A framework for application of metabolic modeling in yeast to predict the effects of nsSNV in human orthologs

Background We have previously suggested a method for proteome wide analysis of variation at functional residues wherein we identified the set of all human genes with nonsynonymous single nucleotide variation (nsSNV) in the active site residue of the corresponding proteins. 34 of these proteins were shown to have a 1:1:1 enzyme:pathway:reaction relationship, making these proteins ideal candidates for laboratory validation through creation and observation of specific yeast active site knock-outs and downstream targeted metabolomics experiments. Here we present the next step in the workflow toward using yeast metabolic modeling to predict human metabolic behavior resulting from nsSNV. Results For the previously identified candidate proteins, we used the reciprocal best BLAST hits method followed by manual alignment and pathway comparison to identify 6 human proteins with yeast orthologs which were suitable for flux balance analysis (FBA). 5 of these proteins are known to be associated with diseases, including ribose 5-phosphate isomerase deficiency, myopathy with lactic acidosis and sideroblastic anaemia, anemia due to disorders of glutathione metabolism, and two porphyrias, and we suspect the sixth enzyme to have disease associations which are not yet classified or understood based on the work described herein. Conclusions Preliminary findings using the Yeast 7.0 FBA model show lack of growth for only one enzyme, but augmentation of the Yeast 7.0 biomass function to better simulate knockout of certain genes suggested physiological relevance of variations in three additional proteins. Thus, we suggest the following four proteins for laboratory validation: delta-aminolevulinic acid dehydratase, ferrochelatase, ribose-5 phosphate isomerase and mitochondrial tyrosyl-tRNA synthetase. This study indicates that the predictive ability of this method will improve as more advanced, comprehensive models are developed. Moreover, these findings will be useful in the development of simple downstream biochemical or mass-spectrometric assays to corroborate these predictions and detect presence of certain known nsSNVs with deleterious outcomes. Results may also be useful in predicting as yet unknown outcomes of active site nsSNVs for enzymes that are not yet well classified or annotated

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Ganoderma lucidum for Improved Extracellular Polysaccharide Production

In this study, we reconstructed for the first time a genome-scale metabolic model (GSMM) of Ganoderma lucidum strain CGMCC5.26, termed model iZBM1060, containing 1060 genes, 1202 metabolites, and 1404 reactions. Important findings based on model iZBM1060 and its predictions are as follows: (i) The extracellular polysaccharide (EPS) biosynthetic pathway was elucidated completely. (ii) A new fermentation strategy is proposed: addition of phenylalanine increased EPS production by 32.80% in simulations and by 38.00% in experiments. (iii) Eight genes for key enzymes were proposed for EPS overproduction. Model iZBM1060 provides a useful platform for regulating EPS production in terms of system metabolic engineering for G. lucidum, as well as a guide for future metabolic pathway construction of other high value-added edible/ medicinal mushroom species

Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond.

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted

Metabolic engineering of Bacillus subtilis for terpenoid production

Terpenoids are the largest group of small-molecule natural products, with more than 60,000 compounds made from isopentenyl diphosphate (IPP) and its isomer dimethylallyl diphosphate (DMAPP). As the most diverse group of small-molecule natural products, terpenoids play an important role in the pharmaceutical, food, and cosmetic industries. For decades, Escherichia coli (E. coli) and Saccharomyces cerevisiae (S. cerevisiae) were extensively studied to biosynthesize terpenoids, because they are both fully amenable to genetic modifications and have vast molecular resources. On the other hand, our literature survey (20 years) revealed that terpenoids are naturally more widespread in Bacillales. In the mid-1990s, an inherent methylerythritol phosphate (MEP) pathway was discovered in Bacillus subtilis (B. subtilis). Since B. subtilis is a generally recognized as safe (GRAS) organism and has long been used for the industrial production of proteins, attempts to biosynthesize terpenoids in this bacterium have aroused much interest in the scientific community. This review discusses metabolic engineering of B. subtilis for terpenoid production, and encountered challenges will be discussed. We will summarize some major advances and outline future directions for exploiting the potential of B. subtilis as a desired "cell factory" to produce terpenoids

Developing methods for the context-specific reconstruction of metabolic models of cancer cells

Dissertação de mestrado em BioinformáticaThe recent advances in genome sequencing technologies and other high-throughput methodologies allowed the identification and quantification of individual cell components. These efforts led to the development of genome-scale metabolic models (GSMMs), not only for humans but also for several other organisms. These models have been used to predict cellular metabolic phenotypes under a variety of physiological conditions and contexts, proving to be useful in tasks such as drug discovery, biomarker identification and interactions between hosts and pathogens. Therefore, these models provide a useful tool for targeting diseases such as cancer, Alzheimer or tuberculosis. However, the usefulness of GSSMs is highly dependent on their capabilities to predict phenotypes in the array of different cell types that compose the human body, making the development of tissue/context-specific models mandatory. To address this issue, several methods have been proposed to integrate omics data, such as transcriptomics or proteomics, to improve the phenotype prediction abilities of GSSMs. Despite these efforts, these methods still have some limitations. In most cases, their usage is locked behind commercially licensed software platforms, or not available in a user-friendly fashion, thus restricting their use to users with programming or command-line knowledge. In this work, an open-source tool was developed for the reconstruction of tissue/context-specific models based on a generic template GSMM and the integration of omics data. The Tissue-Specific Model Reconstruction (TSM-Rec) tool was developed under the Python programming language and features the FASTCORE algorithm for the reconstruction of tissue/context-specific metabolic models. Its functionalities include the loading of omics data from a variety of omics databases, a set of filtering and transformation methods to adjust the data for integration with a template metabolic model, and finally the reconstruction of tissue/context-specific metabolic models. To evaluate the functionality of the developed tool, a cancer related case-study was carried. Using omics data from 314 glioma patients, the TSM-Rec tool was used to reconstruct metabolic models of different grade gliomas. A total of three models were generated, corresponding to grade II, III and IV gliomas. These models were analysed regarding their differences and similarities in reactions and pathways. This comparison highlighted biological processes common to all glioma grades, and pathways that are more prominent in each glioma model. The results show that the tool developed during this work can be useful for the reconstruction of cancer metabolic models, in a search for insights into cancer metabolism and possible approaches towards drug-target discovery.Os avanços recentes nas tecnologias de sequenciação de genomas e noutras metodologias experimentais de alto rendimento permitiram a identificação e quantificação dos diversos componentes celulares. Estes esforços levaram ao desenvolvimento de Modelos Metabólicos à Escala Genómica (MMEG) não só de humanos, mas também de diversos organismos. Estes modelos têm sido utilizados para a previsão de fenótipos metabólicos sob uma variedade de contextos e condições fisiológicas, mostrando a sua utilidade em áreas como a descoberta de fármacos, a identificação de biomarcadores ou interações entre hóspede e patógeno. Desta forma, estes modelos revelam-se ferramentas úteis para o estudo de doenças como o cancro, Alzheimer ou a tuberculose. Contudo, a utilidade dos MMEG está altamente dependente das suas capacidades de previsão de fenótipos nos diversostipos celulares que compõem o corpo humano, tornando o desenvolvimento de modelos específicos de tecidos uma tarefa obrigatória. Para resolver este problema, vários métodos têm proposto a integração de dados ómicos como os de transcriptómica ou proteómica para melhorar as capacidades preditivas dos MMEG. Apesar disso, estes métodos ainda sofrem de algumas limitações. Na maioria dos casos o seu uso está confinado a plataformas ou softwares com licenças comerciais, ou não está disponível numa ferramenta de fácil uso, limitando a sua utilização a utilizadores com conhecimentos de programação ou de linha de comandos. Neste trabalho, foi desenvolvida uma ferramenta de acesso livre para a reconstrução de modelos metabólicos específicos para tecidos tendo por base um MMEG genérico e a integração de dados ómicos. A ferramenta TSM-Rec (Tissue-Specific Model Reconstruction), foi desenvolvida na linguagem de programação Python e recorre ao algoritmo FASTCORE para efetuar a reconstrução de modelos metabólicos específicos. As suas funcionalidades permitem a leitura de dados ómicos de diversas bases de dados ómicas, a filtragem e transformação dos mesmos para permitir a sua integração com um modelo metabólico genérico e por fim, a reconstrução de modelos metabólicos específicos. De forma a avaliar o funcionamento da ferramenta desenvolvida, esta foi aplicada num caso de estudo de cancro. Recorrendo a dados ómicos de 314 pacientes com glioma, usou-se a ferramenta TSM-Rec para a reconstrução de modelos metabólicos de gliomas de diferentes graus. No total, foram desenvolvidos três modelos correspondentes a gliomas de grau II, grau III e grau IV. Estes modelos foram analisados no sentido de perceber as diferenças e as similaridades entre as reações e as vias metabólicas envolvidas em cada um dos modelos. Esta comparação permitiu isolar processos biológicos comuns a todos os graus de glioma, assim como vias metabólicas que se destacam em cada um dos graus. Os resultados obtidos demonstram que a ferramenta desenvolvida pode ser útil para a reconstrução de modelos metabólicos de cancro, na procura de um melhor conhecimento do metabolismo do cancro e possíveis abordagens para a descoberta de fármacos