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Els passatemps solen ser molt apreciats, doncs permeten un bon autoaprenentatge. Els jocs de lletres més famosos són els mots encreuats. Altres jocs són la sopa de lletres, salt del cavall, unir punts, laberints, relacionar dos conjunts entre sí, l'amidakuji, jocs de lògica. Al grup de innovació docent GINDO-UB180 vam crear i aplicar com a tasques d'avaluació continuada de Bioquímica alguns jocs, i van tenir un gran èxit entre els alumnes, que van aprendre l'assignatura tot gaudint de l'estudi.

The liver performs many essential metabolic functions, which can be studied using computational models of hepatocytes. Here we present HepatoDyn, a highly detailed dynamic model of hepatocyte metabolism. HepatoDyn includes a large metabolic network, highly detailed kinetic laws, and is capable of dynamically simulating the redox and energy metabolism of hepatocytes. Furthermore, the model was coupled to the module for isotopic label propagation of the software package IsoDyn, allowing HepatoDyn to integrate data derived from 13C based experiments. As an example of dynamical simulations applied to hepatocytes, we studied the effects of high fructose concentrations on hepatocyte metabolism by integrating data from experiments in which rat hepatocytes were incubated with 20 mM glucose supplemented with either 3 mM or 20 mM fructose. These experiments showed that glycogen accumulation was significantly lower in hepatocytes incubated with medium supplemented with 20 mM fructose than in hepatocytes incubated with medium supplemented with 3 mM fructose. Through the integration of extracellular fluxes and 13C enrichment measurements, HepatoDyn predicted that this phenomenon can be attributed to a depletion of cytosolic ATP and phosphate induced by high fructose concentrations in the medium., Author Summary Despite the key role of hepatocytes in carbohydrate and lipid homeostasis, available dynamic models of hepatocyte metabolism tend to be limited to a single pathway and/or are based on assumptions of constant concentrations of key metabolites involved in redox and energy metabolism (ATP, NAD, NADPH etc.). Furthermore, most dynamic models are unable to integrate information from 13C based experiments. 13C based experiments allow us to infer the relative activity of alternative pathways and hence are highly useful for indicating flux distributions. To overcome these limitations, we developed HepatoDyn, a dynamic model of hepatic metabolism. HepatoDyn uses a large metabolic network including key pathways such as glycolysis, the Krebs cycle, the pentose phosphate pathway and fatty acid metabolism, and dynamically models the concentrations of metabolites involved in the redox and energy metabolism of hepatocytes. In addition, the model was coupled to the label propagation module of the package IsoDyn, allowing it to integrate data from 13C based experiments to assist in the parametrization process. These features make HepatoDyn a powerful tool for studying the dynamics of hepatocyte metabolism.

The effects of pre-incubation with mercury (Hg(2+)) and cadmium (Cd(2+)) on the activities of individual glycolytic enzymes, on the flux and on internal metabolite concentrations of the upper part of glycolysis were investigated in mouse muscle extracts. In the range of metal concentrations analysed we found that only hexokinase and phosphofructokinase, the enzymes that shared the control of the flux, were inhibited by Hg(2+) and Cd(2+). The concentrations of the internal metabolites glucose-6-phosphate and fructose-6-phosphate did not change significantly when Hg(2+) and Cd(2+) were added. A mathematical model was constructed to explore the mechanisms of inhibition of Hg(2+) and Cd(2+) on hexokinase and phosphofructokinase. Equations derived from detailed mechanistic models for each inhibition were fitted to the experimental data. In a concentration-dependent manner these equations describe the observed inhibition of enzyme activity. Under the conditions analysed, the integral model showed that the simultaneous inhibition of hexokinase and phosphofructokinase explains the observation that the concentrations of glucose-6-phosphate and fructose-6-phosphate did not change as the heavy metals decreased the glycolytic flux.

BACKGROUND: Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. FINDINGS: PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. CONCLUSIONS: PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and 'omics research domains.

Treballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2023, Tutors: Marta Cascante Serratosa, Pedro de Atauri Carulla, Colorectal cancer (CRC) is one of the cancers with the most reported cases and mortality rates. Resistance to antitumoral drugs is one of the main reasons for its mortality, leading to the failure of conventional therapies. The understanding of the metabolic changes that encompass metabolic reprogramming upon chemotherapy treatments, such as those with Palbociclib, may unveil new therapeutic targets to enhance Palbociclib effectivity and/or avoid the acquisition of resistance. In this study, we used a mathematical modelling approach based on the reconstruction of tumor cell metabolic network reactions at Genome Scale (GSMMs). The reconstruction was based on a template containing all the possible reactions described in human organisms (Recon3D) and constrained using experimental measurements of tumor cells. Using this approach, first we reconstructed a simplification of the central metabolism, creating a type of GSMM referred to as “toy model”. This step served to understand the correct usage of the algorithms used in the creation of GSMMs, and provided further knowledge to comprehend the key role that transcriptomic data has on constraining and mathematically determining the model. Subsequently, GSMMs for both untreated and Palbociclib-resistant tumor cells were reconstructed using experimental data on consumption and production rates, metabolite concentrations in cell culture media and gene expression information. Linear programming-based algorithms were employed to refine the models. Then, gene knock-out and knock-down techniques were used to identify potential therapeutic targets capable of inducing cellular death or preventing the metabolic reprogramming triggered by Palbociclib. Analysis of the metabolic fluxes estimated from the reconstructed GSMMs showed that Palbociclib-resistant cells exhibit a decrease in glycolytic activity and an increase in cellular respiration, nucleotide metabolism and fatty acid metabolisms. These changes revealed several potential targets, some of which associated with approved drugs, such as cholic acid, Quercetin, Sulfasalazine and Aspirin.

Metabolism is a hallmark of life and underlies most biological processes in both health and disease. For instance, dysregulation of liver metabolism underlies multifactorial disorders such as diabetes or obesity. Similarly, cancer progression involves a reprogramming of metabolism to support unchecked proliferation, metastatic spread and other facets of the cancer phenotype. Hence, the study of metabolism is of great biomedical interest. The metabolic phenotype emerges from the complex interactions of metabolites, enzymes, and the signaling cascades regulating their expression and thus must be studied following a holistic approach. With this aim, Systems Biology formulates the interactions between the molecular components of metabolism as a set of mathematical expressions, termed metabolic models, and uses them as a framework to integrate multiple layers of data (e.g., transcriptomics, proteomics and metabolomics) and simulate the emergent metabolic phenotype. The Systems Biology toolbox for the analysis of metabolism consists of several complementary model-based approaches, each with its strengths and limitations. For instance, constraint-based modeling can predict flux distributions at a genome-scale, whereas kinetic modeling and 13C metabolic flux analysis (13C MFA) can more accurately model central carbon metabolism. As part of this Ph.D. thesis, we have expanded this toolbox through the development of new model-based approaches for computing both detailed metabolic maps of central carbon metabolism and genome-scale flux maps. With this aim, we developed HepatoDyn, a highly detailed kinetic model of hepatocyte metabolism capable of dynamic 13C MFA and used it to characterize the negative effects of fructose in hepatic metabolic function. Similarly, we also developed Iso2Flux, a novel steady-state 13C MFA software, and parsimonious 13C MFA, a new 13C MFA algorithm that can integrate transcriptomics to trace flux through large metabolic networks. Even more, we developed r2MTA a constraint-based modeling algorithm to robustly identify the optimal interventions to induce a transition towards a therapeutically desirable metabolic state. Finally, we also developed a workflow for integrating transcriptomics, metabolomics, gene dependencies, and 13C MFA to predict genome-scale flux maps. Furthermore, we apply the systems biology toolbox, using both newly developed and existing tools, to the genome-scale analysis of the molecular drivers underlying cancer stem cells and metastasis in prostate and colorectal cancer, respectively. We identify putative therapeutic interventions against both phenotypes paving the way for a new generation of anticancer drugs.

[eng] Cancer is characterised by the lost of physiological control and the malignant transformation of cells that acquire functional and genetic abnormalities, leading to tumour development and progression. Colon and lung cancer are two of the most common cancers worldwide. In early stages of the disease, surgery is the common choice while chemotherapy is the main treatment for advanced stage cancer. However, the currently available chemotherapeutic treatments exhibit modest efficacy due to their side effects and drug resistance. Therefore, the search for combined chemotherapies with low systemic toxicity and high efficiency holds great promise to decrease the morbidity and mortality of cancer. Tumour cells present common biological capabilities sequentially acquired during the development of cancer that are considered essential to drive malignancy. In particular, tumour cells switch their core metabolism to meet the increased requirements of cell growth and division. Indeed, oncogenic signals converge to reprogram tumour metabolism by enhancing key metabolic pathways such as glycolysis, pentose phosphate pathway (PPP), glutaminolysis and lipid, nucleic acid and amino acid metabolism. Several oncogenes including c-MYC, hypoxia inducible factor 1 (HIF1), phosphoinositide-3-kinase (PI3K), protein kinase B (PBK or Akt) and the mechanistic target of rapamycin (mTOR), have been known to be involved in the regulation of tumour metabolic reprogramming. Then, the study of the tumour metabolic reprogramming and its connection with oncogenic signalling is an essential strategy to identify new targets for cancer therapy. Thus, the main objective of this thesis was to explore new possibilities for cancer treatment and diagnosis. To this end, we have analysed the links between metabolism and tumour progression, the tumour metabolic reprogramming associated to the dysregulation of cell cycle, and the use of combination therapies for cancer treatment. In order to accomplish our main objective, the results of this thesis are divided in three chapters: 1. We have identified glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a potential predictive biomarker for tumour staging and prognosis of human colorectal cancer. In addition, our results clearly discourage the use of GAPDH as a housekeeping marker in colorectal cancer. 2. We have characterised the metabolic reprogramming associated to the inhibition of cyclin-dependent kinases 4 and 6 (CDK4/6) in colon cancer cells. CDK4/6 inhibition causes a shift towards enhanced metabolism of glucose, glutamine and amino acids by increasing mitochondrial metabolism and function as well as glycolytic flux. Fluxomics and transcriptomics integrated data analysis revealed that this metabolic reprogramming is directed by MYC, which is accumulated when CDK4/6 are inhibited. In fact, the identification of the tumour metabolic adaptations associated to CDK4/6 inhibition reveals potential metabolic vulnerabilities that can be exploited in combination therapies with CDK4/6 inhibitors. Accordingly, we have obtained synergistic and selective antiproliferative effects in vitro by inhibiting mTOR, PI3K/Akt axis or MYC target genes in combination with CDK4/6 inhibitors. Therefore, we propose new combination therapies that simultaneously target cell cycle and metabolism of cancer cells. 3. We have determined the molecular mechanism of action of the selenium compound methylseleninic acid (MSA) in cancer cells. MSA effects are associated with the inhibition of the Akt pathway, leading to dephosphorylation of FOXO transcription factors and their nuclear translocation which, in turn, activate the expression of FOXO target genes. By targeting the PI3K/Akt/FOXO pathway, MSA synergises with cisplatin in combination therapies to reduce the commonly observed toxicity and overcome the resistance of cisplatin-based chemotherapy. The completion of these objectives has shed new light on the understanding of tumour metabolic reprogramming as well as the mechanisms of action of compounds potentially useful as antitumour agents. We have used this information to develop new strategies complementing conventional and existing chemotherapies, providing new approaches for cancer treatment and diagnosis.