Serum oxidative stress-induced repression of Nrf2 and GSH depletion: a mechanism potentially involved in endothelial dysfunction of young smokers

AbstractBackground: Although oxidative stress plays a major role in endothelial dysfunction (ED), the role of glutathione (GSH), ofnuclear erythroid-related factor 2 (Nrf2) and of related antioxidant genes (ARE) are yet unknown. In this study we combined an in vivo with an in vitro model to assess whether cigarette smoking affects flow-mediated vasodilation (FMD), GSHconcentrations and the Nrf2/ARE pathway in human umbilical vein endothelial cells (HUVECs).Methods and Results: 52 healthy subjects (26 non-smokers and 26 heavy smokers) were enrolled in this study. In smokerswe demonstrated increased oxidative stress, i.e., reduced concentrations of GSH and increased concentrations of oxidationproducts of the phospholipid 1-palmitoyl-2-arachidonyl-sn-glycero-3-phosphorylcholine (oxPAPC) in serum and inperipheral blood mononuclear cells (PBMC), used as in vivo surrogates of endothelial cells. Moreover we showedimpairment of FMD in smokers and a positive correlation with the concentration of GSH in PBMC of all subjects. In HUVECsexposed to smokers\u2019 serum but not to non-smokers\u2019 serum we found that oxidative stress increased, whereas nitric oxideand GSH concentrations decreased; interestingly the expression of Nrf2, of heme oxygenase-1 (HO-1) and of glutamatecysteineligase catalytic (GCLC) subunit, the rate-limiting step of synthesis of GSH, was decreased. To test the hypothesisthat the increased oxidative stress in smokers may have a causal role in the repression of Nrf2/ARE pathway, we exposedHUVECs to increasing concentrations of oxPAPC and found that at the highest concentration (similar to that found insmokers\u2019 serum) the expression of Nrf2/ARE pathway was reduced. The knockdown of Nrf2 was associated to a significantreduction of HO-1 and GCLC expression induced by oxPAPC in ECs.Conclusions: In young smokers with ED a novel further consequence of increased oxidative stress is a repression of Nrf2/ARE pathway leading to GSH depletion

Iron Behaving Badly: Inappropriate Iron Chelation as a Major Contributor to the Aetiology of Vascular and Other Progressive Inflammatory and Degenerative Diseases

The production of peroxide and superoxide is an inevitable consequence of aerobic metabolism, and while these particular "reactive oxygen species" (ROSs) can exhibit a number of biological effects, they are not of themselves excessively reactive and thus they are not especially damaging at physiological concentrations. However, their reactions with poorly liganded iron species can lead to the catalytic production of the very reactive and dangerous hydroxyl radical, which is exceptionally damaging, and a major cause of chronic inflammation. We review the considerable and wide-ranging evidence for the involvement of this combination of (su)peroxide and poorly liganded iron in a large number of physiological and indeed pathological processes and inflammatory disorders, especially those involving the progressive degradation of cellular and organismal performance. These diseases share a great many similarities and thus might be considered to have a common cause (i.e. iron-catalysed free radical and especially hydroxyl radical generation). The studies reviewed include those focused on a series of cardiovascular, metabolic and neurological diseases, where iron can be found at the sites of plaques and lesions, as well as studies showing the significance of iron to aging and longevity. The effective chelation of iron by natural or synthetic ligands is thus of major physiological (and potentially therapeutic) importance. As systems properties, we need to recognise that physiological observables have multiple molecular causes, and studying them in isolation leads to inconsistent patterns of apparent causality when it is the simultaneous combination of multiple factors that is responsible. This explains, for instance, the decidedly mixed effects of antioxidants that have been observed, etc...Comment: 159 pages, including 9 Figs and 2184 reference

Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case

Background: Model based design plays a fundamental role in synthetic biology. Exploiting modularity, i.e. using biological parts and interconnecting them to build new and more complex biological circuits is one of the key issues. In this context, mathematical models have been used to generate predictions of the behavior of the designed device. Designers not only want the ability to predict the circuit behavior once all its components have been determined, but also to help on the design and selection of its biological parts, i.e. to provide guidelines for the experimental implementation. This is tantamount to obtaining proper values of the model parameters, for the circuit behavior results from the interplay between model structure and parameters tuning. However, determining crisp values for parameters of the involved parts is not a realistic approach. Uncertainty is ubiquitous to biology, and the characterization of biological parts is not exempt from it. Moreover, the desired dynamical behavior for the designed circuit usually results from a trade-off among several goals to be optimized. Results: We propose the use of a multi-objective optimization tuning framework to get a model-based set of guidelines for the selection of the kinetic parameters required to build a biological device with desired behavior. The design criteria are encoded in the formulation of the objectives and optimization problem itself. As a result, on the one hand the designer obtains qualitative regions/intervals of values of the circuit parameters giving rise to the predefined circuit behavior; on the other hand, he obtains useful information for its guidance in the implementation process. These parameters are chosen so that they can effectively be tuned at the wet-lab, i.e. they are effective biological tuning knobs. To show the proposed approach, the methodology is applied to the design of a well known biological circuit: a genetic incoherent feed-forward circuit showing adaptive behavior. Conclusion: The proposed multi-objective optimization design framework is able to provide effective guidelines to tune biological parameters so as to achieve a desired circuit behavior. Moreover, it is easy to analyze the impact of the context on the synthetic device to be designed. That is, one can analyze how the presence of a downstream load influences the performance of the designed circuit, and take it into account.Research in this area is partially supported by Spanish government and European Union (FEDER-CICYT DPI2011-28112-C04-01, and DPI2014-55276-C5-1-R). Yadira Boada thanks grant FPI/2013-3242 of Universitat Politecnica de Valencia; Gilberto Reynoso-Meza gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work. We are grateful to Alejandra Gonzalez-Bosca for her collaboration on this topic while doing her Bachelor thesis, and to Dr. Jose Luis Pitarch from Universidad de Valladolid for his advise in algorithmic implementations and for proof reading the manuscript.Boada Acosta, YF.; Reynoso Meza, G.; Picó Marco, JA.; Vignoni, A. (2016). Multi-objective optimization framework to obtain model-based guidelines for tuning biological synthetic devices: an adaptive network case. BMC Systems Biology. 10:1-19. https://doi.org/10.1186/s12918-016-0269-0S11910ERASynBio. Next steps for european synthetic biology: a strategic vision from erasynbio. 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Data-driven digital twin of a chemical production site for production and utilities planning

The BASF Antwerp site is one of the most advanced and integrated chemical production sites of the BASF group and worldwide. The chemical plants on site are heavily interconnected, i.e., the product of one plant is the raw material used in the next one and some of the plants use the steam produced from neighboring ones. This high degree of interconnection makes it quite difficult to assess the feasibility let alone the economic optimality of production and maintenance plans for the entire site. This research explores the use of a data-driven digital twin to simulate and assess these plans. Two value chains, i.e., a collection of plants converting raw material to valuable end products have been selected as a proof-of-concept for the entire site. Each plant has been modeled by utilizing simple or multiple regression. Each regression model correlates the final product of a plant with the needed raw materials or utilities (e.g., steam or electricity). All regression models have been found using the software JMP. Additionally, all relevant tanks used to stock raw materials, intermediates and final products have been modelled. This allowed for the visualization and troubleshooting of a particular component excess or shortage. The resulting system of 76 variables has been solved in MATLAB in a multi-period fashion, where a period represents a day. The simulation has been first performed on the training data set, and then on a validation period to verify the models' performance

Value chain planning optimization: A data driven digital twin approach

The long term production planning for a large chemical production site, where 10+ different chemical plants share raw materials, infrastructure (e.g., tank farm, filling stations) and utilities (e.g. steam, electricity, technical gasses) might prove to be a challenging task. This paper introduces a data driven approach to build a digital twin of a chemical production site to aid the relevant decision makers in defining and evaluating the economic impact of a long term (i.e. several months ahead) production planning. Each chemical plant and energy production unit on site is represented by simple regression models relating the consumption of raw materials and utilities to its products. The resulting system of algebraic equations has been inserted in an optimization environment with the objective of maximizing the profit. In the optimization, also the electricity and steam generation were introduced to obtain a global energy balance of the production site. This combination resulted in a multi period Mixed-Integer Linear Programming (MILP) problem. The effect of electricity price and external temperature on the optimization results are also investigated

Antiplatelet and Anticoagulation Treatment in Patients with Thrombocytopenia

Thrombocytopenia (TP) is a common finding in patients hospitalized for cardiovascular causes and needing antiplatelet and anticoagulant therapies. However, TP is not only a numeric parameter, but mostly a dynamic condition affected by the patients' underlying disorders and concomitant treatments. Platelets are important players in the hemostatic process, taking part to both primary and secondary hemostasis. Although both TP and antithrombotic treatment contribute to the risk of bleeding, the complexity of the pathogenesis of bleeding events makes it difficult to predict them accurately simply based on these two parameters. It should be considered that, under certain clinical conditions, TP may be associated with an increased risk of thrombosis. In order to manage antithrombotic therapies in patients with TP, the frail balance between bleeding and thrombotic complications needs to be estimated. A joint hematological and cardiological evaluation is mandatory in order to avoid stopping an otherwise lifesaving treatment and to decrease the individual patient risk for both thrombotic and/or bleeding events, in each different setting. The purpose of this review is to describe an operative work flow aimed at helping clinicians to face this challenging issue

Endothelial progenitor cells in patients with essential hypertension.

OBJECTIVE(S): The eventual role of blood pressure on the endothelial progenitor cell (EPC) has rarely been evaluated and data collected so far relate to patients with co-existing coronary heart disease. METHODS: We have studied the number and functional activity of EPC as well as the number of EPC endothelial colony-forming units (CFU) in a carefully selected group of 36 patients with essential hypertension and 24 normotensive control subjects. RESULTS: In patients with essential hypertension, the EPC number was not statistically different from that found in control subjects (mean +/- SD, essential hypertension 58 +/- 29, controls 53 +/- 20; EPC/high power field). CFU per well were not statistically different in patients with essential hypertension compared with normotensive controls (mean +/- SD, patients with essential hypertension 2.4 +/- 2.6, normotensive controls 3 +/- 3.3 CFU/well). In essential hypertension patients, the EPC number was inversely correlated with both total (R=0.635, P < 0.0001) and low-density lipoprotein (LDL)-cholesterol (R=0.486, P < 0.05). Neither the EPC number nor the EPC CFU were correlated with age, systolic blood pressure, diastolic blood pressure, body mass index, lipoprotein(a), high-sensitivity C-reactive protein or homocysteine. CONCLUSIONS: The present study shows that essential hypertension is not characterized by the altered number or functional activity of EPC. Plasma total and LDL-cholesterol are independent predictors of reduced numbers of circulating EPC in essential hypertension patients. The absence of any correlation between the characteristics of EPC and several markers predictive of cardiovascular damage merits further investigation

Model Predictive Control of a CVD Reactor for Production of Polysilicon Rods

Production of polysilicon plays a key role in the development of hi-tech and renewable energy industry. Massive production is obtained by chemical vapor deposition (CVD) in semi-batch reactors, traditionally called Siemens reactors, where silicon rods are grown. Following recent increase in market demand for polysilicon, a fine process control on industrial processes for improving production yield and reducing energy consumption is required. In this work, a technique for a real-time model-based predictive control applied to a laboratory-scale Siemens reactor is presented; a lumped model is used for describing the CVD process. Discussion is based on numerical results