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      HTT-OMNI: A Web-based Platform for Huntingtin Interaction Exploration and Multi-omics Data Integration

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          Abstract

          Huntington's disease (HD) is a progressive neurological disorder that is caused by polyglutamine expansion of the huntingtin (HTT) protein. With the hope to uncover key modifiers of disease, a focus of the field of HD research has been on characterizing HTT-interacting proteins (HIPs) and the effect of the HTT polyglutamine expansion on the cellular omics landscape. However, while hundreds of studies have uncovered over 3000 potential HIPs to date, a means to interrogate these complementary interaction and omics datasets does not exist. The lack of a unified platform for exploring this breadth of potential HIPs and associated omics data represents a substantial barrier toward understanding the impact of HTT polyQ expansion and identifying interactions proximal to HD pathogenesis. Here, we describe the development of a web-based platform called HTT-OMNI (HTT OMics and Network Integration). This application facilitates the visualization and exploration of ∼3400 potential HTT interactors (from the HINT database) and their associated polyQ-dependent omics measurements, such as transcriptome and proteome abundances. Additionally, HTT-OMNI allows for the integration of user-generated datasets with existing HIPs and omic measurements. We first demonstrate the utility of HTT-OMNI for filtering existing HTT PPIs based on a variety of experimental metadata parameters, highlighting its capacity to select for HIPs detected in specific model organisms and tissues. Next, we leverage our application to visualize the relationships between HTT PPIs, genetic disease modifiers, and their multiomic landscape. Finally, we generate and analyze a previously unreported dataset of HTT PPIs, aimed at defining tissue-specific HTT interactions and the polyQ-dependent modulation of their relative stabilities in the cortex and striatum of HD mouse models.

          Graphical Abstract

          Highlights

          • HTT-OMNI facilitates the exploration of huntingtin (HTT) PPIs and multi-omic data.

          • The platform allows for the analysis of both existing studies and user-uploaded data.

          • HTT-OMNI enables filtering of PPIs via metadata (model organism, method, etc.).

          • Analysis of tissue-specific HTT interactomes in mouse models of Huntington’s disease.

          • Relative stabilities of HTT PPIs in the cortex differ from those in the striatum.

          In Brief

          Kennedy et al. develop HTT-OMNI, a web-based platform for the visualization and analysis of huntingtin (HTT) PPIs and multi-omic data integration. They demonstrate the utility of HTT-OMNI for analyzing existing, as well as user-uploaded, datasets.

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          Most cited references45

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          Cytoscape: a software environment for integrated models of biomolecular interaction networks.

          Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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            UniProt: a worldwide hub of protein knowledge

            (2018)
            Abstract The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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              The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

              Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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                Author and article information

                Contributors
                Journal
                Mol Cell Proteomics
                Mol Cell Proteomics
                Molecular & Cellular Proteomics : MCP
                American Society for Biochemistry and Molecular Biology
                1535-9476
                1535-9484
                03 August 2022
                October 2022
                03 August 2022
                : 21
                : 10
                : 100275
                Affiliations
                [1]Department of Molecular Biology, Princeton University, Lewis Thomas Laboratory, Princeton, New Jersey, USA
                Author notes
                []For correspondence: Ileana M. Cristea icristea@ 123456princeton.edu
                [‡]

                These authors contributed equally to this work.

                Article
                S1535-9476(22)00083-4 100275
                10.1016/j.mcpro.2022.100275
                9540350
                35932982
                2ffb2ec4-6fdb-4c27-a181-f0c41b7d4d15
                © 2022 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 27 May 2022
                : 25 July 2022
                Categories
                Technological Innovation and Resources

                Molecular biology
                protein–protein interactions,huntingtin,huntington's disease,computational platform,multiomics,hd, huntington’s disease,hint, huntingtin protein-protein interaction database,hip, huntingtin-interacting protein,htt, huntingtin gene,htt, huntingtin protein,htt-omni, huntingtin omics and network integration viewer,ms, mass spectrometry,ppi, protein–protein interaction,snrna-seq, single-nucleus rna sequencing

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