Recipient Organization
UNIV OF MINNESOTA
(N/A)
ST PAUL,MN 55108
Performing Department
Plant Pathology
Non Technical Summary
Changing global climate, shifting patterns of crop production, diminishing arable land and other natural resources, and a growing world population represent a perfect storm that threatens long-term food security. Domestication of our major crop plants at the beginning of agriculture entailed selection of plants under the comparatively stable environmental and production conditions encountered over the past 10,000 years. This, in turn, resulted in a significant narrowing of the genepool. This means current crops, while well adapted to a stable climate and production practices that include steady input of water and fertilizer, are genetically less well suited to a variable climate and limited resource availability. Meanwhile, plant pathogens continue to evolve and represent an ever-present threat to food production.Fortunately, genebank collections, landraces, and wild populations of plant species related to our crop plants (collectively referred to here as "plant genetic resources") are treasure troves of alleles useful to improve crop adaptability to changing climate and shifting pathogen pressures. Because plant genetic resources originate from and are adapted to a wide variety of environments, the alleles they encompass can be useful in making crops more tolerant of erratic environmental (e.g., temperature extremes) and biotic (e.g., pathogen) pressures. Using plant genetic resources for crop improvement is not a new concept. For example, for a century scientists have studied wild potato species as sources of resistance to late blight disease (caused by Phytophthora infestans), crossing resistant species with the cultivated potato. But identifying which alleles in which accessions of which plant genetic resources collection are useful for crop improvement has been a challenge akin to finding a needle in a haystack. Additionally, plant genetic resources have not been selected for production agriculture and frequently encompass alleles, such as those conditioning seed shatter or dormancy, that are undesirable in our crops. Finding the few alleles that might improve our crops and separating them from the many alleles that hinder crop productivity has been a challenge that has limited the use of plant genetic resources in crop improvement.In recent years, advancements in understanding the genetic control of plant phenotypes such as disease resistance, enhanced accessibility to plant genomics tools and data, and improved informatics have created new opportunity to mine plant genetic resources collections for alleles useful for crop improvement. At the same time, improvements in marker technologies and marker-assisted breeding, plant transformation technologies, and gene editing are making it easier to transfer alleles from plant genetic resources to crop plants without incorporating undesirable traits. These exciting new possibilities in crop improvement come just in time to enable scientists to respond to the demands on and threats to global crop production.This project builds on two decades of success in identifying useful disease resistance (R) alleles encoding proteins of the nucleotide binding-leucine rice repeat (NB-LRR) architecture in wild relatives of crop plants and understanding R allele function. Extending our previous results, this project further enhances analytical tools for the discovery of useful NB-LRR genes; examines how NB-LRR allelic diversity is apportioned across genotypes, populations, and species; and conducts functional analyses of a core set of highly conserved NB-LRR genes. This project is positioned to directly impact agriculture on a local and global scale by enabling breeding of crop plants that are more resistant to diseases.
Animal Health Component
25%
Research Effort Categories
Basic
65%
Applied
25%
Developmental
10%
Goals / Objectives
This project enhances analytical tools for the discovery of useful NB-LRR (plant disease resistance) genes; examines how NB-LRR allelic diversity is apportioned across genotypes, populations, and species; and conducts functional analyses of a core set of highly conserved NB-LRR genes. Specific objectives are:Objective 1. Expansion of the RosaR80 and SolaR80 systems a) Further improvement of our analytical pipeline (New analytical approaches, Expanded analytical tools, Improved usability) b) Integration of additional R gene data (New whole genome sequences and/or cloned R genes, Data generation by whole genome sequencing, RenSeq, etc.)Objective 2. Evaluate agricultural intensification as a driver of R gene allelic diversification a) Comparison of R gene content, diversification in the genomes of cultivated vs. wild plant species via meta-analysis b) Generation and comparison of R allelic sequence data in a well stratified selection of cultivated vs. wild plant genotypesObjective 3. Demonstrate functional significance of Highly Conserved R genes a) Comparative structural genomics (Within Rosaceae, Across other plant families); b) Transcriptome studies: when, where transcribed; c) Knockouts/knockdown experiments (Under non-stress conditions; In response to known pathogens; In response to pathogens of other plant species; In response to environmental stress)
Project Methods
Objective 1: a) Further improvement of our analytical pipelineIn the next phase of this project, the existing analytical pipeline will be augmented to allow analysis of clustering criteria (i.e., sequence identity percentages) as a function of reduced data set complexity, allowing informed decision of ideal clustering criteria for a given analysis. This approach will allow identification of biases introduced based on hardcoded filtering criteria, allowing us to address these issues as they arise. Out pipeline will also be augmented to allow input of user-specified clustering criteria and rapid assignment of naïve R gene datasets to previously defined R gene lineages. Improved usability will include better program annotation and development of user guides.b) Integration of additional R gene dataThe existing RosaR80 and SolaR80 systems will be further augmented with the incorporation of additional R gene data sets. Sources of data may include mining of publicly available whole genome sequences; whole genome sequencing of novel Rosaceae or Solanaceae species with an emphasis on wild relatives of crop plants with breeding value for crop improvement; and targeted R gene sequencing using RenSeq (Witek et al. 2016) or similar technologies.Objective 2:a) Comparison of R gene content, diversification in the genomes of cultivated vs. wild plant species via meta-analysisComparison of R gene content from genome sequences of crop plants and their wild relatives provides a direct test of our hypothesis that agricultural intensification increases allelic diversification in the genomes of cultivated plants. We will conduct a meta-analysis of R gene content in crops and their wild relatives, leveraging publicly-available whole genome data. This analysis will be conducted across monocots and dicots, representing broad phylogenetic space. This is both necessary to ensure sufficient data and strategic in that we anticipate agriculture to have a similar impact regardless of crop species. While individual genotypes will vary in total R gene numbers, it is anticipated that, on average, wild species genomes will comprise fewer R genes. Wild genome R genes will represent the same broad clades found in the genomes of cultivated plants (e.g., clades A-I in the RosaR80 system), but specific lineages will be less densely represented. In these analyses, predicted genes (or predicted proteins) reported by sequencing consortia will be the starting point for analysis through our analytical pipeline, ensuring the same approaches and criteria are employed in analysis of genes from both cultivated and wild species. Confounding factors that will need to be addressed including biological (e.g., ploidy, mating habit, native environment, etc.) and technical (e.g., influence of sequencing technology, genome assembly approach, gene calling approach, etc.) aspects. In interpreting our results, global or regional production statistics (e.g., total acreage, total production, etc.) will be used as a proxy for agricultural intensification. Additional factors, including abiotic environmental factors (e.g., degree days, rainfall statistics, etc.), may also be incorporated into our findings.b) Generation and comparison of R allelic sequence data in a well stratified selection of cultivated vs. wild plant genotypesGiven the research bias toward sequencing crop genomes and the inherent genotype-to-genotype variation in total R gene numbers we are likely to see in both wild and cultivated genomes, the public availability of sufficient whole genome data represents the most significant challenge to completing the research described above (objective 2a). To augment available data, as resources allow, we will optimize and employ RenSeq (Witek et al. 2016) or a similar targeted sequencing approach to survey the R gene space of selected plant species. Targeted sequencing will be employed to survey both cultivated crop species and their close wild relatives. Our experimental approach will entail first developing a collection of genotypes that will allow both determination of total R gene numbers and allelic diversity within the genomes of individual genotypes and comparison of genotype-to-genotype variation within populations and species. Establishment of a genotype collection for this study will be informed by available phylogenies, accessibility of alleles harbored by wild species through traditional breeding approaches, and depth of representation in available germplasm collections. In the previous phase of this project, a RenSeq probe set for the Rosaceae was designed but never experimentally tested (unpublished). A similar RenSeq probe set will be developed for the Solanaceae based on all available whole genome sequences and cloned NB-LRR R gene sequences. Standard RenSeq experimental and analytical protocols (Witek et al. 2016) will be employed.Objective 3:a) Comparative structural genomicsThe five conserved R gene lineages identified in our previousl analysis of the Rosaceae are likely to be represented in more distantly related families as well. To document the distribution of these R gene lineages, we will survey a phylogenetically structured collection of whole genome sequences, deploying a reciprocal best BLAST approach and analysis of the upstream and downstream gene content to establish orthology and to measure gene representation within the Rosaceae across other families. This set of experiments may give insight into the evolutionary origin of the conserved lineages and might provide clues to their function.b) Transcriptome studiesWe anticipate that the conserved R gene sequences we identified in the Rosaceae are actively transcribed and translated into proteins upon which selective forces act. To test this assumption we will analyze existing transcriptome data from Rosaceous species to document gene transcription. As appropriate, we may generate novel transcriptome datasets for analysis. We will also develop qRT-PCR diagnostic tools and conduct a set of experiments to track gene transcription prior to and during pathogen attack, in response to non-pathogens, in response to environmental stresses, and as a function of plant age and tissue type.c) Knockouts/knockdown experimentsAssuming successful demonstration that the conserved R genes are actively transcribed, one line of research to elucidate function is disruption of normal gene transcription. This may be accomplished, for example, through modification of the gene or genes themselves through gene editing methodologies (e.g., CRISPR/Cas9) or suppression of gene transcripts using RNAi approaches. The woodland strawberry, Fragaria vesca, is a tractable system for these experiments. In these approaches, the implicit expectation is that reduction of gene transcription will lead to reduced protein accumulation, altering plant phenotypes in measurable ways. As a course of this project, we will develop a standard set of pathogens, nonpathogens, and environmental conditions to test for phenotypic perturbations. As warranted and feasible, complementary experimental approaches including over expression of the conserved R genes may be tested as well.