Location: Plant Genetics Research2019 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.
Changes in plant genetics and the environment can have a strong impact on provisions to developing seeds and alter the compositions and thus the agronomic value of a crop. Lipids are the most energy-dense component stored in significant quantities in seeds. However, the accumulation of lipids reflects the rate of biosynthesis and turnover which are dynamic and therefore more challenging to measure. Methods using enzymatic hydrolysis and mass spectrometry were established to assess the relative levels of acyl-acyl carrier protein levels in oilseeds and are currently being leveraged to characterize the relative dynamic rate of fatty acid and lipid biosynthesis in different circumstances by examining isotopically labeled tissues. The acyl-acyl carrier proteins are the first committed compound in lipid production and are an important readout for assessing seed metabolism that establishes composition and can guide metabolic engineering efforts for lipid production in seeds. Because the protein sequence is highly conserved across organisms, in addition to studies in plants, the methods developed here are potentially important to studies on obesity and lipid metabolism in human health and disease.
1. Protein, oil, and carbohydrate establish the seed’s market value and are the result of plant metabolism. Current understanding of biochemical pathway flux to produce valuable components such as lipid is incomplete. ARS researchers in St. Louis, Missouri, developed an analytical approach with enzymatic hydrolysis and mass spectrometry to assess a series of important intermediates that control lipid biosynthesis. The resulting method can sensitively quantify the levels of intermediates at very low levels (i.e. femtomoles) in plants, but also is universally applicable to other species. This method is important because oil is a valuable component in seeds that can provide energy for animal and human diets and as a renewable source of biofuel that can reduce petroleum dependencies.
Kambhampati, S., Li, J., Evans, B.S., Allen, D.K. 2019. Accurate and efficient amino acid analysis for protein quantification using hydrophilic interaction chromatography coupled tandem mass spectrometry. Plant Methods. 15:46. https://doi.org/10.1186/s13007-019-0430-z.
Abernathy, M., Czajka, J., Allen, D.K., Hill, N.C., Cameron, J.C., Tang, Y.J. 2019. Cyanobacterial carboxysome mutant analysis reveals the influence of enzyme compartmentalization on cellular metabolism and metabolic network rigidity. Metabolic Engineering. 54:222-231. https://doi.org/10.1016/j.ymben.2019.04.010.