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      Indoxyl Sulfate-Mediated Metabolic Alteration of Transcriptome Signatures in Monocytes of Patients with End-Stage Renal Disease (ESRD)

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          Abstract

          End-stage renal disease (ESRD) is the final stage of chronic kidney disease, which is increasingly prevalent worldwide and is associated with the progression of cardiovascular disease (CVD). Indoxyl sulfate (IS), a major uremic toxin, plays a key role in the pathology of CVD via adverse effects in endothelial and immune cells. Thus, there is a need for a transcriptomic overview of IS responsive genes in immune cells of ESRD patients. Here, we investigated IS-mediated alterations in gene expression in monocytes from ESRD patients. Transcriptomic analysis of ESRD patient-derived monocytes and IS-stimulated monocytes from healthy controls was performed, followed by analysis of differentially expressed genes (DEGs) and gene ontology (GO). We found that 148 upregulated and 139 downregulated genes were shared between ESRD patient-derived and IS-stimulated monocytes. Interaction network analysis using STRING and ClueGo suggests that mainly metabolic pathways, such as the pentose phosphate pathway, are modified by IS in ESRD patient-derived monocytes. These findings were confirmed in IS-stimulated monocytes by the increased mRNA expression of genes including G6PD, PGD, and TALDO1. Our data suggest that IS causes alteration of metabolic pathways in monocytes of ESRD patients and, thus, these altered genes may be therapeutic targets.

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

                Journal
                Toxins (Basel)
                Toxins (Basel)
                toxins
                Toxins
                MDPI
                2072-6651
                28 September 2020
                October 2020
                : 12
                : 10
                : 621
                Affiliations
                [1 ]Department of Microbiology and Immunology, Seoul National University College of Medicine, Seoul 03080, Korea; hyk0801@ 123456hotmail.com (H.Y.K.); yuri.hwang@ 123456genexine.com (Y.H.); zcbtcey@ 123456ucl.ac.uk (C.E.Y.)
                [2 ]Institute of Infectious Diseases, Seoul National University College of Medicine, Seoul 03080, Korea
                [3 ]Laboratory of Inflammation and Autoimmunity (LAI), Department of Biomedical Sciences and BK21Plus Biomedical Science Project, Seoul National University College of Medicine, Seoul 03080, Korea; minyahile@ 123456naver.com (S.J.L.); icefallv@ 123456hanmail.net (G.H.L.)
                [4 ]Cardiovascular and Metabolic Diseases Etiology Research Center and Department of Preventive Medicine, Yonsei University College of Medicine, Seoul 03722, Korea; HCKIM@ 123456yuhs.ac
                [5 ]Division of Nephrology, Department of Internal Medicine, Yonsei University College of Medicine, Seoul 03722, Korea; YOOSY0316@ 123456yuhs.ac
                [6 ]Cancer Research Institute and Ischemic/Hypoxic Disease Institute, Seoul National University College of Medicine, Seoul National University Hospital Biomedical Research Institute, Seoul 03080, Korea
                Author notes
                [* ]Correspondence: wonwoolee@ 123456snu.ac.kr ; Tel.: +82-2-740-8303; Fax: +82-2-743-0881
                [†]

                H.Y.K. and S.J.L. contributed equally.

                Author information
                https://orcid.org/0000-0001-7867-1240
                https://orcid.org/0000-0002-5347-9591
                Article
                toxins-12-00621
                10.3390/toxins12100621
                7601745
                32998431
                29c36440-57d8-4de3-854a-a673191a55bb
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 18 August 2020
                : 25 September 2020
                Categories
                Article

                Molecular medicine
                end-stage renal disease (esrd),monocytes,uremic toxins,indoxyl sulfate (is),aryl hydrocarbon receptor,transcriptomic analysis,metabolic pathway

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