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. 2018 Dec 14;8:17870. doi: 10.1038/s41598-018-35943-x

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© The Author(s) 2018

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Correspondence analysis of mRNA expression variability across 120 candidate genes in acute kidney injury (AKI) and chronic kidney disease (CKD). Total RNA from injured kidneys and controls (contralateral) kidneys (n = 6 mice) was used in RT reactions with oligo-dT primers. cDNA was used in real time qPCR with gene specific primers (120 genes, Table S1, Supplement). mRNA level of a given gene in each sample was normalized to ribosomal RNA protein L32. For each experimental condition, the mean gene expression values from n = 6 mice were used for correspondence analysis. The analysis was based on assessing whether variability in gene expression across the experimental conditions distinguishes AKI models (magenta) from CKD models (green). Separation along each axis indicates differences in gene expression variability, with the first axis capturing the largest differences. As seen in the figure, AKI and CKD models segregated distinctly (axis 1), implying profound alterations in transcriptional signal between these two conditions. However, we also observed separation within the AKI models (axes 2 and 3) indicating gene expression variability between these acute renal injury conditions.