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Rogliani P, Ritondo BL, Laitano R, Chetta A, Calzetta L. Advances in understanding of mechanisms related to increased cardiovascular risk in COPD. Expert Rev Respir Med 2020; 15:59-70. [PMID: 33084434 DOI: 10.1080/17476348.2021.1840982] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
INTRODUCTION Chronic obstructive pulmonary disease (COPD) represents a serious global health issue that is commonly associated with cardiovascular (CV) disease (CVD). The close relationship between COPD and CVD could be explained by different factors, first and foremost a chronic low-grade systemic inflammation implicated in the pathogenesis of both diseases and several stimuli enhancing the inflammatory processes and causing a mixed condition with worse outcomes than either disorder alone. AREAS COVERED The present narrative review considers the mechanisms underlying the increased CV risk in COPD, and it provides insights on biomarkers and predictive models to predict CVD in COPD patients. EXPERT OPINION COPD patients often remain asymptomatic of CVD, with respiratory symptoms generally attributed to the preexisting pulmonary disease. It is fundamental to understand the mechanistic pathways that underpin the intimate relationship between the two disorders. However, it is still not clear what is the origin of the common background of low-grade systemic inflammation, it could be a 'spillover' or a general inflammatory state. Primary prevention, cross-collaboration between specialists and early detection via predictive biomarkers and validated models are fundamental to stratify COPD patients according to CV risk.
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Affiliation(s)
- Paola Rogliani
- Unit of Respiratory Medicine, Department of Experimental Medicine, University of Rome "Tor Vergata" , Rome, Italy
| | - Beatrice Ludovica Ritondo
- Unit of Respiratory Medicine, Department of Experimental Medicine, University of Rome "Tor Vergata" , Rome, Italy
| | - Rossella Laitano
- Unit of Respiratory Medicine, Department of Experimental Medicine, University of Rome "Tor Vergata" , Rome, Italy
| | - Alfredo Chetta
- Department of Medicine and Surgery, Respiratory Disease and Lung Function Unit, University of Parma , Parma, Italy
| | - Luigino Calzetta
- Department of Medicine and Surgery, Respiratory Disease and Lung Function Unit, University of Parma , Parma, Italy
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Gomez-Cabrero D, Marabita F, Tarazona S, Cano I, Roca J, Conesa A, Sabatier P, Tegnér J. Guidelines for Developing Successful Short Advanced Courses in Systems Medicine and Systems Biology. Cell Syst 2017; 5:168-175. [PMID: 28843483 DOI: 10.1016/j.cels.2017.05.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2016] [Revised: 02/21/2017] [Accepted: 05/31/2017] [Indexed: 12/15/2022]
Abstract
Systems medicine and systems biology have inherent educational challenges. These have largely been addressed either by providing new masters programs or by redesigning undergraduate programs. In contrast, short courses can respond to a different need: they can provide condensed updates for professionals across academia, the clinic, and industry. These courses have received less attention. Here, we share our experiences in developing and providing such courses to current and future leaders in systems biology and systems medicine. We present guidelines for how to reproduce our courses, and we offer suggestions for how to select students who will nurture an interdisciplinary learning environment and thrive there.
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Affiliation(s)
- David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden; Mucosal and Salivary Biology Division, King's College London Dental Institute, London SE1 9RT, UK.
| | - Francesco Marabita
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden
| | - Sonia Tarazona
- Centro de Investigacion Principe Felipe, 46012 Valencia, Spain; Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Camí de Vera, 46022 Valencia, Spain
| | - Isaac Cano
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, 08007 Barcelona, Spain; Center for Biomedical Network Research in Respiratory Diseases (CIBERES), 28029 Madrid, Spain
| | - Josep Roca
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, 08007 Barcelona, Spain; Center for Biomedical Network Research in Respiratory Diseases (CIBERES), 28029 Madrid, Spain
| | - Ana Conesa
- Centro de Investigacion Principe Felipe, 46012 Valencia, Spain; Microbiology and Cell Science Department, Institute for Food and Agricultural Sciences, University of Florida, Gainesville, FL 32603, USA
| | - Philippe Sabatier
- TIMC-IMAG Laboratory, UMR 5525, Centre National de la Recherche Scientifique, Vetagro Sup, Université Grenoble-Alpes, 38400 Saint-Martin-d'Hères, France
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Center for Molecular Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden; Unit of Clinical Epidemiology, Department of Medicine, Karolinska University Hospital, L8, 17176 Stockholm, Sweden; Science for Life Laboratory, 17121 Solna, Sweden; Biological and Environmental Sciences and Engineering Division (BESE), Computer, Electrical and Mathematical Sciences and Engineering Division (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia.
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Gomez-Cabrero D, Tegnér J. Iterative Systems Biology for Medicine – Time for advancing from network signatures to mechanistic equations. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.05.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Lin Y, Chen J, Shen B. Interactions Between Genetics, Lifestyle, and Environmental Factors for Healthcare. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1005:167-191. [PMID: 28916933 DOI: 10.1007/978-981-10-5717-5_8] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
The occurrence and progression of diseases are strongly associated with a combination of genetic, lifestyle, and environmental factors. Understanding the interplay between genetic and nongenetic components provides deep insights into disease pathogenesis and promotes personalized strategies for people healthcare. Recently, the paradigm of systems medicine, which integrates biomedical data and knowledge at multidimensional levels, is considered to be an optimal way for disease management and clinical decision-making in the era of precision medicine. In this chapter, epigenetic-mediated genetics-lifestyle-environment interactions within specific diseases and different ethnic groups are systematically discussed, and data sources, computational models, and translational platforms for systems medicine research are sequentially presented. Moreover, feasible suggestions on precision healthcare and healthy longevity are kindly proposed based on the comprehensive review of current studies.
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Affiliation(s)
- Yuxin Lin
- Center for Systems Biology, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China
| | - Jiajia Chen
- School of Chemistry, Biology and Materials Engineering, Suzhou University of Science and Technology, No.1 Kerui road, Suzhou, Jiangsu, 215011, China
| | - Bairong Shen
- Center for Systems Biology, Soochow University, No.1 Shizi Street, Suzhou, Jiangsu, 215006, China. .,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu, 215163, China. .,Medical College of Guizhou University, Guiyang, 550025, China.
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5
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Marín de Mas I, Fanchon E, Papp B, Kalko S, Roca J, Cascante M. Molecular mechanisms underlying COPD-muscle dysfunction unveiled through a systems medicine approach. Bioinformatics 2016; 33:95-103. [PMID: 27794560 DOI: 10.1093/bioinformatics/btw566] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 08/26/2016] [Accepted: 08/29/2016] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Skeletal muscle dysfunction is a systemic effect in one-third of patients with chronic obstructive pulmonary disease (COPD), characterized by high reactive-oxygen-species (ROS) production and abnormal endurance training-induced adaptive changes. However, the role of ROS in COPD remains unclear, not least because of the lack of appropriate tools to study multifactorial diseases. RESULTS We describe a discrete model-driven method combining mechanistic and probabilistic approaches to decipher the role of ROS on the activity state of skeletal muscle regulatory network, assessed before and after an 8-week endurance training program in COPD patients and healthy subjects. In COPD, our computational analysis indicates abnormal training-induced regulatory responses leading to defective tissue remodeling and abnormal energy metabolism. Moreover, we identified tnf, insr, inha and myc as key regulators of abnormal training-induced adaptations in COPD. The tnf-insr pair was identified as a promising target for therapeutic interventions. Our work sheds new light on skeletal muscle dysfunction in COPD, opening new avenues for cost-effective therapies. It overcomes limitations of previous computational approaches showing high potential for the study of other multi-factorial diseases such as diabetes or cancer. CONTACT jroca@clinic.ub.es or martacascante@ub.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Igor Marín de Mas
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS, Diagonal 645, Barcelona 08028, Spain.,Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain.,Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Eric Fanchon
- Université Grenoble Alpes-CNRS, TIMC-IMAG UMR 5525, Faculté de Médecine, Grenoble 38041, France
| | - Balázs Papp
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Susana Kalko
- Bioinformatics Core Facility, IDIBAPS-CEK, Hospital Clínic, University de Barcelona, Barcelona 08036, Spain
| | - Josep Roca
- Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain.,Department of Pulmonary Medicine, Hospital Clínic, IDIBAPS, CIBERES, Universitat de Barcelona, Barcelona 08036, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS, Diagonal 645, Barcelona 08028, Spain.,Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain
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Saqi M, Pellet J, Roznovat I, Mazein A, Ballereau S, De Meulder B, Auffray C. Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness. Methods Mol Biol 2016; 1386:43-60. [PMID: 26677178 DOI: 10.1007/978-1-4939-3283-2_3] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Recent advances in genomics have led to the rapid and relatively inexpensive collection of patient molecular data including multiple types of omics data. The integration of these data with clinical measurements has the potential to impact on our understanding of the molecular basis of disease and on disease management. Systems medicine is an approach to understanding disease through an integration of large patient datasets. It offers the possibility for personalized strategies for healthcare through the development of a new taxonomy of disease. Advanced computing will be an important component in effectively implementing systems medicine. In this chapter we describe three computational challenges associated with systems medicine: disease subtype discovery using integrated datasets, obtaining a mechanistic understanding of disease, and the development of an informatics platform for the mining, analysis, and visualization of data emerging from translational medicine studies.
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Affiliation(s)
- Mansoor Saqi
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Johann Pellet
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Irina Roznovat
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Stéphane Ballereau
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Bertrand De Meulder
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France. .,Université Claude Bernard, 3e étage plot 2, 50 Avenue Tony Garnier, Lyon, Cedex 07, 69366, France.
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Roca J, Cano I, Gomez-Cabrero D, Tegnér J. From Systems Understanding to Personalized Medicine: Lessons and Recommendations Based on a Multidisciplinary and Translational Analysis of COPD. Methods Mol Biol 2016; 1386:283-303. [PMID: 26677188 DOI: 10.1007/978-1-4939-3283-2_13] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Systems medicine, using and adapting methods and approaches as developed within systems biology, promises to be essential in ongoing efforts of realizing and implementing personalized medicine in clinical practice and research. Here we review and critically assess these opportunities and challenges using our work on COPD as a case study. We find that there are significant unresolved biomedical challenges in how to unravel complex multifactorial components in disease initiation and progression producing different clinical phenotypes. Yet, while such a systems understanding of COPD is necessary, there are other auxiliary challenges that need to be addressed in concert with a systems analysis of COPD. These include information and communication technology (ICT)-related issues such as data harmonization, systematic handling of knowledge, computational modeling, and importantly their translation and support of clinical practice. For example, clinical decision-support systems need a seamless integration with new models and knowledge as systems analysis of COPD continues to develop. Our experience with clinical implementation of systems medicine targeting COPD highlights the need for a change of management including design of appropriate business models and adoption of ICT providing and supporting organizational interoperability among professional teams across healthcare tiers, working around the patient. In conclusion, in our hands the scope and efforts of systems medicine need to concurrently consider these aspects of clinical implementation, which inherently drives the selection of the most relevant and urgent issues and methods that need further development in a systems analysis of disease.
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Affiliation(s)
- Josep Roca
- IDIBAPS, Hospital Clínic, CIBERES, Universitat de Barcelona, Villarroel, 170, Barcelona, Catalunya, 08036, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands.
| | - Isaac Cano
- IDIBAPS, Hospital Clínic, CIBERES, Universitat de Barcelona, Villarroel, 170, Barcelona, Catalunya, 08036, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden. .,L8:05 Karolinska University Hospital, Stockholm, 17176, Sweden.
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Abstract
The cause of a complex disease cannot be pinpointed to a single origin; rather, a highly complex network of many factors that interact on different levels over time and space is disturbed. This complexity requires novel approaches to diagnosis, treatment, and prevention. To foster the necessary shift to a pro-active systems medicine, proof-of-concept studies are needed. Here, we highlight several systems approaches that have been shown to work within the field of respiratory medicine, and we propose the next steps for broader implementation.
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Huertas-Migueláñez M, Mora D, Cano I, Maier D, Gomez-Cabrero D, Lluch-Ariet M, Miralles F. Simulation environment and graphical visualization environment: a COPD use-case. J Transl Med 2014; 12 Suppl 2:S7. [PMID: 25471327 PMCID: PMC4255913 DOI: 10.1186/1479-5876-12-s2-s7] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND Today, many different tools are developed to execute and visualize physiological models that represent the human physiology. Most of these tools run models written in very specific programming languages which in turn simplify the communication among models. Nevertheless, not all of these tools are able to run models written in different programming languages. In addition, interoperability between such models remains an unresolved issue. RESULTS In this paper we present a simulation environment that allows, first, the execution of models developed in different programming languages and second the communication of parameters to interconnect these models. This simulation environment, developed within the Synergy-COPD project, aims at helping and supporting bio-researchers and medical students understand the internal mechanisms of the human body through the use of physiological models. This tool is composed of a graphical visualization environment, which is a web interface through which the user can interact with the models, and a simulation workflow management system composed of a control module and a data warehouse manager. The control module monitors the correct functioning of the whole system. The data warehouse manager is responsible for managing the stored information and supporting its flow among the different modules. CONCLUSION It has been proved that the simulation environment presented here allows the user to research and study the internal mechanisms of the human physiology by the use of models via a graphical visualization environment. A new tool for bio-researchers is ready for deployment in various use cases scenarios.
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Affiliation(s)
| | - Daniel Mora
- Barcelona Digital Technology Centre, 08018 Barcelona, Spain
| | - Isaac Cano
- Hospital Clinic, IDIBAPS, Universitat de Barcelona, 08036 Barcelona, Spain
| | - Dieter Maier
- Biomax Informatics, AG, D-82152 Planegg, Germany
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Karolinska Institutet, 171 77 Solna, Sweden
| | | | - Felip Miralles
- Barcelona Digital Technology Centre, 08018 Barcelona, Spain
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Gomez-Cabrero D, Menche J, Cano I, Abugessaisa I, Huertas-Migueláñez M, Tenyi A, Marin de Mas I, Kiani NA, Marabita F, Falciani F, Burrowes K, Maier D, Wagner P, Selivanov V, Cascante M, Roca J, Barabási AL, Tegnér J. Systems Medicine: from molecular features and models to the clinic in COPD. J Transl Med 2014; 12 Suppl 2:S4. [PMID: 25471042 PMCID: PMC4255907 DOI: 10.1186/1479-5876-12-s2-s4] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
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.
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Cano I, Tényi Á, Schueller C, Wolff M, Huertas Migueláñez MM, Gomez-Cabrero D, Antczak P, Roca J, Cascante M, Falciani F, Maier D. The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research. J Transl Med 2014; 12 Suppl 2:S6. [PMID: 25471253 PMCID: PMC4255911 DOI: 10.1186/1479-5876-12-s2-s6] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Background 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. Results 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. Conclusions 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.
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Miralles F, Gomez-Cabrero D, Lluch-Ariet M, Tegnér J, Cascante M, Roca J. Predictive medicine: outcomes, challenges and opportunities in the Synergy-COPD project. J Transl Med 2014; 12 Suppl 2:S12. [PMID: 25472742 PMCID: PMC4255885 DOI: 10.1186/1479-5876-12-s2-s12] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND 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. OBJECTIVES 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. METHOD 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. RESULTS (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. CONCLUSIONS 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.
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Cano I, Lluch-Ariet M, Gomez-Cabrero D, Maier D, Kalko S, Cascante M, Tegnér J, Miralles F, Herrera D, Roca J. Biomedical research in a Digital Health Framework. J Transl Med 2014; 12 Suppl 2:S10. [PMID: 25472554 PMCID: PMC4255881 DOI: 10.1186/1479-5876-12-s2-s10] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
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.
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Affiliation(s)
- Isaac Cano
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
| | - Magí Lluch-Ariet
- Department of eHealth, Barcelona Digital, Roc Boronat 117, 08017 Barcelona, Catalunya, Spain
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, Planegg, Germany
| | - Susana Kalko
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
| | - Marta Cascante
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
- Departament de Bioquimica i Biologia Molecular i IBUB, Facultat de Biologia, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Felip Miralles
- Department of eHealth, Barcelona Digital, Roc Boronat 117, 08017 Barcelona, Catalunya, Spain
| | - Diego Herrera
- Departament de Bioquimica i Biologia Molecular i IBUB, Facultat de Biologia, Universitat de Barcelona, 08028 Barcelona, Spain
- Almirall R&D, 08980 Sant Feliu de Llobregat, Barcelona, Spain
| | - Josep Roca
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
- Centro de Investigacíon Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
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Burrowes KS, Doel T, Brightling C. Computational modeling of the obstructive lung diseases asthma and COPD. J Transl Med 2014; 12 Suppl 2:S5. [PMID: 25471125 PMCID: PMC4255909 DOI: 10.1186/1479-5876-12-s2-s5] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
Asthma and chronic obstructive pulmonary disease (COPD) are characterized by airway obstruction and airflow limitation and pose a huge burden to society. These obstructive lung diseases impact the lung physiology across multiple biological scales. Environmental stimuli are introduced via inhalation at the organ scale, and consequently impact upon the tissue, cellular and sub-cellular scale by triggering signaling pathways. These changes are propagated upwards to the organ level again and vice versa. In order to understand the pathophysiology behind these diseases we need to integrate and understand changes occurring across these scales and this is the driving force for multiscale computational modeling. There is an urgent need for improved diagnosis and assessment of obstructive lung diseases. Standard clinical measures are based on global function tests which ignore the highly heterogeneous regional changes that are characteristic of obstructive lung disease pathophysiology. Advances in scanning technology such as hyperpolarized gas MRI has led to new regional measurements of ventilation, perfusion and gas diffusion in the lungs, while new image processing techniques allow these measures to be combined with information from structural imaging such as Computed Tomography (CT). However, it is not yet known how to derive clinical measures for obstructive diseases from this wealth of new data. Computational modeling offers a powerful approach for investigating this relationship between imaging measurements and disease severity, and understanding the effects of different disease subtypes, which is key to developing improved diagnostic methods. Gaining an understanding of a system as complex as the respiratory system is difficult if not impossible via experimental methods alone. Computational models offer a complementary method to unravel the structure-function relationships occurring within a multiscale, multiphysics system such as this. Here we review the current state-of-the-art in techniques developed for pulmonary image analysis, development of structural models of the respiratory system and predictions of function within these models. We discuss application of modeling techniques to obstructive lung diseases, namely asthma and emphysema and the use of models to predict response to therapy. Finally we introduce a large European project, AirPROM that is developing multiscale models to investigate structure-function relationships in asthma and COPD.
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Roca J, Vargas C, Cano I, Selivanov V, Barreiro E, Maier D, Falciani F, Wagner P, Cascante M, Garcia-Aymerich J, Kalko S, De Mas I, Tegnér J, Escarrabill J, Agustí A, Gomez-Cabrero D. Chronic Obstructive Pulmonary Disease heterogeneity: challenges for health risk assessment, stratification and management. J Transl Med 2014; 12 Suppl 2:S3. [PMID: 25472887 PMCID: PMC4255905 DOI: 10.1186/1479-5876-12-s2-s3] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
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. Results (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. Conclusions 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.
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