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1.
Sci Rep ; 9(1): 16543, 2019 Nov 12.
Artigo em Inglês | MEDLINE | ID: mdl-31719623

RESUMO

Scientific evidence suggests that α-synuclein and tau have prion-like properties and that prion-like spreading and seeding of misfolded protein aggregates constitutes a central mechanism for neurodegeneration. Heparan sulfate proteoglycans (HSPGs) in the plasma membrane support this process by attaching misfolded protein fibrils. Despite of intense studies, contribution of specific HSPGs to seeding and spreading of α-synuclein and tau has not been explored yet. Here we report that members of the syndecan family of HSPGs mediate cellular uptake of α-synuclein and tau fibrils via a lipid-raft dependent and clathrin-independent endocytic route. Among syndecans, the neuron predominant syndecan-3 exhibits the highest affinity for both α-synuclein and tau. Syndecan-mediated internalization of α-synuclein and tau depends heavily on conformation as uptake via syndecans start to dominate once fibrils are formed. Overexpression of syndecans, on the other hand, reduces cellular uptake of monomeric α-synuclein and tau, yet exerts a fibril forming effect on both proteins. Data obtained from syndecan overexpressing cellular models presents syndecans, especially the neuron predominant syndecan-3, as important mediators of seeding and spreading of α-synuclein and tau and reveal how syndecans contribute to fundamental molecular events of α-synuclein and tau pathology.

2.
BMC Bioinformatics ; 20(1): 494, 2019 Oct 11.
Artigo em Inglês | MEDLINE | ID: mdl-31604427

RESUMO

BACKGROUND: Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking. RESULTS: Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD. CONCLUSIONS: This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.


Assuntos
Algoritmos , Encéfalo/metabolismo , Doenças Neurodegenerativas/metabolismo , Biologia de Sistemas/métodos , Doença de Alzheimer/genética , Doença de Alzheimer/metabolismo , Regulação da Expressão Gênica , Humanos , Redes e Vias Metabólicas , Mitocôndrias/metabolismo , Mitocôndrias/fisiologia , Doenças Neurodegenerativas/genética , Doenças Neurodegenerativas/fisiopatologia , Doença de Parkinson/genética , Doença de Parkinson/metabolismo , Doença de Parkinson/fisiopatologia , Mapas de Interação de Proteínas , Transdução de Sinais
3.
Database (Oxford) ; 20192019 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-31260040

RESUMO

The PTSD Biomarker Database (PTSDDB) is a database that provides a landscape view of physiological markers being studied as putative biomarkers in the current post-traumatic stress disorder (PTSD) literature to enable researchers to explore and compare findings quickly. The PTSDDB currently contains over 900 biomarkers and their relevant information from 109 original articles published from 1997 to 2017. Further, the curated content stored in this database is complemented by a web application consisting of multiple interactive visualizations that enable the investigation of biomarker knowledge in PTSD (e.g. clinical study metadata, biomarker findings, experimental methods, etc.) by compiling results from biomarker studies to visualize the level of evidence for single biomarkers and across functional categories. This resource is the first attempt, to the best of our knowledge, to capture and organize biomarker and metadata in the area of PTSD for storage in a comprehensive database that may, in turn, facilitate future analysis and research in the field.


Assuntos
Bases de Dados Factuais , Metadados , Transtornos de Estresse Pós-Traumáticos , Biomarcadores , Humanos
5.
J Alzheimers Dis ; 60(2): 721-731, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28922161

RESUMO

BACKGROUND: Various studies suggest a comorbid association between Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) indicating that there could be shared underlying pathophysiological mechanisms. OBJECTIVE: This study aims to systematically model relevant knowledge at the molecular level to find a mechanistic rationale explaining the existing comorbid association between AD and T2DM. METHOD: We have used a knowledge-based modeling approach to build two network models for AD and T2DM using Biological Expression Language (BEL), which is capable of capturing and representing causal and correlative relationships at both molecular and clinical levels from various knowledge resources. RESULTS: Using comparative analysis, we have identified several putative "shared pathways". We demonstrate, at a mechanistic level, how the insulin signaling pathway is related to other significant AD pathways such as the neurotrophin signaling pathway, PI3K/AKT signaling, MTOR signaling, and MAPK signaling and how these pathways do cross-talk with each other both in AD and T2DM. In addition, we present a mechanistic hypothesis that explains both favorable and adverse effects of the anti-diabetic drug metformin in AD. CONCLUSION: The two computable models introduced here provide a powerful framework to identify plausible mechanistic links shared between AD and T2DM and thereby identify targeted pathways for new therapeutics. Our approach can also be used to provide mechanistic answers to the question of why some T2DM treatments seem to increase the risk of AD.


Assuntos
Doença de Alzheimer/epidemiologia , Diabetes Mellitus Tipo 2/epidemiologia , Diabetes Mellitus Tipo 2/fisiopatologia , Insulina/metabolismo , Transdução de Sinais/fisiologia , Doença de Alzheimer/induzido quimicamente , Doença de Alzheimer/metabolismo , Comorbidade , Simulação por Computador , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/metabolismo , Feminino , Humanos , Hipoglicemiantes/efeitos adversos , Masculino , Redes e Vias Metabólicas , Metformina/efeitos adversos , Modelos Biológicos
6.
J Alzheimers Dis ; 59(3): 1045-1055, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28731442

RESUMO

Perturbance in inflammatory pathways have been identified as one of the major factors which leads to neurodegenerative diseases (NDD). Owing to the limited access of human brain tissues and the immense complexity of the brain, animal models, specifically mouse models, play a key role in advancing the NDD field. However, many of these mouse models fail to reproduce the clinical manifestations and end points of the disease. NDD drugs, which passed the efficacy test in mice, were repeatedly not successful in clinical trials. There are numerous studies which are supporting and opposing the applicability of mouse models in neuroinflammation and NDD. In this paper, we assessed to what extend a mouse can mimic the cellular and molecular interactions in humans at a mechanism level. Based on our mechanistic modeling approach, we investigate the failure of a neuroinflammation targeted drug in the late phases of clinical trials based on the comparative analyses between the two species.


Assuntos
Citocinas/metabolismo , Modelos Animais de Doenças , Encefalite/etiologia , Doenças Neurodegenerativas/complicações , Transdução de Sinais/fisiologia , Animais , Encéfalo/metabolismo , Encéfalo/patologia , Encefalite/genética , Humanos , Camundongos , Doenças Neurodegenerativas/genética , Transdução de Sinais/genética , Especificidade da Espécie
7.
Bioinformatics ; 33(22): 3679-3681, 2017 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-28651363

RESUMO

Motivation: The concept of a 'mechanism-based taxonomy of human disease' is currently replacing the outdated paradigm of diseases classified by clinical appearance. We have tackled the paradigm of mechanism-based patient subgroup identification in the challenging area of research on neurodegenerative diseases. Results: We have developed a knowledge base representing essential pathophysiology mechanisms of neurodegenerative diseases. Together with dedicated algorithms, this knowledge base forms the basis for a 'mechanism-enrichment server' that supports the mechanistic interpretation of multiscale, multimodal clinical data. Availability and implementation: NeuroMMSig is available at http://neurommsig.scai.fraunhofer.de/. Contact: martin.hofmann-apitius@scai.fraunhofer.de. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Algoritmos , Bases de Conhecimento , Doenças Neurodegenerativas/metabolismo , Doenças Neurodegenerativas/fisiopatologia , Humanos , Internet , Modelos Biológicos , Doenças Neurodegenerativas/genética , Software
8.
J Alzheimers Dis ; 56(2): 677-686, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28035920

RESUMO

Neurodegenerative diseases including Alzheimer's disease are complex to tackle because of the complexity of the brain, both in structure and function. Such complexity is reflected by the involvement of various brain regions and multiple pathways in the etiology of neurodegenerative diseases that render single drug target approaches ineffective. Particularly in the area of neurodegeneration, attention has been drawn to repurposing existing drugs with proven efficacy and safety profiles. However, there is a lack of systematic analysis of the brain chemical space to predict the feasibility of repurposing strategies. Using a mechanism-based, drug-target interaction modeling approach, we have identified promising drug candidates for repositioning. Mechanistic cause-and-effect models consolidate relevant prior knowledge on drugs, targets, and pathways from the scientific literature and integrate insights derived from experimental data. We demonstrate the power of this approach by predicting two repositioning candidates for Alzheimer's disease and one for amyotrophic lateral sclerosis.


Assuntos
Doença de Alzheimer/tratamento farmacológico , Esclerose Amiotrófica Lateral/tratamento farmacológico , Reposicionamento de Medicamentos , Fármacos Neuroprotetores/farmacologia , Fármacos Neuroprotetores/uso terapêutico , Doença de Alzheimer/metabolismo , Esclerose Amiotrófica Lateral/metabolismo , Biologia Computacional , Simulação por Computador , Ciclosporina/farmacologia , Ciclosporina/uso terapêutico , Donepezila , Reposicionamento de Medicamentos/métodos , Humanos , Indanos/farmacologia , Indanos/uso terapêutico , Sondas Moleculares , Piperidinas/farmacologia , Piperidinas/uso terapêutico , Riluzol/farmacologia , Riluzol/uso terapêutico , Relação Estrutura-Atividade
9.
Brief Bioinform ; 17(3): 505-16, 2016 05.
Artigo em Inglês | MEDLINE | ID: mdl-26249223

RESUMO

The work we present here is based on the recent extension of the syntax of the Biological Expression Language (BEL), which now allows for the representation of genetic variation information in cause-and-effect models. In our article, we describe, how genetic variation information can be used to identify candidate disease mechanisms in diseases with complex aetiology such as Alzheimer's disease and Parkinson's disease. In those diseases, we have to assume that many genetic variants contribute moderately to the overall dysregulation that in the case of neurodegenerative diseases has such a long incubation time until the first clinical symptoms are detectable. Owing to the multilevel nature of dysregulation events, systems biomedicine modelling approaches need to combine mechanistic information from various levels, including gene expression, microRNA (miRNA) expression, protein-protein interaction, genetic variation and pathway. OpenBEL, the open source version of BEL, has recently been extended to match this requirement, and we demonstrate in our article, how candidate mechanisms for early dysregulation events in Alzheimer's disease can be identified based on an integrative mining approach that identifies 'chains of causation' that include single nucleotide polymorphism information in BEL models.


Assuntos
Variação Genética , Expressão Gênica , Humanos , Doenças Neurodegenerativas
10.
J Immunol Res ; 2015: 737168, 2015.
Artigo em Inglês | MEDLINE | ID: mdl-26636108

RESUMO

Neurodegenerative as well as autoimmune diseases have unclear aetiologies, but an increasing number of evidences report for a combination of genetic and epigenetic alterations that predispose for the development of disease. This review examines the major milestones in epigenetics research in the context of diseases and various computational approaches developed in the last decades to unravel new epigenetic modifications. However, there are limited studies that systematically link genetic and epigenetic alterations of DNA to the aetiology of diseases. In this work, we demonstrate how disease-related epigenetic knowledge can be systematically captured and integrated with heterogeneous information into a functional context using Biological Expression Language (BEL). This novel methodology, based on BEL, enables us to integrate epigenetic modifications such as DNA methylation or acetylation of histones into a specific disease network. As an example, we depict the integration of epigenetic and genetic factors in a functional context specific to Parkinson's disease (PD) and Multiple Sclerosis (MS).


Assuntos
Doenças Autoimunes/genética , Epigênese Genética , Modelos Biológicos , Esclerose Múltipla/genética , Doença de Parkinson/genética , Acetilação , Animais , Biologia Computacional , Metilação de DNA , Predisposição Genética para Doença , Histonas/metabolismo , Humanos
11.
Int J Mol Sci ; 16(12): 29179-206, 2015 Dec 07.
Artigo em Inglês | MEDLINE | ID: mdl-26690135

RESUMO

Since the decoding of the Human Genome, techniques from bioinformatics, statistics, and machine learning have been instrumental in uncovering patterns in increasing amounts and types of different data produced by technical profiling technologies applied to clinical samples, animal models, and cellular systems. Yet, progress on unravelling biological mechanisms, causally driving diseases, has been limited, in part due to the inherent complexity of biological systems. Whereas we have witnessed progress in the areas of cancer, cardiovascular and metabolic diseases, the area of neurodegenerative diseases has proved to be very challenging. This is in part because the aetiology of neurodegenerative diseases such as Alzheimer´s disease or Parkinson´s disease is unknown, rendering it very difficult to discern early causal events. Here we describe a panel of bioinformatics and modeling approaches that have recently been developed to identify candidate mechanisms of neurodegenerative diseases based on publicly available data and knowledge. We identify two complementary strategies-data mining techniques using genetic data as a starting point to be further enriched using other data-types, or alternatively to encode prior knowledge about disease mechanisms in a model based framework supporting reasoning and enrichment analysis. Our review illustrates the challenges entailed in integrating heterogeneous, multiscale and multimodal information in the area of neurology in general and neurodegeneration in particular. We conclude, that progress would be accelerated by increasing efforts on performing systematic collection of multiple data-types over time from each individual suffering from neurodegenerative disease. The work presented here has been driven by project AETIONOMY; a project funded in the course of the Innovative Medicines Initiative (IMI); which is a public-private partnership of the European Federation of Pharmaceutical Industry Associations (EFPIA) and the European Commission (EC).


Assuntos
Mineração de Dados , Doenças Neurodegenerativas/genética , Animais , Biologia Computacional , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Bases de Conhecimento , Polimorfismo de Nucleotídeo Único
12.
Theor Biol Med Model ; 12: 20, 2015 Sep 22.
Artigo em Inglês | MEDLINE | ID: mdl-26395080

RESUMO

BACKGROUND: Despite the unprecedented and increasing amount of data, relatively little progress has been made in molecular characterization of mechanisms underlying Parkinson's disease. In the area of Parkinson's research, there is a pressing need to integrate various pieces of information into a meaningful context of presumed disease mechanism(s). Disease ontologies provide a novel means for organizing, integrating, and standardizing the knowledge domains specific to disease in a compact, formalized and computer-readable form and serve as a reference for knowledge exchange or systems modeling of disease mechanism. METHODS: The Parkinson's disease ontology was built according to the life cycle of ontology building. Structural, functional, and expert evaluation of the ontology was performed to ensure the quality and usability of the ontology. A novelty metric has been introduced to measure the gain of new knowledge using the ontology. Finally, a cause-and-effect model was built around PINK1 and two gene expression studies from the Gene Expression Omnibus database were re-annotated to demonstrate the usability of the ontology. RESULTS: The Parkinson's disease ontology with a subclass-based taxonomic hierarchy covers the broad spectrum of major biomedical concepts from molecular to clinical features of the disease, and also reflects different views on disease features held by molecular biologists, clinicians and drug developers. The current version of the ontology contains 632 concepts, which are organized under nine views. The structural evaluation showed the balanced dispersion of concept classes throughout the ontology. The functional evaluation demonstrated that the ontology-driven literature search could gain novel knowledge not present in the reference Parkinson's knowledge map. The ontology was able to answer specific questions related to Parkinson's when evaluated by experts. Finally, the added value of the Parkinson's disease ontology is demonstrated by ontology-driven modeling of PINK1 and re-annotation of gene expression datasets relevant to Parkinson's disease. CONCLUSIONS: Parkinson's disease ontology delivers the knowledge domain of Parkinson's disease in a compact, computer-readable form, which can be further edited and enriched by the scientific community and also to be used to construct, represent and automatically extend Parkinson's-related computable models. A practical version of the Parkinson's disease ontology for browsing and editing can be publicly accessed at http://bioportal.bioontology.org/ontologies/PDON .


Assuntos
Ontologia Genética , Conhecimento , Doença de Parkinson/genética , Software , Animais , Bases de Dados Genéticas , Modelos Animais de Doenças , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Anotação de Sequência Molecular , Doença de Parkinson/etiologia
13.
Alzheimers Dement ; 11(11): 1329-39, 2015 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-25849034

RESUMO

INTRODUCTION: The discovery and development of new treatments for Alzheimer's disease (AD) requires a profound mechanistic understanding of the disease. Here, we propose a model-driven approach supporting the systematic identification of putative disease mechanisms. METHODS: We have created a model for AD and a corresponding model for the normal physiology of neurons using biological expression language to systematically model causal and correlative relationships between biomolecules, pathways, and clinical readouts. Through model-model comparison we identify "chains of causal relationships" that lead to new insights into putative disease mechanisms. RESULTS: Using differential analysis of our models we identified a new mechanism explaining the effect of amyloid-beta on apoptosis via both the neurotrophic tyrosine kinase receptor, type 2 and nerve growth factor receptor branches of the neurotrophin signaling pathway. We also provide the example of a model-guided interpretation of genetic variation data for a comorbidity analysis between AD and type 2 diabetes mellitus. DISCUSSION: The two computable, literature-based models introduced here provide a powerful framework for the generation and validation of rational, testable hypotheses across disease areas.


Assuntos
Doença de Alzheimer/fisiopatologia , Modelos Neurológicos , Neurônios/fisiologia , Doença de Alzheimer/complicações , Doença de Alzheimer/epidemiologia , Doença de Alzheimer/genética , Precursor de Proteína beta-Amiloide/metabolismo , Animais , Encéfalo/fisiologia , Encéfalo/fisiopatologia , Comorbidade , Humanos , Polimorfismo de Nucleotídeo Único
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