MetaboTools: A comprehensive toolbox for analysis of genome-scale metabolic models

Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Our previous work revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. Through this work, which consists of a protocol, a toolbox, and tutorials of two use cases, we make our methods available to the broader scientific community. The protocol describes, in a step-wise manner, the workflow of data integration and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, this protocol constitutes a comprehensive guide to the intra-model analysis of extracellular metabolomic data and a resource offering a broad set of computational analysis tools for a wide biomedical and non-biomedical research community

Drug Off-Target Effects Predicted Using Structural Analysis in the Context of a Metabolic Network Model

Recent advances in structural bioinformatics have enabled the prediction of protein-drug off-targets based on their ligand binding sites. Concurrent developments in systems biology allow for prediction of the functional effects of system perturbations using large-scale network models. Integration of these two capabilities provides a framework for evaluating metabolic drug response phenotypes in silico. This combined approach was applied to investigate the hypertensive side effect of the cholesteryl ester transfer protein inhibitor torcetrapib in the context of human renal function. A metabolic kidney model was generated in which to simulate drug treatment. Causal drug off-targets were predicted that have previously been observed to impact renal function in gene-deficient patients and may play a role in the adverse side effects observed in clinical trials. Genetic risk factors for drug treatment were also predicted that correspond to both characterized and unknown renal metabolic disorders as well as cryptic genetic deficiencies that are not expected to exhibit a renal disorder phenotype except under drug treatment. This study represents a novel integration of structural and systems biology and a first step towards computational systems medicine. The methodology introduced herein has important implications for drug development and personalized medicine

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

Molecular mechanisms of PAH function in response to phenylalanine and tetrahydrobiopterin binding

Phenylketonuria (PKU) is an autosomal recessive inborn error of metabolism (IEM) caused by mutations in the phenylalanine hydroxylase (PAH) gene. The molecular mechanism underlying deficiency of the PAH protein is, in most of the cases, loss of function due to protein misfolding. PAH mutations induce disturbed oligomerisation, decreased stability and accelerated degradation of hepatic PAH, a key enzyme in phenylalanine metabolism. Since the development of a phenylalanine-restricted diet in the 1950ies, PKU is a prototype for treatable inherited diseases. About 60 years later, the natural PAH cofactor tetrahydrobiopterin (BH4) was shown to act as a pharmacological chaperone stabilising the misfolded PAH protein. In consequence, BH4 (KUVAN®) was introduced to the pharmaceutical market as an alternative treatment for BH4-responsive PAH deficiency. Therefore, PKU is also regarded as a prototype for a pharmacologically treatable protein misfolding disease. Despite the progress in PKU therapy, knowledge on the molecular basis of PKU and the BH4 mode of action was still incomplete. Biochemical and biophysical characterisation of purified variant PAH proteins, which were derived from patient’s mutations, aimed at a better understanding of the molecular mechanisms of PAH loss of function. We showed that local side-chain replacements induce global conformational changes with negative impact on molecular motions that are essential for physiological enzyme function. The development of a continuous real-time fluorescence-based assay of PAH activity allowed for robust analysis of steady state kinetics and allosteric behaviour of recombinantly expressed PAH proteins. We identified positive cooperativity of the PAH enzyme towards BH4, where cooperativity does not rely on the presence of phenylalanine but is determined by activating conformational rearrangements. In vivo investigations on the mode-of-action of BH4 revealed differences in pharmacodynamics but not in pharmacokinetics between different strains of PAH-deficient mice (wild-type, Pahenu1/1 and Pahenu1/2). These observations pointed to a significant impact of the genotype on responsiveness to BH4. The available database information on PAH function associated with PAH mutations was based on non-standardised enzyme activity assays performed in different cellular systems and under different conditions usually focusing on single PAH mutations. These inconsistent data on PAH enzyme activity hindered robust prediction of the patient’s phenotype. Furthermore, assays on single PAH mutations do not reflect the high allelic and phenotypic heterogeneity of PKU with 89 % of patients being compound heterozygotes. In addition, the knowledge on enzyme function and regulation in the therapeutic and pathologic metabolic context was still scarce. In order to get more insight into the interplay of the PAH genotype, the phenylalanine concentration and BH4 treatment, we performed functional analyses of both, single, purified PAH variants as well as PAH full genotypes in the physiological, pathological and therapeutic context. The analysis of PAH activity as a function of phenylalanine and BH4 concentrations enabled determination of the optimal working ranges of the enzyme and visualisation of differences in the regulation of PAH activity by BH4 and phenylalanine depending on the underlying genotype. Moreover, these PAH activity landscapes allowed for setting rules for dietary regimens and pharmacological treatment based on the genotype of the patient. Taken together, precise knowledge on the mechanism of the misfolding-induced loss of function in PAH deficiency enabled a better understanding of the molecular mode of action of pharmacological rescue of enzyme function by BH4. We implemented the combination of genotype-specific functional analyses together with biochemical, clinical and therapeutic data of individual patients as a powerful tool for phenotype prediction and paved the way for personalised medicine strategies in phenylketonuria

GPS analysis of a team competing at a national Under 18 field hockey tournament

The purpose of this study was to utilise global-positioning system (GPS) technology to quantify the running demands of national Under 18 field hockey players competing in a regional field hockey tournament. Ten male players (mean ± SD; age 17.2 ± 0.4 years; stature 178.1 ± 5.2 cm; body mass 78.8 ± 8.8 kg) wore GPS units while competing in six matches over seven days at the 2018 New Zealand national under 18 field hockey tournament. GPS enabled the measurement of total distance (TD), low-speed activity (LSA; 0 -14.9 km/hr), and high-speed running (HSR; ≥ 15 km/hr) distances. Differences in running demands (TD, LSA, HSR) between positions were assessed using effect size and percent difference ± 90% confidence intervals. Midfielders covered the most TD and LSA per game and strikers the most HSR during the 6 matches. There were “very large” differences between strikers and midfielders for TD and LSA, strikers and defenders for LSA and HSR, and defenders and midfielders for LSA. These results suggest that these playing positions are sufficiently different to warrant specialised position-specific conditioning training leading into a field hockey tournament

The effects of morning preconditioning protocols on testosterone, cortisol and afternoon sprint cycling performance [conference presentation]

Opportunities exist for athletes to undertake morning exercise protocols in an attempt to potentate afternoon performance. Four sub elite track sprint cyclists completed a morning cycling (Cyc) or weights-based protocol (WP) prior to an afternoon cycling time trial (500m) in a repeated measures, counterbalance crossover design. Measured variables included heart rate, blood lactate, cycling peak power, salivary testosterone (T) and cortisol levels along with time trial performance. Standardised differences in means via magnitude-based inferences were calculated using paired samples T-tests in SPSS version 24 with statistical significance set at p < 0.05. The WP produced significantly faster times in the final 250m in comparison to CycP. The anticipated circadian decline of T was observed after the CycP but was however mitigated following the WP. While slight decreases in 500m times were experienced during the WP, they were not significant and were considered within the normal variations experienced between performances by elite athletes. The effect of the WP on the circadian rhythm of T could be linked to a greater recruitment of muscle fibres. Results suggest a morning resistance protocol can positively affect testosterone levels for afternoon performance. Possible gender and individual responses from conducting a W over Cyc protocol were observed and require further investigation

A Dynamic Analysis of IRS-PKR Signaling in Liver Cells: A Discrete Modeling Approach

A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network

A model validation pipeline for healthy tissue genome-scale metabolic models

Dissertação de mestrado em BioinformáticaNos últimos anos, os métodos de alto rendimento disponibilizaram dados ómicos referentes a várias camadas da organização biológica, permitindo a integração do conhecimento de componentes individuais em modelos complexos, como modelos metabólicos à escala genómica (GSMMs). Estes podem ser analisados por métodos de modelação baseada em restrições(CBM), que facilitam abordagens preditivas in silico. Os modelos metabólicos humanos têm sido usados para estudar tecidos saudáveis e as suas doenças metabólicas associadas, como obesidade, diabetes e cancro. Modelos humanos genéricos podem ser integrados com dados contextuais por meio de algoritmos de reconstrução, com vista a produzir modelos metabólicos contextualizados (CSMs), que são normalmente melhores a capturar a variação entre diferentes tecidos e tipos de células. Como o corpo humano contém uma grande variedade de tecidos e tipos de células, os CSMs são frequentemente adotados como um meio de obter modelos metabólicos mais precisos de tecido humano saudável. No entanto, ao contrário de modelos de microrganismos e cancro, que acomodam vários métodos de validação, como a comparação de fluxos in silico ou de previsões de genes essenciais com dados experimentais, os métodos de validação facilmente aplicáveis a CSMs de tecido humano saudável podem ser mais limitados. Consequentemente, apesar de esforços continuados para atualizar os modelos humanos genéricos e algoritmos de reconstrução para extrair CSMs de alta qualidade, a sua validação continua a ser uma preocupação. Este trabalho apresenta uma pipeline para a extração e validação básica de CSMs de tecidos humanos normais derivados da integração de dados transcriptómicos com um modelo humano genérico. Todos os CSMs foram extraídos do modelo genérico Human-GEM publicado recentemente por Robinson et al. (2020), usando o package Troppo em Python e nos algoritmos de reconstrução fastCORE e tINIT nele implementados. Os CSMs extraídos correspondem a 11 tecidos saudáveis disponíveis no conjunto de dados GTEx v8. Antes da extração, métodos de aprendizagem máquina foram aplicados à seleção de um limiar para conversão em gene scores. Os modelos de maior qualidade foram obtidos com um limite mínimo global aplicado diretamente aos dados ómicos. A estratégia de validação focou-se no número de tarefas metabólicas passadas como um indicador de desempenho. Por último, este trabalho é acompanhado por Jupyter Notebooks, que incluem um guia de extração de modelos para novos utilizadores.n the past few years, high-throughput experimental methods have made omics data available for several layers of biological organization, enabling the integration of knowledge from individual components into complex modelssuch as genome-scale metabolic models (GSMMs). These can be analysed by constraint based modelling (CBM) methods, which facilitate in silico predictive approaches. Human metabolic models have been used to study healthy human tissues and their associated metabolic diseases, such as obesity, diabetes, and cancer. Generic human models can be integrated with contextual data through reconstruction algorithms to produce context-specific models (CSMs), which are typically better at capturing the variation between different tissues and cell types. As the human body contains a multitude of tissues and cell types, CSMs are frequently adopted as a means to obtain accurate metabolic models of healthy human tissues. However, unlike microorganisms’ or cancer models, which allow several methods of validation such as the comparison of in silico fluxes or gene essentiality predictions to experimental data, the validation methods easily applicable to CSMs of healthy human tissue are more limited. Consequently, despite continued efforts to update generic human models and reconstruction algorithms to extract high quality CSMs, their validation remains a concern. This work presents a pipeline for the extraction and basic validation of CSMs of normal human tissues derived from the integration of transcriptomics data with a generic human model. All CSMs were extracted from the Human-GEM generic model recently published by Robinson et al. (2020), relied on the open-source Troppo Python package and in the fastCORE and tINIT reconstruction algorithms implemented therein. CSMs were extracted for 11 healthy tissues available in the GTEx v8 dataset. Prior to extraction, machine learning methods were applied to threshold selection for gene scores conversion. The highest quality models were obtained with a global threshold applied to the omics data directly. The CSM validation strategy focused on the total number of metabolic tasks passed as a performance indicator. Lastly, this work is accompanied by Jupyter Notebooks, which include a beginner friendly model extraction guide