Abstract: Lipidomic approaches are now widely used to investigate the relationship between lipid metabolism, health and disease. Large-scale lipidomics studies typically aim to quantify hundreds to thousands of lipid molecular species in a large number of samples. Consequently, high throughput methodology that can efficiently extract a wide range of lipids from biological samples is required. Current methods often rely on extraction in chloroform:methanol with or without two phase partitioning or other solvents, which are often incompatible with liquid chromatography electrospray ionization-tandem mass spectrometry (LC ESI-MS/MS). Here, we present a fast, simple extraction method that is suitable for high throughput LC ESI-MS/MS. Plasma (10 μL) was mixed with 100 μL 1-butanol:methanol (1:1 v/v) containing internal standards resulting in efficient extraction of all major lipid classes (including sterols, glycerolipids, glycerophospholipids and sphingolipids). Lipids were quantified using positive-ion mode LC ESI-MS/MS. The method showed high recovery (>90%) and reproducibility (%CV < 20%). It showed a strong correlation of all lipid measures with an established chloroform:methanol extraction method (R2 = 0.976). This method uses non-halogenated solvents, requires no drying or reconstitution steps and is suitable for large-scale LC ESI-MS/MS-based lipidomic analyses in research and clinical laboratories.
Abstract: Increased consumption of sugar and fructose as sweeteners has resulted in the utilization of fructose as an alternative metabolic fuel that may compete with glucose and alter its metabolism. To explore this, human Simpson-Golabi-Behmel Syndrome (SGBS) preadipocytes were differentiated to adipocytes in the presence of 0, 1, 2.5, 5 or 10 mM of fructose added to a medium containing 5 mM of glucose representing the normal blood glucose concentration. Targeted tracer [1,2-13C2]-d-glucose fate association approach was employed to examine the influence of fructose on the intermediary metabolism of glucose. Increasing concentrations of fructose robustly increased the oxidation of [1,2-13C2]-d-glucose to 13CO2 (p < 0.000001). However, glucose-derived 13CO2 negatively correlated with 13C labeled glutamate, 13C palmitate, and M+1 labeled lactate. These are strong markers of limited tricarboxylic acid (TCA) cycle, fatty acid synthesis, pentose cycle fluxes, substrate turnover and NAD+/NADP+ or ATP production from glucose via complete oxidation, indicating diminished mitochondrial energy metabolism. Contrarily, a positive correlation was observed between glucose-derived 13CO2 formed and 13C oleate and doses of fructose which indicate the elongation and desaturation of palmitate to oleate for storage. Collectively, these results suggest that fructose preferentially drives glucose through serine oxidation glycine cleavage (SOGC pathway) one-carbon cycle for NAD+/NADP+ production that is utilized in fructose-induced lipogenesis and storage in adipocytes.
Abstract: Computational breath analysis is a growing research area aiming at identifying volatile organic compounds (VOCs) in human breath to assist medical diagnostics of the next generation. While inexpensive and non-invasive bioanalytical technologies for metabolite detection in exhaled air and bacterial/fungal vapor exist and the first studies on the power of supervised machine learning methods for profiling of the resulting data were conducted, we lack methods to extract hidden data features emerging from confounding factors. Here, we present Carotta, a new cluster analysis framework dedicated to uncovering such hidden substructures by sophisticated unsupervised statistical learning methods. We study the power of transitivity clustering and hierarchical clustering to identify groups of VOCs with similar expression behavior over most patient breath samples and/or groups of patients with a similar VOC intensity pattern. This enables the discovery of dependencies between metabolites. On the one hand, this allows us to eliminate the effect of potential confounding factors hindering disease classification, such as smoking. On the other hand, we may also identify VOCs associated with disease subtypes or concomitant diseases. Carotta is an open source software with an intuitive graphical user interface promoting data handling, analysis and visualization. The back-end is designed to be modular, allowing for easy extensions with plugins in the future, such as new clustering methods and statistics. It does not require much prior knowledge or technical skills to operate. We demonstrate its power and applicability by means of one artificial dataset. We also apply Carotta exemplarily to a real-world example dataset on chronic obstructive pulmonary disease (COPD). While the artificial data are utilized as a proof of concept, we will demonstrate how Carotta finds candidate markers in our real dataset associated with confounders rather than the primary disease (COPD) and bronchial carcinoma (BC). Carotta is publicly available at http://carotta.compbio.sdu.dk .
Abstract: Metabolism is generally required for cellular maintenance and for the generation of offspring under conditions that support growth. The rates, yields (efficiencies), adaptation time and robustness of metabolism are therefore key determinants of cellular fitness. For biotechnological applications and our understanding of the evolution of metabolism, it is necessary to figure out how the functional system properties of metabolism can be optimized, via adjustments of the kinetics and expression of enzymes, and by rewiring metabolism. The trade-offs that can occur during such optimizations then indicate fundamental limits to evolutionary innovations and bioengineering. In this paper, we review several theoretical and experimental findings about mechanisms for metabolic optimization.
Abstract: BioCyc.org is a genome and metabolic pathway web portal covering 5500 organisms, including Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. These organism-specific databases have undergone variable degrees of curation. The EcoCyc (Escherichia coli Encyclopedia) database is the most highly curated; its contents have been derived from 27,000 publications. The MetaCyc (Metabolic Encyclopedia) database within BioCyc is a “universal” metabolic database that describes pathways, reactions, enzymes and metabolites from all domains of life. Metabolic pathways provide an organizing framework for analyzing metabolomics data, and the BioCyc website provides computational operations for metabolomics data that include metabolite search and translation of metabolite identifiers across multiple metabolite databases. The site allows researchers to store and manipulate metabolite lists using a facility called SmartTables, which supports metabolite enrichment analysis. That analysis operation identifies metabolite sets that are statistically over-represented for the substrates of specific metabolic pathways. BioCyc also enables visualization of metabolomics data on individual pathway diagrams and on the organism-specific metabolic map diagrams that are available for every BioCyc organism. Most of these operations are available both interactively and as programmatic web services.