A Comprehensive Metabolic Profile of Cultured Astrocytes Using Isotopic Transient Metabolic Flux Analysis and 13C-Labeled Glucose

Metabolic models have been used to elucidate important aspects of brain metabolism in recent years. This work applies for the first time the concept of isotopic transient 13C metabolic flux analysis (MFA) to estimate intracellular fluxes in primary cultures of astrocytes. This methodology comprehensively explores the information provided by 13C labeling time-courses of intracellular metabolites after administration of a 13C-labeled substrate. Cells were incubated with medium containing [1-13C]glucose for 24 h and samples of cell supernatant and extracts collected at different time points were then analyzed by mass spectrometry and/or high performance liquid chromatography. Metabolic fluxes were estimated by fitting a carbon labeling network model to isotopomer profiles experimentally determined. Both the fast isotopic equilibrium of glycolytic metabolite pools and the slow labeling dynamics of TCA cycle intermediates are described well by the model. The large pools of glutamate and aspartate which are linked to the TCA cycle via reversible aminotransferase reactions are likely to be responsible for the observed delay in equilibration of TCA cycle intermediates. Furthermore, it was estimated that 11% of the glucose taken up by astrocytes was diverted to the pentose phosphate pathway. In addition, considerable fluxes through pyruvate carboxylase [PC; PC/pyruvate dehydrogenase (PDH) ratio = 0.5], malic enzyme (5% of the total pyruvate production), and catabolism of branched-chained amino acids (contributing with ∼40% to total acetyl-CoA produced) confirmed the significance of these pathways to astrocytic metabolism. Consistent with the need of maintaining cytosolic redox potential, the fluxes through the malate–aspartate shuttle and the PDH pathway were comparable. Finally, the estimated glutamate/α-ketoglutarate exchange rate (∼0.7 μmol mg prot−1 h−1) was similar to the TCA cycle flux. In conclusion, this work demonstrates the potential of isotopic transient MFA for a comprehensive analysis of energy metabolism

Internal Logistics Process Improvement using PDCA: A Case Study in the Automotive Sector

Background: The Plan-do-check-act (PDCA) cycle methodology for a continuous improvement project implementation aims for the internal logistics upgrade, which is especially important in the industrial context of a component manufacturing company for the automotive sector. Objectives: The goal is to quantify the gains from waste reduction based on the usage of the PDCA cycle as a tool in the implementation and optimisation of a milk run in an assembly line of a company in the automotive sector by determining the optimal cycle time of supply and the standardisation of the logistic supply process and the materials’ flow. Methods/Approach: The research was conducted through observation and data collection in loco, involving two main phases: planning and implementation. According to the phases of the PDCA cycle, the process was analysed, and tools such as the SIPOC matrix, process stratification, 5S, and visual management were implemented. Results: Using Lean tools, it was possible to reduce waste by establishing concise flows and defining a supply pattern, which resulted in a reduction of movements. The transportation waste was reduced by fixing the position of more than half of the materials in the logistic trailers. The developed Excel simulator provided the logistic train\u27s optimal cycle time. Conclusions: The assembly line supplied by milk-run was fundamental to highlight a range of improvements in the process of internal supply, such as better integration of stock management systems, greater application of quality, or the adoption of better communication systems between the different areas and employees

Copper Acts Synergistically With Fluconazole in Candida glabrata by Compromising Drug Efflux, Sterol Metabolism, and Zinc Homeostasis

Funding Information: This work was supported by (1) Project LISBOA-01-0145-FEDER-007660 (“Microbiologia Molecular, Estrutural e Celular”) funded by FEDER funds through COMPETE2020 – “Programa Operacional Competitividade e Internacionalização” (POCI); (2) “Fundação para a Ciência e a Tecnologia” (FCT) through programme IF (IF/00124/2015) to CP; (3) the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No. 810856; (4) COST Action CA15133, supported by COST (European Cooperation in Science and Technology); and (5) PPBI – Portuguese Platform of BioImaging (PPBI-POCI-01-0145-FEDER-022122) co-funded by national funds from OE – “Orçamento de Estado” and by FEDER. AG-C was supported by a FCT Ph.D. fellowship (SFRH/BD/118866/2016), and CA and VP by a FCT contract according to DL57/2016 (SFRH/BPD/74294/2010 and SFRH/BPD/87188/2012, respectively). Publisher Copyright: Copyright © 2022 Gaspar-Cordeiro, Amaral, Pobre, Antunes, Petronilho, Paixão, Matos and Pimentel.The synergistic combinations of drugs are promising strategies to boost the effectiveness of current antifungals and thus prevent the emergence of resistance. In this work, we show that copper and the antifungal fluconazole act synergistically against Candida glabrata, an opportunistic pathogenic yeast intrinsically tolerant to fluconazole. Analyses of the transcriptomic profile of C. glabrata after the combination of copper and fluconazole showed that the expression of the multidrug transporter gene CDR1 was decreased, suggesting that fluconazole efflux could be affected. In agreement, we observed that copper inhibits the transactivation of Pdr1, the transcription regulator of multidrug transporters and leads to the intracellular accumulation of fluconazole. Copper also decreases the transcriptional induction of ergosterol biosynthesis (ERG) genes by fluconazole, which culminates in the accumulation of toxic sterols. Co-treatment of cells with copper and fluconazole should affect the function of proteins located in the plasma membrane, as several ultrastructural alterations, including irregular cell wall and plasma membrane and loss of cell wall integrity, were observed. Finally, we show that the combination of copper and fluconazole downregulates the expression of the gene encoding the zinc-responsive transcription regulator Zap1, which possibly, together with the membrane transporters malfunction, generates zinc depletion. Supplementation with zinc reverts the toxic effect of combining copper with fluconazole, underscoring the importance of this metal in the observed synergistic effect. Overall, this work, while unveiling the molecular basis that supports the use of copper to enhance the effectiveness of fluconazole, paves the way for the development of new metal-based antifungal strategies.publishe

Stalked protozoa identification by image analysis and multivariable statistical techniques

Protozoa are considered good indicators of the treatment quality in activated sludge systems as they are sensitive to physical, chemical and operational processes. Therefore, it is possible to correlate the predominance of certain species or groups and several operational parameters of the plant. This work presents a semi-automatic image analysis procedure for the recognition of the stalked protozoa species most frequently found in WWTP by determining the physical, morphological and signature data and subsequent processing by discriminant analysis and neural network techniques. Physical descriptors were found to be responsible the largest identification ability and the crucial Opercularia and V. microstoma micro-organisms identification provided some degree of confidence to establish their presence in WWTP

Development of an image analysis procedure for identifying protozoa and metazoa typical of activated sludge system

A procedure for the semi-automatic identification of the main protozoa and metazoa species present in the activated sludge of wastewater treatment plants was developed. This procedure was based on both image processing and multivariable statistical methodologies, leading to the use of the image analysis morphological descriptors by discriminant analysis and neural network techniques. The image analysis programwritten in Matlab has proved to be adequate in terms of protozoa and metazoa recognition, as well as for the operating conditions assessment.National Council of Scientific and Technological Development of Brazil (CNPq); BIEURAM III ALFA co-operation project (European Commission); Fundação para a Ciência e a Tecnologia (FCT

Recognition of protozoa and metazoa using image analysis tools, discriminant analysis, neural networks and decision trees

Protozoa and metazoa are considered good indicators of the treatment quality in activated sludge systems due to the fact that these organisms are fairly sensitive to physical, chemical and operational processes. Therefore, it is possible to establish close relationships between the predominance of certain species or groups of species and several operational parameters of the plant, such as the biotic indices, namely the Sludge Biotic Index (SBI). This procedure requires the identification, classification and enumeration of the different species, which is usually achieved manually implying both time and expertise availability. Digital image analysis combined with multivariate statistical techniques has proved to be a useful tool to classify and quantify organisms in an automatic and not subjective way. Thiswork presents a semi-automatic image analysis procedure for protozoa and metazoa recognition developed in Matlab language. The obtained morphological descriptors were analyzed using discriminant analysis, neural network and decision trees multivariable statistical techniques to identify and classify each protozoan or metazoan. The obtained procedure was quite adequate for distinguishing between the non-sessile protozoa classes and also for the metazoa classes, with high values for the overall species recognition with the exception of sessile protozoa. In terms of the wastewater conditions assessment the obtained results were found to be suitable for the prediction of these conditions. Finally, the discriminant analysis and neural networks results were found to be quite similar whereas the decision trees technique was less appropriate.National Council of Scientific and Technological Development of Brazil (CNPq); BI-EURAM III ALFA co-operation project (European Commission); Fundação para a Ciência e a Tecnologia (FCT)

Aplicação de técnicas de análise de imagem e de estatística multivariável no reconhecimento de protozoários e metazoários típicos de sistemas por lodos ativados

Os protozoários e pequenos metazoários são organismos abundantes nos tanques de aeração das Estações de Tratamento de Efluentes (ETEs) por lodos ativados. A distribuição de espécies e sua abundância têm sido apontadas como indicadores da qualidade do tratamento fornecendo um instrumento útil para avaliar o desempenho destes sistemas. O presente trabalho teve como objetivo o desenvolvimento em ambiente Matlab de um procedimento de análise digital de imagens combinado com as técnicas multivariáveis de Redes Neurais, Análise Discriminante e Árvores de Decisão para efetuar o reconhecimento dos principais grupos de protozoários e pequenos metazoários típicos dos sistemas de tratamento de efluentes por lodos ativados. O procedimento obtido mostrou-se adequado para distinguir entre as classes de protozoários e metazoários incluídos no estudo. Os desempenhos globais de reconhecimento alcançados podem ser considerados de razoáveis a bons para todos as espécies avaliadas com exceção dos organismos pedunculados. Em relação com o reconhecimento dos organismos indicadores das condições operacionais das ETEs os resultados obtidos foram razoáveis para efetuar o seu diagnóstico, com melhoras na identificação dos organismos indicadores de condições críticas de operação em relação a estudos anteriores. Dentre as técnicas de análise estatística multivariável aplicadas, as Redes Neurais e a Análise Discriminante alcançaram níveis de Desempenho Global de Reconhecimento comparáveis, enquanto que as Árvores de Decisão mostraram-se menos apropriadas para os objetivos deste estudo. Por último, os resultados obtidos provaram que a técnica de análise digital de imagens combinada com as técnicas estatísticas de análise multivariável constitui uma ferramenta promissora para avaliar e monitorar populações de protozoários e matazoários nas ETEs por lodos ativados

Recognition of protozoa and metazoa using image analysis tools, discriminant analysis and neural network

A mixed culture of microorganisms is usually present in biological wastewater treatment processes such as the activated sludge system in aeration tanks. These microorganisms are capable of reducing the organic matter and other pollutants in the sewage. Protozoa and metazoa play an important role in this system because they maintain the density of bacterial populations by predation and contribute to the flocculation process, being responsible for an mprovement in the quality of the effluent. Moreover, protozoa and metazoa are considered to be important bioindicators of the activated sludge process due to their association with physical, chemical and operational parameters of the treatment plant. Furthermore, the analysis of the number and classes of the predominant groups of these organisms is used to predict the effectiveness of the aeration, extent of the nitrification process, sludge age and final effluent conditions1,2. Classical microfauna analysis is frequently done by microscopic observation and assessment of the different protozoa and metazoa species present. However, this task is not only timeconsuming and labour intensive but also requires the expertise of a zoologist or protozoologist. Therefore, digital image analysis can be seen as a useful tool to achieve taxonomic classification and organism’s quantification in an automatic, non subjective manner. Some studies have already been carried out using this technique combined with statistic multivariable analysis such as Neural Networks, Discriminant Analysis, and Principal Components Analysis to perform the recognition of protozoa and metazoa commonly present in the aeration tank of wastewater treatment plants activated sludge, including the works of Amaral et al. (2004)3. In this work an image analysis programme was developed in MATLAB code for the semi-automatic recognition of several groups of protozoa and metazoa commonly present in wastewater treatment plants. The protozoa and metazoa were characterized by different morphological parameters of Euclidean and fractal geometry, with or without their external structures (peduncles, cirri, tentacles). Finally, the morphological parameters (around 40) of the above-mentioned geometries were analysed using the multivariable statistical techniques Discriminant Analysis and Neural Network to identify and classify each protozoan or metazoan image. The procedure obtained was adequate for distinguishing between amoebas, sessile ciliates, crawling ciliates, large flagellates and free swimming ciliates in terms of the protozoa classes and also for the metazoa. Furthermore, with the exception of some sessile species, the value of overall species recognition was high. In terms of the wastewater conditions assessment such as aeration, nitrification, sludge age and effluent quality the obtained results were found to be suitable for the prediction of these conditions.ALFA cooperation project; the Biological Engineering Department of Minho University; Chemistry School – Federal University of Rio de Janeiro

Monitoring biotechnological processes through quantitative image analysis: application to 2-phenylethanol production by Yarrowia lipolytica

Available online 20 March 2023Quantitative image analysis (QIA) is a simple and automated tool for process monitoring that, when combined with chemometric techniques, enables the association of changes in microbiota morphology to various operational parameters. To that effect, principal component analysis, multilinear regression, and ordinary least squares methods were applied to the obtained dataset of the biotransformation conditions for Y. lipolytica through the monitor of yeast morphology, substrates (glycerol, L-phenylalanine - L-Phe) consumption and metabolites (2-phenylethanol 2-PE) production was developed. Glycerol and L-Phe were successfully monitored by the proposed approach, though with a lower monitoring ability for 2-PE, and mostly related to yeast and cluster size and proportion, yeasts contents and cluster morphology. The chemometric approach also allowed to identify significant morphological modifications related with the change in the stirring speed in the experiments at 600rpm, 600/400rpm (600rpm for 24h, and 400rpm until the end of the experiment) and in pH from 5.5 to 7.5. This work demonstrated, for the first time, that QIA combined with chemometric analysis can be considered a valuable tool to monitor biotechnological processes, namely the 2-PE production by Y. lipolytica, by analyzing yeast and cluster morphology.info:eu-repo/semantics/publishedVersio

Combining high pressure and electric fields towards nannochloropsis oculata eicosapentaenoic acid-rich extracts

Nannochloropsis oculata is naturally rich in eicosapentaenoic acid (EPA). To turn this microalga into an economically viable source for commercial applications, extraction efficiency must be achieved. Pursuing this goal, emerging technologies such as high hydrostatic pressure (HHP) and moderate electric fields (MEF) were tested, aiming to increase EPA accessibility and subsequent extraction yields. The innovative approach used in this study combined these technologies and associated tailored, less hazardous different solvent mixtures (SM) with distinct polarity indexes. Although the classical Folch SM with chloroform: methanol (PI 4.4) provided the highest yield concerning total lipids (166.4 mglipid/gbiomass), diethyl ether: ethanol (PI 3.6) presented statistically higher values in terms of EPA per biomass, corresponding to 1.3-fold increase. When SM were used in HHP and MEF, neither technology independently improved EPA extraction yields, although the sequential combination of technologies did result in 62% increment in EPA extraction. Overall, the SM and extraction methodologies tested (HHP—200 MPa, 21 °C, 15 min, followed by MEF processing at 40 °C, 15 min) enabled increased EPA extraction yields from wet N. oculata biomass. These findings are of high relevance for the food and pharmaceutical industries, providing viable alternatives to the “classical” extraction methodologies and solvents, with increased yields and lower environmental impact.info:eu-repo/semantics/publishedVersio