Reconstruction of a genome-scale metabolic model for Actinobacillus succinogenes 130Z.

BACKGROUND: Actinobacillus succinogenes is a promising bacterial catalyst for the bioproduction of succinic acid from low-cost raw materials. In this work, a genome-scale metabolic model was reconstructed and used to assess the metabolic capabilities of this microorganism under producing conditions. RESULTS: The model, iBP722, was reconstructed based on the functional reannotation of the complete genome sequence of A. succinogenes 130Z and manual inspection of metabolic pathways, covering 1072 enzymatic reactions associated with 722 metabolic genes that involve 713 metabolites. The highly curated model was effective in capturing the growth of A. succinogenes on various carbon sources, as well as the SA production under various growth conditions with fair agreement between experimental and predicted data. Calculated flux distributions under different conditions show that a number of metabolic pathways are affected by the activity of some metabolic enzymes at key nodes in metabolism, including the transport mechanism of carbon sources and the ability to fix carbon dioxide. CONCLUSIONS: The established genome-scale metabolic model can be used for model-driven strain design and medium alteration to improve succinic acid yields.

National Food, Nutrition, and Physical Activity Survey of the Portuguese General Population (2015-2016): Protocol for Design and Development.

BACKGROUND: The assessment of food consumption data using harmonized methodologies at the European level is fundamental to support the development of public policies. Portugal is one of the countries with the most outdated information on individual food consumption. OBJECTIVE: The objective of this study was to describe the design and methodology of the National Food, Nutrition and Physical Activity Survey, 2015-2016, developed to collect national and regional data on dietary habits, physical activity (PA), and nutritional status, in a representative sample of the Portuguese general population (3 months-84 years). METHODS: Participants were selected by multistage sampling, using the National Heath Registry as the sampling frame. Data collection, during 12 months, was harmonized according to European guidelines (EU-MENU, European Food Safety Authority [EFSA]). Computer-assisted personal interviewing (CAPI) was performed on a specific electronic platform synchronized with nutritional composition data and considering the FoodEx2 classification system. Dietary assessment was performed using 24-hour recalls (two nonconsecutive, 8-15 days apart) or food diaries in the case of children aged <10 years, complemented with a food propensity questionnaire; PA data (International Physical Activity Questionnaire [IPAQ], the Activity Choice Index [ACI], and 4-days PA diaries); sociodemographic data, and other health-related data were also collected. RESULTS: A sample of 6553 individuals completed the first interview, and 5811 participants completed two dietary assessments. The participation rate among eligible individuals was 33.38% (6553/19,635), considering the first interview, and 29.60% (5811/19,635) for the participants with two completed interviews (about 40% in children and adolescents and 20% in elderly individuals). Results of the survey will be disseminated in national and international scientific journals during 2018-2019. CONCLUSIONS: The survey will assist policy planning and management of national and European health programs on the improvement of nutritional status and risk assessment related to food hazards, and the enhancement of PA. The infrastructures and data driven from this Survey are a solid basis to the development of a future national surveillance system on diet, PA, and other health behaviors reproducible over time.

OptFlux: an open-source software platform for in silico metabolic engineering.

BACKGROUND: Over the last few years a number of methods have been proposed for the phenotype simulation of microorganisms under different environmental and genetic conditions. These have been used as the basis to support the discovery of successful genetic modifications of the microbial metabolism to address industrial goals. However, the use of these methods has been restricted to bioinformaticians or other expert researchers. The main aim of this work is, therefore, to provide a user-friendly computational tool for Metabolic Engineering applications. RESULTS: OptFlux is an open-source and modular software aimed at being the reference computational application in the field. It is the first tool to incorporate strain optimization tasks, i.e., the identification of Metabolic Engineering targets, using Evolutionary Algorithms/Simulated Annealing metaheuristics or the previously proposed OptKnock algorithm. It also allows the use of stoichiometric metabolic models for (i) phenotype simulation of both wild-type and mutant organisms, using the methods of Flux Balance Analysis, Minimization of Metabolic Adjustment or Regulatory on/off Minimization of Metabolic flux changes, (ii) Metabolic Flux Analysis, computing the admissible flux space given a set of measured fluxes, and (iii) pathway analysis through the calculation of Elementary Flux Modes. OptFlux also contemplates several methods for model simplification and other pre-processing operations aimed at reducing the search space for optimization algorithms. The software supports importing/exporting to several flat file formats and it is compatible with the SBML standard. OptFlux has a visualization module that allows the analysis of the model structure that is compatible with the layout information of Cell Designer, allowing the superimposition of simulation results with the model graph. CONCLUSIONS: The OptFlux software is freely available, together with documentation and other resources, thus bridging the gap from research in strain optimization algorithms and the final users. It is a valuable platform for researchers in the field that have available a number of useful tools. Its open-source nature invites contributions by all those interested in making their methods available for the community. Given its plug-in based architecture it can be extended with new functionalities. Currently, several plug-ins are being developed, including network topology analysis tools and the integration with Boolean network based regulatory models.

Metabolic network topology reveals transcriptional regulatory signatures of type 2 diabetes.

Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites--metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM.