changes chemicals inside tomato plants that help them defend against psyllids and Lso. We also used computer tools to identify 900 potential effectors across many types of plant-infecting bacteria and visualized the features they have in common. We found 30 effectors that are good candidates to test in plants to determine how they change plant traits and have started doing this work. We also sequenced the genomes of new Lso pathogens that do not cause disease in crops to add these to our analyses. Comparing disease-causing Lso to Lso that does not make plants sick will also help us to identify which effectors are most involved in causing crop loss. Once we know this, we can devise ways to prevent these effectors from working, reducing losses in crops from disease-causing Lso. Obj. 1. 1) Major activities We performed RNAseq in microtom tomato after Lso infection (5 weeks post infection) and also general phytohormone profiling. Experiments for longer timelines are underway in the Mauck lab and we will also perform profiling on plants expressing selected effectors. 2) Data collected RNAseq data, phytohormone profiling (outsourced) 3) Results RNAseq data analysis is in progress. We found that Lso impacts plant defense and hormone pathways (particularly auxin and cytokinin pathways). 4) Key outcomes We will use this data to determine the time course for more detailed dual RNAseq and phytohormone profiling. Obj. 2 1) Major activities We have completed computational and structural prediction of Liberibacter effectors, have identified common folds found across vector-borne bacterial effectors and synthesized 26 effectors for testing their ability to alter plant architecture. We also sequenced an additional 20 genomes of non-pathogenic Lso haplotypes isolated throughout the Southwestern USA to add to our analysis pipeline and perform more targeted comparisons of putative Lso effectors between genotypes. 2) Data collected This objective was expanded to include all bacterial vector-borne plant pathogens as well as free-living Pseudomonas and Xanthomonas pathogens as a reference. Filtered for high quality genomes, predicted their effectors (signal, deepTMHMM, size), used AlphaFold3 to predict their three-dimensional structure using its artificial intelligence capabilities. Next, effectors with high confidence AlphaFold3 pLDDT scores were clustered based on their structural similarity. This included ~900 effectors from vector-borne pathogens from a total of 26 genomes. 3) Results Vector-borne pathogen effectors were found to not cluster with free-living pathogen effectors, indicating they have different functions. Several Liberibacter effectors cluster with Phytoplasma effectors known to alter plant architecture and have helix-loop-helix predicted structure. Thus, we were successful in identifying sequence unrelated but structurally similar effectors. We narrowed down 30 candidate Liberibacter effectors, synthesized them for expression in plants to identify their effects on plant architecture and psyllid attractiveness in future experiments. We have identified two effectors that induce visual phenotypes in tomato hosts thus far. 4) Key outcomes Plant vector-borne pathogens secrete sequence unrelated, but structurally similar classes of effectors. Effector clustering can be used to help predict function. Obj. 3 1) Major activities We have initiated TurboID proximity labeling for one effector and have yeast-two-hybrid data in hand. 2) Data collected See above. 3) Results We were unable to get proximity labeling to work for the current effector since the addition of the tag affected the plant phenotype. We do have several targets in hand based on yeast-two hybrid data that also match with predicted effector function based on the results of Objective 2. We are continuing with targeted assays based on this data. 4) Key outcomes See results above

Publications