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.

BackgroundVolatile 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.DescriptionThe 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).ConclusionsThe 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.

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.

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.

This study attempts to clarify the consequences for wine flavour that result from harvesting fruit at different maturities. The grapes were harvested from a single vineyard in Paso Robles, and the samples spanned maturity levels from what would be considered early harvest (about 21 °Brix) to late harvest (about 30 °Brix). The wines made from these grapes were analysed using descriptive analysis to investigate the relationships between fruit maturity and wine sensory attributes. In addition, musts and/or wines were chaptalised and/or fortified or watered back to determine the effect of these manipulations on wine sensory properties. This research showed that the sensory attributes of wines made from grapes at different stages of maturation, from about 20 to 30 °Brix, varied in a systematic fashion. Specifically, the wines made from the grapes with a lower Brix were more sour and had more fresh vegetative flavours, while the wines made from the fruit with a high Brix were more hot and bitter and in some cases had more dark fruit flavours and sweetness. Fortifying wines made from lower Brix musts changed the perceptions of the wine sensory profiles more than chaptalising the musts. On the other hand, adding water to higher °Brix musts to mimic 24 °Brix musts resulted in wines with similar sensory profiles to wines made from grapes picked at a sugar content of close to 24 °Brix. This study shows that wine sensory attributes differ more when grapes are picked early in ripening rather than after 24 °Brix.