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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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2
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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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3
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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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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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5
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Modelling of Immune Checkpoint Network Explains Synergistic Effects of Combined Immune Checkpoint Inhibitor Therapy and the Impact of Cytokines in Patient Response. Cancers (Basel) 2020; 12:cancers12123600. [PMID: 33276543 PMCID: PMC7761568 DOI: 10.3390/cancers12123600] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 02/07/2023] Open
Abstract
Simple Summary The future of cancer immunotherapy relies on a combination of individually targeted therapies. However, a lot of experiments are needed to define the most effective combinations of drugs. A computational and modelling approach could help reduce the number of experiments and suggest optimal treatments to test. This article presents a logical model of T cell activation influenced by immune checkpoints, and explores the effect of these checkpoints, suggests mechanisms that would explain why some treatments might be better suited than others. The model includes not only programmed cell death protein 1 (PD1) and cytotoxic T-lymphocyte-associated protein 4 (CTL4) downstream pathways but also those of other immune checkpoints such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), lymphocyte activation gene 3 (LAG3), T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), cluster of differentiation 226 (CD226), inducible T-cell costimulator (ICOS), and tumour necrosis factor receptors (TNFRs). Abstract After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have shown some limitations since a majority of patients have continued to show resistance. Other immune checkpoints have raised some interest and are under investigation, such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), inducible T-cell costimulator (ICOS), and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), which appear as promising targets for immunotherapy. To explore their role and study possible synergetic effects of these different checkpoints, we have built a model of T cell receptor (TCR) regulation including not only PD1 and CTLA4, but also other well studied checkpoints (TIGIT, TIM3, lymphocyte activation gene 3 (LAG3), cluster of differentiation 226 (CD226), ICOS, and tumour necrosis factor receptors (TNFRs)) and simulated different aspects of T cell biology. Our model shows good correspondence with observations from available experimental studies of anti-PD1 and anti-CTLA4 therapies and suggest efficient combinations of immune checkpoint inhibitors (ICI). Among the possible candidates, TIGIT appears to be the most promising drug target in our model. The model predicts that signal transducer and activator of transcription 1 (STAT1)/STAT4-dependent pathways, activated by cytokines such as interleukin 12 (IL12) and interferon gamma (IFNG), could improve the effect of ICI therapy via upregulation of Tbet, suggesting that the effect of the cytokines related to STAT3/STAT1 activity is dependent on the balance between STAT1 and STAT3 downstream signalling.
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Hanspers K, Riutta A, Summer-Kutmon M, Pico AR. Pathway information extracted from 25 years of pathway figures. Genome Biol 2020; 21:273. [PMID: 33168034 PMCID: PMC7649569 DOI: 10.1186/s13059-020-02181-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/16/2020] [Indexed: 12/16/2022] Open
Abstract
Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figures published between 1995 and 2019 and extracted 1,112,551 instances of human genes, comprising 13,464 unique NCBI genes, participating in a wide variety of biological processes. This collection represents an order of magnitude more genes than found in the text of the same papers, and thousands of genes missing from other pathway databases, thus presenting new opportunities for discovery and research.
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Affiliation(s)
- Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Anders Riutta
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Martina Summer-Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands.,Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Alexander R Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA.
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Ravel JM, Monraz Gomez LC, Sompairac N, Calzone L, Zhivotovsky B, Kroemer G, Barillot E, Zinovyev A, Kuperstein I. Comprehensive Map of the Regulated Cell Death Signaling Network: A Powerful Analytical Tool for Studying Diseases. Cancers (Basel) 2020; 12:E990. [PMID: 32316560 PMCID: PMC7226067 DOI: 10.3390/cancers12040990] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2020] [Accepted: 03/10/2020] [Indexed: 12/25/2022] Open
Abstract
The processes leading to, or avoiding cell death are widely studied, because of their frequent perturbation in various diseases. Cell death occurs in three highly interconnected steps: Initiation, signaling and execution. We used a systems biology approach to gather information about all known modes of regulated cell death (RCD). Based on the experimental data retrieved from literature by manual curation, we graphically depicted the biological processes involved in RCD in the form of a seamless comprehensive signaling network map. The molecular mechanisms of each RCD mode are represented in detail. The RCD network map is divided into 26 functional modules that can be visualized contextually in the whole seamless network, as well as in individual diagrams. The resource is freely available and accessible via several web platforms for map navigation, data integration, and analysis. The RCD network map was employed for interpreting the functional differences in cell death regulation between Alzheimer's disease and non-small cell lung cancer based on gene expression data that allowed emphasizing the molecular mechanisms underlying the inverse comorbidity between the two pathologies. In addition, the map was used for the analysis of genomic and transcriptomic data from ovarian cancer patients that provided RCD map-based signatures of four distinct tumor subtypes and highlighted the difference in regulations of cell death molecular mechanisms.
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Affiliation(s)
- Jean-Marie Ravel
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
- Laboratoire de génétique médicale, CHRU-Nancy, F-54000 Nancy, France
- Inserm, NGERE, Université de Lorraine, F-54000 Nancy, France
| | - L. Cristobal Monraz Gomez
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
- Centre de Recherches Interdisciplinaires, Université Paris Descartes, 75006 Paris, France
| | - Laurence Calzone
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Boris Zhivotovsky
- Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia;
- Division of Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Box 210, 17177 Stockholm, Sweden
| | - Guido Kroemer
- Centre de Recherche des Cordeliers, Equipe labellisée par la Ligue contre le cancer, Université de Paris, Sorbonne Université, Inserm U1138, Institut Universitaire de France, 75006 Paris, France;
- Metabolomics and Cell Biology Platforms, Institut Gustave Roussy, 94805 Villejuif, France
- Pôle de Biologie, Hôpital Européen Georges Pompidou, AP-HP, 75015 Paris, France
- Suzhou Institute for Systems Medicine, Chinese Academy of Medical Sciences, Suzhou 215163, China
- Karolinska Institute, Department of Women’s and Children’s Health, Karolinska University Hospital, 171 77 Stockholm, Sweden
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005 Paris, France; (J.-M.R.); (L.C.M.G.); (N.S.); (L.C.); (E.B.); (A.Z.)
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8
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Kondratova M, Czerwinska U, Sompairac N, Amigorena SD, Soumelis V, Barillot E, Zinovyev A, Kuperstein I. A multiscale signalling network map of innate immune response in cancer reveals cell heterogeneity signatures. Nat Commun 2019; 10:4808. [PMID: 31641119 PMCID: PMC6805895 DOI: 10.1038/s41467-019-12270-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2018] [Accepted: 08/02/2019] [Indexed: 12/14/2022] Open
Abstract
The lack of integrated resources depicting the complexity of the innate immune response in cancer represents a bottleneck for high-throughput data interpretation. To address this challenge, we perform a systematic manual literature mining of molecular mechanisms governing the innate immune response in cancer and represent it as a signalling network map. The cell-type specific signalling maps of macrophages, dendritic cells, myeloid-derived suppressor cells and natural killers are constructed and integrated into a comprehensive meta map of the innate immune response in cancer. The meta-map contains 1466 chemical species as nodes connected by 1084 biochemical reactions, and it is supported by information from 820 articles. The resource helps to interpret single cell RNA-Seq data from macrophages and natural killer cells in metastatic melanoma that reveal different anti- or pro-tumor sub-populations within each cell type. Here, we report a new open source analytic platform that supports data visualisation and interpretation of tumour microenvironment activity in cancer. The complexity of the innate immune response to cancer makes interpretation of large data sets challenging. Here, the authors provide an integrated multi-scale map of signalling networks representing the different immune cells and their interactions and show its utility for data interpretation.
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Affiliation(s)
- Maria Kondratova
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005, Paris, France
| | - Urszula Czerwinska
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005, Paris, France.,Université Paris Descartes, Centre de Recherches Interdisciplinaires, Paris, France
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005, Paris, France.,Université Paris Descartes, Centre de Recherches Interdisciplinaires, Paris, France
| | | | - Vassili Soumelis
- Institut Curie, PSL Research University, Inserm, U932, 75005, Paris, France
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005, Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005, Paris, France
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, 75005, Paris, France.
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Duciel L, Monraz Gomez LC, Kondratova M, Kuperstein I, Saule S. The Phosphatase PRL-3 Is Involved in Key Steps of Cancer Metastasis. J Mol Biol 2019; 431:3056-3067. [DOI: 10.1016/j.jmb.2019.06.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2018] [Revised: 05/24/2019] [Accepted: 06/06/2019] [Indexed: 12/17/2022]
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10
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Zhao C, Zhang Y, Popel AS. Mechanistic Computational Models of MicroRNA-Mediated Signaling Networks in Human Diseases. Int J Mol Sci 2019; 20:ijms20020421. [PMID: 30669429 PMCID: PMC6358731 DOI: 10.3390/ijms20020421] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2018] [Revised: 01/14/2019] [Accepted: 01/15/2019] [Indexed: 12/17/2022] Open
Abstract
MicroRNAs (miRs) are endogenous non-coding RNA molecules that play important roles in human health and disease by regulating gene expression and cellular processes. In recent years, with the increasing scientific knowledge and new discovery of miRs and their gene targets, as well as the plentiful experimental evidence that shows dysregulation of miRs in a wide variety of human diseases, the computational modeling approach has emerged as an effective tool to help researchers identify novel functional associations between differential miR expression and diseases, dissect the phenotypic expression patterns of miRs in gene regulatory networks, and elucidate the critical roles of miRs in the modulation of disease pathways from mechanistic and quantitative perspectives. Here we will review the recent systems biology studies that employed different kinetic modeling techniques to provide mechanistic insights relating to the regulatory function and therapeutic potential of miRs in human diseases. Some of the key computational aspects to be discussed in detail in this review include (i) models of miR-mediated network motifs in the regulation of gene expression, (ii) models of miR biogenesis and miR–target interactions, and (iii) the incorporation of such models into complex disease pathways in order to generate mechanistic, molecular- and systems-level understanding of pathophysiology. Other related bioinformatics tools such as computational platforms that predict miR-disease associations will also be discussed, and we will provide perspectives on the challenges and opportunities in the future development and translational application of data-driven systems biology models that involve miRs and their regulatory pathways in human diseases.
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Affiliation(s)
- Chen Zhao
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Yu Zhang
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
| | - Aleksander S Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
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