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      Single-cell-led drug repurposing for Alzheimer’s disease

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          Abstract

          Alzheimer’s disease is the most common form of dementia. Notwithstanding the huge investments in drug development, only one disease-modifying treatment has been recently approved. Here we present a single-cell-led systems biology pipeline for the identification of drug repurposing candidates. Using single-cell RNA sequencing data of brain tissues from patients with Alzheimer’s disease, genome-wide association study results, and multiple gene annotation resources, we built a multi-cellular Alzheimer’s disease molecular network that we leveraged for gaining cell-specific insights into Alzheimer’s disease pathophysiology and for the identification of drug repurposing candidates. Our computational approach pointed out 54 candidate drugs, mainly targeting MAPK and IGF1R signaling pathways, which could be further evaluated for their potential as Alzheimer’s disease therapy.

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          SCENIC: Single-cell regulatory network inference and clustering

          Although single-cell RNA-seq is revolutionizing biology, data interpretation remains a challenge. We present SCENIC for the simultaneous reconstruction of gene regulatory networks and identification of cell states. We apply SCENIC to a compendium of single-cell data from tumors and brain, and demonstrate that the genomic regulatory code can be exploited to guide the identification of transcription factors and cell states. SCENIC provides critical biological insights into the mechanisms driving cellular heterogeneity.
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            KEGG: integrating viruses and cellular organisms

            Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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              CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

              Cell-cell communication mediated by ligand-receptor complexes is critical to coordinating diverse biological processes, such as development, differentiation and inflammation. To investigate how the context-dependent crosstalk of different cell types enables physiological processes to proceed, we developed CellPhoneDB, a novel repository of ligands, receptors and their interactions. In contrast to other repositories, our database takes into account the subunit architecture of both ligands and receptors, representing heteromeric complexes accurately. We integrated our resource with a statistical framework that predicts enriched cellular interactions between two cell types from single-cell transcriptomics data. Here, we outline the structure and content of our repository, provide procedures for inferring cell-cell communication networks from single-cell RNA sequencing data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v.2.0 is an updated version of our resource that incorporates additional functionalities to enable users to introduce new interacting molecules and reduces the time and resources needed to interrogate large datasets. CellPhoneDB v.2.0 is publicly available, both as code and as a user-friendly web interface; it can be used by both experts and researchers with little experience in computational genomics. In our protocol, we demonstrate how to evaluate meaningful biological interactions with CellPhoneDB v.2.0 using published datasets. This protocol typically takes ~2 h to complete, from installation to statistical analysis and visualization, for a dataset of ~10 GB, 10,000 cells and 19 cell types, and using five threads.
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                Author and article information

                Contributors
                parolo@cosbi.eu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 January 2023
                5 January 2023
                2023
                : 13
                : 222
                Affiliations
                [1 ]GRID grid.491181.4, Fondazione the Microsoft Research-University of Trento Centre for Computational and Systems Biology (COSBI), ; 38068 Rovereto, Italy
                [2 ]GRID grid.6292.f, ISNI 0000 0004 1757 1758, Department of Pharmacy and Biotechnology, , Alma Mater Studiorum University of Bologna, ; 40126 Bologna, Italy
                [3 ]GRID grid.11696.39, ISNI 0000 0004 1937 0351, Department of Cellular, Computational and Integrative Biology (CIBIO), , University of Trento, ; 38123 Trento, Italy
                Author information
                http://orcid.org/0000-0002-3671-5353
                Article
                27420
                10.1038/s41598-023-27420-x
                9816180
                36604493
                2849e0d4-9ed2-4839-aab2-30b6180c0d1f
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 June 2022
                : 29 December 2022
                Categories
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                Custom metadata
                © The Author(s) 2023

                Uncategorized
                data integration,gene regulatory networks,network topology
                Uncategorized
                data integration, gene regulatory networks, network topology

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