Uptake and metabolism of sulphated steroids by the blood-brain barrier in the adult male rat.

Little is known about the origin of the neuroactive steroids dehydroepiandrosterone sulphate (DHEAS) and pregnenolone sulphate (PregS) in the brain or of their subsequent metabolism. Using rat brain perfusion in situ, we have found 3 H-PregS to enter more rapidly than 3 H-DHEAS and both to undergo extensive (> 50%) desulphation within 0.5 min of uptake. Enzyme activity for the steroid sulphatase catalysing this deconjugation was enriched in the capillary fraction of the blood-brain barrier and its mRNA expressed in cultures of rat brain endothelial cells and astrocytes. Although permeability measurements suggested a net efflux, addition of the efflux inhibitors GF120918 and/or MK571 to the perfusate reduced rather than enhanced the uptake of 3 H-DHEAS and 3 H-PregS; a further reduction was seen upon the addition of unlabelled steroid sulphate, suggesting a saturable uptake transporter. Analysis of brain fractions after 0.5 min perfusion with the 3 H-steroid sulphates showed no further metabolism of PregS beyond the liberation of free steroid pregnenolone. By contrast, DHEAS underwent 17-hydroxylation to form androstenediol in both the steroid sulphate and the free steroid fractions, with some additional formation of androstenedione in the latter. Our results indicate a gain of free steroid from circulating steroid sulphates as hormone precursors at the blood-brain barrier, with implications for ageing, neurogenesis, neuronal survival, learning and memory.

(1)H-NMR screening for the high-throughput determination of genotype and environmental effects on the content of asparagine in wheat grain.

Free asparagine in cereals is known to be the precursor of acrylamide, a neurotoxic and carcinogenic product formed during cooking processes. Thus, the development of crops with lower asparagine is of considerable interest to growers and the food industry. In this study, we describe the development and application of a rapid (1)H-NMR-based analysis of cereal flour, that is, suitable for quantifying asparagine levels, and hence acrylamide-forming potential, across large numbers of samples. The screen was applied to flour samples from 150 bread wheats grown at a single site in 2005, providing the largest sample set to date. Additionally, screening of 26 selected cultivars grown for two further years in the same location and in three additional European locations in the third year (2007) provided six widely different environments to allow estimation of the environmental (E) and G x E effects on asparagine levels. Asparagine concentrations in the 150 genotypes ranged from 0.32 to 1.56 mg/g dry matter in wholemeal wheat flours. Asparagine levels were correlated with plant height and therefore, due to recent breeding activities to produce semi-dwarf varieties, a negative relationship with the year of registration of the cultivar was also observed. The multisite study indicated that only 13% of the observed variation in asparagine levels was heritable, whilst the environmental contribution was 36% and the GxE component was 43%. Thus, compared to some other phenotypic traits, breeding for low asparagine wheats presents a difficult challenge.

Enhancement of plant metabolite fingerprinting by machine learning.

Metabolite fingerprinting of Arabidopsis (Arabidopsis thaliana) mutants with known or predicted metabolic lesions was performed by (1)H-nuclear magnetic resonance, Fourier transform infrared, and flow injection electrospray-mass spectrometry. Fingerprinting enabled processing of five times more plants than conventional chromatographic profiling and was competitive for discriminating mutants, other than those affected in only low-abundance metabolites. Despite their rapidity and complexity, fingerprints yielded metabolomic insights (e.g. that effects of single lesions were usually not confined to individual pathways). Among fingerprint techniques, (1)H-nuclear magnetic resonance discriminated the most mutant phenotypes from the wild type and Fourier transform infrared discriminated the fewest. To maximize information from fingerprints, data analysis was crucial. One-third of distinctive phenotypes might have been overlooked had data models been confined to principal component analysis score plots. Among several methods tested, machine learning (ML) algorithms, namely support vector machine or random forest (RF) classifiers, were unsurpassed for phenotype discrimination. Support vector machines were often the best performing classifiers, but RFs yielded some particularly informative measures. First, RFs estimated margins between mutant phenotypes, whose relations could then be visualized by Sammon mapping or hierarchical clustering. Second, RFs provided importance scores for the features within fingerprints that discriminated mutants. These scores correlated with analysis of variance F values (as did Kruskal-Wallis tests, true- and false-positive measures, mutual information, and the Relief feature selection algorithm). ML classifiers, as models trained on one data set to predict another, were ideal for focused metabolomic queries, such as the distinctiveness and consistency of mutant phenotypes. Accessible software for use of ML in plant physiology is highlighted.

Defining genetic and chemical diversity in wheat grain by 1H-NMR spectroscopy of polar metabolites.

SCOPE: The application of high-throughput 1H nuclear magnetic resonance (1H-NMR) of unpurified extracts to determine genetic diversity and the contents of polar components in grain of wheat. METHODS AND RESULTS: Milled whole wheat grain was extracted with 80:20 D2 O:CD3 OD containing 0.05% d4 -trimethylsilylpropionate. 1H-NMR spectra were acquired under automation at 300°K using an Avance Spectrometer operating at 600.0528 MHz. Regions for individual metabolites were identified by comparison to a library of known standards run under identical conditions. The individual 1H-NMR peaks or levels of known metabolites were then compared by Principal Component Analysis using SIMCA-P software. CONCLUSIONS: High-throughput 1H-NMR is an excellent tool to compare the extent of genetic diversity within and between wheat species, and to quantify specific components (including glycine betaine, choline, and asparagine) in individual genotypes. It can also be used to monitor changes in composition related to environmental factors and to support comparisons of the substantial equivalence of transgenic lines.

An efficient high throughput metabotyping platform for screening of biomass willows.

Future improvement of woody biomass crops such as willow and poplar relies on our ability to select for metabolic traits that sequester more atmospheric carbon into biomass, or into useful products to replace petrochemical streams. We describe the development of metabotyping screens for willow, using combined 1D 1H-NMR-MS. A protocol was developed to overcome 1D 1H-NMR spectral alignment problems caused by variable pH and peak broadening arising from high organic acid levels and metal cations. The outcome was a robust method to allow direct statistical comparison of profiles arising from source (leaf) and sink (stem) tissues allowing data to be normalised to a constant weight of the soluble metabolome. We also describe the analysis of two willow biomass varieties, demonstrating how fingerprints from 1D 1H-NMR-MS vary from the top to the bottom of the plant. Automated extraction of quantitative data of 56 primary and secondary metabolites from 1D 1H-NMR spectra was realised by the construction and application of a Salix metabolite spectral library using the Chenomx software suite. The optimised metabotyping screen in conjunction with automated quantitation will enable high-throughput screening of genetic collections. It also provides genotype and tissue specific data for future modelling of carbon flow in metabolic networks.

A genome-wide metabolomic resource for tomato fruit from Solanum pennellii.

Tomato and its processed products are one of the most widely consumed fruits. Its domestication, however, has resulted in the loss of some 95% of the genetic and chemical diversity of wild relatives. In order to elucidate this diversity, exploit its potential for plant breeding, as well as understand its biological significance, analytical approaches have been developed, alongside the production of genetic crosses of wild relatives with commercial varieties. In this article, we describe a multi-platform metabolomic analysis, using NMR, mass spectrometry and HPLC, of introgression lines of Solanum pennellii with a domesticated line in order to analyse and quantify alleles (QTL) responsible for metabolic traits. We have identified QTL for health-related antioxidant carotenoids and tocopherols, as well as molecular signatures for some 2000 compounds. Correlation analyses have revealed intricate interactions in isoprenoid formation in the plastid that can be extrapolated to other crop plants.