Journal Article
Research Support, Non-U.S. Gov't
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The Integrated RNA Landscape of Renal Preconditioning against Ischemia-Reperfusion Injury.

BACKGROUND: Although AKI lacks effective therapeutic approaches, preventive strategies using preconditioning protocols, including caloric restriction and hypoxic preconditioning, have been shown to prevent injury in animal models. A better understanding of the molecular mechanisms that underlie the enhanced resistance to AKI conferred by such approaches is needed to facilitate clinical use. We hypothesized that these preconditioning strategies use similar pathways to augment cellular stress resistance.

METHODS: To identify genes and pathways shared by caloric restriction and hypoxic preconditioning, we used RNA-sequencing transcriptome profiling to compare the transcriptional response with both modes of preconditioning in mice before and after renal ischemia-reperfusion injury.

RESULTS: The gene expression signatures induced by both preconditioning strategies involve distinct common genes and pathways that overlap significantly with the transcriptional changes observed after ischemia-reperfusion injury. These changes primarily affect oxidation-reduction processes and have a major effect on mitochondrial processes. We found that 16 of the genes differentially regulated by both modes of preconditioning were strongly correlated with clinical outcome; most of these genes had not previously been directly linked to AKI.

CONCLUSIONS: This comparative analysis of the gene expression signatures in preconditioning strategies shows overlapping patterns in caloric restriction and hypoxic preconditioning, pointing toward common molecular mechanisms. Our analysis identified a limited set of target genes not previously known to be associated with AKI; further study of their potential to provide the basis for novel preventive strategies is warranted. To allow for optimal interactive usability of the data by the kidney research community, we provide an online interface for user-defined interrogation of the gene expression datasets (https://shiny.cecad.uni-koeln.de:3838/IRaP/).

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