Metabolomics Applied to Diabetes Research: Moving From Information to Knowledge

Type 2 diabetes is caused by a complex set ofinteractions between genetic and environmentalfactors. Recent work has shown that human type2 diabetes is a constellation of disorders associ-ated with polymorphisms in a wide array of genes, with each individual gene accounting for 1 % of disease risk (1). Moreover, type 2 diabetes involves dysfunction of multiple organ systems, including impaired insulin action in muscle and adipose, defective control of hepatic glu-cose production, and insulin deficiency caused by loss of -cell mass and function (2). This complexity presents challenges for a full understanding of the molecular path-ways that contribute to the development of this major disease. Progress in this area may be aided by the recent advent of technologies for comprehensive metabolic anal-ysis, sometimes termed “metabolomics. ” Herein, we sum-marize key metabolomics methodologies, including nuclear magnetic resonance (NMR) and mass spectrome

Analysis of heterogeneity in T2_2-weighted MR images can differentiate pseudoprogression from progression in glioblastoma

$\textbf{Purpose}$ To develop an image analysis technique that distinguishes pseudoprogression from true progression by analyzing tumour heterogeneity in $T_2$-weighted images using topological descriptors of image heterogeneity called Minkowski functionals (MFs). $\textbf{Methods}$ Using a retrospective patient cohort ($n$ = 50), and blinded to treatment response outcome, unsupervised feature estimation was performed to investigate MFs for the presence of outliers, potential confounders, and sensitivity to treatment response. The progression and pseudoprogression groups were then unblinded and supervised feature selection was performed using MFs, size and signal intensity features. A support vector machine model was obtained and evaluated using a prospective test cohort. $\textbf{Results}$ The model gave a classification accuracy, using a combination of MFs and size features, of more than 85% in both retrospective and prospective datasets. A different feature selection method (Random Forest) and classifier (Lasso) gave the same results. Although not apparent to the reporting radiologist, the $T_2$-weighted hyperintensity phenotype of those patients with progression was heterogeneous, large and frond-like when compared to those with pseudoprogression. $\textbf{Conclusion}$ Analysis of heterogeneity, in $T_2$-weighted MR images, which are acquired routinely in the clinic, has the potential to detect an earlier treatment response allowing an early change in treatment strategy. Prospective validation of this technique in larger datasets is required.Funded by Medical Research Council/ Royal College of Radiologists (UK) Clinical Research Fellowship (G1000265); Cancer Research UK Clinical Research Fellowship; Addenbrookes Charitable Trust Award to TCB. Cancer Research UK Programme grant (C197/ A3514) to KMB

A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data

<p>Abstract</p> <p>Background</p> <p>A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk assessment and diagnostics. Here we focus on clinically relevant estimation of lipoprotein lipids by ¹H NMR spectroscopy of serum.</p> <p>Results</p> <p>A Bayesian methodology, with a biochemical motivation, is presented for a real ¹H NMR metabonomics data set of 75 serum samples. Lipoprotein lipid concentrations were independently obtained for these samples via ultracentrifugation and specific biochemical assays. The Bayesian models were constructed by Markov chain Monte Carlo (MCMC) and they showed remarkably good quantitative performance, the predictive R-values being 0.985 for the very low density lipoprotein triglycerides (VLDL-TG), 0.787 for the intermediate, 0.943 for the low, and 0.933 for the high density lipoprotein cholesterol (IDL-C, LDL-C and HDL-C, respectively). The modelling produced a kernel-based reformulation of the data, the parameters of which coincided with the well-known biochemical characteristics of the ¹H NMR spectra; particularly for VLDL-TG and HDL-C the Bayesian methodology was able to clearly identify the most characteristic resonances within the heavily overlapping information in the spectra. For IDL-C and LDL-C the resulting model kernels were more complex than those for VLDL-TG and HDL-C, probably reflecting the severe overlap of the IDL and LDL resonances in the ¹H NMR spectra.</p> <p>Conclusion</p> <p>The systematic use of Bayesian MCMC analysis is computationally demanding. Nevertheless, the combination of high-quality quantification and the biochemical rationale of the resulting models is expected to be useful in the field of metabonomics.</p

Metabolomic analysis of human disease and its application to the eye

Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach

Darstellung von N-Acetyl-lactosamin (4-β-D-Galaktopyranosyl-2-desoxy-2-acetamino-D-glucopyranose) aus Lactose. Aminozucker-synthesen IV

Aus dem Anilid der 3‐β‐D‐Galaktopyranosyl‐D‐arabinose läßt sich durch Addition von Blausäure, katalytische Halbhydrierung und Acetylierung das N‐Acetyl‐lactosamin (4‐β‐D‐Galaktopyranosyl‐N‐acetyl‐D‐glucosamin) in einer Ausbeute von 42% d. Th. gewinnen. Die synthetische Substanz ist mit dem aus Mekonium, aus Frauenmilch und aus dem Mucin des Schweinemagens erhaltenen Disaccharid identisch. Die Darstellung des genannten Anilids aus Lactose wird beschrieben

D-Galaktosamin aus D-Lyxose. Aminozucker-synthesen III

Ein durch Umsetzung von D‐Lyxose mit NH3 und HCN leicht erhältliches Aminonitril läßt sich durch katalytische Halbhydrierung in D‐Galaktosamin‐hydrochlorid (64–70% d. Th.) verwandeln. Das N‐Benzyl‐D‐galaktosaminsäure‐nitril liefert unter denselben Bedingungen nicht den Aminozucker, sondern D‐Galaktosaminsäure. Das Benzylamino‐nitril ist offenbar im Gegensatz zum Amino‐ und zum Phenylamino‐nitril ein cyclisches Imino‐lacton (IR‐Spektrum)