Location: Plant Genetics Research2020 Annual Report
Objective 1: Identify genes governing elemental accumulation in plants, elucidate factors affecting co-regulation of elemental accumulation, and link those to allelic variation in existing soybean germplasm. Sub-objective 1.1: Develop bioinformatics tools to identify candidate genes from GWAS experiments. (Non-hypothesis driven) Sub-objective 1.2: Rapidly identify novel genes underlying elemental accumulation in plants. (Non-hypothesis driven) Sub-objective 1.3: Profile seeds from diverse locations to identify environmental parameters effect on soy elemental composition. Objective 2: Develop novel analytical methods to understand the dynamics that underpin lipid metabolism to guide metabolic engineering efforts for lipid production in seeds. Sub-objective 2.1: Characterize acyl-acyl carrier protein levels to assess the dynamics of lipid metabolism (Non-hypothesis driven) Sub-objective 2.2: Characterize glycerolipid pools in developing seeds for development of lipid flux maps (Non-hypothesis driven) Objective 3: Assess central carbon metabolism in metabolically altered plant tissues, and develop strategies that can be used to assess plant metabolic changes for improving agriculturally-relevant seed composition traits or yield. Sub-objective 3.1: Characterize changes in central and lipid metabolism during the course of development (non-hypothesis driven)
Goal 1.1 Develop the CGAS computational tool to identify candidate genes in soybean. We will leverage data from GWAS experiments through computational approaches including an analysis platform we are developing (CGAS; Comparative Genomics of ASsociation experiments). The platform will use ortholog tables from Phytozome to search for orthologs under GWAS peaks then test for significant interactions by creating randomized datasets of the same size to obtain a background distribution. Goal 1.2.: Screen 7500 Arabidopsis T-DNA lines for elemental phenotypes. Recent advances in the manipulation of genes and genomes must be complemented by phenotyping measurements such including those for elemental uptake and movement. We will analyze 7500 lines within our ionomics platform to provide this information and link genotype and phenotype. Goal 1.3: Profile a subset of the Uniform Soybean trials. We will analyze soil from triplicate plots for elemental profiles and correlate with collaborators’ data to get a comprehensive view of elemental profiles for a given line and environmental parameters. We will do this at the level of single element traits and multi element traits determined through principle component analysis. Goal 2.1: Quantify major acyl-Acyl Carrier Protein (ACP) species of fatty acid biosynthesis in soybeans. We will develop biochemical methods with mass spectrometry to rigorously quantify acyl-ACPs. Acyl-ACPs connect central metabolism with lipid metabolism and will provide an indication of when acyl-ACP synthesis may be bottlenecking the production of lipids under different circumstances which will be further considered through isotopic labeling and measurement of labeled acyl-ACPs. Goal 2.2: Quantify labeling in phospholipid and neutral lipid pools. We will isotopically label seeds and investigate the labeling in phospholipid and neutral lipid intermediates that we hypothesize are most indicative of specific pathway use for lipid production and that can be informative to engineer increased lipid production in the future. The mass spectrometry methods will involve optimization with high resolution instruments. Goal 3: Analyze labeling in organic and amino acid pools in developing soybeans. We will build a platform to transiently label seeds with 13C over short durations (minutes to hours) to investigate the allocation of carbon during specific aspects of seed development. These stages of development contribute to the final composition and are therefore important in establishing the final composition. Methods to rigorously analyze important intermediates including amino acids and organic acids will include fragment evaluation with direct injection mass spectrometry and validation with standards prior to quantification of differences in seeds of different ages.
The composition of seeds and the yield of crops are impacted by alterations in plant genetics as well as environmental conditions. Thus, levels of lipid, protein, and carbohydrate reflect these changes; however, at the level of cell, production of protein, lipid, or carbohydrates represented a difference between the biosynthetic and turnover rates that can be more difficult to assess than just the accumulated levels. To quantify the biosynthetic rates of fatty acids for lipids, the absolute levels of acyl-acyl carrier proteins were measured in oilseeds and leaves. The methodology involves highly conserved acyl carrier protein sequences that are the same across plants and many other species, therefore the developed and recently published methods are applicable to many biological systems. Such methods will be important to characterizing the value-added compositions in seeds but also for human health, considering important diseases and obesity. A method to quantify protein levels was also developed, published, and used in subsequent studies to understand metabolism. The protein levels were determined by quantifying individual amino acid levels from protein that had been hydrolyzed and then summing amino acid levels to get a total quantity. The method is an advance on other approaches that require additional derivatization steps and that are unstable. The method which can be completed on a mass spectrometer with a 20-minute run time is increased throughput than other methods in addition to being more accurate and can measure all 20 amino acids, whereas other approaches frequently degrade some amino acids significantly to quantify accurately.
1. A new method to quantify fatty acid production for oil in seeds. The value of soybeans is largely established from the levels of protein and oil in the seed. However, the understanding of how protein and oil are made and regulation of their production occurs at the cellular level and is a function of metabolic pathways and flux. ARS scientists in St. Louis, Missouri, developed an analytical approach to quantify the absolute levels of important compounds that indicate the production rate of fatty acids for lipids. The methods required generation of standards enzymatically as part of the development because no commercial standards are available. The method provides a novel approach to quantify oil biosynthesis in plant leaves, seeds and lipid production in other systems that extend to human health, disease and obesity.
2. Oil and protein levels change over development impacting seed value. Protein and oil are described as being inversely correlated based on comparisons of mature seeds from different varieties. However, during the growth of the plant when seeds are receiving nutrients and growing in size, both protein and oil ramp up in production and accumulate significantly. ARS scientists in St. Louis, Missouri, examined the tradeoff in protein for lipid in soybeans over the course of seed development and maturation, focusing on several mutant lines to understand better some of the tradeoffs that might be explained by mutations in genes. The seeds had changes in turnover of lipids and protein late in development and that may explain an observed increase in oil and protein in the mature seeds. This would be important for adding value to seeds to benefit the farm, and also the supply of food and feed to the consumer.
Czajka, J.J., Kambhampati, S., Tang, Y.J., Wang, Y., Allen, D.K. 2020. Application of stable isotope tracing to elucidate metabolic dynamics during Yarrowia lipolytica a-ionone fermentation. iScience. 23(2):100854. https://doi.org/10.1016/j.isci.2020.100854.
Kambhampati, S., Aznar-Moreno, J.A., Hostetler, C., Caso, T., Bailey, S.R., Hubbard, A.H., Durrett, T.P., Allen, D.K. 2019. On the inverse correlation of protein and oil: Examining the effects of altered central carbon metabolism on seed composition using soybean fast neutron mutants. Metabolites. 10(1):18. https://doi.org/10.3390/metabo10010018.
Allen, D.K., Young, J.D. 2020. Tracing metabolic flux through time and space with isotope labeling experiments. Current Opinion in Biotechnology. 64:92-100. https://doi.org/10.1016/j.copbio.2019.11.003.
Zhou, X., Bhandari, S., Johnson, B.S., Kotapati, H., Allen, D.K., Vanhercke, T., Bates, P.D. 2020. Reorganization of acyl flux through the lipid metabolic network in oil-accumulating tobacco leaves. Plant Physiology. 182(2):739-755. https://doi.org/10.1104/pp.19.00667.
Nam, J., Jenkins, L.M., Li, J., Evans, B.S., Jaworski, J., Allen, D.K. 2020. A general method for quantification and discovery of acyl groups attached to acyl carrier proteins in fatty acid metabolism using LC-MS/MS. The Plant Cell. 32:820–832. https://doi.org/10.1105/tpc.19.00954.