Pathophysiology of the Desmo-Adhesome
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Advances in our understanding of desmosomal diseases have provided a clear demonstration of the key role played by desmosomes in tissue and organ physiology, highlighting the importance of their dynamic and finely regulated structure. In this context, non-desmosomal regulatory molecules have acquired increasing relevance in the study of this organelle resulting in extending the desmosomal interactome, named the "desmo-adhesome." Spatiotemporal changes in the expression and regulation of the desmo-adhesome underlie a number of genetic, infectious, autoimmune, and malignant conditions. The aim of the present article was to examine the structural and functional relationship of the desmosome, by providing a comprehensive, yet focused overview of the constituents targeted in human disease. The inclusion of the novel regulatory network in the desmo-adhesome pathophysiology opens new avenues to a deeper understanding of desmosomal diseases, potentially unveiling pathogenic mechanisms waiting to be explored. J. Cell. Physiol. 232: 496-505, 2017. © 2016 Wiley Periodicals, Inc.Keywords:
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Interactome
Protein-protein interaction network represents an important aspect of systems biology. The understanding of the plant protein-protein interaction network and interactome will provide crucial insights into the regulation of plant developmental, physiological, and pathological processes. In this review, we will first define the concept of plant interactome and the protein-protein interaction network. The significance of the plant interactome study will be discussed. We will then compare the pros and cons for different strategies for interactome mapping including yeast two-hybrid system (Y2H), affinity purification mass spectrometry (AP-MS), bimolecular fluorescence complementation (BiFC), and in silico prediction. The application of these platforms on specific plant biology questions will be further discussed. The recent advancements revealed the great potential for plant protein-protein interaction network and interactome to elucidate molecular mechanisms for signal transduction, stress responses, cell cycle control, pattern formation, and others. Mapping the plant interactome in model species will provide important guideline for the future study of plant biology.
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Bimolecular fluorescence complementation
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14-3-3 protein
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Cellular functions are mediated by protein-protein interactions, and mapping the interactome provides fundamental insights into biological systems. Affinity purification coupled to mass spectrometry is an ideal tool for such mapping, but it has been difficult to identify low copy number complexes, membrane complexes and complexes that are disrupted by protein tagging. As a result, our current knowledge of the interactome is far from complete, and assessing the reliability of reported interactions is challenging. Here we develop a sensitive high-throughput method using highly reproducible affinity enrichment coupled to mass spectrometry combined with a quantitative two-dimensional analysis strategy to comprehensively map the interactome of Saccharomyces cerevisiae. Thousand-fold reduced volumes in 96-well format enabled replicate analysis of the endogenous GFP-tagged library covering the entire expressed yeast proteome1. The 4,159 pull-downs generated a highly structured network of 3,927 proteins connected by 31,004 interactions, doubling the number of proteins and tripling the number of reliable interactions compared with existing interactome maps2. This includes very-low-abundance epigenetic complexes, organellar membrane complexes and non-taggable complexes inferred by abundance correlation. This nearly saturated interactome reveals that the vast majority of yeast proteins are highly connected, with an average of 16 interactors. Similar to social networks between humans, the average shortest distance between proteins is 4.2 interactions. AlphaFold-Multimer provided novel insights into the functional roles of previously uncharacterized proteins in complexes. Our web portal ( www.yeast-interactome.org ) enables extensive exploration of the interactome dataset.
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Apart from some model organisms, the interactome of most organisms is largely unidentified. High-throughput experimental techniques to determine protein-protein interactions (PPIs) are resource intensive and highly susceptible to noise. Computational methods of PPI determination can accelerate biological discovery by identifying the most promising interacting pairs of proteins and by assessing the reliability of identified PPIs. Here we present a first in-depth study describing a global view of the ant Camponotus floridanus interactome. Although several ant genomes have been sequenced in the last eight years, studies exploring and investigating PPIs in ants are lacking. Our study attempts to fill this gap and the presented interactome will also serve as a template for determining PPIs in other ants in future. Our C. floridanus interactome covers 51,866 non-redundant PPIs among 6,274 proteins, including 20,544 interactions supported by domain-domain interactions (DDIs), 13,640 interactions supported by DDIs and subcellular localization, and 10,834 high confidence interactions mediated by 3,289 proteins. These interactions involve and cover 30.6% of the entire C. floridanus proteome.
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Protein-protein interaction (PPI) networks (interactome networks) have successfully advanced our knowledge of molecular function, disease and evolution. While much progress has been made in quantifying errors and biases in experimental PPI datasets, it remains unknown what fraction of the error-free PPIs in the cell are completely dispensable, i.e., effectively neutral upon disruption. Here, we estimate dispensable content in the human interactome by calculating the fractions of PPIs disrupted by neutral and non-neutral mutations. Starting with the human reference interactome determined by experiments, we construct a human structural interactome by building homology-based three-dimensional structural models for PPIs. Next, we map common mutations from healthy individuals as well as Mendelian disease-causing mutations onto the human structural interactome, and perform structure-based calculations of how these mutations perturb the interactome. Using our predicted as well as experimentally-determined interactome perturbation patterns by common and disease mutations, we estimate that <~20% of the human interactome is completely dispensable.
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This chapter contains sections titled: Introduction The ORFeome: the First Step toward the interactome of C. elegans Large-Scale High-Throughput Yeast Two-Hybrid Screens to Map the C. elegans Protein–Protein Interaction (Interactome) Network: Technical Aspects Visualization and Topology of Protein–Protein Interaction Networks Cross-Talk between the C. elegans Interactome and other Large-Scale Genomics and Post-Genomics Data Sets Conclusion: From Interactions to Therapies References
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Physical interactions among proteins constitute the backbone of cellular function, making them an attractive source of therapeutic targets. Although the challenges associated with targeting protein-protein interactions (PPIs) -in particular with small molecules - are considerable, a growing number of functional PPI modulators is being reported and clinically evaluated. An essential starting point for PPI inhibitor screening or design projects is the generation of a detailed map of the human interactome and the interactions between human and pathogen proteins. Different routes to produce these biological networks are being combined, including literature curation and computational methods. Experimental approaches to map PPIs mainly rely on the yeast two-hybrid (Y2H) technology, which have recently shown to produce reliable protein networks. However, other genetic and biochemical methods will be essential to increase both coverage and resolution of current protein networks in order to increase their utility towards the identification of novel disease-related proteins and PPIs, and their potential use as therapeutic targets. Keywords: Protein-protein interaction, interactome, drug discovery, small molecule, oncology, virology, PPI modulators, PPI inhibitor, pathogen proteins, yeast two-hybrid (Y2H) technology.
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In living systems, a complex network of protein-protein interactions (PPIs) underlies most biochemical events. The human protein-protein interactome has been surveyed using yeast two-hybrid (Y2H)- and mass spectrometry (MS)-based approaches such as affinity purification coupled to MS (AP-MS). Despite decades of systematic investigations and collaborative multi-disciplinary efforts, there is no "gold standard" for documenting PPIs. A surprisingly large fraction of the human interactome remains uncharted, which we refer to as the "dark interactome." In this review, we highlight the complexity of the human interactome and discuss the current status of the human reference interactome maps. We discuss why a large proportion of the human interactome has remained refractory to traditional approaches. We propose an experimental model that can enable the identification of the dark interactome in a cell-type-specific manner. We also propose a framework to implement when embarking on studies designed to rigorously identify and characterize protein interactions.
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It is currently difficult to determine the effect of oncogenic viruses on the global function and regulation of pathways within mammalian cells. A thorough understanding of the molecular pathways and individual genes altered by oncogenic viruses is needed for the identification of targets that can be utilised for early diagnosis, prevention, and treatment methods. We detail a logical step-by-step guide to uncover viral-protein-miRNA interactions using publically available datasets and the network building program, Cytoscape. This method may be applied to identify specific pathways that are altered in viral infection, and contribute to the oncogenic transformation of cells. To demonstrate this, we constructed a gene regulatory interactome encompassing Human Papillomavirus Type 16 (HPV16) and its control of specific miRNAs. This approach can be broadly applied to understand and map the regulatory functions of other oncogenic viruses, and determine their role in altering the cellular environment in cancer. Availability and Implementation Cytoscape (Shannon et al. (2003), Smoot et al. (2010)) is freely available at https://cytoscape.org/. •This method allows for the analysis and visualization of large datasets to generate an interactome that integrates key players of molecular biology•This approach may be applied to any oncogenic virus to map its regulatory functions, and its secondary impact on gene regulation via microRNAs.
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Abstract miR-675 is a microRNA expressed from exon 1 of H19 long non-coding RNA. H19 lncRNA is temporally expressed in humans and atypical expression of miR-675 has been linked with several diseases and disorders. To execute its function inside the cell, miR-675 is folded into a particular conformation which aids in its interaction with several other biological molecules. However, the exact folding dynamics of miR-675 and its complete interaction map are currently unknown. Moreover, how H19 lncRNA and miR-675 crosstalk and modulate each other’s activities is also unclear. Detailed structural analysis of miR-675 in this study determines its conformation and identifies novel protein binding sites on miR-675 which can make it an excellent therapeutic target against numerous diseases. Mapping of the interactome identified some of known and unknown interactors of miR-675 which aid in expanding our repertoire of miR-675 involved pathways in the cell. This analysis also identified some of the previously unknown and yet to be characterised proteins as probable interactors of miR-675. Structural and base pair conservation analysis between H19 lncRNA and miR-675 results in structural transformations in miR-675 thus describing the earlier unknown mechanism of interaction between these two molecules. Comprehensively, this study details the conformation of miR-675, its interacting biological partners and explains its relationship with H19 lncRNA which can be interpreted to understand the role of miR-675 in the development and progression of various diseases.
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