Multiscale modelling of cerebrovascular injury reveals the role of vascular anatomy and parenchymal shear stresses.
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ABSTRACT:
SUBMITTER: Farajzadeh Khosroshahi S
PROVIDER: S-EPMC8217506 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Shear distortion of amyloid-beta (A?) solutions accelerates amyloid cascade reactions that may yield different toxic oligomers than those formed in quiescent solutions. Recent experiments indicate that cerebrospinal fluid (CSF) and interstitial fluid (ISF) containing A? flow through narrow brain perivascular pathways and brain parenchyma. This paper suggests that such flow causes shear distortion of A? molecules involving conformation changes that may be one of the initiating events in the etiology of Alzheimer's disease. A? shearing can occur in or around brain arteries and arterioles and is suggested as the origin of cerebral amyloid angiopathy deposits in cerebrovascular walls. Comparatively low flow rates of ISF within the narrow extracellular spaces (ECS) of the brain parenchyma are suggested as a possible initiating factor in both the formation of neurotoxic A?42 oligomers and amyloid fibrils. A?42 in slow-flowing ISF can gain significant shear energy at or near the walls of tortuous brain ECS flow paths, promoting the formation of a shear-distorted, excited state hydrophobic A?42* conformation. This A?42* molecule could possibly be involved in one of two paths, one involving rapid adsorption to a brain membrane surface, ultimately forming neurotoxic oligomers on membranes, and the other ultimately forming plaque within the ECS flow pathways. Rising A? concentrations combined with shear at or near critical brain membranes are proposed as contributing factors to Alzheimer's disease neurotoxicity. These hypotheses may be applicable in other neurodegenerative diseases, including tauopathies and alpha-synucleinopathies, in which shear-distorted proteins also may form in the brain ECS.
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Project description:Lytic polysaccharide monooxygenase (LPMO) enzymes have attracted considerable attention owing to their ability to enhance polysaccharide depolymerization, making them interesting with respect to production of biofuel from cellulose. LPMOs are metalloenzymes that contain a mononuclear copper active site, capable of activating dioxygen. However, many details of this activation are unclear. Some aspects of the mechanism have previously been investigated from a computational angle. Yet, either these studies have employed only molecular mechanics (MM), which are inaccurate for metal active sites, or they have described only the active site with quantum mechanics (QM) and neglected the effect of the protein. Here, we employ hybrid QM and MM (QM/MM) methods to investigate the first steps of the LPMO mechanism, which is reduction of CuII to CuI and the formation of a CuII-superoxide complex. In the latter complex, the superoxide can bind either in an equatorial or an axial position. For both steps, we obtain structures that are markedly different from previous suggestions, based on small QM-cluster calculations. Our calculations show that the equatorial isomer of the superoxide complex is over 60 kJ/mol more stable than the axial isomer because it is stabilized by interactions with a second-coordination-sphere glutamine residue, suggesting a possible role for this residue. The coordination of superoxide in this manner agrees with recent experimental suggestions.
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Project description:The extracellular matrix is a complex three-dimensional network of molecules that provides cells with a complex microenvironment. The major constituents of the extracellular matrix such as collagen, elastin and associated proteins form supramolecular assemblies contributing to its physicochemical properties and organization. The structure of proteins and their supramolecular assemblies such as fibrils have been studied at the atomic level (e.g., by X-ray crystallography, Nuclear Magnetic Resonance and cryo-Electron Microscopy) or at the microscopic scale. However, many protein complexes are too large to be studied at the atomic level and too small to be studied by microscopy. Most extracellular matrix components fall into this intermediate scale, so-called the mesoscopic scale, preventing their detailed characterization. Simulation and modelling are some of the few powerful and promising approaches that can deepen our understanding of mesoscale systems. We have developed a set of modelling tools to study the self-organization of the extracellular matrix and large motion of macromolecules at the mesoscale level by taking advantage of the dynamics of articulated rigid bodies as a mean to study a larger range of motions at the cost of atomic resolution.
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Project description:Cerebral ischemia remains a major cause of morbidity and mortality with little advancement in subacute treatment options. This review aims to cover and discuss novel insight obtained during the last decade into plastic changes in the vasoconstrictor receptor profiles of cerebral arteries and microvessels that takes place after different types of stroke. Receptors like the endothelin type B, angiotensin type 1, and 5-hydroxytryptamine type 1B/1D receptors are upregulated in the smooth muscle layer of cerebral arteries after different types of ischemic stroke as well as after subarachnoid hemorrhage, yielding rather dramatic changes in the contractility of the vessels. Some of the signal transduction processes mediating this receptor upregulation have been elucidated. In particular the extracellular regulated kinase 1/2 pathway, which is activated early in the process, has proven to be a promising therapeutic target for prevention of vasoconstrictor receptor upregulation after stroke. Together, those findings provide new perspectives on the pathophysiology of ischemic stroke and point toward a novel way of reducing vasoconstriction, neuronal cell death, and thus neurologic deficits after stroke.
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Project description:Collective behaviour is of fundamental importance in the life sciences, where it appears at levels of biological complexity from single cells to superorganisms, in demography and the social sciences, where it describes the behaviour of populations, and in the physical and engineering sciences, where it describes physical phenomena and can be used to design distributed systems. Reasoning about collective behaviour is inherently difficult, as the non-linear interactions between individuals give rise to complex emergent dynamics. Mathematical techniques have been developed to analyse systematically collective behaviour in such systems, yet these frequently require extensive formal training and technical ability to apply. Even for those with the requisite training and ability, analysis using these techniques can be laborious, time-consuming and error-prone. Together these difficulties raise a barrier-to-entry for practitioners wishing to analyse models of collective behaviour. However, rigorous modelling of collective behaviour is required to make progress in understanding and applying it. Here we present an accessible tool which aims to automate the process of modelling and analysing collective behaviour, as far as possible. We focus our attention on the general class of systems described by reaction kinetics, involving interactions between components that change state as a result, as these are easily understood and extracted from data by natural, physical and social scientists, and correspond to algorithms for component-level controllers in engineering applications. By providing simple automated access to advanced mathematical techniques from statistical physics, nonlinear dynamical systems analysis, and computational simulation, we hope to advance standards in modelling collective behaviour. At the same time, by providing expert users with access to the results of automated analyses, sophisticated investigations that could take significant effort are substantially facilitated. Our tool can be accessed online without installing software, uses a simple programmatic interface, and provides interactive graphical plots for users to develop understanding of their models.
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Project description:BACKGROUND AND AIMS: Functional-structural plant models (FSPMs) simulate biological processes at different spatial scales. Methods exist for multiscale data representation and modification, but the advantages of using multiple scales in the dynamic aspects of FSPMs remain unclear. Results from multiscale models in various other areas of science that share fundamental modelling issues with FSPMs suggest that potential advantages do exist, and this study therefore aims to introduce an approach to multiscale modelling in FSPMs. METHODS: A three-part graph data structure and grammar is revisited, and presented with a conceptual framework for multiscale modelling. The framework is used for identifying roles, categorizing and describing scale-to-scale interactions, thus allowing alternative approaches to model development as opposed to correlation-based modelling at a single scale. Reverse information flow (from macro- to micro-scale) is catered for in the framework. The methods are implemented within the programming language XL. KEY RESULTS: Three example models are implemented using the proposed multiscale graph model and framework. The first illustrates the fundamental usage of the graph data structure and grammar, the second uses probabilistic modelling for organs at the fine scale in order to derive crown growth, and the third combines multiscale plant topology with ozone trends and metabolic network simulations in order to model juvenile beech stands under exposure to a toxic trace gas. CONCLUSIONS: The graph data structure supports data representation and grammar operations at multiple scales. The results demonstrate that multiscale modelling is a viable method in FSPM and an alternative to correlation-based modelling. Advantages and disadvantages of multiscale modelling are illustrated by comparisons with single-scale implementations, leading to motivations for further research in sensitivity analysis and run-time efficiency for these models.
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