The availability of well-assembled genome sequences and reduced sequencing costs have enabled the resequencing of many additional accessions in several crops, thus facilitating the rapid discovery and development of simple sequence repeat (SSR) markers. Although the genome sequence of inbred spinach line Sp75 is available, previous efforts have resulted in a limited number of useful SSR markers. Identification of additional polymorphic SSR markers will support genetics and breeding research in spinach. This study aimed to use the available genomic resources to mine and catalog a large number of polymorphic SSR markers. A search for SSR loci on six chromosome sequences of spinach line Sp75 using GMATA identified a total of 42,155 loci with repeat motifs of two to six nucleotides in the Sp75 reference genome. Whole-genome sequences (30x) of additional 21 accessions were aligned against the chromosome sequences of the reference genome and in silico genotyped using the HipSTR program by comparing and counting repeat numbers variation across the SSR loci among the accessions. The HipSTR program generated SSR genotype data were filtered for monomorphic and high missing loci, and a final set of the 5986 polymorphic SSR loci were identified. The polymorphic SSR loci were present at a density of 12.9 SSRs/Mb and were physically mapped. Out of 36 randomly selected SSR loci for validation, two failed to amplify, while the remaining were all polymorphic in a set of 48 spinach accessions from 34 countries. Genetic diversity analysis performed using the SSRs allele score data on the 48 spinach accessions showed three main population groups. This strategy to mine and develop polymorphic SSR markers by a comparative analysis of the genome sequences of multiple accessions and computational genotyping of the candidate SSR loci eliminates the need for laborious experimental screening. Our approach increased the efficiency of discovering a large set of novel polymorphic SSR markers, as demonstrated in this report.

A major bottleneck in identifying therapies to control citrus greening and other devastating plant diseases caused by fastidious pathogens is our inability to culture the pathogens in defined media or axenic cultures. As such, conventional approaches for antimicrobial evaluation (genetic or chemical) rely on time-consuming, low-throughput and inherently variable whole-plant assays. Here, we report that plant hairy roots support the growth of fastidious pathogens likeCandidatusLiberibacter spp., the presumptive causal agents of citrus greening, potato zebra chip and tomato vein greening diseases. Importantly, we leverage the microbial hairy roots for rapid, reproducible efficacy screening of multiple therapies. We identify six antimicrobial peptides, two plant immune regulators and eight chemicals which inhibitCandidatusLiberibacter spp. in plant tissues. The antimicrobials, either singly or in combination, can be used as near- and long-term therapies to control citrus greening, potato zebra chip and tomato vein greening diseases.

White rust, caused by Albugo occidentalis, is one of the major yield-limiting diseases of spinach (Spinacia oleracea) in some major commercial production areas, particularly in southern Texas in the United States. The use of host resistance is the most economical and environment-friendly approach to managing white rust in spinach production. The objectives of this study were to conduct a genome-wide associating study (GWAS), to identify single nucleotide polymorphism (SNP) markers associated with white rust resistance in spinach, and to perform genomic prediction (GP) to estimate the prediction accuracy (PA). A GWAS panel of 346 USDA (US Dept. of Agriculture) germplasm accessions was phenotyped for white rust resistance under field conditions and GWAS was performed using 13 235 whole-genome resequencing (WGR) generated SNPs. Nine SNPs, chr2_53 049 132, chr3_58 479 501, chr3_95 114 909, chr4_9 176 069, chr4_17 807 168, chr4_83 938 338, chr4_87 601 768, chr6_1 877 096, and chr6_31 287 118, located on chromosomes 2, 3, 4, and 6 were associated with white rust resistance in this GWAS panel. Four scenarios were tested for PA using Pearson’s correlation coefficient (r) between the genomic estimation breeding value (GEBV) and the observed values: (1) different ratios between the training set and testing set (fold), (2) different GP models, (3) different SNP numbers in three different SNP sets, and (4) the use of GWAS-derived significant SNP markers. The results indicated that a 2- to 10-fold difference in the various GP models had similar, although not identical, averaged r values in each SNP set; using GWAS-derived significant SNP markers would increase PA with a high r-value up to 0.84. The SNP markers and the high PA can provide valuable information for breeders to improve spinach by marker-assisted selection (MAS) and genomic selection (GS).

The efficient acquisition and transport of nutrients by plants largely depend on the root architecture. Due to the absence of complex microbial network interactions and soil heterogeneity in a restricted soilless medium, the architecture of roots is a function of genetics defined by the soilless matrix and exogenously supplied nutrients such as nitrogen (N). The knowledge of root trait combinations that offer the optimal nitrogen use efficiency (NUE) is far from being conclusive. The objective of this study was to define the root trait(s) that best predicts and correlates with vegetative biomass under differed N treatments. We used eight image-derived root architectural traits of 202 diverse spinach lines grown in two N concentrations (high N, HN, and low N, LN) in randomized complete blocks design. Supervised random forest (RF) machine learning augmented by ranger hyperparameter grid search was used to predict the variable importance of the root traits. We also determined the broad-sense heritability (H) and genetic (r_g) and phenotypic (r_p) correlations between root traits and the vegetative biomass (shoot weight, SWt). Each root trait was assigned a predicted importance rank based on the trait’s contribution to the cumulative reduction in the mean square error (MSE) in the RF tree regression models for SWt. The root traits were further prioritized for potential selection based on the r_g and SWt correlated response (CR). The predicted importance of the eight root traits showed that the number of root tips (Tips) and root length (RLength) under HN and crossings (Xsings) and root average diameter (RAvdiam) under LN were the most relevant. SWt had a highly antagonistic r_g (− 0.83) to RAvdiam, but a high predicted indirect selection efficiency (− 112.8%) with RAvdiam under LN; RAvdiam showed no significant rg or rp to SWt under HN. In limited N availability, we suggest that selecting against larger RAvdiam as a secondary trait might improve biomass and, hence, NUE with no apparent yield penalty under HN.

Core Ideas: High‐throughput imaging and genomic information can be combined to optimize marker development. Genome‐wide association studies identified loci associated with plant growth traits. We identified candidate genes associated with plant growth and development. Despite advances in sequencing for genotyping, the lack of rapid, accurate, and reproducible phenotyping platforms has hampered efforts to use genetic analysis to predict traits of interest. Therefore, the use of high‐throughput systems to phenotype traits related to crop growth, yield, quality, and resistance to biotic and abiotic stresses has become a major asset for breeding. Here, we assessed the efficacy of unmanned aircraft system (UAS)‐based high‐throughput phenotyping to obtain data for molecular marker development for spinach ( Spinacia oleracea L.) improvement. We used a UAS equipped with a red–green–blue sensor to capture raw images of 284 spinach accessions throughout the crop cycle. Processed images generated orthomosaic and digital surface models for estimating canopy cover, canopy volume, and excess greenness index models. In addition, we manually recorded the number of days to bolting. Genome‐wide association studies against a single‐nucleotide polymorphism (SNP) panel obtained by ddRADseq identified 99 SNPs significantly associated with growth parameters. Some of these SNPs are in transcription factor and stress‐response genes with possible roles in plant growth and development. The results underscore the utility of combining aerial imaging and genomic data analysis to optimize marker development. This study lays the foundation for the use of UAS‐based high‐throughput phenotyping for the molecular breeding of spinach.