Assessments of genes associated with plant host defense responses can be challenging as the defensive mechanisms that enable the host-mediated defense can be the very compounds that make gene expression assays particularly challenging. RNA extraction from woody plant tissues presents significant challenges due to endogenous phenolics, secondary metabolites, and stem polysaccharides. We have established an improved extraction protocol for Fraxinus species, yielding superior results to commercial kits. Our optimized approach, validated across diverse tissue types from over 10 Fraxinus species, consistently produces high-purity RNA with exceptional concentrations (>3000 ng/μL) and integrity (RIN scores 8.0-10.0). The RNA quality we have achieved allows us to detect and analyze rare transcripts that may play crucial roles in emerald ash borer resistance mechanisms. Our approach enables us to quantify copy numbers of defense genes triggered during insect attacks, shedding new light on the molecular basis of resistance pathways in ash trees. By employing digital droplet PCR and RNA-seq calibrated, we can determine the key defense genes' exact transcript copy numbers, including those encoding protease inhibitors, phenolic compounds, and terpenoid synthases central to anti-herbivory responses. We have validated a stable reference gene suite with reliable quality control and consistent expression benchmarks. These references serve as crucial yardsticks when measuring expression patterns across ash trees with varying levels of EAB vulnerability. By determining actual transcript numbers, we can make more meaningful comparisons between ash species and genotypes, helping us identify the critical expression thresholds needed for effective resistance. This work strengthens our partnership with the Chicago Region Tree Initiative, supporting efforts to build more resilient urban forests and protect endangered ash species. Through our detailed analysis of gene expression profiles across diverse ash populations, we are working to pinpoint the genetic signatures that confer EAB resistance. These findings will enable the development of efficient molecular screening tools (e.g., SNP marker panels, transcriptomic signature profiles, or RNA expression ratio tests) for large-scale population assessment and accelerate efforts for this important genus. (Co-authored by Dr. Nathan Maren, Woody Plant Breeder and Genomics Specialist at The Morton Arboretum).
Stomatal function is a critical determinant of overall plant vigor, health, and yield. Higher stomatal conductance is associated with higher yields, and therefore is a trait of interest for plant improvement. Although stomatal conductance is governed by a complex balance between many factors, stomatal size and density are two traits that set the foundation for a genotype’s response to the external/internal factors. Understanding the genetic architecture of these traits is a key first step in the process of genetic selection; unfortunately, phenotyping stomatal traits on the scale required for mapping studies can be logistically challenging. In this experiment, we microscopically imaged stomata in apple leaves and used two computer vision methods to rapidly phenotype stomatal traits- a convolutional neural network (CNN) and the web-based computer vision platform BioDock. Two apple populations with existing molecular marker information were phenotyped: a biparental mapping population of approximately 400 individuals and the USDA’s Malus germplasm collection. Genetic mapping was carried out using the ‘r/qtl’ and ‘GWASpoly’ packages in R for the mapping population and germplasm collection, respectively. Both computer vision models yielded accuracies >90% for phenotyping stomatal density in the training and validation datasets, demonstrating that these models are effective methods for quickly phenotyping large stomatal image datasets. Preliminary results indicated peaks associated with stomatal density on chromosome 1 and chromosome 7. Furthermore, stomatal density was negatively correlated with stomatal size- resulting in less variation in total stomatal area than either the distributions of distribution or size would indicate. Future work in this project will focus on identifying the genes involved in regulating stomatal density in apples, as well as generalizing the computer vision models to function on multiple plant species.
Pea (Pisum sativum) is a valuable legume crop recognized for its rich nutritional profile, offering plant-based protein, fiber, vitamins, and essential minerals. It holds a significant place in the growing plant-based protein industry, which is projected to reach $313.5 million by 2025. However, global pea production is declining due to soilborne diseases, notably root rots caused by Fusarium solani f. sp. pisi (Fsp). In our earlier study, we performed time-course transcriptome analysis on four Fsp-tolerant and four Fsp-susceptible pea genotypes during pathogen infection, identifying several Fsp-responsive genes. Interestingly, the dataset also contained Fusarium-derived genes, many of which encode ubiquitin, ubiquitin-like proteins, and the ubiquitin-40S ribosomal protein S31 fusion protein. EffectorP analysis revealed that these proteins are secretory in nature. We hypothesize that Fusarium secretes these proteins into host cells to manipulate the host’s ubiquitin-proteasome system, leading to the degradation of plant defense proteins. To explore this further, we investigated RING-type E3 ligase proteins in Pisum sativum, which play key roles in protein ubiquitination. A total of 663 genes encoding RING-type E3 ligases were identified, each containing at least one RING domain as predicted by the SMART database. Domain analysis revealed additional conserved motifs within these proteins. An Un-rooted Neighbor-Joining phylogenetic tree grouped the RING proteins based on shared domain architecture. Transcriptomic data indicates that these genes are differentially expressed during Fsp infection. The E3 Ligase genes are upregulated in Fsp-susceptible cultivars and downregulated in Fsp-tolerant cultivars. These genes can be used to generate future knock-out mutants and perform functional studies to enhance pea resistance to Fsp-induced root rot.
Detecting Phytophthora capsici (P. capsici) based solely on visual symptoms is challenging and often leads to misdiagnosis. Farmers frequently harvest seemingly healthy fruits, only for fruit rot to develop after shipping. Furthermore, other pathogens can mimic P. capsici symptoms on cucurbits and peppers, and plants may even suffer simultaneous attacks by multiple pathogens, complicating identification. Without timely and accurate diagnosis, P. capsici can spread rapidly, causing significant crop losses. Current diagnostic methods, including traditional microscopy-based culture techniques and polymerase chain reaction (PCR), are time-intensive and lack sensitivity for early-stage infections. This study introduces an optimized Oxford Nanopore Technology (ONT) genomic approach for rapid and precise detection of P. capsici in plant samples, both in laboratory and field settings. Designed for portability and capable of sequencing reads up to 100 kb, the ONT MinION device—smaller than a smartphone—provides a promising solution for in-field diagnostics. Plant tissue samples, symptomatic and non-symptomatic, were collected from cucurbit and pepper fields through collaborators during late summer and early fall. Total DNA was extracted using a magnetic bead-based kit (Primerdesign, Southampton, UK). Sequencing libraries were prepared using ONT’s 1D-cDNA sequencing kit, loaded onto a MinION 107 v9.5 Flow Cell, and analyzed using the Mk1B MinION device. Raw sequence reads in fast5 format were converted to fastq or fasta, with high-quality reads subjected to BLAST searches against the NCBI database for P. capsici identification. The deployment of ONT enables the generation of actionable genomic data in real-time, enhancing our understanding of P. capsici and its role in Phytophthora blight disease development in cucurbits. This technology represents a breakthrough in the rapid, field-based diagnosis of P. capsici, providing farmers with an efficient tool to mitigate crop losses. Keywords: Oxford Nanopore Sequencing Technology (ONT), Phytophthora capsici, Raw sequence reads, Phytophthora blight.
Ashy stem blight and white mold caused by Macrophomina phaseolina (Tassi) Goidanich and Sclerotinia sclerotiorum L. de Bary, respectively are important fungi pathogens affecting common bean (Phaseolus vulgaris L.) worldwide. Genetic resistance is the most environmental friendly approach to control both diseases. Our objective was to evaluate the response of Phaseolus spp. germplasm to three fungal isolates. Two runner bean accessions (P. coccineus L.), and 23 common bean genotypes including 10 UPR-Mp breeding lines derived from multiple-parent crosses were inoculated with the NY133 S. sclerotiorum isolate and PRI21 and PRI24M M. phaseolina isolates by the cut-stem method in the greenhouse. The disease severity was evaluated at 35 days post-inoculation. Middle American common beans ‘Othello’, TARS-MST1, and ‘Verano’ were susceptible (mean scores > 6.5) to all fungal isolates whereas the runner beans PI 183412 (Sel-1 and Sel-2) and breeding line UPR-Mp-57 were susceptible to NY133. Andean common beans A 195, ‘PC 50’, PRA154, PRA155, and VA 19 were intermediate (scores 4-6) to NY133, PRI21, and PRI24M. In contrast, Middle American beans 92BG-7 and BAT 477 were intermediate to NY133 and PRI21, and susceptible to PRI24M. The runner beans PI 183412-Sel-1 and PI 183412-Sel-2, and common bean breeding lines UPR-Mp-22, UPR-Mp-48, UPR-Mp-54, and UPR-Mp-57 were resistant (scores < 3.5) to PRI21 and intermediate to PRI24M. Conversely, UPR-Mp-34 and UPR-Mp-54 were resistant to NY133. This information should help to select parents with higher levels of resistance that may be used in breeding programs for both diseases.
In light of the increasing demand for resilient crops amid global food security concerns, recent advances in omics technologies have accelerated plant breeding efforts. Nonetheless, their effectiveness is often undermined by limited phenotypic resolution, particularly under field conditions. Traditional approaches based on single daily measurements are insufficient to capture the full spectrum of genotypic responses, especially when environmental stress is present. This study explores the potential of thermal imaging using unmanned aerial vehicles (UAVs) to monitor canopy temperature (CT) in wheat, providing a non-invasive proxy for assessing plant water status. A collection of 184 genetically distinct wheat genotypes was examined under both irrigated and rainfed conditions within a Mediterranean agroecosystem. Thermal data were recorded across multiple phenological phases (from anthesis to grain filling) and at various times throughout the day. The analysis revealed that both developmental stage and time of observation substantially influenced CT patterns, thereby impacting the detection of genotype-specific responses to drought. The most pronounced thermal contrasts between irrigation regimes were observed during the milk-dough and dough stages, particularly in the mid-afternoon when vapor pressure deficit (VPD) reached its peak. These insights support the integration of diurnal thermal phenotyping into breeding pipelines as a means to enhance the identification of drought-adaptive traits in cereal crops.
To fully evaluate jujube germplasm, we sampled sour jujubes both from Las Cruces, NM and western Texas to examine their fruit and seed metabolomic profiles to facilitate further employment of those jujube germplasm trees. Samples were taken from the NMSU campus and Tornillo/Fabens, TX which had both the wild type and middle types (cross between wild ones and cultivars). Jujube germplasm fruit metabolomic profile reveals that jujube cultivar samples were similar to germplasm samples from Texas. Sour jujube samples in NM were separated from sour jujube from TX. Sour jujube in TX were mingled together with Cross in TX. So-called Cross and sour jujube were arbitrary classifications. Without cultivars, germplasm was separated by location NM vs TX, not by sour jujube or Cross. For significant compounds, there were only 110 significant different compounds between TX sour jujube vs Cross, while Cross vs NM sour jujube, TX sour jujube vs NM sour jujube or TX vs NM, had over 700. TXS and Cross group overlaid and NM group was totally separated from the other two groups. TX samples had significantly higher contents of large numbers of amino acids and derivatives. More compounds were identified from seed samples and their grouping/PCA results were similar to fruit metabolomic results. Cross samples were mixed together with TX sour jujubes and NM sour jujubes were separated from TX samples. New Mexico samples in Las Crues near graduate student housing area were planted at similar time which could be from one nursery, closed related to each other. Texas germplasm was the result of human selection, not the original sour jujubes but cross between sour jujubes or sour jujube and cultivar-like germplasm. The dominant triterpenes were different between fruit and seeds. In fruit flesh, pomolic acid was the dominant one with Honeyjar as the highest, followed by rutundic acid, Cleanothic acid, 2,3,23-Trihydroxyurs-12-en-28-oic acid, 2,3,23-Trihydroxyolean-12-en-28-oic acid, madasiatic acid, which were higher for NM samples than TX samples. In seeds, the dominant triterpenes were oleanolic acid, mangiferotic acid, momordicoside I aglycone, 3,13,15-trihydroxyolenonane-12-one, jujubogenin, and pomolic acid. The contents of the first three metabolites were equivalent and much higher than the rest, ranging from 0.5X108 to 1.5x108 depending on germplasm. Pomolic acid was much lower in seeds than in fruit. The data contained over 1600 metabolites in fruit and over 2000 for seeds which would be good references for future utilization of those jujube germplasm for horticultural or pharmaceutical purposes.
The accumulated genetic, genomic, and breeding data for Prunus species is often underutilized in breeding applications. This study examines 25 years of curated Prunus data in the Genome Database for Rosaceae (GDR, rosaceae.org) to uncover the genetic architecture of key traits, and provide actionable insights for Prunus breeding. The curated dataset includes 177 genetic maps, primarily for almond, apricot, peach, and sweet cherry, and 28,971 trait-associated loci. Most of the trait associations (72.4%) were from genome-wide association studies (GWAS), 18.7% from quantitative trait loci (QTL), and 8.9% from Mendelian trait loci. We identified 17 potential QTL hotspots for fruit morphology, fruit quality, and disease resistance, as well as 17 syntenic regions among peach, sweet cherry, and almond. These findings provide valuable resources for tool development for Prunus breeding, particularly for complex polyploid genomes and less-studied species.
Pears (genus Pyrus) are one of the most widely cultivated temperate fruit. Both abiotic and biotic stress, however, can be harsh constraints on pear cultivation; in America pear production has nearly ceased in the Eastern half of the nation, and in Europe extreme weather has become a growing threat to production, especially in Southern growing regions. Currently, high-quality reference genomes exist for the most widely cultivated Pyrus species, but little genomic information is available on ornamental, less cultivated, and wild Pyrus species. These species inhabit a wide range of climates across Eurasia, exhibiting diverse physiological adaptations to disease, high temperature, and water stress, while also showing variation in fruiting physiology and tree architecture. Discovery of genomic features responsible for this wide functional diversity could be applied to accelerating the genetic improvement of commercially cultivated Pyrus species. In order to characterize the genetic diversity within Pyrus, Nanopore whole genome DNA sequencing has been completed on 24 Pyrus accessions collected from the National Clonal Germplasm Repository, enabling highly contiguous (median N50 ~30Mb) and complete (median ~99% BUSCO assessed completeness), telomere-to-telomere assemblies with Hifiasm. Ab initio gene prediction via the BRAKER pipeline followed by comparative analysis with OrthoFinder has been used to find biome specific genes, while synteny analysis via MCScanX allows for the exploration of structural alterations in the evolution of Pyrus. These newly characterized Pyrus accessions represent an expansion of genomic resources to aid in the development of more resilient pears for the future.
Peach (Prunus persica (L.) Batsch) is a member of the genus Prunus within the Rosaceae family and represents one of the most extensively cultivated temperate deciduous fruit crops, ranking after apples and pears in global production. Due to its diploid genome (2n = 16) and relatively small genome size (~230 Mb), peach serves as a model species for fruit tree genome research. In this study, we performed whole-genome sequencing (WGS) on 445 peach genetic resources using the Illumina NovaSeq 6000 platform at a sequencing depth of 15´ coverage. Single nucleotide polymorphisms (SNPs) were identified from the WGS data and used to establish a core collection of peach genetic resources. Additionally, these SNPs will be utilized in a genome-wide association study (GWAS) to investigate key agronomic traits, including fruit shape, pollen fertility, flower morphology, maturity timing and so on. SNP filtration was conducted based on the following criteria: (1) SNPs with a missing rate exceeding 30% were removed, and (2) SNPs with a minor allele frequency (MAF) below 0.05 were excluded. As a result, 944,670 high-confidence SNPs were identified across the peach genetic resources. Based on this dataset, we established a core collection consisting of 150 accessions that retained over 99% of the total genetic diversity observed within the 445 peach genetic resources. Furthermore, we developed a high-resolution melting (HRM) marker derived from WGS-identified SNPs, which enables differentiation between round and flat peach fruit shapes. The SNP regions that can distinguish the fruit shape (round and flat shape) identified in this study were confirmed to be the same regions as the results of previously reported papers. Collectively, we successfully constructed a peach core collection through WGS analysis and developed a HRM marker for fruit shape classification. Also, our results produced in this study should be valuable for peach breeding program, identifying of agriculturally important genes, GWAS analyses, and further genomic studies in peach.
Although peach production worldwide has been increasing for decades, peach production in the United States continues to decline in the face of changing climate, disease pressures, and reduced consumption. Novel and diverse germplasm is required to improve peach breeding efforts with the goal of developing new cultivars better adapted to these challenges. Unfortunately, current peach SNP genotyping platforms are expensive and need to be outsourced to specialized laboratories. The purpose of this project is to use SNPs generated using Capture-Seq technology to evaluate the diversity of potential new sources of breeding material in comparison with germplasm from different regions of the world. In addition, our goal is to create a panel of SNP-based markers that can be used in-house for future studies. Capture-Seq technology yielded 134,424 SNPs when comparing P. persica (221 genotypes) and related Prunus species (29 genotypes). A PCA from these SNPs yielded different clusters representing Asian, Australian, European, and North American germplasm. AMOVA indicated that, among P. persica samples, 21.3% of the genetic variation was between regions with 78.7% of the variation present within regions. STRUCTURE analysis showed differences between regional groups, where the Asian group composition was different to the other regions, North American and European group composition were similar to each other, and the Australian group composition had a large percentage of genotypes sharing a group mostly present in Asia. This study confirms that Australia’s peach populations could be a valuable source of novel germplasm to bolster worldwide peach breeding efforts. Furthermore, a panel of informative SNP markers can be converted into KASP markers, which can be used in-house for numerous applications, including genetic fingerprinting, MAS, GWAS, among others.
‘Hamlin 1-4-1’ sweet orange (Citrus sinensis L. Osbeck) is one of the major varieties cultivated in Florida and is of relevant importance for the orange juice industry as an early maturing variety. While this cultivar does not produce juice of sufficient quality to meet USDA Grade A orange juice standards, it performs relatively well in semitropical climates characterized by high temperatures and humidity levels. To provide the bioinformatics tools required to support the genetic improvement of modern citrus varieties, we present the de novo and fully phased ‘Hamlin’ genome. The DNA of the plant was sequenced using two different platforms. PacBio technology was adopted to generate long reads sequencing, while Oxford Nanopore was employed to produce ultra-long reads. Hi-C technique was used to capture chromosome conformation and facilitate the correct assembly of contigs into two haplotypes. RNA samples were collected from five different tissues (leaves, petals, ovaries, peel, and bark) and sequenced with the Illumina platform. These RNA sequences enabled the identification and annotation of as many functional genes as possible. The results of this study will provide the genomic information required to compare the ‘Hamlin 1-4-1 genome with the more commonly grown industry standard ‘Valencia’ and to investigate the differences between the genomes of these two clonally derived sweet oranges. These data will also aid in comparing budlines of Hamlin and other sweet orange accessions that appear to be HLB tolerant. This research will facilitate the detection of DNA variants related to traits of interest and their integration in new germplasm resources. In addition, it will allow breeders to get further insights into mutations that may have occurred to new budlines originating from ‘Hamlin’.
Aneuploidy refers to a condition in which a cell or organism that has an abnormal chromosome number compared to the base chromosome number. This can cause gene dosage imbalances and a potential decrease in fitness. Most potato (Solanum tuberosum) cultivars are tetraploid (2n=4x=48) with a base chromosome number of 12. In this study, we analyzed 1,014 potato genotypes, 422 from two autotetraploid bi-parental full-sib populations and 592 from a diversity panel. We used allele SNP fluorescent intensity data for each individual to determine their ploidy and identify aneuploid individuals using the R package Qploidy. This package estimates the copy number by evaluating the standardized B allele frequencies (BAF) distributions across a sample, chromosome, or chromosome arm. Within the mapping populations, 41% of the members were aneuploids, compared to only 17% of the in the diversity panel; with an average of 27% aneuploidy level across all individuals included in the study. However, the frequency of aneuploidy for any given chromosome was 3%. As a measure of fitness, we compared 19 phenotypic traits related to tuber yield and quality in one of the full-sib mapping populations. There were significant differences between aneuploid and euploid family members for six traits. Aneuploid genotypes had significantly lower total tuber weight/plant, marketable tuber weight/plant, non-marketable tuber number/plant, tuber density, and overall appearance, while having higher percentage of tubers with heat sprouts compared to euploid family members. Chromosome additions were more common than chromosome losses in aneuploid individuals accounting for 57% and 39% of the aneuploid chromosomes, respectively. By analyzing this large potato genotypic dataset (most autotetraploids), we gained a better understanding of patterns of aneuploidy and their impact on crop performance in polyploid crops.
