Sugar beet crown and root rot caused by Rhizoctonia solani is a major yield constraint. Root rot is highly increased when R. solani and Leuconostoc mesenteroides co-infect roots. We hypothesized that the absence of plant cell-wall-degrading enzymes in L. mesenteroides and their supply by R. solani during close contact, causes increased damage. In planta root inoculation with or without cell-wall-degrading enzymes showed greater rot when L. mesenteroides was combined with cellulase (22 mm rot), polygalacturonase (47 mm), and pectin lyase (57 mm) versus these enzymes (0–26 mm), R. solani (20 mm), and L. mesenteroides (13 mm) individually. Carbohydrate analysis revealed increased simpler carbohydrates (namely glucose + galactose, and fructose) in the infected roots versus mock control, possibly due to the degradation of complex cell wall carbohydrates. Expression of R. solani cellulase, polygalacturonase, and pectin lyase genes during root infection corroborated well with the enzyme data. Global mRNAseq analysis identified candidate genes and highly co-expressed gene modules in all three organisms that might be critical in host plant defense and pathogenesis. Targeting R. solani cell-wall-degrading enzymes in the future could be an effective strategy to mitigate root damage during its interaction with L. mesenteroides.

Understanding the genetic basis of polygenic traits is a major challenge in agricultural species, especially in non-model systems. Select and sequence (SnS) experiments carried out within existing breeding programs provide a means to simultaneously identify the genomic background of a trait while improving the mean phenotype for a population. Using pooled whole genome sequencing (WGS) of selected and unselected bulks derived from a synthetic outcrossing sugar beet population EL57 (PI 663212), which segregates for seedling rhizoctonia resistance, we identified a putative genomic background involved in conditioning a resistance phenotype. Population genomic parameters were estimated to measure fixation (He), genome divergence (F_ST), and allele frequency changes between bulks (DeltaAF). We report on the genome wide patterns of variation resulting from selection and highlight specific genomic features associated with resistance. Expected heterozygosity (He) showed an increased level of fixation in the resistant bulk, indicating a greater selection pressure was applied. In total, 1,311 biallelic loci were detected as significant F_ST outliers (p < 0.01) in comparisons between the resistant and susceptible bulks. These loci were detected in 206 regions along the chromosomes and contained 275 genes. We estimated changes in allele frequency between bulks resulting from selection for resistance by leveraging the allele frequencies of an unselected bulk. DeltaAF was a more stringent test of selection and recovered 186 significant loci, representing 32 genes, all of which were also detected using F_ST. Estimates of population genetic parameters and statistical significance were visualized with respect to the EL10.2 physical map and produced a candidate gene list that was enriched for function in cell wall metabolism and plant disease resistance, including pathogen perception, signal transduction, and pathogen response. Specific variation associated with these genes was also reported and represents genetic markers for validation and prediction of resistance to Rhizoctonia. Select and sequence experiments offer a means to characterize the genetic base of sugar beet, inform selection within breeding programs, and prioritize candidate variation for functional studies.

Beet Curly Top (BCT) is a viral disease which negatively impacts crop productivity for sugar beet growers and the sugar beet industry in the western USA and dry regions worldwide. Current varieties exhibit little genetic resistance to the Beet Curly Top Virus (BCTV), suggesting there is a large potential for improvement. KDH13 (PI 663862) is a double-haploid line created from a population (C762-17/PI 560130) which segregates for resistance to BCTV and was identified as genetic stock for the improvement of sugar beet varieties. PacBio sequences were generated and assembled to better define the content and organization of variation within the KDH13 genome and to provide resources to identify specific variation underpinning durable genetic resistance. Using ab initio predicted proteins as anchors, the assembled KDH13 contigs were placed in a more contiguous order using the EL10.1 reference genome, which leveraged Bio-Nano optical maps and Hi-C proximity information for chromosome level scaffolding. In total, 4681 (75%) of the 6245 contigs were placed in the order and orientation of the EL10.1 genome. The anchored contigs represented 502,929,268 bp (87.7%), the KDH13 genome assembly. An F₁ hybrid and parental lines KDH13 (resistant) and KDH19-17 (susceptible) were sequenced using Illumina technology in order to characterize the SNP, indel, and structural variation between parental lines and allow for a more detailed investigation into causal variation linked to important phenotypes. In total, 3,086,720 variants were detected, including 2,259,324 single-nucleotide polymorphisms, 191,448 insertions, 198,057 deletions, 268,090 complex substitutions, 90,004 multi allelic variants, and 79,797 structural variants. Of the total variation, 1,158,491 were informative in the F1 and were able to discriminate between the two parents. This information represents a high-density marker dataset distributed globally across the sugar beet genome and can be used to track genomic segments in populations where KDH13 is used as parental material to improve BCTV resistance.

Sugar beet is susceptible to Beet curly top virus (BCTV), which significantly reduces yield and sugar production in the semi-arid growing regions worldwide. Sources of genetic resistance to BCTV is limited and control depends upon insecticide seed treatments with neonicotinoids. Through double haploid production and genetic selection, BCTV resistant breeding lines have been developed. Using BCTV resistant (R) [KDH13; Line 13 and KDH4-9; Line 4] and susceptible (S) [KDH19-17; Line 19] lines, beet leafhopper mediated natural infection, mRNA/sRNA sequencing, and metabolite analyses, potential mechanisms of resistance against the virus and vector were identified. At early infection stages (2- and 6-days post inoculation), examples of differentially expressed genes highly up-regulated in the ‘R’ lines (vs. ‘S’) included EL10Ac5g10437 (inhibitor of trypsin and hageman factor), EL10Ac6g14635 (jasmonate-induced protein), EL10Ac3g06016 (ribosome related), EL10Ac2g02812 (probable prolyl 4-hydroxylase 10), etc. Pathway enrichment analysis showed differentially expressed genes were predominantly involved with peroxisome, amino acids metabolism, fatty acid degradation, amino/nucleotide sugar metabolism, etc. Metabolite analysis revealed significantly higher amounts of specific isoflavonoid O-glycosides, flavonoid 8-C glycosides, triterpenoid, and iridoid-O-glycosides in the leaves of the ‘R’ lines (vs. ‘S’). These data suggest that a combination of transcriptional regulation and production of putative antiviral metabolites might contribute to BCTV resistance. In addition, genome divergence among BCTV strains differentially affects the production of small non-coding RNAs (sncRNAs) and small peptides which may potentially affect pathogenicity and disease symptom development.

Beet curly top virus (BCTV) significantly reduces sugar beet yield in semi-arid production areas. Genetic resistance to BCTV is limited; therefore, identification of additional resistance-associated factors is highly desired. Using 16S rRNA sequencing and BCTV resistant (R) genotypes (KDH13, KDH4-9) along with a susceptible (S) genotype (KDH19-17), we investigated leaf bacteriome changes during BCTV post inoculation (pi). At day 6 (~6-week-old plants), Cyanobacteria were predominant (~90%); whereas, at week 4 (~10-week-old plants) Firmicutes (11–66%), Bacteroidetes (17–26%), and Verrucomicrobia (12–29%) were predominant phyla and genotype dependent. Both Bacteroidetes and Verrucomicrobia, increased post infection only in the R lines. The bacterial genera Brevibacillus increased at 6 dpi, and Akkermansia and Bacteroides at 4 wkpi in the R lines. Linear discriminant analysis effect size (LEfSe) identified potential biomarkers in the R vs. S lines. Functional profiling revealed bacterial enrichment associated with the TCA cycle, polyisoprenoid, and L-methionine biosynthesis pathways only in KDH4-9 at 6 dpi. At 4 wkpi, bacteria associated with tryptophan and palmitate biosynthesis in the R lines, and uridine monophosphate, phosphatidyl glycerol, and phospholipid biosynthesis in the S line, were enriched. Future characterization of bacterial genera with antiviral properties will help establish their use as biocontrol agents/biomarkers against BCTV.

Plant Genetic Resources (PGR) are material containing useful genetic variation in plant species, including their weedy and wild relatives. Collections of PGR are mined for useful traits as needed to respond to new biotic and abiotic stresses; incorporate new nutritional, yield, or value‐added traits; and create new markets or industries. PGR are also useful to identify genes encoding traits of interest; serve as training populations for new breeding methodologies such as genomic selection that integrate the latest genomic data with new statistical modeling; and underpin evolutionary and taxonomic studies. The genebanks of the USDA ARS National Plant Germplasm System have experienced progress and challenges in applying such tools and technologies to PGR maintenance and creation of data that will increase their utility. These tools include high‐throughput genotyping and phenotyping technologies that could be used by breeders to unlock their genetic potential, and to more efficiently and effectively manage the collections. Examples of these technologies used on PGR of the USDA ARS Plant Germplasm Introduction and Testing Research Unit are presented, as are genebank success stories of entries that were used to create new breeding lines and cultivars to solve a problem faced by US farmers. Finally, current challenges and opportunities in PGR management and utilization are offered.