Susan MacIsaac

Application of 1H NMR Profiling To Assess Seed Metabolomic Diversity. A Case Study on a Soybean Era Population

(1)H NMR spectroscopy offers advantages in metabolite quantitation and platform robustness when applied in food metabolomics studies. This paper provides a (1)H NMR-based assessment of seed metabolomic diversity in conventional and glyphosate-resistant genetically modified (GM) soybean from a genetic lineage representing ∼35 years of breeding and differing yield potential. (1)H NMR profiling of harvested seed allowed quantitation of 27 metabolites, including free amino acids, sugars, and organic acids, as well as choline, O-acetylcholine, dimethylamine, trigonelline, and p-cresol. Data were analyzed by canonical discriminant analysis (CDA) and principal variance component analysis (PVCA). Results demonstrated that (1)H NMR spectroscopy was effective in highlighting variation in metabolite levels in the genetically diverse sample set presented. The results also confirmed that metabolite variability is influenced by selective breeding and environment, but not genetic modification. Therefore, metabolite variability is an integral part of crop improvement that has occurred for decades and is associated with a history of safe use.

Evaluation of label-free quantitative proteomics in a plant matrix: A case study of the night-to-day transition in corn leaf

The application of a label-free, LC-MS/MS based proteomics method for analysis of plant tissues was evaluated using both a spike study and case study in corn (Zea mays) leaf tissue. The spike study was utilized to establish a label-free proteomics protocol for corn leaf tissue, with focus on the assessment of sensitivity and accuracy. The data from this spike study indicated that this protocol had quantitative accuracy within ±20% of the true values and was able to differentiate 1.5 fold changes in protein abundance in a corn leaf matrix. Furthermore, the applicability of this protocol as a useful tool for answering biologically relevant questions was tested in a case study of the response of the proteome to night-to-day transition in corn leaf tissue. The label-free proteomics approach detected 136 differentially abundant proteins (FDR = 0.01 with an absolute log fold change ≥ 0.8) and 313 proteins whose abundance did not change in response to the diurnal cycle using ANOVA fixed effects model analysis. Identified proteins were mapped to their Gene Ontology (GO) biological processes and compared with expected diurnal biology. Many observed changes, including an increase in photosynthetic processes, were consistent with anticipated biological responses to the night-to-day transition. The results from the spike and case studies show that the label-free method can reliably provide a means to detect changes in protein abundance in plant tissue.

In-gel protein N- and C-termini identification and its application for transgenic protein characterization

A new method for the determination of N- and C-termini of a protein isolated in a polyacrylamide gel is introduced. In-gel partial protein hydrolysis by hydrochloric acid is used to generate N- and C-terminal peptides for identification. This new method is complementary to existing techniques. The application of the in-gel protein termini identification technique to the characterization of the transgenic protein diacylglycerol acyltransferase (UrDGAT2A) purified from soybean seeds is also reported here. Both N- and C-termini of UrDGAT2A were successfully identified and the N-terminus was found to be blocked by acetylation. The analysis results of UrDGAT2A and two commercial proteins (bovine serum albumin (BSA) and alcohol dehydrogenase) are used to demonstrate the effectiveness of the method in identifying actual N- and C-termini, terminal truncation and blocking.

High Fermentable Corn Hybrids for the Dry-Grind Corn Ethanol Industry

The biofuel corn ethanol helps provide a sustainable and secure non-petroleum source of energy. The dry-grind ethanol industry is the customer for about one-third of US-produced corn grain. Getting the most ethanol from sourced corn grain is important to the economics of a commercial ethanol plant. Near infrared transmittance spectroscopy (NIT), backed by calibrations built with robust reference chemistry, is used to predict the fermentability of whole corn grain. Ethanol yield predicted by NIT has been shown to be highly correlated with commercial ethanol yield. High fermentable corn hybrids identified using NIT have been designated by commercial seed producers and made available to corn growers. The combination of a robust, commercially validated NIT calibration and a rigorous corn hybrid designation process has been used to identify high fermentable corn hybrids to enable higher ethanol yields for the dry-grind ethanol industry.