51
|
Mazein A, Ostaszewski M, Kuperstein I, Watterson S, Le Novère N, Lefaudeux D, De Meulder B, Pellet J, Balaur I, Saqi M, Nogueira MM, He F, Parton A, Lemonnier N, Gawron P, Gebel S, Hainaut P, Ollert M, Dogrusoz U, Barillot E, Zinovyev A, Schneider R, Balling R, Auffray C. Systems medicine disease maps: community-driven comprehensive representation of disease mechanisms. NPJ Syst Biol Appl 2018; 4:21. [PMID: 29872544 PMCID: PMC5984630 DOI: 10.1038/s41540-018-0059-y] [Citation(s) in RCA: 64] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2018] [Revised: 04/26/2018] [Accepted: 05/04/2018] [Indexed: 12/18/2022] Open
Abstract
The development of computational approaches in systems biology has reached a state of maturity that allows their transition to systems medicine. Despite this progress, intuitive visualisation and context-dependent knowledge representation still present a major bottleneck. In this paper, we describe the Disease Maps Project, an effort towards a community-driven computationally readable comprehensive representation of disease mechanisms. We outline the key principles and the framework required for the success of this initiative, including use of best practices, standards and protocols. We apply a modular approach to ensure efficient sharing and reuse of resources for projects dedicated to specific diseases. Community-wide use of disease maps will accelerate the conduct of biomedical research and lead to new disease ontologies defined from mechanism-based disease endotypes rather than phenotypes.
Collapse
Affiliation(s)
- Alexander Mazein
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Marek Ostaszewski
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Inna Kuperstein
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Steven Watterson
- 7Northern Ireland Centre for Stratified Medicine, Ulster University, C-Tric, Altnagelvin Hospital Campus, Derry, Co Londonderry, Northern Ireland, BT47 6SB UK
| | - Nicolas Le Novère
- 8The Babraham Institute, Babraham Research Campus, Cambridge, CB22 3AT UK
| | - Diane Lefaudeux
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Bertrand De Meulder
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Johann Pellet
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Irina Balaur
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Mansoor Saqi
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Maria Manuela Nogueira
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| | - Feng He
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), House of BioHealth, 29 Rue Henri Koch, L-4354 Esch-Sur-Alzette, Luxembourg
| | - Andrew Parton
- 7Northern Ireland Centre for Stratified Medicine, Ulster University, C-Tric, Altnagelvin Hospital Campus, Derry, Co Londonderry, Northern Ireland, BT47 6SB UK
| | - Nathanaël Lemonnier
- 10Institute for Advanced Biosciences, University Grenoble-Alpes-INSERM U1209-CNRS UMR5309, Site Santé - Allée des Alpes, 38700 La Tronche, France
| | - Piotr Gawron
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Pierre Hainaut
- 10Institute for Advanced Biosciences, University Grenoble-Alpes-INSERM U1209-CNRS UMR5309, Site Santé - Allée des Alpes, 38700 La Tronche, France
| | - Markus Ollert
- Department of Infection and Immunity, Luxembourg Institute of Health (LIH), House of BioHealth, 29 Rue Henri Koch, L-4354 Esch-Sur-Alzette, Luxembourg.,11Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis, University of Southern Denmark, Odense, Denmark
| | - Ugur Dogrusoz
- 12Faculty of Engineering, Computer Engineering Department, Bilkent University, Ankara, 06800 Turkey
| | - Emmanuel Barillot
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Andrei Zinovyev
- 3Institut Curie, Paris, France.,4INSERM, U900 Paris, France.,5Mines ParisTech, Fontainebleau, France.,6PSL Research University, Paris, France
| | - Reinhard Schneider
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Rudi Balling
- 2Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 7 Avenue des Hauts-Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg
| | - Charles Auffray
- 1European Institute for Systems Biology and Medicine, CIRI UMR5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, 69007 Lyon, France
| |
Collapse
|
52
|
De Meulder B, Lefaudeux D, Bansal AT, Mazein A, Chaiboonchoe A, Ahmed H, Balaur I, Saqi M, Pellet J, Ballereau S, Lemonnier N, Sun K, Pandis I, Yang X, Batuwitage M, Kretsos K, van Eyll J, Bedding A, Davison T, Dodson P, Larminie C, Postle A, Corfield J, Djukanovic R, Chung KF, Adcock IM, Guo YK, Sterk PJ, Manta A, Rowe A, Baribaud F, Auffray C. A computational framework for complex disease stratification from multiple large-scale datasets. BMC SYSTEMS BIOLOGY 2018; 12:60. [PMID: 29843806 PMCID: PMC5975674 DOI: 10.1186/s12918-018-0556-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 02/21/2018] [Indexed: 01/05/2023]
Abstract
BACKGROUND Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.
Collapse
Affiliation(s)
- Bertrand De Meulder
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France.
| | - Diane Lefaudeux
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Aruna T Bansal
- Acclarogen Ltd, St John's Innovation Centre, Cambridge, CB4 OWS, UK
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Amphun Chaiboonchoe
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Hassan Ahmed
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Irina Balaur
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Mansoor Saqi
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Johann Pellet
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Stéphane Ballereau
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Nathanaël Lemonnier
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France
| | - Kai Sun
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | - Ioannis Pandis
- Data Science Institute, Imperial College, London, SW7 2AZ, UK.,Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | - Xian Yang
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | | | | | | | | | - Timothy Davison
- Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | - Paul Dodson
- AstraZeneca Ltd, Alderley Park, Macclesfield, SK10 4TG, UK
| | | | - Anthony Postle
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Julie Corfield
- AstraZeneca R & D, 43150, Mölndal, Sweden.,Arateva R & D Ltd, Nottingham, NG1 1GF, UK
| | - Ratko Djukanovic
- Faculty of Medicine, University of Southampton, Southampton, SO17 1BJ, UK
| | - Kian Fan Chung
- National Hearth and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Ian M Adcock
- National Hearth and Lung Institute, Imperial College London, London, SW3 6LY, UK
| | - Yi-Ke Guo
- Data Science Institute, Imperial College, London, SW7 2AZ, UK
| | - Peter J Sterk
- Department of Respiratory Medicine, Academic Medical Centre, University of Amsterdam, Amsterdam, AZ1105, The Netherlands
| | - Alexander Manta
- Research Informatics, Roche Diagnostics GmbH, 82008, Unterhaching, Germany
| | - Anthony Rowe
- Janssen Research and Development Ltd, High Wycombe, HP12 4DP, UK
| | | | - Charles Auffray
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, EISBM, 50 Avenue Tony Garnier, 69007, Lyon, France.
| | | |
Collapse
|
53
|
van Maanen EMT, van Steeg TJ, Ahsman MJ, Michener MS, Savage MJ, Kennedy ME, Kleijn HJ, Stone J, Danhof M. Extending a Systems Model of the APP Pathway: Separation of β- and γ-Secretase Sequential Cleavage Steps of APP. J Pharmacol Exp Ther 2018; 365:507-518. [PMID: 29563326 DOI: 10.1124/jpet.117.244699] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2017] [Accepted: 02/05/2018] [Indexed: 11/22/2022] Open
Abstract
The abnormal accumulation of amyloid-β (Aβ) in the brain parenchyma has been posited as a central event in the pathophysiology of Alzheimer's disease. Recently, we have proposed a systems pharmacology model of the amyloid precursor protein (APP) pathway, describing the Aβ APP metabolite responses (Aβ40, Aβ42, sAPPα, and sAPPβ) to β-secretase 1 (BACE1) inhibition. In this investigation this model was challenged to describe Aβ dynamics following γ-secretase (GS) inhibition. This led an extended systems pharmacology model, with separate descriptions to characterize the sequential cleavage steps of APP by BACE1 and GS, to describe the differences in Aβ response to their respective inhibition. Following GS inhibition, a lower Aβ40 formation rate constant was observed, compared with BACE1 inhibition. Both BACE1 and GS inhibition were predicted to lower Aβ oligomer levels. Further model refinement and new data may be helpful to fully understand the difference in Aβ dynamics following BACE1 versus GS inhibition.
Collapse
Affiliation(s)
- Eline M T van Maanen
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| | - Tamara J van Steeg
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| | - Maurice J Ahsman
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| | - Maria S Michener
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| | - Mary J Savage
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| | - Matthew E Kennedy
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| | - Huub Jan Kleijn
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| | - Julie Stone
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| | - Meindert Danhof
- Division of Pharmacology, Leiden Academic Center for Drug Research, Leiden University, Leiden, The Netherlands (E.M.T.v.M., M.D.); Leiden Experts on Advanced Pharmacokinetics and Pharmacodynamics, Leiden, The Netherlands (E.M.T.v.M., T.J.v.S., M.J.A.); and Merck & Company, Inc., Kenilworth, New Jersey (M.S.M., M.J.S., M.E.K., H.J.K., J.S.)
| |
Collapse
|
54
|
Mendiola-Precoma J, Padilla K, Rodríguez-Cruz A, Berumen LC, Miledi R, García-Alcocer G. Theobromine-Induced Changes in A1 Purinergic Receptor Gene Expression and Distribution in a Rat Brain Alzheimer's Disease Model. J Alzheimers Dis 2018; 55:1273-1283. [PMID: 27792010 DOI: 10.3233/jad-160569] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Dementia caused by Alzheimer's disease (AD) is mainly characterized by accumulation in the brain of extra- and intraneuronal amyloid-β (Aβ) and tau proteins, respectively, which selectively affect specific regions, particularly the neocortex and the hippocampus. Sporadic AD is mainly caused by an increase in apolipoprotein E, a component of chylomicrons, which are cholesterol transporters in the brain. Recent studies have shown that high lipid levels, especially cholesterol, are linked to AD. Adenosine is an atypical neurotransmitter that regulates a wide range of physiological functions by activating four P1 receptors (A1, A2A, A2B, and A3) and P2 purinergic receptors that are G protein-coupled. A1 receptors are involved in the inhibition of neurotransmitter release, which could be related to AD. The aim of the present work was to study the effects of a lard-enriched diet (LED) on cognitive and memory processes in adult rats (6 months of age) as well as the effect of theobromine on these processes. The results indicated that the fat-enriched diet resulted in a long-term deterioration in cognitive and memory functions. Increased levels of Aβ protein and IL-1β were also observed in the rats fed with a high-cholesterol diet, which were used to validate the AD animal model. In addition, the results of qPCR and immunohistochemistry indicated a decrease in gene expression and distribution of A1 purinegic receptor, respectively, in the hippocampus of LED-fed rats. Interestingly, theobromine, at both concentrations tested, restored A1 receptor levels and improved cognitive functions and Aβ levels for a dose of 30 mg/L drinking water.
Collapse
Affiliation(s)
- Jesus Mendiola-Precoma
- Laboratorio de Investigación Genética, Facultad de Química, Universidad Autónoma de Querétaro, Centro Universitario S/N, Querétaro, México
| | - Karla Padilla
- Laboratorio de Investigación Genética, Facultad de Química, Universidad Autónoma de Querétaro, Centro Universitario S/N, Querétaro, México
| | - Alfredo Rodríguez-Cruz
- Laboratorio de Investigación Genética, Facultad de Química, Universidad Autónoma de Querétaro, Centro Universitario S/N, Querétaro, México
| | - Laura C Berumen
- Laboratorio de Investigación Genética, Facultad de Química, Universidad Autónoma de Querétaro, Centro Universitario S/N, Querétaro, México
| | - Ricardo Miledi
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Campus Juriquilla, Juriquilla, Querétaro, CP, Mexico
| | - Guadalupe García-Alcocer
- Laboratorio de Investigación Genética, Facultad de Química, Universidad Autónoma de Querétaro, Centro Universitario S/N, Querétaro, México
| |
Collapse
|
55
|
Delhalle S, Bode SFN, Balling R, Ollert M, He FQ. A roadmap towards personalized immunology. NPJ Syst Biol Appl 2018; 4:9. [PMID: 29423275 PMCID: PMC5802799 DOI: 10.1038/s41540-017-0045-9] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 11/29/2017] [Accepted: 12/19/2017] [Indexed: 12/30/2022] Open
Abstract
Big data generation and computational processing will enable medicine to evolve from a "one-size-fits-all" approach to precise patient stratification and treatment. Significant achievements using "Omics" data have been made especially in personalized oncology. However, immune cells relative to tumor cells show a much higher degree of complexity in heterogeneity, dynamics, memory-capability, plasticity and "social" interactions. There is still a long way ahead on translating our capability to identify potentially targetable personalized biomarkers into effective personalized therapy in immune-centralized diseases. Here, we discuss the recent advances and successful applications in "Omics" data utilization and network analysis on patients' samples of clinical trials and studies, as well as the major challenges and strategies towards personalized stratification and treatment for infectious or non-communicable inflammatory diseases such as autoimmune diseases or allergies. We provide a roadmap and highlight experimental, clinical, computational analysis, data management, ethical and regulatory issues to accelerate the implementation of personalized immunology.
Collapse
Affiliation(s)
- Sylvie Delhalle
- 1Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
| | - Sebastian F N Bode
- 1Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg.,2Center for Pediatrics-Department of General Pediatrics, Adolescent Medicine, and Neonatology, Medical Center, Faculty of Medicine, University of Freiburg, Mathildenstrasse 1, 79106 Freiburg, Germany
| | - Rudi Balling
- 3Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Campus Belval, 6, Avenue du Swing, 4367 Belvaux, Luxembourg
| | - Markus Ollert
- 1Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg.,4Department of Dermatology and Allergy Center, Odense Research Center for Anaphylaxis (ORCA), University of Southern Denmark, 5000 Odense C, Denmark
| | - Feng Q He
- 1Department of Infection and Immunity, Luxembourg Institute of Health (LIH), 29, rue Henri Koch, 4354 Esch-sur-Alzette, Luxembourg
| |
Collapse
|
56
|
Hampel H, Toschi N, Babiloni C, Baldacci F, Black KL, Bokde AL, Bun RS, Cacciola F, Cavedo E, Chiesa PA, Colliot O, Coman CM, Dubois B, Duggento A, Durrleman S, Ferretti MT, George N, Genthon R, Habert MO, Herholz K, Koronyo Y, Koronyo-Hamaoui M, Lamari F, Langevin T, Lehéricy S, Lorenceau J, Neri C, Nisticò R, Nyasse-Messene F, Ritchie C, Rossi S, Santarnecchi E, Sporns O, Verdooner SR, Vergallo A, Villain N, Younesi E, Garaci F, Lista S. Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. J Alzheimers Dis 2018; 64:S47-S105. [PMID: 29562524 PMCID: PMC6008221 DOI: 10.3233/jad-179932] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular, and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an "omics"-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical, and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer's disease. The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group "Alzheimer Precision Medicine" (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development toward breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.
Collapse
Affiliation(s)
- Harald Hampel
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Department of Radiology, “Athinoula A. Martinos” Center for Biomedical Imaging, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Claudio Babiloni
- Department of Physiology and Pharmacology “Vittorio Erspamer”, University of Rome “La Sapienza”, Rome, Italy
- Institute for Research and Medical Care, IRCCS “San Raffaele Pisana”, Rome, Italy
| | - Filippo Baldacci
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Keith L. Black
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Arun L.W. Bokde
- Discipline of Psychiatry, School of Medicine and Trinity College Institute of Neuroscience (TCIN), Trinity College Dublin, Dublin, Ireland
| | - René S. Bun
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Francesco Cacciola
- Unit of Neurosurgery, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Enrica Cavedo
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
- IRCCS “San Giovanni di Dio-Fatebenefratelli”, Brescia, Italy
| | - Patrizia A. Chiesa
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Olivier Colliot
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France; Department of Neuroradiology, AP-HP, Hôpital de la Pitié-Salpêtrière, Paris, France; Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Paris, France
| | - Cristina-Maria Coman
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Bruno Dubois
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
| | - Stanley Durrleman
- Inserm, U1127, Paris, France; CNRS, UMR 7225 ICM, Paris, France; Sorbonne Universités, UPMC Univ Paris 06, UMR S 1127, Paris, France; Institut du Cerveau et de la Moelle Épinière (ICM) Paris, France; Inria, Aramis project-team, Centre de Recherche de Paris, France
| | - Maria-Teresa Ferretti
- IREM, Institute for Regenerative Medicine, University of Zurich, Zürich, Switzerland
- ZNZ Neuroscience Center Zurich, Zürich, Switzerland
| | - Nathalie George
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, Institut du Cerveau et de la Moelle Épinière, ICM, Ecole Normale Supérieure, ENS, Centre MEG-EEG, F-75013, Paris, France
| | - Remy Genthon
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Marie-Odile Habert
- Département de Médecine Nucléaire, Hôpital de la Pitié-Salpêtrière, AP-HP, Paris, France
- Laboratoire d’Imagerie Biomédicale, Sorbonne Universités, UPMC Univ Paris 06, Inserm U 1146, CNRS UMR 7371, Paris, France
| | - Karl Herholz
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
- Division of Informatics, Imaging and Data Sciences, University of Manchester, Wolfson Molecular Imaging Centre, Manchester, UK
| | - Yosef Koronyo
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Foudil Lamari
- AP-HP, UF Biochimie des Maladies Neuro-métaboliques, Service de Biochimie Métabolique, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | | | - Stéphane Lehéricy
- Centre de NeuroImagerie de Recherche - CENIR, Institut du Cerveau et de la Moelle Épinière - ICM, F-75013, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06 UMR S 1127, Inserm U 1127, CNRS UMR 7225, ICM, F-75013, Paris, France
| | - Jean Lorenceau
- Institut de la Vision, INSERM, Sorbonne Universités, UPMC Univ Paris 06, UMR_S968, CNRS UMR7210, Paris, France
| | - Christian Neri
- Sorbonne Universités, Université Pierre et Marie Curie (UPMC) Paris 06, CNRS UMR 8256, Institut de Biologie Paris-Seine (IBPS), Place Jussieu, F-75005, Paris, France
| | - Robert Nisticò
- Department of Biology, University of Rome “Tor Vergata” & Pharmacology of Synaptic Disease Lab, European Brain Research Institute (E.B.R.I.), Rome, Italy
| | - Francis Nyasse-Messene
- Sorbonne Université, Inserm, CNRS, Institut du Cerveau et de la Moelle Épinière (ICM), Département de Neurologie, Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
| | - Craig Ritchie
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Simone Rossi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Department of Medicine, Surgery and Neurosciences, Section of Human Physiology University of Siena, Siena, Italy
| | - Emiliano Santarnecchi
- Department of Medicine, Surgery and Neurosciences, Unit of Neurology and Clinical Neurophysiology, Brain Investigation & Neuromodulation Lab. (Si-BIN Lab.), University of Siena, Siena, Italy
- Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
- IU Network Science Institute, Indiana University, Bloomington, IN, USA
| | | | - Andrea Vergallo
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | - Nicolas Villain
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| | | | - Francesco Garaci
- Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, Rome, Italy
- Casa di Cura “San Raffaele Cassino”, Cassino, Italy
| | - Simone Lista
- AXA Research Fund & Sorbonne Université Chair, Paris, France
- Sorbonne Université, AP-HP, GRC n° 21, Alzheimer Precision Medicine (APM), Hôpital de la Pitié-Salpêtrière, Boulevard de l’hôpital, F-75013, Paris, France
- Institut du Cerveau et de la Moelle Épinière (ICM), INSERM U 1127, CNRS UMR 7225, Boulevard de l’hôpital, F-75013, Paris, France
- Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Département de Neurologie, Hôpital de la Pitié-Salpêtrière, AP-HP, Boulevard de l’hôpital, F-75013, Paris, France
| |
Collapse
|
57
|
Csabai L, Ölbei M, Budd A, Korcsmáros T, Fazekas D. SignaLink: Multilayered Regulatory Networks. Methods Mol Biol 2018; 1819:53-73. [PMID: 30421399 DOI: 10.1007/978-1-4939-8618-7_3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Biological networks are graphs used to represent the inner workings of a biological system. Networks describe the relationships of the elements of biological systems using edges and nodes. However, the resulting representation of the system can sometimes be too simplistic to usefully model reality. By combining several different interaction types within one larger multilayered biological network, tools such as SignaLink provide a more nuanced view than those relying on single-layer networks (where edges only describe one kind of interaction). Multilayered networks display connections between multiple networks (i.e., protein-protein interactions and their transcriptional and posttranscriptional regulators), each one of them describing a specific set of connections. Multilayered networks also allow us to depict cross talk between cellular systems, which is a more realistic way of describing molecular interactions. They can be used to collate networks from different sources into one multilayered structure, which makes them useful as an analytic tool as well.
Collapse
Affiliation(s)
| | - Márton Ölbei
- Earlham Institute, Norwich Research Park, Norwich, UK.,Quadram Institute, Norwich Research Park, Norwich, UK
| | - Aidan Budd
- Earlham Institute, Norwich Research Park, Norwich, UK
| | - Tamás Korcsmáros
- Eötvös Loránd University, Budapest, Hungary. .,Earlham Institute, Norwich Research Park, Norwich, UK. .,Quadram Institute, Norwich Research Park, Norwich, UK.
| | - Dávid Fazekas
- Eötvös Loránd University, Budapest, Hungary.,Earlham Institute, Norwich Research Park, Norwich, UK
| |
Collapse
|
58
|
Felgueiras J, Silva JV, Fardilha M. Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools. J Proteomics 2018; 171:127-140. [DOI: 10.1016/j.jprot.2017.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/26/2017] [Accepted: 05/13/2017] [Indexed: 02/02/2023]
|
59
|
Geerts H, Hofmann-Apitius M, Anastasio TJ. Knowledge-driven computational modeling in Alzheimer's disease research: Current state and future trends. Alzheimers Dement 2017; 13:1292-1302. [PMID: 28917669 DOI: 10.1016/j.jalz.2017.08.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Revised: 07/05/2017] [Accepted: 08/01/2017] [Indexed: 11/24/2022]
Abstract
Neurodegenerative diseases such as Alzheimer's disease (AD) follow a slowly progressing dysfunctional trajectory, with a large presymptomatic component and many comorbidities. Using preclinical models and large-scale omics studies ranging from genetics to imaging, a large number of processes that might be involved in AD pathology at different stages and levels have been identified. The sheer number of putative hypotheses makes it almost impossible to estimate their contribution to the clinical outcome and to develop a comprehensive view on the pathological processes driving the clinical phenotype. Traditionally, bioinformatics approaches have provided correlations and associations between processes and phenotypes. Focusing on causality, a new breed of advanced and more quantitative modeling approaches that use formalized domain expertise offer new opportunities to integrate these different modalities and outline possible paths toward new therapeutic interventions. This article reviews three different computational approaches and their possible complementarities. Process algebras, implemented using declarative programming languages such as Maude, facilitate simulation and analysis of complicated biological processes on a comprehensive but coarse-grained level. A model-driven Integration of Data and Knowledge, based on the OpenBEL platform and using reverse causative reasoning and network jump analysis, can generate mechanistic knowledge and a new, mechanism-based taxonomy of disease. Finally, Quantitative Systems Pharmacology is based on formalized implementation of domain expertise in a more fine-grained, mechanism-driven, quantitative, and predictive humanized computer model. We propose a strategy to combine the strengths of these individual approaches for developing powerful modeling methodologies that can provide actionable knowledge for rational development of preventive and therapeutic interventions. Development of these computational approaches is likely to be required for further progress in understanding and treating AD.
Collapse
Affiliation(s)
- Hugo Geerts
- In Silico Biosciences, Berwyn, PA, USA; Perelman School of Medicine, Univ. of Pennsylvania.
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
| | - Thomas J Anastasio
- Department of Molecular and Integrative Physiology, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | |
Collapse
|
60
|
Hampel H, O’Bryant SE, Durrleman S, Younesi E, Rojkova K, Escott-Price V, Corvol JC, Broich K, Dubois B, Lista S. A Precision Medicine Initiative for Alzheimer’s disease: the road ahead to biomarker-guided integrative disease modeling. Climacteric 2017; 20:107-118. [DOI: 10.1080/13697137.2017.1287866] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- H. Hampel
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - S. E. O’Bryant
- Institute for Healthy Aging, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - S. Durrleman
- ARAMIS Lab, Inria Paris, Paris, France
- Sorbonne Universités, UPMC Univ Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM), Hôpital Pitié-Salpêtrière, Boulevard de l’hôpital, Paris, France
| | - E. Younesi
- European Society for Translational Medicine, Vienna, Austria
| | - K. Rojkova
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - V. Escott-Price
- Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK
| | - J-C. Corvol
- Département de Neurologie, Sorbonne Université, Université Pierre et Marie Curie, Paris 06 UMR S 1127, Institut National de Santé et en Recherche Médicale (INSERM) U 1127 and CIC-1422, Centre National de Recherche Scientifique U 7225, Institut du Cerveau et de la Moelle Epinière, Assistance Publique Hôpitaux de Paris, Hôpital Pitié-Salpêtrière, Paris, France
| | - K. Broich
- President, Federal Institute for Drugs and Medical Devices (BfArM), Bonn, Germany
| | - B. Dubois
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | - S. Lista
- AXA Research Fund & UPMC Chair, Paris, France
- Département de Neurologie, Sorbonne Universités, Université Pierre et Marie Curie (UPMC), Paris 06, Inserm, CNRS, Institut du cerveau et de la moelle (ICM), Institut de la Mémoire et de la Maladie d’Alzheimer (IM2A), Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
| | | |
Collapse
|
61
|
Lloret‐Villas A, Varusai TM, Juty N, Laibe C, Le NovÈre N, Hermjakob H, Chelliah V. The Impact of Mathematical Modeling in Understanding the Mechanisms Underlying Neurodegeneration: Evolving Dimensions and Future Directions. CPT Pharmacometrics Syst Pharmacol 2017; 6:73-86. [PMID: 28063254 PMCID: PMC5321808 DOI: 10.1002/psp4.12155] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 10/14/2016] [Accepted: 10/30/2016] [Indexed: 12/14/2022] Open
Abstract
Neurodegenerative diseases are a heterogeneous group of disorders that are characterized by the progressive dysfunction and loss of neurons. Here, we distil and discuss the current state of modeling in the area of neurodegeneration, and objectively compare the gaps between existing clinical knowledge and the mechanistic understanding of the major pathological processes implicated in neurodegenerative disorders. We also discuss new directions in the field of neurodegeneration that hold potential for furthering therapeutic interventions and strategies.
Collapse
Affiliation(s)
- A Lloret‐Villas
- European Bioinformatics Institute (EMBL‐EBI), European Molecular Biology LaboratoryWellcome Trust Genome Campus, HinxtonCambridgeUK
| | - TM Varusai
- European Bioinformatics Institute (EMBL‐EBI), European Molecular Biology LaboratoryWellcome Trust Genome Campus, HinxtonCambridgeUK
| | - N Juty
- European Bioinformatics Institute (EMBL‐EBI), European Molecular Biology LaboratoryWellcome Trust Genome Campus, HinxtonCambridgeUK
| | - C Laibe
- European Bioinformatics Institute (EMBL‐EBI), European Molecular Biology LaboratoryWellcome Trust Genome Campus, HinxtonCambridgeUK
| | - N Le NovÈre
- Babraham Institute, Babraham Research CampusCambridgeUK
| | - H Hermjakob
- European Bioinformatics Institute (EMBL‐EBI), European Molecular Biology LaboratoryWellcome Trust Genome Campus, HinxtonCambridgeUK
| | - V Chelliah
- European Bioinformatics Institute (EMBL‐EBI), European Molecular Biology LaboratoryWellcome Trust Genome Campus, HinxtonCambridgeUK
| |
Collapse
|
62
|
Touré V, Mazein A, Waltemath D, Balaur I, Saqi M, Henkel R, Pellet J, Auffray C. STON: exploring biological pathways using the SBGN standard and graph databases. BMC Bioinformatics 2016; 17:494. [PMID: 27919219 PMCID: PMC5139139 DOI: 10.1186/s12859-016-1394-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2016] [Accepted: 11/29/2016] [Indexed: 01/16/2023] Open
Abstract
Background When modeling in Systems Biology and Systems Medicine, the data is often extensive, complex and heterogeneous. Graphs are a natural way of representing biological networks. Graph databases enable efficient storage and processing of the encoded biological relationships. They furthermore support queries on the structure of biological networks. Results We present the Java-based framework STON (SBGN TO Neo4j). STON imports and translates metabolic, signalling and gene regulatory pathways represented in the Systems Biology Graphical Notation into a graph-oriented format compatible with the Neo4j graph database. Conclusion STON exploits the power of graph databases to store and query complex biological pathways. This advances the possibility of: i) identifying subnetworks in a given pathway; ii) linking networks across different levels of granularity to address difficulties related to incomplete knowledge representation at single level; and iii) identifying common patterns between pathways in the database. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1394-x) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Vasundra Touré
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051, Germany. .,European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France.
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Dagmar Waltemath
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, 18051, Germany
| | - Irina Balaur
- European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Mansoor Saqi
- European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Ron Henkel
- Scientific Databases and Visualization, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.,Department of Business Information Systems, University of Rostock, Rostock, 18051, Germany
| | - Johann Pellet
- European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine (EISBM), CIRI UMR 5308, CNRS-ENS-UCBL-INSERM, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| |
Collapse
|
63
|
THD-Module Extractor: An Application for CEN Module Extraction and Interesting Gene Identification for Alzheimer's Disease. Sci Rep 2016; 6:38046. [PMID: 27901073 PMCID: PMC5128915 DOI: 10.1038/srep38046] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2016] [Accepted: 11/03/2016] [Indexed: 01/08/2023] Open
Abstract
There exist many tools and methods for construction of co-expression network from gene expression data and for extraction of densely connected gene modules. In this paper, a method is introduced to construct co-expression network and to extract co-expressed modules having high biological significance. The proposed method has been validated on several well known microarray datasets extracted from a diverse set of species, using statistical measures, such as p and q values. The modules obtained in these studies are found to be biologically significant based on Gene Ontology enrichment analysis, pathway analysis, and KEGG enrichment analysis. Further, the method was applied on an Alzheimer’s disease dataset and some interesting genes are found, which have high semantic similarity among them, but are not significantly correlated in terms of expression similarity. Some of these interesting genes, such as MAPT, CASP2, and PSEN2, are linked with important aspects of Alzheimer’s disease, such as dementia, increase cell death, and deposition of amyloid-beta proteins in Alzheimer’s disease brains. The biological pathways associated with Alzheimer’s disease, such as, Wnt signaling, Apoptosis, p53 signaling, and Notch signaling, incorporate these interesting genes. The proposed method is evaluated in regard to existing literature.
Collapse
|
64
|
Gawron P, Ostaszewski M, Satagopam V, Gebel S, Mazein A, Kuzma M, Zorzan S, McGee F, Otjacques B, Balling R, Schneider R. MINERVA-a platform for visualization and curation of molecular interaction networks. NPJ Syst Biol Appl 2016; 2:16020. [PMID: 28725475 PMCID: PMC5516855 DOI: 10.1038/npjsba.2016.20] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Revised: 06/15/2016] [Accepted: 06/24/2016] [Indexed: 12/11/2022] Open
Abstract
Our growing knowledge about various molecular mechanisms is becoming increasingly more structured and accessible. Different repositories of molecular interactions and available literature enable construction of focused and high-quality molecular interaction networks. Novel tools for curation and exploration of such networks are needed, in order to foster the development of a systems biology environment. In particular, solutions for visualization, annotation and data cross-linking will facilitate usage of network-encoded knowledge in biomedical research. To this end we developed the MINERVA (Molecular Interaction NEtwoRks VisuAlization) platform, a standalone webservice supporting curation, annotation and visualization of molecular interaction networks in Systems Biology Graphical Notation (SBGN)-compliant format. MINERVA provides automated content annotation and verification for improved quality control. The end users can explore and interact with hosted networks, and provide direct feedback to content curators. MINERVA enables mapping drug targets or overlaying experimental data on the visualized networks. Extensive export functions enable downloading areas of the visualized networks as SBGN-compliant models for efficient reuse of hosted networks. The software is available under Affero GPL 3.0 as a Virtual Machine snapshot, Debian package and Docker instance at http://r3lab.uni.lu/web/minerva-website/. We believe that MINERVA is an important contribution to systems biology community, as its architecture enables set-up of locally or globally accessible SBGN-oriented repositories of molecular interaction networks. Its functionalities allow overlay of multiple information layers, facilitating exploration of content and interpretation of data. Moreover, annotation and verification workflows of MINERVA improve the efficiency of curation of networks, allowing life-science researchers to better engage in development and use of biomedical knowledge repositories.
Collapse
Affiliation(s)
- Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, Université de Lyon, eTRIKS Consortium, Lyon, France
| | - Michal Kuzma
- Institute of Computing Science, Poznan University of Technology, Poznan, Poland
| | - Simone Zorzan
- Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
| | - Fintan McGee
- Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
| | - Benoît Otjacques
- Luxembourg Institute of Science and Technology, Belvaux, Luxembourg
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Esch-sur-Alzette, Luxembourg
| |
Collapse
|
65
|
Hsin KY, Matsuoka Y, Asai Y, Kamiyoshi K, Watanabe T, Kawaoka Y, Kitano H. systemsDock: a web server for network pharmacology-based prediction and analysis. Nucleic Acids Res 2016; 44:W507-13. [PMID: 27131384 PMCID: PMC4987901 DOI: 10.1093/nar/gkw335] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 04/08/2016] [Accepted: 04/15/2016] [Indexed: 11/14/2022] Open
Abstract
We present systemsDock, a web server for network pharmacology-based prediction and analysis, which permits docking simulation and molecular pathway map for comprehensive characterization of ligand selectivity and interpretation of ligand action on a complex molecular network. It incorporates an elaborately designed scoring function for molecular docking to assess protein-ligand binding potential. For large-scale screening and ease of investigation, systemsDock has a user-friendly GUI interface for molecule preparation, parameter specification and result inspection. Ligand binding potentials against individual proteins can be directly displayed on an uploaded molecular interaction map, allowing users to systemically investigate network-dependent effects of a drug or drug candidate. A case study is given to demonstrate how systemsDock can be used to discover a test compound's multi-target activity. systemsDock is freely accessible at http://systemsdock.unit.oist.jp/.
Collapse
Affiliation(s)
- Kun-Yi Hsin
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0412, Japan
| | - Yukiko Matsuoka
- The Systems Biology Institute, Minato, Tokyo 108-0071, Japan
| | - Yoshiyuki Asai
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0412, Japan
| | - Kyota Kamiyoshi
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0412, Japan
| | - Tokiko Watanabe
- Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, The University of Tokyo, Minato, Tokyo 108-8639, Japan
| | - Yoshihiro Kawaoka
- Division of Virology, Department of Microbiology and Immunology, Institute of Medical Science, The University of Tokyo, Minato, Tokyo 108-8639, Japan Department of Pathobiological Sciences, Influenza Research Institute, School of Veterinary Medicine, University of Wisconsin-Madison, Madison, Wisconsin 53711, USA
| | - Hiroaki Kitano
- Integrated Open Systems Unit, Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0412, Japan The Systems Biology Institute, Minato, Tokyo 108-0071, Japan Laboratory for Disease Systems Modeling, RIKEN Center for Integrative Medical Sciences, Yokohama City, Kanagawa 230-0045, Japan
| |
Collapse
|
66
|
Satagopam V, Gu W, Eifes S, Gawron P, Ostaszewski M, Gebel S, Barbosa-Silva A, Balling R, Schneider R. Integration and Visualization of Translational Medicine Data for Better Understanding of Human Diseases. BIG DATA 2016; 4:97-108. [PMID: 27441714 PMCID: PMC4932659 DOI: 10.1089/big.2015.0057] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Translational medicine is a domain turning results of basic life science research into new tools and methods in a clinical environment, for example, as new diagnostics or therapies. Nowadays, the process of translation is supported by large amounts of heterogeneous data ranging from medical data to a whole range of -omics data. It is not only a great opportunity but also a great challenge, as translational medicine big data is difficult to integrate and analyze, and requires the involvement of biomedical experts for the data processing. We show here that visualization and interoperable workflows, combining multiple complex steps, can address at least parts of the challenge. In this article, we present an integrated workflow for exploring, analysis, and interpretation of translational medicine data in the context of human health. Three Web services-tranSMART, a Galaxy Server, and a MINERVA platform-are combined into one big data pipeline. Native visualization capabilities enable the biomedical experts to get a comprehensive overview and control over separate steps of the workflow. The capabilities of tranSMART enable a flexible filtering of multidimensional integrated data sets to create subsets suitable for downstream processing. A Galaxy Server offers visually aided construction of analytical pipelines, with the use of existing or custom components. A MINERVA platform supports the exploration of health and disease-related mechanisms in a contextualized analytical visualization system. We demonstrate the utility of our workflow by illustrating its subsequent steps using an existing data set, for which we propose a filtering scheme, an analytical pipeline, and a corresponding visualization of analytical results. The workflow is available as a sandbox environment, where readers can work with the described setup themselves. Overall, our work shows how visualization and interfacing of big data processing services facilitate exploration, analysis, and interpretation of translational medicine data.
Collapse
Affiliation(s)
- Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Wei Gu
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Serge Eifes
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
- Information Technology for Translational Medicine (ITTM) S.A., Esch-Belval, Luxembourg
| | - Piotr Gawron
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Stephan Gebel
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Adriano Barbosa-Silva
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Belval, Luxembourg
| |
Collapse
|
67
|
Thomas J, Seo D, Sael L. Review on Graph Clustering and Subgraph Similarity Based Analysis of Neurological Disorders. Int J Mol Sci 2016; 17:ijms17060862. [PMID: 27258269 PMCID: PMC4926396 DOI: 10.3390/ijms17060862] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Revised: 05/10/2016] [Accepted: 05/24/2016] [Indexed: 01/03/2023] Open
Abstract
How can complex relationships among molecular or clinico-pathological entities of neurological disorders be represented and analyzed? Graphs seem to be the current answer to the question no matter the type of information: molecular data, brain images or neural signals. We review a wide spectrum of graph representation and graph analysis methods and their application in the study of both the genomic level and the phenotypic level of the neurological disorder. We find numerous research works that create, process and analyze graphs formed from one or a few data types to gain an understanding of specific aspects of the neurological disorders. Furthermore, with the increasing number of data of various types becoming available for neurological disorders, we find that integrative analysis approaches that combine several types of data are being recognized as a way to gain a global understanding of the diseases. Although there are still not many integrative analyses of graphs due to the complexity in analysis, multi-layer graph analysis is a promising framework that can incorporate various data types. We describe and discuss the benefits of the multi-layer graph framework for studies of neurological disease.
Collapse
Affiliation(s)
- Jaya Thomas
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.
- Department of Computer Science, State University New York Korea, Incheon 406-840, Korea.
| | - Dongmin Seo
- Korea Institute of Science and Technology Information, 245 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea.
| | - Lee Sael
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11794, USA.
- Department of Computer Science, State University New York Korea, Incheon 406-840, Korea.
| |
Collapse
|
68
|
Meng G, Zhong X, Mei H. A Systematic Investigation into Aging Related Genes in Brain and Their Relationship with Alzheimer's Disease. PLoS One 2016; 11:e0150624. [PMID: 26937969 PMCID: PMC4777381 DOI: 10.1371/journal.pone.0150624] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2015] [Accepted: 02/17/2016] [Indexed: 01/08/2023] Open
Abstract
Aging, as a complex biological process, is accompanied by the accumulation of functional loses at different levels, which makes age to be the biggest risk factor to many neurological diseases. Even following decades of investigation, the process of aging is still far from being fully understood, especially at a systematic level. In this study, we identified aging related genes in brain by collecting the ones with sustained and consistent gene expression or DNA methylation changes in the aging process. Functional analysis with Gene Ontology to these genes suggested transcriptional regulators to be the most affected genes in the aging process. Transcription regulation analysis found some transcription factors, especially Specificity Protein 1 (SP1), to play important roles in regulating aging related gene expression. Module-based functional analysis indicated these genes to be associated with many well-known aging related pathways, supporting the validity of our approach to select aging related genes. Finally, we investigated the roles of aging related genes on Alzheimer's Disease (AD). We found that aging and AD related genes both involved some common pathways, which provided a possible explanation why aging made the brain more vulnerable to Alzheimer's Disease.
Collapse
Affiliation(s)
- Guofeng Meng
- Computational Modeling Sciences, Platform Technologies and Science, GlaxoSmithKline Research & Development, Shanghai, China
| | - Xiaoyan Zhong
- Neurodegeneration DPU, GlaxoSmithKline Research & Development, Shanghai, China
| | - Hongkang Mei
- Computational Modeling Sciences, Platform Technologies and Science, GlaxoSmithKline Research & Development, Shanghai, China
| |
Collapse
|
69
|
Calderone A, Formenti M, Aprea F, Papa M, Alberghina L, Colangelo AM, Bertolazzi P. Comparing Alzheimer's and Parkinson's diseases networks using graph communities structure. BMC SYSTEMS BIOLOGY 2016; 10:25. [PMID: 26935435 PMCID: PMC4776441 DOI: 10.1186/s12918-016-0270-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2015] [Accepted: 02/16/2016] [Indexed: 01/01/2023]
Abstract
BACKGROUND Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson's Disease (PD) and Alzheimer's Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences. RESULTS In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine). CONCLUSIONS This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.
Collapse
Affiliation(s)
- Alberto Calderone
- Institute of Systems Analysis and Computer Science, National Research Council of Italy, Via dei Taurini, 19, Roma, 00185, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| | - Matteo Formenti
- Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| | - Federica Aprea
- Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| | - Michele Papa
- Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, Second University of Naples, Naples, Italy, Via L. Armanni, 5, Napoli, 80138, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| | - Lilia Alberghina
- Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. .,NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, Piazza della Scienza, 4, Milano, 20126, Italy.
| | - Anna Maria Colangelo
- Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. .,NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, Piazza della Scienza, 4, Milano, 20126, Italy.
| | - Paola Bertolazzi
- Institute of Systems Analysis and Computer Science, National Research Council of Italy, Via dei Taurini, 19, Roma, 00185, Italy. .,SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy.
| |
Collapse
|
70
|
Jagannadham J, Jaiswal HK, Agrawal S, Rawal K. Comprehensive Map of Molecules Implicated in Obesity. PLoS One 2016; 11:e0146759. [PMID: 26886906 PMCID: PMC4757102 DOI: 10.1371/journal.pone.0146759] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2014] [Accepted: 12/22/2015] [Indexed: 01/22/2023] Open
Abstract
Obesity is a global epidemic affecting over 1.5 billion people and is one of the risk factors for several diseases such as type 2 diabetes mellitus and hypertension. We have constructed a comprehensive map of the molecules reported to be implicated in obesity. A deep curation strategy was complemented by a novel semi-automated text mining system in order to screen 1,000 full-length research articles and over 90,000 abstracts that are relevant to obesity. We obtain a scale free network of 804 nodes and 971 edges, composed of 510 proteins, 115 genes, 62 complexes, 23 RNA molecules, 83 simple molecules, 3 phenotype and 3 drugs in "bow-tie" architecture. We classify this network into 5 modules and identify new links between the recently discovered fat mass and obesity associated FTO gene with well studied examples such as insulin and leptin. We further built an automated docking pipeline to dock orlistat as well as other drugs against the 24,000 proteins in the human structural proteome to explain the therapeutics and side effects at a network level. Based upon our experiments, we propose that therapeutic effect comes through the binding of one drug with several molecules in target network, and the binding propensity is both statistically significant and different in comparison with any other part of human structural proteome.
Collapse
Affiliation(s)
- Jaisri Jagannadham
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida [UP]-201 307, India
| | - Hitesh Kumar Jaiswal
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida [UP]-201 307, India
| | - Stuti Agrawal
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida [UP]-201 307, India
| | - Kamal Rawal
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida [UP]-201 307, India
| |
Collapse
|
71
|
Mizuno S, Ogishima S, Kitatani K, Kikuchi M, Tanaka H, Yaegashi N, Nakaya J. Network Analysis of a Comprehensive Knowledge Repository Reveals a Dual Role for Ceramide in Alzheimer's Disease. PLoS One 2016; 11:e0148431. [PMID: 26849355 PMCID: PMC4752297 DOI: 10.1371/journal.pone.0148431] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2015] [Accepted: 01/18/2016] [Indexed: 12/13/2022] Open
Abstract
Alzheimer’s disease (AD) is the most common cause of senile dementia. Many inflammatory factors such as amyloid-β and pro-inflammatory cytokines are known to contribute to the inflammatory response in the AD brain. Sphingolipids are widely known to have roles in the pathogenesis of inflammatory diseases, where the precise roles for sphingolipids in inflammation-associated pathogenesis of AD are not well understood. Here we performed a network analysis to clarify the importance of sphingolipids and to model relationships among inflammatory factors and sphingolipids in AD. In this study, we have updated sphingolipid signaling and metabolic cascades in a map of AD signaling networks that we named “AlzPathway,” a comprehensive knowledge repository of signaling pathways in AD. Our network analysis of the updated AlzPathway indicates that the pathways related to ceramide are one of the primary pathways and that ceramide is one of the important players in the pathogenesis of AD. The results of our analysis suggest the following two prospects about inflammation in AD: (1) ceramide could play important roles in both inflammatory and anti-inflammatory pathways of AD, and (2) several factors such as Sphingomyelinase and Siglec-11 may be associated with ceramide related inflammation and anti-inflammation pathways in AD. In this study, network analysis of comprehensive knowledge repository reveals a dual role for ceramide in AD. This result provides a clue to clarify sphingolipids related inflammatory and anti-inflammatory pathways in AD.
Collapse
Affiliation(s)
- Satoshi Mizuno
- Department of Clinical Informatics, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
- Department of Clinical Record Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
- * E-mail: (SM); (SO)
| | - Soichi Ogishima
- Department of Clinical Record Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
- * E-mail: (SM); (SO)
| | - Kazuyuki Kitatani
- Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Masataka Kikuchi
- Department of Genome Informatics, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Hiroshi Tanaka
- Department of Clinical Record Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi, Japan
| | - Nobuo Yaegashi
- Department of Gynecology and Obstetrics, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| | - Jun Nakaya
- Department of Clinical Informatics, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan
| |
Collapse
|
72
|
Kikuchi M, Ogishima S, Mizuno S, Miyashita A, Kuwano R, Nakaya J, Tanaka H. Network-Based Analysis for Uncovering Mechanisms Underlying Alzheimer's Disease. Methods Mol Biol 2016; 1303:479-91. [PMID: 26235086 DOI: 10.1007/978-1-4939-2627-5_29] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/08/2022]
Abstract
Alzheimer's disease (AD) is known to be a multifactorial neurodegenerative disorder, and is one of the main causes of dementia in the elderly. Many studies have demonstrated molecules involved in the pathogenesis of AD, however its underlying mechanisms remain obscure. It may be simplistic to try to explain the disease based on the role of a few genes only. Accumulating new, huge amount of information from e.g. genome, proteome and interactome datasets and new knowledge, we are now able to clarify and characterize diseases essentially as a result of dysfunction of molecular networks. Recent studies have indicated that relevant genes affected in human diseases concentrate in a part of the network, often called as "disease module." In the case of AD, some disease-associated pathways seem different, but some of them are clearly disease-related and coherent. This suggests the existence of a common pathway that negatively drives from healthy state to disease state (i.e., the disease module(s)). Additionally, such disease modules should dynamically change through AD progression. Thus, network-level approaches are indispensable to address unknown mechanisms of AD. In this chapter, we introduce network strategies using gene co-expression and protein interaction networks.
Collapse
Affiliation(s)
- Masataka Kikuchi
- Department of Molecular Genetics, Center for Bioresources, Brain Research Institute, Niigata University, Niigata, 951-8585, Japan
| | | | | | | | | | | | | |
Collapse
|
73
|
AlzPathway, an Updated Map of Curated Signaling Pathways: Towards Deciphering Alzheimer's Disease Pathogenesis. Methods Mol Biol 2016; 1303:423-32. [PMID: 26235082 DOI: 10.1007/978-1-4939-2627-5_25] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Alzheimer's disease (AD) is a complex neurodegenerative disorder in which loss of neurons and synaptic function causes dementia in the elderly. To clarify AD pathogenesis and develop drugs for AD, thousands of studies have elucidated signaling pathways involved. However, knowledge of AD signaling pathways has not been compiled as a pathway map. In this chapter, we introduce the manual construction of a pathway map in AD which we call "AlzPathway", that comprehensively catalogs signaling pathways in the field of AD. We have collected and manually curated over 100 review articles related to AD, and have built the AD pathway map. AlzPathway is currently composed of thousands of molecules and reactions in neurons, brain blood barrier, presynaptic, postsynaptic, astrocyte, and microglial cells, with their cellular localizations. AlzPathway provides a systems-biology platform of comprehensive AD signaling and related pathways which is expected to contribute to clarification of AD pathogenesis and AD drug development.
Collapse
|
74
|
Castrillo JI, Oliver SG. Alzheimer's as a Systems-Level Disease Involving the Interplay of Multiple Cellular Networks. Methods Mol Biol 2016; 1303:3-48. [PMID: 26235058 DOI: 10.1007/978-1-4939-2627-5_1] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Alzheimer's disease (AD), and many neurodegenerative disorders, are multifactorial in nature. They involve a combination of genomic, epigenomic, interactomic and environmental factors. Progress is being made, and these complex diseases are beginning to be understood as having their origin in altered states of biological networks at the cellular level. In the case of AD, genomic susceptibility and mechanisms leading to (or accompanying) the impairment of the central Amyloid Precursor Protein (APP) processing and tau networks are widely accepted as major contributors to the diseased state. The derangement of these networks may result in both the gain and loss of functions, increased generation of toxic species (e.g., toxic soluble oligomers and aggregates) and imbalances, whose effects can propagate to supra-cellular levels. Although well sustained by empirical data and widely accepted, this global perspective often overlooks the essential roles played by the main counteracting homeostatic networks (e.g., protein quality control/proteostasis, unfolded protein response, protein folding chaperone networks, disaggregases, ER-associated degradation/ubiquitin proteasome system, endolysosomal network, autophagy, and other stress-protective and clearance networks), whose relevance to AD is just beginning to be fully realized. In this chapter, an integrative perspective is presented. Alzheimer's disease is characterized to be a result of: (a) intrinsic genomic/epigenomic susceptibility and, (b) a continued dynamic interplay between the deranged networks and the central homeostatic networks of nerve cells. This interplay of networks will underlie both the onset and rate of progression of the disease in each individual. Integrative Systems Biology approaches are required to effect its elucidation. Comprehensive Systems Biology experiments at different 'omics levels in simple model organisms, engineered to recapitulate the basic features of AD may illuminate the onset and sequence of events underlying AD. Indeed, studies of models of AD in simple organisms, differentiated cells in culture and rodents are beginning to offer hope that the onset and progression of AD, if detected at an early stage, may be stopped, delayed, or even reversed, by activating or modulating networks involved in proteostasis and the clearance of toxic species. In practice, the incorporation of next-generation neuroimaging, high-throughput and computational approaches are opening the way towards early diagnosis well before irreversible cell death. Thus, the presence or co-occurrence of: (a) accumulation of toxic Aβ oligomers and tau species; (b) altered splicing and transcriptome patterns; (c) impaired redox, proteostatic, and metabolic networks together with, (d) compromised homeostatic capacities may constitute relevant 'AD hallmarks at the cellular level' towards reliable and early diagnosis. From here, preventive lifestyle changes and tailored therapies may be investigated, such as combined strategies aimed at both lowering the production of toxic species and potentiating homeostatic responses, in order to prevent or delay the onset, and arrest, alleviate, or even reverse the progression of the disease.
Collapse
Affiliation(s)
- Juan I Castrillo
- Department of Biochemistry & Cambridge Systems Biology Centre, University of Cambridge, Sanger Building, 80 Tennis Court Road, Cambridge, CB2 1GA, UK,
| | | |
Collapse
|
75
|
Saqi M, Pellet J, Roznovat I, Mazein A, Ballereau S, De Meulder B, Auffray C. Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness. Methods Mol Biol 2016; 1386:43-60. [PMID: 26677178 DOI: 10.1007/978-1-4939-3283-2_3] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Recent advances in genomics have led to the rapid and relatively inexpensive collection of patient molecular data including multiple types of omics data. The integration of these data with clinical measurements has the potential to impact on our understanding of the molecular basis of disease and on disease management. Systems medicine is an approach to understanding disease through an integration of large patient datasets. It offers the possibility for personalized strategies for healthcare through the development of a new taxonomy of disease. Advanced computing will be an important component in effectively implementing systems medicine. In this chapter we describe three computational challenges associated with systems medicine: disease subtype discovery using integrated datasets, obtaining a mechanistic understanding of disease, and the development of an informatics platform for the mining, analysis, and visualization of data emerging from translational medicine studies.
Collapse
Affiliation(s)
- Mansoor Saqi
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Johann Pellet
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Irina Roznovat
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Stéphane Ballereau
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Bertrand De Meulder
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France. .,Université Claude Bernard, 3e étage plot 2, 50 Avenue Tony Garnier, Lyon, Cedex 07, 69366, France.
| |
Collapse
|
76
|
Gabr H, Kahveci T. Signal reachability facilitates characterization of probabilistic signaling networks. BMC Bioinformatics 2015; 16 Suppl 17:S6. [PMID: 26679404 PMCID: PMC4674881 DOI: 10.1186/1471-2105-16-s17-s6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Background Studying biological networks is of extreme importance in understanding cellular functions. These networks model interactions between molecules in each cell. A large volume of research has been done to uncover different characteristics of biological networks, such as large-scale organization, node centrality and network robustness. Nevertheless, the vast majority of research done in this area assume that biological networks have deterministic topologies. Biological interactions are however probabilistic events that may or may not appear at different cells or even in the same cell at different times. Results In this paper, we present novel methods for characterizing probabilistic signaling networks. Our methods do this by computing the probability that a signal propagates successfully from receptor to reporter genes through interactions in the network. We characterize such networks with respect to (i) centrality of individual nodes, (ii) stability of the entire network, and (iii) important functions served by the network. We use these methods to characterize major H. sapiens signaling networks including Wnt, ErbB and MAPK.
Collapse
|
77
|
Rollo JL, Banihashemi N, Vafaee F, Crawford JW, Kuncic Z, Holsinger RMD. Unraveling the mechanistic complexity of Alzheimer's disease through systems biology. Alzheimers Dement 2015; 12:708-18. [PMID: 26703952 DOI: 10.1016/j.jalz.2015.10.010] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2014] [Revised: 08/18/2015] [Accepted: 10/21/2015] [Indexed: 11/16/2022]
Abstract
Alzheimer's disease (AD) is a complex, multifactorial disease that has reached global epidemic proportions. The challenge remains to fully identify its underlying molecular mechanisms that will enable development of accurate diagnostic tools and therapeutics. Conventional experimental approaches that target individual or small sets of genes or proteins may overlook important parts of the regulatory network, which limits the opportunity of identifying multitarget interventions. Our perspective is that a more complete insight into potential treatment options for AD will only be made possible through studying the disease as a system. We propose an integrative systems biology approach that we argue has been largely untapped in AD research. We present key publications to demonstrate the value of this approach and discuss the potential to intensify research efforts in AD through transdisciplinary collaboration. We highlight challenges and opportunities for significant breakthroughs that could be made if a systems biology approach is fully exploited.
Collapse
Affiliation(s)
- Jennifer L Rollo
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; Laboratory of Molecular Neuroscience, Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Department of Molecular Neuroscience, Institute of Neurology, University College of London, London, UK.
| | - Nahid Banihashemi
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Fatemeh Vafaee
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Mathematics and Statistics, University of Sydney, Sydney, NSW, Australia
| | | | - Zdenka Kuncic
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia; School of Physics, The University of Sydney, Sydney, NSW, Australia
| | - R M Damian Holsinger
- Laboratory of Molecular Neuroscience, Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia; Discipline of Biomedical Science, School of Medical Sciences, Sydney Medical School, The University of Sydney, Lidcombe, NSW, Australia
| |
Collapse
|
78
|
Molecular Targets in Alzheimer's Disease: From Pathogenesis to Therapeutics. BIOMED RESEARCH INTERNATIONAL 2015; 2015:760758. [PMID: 26665008 PMCID: PMC4668300 DOI: 10.1155/2015/760758] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2015] [Revised: 11/02/2015] [Accepted: 11/03/2015] [Indexed: 11/17/2022]
Abstract
Alzheimer's disease (AD) is characterized by progressive cognitive decline usually beginning with impairment in the ability to form recent memories. Nonavailability of definitive therapeutic strategy urges developing pharmacological targets based on cell signaling pathways. A great revival of interest in nutraceuticals and adjuvant therapy has been put forward. Tea polyphenols for their multiple health benefits have also attracted the attention of researchers. Tea catechins showed enough potentiality to be used in future as therapeutic targets to provide neuroprotection against AD. This review attempts to present a concise map of different receptor signaling pathways associated with AD with an insight into drug designing based on the proposed signaling pathways, molecular mechanistic details of AD pathogenesis, and a scientific rationale for using tea polyphenols as proposed therapeutic agents in AD.
Collapse
|
79
|
Tripathi S, Flobak Å, Chawla K, Baudot A, Bruland T, Thommesen L, Kuiper M, Lægreid A. The gastrin and cholecystokinin receptors mediated signaling network: a scaffold for data analysis and new hypotheses on regulatory mechanisms. BMC SYSTEMS BIOLOGY 2015. [PMID: 26205660 PMCID: PMC4513977 DOI: 10.1186/s12918-015-0181-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background The gastrointestinal peptide hormones cholecystokinin and gastrin exert their biological functions via cholecystokinin receptors CCK1R and CCK2R respectively. Gastrin, a central regulator of gastric acid secretion, is involved in growth and differentiation of gastric and colonic mucosa, and there is evidence that it is pro-carcinogenic. Cholecystokinin is implicated in digestion, appetite control and body weight regulation, and may play a role in several digestive disorders. Results We performed a detailed analysis of the literature reporting experimental evidence on signaling pathways triggered by CCK1R and CCK2R, in order to create a comprehensive map of gastrin and cholecystokinin-mediated intracellular signaling cascades. The resulting signaling map captures 413 reactions involving 530 molecular species, and incorporates the currently available knowledge into one integrated signaling network. The decomposition of the signaling map into sub-networks revealed 18 modules that represent higher-level structures of the signaling map. These modules allow a more compact mapping of intracellular signaling reactions to known cell behavioral outcomes such as proliferation, migration and apoptosis. The integration of large-scale protein-protein interaction data to this literature-based signaling map in combination with topological analyses allowed us to identify 70 proteins able to increase the compactness of the map. These proteins represent experimentally testable hypotheses for gaining new knowledge on gastrin- and cholecystokinin receptor signaling. The CCKR map is freely available both in a downloadable, machine-readable SBML-compatible format and as a web resource through PAYAO (http://sblab.celldesigner.org:18080/Payao11/bin/). Conclusion We have demonstrated how a literature-based CCKR signaling map together with its protein interaction extensions can be analyzed to generate new hypotheses on molecular mechanisms involved in gastrin- and cholecystokinin-mediated regulation of cellular processes. Electronic supplementary material The online version of this article (doi:10.1186/s12918-015-0181-z) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Sushil Tripathi
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| | - Åsmund Flobak
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| | - Konika Chawla
- Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway.
| | - Anaïs Baudot
- I2M, Marseilles Institute of Mathematics CNRS - AMU, Case 907, 13288, Marseille, Cedex 9, France.
| | - Torunn Bruland
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| | - Liv Thommesen
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway. .,Department of Technology, Sør-Trøndelag University College, N-7004, Trondheim, Norway.
| | - Martin Kuiper
- Department of Biology, Norwegian University of Science and Technology (NTNU), N-7491, Trondheim, Norway.
| | - Astrid Lægreid
- Department of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway. .,Institute of Cancer Research and Molecular Medicine, Norwegian University of Science and Technology (NTNU), N-7489, Trondheim, Norway.
| |
Collapse
|
80
|
Kuperstein I, Bonnet E, Nguyen HA, Cohen D, Viara E, Grieco L, Fourquet S, Calzone L, Russo C, Kondratova M, Dutreix M, Barillot E, Zinovyev A. Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps. Oncogenesis 2015; 4:e160. [PMID: 26192618 PMCID: PMC4521180 DOI: 10.1038/oncsis.2015.19] [Citation(s) in RCA: 87] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2014] [Revised: 05/29/2015] [Accepted: 06/08/2015] [Indexed: 02/07/2023] Open
Abstract
Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless ‘geographic-like' map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.
Collapse
Affiliation(s)
- I Kuperstein
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | - E Bonnet
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | - H-A Nguyen
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | - D Cohen
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | | | - L Grieco
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France [4] Ecole Normale Supérieure, IBENS, Paris, France [5] CNRS, UMR8197, Paris, France [6] INSERM, U1024, Paris, France
| | - S Fourquet
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | - L Calzone
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | - C Russo
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | - M Kondratova
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | - M Dutreix
- 1] Institut Curie, Paris, France [2] CNRS, UMR3347, Orsay, France [3] INSERM, U1021, Orsay, France
| | - E Barillot
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| | - A Zinovyev
- 1] Institut Curie, Paris, France [2] INSERM, U900, Paris, France [3] Mines ParisTech, Fontainebleau, France
| |
Collapse
|
81
|
Glaab E. Using prior knowledge from cellular pathways and molecular networks for diagnostic specimen classification. Brief Bioinform 2015; 17:440-52. [PMID: 26141830 PMCID: PMC4870394 DOI: 10.1093/bib/bbv044] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Indexed: 12/27/2022] Open
Abstract
For many complex diseases, an earlier and more reliable diagnosis is considered a key prerequisite for developing more effective therapies to prevent or delay disease progression. Classical statistical learning approaches for specimen classification using omics data, however, often cannot provide diagnostic models with sufficient accuracy and robustness for heterogeneous diseases like cancers or neurodegenerative disorders. In recent years, new approaches for building multivariate biomarker models on omics data have been proposed, which exploit prior biological knowledge from molecular networks and cellular pathways to address these limitations. This survey provides an overview of these recent developments and compares pathway- and network-based specimen classification approaches in terms of their utility for improving model robustness, accuracy and biological interpretability. Different routes to translate omics-based multifactorial biomarker models into clinical diagnostic tests are discussed, and a previous study is presented as example.
Collapse
|
82
|
Chiaradonna F, Cirulli C, Palorini R, Votta G, Alberghina L. New Insights into the Connection Between Histone Deacetylases, Cell Metabolism, and Cancer. Antioxid Redox Signal 2015; 23:30-50. [PMID: 24483782 DOI: 10.1089/ars.2014.5854] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
SIGNIFICANCE Histone deacetylases (HDACs) activity and cell metabolism are considered important targets for cancer therapy, as both are deregulated and associated with the onset and maintenance of tumors. RECENT ADVANCES Besides the classical function of HDACs as HDAC enzymes controlling the transcription, it is becoming increasingly evident that these proteins are involved in the regulation of several other cellular processes by their ability to deacetylate hundreds of proteins with different functions in both the cytoplasm and the nucleus. Importantly, recent high-throughput studies have identified as important target proteins several enzymes involved in different metabolic pathways. Conversely, it has been also shown that metabolic intermediates may control HDACs activity. Consequently, the acetylation/deacetylation of metabolic enzymes and the ability of metabolic intermediates to modulate HDACs may represent a cross-talk connecting cell metabolism, transcription, and other HDACs-controlled processes in physiological and pathological conditions. CRITICAL ISSUES Since metabolic alterations and HDACs deregulation are important cancer hallmarks, disclosing connections among them may improve our understanding on cancer mechanisms and reveal novel therapeutic protocols against this disease. FUTURE DIRECTIONS High-throughput metabolic studies performed by using more sophisticated technologies applied to the available models of conditional deletion of HDACs in cell lines or in mice will fill the gap in the current understanding and open directions for future research.
Collapse
Affiliation(s)
- Ferdinando Chiaradonna
- 1 SYSBIO Centre of Systems Biology , Milan, Italy .,2 Department of Biotechnology and Biosciences, University of Milano-Bicocca , Milan, Italy
| | - Claudia Cirulli
- 1 SYSBIO Centre of Systems Biology , Milan, Italy .,2 Department of Biotechnology and Biosciences, University of Milano-Bicocca , Milan, Italy
| | - Roberta Palorini
- 1 SYSBIO Centre of Systems Biology , Milan, Italy .,2 Department of Biotechnology and Biosciences, University of Milano-Bicocca , Milan, Italy
| | - Giuseppina Votta
- 1 SYSBIO Centre of Systems Biology , Milan, Italy .,2 Department of Biotechnology and Biosciences, University of Milano-Bicocca , Milan, Italy
| | - Lilia Alberghina
- 1 SYSBIO Centre of Systems Biology , Milan, Italy .,2 Department of Biotechnology and Biosciences, University of Milano-Bicocca , Milan, Italy
| |
Collapse
|
83
|
Bonnet E, Viara E, Kuperstein I, Calzone L, Cohen DPA, Barillot E, Zinovyev A. NaviCell Web Service for network-based data visualization. Nucleic Acids Res 2015; 43:W560-5. [PMID: 25958393 PMCID: PMC4489283 DOI: 10.1093/nar/gkv450] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 04/24/2015] [Indexed: 12/17/2022] Open
Abstract
Data visualization is an essential element of biological research, required for obtaining insights and formulating new hypotheses on mechanisms of health and disease. NaviCell Web Service is a tool for network-based visualization of ‘omics’ data which implements several data visual representation methods and utilities for combining them together. NaviCell Web Service uses Google Maps and semantic zooming to browse large biological network maps, represented in various formats, together with different types of the molecular data mapped on top of them. For achieving this, the tool provides standard heatmaps, barplots and glyphs as well as the novel map staining technique for grasping large-scale trends in numerical values (such as whole transcriptome) projected onto a pathway map. The web service provides a server mode, which allows automating visualization tasks and retrieving data from maps via RESTful (standard HTTP) calls. Bindings to different programming languages are provided (Python and R). We illustrate the purpose of the tool with several case studies using pathway maps created by different research groups, in which data visualization provides new insights into molecular mechanisms involved in systemic diseases such as cancer and neurodegenerative diseases.
Collapse
Affiliation(s)
- Eric Bonnet
- Institut Curie, 26 rue d'Ulm, 75248 Paris, France INSERM, U900, 75248 Paris, France Mines ParisTech, 77300 Fontainebleau, France
| | | | - Inna Kuperstein
- Institut Curie, 26 rue d'Ulm, 75248 Paris, France INSERM, U900, 75248 Paris, France Mines ParisTech, 77300 Fontainebleau, France
| | - Laurence Calzone
- Institut Curie, 26 rue d'Ulm, 75248 Paris, France INSERM, U900, 75248 Paris, France Mines ParisTech, 77300 Fontainebleau, France
| | - David P A Cohen
- Institut Curie, 26 rue d'Ulm, 75248 Paris, France INSERM, U900, 75248 Paris, France Mines ParisTech, 77300 Fontainebleau, France
| | - Emmanuel Barillot
- Institut Curie, 26 rue d'Ulm, 75248 Paris, France INSERM, U900, 75248 Paris, France Mines ParisTech, 77300 Fontainebleau, France
| | - Andrei Zinovyev
- Institut Curie, 26 rue d'Ulm, 75248 Paris, France INSERM, U900, 75248 Paris, France Mines ParisTech, 77300 Fontainebleau, France
| |
Collapse
|
84
|
Abstract
The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.
Collapse
Affiliation(s)
- Ralf Herwig
- Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany
| |
Collapse
|
85
|
Gabr H, Todor A, Dobra A, Kahveci T. Reachability Analysis in Probabilistic Biological Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2015; 12:53-66. [PMID: 26357078 DOI: 10.1109/tcbb.2014.2343967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Extra-cellular molecules trigger a response inside the cell by initiating a signal at special membrane receptors (i.e., sources), which is then transmitted to reporters (i.e., targets) through various chains of interactions among proteins. Understanding whether such a signal can reach from membrane receptors to reporters is essential in studying the cell response to extra-cellular events. This problem is drastically complicated due to the unreliability of the interaction data. In this paper, we develop a novel method, called PReach (Probabilistic Reachability), that precisely computes the probability that a signal can reach from a given collection of receptors to a given collection of reporters when the underlying signaling network is uncertain. This is a very difficult computational problem with no known polynomial-time solution. PReach represents each uncertain interaction as a bi-variate polynomial. It transforms the reachability problem to a polynomial multiplication problem. We introduce novel polynomial collapsing operators that associate polynomial terms with possible paths between sources and targets as well as the cuts that separate sources from targets. These operators significantly shrink the number of polynomial terms and thus the running time. PReach has much better time complexity than the recent solutions for this problem. Our experimental results on real data sets demonstrate that this improvement leads to orders of magnitude of reduction in the running time over the most recent methods. Availability: All the data sets used, the software implemented and the alignments found in this paper are available at http://bioinformatics.cise.ufl.edu/PReach/.
Collapse
|
86
|
Wu C, Schwartz JM, Brabant G, Nenadic G. Molecular profiling of thyroid cancer subtypes using large-scale text mining. BMC Med Genomics 2014; 7 Suppl 3:S3. [PMID: 25521965 PMCID: PMC4290788 DOI: 10.1186/1755-8794-7-s3-s3] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Background Thyroid cancer is the most common endocrine tumor with a steady increase in incidence. It is classified into multiple histopathological subtypes with potentially distinct molecular mechanisms. Identifying the most relevant genes and biological pathways reported in the thyroid cancer literature is vital for understanding of the disease and developing targeted therapeutics. Results We developed a large-scale text mining system to generate a molecular profiling of thyroid cancer subtypes. The system first uses a subtype classification method for the thyroid cancer literature, which employs a scoring scheme to assign different subtypes to articles. We evaluated the classification method on a gold standard derived from the PubMed Supplementary Concept annotations, achieving a micro-average F1-score of 85.9% for primary subtypes. We then used the subtype classification results to extract genes and pathways associated with different thyroid cancer subtypes and successfully unveiled important genes and pathways, including some instances that are missing from current manually annotated databases or most recent review articles. Conclusions Identification of key genes and pathways plays a central role in understanding the molecular biology of thyroid cancer. An integration of subtype context can allow prioritized screening for diagnostic biomarkers and novel molecular targeted therapeutics. Source code used for this study is made freely available online at https://github.com/chengkun-wu/GenesThyCan.
Collapse
|
87
|
Abstract
A significant portion of the world's population suffers from sporadic Alzheimer's disease (AD) with available present therapies limited to symptomatic care that does not alter disease progression. Over the next decade, advancing age of the global population will dramatically increase the incidence of AD and severely impact health care resources, necessitating novel, safe, and efficacious strategies for AD. The mammalian target of rapamycin (mTOR) and its protein complexes mTOR Complex 1 (mTORC1) and mTOR Complex 2 (mTORC2) offer exciting and unique avenues of intervention for AD through the oversight of programmed cell death pathways of apoptosis, autophagy, and necroptosis. mTOR modulates multi-faceted signal transduction pathways that involve phosphoinositide 3-kinase (PI 3-K), protein kinase B (Akt), hamartin (tuberous sclerosis 1)/ tuberin (tuberous sclerosis 2) (TSC1/TSC2) complex, proline-rich Akt substrate 40 kDa (PRAS40), and p70 ribosomal S6 kinase (p70S6K) and can interface with the neuroprotective pathways of growth factors, sirtuins, wingless, forkhead transcription factors, and glycogen synthase kinase-3β. With the ability of mTOR to broadly impact cellular function, clinical strategies for AD that implement mTOR must achieve parallel objectives of protecting neuronal, vascular, and immune cell survival in conjunction with preserving networks that determine memory and cognitive function.
Collapse
Affiliation(s)
- Kenneth Maiese
- Cellular and Molecular Signaling , Newark, New Jersey 07101 , USA
| |
Collapse
|
88
|
Abstract
Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal.
Collapse
Affiliation(s)
- Scott W Robinson
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, BHF Glasgow Cardiovascular Research Centre, 126 University Place, Glasgow G12 8TA, UK
| | - Marco Fernandes
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, BHF Glasgow Cardiovascular Research Centre, 126 University Place, Glasgow G12 8TA, UK
| | - Holger Husi
- Institute of Cardiovascular and Medical Sciences, University of Glasgow, BHF Glasgow Cardiovascular Research Centre, 126 University Place, Glasgow G12 8TA, UK
| |
Collapse
|
89
|
Van Dooren T, Princen K, De Witte K, Griffioen G. Derailed intraneuronal signalling drives pathogenesis in sporadic and familial Alzheimer's disease. BIOMED RESEARCH INTERNATIONAL 2014; 2014:167024. [PMID: 25243118 PMCID: PMC4160617 DOI: 10.1155/2014/167024] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2014] [Revised: 07/31/2014] [Accepted: 08/03/2014] [Indexed: 02/01/2023]
Abstract
Although a wide variety of genetic and nongenetic Alzheimer's disease (AD) risk factors have been identified, their role in onset and/or progression of neuronal degeneration remains elusive. Systematic analysis of AD risk factors revealed that perturbations of intraneuronal signalling pathways comprise a common mechanistic denominator in both familial and sporadic AD and that such alterations lead to increases in Aβ oligomers (Aβo) formation and phosphorylation of TAU. Conversely, Aβo and TAU impact intracellular signalling directly. This feature entails binding of Aβo to membrane receptors, whereas TAU functionally interacts with downstream transducers. Accordingly, we postulate a positive feedback mechanism in which AD risk factors or genes trigger perturbations of intraneuronal signalling leading to enhanced Aβo formation and TAU phosphorylation which in turn further derange signalling. Ultimately intraneuronal signalling becomes deregulated to the extent that neuronal function and survival cannot be sustained, whereas the resulting elevated levels of amyloidogenic Aβo and phosphorylated TAU species self-polymerizes into the AD plaques and tangles, respectively.
Collapse
|
90
|
Kwak JW, Jeong H, Han SH, Kim Y, Son SM, Mook-Jung I, Hwang D, Park JW. Phosphokinase antibody arrays on dendron-coated surface. PLoS One 2014; 9:e96456. [PMID: 24802362 PMCID: PMC4011796 DOI: 10.1371/journal.pone.0096456] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2013] [Accepted: 04/08/2014] [Indexed: 12/04/2022] Open
Abstract
Monitoring protein phosphorylation at the cellular level is important to understand the intracellular signaling. Among the phosphoproteomics methods, phosphokinase antibody arrays have emerged as preferred tools to measure well-characterized phosphorylation in the intracellular signaling. Here, we present a dendron-coated phosphokinase antibody array (DPA) in which the antibodies are immobilized on a dendron-coated glass slide. Self-assembly of conically shaped dendrons well-controlled in size and structure resulted in precisely controlled lateral spacing between the immobilized phosphosite-specific antibodies, leading to minimized steric hindrance and improved antigen-antibody binding kinetics. These features increased sensitivity, selectivity, and reproducibility in measured amounts of protein phosphorylation. To demonstrate the utility of the DPA, we generated the phosphorylation profiles of brain tissue samples obtained from Alzheimer's disease (AD) model mice. The analysis of the profiles revealed signaling pathways deregulated during the course of AD progression.
Collapse
Affiliation(s)
- Ju-Won Kwak
- Department of Chemistry, POSTECH, Pohang, Republic of Korea
| | - Hyobin Jeong
- School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang, Republic of Korea
| | - Sun-Ho Han
- Department of Biochemistry and Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Youngkyu Kim
- School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang, Republic of Korea
| | - Sung Min Son
- Department of Biochemistry and Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Inhee Mook-Jung
- Department of Biochemistry and Biomedical Sciences, Seoul National University, Seoul, Republic of Korea
- * E-mail: (IM-J); (DH); (JWP)
| | - Daehee Hwang
- School of Interdisciplinary Bioscience and Bioengineering, POSTECH, Pohang, Republic of Korea
- Center for Plant Aging Research, Institute for Basic Science, DGIST, Daegu, Republic of Korea
- * E-mail: (IM-J); (DH); (JWP)
| | - Joon Won Park
- Department of Chemistry, POSTECH, Pohang, Republic of Korea
- * E-mail: (IM-J); (DH); (JWP)
| |
Collapse
|
91
|
Colangelo AM, Alberghina L, Papa M. Astrogliosis as a therapeutic target for neurodegenerative diseases. Neurosci Lett 2014; 565:59-64. [DOI: 10.1016/j.neulet.2014.01.014] [Citation(s) in RCA: 105] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2013] [Revised: 01/08/2014] [Accepted: 01/13/2014] [Indexed: 01/16/2023]
|
92
|
Liu H, Wang L, Lv M, Pei R, Li P, Pei Z, Wang Y, Su W, Xie XQ. AlzPlatform: an Alzheimer's disease domain-specific chemogenomics knowledgebase for polypharmacology and target identification research. J Chem Inf Model 2014; 54:1050-60. [PMID: 24597646 PMCID: PMC4010297 DOI: 10.1021/ci500004h] [Citation(s) in RCA: 160] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
![]()
Alzheimer’s
disease (AD) is one of the most complicated progressive neurodegeneration
diseases that involve many genes, proteins, and their complex interactions.
No effective medicines or treatments are available yet to stop or
reverse the progression of the disease due to its polygenic nature.
To facilitate discovery of new AD drugs and better understand the
AD neurosignaling pathways involved, we have constructed an Alzheimer’s
disease domain-specific chemogenomics knowledgebase, AlzPlatform (www.cbligand.org/AD/) with cloud computing and sourcing
functions. AlzPlatform is implemented with powerful computational
algorithms, including our established TargetHunter, HTDocking, and
BBB Predictor for target identification and polypharmacology analysis
for AD research. The platform has assembled various AD-related chemogenomics
data records, including 928 genes and 320 proteins related to AD,
194 AD drugs approved or in clinical trials, and 405 188 chemicals
associated with 1 023 137 records of reported bioactivities
from 38 284 corresponding bioassays and 10 050 references.
Furthermore, we have demonstrated the application of the AlzPlatform
in three case studies for identification of multitargets and polypharmacology
analysis of FDA-approved drugs and also for screening and prediction
of new AD active small chemical molecules and potential novel AD drug
targets by our established TargetHunter and/or HTDocking programs.
The predictions were confirmed by reported bioactivity data and our
in vitro experimental validation. Overall, AlzPlatform will enrich
our knowledge for AD target identification, drug discovery, and polypharmacology
analyses and, also, facilitate the chemogenomics data sharing and
information exchange/communications in aid of new anti-AD drug discovery
and development.
Collapse
Affiliation(s)
- Haibin Liu
- Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy; Drug Discovery Institute; University of Pittsburgh , Pittsburgh, Pennsylvania 15260, United States
| | | | | | | | | | | | | | | | | |
Collapse
|
93
|
Fujita KA, Ostaszewski M, Matsuoka Y, Ghosh S, Glaab E, Trefois C, Crespo I, Perumal TM, Jurkowski W, Antony PMA, Diederich N, Buttini M, Kodama A, Satagopam VP, Eifes S, del Sol A, Schneider R, Kitano H, Balling R. Integrating pathways of Parkinson's disease in a molecular interaction map. Mol Neurobiol 2014; 49:88-102. [PMID: 23832570 PMCID: PMC4153395 DOI: 10.1007/s12035-013-8489-4] [Citation(s) in RCA: 164] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2013] [Accepted: 06/13/2013] [Indexed: 12/12/2022]
Abstract
Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map .
Collapse
Affiliation(s)
| | - Marek Ostaszewski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
- Integrated Biobank of Luxembourg, Luxembourg City, Luxembourg
| | | | - Samik Ghosh
- The Systems Biology Institute, Minato-ku, Tokyo, Japan
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Christophe Trefois
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Isaac Crespo
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Thanneer M. Perumal
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Wiktor Jurkowski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Paul M. A. Antony
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Nico Diederich
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
- Department of Neuroscience, Centre Hospitalier Luxembourg, Luxembourg City, Luxembourg
| | - Manuel Buttini
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Akihiko Kodama
- Faculty of Medicine, Tokyo Medical and Dental University, Tokyo, Japan
| | - Venkata P. Satagopam
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
- Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Serge Eifes
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Antonio del Sol
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
- Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Hiroaki Kitano
- The Systems Biology Institute, Minato-ku, Tokyo, Japan
- Sony Computer Science Laboratories, Shinagawa-ku, Tokyo, Japan
- Division of Systems Biology, Cancer Institute, Tokyo, Japan
- Open Biology Unit, Okinawa Institute of Science and Technology, Kunigami, Okinawa Japan
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, Avenue des Hauts-Fourneaux, Esch-sur-Alzette, Luxembourg
| |
Collapse
|
94
|
Caberlotto L, Lauria M, Nguyen TP, Scotti M. The central role of AMP-kinase and energy homeostasis impairment in Alzheimer's disease: a multifactor network analysis. PLoS One 2013; 8:e78919. [PMID: 24265728 PMCID: PMC3827084 DOI: 10.1371/journal.pone.0078919] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2013] [Accepted: 09/23/2013] [Indexed: 12/15/2022] Open
Abstract
Alzheimer’s disease is the most common cause of dementia worldwide, affecting the elderly population. It is characterized by the hallmark pathology of amyloid-β deposition, neurofibrillary tangle formation, and extensive neuronal degeneration in the brain. Wealth of data related to Alzheimer’s disease has been generated to date, nevertheless, the molecular mechanism underlying the etiology and pathophysiology of the disease is still unknown. Here we described a method for the combined analysis of multiple types of genome-wide data aimed at revealing convergent evidence interest that would not be captured by a standard molecular approach. Lists of Alzheimer-related genes (seed genes) were obtained from different sets of data on gene expression, SNPs, and molecular targets of drugs. Network analysis was applied for identifying the regions of the human protein-protein interaction network showing a significant enrichment in seed genes, and ultimately, in genes associated to Alzheimer’s disease, due to the cumulative effect of different combinations of the starting data sets. The functional properties of these enriched modules were characterized, effectively considering the role of both Alzheimer-related seed genes and genes that closely interact with them. This approach allowed us to present evidence in favor of one of the competing theories about AD underlying processes, specifically evidence supporting a predominant role of metabolism-associated biological process terms, including autophagy, insulin and fatty acid metabolic processes in Alzheimer, with a focus on AMP-activated protein kinase. This central regulator of cellular energy homeostasis regulates a series of brain functions altered in Alzheimer’s disease and could link genetic perturbation with neuronal transmission and energy regulation, representing a potential candidate to be targeted by therapy.
Collapse
Affiliation(s)
- Laura Caberlotto
- The Microsoft Research - University of Trento Centre for Computational Systems Biology, Rovereto, Italy
- * E-mail:
| | - Mario Lauria
- The Microsoft Research - University of Trento Centre for Computational Systems Biology, Rovereto, Italy
| | - Thanh-Phuong Nguyen
- The Microsoft Research - University of Trento Centre for Computational Systems Biology, Rovereto, Italy
| | - Marco Scotti
- The Microsoft Research - University of Trento Centre for Computational Systems Biology, Rovereto, Italy
| |
Collapse
|
95
|
Kariya Y, Honma M, Suzuki H. Systems-based understanding of pharmacological responses with combinations of multidisciplinary methodologies. Biopharm Drug Dispos 2013; 34:489-507. [DOI: 10.1002/bdd.1865] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2013] [Accepted: 10/06/2013] [Indexed: 12/25/2022]
Affiliation(s)
- Yoshiaki Kariya
- Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine; The University of Tokyo; 113-8655 Tokyo Japan
| | - Masashi Honma
- Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine; The University of Tokyo; 113-8655 Tokyo Japan
| | - Hiroshi Suzuki
- Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine; The University of Tokyo; 113-8655 Tokyo Japan
| |
Collapse
|
96
|
Wu C, Schwartz JM, Nenadic G. PathNER: a tool for systematic identification of biological pathway mentions in the literature. BMC SYSTEMS BIOLOGY 2013; 7 Suppl 3:S2. [PMID: 24555844 PMCID: PMC3852116 DOI: 10.1186/1752-0509-7-s3-s2] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Background Biological pathways are central to many biomedical studies and are frequently discussed in the literature. Several curated databases have been established to collate the knowledge of molecular processes constituting pathways. Yet, there has been little focus on enabling systematic detection of pathway mentions in the literature. Results We developed a tool, named PathNER (Pathway Named Entity Recognition), for the systematic identification of pathway mentions in the literature. PathNER is based on soft dictionary matching and rules, with the dictionary generated from public pathway databases. The rules utilise general pathway-specific keywords, syntactic information and gene/protein mentions. Detection results from both components are merged. On a gold-standard corpus, PathNER achieved an F1-score of 84%. To illustrate its potential, we applied PathNER on a collection of articles related to Alzheimer's disease to identify associated pathways, highlighting cases that can complement an existing manually curated knowledgebase. Conclusions In contrast to existing text-mining efforts that target the automatic reconstruction of pathway details from molecular interactions mentioned in the literature, PathNER focuses on identifying specific named pathway mentions. These mentions can be used to support large-scale curation and pathway-related systems biology applications, as demonstrated in the example of Alzheimer's disease. PathNER is implemented in Java and made freely available online at http://sourceforge.net/projects/pathner/.
Collapse
|
97
|
Ogishima S, Mizuno S, Kikuchi M, Miyashita A, Kuwano R, Tanaka H, Nakaya J. A map of Alzheimer's disease-signaling pathways: a hope for drug target discovery. Clin Pharmacol Ther 2013; 93:399-401. [PMID: 23511713 DOI: 10.1038/clpt.2013.37] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Alzheimer’s disease (AD) is a complex neurodegenerative condition, and its drug therapy is challenging. To inform AD drug discovery, we developed the “AlzPathway,” a prototype of a comprehensive map of AD-related signaling pathways, from information obtained through studies in the public domain. The AlzPathway provides an integrated platform for systems analyses of AD-signaling pathways and networks.
Collapse
Affiliation(s)
- S Ogishima
- Department of Bioclinical Informatics, Tohoku Medical Megabank Organization, Tohoku University, Sendai-shi, Japan.
| | | | | | | | | | | | | |
Collapse
|
98
|
The 3rd DBCLS BioHackathon: improving life science data integration with Semantic Web technologies. J Biomed Semantics 2013; 4:6. [PMID: 23398680 PMCID: PMC3598643 DOI: 10.1186/2041-1480-4-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2012] [Accepted: 02/05/2013] [Indexed: 01/20/2023] Open
Abstract
BACKGROUND BioHackathon 2010 was the third in a series of meetings hosted by the Database Center for Life Sciences (DBCLS) in Tokyo, Japan. The overall goal of the BioHackathon series is to improve the quality and accessibility of life science research data on the Web by bringing together representatives from public databases, analytical tool providers, and cyber-infrastructure researchers to jointly tackle important challenges in the area of in silico biological research. RESULTS The theme of BioHackathon 2010 was the 'Semantic Web', and all attendees gathered with the shared goal of producing Semantic Web data from their respective resources, and/or consuming or interacting those data using their tools and interfaces. We discussed on topics including guidelines for designing semantic data and interoperability of resources. We consequently developed tools and clients for analysis and visualization. CONCLUSION We provide a meeting report from BioHackathon 2010, in which we describe the discussions, decisions, and breakthroughs made as we moved towards compliance with Semantic Web technologies - from source provider, through middleware, to the end-consumer.
Collapse
|