Comparison of Emulsion Stabilizers: Application for the Enhancement of the Bioactivity of Lemongrass Essential Oil

Recent focus on cellulose nanomaterials, particularly biodegradable and biocompatible cellulose nanocrystals (CNCs), has prompted their use as emulsion stabilizers. CNCs, when combined with salt, demonstrate enhanced emulsion stabilization. This study explored three emulsion stabilizers: Tween 80, soybean CNCs with salt (salted CNCs), and a combination of salted CNCs with Tween 80. Soybean CNCs, derived from soybean stover, were characterized by Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). Antifungal testing against Aspergillus flavus revealed increased bioactivity in all lemongrass essential oil (EO)-loaded emulsions compared to pure essential oil. In addition, all three emulsions exhibited a slight reduction in antifungal activity after 30 days of room temperature storage. The release experiment revealed that the EO-loaded nanoemulsion exhibited a slow-release profile. The nanoemulsion stabilized by salted CNCs and Tween 80 exhibited significantly lower release rates when compared to the nanoemulsion stabilized solely by Tween 80, attributed to the gel network formed by salted CNCs. The findings of this study highlight the efficacy of cellulose nanocrystals procured from soybean byproducts in conjunction with synthetic surfactants to create nanoencapsulated essential oils, resulting in improved antimicrobial efficacy and the achievement of sustained release properties.

Synergistic Stabilization of Nanoemulsion Using Nonionic Surfactants and Salt-Sensitive Cellulose Nanocrystals

Soybean stover is a lignocellulose biomass that is rich in cellulose. In the present study, soybean cellulose nanocrystals (CNCs) were prepared from soybean stover by alkaline treatment, bleaching treatment, acid hydrolysis, dialysis and ultrasonication. The as-prepared soybean CNC was characterized by transmission electron microscopy (TEM), zetasizer and rheometer. The effects of NaCl on the particle size, zeta potential, and viscosity of soybean CNC was studied. Soybean CNC was explored as an emulsion stabilizer for lemongrass-essential-oil-loaded emulsions. Soybean CNCs could stabilize the oil-in-water emulsion against coalescence but not flocculation. The addition of NaCl reduced the creaming index and enhanced the encapsulation efficiency and freeze-thaw stability of the CNC-stabilized emulsion. Salted CNC (i.e., CNC in the presence of NaCl) enhanced the thermodynamic stability (i.e., heating-cooling and freeze-thaw stability) of Tween 80 stabilized emulsion, while unsalted CNC did not. Synergistic effects existed between Tween 80 and salted CNC in stabilizing oil-in-water emulsions. The nanoemulsion stabilized with Tween 80 and salted CNC had a mean particle size of ~70 nm, and it was stable against all thermodynamic stability tests. This is the first study to report the synergistic interaction between salted CNC and small molecular weight surfactants (e.g., Tween 80) to improve the thermodynamic stability of nanoemulsion.

Comparison of sampling methods for small oxbow wetland fish communities

Throughout the world, wetlands have experienced degradation and declines in areal coverage. Fortunately, recognition of the value of wetlands has generated interest in preserving and restoring them. Post-restoration monitoring is necessary to analyze success or failure, thereby informing subsequent management decisions. Restoration of oxbow wetlands has become the focus of targeted restoration efforts to promote recovery of biodiversity and sensitive species, and to enhance ecosystem services. The fish communities of oxbows have been the subject of many monitoring studies. However, a recommended sampling methodology for monitoring the fish communities of oxbows has not been described, thereby limiting our capacity to effectively monitor these ecosystems. We compared four sampling methodologies (backpack electrofishing, fyke netting, minnow trapping, and seining) for fish community data collection with a primary objective of determining an effective method for sampling fish communities in small oxbow wetlands. Seining and fyke netting were determined to be effective methods for sampling oxbow fish communities. Backpack electrofishing and minnow trapping produced low taxonomic richness values and sampled a smaller proportion of species present than seining and fyke netting. Although seining and fyke netting produced similar taxonomic diversity and abundance values, these two gears differ in their ease of implementation and potential habitat disturbance generated by sampling. Therefore, consideration must be given to how species present (especially sensitive species) within the wetland could be impacted by sampling disturbance when choosing between seining and fyke netting.

A Phosphoproteomics Study of the Soybean root necrosis 1 Mutant Revealed Type II Metacaspases Involved in Cell Death Pathway

The soybean root necrosis 1 (rn1) mutation causes progressive browning of the roots soon after germination and provides increased tolerance to the soil-borne oomycete pathogen Phytophthora sojae in soybean. Toward understanding the molecular basis of the rn1 mutant phenotypes, we conducted tandem mass tag (TMT)-labeling proteomics and phosphoproteomics analyses of the root tissues of the rn1 mutant and progenitor T322 line to identify potential proteins involved in manifestation of the mutant phenotype. We identified 3,160 proteins. When the p-value was set at ≤0.05 and the fold change of protein accumulation between rn1 and T322 at ≥1.5 or ≤0.67, we detected 118 proteins that showed increased levels and 32 proteins decreased levels in rn1 as compared to that in T322. The differentially accumulated proteins (DAPs) are involved in several pathways including cellular processes for processing environmental and genetic information, metabolism and organismal systems. Five pathogenesis-related proteins were accumulated to higher levels in the mutant as compared to that in T322. Several of the DAPs are involved in hormone signaling, redox reaction, signal transduction, and cell wall modification processes activated in plant-pathogen interactions. The phosphoproteomics analysis identified 22 phosphopeptides, the levels of phosphorylation of which were significantly different between rn1 and T322 lines. The phosphorylation levels of two type II metacaspases were reduced in rn1 as compared to T322. Type II metacaspase has been shown to be a negative regulator of hypersensitive cell death. In absence of the functional Rn1 protein, two type II metacaspases exhibited reduced phosphorylation levels and failed to show negative regulatory cell death function in the soybean rn1 mutant. We hypothesize that Rn1 directly or indirectly phosphorylates type II metacaspases to negatively regulate the cell death process in soybean roots.

Evidence of enhanced reproductive performance and lack-of-fitness costs among soybean aphids, Aphis glycines, with varying levels of pyrethroid resistance

BACKGROUND

Foliar application of insecticides is the main strategy to manage soybean aphid, Aphis glycines (Hemiptera: Aphididae), in the northcentral United States. Subpopulations of A. glycines have multiple nonsynonymous mutations in the voltage-gated sodium channel (vgsc) genes that are associated with pyrethroid resistance. We explored if fitness costs are associated with phenotypes conferred by vgsc mutations using life table analyses. We predicted that there would be significant differences between pyrethroid susceptibility and field-collected, parthenogenetic isofemale clones with differing, nonsynonymous mutations in vgsc genes.

RESULTS

Estimated resistance ratios for the pyrethroid-resistant clones ranged from 3.1 to 37.58 and 5.6 to 53.91 for lambda-cyhalothrin and bifenthrin, respectively. Although life table analyses revealed some biological and demographic parameters to be significantly different among the clonal lines, there was no association between levels of pyrethroid resistance and a decline in fitness. By contrast, one of the most resistant clonal lines (SBA-MN1-2017) had a significantly higher finite rate of increase, intrinsic rate of increase and greater overall fitness compared to the susceptible control and other pyrethroid-resistant clonal lines.

CONCLUSIONS

Our life history analysis suggests that there are no negative pleotropic effects associated with the pyrethroid resistance in the clonal A. glycines lines used in this study. We discuss the potential impact of these results on efficacies of insecticide resistance management (IRM) and integrated pest management (IPM) plans directed at delaying the spread of pyrethroid-resistant A. glycines.

Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020-2080) increased N mineralization (24%) and N₂O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems' performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems.

Interaction between Rag genes results in a unique synergistic transcriptional response that enhances soybean resistance to soybean aphids

BACKGROUND

Pyramiding different resistance genes into one plant genotype confers enhanced resistance at the phenotypic level, but the molecular mechanisms underlying this effect are not well-understood. In soybean, aphid resistance is conferred by Rag genes. We compared the transcriptional response of four soybean genotypes to aphid feeding to assess how the combination of Rag genes enhanced the soybean resistance to aphid infestation.

RESULTS

A strong synergistic interaction between Rag1 and Rag2, defined as genes differentially expressed only in the pyramid genotype, was identified. This synergistic effect in the Rag1/2 phenotype was very evident early (6 h after infestation) and involved unique biological processes. However, the response of susceptible and resistant genotypes had a large overlap 12 h after aphid infestation. Transcription factor (TF) analyses identified a network of interacting TF that potentially integrates signaling from Rag1 and Rag2 to produce the unique Rag1/2 response. Pyramiding resulted in rapid induction of phytochemicals production and deposition of lignin to strengthen the secondary cell wall, while repressing photosynthesis. We also identified Glyma.07G063700 as a novel, strong candidate for the Rag1 gene.

CONCLUSIONS

The synergistic interaction between Rag1 and Rag2 in the Rag1/2 genotype can explain its enhanced resistance phenotype. Understanding molecular mechanisms that support enhanced resistance in pyramid genotypes could facilitate more directed approaches for crop improvement.

Tightly linked Rps12 and Rps13 genes provide broad-spectrum Phytophthora resistance in soybean

The Phytophtora root and stem rot is a serious disease in soybean. It is caused by the oomycete pathogen Phytophthora sojae. Growing Phytophthora resistant cultivars is the major method of controlling this disease. Resistance is race- or gene-specific; a single gene confers immunity against only a subset of the P. sojae isolates. Unfortunately, rapid evolution of new Phytophthora sojae virulent pathotypes limits the effectiveness of an Rps ("resistance to Phytophthora sojae") gene to 8-15 years. The current study was designed to investigate the effectiveness of Rps12 against a set of P. sojae isolates using recombinant inbred lines (RILs) that contain recombination break points in the Rps12 region. Our study revealed a unique Rps gene linked to the Rps12 locus. We named this novel gene as Rps13 that confers resistance against P. sojae isolate V13, which is virulent to recombinants that contains Rps12 but lack Rps13. The genetic distance between the two Rps genes is 4 cM. Our study revealed that two tightly linked functional Rps genes with distinct race-specificity provide broad-spectrum resistance in soybean. We report here the molecular markers for incorporating the broad-spectrum Phytophthora resistance conferred by the two Rps genes in commercial soybean cultivars.

Crop yield prediction integrating genotype and weather variables using deep learning

Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)-Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.

GmNF-YC4-2 Increases Protein, Exhibits Broad Disease Resistance and Expedites Maturity in Soybean

The NF-Y gene family is a highly conserved set of transcription factors. The functional transcription factor complex is made up of a trimer between NF-YA, NF-YB, and NF-YC proteins. While mammals typically have one gene for each subunit, plants often have multigene families for each subunit which contributes to a wide variety of combinations and functions. Soybean plants with an overexpression of a particular NF-YC isoform GmNF-YC4-2 (Glyma.04g196200) in soybean cultivar Williams 82, had a lower amount of starch in its leaves, a higher amount of protein in its seeds, and increased broad disease resistance for bacterial, viral, and fungal infections in the field, similar to the effects of overexpression of its isoform GmNF-YC4-1 (Glyma.06g169600). Interestingly, GmNF-YC4-2-OE (overexpression) plants also filled pods and senesced earlier, a novel trait not found in GmNF-YC4-1-OE plants. No yield difference was observed in GmNF-YC4-2-OE compared with the wild-type control. Sequence alignment of GmNF-YC4-2, GmNF-YC4-1 and AtNF-YC1 indicated that faster maturation may be a result of minor sequence differences in the terminal ends of the protein compared to the closely related isoforms.

Interactive Web-based Data Visualization and Analysis Tool for Synthetizing on-farm Research Networks Data

The on-farm research network concept enables a group of farmers to test new agricultural management practices under local conditions with support from local researchers or agronomists. Different on-farm trials based on the same experimental design are conducted over several years and sites to test the effectiveness of different innovative management practices aimed at increasing crop productivity and profitability. As a larger amount of historical trial data are being accumulated, data of all the trials require analyses and summarization. Summaries of on-farm trials are usually presented to farmers as individual field reports, which are not optimal for the dissemination of results and decision making. A more practical communication method is needed to enhance result communication and decision making. R Shiny is a new rapidly developing technology for turning R data analyses into interactive web applications. For the first time for on-farm research networks, we developed and launched an interactive web tool called ISOFAST using R Shiny. ISOFAST simultaneously reports all trial results about the same management practice to simplify interpretation of multi-site and multi-year summaries. We used a random-effects model to synthetize treatment differences at both the individual trial and network levels and generate new knowledge for farmers and agronomists. The friendly interface enables users to explore trial summaries, access model outputs, and perform economic analysis at their fingertips. This paper describes a case-study to illustrate how to use the tool and make agronomic management decisions based on the on-farm trial data. We also provided technical details and guidance for developing a similar interactive visualization tool customized for on-farm research network. ISOFAST is currently available at https://analytics.iasoybeans.com/cool-apps/ISOFAST/.

Soybean aphid (Hemiptera: Aphididae) response to lambda-cyhalothrin varies with its virulence status to aphid-resistant soybean

BACKGROUND

Soybean aphid, Aphis glycines, is an invasive insect in North America, considered one of the most important pests of soybean. Their management relies heavily on foliar insecticides, but there is growing effort to expand these tools to include aphid-resistant varieties. We explored if the LC50 and LC25 of lambda-cyhalothrin varied between virulent (resistant to Aphis glycines (Rag) soybeans) and avirulent (susceptible to Rag-genes soybeans) populations of soybean aphid with a leaf-dip bioassay. We also investigated the response to the LC25 of lambda-cyhalothrin on adults (F0) and their progeny (F1) for both avirulent and virulent soybean aphid.

RESULTS

The LC50 of the virulent aphid population was significantly higher compared with the LC50 of the avirulent population. The LC25 significantly reduced fecundity of the F0 generation of avirulent soybean aphid, but no significant effect was observed for virulent aphids. In addition, the LC25 significantly shortened the adult pre-oviposition period (APOP) and lengthened the total pre-oviposition period (TPOP) of avirulent aphids, while the mean generation time (T) was significantly increased. For the virulent aphid, sublethal exposure significantly lengthened development time of first and third instars, TPOP, and adult longevity. In addition, all demographic parameters of virulent soybean aphid were significantly affected when they were exposed to the LC25 of lambda-cyhalothrin.

CONCLUSION

Our results demonstrate lambda-cyhalothrin is less toxic to virulent aphids and exposure to the LC25 can trigger hormesis, which may have implications for the long-term management of this pest with this insecticide as well as with aphid-resistant varieties of soybean. © 2019 Society of Chemical Industry.

Deconstructing the genetic architecture of iron deficiency chlorosis in soybean using genome-wide approaches

BACKGROUND

Iron (Fe) is an essential micronutrient for plant growth and development. Iron deficiency chlorosis (IDC), caused by calcareous soils or high soil pH, can limit iron availability, negatively affecting soybean (Glycine max) yield. This study leverages genome-wide association study (GWAS) and a genome-wide epistatic study (GWES) with previous gene expression studies to identify regions of the soybean genome important in iron deficiency tolerance.

RESULTS

A GWAS and a GWES were performed using 460 diverse soybean PI lines from 27 countries, in field and hydroponic iron stress conditions, using more than 36,000 single nucleotide polymorphism (SNP) markers. Combining this approach with available RNA-sequencing data identified significant markers, genomic regions, and novel genes associated with or responding to iron deficiency. Sixty-nine genomic regions associated with IDC tolerance were identified across 19 chromosomes via the GWAS, including the major-effect quantitative trait locus (QTL) on chromosome Gm03. Cluster analysis of significant SNPs in this region deconstructed this historically prominent QTL into four distinct linkage blocks, enabling the identification of multiple candidate genes for iron chlorosis tolerance. The complementary GWES identified SNPs in this region interacting with nine other genomic regions, providing the first evidence of epistatic interactions impacting iron deficiency tolerance.

CONCLUSIONS

This study demonstrates that integrating cutting edge genome wide association (GWA), genome wide epistasis (GWE), and gene expression studies is a powerful strategy to identify novel iron tolerance QTL and candidate loci from diverse germplasm. Crops, unlike model species, have undergone selection for thousands of years, constraining and/or enhancing stress responses. Leveraging genomics-enabled approaches to study these adaptations is essential for future crop improvement.

Computer vision and machine learning enabled soybean root phenotyping pipeline

BACKGROUND

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis.

RESULTS

This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle.

CONCLUSIONS

This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

Efficacy of afidopyropen against soybean aphid (Hemiptera: Aphididae) and toxicity to natural enemies

BACKGROUND

Soybean aphid, Aphis glycines (Hemiptera: Aphididae), remains the most significant soybean insect pest in the North Central Region of the USA. The sustainability of reliance on only a few insecticide groups for this pest is questionable. We evaluate afidopyropen, a novel pyropene insecticide (Group 9D), for efficacy against A. glycines in field and greenhouse experiments and toxicity to common natural enemies in laboratory experiments.

RESULTS

Across 4 site-years of field experiments and a greenhouse experiment, afidopyropen reduced A. glycines populations similar to commonly used broad-spectrum [i.e. lambda-cyhalothrin (Group 3A) and chlorpyrifos (Group 1B)] insecticides and potential selective insecticides [i.e. sulfoxaflor (Group 4C) and flupyradifurone (Group 4D)]. In the greenhouse, however, A. glycines mortality was delayed slightly for afidopyropen compared to the other insecticides. In laboratory experiments with natural enemies of A. glycines, afidopyropen was not toxic to adult or third instar Hippodamia convergens (Coleoptera: Coccinellidae) or adult Orius insidiosus (Hemiptera: Anthocoridae), and was only moderately toxic to Aphelinus certus (Hymenoptera: Aphelinidae).

CONCLUSION

Afidopyropen is effective against A. glycines and relatively non-toxic to natural enemies, and appears to be an effective option for integrated pest management and insecticide resistance management programs for A. glycines. © 2019 Society of Chemical Industry.

Plant disease identification using explainable 3D deep learning on hyperspectral images

BACKGROUND

Hyperspectral imaging is emerging as a promising approach for plant disease identification. The large and possibly redundant information contained in hyperspectral data cubes makes deep learning based identification of plant diseases a natural fit. Here, we deploy a novel 3D deep convolutional neural network (DCNN) that directly assimilates the hyperspectral data. Furthermore, we interrogate the learnt model to produce physiologically meaningful explanations. We focus on an economically important disease, charcoal rot, which is a soil borne fungal disease that affects the yield of soybean crops worldwide.

RESULTS

Based on hyperspectral imaging of inoculated and mock-inoculated stem images, our 3D DCNN has a classification accuracy of 95.73% and an infected class F1 score of 0.87. Using the concept of a saliency map, we visualize the most sensitive pixel locations, and show that the spatial regions with visible disease symptoms are overwhelmingly chosen by the model for classification. We also find that the most sensitive wavelengths used by the model for classification are in the near infrared region (NIR), which is also the commonly used spectral range for determining the vegetative health of a plant.

CONCLUSION

The use of an explainable deep learning model not only provides high accuracy, but also provides physiological insight into model predictions, thus generating confidence in model predictions. These explained predictions lend themselves for eventual use in precision agriculture and research application using automated phenotyping platforms.

Induction of ethylene inhibits development of soybean sudden death syndrome by inducing defense-related genes and reducing Fusarium virguliforme growth

Ethylene is a gaseous hormone that regulates plant responses to biotic and abiotic stresses. To investigate the importance of ethylene in soybean resistance to Fusarium virguliforme (Fv), the causal agent of sudden death syndrome (SDS), soybean cultivars Williams 82 (SDS-susceptible) and MN1606 (SDS-resistant) were treated 24 h before and 24h after Fv inoculation with either ethephon (ethylene inducer), cobalt chloride (ethylene biosynthesis inhibitor), or 1-MCP (ethylene perception inhibitor). Inoculated plants were grown for 21 days at 24°C in the greenhouse and then evaluated for SDS severity and expression of soybean defense genes. In both cultivars, plants treated with ethephon showed lower SDS foliar severity compared to the other treatments, whereas those treated with cobalt chloride or 1-MCP showed the same or higher SDS foliar severity compared to the water-treated control. Ethephon application resulted in activation of genes involved in ethylene biosynthesis, such as ethylene synthase (ACS) and ethylene oxidase (ACO), and genes involved in soybean defense response, such as pathogenesis-related protein (PR), basic peroxidase (IPER), chalcone synthase (CHS), and defense-associated transcription factors. Cobalt chloride and 1-MCP treatments had little or no effect on the expression of these genes. In addition, ethephon had a direct inhibitory effect on in-vitro growth of Fv on PDA media. Our results suggest that ethephon application inhibits SDS development directly by slowing Fv growth and/or by inducing soybean ethylene signaling and the expression of defense related genes.

QQS orphan gene and its interactor NF-YC4 reduce susceptibility to pathogens and pests

Enhancing the nutritional quality and disease resistance of crops without sacrificing productivity is a key issue for developing varieties that are valuable to farmers and for simultaneously improving food security and sustainability. Expression of the Arabidopsis thaliana species-specific AtQQS (Qua-Quine Starch) orphan gene or its interactor, NF-YC4 (Nuclear Factor Y, subunit C4), has been shown to increase levels of leaf/seed protein without affecting the growth and yield of agronomic species. Here, we demonstrate that overexpression of AtQQS and NF-YC4 in Arabidopsis and soybean enhances resistance/reduces susceptibility to viruses, bacteria, fungi, aphids and soybean cyst nematodes. A series of Arabidopsis mutants in starch metabolism were used to explore the relationships between QQS expression, carbon and nitrogen partitioning, and defense. The enhanced basal defenses mediated by QQS were independent of changes in protein/carbohydrate composition of the plants. We demonstrate that either AtQQS or NF-YC4 overexpression in Arabidopsis and in soybean reduces susceptibility of these plants to pathogens/pests. Transgenic soybean lines overexpressing NF-YC4 produce seeds with increased protein while maintaining healthy growth. Pull-down studies reveal that QQS interacts with human NF-YC, as well as with Arabidopsis NF-YC4, and indicate two QQS binding sites near the NF-YC-histone-binding domain. A new model for QQS interaction with NF-YC is speculated. Our findings illustrate the potential of QQS and NF-YC4 to increase protein and improve defensive traits in crops, overcoming the normal growth-defense trade-offs.

Leveraging genomic prediction to scan germplasm collection for crop improvement

The objective of this study was to explore the potential of genomic prediction (GP) for soybean resistance against Sclerotinia sclerotiorum (Lib.) de Bary, the causal agent of white mold (WM). A diverse panel of 465 soybean plant introduction accessions was phenotyped for WM resistance in replicated field and greenhouse tests. All plant accessions were previously genotyped using the SoySNP50K BeadChip. The predictive ability of six GP models were compared, and the impact of marker density and training population size on the predictive ability was investigated. Cross-prediction among environments was tested to determine the effectiveness of the prediction models. GP models had similar prediction accuracies for all experiments. Predictive ability did not improve significantly by using more than 5k SNPs, or by increasing the training population size (from 50% to 90% of the total of individuals). The GP model effectively predicted WM resistance across field and greenhouse experiments when each was used as either the training or validation population. The GP model was able to identify WM-resistant accessions in the USDA soybean germplasm collection that had previously been reported and were not included in the study panel. This study demonstrated the applicability of GP to identify useful genetic sources of WM resistance for soybean breeding. Further research will confirm the applicability of the proposed approach to other complex disease resistance traits and in other crops.

A real-time phenotyping framework using machine learning for plant stress severity rating in soybean

BACKGROUND

Phenotyping is a critical component of plant research. Accurate and precise trait collection, when integrated with genetic tools, can greatly accelerate the rate of genetic gain in crop improvement. However, efficient and automatic phenotyping of traits across large populations is a challenge; which is further exacerbated by the necessity of sampling multiple environments and growing replicated trials. A promising approach is to leverage current advances in imaging technology, data analytics and machine learning to enable automated and fast phenotyping and subsequent decision support. In this context, the workflow for phenotyping (image capture → data storage and curation → trait extraction → machine learning/classification → models/apps for decision support) has to be carefully designed and efficiently executed to minimize resource usage and maximize utility. We illustrate such an end-to-end phenotyping workflow for the case of plant stress severity phenotyping in soybean, with a specific focus on the rapid and automatic assessment of iron deficiency chlorosis (IDC) severity on thousands of field plots. We showcase this analytics framework by extracting IDC features from a set of ~4500 unique canopies representing a diverse germplasm base that have different levels of IDC, and subsequently training a variety of classification models to predict plant stress severity. The best classifier is then deployed as a smartphone app for rapid and real time severity rating in the field.

RESULTS

We investigated 10 different classification approaches, with the best classifier being a hierarchical classifier with a mean per-class accuracy of ~96%. We construct a phenotypically meaningful 'population canopy graph', connecting the automatically extracted canopy trait features with plant stress severity rating. We incorporated this image capture → image processing → classification workflow into a smartphone app that enables automated real-time evaluation of IDC scores using digital images of the canopy.

CONCLUSION

We expect this high-throughput framework to help increase the rate of genetic gain by providing a robust extendable framework for other abiotic and biotic stresses. We further envision this workflow embedded onto a high throughput phenotyping ground vehicle and unmanned aerial system that will allow real-time, automated stress trait detection and quantification for plant research, breeding and stress scouting applications.

Validation of a hairy roots system to study soybean-soybean aphid interactions

The soybean aphid (Aphis glycines) is one of the main insect pests of soybean (Glycine max) worldwide. Genomics approaches have provided important data on transcriptome changes, both in the insect and in the plant, in response to the plant-aphid interaction. However, the difficulties to transform soybean and to rear soybean aphid on artificial media have hindered our ability to systematically test the function of genes identified by those analyses as mediators of plant resistance to the insect. An efficient approach to produce transgenic soybean material is the production of transformed hairy roots using Agrobacterium rhizogenes; however, soybean aphids colonize leaves or stems and thus this approach has not been utilized. Here, we developed a hairy root system that allowed effective aphid feeding. We show that this system supports aphid performance similar to that observed in leaves. The use of hairy roots to study plant resistance is validated by experiments showing that roots generated from cotyledons of resistant lines carrying the Rag1 or Rag2 resistance genes are also resistant to aphid feeding, while related susceptible lines are not. Our results demonstrate that hairy roots are a good system to study soybean aphid-soybean interactions, providing a quick and effective method that could be used for functional analysis of the resistance response to this insect.

Investigation of the Fusarium virguliforme Transcriptomes Induced during Infection of Soybean Roots Suggests that Enzymes with Hydrolytic Activities Could Play a Major Role in Root Necrosis

Sudden death syndrome (SDS) is caused by the fungal pathogen, Fusarium virguliforme, and is a major threat to soybean production in North America. There are two major components of this disease: (i) root necrosis and (ii) foliar SDS. Root symptoms consist of root necrosis with vascular discoloration. Foliar SDS is characterized by interveinal chlorosis and leaf necrosis, and in severe cases by flower and pod abscission. A major toxin involved in initiating foliar SDS has been identified. Nothing is known about how root necrosis develops. In order to unravel the mechanisms used by the pathogen to cause root necrosis, the transcriptome of the pathogen in infected soybean root tissues of a susceptible cultivar, 'Essex', was investigated. The transcriptomes of the germinating conidia and mycelia were also examined. Of the 14,845 predicted F. virguliforme genes, we observed that 12,017 (81%) were expressed in germinating conidia and 12,208 (82%) in mycelia and 10,626 (72%) in infected soybean roots. Of the 10,626 genes induced in infected roots, 224 were transcribed only following infection. Expression of several infection-induced genes encoding enzymes with oxidation-reduction properties suggests that degradation of antimicrobial compounds such as the phytoalexin, glyceollin, could be important in early stages of the root tissue infection. Enzymes with hydrolytic and catalytic activities could play an important role in establishing the necrotrophic phase. The expression of a large number of genes encoding enzymes with catalytic and hydrolytic activities during the late infection stages suggests that cell wall degradation could be involved in root necrosis and the establishment of the necrotrophic phase in this pathogen.

A Novel Phytophthora sojae Resistance Rps12 Gene Mapped to a Genomic Region That Contains Several Rps Genes

Phytophthora sojae Kaufmann and Gerdemann, which causes Phytophthora root rot, is a widespread pathogen that limits soybean production worldwide. Development of Phytophthora resistant cultivars carrying Phytophthora resistance Rps genes is a cost-effective approach in controlling this disease. For this mapping study of a novel Rps gene, 290 recombinant inbred lines (RILs) (F7 families) were developed by crossing the P. sojae resistant cultivar PI399036 with the P. sojae susceptible AR2 line, and were phenotyped for responses to a mixture of three P. sojae isolates that overcome most of the known Rps genes. Of these 290 RILs, 130 were homozygous resistant, 12 heterzygous and segregating for Phytophthora resistance, and 148 were recessive homozygous and susceptible. From this population, 59 RILs homozygous for Phytophthora sojae resistance and 61 susceptible to a mixture of P. sojae isolates R17 and Val12-11 or P7074 that overcome resistance encoded by known Rps genes mapped to Chromosome 18 were selected for mapping novel Rps gene. A single gene accounted for the 1:1 segregation of resistance and susceptibility among the RILs. The gene encoding the Phytophthora resistance mapped to a 5.8 cM interval between the SSR markers BARCSOYSSR_18_1840 and Sat_064 located in the lower arm of Chromosome 18. The gene is mapped 2.2 cM proximal to the NBSRps4/6-like sequence that was reported to co-segregate with the Phytophthora resistance genes Rps4 and Rps6. The gene is mapped to a highly recombinogenic, gene-rich genomic region carrying several nucleotide binding site-leucine rich repeat (NBS-LRR)-like genes. We named this novel gene as Rps12, which is expected to be an invaluable resource in breeding soybeans for Phytophthora resistance.

A Small Cysteine-Rich Protein from the Asian Soybean Rust Fungus, Phakopsora pachyrhizi, Suppresses Plant Immunity

The Asian soybean rust fungus, Phakopsora pachyrhizi, is an obligate biotrophic pathogen causing severe soybean disease epidemics. Molecular mechanisms by which P. pachyrhizi and other rust fungi interact with their host plants are poorly understood. The genomes of all rust fungi encode many small, secreted cysteine-rich proteins (SSCRP). While these proteins are thought to function within the host, their roles are completely unknown. Here, we present the characterization of P. pachyrhizi effector candidate 23 (PpEC23), a SSCRP that we show to suppress plant immunity. Furthermore, we show that PpEC23 interacts with soybean transcription factor GmSPL12l and that soybean plants in which GmSPL12l is silenced have constitutively active immunity, thereby identifying GmSPL12l as a negative regulator of soybean defenses. Collectively, our data present evidence for a virulence function of a rust SSCRP and suggest that PpEC23 is able to suppress soybean immune responses and physically interact with soybean transcription factor GmSPL12l, a negative immune regulator.

Identification of Highly Variable Supernumerary Chromosome Segments in an Asexual Pathogen

Supernumerary chromosome segments are known to harbor different transposons from their essential counterparts. The aim of this study was to investigate the role of transposons in the origin and evolution of supernumerary segments in the asexual fungal pathogen Fusarium virguliforme. We compared the genomes of 11 isolates comprising six Fusarium species that cause soybean sudden death syndrome (SDS) or bean root rot (BRR), and identified significant levels of genetic variation in A+T-rich repeat blocks of the essential chromosomes and in A+T-neutral regions of the supernumerary segments. The A+T-rich repeat blocks in the essential chromosomes were highly variable between F. virguliforme and non-F. virguliforme isolates, but were scarcely variable between F. virguliforme isolates. The A+T-neutral regions in the supernumerary segments, however, were highly variable between F. virguliforme isolates, with a statistically significant number (21 standard deviations above the mean) of single nucleotide polymorphisms (SNPs). And supernumerary sequence types and rearrangement patterns of some F. virguliforme isolates were present in an isolate of F. cuneirostrum but not in the other F. virguliforme isolates. The most variable and highly expressed region in the supernumerary segments contained an active DNA transposon that was a most conserved match between F. virguliforme and the unrelated fungus Tolypocladium inflatum. This transposon was absent from two of the F. virguliforme isolates. Furthermore, transposons in the supernumerary segments of some F. virguliforme isolates were present in non-F. virguliforme isolates, but were absent from the other F. virguliforme isolates. Two supernumerary P450 enzymes were 43% and 57% identical to their essential counterparts. This study has raised the possibility that transposons generate genetic variation in supernumerary chromosome segments by frequent horizontal transfer within and between closely related species.

Modification of Cry4Aa toward Improved Toxin Processing in the Gut of the Pea Aphid, Acyrthosiphon pisum

Aphids are sap-sucking insects (order: Hemiptera) that cause extensive damage to a wide range of agricultural crops. Our goal was to optimize a naturally occurring insecticidal crystalline (Cry) toxins produced by the soil-dwelling bacterium Bacillus thuringiensis for use against the pea aphid, Acyrthosiphon pisum. On the basis that activation of the Cry4Aa toxin is a rate-limiting factor contributing to the relatively low aphicidal activity of this toxin, we introduced cathepsin L and cathepsin B cleavage sites into Cry4Aa for rapid activation in the aphid gut environment. Incubation of modified Cry4Aa and aphid proteases in vitro demonstrated enhanced processing of the toxin into the active form for some of the modified constructs relative to non-modified Cry4Aa. Aphids fed artificial diet with toxin at a final concentration of 125 μg/ml showed enhanced mortality after two days for one of the four modified constructs. Although only modest toxin improvement was achieved by use of this strategy, such specific toxin modifications designed to overcome factors that limit aphid toxicity could be applied toward managing aphid populations via transgenic plant resistance.