A Systems Approach to Predict Oncometabolites via Context-Specific Genome-Scale Metabolic Networks

Altered metabolism in cancer cells has been viewed as a passive response required for a malignant transformation. However, this view has changed through the recently described metabolic oncogenic factors: mutated isocitrate dehydrogenases (IDH), succinate dehydrogenase (SDH), and fumarate hydratase (FH) that produce oncometabolites that competitively inhibit epigenetic regulation. In this study, we demonstrate in silico predictions of oncometabolites that have the potential to dysregulate epigenetic controls in nine types of cancer by incorporating massive scale genetic mutation information (collected from more than 1,700 cancer genomes), expression profiling data, and deploying Recon 2 to reconstruct context-specific genome-scale metabolic models. Our analysis predicted 15 compounds and 24 substructures of potential oncometabolites that could result from the loss-of-function and gain-of-function mutations of metabolic enzymes, respectively. These results suggest a substantial potential for discovering unidentified oncometabolites in various forms of cancers.     

From Metabolomics to Fluxomics: A Computational Procedure to Translate Metabolite Profiles into Metabolic Fluxes

We describe a believed-novel procedure for translating metabolite profiles (metabolome) into the set of metabolic fluxes (fluxome) from which they originated. Methodologically, computational modeling is integrated with an analytical platform comprising linear optimization, continuation and dynamic analyses, and metabolic control. The procedure was tested with metabolite profiles obtained from ex vivo mice Langendorff-heart preparations perfused with glucose. The metabolic profiles were analyzed using a detailed kinetic model of the glucose catabolic pathways including glycolysis, pentose phosphate (PP), glycogenolysis, and polyols to translate the glucose metabolome of the heart into the fluxome. After optimization, the ability of the model to simulate the initial metabolite profile was confirmed, and metabolic fluxes as well as the structure of control and regulation of the glucose catabolic network could be calculated. We show that the step catalyzed by phosphofructokinase together with ATP demand and glycogenolysis exert the highest control on the glycolytic flux. The negative flux control exerted by phosphofructokinase on the PP and polyol pathways revealed that the extent of glycolytic flux directly affects flux redirection through these pathways, i.e., the higher the glycolytic flux the lower the PP and polyols. This believed-novel methodological approach represents a step forward that may help in designing therapeutic strategies targeted to diagnose, prevent, and treat metabolic diseases.     

A Genetic Basis Behind Combining Ability and Breeding Values in Mono- and Di-genic Systems

Combining ability and breeding values are concepts of crucial importance in practical breeding. However, in early literature, methods of partitioning the genetic value into additive and dominance effects were described mostly in single gene and under random mating. Though average effect and average excess of gene substitution were defined by Fisher and a practial method of obtaining breeding values outlined by Falconer, concerted attempts are scarce in integrating the concepts of combining ability, breeding value and additive effects. Only a few workers have reported the values of those parameters for inbred populations while published reports on those effects in digenic systems with linkage appear to be not readily available. Practical breeders continue to use those concepts as if they were independent. This paper is therefore an attempt at filling the research gaps and setting those concepts in proper practical perspective.     

A Metabolomic Approach to Understanding the Metabolic Link between Obesity and Diabetes

Obesity and diabetes arise from an intricate interplay between both genetic and environmental factors. It is well recognized that obesity plays an important role in the development of insulin resistance and diabetes. Yet, the exact mechanism of the connection between obesity and diabetes is still not completely understood. Metabolomics is an analytical approach that aims to detect and quantify small metabolites. Recently, there has been an increased interest in the application of metabolomics to the identification of disease biomarkers, with a number of well-known biomarkers identified. Metabolomics is a potent approach to unravel the intricate relationships between metabolism, obesity and progression to diabetes and, at the same time, has potential as a clinical tool for risk evaluation and monitoring of disease. Moreover, metabolomics applications have revealed alterations in the levels of metabolites related to obesity-associated diabetes. This review focuses on the part that metabolomics has played in elucidating the roles of metabolites in the regulation of systemic metabolism relevant to obesity and diabetes. It also explains the possible metabolic relation and association between the two diseases. The metabolites with altered profiles in individual disorders and those that are specifically and similarly altered in both disorders are classified, categorized and summarized.     

A Genetic Approach to the Study of Neurogenesis and Myogenesis

Extensive sequences of morphological differentiation have beendescribed for Drosophila melanogaster neurogenesis and myogenesis.Neuroblasts and myoblasts become determined in the blastodermor shortly thereafter. Each blast cell will then morphologicallydifferentiate in vitro in sequences that closely resemble theevents in vivo. Functional neurons, muscle cells and neuromuscularjunctions form and these have normal biochemical and ultrastructuralcharacteristics. A mutant hunt was conducted which was designedto generate mutations specific to neurogenesis or myogenesis.One mutation was recovered that apparently interfered only withmyogenesis. The frequency of recovered mutations that affectedcell differentiation was low and led to estimates that relativelyfew genes are required to preserve the structural integrityof cells during neurogenesis and myogenesis.     

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.     

A Systems Biology Approach to Learning Autophagy

With its relevance to our understanding of eukaryotic cell function in the normal and disease state, autophagy is an important topic in modern cell biology; yet, few textbooks discuss autophagy beyond a two- or three-sentence summary. Here, we report an undergraduate/graduate class lesson for the in-depth presentation of autophagy using an active learning approach. By our method, students will work in small groups to solve problems and interpret an actual data set describing genes involved in autophagy. The problem-solving exercises and data set analysis will instill within the students a much greater understanding of the autophagy pathway than can be achieved by simple rote memorization of lecture materials; furthermore, the students will gain a general appreciation of the process by which data are interpreted and eventually formed into an understanding of a given pathway. As the data sets used in these class lessons are largely genomic and complementary in content, students will also understand first-hand the advantage of an integrative or systems biology study: No single data set can be used to define the pathway in full æ the information from multiple complementary studies must be integrated in order to recapitulate our present understanding of the pathways mediating autophagy. In total, our teaching methodology offers an effective presentation of autophagy as well as a general template for the discussion of nearly any signaling pathway within the eukaryotic kingdom.     

A Langevin Canonical Approach to the Dynamics of Chiral Systems: Populations and Coherences

A canonical framework for chiral two–level systems coupled to a bath of harmonic oscillators is developed to extract, from a stochastic dynamics, the thermodynamic equilibrium values of both the population difference and coherences. The incoherent and coherent tunneling regimes are analyzed for an Ohmic environment in terms of a critical temperature defined by the maximum of the heat capacity. The corresponding numerical results issued from solving a non‐linear coupled system of equations are fitted to approximate path–integral analytical expressions beyond the so‐called non‐interacting blip approximation in order to determine the different time scales governing both regimes. Chirality 25:514–520, 2013. © 2013 Wiley Periodicals, Inc.     

Systematic NMR Analysis of Stable Isotope Labeled Metabolite Mixtures in Plant and Animal Systems: Coarse Grained Views of Metabolic Pathways

Background

Metabolic phenotyping has become an important ‘bird''s-eye-view’ technology which can be applied to higher organisms, such as model plant and animal systems in the post-genomics and proteomics era. Although genotyping technology has expanded greatly over the past decade, metabolic phenotyping has languished due to the difficulty of ‘top-down’ chemical analyses. Here, we describe a systematic NMR methodology for stable isotope-labeling and analysis of metabolite mixtures in plant and animal systems.

Methodology/Principal Findings

The analysis method includes a stable isotope labeling technique for use in living organisms; a systematic method for simultaneously identifying a large number of metabolites by using a newly developed HSQC-based metabolite chemical shift database combined with heteronuclear multidimensional NMR spectroscopy; Principal Components Analysis; and a visualization method using a coarse-grained overview of the metabolic system. The database contains more than 1000 ¹H and ¹³C chemical shifts corresponding to 142 metabolites measured under identical physicochemical conditions. Using the stable isotope labeling technique in Arabidopsis T87 cultured cells and Bombyx mori, we systematically detected >450 HSQC peaks in each ¹³C-HSQC spectrum derived from model plant, Arabidopsis T87 cultured cells and the invertebrate animal model Bombyx mori. Furthermore, for the first time, efficient ¹³C labeling has allowed reliable signal assignment using analytical separation techniques such as 3D HCCH-COSY spectra in higher organism extracts.

Conclusions/Significance

Overall physiological changes could be detected and categorized in relation to a critical developmental phase change in B. mori by coarse-grained representations in which the organization of metabolic pathways related to a specific developmental phase was visualized on the basis of constituent changes of 56 identified metabolites. Based on the observed intensities of ¹³C atoms of given metabolites on development-dependent changes in the 56 identified ¹³C-HSQC signals, we have determined the changes in metabolic networks that are associated with energy and nitrogen metabolism.     

HPLC/HR-MS-Based Metabolite Profiling and Chemometrics: A Powerful Approach to Identify Bioactive Compounds from Abarema cochliacarpos

Despite its importance as a medicinal plant, there is a lack of studies that assessed the chemical composition of A. cochliacarpos extracts. Herein, we used a metabolite profiling approach and chemometrics as a powerful strategy to correlate the chemical composition with the antioxidant activity of A. cochliacarpos extracts. Extracts obtained with ethyl acetate showed greater antioxidant activity and higher total phenolic content than extracts obtained with hexane. The chemical composition was assessed by HPLC/HR-MS and it encompassed fatty alcohols, terpenoids, phenolic derivatives, lipids, carotenoid-like compounds, alkaloids, flavonoids, polyketides, and glycerophospholipids. Chemometrics successfully differentiated not only the chemical composition of extracts in response to the nature of the extraction solvent and the botanical part used during extraction but also it allowed us to associate the chemical composition with the antioxidant activity of the extracts, which might be particularly helpful for drug discovery and development programs.     

A Bayesian Approach to the Evolution of Metabolic Networks on a Phylogeny

The availability of genomes of many closely related bacteria with diverse metabolic capabilities offers the possibility of tracing metabolic evolution on a phylogeny relating the genomes to understand the evolutionary processes and constraints that affect the evolution of metabolic networks. Using simple (independent loss/gain of reactions) or complex (incorporating dependencies among reactions) stochastic models of metabolic evolution, it is possible to study how metabolic networks evolve over time. Here, we describe a model that takes the reaction neighborhood into account when modeling metabolic evolution. The model also allows estimation of the strength of the neighborhood effect during the course of evolution. We present Gibbs samplers for sampling networks at the internal node of a phylogeny and for estimating the parameters of evolution over a phylogeny without exploring the whole search space by iteratively sampling from the conditional distributions of the internal networks and parameters. The samplers are used to estimate the parameters of evolution of metabolic networks of bacteria in the genus Pseudomonas and to infer the metabolic networks of the ancestral pseudomonads. The results suggest that pathway maps that are conserved across the Pseudomonas phylogeny have a stronger neighborhood structure than those which have a variable distribution of reactions across the phylogeny, and that some Pseudomonas lineages are going through genome reduction resulting in the loss of a number of reactions from their metabolic networks.     

A Systems Approach to Sustainable Technical Product Design

Many existing methods for sustainable technical product design focus on environmental efficiency while lacking a framework for a holistic, sustainable design approach that includes combined social, technical, economic, and environmental aspects in the whole product life cycle, and that provides guidance on a technical product development level. This research proposes a framework for sustainable technical product design in the case of skis. We developed a ski under the Grown brand, benchmarked according to social, environmental, economic, and technical targets, following an initial sustainability assessment, and delivered the first environmental life cycle assessment (ELCA) and the first social life cycle assessment (SLCA) of skis. The framework applies a virtual development process as a combination of ELCA to calculate the environmental footprint as carbon equivalents of all materials and processes and a technical computer‐aided design (CAD) and computer‐aided engineering (CAE) simulation and virtual optimization using parameter studies for the nearly prototype‐free development of the benchmarked skis. The feedback loops between life cycle assessment (LCA) and virtual simulation led to the elimination of highly energy intensive materials, to the pioneering use of basalt fibers in skis, to the optimization of the use of natural materials using protective coatings from natural resins, and to the optimization of the production process. From an environmental perspective, a minimum 32% reduction in carbon equivalent emissions of materials in relation to other comparably performing skis has been achieved, as well as a pioneering step forward toward transparent communication of the environmental performance by the individual, comparable, and first published ski carbon footprint per volume unit.     

Arabidopsis Trichome Morphogenesis: A Genetic Approach to Studying Cytoskeletal Function

   apd: 3 July 2001