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      Exploring the mechanism of Celastrol in the treatment of rheumatoid arthritis based on systems pharmacology and multi-omics

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          Abstract

          To explore the molecular network mechanism of Celastrol in the treatment of rheumatoid arthritis (RA) based on a novel strategy (integrated systems pharmacology, proteomics, transcriptomics and single-cell transcriptomics). Firstly, the potential targets of Celastrol and RA genes were predicted through the database, and the Celastrol-RA targets were obtained by taking the intersection. Then, transcriptomic data and proteomic data of Celastrol treatment of RA were collected. Subsequently, Celastrol-RA targets, differentially expressed genes, and differentially expressed proteins were imported into Metascape for enrichment analysis, and related networks were constructed. Finally, the core targets of Celastrol-RA targets, differentially expressed genes, and differentially expressed proteins were mapped to synoviocytes of RA mice to find potential cell populations for Celastrol therapy. A total of 195 Celastrol-RA targets, 2068 differential genes, 294 differential proteins were obtained. The results of enrichment analysis showed that these targets, genes and proteins were mainly related to extracellular matrix organization, TGF-β signaling pathway, etc. The results of single cell sequencing showed that the main clusters of these targets, genes, and proteins could be mapped to RA synovial cells. For example, Mmp9 was mainly distributed in Hematopoietic cells, especially in Ptprn+fibroblast. The results of molecular docking also suggested that Celastrol could stably combine with molecules predicted by network pharmacology. In conclusion, this study used systems pharmacology, transcriptomics, proteomics, single-cell transcriptomics to reveal that Celastrol may regulate the PI3K/AKT signaling pathway by regulating key targets such as TNF and IL6, and then play an immune regulatory role.

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          Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

          A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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            Integrated analysis of multimodal single-cell data

            Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.
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              STRING v10: protein–protein interaction networks, integrated over the tree of life

              The many functional partnerships and interactions that occur between proteins are at the core of cellular processing and their systematic characterization helps to provide context in molecular systems biology. However, known and predicted interactions are scattered over multiple resources, and the available data exhibit notable differences in terms of quality and completeness. The STRING database (http://string-db.org) aims to provide a critical assessment and integration of protein–protein interactions, including direct (physical) as well as indirect (functional) associations. The new version 10.0 of STRING covers more than 2000 organisms, which has necessitated novel, scalable algorithms for transferring interaction information between organisms. For this purpose, we have introduced hierarchical and self-consistent orthology annotations for all interacting proteins, grouping the proteins into families at various levels of phylogenetic resolution. Further improvements in version 10.0 include a completely redesigned prediction pipeline for inferring protein–protein associations from co-expression data, an API interface for the R computing environment and improved statistical analysis for enrichment tests in user-provided networks.
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                Author and article information

                Contributors
                xjtang09@163.com
                lingyunsun@nju.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 January 2024
                18 January 2024
                2024
                : 14
                : 1604
                Affiliations
                [1 ]GRID grid.428392.6, ISNI 0000 0004 1800 1685, Department of Rheumatology and Immunology, , Nanjing Drum Tower Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Graduate School of Peking Union Medical College, ; Nanjing, China
                [2 ]GRID grid.513126.2, People’s Hospital of Ningxiang City, ; Ningxiang, China
                [3 ]Key Laboratory of Hunan Province for Integrated Traditional Chinese and Western Medicine on Prevention and Treatment of Cardio-Cerebral Diseases, School of Integrated Chinese and Western Medicine, Hunan University of Chinese Medicine, ( https://ror.org/02my3bx32) Changsha, China
                [4 ]GRID grid.459864.2, ISNI 0000 0004 6005 705X, Department of Rehabilitation Medicine, , Guangzhou Panyu Central Hospital, ; Guangzhou, China
                [5 ]Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, ( https://ror.org/02drdmm93) Beijing, China
                [6 ]Department of Rheumatology, The First People′s Hospital Changde City, ( https://ror.org/02h2ywm64) Changde, China
                [7 ]Department of Rheumatology and Immunology, The First Affiliated Hospital of Anhui Medical University, ( https://ror.org/03t1yn780) Anhui, China
                Article
                48248
                10.1038/s41598-023-48248-5
                10796403
                38238321
                ee17e844-9723-4cc2-bde2-4846744ec25b
                © The Author(s) 2024

                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
                : 4 April 2023
                : 23 November 2023
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                © Springer Nature Limited 2024

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                biochemistry,immunology
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                biochemistry, immunology

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