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      Mesenchymal Stem Cell Derived Exosomes Repair Uterine Injury by Targeting Transforming Growth Factor-β Signaling

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          Abstract

          <p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d6846651e142">Intrauterine adhesions (IUA) refer to adhesions within the uterine cavity and cervix caused by injuries from uterine surgery. They are a significant cause of female infertility. Exosomes derived from mesenchymal stem cells (MSCs) play an active role in the treatment of IUA. However, the mechanism by which they reduce fibrosis in the damaged endometrium remains unclear. In this paper, we demonstrate that exosomes derived from placental mesenchymal stem cells (PMSCs) can restore uterine functions and improve the fertility rate of injured animals. This is achieved by promoting cell proliferation, increasing endometrial thickness, and reversing fibrosis. Regarding the molecular mechanism behind these therapeutic effects, we identify three specific miRNAs, namely, miR-125b-5p, miR-30c-5p, and miR-23a-3p, enriched in PMSC-exosomes, as the key players in the treatment of IUA. Specifically, miR-125b-5p/miR-30c-5p and miR-23a-3p inhibit the expression of smad2 and smad3 by targeting their 3'-untranslated regions, resulting in the downregulation of the transforming growth factor-β (TGF-β)/smad signaling pathway and the reversal of fibrosis. Notably, the safety of PMSC-exosomes in intrauterine treatment was also been confirmed. In conclusion, we illustrate that exosomes derived from PMSCs possess the capability to repair endometrial damage and enhance fertility in injured animals by regulating the TGF-β/smad pathway via miR-125b-5p, miR-30c-5p, and miR-23a-3p. This provides insights into the precision treatment of IUA through exosome-based cell-free therapy. </p>

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          Most cited references38

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          clusterProfiler: an R package for comparing biological themes among gene clusters.

          Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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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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              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Contributors
                Journal
                ACS Nano
                ACS Nano
                American Chemical Society (ACS)
                1936-0851
                1936-086X
                January 30 2024
                January 19 2024
                January 30 2024
                : 18
                : 4
                : 3509-3519
                Affiliations
                [1 ]Department of Gynecology and Obstetrics, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences &Peking Union Medical College,Beijing 100005, China
                [2 ]The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Beijing Hospital, National Center of Gerontology, National Health Commission; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing 100005, China
                [3 ]Department of Biomedical Engineering, Columbia University, New York, New York 10032,United States
                Article
                10.1021/acsnano.3c10884
                38241636
                aeb0bc50-c632-4d7d-a524-bf9b23ef375c
                © 2024

                https://doi.org/10.15223/policy-029

                https://doi.org/10.15223/policy-037

                https://doi.org/10.15223/policy-045

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