Abstract

Plant pathology research has long placed a focus on pathogen-derived effector proteins: small, secreted proteins translocated into host cells where they subvert host basal immunity and promote infection. Recent studies suggest that some plant species secrete similar effector-like proteins during mutualistic plant-fungal interactions and affect fungal growth. In this project, a computational tool named SecretePEPPr (Secreted Plant Effector-like Protein Predictor) was written, evaluated, and tested for the purpose of predicting candidate plant effector-like proteins from a set of whole genome annotations. Among other factors, this prediction tool considered classical and non-classical secretion, protein size, and the prevalence and presence of clathrin-mediated endocytic motifs. Analysis on testing data revealed a subcellular localization prediction specificity of 90% on a set of over 500 intracellular plant proteins and sensitivity of 55% on a set of experimentally validated secreted plant proteins. Across four analyzed grape proteomes, several germin-like proteins were identified as potentially haustorially localized through clathrin-mediated endocytic means. Protein length distributions revealed that effector-like candidates containing clathrin-mediated endocytic motifs were mostly in the 150-300 amino acid length range. Follow up in vivo validation was conducted in Erysiphe necator-infected Chardonnay grape leaves. Through this process, the first Erysiphe necator haustorial extraction method was devised using a Percoll Density Gradient followed by fluorescent labeling and fluorescence-activated cell sorting (FACS), resulting in 14 and 18 million purified fungal haustorial cells from 20 grams of heavily infected leaves. Computational streamlining of plant effector-like protein prediction established in this project provides a foundation for the top-down discovery and characterization of this recently discovered class of plant proteins, which have important implications in plant-microbe interactions and may act as targets for breeding and gene editing in plants.

Library of Congress Subject Headings

Plant proteins--Data processing; Plant proteomics; Grape powdery mildew disease--Genetics

Publication Date

5-7-2021

Document Type

Thesis

Student Type

Graduate

Degree Name

Bioinformatics (MS)

Department, Program, or Center

Thomas H. Gosnell School of Life Sciences (COS)

Advisor

Michael V. Osier

Advisor/Committee Member

Eli J. Borrego

Advisor/Committee Member

Andre O. Hudson

Campus

RIT – Main Campus

Plan Codes

BIOINFO-MS

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