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Virtuoso A, Giovannoni R, De Luca C, Gargano F, Cerasuolo M, Maggio N, Lavitrano M, Papa M. The Glioblastoma Microenvironment: Morphology, Metabolism, and Molecular Signature of Glial Dynamics to Discover Metabolic Rewiring Sequence. Int J Mol Sci 2021; 22:3301. [PMID: 33804873 PMCID: PMC8036663 DOI: 10.3390/ijms22073301] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/17/2021] [Accepted: 03/22/2021] [Indexed: 02/07/2023] Open
Abstract
Different functional states determine glioblastoma (GBM) heterogeneity. Brain cancer cells coexist with the glial cells in a functional syncytium based on a continuous metabolic rewiring. However, standard glioma therapies do not account for the effects of the glial cells within the tumor microenvironment. This may be a possible reason for the lack of improvements in patients with high-grade gliomas therapies. Cell metabolism and bioenergetic fitness depend on the availability of nutrients and interactions in the microenvironment. It is strictly related to the cell location in the tumor mass, proximity to blood vessels, biochemical gradients, and tumor evolution, underlying the influence of the context and the timeline in anti-tumor therapeutic approaches. Besides the cancer metabolic strategies, here we review the modifications found in the GBM-associated glia, focusing on morphological, molecular, and metabolic features. We propose to analyze the GBM metabolic rewiring processes from a systems biology perspective. We aim at defining the crosstalk between GBM and the glial cells as modules. The complex networking may be expressed by metabolic modules corresponding to the GBM growth and spreading phases. Variation in the oxidative phosphorylation (OXPHOS) rate and regulation appears to be the most important part of the metabolic and functional heterogeneity, correlating with glycolysis and response to hypoxia. Integrated metabolic modules along with molecular and morphological features could allow the identification of key factors for controlling the GBM-stroma metabolism in multi-targeted, time-dependent therapies.
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Affiliation(s)
- Assunta Virtuoso
- Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, University of Campania ‘‘Luigi Vanvitelli”, 80138 Naples, Italy; (A.V.); (F.G.); (M.C.); (M.P.)
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy;
| | | | - Ciro De Luca
- Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, University of Campania ‘‘Luigi Vanvitelli”, 80138 Naples, Italy; (A.V.); (F.G.); (M.C.); (M.P.)
| | - Francesca Gargano
- Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, University of Campania ‘‘Luigi Vanvitelli”, 80138 Naples, Italy; (A.V.); (F.G.); (M.C.); (M.P.)
| | - Michele Cerasuolo
- Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, University of Campania ‘‘Luigi Vanvitelli”, 80138 Naples, Italy; (A.V.); (F.G.); (M.C.); (M.P.)
| | - Nicola Maggio
- Department of Neurology, Sackler Faculty of Medicine, Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel;
- Department of Neurology, The Chaim Sheba Medical Center, Ramat Gan 5211401, Israel
| | - Marialuisa Lavitrano
- School of Medicine and Surgery, University of Milano-Bicocca, 20900 Monza, Italy;
| | - Michele Papa
- Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, University of Campania ‘‘Luigi Vanvitelli”, 80138 Naples, Italy; (A.V.); (F.G.); (M.C.); (M.P.)
- SYSBIO Centre of Systems Biology ISBE-IT, University of Milano-Bicocca, 20126 Milan, Italy
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2
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Jiang Y, Li M, Fan Y, Di Z. Characterizing dissimilarity of weighted networks. Sci Rep 2021; 11:5768. [PMID: 33707620 PMCID: PMC7952696 DOI: 10.1038/s41598-021-85175-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Accepted: 02/22/2021] [Indexed: 11/09/2022] Open
Abstract
Measuring the dissimilarities between networks is a basic problem and wildly used in many fields. Based on method of the D-measure which is suggested for unweighted networks, we propose a quantitative dissimilarity metric of weighted network (WD-metric). Crucially, we construct a distance probability matrix of weighted network, which can capture the comprehensive information of weighted network. Moreover, we define the complementary graph and alpha centrality of weighted network. Correspondingly, several synthetic and real-world networks are used to verify the effectiveness of the WD-metric. Experimental results show that WD-metric can effectively capture the influence of weight on the network structure and quantitatively measure the dissimilarity of weighted networks. It can also be used as a criterion for backbone extraction algorithms of complex network.
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Affiliation(s)
- Yuanxiang Jiang
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Meng Li
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Ying Fan
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Zengru Di
- School of Systems Science, Beijing Normal University, Beijing, 100875, China.
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3
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Neurodegenerative Implications of Neuronal Cytoplasmic Protein Dysfunction in Response to Environmental Contaminants. Neurotox Res 2020; 39:533-541. [PMID: 33175324 DOI: 10.1007/s12640-020-00308-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 09/10/2020] [Accepted: 10/29/2020] [Indexed: 10/23/2022]
Abstract
Neurodegenerative diseases account for a significant portion of public health concerns particularly in the aging population. The dysfunction of interfilament proteins has been identified as a key event in the initiation of neurodegeneration and subsequent progression to neurodegenerative diseases. In addition, several studies have found associations between the dysfunction of interfilament proteins and exposure to environmental contaminants. Therefore, in this review, the role of interfilament proteins in neuronal cells, their connection to neurotoxicity from environmental contaminants, and finally the resulting neurodegeneration are discussed.
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4
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N Kolodkin A, Sharma RP, Colangelo AM, Ignatenko A, Martorana F, Jennen D, Briedé JJ, Brady N, Barberis M, Mondeel TDGA, Papa M, Kumar V, Peters B, Skupin A, Alberghina L, Balling R, Westerhoff HV. ROS networks: designs, aging, Parkinson's disease and precision therapies. NPJ Syst Biol Appl 2020; 6:34. [PMID: 33106503 PMCID: PMC7589522 DOI: 10.1038/s41540-020-00150-w] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Accepted: 08/28/2020] [Indexed: 12/11/2022] Open
Abstract
How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.
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Affiliation(s)
- Alexey N Kolodkin
- Infrastructure for Systems Biology Europe (ISBE.NL), Amsterdam, The Netherlands.
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
| | - Raju Prasad Sharma
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands
- Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain
| | - Anna Maria Colangelo
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuroscience "R Levi-Montalcini" Dept of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Andrew Ignatenko
- Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg
| | - Francesca Martorana
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Laboratory of Neuroscience "R Levi-Montalcini" Dept of Biotechnology and Biosciences, University of Milano-Bicocca, Milan, Italy
| | - Danyel Jennen
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Jacco J Briedé
- Department of Toxicogenomics, GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Nathan Brady
- Department of Molecular Microbiology & Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Matteo Barberis
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Surrey, UK
| | - Thierry D G A Mondeel
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
- Systems Biology, School of Biosciences and Medicine, Faculty of Health and Medical Sciences, University of Surrey, Surrey, UK
- Centre for Mathematical and Computational Biology, CMCB, University of Surrey, Surrey, UK
| | - Michele Papa
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
- Infrastructure for Systems Biology Europe (ISBE.IT), Naples, Italy
- Department of Mental and Physical Health, University of Campania-L. Vanvitelli, Napoli, Italia
| | - Vikas Kumar
- Environmental Engineering Laboratory, Departament d'Enginyeria Quimica, Universitat Rovira i Virgili, Tarragona, Spain
- IISPV, Hospital Universitari Sant Joan de Reus, Universitat Rovira I Virgili, Reus, Spain
| | - Bernhard Peters
- Faculty of Science, Technology and Communication, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Alexander Skupin
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Lilia Alberghina
- Infrastructure for Systems Biology Europe (ISBE.IT), Milan, Italy
- SysBio Centre of Systems Biology (ISBE.IT), University of Milano-Bicocca, Milan, Italy
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Hans V Westerhoff
- Infrastructure for Systems Biology Europe (ISBE.NL), Amsterdam, The Netherlands.
- Molecular Cell Physiology, VU University Amsterdam, Amsterdam, The Netherlands.
- Synthetic Systems Biology and Nuclear Organization, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
- Manchester Centre for Integrative Systems Biology, School for Chemical Engineering and Analytical Science, The University of Manchester, Manchester, UK.
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5
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Mheich A, Wendling F, Hassan M. Brain network similarity: methods and applications. Netw Neurosci 2020; 4:507-527. [PMID: 32885113 PMCID: PMC7462433 DOI: 10.1162/netn_a_00133] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Accepted: 02/26/2020] [Indexed: 12/11/2022] Open
Abstract
Graph theoretical approach has proved an effective tool to understand, characterize, and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths, and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a “network of networks” that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications.
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Affiliation(s)
- Ahmad Mheich
- Laboratoire Traitement du Signal et de l'Image, Institut National de la Santé et de la Recherche Médicale, Rennes, France
| | - Fabrice Wendling
- Laboratoire Traitement du Signal et de l'Image, Institut National de la Santé et de la Recherche Médicale, Rennes, France
| | - Mahmoud Hassan
- Laboratoire Traitement du Signal et de l'Image, Institut National de la Santé et de la Recherche Médicale, Rennes, France
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6
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Neurons, Glia, Extracellular Matrix and Neurovascular Unit: A Systems Biology Approach to the Complexity of Synaptic Plasticity in Health and Disease. Int J Mol Sci 2020; 21:ijms21041539. [PMID: 32102370 PMCID: PMC7073232 DOI: 10.3390/ijms21041539] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2019] [Revised: 02/19/2020] [Accepted: 02/20/2020] [Indexed: 02/06/2023] Open
Abstract
The synaptic cleft has been vastly investigated in the last decades, leading to a novel and fascinating model of the functional and structural modifications linked to synaptic transmission and brain processing. The classic neurocentric model encompassing the neuronal pre- and post-synaptic terminals partly explains the fine-tuned plastic modifications under both pathological and physiological circumstances. Recent experimental evidence has incontrovertibly added oligodendrocytes, astrocytes, and microglia as pivotal elements for synapse formation and remodeling (tripartite synapse) in both the developing and adult brain. Moreover, synaptic plasticity and its pathological counterpart (maladaptive plasticity) have shown a deep connection with other molecular elements of the extracellular matrix (ECM), once considered as a mere extracellular structural scaffold altogether with the cellular glue (i.e., glia). The ECM adds another level of complexity to the modern model of the synapse, particularly, for the long-term plasticity and circuit maintenance. This model, called tetrapartite synapse, can be further implemented by including the neurovascular unit (NVU) and the immune system. Although they were considered so far as tightly separated from the central nervous system (CNS) plasticity, at least in physiological conditions, recent evidence endorsed these elements as structural and paramount actors in synaptic plasticity. This scenario is, as far as speculations and evidence have shown, a consistent model for both adaptive and maladaptive plasticity. However, a comprehensive understanding of brain processes and circuitry complexity is still lacking. Here we propose that a better interpretation of the CNS complexity can be granted by a systems biology approach through the construction of predictive molecular models that enable to enlighten the regulatory logic of the complex molecular networks underlying brain function in health and disease, thus opening the way to more effective treatments.
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7
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Tran QH, Vo VT, Hasegawa Y. Scale-variant topological information for characterizing the structure of complex networks. Phys Rev E 2019; 100:032308. [PMID: 31640058 DOI: 10.1103/physreve.100.032308] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Indexed: 06/10/2023]
Abstract
The structure of real-world networks is usually difficult to characterize owing to the variation of topological scales, the nondyadic complex interactions, and the fluctuations in the network. We aim to address these problems by introducing a general framework using a method based on topological data analysis. By considering the diffusion process at a single specified timescale in a network, we map the network nodes to a finite set of points that contains the topological information of the network at a single scale. Subsequently, we study the shape of these point sets over variable timescales that provide scale-variant topological information, to understand the varying topological scales and the complex interactions in the network. We conduct experiments on synthetic and real-world data to demonstrate the effectiveness of the proposed framework in identifying network models, classifying real-world networks, and detecting transition points in time-evolving networks. Overall, our study presents a unified analysis that can be applied to more complex network structures, as in the case of multilayer and multiplex networks.
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Affiliation(s)
- Quoc Hoan Tran
- Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
| | - Van Tuan Vo
- Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
| | - Yoshihiko Hasegawa
- Department of Information and Communication Engineering, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo 113-8656, Japan
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8
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Lo Surdo P, Calderone A, Iannuccelli M, Licata L, Peluso D, Castagnoli L, Cesareni G, Perfetto L. DISNOR: a disease network open resource. Nucleic Acids Res 2019; 46:D527-D534. [PMID: 29036667 PMCID: PMC5753342 DOI: 10.1093/nar/gkx876] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 09/25/2017] [Indexed: 12/13/2022] Open
Abstract
DISNOR is a new resource that aims at exploiting the explosion of data on the identification of disease-associated genes to assemble inferred disease pathways. This may help dissecting the signaling events whose disruption causes the pathological phenotypes and may contribute to build a platform for precision medicine. To this end we combine the gene-disease association (GDA) data annotated in the DisGeNET resource with a new curation effort aimed at populating the SIGNOR database with causal interactions related to disease genes with the highest possible coverage. DISNOR can be freely accessed at http://DISNOR.uniroma2.it/ where >3700 disease-networks, linking ∼2600 disease genes, can be explored. For each disease curated in DisGeNET, DISNOR links disease genes by manually annotated causal relationships and offers an intuitive visualization of the inferred ‘patho-pathways’ at different complexity levels. User-defined gene lists are also accepted in the query pipeline. In addition, for each list of query genes—either annotated in DisGeNET or user-defined—DISNOR performs a gene set enrichment analysis on KEGG-defined pathways or on the lists of proteins associated with the inferred disease pathways. This function offers additional information on disease-associated cellular pathways and disease similarity.
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Affiliation(s)
- Prisca Lo Surdo
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Alberto Calderone
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Marta Iannuccelli
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Luana Licata
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Daniele Peluso
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy.,Laboratory of Bioinformatic, IRCCS Fondazione Santa Lucia, 00143 Rome, Italy
| | - Luisa Castagnoli
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Gianni Cesareni
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
| | - Livia Perfetto
- Bioinformatics and Computational Biology Unit, Department of Biology, University of Rome 'Tor Vergata', 00133 Rome, Italy
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9
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Community detection in large-scale social networks: state-of-the-art and future directions. SOCIAL NETWORK ANALYSIS AND MINING 2019. [DOI: 10.1007/s13278-019-0566-x] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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10
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Lumsden AL, Rogers JT, Majd S, Newman M, Sutherland GT, Verdile G, Lardelli M. Dysregulation of Neuronal Iron Homeostasis as an Alternative Unifying Effect of Mutations Causing Familial Alzheimer's Disease. Front Neurosci 2018; 12:533. [PMID: 30150923 PMCID: PMC6099262 DOI: 10.3389/fnins.2018.00533] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 07/16/2018] [Indexed: 12/12/2022] Open
Abstract
The overwhelming majority of dominant mutations causing early onset familial Alzheimer’s disease (EOfAD) occur in only three genes, PSEN1, PSEN2, and APP. An effect-in-common of these mutations is alteration of production of the APP-derived peptide, amyloid β (Aβ). It is this key fact that underlies the authority of the Amyloid Hypothesis that has informed Alzheimer’s disease research for over two decades. Any challenge to this authority must offer an alternative explanation for the relationship between the PSEN genes and APP. In this paper, we explore one possible alternative relationship – the dysregulation of cellular iron homeostasis as a common effect of EOfAD mutations in these genes. This idea is attractive since it provides clear connections between EOfAD mutations and major characteristics of Alzheimer’s disease such as dysfunctional mitochondria, vascular risk factors/hypoxia, energy metabolism, and inflammation. We combine our ideas with observations by others to describe a “Stress Threshold Change of State” model of Alzheimer’s disease that may begin to explain the existence of both EOfAD and late onset sporadic (LOsAD) forms of the disease. Directing research to investigate the role of dysregulation of iron homeostasis in EOfAD may be a profitable way forward in our struggle to understand this form of dementia.
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Affiliation(s)
- Amanda L Lumsden
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia.,South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Jack T Rogers
- Neurochemistry Laboratory, Department of Psychiatry-Neuroscience, Massachusetts General Hospital (East), Harvard Medical School, Harvard University, Charlestown, MA, United States
| | - Shohreh Majd
- Neuronal Injury and Repair Laboratory, Centre for Neuroscience, College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
| | - Morgan Newman
- Centre for Molecular Pathology, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Greg T Sutherland
- Discipline of Pathology, Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Giuseppe Verdile
- School of Pharmacy and Biomedical Sciences, Faculty of Health Sciences, Curtin Health Innovation Research Institute, Curtin University, Bentley, WA, Australia
| | - Michael Lardelli
- Centre for Molecular Pathology, School of Biological Sciences, University of Adelaide, Adelaide, SA, Australia
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11
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Garcia-Vaquero ML, Gama-Carvalho M, Rivas JDL, Pinto FR. Searching the overlap between network modules with specific betweeness (S2B) and its application to cross-disease analysis. Sci Rep 2018; 8:11555. [PMID: 30068933 PMCID: PMC6070533 DOI: 10.1038/s41598-018-29990-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 07/23/2018] [Indexed: 12/14/2022] Open
Abstract
Discovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B .
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Affiliation(s)
- Marina L Garcia-Vaquero
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal
| | - Margarida Gama-Carvalho
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal
| | - Javier De Las Rivas
- Cancer Research Center (CiC-IBMCC, CSIC/USAL/IBSAL), Consejo Superior de Investigaciones Científicas (CSIC) and Universidad de Salamanca (USAL), Salamanca, Spain
| | - Francisco R Pinto
- University of Lisboa, Faculty of Sciences, BioISI - Biosystems & Integrative Sciences Institute, Campo Grande, C8 bdg, 1749-016, Lisboa, Portugal.
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12
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Glaab E. Computational systems biology approaches for Parkinson's disease. Cell Tissue Res 2018; 373:91-109. [PMID: 29185073 PMCID: PMC6015628 DOI: 10.1007/s00441-017-2734-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 11/06/2017] [Indexed: 12/26/2022]
Abstract
Parkinson's disease (PD) is a prime example of a complex and heterogeneous disorder, characterized by multifaceted and varied motor- and non-motor symptoms and different possible interplays of genetic and environmental risk factors. While investigations of individual PD-causing mutations and risk factors in isolation are providing important insights to improve our understanding of the molecular mechanisms behind PD, there is a growing consensus that a more complete understanding of these mechanisms will require an integrative modeling of multifactorial disease-associated perturbations in molecular networks. Identifying and interpreting the combinatorial effects of multiple PD-associated molecular changes may pave the way towards an earlier and reliable diagnosis and more effective therapeutic interventions. This review provides an overview of computational systems biology approaches developed in recent years to study multifactorial molecular alterations in complex disorders, with a focus on PD research applications. Strengths and weaknesses of different cellular pathway and network analyses, and multivariate machine learning techniques for investigating PD-related omics data are discussed, and strategies proposed to exploit the synergies of multiple biological knowledge and data sources. A final outlook provides an overview of specific challenges and possible next steps for translating systems biology findings in PD to new omics-based diagnostic tools and targeted, drug-based therapeutic approaches.
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Affiliation(s)
- Enrico Glaab
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7 avenue des Hauts Fourneaux, L-4362, Esch-sur-Alzette, Luxembourg.
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13
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Santiago JA, Bottero V, Potashkin JA. Dissecting the Molecular Mechanisms of Neurodegenerative Diseases through Network Biology. Front Aging Neurosci 2017; 9:166. [PMID: 28611656 PMCID: PMC5446999 DOI: 10.3389/fnagi.2017.00166] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Accepted: 05/12/2017] [Indexed: 12/27/2022] Open
Abstract
Neurodegenerative diseases are rarely caused by a mutation in a single gene but rather influenced by a combination of genetic, epigenetic and environmental factors. Emerging high-throughput technologies such as RNA sequencing have been instrumental in deciphering the molecular landscape of neurodegenerative diseases, however, the interpretation of such large amounts of data remains a challenge. Network biology has become a powerful platform to integrate multiple omics data to comprehensively explore the molecular networks in the context of health and disease. In this review article, we highlight recent advances in network biology approaches with an emphasis in brain-networks that have provided insights into the molecular mechanisms leading to the most prevalent neurodegenerative diseases including Alzheimer’s (AD), Parkinson’s (PD) and Huntington’s diseases (HD). We discuss how integrative approaches using multi-omics data from different tissues have been valuable for identifying biomarkers and therapeutic targets. In addition, we discuss the challenges the field of network medicine faces toward the translation of network-based findings into clinically actionable tools for personalized medicine applications.
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Affiliation(s)
- Jose A Santiago
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and ScienceNorth Chicago, IL, United States
| | - Virginie Bottero
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and ScienceNorth Chicago, IL, United States
| | - Judith A Potashkin
- Department of Cellular and Molecular Pharmacology, The Chicago Medical School, Rosalind Franklin University of Medicine and ScienceNorth Chicago, IL, United States
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Quantification of network structural dissimilarities. Nat Commun 2017; 8:13928. [PMID: 28067266 PMCID: PMC5227707 DOI: 10.1038/ncomms13928] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 11/15/2016] [Indexed: 11/22/2022] Open
Abstract
Identifying and quantifying dissimilarities among graphs is a fundamental and challenging problem of practical importance in many fields of science. Current methods of network comparison are limited to extract only partial information or are computationally very demanding. Here we propose an efficient and precise measure for network comparison, which is based on quantifying differences among distance probability distributions extracted from the networks. Extensive experiments on synthetic and real-world networks show that this measure returns non-zero values only when the graphs are non-isomorphic. Most importantly, the measure proposed here can identify and quantify structural topological differences that have a practical impact on the information flow through the network, such as the presence or absence of critical links that connect or disconnect connected components. Identifying and quantifying dissimilarities among graphs is a problem of practical importance, but current approaches are either limited or computationally demanding. Here, the authors propose an efficiently computable measure for network comparison that can identify structural topological differences.
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