774 research outputs found

    A Systems-Based Approach for Cyanide Overproduction by Bacillus megaterium for Gold Bioleaching Enhancement

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    With the constant accumulation of electronic waste, extracting precious metals contained therein is becoming a major challenge for sustainable development. Bacillus megaterium is currently one of the microbes used for the production of cyanide, which is the main leaching agent for gold recovery. The present study aimed to propose a strategy for metabolic engineering of B. megaterium to overproduce cyanide, and thus ameliorate the bioleaching process. For this, we employed constraint-based modeling, running in silico simulations on iJA1121, the genome-scale metabolic model of B. megaterium DSM319. Flux balance analysis (FBA) was initially used to identify amino acids to be added to the culture medium. Considering cyanide as the desired product, we used growth-coupled methods, constrained minimal cut sets (cMCSs) and OptKnock to identify gene inactivation targets. To identify gene overexpression targets, flux scanning based on enforced objective flux (FSEOF) was performed. Further analysis was carried out on the identified targets to determine compounds with beneficial regulatory effects. We have proposed a chemical-defined medium for accelerating cyanide production on the basis of microplate assays to evaluate the components with the greatest improving effects. Accordingly, the cultivation of B. megaterium DSM319 in a chemically-defined medium with 5.56 mM glucose as the carbon source, and supplemented with 413 μM cysteine, led to the production of considerably increased amounts of cyanide. Bioleaching experiments were successfully performed in this medium to recover gold and copper from telecommunication printed circuit boards. The results of inductively coupled plasma (ICP) analysis confirmed that gold recovery peaked out at around 55% after 4 days, whereas copper recovery continued to increase for several more days, peaking out at around 85%. To further validate the bioleaching results, FESEM, XRD, FTIR, and EDAX mapping analyses were performed. We concluded that the proposed strategy represents a viable route for improving the performance of the bioleaching processes

    Relative flux trade-offs and optimization of metabolic network functionalities

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    Trade-offs between traits are present across different levels of biological systems and ultimately reflect constraints imposed by physicochemical laws and the structure of underlying biochemical networks. Yet, mechanistic explanation of how trade-offs between molecular traits arise and how they relate to optimization of fitness-related traits remains elusive. Here, we introduce the concept of relative flux trade-offs and propose a constraint-based approach, termed FluTOr, to identify metabolic reactions whose fluxes are in relative trade-off with respect to an optimized fitness-related cellular task, like growth. We then employed FluTOr to identify relative flux trade-offs in the genome-scale metabolic networks of Escherichia coli, Saccharomyces cerevisiae, and Arabidopsis thaliana. For the metabolic models of E. coli and S. cerevisiae we showed that: (i) the identified relative flux trade-offs depend on the carbon source used and that (ii) reactions that participated in relative trade-offs in both species were implicated in cofactor biosynthesis. In contrast to the two microorganisms, the relative flux trade-offs for the metabolic model of A. thaliana did not depend on the available nitrogen sources, reflecting the differences in the underlying metabolic network as well as the considered environments. Lastly, the established connection between relative flux trade-offs allowed us to identify overexpression targets that can be used to optimize fitness-related traits. Altogether, our computational approach and findings demonstrate how relative flux trade-offs can shape optimization of metabolic tasks, important in biotechnological applications. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.Peer reviewe

    Determination of key enzymes for threonine synthesis through in vitro metabolic pathway analysis

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    Figure S1. The pathway flux (J) in the in vitro system when one enzyme concentration was increased. (A) The pathway flux when purified ThrA was added to the crude enzyme extract. (B) The pathway flux when purified Asd was added to the crude enzyme extract. (C) The pathway flux when purified ThrB was added to the crude enzyme extract. (D) The pathway flux when purified ThrC was added to the crude enzyme extract

    A systems biology understanding of protein constraints in the metabolism of budding yeasts

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    Fermentation technologies, such as bread making and production of alcoholic beverages, have been crucial for development of humanity throughout history. Saccharomyces cerevisiae provides a natural platform for this, due to its capability to transform sugars into ethanol. This, and other yeasts, are now used for production of pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, nutraceuticals, and fuel precursors. In this thesis, different systems biology methods were developed to study interactions between metabolism, enzymatic capabilities, and regulation of gene expression in budding yeasts. In paper I, a study of three different yeast species (S. cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus), exposed to multiple conditions, was carried out to understand their adaptation to environmental stress. Paper II revises the use of genome-scale metabolic models (GEMs) for the study and directed engineering of diverse yeast species. Additionally, 45 GEMs for different yeasts were collected, analyzed, and tested. In paper III, GECKO 2.0, a toolbox for integration of enzymatic constraints and proteomics data into GEMs, was developed and used for reconstruction of enzyme-constrained models (ecGEMs) for three yeast species and model organisms. Proteomics data and ecGEMs were used to further characterize the impact of environmental stress over metabolism of budding yeasts. On paper IV, gene engineering targets for increased accumulation of heme in S. cerevisiae cells were predicted with an ecGEM. Predictions were experimentally validated, yielding a 70-fold increase in intracellular heme. The prediction method was systematized and applied to the production of 102 chemicals in S. cerevisiae (Paper V). Results highlighted general principles for systems metabolic engineering and enabled understanding of the role of protein limitations in bio-based chemical production. Paper VI presents a hybrid model integrating an enzyme-constrained metabolic network, coupled to a gene regulatory model of nutrient-sensing mechanisms in S. cerevisiae. This model improves prediction of protein expression patterns while providing a rational connection between metabolism and the use of nutrients from the environment.This thesis demonstrates that integration of multiple systems biology approaches is valuable for understanding the connection of cell physiology at different levels, and provides tools for directed engineering of cells for the benefit of society

    Improvement of in silico strain engineering methods in Saccharomyces cerevisiae

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    PhD thesis in BioengineeringThe buildup of knowledge about microbial metabolism and the development of genome engineering techniques gave rise to the rational modification of microorganisms in order to use them to biosynthesize chemicals of industrial interest. Recently, the construction of genome-scale metabolic models (GSMMs) allowed the design of strain engineering strategies in silico. This thesis focused on the study and improvement of in silico strain engineering methodologies using Saccharomyces cerevisiae as a case study organism. Firstly, in order to investigate the accuracy of the GSMMs available for S. cerevisiae, their capacity to simulate the intracellular fluxes in central metabolism was tested. The results revealed that the simulations contained relevant errors in important areas of the central metabolism. A careful manual curation of the feasibility of all reactions producing or consuming NADH / NADPH resulted in the improvement of many fluxes in central metabolic pathways when compared to fluxes measured experimentally. The lack of a simulation method that could predict in quantitative terms the phenotype of strains with complex engineered genotypes, led to the development of a novel simulation method called turnover dependent phenotypic simulation (TDPS). This method was designed with the goal of simulating the majority of the genetic modifications usually implemented in engineered strains. The assumption that the production turnover of a metabolite can be used as an indication of its abundance was used in the formulation of TDPS in order to take into account the availability of resources when modelling genetic modifications. TDPS was validated using metabolically engineered S. cerevisiae strains available in the literature by comparing the production yields of the target metabolite. TDPS was then applied to the optimization of the availability of cytosolic acetyl-CoA in S. cerevisiae, by using an evolutionary algorithm to search for sets of genetic alterations that could improve the production yield of 3-hydroxypropionic acid (3-HP) derived from acetyl-CoA. Although the yields obtained experimentally were considerably lower than the simulations suggested, a positive effect on the 3-HP yield was observed for the downregulation of the pyruvate dehydrogenase complex and the deletion of ACH1 (succinyl- CoA:acetate CoA-transferase).O progresso que tem sido feito na área da fisiologia microbiana, juntamente com o desenvolvimento de técnicas de engenharia genética, permitiu a criação de estirpes microbianas modificadas racionalmente com o intuito de optimizar a produção de compostos de interesse industrial. Mais recentemente, a construção de modelos metabólicos à escala genómica (MMEG) proporcionou o desenho de estirpes modificadas in silico. Esta tese focou-se no estudo e melhoramento de metodologias de manipulação de estirpes in silico, usando Saccharomyces cerevisiae como caso de estudo. De forma a investigar a precisão dos MMEG disponíveis para S. cerevisiae, a sua capacidade para simular os fluxos intracelulares foi testada. Os resultados mostraram que os fluxos simulados continham erros em áreas importantes do metabolismo central e que a curação manual das reacções envolvidas no metabolismo de NADH e NADPH resulta em melhorias significativas nos fluxos metabólicos centrais. A ausência de um método de simulação que conseguisse prever quantitativamente o fenótipo de estirpes com genótipos complexos, levou ao desenvolvimento de um método novo designado por turnover dependent phenotypic simulation (TDPS). Este método foi concebido com o objectivo de simular a maior parte das modificações genéticas normalmente implementadas em estripes modificadas. A formulação do TDPS teve como base o uso do nível de produção de um metabolito como indicador da sua abundancia, de forma a modelar as modificações genéticas em função da disponibilidade de recursos. A validação deste método foi feita usando dados da literatura sobre estirpes geneticamente modificadas de S. cerevisiae, através da comparação dos rendimentos simulados e reais. O método de simulação TDPS foi posteriormente aplicado na optimização da produção de acetil-CoA no citosol de S. cerevisiae, usando um algoritmo evolucionário para procurar conjuntos de alterações genéticas que aumentassem a produção de ácido 3- hidroxipropiónico derivado de acetil-CoA. Apesar dos rendimentos experimentais serem mais baixos que as simulações sugeriam, observou-se um efeito positivo da sub-regulação do complexo da piruvato desidrogenase e da eliminação do gene ACH1 (succinil- CoA:acetato CoA-transferase).Esta investigação foi financiada pela Fundação para a Ciência e Tecnologia através da concessão de uma bolsa de doutoramento (SFRH/BD/51111/2010), co-financiada pelo POPH - QREN - Tipologia 4.1 -Formação Avançada - e comparticipados pelo Fundo Social Europeu (FSE) e por fundos nacionais do Ministério da Ciência, Tecnologia e Ensino Superior (MCTES)

    Development of an integrated platform for the in silico phenotype simulation of microbial strains

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    Dissertação de mestrado em BioinformáticaRecentes técnicas de sequenciação genómica e abordagens estão a aumentar constantemente, a uma taxa exponencial, os dados biológicos disponíveis. Esta informação está se tornar ainda mais acessível com o desenvolvimento de novas tecnologias de high-throughout, fazendo com que a simulação in silico de sistemas vivos ou sintéticos se torne mais atrativa. A crescente investigação dos últimos dez anos permitiu o desenvolvimento de modelos matemáticos sofisticados que, por meio de engenharia metabólica, são utilizados numa tentativa de otimizar as funções do organismo, modificando-os geneticamente para produzir compostos de interesse industrial. Em relação ao uso dos modelos de simulação de fenótipo, métodos como o Flux Balance Analysis são utilizados para identificar conjuntos de manipulações genéticas que resultam em estirpes mutantes capazes de produzir compostos desejados. Variações desses métodos têm rapidamente surgido e, em paralelo, um grande número de ferramentas computacionais surgiram com a capacidade de realizar esses métodos. Todas estas ferramentas estão disponíveis para a comunidade e construídas em diferentes sistemas e linguagens. No entanto, nenhuma inclui todos os métodos relevantes, o que resulta numa necessidade de recurso a mais do que uma ferramenta para realizar certas tarefas. Neste trabalho, uma plataforma que pode integrar todos estes métodos é apresentada com o objetivo de centralizar e integrar os métodos de simulação de fenótipo mais relevantes e também permitir a sua extensão fácil com outros métodos. Para este trabalho, foi realizado um estudo dos métodos e ferramentas mais relevantes de modo a que esta plataforma possa integrar os métodos e ferramentas mais significativas. Esta plataforma é dividida em duas camadas: uma camada de ligação que é responsável pela troca de informação através das ferramentas computacionais e línguas, e uma camada de formulação responsável por executar os métodos dessas ferramentas e também apresentar os seus resultados. Através destas camadas, a plataforma pode executar métodos de qualquer ferramenta disponível construída em qualquer linguagem computacional e fornecer aos investigadores o acesso a todos os métodos em uma única plataforma. Como aplicação prática desta plataforma, foi desenvolvido um plugin e integrado no OptFlux, um software de código aberto para apoiar as tarefas de engenharia metabólica, e encontra-se disponível em www.optflux.org. Este plugin oferece uma ligação com a ferramenta COBRA e executa os métodos de simulação metabólicas mais relevantes presentes no último.Modern sequencing techniques and omics approaches are constantly increasing available biological data at an exponential rate. This information is becoming even more accessible with the development of new high-throughout technologies, making in silico simulation of living and synthetic systems more attractive. The growing research in the past decade allowed the development of sophisticated mathematical models that, through Metabolic Engineering, are used in an attempt to optimize organism's functions, genetically modifying them to produce compounds of industrial interest. Regarding the use of models for phenotype simulation, methods such as Flux Balance Analysis are used to identify sets of genetic manipulations that result in mutant strains capable of producing desired compounds. Variations of these methods have rapidly appeared and in parallel, a great number of computational tools emerged with the capability of performing these methods. All these tools are available for the community and built in different systems and languages. However, none includes all the relevant methods, which results in a need of recurring to more than one tool to accomplish certain tasks. In this work, a platform that can integrate all these methods is presented with the goal of centralizing and integrating the most relevant existing phenotype simulation methods and also enabling their easy extension with other methods. For this work, a study of the most relevant methods and tools was made so that this platform could integrate the most significant methods and tools. This platform is divided in two layers: a connection layer that is responsible for exchanging information through the computational tools and languages, and a formulation layer responsible for performing the methods from these tools and also presenting the results. Through these layers, the platform can perform methods from any available tool built in any computational language and provide to the researchers the access to all methods in a single platform. As a practical implementation of this platform, a plugin was developed and integrated in OptFlux, an open-source software to support metabolic engineering tasks, available at www.optflux.org. This plugin provides a connection with the COBRA Toolbox and performs the most relevant metabolic simulation methods present in the latter

    Microbial production of advanced biofuels

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    Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels are biofuels produced from a renewable carbon source using engineered microorganisms. Two biofuels, ethanol and biodiesel, have been made inroads to displacing petroleum-based fuels, but their penetration has been limited by the amounts that can be used in conventional engines and by cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure, but have had limited penetration due to costs. In this review, we will discuss the advances in engineering microbial metabolism to produce advanced biofuels and prospects for reducing their costs
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