Agricultural crops can endure a matrix of stress resultant from a variety of sources including biotic or abiotic stressors such as drought, flooding, salinity, or nutrient availability. Among the different sources of stress plants can undergo, varying levels of water deficiency and drought have the most prolific and detrimental effect to agricultural farms on the global and national scale. Numerous plant traits have been identified for the potential of improving the performance of drought-affected crops, mainly through conservation of water, with more recent works identifying the importance of slow canopy wilting (SW) phenotypes for their potential stress tolerance in water deficient environments. Legumes have a particular intolerance to water deficiency in the early stages of growth and flowering, where a decrease in water availability by half can result in up to a loss of half the expected yields.
An exotic soybean germplasm, plant introduction (PI) 567731 in maturity group III (MG III), was identified to consistently express the SW phenotype in the field compared to the drought sensitive cultivar Pana; PI 567731 showed lower yield loss than Pana under drought stress with greater than 13% more yield index (yield under rain-fed/ yield under irrigation). PI 567731 uses significantly less water under drought, and this water conservation strategy was identified to be associated with limited-maximum transpiration rates. The transpiration of PI 567731 was found to be sensitive to an aquaporin inhibitor (silver-nitrate) indicating the independence of a limited-maximum transpiration to a lack of silver-sensitive aquaporins in these SW genotypes. In efforts to understand many findings from field trials and further mapping of QTLs, many researchers have adopted use of proteomic and metabolomic techniques and platforms for data analysis to further reinforce and probe the mechanisms of plant stress responses. As responses are characteristic to either acute or prolonged effects to drought stress, the initial impacts primarily effect net photosynthesis and photosynthetic performance of the plants. Under drought, stomatal closures and hormonal signaling through abscisic acid have been identified as the key reductants to net photosynthesis, with increased efficiency of the photosystem (PS) II denoted in stress tolerance. Even though water deficiencies do not directly impact the primary components of C3 plants PSI or PSII directly, these secondary impacts are well known in a variety of crops to reversibly impact photosynthesis, prior to photosynthetic decay. This emphasizes the need for targeted approaches of profiling phytochemicals as a reliable means of screening for stress tolerances.
Targeted and non-targeted approaches for determining metabolic profiles of agronomical crops have been entailed with instrumental approaches ranging from gas or liquid chromatography (GC/LC) coupled with mass spectrometry (MS), nuclear magnetic resonance (NMR) and a variety of spectroscopic techniques. No all-inclusive method for simultaneous detection of all metabolites is available. With a broad array of expression in a variety of primary and secondary metabolites in model plants and agricultural crops, methods either prove to be either moderate throughput with high specificity in extracts, or lack specificity with high-throughput analysis. High-resolution accurate mass MS platforms allows for the determination of molecular formulas; when combined with tandem mass spectrometry (MS/MS) molecular structures can be proposed.
Research supported by the sponsors in the previous grant period led to a number of important metabolomics findings for the drought-susceptible cultivar Pana and the drought-tolerant cultivar PI 567731. The plant introduction (PI) PI 567731, which demonstrates a slow wilting canopy phenotype in maturity group III, was profiled in drought stress field trials against a drought susceptible check cultivar, Pana. Relative phytochemical content of chlorophyll (chl) a/b, and pheophytin (pheo) was profiled by direct infusion electrospray Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometry. High-throughput detection of metabolic profiles in twenty-four experimental groups occurred in triplicate within a few hours, without chromatographic separation. Multivariate analysis was able to form predictive models, encompassing the variance of growth and drought stress, within the experimental groupings at two physiological ages. Statistically significant increases within the Chl content in control conditions were detected, and an expanded photosynthetic antenna within the drought affected treatment condition could account for increased photosynthetic content; in particular, the distinct enhancement of chl b is noted from PI 567731. Moreover, the existence of unique chl-related metabolites (m/z >900) were confirmed through tandem mass spectrometry. The chlorophyll-related metabolite at m/z 1073.71 is a metabolic product of chlorophyll a with an additional C12H20O- group.
We completed the metabolite libraries of the methanolic leaf extracts of Pana and PI 567731 by filtering the high mass resolution data obtained from each through the SoyCyc and Human Metabolome Database and performing Kendrick mass defect analysis (KMD) to identify ionic formulas. Direct infusion electrospray ionization FT-ICR mass spectra for both cultivars is shown in Figure 1 of the attachment.
A total of 60 ionic formulas certified to be present in soybeans are shared by both Pana and PI 567731 from the over 460 found for Pana and 350 for PI 567731. Prominent amongst these are mono- and diacylglycerols, pheophytin a and chlorophyll a, monosaccharides, disaccharides, xanthins, and vicenin-2 (a flavonoid diglucosylation product). Notable also is the simultaneous presence of plastoquinone, detected with products echinone and plastoquinol, essential components of photosynthetic electron transfer. Likewise, ubiquinol-8 and -9 are detected along with 3-demethylubiquinol-9 and demethylmenaquinol-8, key components of aerobic respiration and photosynthethic electron transfer. The metabolite cycloeucalenone is involved in phytosterol biosynethesis.
In addition, 23 species are unique to Pana in controls, and are shown in Table 1 of the attachmebt, while five are unique to PI 567731 in controls, and are shown in Table 2 of the attachment.
Pana is a drought-sensitive soybean cultivar. Using the known soybean metabolites identified in Table 1, there are several carboxylic acid molecules present in Pana that were not detected in PI 567731; these are essential precursors to lipids. Carlactone is an oxidation product of cartenal, possibly indicating oxidative stress in Pana even in the control which has not experienced drought. This is further supported by the presence of glutathione disulfide, the oxidized dimer of glutathione. Galactopinitols are required substrates and products of galactosylcyclitol biosynthesis. The compound 15-cis-phytoene is needed for production of plastoquinol and carotenes. Likewise, the substance menoquinol-8 is a polyprenyl quinone required for electron transport. A richer complement of pheophytins and chlorophylls are detected in Pana in comparison to PI 567731 (e.g., chlorophyll b was only detected in Pana). However, our earlier work showed that PI 567731 maintains greater levels of pheophytins and chlorophylls during drought.
In contrast, PI 567731 is a drought-tolerant soybean cultivar. The metabolites uniquely detected in the methanolic extract of PI 567731 are shown in Table 2. The galactosyl glycerol compound is 3-ß-D-galactosyl-sn-glycerol, formed from the degradation of diacyl glycerols. Soyasapogenol B is a key precursor in the formation of its glucuronide. There are many possible structures for the trisaccharides, so anabolism of more complex saccharides from mono- and disaccharides might explain the appearance of trisaccharides here. Plastoquinones are electron carriers that are necessary building blocks for plastoquinol, and are found in chloroplasts, thus playing a central role in the photosynthetic electron transport chain. Therefore, PI 567731 may adapt better to drought conditions because of how it processes sugar molecules and builds a reservoir of electron transport carriers.
Having established a methodology to catalog the leaf metabolites, the next phase of this research is to apply high resolution mass spectrometry to identify the novel molecular structures of the metabolites detected in soybean seeds from the drought susceptible cultivar Pana and drought tolerant cultivar PI 567731. In order to accomplish this, these metabolites will be separated from extracts using liquid chromatography (LC) and further be characterized using tandem mass spectrometry (MS/MS). LC is essential here, as noted in Tables 1 and 2, some of the unique ionic formulas detected in methanolic leaf extracts have multiple possible isomeric structures, and indeed a multiplicity of these structures may be present! A previous study of different methanolic, acetone, and methylene chloride extracts of soybean seeds using LC and MS/MS showed that key metabolites such as phytochemicals, flavones and isoflavones, and soyasaponins could be distinguished using this method. Interestingly, the authors found that use of low polarity solvents such as methylene chloride and acetone did not impact the overall species detected in the seed extracts but did lower their abundances; thus, we will exclusively utilize methanol as the extract solvent for the Pana and PI 567731 seeds.
Based on the metabolite identities established by LC-MS/MS from the methanolic extracts of the Pana and PI 567731 seeds, we will compare the known identifications for correlations linking these metabolites to various metabolic pathways; in essence, we are using molecular profiling as an approach to distinguish soybean phenotypes, particularly for the drought-tolerant cultivar so that plant biologists could develop soybean cultivars that possess enhanced drought tolerance. We will compare and contrast the molecular composition of the seeds of the drought tolerant and drought susceptible cultivars. An emphasis in the analysis of this data is to determine whether there may be key nutritional differences between the seeds.