Microbial carbon use efficiency predicted from genome-scale metabolic models

Respiration by soil bacteria and fungi is one of the largest fluxes of carbon (C) from the land surface. Although this flux is a direct product of microbial metabolism, controls over metabolism and their responses to global change are a major uncertainty in the global C cycle. Here, we explore an in silico approach to predict bacterial C-use efficiency (CUE) for over 200 species using genome-specific constraint-based metabolic modeling. We find that potential CUE averages 0.62 ± 0.17 with a range of 0.22 to 0.98 across taxa and phylogenetic structuring at the subphylum levels. Potential CUE is negatively correlated with genome size, while taxa with larger genomes are able to access a wider variety of C substrates. Incorporating the range of CUE values reported here into a next-generation model of soil biogeochemistry suggests that these differences in physiology across microbial taxa can feed back on soil-C cycling.Published versio

Computational strategies for a system-level understanding of metabolism

Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided

Improved Evidence-based Genome-scale Metabolic Models for Maize Leaf, Embryo, and Endosperm

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes

Consistency, inconsistency, and ambiguity of metabolite names in biochemical databases used for genome-scale metabolic modelling

Genome-scale metabolic models (GEMs) are manually curated repositories describing the metabolic capabilities of an organism. GEMs have been successfully used in different research areas, ranging from systems medicine to biotechnology. However, the different naming conventions (namespaces) of databases used to build GEMs limit model reusability and prevent the integration of existing models. This problem is known in the GEM community, but its extent has not been analyzed in depth. In this study, we investigate the name ambiguity and the multiplicity of non-systematic identifiers and we highlight the (in)consistency in their use in 11 biochemical databases of biochemical reactions and the problems that arise when mapping between different namespaces and databases. We found that such inconsistencies can be as high as 83.1%, thus emphasizing the need for strategies to deal with these issues. Currently, manual verification of the mappings appears to be the only solution to remove inconsistencies when combining models. Finally, we discuss several possible approaches to facilitate (future) unambiguous mapping.</p

Genome-scale metabolic modelling of an extremophile microbial community

Dissertação de mestrado em BioinformaticsBiomining offers an ecological alternative to the standard mining practices by using ex tremophiles that can endure elevated temperatures and low pH values. Several studies have been performed using Acidithiobacillus caldus SM-1 and Acidimicrobium ferrooxidans DSM 10331, suggesting that these bacteria in a community offer several advantages in bioleaching environ ments. Genome-Scale Metabolic (GSM) models simulate the organisms’ metabolism through constraint based approaches. Therefore, the reconstruction of GSM models for A. caldus and A. ferroox idans and their integration into a community will offer, besides valuable insights into their metabolism, a unique perspective on the potential interaction mechanisms between the two organisms within the community. In this work, we developed manually curated GSM models for A. caldus with 416 genes, 846 reactions and 646 metabolites, and A. ferrooxidans with 408 genes, 817 reactions and 640 metabolites. Both models were reconstructed using the user-friendly software merlin. We performed the functional annotation of both organisms’ genomes to identify their metabolic characteristics, which allowed generating a draft of the metabolic network. Manual curation efforts through literature, genomic information, phylogenetically close organisms and biological databases allowed refining the metabolic network. Furthermore, the models were validated using Cobrapy and Mewpy which allowed analysing flux distribution and interactions in different environmental conditions, and the results were compared with the literature and experimental data. Lastly, the community model was built using the organisms’ validated GSM models. In silico phenotypic simulations of the community model revealed that A. caldus exchanged lipid-production related compounds whilst A. ferrooxidans donated hydrogen sulfide assisting the former with its more complex sulfur metabolism. Moreover, the results suggest a more significant influence of A. ferrooxidans in the community’s growth rate whilst A. caldus assists A. ferrooxidans in biomass production. These models can serve as a starting point to study and model the community’s behaviour in several bioleaching conditions.A biomineração oferece uma alternativa ecológica às práticas de mineração comuns através do uso de extremófilos que são capazes suportar elevadas temperaturas e baixos valores de pH. Vários estudos foram realizados usando os microrganismos Acidithiobacillus caldus SM-1 e Acidimicrobium ferrooxidans DSM 10331 em comunidade, sugerindo várias vantagens em ambientes de biolixiviação. Os modelos metabólicos à escala genómica permitem a modelação do metabolismo através de abordagens baseadas em restrições. Portanto, a construção de uma comunidade com contendo o modelo da A. caldus e outro da A. ferrooxidans poderá oferecer novas perspetivas sobre os seus respetivos metabolismos, assim como sobre os mecanismos de interação entre os dois organismos dentro da comunidade. Neste trabalho, foram reconstruídos dois modelos metabólicos à escala genómica com um elevado nível de curação manual utilizando a ferramenta merlin. O modelo da A. caldus conta com 416 genes e 846 reações enquanto que o da A. ferrooxidans possuí 408 genes e 817 reações. Os modelos foram funcionalmente anotados a fim de identificar as características metabólicas dos organismos, gerando um esboço da rede metabólica. Esta rede metabólica foi depois curada manualmente a fim de a refinar. Para isto foi usado informação presente na literatura, dados genómicos, organismos filogeneticamente próximos e bases de dados biológicas. Posteriormente, os modelos foram validados através de uma análise da distribuição de fluxo com diferentes condições ambientais e os resultados foram comparados com a literatura e dados experimentais. Por fim, o modelo da comunidade foi construído usando os modelos validados dos dois organismos. Simulações fenotípicas in silico do modelo da comunidade revelaram uma troca de compostos relacionados com produção de lípidios por parte da A. caldus, enquanto A. ferrooxidans doou sulfato de hidrogénio, auxiliando o primeiro no seu metabolismo de enxofre mais complexo. Por fim, os resultados sugerem uma maior influência de A. ferrooxidans na taxa de crescimento da comunidade enquanto que A. caldus auxilia o primeiro na produção de biomassa

Relationships between soil microbial physiology, community structure and carbon and nitrogen cycling in temperate forest ecosystems

Soil bacteria and fungi play a central role in the biogeochemical cycling of both carbon (C) and nitrogen (N) through terrestrial ecosystems. In the C cycle, soil microbial groups regulate the depolymerization of large stocks of soil organic matter and contribute 35-69 Pg C to the atmosphere annually through heterotrophic respiration. Soil microbial groups also mediate several important transformations of N, including making limiting nutrients available for uptake by plants through N-fixation, converting N between inorganic forms through nitrification, and returning N to the atmosphere through denitrification. While each of these functions is performed by soil microbes, scaling microbial physiology and community structure to biogeochemical cycling remains a significant research challenge. This dissertation integrates three distinct approaches to characterizing relationships between microbial physiology, microbial community structure and biogeochemical cycling. First, I explore the role of microbial physiology in C cycling by developing a novel method to predict bacterial carbon use efficiency (CUE) from genomes using metabolic modeling. I find that bacterial CUE is phylogenetically structured, with the class and order levels explaining the greatest proportion of variance in CUE, and I identify particular bacterial traits that most strongly predict CUE. These findings highlight the importance of accounting for microbial physiology when modeling soil C cycling. Second, I explore how differences in the abundance and activity of microbial functional groups and their interactions with mycorrhizal fungi impact temperate forest N cycling. I find that N availability and rates of N-fixation, nitrification and denitrification are structured in relation to mycorrhizal fungal types, but that the abundances of bacterial functional groups are not correlated with biogeochemical fluxes. Finally, I use a soil biogeochemical model to identify sources of uncertainty and data needs in advancing our understanding of microbially-mediated soil biogeochemical cycling. I isolate specific microbial physiological and enzyme kinetic parameters that have disproportionately large impacts on projections of coupled C and N cycling, and I quantify the potential for particular types of data to help reduce uncertainties. Overall, this dissertation advances our understanding of how microbial processes impact the biogeochemical cycling of C and N in terrestrial ecosystems

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

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