Study of the differences in the fermentative metabolism of S. cerevisiae, S. uvarum and S. kudriavzevii species

Tesis por compendio[ES] Saccharomyces cerevisiae, además de ser un importante organismo modelo en biología, es indiscutiblemente la especie de levadura más utilizada en procesos fermentativos industriales, incluyendo el sector enológico. Su capacidad de fermentar en concentraciones elevadas de azúcares, tolerar concentraciones altas de etanol y soportar la adición de sulfitos, son algunos de los factores que explican su éxito en fermentaciones vínicas. El metabolismo fermentativo de S. cerevisiae en condiciones enológicas se conoce bien gracias a una amplia bibliografía científica. En cambio, aún se sabe poco sobre el metabolismo de las especies de Saccharomyces criotolerantes, S. uvarum y S. kudriavzevii, quienes han suscitado recientemente el interés del sector vitivinícola por sus buenas propiedades fermentativas a bajas temperaturas, tales como la producción de vinos con mayor contenido en glicerol y alta complejidad aromática, llegando a veces a reducir su contenido en etanol. En este contexto, esta tesis pretende ampliar nuestros conocimientos sobre el metabolismo fermentativo de S. uvarum y S. kudriavzevii en condiciones enológicas, profundizando en el entendimiento de las diferencias existentes con el de S. cerevisiae, así como entre cepas de S. cerevisiae de distintos orígenes. Para ello, hemos utilizado varias técnicas ómicas para analizar la dinámica de los metabolomas (intra- y extracelulares) y/o transcriptomas de cepas representativas de S. cerevisiae, S. uvarum y S. kudriavzevii a alta (25 °C) y baja (12 °C) temperatura de fermentación. También, hemos desarrollado un modelo metabólico a escala de genoma que, junto a un análisis de balance de flujos, es capaz de cuantificar los flujos a través del metabolismo del carbono y del nitrógeno de levaduras en cultivo de tipo batch. Así, el conjunto de estos trabajos nos ha permitido identificar rasgos metabólicos y/o transcriptómicos relevantes para el sector enológico en estas especies. También se aporta nueva información sobre las especificidades de redistribución de flujos en la red metabólica de levaduras del género Saccharomyces acorde a la especie y las fluctuaciones ambientales que ocurren durante una fermentación vínica.[CAT] Saccharomyces cerevisiae, a més de ser un important organisme model en biologia, és indiscutiblement l'espècie de llevat més utilitzat en processos fermentatius industrials, incloent el sector enològic. La seua capacitat de fermentar grans concentracions de sucres, tolerar concentracions altes d'etanol i suportar l'addició de sulfits, són alguns dels factors que expliquen el seu èxit en fermentacions víniques. D'aquesta manera, el metabolisme fermentatiu de S. cerevisiae en condicions enològiques està ben descrit i es beneficia d'una àmplia bibliografia científica. En canvi, poc se sap encara sobre el metabolisme de les espècies de Saccharomyces criotolerants, S. uvarum i S. kudriavzevii, els qui han recentment suscitat l'interés del sector vitivinícola per les seues bones propietats fermentatives a baixes temperatures, com ara la producció de vins amb major contingut en glicerol, alta complexitat aromàtica i arribant a vegades a reduir el seu contingut en etanol. En aquest context, aquesta tesi pretén ampliar els nostres coneixements sobre el metabolisme fermentatiu de S. uvarum i S. kudriavzevii en condicions enològiques, aprofundint en l'enteniment de les diferències existents amb el de S. cerevisiae, així també com entre ceps de S. cerevisiae de diferents orígens. Per a això, hem utilitzat diverses tècniques omiques per a analitzar la dinàmica dels metabolomes (intra- i extracelul·lars) i/o transcriptomes de ceps representatius de S. cerevisiae, S. uvarum i S. kudriavzevii a alta (25 °C) i baixa (12 °C) temperatures de fermentació. També, hem desenvolupat un model metabòlic a escala del genoma que, al costat d'una anàlisi de balanç de fluxos, és capaç de quantificar els fluxos a través del metabolisme carbonat i nitrogenat de llevats en cultius de tipus batch. Així, el conjunt d'aquests treballs ens ha permés identificar trets metabòlics i/o transcriptómics rellevants per al sector enològic en aquestes espècies. També aporta nova informació sobre les especificitats de redistribució de fluxos en la xarxa metabòlica de llevats del gènere Saccharomyces concorde a l'espècie i les fluctuacions ambientals ocorrent durant una fermentació vínica.[EN] Saccharomyces cerevisiae, besides being an important model organism in biology, is undoubtedly the most widely used yeast species in industrial fermentation processes, including the winemaking sector. Its ability to ferment at high levels of sugars, tolerate high ethanol concentrations and withstand the addition of sulfites are some of the factors explaining its success in wine fermentation. Accordingly, the fermentative metabolism of S. cerevisiae under oenological conditions is well described and benefits from a large scientific literature. In contrast, little is known about the metabolism of the cryotolerant Saccharomyces species, S. uvarum and S. kudriavzevii, which have recently attracted the interest of the wine industry for their good fermentative properties at low temperatures, such as the production of wines with higher glycerol content, high aromatic complexity and sometimes even reduced ethanol content. In this context, this thesis aims to expand our knowledge on the fermentative metabolism of S. uvarum and S. kudriavzevii under oenological conditions, deepening our understanding of the existing differences with that of S. cerevisiae, as well as between S. cerevisiae strains of different origins. For this purpose, we have used several omics techniques to analyze the dynamics of the (intra- and extracellular) metabolomes and/or transcriptomes of representative strains of S. cerevisiae, S. uvarum and S. kudriavzevii at high (25 °C) and low (12 °C) fermentation temperatures. Also, we have developed a genome-scale metabolic model that, together with a flux balance analysis, is able to quantify fluxes through carbon and nitrogen metabolism of yeast in batch culture. Taken together, this work has allowed us to identify metabolic and/or transcriptomic traits relevant to the oenological sector in these species. It also provides new information on the specificities of flux redistribution in the metabolic network of Saccharomyces yeasts according to the species and environmental fluctuations occurring during wine fermentation.The present work has been carried out at the Department of Food Biotechnology of the IATA (CSIC). Romain Minebois was funded by a FPI grant (REF: BES-2016-078202) and supported by projects AGL2015-67504-C3-1R and RTI2018-093744-BC31 of the Ministerio de Ciencia e Inovación awarded to Amparo Querol.Minebois, RCM. (2021). Study of the differences in the fermentative metabolism of S. cerevisiae, S. uvarum and S. kudriavzevii species [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/176018TESISCompendi

Systems biology of yeast metabolism - Understanding metabolism through proteomics and constraint-based modeling

Metabolism is the set of all chemical reactions that occur inside of cells. By providing all the building blocks that are required for sustaining a cellular state and cell proliferation, metabolism is at the core of cellular function. Therefore, in order to understand cellular function it is important to understand cellular metabolism. The cellular metabolic network comprises thousands of reactions even in the simplest of organisms. Due to the high complexity, a holistic approach is required to study and understand the interactions between different parts of metabolism giving rise to cellular phenotypes.In this thesis, a systems biology approach to study metabolism in yeast, mainly with a focus on Saccharomyces cerevisiae (baker’s yeast), was used. This approach consisted of combining proteomic analysis with constraint-based modeling to gain insights into different aspects of metabolism. First, the role of mitochondria in cellular metabolism throughout diauxic growth was evaluated, showing that mitochondria balance their role as a biosynthetic hub and center for energy generation depending on the mode of cellular metabolism. Next, the construction of a model of mitochondrial metabolism describing the essential mitochondrial processes of protein import and cofactor metabolism as well as proton motive force driving the generation of free energy (in the form of ATP) is described and evaluated. The model was used to investigate the dynamics in mitochondrial metabolism and the requirement of these processes.Second, the constraints placed on cellular metabolism arising from finite protein resources is investigated in two studies. The first study evaluates the effect of amino acid supplementation of the physiology and allocation of protein resources. This study showed that as the burden of producing amino acids is relieved, the cells can allocate more protein to the translation, which allows the cells to grow faster. In the second study, a quantitative comparison of four yeast species was performed to evaluate the underlying causes of overflow metabolism, which is the seemingly wasteful strategy of aerobic fermentation instead of using the more efficient respiratory pathway for glucose utilization. We showed that overflow metabolism in yeast is linked to adaptations in metabolism and protein translation This phenomenon is seen in cells ranging from bacteria to yeast and cancer cells, and the insights provided in our study could therefore be valuable in understanding the metabolism not only in yeast but in more complex systems

Yeast synthetic biology advances biofuel production

Increasing concerns of environmental impacts and global warming calls for urgent need to switch from use of fossil fuels to renewable technologies. Biofuels represent attractive alternatives of fossil fuels and have gained continuous attentions. Through the use of synthetic biology it has become possible to engineer microbial cell factories for efficient biofuel production in a more precise and efficient manner. Here, we review advances on yeast-based biofuel production. Following an overview of synthetic biology impacts on biofuel production, we review recent advancements on the design, build, test, learn steps of yeast-based biofuel production, and end with discussion of challenges associated with use of synthetic biology for developing novel processes for biofuel production

Simulation and optimization of dynamic flux balance analysis models using an interior point method reformulation

This work presents a novel, differentiable, way of solving dynamic Flux Balance Analysis (dFBA) problems by embedding flux balance analysis of metabolic network models within lumped bulk kinetics for biochemical processes. The proposed methodology utilizes transformation of the bounds of the embedded linear programming problem of flux balance analysis via a logarithmic barrier (interior point) approach. By exploiting the first-order optimality conditions of the interior-point problem, and with further transformations, the approach results in a system of implicit ordinary differential equations. Results from four case studies, show that the CPU and wall-times obtained using the proposed method are competitive with existing state-of-the-art approaches for solving dFBA simulations, for problem sizes up to genome-scale. The differentiability of the proposed approach allows, using existing commercial packages, its application to the optimal control of dFBA problems at a genome-scale size, thus outperforming existing formulations as shown by two dynamic optimization case studies.• R. Conejeros would like to thank CONICYT’s research grant FONDECYT 1151295 for funding this research. • F. Scott gratefully acknowledges financial support from CONICYT (Proyectos REDES ETAPA INICIAL, Convocatoria 2017, REDI170254)

Towards a comprehensive modeling framework for studying glucose repression in yeast

The yeast Saccharomyces cerevisiae is an important model organism for human health and for industry applications as a cell factory. For both purposes, it has been an important organism for studying glucose repression. Glucose sensing and signaling is a complex biological system, where the SNF1 pathway is the main pathway responsible for glucose repression. However, it is highly interconnected with the cAMP-PKA, Snf3-Rgt2 and TOR pathways. To handle the complexity, mathematical modeling has successfully aided in elucidating the structure, mechanism, and dynamics of the pathway. In this thesis, I aim to elucidate what the effect of the interconnection of glucose repression with sensory and metabolic pathways in yeast is, specifically, how crosstalk influences the signaling cascade; what the main effects of nutrient signaling on the metabolism are and how those are affected by intrinsic stress, such as damage accumulation. Here, I have addressed these questions by developing new frameworks for mathematical modeling. A vector based method for Boolean representation of complex signaling events is presented. The method reduces the amount of necessary nodes and eases the interpretation of the Boolean states by separating different events that could alter the activity of a protein. This method was used to study how crosstalk influences the signaling cascade.To be able to represent a diverse biological network using methods suitable for respective pathways, we also developed two hybrid models. The first is demonstrating a framework to connect signaling pathways with metabolic networks, enabling the study of long-term signaling effects on the metabolism. The second hybrid model is demonstrating a framework to connect models of signaling and metabolism to growth and damage accumulation, enabling the study of how the long-term signaling effects on the metabolism influence the lifespan. This thesis represents a step towards comprehensive models of glucose repression. In addition, the methods and frameworks in this thesis can be applied and extended to other signaling pathways

Unveiling the production of metabolites with flavor enhancement through genome-scale metabolic modelling of a lambic beer microbial community

Dissertação de mestrado em BioinformaticsSystems biology studies biological processes on a global scale, involving different omics. It uses bioinformatic approaches, such as the reconstruction of genome-scale metabolic models to understand the biological system of a cell, organism, or microbial community. Genome-scale metabolic models are metabolic models based on the well-known stoichiometry of biochemical reactions. It offers a whole system view, predicting the metabolic phenotype, based on the genome and biochemical information. These models haves several applications in different areas such as biotechnological and pharmaceutical. Lambic beers are commercial beers from Belgium that still use old brewing styles. This type of beer is gaining interest worldwide due to its unique flavour profile obtained by a mixed yeast-bacteria culture fermentation. Therefore, in this thesis, the lactic acid bacterium Pediococcus damnosus and Brettanomyces bruxellensis yeast, which play an important role in the acidification and maturation phase of the lambic beer fermentation, will be studied. Pediococcus damnosus is a gram-positive bacterium belonging to the lactic acid bacteria group commonly found in brewery environments. Pediococcus damnosus produces only lactate by the sugar metabolism, which confers an acidic and tart flavour to the beer. In turn, Brettanomyces bruxellensis is a facultatively anaerobic yeast, also responsible for the typical aroma of lambic beer. It uses several carbon sources and produces several volatile phenolic compounds not desired in common fermentations crucial in this type of beer. Usually, in nature, the microorganisms appear in communities. Thus, the study of microbial communities is essential to understand their development, interaction and evolution. The main aim of this thesis is to unveil the production of metabolites with flavour enhancement in the acid lambic beer through the reconstruction and simulation of genome-scale metabolic models for each microorganism and therefore for the microbial community composed by them, to understand the interactions between the species and how these affects the lambic beer flavour. Two genome-scale metabolic models were reconstructed: the model of the bacterium Pediococcus damnosus and the model of the yeast Brettanomyces bruxellensis. The tool used for model reconstruction was merlin, which automates several reconstruction processes and having a user-friendly interface. The Pediococcus damnosus genome-scale metabolic model consists of 809 reactions and 589 metabolites. In turn, the Brettanomyces bruxellensis genome-scale metabolic model has 2095 reactions and 1249 metabolites. In the simulations performances, the genome-scale metabolic models showed the ability to grow in the minimal medium provided, as described in the literature. Furthermore, simulations predicted the production of certain compounds, such as butanediol in the bacterium Pediococcus damnosus and 4-ethylphenol in the yeast B. bruxellensis, which may influence the Lambic beer flavour. Interactions between the genome-scale metabolic models, especially amino acid exchanges, were predicted. The model of the Pediococcus damnosus-Brettanomyces bruxellensis community was assembled using ReFramed. The community model’s simulation results show that the interaction of these microorganisms results in the production of compounds that may flavour and thus be responsible for the unique flavour profile of Lambic beer.A biologia de sistemas estuda os processos biológicos numa escala global, envolvendo diferentes ómicas. Utiliza abordagens bioinformáticas, como a construção de modelos metabólicos à escala genómica, de modo a perceber o sistema biológico de uma célula, organismo ou comunidade microbiana. Os modelos metabólicos à escala genómica são baseados na estequiometria bem conhecida das reações bioquímicas de um dado organismo. Oferece uma perspetiva do sistema como um todo, sendo capaz de prever o fenótipo do metabolismo, baseando-se no genoma e em informações bioquímicas. Estes modelos tem várias aplicações em diferentes áreas como a indústria biotecnológica e farmacêutica. As cervejas lambic são cervejas comerciais típicas da Bélgica que ainda utilizam processos de produção de cerveja antigos. Esta cerveja tem vindo a ganhar interesse a nível mundial devido ao seu perfil aromático único que é obtido através da fermentação de uma cultura de bactérias e leveduras. Nesta tese serão estudadas a bactéria ácida láctica Pediococcus damnosus e a levedura Brettanomyces bruxellensis, que possuem um papel importante nas fases de acidificação e maturação da fermentação desta cerveja. Pediococcus damnosus é uma bactéria gram-positiva que pertence ao grupo das bactérias ácidas lácticas e é geralmente encontrada em ambientes de fermentação de cerveja. A bactéria Pediococcus damnosus produz apenas lactato pelo metabolismo dos açucares, conferindo um sabor ácido e azedo à cerveja. Por sua vez, a levedura Brettanomyces bruxellensis é uma levedura anaeróbica facultativa, também responsável pelo aroma típico da cerveja lambic. Utiliza inúmeras fontes de carbono e produz muitos compostos fenólicos voláteis que não são desejados em fermentações comuns, mas são cruciais neste tipo de cerveja. Geralmente, os microrganismos aparecem na natureza, em comunidades. O estudo de comunidades microbianas é importante para perceber o seu desenvolvimento, interação e evolução. O objetivo desta dissertação de mestrado é encontrar metabolitos que conferem o aroma característico da cerveja ácida lambic, de modo a melhorar a sua produção, usando para isso construção e simulação de modelos metabólicos à escala genómica para cada microrganismo e para a comunidade microbiana, de forma a perceber as interações entre espécies e como estas influenciam o aroma da cerveja lambic. Assim foram construídos dois modelos metabólicos à escala genomA ferramenta utilizada para a construção destes modelos metabólicos à escala genómica foi o merlin, uma vez que automatiza vários processo de construção e possui um interface intuitiva. O modelo metabólico à escala genómica da bactéria Pediococcus damnosus é constituído por 809 reações e 589 metabolitos. Por sua vez, o modelo metabólico à escala genómica da levedura Brettanomyces bruxellensis possui 2095 reações e 1249 metabolitos. Nas simulações executadas, os modelos metabólicos à escala genómica mostraram capacidade de crescer no meio mínimo fornecido, como descrito na literatura. Além disso, as simulações preveram a produção de certos compostos, como o butanodiol na bactéria Pediococcus damnosus e o 4-etilfenol na levedura B. bruxellensis, que podem influenciar o sabor da cerveja Lambic. Foram previstas interações entre os modelos metabólicos à escala genómica, sobretudo trocas de aminoácidos. O modelo da comunidade Pediococcus damnosus-Brettanomyces bruxellensis foi construído usando o ReFramed. Analisando os resultados da simulação do modelo da comunidade, pode-se concluir que a interação dos dois microorganismos resulta na produção de compostos que tem a capacidade de conferir sabor e assim serem responsáveis pelo aroma tão único da cerveja lambic

Nitrogen limitation reveals large reserves in metabolic and translational capacities of yeast

Cells maintain reserves in their metabolic and translational capacities as a strategy to quickly respond to changing environments. Here we quantify these reserves by stepwise\ua0reducing nitrogen availability in yeast steady-state chemostat cultures, imposing severe restrictions on total cellular protein and transcript content. Combining multi-omics analysis with metabolic modeling, we find that seven metabolic superpathways maintain >50% metabolic capacity in reserve, with glucose metabolism maintaining >80% reserve capacity. Cells maintain >50% reserve in translational capacity for 2490 out of 3361 expressed genes (74%), with a disproportionately large reserve dedicated to translating metabolic proteins. Finally, ribosome reserves contain up to 30% sub-stoichiometric ribosomal proteins, with activation of reserve translational capacity associated with selective upregulation of 17 ribosomal proteins. Together, our dataset provides a quantitative link between yeast physiology and cellular economics, which could be leveraged in future cell engineering through targeted proteome streamlining