1
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Veyssiere M, Sadat Aghamiri S, Hernandez Cervantes A, Henry T, Soumelis V. A mathematical model of Familial Mediterranean Fever predicts mechanisms controlling inflammation. Clin Immunol 2023; 257:109839. [PMID: 37952562 DOI: 10.1016/j.clim.2023.109839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/14/2023]
Abstract
BACKGROUND Familial Mediterranean Fever (FMF) is a monogenic disease caused by gain-of-function mutations in the MEditerranean FeVer (MEFV) gene. The molecular dysregulations induced by these mutations and the associated causal mechanisms are complex and intricate. OBJECTIVE We sought to provide a computational model capturing the mechanistic details of biological pathways involved in FMF physiopathology and enabling the study of the patient's immune cell dynamics. METHODS We carried out a literature survey to identify experimental studies published from January 2000 to December 2020, and integrated its results into a molecular map and a mathematical model. Then, we studied the network of molecular interactions and the dynamic of monocytes to identify key players for inflammation phenotype in FMF patients. RESULTS We built a molecular map of FMF integrating in a structured manner the current knowledge regarding pathophysiological processes participating in the triggering and perpetuation of the disease flares. The mathematical model derived from the map reproduced patient's monocyte behavior, in particular its proinflammatory role via the Pyrin inflammasome activation. Network analysis and in silico experiments identified NF-κB and JAK1/TYK2 as critical to modulate IL-1β- and IL-18-mediated inflammation. CONCLUSION The in silico model of FMF monocyte proved its ability to reproduce in vitro observations. Considering the difficulties related to experimental settings and financial investments to test combinations of stimuli/perturbation in vitro, this model could be used to test complex hypotheses in silico, thus narrowing down the number of in vitro and ex vivo experiments to perform.
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Affiliation(s)
| | - Sara Sadat Aghamiri
- Université Paris Cité, INSERM U976, Paris, France; University of Nebraska-Lincoln, Lincoln, NE, United States
| | | | - Thomas Henry
- CIRI, Centre International de Recherche en Infectiologie, Inserm U1111, Université Claude Bernard Lyon 1, CNRS, UMR5308, ENS de Lyon, Univ Lyon, Lyon F-69007, France
| | - Vassili Soumelis
- Université Paris Cité, INSERM U976, Paris, France; Owkin, 14 boulevard Poissonniere, Paris 75009, France.
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2
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Ramadan Q, Hazaymeh R, Zourob M. Immunity-on-a-Chip: Integration of Immune Components into the Scheme of Organ-on-a-Chip Systems. Adv Biol (Weinh) 2023; 7:e2200312. [PMID: 36866511 DOI: 10.1002/adbi.202200312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 01/16/2023] [Indexed: 03/04/2023]
Abstract
Studying the immune system in vitro aims to understand how, when, and where the immune cells migrate/differentiate and respond to the various triggering events and the decision points along the immune response journey. It becomes evident that organ-on-a-chip (OOC) technology has a superior capability to recapitulate the cell-cell and tissue-tissue interaction in the body, with a great potential to provide tools for tracking the paracrine signaling with high spatial-temporal precision and implementing in situ real-time, non-destructive detection assays, therefore, enabling extraction of mechanistic information rather than phenotypic information. However, despite the rapid development in this technology, integration of the immune system into OOC devices stays among the least navigated tasks, with immune cells still the major missing components in the developed models. This is mainly due to the complexity of the immune system and the reductionist methodology of the OOC modules. Dedicated research in this field is demanded to establish the understanding of mechanism-based disease endotypes rather than phenotypes. Herein, we systemically present a synthesis of the state-of-the-art of immune-cantered OOC technology. We comprehensively outlined what is achieved and identified the technology gaps emphasizing the missing components required to establish immune-competent OOCs and bridge these gaps.
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Affiliation(s)
- Qasem Ramadan
- Alfaisal University, Riyadh, 11533, Kingdom of Saudi Arabia
| | - Rana Hazaymeh
- Almaarefa University, Diriyah, 13713, Kingdom of Saudi Arabia
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3
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Viswan NA, Bhalla US. Understanding molecular signaling cascades in neural disease using multi-resolution models. Curr Opin Neurobiol 2023; 83:102808. [PMID: 37972535 DOI: 10.1016/j.conb.2023.102808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 10/10/2023] [Accepted: 10/19/2023] [Indexed: 11/19/2023]
Abstract
If the genome defines the program for the operations of a cell, signaling networks execute it. These cascades of chemical, cell-biological, structural, and trafficking events span milliseconds (e.g., synaptic release) to potentially a lifetime (e.g., stabilization of dendritic spines). In principle almost every aspect of neuronal function, particularly at the synapse, depends on signaling. Thus dysfunction of these cascades, whether through mutations, local dysregulation, or infection, leads to disease. The sheer complexity of these pathways is matched by the range of diseases and the diversity of their phenotypes. In this review, we discuss how to build computational models, how these models are essential to tackle this complexity, and the benefits of using families of models at different levels of detail to understand signaling in health and disease.
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Affiliation(s)
- Nisha Ann Viswan
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bengaluru, 560065, India; The University of Trans-Disciplinary Health Sciences and Technology, Bangalore, India. https://twitter.com/nishanna
| | - Upinder Singh Bhalla
- National Centre for Biological Sciences, Tata Institute of Fundamental Research, Bellary Road, Bengaluru, 560065, India.
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4
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Scheel J, Hoch M, Wolfien M, Gupta S. NaviCenta - The disease map for placental research. Placenta 2023; 143:12-15. [PMID: 37793322 DOI: 10.1016/j.placenta.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 09/14/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023]
Abstract
The placenta remains the key organ to pregnancy complications, such as preeclampsia, contrarily the pathophysiology underlying the placental dysfunctions remains elusive. Here, we present our Disease Map "NaviCenta", which is an online resource based on the interactions between tissues, cellular compartments, and molecules that mediate disease-related processes in the placenta. We built cellular and molecular interaction networks based upon manual curation and annotation of publicly available information in the scientific literature, pathways resources, and Omics data. NaviCenta (Navigate the plaCenta) serves as an open access, spatio-temporal, multi-scale knowledge base, and analytical tool for enhanced interpretation and hypothesis testing on various placental disease phenotypes.
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Affiliation(s)
- Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany.
| | - Matti Hoch
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
| | - Markus Wolfien
- Institute for Medical Informatics and Biometry, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, Dresden, Germany; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
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5
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Acencio ML, Ostaszewski M, Mazein A, Rosenstiel P, Aden K, Mishra N, Andersen V, Sidiropoulos P, Banos A, Filia A, Rahmouni S, Finckh A, Gu W, Schneider R, Satagopam V. The SYSCID map: a graphical and computational resource of molecular mechanisms across rheumatoid arthritis, systemic lupus erythematosus and inflammatory bowel disease. Front Immunol 2023; 14:1257321. [PMID: 38022524 PMCID: PMC10646502 DOI: 10.3389/fimmu.2023.1257321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Accepted: 10/16/2023] [Indexed: 12/01/2023] Open
Abstract
Chronic inflammatory diseases (CIDs), including inflammatory bowel disease (IBD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are thought to emerge from an impaired complex network of inter- and intracellular biochemical interactions among several proteins and small chemical compounds under strong influence of genetic and environmental factors. CIDs are characterised by shared and disease-specific processes, which is reflected by partially overlapping genetic risk maps and pathogenic cells (e.g., T cells). Their pathogenesis involves a plethora of intracellular pathways. The translation of the research findings on CIDs molecular mechanisms into effective treatments is challenging and may explain the low remission rates despite modern targeted therapies. Modelling CID-related causal interactions as networks allows us to tackle the complexity at a systems level and improve our understanding of the interplay of key pathways. Here we report the construction, description, and initial applications of the SYSCID map (https://syscid.elixir-luxembourg.org/), a mechanistic causal interaction network covering the molecular crosstalk between IBD, RA and SLE. We demonstrate that the map serves as an interactive, graphical review of IBD, RA and SLE molecular mechanisms, and helps to understand the complexity of omics data. Examples of such application are illustrated using transcriptome data from time-series gene expression profiles following anti-TNF treatment and data from genome-wide associations studies that enable us to suggest potential effects to altered pathways and propose possible mechanistic biomarkers of treatment response.
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Affiliation(s)
- Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Philip Rosenstiel
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Konrad Aden
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Neha Mishra
- Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Vibeke Andersen
- Diagnostics and Clinical Research Unit, Institute of Regional Health Research, University Hospital of Southern Denmark, Aabenraa, Denmark
- Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark
| | - Prodromos Sidiropoulos
- Rheumatology and Clinical Immunology, Medical School, University of Crete, Heraklion, Greece
- Laboratory of Rheumatology, Autoimmunity and Inflammation, Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology (IMBB-FORTH), Heraklion, Greece
| | - Aggelos Banos
- Autoimmunity and Inflammation Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens and Laboratory of Molecular Hematology, Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupolis, Greece
| | - Anastasia Filia
- Autoimmunity and Inflammation Laboratory, Biomedical Research Foundation of the Academy of Athens, Athens and Laboratory of Molecular Hematology, Democritus University of Thrace, University Hospital of Alexandroupolis, Alexandroupolis, Greece
| | - Souad Rahmouni
- Unit of Animal Genomics, GIGA-Institute, University of Liège, Liège, Belgium
| | - Axel Finckh
- Rheumatology Division, Geneva University Hospital (HUG), Geneva, Switzerland
- Geneva Center for Inflammation Research (GCIR), University of Geneva (UNIGE), Geneva, Switzerland
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Esch-sur-Alzette, Luxembourg
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6
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Singh V, Naldi A, Soliman S, Niarakis A. A large-scale Boolean model of the rheumatoid arthritis fibroblast-like synoviocytes predicts drug synergies in the arthritic joint. NPJ Syst Biol Appl 2023; 9:33. [PMID: 37454172 DOI: 10.1038/s41540-023-00294-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023] Open
Abstract
Rheumatoid arthritis (RA) is a complex autoimmune disease with an unknown aetiology. However, rheumatoid arthritis fibroblast-like synoviocytes (RA-FLS) play a significant role in initiating and perpetuating destructive joint inflammation by expressing immuno-modulating cytokines, adhesion molecules, and matrix remodelling enzymes. In addition, RA-FLS are primary drivers of inflammation, displaying high proliferative rates and an apoptosis-resistant phenotype. Thus, RA-FLS-directed therapies could become a complementary approach to immune-directed therapies by predicting the optimal conditions that would favour RA-FLS apoptosis, limit inflammation, slow the proliferation rate and minimise bone erosion and cartilage destruction. In this paper, we present a large-scale Boolean model for RA-FLS that consists of five submodels focusing on apoptosis, cell proliferation, matrix degradation, bone erosion and inflammation. The five-phenotype-specific submodels can be simulated independently or as a global model. In silico simulations and perturbations reproduced the expected biological behaviour of the system under defined initial conditions and input values. The model was then used to mimic the effect of mono or combined therapeutic treatments and predict novel targets and drug candidates through drug repurposing analysis.
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Affiliation(s)
- Vidisha Singh
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, Univ Evry, Evry, France
| | - Aurelien Naldi
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - Sylvain Soliman
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, Univ Evry, Evry, France.
- Lifeware Group, Inria, Saclay-île de France, 91120, Palaiseau, France.
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7
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Gawron P, Hoksza D, Piñero J, Peña-Chilet M, Esteban-Medina M, Fernandez-Rueda JL, Colonna V, Smula E, Heirendt L, Ancien F, Groues V, Satagopam VP, Schneider R, Dopazo J, Furlong LI, Ostaszewski M. Visualization of automatically combined disease maps and pathway diagrams for rare diseases. FRONTIERS IN BIOINFORMATICS 2023; 3:1101505. [PMID: 37502697 PMCID: PMC10369067 DOI: 10.3389/fbinf.2023.1101505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 05/05/2023] [Indexed: 07/29/2023] Open
Abstract
Introduction: Investigation of molecular mechanisms of human disorders, especially rare diseases, require exploration of various knowledge repositories for building precise hypotheses and complex data interpretation. Recently, increasingly more resources offer diagrammatic representation of such mechanisms, including disease-dedicated schematics in pathway databases and disease maps. However, collection of knowledge across them is challenging, especially for research projects with limited manpower. Methods: In this article we present an automated workflow for construction of maps of molecular mechanisms for rare diseases. The workflow requires a standardized definition of a disease using Orphanet or HPO identifiers to collect relevant genes and variants, and to assemble a functional, visual repository of related mechanisms, including data overlays. The diagrams composing the final map are unified to a common systems biology format from CellDesigner SBML, GPML and SBML+layout+render. The constructed resource contains disease-relevant genes and variants as data overlays for immediate visual exploration, including embedded genetic variant browser and protein structure viewer. Results: We demonstrate the functionality of our workflow on two examples of rare diseases: Kawasaki disease and retinitis pigmentosa. Two maps are constructed based on their corresponding identifiers. Moreover, for the retinitis pigmentosa use-case, we include a list of differentially expressed genes to demonstrate how to tailor the workflow using omics datasets. Discussion: In summary, our work allows for an ad-hoc construction of molecular diagrams combined from different sources, preserving their layout and graphical style, but integrating them into a single resource. This allows to reduce time consuming tasks of prototyping of a molecular disease map, enabling visual exploration, hypothesis building, data visualization and further refinement. The code of the workflow is open and accessible at https://gitlab.lcsb.uni.lu/minerva/automap/.
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Affiliation(s)
- Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - David Hoksza
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
- Faculty of Mathematics and Physics, Charles University, Prague, Czechia
| | - Janet Piñero
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Maria Peña-Chilet
- Computational Medicine Platform, Fundacion Progreso y Salud, Sevilla, Spain
- Spanish Network of Research in Rare Diseases (CIBERER), Sevilla, Spain
| | | | | | - Vincenza Colonna
- Institute of Genetics and Biophysics, National Research Council of Italy, Naples, Rome
- Department of Genetics, Genomics and Informatics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, United States
| | - Ewa Smula
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - François Ancien
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Valentin Groues
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Venkata P. Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
| | - Joaquin Dopazo
- Computational Medicine Platform, Fundacion Progreso y Salud, Sevilla, Spain
- Spanish Network of Research in Rare Diseases (CIBERER), Sevilla, Spain
| | - Laura I. Furlong
- Research Programme on Biomedical Informatics (GRIB), Hospital del Mar Medical Research Institute (IMIM), Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
- MedBioinformatics Solutions SL, Barcelona, Spain
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Luxembourg, Luxembourg
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8
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Mazein A, Acencio ML, Balaur I, Rougny A, Welter D, Niarakis A, Ramirez Ardila D, Dogrusoz U, Gawron P, Satagopam V, Gu W, Kremer A, Schneider R, Ostaszewski M. A guide for developing comprehensive systems biology maps of disease mechanisms: planning, construction and maintenance. FRONTIERS IN BIOINFORMATICS 2023; 3:1197310. [PMID: 37426048 PMCID: PMC10325725 DOI: 10.3389/fbinf.2023.1197310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 06/09/2023] [Indexed: 07/11/2023] Open
Abstract
As a conceptual model of disease mechanisms, a disease map integrates available knowledge and is applied for data interpretation, predictions and hypothesis generation. It is possible to model disease mechanisms on different levels of granularity and adjust the approach to the goals of a particular project. This rich environment together with requirements for high-quality network reconstruction makes it challenging for new curators and groups to be quickly introduced to the development methods. In this review, we offer a step-by-step guide for developing a disease map within its mainstream pipeline that involves using the CellDesigner tool for creating and editing diagrams and the MINERVA Platform for online visualisation and exploration. We also describe how the Neo4j graph database environment can be used for managing and querying efficiently such a resource. For assessing the interoperability and reproducibility we apply FAIR principles.
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Affiliation(s)
- Alexander Mazein
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | | | - Danielle Welter
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche Pour la Polyarthrite Rhumatoïde–Genhotel, University Evry, Evry, France
- Lifeware Group, Inria Saclay-Ile de France, Palaiseau, France
| | - Diana Ramirez Ardila
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara, Türkiye
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Andreas Kremer
- ITTM Information Technology for Translational Medicine, Esch-sur-Alzette, Luxemburg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- ELIXIR Luxembourg, Belvaux, Luxembourg
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Hemedan AA, Schneider R, Ostaszewski M. Applications of Boolean modeling to study the dynamics of a complex disease and therapeutics responses. FRONTIERS IN BIOINFORMATICS 2023; 3:1189723. [PMID: 37325771 PMCID: PMC10267406 DOI: 10.3389/fbinf.2023.1189723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Accepted: 05/18/2023] [Indexed: 06/17/2023] Open
Abstract
Computational modeling has emerged as a critical tool in investigating the complex molecular processes involved in biological systems and diseases. In this study, we apply Boolean modeling to uncover the molecular mechanisms underlying Parkinson's disease (PD), one of the most prevalent neurodegenerative disorders. Our approach is based on the PD-map, a comprehensive molecular interaction diagram that captures the key mechanisms involved in the initiation and progression of PD. Using Boolean modeling, we aim to gain a deeper understanding of the disease dynamics, identify potential drug targets, and simulate the response to treatments. Our analysis demonstrates the effectiveness of this approach in uncovering the intricacies of PD. Our results confirm existing knowledge about the disease and provide valuable insights into the underlying mechanisms, ultimately suggesting potential targets for therapeutic intervention. Moreover, our approach allows us to parametrize the models based on omics data for further disease stratification. Our study highlights the value of computational modeling in advancing our understanding of complex biological systems and diseases, emphasizing the importance of continued research in this field. Furthermore, our findings have potential implications for the development of novel therapies for PD, which is a pressing public health concern. Overall, this study represents a significant step forward in the application of computational modeling to the investigation of neurodegenerative diseases, and underscores the power of interdisciplinary approaches in tackling challenging biomedical problems.
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10
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Hoch M, Smita S, Cesnulevicius K, Schultz M, Lescheid D, Wolkenhauer O, Gupta S. Network analyses reveal new insights into the effect of multicomponent Tr14 compared to single-component diclofenac in an acute inflammation model. J Inflamm (Lond) 2023; 20:12. [PMID: 36973809 PMCID: PMC10044762 DOI: 10.1186/s12950-023-00335-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 02/21/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND Modifying the acute inflammatory response has wide clinical benefits. Current options include non-steroidal anti-inflammatory drugs (NSAIDs) and therapies that may resolve inflammation. Acute inflammation involves multiple cell types and various processes. We, therefore, investigated whether an immunomodulatory drug that acts simultaneously at multiple sites shows greater potential to resolve acute inflammation more effectively and with fewer side effects than a common anti-inflammatory drug developed as a small molecule for a single target. In this work, we used time-series gene expression profiles from a wound healing mouse model to compare the effects of Traumeel (Tr14), a multicomponent natural product, to diclofenac, a single component NSAID on inflammation resolution. RESULTS We advance previous studies by mapping the data onto the "Atlas of Inflammation Resolution", followed by in silico simulations and network analysis. We found that Tr14 acts primarily on the late phase of acute inflammation (during resolution) compared to diclofenac, which suppresses acute inflammation immediately after injury. CONCLUSIONS Our results provide new insights how network pharmacology of multicomponent drugs may support inflammation resolution in inflammatory conditions.
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Affiliation(s)
- Matti Hoch
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
| | - Suchi Smita
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
| | | | | | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany
- Leibniz-Institute for Food Systems Biology at the Technical University of Munich, Freising, 85354, Germany
- Stellenbosch Institute of Advanced Study, Wallenberg Research Centre, Stellenbosch University, Stellenbosch, 7602, South Africa
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18055, Germany.
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11
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Gawron P, Smula E, Schneider R, Ostaszewski M. Exploration and comparison of molecular mechanisms across diseases using MINERVA Net. Protein Sci 2023; 32:e4565. [PMID: 36648161 PMCID: PMC9885449 DOI: 10.1002/pro.4565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/16/2022] [Accepted: 01/11/2023] [Indexed: 01/18/2023]
Abstract
Protein function is often interpreted using molecular interaction diagrams, encoding roles a given protein plays in various molecular mechanisms. Information about disease-related mechanisms can be inferred from disease maps, knowledge repositories containing manually constructed systems biology diagrams. Disease maps hosted on the Molecular Interaction Network VisuAlization (MINERVA) Platform are individually accessible through a REST API interface of each instance, making it challenging to systematically explore their contents. To address this challenge, we introduce the MINERVA Net web service, a repository of open-access disease maps allowing users to publicly share minimal information about their maps. The MINERVA Net repository provides REST API endpoints of particular disease maps, which then can be individually queried for content. In this article, we describe the concept of MINERVA Net and illustrate its use by comparing proteins and their interactions in three different disease maps.
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Affiliation(s)
- Piotr Gawron
- LCSB, Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Ewa Smula
- LCSB, Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Reinhard Schneider
- LCSB, Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Marek Ostaszewski
- LCSB, Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
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Manna S, Scheel J, Noone A, McElwain CJ, Scaife C, Gupta S, English J, McCarthy C, McCarthy FP. A proteomic profile of the healthy human placenta. Clin Proteomics 2023; 20:1. [PMID: 36593452 PMCID: PMC9808999 DOI: 10.1186/s12014-022-09388-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 12/09/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND The placenta remains one of the least studied organs within the human body. Yet, placental dysfunction has been associated with various pregnancy complications leading to both maternal and fetal death and long-term health consequences. The aim of this study was to characterise the protein networks of healthy term placental sub-anatomical regions using label free quantification mass spectrometry. METHODS Three healthy placentae were sampled at five sample sites and each biopsy was dissected into maternal-, middle-, and fetal- sub-anatomical regions. Quadrupole-orbitrap mass spectrometer was used in data dependant analysis mode to identify 1859 unique proteins before detailed differential expression between regions. RESULTS Protein profiling identified 1081, 1086, and 1101 proteins in maternal, middle, and fetal sub-anatomical regions respectively. Differentially expressed proteins were identified considering the effect between sample site location and sub-anatomical region on protein expression. Of these, 374 differentially expressed proteins (Two-way ANOVA adjusted p-value < 0.05, HSD Tukey adjusted p-value 0.05) were identified between sample site locations and sub-anatomical regions. The placenta specific disease map NaviCenta ( https://www.sbi.uni-rostock.de/minerva/index.xhtml?id=NaviCenta ) was used to focus functional analysis results to the placenta specific context. Subsequently, functional analysis with a focus on senescence, and mitochondrial function were performed. Significant differences were observed between sub-anatomical regions in protein intensity and composition. A decrease in anti-senescent proteins within the maternal sub-anatomical region, and an increase in proteins associated with a switch from ATP to fatty acid consumption as a source of energy between middle and fetal sub-anatomical regions were observed. CONCLUSION These results suggest that normal proteomic variations exist within the anatomical structure of the placenta, thus recommending serial sectioning methodology for consistent placental research.
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Affiliation(s)
- Samprikta Manna
- grid.411916.a0000 0004 0617 6269Department of Obstetrics and Gynaecology, Cork University Maternity Hospital, University College Cork, Cork, Ireland
| | - Julia Scheel
- grid.10493.3f0000000121858338Dept. of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany
| | - Aisling Noone
- grid.7872.a0000000123318773Department of Anatomy and Neuroscience, Western Gateway Building, University College Cork, Cork, Ireland
| | - Colm J. McElwain
- grid.7872.a0000000123318773Department of Pharmacology and Therapeutics, Western Gateway Building, University College Cork, Cork, Ireland
| | - Caitriona Scaife
- grid.7886.10000 0001 0768 2743UCD Conway Institute for Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland
| | - Shailendra Gupta
- grid.10493.3f0000000121858338Dept. of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany
| | - Jane English
- grid.512512.0INFANT Research Centre, Cork, Ireland ,grid.7872.a0000000123318773Department of Anatomy and Neuroscience, Western Gateway Building, University College Cork, Cork, Ireland
| | - Cathal McCarthy
- grid.7872.a0000000123318773Department of Pharmacology and Therapeutics, Western Gateway Building, University College Cork, Cork, Ireland
| | - Fergus P. McCarthy
- grid.411916.a0000 0004 0617 6269Department of Obstetrics and Gynaecology, Cork University Maternity Hospital, University College Cork, Cork, Ireland ,grid.512512.0INFANT Research Centre, Cork, Ireland
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Kumari P, Mamud A, Jha AN. Review on the Drug Intolerance and Vaccine Development for the Leishmaniasis. Curr Drug Targets 2023; 24:1023-1031. [PMID: 37823567 DOI: 10.2174/0113894501254585230927100440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/07/2023] [Accepted: 09/01/2023] [Indexed: 10/13/2023]
Abstract
Leishmaniasis is one of the Neglected Tropical Diseases (NTDs), a zoonotic disease of vector-borne nature that is caused by a protozoan parasite Leishmania. This parasite is transmitted by the vector sandfly into the human via a bite. Visceral leishmaniasis (VL), also called kala-azar, is the most fatal among the types of leishmaniasis, with high mortality mostly spread in the East Africa and South Asia regions. WHO report stated that approximately 3.3 million disabilities occur every year due to the disease along with approximately 50,000 annual deaths. The real matter of concern is that there is no particular effective medicine/vaccine available against leishmaniasis to date except a few approved drugs and chemotherapy for the infected patient. The current selection of small compounds was constrained, and their growing drug resistance had been a major worry. Additionally, the serious side effects on humans of the available therapy or drugs have made it essential to discover efficient and low-cost methods to speed up the development of new drugs against leishmaniasis. Ideally, the vaccine could be a low risk and effective alternative for both CL and VL and elicit long-lasting immunity against the disease. There are a number of vaccine candidates at various stages of clinical development and preclinical stage. However, none has successfully passed all clinical trials. But, the successful development and approval of commercially available vaccines for dogs against canine leishmaniasis (CanL) provides evidence that it can be possible for humans in distant future. In the present article, the approaches used for the development of vaccines for leishmaniasis are discussed and the progress being made is briefly reviewed.
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Affiliation(s)
- Priya Kumari
- Centre of Sustainable Polymers, Indian Institute of Technology, Guwahati, Assam, India
| | - Afrin Mamud
- Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam, India
| | - Anupam Nath Jha
- Department of Molecular Biology and Biotechnology, Tezpur University, Tezpur, Assam, India
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/24/2022] [Indexed: 11/09/2022] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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Martins dos Santos V, Anton M, Szomolay B, Ostaszewski M, Arts I, Benfeitas R, Dominguez Del Angel V, Domínguez-Romero E, Ferk P, Fey D, Goble C, Golebiewski M, Gruden K, Heil KF, Hermjakob H, Kahlem P, Klapa MI, Koehorst J, Kolodkin A, Kutmon M, Leskošek B, Moretti S, Müller W, Pagni M, Rezen T, Rocha M, Rozman D, Šafránek D, T. Scott W, Sheriff RSM, Suarez Diez M, Van Steen K, Westerhoff HV, Wittig U, Wolstencroft K, Zupanic A, Evelo CT, Hancock JM. Systems Biology in ELIXIR: modelling in the spotlight. F1000Res 2022; 11:ELIXIR-1265. [PMID: 36742342 PMCID: PMC9871403 DOI: 10.12688/f1000research.126734.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 05/20/2024] [Indexed: 06/05/2024] Open
Abstract
In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR's future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.
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Affiliation(s)
- Vitor Martins dos Santos
- Laboratory of Bioprocess Engineering, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Gothenburg, SE-41258, Sweden
| | - Barbara Szomolay
- Division of Infection and Immunity, School of Medicine, Cardiff University, Cardiff, UK
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Belvaux, L-4367, Luxembourg
| | - Ilja Arts
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Rui Benfeitas
- National Bioinformatics Infrastructure Sweden (NBIS), Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | | | | | - Polonca Ferk
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Dirk Fey
- Systems Biology Ireland, School of Medicine, University College Dublin, Dublin, 4, Ireland
| | - Carole Goble
- Department of Computer Science, The University of Manchester, Manchester, M13 9PL, UK
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Kristina Gruden
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | | | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Pascal Kahlem
- Scientific Network Management SL, Barcelona, 08015, Spain
| | - Maria I. Klapa
- Metabolic Engineering & Systems Biology Laboratory, Institute of Chemical Engineering Sciences, Foundation for Research & Technology - Hellas (FORTH/ICE-HT), Patras, 26504, Greece
| | - Jasper Koehorst
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Alexey Kolodkin
- Competence Center for Methodology and Statistics; Transversal Translational Medicine, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, L-1445, Luxembourg
- ISBE.NL, VU University of Amsterdam, Amsterdam, The Netherlands
| | - Martina Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, 6200 MD, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - Brane Leskošek
- Faculty of Medicine, Institute for Biostatistics and Medical Informatics, Centre ELIXIR-SI, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | | | - Wolfgang Müller
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Marco Pagni
- SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Tadeja Rezen
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho, Braga, Portugal
| | - Damjana Rozman
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
| | - David Šafránek
- Faculty of Informatics, Masaryk University, Brno, 602 00, Czech Republic
| | - William T. Scott
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
- UNLOCK, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Rahuman S. Malik Sheriff
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, CB10 1SD, UK
| | - Maria Suarez Diez
- Laboratory of Systems and Synthetic Biology, Wageningen University & Research, Wageningen, 6708WE, The Netherlands
| | - Kristel Van Steen
- BIO3 - Laboratory for Systems Medicine, Department of Human Genetics, KU Leuven, Leuven, 3000, Belgium
- BIO3 - Systems Genetics, GIGA-R Medical Genomics, University of Liege, Liege, 4000, Belgium
| | | | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies - HITS, Heidelberg, 69118, Germany
| | - Katherine Wolstencroft
- Leiden Institute of Advanced Computer Science, Leiden University, Leiden, 2333 CA, The Netherlands
| | - Anze Zupanic
- Department of Biotechnology and Systems Biology, National Institute of Biology, Ljubljana, SI-1000, Slovenia
| | - Chris T. Evelo
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, 6200 MD, The Netherlands
| | - John M. Hancock
- Faculty of Medicine, University of Ljubljana, Ljubljana, SI-1000, Slovenia
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Hoch M, Ehlers L, Bannert K, Stanke C, Brauer D, Caton V, Lamprecht G, Wolkenhauer O, Jaster R, Wolfien M. In silico investigation of molecular networks linking gastrointestinal diseases, malnutrition, and sarcopenia. Front Nutr 2022; 9:989453. [DOI: 10.3389/fnut.2022.989453] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 09/28/2022] [Indexed: 11/06/2022] Open
Abstract
Malnutrition (MN) is a common primary or secondary complication in gastrointestinal diseases. The patient’s nutritional status also influences muscle mass and function, which can be impaired up to the degree of sarcopenia. The molecular interactions in diseases leading to sarcopenia are complex and multifaceted, affecting muscle physiology, the intestine (nutrition), and the liver at different levels. Although extensive knowledge of individual molecular factors is available, their regulatory interplay is not yet fully understood. A comprehensive overall picture of pathological mechanisms and resulting phenotypes is lacking. In silico approaches that convert existing knowledge into computationally readable formats can help unravel mechanisms, underlying such complex molecular processes. From public literature, we manually compiled experimental evidence for molecular interactions involved in the development of sarcopenia into a knowledge base, referred to as the Sarcopenia Map. We integrated two diseases, namely liver cirrhosis (LC), and intestinal dysfunction, by considering their effects on nutrition and blood secretome. We demonstrate the performance of our model by successfully simulating the impact of changing dietary frequency, glycogen storage capacity, and disease severity on the carbohydrate and muscle systems. We present the Sarcopenia Map as a publicly available, open-source, and interactive online resource, that links gastrointestinal diseases, MN, and sarcopenia. The map provides tools that allow users to explore the information on the map and perform in silico simulations.
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Voskamp M, Vinhoven L, Stanke F, Hafkemeyer S, Nietert MM. Integrating Text Mining into the Curation of Disease Maps. Biomolecules 2022; 12:biom12091278. [PMID: 36139119 PMCID: PMC9496510 DOI: 10.3390/biom12091278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 09/02/2022] [Accepted: 09/07/2022] [Indexed: 11/16/2022] Open
Abstract
An adequate visualization form is required to gain an overview and ultimately understand the complex and diverse biological mechanisms of diseases. Recently, disease maps have been introduced for this purpose. A disease map is defined as a systems biological map or model that combines metabolic, signaling, and physiological pathways to create a comprehensive overview of known disease mechanisms. With the increase in publications describing biological interactions, efforts in creating and curating comprehensive disease maps is growing accordingly. Therefore, new computational approaches are needed to reduce the time that manual curation takes. Test mining algorithms can be used to analyse the natural language of scientific publications. These types of algorithms can take humanly readable text passages and convert them into a more ordered, machine-usable data structure. To support the creation of disease maps by text mining, we developed an interactive, user-friendly disease map viewer. The disease map viewer displays text mining results in a systems biology map, where the user can review them and either validate or reject identified interactions. Ultimately, the viewer brings together the time-saving advantages of text mining with the accuracy of manual data curation.
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Affiliation(s)
- Malte Voskamp
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany
| | - Liza Vinhoven
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany
| | - Frauke Stanke
- Clinic for Pediatric Pneumology, Allergology and Neonatology, Hannover Medical School, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany
- Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), the German Center for Lung Research, Carl-Neuberg-Strasse 1, 30625 Hannover, Germany
| | | | - Manuel Manfred Nietert
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany
- CIDAS Campus Institute Data Science, Goldschmidtstraße 1, 37077 Göttingen, Germany
- Correspondence: ; Tel.: +49-551-39-14920
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Ladeira L, Gamba A, Lesage R, Ertvelde JV, Jiang J, Verhoeven A, Roodzant D, Teunis M, Jover R, Vanhaecke T, Vinken M, Geris L, Staumont B. P11-09 Physiology-based framework to study chemical-induced cholestasis. Toxicol Lett 2022. [DOI: 10.1016/j.toxlet.2022.07.459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Staumont B, Ladeira L, Gamba A, Lesage R, Verhoeven A, Jiang J, Ertvelde JV, Barnes D, Janssen M, Kuchovska E, Berkhout J, Roodzant D, Teunis M, Bozada T, Luechtefeld T, Jover R, Vanhaecke T, Vinken M, Masereeuw R, Fritsche E, Piersma A, Heusinkveld H, Geris L. SOC-VI-08 Physiological maps: a benchmark tool for adverse outcome pathways and a cornerstone for the development of disease ontologies. Toxicol Lett 2022. [DOI: 10.1016/j.toxlet.2022.07.228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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20
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Atehortúa A, Romero E, Garreau M. Characterization of motion patterns by a spatio-temporal saliency descriptor in cardiac cine MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 218:106714. [PMID: 35263659 DOI: 10.1016/j.cmpb.2022.106714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 02/03/2022] [Accepted: 02/23/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Abnormalities of the heart motion reveal the presence of a disease. However, a quantitative interpretation of the motion is still a challenge due to the complex dynamics of the heart. This work proposes a quantitative characterization of regional cardiac motion patterns in cine magnetic resonance imaging (MRI) by a novel spatio-temporal saliency descriptor. METHOD The strategy starts by dividing the cardiac sequence into a progression of scales which are in due turn mapped to a feature space of regional orientation changes, mimicking the multi-resolution decomposition of oriented primitive changes of visual systems. These changes are estimated as the difference between a particular time and the rest of the sequence. This decomposition is then temporarily and regionally integrated for a particular orientation and then for the set of different orientations. A final spatio-temporal 4D saliency map is obtained as the summation of the previously integrated information for the available scales. The saliency dispersion of this map was computed in standard cardiac locations as a measure of the regional motion pattern and was applied to discriminate control and hypertrophic cardiomyopathy (HCM) subjects during the diastolic phase. RESULTS Salient motion patterns were estimated from an experimental set, which consisted of 3D sequences acquired by MRI from 108 subjects (33 control, 35 HCM, 20 dilated cardiomyopathy (DCM), and 20 myocardial infarction (MINF) from heterogeneous datasets). HCM and control subjects were classified by an SVM that learned the salient motion patterns estimated from the presented strategy, by achieving a 94% AUC. In addition, statistical differences (test t-student, p<0.05) were found among groups of disease in the septal and anterior ventricular segments at both the ED and ES, with salient motion characteristics aligned with existing knowledge on the diseases. CONCLUSIONS Regional wall motion abnormality in the apical, anterior, basal, and inferior segments was associated with the saliency dispersion in HCM, DCM, and MINF compared to healthy controls during the systolic and diastolic phases. This saliency analysis may be used to detect subtle changes in heart function.
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Affiliation(s)
- Angélica Atehortúa
- Universidad Nacional de Colombia, Bogotá, Colombia; Univ Rennes, Inserm, LTSI UMR 1099, Rennes F-35000, France
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Network- and enrichment-based inference of phenotypes and targets from large-scale disease maps. NPJ Syst Biol Appl 2022; 8:13. [PMID: 35473910 PMCID: PMC9042890 DOI: 10.1038/s41540-022-00222-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 03/22/2022] [Indexed: 01/09/2023] Open
Abstract
Complex diseases are inherently multifaceted, and the associated data are often heterogeneous, making linking interactions across genes, metabolites, RNA, proteins, cellular functions, and clinically relevant phenotypes a high-priority challenge. Disease maps have emerged as knowledge bases that capture molecular interactions, disease-related processes, and disease phenotypes with standardized representations in large-scale molecular interaction maps. Various tools are available for disease map analysis, but an intuitive solution to perform in silico experiments on the maps in a wide range of contexts and analyze high-dimensional data is currently missing. To this end, we introduce a two-dimensional enrichment analysis (2DEA) approach to infer downstream and upstream elements through the statistical association of network topology parameters and fold changes from molecular perturbations. We implemented our approach in a plugin suite for the MINERVA platform, providing an environment where experimental data can be mapped onto a disease map and predict potential regulatory interactions through an intuitive graphical user interface. We show several workflows using this approach and analyze two RNA-seq datasets in the Atlas of Inflammation Resolution (AIR) to identify enriched downstream processes and upstream transcription factors. Our work improves the usability of disease maps and increases their functionality by facilitating multi-omics data integration and exploration.
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Manna S, Ruano CSM, Hegenbarth JC, Vaiman D, Gupta S, McCarthy FP, Méhats C, McCarthy C, Apicella C, Scheel J. Computational Models on Pathological Redox Signalling Driven by Pregnancy: A Review. Antioxidants (Basel) 2022; 11:antiox11030585. [PMID: 35326235 PMCID: PMC8945226 DOI: 10.3390/antiox11030585] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 02/04/2023] Open
Abstract
Oxidative stress is associated with a myriad of diseases including pregnancy pathologies with long-term cardiovascular repercussions for both the mother and baby. Aberrant redox signalling coupled with deficient antioxidant defence leads to chronic molecular impairment. Abnormal placentation has been considered the primary source for reactive species; however, placental dysfunction has been deemed secondary to maternal cardiovascular maladaptation in pregnancy. While various therapeutic interventions, aimed at combating deregulated oxidative stress during pregnancy have shown promise in experimental models, they often result as inconclusive or detrimental in clinical trials, warranting the need for further research to identify candidates. The strengths and limitations of current experimental methods in redox research are discussed. Assessment of redox status and oxidative stress in experimental models and in clinical practice remains challenging; the state-of-the-art of computational models in this field is presented in this review, comparing static and dynamic models which provide functional information such as protein-protein interactions, as well as the impact of changes in molecular species on the redox-status of the system, respectively. Enhanced knowledge of redox biology in during pregnancy through computational modelling such as generation of Systems Biology Markup Language model which integrates existing models to a larger network in the context of placenta physiology.
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Affiliation(s)
- Samprikta Manna
- Department of Obstetrics and Gynaecology, Cork University Maternity Hospital, University College Cork, T12 YE02 Cork, Ireland;
- Correspondence:
| | - Camino S. M. Ruano
- Institut Cochin, Inserm U1016, UMR8104 CNRS, Université de Paris, 75014 Paris, France; (C.S.M.R.); (D.V.); (C.M.); (C.A.)
| | - Jana-Charlotte Hegenbarth
- Department of Molecular Genetics, Faculty of Science and Engineering, Faculty of Health, Medicine and Life Sciences, Maastricht University, 6211 KH Maastricht, The Netherlands;
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Faculty of Health, Medicine and Life Sciences, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Daniel Vaiman
- Institut Cochin, Inserm U1016, UMR8104 CNRS, Université de Paris, 75014 Paris, France; (C.S.M.R.); (D.V.); (C.M.); (C.A.)
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, Rostock University, 18051 Rostock, Germany; (S.G.); (J.S.)
| | - Fergus P. McCarthy
- Department of Obstetrics and Gynaecology, Cork University Maternity Hospital, University College Cork, T12 YE02 Cork, Ireland;
| | - Céline Méhats
- Institut Cochin, Inserm U1016, UMR8104 CNRS, Université de Paris, 75014 Paris, France; (C.S.M.R.); (D.V.); (C.M.); (C.A.)
| | - Cathal McCarthy
- Department of Pharmacology and Therapeutics, Western Gateway Building, University College Cork, T12 K8AF Cork, Ireland;
| | - Clara Apicella
- Institut Cochin, Inserm U1016, UMR8104 CNRS, Université de Paris, 75014 Paris, France; (C.S.M.R.); (D.V.); (C.M.); (C.A.)
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, Rostock University, 18051 Rostock, Germany; (S.G.); (J.S.)
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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24
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Madhavan M, Mustafa S. Systems biology–the transformative approach to integrate sciences across disciplines. PHYSICAL SCIENCES REVIEWS 2022. [DOI: 10.1515/psr-2021-0102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
Life science is the study of living organisms, including bacteria, plants, and animals. Given the importance of biology, chemistry, and bioinformatics, we anticipate that this chapter may contribute to a better understanding of the interdisciplinary connections in life science. Research in applied biological sciences has changed the paradigm of basic and applied research. Biology is the study of life and living organisms, whereas science is a dynamic subject that as a result of constant research, new fields are constantly emerging. Some fields come and go, whereas others develop into new, well-recognized entities. Chemistry is the study of composition of matter and its properties, how the substances merge or separate and also how substances interact with energy. Advances in biology and chemistry provide another means to understand the biological system using many interdisciplinary approaches. Bioinformatics is a multidisciplinary or rather transdisciplinary field that encourages the use of computer tools and methodologies for qualitative and quantitative analysis. There are many instances where two fields, biology and chemistry have intersection. In this chapter, we explain how current knowledge in biology, chemistry, and bioinformatics, as well as its various interdisciplinary domains are merged into life sciences and its applications in biological research.
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Affiliation(s)
- Maya Madhavan
- Department of Biochemistry , Government College for Women , Thiruvananthapuram , Kerala , India
| | - Sabeena Mustafa
- Department of Biostatistics and Bioinformatics , King Abdullah International Medical Research Center (KAIMRC), King Saud Bin Abdulaziz University for Health Sciences, King Abdulaziz Medical City, Ministry of National Guard Health Affairs (MNGHA) , Riyadh , Kingdom of Saudi Arabia
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25
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Hemedan AA, Niarakis A, Schneider R, Ostaszewski M. Boolean modelling as a logic-based dynamic approach in systems medicine. Comput Struct Biotechnol J 2022; 20:3161-3172. [PMID: 35782730 PMCID: PMC9234349 DOI: 10.1016/j.csbj.2022.06.035] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Revised: 06/14/2022] [Accepted: 06/14/2022] [Indexed: 11/17/2022] Open
Abstract
Molecular mechanisms of health and disease are often represented as systems biology diagrams, and the coverage of such representation constantly increases. These static diagrams can be transformed into dynamic models, allowing for in silico simulations and predictions. Boolean modelling is an approach based on an abstract representation of the system. It emphasises the qualitative modelling of biological systems in which each biomolecule can take two possible values: zero for absent or inactive, one for present or active. Because of this approximation, Boolean modelling is applicable to large diagrams, allowing to capture their dynamic properties. We review Boolean models of disease mechanisms and compare a range of methods and tools used for analysis processes. We explain the methodology of Boolean analysis focusing on its application in disease modelling. Finally, we discuss its practical application in analysing signal transduction and gene regulatory pathways in health and disease.
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Affiliation(s)
- Ahmed Abdelmonem Hemedan
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Anna Niarakis
- Université Paris-Saclay, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde – Genhotel, Univ Evry, Evry, France
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Corresponding author at: Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, L-4367 Belvaux, Luxembourg.
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Montaldo C, Messina F, Abbate I, Antonioli M, Bordoni V, Aiello A, Ciccosanti F, Colavita F, Farroni C, Najafi Fard S, Giombini E, Goletti D, Matusali G, Rozera G, Rueca M, Sacchi A, Piacentini M, Agrati C, Fimia GM, Capobianchi MR, Lauria FN, Ippolito G. Multi-omics approach to COVID-19: a domain-based literature review. J Transl Med 2021; 19:501. [PMID: 34876157 PMCID: PMC8649311 DOI: 10.1186/s12967-021-03168-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 11/23/2021] [Indexed: 12/15/2022] Open
Abstract
Background Omics data, driven by rapid advances in laboratory techniques, have been generated very quickly during the COVID-19 pandemic. Our aim is to use omics data to highlight the involvement of specific pathways, as well as that of cell types and organs, in the pathophysiology of COVID-19, and to highlight their links with clinical phenotypes of SARS-CoV-2 infection. Methods The analysis was based on the domain model, where for domain it is intended a conceptual repository, useful to summarize multiple biological pathways involved at different levels. The relevant domains considered in the analysis were: virus, pathways and phenotypes. An interdisciplinary expert working group was defined for each domain, to carry out an independent literature scoping review. Results The analysis revealed that dysregulated pathways of innate immune responses, (i.e., complement activation, inflammatory responses, neutrophil activation and degranulation, platelet degranulation) can affect COVID-19 progression and outcomes. These results are consistent with several clinical studies. Conclusions Multi-omics approach may help to further investigate unknown aspects of the disease. However, the disease mechanisms are too complex to be explained by a single molecular signature and it is necessary to consider an integrated approach to identify hallmarks of severity. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-021-03168-8.
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Affiliation(s)
- Chiara Montaldo
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Francesco Messina
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Isabella Abbate
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Manuela Antonioli
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Veronica Bordoni
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Alessandra Aiello
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Fabiola Ciccosanti
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Francesca Colavita
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Chiara Farroni
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Saeid Najafi Fard
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Emanuela Giombini
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Delia Goletti
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Giulia Matusali
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Gabriella Rozera
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Martina Rueca
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Alessandra Sacchi
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Mauro Piacentini
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy.,Dept. Biology, University of Rome Tor Vergata, Via della Ricerca scientifica 1, Rome, Italy
| | - Chiara Agrati
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Gian Maria Fimia
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy.,Dept. Molecular Medicine, Sapienza University of Rome, 00185, Rome, Italy
| | - Maria Rosaria Capobianchi
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy.
| | - Francesco Nicola Lauria
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
| | - Giuseppe Ippolito
- National Institute for Infectious Diseases, "Lazzaro Spallanzani" - IRCCS, Via Portuense, 292, 00149, Rome, Italy
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Pereira C, Mazein A, Farinha CM, Gray MA, Kunzelmann K, Ostaszewski M, Balaur I, Amaral MD, Falcao AO. CyFi-MAP: an interactive pathway-based resource for cystic fibrosis. Sci Rep 2021; 11:22223. [PMID: 34782688 PMCID: PMC8592983 DOI: 10.1038/s41598-021-01618-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 10/27/2021] [Indexed: 12/11/2022] Open
Abstract
Cystic fibrosis (CF) is a life-threatening autosomal recessive disease caused by more than 2100 mutations in the CF transmembrane conductance regulator (CFTR) gene, generating variability in disease severity among individuals with CF sharing the same CFTR genotype. Systems biology can assist in the collection and visualization of CF data to extract additional biological significance and find novel therapeutic targets. Here, we present the CyFi-MAP-a disease map repository of CFTR molecular mechanisms and pathways involved in CF. Specifically, we represented the wild-type (wt-CFTR) and the F508del associated processes (F508del-CFTR) in separate submaps, with pathways related to protein biosynthesis, endoplasmic reticulum retention, export, activation/inactivation of channel function, and recycling/degradation after endocytosis. CyFi-MAP is an open-access resource with specific, curated and continuously updated information on CFTR-related pathways available online at https://cysticfibrosismap.github.io/ . This tool was developed as a reference CF pathway data repository to be continuously updated and used worldwide in CF research.
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Affiliation(s)
- Catarina Pereira
- Faculty of Sciences, BioISI-Biosystems Integrative Sciences Institute, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
- LASIGE, Faculty of Sciences, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
| | - Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- CIRI UMR5308, CNRS-ENS-UCBL-INSERM, European Institute for Systems Biology and Medicine, Université de Lyon, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Carlos M Farinha
- Faculty of Sciences, BioISI-Biosystems Integrative Sciences Institute, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
| | - Michael A Gray
- Biosciences Institute, University Medical School, Newcastle University, Framlington Place, Newcastle upon Tyne, NE2 4HH, UK
| | | | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Irina Balaur
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
- CIRI UMR5308, CNRS-ENS-UCBL-INSERM, European Institute for Systems Biology and Medicine, Université de Lyon, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Margarida D Amaral
- Faculty of Sciences, BioISI-Biosystems Integrative Sciences Institute, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal
| | - Andre O Falcao
- Faculty of Sciences, BioISI-Biosystems Integrative Sciences Institute, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal.
- LASIGE, Faculty of Sciences, University of Lisboa, Campo Grande, 1749-016, Lisbon, Portugal.
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Vinhoven L, Voskamp M, Nietert MM. Mapping Compound Databases to Disease Maps-A MINERVA Plugin for CandActBase. J Pers Med 2021; 11:jpm11111072. [PMID: 34834423 PMCID: PMC8625161 DOI: 10.3390/jpm11111072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/19/2021] [Accepted: 10/22/2021] [Indexed: 11/16/2022] Open
Abstract
The MINERVA platform is currently the most widely used platform for visualizing and providing access to disease maps. Disease maps are systems biological maps of molecular interactions relevant in a certain disease context, where they can be used to support drug discovery. For this purpose, we extended MINERVA's own drug and chemical search using the MINERVA plugin starter kit. We developed a plugin to provide a linkage between disease maps in MINERVA and application-specific databases of candidate therapeutics. The plugin has three main functionalities; one shows all the targets of all the compounds in the database, the second is a compound-based search to highlight targets of specific compounds, and the third can be used to find compounds that affect a certain target. As a use case, we applied the plugin to link a disease map and compound database we previously established in the context of cystic fibrosis and, herein, point out possible issues and difficulties. The plugin is publicly available on GitLab; the use-case application to cystic fibrosis, connecting disease maps and the compound database CandActCFTR, is available online.
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Affiliation(s)
- Liza Vinhoven
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany; (L.V.); (M.V.)
| | - Malte Voskamp
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany; (L.V.); (M.V.)
| | - Manuel Manfred Nietert
- Department of Medical Bioinformatics, University Medical Center Göttingen, Goldschmidtstraße 1, 37077 Göttingen, Germany; (L.V.); (M.V.)
- CIDAS Campus Institute Data Science, Goldschmidtstraße 1, 37077 Göttingen, Germany
- Correspondence: ; Tel.: +49-551-39-14920
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Glavaški M, Velicki L. Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms' representation. BioData Min 2021; 14:45. [PMID: 34600580 PMCID: PMC8487578 DOI: 10.1186/s13040-021-00279-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 09/14/2021] [Indexed: 11/25/2022] Open
Abstract
Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale. Supplementary Information The online version contains supplementary material available at 10.1186/s13040-021-00279-2.
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Affiliation(s)
- Mila Glavaški
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.
| | - Lazar Velicki
- Faculty of Medicine, University of Novi Sad, Novi Sad, Serbia.,Institute of Cardiovascular Diseases Vojvodina, Sremska Kamenica, Serbia
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Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta‐Resendiz A, Singh V, Aghamiri SS, Acencio ML, Glaab E, Ruepp A, Fobo G, Montrone C, Brauner B, Frishman G, Monraz Gómez LC, Somers J, Hoch M, Kumar Gupta S, Scheel J, Borlinghaus H, Czauderna T, Schreiber F, Montagud A, Ponce de Leon M, Funahashi A, Hiki Y, Hiroi N, Yamada TG, Dräger A, Renz A, Naveez M, Bocskei Z, Messina F, Börnigen D, Fergusson L, Conti M, Rameil M, Nakonecnij V, Vanhoefer J, Schmiester L, Wang M, Ackerman EE, Shoemaker JE, Zucker J, Oxford K, Teuton J, Kocakaya E, Summak GY, Hanspers K, Kutmon M, Coort S, Eijssen L, Ehrhart F, Rex DAB, Slenter D, Martens M, Pham N, Haw R, Jassal B, Matthews L, Orlic‐Milacic M, Senff Ribeiro A, Rothfels K, Shamovsky V, Stephan R, Sevilla C, Varusai T, Ravel J, Fraser R, Ortseifen V, Marchesi S, Gawron P, Smula E, Heirendt L, Satagopam V, Wu G, Riutta A, Golebiewski M, Owen S, Goble C, Hu X, Overall RW, Maier D, Bauch A, Gyori BM, Bachman JA, Vega C, Grouès V, Vazquez M, Porras P, Licata L, Iannuccelli M, Sacco F, Nesterova A, Yuryev A, de Waard A, Turei D, Luna A, Babur O, Soliman S, Valdeolivas A, Esteban‐Medina M, Peña‐Chilet M, Rian K, Helikar T, Puniya BL, Modos D, Treveil A, Olbei M, De Meulder B, Ballereau S, Dugourd A, Naldi A, Noël V, Calzone L, Sander C, Demir E, Korcsmaros T, Freeman TC, Augé F, Beckmann JS, Hasenauer J, Wolkenhauer O, Wilighagen EL, Pico AR, Evelo CT, Gillespie ME, Stein LD, Hermjakob H, D'Eustachio P, Saez‐Rodriguez J, Dopazo J, Valencia A, Kitano H, Barillot E, Auffray C, Balling R, Schneider R. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. Mol Syst Biol 2021; 17:e10387. [PMID: 34664389 PMCID: PMC8524328 DOI: 10.15252/msb.202110387] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 12/13/2022] Open
Abstract
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
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Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Anna Niarakis
- Université Paris‐SaclayLaboratoire Européen de Recherche pour la Polyarthrite rhumatoïde ‐ GenhotelUniv EvryEvryFrance
- Lifeware GroupInria Saclay‐Ile de FrancePalaiseauFrance
| | - Alexander Mazein
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Inna Kuperstein
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Robert Phair
- Integrative Bioinformatics, Inc.Mountain ViewCAUSA
| | - Aurelio Orta‐Resendiz
- Institut PasteurUniversité de Paris, Unité HIVInflammation et PersistanceParisFrance
- Bio Sorbonne Paris CitéUniversité de ParisParisFrance
| | - Vidisha Singh
- Université Paris‐SaclayLaboratoire Européen de Recherche pour la Polyarthrite rhumatoïde ‐ GenhotelUniv EvryEvryFrance
| | - Sara Sadat Aghamiri
- Inserm‐ Institut national de la santé et de la recherche médicaleParisFrance
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Enrico Glaab
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Andreas Ruepp
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Gisela Fobo
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Corinna Montrone
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Barbara Brauner
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Goar Frishman
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Luis Cristóbal Monraz Gómez
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Julia Somers
- Department of Molecular and Medical GeneticsOregon Health & Sciences UniversityPortlandORUSA
| | - Matti Hoch
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | | | - Julia Scheel
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | - Hanna Borlinghaus
- Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
| | - Tobias Czauderna
- Faculty of Information TechnologyDepartment of Human‐Centred ComputingMonash UniversityClaytonVic.Australia
| | - Falk Schreiber
- Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
- Faculty of Information TechnologyDepartment of Human‐Centred ComputingMonash UniversityClaytonVic.Australia
| | | | | | - Akira Funahashi
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Yusuke Hiki
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Noriko Hiroi
- Graduate School of Media and GovernanceResearch Institute at SFCKeio UniversityKanagawaJapan
| | - Takahiro G Yamada
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial‐Resistant PathogensInstitute for Bioinformatics and Medical Informatics (IBMI)University of TübingenTübingenGermany
- Department of Computer ScienceUniversity of TübingenTübingenGermany
- German Center for Infection Research (DZIF), partner siteTübingenGermany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial‐Resistant PathogensInstitute for Bioinformatics and Medical Informatics (IBMI)University of TübingenTübingenGermany
- Department of Computer ScienceUniversity of TübingenTübingenGermany
| | - Muhammad Naveez
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
- Institute of Applied Computer SystemsRiga Technical UniversityRigaLatvia
| | - Zsolt Bocskei
- Sanofi R&DTranslational SciencesChilly‐MazarinFrance
| | - Francesco Messina
- Dipartimento di Epidemiologia Ricerca Pre‐Clinica e Diagnostica AvanzataNational Institute for Infectious Diseases 'Lazzaro Spallanzani' I.R.C.C.S.RomeItaly
- COVID‐19 INMI Network Medicine for IDs Study GroupNational Institute for Infectious Diseases 'Lazzaro Spallanzani' I.R.C.C.SRomeItaly
| | - Daniela Börnigen
- Bioinformatics Core FacilityUniversitätsklinikum Hamburg‐EppendorfHamburgGermany
| | - Liam Fergusson
- Royal (Dick) School of Veterinary MedicineThe University of EdinburghEdinburghUK
| | - Marta Conti
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Marius Rameil
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Vanessa Nakonecnij
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Jakob Vanhoefer
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Leonard Schmiester
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
- Center for MathematicsChair of Mathematical Modeling of Biological SystemsTechnische Universität MünchenGarchingGermany
| | - Muying Wang
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
| | - Emily E Ackerman
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
| | - Jason E Shoemaker
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
- Department of Computational and Systems BiologyUniversity of PittsburghPittsburghPAUSA
| | | | | | | | | | | | - Kristina Hanspers
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | - Martina Kutmon
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht Centre for Systems Biology (MaCSBio)Maastricht UniversityMaastrichtThe Netherlands
| | - Susan Coort
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Lars Eijssen
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht University Medical CentreMaastrichtThe Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht University Medical CentreMaastrichtThe Netherlands
| | | | - Denise Slenter
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Marvin Martens
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Nhung Pham
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Robin Haw
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | - Bijay Jassal
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | | | | | - Andrea Senff Ribeiro
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- Universidade Federal do ParanáCuritibaBrasil
| | - Karen Rothfels
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | | | - Ralf Stephan
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | - Cristoffer Sevilla
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Thawfeek Varusai
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Jean‐Marie Ravel
- INSERM UMR_S 1256Nutrition, Genetics, and Environmental Risk Exposure (NGERE)Faculty of Medicine of NancyUniversity of LorraineNancyFrance
- Laboratoire de génétique médicaleCHRU NancyNancyFrance
| | - Rupsha Fraser
- Queen's Medical Research InstituteThe University of EdinburghEdinburghUK
| | - Vera Ortseifen
- Senior Research Group in Genome Research of Industrial MicroorganismsCenter for BiotechnologyBielefeld UniversityBielefeldGermany
| | - Silvia Marchesi
- Department of Surgical ScienceUppsala UniversityUppsalaSweden
| | - Piotr Gawron
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
- Institute of Computing SciencePoznan University of TechnologyPoznanPoland
| | - Ewa Smula
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Guanming Wu
- Department of Medical Informatics and Clinical EpidemiologyOregon Health & Science UniversityPortlandORUSA
| | - Anders Riutta
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | | | - Stuart Owen
- Department of Computer ScienceThe University of ManchesterManchesterUK
| | - Carole Goble
- Department of Computer ScienceThe University of ManchesterManchesterUK
| | - Xiaoming Hu
- Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
| | - Rupert W Overall
- German Center for Neurodegenerative Diseases (DZNE) DresdenDresdenGermany
- Center for Regenerative Therapies Dresden (CRTD)Technische Universität DresdenDresdenGermany
- Institute for BiologyHumboldt University of BerlinBerlinGermany
| | | | | | - Benjamin M Gyori
- Harvard Medical SchoolLaboratory of Systems PharmacologyBostonMAUSA
| | - John A Bachman
- Harvard Medical SchoolLaboratory of Systems PharmacologyBostonMAUSA
| | - Carlos Vega
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Valentin Grouès
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | | | - Pablo Porras
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Luana Licata
- Department of BiologyUniversity of Rome Tor VergataRomeItaly
| | | | - Francesca Sacco
- Department of BiologyUniversity of Rome Tor VergataRomeItaly
| | | | | | | | - Denes Turei
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Augustin Luna
- cBio Center, Divisions of Biostatistics and Computational BiologyDepartment of Data SciencesDana‐Farber Cancer InstituteBostonMAUSA
- Department of Cell BiologyHarvard Medical SchoolBostonMAUSA
| | - Ozgun Babur
- Computer Science DepartmentUniversity of Massachusetts BostonBostonMAUSA
| | | | - Alberto Valdeolivas
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Marina Esteban‐Medina
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
| | - Maria Peña‐Chilet
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
- Bioinformatics in Rare Diseases (BiER)Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)FPS, Hospital Virgen del RocíoSevillaSpain
| | - Kinza Rian
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
| | - Tomáš Helikar
- Department of BiochemistryUniversity of Nebraska‐LincolnLincolnNEUSA
| | | | - Dezso Modos
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Agatha Treveil
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Marton Olbei
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Stephane Ballereau
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Aurélien Dugourd
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Institute of Experimental Medicine and Systems BiologyFaculty of Medicine, RWTHAachen UniversityAachenGermany
| | | | - Vincent Noël
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Laurence Calzone
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Chris Sander
- cBio Center, Divisions of Biostatistics and Computational BiologyDepartment of Data SciencesDana‐Farber Cancer InstituteBostonMAUSA
- Department of Cell BiologyHarvard Medical SchoolBostonMAUSA
| | - Emek Demir
- Department of Molecular and Medical GeneticsOregon Health & Sciences UniversityPortlandORUSA
| | | | - Tom C Freeman
- The Roslin InstituteUniversity of EdinburghEdinburghUK
| | - Franck Augé
- Sanofi R&DTranslational SciencesChilly‐MazarinFrance
| | | | - Jan Hasenauer
- Helmholtz Zentrum München – German Research Center for Environmental HealthInstitute of Computational BiologyNeuherbergGermany
- Interdisciplinary Research Unit Mathematics and Life SciencesUniversity of BonnBonnGermany
| | - Olaf Wolkenhauer
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | - Egon L Wilighagen
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Alexander R Pico
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | - Chris T Evelo
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht Centre for Systems Biology (MaCSBio)Maastricht UniversityMaastrichtThe Netherlands
| | - Marc E Gillespie
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- St. John’s University College of Pharmacy and Health SciencesQueensNYUSA
| | - Lincoln D Stein
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Henning Hermjakob
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | | | | | - Joaquin Dopazo
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
- Bioinformatics in Rare Diseases (BiER)Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)FPS, Hospital Virgen del RocíoSevillaSpain
- FPS/ELIXIR‐esHospital Virgen del RocíoSevillaSpain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
| | - Hiroaki Kitano
- Systems Biology InstituteTokyoJapan
- Okinawa Institute of Science and Technology Graduate SchoolOkinawaJapan
| | - Emmanuel Barillot
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Charles Auffray
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Rudi Balling
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
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A Vision of Future Healthcare: Potential Opportunities and Risks of Systems Medicine from a Citizen and Patient Perspective-Results of a Qualitative Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18189879. [PMID: 34574802 PMCID: PMC8465522 DOI: 10.3390/ijerph18189879] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 09/13/2021] [Accepted: 09/17/2021] [Indexed: 12/26/2022]
Abstract
Advances in (bio)medicine and technological innovations make it possible to combine high-dimensional, heterogeneous health data to better understand causes of diseases and make them usable for predictive, preventive, and precision medicine. This study aimed to determine views on and expectations of “systems medicine” from the perspective of citizens and patients in six focus group interviews, all transcribed verbatim and content analyzed. A future vision of the use of systems medicine in healthcare served as a stimulus for the discussion. The results show that although certain aspects of systems medicine were seen positive (e.g., use of smart technology, digitalization, and networking in healthcare), the perceived risks dominated. The high degree of technification was perceived as emotionally burdensome (e.g., reduction of people to their data, loss of control, dehumanization). The risk-benefit balance for the use of risk-prediction models for disease events and trajectories was rated as rather negative. There were normative and ethical concerns about unwanted data use, discrimination, and restriction of fundamental rights. These concerns and needs of citizens and patients must be addressed in policy frameworks and health policy implementation strategies to reduce negative emotions and attitudes toward systems medicine and to take advantage of its opportunities.
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Abstract
Multiscale computational modeling aims to connect the complex networks of effects at different length and/or time scales. For example, these networks often include intracellular molecular signaling, crosstalk, and other interactions between neighboring cell populations, and higher levels of emergent phenomena across different regions of tissues and among collections of tissues or organs interacting with each other in the whole body. Recent applications of multiscale modeling across intracellular, cellular, and/or tissue levels are highlighted here. These models incorporated the roles of biochemical and biomechanical modulation in processes that are implicated in the mechanisms of several diseases including fibrosis, joint and bone diseases, respiratory infectious diseases, and cancers.
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Miagoux Q, Singh V, de Mézquita D, Chaudru V, Elati M, Petit-Teixeira E, Niarakis A. Inference of an Integrative, Executable Network for Rheumatoid Arthritis Combining Data-Driven Machine Learning Approaches and a State-of-the-Art Mechanistic Disease Map. J Pers Med 2021; 11:785. [PMID: 34442429 PMCID: PMC8400381 DOI: 10.3390/jpm11080785] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 08/02/2021] [Accepted: 08/10/2021] [Indexed: 02/07/2023] Open
Abstract
Rheumatoid arthritis (RA) is a multifactorial, complex autoimmune disease that involves various genetic, environmental, and epigenetic factors. Systems biology approaches provide the means to study complex diseases by integrating different layers of biological information. Combining multiple data types can help compensate for missing or conflicting information and limit the possibility of false positives. In this work, we aim to unravel mechanisms governing the regulation of key transcription factors in RA and derive patient-specific models to gain more insights into the disease heterogeneity and the response to treatment. We first use publicly available transcriptomic datasets (peripheral blood) relative to RA and machine learning to create an RA-specific transcription factor (TF) co-regulatory network. The TF cooperativity network is subsequently enriched in signalling cascades and upstream regulators using a state-of-the-art, RA-specific molecular map. Then, the integrative network is used as a template to analyse patients' data regarding their response to anti-TNF treatment and identify master regulators and upstream cascades affected by the treatment. Finally, we use the Boolean formalism to simulate in silico subparts of the integrated network and identify combinations and conditions that can switch on or off the identified TFs, mimicking the effects of single and combined perturbations.
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Affiliation(s)
- Quentin Miagoux
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Vidisha Singh
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Dereck de Mézquita
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Valerie Chaudru
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Mohamed Elati
- CANTHER, University of Lille, CNRS UMR 1277, Inserm U9020, 59045 Lille, France;
| | - Elisabeth Petit-Teixeira
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
| | - Anna Niarakis
- Université Paris-Saclay, Univ Evry, Laboratoire Européen de Recherche pour la Polyarthrite rhumatoïde-Genhotel, 91057 Evry, France; (Q.M.); (V.S.); (D.d.M.); (V.C.); (E.P.-T.)
- Lifeware Group, Inria, Saclay-île de France, 91120 Palaiseau, France
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Looking for pathways related to COVID-19: confirmation of pathogenic mechanisms by SARS-CoV-2-host interactome. Cell Death Dis 2021; 12:788. [PMID: 34385425 PMCID: PMC8357963 DOI: 10.1038/s41419-021-03881-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/21/2021] [Accepted: 05/24/2021] [Indexed: 12/15/2022]
Abstract
In the last months, many studies have clearly described several mechanisms of SARS-CoV-2 infection at cell and tissue level, but the mechanisms of interaction between host and SARS-CoV-2, determining the grade of COVID-19 severity, are still unknown. We provide a network analysis on protein–protein interactions (PPI) between viral and host proteins to better identify host biological responses, induced by both whole proteome of SARS-CoV-2 and specific viral proteins. A host-virus interactome was inferred, applying an explorative algorithm (Random Walk with Restart, RWR) triggered by 28 proteins of SARS-CoV-2. The analysis of PPI allowed to estimate the distribution of SARS-CoV-2 proteins in the host cell. Interactome built around one single viral protein allowed to define a different response, underlining as ORF8 and ORF3a modulated cardiovascular diseases and pro-inflammatory pathways, respectively. Finally, the network-based approach highlighted a possible direct action of ORF3a and NS7b to enhancing Bradykinin Storm. This network-based representation of SARS-CoV-2 infection could be a framework for pathogenic evaluation of specific clinical outcomes. We identified possible host responses induced by specific proteins of SARS-CoV-2, underlining the important role of specific viral accessory proteins in pathogenic phenotypes of severe COVID-19 patients.
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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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Messina F, Montaldo C, Abbate I, Antonioli M, Bordoni V, Matusali G, Sacchi A, Giombini E, Fimia GM, Piacentini M, Capobianchi MR, Lauria FN, Ippolito G. Rationale and Criteria for a COVID-19 Model Framework. Viruses 2021; 13:1309. [PMID: 34372515 PMCID: PMC8309961 DOI: 10.3390/v13071309] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 12/17/2022] Open
Abstract
Complex systems are inherently multilevel and multiscale systems. The infectious disease system is considered a complex system resulting from the interaction between three sub-systems (host, pathogen, and environment) organized into a hierarchical structure, ranging from the cellular to the macro-ecosystem level, with multiscales. Therefore, to describe infectious disease phenomena that change through time and space and at different scales, we built a model framework where infectious disease must be considered the set of biological responses of human hosts to pathogens, with biological pathways shared with other pathologies in an ecological interaction context. In this paper, we aimed to design a framework for building a disease model for COVID-19 based on current literature evidence. The model was set up by identifying the molecular pathophysiology related to the COVID-19 phenotypes, collecting the mechanistic knowledge scattered across scientific literature and bioinformatic databases, and integrating it using a logical/conceptual model systems biology. The model framework building process began from the results of a domain-based literature review regarding a multiomics approach to COVID-19. This evidence allowed us to define a framework of COVID-19 conceptual model and to report all concepts in a multilevel and multiscale structure. The same interdisciplinary working groups that carried out the scoping review were involved. The conclusive result is a conceptual method to design multiscale models of infectious diseases. The methodology, applied in this paper, is a set of partially ordered research and development activities that result in a COVID-19 multiscale model.
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Affiliation(s)
- Francesco Messina
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Chiara Montaldo
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Isabella Abbate
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Manuela Antonioli
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Veronica Bordoni
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Giulia Matusali
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Alessandra Sacchi
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Emanuela Giombini
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Gian Maria Fimia
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
- Department of Molecular Medicine, Sapienza University of Rome, 00185 Rome, Italy
| | - Mauro Piacentini
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
- Department of Biology, University of Rome Tor Vergata, Via della Ricerca Scientifica 1, 00133 Rome, Italy
| | - Maria Rosaria Capobianchi
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Francesco Nicola Lauria
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
| | - Giuseppe Ippolito
- National Institute for Infectious Diseases, “Lazzaro Spallanzani”–IRCCS, Via Portuense, 292, 00149 Rome, Italy; (F.M.); (C.M.); (I.A.); (M.A.); (V.B.); (G.M.); (A.S.); (E.G.); (G.M.F.); (M.P.); (M.R.C.); (F.N.L.)
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Correia Y, Scheel J, Gupta S, Wang K. Placental mitochondrial function as a driver of angiogenesis and placental dysfunction. Biol Chem 2021; 402:887-909. [PMID: 34218539 DOI: 10.1515/hsz-2021-0121] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022]
Abstract
The placenta is a highly vascularized and complex foetal organ that performs various tasks, crucial to a healthy pregnancy. Its dysfunction leads to complications such as stillbirth, preeclampsia, and intrauterine growth restriction. The specific cause of placental dysfunction remains unknown. Recently, the role of mitochondrial function and mitochondrial adaptations in the context of angiogenesis and placental dysfunction is getting more attention. The required energy for placental remodelling, nutrient transport, hormone synthesis, and the reactive oxygen species leads to oxidative stress, stemming from mitochondria. Mitochondria adapt to environmental changes and have been shown to adjust their oxygen and nutrient use to best support placental angiogenesis and foetal development. Angiogenesis is the process by which blood vessels form and is essential for the delivery of nutrients to the body. This process is regulated by different factors, pro-angiogenic factors and anti-angiogenic factors, such as sFlt-1. Increased circulating sFlt-1 levels have been linked to different preeclamptic phenotypes. One of many effects of increased sFlt-1 levels, is the dysregulation of mitochondrial function. This review covers mitochondrial adaptations during placentation, the importance of the anti-angiogenic factor sFlt-1in placental dysfunction and its role in the dysregulation of mitochondrial function.
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Affiliation(s)
- Yolanda Correia
- Aston Medical School, College of Health & Life Sciences, Aston University, Aston Triangle, BirminghamB4 7ET, UK
| | - Julia Scheel
- Department of Systems Biology and Bioinformatics, University of Rostock, D-18051Rostock, Germany
| | - Shailendra Gupta
- Department of Systems Biology and Bioinformatics, University of Rostock, D-18051Rostock, Germany
| | - Keqing Wang
- Aston Medical School, College of Health & Life Sciences, Aston University, Aston Triangle, BirminghamB4 7ET, UK
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Kundu K, Darden L, Moult J. MecCog: A knowledge representation framework for genetic disease mechanism. Bioinformatics 2021; 37:4180-4186. [PMID: 34117883 DOI: 10.1093/bioinformatics/btab432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/11/2021] [Accepted: 06/11/2021] [Indexed: 12/16/2022] Open
Abstract
MOTIVATION Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams, and network graphs. Integration and structuring of such mechanistic information greatly enhances its utility. RESULTS MecCog is a graphical framework for building integrated representations (mechanism schemas) of mechanisms by which a genetic variant causes a disease phenotype. A MecCog mechanism schema displays the propagation of system perturbations across stages of biological organization, using graphical notations to symbolize perturbed entities and activities, hyperlinked evidence tagging, a mechanism ontology, and depiction of knowledge gaps, ambiguities, and uncertainties. The web platform enables a user to construct, store, publish, browse, query, and comment on schemas. MecCog facilitates the identification of potential biomarkers, therapeutic intervention sites, and critical future experiments. AVAILABILITY The MecCog framework is freely available at http://www.meccog.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kunal Kundu
- Computational Biology, Bioinformatics and Genomics, Biological Sciences Graduate Program, University of Maryland, College Park, MD, 20742, USA.,Institute for Bioscience and Biotechnology Research, University of Maryland, 9600 Gudelsky Drive, Rockville, MD, 20850, USA
| | - Lindley Darden
- Department of Philosophy, University of Maryland, College Park, MD, 20742, USA
| | - John Moult
- Institute for Bioscience and Biotechnology Research, University of Maryland, 9600 Gudelsky Drive, Rockville, MD, 20850, USA.,Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, MD, 20742, USA
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Abstract
BACKGROUND Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results. METHODS Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed. RESULTS The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies. CONCLUSIONS The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.
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Affiliation(s)
- Claudio Fiocchi
- Department of Inflammation & Immunity, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, USA.,Department of Gastroenterology, Hepatology and Nutrition, Digestive Disease and Surgery Institute, Cleveland Clinic, Cleveland, Ohio, USA
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Cremades-Jimeno L, de Pedro MÁ, López-Ramos M, Sastre J, Mínguez P, Fernández IM, Baos S, Cárdaba B. Prioritizing Molecular Biomarkers in Asthma and Respiratory Allergy Using Systems Biology. Front Immunol 2021; 12:640791. [PMID: 33936056 PMCID: PMC8081895 DOI: 10.3389/fimmu.2021.640791] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/15/2021] [Indexed: 01/29/2023] Open
Abstract
Highly prevalent respiratory diseases such as asthma and allergy remain a pressing health challenge. Currently, there is an unmet need for precise diagnostic tools capable of predicting the great heterogeneity of these illnesses. In a previous study of 94 asthma/respiratory allergy biomarker candidates, we defined a group of potential biomarkers to distinguish clinical phenotypes (i.e. nonallergic asthma, allergic asthma, respiratory allergy without asthma) and disease severity. Here, we analyze our experimental results using complex algorithmic approaches that establish holistic disease models (systems biology), combining these insights with information available in specialized databases developed worldwide. With this approach, we aim to prioritize the most relevant biomarkers according to their specificity and mechanistic implication with molecular motifs of the diseases. The Therapeutic Performance Mapping System (Anaxomics’ TPMS technology) was used to generate one mathematical model per disease: allergic asthma (AA), non-allergic asthma (NA), and respiratory allergy (RA), defining specific molecular motifs for each. The relationship of our molecular biomarker candidates and each disease was analyzed by artificial neural networks (ANNs) scores. These analyses prioritized molecular biomarkers specific to the diseases and to particular molecular motifs. As a first step, molecular characterization of the pathophysiological processes of AA defined 16 molecular motifs: 2 specific for AA, 2 shared with RA, and 12 shared with NA. Mechanistic analysis showed 17 proteins that were strongly related to AA. Eleven proteins were associated with RA and 16 proteins with NA. Specificity analysis showed that 12 proteins were specific to AA, 7 were specific to RA, and 2 to NA. Finally, a triggering analysis revealed a relevant role for AKT1, STAT1, and MAPK13 in all three conditions and for TLR4 in asthmatic diseases (AA and NA). In conclusion, this study has enabled us to prioritize biomarkers depending on the functionality associated with each disease and with specific molecular motifs, which could improve the definition and usefulness of new molecular biomarkers.
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Affiliation(s)
- Lucía Cremades-Jimeno
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - María Ángeles de Pedro
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - María López-Ramos
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Joaquín Sastre
- Allergy Department, Fundación Jiménez Díaz, Madrid, Spain.,Center for Biomedical Network of Respiratory Diseases (CIBERES), ISCIII, Madrid, Spain
| | - Pablo Mínguez
- Department of Genetics, IIS-Fundación Jiménez Díaz, UAM, Madrid, Spain.,Center for Biomedical Network Research on Rare Diseases (CIBERER), ISCIII, Madrid, Spain
| | | | - Selene Baos
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain
| | - Blanca Cárdaba
- Immunology Department, IIS-Fundación Jiménez Díaz, Universidad Autónoma de Madrid (UAM), Madrid, Spain.,Center for Biomedical Network of Respiratory Diseases (CIBERES), ISCIII, Madrid, Spain
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Mazein A, Rougny A, Karr JR, Saez-Rodriguez J, Ostaszewski M, Schneider R. Reusability and composability in process description maps: RAS-RAF-MEK-ERK signalling. Brief Bioinform 2021; 22:6217719. [PMID: 33834185 PMCID: PMC8425390 DOI: 10.1093/bib/bbab103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/05/2022] Open
Abstract
Detailed maps of the molecular basis of the disease are powerful tools for interpreting data and building predictive models. Modularity and composability are considered necessary network features for large-scale collaborative efforts to build comprehensive molecular descriptions of disease mechanisms. An effective way to create and manage large systems is to compose multiple subsystems. Composable network components could effectively harness the contributions of many individuals and enable teams to seamlessly assemble many individual components into comprehensive maps. We examine manually built versions of the RAS–RAF–MEK–ERK cascade from the Atlas of Cancer Signalling Network, PANTHER and Reactome databases and review them in terms of their reusability and composability for assembling new disease models. We identify design principles for managing complex systems that could make it easier for investigators to share and reuse network components. We demonstrate the main challenges including incompatible levels of detail and ambiguous representation of complexes and highlight the need to address these challenges.
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Affiliation(s)
- Alexander Mazein
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.,European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Adrien Rougny
- Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Aomi, Tokyo, Japan.,Com. Bio Big Data Open Innovation Lab. (CBBD-OIL), AIST, Aomi, Tokyo, Japan
| | - Jonathan R Karr
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA.,Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, NY, USA
| | - Julio Saez-Rodriguez
- Joint Research Centre for Computational Biomedicine (JRC-COMBINE), RWTH Aachen University, Faculty of Medicine, 52074 Aachen, Germany.,Institute for Computational Biomedicine, Heidelberg University Hospital and Heidelberg University, Faculty of Medicine, Bioquant Heidelberg, Heidelberg 69120, Germany
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
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Aghamiri SS, Delaplace F. TaBooN Boolean Network Synthesis Based on Tabu Search. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; PP:2499-2511. [PMID: 33661736 DOI: 10.1109/tcbb.2021.3063817] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recent developments in Omics-technologies revolutionized the investigation of biology by producing molecular data in multiple dimensions and scale. This breakthrough in biology raises the crucial issue of their interpretation based on modelling. In this undertaking, network provides a suitable framework for modelling the interactions between molecules. Basically a Biological network is composed of nodes referring to the components such as genes or proteins, and the edges/arcs formalizing interactions between them. The evolution of the interactions is then modelled by the definition of a dynamical system. Among the different categories of network, the Boolean network offers a reliable qualitative framework for the modelling. Automatically synthesizing a Boolean network from experimental data therefore remains a necessary but challenging issue. In this study, we present Taboon, an original work-flow for synthesizing Boolean Networks from biological data. The methodology uses the data in the form of boolean profiles for inferring all the potential local formula inference. They combine to form the model space from which the most truthful model with regards to biological knowledge and experiments must be found. In the TaBooN work-flow the selection of the fittest model is achieved by a Tabu-search algorithm. TaBooN is an automated method for Boolean Network inference from.
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Rian K, Esteban-Medina M, Hidalgo MR, Çubuk C, Falco MM, Loucera C, Gunyel D, Ostaszewski M, Peña-Chilet M, Dopazo J. Mechanistic modeling of the SARS-CoV-2 disease map. BioData Min 2021; 14:5. [PMID: 33478554 PMCID: PMC7817765 DOI: 10.1186/s13040-021-00234-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 01/05/2021] [Indexed: 12/13/2022] Open
Abstract
Here we present a web interface that implements a comprehensive mechanistic model of the SARS-CoV-2 disease map. In this framework, the detailed activity of the human signaling circuits related to the viral infection, covering from the entry and replication mechanisms to the downstream consequences as inflammation and antigenic response, can be inferred from gene expression experiments. Moreover, the effect of potential interventions, such as knock-downs, or drug effects (currently the system models the effect of more than 8000 DrugBank drugs) can be studied. This freely available tool not only provides an unprecedentedly detailed view of the mechanisms of viral invasion and the consequences in the cell but has also the potential of becoming an invaluable asset in the search for efficient antiviral treatments.
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Affiliation(s)
- Kinza Rian
- Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain
| | - Marina Esteban-Medina
- Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Sevilla, Spain
| | - Marta R Hidalgo
- Bioinformatics and Biostatistics Unit, Centro de Investigación Príncipe Felipe (CIPF), 46012, Valencia, Spain
| | - Cankut Çubuk
- Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain
| | - Matias M Falco
- Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla, Spain
| | - Carlos Loucera
- Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Sevilla, Spain
| | - Devrim Gunyel
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg
| | - María Peña-Chilet
- Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain.
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Sevilla, Spain.
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla, Spain.
| | - Joaquín Dopazo
- Bioinformatics Area, Fundación Progreso y Salud (FPS), Hospital Virgen del Rocío, Sevilla, Spain.
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBIS), Hospital Virgen del Rocio, 41013, Sevilla, Spain.
- Bioinformatics in RareDiseases (BiER), Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Sevilla, Spain.
- Functional Genomics Node (INB-ELIXIR-es), Sevilla, Spain.
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Touré V, Dräger A, Luna A, Dogrusoz U, Rougny A. The Systems Biology Graphical Notation: Current Status and Applications in Systems Medicine. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11515-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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46
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MINERVA, A Platform for the Exploration of Disease Maps. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11685-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
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47
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Henry V, Moszer I, Dameron O, Vila Xicota L, Dubois B, Potier MC, Hofmann-Apitius M, Colliot O. Converting disease maps into heavyweight ontologies: general methodology and application to Alzheimer's disease. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6137817. [PMID: 33590873 DOI: 10.1093/database/baab004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 01/17/2021] [Accepted: 01/27/2021] [Indexed: 11/12/2022]
Abstract
Omics technologies offer great promises for improving our understanding of diseases. The integration and interpretation of such data pose major challenges, calling for adequate knowledge models. Disease maps provide curated knowledge about disorders' pathophysiology at the molecular level adapted to omics measurements. However, the expressiveness of disease maps could be increased to help in avoiding ambiguities and misinterpretations and to reinforce their interoperability with other knowledge resources. Ontology is an adequate framework to overcome this limitation, through their axiomatic definitions and logical reasoning properties. We introduce the Disease Map Ontology (DMO), an ontological upper model based on systems biology terms. We then propose to apply DMO to Alzheimer's disease (AD). Specifically, we use it to drive the conversion of AlzPathway, a disease map devoted to AD, into a formal ontology: Alzheimer DMO. We demonstrate that it allows one to deal with issues related to redundancy, naming, consistency, process classification and pathway relationships. Furthermore, we show that it can store and manage multi-omics data. Finally, we expand the model using elements from other resources, such as clinical features contained in the AD Ontology, resulting in an enriched model called ADMO-plus. The current versions of DMO, ADMO and ADMO-plus are freely available at http://bioportal.bioontology.org/ontologies/ADMO.
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Affiliation(s)
- Vincent Henry
- Inria Paris, Aramis Project-Team, Paris 75013, France.,Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,ICONICS Core Facility, Paris Brain Institute, Paris 75013, France
| | - Ivan Moszer
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,ICONICS Core Facility, Paris Brain Institute, Paris 75013, France
| | - Olivier Dameron
- Univ Rennes, CNRS, Inria, IRISA-UMR 6074, Rennes 35000, France
| | - Laura Vila Xicota
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,Alzheimer's and Prion Diseases Team, Paris Brain Institute, Paris 75013, France
| | - Bruno Dubois
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,AP-HP, Hôpital de la Pitié-Salpêtrière, Department of Neurology, Institut de la Mémoire et de la Maladie d'Alzheimer (IM2A), Paris 75013, France
| | - Marie-Claude Potier
- Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France.,Alzheimer's and Prion Diseases Team, Paris Brain Institute, Paris 75013, France
| | | | - Olivier Colliot
- Inria Paris, Aramis Project-Team, Paris 75013, France.,Institut du Cerveau et de la Moelle épinière, ICM, Paris 75013, France.,Inserm, U 1127, Paris 75013, France.,CNRS, UMR 7225, Paris 75013, France.,Sorbonne Université, Paris 75013, France
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48
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Mazein A, Ivanova O, Balaur I, Ostaszewski M, Berzhitskaya V, Serebriyskaya T, Ligon T, Hasenauer J, De Meulder B, Overall RW, Roy L, Knowles RG, Wheelock CE, Dahlen SE, Chung KF, Adcock IM, Roberts G, Djukanovic R, Pellet J, Gawron P, Balling R, Maitland-van der Zee AH, Schneider R, Sterk PJ, Auffray C. AsthmaMap: An interactive knowledge repository for mechanisms of asthma. J Allergy Clin Immunol 2020; 147:853-856. [PMID: 33309742 DOI: 10.1016/j.jaci.2020.11.032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/17/2020] [Accepted: 11/10/2020] [Indexed: 10/22/2022]
Affiliation(s)
- Alexander Mazein
- European Institute for Systems Biology and Medicine, Université de Lyon, Lyon, France; Institute of Cell Biophysics, Russian Academy of Sciences, Pushchino, Moscow, Russia; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
| | - Olga Ivanova
- European Institute for Systems Biology and Medicine, Université de Lyon, Lyon, France; Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Respiratory Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Irina Balaur
- European Institute for Systems Biology and Medicine, Université de Lyon, Lyon, France
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | | | | | - Thomas Ligon
- Faculty of Physics and Center for NanoScience (CeNS), Ludwig-Maximilians-Universität, Munich, Germany
| | - Jan Hasenauer
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany; Faculty of Mathematics and Natural Sciences, University of Bonn, Endenicher, Bonn, Germany
| | - Bertrand De Meulder
- European Institute for Systems Biology and Medicine, Université de Lyon, Lyon, France
| | - Rupert W Overall
- Center for Regenerative Therapies Dresden (CRTD), Technische Universität Dresden, Dresden, Germany; German Center for Neurodegenerative Diseases (DZNE) Dresden, Dresden, Germany
| | - Ludovic Roy
- European Institute for Systems Biology and Medicine, Université de Lyon, Lyon, France
| | - Richard G Knowles
- Knowles Consulting, Stevenage Bioscience Catalyst, Stevenage, United Kingdom
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden
| | - Sven-Erik Dahlen
- Unit for Experimental Asthma and Allergy Research, Institute of Environmental Medicine (IMM), Karolinska Institutet, Solna, Sweden
| | - Kian Fan Chung
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Ian M Adcock
- National Heart & Lung Institute, Imperial College London, London, United Kingdom
| | - Graham Roberts
- Clinical and Experimental Sciences and Human Development and Health, Faculty of Medicine, University of Southampton, Southampton, United Kingdom; NIHR Southampton Biomedical Research Centre, Southampton, United Kingdom
| | - Ratko Djukanovic
- Clinical and Experimental Sciences, University of Southampton Faculty of Medicine, University of Southampton, Southampton, United Kingdom; NIHR Southampton Respiratory Biomedical Research Unit, Southampton University Hospital, Southampton, United Kingdom
| | - Johann Pellet
- European Institute for Systems Biology and Medicine, Université de Lyon, Lyon, France
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Anke H Maitland-van der Zee
- Respiratory Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Peter J Sterk
- Respiratory Medicine, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, The Netherlands
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, Université de Lyon, Lyon, France
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49
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Lemmen C, Woopen C, Stock S. Systems medicine 2030: A Delphi study on implementation in the German healthcare system. Health Policy 2020; 125:104-114. [PMID: 33288301 DOI: 10.1016/j.healthpol.2020.11.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 11/06/2020] [Accepted: 11/11/2020] [Indexed: 12/13/2022]
Abstract
INTRODUCTION Systems medicine is based on approaches taken from systems biology, omics research, bio-informatics and network theory. It promises to facilitate a better understanding of the causes of diseases, detection at an earlier stage, and the use of tailor-made approaches to prevention and therapy. This study provides information on how systems medicine could be incorporated into the German healthcare system. METHODS In a Policy Delphi, consensus and divergence was elicited on whether experts believed systems medicine could be incorporated into the German healthcare system by 2030. Additionally, factors that could influence the implementation process were analysed. 11 theses on potential systems medicine developments and 193 arguments on influencing factors were evaluated. Experts from health and health-related fields were selected using "purposive sampling". RESULTS The experts interviewed expressed their trust in the provision of a legal-political framework, though they remained uncertain as to whether the necessary social discourse on the ethical and cultural questions surrounding systems medicine would occur. They do not (currently) expect systems medicine to be implemented by 2030. CONCLUSION Systems medicine is currently regarded as a visionary concept. As such, it would be premature to attempt to judge the success of the translation process at this stage. The results can help with the identification of the challenges involved in implementation, and the action required to achieve this aim.
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Affiliation(s)
- Clarissa Lemmen
- Institute for Health Economics and Clinical Epidemiology, University Hospital Cologne, Cologne, Germany.
| | - Christiane Woopen
- Research Unit Ethics, Institute for the History of Medicine and Medical Ethics, Faculty of Medicine, University of Cologne and University Hospital Cologne, Cologne, Germany; Ceres, Cologne Center for Ethics, Rights, Economics, and Social Sciences of Health, University of Cologne, Universitätsstr. 91, 50931 Cologne, Germany.
| | - Stephanie Stock
- Institute for Health Economics and Clinical Epidemiology, University Hospital Cologne, Cologne, Germany.
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50
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NOX5-induced uncoupling of endothelial NO synthase is a causal mechanism and theragnostic target of an age-related hypertension endotype. PLoS Biol 2020; 18:e3000885. [PMID: 33170835 PMCID: PMC7654809 DOI: 10.1371/journal.pbio.3000885] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Accepted: 09/18/2020] [Indexed: 02/07/2023] Open
Abstract
Hypertension is the most important cause of death and disability in the elderly. In 9 out of 10 cases, the molecular cause, however, is unknown. One mechanistic hypothesis involves impaired endothelium-dependent vasodilation through reactive oxygen species (ROS) formation. Indeed, ROS forming NADPH oxidase (Nox) genes associate with hypertension, yet target validation has been negative. We re-investigate this association by molecular network analysis and identify NOX5, not present in rodents, as a sole neighbor to human vasodilatory endothelial nitric oxide (NO) signaling. In hypertensive patients, endothelial microparticles indeed contained higher levels of NOX5—but not NOX1, NOX2, or NOX4—with a bimodal distribution correlating with disease severity. Mechanistically, mice expressing human Nox5 in endothelial cells developed—upon aging—severe systolic hypertension and impaired endothelium-dependent vasodilation due to uncoupled NO synthase (NOS). We conclude that NOX5-induced uncoupling of endothelial NOS is a causal mechanism and theragnostic target of an age-related hypertension endotype. Nox5 knock-in (KI) mice represent the first mechanism-based animal model of hypertension. The causes of hypertension are not understood; treatments are symptomatic and prevent only few of the associated risks. This study applies network medicine to identify a subgroup of patients with NADPH oxidase 5-induced uncoupling of nitric oxide synthase as the cause of age-related hypertension, enabling a first-in-class mechanism-based treatment of hypertension.
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