Location: Soil Management and Sugarbeet Research
2024 Annual Report
Objectives
Objective 1: Phenotype and genotype global germplasm resources for Beta vulgaris spp. maritima (Bvm), Beta corolliflora (Bcor), and Patellifolia procumbens (Ppro) to enable trait discovery of resistance to Rhizoctonia Crown and Root Rot (RCRR), Fusarium Yellows (FY), and Sugar Beet Cyst Nematode (SBCN).
Sub-objective 1.A: Phenotypic evaluation of Beta vulgaris spp. maritima, Beta corolliflora, and Patellifolia procumbens accessions for resistance to Rhizoctonia Crown and Root Rot, Fusarium Yellows, and Sugar Beet Cyst Nematode.
Sub-objective 1.B: Whole genome sequencing of Beta vulgaris spp. maritima, Beta corolliflora, and Patellifolia procumbens accession pools to catalog genome-wide diversity.
Sub-objective 1.C: Develop and deploy BeetBase: a centralized, cloud-based germplasm, genomics and phenomics database for the Beta research community.
Objective 2: Develop inbred lines homozygous for new resistance traits for genome assembly and annotation to drive functional genomics studies to understand the molecular mechanisms of each gene and unravel the plant-pathogen interaction at a systems biology level.
Sub-objective 2.A: Develop and release genetic stocks of sugar beet and crop wild relatives harboring disease resistance traits of interest alongside genomic resources for gene discovery and functional characterization.
Approach
Sugar beets provide more than half the sucrose consumed in the United States and can be one of the most profitable crops in the regions where they are grown. Susceptibility to diseases remains a dominant concern to sugar beet production in the United States. While successful examples of breeding disease resistance exist, including those using ARS germplasm as the foundation for breeding resistance, current processes for breeding are relatively slow and can take well over a decade. We aim to combine state-of-the-art phenomic and genomic research, leveraging germplasm derived from the public plant repositories, to accelerate the breeding of disease resistance into sugar beets. Specifically, we will phenotype and genotype global germplasm resources for Beta vulgaris spp. maritima, Beta corolliflora and Patellifolia procumbens to enable trait discovery of resistance to Rhizoctonia Crown and Root Rot, Fusarium Yellows and Sugar Beet Cyst Nematode. While doing so, we will develop and deploy imaging pipelines to accelerate the rate of phenotypic screening of sugar beet germplasm resources. To host the genomic and phenomic data that we produce, we will develop both a repository and toolkit for the sugar beet community, BeetBase, housed within ARS’s publicly available platform. Upon completion of the planned research, we anticipate shortening the disease resistance trait discovery cycle to 3-4 years. Results from this program address the immediate needs of sugar beet growers who are facing the aforementioned disease pressures, seed companies who are striving to develop improved lines of sugar beet, the sugar beet and broader scientific community who will benefit from our collaboration, techniques and data platforms and the American consumer.
Progress Report
ARS sugar beet researchers in Fort Collins, Colorado, began a new effort to comprehensively characterize the beet gene bank to streamline the discovery of resistance to multiple diseases. This research expands upon decades-long phenotyping efforts, and now incorporates cutting edge remote sensing and genome sequencing technologies, high performance computing and bioinformatics, and cloud-based database development. With these new tools, we are continuing the long-term mission of identifying and deploying new disease resistance traits to improve American beet sugar production.
On Objective 1A, the research team completed both greenhouse and field-based phenotyping experiments for both Rhizoctonia Root and Crown Rot (RRCR) and Fusarium Yellows (FY) to develop accurate high throughput phenotyping pipelines for each disease. Hyperspectral imaging experiments on greenhouse-grown plants for both RRCR and FY were conducted to identify precise regions of the electromagnetic spectrum that can accurately differentiate resistant versus susceptible plants using only above-ground imaging. Combinations of these spectral regions will continue to be tested to validate a disease-specific spectral indices that can be deployed in large-scale greenhouse and/or field experiments. The first field experiment to collect hyperspectral datasets for RRCR phenotyping was completed in the reporting period, which consisted of two separate experiments using both sugar beet pre-breeding lines and wild beet accessions from the National Plant Germplasm System (NPGS) Beta gene bank. This dataset is being used to validate the disease-specific spectral indices from the greenhouse experiments described above.
For Objective 1B, the research team made substantial progress to characterize the full genomic diversity of available accessions for the beet wild relative species Beta vulgaris spp. maritima, Beta corolliflora, and Patellifolia procumbens. Utilizing a recently published public high density genotyping dataset from 602 Beta vulgaris spp. maritima, we are utilizing an alternative approach to develop the Beta vulgaris spp. maritima core collection for later phenotyping and pooled whole genome sequencing. Using the 148,317 biallelic single nucleotide polymorphisms (SNPs) identified in this study, 160 Beta vulgaris spp. maritima accessions available from the NPGS Beta gene bank were identified that maximize the genetic variation in the 602 genotyped accessions. DNA was collected from replicate plants of all 160 accessions during the reporting period and will be used for whole genome sequencing. DNA was also collected for future whole genome sequencing for all publicly available accessions of Beta corolliflora and Patellifolia procumbens.
On Objective 1C, the team completed the development and deployment of the first version of BeetBase. Backend software and database development was completed to incorporate inventory, phenotyping, and genomic datasets from 20 years of pre-breeding efforts by the Fort Collins program. In close collaboration with the USDA-ARS Partnerships for Data Innovation team, these tools were deployed to USDA’s Microsoft Azure Cloud environment for long term hosting. Ongoing efforts to onboard additional ARS breeding station phenotyping and genotyping datasets will be completed prior to publication.
Objective 2 focuses on continued germplasm and genomic resource development research efforts for both sugar beet pre-breeding lines and beet crop wild relative species (Beta corolliflora and Beta vulgaris spp. maritima), with the long-term goal of identifying useful variation and candidate disease resistance genes. For this objective, the research team successfully identified individual plants from Beta corolliflora and Beta vulgaris spp. maritima accessions with resistance to each disease, as well as from two additional sugar beet pre-breeding lines with Beta vulgaris spp. maritima introgressions. High molecular weight DNA was successfully isolated from Beta corolliflora accession PI604030 and Beta vulgaris spp. maritima accession PI 604511, and sugar beet pre-breeding lines FC309 and FC709-3. PacBio long read DNA sequencing reads sufficient for genome assembly from all four accessions were also successfully generated ahead of schedule. This research objective was also extended to include continued pre-breeding of the FC709-2 lineage, from which the FC709-3 genomic resources are being developed. An additional cycle phenotypic selection for RRCR resistance was completed in the field. Seed production from this selection has been completed during the reporting period, and this line will be released and registered as germplasm FC709-4. Whole genome sequencing data of the FC709-4 germplasm was also obtained, which has been submitted to the National Center for Biotechnology Information Sequence Read Archive under accession SRR27945568.
Accomplishments
1. New disease-resistance traits for sugar beets. American beet sugar producers face ongoing threats from multiple pests and pathogens. Sugar beet breeders have traditionally used mass selection to gradually improve resistance to pests and pathogens. ARS researchers in Fort Collins, Colorado, and collaborators used cutting-edge molecular-assisted breeding and modern genome sequencing technologies to develop two new sugar beet lines. These new lines have improved levels and uniformity of resistance to Fusarium Yellows and Sugar Beet Cyst Nematode, and they provide a new genetic resource for scientists studying plant-pest interactions and for commercial breeders aiming to develop improved commercial hybrids.
2. Improved sugar beet disease rating tool. The Fusarium oxysporum species complex is a group of harmful filamentous fungi that devastate sugar beets. These fungi cause Fusarium Yellows, a disease which causes reduced plant growth, low crop yield, and reduced sugar production. To develop better resistant germplasm and manage this disease, scientists and growers need accurate methods to assess disease severity. ARS researchers in Fort Collins, Colorado, and East Lansing, Michigan, developed a photo-based, novel rating method for Fusarium Wilt in sugar beet that is both accurate and precise. This tool, referred to as a Standard Area Diagram, allows ease of use in the field regardless of experience level with rating Fusarium diseases using a picture-based method. This Standard Area Diagram is applicable to growers, researchers, and seed companies who wish to identify the degree of Fusarium Wilt severity in their fields. The use of this tool also supports repeatable and consistent disease screening within USDA prebreeding programs to ensure production of highly resistant germplasm.
Review Publications
Todd, O.E., Nielson, A.L., Fenwick, A.L., Hanson, L.E., Richardson, K.L., Dorn, K.M. 2024. Registration of sugar beet genetic stocks FC308 (PI701378) and FC309 (PI700990). Journal of Plant Registrations. https://doi.org/10.1002/plr2.20345.
Dorn, K.M., Strausbaugh, C.A., Majumdar, R. 2023. Evaluation of USDA-ARS sugar beet germplasm for resistance to rhizomania and storage rot in Idaho, 2022. Plant Disease Management Reports. 17. Article eV153.
Dorn, K.M., Strausbaugh, C.A., Majumdar, R. 2023. USDA-ARS Plant Introduction lines evaluated for rhizomania and storage rot resistance in Idaho, 2022. Plant Disease Management Reports. 17. Article eV114.
Todd, O.E., Creech, C.F., Kumar, V., Mahood, A.L., Peirce, E.S. 2024. Future outlook of dryland crop production systems in the semi-arid High Plains amid climate change. Outlooks on Pest Management. 35(1):4-10. https://doi.org/10.1564/v35_feb_02.