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      Alkali therapy protects renal function, suppresses inflammation, and improves cellular metabolism in kidney disease

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          Abstract

          Chronic kidney disease (CKD) affects approximately 10–13% of the population worldwide and halting its progression is a major clinical challenge. Metabolic acidosis is both a consequence and a possible driver of CKD progression. Alkali therapy counteracts these effects in CKD patients, but underlying mechanisms remain incompletely understood. Here we show that bicarbonate supplementation protected renal function in a murine CKD model induced by an oxalate-rich diet. Alkali therapy had no effect on the aldosterone–endothelin axis but promoted levels of the anti-aging protein klotho; moreover, it suppressed adhesion molecules required for immune cell invasion along with reducing T-helper cell and inflammatory monocyte invasion. Comparing transcriptomes from the murine crystallopathy model and from human biopsies of kidney transplant recipients (KTRs) suffering from acidosis with or without alkali therapy unveils parallel transcriptome responses mainly associated with lipid metabolism and oxidoreductase activity. Our data reveal novel pathways associated with acidosis in kidney disease and sensitive to alkali therapy and identifies potential targets through which alkali therapy may act on CKD and that may be amenable for more targeted therapies.

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          Most cited references67

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          Trimmomatic: a flexible trimmer for Illumina sequence data

          Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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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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              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Clinical Science
                Portland Press Ltd.
                0143-5221
                1470-8736
                April 2022
                April 29 2022
                April 2022
                April 29 2022
                April 21 2022
                : 136
                : 8
                : 557-577
                Affiliations
                [1 ]Institute of Physiology, University of Zurich, Zurich, Switzerland
                [2 ]National Center of Competence in Research, NCCR Kidney.CH, Switzerland
                [3 ]Institute of Experimental Immunology, University of Zurich, Zurich, Switzerland
                [4 ]Functional Genomics Center Zürich, University of Zürich and ETH Zurich, Zurich, Switzerland
                [5 ]EMBL Partner Institute for Genome, Editing Life Science Center, Vilnius University, Vilnius, Lithuania
                [6 ]Division of Nephrology, University Hospital Zurich, Zurich, Switzerland
                [7 ]Department of Nephropathology, Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander-University Erlangen-Nürnberg, Erlangen, Germany
                Article
                10.1042/CS20220095
                35389462
                254c3564-82a3-4359-a7c7-b76f7df187ad
                © 2022

                http://creativecommons.org/licenses/by/2.0/

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