1
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Sanchez-Rodriguez LM, Khan AF, Adewale Q, Bezgin G, Therriault J, Fernandez-Arias J, Servaes S, Rahmouni N, Tissot C, Stevenson J, Jiang H, Chai X, Carbonell F, Rosa-Neto P, Iturria-Medina Y. In-vivo neuronal dysfunction by Aβ and tau overlaps with brain-wide inflammatory mechanisms in Alzheimer's disease. Front Aging Neurosci 2024; 16:1383163. [PMID: 38966801 PMCID: PMC11223503 DOI: 10.3389/fnagi.2024.1383163] [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: 02/07/2024] [Accepted: 05/09/2024] [Indexed: 07/06/2024] Open
Abstract
The molecular mechanisms underlying neuronal dysfunction in Alzheimer's disease (AD) remain uncharacterized. Here, we identify genes, molecular pathways and cellular components associated with whole-brain dysregulation caused by amyloid-beta (Aβ) and tau deposits in the living human brain. We obtained in-vivo resting-state functional MRI (rs-fMRI), Aβ- and tau-PET for 47 cognitively unimpaired and 16 AD participants from the Translational Biomarkers in Aging and Dementia cohort. Adverse neuronal activity impacts by Aβ and tau were quantified with personalized dynamical models by fitting pathology-mediated computational signals to the participant's real rs-fMRIs. Then, we detected robust brain-wide associations between the spatial profiles of Aβ-tau impacts and gene expression in the neurotypical transcriptome (Allen Human Brain Atlas). Within the obtained distinctive signature of in-vivo neuronal dysfunction, several genes have prominent roles in microglial activation and in interactions with Aβ and tau. Moreover, cellular vulnerability estimations revealed strong association of microglial expression patterns with Aβ and tau's synergistic impact on neuronal activity (q < 0.001). These results further support the central role of the immune system and neuroinflammatory pathways in AD pathogenesis. Neuronal dysregulation by AD pathologies also associated with neurotypical synaptic and developmental processes. In addition, we identified drug candidates from the vast LINCS library to halt or reduce the observed Aβ-tau effects on neuronal activity. Top-ranked pharmacological interventions target inflammatory, cancer and cardiovascular pathways, including specific medications undergoing clinical evaluation in AD. Our findings, based on the examination of molecular-pathological-functional interactions in humans, may accelerate the process of bringing effective therapies into clinical practice.
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Affiliation(s)
- Lazaro M. Sanchez-Rodriguez
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
| | - Ahmed F. Khan
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
| | - Quadri Adewale
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
| | - Gleb Bezgin
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Joseph Therriault
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Jaime Fernandez-Arias
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Stijn Servaes
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Nesrine Rahmouni
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Cécile Tissot
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
- Lawrence Berkeley National Laboratory, Berkeley, CA, United States
| | - Jenna Stevenson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Hongxiu Jiang
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
| | - Xiaoqian Chai
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
| | | | - Pedro Rosa-Neto
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- McGill University Research Centre for Studies in Aging, Douglas Research Centre, Montreal, QC, Canada
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
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2
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Bertsch M, Franchi B, Tesi MC, Tora V. The role of A[Formula: see text] and Tau proteins in Alzheimer's disease: a mathematical model on graphs. J Math Biol 2023; 87:49. [PMID: 37646953 PMCID: PMC10468937 DOI: 10.1007/s00285-023-01985-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/25/2023] [Accepted: 08/13/2023] [Indexed: 09/01/2023]
Abstract
In this Note we study a mathematical model for the progression of Alzheimer's Disease in the human brain. The novelty of our approach consists in the representation of the brain as two superposed graphs where toxic proteins diffuse, the connectivity graph which represents the neural network, and the proximity graph which takes into account the extracellular space. Toxic proteins such as [Formula: see text] amyloid and Tau play in fact a crucial role in the development of Alzheimer's disease and, separately, have been targets of medical treatments. Recent biomedical literature stresses the potential impact of the synergetic action of these proteins. We numerically test various modelling hypotheses which confirm the relevance of this synergy.
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Affiliation(s)
- Michiel Bertsch
- Department of Mathematics, University of Roma “Tor Vergata”, Rome, Italy
- Istituto per le Applicazioni del Calcolo “M. Picone”, Consiglio Nazionale delle Ricerche, Rome, Italy
| | - Bruno Franchi
- Department of Mathematics, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Maria Carla Tesi
- Department of Mathematics, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Veronica Tora
- Department of Mathematics, University of Roma “Tor Vergata”, Rome, Italy
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3
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Ciuro M, Sangiorgio M, Leanza G, Gulino R. A Meta-Analysis Study of SOD1-Mutant Mouse Models of ALS to Analyse the Determinants of Disease Onset and Progression. Int J Mol Sci 2022; 24:ijms24010216. [PMID: 36613659 PMCID: PMC9820332 DOI: 10.3390/ijms24010216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/10/2022] [Accepted: 12/15/2022] [Indexed: 12/25/2022] Open
Abstract
A complex interaction between genetic and external factors determines the development of amyotrophic lateral sclerosis (ALS). Epidemiological studies on large patient cohorts have suggested that ALS is a multi-step disease, as symptom onset occurs only after exposure to a sequence of risk factors. Although the exact nature of these determinants remains to be clarified, it seems clear that: (i) genetic mutations may be responsible for one or more of these steps; (ii) other risk factors are probably linked to environment and/or to lifestyle, and (iii) compensatory plastic changes taking place during the ALS etiopathogenesis probably affect the timing of onset and progression of disease. Current knowledge on ALS mechanisms and therapeutic targets, derives mainly from studies involving superoxide dismutase 1 (SOD1) transgenic mice; therefore, it would be fundamental to verify whether a multi-step disease concept can also be applied to these animal models. With this aim, a meta-analysis study has been performed using a collection of primary studies (n = 137), selected according to the following criteria: (1) the studies should employ SOD1 transgenic mice; (2) the studies should entail the presence of a disease-modifying experimental manipulation; (3) the studies should make use of Kaplan-Meier plots showing the distribution of symptom onset and lifespan. Then, using a subset of this study collection (n = 94), the effects of treatments on key molecular mechanisms, as well as on the onset and progression of disease have been analysed in a large population of mice. The results are consistent with a multi-step etiopathogenesis of disease in ALS mice (including two to six steps, depending on the particular SOD1 mutation), closely resembling that observed in patient cohorts, and revealed an interesting relationship between molecular mechanisms and disease manifestation. Thus, SOD1 mouse models may be considered of high predictive value to understand the determinants of disease onset and progression, as well as to identify targets for therapeutic interventions.
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Affiliation(s)
- Maria Ciuro
- Department of Biomedical and Biotechnological Sciences, Physiology Section, University of Catania, 95123 Catania, Italy
| | - Maria Sangiorgio
- Department of Biomedical and Biotechnological Sciences, Physiology Section, University of Catania, 95123 Catania, Italy
| | - Giampiero Leanza
- Department of Drug and Health Sciences, University of Catania, 95125 Catania, Italy
- Molecular Preclinical and Translational Imaging Research Centre—IMPRonTE, University of Catania, 95125 Catania, Italy
| | - Rosario Gulino
- Department of Biomedical and Biotechnological Sciences, Physiology Section, University of Catania, 95123 Catania, Italy
- Molecular Preclinical and Translational Imaging Research Centre—IMPRonTE, University of Catania, 95125 Catania, Italy
- Correspondence:
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4
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van der Vegt SA, Wang YJ, Polonchuk L, Wang K, Waters SL, Baker RE. A model-informed approach to assess the risk of immune checkpoint inhibitor-induced autoimmune myocarditis. Front Pharmacol 2022; 13:966180. [PMID: 36249751 PMCID: PMC9555336 DOI: 10.3389/fphar.2022.966180] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 08/23/2022] [Indexed: 11/17/2022] Open
Abstract
Immune checkpoint inhibitors (ICIs), as a novel immunotherapy, are designed to modulate the immune system to attack malignancies. Despite their promising benefits, immune-related adverse events (IRAEs) may occur, and incidences are bound to increase with surging demand of this class of drugs in treating cancer. Myocarditis, although rare compared to other IRAEs, has a significantly higher fatal frequency. Due to the overwhelming complexity of the immune system, this condition is not well understood, despite the significant research efforts devoted to it. To better understand the development and progression of autoimmune myocarditis and the roles of ICIs therein, we suggest a new approach: mathematical modelling. Mathematical modelling of myocarditis has enormous potential to determine which parts of the immune system are critical to the development and progression of the disease, and therefore warrant further investigation. We provide the immunological background needed to develop a mathematical model of this disease and review relevant existing models of immunology that serve as the mathematical inspiration needed to develop this field.
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Affiliation(s)
- Solveig A. van der Vegt
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
- *Correspondence: Solveig A. van der Vegt,
| | - Ying-Jie Wang
- Department of Cardiovascular Medicine, Radcliffe Department of Medicine, Wellcome Centre of Human Genetics, University of Oxford, Oxford, United Kingdom
| | - Liudmila Polonchuk
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Ken Wang
- Pharmaceutical Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Sarah L. Waters
- Oxford Centre for Industrial and Applied Mathematics, Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Ruth E. Baker
- Wolfson Centre for Mathematical Biology, Mathematical Institute, University of Oxford, Oxford, United Kingdom
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5
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Shafiei G, Bazinet V, Dadar M, Manera AL, Collins DL, Dagher A, Borroni B, Sanchez-Valle R, Moreno F, Laforce R, Graff C, Synofzik M, Galimberti D, Rowe JB, Masellis M, Tartaglia MC, Finger E, Vandenberghe R, de Mendonça A, Tagliavini F, Santana I, Butler C, Gerhard A, Danek A, Levin J, Otto M, Sorbi S, Jiskoot LC, Seelaar H, van Swieten JC, Rohrer JD, Misic B, Ducharme S. Network structure and transcriptomic vulnerability shape atrophy in frontotemporal dementia. Brain 2022; 146:321-336. [PMID: 35188955 PMCID: PMC9825569 DOI: 10.1093/brain/awac069] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/14/2021] [Accepted: 01/30/2022] [Indexed: 01/13/2023] Open
Abstract
Connections among brain regions allow pathological perturbations to spread from a single source region to multiple regions. Patterns of neurodegeneration in multiple diseases, including behavioural variant of frontotemporal dementia (bvFTD), resemble the large-scale functional systems, but how bvFTD-related atrophy patterns relate to structural network organization remains unknown. Here we investigate whether neurodegeneration patterns in sporadic and genetic bvFTD are conditioned by connectome architecture. Regional atrophy patterns were estimated in both genetic bvFTD (75 patients, 247 controls) and sporadic bvFTD (70 patients, 123 controls). First, we identified distributed atrophy patterns in bvFTD, mainly targeting areas associated with the limbic intrinsic network and insular cytoarchitectonic class. Regional atrophy was significantly correlated with atrophy of structurally- and functionally-connected neighbours, demonstrating that network structure shapes atrophy patterns. The anterior insula was identified as the predominant group epicentre of brain atrophy using data-driven and simulation-based methods, with some secondary regions in frontal ventromedial and antero-medial temporal areas. We found that FTD-related genes, namely C9orf72 and TARDBP, confer local transcriptomic vulnerability to the disease, modulating the propagation of pathology through the connectome. Collectively, our results demonstrate that atrophy patterns in sporadic and genetic bvFTD are jointly shaped by global connectome architecture and local transcriptomic vulnerability, providing an explanation as to how heterogenous pathological entities can lead to the same clinical syndrome.
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Affiliation(s)
| | | | - Mahsa Dadar
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada,Radiology and Nuclear Medicine, Laval University, Quebec City, QC, Canada
| | - Ana L Manera
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - D Louis Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Barbara Borroni
- Centre for Neurodegenerative Disorders, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
| | - Raquel Sanchez-Valle
- Alzheimer’s Disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d’Investigacións Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain
| | - Fermin Moreno
- Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, San Sebastian, Gipuzkoa, Spain,Neuroscience Area, Biodonostia Health Research Institute, San Sebastian, Gipuzkoa, Spain
| | - Robert Laforce
- Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de Médecine, Université Laval, Quebec, QC, Canada
| | - Caroline Graff
- Department of Geriatric Medicine, Karolinska University Hospital-Huddinge, Stockholm, Sweden,Unit for Hereditary Dementias, Theme Aging, Karolinska University Hospital, Solna, Sweden
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany,Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
| | - Daniela Galimberti
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy,Department of Biomedical, Surgical and Dental Sciences, University of Milan, Dino Ferrari Center, Milan, Italy
| | - James B Rowe
- University of Cambridge, Department of Clinical Neurosciences, Cambridge University Hospitals NHS Trust, and MRC Cognition and Brain Sciences Unit, Cambridge, UK
| | - Mario Masellis
- Sunnybrook Health Sciences Centre, Sunnybrook Research Institute, University of Toronto, Toronto, ON, Canada
| | - Maria Carmela Tartaglia
- Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease, Toronto, ON, Canada
| | - Elizabeth Finger
- Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium,Neurology Service, University Hospitals Leuven, Leuven, Belgium,Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | | | - Fabrizio Tagliavini
- Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, Italy
| | - Isabel Santana
- Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal,Center for Neuroscience and Cell Biology, Faculty of Medicine, University of Coimbra, Coimbra, Portugal
| | - Chris Butler
- Department of Clinical Neurology, University of Oxford, Oxford, UK,Department of Brain Sciences, Imperial College London, London, UK
| | - Alex Gerhard
- Division of Neuroscience and Experimental Psychology, Faculty of Medicine, Biology and Health, University of Manchester, Manchester, UK,Department of Geriatric Medicine and Nuclear Medicine, University of Duisburg-Essen, Duisburg and Essen, Germany
| | - Adrian Danek
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Johannes Levin
- Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany,Clinical Research Unit, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany,Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Markus Otto
- Department of Neurology, University Hospital Ulm, Ulm, Germany
| | - Sandro Sorbi
- Department of Neurofarba, University of Florence, Florence, Italy,IRCCS Fondazione Don Carlo Gnocchi, Florence, Italy
| | - Lize C Jiskoot
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Harro Seelaar
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - John C van Swieten
- Department of Neurology, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Jonathan D Rohrer
- Department of Neurodegenerative Disease, Dementia Research Centre, UCL Institute of Neurology, Queen Square, London, UK
| | - Bratislav Misic
- Correspondence to: Bratislav Misic 3801 Rue University Webster 211, Montreal QC H3A 2B4, Canada E-mail:
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6
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Mathematical modelling of autoimmune myocarditis and the effects of immune checkpoint inhibitors. J Theor Biol 2022; 537:111002. [PMID: 35007511 DOI: 10.1016/j.jtbi.2021.111002] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 12/20/2021] [Accepted: 12/27/2021] [Indexed: 12/26/2022]
Abstract
Autoimmune myocarditis is a rare, but frequently fatal, side effect of immune checkpoint inhibitors (ICIs), a class of cancer therapies. Despite extensive experimental work on the causes, development and progression of this disease, much still remains unknown about the importance of the different immunological pathways involved. We present a mathematical model of autoimmune myocarditis and the effects of ICIs on its development and progression to either resolution or chronic inflammation. From this, we gain a better understanding of the role of immune cells, cytokines and other components of the immune system in driving the cardiotoxicity of ICIs. We parameterise the model using existing data from the literature, and show that qualitative model behaviour is consistent with disease characteristics seen in patients in an ICI-free context. The bifurcation structures of the model show how the presence of ICIs increases the risk of developing autoimmune myocarditis. This predictive modelling approach is a first step towards determining treatment regimens that balance the benefits of treating cancer with the risk of developing autoimmune myocarditis.
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7
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Zeiler FA, Iturria-Medina Y, Thelin EP, Gomez A, Shankar JJ, Ko JH, Figley CR, Wright GEB, Anderson CM. Integrative Neuroinformatics for Precision Prognostication and Personalized Therapeutics in Moderate and Severe Traumatic Brain Injury. Front Neurol 2021; 12:729184. [PMID: 34557154 PMCID: PMC8452858 DOI: 10.3389/fneur.2021.729184] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 08/09/2021] [Indexed: 01/13/2023] Open
Abstract
Despite changes in guideline-based management of moderate/severe traumatic brain injury (TBI) over the preceding decades, little impact on mortality and morbidity have been seen. This argues against the "one-treatment fits all" approach to such management strategies. With this, some preliminary advances in the area of personalized medicine in TBI care have displayed promising results. However, to continue transitioning toward individually-tailored care, we require integration of complex "-omics" data sets. The past few decades have seen dramatic increases in the volume of complex multi-modal data in moderate and severe TBI care. Such data includes serial high-fidelity multi-modal characterization of the cerebral physiome, serum/cerebrospinal fluid proteomics, admission genetic profiles, and serial advanced neuroimaging modalities. Integrating these complex and serially obtained data sets, with patient baseline demographics, treatment information and clinical outcomes over time, can be a daunting task for the treating clinician. Within this review, we highlight the current status of such multi-modal omics data sets in moderate/severe TBI, current limitations to the utilization of such data, and a potential path forward through employing integrative neuroinformatic approaches, which are applied in other neuropathologies. Such advances are positioned to facilitate the transition to precision prognostication and inform a top-down approach to the development of personalized therapeutics in moderate/severe TBI.
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Affiliation(s)
- Frederick A. Zeiler
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Biomedical Engineering, Faculty of Engineering, University of Manitoba, Winnipeg, MB, Canada
- Centre on Aging, University of Manitoba, Winnipeg, MB, Canada
- Division of Anaesthesia, Department of Medicine, Addenbrooke's Hospital, University of Cambridge, Cambridge, United Kingdom
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Montreal, QC, Canada
| | - Eric P. Thelin
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Neurology, Karolinska University Hospital, Stockholm, Sweden
| | - Alwyn Gomez
- Section of Neurosurgery, Department of Surgery, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Jai J. Shankar
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Chase R. Figley
- Department of Radiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Galen E. B. Wright
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Pharmacology and Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Chris M. Anderson
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Pharmacology and Therapeutics, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
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8
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Raj A, Tora V, Gao X, Cho H, Choi JY, Ryu YH, Lyoo CH, Franchi B. Combined Model of Aggregation and Network Diffusion Recapitulates Alzheimer's Regional Tau-Positron Emission Tomography. Brain Connect 2021; 11:624-638. [PMID: 33947253 DOI: 10.1089/brain.2020.0841] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Background: Alzheimer's disease involves widespread and progressive deposition of misfolded protein tau (τ), first appearing in the entorhinal cortex, coagulating in longer polymers and insoluble fibrils. There is mounting evidence for "prion-like" trans-neuronal transmission, whereby misfolded proteins cascade along neuronal pathways, giving rise to networked spread. However, the cause-effect mechanisms by which various oligomeric τ species are produced, aggregate, and disseminate are unknown. The question of how protein aggregation and subsequent spread lead to stereotyped progression in the Alzheimer brain remains unresolved. Materials and Methods: We address these questions by using mathematically precise parsimonious modeling of these pathophysiological processes, extrapolated to the whole brain. We model three key processes: τ monomer production; aggregation into oligomers and then into tangles; and the spatiotemporal progression of misfolded τ as it ramifies into neural circuits via the brain connectome. We model monomer seeding and production at the entorhinal cortex, aggregation using Smoluchowski equations; and networked spread using our prior Network-Diffusion model. Results: This combined aggregation-network-diffusion model exhibits all hallmarks of τ progression seen in human patients. Unlike previous theoretical studies of protein aggregation, we present here an empirical validation on in vivo imaging and fluid τ measurements from large datasets. The model accurately captures not just the spatial distribution of empirical regional τ and atrophy but also patients' cerebrospinal fluid phosphorylated τ profiles as a function of disease progression. Conclusion: This unified quantitative and testable model has the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA
| | - Veronica Tora
- Dipartimento di Matematica, Universita' di Bologna, Bologna, Italy
| | - Xiao Gao
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Jae Yong Choi
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
- Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul, Republic of Korea
| | - Young Hoon Ryu
- Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Chul Hyoung Lyoo
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seodaemun-gu, Republic of Korea
| | - Bruno Franchi
- Dipartimento di Matematica, Universita' di Bologna, Bologna, Italy
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9
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Raj A. Graph Models of Pathology Spread in Alzheimer's Disease: An Alternative to Conventional Graph Theoretic Analysis. Brain Connect 2021; 11:799-814. [PMID: 33858198 DOI: 10.1089/brain.2020.0905] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Background: Graph theory and connectomics are new techniques for uncovering disease-induced changes in the brain's structural network. Most prior studied have focused on network statistics as biomarkers of disease. However, an emerging body of work involves exploring how the network serves as a conduit for the propagation of disease factors in the brain and has successfully mapped the functional and pathological consequences of disease propagation. In Alzheimer's disease (AD), progressive deposition of misfolded proteins amyloid and tau is well-known to follow fiber projections, under a "prion-like" trans-neuronal transmission mechanism, through which misfolded proteins cascade along neuronal pathways, giving rise to network spread. Methods: In this review, we survey the state of the art in mathematical modeling of connectome-mediated pathology spread in AD. Then we address several open questions that are amenable to mathematically precise parsimonious modeling of pathophysiological processes, extrapolated to the whole brain. We specifically identify current formal models of how misfolded proteins are produced, aggregate, and disseminate in brain circuits, and attempt to understand how this process leads to stereotyped progression in Alzheimer's and other related diseases. Conclusion: This review serves to unify current efforts in modeling of AD progression that together have the potential to explain observed phenomena and serve as a test-bed for future hypothesis generation and testing in silico. Impact statement Graph theory is a powerful new approach that is transforming the study of brain processes. There do not exist many focused reviews of the subfield of graph modeling of how Alzheimer's and other dementias propagate within the brain network, and how these processes can be mapped mathematically. By providing timely and topical review of this subfield, we fill a critical gap in the community and present a unified view that can serve as an in silico test-bed for future hypothesis generation and testing. We also point to several open and unaddressed questions and controversies that future practitioners can tackle.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, California, USA
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10
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Integrating molecular, histopathological, neuroimaging and clinical neuroscience data with NeuroPM-box. Commun Biol 2021; 4:614. [PMID: 34021244 PMCID: PMC8140107 DOI: 10.1038/s42003-021-02133-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/22/2021] [Indexed: 02/04/2023] Open
Abstract
Understanding and treating heterogeneous brain disorders requires specialized techniques spanning genetics, proteomics, and neuroimaging. Designed to meet this need, NeuroPM-box is a user-friendly, open-access, multi-tool cross-platform software capable of characterizing multiscale and multifactorial neuropathological mechanisms. Using advanced analytical modeling for molecular, histopathological, brain-imaging and/or clinical evaluations, this framework has multiple applications, validated here with synthetic (N > 2900), in-vivo (N = 911) and post-mortem (N = 736) neurodegenerative data, and including the ability to characterize: (i) the series of sequential states (genetic, histopathological, imaging or clinical alterations) covering decades of disease progression, (ii) concurrent intra-brain spreading of pathological factors (e.g., amyloid, tau and alpha-synuclein proteins), (iii) synergistic interactions between multiple biological factors (e.g., toxic tau effects on brain atrophy), and (iv) biologically-defined patient stratification based on disease heterogeneity and/or therapeutic needs. This freely available toolbox ( neuropm-lab.com/neuropm-box.html ) could contribute significantly to a better understanding of complex brain processes and accelerating the implementation of Precision Medicine in Neurology.
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11
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Andrade-Restrepo M, Ciuperca IS, Lemarre P, Pujo-Menjouet L, Tine LM. A reaction-diffusion model of spatial propagation of A[Formula: see text] oligomers in early stage Alzheimer's disease. J Math Biol 2021; 82:39. [PMID: 33768404 DOI: 10.1007/s00285-021-01593-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 10/30/2020] [Accepted: 03/12/2021] [Indexed: 11/28/2022]
Abstract
The misconformation and aggregation of the protein Amyloid-Beta (A[Formula: see text]) is a key event in the propagation of Alzheimer's Disease (AD). Different types of assemblies are identified, with long fibrils and plaques deposing during the late stages of AD. In the earlier stages, the disease spread is driven by the formation and the spatial propagation of small amorphous assemblies called oligomers. We propose a model dedicated to studying those early stages, in the vicinity of a few neurons and after a polymer seed has been formed. We build a reaction-diffusion model, with a Becker-Döring-like system that includes fragmentation and size-dependent diffusion. We hereby establish the theoretical framework necessary for the proper use of this model, by proving the existence of solutions using a fixed point method.
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Affiliation(s)
- Martin Andrade-Restrepo
- Department of Applied Mathematics and Computer Science, Universidad del Rosario, Bogotá, 111711, Colombia.,Institut Jacques Monod, CNRS UMR 7592, Université Paris Diderot, Université de Paris, 750205, Paris, France
| | - Ionel Sorin Ciuperca
- Institut Camille Jordan, CNRS UMR 5208, Université Claude Bernard Lyon 1, Univ Lyon, 69622, Villeurbanne, France
| | - Paul Lemarre
- Institut Camille Jordan, CNRS UMR 5208, Université Claude Bernard Lyon 1, Univ Lyon, 69622, Villeurbanne, France
| | - Laurent Pujo-Menjouet
- Institut Camille Jordan, CNRS UMR 5208, Université Claude Bernard Lyon 1, Univ Lyon, 69622, Villeurbanne, France
| | - Léon Matar Tine
- Institut Camille Jordan, CNRS UMR 5208, Université Claude Bernard Lyon 1, Univ Lyon, 69622, Villeurbanne, France.
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12
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Bertsch M, Franchi B, Raj A, Tesi MC. Macroscopic modelling of Alzheimer’s disease: difficulties and challenges. BRAIN MULTIPHYSICS 2021. [DOI: 10.1016/j.brain.2021.100040] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
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13
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Braun GA, Ary BE, Dear AJ, Rohn MCH, Payson AM, Lee DSM, Parry RC, Friedman C, Knowles TPJ, Linse S, Åkerfeldt KS. On the Mechanism of Self-Assembly by a Hydrogel-Forming Peptide. Biomacromolecules 2020; 21:4781-4794. [PMID: 33170649 DOI: 10.1021/acs.biomac.0c00989] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Self-assembling peptide-based hydrogels are a class of tunable soft materials that have been shown to be highly useful for a number of biomedical applications. The dynamic formation of the supramolecular fibrils that compose these materials has heretofore remained poorly characterized. A better understanding of this process would provide important insights into the behavior of these systems and could aid in the rational design of new peptide hydrogels. Here, we report the determination of the microscopic steps that underpin the self-assembly of a hydrogel-forming peptide, SgI37-49. Using theoretical models of linear polymerization to analyze the kinetic self-assembly data, we show that SgI37-49 fibril formation is driven by fibril-catalyzed secondary nucleation and that all the microscopic processes involved in SgI37-49 self-assembly display an enzyme-like saturation behavior. Moreover, this analysis allows us to quantify the rates of the underlying processes at different peptide concentrations and to calculate the time evolution of these reaction rates over the time course of self-assembly. We demonstrate here a new mechanistic approach for the study of self-assembling hydrogel-forming peptides, which is complementary to commonly used materials science characterization techniques.
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Affiliation(s)
- Gabriel A Braun
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States.,Department of Biochemistry and Structural Biology, Centre for Molecular Protein Science, Lund University, Lund SE-22100, Sweden
| | - Beatrice E Ary
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - Alexander J Dear
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.,Paulson School of Engineering and Applied Science, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Matthew C H Rohn
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - Abigail M Payson
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - David S M Lee
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - Robert C Parry
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - Connie Friedman
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
| | - Tuomas P J Knowles
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.,Cavendish Laboratory, University of Cambridge, J J Thomson Avenue, Cambridge CB3 0HE, U.K
| | - Sara Linse
- Department of Biochemistry and Structural Biology, Centre for Molecular Protein Science, Lund University, Lund SE-22100, Sweden
| | - Karin S Åkerfeldt
- Department of Chemistry, Haverford College, Haverford, Pennsylvania 19041, United States
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14
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Abstract
Communication models describe the flow of signals among nodes of a network. In neural systems, communication models are increasingly applied to investigate network dynamics across the whole brain, with the ultimate aim to understand how signal flow gives rise to brain function. Communication models range from diffusion-like processes to those related to infectious disease transmission and those inspired by engineered communication systems like the internet. This Focus Feature brings together novel investigations of a diverse range of mechanisms and strategies that could shape communication in mammal whole-brain networks.
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Affiliation(s)
- Daniel Graham
- Department of Psychology, Hobart and William Smith Colleges, Geneva, NY, USA
| | | | - Bratislav Mišić
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
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15
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Thompson TB, Chaggar P, Kuhl E, Goriely A. Protein-protein interactions in neurodegenerative diseases: A conspiracy theory. PLoS Comput Biol 2020; 16:e1008267. [PMID: 33048932 PMCID: PMC7584458 DOI: 10.1371/journal.pcbi.1008267] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 10/23/2020] [Accepted: 08/17/2020] [Indexed: 12/13/2022] Open
Abstract
Neurodegenerative diseases such as Alzheimer's or Parkinson's are associated with the prion-like propagation and aggregation of toxic proteins. A long standing hypothesis that amyloid-beta drives Alzheimer's disease has proven the subject of contemporary controversy; leading to new research in both the role of tau protein and its interaction with amyloid-beta. Conversely, recent work in mathematical modeling has demonstrated the relevance of nonlinear reaction-diffusion type equations to capture essential features of the disease. Such approaches have been further simplified, to network-based models, and offer researchers a powerful set of computationally tractable tools with which to investigate neurodegenerative disease dynamics. Here, we propose a novel, coupled network-based model for a two-protein system that includes an enzymatic interaction term alongside a simple model of aggregate transneuronal damage. We apply this theoretical model to test the possible interactions between tau proteins and amyloid-beta and study the resulting coupled behavior between toxic protein clearance and proteopathic phenomenology. Our analysis reveals ways in which amyloid-beta and tau proteins may conspire with each other to enhance the nucleation and propagation of different diseases, thus shedding new light on the importance of protein clearance and protein interaction mechanisms in prion-like models of neurodegenerative disease.
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Affiliation(s)
| | - Pavanjit Chaggar
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
| | - Ellen Kuhl
- Living Matter Laboratory, Stanford University, Stanford, California, USA
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, United Kingdom
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16
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Taylor AIP, Gahan LD, Chakrabarti B, Staniforth RA. A two-step biopolymer nucleation model shows a nonequilibrium critical point. J Chem Phys 2020; 153:025102. [PMID: 32668930 DOI: 10.1063/5.0009394] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Biopolymer self-assembly pathways are complicated by the ability of their monomeric subunits to adopt different conformational states. This means nucleation often involves a two-step mechanism where the monomers first condense to form a metastable intermediate, which then converts to a stable polymer by conformational rearrangement of constituent monomers. Nucleation intermediates play a causative role in amyloid diseases such as Alzheimer's and Parkinson's. While existing mathematical models neglect the conversion dynamics, experiments show that conversion events frequently occur on comparable timescales to the condensation of intermediates and growth of mature polymers and thus cannot be ignored. We present a model that explicitly accounts for simultaneous assembly and conversion. To describe conversion, we propose an experimentally motivated initiation-propagation mechanism in which the stable phase arises locally within the intermediate and then spreads by nearest-neighbor interactions, in a manner analogous to one-dimensional Glauber dynamics. Our analysis shows that the competing timescales of assembly and conversion result in a nonequilibrium critical point, separating a regime where intermediates are kinetically unstable from one where conformationally mixed intermediates accumulate. This strongly affects the accumulation rate of the stable biopolymer phase. Our model is uniquely able to explain experimental phenomena such as the formation of mixed intermediates and abrupt changes in the scaling exponent γ, which relates the total monomer concentration to the accumulation rate of the stable phase. This provides a first step toward a general model of two-step biopolymer nucleation, which can quantitatively predict the concentration and composition of biologically crucial intermediates.
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Affiliation(s)
- Alexander I P Taylor
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Lianne D Gahan
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield S10 2TN, United Kingdom
| | - Buddhapriya Chakrabarti
- Department of Physics and Astronomy, University of Sheffield, Sheffield S3 7RH, United Kingdom
| | - Rosemary A Staniforth
- Department of Molecular Biology and Biotechnology, University of Sheffield, Sheffield S10 2TN, United Kingdom
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17
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Banwarth-Kuhn M, Sindi S. How and why to build a mathematical model: A case study using prion aggregation. J Biol Chem 2020; 295:5022-5035. [PMID: 32005659 PMCID: PMC7152750 DOI: 10.1074/jbc.rev119.009851] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Biological systems are inherently complex, and the increasing level of detail with which we are able to experimentally probe such systems continually reveals new complexity. Fortunately, mathematical models are uniquely positioned to provide a tool suitable for rigorous analysis, hypothesis generation, and connecting results from isolated in vitro experiments with results from in vivo and whole-organism studies. However, developing useful mathematical models is challenging because of the often different domains of knowledge required in both math and biology. In this work, we endeavor to provide a useful guide for researchers interested in incorporating mathematical modeling into their scientific process. We advocate for the use of conceptual diagrams as a starting place to anchor researchers from both domains. These diagrams are useful for simplifying the biological process in question and distinguishing the essential components. Not only do they serve as the basis for developing a variety of mathematical models, but they ensure that any mathematical formulation of the biological system is led primarily by scientific questions. We provide a specific example of this process from our own work in studying prion aggregation to show the power of mathematical models to synergistically interact with experiments and push forward biological understanding. Choosing the most suitable model also depends on many different factors, and we consider how to make these choices based on different scales of biological organization and available data. We close by discussing the many opportunities that abound for both experimentalists and modelers to take advantage of collaborative work in this field.
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Affiliation(s)
- Mikahl Banwarth-Kuhn
- Department of Applied Mathematics, School of Natural Sciences, University of California, Merced, California 95343
| | - Suzanne Sindi
- Department of Applied Mathematics, School of Natural Sciences, University of California, Merced, California 95343
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18
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Abstract
Proteins with a high degree of sequence similarity representing different structures provide a key to understand how protein sequence codes for 3D structure. An analysis using the fuzzy oil drop model was carried out on two pairs of proteins with different secondary structures and with high sequence identities. It has been shown that distributions of hydrophobicity for these proteins are approximated well using single 3D Gaussian function. In other words, the similar sequences fold into different 3D structures, however, alternative structures also have symmetric and monocentric hydrophobic cores. It should be noted that a significant change in the helical to beta-structured form in the N-terminal section takes places in the fragment much preceding the location of the mutated regions. It can be concluded that the final structure is the result of a complicated synergy effect in which the whole chain participates simultaneously.
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19
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Gandhi J, Antonelli AC, Afridi A, Vatsia S, Joshi G, Romanov V, Murray IVJ, Khan SA. Protein misfolding and aggregation in neurodegenerative diseases: a review of pathogeneses, novel detection strategies, and potential therapeutics. Rev Neurosci 2019; 30:339-358. [PMID: 30742586 DOI: 10.1515/revneuro-2016-0035] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 08/03/2018] [Indexed: 12/13/2022]
Abstract
Protein folding is a complex, multisystem process characterized by heavy molecular and cellular footprints. Chaperone machinery enables proper protein folding and stable conformation. Other pathways concomitant with the protein folding process include transcription, translation, post-translational modifications, degradation through the ubiquitin-proteasome system, and autophagy. As such, the folding process can go awry in several different ways. The pathogenic basis behind most neurodegenerative diseases is that the disruption of protein homeostasis (i.e. proteostasis) at any level will eventually lead to protein misfolding. Misfolded proteins often aggregate and accumulate to trigger neurotoxicity through cellular stress pathways and consequently cause neurodegenerative diseases. The manifestation of a disease is usually dependent on the specific brain region that the neurotoxicity affects. Neurodegenerative diseases are age-associated, and their incidence is expected to rise as humans continue to live longer and pursue a greater life expectancy. We presently review the sequelae of protein misfolding and aggregation, as well as the role of these phenomena in several neurodegenerative diseases including Alzheimer's disease, Huntington's disease, amyotrophic lateral sclerosis, Parkinson's disease, transmissible spongiform encephalopathies, and spinocerebellar ataxia. Strategies for treatment and therapy are also conferred with respect to impairing, inhibiting, or reversing protein misfolding.
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Affiliation(s)
- Jason Gandhi
- Department of Physiology and Biophysics, Stony Brook University School of Medicine, 101 Nicolls Road, Health Sciences Center, Stony Brook, NY 11794-8434, USA.,Medical Student Research Institute, St. George's University School of Medicine, Grenada, West Indies
| | - Anthony C Antonelli
- Department of Pathology, Stony Brook University School of Medicine, 101 Nicolls Road, Health Sciences Center, Stony Brook, NY 11794-8434, USA
| | - Adil Afridi
- Department of Physiology and Biophysics, Stony Brook University School of Medicine, 101 Nicolls Road, Health Sciences Center, Stony Brook, NY 11794-8434, USA
| | - Sohrab Vatsia
- Department of Cardiothoracic Surgery, Lenox Hill Hospital, 130 East 77th Street, New York, NY 10075, USA
| | - Gunjan Joshi
- Department of Internal Medicine, Stony Brook Southampton Hospital, 240 Meeting House Lane, Southampton, NY 11968, USA
| | - Victor Romanov
- Department of Urology, Health Sciences Center T9-040, Stony Brook University School of Medicine, 101 Nicolls Road, Stony Brook, NY 11794-8093, USA
| | - Ian V J Murray
- Department of Physiology and Neuroscience, St. George's University School of Medicine, Grenada, West Indies
| | - Sardar Ali Khan
- Department of Physiology and Biophysics, Stony Brook University School of Medicine, 101 Nicolls Road, Health Sciences Center, Stony Brook, NY 11794-8434, USA.,Department of Urology, Health Sciences Center T9-040, Stony Brook University School of Medicine, 101 Nicolls Road, Stony Brook, NY 11794-8093, USA
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20
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Kawahara H, Hanayama R. The Role of Exosomes/Extracellular Vesicles in Neural Signal Transduction. Biol Pharm Bull 2018; 41:1119-1125. [PMID: 30068858 DOI: 10.1248/bpb.b18-00167] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Exosomes, in a broad sense extracellular vesicles (EVs), are secreted from several cells and also exist in cerebrospinal fluid (CSF); they contribute to signal transduction not only between neural cells but also among hematopoietic cells. In addition to the peripheral nervous system, the association of regeneration and EVs has also been reported in the central nervous system, for example, following a spinal cord injury. Furthermore, it has become clear that major causative factors of neurodegenerative diseases are transmitted by EVs; thus, EVs are involved in the pathogenesis of neurodegenerative diseases. In particular, we would like to outline the relationship between neurophysiology and neurological disorders centered on EV-mediated communication between neural and glial cells.
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Affiliation(s)
- Hironori Kawahara
- Department of Immunology, Graduate School of Medical Sciences, Kanazawa University.,Nano Life Science Institute (WPI-NanoLSI), Kanazawa University
| | - Rikinari Hanayama
- Department of Immunology, Graduate School of Medical Sciences, Kanazawa University.,Nano Life Science Institute (WPI-NanoLSI), Kanazawa University
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21
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Fornito A, Zalesky A. Computational Approaches to Understanding Mental Dysfunction: Progress, Challenges, and New Frontiers. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:728-730. [PMID: 30170710 DOI: 10.1016/j.bpsc.2018.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Accepted: 07/20/2018] [Indexed: 10/28/2022]
Affiliation(s)
- Alex Fornito
- Brain and Mental Health Research Hub, Monash Institute of Cognitive and Clinical Neuroscience, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, Australia.
| | - Andrew Zalesky
- Departments of Biomedical Engineering and Psychiatry, University of Melbourne, Melbourne, Australia
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22
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Raj A, Powell F. Models of Network Spread and Network Degeneration in Brain Disorders. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2018; 3:788-797. [PMID: 30170711 DOI: 10.1016/j.bpsc.2018.07.012] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 07/11/2018] [Accepted: 07/11/2018] [Indexed: 01/01/2023]
Abstract
Network analysis can provide insight into key organizational principles of brain structure and help identify structural changes associated with brain disease. Though static differences between diseased and healthy networks are well characterized, the study of network dynamics, or how brain networks change over time, is increasingly central to understanding ongoing brain changes throughout disease. Accordingly, we present a short review of network models of spread, network dynamics, and network degeneration. Borrowing from recent suggestions, we divide this review into two processes by which brain networks can change: dynamics on networks, which are functional and pathological consequences taking place atop a static structural brain network; and dynamics of networks, which constitutes a changing structural brain network. We focus on diffusion magnetic resonance imaging-based structural or anatomic connectivity graphs. We address psychiatric disorders like schizophrenia; developmental disorders like epilepsy; stroke; and Alzheimer's disease and other neurodegenerative diseases.
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Affiliation(s)
- Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, California.
| | - Fon Powell
- Department of Radiology, Weill Cornell Medicine, New York, New York
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23
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Dułak D, Banach M, Wiśniowski Z, Konieczny L, Roterman I. Comparison of the structure of Aβ(1-40) amyloid with the one in complex with polyphenol ε-viniferin glucoside (EVG). BIO-ALGORITHMS AND MED-SYSTEMS 2018. [DOI: 10.1515/bams-2018-0008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
The analysis of amyloid structures is much easier recently due to the availability of the solid-state nuclear magnetic resonance technique, which allows the determination of the 3D structure of amyloid forms. The amyloidogenic polypeptide Aβ(1-40) (PDB ID 2M9R, 2M9S) in its soluble form is the object of analysis in this paper. The solubility of this polypeptide is reached due to the presence of a complexed ligand: polyphenol ε-viniferin glucoside. Two forms of complexes available in the PDB were taken for analysis with respect to the presence of a hydrophobic core in the 3D structure of these complexes. The idealized hydrophobic core structure assumed to be accordant with the 3D Gauss function distribution was taken as the pattern. The aim of this analysis is the possible further comparison to the structures of the hydrophobic core present in amyloids. It is shown that the discordant (versus the 3D Gauss function) fragments present in amyloids appear accordant in the discussed complexes.
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