Next Issue
Previous Issue

Table of Contents

Metabolites, Volume 5, Issue 4 (December 2015) , Pages 536-813

  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Readerexternal link to open them.
View options order results:
result details:
Displaying articles 1-11
Export citation of selected articles as:
Open AccessArticle Flux Balance Analysis Inspired Bioprocess Upgrading for Lycopene Production by a Metabolically Engineered Strain of Yarrowia lipolytica
Metabolites 2015, 5(4), 794-813; https://doi.org/10.3390/metabo5040794
Received: 29 August 2015 / Revised: 1 December 2015 / Accepted: 10 December 2015 / Published: 21 December 2015
Cited by 11 | Viewed by 1892 | PDF Full-text (318 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Genome-scale metabolic models embody a significant advantage of systems biology since their applications as metabolic flux simulation models enable predictions for the production of industrially-interesting metabolites. The biotechnological production of lycopene from Yarrowia lipolytica is an emerging scope that has not been fully [...] Read more.
Genome-scale metabolic models embody a significant advantage of systems biology since their applications as metabolic flux simulation models enable predictions for the production of industrially-interesting metabolites. The biotechnological production of lycopene from Yarrowia lipolytica is an emerging scope that has not been fully scrutinized, especially for what concerns cultivation conditions of newly generated engineered strains. In this study, by combining flux balance analysis (FBA) and Plackett-Burman design, we screened chemicals for lycopene production from a metabolically engineered strain of Y. lipolytica. Lycopene concentrations of 126 and 242 mg/L were achieved correspondingly from the FBA-independent and the FBA-assisted designed media in fed-batch cultivation mode. Transcriptional studies revealed upregulations of heterologous genes in media designed according to FBA, thus implying the efficiency of model predictions. Our study will potentially support upgraded lycopene and other terpenoids production from existing or prospect bioengineered strains of Y. lipolytica and/or closely related yeast species. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
Figures

Figure 1

Open AccessArticle Quasi-Steady-State Analysis based on Structural Modules and Timed Petri Net Predict System’s Dynamics: The Life Cycle of the Insulin Receptor
Metabolites 2015, 5(4), 766-793; https://doi.org/10.3390/metabo5040766
Received: 11 September 2015 / Revised: 23 November 2015 / Accepted: 9 December 2015 / Published: 17 December 2015
Cited by 3 | Viewed by 1954 | PDF Full-text (3475 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The insulin-dependent activation and recycling of the insulin receptor play an essential role in the regulation of the energy metabolism, leading to a special interest for pharmaceutical applications. Thus, the recycling of the insulin receptor has been intensively investigated, experimentally as well as [...] Read more.
The insulin-dependent activation and recycling of the insulin receptor play an essential role in the regulation of the energy metabolism, leading to a special interest for pharmaceutical applications. Thus, the recycling of the insulin receptor has been intensively investigated, experimentally as well as theoretically. We developed a time-resolved, discrete model to describe stochastic dynamics and study the approximation of non-linear dynamics in the context of timed Petri nets. Additionally, using a graph-theoretical approach, we analyzed the structure of the regulatory system and demonstrated the close interrelation of structural network properties with the kinetic behavior. The transition invariants decomposed the model into overlapping subnetworks of various sizes, which represent basic functional modules. Moreover, we computed the quasi-steady states of these subnetworks and demonstrated that they are fundamental to understand the dynamic behavior of the system. The Petri net approach confirms the experimental results of insulin-stimulated degradation of the insulin receptor, which represents a common feature of insulin-resistant, hyperinsulinaemic states. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
Figures

Figure 1

Open AccessFeature PaperReview Bioactive Plant Metabolites in the Management of Non-Communicable Metabolic Diseases: Looking at Opportunities beyond the Horizon
Metabolites 2015, 5(4), 733-765; https://doi.org/10.3390/metabo5040733
Received: 8 October 2015 / Revised: 2 December 2015 / Accepted: 9 December 2015 / Published: 12 December 2015
Cited by 7 | Viewed by 2164 | PDF Full-text (427 KB) | HTML Full-text | XML Full-text
Abstract
There has been an unprecedented worldwide rise in non-communicable metabolic diseases (NCDs), particularly cardiovascular diseases (CVD) and diabetes. While modern pharmacotherapy has decreased the mortality in the existing population, it has failed to stem the rise. Furthermore, a large segment of the world [...] Read more.
There has been an unprecedented worldwide rise in non-communicable metabolic diseases (NCDs), particularly cardiovascular diseases (CVD) and diabetes. While modern pharmacotherapy has decreased the mortality in the existing population, it has failed to stem the rise. Furthermore, a large segment of the world population cannot afford expensive pharmacotherapy. Therefore, there is an urgent need for inexpensive preventive measures to control the rise in CVD and diabetes and associated co-morbidities. The purpose of this review is to explore the role of food bioactives in prevention of NCDs. To this end, we have critically analyzed the possible utility of three classes of food bioactives: (a) resistant starch, a metabolically resistant carbohydrate known to favorably modulate insulin secretion and glucose metabolism; (b) cyclo (His-Pro), a food-derived cyclic dipeptides; and (c) polyphenol-rich berries. Finally, we have also briefly outlined the strategies needed to prepare these food-bioactives for human use. Full article
(This article belongs to the Special Issue Metabolomic Studies in Metabolic Diseases)
Figures

Figure 1

Open AccessReview Obesity-Related Chronic Kidney Disease—The Role of Lipid Metabolism
Metabolites 2015, 5(4), 720-732; https://doi.org/10.3390/metabo5040720
Received: 3 November 2015 / Revised: 1 December 2015 / Accepted: 8 December 2015 / Published: 11 December 2015
Cited by 25 | Viewed by 2882 | PDF Full-text (803 KB) | HTML Full-text | XML Full-text
Abstract
Obesity is an independent risk factor for chronic kidney disease (CKD). The mechanisms linking obesity and CKD include systemic changes such as high blood pressure and hyperglycemia, and intrarenal effects relating to lipid accumulation. Normal lipid metabolism is integral to renal physiology and [...] Read more.
Obesity is an independent risk factor for chronic kidney disease (CKD). The mechanisms linking obesity and CKD include systemic changes such as high blood pressure and hyperglycemia, and intrarenal effects relating to lipid accumulation. Normal lipid metabolism is integral to renal physiology and disturbances of renal lipid and energy metabolism are increasingly being linked with kidney disease. AMP-activated protein kinase (AMPK) and acetyl-CoA carboxylase (ACC) are important regulators of fatty acid oxidation, which is frequently abnormal in the kidney with CKD. A high fat diet reduces renal AMPK activity, thereby contributing to reduced fatty acid oxidation and energy imbalance, and treatments to activate AMPK are beneficial in animal models of obesity-related CKD. Studies have found that the specific cell types affected by excessive lipid accumulation are proximal tubular cells, podocytes, and mesangial cells. Targeting disturbances of renal energy metabolism is a promising approach to addressing the current epidemic of obesity-related kidney disease. Full article
(This article belongs to the Special Issue Metabolomic Studies in Metabolic Diseases)
Figures

Figure 1

Open AccessArticle Effective Estimation of Dynamic Metabolic Fluxes Using 13C Labeling and Piecewise Affine Approximation: From Theory to Practical Applicability
Metabolites 2015, 5(4), 697-719; https://doi.org/10.3390/metabo5040697
Received: 1 October 2015 / Revised: 11 November 2015 / Accepted: 26 November 2015 / Published: 4 December 2015
Cited by 1 | Viewed by 2288 | PDF Full-text (757 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The design of microbial production processes relies on rational choices for metabolic engineering of the production host and the process conditions. These require a systematic and quantitative understanding of cellular regulation. Therefore, a novel method for dynamic flux identification using quantitative metabolomics and [...] Read more.
The design of microbial production processes relies on rational choices for metabolic engineering of the production host and the process conditions. These require a systematic and quantitative understanding of cellular regulation. Therefore, a novel method for dynamic flux identification using quantitative metabolomics and 13C labeling to identify piecewise-affine (PWA) flux functions has been described recently. Obtaining flux estimates nevertheless still required frequent manual reinitalization to obtain a good reproduction of the experimental data and, moreover, did not optimize on all observables simultaneously (metabolites and isotopomer concentrations). In our contribution we focus on measures to achieve faster and robust dynamic flux estimation which leads to a high dimensional parameter estimation problem. Specifically, we address the following challenges within the PWA problem formulation: (1) Fast selection of sufficient domains for the PWA flux functions, (2) Control of over-fitting in the concentration space using shape-prescriptive modeling and (3) robust and efficient implementation of the parameter estimation using the hybrid implicit filtering algorithm. With the improvements we significantly speed up the convergence by efficiently exploiting that the optimization problem is partly linear. This allows application to larger-scale metabolic networks and demonstrates that the proposed approach is not purely theoretical, but also applicable in practice. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
Figures

Figure 1

Open AccessArticle Comparative Lipidomics of Caenorhabditis elegans Metabolic Disease Models by SWATH Non-Targeted Tandem Mass Spectrometry
Metabolites 2015, 5(4), 677-696; https://doi.org/10.3390/metabo5040677
Received: 30 September 2015 / Revised: 29 October 2015 / Accepted: 4 November 2015 / Published: 11 November 2015
Cited by 14 | Viewed by 3382 | PDF Full-text (641 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Tandem mass spectrometry (MS/MS) with Sequential Window Acquisition of all Theoretical (SWATH) mass spectra generates a comprehensive archive of lipid species within an extract for retrospective, quantitative MS/MS analysis. Here we apply this new technology in Caenorhabditis elegans (C. elegans) to [...] Read more.
Tandem mass spectrometry (MS/MS) with Sequential Window Acquisition of all Theoretical (SWATH) mass spectra generates a comprehensive archive of lipid species within an extract for retrospective, quantitative MS/MS analysis. Here we apply this new technology in Caenorhabditis elegans (C. elegans) to identify potential lipid mediators and pathways. The DAF-1 type I TGF-β and DAF-2 insulin receptors transmit endocrine signals that couple metabolic status to fertility and lifespan. Mutations in daf-1 and daf-2 reduce prostaglandin-endoperoxide synthase (i.e., Cox)-independent prostaglandin synthesis, increase triacylglyceride storage, and alter transcription of numerous lipid metabolism genes. However, the extent to which DAF-1 and DAF-2 signaling modulate lipid metabolism and the underlying mechanisms are not well understood. MS/MSALL with SWATH analysis across the groups identified significant changes in numerous lipids, including specific triacylglycerols, diacylglycerols, and phosphatidylinositols. Examples are provided, using retrospective neutral loss and precursor ion scans as well as MS/MS spectra, to help identify annotated lipids and search libraries for lipids of interest. As proof of principle, we used comparative lipidomics to investigate the prostaglandin metabolism pathway. SWATH data support an unanticipated model: Cox-independent prostaglandin synthesis may involve lysophosphatidylcholine and other lyso glycerophospholipids. This study showcases the power of comprehensive, retrospectively searchable lipid archives as a systems approach for biological discovery in genetic animal models. Full article
Figures

Figure 1

Open AccessArticle Modeling and Simulation of Optimal Resource Management during the Diurnal Cycle in Emiliania huxleyi by Genome-Scale Reconstruction and an Extended Flux Balance Analysis Approach
Metabolites 2015, 5(4), 659-676; https://doi.org/10.3390/metabo5040659
Received: 13 June 2015 / Revised: 14 October 2015 / Accepted: 22 October 2015 / Published: 28 October 2015
Cited by 8 | Viewed by 2096 | PDF Full-text (1548 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The coccolithophorid unicellular alga Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For [...] Read more.
The coccolithophorid unicellular alga Emiliania huxleyi is known to form large blooms, which have a strong effect on the marine carbon cycle. As a photosynthetic organism, it is subjected to a circadian rhythm due to the changing light conditions throughout the day. For a better understanding of the metabolic processes under these periodically-changing environmental conditions, a genome-scale model based on a genome reconstruction of the E. huxleyi strain CCMP 1516 was created. It comprises 410 reactions and 363 metabolites. Biomass composition is variable based on the differentiation into functional biomass components and storage metabolites. The model is analyzed with a flux balance analysis approach called diurnal flux balance analysis (diuFBA) that was designed for organisms with a circadian rhythm. It allows storage metabolites to accumulate or be consumed over the diurnal cycle, while keeping the structure of a classical FBA problem. A feature of this approach is that the production and consumption of storage metabolites is not defined externally via the biomass composition, but the result of optimal resource management adapted to the diurnally-changing environmental conditions. The model in combination with this approach is able to simulate the variable biomass composition during the diurnal cycle in proximity to literature data. Full article
(This article belongs to the Special Issue Metabolism and Systems Biology)
Figures

Figure 1

Open AccessReview Advances in Metabolic Engineering of Cyanobacteria for Photosynthetic Biochemical Production
Metabolites 2015, 5(4), 636-658; https://doi.org/10.3390/metabo5040636
Received: 2 July 2015 / Revised: 30 September 2015 / Accepted: 22 October 2015 / Published: 27 October 2015
Cited by 23 | Viewed by 3139 | PDF Full-text (818 KB) | HTML Full-text | XML Full-text
Abstract
Engineering cyanobacteria into photosynthetic microbial cell factories for the production of biochemicals and biofuels is a promising approach toward sustainability. Cyanobacteria naturally grow on light and carbon dioxide, bypassing the need of fermentable plant biomass and arable land. By tapping into the central [...] Read more.
Engineering cyanobacteria into photosynthetic microbial cell factories for the production of biochemicals and biofuels is a promising approach toward sustainability. Cyanobacteria naturally grow on light and carbon dioxide, bypassing the need of fermentable plant biomass and arable land. By tapping into the central metabolism and rerouting carbon flux towards desirable compound production, cyanobacteria are engineered to directly convert CO2 into various chemicals. This review discusses the diversity of bioproducts synthesized by engineered cyanobacteria, the metabolic pathways used, and the current engineering strategies used for increasing their titers. Full article
(This article belongs to the Special Issue Metabolic Engineering and Synthetic Biology)
Figures

Figure 1

Open AccessArticle Design Principles as a Guide for Constraint Based and Dynamic Modeling: Towards an Integrative Workflow
Metabolites 2015, 5(4), 601-635; https://doi.org/10.3390/metabo5040601
Received: 14 August 2015 / Accepted: 10 October 2015 / Published: 16 October 2015
Cited by 2 | Viewed by 2006 | PDF Full-text (1526 KB) | HTML Full-text | XML Full-text
Abstract
During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it [...] Read more.
During the last 10 years, systems biology has matured from a fuzzy concept combining omics, mathematical modeling and computers into a scientific field on its own right. In spite of its incredible potential, the multilevel complexity of its objects of study makes it very difficult to establish a reliable connection between data and models. The great number of degrees of freedom often results in situations, where many different models can explain/fit all available datasets. This has resulted in a shift of paradigm from the initially dominant, maybe naive, idea of inferring the system out of a number of datasets to the application of different techniques that reduce the degrees of freedom before any data set is analyzed. There is a wide variety of techniques available, each of them can contribute a piece of the puzzle and include different kinds of experimental information. But the challenge that remains is their meaningful integration. Here we show some theoretical results that enable some of the main modeling approaches to be applied sequentially in a complementary manner, and how this workflow can benefit from evolutionary reasoning to keep the complexity of the problem in check. As a proof of concept, we show how the synergies between these modeling techniques can provide insight into some well studied problems: Ammonia assimilation in bacteria and an unbranched linear pathway with end-product inhibition. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
Figures

Figure 1

Open AccessReview Cancer Metabolism and Drug Resistance
Metabolites 2015, 5(4), 571-600; https://doi.org/10.3390/metabo5040571
Received: 28 August 2015 / Revised: 25 September 2015 / Accepted: 28 September 2015 / Published: 30 September 2015
Cited by 41 | Viewed by 3736 | PDF Full-text (419 KB) | HTML Full-text | XML Full-text
Abstract
Metabolic alterations, driven by genetic and epigenetic factors, have long been known to be associated with the etiology of cancer. Furthermore, accumulating evidence suggest that cancer metabolism is intimately linked to drug resistance, which is currently one of the most important challenges in [...] Read more.
Metabolic alterations, driven by genetic and epigenetic factors, have long been known to be associated with the etiology of cancer. Furthermore, accumulating evidence suggest that cancer metabolism is intimately linked to drug resistance, which is currently one of the most important challenges in cancer treatment. Altered metabolic pathways help cancer cells to proliferate at a rate higher than normal, adapt to nutrient limited conditions, and develop drug resistance phenotypes. Application of systems biology, boosted by recent advancement of novel high-throughput technologies to obtain cancer-associated, transcriptomic, proteomic and metabolomic data, is expected to make a significant contribution to our understanding of metabolic properties related to malignancy. Indeed, despite being at a very early stage, quantitative data obtained from the omics platforms and through applications of 13C metabolic flux analysis (MFA) in in vitro studies, researchers have already began to gain insight into the complex metabolic mechanisms of cancer, paving the way for selection of molecular targets for therapeutic interventions. In this review, we discuss some of the major findings associated with the metabolic pathways in cancer cells and also discuss new evidences and achievements on specific metabolic enzyme targets and target-directed small molecules that can potentially be used as anti-cancer drugs. Full article
(This article belongs to the Special Issue Metabolic Flux Analysis)
Figures

Figure 1

Open AccessArticle Using Gene Essentiality and Synthetic Lethality Information to Correct Yeast and CHO Cell Genome-Scale Models
Metabolites 2015, 5(4), 536-570; https://doi.org/10.3390/metabo5040536
Received: 28 July 2015 / Revised: 4 September 2015 / Accepted: 23 September 2015 / Published: 29 September 2015
Cited by 10 | Viewed by 2316 | PDF Full-text (2436 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. [...] Read more.
Essentiality (ES) and Synthetic Lethality (SL) information identify combination of genes whose deletion inhibits cell growth. This information is important for both identifying drug targets for tumor and pathogenic bacteria suppression and for flagging and avoiding gene deletions that are non-viable in biotechnology. In this study, we performed a comprehensive ES and SL analysis of two important eukaryotic models (S. cerevisiae and CHO cells) using a bilevel optimization approach introduced earlier. Information gleaned from this study is used to propose specific model changes to remedy inconsistent with data model predictions. Even for the highly curated Yeast 7.11 model we identified 50 changes (metabolic and GPR) leading to the correct prediction of an additional 28% of essential genes and 36% of synthetic lethals along with a 53% reduction in the erroneous identification of essential genes. Due to the paucity of mutant growth phenotype data only 12 changes were made for the CHO 1.2 model leading to an additional correctly predicted 11 essential and eight non-essential genes. Overall, we find that CHO 1.2 was 76% less accurate than the Yeast 7.11 metabolic model in predicting essential genes. Based on this analysis, 14 (single and double deletion) maximally informative experiments are suggested to improve the CHO cell model by using information from a mouse metabolic model. This analysis demonstrates the importance of single and multiple knockout phenotypes in assessing and improving model reconstructions. The advent of techniques such as CRISPR opens the door for the global assessment of eukaryotic models. Full article
Figures

Figure 1

Metabolites EISSN 2218-1989 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top