Unveiling the Metabolic changes on muscle cell metabolism underlying p-phenylenediamine toxicity

Rhabdomyolysis is a disorder characterized by acute damage of the sarcolemma of the skeletal muscle leading to release of potentially toxic muscle cell components into the circulation, most notably creatine phosphokinase (CK) and myoglobulin, and is frequently accompanied by myoglobinuria. In the present work, we evaluated the toxicity of p-phenylenediamine (PPD), a main component of hair dyes which is reported to induce rhabdomyolysis. We studied the metabolic effect of this compound in vivo with Wistar rats and in vitro with C2C12 muscle cells. To this aim we have combined multi-omic experimental measurements with computational approaches using model-driven methods. The integrative study presented here has unveiled the metabolic disorders associated to PPD exposure that may underlay the aberrant metabolism observed in rhabdomyolys disease. Animals treated with lower doses of PPD (10 and 20 mg/kg) showed depressed activity and myoglobinuria after 10 h of treatment. We measured the serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and creatine kinase (CK) in rats after 24, 48, and 72 h of PPD exposure. At all times, treatment with PPD at higher doses (40 and 60 mg/kg) showed an increase of AST and ALT, and also an increase of lactate dehydrogenase (LDH) and CK after 24 h. Blood packed cell volume and hemoglobin levels, as well as organs weight at 48 and 72 h, were also measured. No significant differences were observed in these parameters under any condition. PPD induce cell cycle arrest in S phase and apoptosis (40% or early apoptotic cells) on mus musculus mouse C2C12 cells after 24 h of treatment. Incubation of mus musculus mouse C2C12 cells with [1,2-13C2]-glucose during 24 h, subsequent quantification of 13C isotopologues distribution in key metabolites of glucose metabolic network and a computational fluxomic analysis using in-house developed software (Isodyn) showed that PPD is inhibiting glycolysis, non-oxidative pentose phosphate pathway, glycogen turnover, and ATPAse reaction leading to a reduction in ATP synthesis. These findings unveil the glucose metabolism collapse, which is consistent with a decrease in cell viability observed in PPD-treated C2C12 cells and with the myoglubinuria and other effects observed in Wistar Rats treated with PPD. These findings shed new light on muscle dysfunction associated to PPD exposure, opening new avenues for cost-effective therapies in Rhabdomyolysis disease

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM , which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development

Workforce preparation: the Biohealth computing model for Master and PhD students

The article addresses the strategic role of workforce preparation in the process of adoption of Systems Medicine as a driver of biomedical research in the new health paradigm. It reports on relevant initiatives, like CASyM, fostering Systems Medicine at EU level. The chapter focuses on the BioHealth Computing Program as a reference for multidisciplinary training of future systems-oriented researchers describing the productive interactions with the Synergy-COPD project

Systems Medicine: from molecular features and models to the clinic in COPD

BACKGROUND AND HYPOTHESIS: Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice. OBJECTIVE AND METHOD: Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework. RESULTS: In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice. CONCLUSIONS: The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them

Development and application of novel model-driven and data-driven approaches to study metabolism in the framework of systems medicine

[spa] La presente tesis doctoral se centra en el desarrollo de herramientas computacionales que permitan el estudio de los mecanismos moleculares que ocurren dentro de la célula. Mas específicamente estudia el metabolismo celular desde diferentes puntos de vista usando y desarrollando métodos computacionales basados en diversas metodologías. Así pues en un primer capitulo se desarrolla un método basado en el analista de los flujos metabólicos en estado no estacional isotópico utilizando modelos cinéticos para estudiar el fenómeno de la canalización metabólica en hepatocitos. Este fenómeno modifica la topología metabólica alterando el fenotipo. Nuestro método nos permitió discriminar varios modelos con distintas topología prediciendo la existencia de canalización metabólica en la glucólisis. En el segundo capitulo se desarrolló un método para analizar el metabolismo tumoral teniendo en cuenta la heterogeneidad de poblaciones. En concreto estudiamos dos subpoblaciones extraídas de una linea celular de cáncer de próstata. Para ello utilizamos un modelo a gran escala de todo el metabolismo celular humano. El análisis reflejó la existencia de diferencias notables a nivel de vías metabólicas concretas, confiriendo a cada subpoblacion sensibilidades distintas a diferentes fármacos. En esta linea se demostró que mientras las células PC-3M eran sensibles al etomoxir e insensibles al calcitriol, las PC-3S presentaban una sensibilidad opuesta. En el tercero y ultimo capitulo de la tesis desarrollamos un nuevo método computacional que integra aproximaciones probabilísticas y mecanicistas para integrar diferentes tipos de datos en un análisis basado en modelos discretos. Para ello utilizamos como caso de concepto el estudio de la adaptación anómala al entrenamiento de pacientes con EPOC. El análisis reveló diferencias importantes a nivel de metabolismo energético en comparación con el grupo control.[eng] The general aim of this thesis is to develop and apply new computational tools to overcome existing limitations in the analysis of metabolism. This thesis is focused on developing new computational strategies to overcome the following identified limitations: i) The existing metabolic flux analysis tools does not account for the existence of metabolic channeling: Here we developed a new computational tool based on non-stationary 13 C-FBA to evaluate different models reflecting different topologies of intracellular metabolism, using the channeling in hepatocytes as case of concep ii) Metabolic drug-target discovery based on GSMM does not consider the different cell subpopulations existing within the tumor: Here we develope a method that integrate trancriptomic data into a comparative genome-scale metabolic network reconstruction analysis in the context of intra-tumoral heterogeneity . We determined subpopulation-specific drug targets . Additionally we determined a metabolic gene signature associated to tumor progression in pc that was correlated with other types of cancer. Iii) Current mechanistic and probabilistic computational approaches are not suitable to study the complexity of the crosstalk between metabolic and gene regulatory networks.: Here we developed a novel computational method combining probabilistic and mechanistic approaches to integrate multi-level omic data into a discrete model-based analysis. This method allowed to analyze the mechanism underlying the crosstalk between metabolism and gene regulation, using as case of concept the study of the abnormal adaptation to training in COPD patients

Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions

Background: Stable isotope tracers are used to assess metabolic flux profiles in living cells. The existing methods of measurement average out the isotopic isomer distribution in metabolites throughout the cell, whereas the knowledge of compartmental organization of analyzed pathways is crucial for the evaluation of true fluxes. That is why we accepted a challenge to create a software tool that allows deciphering the compartmentation of metabolites based on the analysis of average isotopic isomer distribution. Results: The software Isodyn, which simulates the dynamics of isotopic isomer distribution in central metabolic pathways, was supplemented by algorithms facilitating the transition between various analyzed metabolic schemes, and by the tools for model discrimination. It simulated 13C isotope distributions in glucose, lactate, glutamate and glycogen, measured by mass spectrometry after incubation of hepatocytes in the presence of only labeled glucose or glucose and lactate together (with label either in glucose or lactate). The simulations assumed either a single intracellular hexose phosphate pool, or also channeling of hexose phosphates resulting in a different isotopic composition of glycogen. Model discrimination test was applied to check the consistency of both models with experimental data. Metabolic flux profiles, evaluated with the accepted model that assumes channeling, revealed the range of changes in metabolic fluxes in liver cells. Conclusions: The analysis of compartmentation of metabolic networks based on the measured 13C distribution was included in Isodyn as a routine procedure. The advantage of this implementation is that, being a part of evaluation of metabolic fluxes, it does not require additional experiments to study metabolic compartmentation. The analysis of experimental data revealed that the distribution of measured 13C-labeled glucose metabolites is inconsistent with the idea of perfect mixing of hexose phosphates in cytosol. In contrast, the observed distribution indicates the presence of a separate pool of hexose phosphates that is channeled towards glycogen synthesis

Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer

Background Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods. Results Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype. Conclusions The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications. Electronic supplementary material The online version of this article (10.1186/s12864-019-5979-4) contains supplementary material, which is available to authorized users

Model-driven discovery of long-chain fatty acid metabolic reprogramming in heterogeneous prostate cancer cells.

Epithelial-mesenchymal-transition promotes intra-tumoral heterogeneity, by enhancing tumor cell invasiveness and promoting drug resistance. We integrated transcriptomic data for two clonal subpopulations from a prostate cancer cell line (PC-3) into a genome-scale metabolic network model to explore their metabolic differences and potential vulnerabilities. In this dual cell model, PC-3/S cells express Epithelial-mesenchymal-transition markers and display high invasiveness and low metastatic potential, while PC-3/M cells present the opposite phenotype and higher proliferative rate. Model-driven analysis and experimental validations unveiled a marked metabolic reprogramming in long-chain fatty acids metabolism. While PC-3/M cells showed an enhanced entry of long-chain fatty acids into the mitochondria, PC-3/S cells used long-chain fatty acids as precursors of eicosanoid metabolism. We suggest that this metabolic reprogramming endows PC-3/M cells with augmented energy metabolism for fast proliferation and PC-3/S cells with increased eicosanoid production impacting angiogenesis, cell adhesion and invasion. PC-3/S metabolism also promotes the accumulation of docosahexaenoic acid, a long-chain fatty acid with antiproliferative effects. The potential therapeutic significance of our model was supported by a differential sensitivity of PC-3/M cells to etomoxir, an inhibitor of long-chain fatty acid transport to the mitochondria

Unveiling the Metabolic changes on muscle cell metabolism underlying p-phenylenediamine toxicity

Rhabdomyolysis is a disorder characterized by acute damage of the sarcolemma of the skeletal muscle leading to release of potentially toxic muscle cell components into the circulation, most notably creatine phosphokinase (CK) and myoglobulin, and is frequently accompanied by myoglobinuria. In the present work, we evaluated the toxicity of p-phenylenediamine (PPD), a main component of hair dyes which is reported to induce rhabdomyolysis. We studied the metabolic effect of this compound in vivo with Wistar rats and in vitro with C2C12 muscle cells. To this aim we have combined multi-omic experimental measurements with computational approaches using model-driven methods. The integrative study presented here has unveiled the metabolic disorders associated to PPD exposure that may underlay the aberrant metabolism observed in rhabdomyolys disease. Animals treated with lower doses of PPD (10 and 20 mg/kg) showed depressed activity and myoglobinuria after 10 h of treatment. We measured the serum levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), and creatine kinase (CK) in rats after 24, 48, and 72 h of PPD exposure. At all times, treatment with PPD at higher doses (40 and 60 mg/kg) showed an increase of AST and ALT, and also an increase of lactate dehydrogenase (LDH) and CK after 24 h. Blood packed cell volume and hemoglobin levels, as well as organs weight at 48 and 72 h, were also measured. No significant differences were observed in these parameters under any condition. PPD induce cell cycle arrest in S phase and apoptosis (40% or early apoptotic cells) on mus musculus mouse C2C12 cells after 24 h of treatment. Incubation of mus musculus mouse C2C12 cells with [1,2-13C2]-glucose during 24 h, subsequent quantification of 13C isotopologues distribution in key metabolites of glucose metabolic network and a computational fluxomic analysis using in-house developed software (Isodyn) showed that PPD is inhibiting glycolysis, non-oxidative pentose phosphate pathway, glycogen turnover, and ATPAse reaction leading to a reduction in ATP synthesis. These findings unveil the glucose metabolism collapse, which is consistent with a decrease in cell viability observed in PPD-treated C2C12 cells and with the myoglubinuria and other effects observed in Wistar Rats treated with PPD. These findings shed new light on muscle dysfunction associated to PPD exposure, opening new avenues for cost-effective therapies in Rhabdomyolysis disease

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM , which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development