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      Identification of Ribonuclease 6 as an immunoinflammatory key gene associated with the glomerular injury in diabetic nephropathy

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          Abstract

          Diabetic nephropathy is one of the major causes of end-stage renal disease, and the pathogenesis of the disease has not been elucidated. While the immunoinflammatory response plays an essential role in the progression of diabetic nephropathy. Glomerular expression dataset in diabetic nephropathy was obtained from the GEO database. Differentially expressed genes were identified and functional enrichment analysis was performed to find genes associated with immunity and inflammation from them. The hub genes of immunoinflammatory were identified using MCODE after establishing the PPI network and gene expression was verified with diabetic nephropathy model rats. Xcell was used to assign immune cells to diabetic nephropathy glomerular samples to detect significant changes in immune cells and to analyze correlations with the hub gene. We found 120 DEGs associated with immunity and inflammation, Ribonuclease 6 was the Hub gene with the highest MCODE score. Xcell analysis revealed significant changes of immune cells in DN glomeruli, including upregulated Activated DCs, Conventional DCs, CD4+ Tem, Epithelial cells, Macrophages, Macrophages M1, and Memory B-cells. RNase6 expression showed the highest positive correlation with Macrophages M1, Activated DCs, and Conventional DCs. We verified through the Nephroseq v5 database that RNase6 expression was elevated in DN glomeruli and negatively correlated with glomerular filtration rate. Animal studies revealed that the kidney of DN model rats showed increased RNase6 expression together with inflammatory factor TNF-alpha and chemokine MCP-1. Our study identified RNase6 as a diagnostic and prognostic biomarker for diabetic nephropathy and found that it may play an essential role in the immunoinflammatory damage to the glomerulus.

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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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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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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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                Author and article information

                Contributors
                chenqiu1005@cdutcm.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 November 2022
                16 November 2022
                2022
                : 12
                : 19709
                Affiliations
                GRID grid.415440.0, Hospital of Chengdu University of Traditional Chinese Medicine, ; Chengdu, 610072 China
                Article
                24289
                10.1038/s41598-022-24289-0
                9668917
                36385487
                dd4eb29c-d3fc-485b-ba7c-8bd21b24ed96
                © The Author(s) 2022

                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
                : 19 July 2022
                : 14 November 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100019014, Chengdu Science and Technology Program;
                Award ID: 2019-YF09-00094-SN
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                endocrine system and metabolic diseases,nephrology
                Uncategorized
                endocrine system and metabolic diseases, nephrology

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