The volatile compound BinBase mass spectral database.

BACKGROUND: Volatile compounds comprise diverse chemical groups with wide-ranging sources and functions. These compounds originate from major pathways of secondary metabolism in many organisms and play essential roles in chemical ecology in both plant and animal kingdoms. In past decades, sampling methods and instrumentation for the analysis of complex volatile mixtures have improved; however, design and implementation of database tools to process and store the complex datasets have lagged behind. DESCRIPTION: The volatile compound BinBase (vocBinBase) is an automated peak annotation and database system developed for the analysis of GC-TOF-MS data derived from complex volatile mixtures. The vocBinBase DB is an extension of the previously reported metabolite BinBase software developed to track and identify derivatized metabolites. The BinBase algorithm uses deconvoluted spectra and peak metadata (retention index, unique ion, spectral similarity, peak signal-to-noise ratio, and peak purity) from the Leco ChromaTOF software, and annotates peaks using a multi-tiered filtering system with stringent thresholds. The vocBinBase algorithm assigns the identity of compounds existing in the database. Volatile compound assignments are supported by the Adams mass spectral-retention index library, which contains over 2,000 plant-derived volatile compounds. Novel molecules that are not found within vocBinBase are automatically added using strict mass spectral and experimental criteria. Users obtain fully annotated data sheets with quantitative information for all volatile compounds for studies that may consist of thousands of chromatograms. The vocBinBase database may also be queried across different studies, comprising currently 1,537 unique mass spectra generated from 1.7 million deconvoluted mass spectra of 3,435 samples (18 species). Mass spectra with retention indices and volatile profiles are available as free download under the CC-BY agreement (http://vocbinbase.fiehnlab.ucdavis.edu). CONCLUSIONS: The BinBase database algorithms have been successfully modified to allow for tracking and identification of volatile compounds in complex mixtures. The database is capable of annotating large datasets (hundreds to thousands of samples) and is well-suited for between-study comparisons such as chemotaxonomy investigations. This novel volatile compound database tool is applicable to research fields spanning chemical ecology to human health. The BinBase source code is freely available at http://binbase.sourceforge.net/ under the LGPL 2.0 license agreement.

Analysis of early host responses for asymptomatic disease detection and management of specialty crops.

The rapid and unabated spread of vector-borne diseases within US specialty crops threatens our agriculture, our economy, and the livelihood of growers and farm workers. Early detection of vector-borne pathogens is an essential step for the accurate surveillance and management of vector-borne diseases of specialty crops. Currently, we lack the tools that would detect the infectious agent at early (primary) stages of infection with a high degree of sensitivity and specificity. In this paper, we outline a strategy for developing an integrated suite of platform technologies to enable rapid, early disease detection and diagnosis of huanglongbing (HLB), the most destructive citrus disease. The research has two anticipated outcomes: i) identification of very early, disease-specific biomarkers using a knowledge base of translational genomic information on host and pathogen responses associated with early (asymptomatic) disease development; and ii) development and deployment of novel sensors that capture these and other related biomarkers and aid in presymptomatic disease detection. By combining these two distinct approaches, it should be possible to identify and defend the crop by interdicting pathogen spread prior to the rapid expansion phase of the disease. We believe that similar strategies can also be developed for the surveillance and management of diseases affecting other economically important specialty crops.

Global metabolic profiling of plant cell wall polysaccharide degradation by Saccharophagus degradans.

Plant cell wall polysaccharides can be used as the main feedstock for the production of biofuels. Saccharophagus degradans 2-40 is considered to be a potent system for the production of sugars from plant biomass due to its high capability to degrade many complex polysaccharides. To understand the degradation metabolism of plant cell wall polysaccharides by S. degradans, the cell growth, enzyme activity profiles, and the metabolite profiles were analyzed by gas chromatography-time of flight mass spectrometry using different carbon sources including cellulose, xylan, glucose, and xylose. The specific activity of cellulase was only found to be significantly higher when cellulose was used as the sole carbon source, but the xylanase activity increased when xylan, xylose, or cellulose was used as the carbon source. In addition, principal component analysis of 98 identified metabolites in S. degradans revealed four distinct groups that differed based on the carbon source used. Furthermore, metabolite profiling showed that the use of cellulose or xylan as polysaccharides led to increased abundances of fatty acids, nucleotides and glucuronic acid compared to the use of glucose or xylose. Finally, intermediates in the pentose phosphate pathway seemed to be up-regulated on xylose or xylan when compared to those on glucose or cellulose. Such metabolic responses of S. degradans under plant cell wall polysaccharides imply that its metabolic system is transformed to more efficiently degrade polysaccharides and conserve energy. This study demonstrates that the gas chromatography-time of flight mass spectrometry-based global metabolomics are useful for understanding microbial metabolism and evaluating its fermentation characteristics.

Metabolomic Assessment of Key Maize Resources: GC-MS and NMR Profiling of Grain from B73 Hybrids of the Nested Association Mapping (NAM) Founders and of Geographically Diverse Landraces.

The present study expands metabolomic assessments of maize beyond commercial lines to include two sets of hybrids used extensively in the scientific community. One set included hybrids derived from the nested association mapping (NAM) founder lines, a collection of 25 inbreds selected on the basis of genetic diversity and used to investigate the genetic basis of complex plant traits. A second set included 24 hybrids derived from a collection of landraces representative of native diversity from North and South America that may serve as a source of new alleles for improving modern maize hybrids. Metabolomic analysis of grain harvested from these hybrids utilized gas chromatography-time-of-flight mass spectrometry (GC-TOF-MS) and (1)H nuclear magnetic resonance spectroscopy ((1)H NMR) techniques. Results highlighted extensive metabolomic variation in grain from both hybrid sets, but also demonstrated that, within each hybrid set, subpopulations could be differentiated in a pattern consistent with the known genetic and compositional variation of these lines. Correlation analysis did not indicate a strong association of the metabolomic data with grain nutrient composition, although some metabolites did show moderately strong correlations with agronomic features such as plant and ear height. Overall, this study provides insights into the extensive metabolomic diversity associated with conventional maize germplasm.

Application of (1)h 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.

Impact of genetics and environment on the metabolite composition of maize grain.

This study sought to assess genetic and environmental impacts on the metabolite composition of maize grain. Gas chromatography coupled to time-of-flight mass spectrometry (GC-TOF-MS) measured 119 identified metabolites including free amino acids, free fatty acids, sugars, organic acids, and other small molecules in a range of hybrids derived from 48 inbred lines crossed against two different tester lines (from the C103 and Iodent heterotic groups) and grown at three locations in Iowa. It was reasoned that expanded metabolite coverage would contribute to a comprehensive evaluation of the grain metabolome, its degree of variability, and, in principle, its relationship to other compositional and agronomic features. The metabolic profiling results established that the small molecule metabolite pool is highly dependent on genotypic variation and that levels of certain metabolite classes may have an inverse genotypic relationship to each other. Different metabolic phenotypes were clearly associated with the two distinct tester populations. Overall, grain from the C103 lines contained higher levels of free fatty acids and organic acids, whereas grain from the Iodent lines were associated with higher levels of amino acids and carbohydrates. In addition, the fold-range of genotype mean values [composed of six samples each (two tester crosses per inbred x three field sites)] for identified metabolites ranged from approximately 1.5- to 93-fold. Interestingly, some grain metabolites showed a non-normal distribution over the entire corn population, which could, at least in part, be attributed to large differences in metabolite values within specific inbred crosses relative to other inbred sets. This study suggests a potential role for metabolic profiling in assisting the process of selecting elite germplasm in biotechnology development, or marker-assisted breeding.

Comparison of gas chromatography-coupled time-of-flight mass spectrometry and 1H nuclear magnetic resonance spectroscopy metabolite identification in white wines from a sensory study investigating wine body.

Metabolite profiles of white wines, including Chardonnay, Pinot gris, Riesling, Sauvignon blanc, and Viognier varieties, were determined using both gas chromatography-coupled time-of-flight mass spectrometry (GC-TOF-MS) and proton nuclear magnetic resonance spectroscopy ((1)H NMR). A total of 108 metabolites were identified by GC-TOF-MS, and 51 metabolites were identified by (1)H NMR; the majority of metabolites identified include the most abundant compounds found in wine (ethanol, glycerol, sugars, organic acids, and amino acids). Compositional differences in these wines correlating to the wine sensory property "body", or viscous mouthfeel, as scored by a trained panel were identified using partial least-squares (PLS) regression. Independently calculated GC-TOF-MS and NMR-based PLS models demonstrate potential for predictive models to replace expensive, time-consuming sensory panels. At the modeling stage, correlations between the measured and predicted values have coefficients of determination of 0.83 and 0.75 for GC-TOF-MS and (1)H NMR, respectively. Additionally, the MS- and NMR-based models present new insights into the chemical basis for wine mouthfeel properties.

Novel peptide inhibitors of angiotensin-converting enzyme 2.

Angiotensin-converting enzyme 2 (ACE2), a recently identified human homolog of ACE, is a novel metallocarboxypeptidase with specificity, tissue distribution, and function distinct from those of ACE. ACE2 may play a unique role in the renin-angiotensin system and mediate cardiovascular and renal function. Here we report the discovery of ACE2 peptide inhibitors through selection of constrained peptide libraries displayed on phage. Six constrained peptide libraries were constructed and selected against FLAG-tagged ACE2 target. ACE2 peptide binders were identified and classified into five groups, based on their effects on ACE2 activity. Peptides from the first three classes exhibited none, weak, or moderate inhibition on ACE2. Peptides from the fourth class exhibited strong inhibition, with equilibrium inhibition constants (K(i) values) from 0.38 to 1.7 microm. Peptides from the fifth class exhibited very strong inhibition, with K(i) values < 0.14 microm. The most potent inhibitor, DX600, had a K(i) of 2.8 nm. Steady-state enzyme kinetic analysis showed that these potent ACE2 inhibitors exhibited a mixed competitive and non-competitive type of inhibition. They were not hydrolyzed by ACE2. Furthermore, they did not inhibit ACE activity, and thus were specific to ACE2. Finally, they also inhibited ACE2 activity toward its natural substrate angiotensin I, suggesting that they would be functional in vivo. As novel ACE2-specific peptide inhibitors, they should be useful in elucidation of ACE2 in vivo function, thus contributing to our better understanding of the biology of cardiovascular regulation. Our results also demonstrate that library selection by phage display technology can be a rapid and efficient way to discover potent and specific protease inhibitors.