930 resultados para Metabolic flux analysis


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The present PhD thesis develops the cell functional enviromics (CFE) method to investigate the relationship between environment and cellular physiology. CFE may be defined as the envirome-wide cellular function reconstruction through the collection and systems-level analysis of dynamic envirome data. Throughout the thesis, CFE is illustrated by two main applications to cultures of a constitutive P. pastoris X33 strain expressing a scFv antibody fragment. The first application addresses the challenge of culture media development. A dataset was built from 26 shake flask experiments, with variations in trace elements concentrations and basal medium dilution based on the standard BSM+PTM1. Protein yield showed high sensitivity to culture medium variations, while biomass was essentially determined by BSM dilution. High scFv yield was associated with high overall metabolic fluxes through central carbon pathways concomitantly with a relative shift of carbon flux from biosynthetic towards energy-generating pathways. CFE identified three cellular functions (growth, energy generation and by-product formation) that together described 98.8% of the variance in observed fluxes. Analyses of how medium factors relate to identified cellular functions showed iron and manganese at concentrations close to PTM1 inhibit overall metabolic activity. The second application addresses bioreactor operation. Pilot 50 L fed-batch cultivations, followed by 1H-NMR exometabolite profiling, allowed the acquisition of data for 21 environmental factors over time. CFE identified five major metabolic pathway groups that are frequently activated by the environment. The resulting functional enviromics map may serve as template for future optimization of media composition and feeding strategies for Pichia pastoris. The present PhD thesis is a step forward towards establishing the foundations of CFE that is still at its infancy. The methods developed herein are a contribution for changing the culture media and process development paradigm towards a holistic and systematic discipline in the future.

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PhD Thesis in Bioengineering

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The chemical composition of propolis is affected by environmental factors and harvest season, making it difficult to standardize its extracts for medicinal usage. By detecting a typical chemical profile associated with propolis from a specific production region or season, certain types of propolis may be used to obtain a specific pharmacological activity. In this study, propolis from three agroecological regions (plain, plateau, and highlands) from southern Brazil, collected over the four seasons of 2010, were investigated through a novel NMR-based metabolomics data analysis workflow. Chemometrics and machine learning algorithms (PLS-DA and RF), including methods to estimate variable importance in classification, were used in this study. The machine learning and feature selection methods permitted construction of models for propolis sample classification with high accuracy (>75%, reaching 90% in the best case), better discriminating samples regarding their collection seasons comparatively to the harvest regions. PLS-DA and RF allowed the identification of biomarkers for sample discrimination, expanding the set of discriminating features and adding relevant information for the identification of the class-determining metabolites. The NMR-based metabolomics analytical platform, coupled to bioinformatic tools, allowed characterization and classification of Brazilian propolis samples regarding the metabolite signature of important compounds, i.e., chemical fingerprint, harvest seasons, and production regions.

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The use of genome-scale metabolic models has been rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation since this reaction will ultimately determine the predictive capacity of the model in terms of essentiality and flux distributions. Thus, in order to obtain a reliable metabolic model the biomass precursors and their coefficients must be as precise as possible. Ideally, determination of the biomass composition would be performed experimentally, but when no experimental data are available this is established by approximation to closely related organisms. Computational methods however, can extract some information from the genome such as amino acid and nucleotide compositions. The main objectives of this study were to compare the biomass composition of several organisms and to evaluate how biomass precursor coefficients affected the predictability of several genome-scale metabolic models by comparing predictions with experimental data in literature. For that, the biomass macromolecular composition was experimentally determined and the amino acid composition was both experimentally and computationally estimated for several organisms. Sensitivity analysis studies were also performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rates and flux distributions. The results obtained suggest that the macromolecular composition is conserved among related organisms. Contrasting, experimental data for amino acid composition seem to have no similarities for related organisms. It was also observed that the impact of macromolecular composition on specific growth rates and flux distributions is larger than the impact of amino acid composition, even when data from closely related organisms are used.

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Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

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OBJECTIVE: To identify the electrocardiographic changes and their associations with metabolic and electrolytic changes in female alcoholics. METHODS: The study comprised 44 female alcoholics with no apparent physical disorder. They underwent the following examinations: conventional electrocardiography; serologic tests for syphilis, Chagas' disease, and hepatitis B and C viruses; urinary pregnancy testing; hematimetric analysis; biochemical measurements of albumin, fibrinogen, fasting and postprandial glycemias, lipids, hepatic enzymes, and markers for tissue necrosis and inflammation. RESULTS: Some type of electrocardiographic change was identified in 33 (75%) patients. In 17 (38.6%) patients, more than one of the following changes were present: prolonged QTc interval in 24 (54.5%), change in ventricular repolarization in 11(25%), left ventricular hypertrophy in 6 (13.6%), sinus bradycardia in 4 (9.1%), sinus tachycardia in 3 (6.8%), and conduction disorder in 3 (6.8%). The patients had elevated mean serum levels of creatine phosphokinase, aspartate aminotransferases, and gamma glutamyl transferase, as well as hypocalcemia and low levels of total cholesterol and LDL-cholesterol. The patients with altered electrocardiograms had a more elevated age, a lower alcohol consumption, hypopotassemia, and significantly elevated levels of triglycerides, postprandial glucose, sodium and gamma glutamyl transferase than those with normal electrocardiograms. The opposite occurred with fasting glycemia, magnesium, and alanine aminotransferase. CONCLUSION: The electrocardiographic changes found were prolonged QTc interval, change in ventricular repolarization, and left ventricular hypertrophy. Patients with normal and abnormal electrocardiograms had different metabolic and electrolytic changes.

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Abstract Background: Truck driver sleepiness is a primary cause of vehicle accidents. Several causes are associated with sleepiness in truck drivers. Obesity and metabolic syndrome (MetS) are associated with sleep disorders and with primary risk factors for cardiovascular diseases (CVD). We analyzed the relationship between these conditions and prevalence of sleepiness in truck drivers. Methods: We analyzed the major risk factors for CVD, anthropometric data and sleep disorders in 2228 male truck drivers from 148 road stops made by the Federal Highway Police from 2006 to 2011. Alcohol consumption, illicit drugs and overtime working hours were also analyzed. Sleepiness was assessed using the Epworth Sleepiness Scale. Results: Mean age was 43.1 ± 10.8 years. From 2006 to 2011, an increase in neck (p = 0.011) and abdominal circumference (p < 0.001), total cholesterol (p < 0.001), triglyceride plasma levels (p = 0.014), and sleepiness was observed (p < 0.001). In addition, a reduction in hypertension (39.6% to 25.9%, p < 0.001), alcohol consumption (32% to 23%, p = 0.033) and overtime hours (52.2% to 42.8%, p < 0.001) was found. Linear regression analysis showed that sleepiness correlated closely with body mass index (β = 0.19, Raj2 = 0.659, p = 0.031), abdominal circumference (β = 0.24, Raj2 = 0.826, p = 0.021), hypertension (β = -0.62, Raj2 = 0.901, p = 0.002), and triglycerides (β = 0.34, Raj2 = 0.936, p = 0.022). Linear multiple regression indicated that hypertension (p = 0.008) and abdominal circumference (p = 0.025) are independent variables for sleepiness. Conclusions: Increased prevalence of sleepiness was associated with major components of the MetS.

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Abstract Background: The use of aortic counterpulsation therapy in advanced heart failure is controversial. Objectives: To evaluate the hemodynamic and metabolic effects of intra-aortic balloon pump (IABP) and its impact on 30-day mortality in patients with heart failure. Methods: Historical prospective, unicentric study to evaluate all patients treated with IABP betwen August/2008 and July/2013, included in an institutional registry named TBRIDGE (The Brazilian Registry of Intra-aortic balloon pump in Decompensated heart failure - Global Evaluation). We analyzed changes in oxygen central venous saturation (ScvO2), arterial lactate, and use of vasoactive drugs at 48 hours after IABP insertion. The 30-day mortality was estimated by the Kaplan-Meier method and diferences in subgroups were evaluated by the Log-rank test. Results: A total of 223 patients (mean age 49 ± 14 years) were included. Mean left ventricle ejection fraction was 24 ± 10%, and 30% of patients had Chagas disease. Compared with pre-IABP insertion, we observed an increase in ScvO2 (50.5% vs. 65.5%, p < 0.001) and use of nitroprusside (33.6% vs. 47.5%, p < 0.001), and a decrease in lactate levels (31.4 vs. 16.7 mg/dL, p < 0.001) and use of vasopressors (36.3% vs. 25.6%, p = 0.003) after IABP insertion. Thirty-day survival was 69%, with lower mortality in Chagas disease patients compared without the disease (p = 0.008). Conclusion: After 48 hours of use, IABP promoted changes in the use of vasoactive drugs, improved tissue perfusion. Chagas etiology was associated with lower 30-day mortality. Aortic counterpulsation therapy is an effective method of circulatory support for patients waiting for heart transplantation.

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Résumé : Les progrès techniques de la spectrométrie de masse (MS) ont contribué au récent développement de la protéomique. Cette technique peut actuellement détecter, identifier et quantifier des milliers de protéines. Toutefois, elle n'est pas encore assez puissante pour fournir une analyse complète des modifications du protéome corrélées à des phénomènes biologiques. Notre objectif était le développement d'une nouvelle stratégie pour la détection spécifique et la quantification des variations du protéome, basée sur la mesure de la synthèse des protéines plutôt que sur celle de la quantité de protéines totale. Pour cela, nous volions associer le marquage pulsé des protéines par des isotopes stables avec une méthode d'acquisition MS basée sur le balayage des ions précurseurs (precursor ion scan, ou PIS), afin de détecter spécifiquement les protéines ayant intégré les isotopes et d'estimer leur abondance par rapport aux protéines non marquées. Une telle approche peut identifier les protéines avec les plus hauts taux de synthèse dans une période de temps donnée, y compris les protéines dont l'expression augmente spécifiquement suite à un événement précis. Nous avons tout d'abord testé différents acides aminés marqués en combinaison avec des méthodes PIS spécifiques. Ces essais ont permis la détection spécifique des protéines marquées. Cependant, en raison des limitations instrumentales du spectromètre de masse utilisé pour les méthodes PIS, la sensibilité de cette approche s'est révélée être inférieure à une analyse non ciblée réalisée sur un instrument plus récent (Chapitre 2.1). Toutefois, pour l'analyse différentielle de deux milieux de culture conditionnés par des cellules cancéreuses humaines, nous avons utilisé le marquage métabolique pour distinguer les protéines d'origine cellulaire des protéines non marquées du sérum présentes dans les milieux de culture (Chapitre 2.2). Parallèlement, nous avons développé une nouvelle méthode de quantification nommée IBIS, qui utilise des paires d'isotopes stables d'acides aminés capables de produire des ions spécifiques qui peuvent être utilisés pour la quantification relative. La méthode IBIS a été appliquée à l'analyse de deux lignées cellulaires cancéreuses complètement marquées, mais de manière différenciée, par des paires d'acides aminés (Chapitre 2.3). Ensuite, conformément à l'objectif initial de cette thèse, nous avons utilisé une variante pulsée de l'IBIS pour détecter des modifications du protéome dans des cellules HeLa infectée par le virus humain Herpes Simplex-1 (Chapitre 2.4). Ce virus réprime la synthèse des protéines des cellules hôtes afin d'exploiter leur mécanisme de traduction pour la production massive de virions. Comme prévu, de hauts taux de synthèse ont été mesurés pour les protéines virales détectées, attestant de leur haut niveau d'expression. Nous avons de plus identifié un certain nombre de protéines humaines dont le rapport de synthèse et de dégradation (S/D) a été modifié par l'infection virale, ce qui peut donner des indications sur les stratégies utilisées par les virus pour détourner la machinerie cellulaire. En conclusion, nous avons montré dans ce travail que le marquage métabolique peut être employé de façon non conventionnelle pour étudier des dimensions peu explorées en protéomique. Summary : In recent years major technical advancements greatly supported the development of mass spectrometry (MS)-based proteomics. Currently, this technique can efficiently detect, identify and quantify thousands of proteins. However, it is not yet sufficiently powerful to provide a comprehensive analysis of the proteome changes correlated with biological phenomena. The aim of our project was the development of ~a new strategy for the specific detection and quantification of proteomé variations based on measurements of protein synthesis rather than total protein amounts. The rationale for this approach was that changes in protein synthesis more closely reflect dynamic cellular responses than changes in total protein concentrations. Our starting idea was to couple "pulsed" stable-isotope labeling of proteins with a specific MS acquisition method based on precursor ion scan (PIS), to specifically detect proteins that incorporated the label and to simultaneously estimate their abundance, relative to the unlabeled protein isoform. Such approach could highlight proteins with the highest synthesis rate in a given time frame, including proteins specifically up-regulated by a given biological stimulus. As a first step, we tested different isotope-labeled amino acids in combination with dedicated PIS methods and showed that this leads to specific detection of labeled proteins. Sensitivity, however, turned out to be lower than an untargeted analysis run on a more recent instrument, due to MS hardware limitations (Chapter 2.1). We next used metabolic labeling to distinguish the proteins of cellular origin from a high background of unlabeled (serum) proteins, for the differential analysis of two serum-containing culture media conditioned by labeled human cancer cells (Chapter 2.2). As a parallel project we developed a new quantification method (named ISIS), which uses pairs of stable-isotope labeled amino acids able to produce specific reporter ions, which can be used for relative quantification. The ISIS method was applied to the analysis of two fully, yet differentially labeled cancer cell lines, as described in Chapter 2.3. Next, in line with the original purpose of this thesis, we used a "pulsed" variant of ISIS to detect proteome changes in HeLa cells after the infection with human Herpes Simplex Virus-1 (Chapter 2.4). This virus is known to repress the synthesis of host cell proteins to exploit the translation machinery for the massive production of virions. As expected, high synthesis rates were measured for the detected viral proteins, confirming their up-regulation. Moreover, we identified a number of human proteins whose synthesis/degradation ratio (S/D) was affected by the viral infection and which could provide clues on the strategies used by the virus to hijack the cellular machinery. Overall, in this work, we showed that metabolic labeling can be employed in alternative ways to investigate poorly explored dimensions in proteomics.

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Body composition, resting energy expenditure (REE), and whole body protein metabolism were studied in 26 young and 28 elderly Gambian men matched for body mass index during the dry season in a rural village in The Gambia. REE was measured by indirect calorimetry (hood system) in the fasting state and after five successive meals. Rates of whole body nitrogen flux, protein synthesis, and protein breakdown were determined in the fed state from the level of isotopic enrichment of urinary ammonia over a period of 12 h after a single oral dose of [15N]glycine. Expressed in absolute value, REE was significantly lower in the elderly compared with the young group (3.21 +/- 0.07 vs. 4.04 +/- 0.07 kJ/min, P < 0.001) and when adjusted to body weight (3.29 +/- 0.05 vs. 3.96 +/- 0.05 kJ/min, P < 0.0001) and fat-free mass (FFM; 3.38 +/- 0.01 vs. 3.87 +/- 0.01 kJ/min, P < 0.0001). The rate of protein synthesis averaged 207 +/- 13 g protein/day in the elderly and 230 +/- 13 g protein/day in the young group, whereas protein breakdown averaged 184 +/- 13 g protein/day in the elderly and 203 +/- 13 g protein/day in the young group (nonsignificant). When values were adjusted for body weight or FFM, they did not reveal any difference between the two groups. It is concluded that the reduced REE adjusted for body composition observed in elderly Gambian men is not explained by a decrease in protein turnover.

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Synthetic chemicals currently used in a variety of industrial and agricultural applications are leading to widespread contamination of the environment. Even though the intended uses of pesticides, plasticizers, antimicrobials, and flame retardants are beneficial, effects on human health are a global concern. These so-called endocrine-disrupting chemicals (EDCs) can disrupt hormonal balance and result in developmental and reproductive abnormalities. New in vitro, in vivo, and epidemiological studies link human EDC exposure with obesity, metabolic syndrome, and type 2 diabetes. Here we review the main chemical compounds that may contribute to metabolic disruption. We then present their demonstrated or suggested mechanisms of action with respect to nuclear receptor signaling. Finally, we discuss the difficulties of fairly assessing the risks linked to EDC exposure, including developmental exposure, problems of high- and low-dose exposure, and the complexity of current chemical environments.

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PURPOSE: Gastric or intestinal patches, commonly used for reconstructive cystoplasty, may induce severe metabolic complications. The use of bladder tissues reconstructed in vitro could avoid these complications. We compared cellular differentiation and permeability characteristics of human native with in vitro cultured stratified urothelium. MATERIALS AND METHODS: Human stratified urothelium was induced in vitro. Morphology was studied with light and electron microscopy and expression of key cellular proteins was assessed using immunohistochemistry. Permeability coefficients were determined by measuring water, urea, ammonia and proton fluxes across the urothelium. RESULTS: As in native urothelium the stratified urothelial construct consisted of basal membrane and basal, intermediate and superficial cell layers. The apical membrane of superficial cells formed villi and glycocalices, and tight junctions and desmosomes were developed. Immunohistochemistry showed similarities and differences in the expression of cytokeratins, integrin and cellular adhesion proteins. In the cultured urothelium cytokeratin 20 and integrin subunits alpha6 and beta4 were absent, and symplekin was expressed diffusely in all layers. Uroplakins were clearly expressed in the superficial umbrella cells of the urothelial constructs, however, they were also present in intermediate and basal cells. Symplekin and uroplakins were expressed only in the superficial cells of native bladder tissue. The urothelial constructs showed excellent viability, and functionally their permeabilities for water, urea and ammonia were no different from those measured in native human urothelium. Proton permeability was even lower in the constructs compared to that of native urothelium. CONCLUSIONS: Although the in vitro cultured human stratified urothelium did not show complete terminal differentiation of its superficial cells, it retained the same barrier characteristics against the principal urine components. These results indicate that such in vitro cultured urothelium, after being grown on a compliant degradable support or in coculture with smooth muscle cells, is suitable for reconstructive cystoplasty.

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Besides CYP2B6, other polymorphic enzymes contribute to efavirenz (EFV) interindividual variability. This study was aimed at quantifying the impact of multiple alleles on EFV disposition. Plasma samples from 169 human immunodeficiency virus (HIV) patients characterized for CYP2B6, CYP2A6, and CYP3A4/5 allelic diversity were used to build up a population pharmacokinetic model using NONMEM (non-linear mixed effects modeling), the aim being to seek a general approach combining genetic and demographic covariates. Average clearance (CL) was 11.3 l/h with a 65% interindividual variability that was explained largely by CYP2B6 genetic variation (31%). CYP2A6 and CYP3A4 had a prominent influence on CL, mostly when CYP2B6 was impaired. Pharmacogenetics fully accounted for ethnicity, leaving body weight as the only significant demographic factor influencing CL. Square roots of the numbers of functional alleles best described the influence of each gene, without interaction. Functional genetic variations in both principal and accessory metabolic pathways demonstrate a joint impact on EFV disposition. Therefore, dosage adjustment in accordance with the type of polymorphism (CYP2B6, CYP2A6, or CYP3A4) is required in order to maintain EFV within the therapeutic target levels.

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This document presents an integrated analysis of the performance of Catalonia based on an analysis of how the energy consumption (measured at the societal level for the Catalan Society) is used within both the productive sectors of the economy and the household, to generate added value, jobs, and to guarantee a given level of material standard of living to the population. The trends found in Catalonia are compared to the trends of other European Countries to contextualize the performance of Catalonia with respect to other societies that have followed different paths of economic development. The first part of the document consists of the Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) approach that has been used to provide this integrated analysis of Catalan Society across different scales (starting from an analysis of the specific sectors of the Catalan economy as an Autonomous Community and scaling up to an intra-regional (European Union 14) comparison) and across different dimensions of analyses of energy consumption coupled with added value generation. Within the scope of this study, we observe the various trajectories of changes in the metabolic pattern for Catalonia and the EU14 countries in the Paid Work Sectors composed of namely, the Agricultural Sector, the Productive Sector and the Services and Government Sector also in comparison with the changes in the household sector. The flow intensities of the exosomatic energy and the added value generated for each specific sector are defined per hour of human activity, thus characterized as exosomatic energy (MJ/hour) (or Exosomatic Metabolic Rate) and added value (€/hour) (Economic Labour Productivity) across multiple levels. Within the second part of the document, the possible usage of the MuSIASEM approach to land use analyses (using a multi-level matrix of categories of land use) has been conducted.

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Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻&supl;³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.