Location: Soybean Genomics & Improvement Laboratory
2021 Annual Report
Objectives
Objective 1: Characterize biochemical processes in rust fungi and hosts during infection, determine relationships with currently used resistance genes, and work with breeders or pathologists to insert multiple resistance genes. [NP301, C1, PS1A; C3, PS3A]
Objective 2: Determine the role of root knot nematode secreted proteins in soybean growth alterations, such as the recently discovered MiIDL1 hormone mimic, to develop genetic resistance to the nematode. [NP301, C3, PS3A]
Objective 3: Assess proteins and metabolite profiles in soybean seeds, determine associations of metabolic pathways with nutritional traits, and identify germplasm or genes that breeders can use for genetic improvement of quality traits. [NP301, C2, PS2A]
Approach
For Objective 1, candidate rust fungus effector proteins identified in infected beans and soybeans will be characterized. A plant virus gene silencing system will be used to deliver fungal effector gene silencing RNAs from the plant to the fungus to block rust fungus infection. The fungal effector genes will be inserted into a plant virus for protein expression in plant leaves, and mass spectrometry will be used to identify plant proteins that interact with the fungal protein. Plants will be treated with plant hormones to induce disease resistance, and mass spectrometry will be used to identify plant proteins that contribute to disease resistance. Transgenic plants expressing proteins that may confer resistance to rust fungi will be screened by mass spectrometry and tested for resistance. For Objective 2, immunocytochemistry on thin root-gall sections will be performed to determine if an effector protein from a nematode pathogenic to soybean is secreted into the plant. The nematode effector gene will be expressed in plant roots, and mass spectrometry will be used to identify plant proteins that interact with the nematode protein. RNA sequencing and mass spectrometry will be used to identify differential transcript and protein accumulation in the galls formed on nematode infected roots. For Objective 3, a systems approach will be used to identify the protein and metabolic pathways that produce protein, oil, and carbohydrate seed traits in soybeans and to ensure that allergens and anti-nutritional proteins do not exceed normal levels. Comparative genomic hybridization will be used to map gene deletions associated with traits. Seeds with high protein content will be investigated by mass spectrometry for changes in the protein profiles with special attention being paid to assure the presence of low amounts of allergens or high methionine content. Seeds selected for oil, carbohydrates, and other (isoflavones, amino acids) traits will be investigated for changes in the metabolite profiles and to identify mutants with low anti-nutritional compounds/high isoflavone content.
Progress Report
Several hypotheses were evaluated as part of Objective 1. For the first hypothesis, we postulated that modulation of fungal effectors will lead to less pathogen accumulation and improved disease resistance. This hypothesis was based on prior reported research where we used mass spectrometry to identify effector proteins from Uromyces appendiculatus, the common bean rust fungus. Suspecting that these effector proteins are required by the fungus to infect plants, we thought the effectors may physically interact with bean plant proteins to disrupt the plant immune system. To test this, we fused the DNA of 13 bean rust effector proteins found by mass spectrometry to the green fluorescent protein (GFP) gene and inserted the fusion gene into the soybean mosaic virus expression vector. We infected beans with the synthetic virus and confirmed by symptomology, RT-PCR, and fluorescent microscopy that the plants were infected and produced RNA of the inserted fungal effector gene fused to GFP. Some virus constructs produced stronger symptoms than others, but most produced symptoms similar to wild-type viruses. We then used GFP antibody to extract GFP-fused effector proteins from infected plants and resolved them on a polymerized gel. In all cases, the GFP fusion proteins migrated to their expected positions based on size. Assuming that effector protein-plant protein interactions were strong, we suspected that bean proteins would co-purify with the fusion proteins and thus could be distinguished on the gel in relation to controls. Despite testing multiple protein extraction procedures employing a range of salt concentrations that either disrupt or allow protein-protein interactions, we could not reproduce any bean proteins alongside any of the 13 GFP-fused effectors at amounts that could confirm positive protein-protein interactions. It is possible that 1) the GFP protein disrupted the function of the effectors such that no plant protein interactions occurred; 2) that effector interactions with plant proteins are transient and not captured by this method; 3) the method is not sensitive enough to capture enough interactors to visualize on a gel; and 4) no plant protein interactions occurred. To address the above, we are working with a collaborator to test a technology that exploits biotinylation to assess transient interactions better and to use a direct mass spectrometry detection method to increase sensitivity over gel-based assays.
For the second hypothesis of Objective 1, we postulated that the expression of fungal growth inhibitors will lead to less pathogen accumulation and improved disease resistance. One fungal growth inhibitor, KP4, is made by a virus that infects the corn smut fungus. Scientists have previously expressed KP4 in transgenic wheat and maize to confer resistance to smut. Therefore, we hypothesized that KP4 could confer protection to soybean rust in transgenic plants. We made transgenic soybean plants harboring the KP4 gene constructs and the Basta resistance gene (for selection). To test whether the transgenic plants produced KP4, we used a targeted mass spectrometry method to screen more than 100 plants from 10 different transgenic lines and discovered that some lines accumulated more transgenic KP4 protein than others. We allowed the plants with the greatest amounts of KP4 to self-fertilize and selected plants from 1 St268-40 line, 2 St268-45 lines, and 1 St268-62 line with KP4 levels as great or greater than their parents. We then allowed each plant to self-fertilize to produce T3 seed and tested Basta-resistant T3 progeny for resistance to soybean rust at the ARS containment greenhouse at Ft. Detrick, Maryland. Unfortunately, none of the transgenic plants exhibited resistance compared to susceptible controls. Unlike some rusts that migrate across a leaf surface and colonize the leaf apoplast, soybean rust directly penetrates soybean cells. Thus, soybean rust may evade KP4 that was presented on the outside of soybean cells.
The third hypothesis of Objective 1 is to modulate plant proteins identified by mass spectrometry. This year’s milestones were linked to the protein-protein interaction studies described above. Because we did not identify plant proteins that interacted with the effectors, we are following the proposed backup plan to perform a proteomics experiment on beans resistant and susceptible to bean rust. From these results, we will attempt to modulate plant proteins that appear in the experiments to validate their roles in disease resistance.
The goals of the second objective were to determine if the root-knot nematode MilDL1 peptide is secreted from the nematode into its plant host, evaluate wherein the host it is secreted, identify if it interacts with plant host proteins, and determine if it acts as a hormone to affect plant cell development. The scientist responsible for this objective retired, and the work was not completed due to a critical vacancy.
The first milestone of the third objective was to extract primary and secondary metabolites from Fast Neutron soybean mutants. We optimized the extraction procedure for metabolites from matured soybean seeds and developing leaves. After crushing the samples, we removed proteins by precipitating with methanol under vigorous shaking followed by centrifugation. The pellets were air-dried to remove the organic solvent and kept at -80 degrees C. The pellets were reconstituted in a solution containing water, methanol, perfluoropentanoic acid, and formic acid. We evaluated the variability of the metabolites in the sample by calculating the median relative standard deviation for the standards that were added to each sample prior to injection into the mass spectrometers.
The second milestone of the third objective was to characterize and statistically analyze metabolites from wild and mutant seeds and leaves. We used ultra-performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) for metabolite identification and quantitation. The statistical analysis consisted of four major components: the laboratory information management system, the data extraction, and peak-identification software, data processing tools for quality control and compound identification, and a collection of information interpretation and visualization tools for use by data analysts. Raw data were extracted, peaks identified, and data processed using Metabolon’s hardware and software. Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. The MS/MS scores were based on a comparison of the ions present in the experimental spectrum to the ions present in the library spectrum. Standard statistical analyses were performed in ArrayStudio on log-transformed data. The false discovery rate was calculated.
The third milestone of the third objective was to correlate the analysis of primary and secondary metabolites from wild and mutant samples. Four biological replicates for each sample (wild and mutant) were used. We identified a total of 684 metabolites (p=0.05), of which 89 appeared only in leaves, 154 appeared only in seeds, and 441 appeared both in seeds and leaves. Based on the pattern of abundance, the metabolites were divided into seven categories: up in leaf only, down in leaf only, up in seed only, down in seed only, down in leaf and up in seed, up in a leaf, and up in seed and up in a leaf and down in seed. The differentially expressed (seven categories) metabolites were mapped to the global metabolic pathways on the background of soybean pathways curated by Kyoto Encyclopedia of Genes and Genomes. We found that the most differentially expressed abundant metabolites mapped to the amino acid metabolism, secondary metabolite metabolism, carbon metabolism, and nucleotide metabolism pathways. Gene duplication in the mutant resulted in significant increases in sulfur-containing metabolites such as S-methylmethionine, methionine, and cysteine. A significant increase of several secondary metabolites such as cosmosiin and apigenin was also observed. Integrating metabolomic and genomic data on the global metabolic pathways unveiled several duplicated genes that might have contributed to the increased S-metabolites in the seeds.
Accomplishments
1. Bean proteins identified that confer resistance to halo blight. Halo blight disease, caused by a bacterium, reduces harvests of the dry, edible common bean. ARS scientists in Beltsville, Maryland, used mass spectrometry to measure the amounts of 4,000 bean proteins in leaves inoculated with a halo blight strain that triggers resistance. Several bean proteins include receptor kinases that can sense bacteria and send signals within the plant to stimulate defense. We reduced the gene expression for one of the kinases, which increased infection, thus providing new evidence that the gene was needed for resistance to halo blight. These results will be of interest to scientists in the government, universities, and private institutions to develop novel means to protect beans from halo blight resistance and develop newdisease-resistant dry bean cultivars.
Review Publications
Cooper, B., Yang, R. 2021. Genomic resources for Pseudomonas syringae pv. phaseolicola races 5 and 8. Phytopathology. 111:893-895. https://doi.org/10.1094/phyto-10-20-0462-a.
Klein, A., Husselmann, L.H., Williams, A., Bell, L., Cooper, B., Ragar, B., Tabb, D.L. 2021. Proteomic identification and meta-analysis in Salvia hispanica RNA-Seq de novo assemblies. Plants. 10(4):765. https://doi.org/10.3390/plants10040765.
Cooper, B., Campbell, K., Beard, H.S., Garrett, W.M., Ferreira, M.E. 2020. The Proteomics of Resistance to Halo Blight in Common Bean. Molecular Plant-Microbe Interactions. 33(9):1161-1175. https://doi.org/10.1094/MPMI-05-20-0112-R.
Islam, N., Luthria, D.L., Kotha, R.R., Natarajan, S.S. 2020. Enhanced protein separation after removing soluble sugars from ground soybean seeds. Analytical Biochemistry. 610:113931. https://doi.org/10.1016/j.ab.2020.113931.
Islam, N., Krishnan, H.B., Natarajan, S.S. 2020. Proteomic profiling of fast neutron-induced soybean mutant unveiled pathways associated with increased seed protein content. Journal of Proteome Research. 19:3936-3944. https://doi.org/10.1021/acs.jproteome.0c00160.
Luthria, D.L., Kotha, R.R., Natarajan, S.S., Wang, D. 2019. Compositional analysis of non-polar and polar metabolites in 14 soybeans using spectroscopy and chromatography tools. Foods. 8: 557-569. https://doi.org/10.3390/foods8110557.
