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Title:Identification of phytotoxins produced by fusarium virguliforme associated with foliar symptoms of sudden death syndrome and genome-wide association studies for soybean disease resistance
Author(s):Chang, Hao-Xun
Director of Research:Hartman, Glen L.
Doctoral Committee Chair(s):Hartman, Glen L.
Doctoral Committee Member(s):Domier, Leslie L.; Hudson, Matthew E.; Zhao, Youfu
Department / Program:Crop Sciences
Discipline:Crop Sciences
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(s):Soybean
Disease
Phytotoxin
Fusarium virguliforme, Sudden death Syndrome
Genome-wide association study
Genomic prediction
Tobacco ringspot virus
Abstract:Plant pathology is a multidisciplinary subject and the scientific progress in plant pathology follows the advent of sciences. Beginning in the 1800s, the father of plant pathology, Dr. Anton de Bary, initiated studies on the identification of oomycetes and fungal plant pathogens. This was the first integration for the subjects of "microbiology" and "botany", which initiated the science of plant pathology. Dr. Flor proposed the gene-for-gene model for plant-microbe interaction at 1940s, which introduced Mendelian "genetics" to the subject that eventually became the foundation for resistance breeding. The development of "molecular biology" and "biotechnology" in the 1950s to 2000s enabled molecular studies using forward and reverse genetics to understand the biological and physiological mechanisms of microbes, plants, and their interactions. Although studies at this period were limited in functional analyses for a single gene or few target genes, the accumulation of knowledge over decades inspired the zigzag plant immunity model including the concepts of pathogen-associated molecular pattern (PAMP)-trigger immunity (PTI) and effector-trigger immunity (ETI), which has become the dogma for plant pathology. With the development of the microarray or biochip technology in early 2000s and the maturity of next-generation sequencing around 2010s, genomic or transcriptomic level of biology studies have become regular experiments when I started my academia career in plant pathology. When "informatics" joined plant pathology; I was provided with an opportunity to "dig" biological information from the big data, with specific interests on soybean pathology. In my first project, I applied the RNA-Seq technology to identify additional phytotoxins produced by the fungus Fusarium virguliforme, which causes soybean sudden death syndrome (SDS). A robust and comprehensive bioinformatics-searching pipeline was established and I successfully identified three secondary metabolites and 11 phytotoxic effectors. One of the effectors, FvNIS1, induced identical foliar symptoms to field-observed SDS through an overexpression system via Soybean mosaic virus. Results of phytotoxicity assay on eighty plant introductions (PIs), genome-wide association study (GWAS), and phytotoxicity assay for FvNIS1 gene knockout mutants supported that FvNIS1 is one of the phytotoxins responsible for SDS foliar symptoms. My second project focused on annotation of carbohydrate-active enzymes and plant cell wall degrading enzymes (PCWDEs) in the genome of F. virguliforme. I focused on the polymorphisms of GH28 polygalacturonase and GH11 xylanase because several Fusarium species have amino acid substitutions on these enzymes that allow them to escape PCWDEs-inhibiting proteins released by plants as a counteract defense mechanism. The results indicated F. virguliforme has conserved xylanases and development of transgenic soybean with wheat xylanase-inhibitor protein might enhance soybean resistance to F. virguliforme. In my third project, I incorporated soybean sensitivity to Tobacco ringspot virus (TRSV) to the single nucleotide polymorphism (SNP) markers from SoySNP50K, and performed a GWAS to identify SNP that associate with TRSV sensitivity. I further applied genomic selection to predict TRSV sensitivity for the unscreened soybean PIs in the USDA Soybean Germplasm Collection. In this project, I identified a single locus and two candidate genes that may involve in TRSV sensitivity, and showed genomic prediction has higher performance than single SNP-based marker-assisted selection. My interests in GWAS extended to my fourth project, for which I adopted phenotypes of 13 soybean diseases deposited in the United States Department of Agriculture of Agriculture Research Service (USDA-ARS) Germplasm Resources Information Network (GRIN) database and performed GWAS for each disease. In the study, I discovered SNPs locate in previously reported loci, I found novel SNPs for diseases such as Diaporthe stem canker, and I presented the power and challenges of GWAS in searching soybean resistance sources. In summary, my dissertation contains demonstrations on the impact of informatics in soybean pathology regarding finding genes involved in soybean-microbes interactions.
Issue Date:2016-04-20
Type:Thesis
URI:http://hdl.handle.net/2142/90778
Rights Information:Copyright 2016 HAO-XUN CHANG
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05


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