Advances in high throughput technologies and growth of biomedical knowledge have contributed to an exponential increase in associative data. These data can be represented in the form of complex networks of biological associations, which are suitable for systems analyses. However, these networks usually lack both, context specificity in time and space as well as the distinctive borders, which are usually assigned in the classical pathway view of molecular events (e.g. signal transduction). This complexity and high interconnectedness call for automated techniques that can identify smaller targeted subnetworks specific to a given research context (e.g. a disease scenario).
Our method, named ChainRank, finds relevant subnetworks by identifying and scoring chains of interactions that link specific network components. Scores can be generated from integrating multiple general and context specific measures (e.g. experimental molecular data from expression to proteomics and metabolomics, literature evidence, network topology). The performance of the novel ChainRank method was evaluated on recreating selected signalling pathways from a human protein interaction network. Specifically, we recreated skeletal muscle specific signaling networks in healthy and chronic obstructive pulmonary disease (COPD) contexts. The analysis showed that ChainRank can identify main mediators of context specific molecular signalling. An improvement of up to factor 2.5 was shown in the precision of finding proteins of the recreated pathways compared to random simulation.
ChainRank provides a framework, which can integrate several user-defined scores and evaluate their combined effect on ranking interaction chains linking input data sets. It can be used to contextualise networks, identify signaling and regulatory path amongst targeted genes or to analyse synthetic lethality in the context of anticancer therapy. ChainRank is implemented in R programming language and freely available at https://github.com/atenyi/ChainRank.
Electronic supplementary material
The online version of this article (doi:10.1186/s12859-015-0864-x) contains supplementary material, which is available to authorized users.
Biological networks; Protein-protein interaction; Data integration; Filtering; Computational biology; Bioinformatics; Systems biology; COPD
Few data are available in regards to the prevalence of pulmonary hypertension (PH) in the broad spectrum of COPD. This study was aimed at assessing the prevalence of PH in a cohort of COPD patients across the severity of airflow limitation, and reporting the hemodynamic characteristics at rest and during exercise.
We performed a retrospective analysis on COPD patients who underwent right-heart catheterization in our center with measurements obtained at rest (n=139) and during exercise (n=85). PH was defined as mean pulmonary artery pressure (mPAP) ≥25 mmHg and pulmonary capillary wedge pressure <15 mmHg. Exercise-induced PH (EIPH) was defined by a ratio of ΔmPAP/Δcardiac output >3.
PH was present in 25 patients (18%). According to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification, PH prevalence in GOLD 2 was 7% (3 patients); 25% (14 patients) in GOLD 3; and 22% (8 patients) in GOLD 4. Severe PH (mPAP ≥35 mmHg) was identified in four patients (2.8%). Arterial partial oxygen pressure was the outcome most strongly associated with PH (r=−0.29, P<0.001). EIPH was observed in 60 patients (71%) and had a similar prevalence in both GOLD 2 and 3, and was present in all GOLD 4 patients. Patients with PH had lower cardiac index during exercise than patients without PH (5.0±1.2 versus 6.7±1.4 L/min/m2, respectively; P=0.001).
PH has a similar prevalence in COPD patients with severe and very-severe airflow limitation, being associated with the presence of arterial hypoxemia. In contrast, EIPH is highly prevalent, even in moderate COPD, and might contribute to limiting exercise tolerance.
pulmonary hypertension; right heart catheterization; cardiac index; GOLD
Chronic obstructive pulmonary disease (COPD) generates a high burden on health care, and hospital admissions represent a substantial proportion of the overall costs of the disease. Integrated care (IC) has shown efficacy to reduce hospitalisations in COPD patients at a pilot level. Deployment strategies for IC services require assessment of effectiveness at the health care system level.
The aim of this study was to explore the effectiveness of a community-based IC service in preventing hospitalisations and emergency department (ED) visits in stable frail COPD patients.
From April to December 2005, 155 frail community-dwelling COPD patients were randomly allocated either to IC (n=76, age 73 (8) years, forced expiratory volume during the first second, FEV1 41(19) % predicted) or usual care (n=84, age 75(9) years, FEV1 44 (20) % predicted) and followed up for 12 months. The IC intervention consisted of the following: (a) patient’s empowerment for self-management; (b) an individualised care plan; (c) access to a call centre; and (d) coordination between the levels of care. Thereafter, hospital admissions, ED visits and mortality were monitored for 6 years.
IC enhanced self-management (P=0.02), reduced anxiety–depression (P=0.001) and improved health-related quality of life (P=0.02). IC reduced both ED visits (P=0.02) and mortality (P=0.03) but not hospital admission. No differences between the two groups were seen after 6 years.
The intervention improved clinical outcomes including survival and decreased the ED visits, but it did not reduce hospital admissions. The study facilitated the identification of two key requirements for adoption of IC services in the community: appropriate risk stratification of patients, and preparation of the community-based work force.
To identify barriers to deployment of four articulated Integrated Care Services supported by Information Technologies in three European sites. The four services covered the entire spectrum of severity of illness. The project targeted chronic patients with obstructive pulmonary disease, cardiac failure and/or type II diabetes mellitus.
One health care sector in Spain (Barcelona) (n = 11.382); six municipalities in Norway (Trondheim) (n = 450); and one hospital in Greece (Athens) (n = 388).
The four services were: (i) Home-based long-term maintenance of rehabilitation effects (n = 337); (ii) Enhanced Care for frail patients, n = 1340); (iii) Home Hospitalization and Early Discharge (n = 2404); and Support for remote diagnosis (forced spirometry testing) in primary care (Support) (n = 8139). Both randomized controlled trials and pragmatic study designs were combined. Two technological approaches were compared. The Model for Assessment of Telemedicine applications was adopted.
The project demonstrated: (i) Sustainability of training effects over time in chronic patients with obstructive pulmonary disease (p < 0.01); (ii) Enhanced care and fewer hospitalizations in chronic respiratory patients (p < 0.05); (iii) Reduced in-hospital days for all types of patients (p < 0.001) in Home Hospitalization/Early Discharge; and (iv) Increased quality of testing (p < 0.01) for patients with respiratory symptoms in Support, with marked differences among sites.
The four integrated care services showed high potential to enhance health outcomes with cost-containment. Change management, technological approach and legal issues were major factors modulating the success of the deployment. The project generated a business plan to foster service sustainability and health innovation. Deployment strategies require site-specific adaptations.
case management; integrated health care systems; chronic disease; long-term care; telemedicine
We hypothesized that the implementation of automatic real-time assessment of quality of forced spirometry (FS) may significantly enhance the potential for extensive deployment of a FS program in the community. Recent studies have demonstrated that the application of quality criteria defined by the ATS/ERS (American Thoracic Society/European Respiratory Society) in commercially available equipment with automatic quality assessment can be markedly improved. To this end, an algorithm for assessing quality of FS automatically was reported. The current research describes the mathematical developments of the algorithm. An innovative analysis of the shape of the spirometric curve, adding 23 new metrics to the traditional 4 recommended by ATS/ERS, was done. The algorithm was created through a two-step iterative process including: (1) an initial version using the standard FS curves recommended by the ATS; and, (2) a refined version using curves from patients. In each of these steps the results were assessed against one expert's opinion. Finally, an independent set of FS curves from 291 patients was used for validation purposes. The novel mathematical approach to characterize the FS curves led to appropriate FS classification with high specificity (95%) and sensitivity (96%). The results constitute the basis for a successful transfer of FS testing to non-specialized professionals in the community.
This article describes a Digital Health Framework (DHF), benefitting from the lessons learnt during the three-year life span of the FP7 Synergy-COPD project. The DHF aims to embrace the emerging requirements - data and tools - of applying systems medicine into healthcare with a three-tier strategy articulating formal healthcare, informal care and biomedical research. Accordingly, it has been constructed based on three key building blocks, namely, novel integrated care services with the support of information and communication technologies, a personal health folder (PHF) and a biomedical research environment (DHF-research). Details on the functional requirements and necessary components of the DHF-research are extensively presented. Finally, the specifics of the building blocks strategy for deployment of the DHF, as well as the steps toward adoption are analyzed. The proposed architectural solutions and implementation steps constitute a pivotal strategy to foster and enable 4P medicine (Predictive, Preventive, Personalized and Participatory) in practice and should provide a head start to any community and institution currently considering to implement a biomedical research platform.
Biomedical Research; Chronic care; Clinical Decision Support Systems; Integrated Health Care Systems; Patient Decision Support Systems; Personal Health Folder
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.
Biomedical Research; Education; Integrated Health Care Systems; Master; PhD; Professionals; Systems Medicine; Training
Chronic Obstructive Pulmonary Disease (COPD) is a major challenge for healthcare. Heterogeneities in clinical manifestations and in disease progression are relevant traits in COPD with impact on patient management and prognosis. It is hypothesized that COPD heterogeneity results from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering.
To assess the potential of systems medicine to better understand non-pulmonary determinants of COPD heterogeneity. To transfer acquired knowledge to healthcare enhancing subject-specific health risk assessment and stratification to improve management of chronic patients.
Underlying mechanisms of skeletal muscle dysfunction and of co-morbidity clustering in COPD patients were explored with strategies combining deterministic modelling and network medicine analyses using the Biobridge dataset. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was done (ICD9-CM data from Medicare, 13 million people). A targeted network analysis using the two studies: skeletal muscle dysfunction and co-morbidity clustering explored shared pathways between them.
(1) Evidence of abnormal regulation of pivotal skeletal muscle biological pathways and increased risk for co-morbidity clustering was observed in COPD; (2) shared abnormal pathway regulation between skeletal muscle dysfunction and co-morbidity clustering; and, (3) technological achievements of the projects were: (i) COPD Knowledge Base; (ii) novel modelling approaches; (iii) Simulation Environment; and, (iv) three layers of Clinical Decision Support Systems.
The project demonstrated the high potential of a systems medicine approach to address COPD heterogeneity. Limiting factors for the project development were identified. They were relevant to shape strategies fostering 4P Medicine for chronic patients. The concept of Digital Health Framework and the proposed roadmap for its deployment constituted relevant project outcomes.
chronic diseases; co-morbidities; chronic obstructive pulmonary disease; information and communication technologies; modelling; network medicine; training
Chronic diseases (CD) are generating a dramatic societal burden worldwide that is expected to persist over the next decades. The challenges posed by the epidemics of CD have triggered a novel health paradigm with major consequences on the traditional concept of disease and with a profound impact on key aspects of healthcare systems.
We hypothesized that the development of a systems approach to understand CD together with the generation of an ecosystem to transfer the acquired knowledge into the novel healthcare scenario may contribute to a cost-effective enhancement of health outcomes. To this end, we designed the Synergy-COPD project wherein the heterogeneity of chronic obstructive pulmonary disease (COPD) was addressed as a use case representative of CD.
The current manuscript describes main features of the project design and the strategies put in place for its development, as well the expected outcomes during the project life-span. Moreover, the manuscript serves as introductory and unifying chapter of the different papers associated to the Supplement describing the characteristics, tools and the objectives of Synergy-COPD
Chronic Diseases; Chronic Obstructive Pulmonary Disease; COPD; Integrated Care; ICT; Systems Medicine; Predictive Medicine; Telemedicine
Background and hypothesis
Heterogeneity in clinical manifestations and disease progression in Chronic Obstructive Pulmonary Disease (COPD) lead to consequences for patient health risk assessment, stratification and management. Implicit with the classical "spill over" hypothesis is that COPD heterogeneity is driven by the pulmonary events of the disease. Alternatively, we hypothesized that COPD heterogeneities result from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering, each of them with their own dynamics.
Objective and method
To explore the potential of a systems analysis of COPD heterogeneity focused on skeletal muscle dysfunction and on co-morbidity clustering aiming at generating predictive modeling with impact on patient management. To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people). Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena.
(1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering.
The results indicate the high potential of a systems approach to address COPD heterogeneity. Significant knowledge gaps were identified that are relevant to shape strategies aiming at fostering 4P Medicine for patients with COPD.
Chronic diseases; COPD; Disease heterogeneity; Integrated Care; Predictive Medicine; Redox disequilibrium; Systems Medicine; VO2max
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.
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.
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.
Chronic diseases; COPD; Disease heterogeneity; Systems Medicine; Predictive Modeling; Co-morbidity
Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data.
The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice.
The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.
The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway.
The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow.
The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team.
A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms.
Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.
decision support; COPD; service oriented architecture; integrated care; rule-based systems
The production of reactive oxygen species (ROS) from the inner mitochondrial membrane is one of many fundamental processes governing the balance between health and disease. It is well known that ROS are necessary signaling molecules in gene expression, yet when expressed at high levels, ROS may cause oxidative stress and cell damage. Both hypoxia and hyperoxia may alter ROS production by changing mitochondrial Po2 (). Because depends on the balance between O2 transport and utilization, we formulated an integrative mathematical model of O2 transport and utilization in skeletal muscle to predict conditions to cause abnormally high ROS generation. Simulations using data from healthy subjects during maximal exercise at sea level reveal little mitochondrial ROS production. However, altitude triggers high mitochondrial ROS production in muscle regions with high metabolic capacity but limited O2 delivery. This altitude roughly coincides with the highest location of permanent human habitation. Above 25,000 ft., more than 90% of exercising muscle is predicted to produce abnormally high levels of ROS, corresponding to the “death zone” in mountaineering.
Background: Limb muscle dysfunction is prevalent in chronic obstructive pulmonary disease (COPD) and it has important clinical implications, such as reduced exercise tolerance, quality of life, and even survival. Since the previous American Thoracic Society/European Respiratory Society (ATS/ERS) statement on limb muscle dysfunction, important progress has been made on the characterization of this problem and on our understanding of its pathophysiology and clinical implications.
Purpose: The purpose of this document is to update the 1999 ATS/ERS statement on limb muscle dysfunction in COPD.
Methods: An interdisciplinary committee of experts from the ATS and ERS Pulmonary Rehabilitation and Clinical Problems assemblies determined that the scope of this document should be limited to limb muscles. Committee members conducted focused reviews of the literature on several topics. A librarian also performed a literature search. An ATS methodologist provided advice to the committee, ensuring that the methodological approach was consistent with ATS standards.
Results: We identified important advances in our understanding of the extent and nature of the structural alterations in limb muscles in patients with COPD. Since the last update, landmark studies were published on the mechanisms of development of limb muscle dysfunction in COPD and on the treatment of this condition. We now have a better understanding of the clinical implications of limb muscle dysfunction. Although exercise training is the most potent intervention to address this condition, other therapies, such as neuromuscular electrical stimulation, are emerging. Assessment of limb muscle function can identify patients who are at increased risk of poor clinical outcomes, such as exercise intolerance and premature mortality.
Conclusions: Limb muscle dysfunction is a key systemic consequence of COPD. However, there are still important gaps in our knowledge about the mechanisms of development of this problem. Strategies for early detection and specific treatments for this condition are also needed.
We recently demonstrated that quality of spirometry in primary care could markedly improve with remote offline support from specialized professionals. It is hypothesized that implementation of automatic online assessment of quality of spirometry using information and communication technologies may significantly enhance the potential for extensive deployment of a high quality spirometry program in integrated care settings.
The objective of the study was to elaborate and validate a Clinical Decision Support System (CDSS) for automatic online quality assessment of spirometry.
The CDSS was done through a three step process including: (1) identification of optimal sampling frequency; (2) iterations to build-up an initial version using the 24 standard spirometry curves recommended by the American Thoracic Society; and (3) iterations to refine the CDSS using 270 curves from 90 patients. In each of these steps the results were checked against one expert. Finally, 778 spirometry curves from 291 patients were analyzed for validation purposes.
The CDSS generated appropriate online classification and certification in 685/778 (88.1%) of spirometry testing, with 96% sensitivity and 95% specificity.
Consequently, only 93/778 (11.9%) of spirometry testing required offline remote classification by an expert, indicating a potential positive role of the CDSS in the deployment of a high quality spirometry program in an integrated care setting.
spirometry; telemedicine; information communication technologies; primary care; quality control
Patients with chronic obstructive pulmonary disease (COPD) often suffer concomitant disorders that worsen significantly their health status and vital prognosis. The pathogenic mechanisms underlying COPD multimorbidities are not completely understood, thus the exploration of potential molecular and biological linkages between COPD and their associated diseases is of great interest.
We developed a novel, unbiased, integrative network medicine approach for the analysis of the diseasome, interactome, the biological pathways and tobacco smoke exposome, which has been applied to the study of 16 prevalent COPD multimorbidities identified by clinical experts.
Our analyses indicate that all COPD multimorbidities studied here are related at the molecular and biological level, sharing genes, proteins and biological pathways. By inspecting the connections of COPD with their associated diseases in more detail, we identified known biological pathways involved in COPD, such as inflammation, endothelial dysfunction or apoptosis, serving as a proof of concept of the methodology. More interestingly, we found previously overlooked biological pathways that might contribute to explain COPD multimorbidities, such as hemostasis in COPD multimorbidities other than cardiovascular disorders, and cell cycle pathway in the association of COPD with depression. Moreover, we also observed similarities between COPD multimorbidities at the pathway level, suggesting common biological mechanisms for different COPD multimorbidities. Finally, chemicals contained in the tobacco smoke target an average of 69% of the identified proteins participating in COPD multimorbidities.
The network medicine approach presented here allowed the identification of plausible molecular links between COPD and comorbid diseases, and showed that many of them are targets of the tobacco exposome, proposing new areas of research for understanding the molecular underpinning of COPD multimorbidities.
Electronic supplementary material
The online version of this article (doi:10.1186/s12931-014-0111-4) contains supplementary material, which is available to authorized users.
Diseasome; Systems biology; Network medicine; Comorbidity; Multimorbidity; COPD; Tobacco chemicals
In chronic obstructive pulmonary disease (COPD), decreased progenitor cells and impairment of systemic vascular function have been suggested to confer higher cardiovascular risk. The origin of these changes and their relationship with alterations in the pulmonary circulation are unknown.
To investigate whether changes in the number of circulating hematopoietic progenitor cells are associated with pulmonary hypertension or changes in endothelial function.
62 COPD patients and 35 controls (18 non-smokers and 17 smokers) without cardiovascular risk factors other than cigarette smoking were studied. The number of circulating progenitors was measured as CD45+CD34+CD133+ labeled cells by flow cytometry. Endothelial function was assessed by flow-mediated dilation. Markers of inflammation and angiogenesis were also measured in all subjects.
Compared with controls, the number of circulating progenitor cells was reduced in COPD patients. Progenitor cells did not differ between control smokers and non-smokers. COPD patients with pulmonary hypertension showed greater number of progenitor cells than those without pulmonary hypertension. Systemic endothelial function was worse in both control smokers and COPD patients. Interleukin-6, fibrinogen, high sensitivity C-reactive protein, vascular endothelial growth factor and tumor necrosis factor were increased in COPD. In COPD patients, the number of circulating progenitor cells was inversely related to the flow-mediated dilation of systemic arteries.
Pulmonary and systemic vascular impairment in COPD is associated with cigarette smoking but not with the reduced number of circulating hematopoietic progenitors. The latter appears to be a consequence of the disease itself not related to smoking habit.
A relatively large percentage of patients with chronic obstructive pulmonary disease (COPD) develop systemic co-morbidities that affect prognosis, among which muscle wasting is particularly debilitating. Despite significant research effort, the pathophysiology of this important extrapulmonary manifestation is still unclear. A key question that remains unanswered is to what extent systemic inflammatory mediators might play a role in this pathology.
Cigarette smoke (CS) is the main risk factor for developing COPD and therefore animal models chronically exposed to CS have been proposed for mechanistic studies and biomarker discovery. Although mice have been successfully used as a pre-clinical in vivo model to study the pulmonary effects of acute and chronic CS exposure, data suggest that they may be inadequate models for studying the effects of CS on peripheral muscle function. In contrast, recent findings indicate that the guinea pig model (Cavia porcellus) may better mimic muscle wasting.
We have used a systems biology approach to compare the transcriptional profile of hindlimb skeletal muscles from a Guinea pig rodent model exposed to CS and/or chronic hypoxia to COPD patients with muscle wasting.
We show that guinea pigs exposed to long-term CS accurately reflect most of the transcriptional changes observed in dysfunctional limb muscle of severe COPD patients when compared to matched controls. Using network inference, we could then show that the expression profile in whole lung of genes encoding for soluble inflammatory mediators is informative of the molecular state of skeletal muscles in the guinea pig smoking model. Finally, we show that CXCL10 and CXCL9, two of the candidate systemic cytokines identified using this pre-clinical model, are indeed detected at significantly higher levels in serum of COPD patients, and that their serum protein level is inversely correlated with the expression of aerobic energy metabolism genes in skeletal muscle.
We conclude that CXCL10 and CXCL9 are promising candidate inflammatory signals linked to the regulation of central metabolism genes in skeletal muscles. On a methodological level, our work also shows that a system level analysis of animal models of diseases can be very effective to generate clinically relevant hypothesis.
Electronic supplementary material
The online version of this article (doi:10.1186/s13073-014-0059-5) contains supplementary material, which is available to authorized users.
Telemedicine seems to offer reliable solutions to health care challenges, but significant contradictory results were recently found. Therefore, it is crucial to carefully select outcomes and target patients who may take advantage of this technology. Continuous positive airway pressure (CPAP) therapy compliance is essential to treat patients with obstructive sleep apnea (OSA). We believe that OSA patients could benefit greatly from a telemedicine approach for CPAP therapy management.
The objective of our study was to evaluate the application of a telemedicine-based approach in the CPAP therapy management, focusing on patients’ CPAP follow-up and training.
We performed two studies. First, (study 1) we enrolled 50 consecutive OSA patients who came to our sleep center for the CPAP follow-up visit. Patients performed a teleconsultation with a physician, and once finalized, they were asked to answer anonymously to a questionnaire regarding their opinion about the teleconsultation. In a second randomized controlled trial (RCT) (study 2), we included 40 OSA patients scheduled for CPAP training. There were 20 that received the usual face-to-face training and 20 that received the training via videoconference. After the session, they were blindly evaluated on what they learned about OSA and mask placement.
More than 95% (49/50) of the interviewed patients were satisfied with the teleconsultation, and 66% (33/50) of them answered that the teleconsultation could replace 50%-100% of their CPAP follow-up visits. Regarding the RCT, patients who received the CPAP training via videoconference demonstrated the same knowledge about OSA and CPAP therapy as the face-to-face group (mean 93.6% of correct answers vs mean 92.1%; P=.935). Performance on practical skills (mask and headgear placement, leaks avoidance) was also similar between the two groups.
OSA patients gave a positive feedback about the use of teleconsultation for CPAP follow-up, and the CPAP training based on a telemedicine approach proved to be as effective as face-to-face training. These results support the use of this telemedicine-based approach as a valuable strategy for patients’ CPAP training and clinical follow-up.
telemedicine; sleep apnea; CPAP therapy; teleconsultation
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.
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.
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.
Chronic Obstructive Pulmonary Disease (COPD) is an inflammatory process of the lung inducing persistent airflow limitation. Extensive systemic effects, such as skeletal muscle dysfunction, often characterize these patients and severely limit life expectancy. Despite considerable research efforts, the molecular basis of muscle degeneration in COPD is still a matter of intense debate. In this study, we have applied a network biology approach to model the relationship between muscle molecular and physiological response to training and systemic inflammatory mediators. Our model shows that failure to co-ordinately activate expression of several tissue remodelling and bioenergetics pathways is a specific landmark of COPD diseased muscles. Our findings also suggest that this phenomenon may be linked to an abnormal expression of a number of histone modifiers, which we discovered correlate with oxygen utilization. These observations raised the interesting possibility that cell hypoxia may be a key factor driving skeletal muscle degeneration in COPD patients.
Chronic Obstructive Pulmonary Disease (COPD) is a major life threatening disease of the lungs, characterized by airflow limitation and chronic inflammation. Progressive reduction of the body muscle mass is a condition linked to COPD that significantly decreases quality of life and survival. Physical exercise has been proposed as a therapeutic option but its utility is still a matter of debate. The mechanisms underlying muscle wasting are also still largely unknown. The results presented in this paper show that diseased muscles are largely unable to coordinate the expression of muscle remodelling and bioenergetics pathways and that the cause of this phenomena may be tissue hypoxia. These findings contrast with current hypotheses based on the role of chronic inflammation and show that a mechanism based on an oxygen driven, epigenetic control of these two important functions may be an important disease mechanism.
We propose an innovative, integrated, cost-effective health system to combat major non-communicable diseases (NCDs), including cardiovascular, chronic respiratory, metabolic, rheumatologic and neurologic disorders and cancers, which together are the predominant health problem of the 21st century. This proposed holistic strategy involves comprehensive patient-centered integrated care and multi-scale, multi-modal and multi-level systems approaches to tackle NCDs as a common group of diseases. Rather than studying each disease individually, it will take into account their intertwined gene-environment, socio-economic interactions and co-morbidities that lead to individual-specific complex phenotypes. It will implement a road map for predictive, preventive, personalized and participatory (P4) medicine based on a robust and extensive knowledge management infrastructure that contains individual patient information. It will be supported by strategic partnerships involving all stakeholders, including general practitioners associated with patient-centered care. This systems medicine strategy, which will take a holistic approach to disease, is designed to allow the results to be used globally, taking into account the needs and specificities of local economies and health systems.
Reactive oxygen species (ROS) produced in the mitochondrial respiratory chain (RC) are primary signals that modulate cellular adaptation to environment, and are also destructive factors that damage cells under the conditions of hypoxia/reoxygenation relevant for various systemic diseases or transplantation. The important role of ROS in cell survival requires detailed investigation of mechanism and determinants of ROS production. To perform such an investigation we extended our rule-based model of complex III in order to account for electron transport in the whole RC coupled to proton translocation, transmembrane electrochemical potential generation, TCA cycle reactions, and substrate transport to mitochondria. It fits respiratory electron fluxes measured in rat brain mitochondria fueled by succinate or pyruvate and malate, and the dynamics of NAD+ reduction by reverse electron transport from succinate through complex I. The fitting of measured characteristics gave an insight into the mechanism of underlying processes governing the formation of free radicals that can transfer an unpaired electron to oxygen-producing superoxide and thus can initiate the generation of ROS. Our analysis revealed an association of ROS production with levels of specific radicals of individual electron transporters and their combinations in species of complexes I and III. It was found that the phenomenon of bistability, revealed previously as a property of complex III, remains valid for the whole RC. The conditions for switching to a state with a high content of free radicals in complex III were predicted based on theoretical analysis and were confirmed experimentally. These findings provide a new insight into the mechanisms of ROS production in RC.
Respiration at the level of mitochondria is considered as delivery of electrons and protons from NADH or succinate to oxygen through a set of transporters constituting the respiratory chain (RC). Mitochondrial respiration, dealing with transfer of unpaired electrons, may produce reactive oxygen species (ROS) such as O2− and subsequently H2O2 as side products. ROS are chemically very active and can cause oxidative damage to cellular components. The production of ROS, normally low, can increase under stress to the levels incompatible with cell survival; thus, understanding the ways of ROS production in the RC represents a vital task in research. We used mathematical modeling to analyze experiments with isolated brain mitochondria aimed to study relations between electron transport and ROS production. Elsewhere we reported that mitochondrial complex III can operate in two distinct steady states at the same microenvironmental conditions, producing either low or high levels of ROS. Here, this property of bistability was confirmed for the whole RC. The associations between measured ROS production and computed individual free radical levels in complexes I and III were established. The discovered phenomenon of bistability is important as a basis for new strategies in organ transplantation and therapy.
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.
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.
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.