Interactions of Pathological Hallmark Proteins

Oláh, Judit; Vincze, Orsolya; Virók, Dezső; Simon, Dóra; Bozsó, Zsolt; Tõkési, Natália; Horváth, István; Hlavanda, Emma; Kovács, János; Magyar, Anna; Szücs, Mária; Orosz, Ferenc; Penke, Botond; Ovádi, Judit

doi:10.1074/jbc.m111.243907

Cited by 128 publications

(64 citation statements)

References 59 publications

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“…48 The interactions with Vamp2 (synaptobrevin), Snap23 (synaptosomal-associated protein 23) and Psma3 (Proteasome subunit alpha type-3), a component of the proteasome, reflect the involvement of STX11 in vesicle transport. 74 However, their interactions are modified by amyloid resulting in modulation of neurotransmitter release.…”

Section: Discussionmentioning

confidence: 99%

Approaches to Investigating Complex Genetic Traits in a Large-Scale Inbred Mouse Aging Study

et al. 2016

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Inbred mice are a unique model system for studying aging because of the genetic homogeneity within inbred strains, the short life span of mice relative to humans, and the rich array of analytical tools that are available. A large-scale aging study was conducted on 28 inbred strains representing great genetic diversity to determine, using histopathology, the type and diversity of spontaneous diseases aging mice develop. Eighteen inbred strains have had their genomes sequenced and many others have been partially sequenced to provide large repositories of data on genetic variation between the strains. This vast amount of genomic information can be utilized in genome-wide association studies (GWAS) to find candidate genes that are involved in the pathogenesis of spontaneous diseases. We present here, as an illustration, a GWAS of the genetic associations of age-related intestinal amyloidosis which implicates three candidate genes Tram1, Sf3b5, and Stx11. Many of the age-related mouse diseases are similar, if not identical, to human diseases and therefore the genetic discoveries have direct translational benefit. Representative photomicrographs are available on the Mouse Tumor Biology Database and Pathbase to serve as a reference when evaluating inbred mice used in other genetic or experimental studies to rule out strain background lesions.

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Section: Discussionmentioning

confidence: 99%

Approaches to Investigating Complex Genetic Traits in a Large-Scale Inbred Mouse Aging Study

et al. 2016

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“…TPPP/p25 expression enhances cellular sensitivity to dopamine toxicity [218], and the interaction of TPPP/p25 and Aβ can produce pathologic aggregates in AD and LB diseases [219], although other proteins, for example, αSyn and tau, have also been shown to interact with p25 [219]. p25α was found in LBs in PD and DLB, but does not co-localize with the inclusions in tauopathies [220].…”

Section: Protein Interactions In Pdmentioning

confidence: 99%

Interaction between pathogenic proteins in neurodegenerative disorders

Jellinger¹

2012

J Cellular Molecular Medi

110

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The misfolding and progressive aggregation of specific proteins in selective regions of the nervous system is a seminal occurrence in many neurodegenerative disorders, and the interaction between pathological/toxic proteins to cause neurodegeneration is a hot topic of current neuroscience research. Despite clinical, genetic and experimental differences, increasing evidence indicates considerable overlap between synucleinopathies, tauopathies and other protein-misfolding diseases. Inclusions, often characteristic hallmarks of these disorders, suggest interactions of pathological proteins enganging common downstream pathways. Novel findings that have shifted our understanding in the role of pathologic proteins in the pathogenesis of Alzheimer, Parkinson, Huntington and prion diseases, have confirmed correlations/overlaps between these and other neurodegenerative disorders. Emerging evidence, in addition to synergistic effects of tau protein, amyloid-β, α-synuclein and other pathologic proteins, suggests that prion-like induction and spreading, involving secreted proteins, are major pathogenic mechanisms in various neurodegenerative diseases, depending on genetic backgrounds and environmental factors. The elucidation of the basic molecular mechanisms underlying the interaction and spreading of pathogenic proteins, suggesting a dualism or triad of neurodegeneration in protein-misfolding disorders, is a major challenge for modern neuroscience, to provide a deeper insight into their pathogenesis as a basis of effective diagnosis and treatment.

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“…37 compared to 16 in PICKLE 1.0 ( S1 File, S5 Table ). A notable increase in PPIs is that of amyloid beta A4 protein (P05067) with 2010 interactions in PICKLE2.0 compared to only 124 in PICKLE 1.0, due to a targeted experiment [24] reported in BioGRID. The larger number of hubs in PICKLE 2.0 is in agreement with its network analysis results ( Table E in S1 File ), which indicate a higher degree of network connectivity compared to PICKLE 1.0.…”

Section: Resultsmentioning

confidence: 99%

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

2017

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It has been acknowledged that source databases recording experimentally supported human protein-protein interactions (PPIs) exhibit limited overlap. Thus, the reconstruction of a comprehensive PPI network requires appropriate integration of multiple heterogeneous primary datasets, presenting the PPIs at various genetic reference levels. Existing PPI meta-databases perform integration via normalization; namely, PPIs are merged after converted to a certain target level. Hence, the node set of the integrated network depends each time on the number and type of the combined datasets. Moreover, the irreversible a priori normalization process hinders the identification of normalization artifacts in the integrated network, which originate from the nonlinearity characterizing the genetic information flow. PICKLE (Protein InteraCtion KnowLedgebasE) 2.0 implements a new architecture for this recently introduced human PPI meta-database. Its main novel feature over the existing meta-databases is its approach to primary PPI dataset integration via genetic information ontology. Building upon the PICKLE principles of using the reviewed human complete proteome (RHCP) of UniProtKB/Swiss-Prot as the reference protein interactor set, and filtering out protein interactions with low probability of being direct based on the available evidence, PICKLE 2.0 first assembles the RHCP genetic information ontology network by connecting the corresponding genes, nucleotide sequences (mRNAs) and proteins (UniProt entries) and then integrates PPI datasets by superimposing them on the ontology network without any a priori transformations. Importantly, this process allows the resulting heterogeneous integrated network to be reversibly normalized to any level of genetic reference without loss of the original information, the latter being used for identification of normalization biases, and enables the appraisal of potential false positive interactions through PPI source database cross-checking. The PICKLE web-based interface (www.pickle.gr) allows for the simultaneous query of multiple entities and provides integrated human PPI networks at either the protein (UniProt) or the gene level, at three PPI filtering modes.

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Interactions of Pathological Hallmark Proteins

Cited by 128 publications

References 59 publications

Approaches to Investigating Complex Genetic Traits in a Large-Scale Inbred Mouse Aging Study

Approaches to Investigating Complex Genetic Traits in a Large-Scale Inbred Mouse Aging Study

Interaction between pathogenic proteins in neurodegenerative disorders

PICKLE 2.0: A human protein-protein interaction meta-database employing data integration via genetic information ontology

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