Learning Via Distance Education

Poster Presentation for EUI Spring 2013 Student Conference, examining the use of online learning software and e-books in UIUC classes.unpublishednot peer reviewe

Direct Agroinoculation of Maize Seedlings by Injection with Recombinant Foxtail Mosaic Virus and Sugarcane Mosaic Virus Infectious Clones

Agrobacterium-based inoculation approaches are widely used for introducing viral vectors into plant tissues. This study details a protocol for the injection of maize seedlings near meristematic tissue with Agrobacterium carrying a viral vector. Recombinant foxtail mosaic virus (FoMV) clones engineered for gene silencing and gene expression were used to optimize this method, and its use was expanded to include a recombinant sugarcane mosaic virus (SCMV) engineered for gene expression. Gene fragments or coding sequences of interest are inserted into a modified, infectious viral genome that has been cloned into the binary T-DNA plasmid vector pCAMBIA1380. The resulting plasmid constructs are transformed into Agrobacterium tumefaciens strain GV3101. Maize seedlings as young as 4 days old can be injected near the coleoptilar node with bacteria resuspended in MgSO4 solution. During infection with Agrobacterium, the T-DNA carrying the viral genome is transferred to maize cells, allowing for the transcription of the viral RNA genome. As the recombinant virus replicates and systemically spreads throughout the plant, viral symptoms and phenotypic changes resulting from the silencing of the target genes lesion mimic 22 (les22) or phytoene desaturase (pds) can be observed on the leaves, or expression of green fluorescent protein (GFP) can be detected upon illumination with UV light or fluorescence microscopy. To detect the virus and assess the integrity of the insert simultaneously, RNA is extracted from the leaves of the injected plant and RT-PCR is conducted using primers flanking the multiple cloning site (MCS) carrying the inserted sequence. This protocol has been used effectively in several maize genotypes and can readily be expanded to other viral vectors, thereby offering an accessible tool for viral vector introduction in maize

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Rust fungal pathogens comprise the largest group of plant pathogenic fungi. Due to limitations of their study, like an inability to be cultured or difficulty in making genetic modifications, there are many gaps in the knowledge base of these organisms. One rust species, Puccinia sorghi, is a worldwide pathogen of maize that can cause significant yield losses. Much of the research for P. sorghi focuses on qualitative disease phenotypes of various isolates on different maize genetic backgrounds, with limited information regarding the key pathogenicity genes (effectors) required for a successful infection within this pathosystem. It is imperative to further develop the genomic and phenomic tools available for P. sorghi for use in effector characterization screens. With the recent advent of long-read sequencing, rust genome assembly has transitioned from exceedingly fragmented contigs based on short-read sequencing to large, repeat-resolved scaffolds. More complete rust genomes have led to many discoveries about the true genome size, repeat content, and gene content of these organisms. Well-annotated assemblies also allow for the prediction of candidate effector proteins that function as pathogenicity and virulence determinants. In this work, the genomic resources for P. sorghi are expanded with a highly contiguous, long-read assembly of a previously undescribed isolate (IA16). Comprehensive annotation utilizing expressed sequence tags from several timepoints across the disease cycle in maize enabled the prediction of additional candidate effectors for this species. Comparison of these candidates to other P. sorghi isolates will lead to discoveries regarding a particular isolate’s virulence. We also report on the characterization of the members of a rust-specific candidate secreted effector protein family present in the P. sorghi IA16 isolate. Of eight candidates, we were able to demonstrate that one is a weak suppressor of the plant hypersensitive immune response in the heterologous system Nicotiana benthamiana. This work also utilized an automated phenotyping setup to acquire time lapse images of leaves during experimental assays. By pairing effector characterization assays with automated phenotyping platforms, we can increase throughput, accuracy, and consistency in results. Lastly, we detail a machine learning approach to quantifying common rust disease on maize leaves. Because plant-pathogen interactions are complex, and small changes to phenotype that are undetectable by human measurements may occur, the development of easy-to-use computer vision-based phenotyping platforms to provide consistent and quantitative results is essential. Additionally, a better understanding of the minimum requirements for a given phenotyping approach is useful for future development, as this can increase the speed at which new platforms are developed. This work demonstrates machine learning is a viable and accurate approach to the quantification of rust disease symptoms, corroborating ground truth experimental results. This work also provides extensive image and annotation data for use in future applications. Overall, this dissertation presents a multi-disciplinary approach to the study of P. sorghi that provides both genomic resources and phenotyping pipelines for the study of candidate effectors and plant-pathogen interactions

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Rust fungal pathogens comprise the largest group of plant pathogenic fungi. Due to limitations of their study, like an inability to be cultured or difficulty in making genetic modifications, there are many gaps in the knowledge base of these organisms. One rust species, Puccinia sorghi, is a worldwide pathogen of maize that can cause significant yield losses. Much of the research for P. sorghi focuses on qualitative disease phenotypes of various isolates on different maize genetic backgrounds, with limited information regarding the key pathogenicity genes (effectors) required for a successful infection within this pathosystem. It is imperative to further develop the genomic and phenomic tools available for P. sorghi for use in effector characterization screens. With the recent advent of long-read sequencing, rust genome assembly has transitioned from exceedingly fragmented contigs based on short-read sequencing to large, repeat-resolved scaffolds. More complete rust genomes have led to many discoveries about the true genome size, repeat content, and gene content of these organisms. Well-annotated assemblies also allow for the prediction of candidate effector proteins that function as pathogenicity and virulence determinants. In this work, the genomic resources for P. sorghi are expanded with a highly contiguous, long-read assembly of a previously undescribed isolate (IA16). Comprehensive annotation utilizing expressed sequence tags from several timepoints across the disease cycle in maize enabled the prediction of additional candidate effectors for this species. Comparison of these candidates to other P. sorghi isolates will lead to discoveries regarding a particular isolate’s virulence. We also report on the characterization of the members of a rust-specific candidate secreted effector protein family present in the P. sorghi IA16 isolate. Of eight candidates, we were able to demonstrate that one is a weak suppressor of the plant hypersensitive immune response in the heterologous system Nicotiana benthamiana. This work also utilized an automated phenotyping setup to acquire time lapse images of leaves during experimental assays. By pairing effector characterization assays with automated phenotyping platforms, we can increase throughput, accuracy, and consistency in results. Lastly, we detail a machine learning approach to quantifying common rust disease on maize leaves. Because plant-pathogen interactions are complex, and small changes to phenotype that are undetectable by human measurements may occur, the development of easy-to-use computer vision-based phenotyping platforms to provide consistent and quantitative results is essential. Additionally, a better understanding of the minimum requirements for a given phenotyping approach is useful for future development, as this can increase the speed at which new platforms are developed. This work demonstrates machine learning is a viable and accurate approach to the quantification of rust disease symptoms, corroborating ground truth experimental results. This work also provides extensive image and annotation data for use in future applications. Overall, this dissertation presents a multi-disciplinary approach to the study of P. sorghi that provides both genomic resources and phenotyping pipelines for the study of candidate effectors and plant-pathogen interactions

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Rust fungal pathogens comprise the largest group of plant pathogenic fungi. Due to limitations of their study, like an inability to be cultured or difficulty in making genetic modifications, there are many gaps in the knowledge base of these organisms. One rust species, Puccinia sorghi, is a worldwide pathogen of maize that can cause significant yield losses. Much of the research for P. sorghi focuses on qualitative disease phenotypes of various isolates on different maize genetic backgrounds, with limited information regarding the key pathogenicity genes (effectors) required for a successful infection within this pathosystem. It is imperative to further develop the genomic and phenomic tools available for P. sorghi for use in effector characterization screens. With the recent advent of long-read sequencing, rust genome assembly has transitioned from exceedingly fragmented contigs based on short-read sequencing to large, repeat-resolved scaffolds. More complete rust genomes have led to many discoveries about the true genome size, repeat content, and gene content of these organisms. Well-annotated assemblies also allow for the prediction of candidate effector proteins that function as pathogenicity and virulence determinants. In this work, the genomic resources for P. sorghi are expanded with a highly contiguous, long-read assembly of a previously undescribed isolate (IA16). Comprehensive annotation utilizing expressed sequence tags from several timepoints across the disease cycle in maize enabled the prediction of additional candidate effectors for this species. Comparison of these candidates to other P. sorghi isolates will lead to discoveries regarding a particular isolate’s virulence. We also report on the characterization of the members of a rust-specific candidate secreted effector protein family present in the P. sorghi IA16 isolate. Of eight candidates, we were able to demonstrate that one is a weak suppressor of the plant hypersensitive immune response in the heterologous system Nicotiana benthamiana. This work also utilized an automated phenotyping setup to acquire time lapse images of leaves during experimental assays. By pairing effector characterization assays with automated phenotyping platforms, we can increase throughput, accuracy, and consistency in results. Lastly, we detail a machine learning approach to quantifying common rust disease on maize leaves. Because plant-pathogen interactions are complex, and small changes to phenotype that are undetectable by human measurements may occur, the development of easy-to-use computer vision-based phenotyping platforms to provide consistent and quantitative results is essential. Additionally, a better understanding of the minimum requirements for a given phenotyping approach is useful for future development, as this can increase the speed at which new platforms are developed. This work demonstrates machine learning is a viable and accurate approach to the quantification of rust disease symptoms, corroborating ground truth experimental results. This work also provides extensive image and annotation data for use in future applications. Overall, this dissertation presents a multi-disciplinary approach to the study of P. sorghi that provides both genomic resources and phenotyping pipelines for the study of candidate effectors and plant-pathogen interactions

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Rust fungal pathogens comprise the largest group of plant pathogenic fungi. Due to limitations of their study, like an inability to be cultured or difficulty in making genetic modifications, there are many gaps in the knowledge base of these organisms. One rust species, Puccinia sorghi, is a worldwide pathogen of maize that can cause significant yield losses. Much of the research for P. sorghi focuses on qualitative disease phenotypes of various isolates on different maize genetic backgrounds, with limited information regarding the key pathogenicity genes (effectors) required for a successful infection within this pathosystem. It is imperative to further develop the genomic and phenomic tools available for P. sorghi for use in effector characterization screens. With the recent advent of long-read sequencing, rust genome assembly has transitioned from exceedingly fragmented contigs based on short-read sequencing to large, repeat-resolved scaffolds. More complete rust genomes have led to many discoveries about the true genome size, repeat content, and gene content of these organisms. Well-annotated assemblies also allow for the prediction of candidate effector proteins that function as pathogenicity and virulence determinants. In this work, the genomic resources for P. sorghi are expanded with a highly contiguous, long-read assembly of a previously undescribed isolate (IA16). Comprehensive annotation utilizing expressed sequence tags from several timepoints across the disease cycle in maize enabled the prediction of additional candidate effectors for this species. Comparison of these candidates to other P. sorghi isolates will lead to discoveries regarding a particular isolate’s virulence. We also report on the characterization of the members of a rust-specific candidate secreted effector protein family present in the P. sorghi IA16 isolate. Of eight candidates, we were able to demonstrate that one is a weak suppressor of the plant hypersensitive immune response in the heterologous system Nicotiana benthamiana. This work also utilized an automated phenotyping setup to acquire time lapse images of leaves during experimental assays. By pairing effector characterization assays with automated phenotyping platforms, we can increase throughput, accuracy, and consistency in results. Lastly, we detail a machine learning approach to quantifying common rust disease on maize leaves. Because plant-pathogen interactions are complex, and small changes to phenotype that are undetectable by human measurements may occur, the development of easy-to-use computer vision-based phenotyping platforms to provide consistent and quantitative results is essential. Additionally, a better understanding of the minimum requirements for a given phenotyping approach is useful for future development, as this can increase the speed at which new platforms are developed. This work demonstrates machine learning is a viable and accurate approach to the quantification of rust disease symptoms, corroborating ground truth experimental results. This work also provides extensive image and annotation data for use in future applications. Overall, this dissertation presents a multi-disciplinary approach to the study of P. sorghi that provides both genomic resources and phenotyping pipelines for the study of candidate effectors and plant-pathogen interactions

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Rust fungal pathogens comprise the largest group of plant pathogenic fungi. Due to limitations of their study, like an inability to be cultured or difficulty in making genetic modifications, there are many gaps in the knowledge base of these organisms. One rust species, Puccinia sorghi, is a worldwide pathogen of maize that can cause significant yield losses. Much of the research for P. sorghi focuses on qualitative disease phenotypes of various isolates on different maize genetic backgrounds, with limited information regarding the key pathogenicity genes (effectors) required for a successful infection within this pathosystem. It is imperative to further develop the genomic and phenomic tools available for P. sorghi for use in effector characterization screens. With the recent advent of long-read sequencing, rust genome assembly has transitioned from exceedingly fragmented contigs based on short-read sequencing to large, repeat-resolved scaffolds. More complete rust genomes have led to many discoveries about the true genome size, repeat content, and gene content of these organisms. Well-annotated assemblies also allow for the prediction of candidate effector proteins that function as pathogenicity and virulence determinants. In this work, the genomic resources for P. sorghi are expanded with a highly contiguous, long-read assembly of a previously undescribed isolate (IA16). Comprehensive annotation utilizing expressed sequence tags from several timepoints across the disease cycle in maize enabled the prediction of additional candidate effectors for this species. Comparison of these candidates to other P. sorghi isolates will lead to discoveries regarding a particular isolate’s virulence. We also report on the characterization of the members of a rust-specific candidate secreted effector protein family present in the P. sorghi IA16 isolate. Of eight candidates, we were able to demonstrate that one is a weak suppressor of the plant hypersensitive immune response in the heterologous system Nicotiana benthamiana. This work also utilized an automated phenotyping setup to acquire time lapse images of leaves during experimental assays. By pairing effector characterization assays with automated phenotyping platforms, we can increase throughput, accuracy, and consistency in results. Lastly, we detail a machine learning approach to quantifying common rust disease on maize leaves. Because plant-pathogen interactions are complex, and small changes to phenotype that are undetectable by human measurements may occur, the development of easy-to-use computer vision-based phenotyping platforms to provide consistent and quantitative results is essential. Additionally, a better understanding of the minimum requirements for a given phenotyping approach is useful for future development, as this can increase the speed at which new platforms are developed. This work demonstrates machine learning is a viable and accurate approach to the quantification of rust disease symptoms, corroborating ground truth experimental results. This work also provides extensive image and annotation data for use in future applications. Overall, this dissertation presents a multi-disciplinary approach to the study of P. sorghi that provides both genomic resources and phenotyping pipelines for the study of candidate effectors and plant-pathogen interactions

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Rust fungal pathogens comprise the largest group of plant pathogenic fungi. Due to limitations of their study, like an inability to be cultured or difficulty in making genetic modifications, there are many gaps in the knowledge base of these organisms. One rust species, Puccinia sorghi, is a worldwide pathogen of maize that can cause significant yield losses. Much of the research for P. sorghi focuses on qualitative disease phenotypes of various isolates on different maize genetic backgrounds, with limited information regarding the key pathogenicity genes (effectors) required for a successful infection within this pathosystem. It is imperative to further develop the genomic and phenomic tools available for P. sorghi for use in effector characterization screens. With the recent advent of long-read sequencing, rust genome assembly has transitioned from exceedingly fragmented contigs based on short-read sequencing to large, repeat-resolved scaffolds. More complete rust genomes have led to many discoveries about the true genome size, repeat content, and gene content of these organisms. Well-annotated assemblies also allow for the prediction of candidate effector proteins that function as pathogenicity and virulence determinants. In this work, the genomic resources for P. sorghi are expanded with a highly contiguous, long-read assembly of a previously undescribed isolate (IA16). Comprehensive annotation utilizing expressed sequence tags from several timepoints across the disease cycle in maize enabled the prediction of additional candidate effectors for this species. Comparison of these candidates to other P. sorghi isolates will lead to discoveries regarding a particular isolate’s virulence. We also report on the characterization of the members of a rust-specific candidate secreted effector protein family present in the P. sorghi IA16 isolate. Of eight candidates, we were able to demonstrate that one is a weak suppressor of the plant hypersensitive immune response in the heterologous system Nicotiana benthamiana. This work also utilized an automated phenotyping setup to acquire time lapse images of leaves during experimental assays. By pairing effector characterization assays with automated phenotyping platforms, we can increase throughput, accuracy, and consistency in results. Lastly, we detail a machine learning approach to quantifying common rust disease on maize leaves. Because plant-pathogen interactions are complex, and small changes to phenotype that are undetectable by human measurements may occur, the development of easy-to-use computer vision-based phenotyping platforms to provide consistent and quantitative results is essential. Additionally, a better understanding of the minimum requirements for a given phenotyping approach is useful for future development, as this can increase the speed at which new platforms are developed. This work demonstrates machine learning is a viable and accurate approach to the quantification of rust disease symptoms, corroborating ground truth experimental results. This work also provides extensive image and annotation data for use in future applications. Overall, this dissertation presents a multi-disciplinary approach to the study of P. sorghi that provides both genomic resources and phenotyping pipelines for the study of candidate effectors and plant-pathogen interactions

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Rust fungal pathogens comprise the largest group of plant pathogenic fungi. Due to limitations of their study, like an inability to be cultured or difficulty in making genetic modifications, there are many gaps in the knowledge base of these organisms. One rust species, Puccinia sorghi, is a worldwide pathogen of maize that can cause significant yield losses. Much of the research for P. sorghi focuses on qualitative disease phenotypes of various isolates on different maize genetic backgrounds, with limited information regarding the key pathogenicity genes (effectors) required for a successful infection within this pathosystem. It is imperative to further develop the genomic and phenomic tools available for P. sorghi for use in effector characterization screens. With the recent advent of long-read sequencing, rust genome assembly has transitioned from exceedingly fragmented contigs based on short-read sequencing to large, repeat-resolved scaffolds. More complete rust genomes have led to many discoveries about the true genome size, repeat content, and gene content of these organisms. Well-annotated assemblies also allow for the prediction of candidate effector proteins that function as pathogenicity and virulence determinants. In this work, the genomic resources for P. sorghi are expanded with a highly contiguous, long-read assembly of a previously undescribed isolate (IA16). Comprehensive annotation utilizing expressed sequence tags from several timepoints across the disease cycle in maize enabled the prediction of additional candidate effectors for this species. Comparison of these candidates to other P. sorghi isolates will lead to discoveries regarding a particular isolate’s virulence. We also report on the characterization of the members of a rust-specific candidate secreted effector protein family present in the P. sorghi IA16 isolate. Of eight candidates, we were able to demonstrate that one is a weak suppressor of the plant hypersensitive immune response in the heterologous system Nicotiana benthamiana. This work also utilized an automated phenotyping setup to acquire time lapse images of leaves during experimental assays. By pairing effector characterization assays with automated phenotyping platforms, we can increase throughput, accuracy, and consistency in results. Lastly, we detail a machine learning approach to quantifying common rust disease on maize leaves. Because plant-pathogen interactions are complex, and small changes to phenotype that are undetectable by human measurements may occur, the development of easy-to-use computer vision-based phenotyping platforms to provide consistent and quantitative results is essential. Additionally, a better understanding of the minimum requirements for a given phenotyping approach is useful for future development, as this can increase the speed at which new platforms are developed. This work demonstrates machine learning is a viable and accurate approach to the quantification of rust disease symptoms, corroborating ground truth experimental results. This work also provides extensive image and annotation data for use in future applications. Overall, this dissertation presents a multi-disciplinary approach to the study of P. sorghi that provides both genomic resources and phenotyping pipelines for the study of candidate effectors and plant-pathogen interactions

Genomic and phenomic approaches for studying Puccinia sorghi-maize interactions

Rust fungal pathogens comprise the largest group of plant pathogenic fungi. Due to limitations of their study, like an inability to be cultured or difficulty in making genetic modifications, there are many gaps in the knowledge base of these organisms. One rust species, Puccinia sorghi, is a worldwide pathogen of maize that can cause significant yield losses. Much of the research for P. sorghi focuses on qualitative disease phenotypes of various isolates on different maize genetic backgrounds, with limited information regarding the key pathogenicity genes (effectors) required for a successful infection within this pathosystem. It is imperative to further develop the genomic and phenomic tools available for P. sorghi for use in effector characterization screens. With the recent advent of long-read sequencing, rust genome assembly has transitioned from exceedingly fragmented contigs based on short-read sequencing to large, repeat-resolved scaffolds. More complete rust genomes have led to many discoveries about the true genome size, repeat content, and gene content of these organisms. Well-annotated assemblies also allow for the prediction of candidate effector proteins that function as pathogenicity and virulence determinants. In this work, the genomic resources for P. sorghi are expanded with a highly contiguous, long-read assembly of a previously undescribed isolate (IA16). Comprehensive annotation utilizing expressed sequence tags from several timepoints across the disease cycle in maize enabled the prediction of additional candidate effectors for this species. Comparison of these candidates to other P. sorghi isolates will lead to discoveries regarding a particular isolate’s virulence. We also report on the characterization of the members of a rust-specific candidate secreted effector protein family present in the P. sorghi IA16 isolate. Of eight candidates, we were able to demonstrate that one is a weak suppressor of the plant hypersensitive immune response in the heterologous system Nicotiana benthamiana. This work also utilized an automated phenotyping setup to acquire time lapse images of leaves during experimental assays. By pairing effector characterization assays with automated phenotyping platforms, we can increase throughput, accuracy, and consistency in results. Lastly, we detail a machine learning approach to quantifying common rust disease on maize leaves. Because plant-pathogen interactions are complex, and small changes to phenotype that are undetectable by human measurements may occur, the development of easy-to-use computer vision-based phenotyping platforms to provide consistent and quantitative results is essential. Additionally, a better understanding of the minimum requirements for a given phenotyping approach is useful for future development, as this can increase the speed at which new platforms are developed. This work demonstrates machine learning is a viable and accurate approach to the quantification of rust disease symptoms, corroborating ground truth experimental results. This work also provides extensive image and annotation data for use in future applications. Overall, this dissertation presents a multi-disciplinary approach to the study of P. sorghi that provides both genomic resources and phenotyping pipelines for the study of candidate effectors and plant-pathogen interactions