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Dainauskas JJ, Vitale P, Moreno S, Marie H, Migliore M, Saudargiene A. Altered synaptic plasticity at hippocampal CA1-CA3 synapses in Alzheimer's disease: integration of amyloid precursor protein intracellular domain and amyloid beta effects into computational models. Front Comput Neurosci 2023; 17:1305169. [PMID: 38130706 PMCID: PMC10733499 DOI: 10.3389/fncom.2023.1305169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive memory loss and cognitive dysfunction brain disorder brought on by the dysfunctional amyloid precursor protein (APP) processing and clearance of APP peptides. Increased APP levels lead to the production of AD-related peptides including the amyloid APP intracellular domain (AICD) and amyloid beta (Aβ), and consequently modify the intrinsic excitability of the hippocampal CA1 pyramidal neurons, synaptic protein activity, and impair synaptic plasticity at hippocampal CA1-CA3 synapses. The goal of the present study is to build computational models that incorporate the effect of AD-related peptides on CA1 pyramidal neuron and hippocampal synaptic plasticity under the AD conditions and investigate the potential pharmacological treatments that could normalize hippocampal synaptic plasticity and learning in AD. We employ a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model that includes the separate receptor contributions on long-term potentiation (LTP) and long-term depression (LTD) and embed it into the a detailed compartmental model of CA1 pyramidal neuron. Modeling results show that partial blockade of Glu2NB-NMDAR-gated channel restores intrinsic excitability of a CA1 pyramidal neuron and rescues LTP in AICD and Aβ conditions. The model provides insight into the complex interactions in AD pathophysiology and suggests the conditions under which the synchronous activation of a cluster of synaptic inputs targeting the dendritic tree of CA1 pyramidal neuron leads to restored synaptic plasticity.
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Affiliation(s)
- Justinas J. Dainauskas
- Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Informatics, Vytautas Magnus University, Kaunas, Lithuania
| | - Paola Vitale
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Sebastien Moreno
- Université Côte d'Azur, Centre National de la Recherche Scientifique (CNRS), Institut de Pharmacologie Moléculaire et Cellulaire (IPMC), Valbonne, France
| | - Hélène Marie
- Université Côte d'Azur, Centre National de la Recherche Scientifique (CNRS), Institut de Pharmacologie Moléculaire et Cellulaire (IPMC), Valbonne, France
| | - Michele Migliore
- Institute of Biophysics, National Research Council, Palermo, Italy
| | - Ausra Saudargiene
- Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Informatics, Vytautas Magnus University, Kaunas, Lithuania
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Stefanovski L, Meier JM, Pai RK, Triebkorn P, Lett T, Martin L, Bülau K, Hofmann-Apitius M, Solodkin A, McIntosh AR, Ritter P. Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain. Front Neuroinform 2021; 15:630172. [PMID: 33867964 PMCID: PMC8047422 DOI: 10.3389/fninf.2021.630172] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Accepted: 03/08/2021] [Indexed: 12/18/2022] Open
Abstract
Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.
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Affiliation(s)
- Leon Stefanovski
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Jil Mona Meier
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Roopa Kalsank Pai
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
| | - Paul Triebkorn
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Institut de Neurosciences des Systèmes, Aix Marseille Université, Marseille, France
| | - Tristram Lett
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Leon Martin
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Konstantin Bülau
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
| | - Ana Solodkin
- Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX, United States
| | | | - Petra Ritter
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
- Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Neurology with Experimental Neurology, Brain Simulation Section, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Einstein Center for Neuroscience Berlin, Berlin, Germany
- Einstein Center Digital Future, Berlin, Germany
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3
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Joshi A, Wang DH, Watterson S, McClean PL, Behera CK, Sharp T, Wong-Lin K. Opportunities for multiscale computational modelling of serotonergic drug effects in Alzheimer's disease. Neuropharmacology 2020; 174:108118. [PMID: 32380022 PMCID: PMC7322519 DOI: 10.1016/j.neuropharm.2020.108118] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 04/13/2020] [Accepted: 04/27/2020] [Indexed: 12/17/2022]
Abstract
Alzheimer's disease (AD) is an age-specific neurodegenerative disease that compromises cognitive functioning and impacts the quality of life of an individual. Pathologically, AD is characterised by abnormal accumulation of beta-amyloid (Aβ) and hyperphosphorylated tau protein. Despite research advances over the last few decades, there is currently still no cure for AD. Although, medications are available to control some behavioural symptoms and slow the disease's progression, most prescribed medications are based on cholinesterase inhibitors. Over the last decade, there has been increased attention towards novel drugs, targeting alternative neurotransmitter pathways, particularly those targeting serotonergic (5-HT) system. In this review, we focused on 5-HT receptor (5-HTR) mediated signalling and drugs that target these receptors. These pathways regulate key proteins and kinases such as GSK-3 that are associated with abnormal levels of Aβ and tau in AD. We then review computational studies related to 5-HT signalling pathways with the potential for providing deeper understanding of AD pathologies. In particular, we suggest that multiscale and multilevel modelling approaches could potentially provide new insights into AD mechanisms, and towards discovering novel 5-HTR based therapeutic targets. Alzheimer's disease (AD) drug treatment is limited, and alternatives are needed. Serotonin (5-HT) mediated signalling pathways may regulate Aβ and tau levels. 5-HT based drugs have the potential to provide as novel therapeutics for AD. Complex 5-HT signalling mechanisms for AD and related drugs hinder understanding. Multiscale models may offer insights into mechanisms and therapeutic targets.
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Affiliation(s)
- Alok Joshi
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK.
| | - Da-Hui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; School of System Science, Beijing Normal University, Beijing, China
| | - Steven Watterson
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Paula L McClean
- Northern Ireland Centre for Stratified Medicine, Biomedical Sciences Research Institute, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Chandan K Behera
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK
| | - Trevor Sharp
- Department of Pharmacology, University of Oxford, Oxford, UK
| | - KongFatt Wong-Lin
- Intelligent Systems Research Centre, Ulster University, Derry~Londonderry, Northern Ireland, UK.
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Anastasio TJ. Exploring the Correlation between the Cognitive Benefits of Drug Combinations in a Clinical Database and the Efficacies of the Same Drug Combinations Predicted from a Computational Model. J Alzheimers Dis 2019; 70:287-302. [PMID: 31177222 PMCID: PMC6700640 DOI: 10.3233/jad-190144] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Identification of drug combinations that could be effective in Alzheimer’s disease treatment is made difficult by the sheer number of possible combinations. This analysis identifies as potentially therapeutic those drug combinations that rank highest when their efficacy is determined jointly from two independent data sources. Estimates of the efficacy of the same drug combinations were derived from a clinical dataset on cognitively impaired elderly participants and from pre-clinical data, in the form of a computational model of neuroinflammation. Linear regression was used to show that the two sets of estimates were correlated, and to rule out confounds. The ten highest ranking, jointly determined drug combinations most frequently consisted of COX2 inhibitors and aspirin, along with various antihypertensive medications. Ten combinations of from five to nine drugs, and the three-drug combination of a COX2 inhibitor, aspirin, and a calcium-channel blocker, are discussed as candidates for consideration in future pre-clinical and clinical studies.
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Affiliation(s)
- Thomas J Anastasio
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
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Hassan M, Abbas Q, Seo SY, Shahzadi S, Ashwal HA, Zaki N, Iqbal Z, Moustafa AA. Computational modeling and biomarker studies of pharmacological treatment of Alzheimer's disease (Review). Mol Med Rep 2018; 18:639-655. [PMID: 29845262 PMCID: PMC6059694 DOI: 10.3892/mmr.2018.9044] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 07/05/2017] [Indexed: 12/31/2022] Open
Abstract
Alzheimer's disease (AD) is a complex and multifactorial disease. In order to understand the genetic influence in the progression of AD, and to identify novel pharmaceutical agents and their associated targets, the present study discusses computational modeling and biomarker evaluation approaches. Based on mechanistic signaling pathway approaches, various computational models, including biochemical and morphological models, are discussed to explore the strategies that may be used to target AD treatment. Different biomarkers are interpreted on the basis of morphological and functional features of amyloid β plaques and unstable microtubule‑associated tau protein, which is involved in neurodegeneration. Furthermore, imaging and cerebrospinal fluids are also considered to be key methods in the identification of novel markers for AD. In conclusion, the present study reviews various biochemical and morphological computational models and biomarkers to interpret novel targets and agonists for the treatment of AD. This review also highlights several therapeutic targets and their associated signaling pathways in AD, which may have potential to be used in the development of novel pharmacological agents for the treatment of patients with AD. Computational modeling approaches may aid the quest for the development of AD treatments with enhanced therapeutic efficacy and reduced toxicity.
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Affiliation(s)
- Mubashir Hassan
- Department of Biology, College of Natural Sciences, Kongju National University, Gongju, Chungcheongnam 32588, Republic of Korea
- Institute of Molecular Science and Bioinformatics, Dyal Singh Trust Library, Lahore 54000, Pakistan
| | - Qamar Abbas
- Department of Physiology, University of Sindh, Jamshoro 76080, Pakistan
| | - Sung-Yum Seo
- Department of Biology, College of Natural Sciences, Kongju National University, Gongju, Chungcheongnam 32588, Republic of Korea
| | - Saba Shahzadi
- Institute of Molecular Science and Bioinformatics, Dyal Singh Trust Library, Lahore 54000, Pakistan
- Department of Bioinformatics, Virtual University Davis Road Campus, Lahore 54000, Pakistan
| | - Hany Al Ashwal
- College of Information Technology, United Arab Emirates University, Al-Ain 15551, United Arab Emirates
| | - Nazar Zaki
- College of Information Technology, United Arab Emirates University, Al-Ain 15551, United Arab Emirates
| | - Zeeshan Iqbal
- Institute of Molecular Science and Bioinformatics, Dyal Singh Trust Library, Lahore 54000, Pakistan
| | - Ahmed A. Moustafa
- School of Social Sciences and Psychology, Western Sydney University, Sydney, NSW 2751, Australia
- MARCS Institute for Brain, Behavior and Development, Western Sydney University, Sydney, NSW 2751, Australia
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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.
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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
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Anwar H. Capturing intracellular Ca 2+ dynamics in computational models of neurodegenerative diseases. ACTA ACUST UNITED AC 2017; 19:37-42. [PMID: 28983320 DOI: 10.1016/j.ddmod.2017.02.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Many signaling pathways crucial for homeostatic regulation, synaptic plasticity, apoptosis and immune response depend on Ca2+. Ca2+ dysregulation disrupts normal function of neurons and neuronal networks. This causes severe motor and cognitive disabilities. Understanding how Ca2+ dysregulation triggers disease onset and progression, and affects downstream processes, can help identify targets for treatments. Because of intermingling of molecular pathways, dissecting the role of individual mechanisms and establishing causality is very challenging. Computational models provide a way to decipher these processes. I review some computational models with Ca2+ dynamics to illustrate their predictive power, and note where extending those models to capture multiscale interaction of Ca2+ dependent molecular pathways can be useful for therapeutic and drug discovery purposes.
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Affiliation(s)
- Haroon Anwar
- Department of Biological Sciences, New Jersey Institute of Technology, 100 Summit St, University Heights, Newark, NJ 07102, United States
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Anastasio TJ. Temporal-logic analysis of microglial phenotypic conversion with exposure to amyloid-β. MOLECULAR BIOSYSTEMS 2016; 11:434-53. [PMID: 25406664 DOI: 10.1039/c4mb00457d] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Alzheimer Disease (AD) remains a leading killer with no adequate treatment. Ongoing research increasingly implicates the brain's immune system as a critical contributor to AD pathogenesis, but the complexity of the immune contribution poses a barrier to understanding. Here I use temporal logic to analyze a computational specification of the immune component of AD. Temporal logic is an extension of logic to propositions expressed in terms of time. It has traditionally been used to analyze computational specifications of complex engineered systems but applications to complex biological systems are now appearing. The inflammatory component of AD involves the responses of microglia to the peptide amyloid-β (Aβ), which is an inflammatory stimulus and a likely causative AD agent. Temporal-logic analysis of the model provides explanations for the puzzling findings that Aβ induces an anti-inflammatory and well as a pro-inflammatory response, and that Aβ is phagocytized by microglia in young but not in old animals. To potentially explain the first puzzle, the model suggests that interferon-γ acts as an "autocrine bridge" over which an Aβ-induced increase in pro-inflammatory cytokines leads to an increase in anti-inflammatory mediators also. To potentially explain the second puzzle, the model identifies a potential instability in signaling via insulin-like growth factor 1 that could explain the failure of old microglia to phagocytize Aβ. The model predicts that augmentation of insulin-like growth factor 1 signaling, and activation of protein kinase C in particular, could move old microglia from a neurotoxic back toward a more neuroprotective and phagocytic phenotype.
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Affiliation(s)
- Thomas J Anastasio
- Computational Neurobiology Laboratory, Department of Molecular and Integrative Physiology, and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
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