A nematode demographics assay in transgenic roots reveals no significant impacts of the Rhg1 locus LRR-Kinase on soybean cyst nematode resistance

<p>Abstract</p> <p>Background</p> <p>Soybean cyst nematode (<it>Heterodera glycines</it>, SCN) is the most economically damaging pathogen of soybean (<it>Glycine max</it>) in the U.S. The <it>Rhg1 </it>locus is repeatedly observed as the quantitative trait locus with the greatest impact on SCN resistance. The Glyma18g02680.1 gene at the <it>Rhg1 </it>locus that encodes an apparent leucine-rich repeat transmembrane receptor-kinase (LRR-kinase) has been proposed to be the SCN resistance gene, but its function has not been confirmed. Generation of fertile transgenic soybean lines is difficult but methods have been published that test SCN resistance in transgenic roots generated with <it>Agrobacterium rhizogenes</it>.</p> <p>Results</p> <p>We report use of artificial microRNA (amiRNA) for gene silencing in soybean, refinements to transgenic root SCN resistance assays, and functional tests of the <it>Rhg1 </it>locus LRR-kinase gene. A nematode demographics assay monitored infecting nematode populations for their progress through developmental stages two weeks after inoculation, as a metric for SCN resistance. Significant differences were observed between resistant and susceptible control genotypes. Introduction of the <it>Rhg1 </it>locus LRR-kinase gene (genomic promoter/coding region/terminator; Peking/PI 437654-derived SCN-resistant source), into <it>rhg1</it>^-SCN-susceptible plant lines carrying the resistant-source <it>Rhg4</it>^{<it>+ </it>}locus, provided no significant increases in SCN resistance. Use of amiRNA to reduce expression of the LRR-kinase gene from the <it>Rhg1 </it>locus of Fayette (PI 88788 source of <it>Rhg1</it>) also did not detectably alter resistance to SCN. However, silencing of the LRR-kinase gene did have impacts on root development.</p> <p>Conclusion</p> <p>The nematode demographics assay can expedite testing of transgenic roots for SCN resistance. amiRNAs and the pSM103 vector that drives interchangeable amiRNA constructs through a soybean polyubiqutin promoter (Gmubi), with an intron-GFP marker for detection of transgenic roots, may have widespread use in legume biology. Studies in which expression of the <it>Rhg1 </it>locus LRR-kinase gene from different resistance sources was either reduced or complemented did not reveal significant impacts on SCN resistance.</p

Metabolic engineering of Arabidopsis for butanetriol production using bacterial genes

Includes bibliographical references (pages 119-120).1,2,4-butanetriol (butanetriol) is a useful precursor for the synthesis of the energetic material butanetriol trinitrate and several pharmaceutical compounds. Bacterial synthesis of butanetriol from xylose or arabinose takes place in a pathway that requires four enzymes. To produce butanetriol in plants by expressing bacterial enzymes, we cloned native bacterial or codon optimized synthetic genes under different promoters into a binary vector and stably transformed Arabidopsis plants. Transgenic lines expressing introduced genes were analyzed for the production of butanetriol using gas chromatography coupled to mass spectrometry (GC-MS). Soil-grown transgenic plants expressing these genes produced up to 20 µg/g of butanetriol. To test if an exogenous supply of pentose sugar precursors would enhance the butanetriol level, transgenic plants were grown in a medium supplemented with either xylose or arabinose and the amount of butanetriol was quantified. Plants expressing synthetic genes in the arabinose pathway showed up to a forty-fold increase in butanetriol levels after arabinose was added to the medium. Transgenic plants expressing either bacterial or synthetic xylose pathways, or the arabinose pathway showed toxicity symptoms when xylose or arabinose was added to the medium, suggesting that a by-product in the pathway or butanetriol affected plant growth. Furthermore, the metabolite profile of plants expressing arabinose and xylose pathways was altered. Our results demonstrate that bacterial pathways that produce butanetriol can be engineered into plants to produce this chemical. This proof-of-concept study for phytoproduction of butanetriol paves the way to further manipulate metabolic pathways in plants to enhance the level of butanetriol production.Published with support from the Colorado State University Libraries Open Access Research and Scholarship Fund

Metabolomic and Functional Genomic Analyses Reveal Varietal Differences in Bioactive Compounds of Cooked Rice

Emerging evidence supports that cooked rice (Oryza sativa L.) contains metabolites with biomedical activities, yet little is known about the genetic diversity that is responsible for metabolite variation and differences in health traits. Metabolites from ten diverse varieties of cooked rice were detected using ultra performance liquid chromatography coupled to mass spectrometry. A total of 3,097 compounds were detected, of which 25% differed among the ten varieties. Multivariate analyses of the metabolite profiles showed that the chemical diversity among the varieties cluster according to their defined subspecies classifications: indica, japonica, and aus. Metabolite-specific genetic diversity in rice was investigated by analyzing a collection of single nucleotide polymorphisms (SNPs) in genes from biochemical pathways of nutritional importance. Two classes of bioactive compounds, phenolics and vitamin E, contained nonsynonymous SNPs and SNPs in the 5′ and 3′ untranslated regions for genes in their biosynthesis pathways. Total phenolics and tocopherol concentrations were determined to examine the effect of the genetic diversity among the ten varieties. Per gram of cooked rice, total phenolics ranged from 113.7 to 392.6 µg (gallic acid equivalents), and total tocopherols ranged between 7.2 and 20.9 µg. The variation in the cooked rice metabolome and quantities of bioactive components supports that the SNP-based genetic diversity influenced nutritional components in rice, and that this approach may guide rice improvement strategies for plant and human health

Comparison of Machine Learning Algorithms for Predictive Modeling of Beef Attributes Using Rapid Evaporative Ionization Mass Spectrometry (REIMS) Data

Ambient mass spectrometry is an analytical approach that enables ionization of molecules under open-air conditions with no sample preparation and very fast sampling times. Rapid evaporative ionization mass spectrometry (REIMS) is a relatively new type of ambient mass spectrometry that has demonstrated applications in both human health and food science. Here, we present an evaluation of REIMS as a tool to generate molecular scale information as an objective measure for the assessment of beef quality attributes. Eight different machine learning algorithms were compared to generate predictive models using REIMS data to classify beef quality attributes based on the United States Department of Agriculture (USDA) quality grade, production background, breed type and muscle tenderness. The results revealed that the optimal machine learning algorithm, as assessed by predictive accuracy, was different depending on the classification problem, suggesting that a “one size fits all” approach to developing predictive models from REIMS data is not appropriate. The highest performing models for each classification achieved prediction accuracies between 81.5–99%, indicating the potential of the approach to complement current methods for classifying quality attributes in beef

Genetic basis of barley contributions to beer flavor

13 Pags.- 1 Fig.- 3 Tabls. © 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.Barley malt is critical for the malting, brewing, and distilling industries, as it is one of the main ingredients of beer and some types of spirits. There is growing evidence that barley genotype - via malt - can impact the flavors of beers and spirits. However, information on the barley genes involved in these flavors is lacking. Therefore, we used quantitative trait locus (QTL) mapping of malt quality traits, beer sensory descriptors and metabolic compounds on a biparental population of doubled haploids derived from the cross of the cultivars Golden Promise and Full Pint. Putative candidate genes for QTLs were identified by alignment with the reference barley genome sequence. There were thirty-seven QTLs across all chromosomes except 4H, with three QTL clusters located on 3H (1 cluster) and 5H (2 clusters: mid-5H and end-5H). Those “hotspots” contained QTLs for multiple phenotypes. Several candidate genes that regulate plant metabolism were identified within the QTLs and included HvAlaAT, HvDep1, HvMKK3, HvGA20ox1 and HvGA20ox2. These genes are involved in seed dormancy and plant height. Alleles at these loci, and perhaps at physically linked loci, can have key downstream effects on malting quality, beer flavor, and abundance of volatile metabolites.Research at Oregon State University was supported by the Agricultural Research Foundation Barley Progress Fund. At Colorado State University, research was supported by CSU's College of Agricultural Sciences, with partial support from the American Malting Barley Association.Peer reviewe

Metabolomics and Ionomics of Potato Tuber Reveals an Influence of Cultivar and Market Class on Human Nutrients and Bioactive Compounds

Potato (Solanum tuberosum L.) is an important global food crop that contains phytochemicals with demonstrated effects on human health. Understanding sources of chemical variation of potato tuber can inform breeding for improved health attributes of the cooked food. Here, a comprehensive metabolomics (UPLC- and GC-MS) and ionomics (ICP-MS) analysis of raw and cooked potato tuber was performed on 60 unique potato genotypes that span 5 market classes including russet, red, yellow, chip, and specialty potatoes. The analyses detected 2,656 compounds that included known bioactives (43 compounds), nutrients (42), lipids (76), and 23 metals. Most nutrients and bioactives were partially degraded during cooking (44 out of 85; 52%), however genotypes with high quantities of bioactives remained highest in the cooked tuber. Chemical variation was influenced by genotype and market class. Specifically, ~53% of all detected compounds from cooked potato varied among market class and 40% varied by genotype. The most notable metabolite profiles were observed in yellow-flesh potato which had higher levels of carotenoids and specialty potatoes which had the higher levels of chlorogenic acid as compared to the other market classes. Variation in several molecules with known association to health was observed among market classes and included vitamins (e.g., pyridoxal, ~2-fold variation), bioactives (e.g., chlorogenic acid, ~40-fold variation), medicinals (e.g., kukoamines, ~6-fold variation), and minerals (e.g., calcium, iron, molybdenum, ~2-fold variation). Furthermore, more metabolite variation was observed within market class than among market class (e.g., α-tocopherol, ~1-fold variation among market class vs. ~3-fold variation within market class). Taken together, the analysis characterized significant metabolite and mineral variation in raw and cooked potato tuber, and support the potential to breed new cultivars for improved health traits

Evaluating plant immunity using mass spectrometry-based metabolomics workflows

Metabolic processes in plants are key components of physiological and biochemical disease resistance. Metabolomics, the analysis of a broad range of small molecule compounds in a biological system, has been used to provide a systems-wide overview of plant metabolism associated with defense responses. Plant immunity has been examined using multiple metabolomics workflows that vary in methods of detection, annotation, and interpretation, and the choice of workflow can significantly impact the conclusions inferred from a metabolomics investigation. The broad range of metabolites involved in plant defense often supports the need for multiple chemical detection platforms and implementation of a non-targeted approach. A review of the current literature reveals a wide range of workflows that are currently used in plant metabolomics, and new methods for analyzing and reporting mass spectrometry data can improve the ability to translate investigative findings among different plant-pathogen systems

Proteome Characterization of Leaves in Common Bean

Dry edible bean (Phaseolus vulgaris L.) is a globally relevant food crop. The bean genome was recently sequenced and annotated allowing for proteomics investigations aimed at characterization of leaf phenotypes important to agriculture. The objective of this study was to utilize a shotgun proteomics approach to characterize the leaf proteome and to identify protein abundance differences between two bean lines with known variation in their physiological resistance to biotic stresses. Overall, 640 proteins were confidently identified. Among these are proteins known to be involved in a variety of molecular functions including oxidoreductase activity, binding peroxidase activity, and hydrolase activity. Twenty nine proteins were found to significantly vary in abundance (p-value &lt; 0.05) between the two bean lines, including proteins associated with biotic stress. To our knowledge, this work represents the first large scale shotgun proteomic analysis of beans and our results lay the groundwork for future studies designed to investigate the molecular mechanisms involved in pathogen resistance