Structural plasticity with preserved topology in the postsynaptic protein network.
Ontology highlight
ABSTRACT:
SUBMITTER: Blanpied TA
PROVIDER: S-EPMC2519044 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Prior studies demonstrate altered organization of functional brain networks in attention-deficit/hyperactivity disorder (ADHD). However, the structural underpinnings of these functional disturbances are poorly understood. In the current study, we applied a graph-theoretic approach to whole-brain diffusion magnetic resonance imaging data to investigate the organization of structural brain networks in adults with ADHD and unaffected controls using deterministic fiber tractography. Groups did not differ in terms of global network metrics - small-worldness, global efficiency and clustering coefficient. However, there were widespread ADHD-related effects at the nodal level in relation to local efficiency and clustering. The affected nodes included superior occipital, supramarginal, superior temporal, inferior parietal, angular and inferior frontal gyri, as well as putamen, thalamus and posterior cerebellum. Lower local efficiency of left superior temporal and supramarginal gyri was associated with higher ADHD symptom scores. Also greater local clustering of right putamen and lower local clustering of left supramarginal gyrus correlated with ADHD symptom severity. Overall, the findings indicate preserved global but altered local network organization in adult ADHD implicating regions underpinning putative ADHD-related neuropsychological deficits.
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Project description:BackgroundIdentifying structurally similar proteins with different chain topologies can aid studies in homology modeling, protein folding, protein design, and protein evolution. These include circular permuted protein structures, and the more general cases of non-cyclic permutations between similar structures, which are related by non-topological rearrangement beyond circular permutation. We present a method based on an approximation algorithm that finds sequence-order independent structural alignments that are close to optimal. We formulate the structural alignment problem as a special case of the maximum-weight independent set problem, and solve this computationally intensive problem approximately by iteratively solving relaxations of a corresponding integer programming problem. The resulting structural alignment is sequence order independent. Our method is also insensitive to insertions, deletions, and gaps.ResultsUsing a novel similarity score and a statistical model for significance p-value, we are able to discover previously unknown circular permuted proteins between nucleoplasmin-core protein and auxin binding protein, between aspartate rasemase and 3-dehydrogenate dehydralase, as well as between migration inhibition factor and arginine repressor which involves an additional strand-swapping. We also report the finding of non-cyclic permuted protein structures existing in nature between AML1/core binding factor and ribofalvin synthase. Our method can be used for large scale alignment of protein structures regardless of the topology.ConclusionThe approximation algorithm introduced in this work can find good solutions for the problem of protein structure alignment. Furthermore, this algorithm can detect topological differences between two spatially similar protein structures. The alignment between MIF and the arginine repressor demonstrates our algorithm's ability to detect structural similarities even when spatial rearrangement of structural units has occurred. The effectiveness of our method is also demonstrated by the discovery of previously unknown circular permutations. In addition, we report in this study the finding of a naturally occurring non-cyclic permuted protein between AML1/Core Binding Factor chain F and riboflavin synthase chain A.
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Project description:Episodic memory is supported by a distributed network of brain regions, and this complex network of regions does not operate in isolation. To date, neuroscience research in this area has typically focused on the activation levels in specific regions or pairwise connectivity between such regions. However, research has yet to investigate how the complex interactions of structural brain networks influence episodic memory abilities. We applied graph theory methods to diffusion-based anatomical networks in order to examine the structural architecture of the medial temporal lobe needed to support effective episodic memory functioning. We examined the relationship between performance on tests of verbal and non-verbal episodic memory with node strength, which indexes how well connected a brain region is in the network. Findings mapped onto the Posterior Medial memory system, subserved by the parahippocampal cortex and overlapped with findings of previous studies of episodic memory employing different methodologies. This expands our current understanding by providing independent evidence for the importance of identified regions and suggesting the particular manner in which these regions support episodic memory.
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Project description:Metastasis is the cause of over 90% of cancer-related deaths. Cancer cells undergoing metastasis can switch dynamically between different phenotypes, enabling them to adapt to harsh challenges, such as overcoming anoikis and evading immune response. This ability, known as phenotypic plasticity, is crucial for the survival of cancer cells during metastasis, as well as acquiring therapy resistance. Various biochemical networks have been identified to contribute to phenotypic plasticity, but how plasticity emerges from the dynamics of these networks remains elusive. Here, we investigated the dynamics of various regulatory networks implicated in Epithelial-mesenchymal plasticity (EMP)-an important arm of phenotypic plasticity-through two different mathematical modelling frameworks: a discrete, parameter-independent framework (Boolean) and a continuous, parameter-agnostic modelling framework (RACIPE). Results from either framework in terms of phenotypic distributions obtained from a given EMP network are qualitatively similar and suggest that these networks are multi-stable and can give rise to phenotypic plasticity. Neither method requires specific kinetic parameters, thus our results emphasize that EMP can emerge through these networks over a wide range of parameter sets, elucidating the importance of network topology in enabling phenotypic plasticity. Furthermore, we show that the ability to exhibit phenotypic plasticity correlates positively with the number of positive feedback loops in a given network. These results pave a way toward an unorthodox network topology-based approach to identify crucial links in a given EMP network that can reduce phenotypic plasticity and possibly inhibit metastasis-by reducing the number of positive feedback loops.
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Project description:Although a large body of research has identified discrete neuroanatomical regions involved in social cognition and behavior (the "social brain"), the existing findings are based largely on studies of specific brain structures defined within the context of particular tasks or for specific types of social behavior. The objective of the current work was to view these regions as nodes of a larger collective network and to quantitatively characterize both the topology of that network and the relative criticality of its many nodes. Large-scale data mining was performed to generate seed regions of the social brain. High-quality diffusion MRI data of typical adults were used to map anatomical networks of the social brain. Network topology and nodal centrality were analyzed using graph theory. The structural social brain network demonstrates a high degree of global functional integration with strong local segregation. Bilateral dorsomedial prefrontal cortices and amygdala play the most central roles in the network. Strong probabilistic evidence supports modular divisions of the social brain into subnetworks bearing good resemblance to functionally classified clusters. The present network-driven approach quantifies the structural topology of the social brain as a whole. This work can serve as a critical benchmark against which to compare (1) developmental change in social brain topology over time (from infancy through adolescence and beyond) and (2) atypical network topologies that may be a sign or symptom of disorder (as in conditions such as autism, Williams syndrome, schizophrenia, and others).
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Project description:ObjectiveAbnormalities in segregative and integrative properties of brain networks have been observed in multiple sclerosis (MS) and are related to clinical functioning. This study aims to investigate the micro-scale correlates of macro-scale network measures of segregation and integration in MS.MethodsEight MS patients underwent post-mortem in situ whole-brain diffusion tensor (DT) imaging and subsequent brain dissection. Macro-scale structural network topology was derived from DT data using graph theory. Clustering coefficient and mean white matter (WM) fiber length were measures of nodal segregation and integration. Thirty-three tissue blocks were collected from five cortical brain regions. Using immunohistochemistry micro-scale tissue properties were evaluated, including, neuronal size, neuronal density, axonal density and total cell density. Nodal network properties and tissue properties were correlated.ResultsA negative correlation between clustering coefficient and WM fiber length was found. Higher clustering coefficient was associated with smaller neuronal size and lower axonal density, and vice versa for fiber length. Higher whole-brain WM lesion load was associated with higher whole-brain clustering, shorter whole-brain fiber length, lower neuronal size and axonal density.ConclusionStructural network properties on MRI associate with neuronal size and axonal density, suggesting that macro-scale network measures may grasp cortical neuroaxonal degeneration in MS.
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Project description:After brain lesions caused by tumors or stroke, or after lasting loss of input (deafferentation), inter- and intra-regional brain networks respond with complex changes in topology. Not only areas directly affected by the lesion but also regions remote from the lesion may alter their connectivity-a phenomenon known as diaschisis. Changes in network topology after brain lesions can lead to cognitive decline and increasing functional disability. However, the principles governing changes in network topology are poorly understood. Here, we investigated whether homeostatic structural plasticity can account for changes in network topology after deafferentation and brain lesions. Homeostatic structural plasticity postulates that neurons aim to maintain a desired level of electrical activity by deleting synapses when neuronal activity is too high and by providing new synaptic contacts when activity is too low. Using our Model of Structural Plasticity, we explored how local changes in connectivity induced by a focal loss of input affected global network topology. In accordance with experimental and clinical data, we found that after partial deafferentation, the network as a whole became more random, although it maintained its small-world topology, while deafferentated neurons increased their betweenness centrality as they rewired and returned to the homeostatic range of activity. Furthermore, deafferentated neurons increased their global but decreased their local efficiency and got longer tailed degree distributions, indicating the emergence of hub neurons. Together, our results suggest that homeostatic structural plasticity may be an important driving force for lesion-induced network reorganization and that the increase in betweenness centrality of deafferentated areas may hold as a biomarker for brain repair.
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Project description:The TOPS database holds topological descriptions of protein structures. These compact and highly abstract descriptions reduce the protein fold to a sequence of Secondary Structure Elements (SSEs) and three sets of pairwise relationships between them, hydrogen bonds relating parallel and anti- parallel beta strands, spatial adjacencies relating neighbouring SSEs, and the chiralities of selected supersecondary structures, including connections in betaalphabeta units and between parallel alpha helices. The database is used as a resource for visualizing folding topologies, fast topological pattern searching and structure comparison. Here, significant enhancements to the TOPS database are described. The topological description has been enhanced to include packing relationships between helices, which significantly improves the description of protein folds with little beta strand content. Further, the topological description has been annotated with sequence information. The query interfaces to the database have been improved and the new version can be found at http://www.tops.leeds.ac.uk/.
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Project description:We use a number of computational and experimental approaches to investigate the membrane topology of the membrane-interacting C-terminal domain of the HIV-1 gp41 fusion protein. Several putative transmembrane regions are identified using hydrophobicity analysis based on the Wimley-White scales, including the membrane-proximal external region (MPER). The MPER region is an important target for neutralizing anti-HIV monoclonal antibodies and is believed to have an interfacial topology in the membrane. To assess the possibility of a transmembrane topology of MPER, we examined the membrane interactions of a peptide corresponding to a 22-residue stretch of the MPER sequence (residues 662-683) using fluorescence spectroscopy and oriented circular dichroism. In addition to the previously reported interfacial location, we identify a stable transmembrane conformation of the peptide in synthetic lipid bilayers. All-atom molecular dynamics simulations of the MPER-derived peptide in a lipid bilayer demonstrate a stable helical structure with an average tilt of 24 degrees, with the five tryptophan residues sampling different environments inside the hydrocarbon core of the lipid bilayer, consistent with the observed spectral properties of intrinsic fluorescence. The degree of lipid bilayer penetration obtained by computer simulation was verified using depth-dependent fluorescence quenching of a selectively attached fluorescence probe. Overall, our data indicate that the MPER sequence can have at least two stable conformations in the lipid bilayer, interfacial and transmembrane, and suggest a possibility that external perturbations can switch the topology during physiological functioning.
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Project description:Behavioural disturbances in attention deficit hyperactivity disorder (ADHD) are thought to be due to dysfunction of spatially distributed, interconnected neural systems. While there is a fast-growing literature on functional dysconnectivity in ADHD, far less is known about the structural architecture underpinning these disturbances and how it may contribute to ADHD symptomology and treatment prognosis. We applied graph theoretical analyses on diffusion MRI tractography data to produce quantitative measures of global network organisation and local efficiency of network nodes. Support vector machines (SVMs) were used for comparison of multivariate graph measures of 37 children and adolescents with ADHD relative to 26 age and gender matched typically developing children (TDC). We also explored associations between graph measures and functionally-relevant outcomes such as symptom severity and prediction of methylphenidate (MPH) treatment response. We found that multivariate patterns of reduced local efficiency, predominantly in subcortical regions (SC), were able to distinguish between ADHD and TDC groups with 76% accuracy. For treatment prognosis, higher global efficiency, higher local efficiency of the right supramarginal gyrus and multivariate patterns of increased local efficiency across multiple networks at baseline also predicted greater symptom reduction after 6 weeks of MPH treatment. Our findings demonstrate that graph measures of structural topology provide valuable diagnostic and prognostic markers of ADHD, which may aid in mechanistic understanding of this complex disorder.
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