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Identifying cell-enriched miRNAs in kidney injury and repair
Katie L. Connor, Oliver Teenan, Carolynn Cairns, Victoria Banwell, Rachel A.B. Thomas, Julie Rodor, Sarah Finnie, Riinu Pius, Gillian M. Tannahill, Vishal Sahni, Caroline O.S. Savage, Jeremy Hughes, Ewen M. Harrison, Robert B. Henderson, Lorna P. Marson, Bryan R. Conway, Stephen J. Wigmore, Laura Denby
Katie L. Connor, Oliver Teenan, Carolynn Cairns, Victoria Banwell, Rachel A.B. Thomas, Julie Rodor, Sarah Finnie, Riinu Pius, Gillian M. Tannahill, Vishal Sahni, Caroline O.S. Savage, Jeremy Hughes, Ewen M. Harrison, Robert B. Henderson, Lorna P. Marson, Bryan R. Conway, Stephen J. Wigmore, Laura Denby
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Research Article Nephrology

Identifying cell-enriched miRNAs in kidney injury and repair

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Abstract

Small noncoding RNAs, miRNAs (miRNAs), are emerging as important modulators in the pathogenesis of kidney disease, with potential as biomarkers of kidney disease onset, progression, or therapeutic efficacy. Bulk tissue small RNA-sequencing (sRNA-Seq) and microarrays are widely used to identify dysregulated miRNA expression but are limited by the lack of precision regarding the cellular origin of the miRNA. In this study, we performed cell-specific sRNA-Seq on tubular cells, endothelial cells, PDGFR-β+ cells, and macrophages isolated from injured and repairing kidneys in the murine reversible unilateral ureteric obstruction model. We devised an unbiased bioinformatics pipeline to define the miRNA enrichment within these cell populations, constructing a miRNA catalog of injury and repair. Our analysis revealed that a significant proportion of cell-specific miRNAs in healthy animals were no longer specific following injury. We then applied this knowledge of the relative cell specificity of miRNAs to deconvolute bulk miRNA expression profiles in the renal cortex in murine models and human kidney disease. Finally, we used our data-driven approach to rationally select macrophage-enriched miR-16-5p and miR-18a-5p and demonstrate that they are promising urinary biomarkers of acute kidney injury in renal transplant recipients.

Authors

Katie L. Connor, Oliver Teenan, Carolynn Cairns, Victoria Banwell, Rachel A.B. Thomas, Julie Rodor, Sarah Finnie, Riinu Pius, Gillian M. Tannahill, Vishal Sahni, Caroline O.S. Savage, Jeremy Hughes, Ewen M. Harrison, Robert B. Henderson, Lorna P. Marson, Bryan R. Conway, Stephen J. Wigmore, Laura Denby

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Figure 2

Development of kidney cell enrichment clusters.

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Development of kidney cell enrichment clusters.
Analysis of single popul...
Analysis of single population sRNA-Seq of macrophage (Mac; n = 16), endothelial (EC; n = 16), PDGFR-β+ (n = 15), and proximal tubular (PT; n = 16) sorted cells (n = 3–4 per time point) from the R-UUO model to identify miRNAs specific for each cell type. (A) For each time point, cell clusters were developed using a combination of unbiased fuzzy clustering (mFuzz) and filtering relative expression (z score) in each cell type (abbreviated example shown of UUO-2, full data Supplemental Figures 2–5). (B) Sankey plot showing the trajectory of each individual miRNA enriched at baseline (Sham). Many miRNAs either became non–cell specific or occasionally switched cell type enrichment during renal injury and repair. (C) On unsupervised clustering, miRNAs in high enrichment consistency clusters were noted to have greater cell specificity when mapped to all time points. Heatmap: Each row represents a miRNA; each column is a sample, with clustering by Euclidian distances. UUO, unilateral ureteric obstruction; R-UUO, reversal of UUO (2 weeks’ reversal); PGF/PDGFR-β, platelet-derived growth factor receptor–β.

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