Final Report Summary - BIOBRIDGE (Integrative Genomics and Chronic Disease Phenotypes: modelling and simulation tools for clinicians)
The BIOBRIDGE project was built up around a well-defined clinical problem that is the analysis of the systemic effects in three highly prevalent chronic conditions: chronic obstructive pulmonary disease (COPD), chronic heart failure (CHF) and diabetes type II. Hallmark features of the systemic effects are skeletal muscle dysfunction and muscle wasting.
The project sought to enhance the biomedical understanding of the underlying mechanisms explaining the systemic effects of the target disorders. Recent publications indicate that the pathogenesis of the systemic effects may share common mechanisms in the three conditions. It has been suggested that the nitroso-redox unbalance of the cardiovascular system accounts for functional heterogeneities at tissue level which may explain both muscle dysfunction (mechanical-energetic uncoupling) and abnormal tissue remodelling seen in these patients.
It is documented that the presence of systemic effects conforms disease phenotypes associated to high use of healthcare resources and to poor prognosis. However, the complexities of the interactions between susceptibility (i.e. genetic determinants) and life style factors (i.e. low physical activity, tobacco smoking, nutrition) put on display the insufficiencies of the classical hypothesis-based research approach to effectively explore the underlying mechanisms determining the clinical problem. It prompts the need for complementary novel strategies based on integrative translational research using systems biology methodologies. Consequently, BIOBRIDGE's goals were aligned in two well differentiated dimensions, biomedical and bioinformatics.
The operational aspects of the project required us to focus on one specific use case. We decided to explore the effects of skeletal muscle endurance training in healthy subjects and in COPD patients. To this end, we analysed different levels of the system (blood, skeletal muscle and whole-body bioenergetics) with multidimensional assessments (physiological measurements, genomics, proteomics and metabolomics) in two different protocols (3-w and 8-w training periods) done in different sets of subjects. Each of the studies including three subsets of individuals: healthy sedentary subjects (C), COPD patients without muscle wasting (COPDN) and COPD patients with muscle wasting (COPDL). A large study aiming at analysing relationships between body mass composition and exercise capacity in COPD patients was also undertaken. Animal experiments and cell culture protocols were done to answer specific questions generated through the bioinformatics approach. Cardiac failure and diabetes type II were approached through data mining. Moreover, muscle dysfunction in diabetes type II patients was explored through inference analysis using public datasets and the results were compared with systemic effects seen in COPD patients and with those observed in other degenerative muscle diseases.
Besides the novelties emerging from the results of the experimental designs, the project has generated biological knowledge through the simulation environment. We have also done relevant contributions in several bioinformatic dimensions. Main achievements in this field were those related with multilevel data integration; development of SBML standards ensuring interoperability among different ICT tools; development of deterministic modelling of oxygen transport-central metabolism and mitochondrial ROS generation; preparing tools for inference analysis and probabilistic modelling; and, integration of simulation environment into the BIOBRIDGE portal.
Last but not least, the elaboration of future business prospective has also been an important component generating the formulation of specific strategies for future developments based on interactions between adoption of novel integrated care services for chronic patients based on open ICT platforms and systems biology developments.
The success in deployment of integrated care services in our area and the networking with other platforms are generating strong synergies that will help to foster the business strategies articulated in BIOBRIDGE. Moreover, the bioinformatics platform allows generalisation, such that the simulation environment generated within the project should be useful to address other clinical problems. The results of the project will help to pave the way for predictive medicine for complex chronic diseases leading to personalised health strategies targeting chronic patients at early disease stages.
The project sought to enhance the biomedical understanding of the underlying mechanisms explaining the systemic effects of the target disorders. Recent publications indicate that the pathogenesis of the systemic effects may share common mechanisms in the three conditions. It has been suggested that the nitroso-redox unbalance of the cardiovascular system accounts for functional heterogeneities at tissue level which may explain both muscle dysfunction (mechanical-energetic uncoupling) and abnormal tissue remodelling seen in these patients.
It is documented that the presence of systemic effects conforms disease phenotypes associated to high use of healthcare resources and to poor prognosis. However, the complexities of the interactions between susceptibility (i.e. genetic determinants) and life style factors (i.e. low physical activity, tobacco smoking, nutrition) put on display the insufficiencies of the classical hypothesis-based research approach to effectively explore the underlying mechanisms determining the clinical problem. It prompts the need for complementary novel strategies based on integrative translational research using systems biology methodologies. Consequently, BIOBRIDGE's goals were aligned in two well differentiated dimensions, biomedical and bioinformatics.
The operational aspects of the project required us to focus on one specific use case. We decided to explore the effects of skeletal muscle endurance training in healthy subjects and in COPD patients. To this end, we analysed different levels of the system (blood, skeletal muscle and whole-body bioenergetics) with multidimensional assessments (physiological measurements, genomics, proteomics and metabolomics) in two different protocols (3-w and 8-w training periods) done in different sets of subjects. Each of the studies including three subsets of individuals: healthy sedentary subjects (C), COPD patients without muscle wasting (COPDN) and COPD patients with muscle wasting (COPDL). A large study aiming at analysing relationships between body mass composition and exercise capacity in COPD patients was also undertaken. Animal experiments and cell culture protocols were done to answer specific questions generated through the bioinformatics approach. Cardiac failure and diabetes type II were approached through data mining. Moreover, muscle dysfunction in diabetes type II patients was explored through inference analysis using public datasets and the results were compared with systemic effects seen in COPD patients and with those observed in other degenerative muscle diseases.
Besides the novelties emerging from the results of the experimental designs, the project has generated biological knowledge through the simulation environment. We have also done relevant contributions in several bioinformatic dimensions. Main achievements in this field were those related with multilevel data integration; development of SBML standards ensuring interoperability among different ICT tools; development of deterministic modelling of oxygen transport-central metabolism and mitochondrial ROS generation; preparing tools for inference analysis and probabilistic modelling; and, integration of simulation environment into the BIOBRIDGE portal.
Last but not least, the elaboration of future business prospective has also been an important component generating the formulation of specific strategies for future developments based on interactions between adoption of novel integrated care services for chronic patients based on open ICT platforms and systems biology developments.
The success in deployment of integrated care services in our area and the networking with other platforms are generating strong synergies that will help to foster the business strategies articulated in BIOBRIDGE. Moreover, the bioinformatics platform allows generalisation, such that the simulation environment generated within the project should be useful to address other clinical problems. The results of the project will help to pave the way for predictive medicine for complex chronic diseases leading to personalised health strategies targeting chronic patients at early disease stages.