Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth

<p>Abstract</p> <p>Background</p> <p>Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known <it>in vivo</it> phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the <it>in silico</it> capability in predicting <it>in vivo</it> phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes.</p> <p>Results</p> <p>Here we employed a systematic and iterative process, designated as Reconciling <it>In silico/in vivo</it> mutaNt Growth (RING), to settle discrepancies between <it>in silico</it> prediction and <it>in vivo</it> observations to a newly reconstructed genome-scale metabolic model of the fission yeast, <it>Schizosaccharomyces pombe</it>, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of <it>S. pombe</it>. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype).</p> <p>Conclusion</p> <p>This study presents validation and refinement of a newly reconstructed metabolic model of the yeast <it>S. pombe</it>, through improving the metabolic model’s predictive capabilities by reconciling the <it>in silico</it> predicted growth phenotypes of single-gene knockout mutants, with experimental <it>in vivo</it> growth data.</p

Mapping condition-dependent regulation of metabolism in yeast through genome-scale modeling

<p>Background:</p> <p>The genome-scale metabolic model of Saccharomyces cerevisiae, first presented in 2003, was the first genome-scale network reconstruction for a eukaryotic organism. Since then continuous efforts have been made in order to improve and expand the yeast metabolic network.</p> <p>Results:</p> <p>Here we present iTO977, a comprehensive genome-scale metabolic model that contains more reactions, metabolites and genes than previous models. The model was constructed based on two earlier reconstructions, namely iIN800 and the consensus network, and then improved and expanded using gap-filling methods and by introducing new reactions and pathways based on studies of the literature and databases. The model was shown to perform well both for growth simulations in different media and gene essentiality analysis for single and double knock-outs. Further, the model was used as a scaffold for integrating transcriptomics, and flux data from four different conditions in order to identify transcriptionally controlled reactions, i.e. reactions that change both in flux and transcription between the compared conditions.</p> <p>Conclusion:</p> <p>We present a new yeast model that represents a comprehensive up-to-date collection of knowledge on yeast metabolism. The model was used for simulating the yeast metabolism under four different growth conditions and experimental data from these four conditions was integrated to the model. The model together with experimental data is a useful tool to identify condition-dependent changes of metabolism between different environmental conditions.</p

Development of computational methods for the determination of biomass composition and evaluation of its impact in genome-scale models predictions

Dissertação de mestrado em BioinformáticaThe use of genome-scale metabolic models is rapidly increasing in fields such as metabolic engineering. An important part of a metabolic model is the biomass equation, since this reaction will be used as the objective function in most simulation approaches. In order to obtain a reliable metabolic model, the biomass precursors and their coefficients must be as precise as possible. Ideally, the determination of the biomass composition would be performed experimentally, but due to technical limitations in cellular components quantification, budget restraints and time limitations, this is often established by approximation to closely related organisms. Computational methods however, can extract some information from the genome, such as amino acid and nucleotide compositions. One main objective in this study was to evaluate how biomass precursor coefficients computationally determined, affected the predictability of several genome-scale metabolic models by comparison with experimental data. Sensitivity analysis studies were performed with the Escherichia coli iAF1260 metabolic model concerning specific growth rate and flux distribution. Several metabolic models, whose biomass composition had been experimentally determined, were used to evaluate the impact of biomass coefficients on growth rates and flux distributions. In this study, biomass precursor coefficients were changed based on data obtained from computational methods and from closely related organisms. The results obtained from these two changes were then compared to the results obtained from the model using the experimentally determined biomass composition. Finally, analytical methods were established for macromolecule quantification and protein, DNA and RNA content of Helicobacter pylori biomass were experimentally determined. The results obtained suggest that small modifications (around 1×10-2) in biomass precursor coefficients have no significant impact on the computed specific growth rate and flux distributions. We also observed that, despite computationally determined biomass coefficients present differences to those experimentally determined, the growth rate and flux distributions have similar results (differences below 1,5 %). Surprisingly, specific growth rates and flux distributions were more distant from experimental data when adopting biomass precursor coefficients from closely related organisms.O uso de modelos metabólicos à escala genómica tem grande importância áreas, tais como a engenharia metabólica. A equação da biomassa é uma das reações fundamentais nestes modelos, uma vez que esta reacção é usada como função objectivo na maioria das abordagens de simulação. Para se obter um modelo à escala genómica coerente, os percursores da biomassa devem ser o mais precisos possível. A composição da biomassa deveria ser determinada experimentalmente; contudo, devido a limitações técnicas de quantificação, limitações de material biológico e tempo, muitos modelos metabólicos adoptam a composição da biomassa de organismos similares. No entanto, alguns métodos computacionais conseguem estimar coeficientes de aminoácidos e nucleótidos, a partir de informação do genoma. Neste trabalho, pretende-se avaliar o impacto que os coeficientes estimados a partir da informação do genoma, têm na previsão destes modelos à escala genómica, comparando-os com dados experimentais. Realizou-se uma análise de sensibilidade aos coeficientes da composição da biomassa do modelo à escala genómica da Escherichia coli iAF1260, comparando valores de taxa específica de crescimento e distribuição de fluxos. Foram também usados outros modelos à escala genómica, que possuem composição da biomassa com dados experimentais, de modo a avaliar o impacto da alteração da composição da biomassa na taxa específica de crescimento e distribuição de fluxos. Neste estudo fez-se a alteração da composição da biomassa com valores estimados in silico e com valores experimentais de organismos similares. Os valores de taxa específica de crescimento e de distribuição de fluxos obtidos para cada composição de biomassa foram comparados com os respectivos valores da composição da biomassa experimental. Por fim, procedeu-se também à implementação de métodos para análise da composição da biomassa em macromoléculas e determinou-se experimentalmente a composição de proteína, DNA e RNA total para o organismo Helicobacter pylori. Os resultados obtidos sugerem que pequenas alterações (na ordem de 1×10-2) nos coeficientes da composição da biomassa não afectam os valores das taxas específicas de crescimento e distribuição de fluxos. Observa-se também que os coeficientes da biomassa estimados a partir da composição do genoma, apesar de não serem muito semelhantes aos determinados experimentalmente, produzem resultados de taxa específica de crescimento e distribuição de fluxos muito semelhantes (diferenças menores que 1,5%). Estas diferenças são menores do que quando se adopta composições de biomassa de organismos semelhantes.Fundação para a Ciência e a Tecnologia (FCT) - Project FCOMP-01-0124-FEDER-009707 (HeliSysBio-molecular Systems Biology in Helicobacter pylori).ERDF - European Regional Development Fund through the COMPETE Programme (operational programme for competitiveness)

Constraint-based modeling of yeast metabolism and protein secretion

Yeasts are extensively exploited as cell factories for producing alcoholic beverages, biofuels, bio-pharmaceutical proteins, and other value-added chemicals. To improve the performance of yeast cell factories, it is necessary to understand their metabolism. Genome-scale metabolic models (GEMs) have been widely used to study cellular metabolism systematically. However, GEMs for yeast species have not been equally developed. GEMs for the well-studied yeasts such as Saccharomyces cerevisiae have been updated several times, while most of the other yeast species have no available GEM. Additionally, classical GEMs only account for the metabolic reactions, which limits their usage to study complex phenotypes that are not controlled by metabolism alone. Thus, other biological processes can be integrated with GEMs to fulfill diverse research purposes. \ua0In this thesis, the GEM for S. cerevisiae was updated to the latest version Yeast8, which serves as the basic model for the remaining work of the thesis including two dimensions: 1) Yeast8 was used as a template for generating GEMs of other yeast species/strains, and 2) Yeast8 was expanded to account for more biological processes. Regarding the first dimension, strain-specific GEMs for 1,011 S. cerevisiae isolates from diverse origins and species-specific GEMs for 343 yeast/fungi species were generated. These GEMs enabled explore the phenotypic diversity of the single species from diverse ecological and geographical origins, and evolution tempo among diverse yeast species. Regarding the second dimension, other biological processes were formulated within Yeast8. Firstly, Yeast8 was expanded to account for enzymatic constraints, resulting in enzyme-constrained GEMs (ecGEMs). Secondly, Yeast8 was expanded to the model CofactorYeast by accounting for enzyme cofactors such as metal ions, which was used to simulate the interaction between metal ions and metabolism, and the cellular responses to metal ion limitation. Lastly, Yeast8 was expanded to include the protein synthesis and secretion processes, named as pcSecYeast. pcSecYeast was used to simulate the competition of the recombinant protein with the native secretory-pathway-processed proteins. Besides that, pcSecYeast enabled the identification of overexpression targets for improving recombinant protein production. \ua0When developing these complex models, issues were identified among which the lack of enzyme turnover rates, i.e., kcatvalues, needs to be solved. Accordingly, a machine learning method for kcat prediction and automated incorporation into GEMs were developed, facilitating the generation of functional ecGEMs in a large scale

Reconstruction of the genome-scale metabolic network of Kluyveromyces lactis

System Biology proposes to study biological components, as well as the interactions between them, to understand and predict systems’ behaviour through the use of mathematical models. Under this scope, Genome-Scale Metabolic Models (GSMMs) can be regarded as mathematical representations of the intrinsic metabolic capabilities of a given organism, encoded in its genome, and can be used in a variety of applications like predicting the phenotypical behaviour of a given organism in different environmental and genetic perturbations. The reconstruction of these models comprehends four fundamental stages, namely Genome Annotation, Assembling of a Metabolic Network from the Genome, the Conversion of the Network to a Stoichiometric Model and finally the Validation of the Metabolic Model. Although this procedure is currently relatively standardized in some stages, a significant amount of work still needs to be done by the community before the reconstruction process becomes semi-automated and reproducible. The present work aims at contributing to this field through the development of several tools for aiding the reconstruction process, while simultaneously applying some of those tools to an industrially relevant organism, the yeast Kluyveromyces lactis. The genome annotation stage is critical, as an inadequate annotation may delay, or even impair, the development of the model. The genome metabolic annotation consists on identifying and attributing functions to metabolic genes, i.e., genes encoding enzymes and transport proteins. While the identification of enzyme encoding genes can be performed by assigning Enzyme Commission numbers to the proteins encoded in the genes, the transport proteins encoding genes annotation is not straightforward. In this work, an automatic system to detect and classify all potential transport proteins from a given genome and integrate the related reactions into GSMMs is proposed, based on the identification and classification of genes that encode transmembrane proteins. The integration of the data provided by this methodology with highly curated models allowed the identification of new transport reactions. This tool was included in the merlin tool, a user-friendly Java application developed under the scope of this thesis that performs the reconstruction of GSMMs for any organism that has its genome sequenced. It performs several steps of the reconstruction process, including the functional genomic annotation of the whole genome. merlin 2.0 also performs the compartmentalisation of the model, predicting the organelle localisation of the proteins encoded in the genome, and thus the localisation of the metabolites involved in the reactions induced by such proteins. Finally, merlin 2.0 expedites the transition from genome-scale data to SBML (the standard Systems Biology Markup Language) metabolic models, allowing the user to have a preliminary view of the biochemical network. The yeast Kluyveromyces lactis has long been considered a model organism for studies in genetics and physiology, mainly due to its ability to metabolize lactose and to express recombinant proteins. Although the genome of Kluyveromyces lactis has been publicly available for some years, until now no complete metabolic functional annotation has been performed to the proteins encoded in the Kluyveromyces lactis genome and consequently no GSMM has been made available. In this work, a new metabolic genome-wide functional re-annotation of the proteins encoded in the Kluyveromyces lactis genome was performed, resulting in the annotation of 1759 genes with metabolic functions, and the development of a methodology supported by merlin. The new annotation includes novelties, such as the assignment of transporter superfamily numbers to genes identified as transporter proteins. The methodology developed throughout this work can be used to re-annotate any yeast or, with a little tweak of the reference organism, the proteins encoded in any sequenced genome. The new annotation provided by this study served as the basis for the reconstruction of a compartmentalized, genome-scale metabolic model for Kluyveromyces lactis. The partially compartmentalised (4 compartments) genome-scale metabolic model of Kluyveromyces lactis, the iOD962 metabolic model, comprises 962 genes, 2038 reactions and 1561 metabolites. Previous chemostat experiments were used to adjust both growth and non-growth associated energy requirements, and the model proved accurate when predicting the biomass, oxygen and carbon dioxide yields. Also, the in silico knockouts predicted accurately the in vivo phenotypes, when compared to published experiments. This model allowed determining a minimal medium for cultivating Kluyveromyces lactis and will surely bring new insights on the milk yeast metabolism, identifying engineering targets for the improvement of the yields of products of interest by performing in silico simulations.A Biologia de Sistemas propõe-se estudar os componentes biológicos e as interações entre eles, para compreender e prever o comportamento dos sistemas através do uso de modelos matemáticos. Nesse âmbito, os Modelos Metabólicos à Escala Genómica (MMEGs) podem ser considerados representações matemáticas das capacidades metabólicas intrínsecas de um dado organismo, codificadas no seu genoma, e podem ser usados numa grande variedade de aplicações tais como a previsão do comportamento fenotípico de um determinado organismo face a diferentes perturbações ambientais e genéticas. O processo de reconstrução destes modelos compreende quatro fases fundamentais: anotação do genoma, desenvolvimento da rede metabólica, conversão da rede num modelo estequiométrico e, finalmente, a validação do modelo metabólico. Apesar de algumas destas fases estarem já relativamente normalizadas, existe ainda uma lacuna significativa na comunidade no que se refere à (semi-) automação e reprodutibilidade deste processo. O presente trabalho apresenta-se como uma contribuição para esta área, através do desenvolvimento de várias ferramentas de apoio à construção de modelos metabólicos e, simultaneamente da sua aplicação ao organismo Kluyveromyces lactis, uma levedura de elevado interesse industrial. A fase de anotação do genoma é uma fase crítica, pois uma anotação inadequada pode atrasar, ou mesmo comprometer o desenvolvimento de um modelo metabólico. A anotação metabólica do genoma consiste na identificação e atribuição de funções aos genes metabólicos, ou seja, genes que codificam enzimas e proteínas de transporte. Enquanto que a identificação de enzimas codificadas nos genes pode ser realizada através da atribuição de números da Comissão para as Enzimas, a anotação de genes que codificam as proteínas de transporte é um processo mais complexo. Neste trabalho é proposto um sistema automático para a deteção e classificação de proteínas de transporte. Este sistema é baseado na identificação e classificação dos genes que codificam proteínas transmembranares. A integração dos dados fornecidos por esta metodologia com modelos metabólicos curados permitiu a identificação de novas reações de transporte em organismos bem estudados. Esta ferramenta está incluída na ferramenta bioinformática merlin desenvolvida no âmbito desta tese, que é uma aplicação Java de fácil utilização, direcionada para a reconstrução de modelos metabólicos à escala genómica. Esta aplicação executa várias etapas do processo de reconstrução, incluindo a anotação funcional do genoma. O merlin 2.0 também efetua a compartimentação do modelo, prevendo a localização das proteínas codificadas no genoma, e consequentemente dos metabolitos envolvidos nas reações induzidas por essas proteínas. Finalmente, merlin 2.0 acelera a transição de dados do genoma para modelos metabólicos no formato SBML (Systems Biology Markup Language), possibilitando uma visão preliminar da rede bioquímica. A levedura Kluyveromyces lactis tem sido considerada um organismo modelo para estudos de genética e fisiologia, principalmente devido à sua capacidade de metabolizar a lactose e pela sua capacidade de expressar proteínas recombinantes. Apesar de o genoma da Kluyveromyces lactis ter sido disponibilizado publicamente há alguns anos, até agora não foi efetuada uma anotação funcional completa para identificar as proteínas codificadas no genoma da Kluyveromyces lactis. Consequentemente, não existe ainda nenhum MMEG para esta levedura. Neste trabalho foi efetuada uma re-anotação funcional das proteínas codificadas no genoma da Kluyveromyces lactis, resultando na anotação de 1759 genes com funções metabólicas, e no desenvolvimento de uma metodologia apoiada na aplicação merlin. A nova anotação do genoma inclui novidades, tais como a atribuição de números de superfamílias de transportadores a genes que codificam proteínas de transporte. A metodologia desenvolvida ao longo deste trabalho pode ser usada para reanotar qualquer levedura ou, com um ajuste do organismo de referência, as proteínas codificadas em qualquer genoma sequenciado. A nova anotação fornecida por este estudo serviu de base para a reconstrução de um modelo metabólico à escala genómica da Kluyveromyces lactis. Este modelo metabólico, parcialmente compartimentado (4 compartimentos), designado iOD962, inclui 962 genes, 2038 reações e 1561 metabolitos. Foram utilizadas experiências em quimiostato publicadas anteriormente para ajustar os requisitos energéticos associados à manutenção celular, e o modelo mostrou precisão na previsão dos rendimentos de biomassa, de dióxido de carbono e de oxigénio. Além disso, as simulações in silico previram com precisão os fenótipos in vivo, quando comparadas com as experiências publicadas. Este modelo permitiu determinar um meio mínimo para o cultivo de Kluyveromyces lactis e certamente trará novas perspectivas sobre o metabolismo desta levedura, identificando alvos de engenharia metabólica para a melhoria dos rendimentos dos produtos de interesse através da realização de simulações in silico

Metabolic studies on Schizosaccharomyces pombe for improved protein secretion

The fission yeast Schizosaccharomyces pombe is an attractive host for heterologous protein secretion but currently still too little developed to compete against industrial cell factories like Pichia pastoris and Saccharomyces cerevisiae. Thus, the present work aimed at increasing the understanding of the metabolism of S. pombe and the metabolic burden associated with protein secretion in order to derive metabolic engineering strategies for improving recombinant protein production. In the first part a system of small-scale parallel bioreactors was constructed to perform studies in continuous culture. This system was used for quantitative metabolic analyses applying 13C-based metabolic flux analysis to S. pombe grown on mixtures of glycerol and acetate compared to respiratory growth on glucose as sole carbon source. Next the methttp://scidok.sulb.uni-saarland.de/volltexte/incoming/2014/5760/abolic burden of protein secretion was investigated, using strains secreting the model protein maltase in varying amounts up to 27 mg per g cells. Quantitative analysis of the metabolic fluxes and the macromolecular cell composition revealed that lipid biosynthesis, TCA cycle and supply of mitochondrial NADPH as bottlenecks in protein secretion. From these data, a feeding strategy was derived for supplementing the media with acetate and glycerol, which enabled the cells to overcome these limitations. The results were transferred to heterologous secretion of GFP and a single-chain antibody fragment, increasing yields 2-fold and 4-fold, respectively.Die Spalthefe Schizosaccharomyces pombe stellt ein attraktives System zur heterologen Proteinsekretion dar, kann derzeit aber noch nicht mit Zellfabriken wie Pichia pastoris und Saccharomyces cerevisiae konkurrieren. Diese Arbeit sollte daher das Verständnis des Metabolismus von S. pombe und dessen Belastung durch Sekretion von Proteinen vergrößern, um Strategien für das Metabolic Engineering abzuleiten, welche die rekombinanten Proteinproduktion verbessern. Zunächst wurde ein System paralleler Bioreaktoren aufgebaut, um Studien in kontinuierlicher Kultur durchzuführen. In diesem System wurden quantitative metabolische Analysen an S. pombe mittels 13C basierter metabolischer Flussanalyse auf Gemischen aus Glycerin und Acetat durchgeführt und mit dem respirativen Wachstum auf Glucose verglichen. Weiter wurde die Belastung des Metabolismus durch Sekretion des Modellproteins Maltase untersucht. Eine quantitative Analyse der metabolischen Flüsse und der makromolekularen Zellzusammensetzung zeigte, dass die Lipid-Biosynthese, der Citratzyklus und die Bereitstellung von mitochondriellem NADPH Engpässe in der Proteinsekretion darstellen. Hieraus wurden Fütterungsstrategien abgeleitet und das Medium mit Acetat und Glycerin supplementiert, wodurch diese Limitierungen überwunden werden konnten. Diese Strategien wurden auf die heterologe Sekretion von GFP und einem Antikörper-Fragment übertragen, wodurch deren Ausbeuten um das 2-fache und 4-fache gesteigert werden konnten