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      The nuclear factor of activated T cells 5 (NFAT5) contributes to the renal corticomedullary differences in gene expression

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          Abstract

          The corticomedullary osmotic gradient between renal cortex and medulla induces a specific spatial gene expression pattern. The factors that controls these differences are not fully addressed. Adaptation to hypertonic environment is mediated by the actions of the nuclear factor of activated T-cells 5 (NFAT5). NFAT5 induces the expression of genes that lead to intracellular accumulation of organic osmolytes. However, a systematical analysis of the NFAT5-dependent gene expression in the kidneys was missing. We used primary cultivated inner medullary collecting duct (IMCD) cells from control and NFAT5 deficient mice as well as renal cortex and inner medulla from principal cell specific NFAT5 deficient mice for gene expression profiling. In primary NFAT5 deficient IMCD cells, hyperosmolality induced changes in gene expression were abolished. The majority of the hyperosmolality induced transcripts in primary IMCD culture were determined to have the greatest expression in the inner medulla. Loss of NFAT5 altered the expression of more than 3000 genes in the renal cortex and more than 5000 genes in the inner medulla. Gene enrichment analysis indicated that loss of NFAT5 is associated with renal inflammation and increased expression of kidney injury marker genes, like lipocalin-2 or kidney injury molecule-1. In conclusion we show that NFAT5 is a master regulator of gene expression in the kidney collecting duct and in vivo loss of NFAT function induces a kidney injury like phenotype.

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          Most cited references78

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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              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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                Author and article information

                Contributors
                bayram.edemir@uk-halle.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 November 2022
                24 November 2022
                2022
                : 12
                : 20304
                Affiliations
                [1 ]GRID grid.9018.0, ISNI 0000 0001 0679 2801, Department of Medicine, Hematology and Oncology, , Martin Luther University Halle-Wittenberg, ; Ernst-Grube-Str. 40, 06120 Halle (Saale), Germany
                [2 ]GRID grid.7048.b, ISNI 0000 0001 1956 2722, Department of Biomedicine, , Aarhus University, ; Aarhus, Denmark
                [3 ]GRID grid.412581.b, ISNI 0000 0000 9024 6397, Institute for Physiology, Pathophysiology and Toxicology, Zentrum für Biomedizinische Ausbildung und Forschung (ZBAF), , Witten/Herdecke University, ; Witten, Germany
                Article
                24237
                10.1038/s41598-022-24237-y
                9700710
                22834cbd-a781-450a-92a7-e6fef6d50b4b
                © 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
                : 12 August 2022
                : 11 November 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft;
                Award ID: Ed 181/9-1
                Award Recipient :
                Funded by: Martin-Luther-Universität Halle-Wittenberg (1043)
                Categories
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                © The Author(s) 2022

                Uncategorized
                nephrology,physiology
                Uncategorized
                nephrology, physiology

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