Previous studies revealed that exposure of mesangial cells to high glucose concentration induces the production of matrix proteins mediated by TGF-beta1. We tested if structural analogues of D-glucose may mimic the high glucose effect and found that D-glucosamine was strikingly more potent than D-glucose itself in enhancing the production of TGF-beta protein and subsequent production of the matrix components heparan sulfate proteoglycan and fibronectin in a time- and dose-dependent manner. D-Glucosamine also promoted conversion of latent TGF-beta to the active form. Therefore, we suggested that the hexosamine biosynthetic pathway (the key enzyme of which is glutamine:fructose-6-phosphate amidotransferase [GFAT]) contributes to the high glucose-induced TGF-beta1 production. Inhibition of GFAT by the substrate analogue azaserine or by inhibition of GFAT protein synthesis with antisense oligonucleotide prevented the high glucose-induced increase in cellular glucosamine metabolites and TGF-beta1 expression and bioactivity and subsequent effects on mesangial cell proliferation and matrix production. Overall, our study indicates that the flux of glucose metabolism through the GFAT catalyzed hexosamine biosynthetic pathway is involved in the glucose-induced mesangial production of TGF-beta leading to increased matrix production.

BACKGROUND:Metabolomics is a powerful tool that is increasingly used in clinical research. Although excellent sample quality is essential, it can easily be compromised by undetected preanalytical errors. We set out to identify critical preanalytical steps and biomarkers that reflect preanalytical inaccuracies.

Every day, analytical and bio-analytical chemists make sustained efforts to improve the sensitivity, specificity, robustness, and reproducibility of their methods. Especially in targeted and non-targeted profiling approaches, including metabolomics analysis, these objectives are not easy to achieve; however, robust and reproducible measurements and low coefficients of variation (CV) are crucial for successful metabolomics approaches. Nevertheless, all efforts from the analysts are in vain if the sample quality is poor, i.e. if preanalytical errors are made by the partner during sample collection. Preanalytical risks and errors are more common than expected, even when standard operating procedures (SOP) are used. This risk is particularly high in clinical studies, and poor sample quality may heavily bias the CV of the final analytical results, leading to disappointing outcomes of the study and consequently, although unjustified, to critical questions about the analytical performance of the approach from the partner who provided the samples. This review focuses on the preanalytical phase of liquid chromatography–mass spectrometry-driven metabolomics analysis of body fluids. Several important preanalytical factors that may seriously affect the profile of the investigated metabolome in body fluids, including factors before sample collection, blood drawing, subsequent handling of the whole blood (transportation), processing of plasma and serum, and inadequate conditions for sample storage, will be discussed. In addition, a detailed description of latent effects on the stability of the blood metabolome and a suggestion for a practical procedure to circumvent risks in the preanalytical phase will be given.Graphical AbstractThe procedures and potential problems in preanalytical aspects of metabolomics studies using blood samples. Bias in the preanalytical phase may lead to unwanted results in the subsequential studies

The oral glucose tolerance test (oGTT) is a common tool to provoke a metabolic challenge for scientific purposes, as well as for diagnostic reasons, to monitor the kinetics of glucose and insulin. Here, we aimed to follow the variety of physiological changes of the whole metabolic pattern in plasma during an oGTT in healthy subjects in a nontargeted reversed-phase ultra performance liquid chromatography coupled to electrospray ionization quadrupole time of flight mass spectrometric metabolomics approach. We detected 11,500 metabolite ion masses/individual. Applying multivariate data analysis, four major groups of metabolites have been detected as the most discriminating oGTT biomarkers: free fatty acids (FFA), acylcarnitines, bile acids, and lysophosphatidylcholines. We found in detail 1) a strong decrease of all saturated and monounsaturated FFA studied during the oGTT; 2) a significant faster decline of palmitoleate (C16:1) and oleate (C18:1) FFA levels than their saturated counterparts; 3) a strong relative increase of polyunsaturated fatty acids in the fatty acid pattern at 120 min; and 4) a clear decrease in plasma C10:0, C12:0, and C14:1 acylcarnitine levels. These data reflect the switch from beta-oxidation to glycolysis and fat storage during the oGTT. Moreover, the bile acids glycocholic acid, glycochenodeoxycholic acid, and glycodeoxycholic acid were highly discriminative, showing a biphasic kinetic with a maximum of a 4.5- to 6-fold increase at 30 min after glucose ingestion, a significant decrease over the next 60 min followed by an increase until the end of the oGTT. Lysophosphatidylcholines were also increased significantly. The findings of our metabolomics study reveal detailed insights in the complex physiological regulation of the metabolism during an oGTT offering novel perspectives of this widely used procedure.

Circulating trimethylamine N-Oxide (TMAO) levels predict cardiovascular disease (CVD), possibly by impacting on cholesterol metabolism and oxidative stress. Because hepatic TMAO production is regulated by insulin signalling and it is unclear whether and to what extent circulating TMAO levels associate with CVD risk, independently of insulin resistance and its important determinants fatty liver and visceral obesity, we have now addressed this question in 220 subjects who participated in the Tübingen Lifestyle Intervention Program. Visceral fat mass (r = 0.40, p < 0.0001), liver fat content (r = 0.23, p = 0.0005) and TMAO levels (r = 0.26, p < 0.0001) associated positively, and insulin sensitivity associated negatively (r = −0.18, p = 0.009) with carotid intima-media thickness (cIMT). Higher TMAO levels (std.−Beta 0.11, p = 0.03) predicted increased cIMT, independently of age, sex and visceral fat mass. While during the lifestyle intervention most cardiovascular risk parameters improved, mean TMAO levels did not change (p = 0.18). However, cIMT decreased significantly (p = 0.0056) only in subjects in the tertile with the largest decrease of TMAO levels (>20%). We provide novel information that increased serum TMAO levels associate with increased cIMT, independently of established cardiovascular risk markers, including insulin resistance, visceral obesity and fatty liver. Furthermore, the decrease of cIMT during a lifestyle intervention may be related to the decrease of TMAO levels.

Sensitive and high-resolution chromatographic-driven metabonomomics studies experienced major growth with the aid of new analytical technologies and bioinformatics software packages. Hence, data collections by LC-MS and data analyses by multivariate statistical methods are by far the most straightforward steps, and the detection of biomarker candidates can easily be achieved. However, the unequivocal identification of the detected metabolite candidates, including isomer elucidation, is still a crux of current metabonomics studies. Here we present a comprehensive analytical strategy for the elucidation of the molecular structure of metabolite biomarkers detected in a metabonomics study, exemplified analyzing spot urine of a cohort of healthy, insulin sensitive subjects and clinically well characterized prediabetic, insulin resistant individuals. An integrated approach of LC-MS fingerprinting, multivariate statistic analysis, LC-MSn experiments, micro preparation, FTICR-MS, GC retention index, database search, and generation of an isotope labeled standard was applied. Overall, we could demonstrate the efficiency of our analytical approach by the unambiguous elucidation of the molecular structure of an isomeric biomarker candidate detected in a complex human biofluid. The proposed strategy is a powerful new analytical tool, which will allow the definite identification of physiologically important molecules in metabonomics studies from basic biochemistry to clinical biomarker discovery.

Impaired glucose tolerance (IGT) which precedes overt type 2 diabetes (T2DM) for decades is associated with multiple metabolic alterations in insulin sensitive tissues. In an UPLC-qTOF-mass spectrometrydriven non-targeted metabonomics approach we investigated plasma as well as spot urine of 51 non-diabetic, overnight fasted individuals aiming to separate subjects with IGT from controls thereby identify pathways affected by the pre-diabetic metabolic state. We could clearly demonstrate that normal glucose tolerant (NGT) and IGT subjects clustered in two distinct groups independent of the investigated metabonome. These findings reflect considerable differences in individual metabolite fingerprints, both in plasma and urine. Pre-diabetes associated alterations in fatty acid-, tryptophan-, uric acid-, bile acid-, and lysophosphatidylcholine-metabolism, as well as the TCA cycle were identified. Of note, individuals with IGT also showed decreased levels of gut flora-associated metabolites namely hippuric acid, methylxanthine, methyluric acid, and 3-hydroxyhippuric acid. The findings of our non-targeted UPLC-qTOF-MS metabonomics analysis in plasma and spot urine of individuals with IGT vs NGT offers novel insights into the metabolic alterations occurring in the long, asymptomatic period preceding the manifestation of T2DM thereby giving prospects for new intervention targets.

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