Independent associations between sedentary time, moderate-to-vigorous physical activity, cardiorespiratory fitness and cardio-metabolic health : a cross-sectional study

We aimed to study the independent associations of sedentary time (ST), moderate-to-vigorous physical activity (MVPA), and objectively measured cardiorespiratory fitness (CRF) with clustered cardio-metabolic risk and its individual components (waist circumference, fasting glucose, HDL-cholesterol, triglycerides and blood pressure). We also investigated whether any associations between MVPA or ST and clustered cardio-metabolic risk were mediated by CRF. MVPA, ST, CRF and individual cardio-metabolic components were measured in a population-based sample of 341 adults (age 53.8 +/- 8.9 years; 61% men) between 2012 and 2014. MVPA and ST were measured with the SenseWear pro 3 Armband and CRF was measured with a maximal exercise test. Multiple linear regression models and the product of coefficients method were used to examine independent associations and mediation effects, respectively. Results showed that low MVPA and low CRF were associated with a higher clustered cardio-metabolic risk (beta = -0.26 and beta = -0.43, both p<0.001, respectively). CRF explained 73% of the variance in the association between MVPA and clustered cardio-metabolic risk and attenuated this association to non-significance. After mutual adjustment for MVPA and ST, CRF was the most important risk factor for a higher clustered cardio-metabolic risk (beta = -0.39, p<0.001). In conclusion, because of the mediating role of CRF, lifestyle-interventions need to be feasible yet challenging enough to lead to increases in CRF to improve someone's cardio-metabolic health

An improved multi-parametric programming algorithm for flux balance analysis of metabolic networks

Flux balance analysis has proven an effective tool for analyzing metabolic networks. In flux balance analysis, reaction rates and optimal pathways are ascertained by solving a linear program, in which the growth rate is maximized subject to mass-balance constraints. A variety of cell functions in response to environmental stimuli can be quantified using flux balance analysis by parameterizing the linear program with respect to extracellular conditions. However, for most large, genome-scale metabolic networks of practical interest, the resulting parametric problem has multiple and highly degenerate optimal solutions, which are computationally challenging to handle. An improved multi-parametric programming algorithm based on active-set methods is introduced in this paper to overcome these computational difficulties. Degeneracy and multiplicity are handled, respectively, by introducing generalized inverses and auxiliary objective functions into the formulation of the optimality conditions. These improvements are especially effective for metabolic networks because their stoichiometry matrices are generally sparse; thus, fast and efficient algorithms from sparse linear algebra can be leveraged to compute generalized inverses and null-space bases. We illustrate the application of our algorithm to flux balance analysis of metabolic networks by studying a reduced metabolic model of Corynebacterium glutamicum and a genome-scale model of Escherichia coli. We then demonstrate how the critical regions resulting from these studies can be associated with optimal metabolic modes and discuss the physical relevance of optimal pathways arising from various auxiliary objective functions. Achieving more than five-fold improvement in computational speed over existing multi-parametric programming tools, the proposed algorithm proves promising in handling genome-scale metabolic models.Comment: Accepted in J. Optim. Theory Appl. First draft was submitted on August 4th, 201

Menadione-induced oxidative stress re-shapes the oxylipin profile of Aspergillus flavus and its lifestyle

Aspergillus flavus is an efficient producer of mycotoxins, particularly aflatoxin B1, probably the most hepatocarcinogenic naturally-occurring compound. Although the inducing agents of toxin synthesis are not unanimously identified, there is evidence that oxidative stress is one of the main actors in play. In our study, we use menadione, a quinone extensively implemented in studies on ROS response in animal cells, for causing stress to A. flavus. For uncovering the molecular determinants that drive A. flavus in challenging oxidative stress conditions, we have evaluated a wide spectrum of several different parameters, ranging from metabolic (ROS and oxylipin profile) to transcriptional analysis (RNA-seq). There emerges a scenario in which A. flavus activates several metabolic processes under oxidative stress conditions for limiting the ROS-associated detrimental effects, as well as for triggering adaptive and escape strategies

Computing the shortest elementary flux modes in genome-scale metabolic networks

This article is available open access through the publisher’s website through the link below. Copyright @ The Author 2009.Motivation: Elementary flux modes (EFMs) represent a key concept to analyze metabolic networks from a pathway-oriented perspective. In spite of considerable work in this field, the computation of the full set of elementary flux modes in large-scale metabolic networks still constitutes a challenging issue due to its underlying combinatorial complexity. Results: In this article, we illustrate that the full set of EFMs can be enumerated in increasing order of number of reactions via integer linear programming. In this light, we present a novel procedure to efficiently determine the K-shortest EFMs in large-scale metabolic networks. Our method was applied to find the K-shortest EFMs that produce lysine in the genome-scale metabolic networks of Escherichia coli and Corynebacterium glutamicum. A detailed analysis of the biological significance of the K-shortest EFMs was conducted, finding that glucose catabolism, ammonium assimilation, lysine anabolism and cofactor balancing were correctly predicted. The work presented here represents an important step forward in the analysis and computation of EFMs for large-scale metabolic networks, where traditional methods fail for networks of even moderate size. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online (http://bioinformatics.oxfordjournals.org/cgi/content/full/btp564/DC1).Fundação Calouste Gulbenkian, Fundação para a Ciência e a Tecnologia (FCT) and Siemens SA Portugal

Deep Proteomics of Mouse Skeletal Muscle Enables Quantitation of Protein Isoforms, Metabolic Pathways, and Transcription Factors

Skeletal muscle constitutes 40% of individual body mass and plays vital roles in locomotion and whole-body metabolism. Proteomics of skeletal muscle is challenging because of highly abundant contractile proteins that interfere with detection of regulatory proteins. Using a state-of-the art MS workflow and a strategy to map identifications from the C2C12 cell line model to tissues, we identified a total of 10,218 proteins, including skeletal muscle specific transcription factors like myod1 and myogenin and circadian clock proteins. We obtain absolute abundances for proteins expressed in a muscle cell line and skeletal muscle, which should serve as a valuable resource. Quantitation of protein isoforms of glucose uptake signaling pathways and in glucose and lipid metabolic pathways provides a detailed metabolic map of the cell line compared with tissue. This revealed unexpectedly complex regulation of AMP-activated protein kinase and insulin signaling in muscle tissue at the level of enzyme isoforms

Energy metabolism in human pluripotent stem cells and their differentiated counterparts

Background: Human pluripotent stem cells have the ability to generate all cell types present in the adult organism, therefore harboring great potential for the in vitro study of differentiation and for the development of cell-based therapies. Nonetheless their use may prove challenging as incomplete differentiation of these cells might lead to tumoregenicity. Interestingly, many cancer types have been reported to display metabolic modifications with features that might be similar to stem cells. Understanding the metabolic properties of human pluripotent stem cells when compared to their differentiated counterparts can thus be of crucial importance. Furthermore recent data has stressed distinct features of different human pluripotent cells lines, namely when comparing embryo-derived human embryonic stem cells (hESCs) and induced pluripotent stem cells (IPSCs) reprogrammed from somatic cells. Methodology/Principal Findings: We compared the energy metabolism of hESCs, IPSCs, and their somatic counterparts. Focusing on mitochondria, we tracked organelle localization and morphology. Furthermore we performed gene expression analysis of several pathways related to the glucose metabolism, including glycolysis, the pentose phosphate pathway and the tricarboxylic acid (TCA) cycle. In addition we determined oxygen consumption rates (OCR) using a metabolic extracellular flux analyzer, as well as total intracellular ATP levels by high performance liquid chromatography (HPLC). Finally we explored the expression of key proteins involved in the regulation of glucose metabolism. Conclusions/Findings: Our results demonstrate that, although the metabolic signature of IPSCs is not identical to that of hESCs, nonetheless they cluster with hESCs rather than with their somatic counterparts. ATP levels, lactate production and OCR revealed that human pluripotent cells rely mostly on glycolysis to meet their energy demands. Furthermore, our work points to some of the strategies which human pluripotent stem cells may use to maintain high glycolytic rates, such as high levels of hexokinase II and inactive pyruvate dehydrogenase (PDH). © 2011 Varum et al