Analysis of metabolic flux distributions in relation to the extracellular environment in Avian cells

Continuous cell lines that proliferate in chemically defined and simple media have been highly regarded as suitable alternatives for vaccine production. One such cell line is the AG1.CR.pIX avian cell line developed by PROBIOGEN. This cell line can be cultivated in a fully scalable suspension culture and adapted to grow in chemically defined, calf serum free, medium [1]–[5]. The medium composition and cultivation strategy are important factors for reaching high virus titers. In this project, a series of computational methods was used to simulate the cell’s response to different environments. The study is based on the metabolic model of the central metabolism proposed in [1]. In a first step, Metabolic Flux Analysis (MFA) was used along with measured uptake and secretion fluxes to estimate intracellular flux values. The network and data were found to be consistent. In a second step, Flux Balance Analysis (FBA) was performed to access the cell’s biological objective. The objective that resulted in the best predicted results fit to the experimental data was the minimization of oxidative phosphorylation. Employing this objective, in the next step Flux Variability Analysis (FVA) was used to characterize the flux solution space. Furthermore, various scenarios, where a reaction deletion (elimination of the compound from the media) was simulated, were performed and the flux solution space for each scenario was calculated. Growth restrictions caused by essential and non-essential amino acids were accurately predicted. Fluxes related to the essential amino acids uptake and catabolism, the lipid synthesis and ATP production via TCA were found to be essential to exponential growth. Finally, the data gathered during the previous steps were analyzed using principal component analysis (PCA), in order to assess potential changes in the physiological state of the cell. Three metabolic states were found, which correspond to zero, partial and maximum biomass growth rate. Elimination of non-essential amino acids or pyruvate from the media showed no impact on the cell’s assumed normal metabolic state.

Genome-scale metabolic network reconstruction of Polaromonas sp. strain JS666: analysis of cDCE degradation rates and design of experiments for bioremediation improvement

Release of chloroethene compounds into the environment often results in groundwater contamination, which puts people at risk of exposure by drinking contaminated water. cDCE (cis-1,2-dichloroethene) accumulation on subsurface environments is a common environmental problem due to stagnation and partial degradation of other precursor chloroethene species. Polaromonas sp. strain JS666 apparently requires no exotic growth factors to be used as a bioaugmentation agent for aerobic cDCE degradation. Although being the only suitable microorganism found capable of such, further studies are needed for improving the intrinsic bioremediation rates and fully comprehend the metabolic processes involved. In order to do so, a metabolic model, iJS666, was reconstructed from genome annotation and available bibliographic data. FVA (Flux Variability Analysis) and FBA (Flux Balance Analysis) techniques were used to satisfactory validate the predictive capabilities of the iJS666 model. The iJS666 model was able to predict biomass growth for different previously tested conditions, allowed to design key experiments which should be done for further model improvement and, also, produced viable predictions for the use of biostimulant metabolites in the cDCE biodegradation.

Pimenta pseudocaryophyllus inhibits virulence factors and promotes metabolic changes in Candidayeast

IntroductionThis is the first study to examine the in vitrosusceptibility and the expression of virulence factors in Candida species in the presence of Pimenta pseudocaryophyllus (Gomes) L.R. Landrum (Myrtaceae), a Brazilian plant known as paucravo. Additionally, the mechanisms of action of the crude ethanol extract and the ethyl acetate and aqueous fractions of this plant were investigated.MethodsThe in vitro susceptibility of Candida was tested using the broth microdilution method, whereas an XTT reduction assay was used for biofilms. Adherence was determined by counting the number of yeast cells that adhered to 100 oral epithelial cells, and hyphal formation was verified in the hyphal induction medium M199. Flow cytometry with propidium iodide and FUN-1 was performed to assess the mechanism of action.ResultsThe results revealed that the crude ethanol extract and the ethyl acetate and aqueous fractions of P. pseudocaryophyllusinhibited the growth of Candida isolates at a minimal inhibitory concentration (MIC) ranging from 64 to 256µg/mL, whereas the 50% sessile minimal inhibitory concentration (SMIC50) ranged from 512 to >1,024µg/mL. Adherence and hyphal formation were significantly reduced in the presence of the crude ethanol extract and both fractions. Although cell membrane injury was detected, the predominant mechanism of action appeared to be the alteration of yeast metabolism, as demonstrated by flow cytometry.ConclusionsOur results indicated that antifungal activity reduced the expression of virulence factors in yeast via the alteration of yeast metabolism, suggesting that the crude extract of P. pseudocaryophyllus and its fractions may contain novel antifungal agents.

Exploring chemical conversions in metabolic networks by tracing atoms transitions

Dissertação de mestrado em Bioinformática

Modeling microbes: New methods for integrated metabolic and regulatory network reconstruction

PhD Thesis in Bioengineering

Metabolic profiling and classification of propolis samples from Southern Brazil: an NMR-based platform coupled with machine learning

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.

The influence of kaolin application on key metabolic pathways associated with grape berry quality: a molecular and biochemical analysis

Dissertação de mestrado em Biologia Molecular, Biotecnologia e Bioempreendedorismo em Plantas

A glycolytic phenotype is associated with prostate cancer progression and aggressiveness: a role for Monocarboxylate Transporters as metabolic targets for therapy.

Metabolic adaptation is considered an emerging hallmark of cancer, whereby cancer cells exhibit high rates of glucose consumption with consequent lactate production. To ensure rapid efflux of lactate, most cancer cells express high levels of monocarboxylate transporters (MCTs), which therefore may constitute suitable therapeutic targets. The impact of MCT inhibition, along with the clinical impact of altered cellular metabolism during prostate cancer (PCa) initiation and progression, has not been described. Using a large cohort of human prostate tissues of different grades, in silico data, in vitro and ex vivo studies, we demonstrate the metabolic heterogeneity of PCa and its clinical relevance. We show an increased glycolytic phenotype in advanced stages of PCa and its correlation with poor prognosis. Finally, we present evidence supporting MCTs as suitable targets in PCa, affecting not only cancer cell proliferation and survival but also the expression of a number of hypoxia-inducible factor target genes associated with poor prognosis. Herein, we suggest that patients with highly glycolytic tumours have poorer outcome, supporting the notion of targeting glycolytic tumour cells in prostate cancer through the use of MCT inhibitors.

Metabolic engineering with multi-objective optimization of kinetic models

Kinetic models have a great potential for metabolic engineering applications. They can be used for testing which genetic and regulatory modifications can increase the production of metabolites of interest, while simultaneously monitoring other key functions of the host organism. This work presents a methodology for increasing productivity in biotechnological processes exploiting dynamic models. It uses multi-objective dynamic optimization to identify the combination of targets (enzymatic modifications) and the degree of up- or down-regulation that must be performed in order to optimize a set of pre-defined performance metrics subject to process constraints. The capabilities of the approach are demonstrated on a realistic and computationally challenging application: a large-scale metabolic model of Chinese Hamster Ovary cells (CHO), which are used for antibody production in a fed-batch process. The proposed methodology manages to provide a sustained and robust growth in CHO cells, increasing productivity while simultaneously increasing biomass production, product titer, and keeping the concentrations of lactate and ammonia at low values. The approach presented here can be used for optimizing metabolic models by finding the best combination of targets and their optimal level of up/down-regulation. Furthermore, it can accommodate additional trade-offs and constraints with great flexibility.

Algorithms and computational tools for metabolic flux analysis

PhD thesis in Biomedical Engineering

Hormonal, metabolic and nutritional alterations in smokers: emergency for smoking abstinence

OBJECTIVE: To evaluate the biochemical and nutritional status of smokers in treatment for smoking cessation and its association with anthropometric parameters. METHODS: This is a cross-sectional study with convenience sample. Adult smokers were assessed at the start of treatment in the Interdisciplinary Center for Tobacco Research and Intervention of the University Hospital of the Federal University of Juiz de Fora (CIPIT/HU-UFJF). We evaluated the body mass index (BMI), conicity index (CI); waist circumference (WC), percentage of body fat (%BF), fasting glycemia, cortisol, insulin, total cholesterol (TC), LDL-c, HDL-c, triglycerides (TG) and metabolic syndrome (MS). RESULTS: Most participants (52.2%) had MS and high cardiovascular risk. The fasting glycemia was abnormal in 30.4%. There was a significant positive correlation between BMI and WC (r = 0.90; p = 0.0001), %BF (r = 0.79; p = 0.0001), CI (r = 0.65; p = 0.0001), glycemia (r = 0.42; p = 0.04) and TG (r = 0.47; p = 0.002). The CI presented positive correction with insulin (r = 0.60; p = 0.001), glycemia (r = 0.55; p = 0.007), TG (r = 0.54; p = 0.008) and %BF (r = 0.43; p = 0.004). Patients with longer duration of smoking had a higher risk of developing MS (OR = 9.6, p = 0.016). CONCLUSION: The smokers evaluated had increased risk for developing MS, especially those with longer duration of smoking, requiring urgent smoking cessation.

Tapping the wealth of microbial data in high-throughput metabolic model reconstruction

Genome-scale metabolic models are valuable tools in the metabolic engineering process, based on the ability of these models to integrate diverse sources of data to produce global predictions of organism behavior. At the most basic level, these models require only a genome sequence to construct, and once built, they may be used to predict essential genes, culture conditions, pathway utilization, and the modifications required to enhance a desired organism behavior. In this chapter, we address two key challenges associated with the reconstruction of metabolic models: (a) leveraging existing knowledge of microbiology, biochemistry, and available omics data to produce the best possible model; and (b) applying available tools and data to automate the reconstruction process. We consider these challenges as we progress through the model reconstruction process, beginning with genome assembly, and culminating in the integration of constraints to capture the impact of transcriptional regulation. We divide the reconstruction process into ten distinct steps: (1) genome assembly from sequenced reads; (2) automated structural and functional annotation; (3) phylogenetic tree-based curation of genome annotations; (4) assembly and standardization of biochemistry database; (5) genome-scale metabolic reconstruction; (6) generation of core metabolic model; (7) generation of biomass composition reaction; (8) completion of draft metabolic model; (9) curation of metabolic model; and (10) integration of regulatory constraints. Each of these ten steps is documented in detail.

Candida tropicalis biofilms: biomass, metabolic activity and secreted aspartyl proteinase production

According to epidemiological data, Candida tropicalis has been related to urinary tract infections and haematological malignancy. Several virulence factors seem to be responsible for C. tropicalis infections, for example: their ability to adhere and to form biofilms onto different indwelling medical devices; their capacity to adhere, invade and damage host human tissues due to enzymes production such as proteinases. The main aim of this work was to study the behaviour of C. tropicalis biofilms of different ages (24120 h) formed in artificial urine (AU) and their ability to express aspartyl proteinase (SAPT) genes. The reference strain C. tropicalis ATCC 750 and two C. tropicalis isolates from urine were used. Biofilms were evaluated in terms of culturable cells by colony-forming units enumeration; total biofilm biomass was evaluated using the crystal violet staining method; metabolic activity was evaluated by XTT assay; and SAPT gene expression was determined by real-time PCR. All strains of C. tropicalis were able to form biofilms in AU, although with differences between strains. Candida tropicalis biofilms showed a decrease in terms of the number of culturable cells from 48 to 72 h. Generally, SAPT3 was highly expressed. C. tropicalis strains assayed were able to form biofilms in the presence of AU although in a strain- and time-dependent way, and SAPT genes are expressed during C. tropicalis biofilm formation.

Metabolic and hemodynamic effects of saline infusion to maintain volemia on temporary abdominal aortic occlusion

OBJECTIVE: To analyze hemodynamic and metabolic effects of saline solution infusion in the maintenance of blood volume in ischemia-reperfusion syndrome during temporary abdominal aortic occlusion in dogs. METHODS: We studied 20 dogs divided into 2 groups: the ischemia-reperfusion group (IRG, n=10) and the ischemia-reperfusion group with saline solution infusion aiming at maintaining mean pulmonary arterial wedge pressure between 10 and 20 mmHg (IRG-SS, n=10). All animals were anesthetized with sodium thiopental and maintained on spontaneous ventilation. Occlusion of the supraceliac aorta was obtained with inflation of a Fogarty catheter inserted through the femoral artery. After 60 minutes of ischemia, the balloon was deflated, and the animals were observed for another 60 minutes of reperfusion. RESULTS: IRG-SS dogs did not have hemodynamic instability after aortic unclamping, and the mean systemic blood pressure and heart rate were maintained. However, acidosis worsened, which was documented by a greater reduction of arterial pH that occurred especially due to the absence of a respiratory response to metabolic acidosis that was greater with the adoption of this procedure. CONCLUSION: Saline solution infusion to maintain blood volume avoided hemodynamic instability after aortic unclamping. This procedure, however, caused worsening in metabolic acidosis in this experimental model.