bioRxivMolecular BiologyPreprint
Shared and Specific miRNA-mRNA in Human and Murine Acute Kidney Injury
Danielle JanoseviciD, Thomas W. McCarthyiD, Ashish Sadolikar, Takashi HatoiD, Michael T. EadoniD, Pierre C. DagheriD, the Kidney Precision Medicine Project
Abstract
Acute kidney injury (AKI) is managed as a single clinical entity despite substantial biological heterogeneity. We established a cross-species framework to define etiology-specific molecular programs and determine how these programs relate to molecular heterogeneity and pathobiology in human AKI. We performed parallel sequencing of totalRNA (mRNA) and miRNA (kidney, whole blood) in 3 murine models (mild AKI: ischemia-reperfusion injury, IRI; cecal-ligation and puncture, CLP; endotoxemia, LPS); kidney mRNA and miRNA clearly distinguished the models, blood mRNA maintained model-specific separation, and blood miRNA was less distinct. IRI was characterized by epithelial injury/repair, and extracellular matrix remodeling (i.e., miR-125b-1-3p, Vim, Sprr2g); CLP, broader inflammatory, metabolic programs (i.e., miR-692, Pfkfb3, Cdo1); LPS, inflammatory signaling (i.e., miR-155-5p, Irf7, Gbp3). We then compared these signatures to human whole blood RNAseq. Human blood samples varied along a shared gene axis that correlated to differing degrees with all three murine models, consistent with the heterogeneous nature of clinical AKI rather than discrete assignment to a single experimental etiology. Orthologs were linked to immune processes (i.e., SIGLEC14, DAAM2, CD177, DEFA3), and the presence of tubular casts in human biopsies, reflecting that mild murine AKI models capture etiology-specific biology relevant to severe human AKI. Integration of injury etiology, severity, and time may provide a framework for developing circulating biomarkers that may resolve the biological heterogeneity of human AKI.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 0
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