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Kidney Week

Abstract: FR-PO137

Validation of Inferred MicroRNA Activity From Kidney scRNAseq

Session Information

  • AKI: Mechanisms - II
    November 04, 2022 | Location: Exhibit Hall, Orange County Convention Center‚ West Building
    Abstract Time: 10:00 AM - 12:00 PM

Category: Acute Kidney Injury

  • 103 AKI: Mechanisms

Authors

  • Janosevic, Danielle, Indiana University School of Medicine, Indianapolis, Indiana, United States
  • De Luca, Thomas, Indiana University School of Medicine, Indianapolis, Indiana, United States
  • Jiang, Guanglong, Indiana University School of Medicine, Indianapolis, Indiana, United States
Background

MicroRNA (miR) regulate renal injury and recovery processes in acute kidney injury (AKI). Single-cell RNAseq (scRNAseq) datasets are broadly available, in contrast to single-cell miRNAseq (scMiRseq). Thus, pipelines that computationally derive miRNA activity (miRa) from scRNAseq are appealing. These pipelines infer a miR’s activity level through enrichment of miR binding motifs on mRNA and changes in the miR targetome, proposed as a surrogate for miR expression. We hypothesize that miRa derived from kidney scRNAseq will strongly correlate with expressed miR from bulk kidney sequencing.

Methods

Murine kidney scRNAseq were downloaded from NCBI’s GEO: GSE151658. This data is a model of sepsis-AKI at 0,1,4,16, and 27 hours after endotoxin (LPS) injection (i.v.). ScRNAseq data were normalized and miRa estimated using “miReact” R package. MiRa were averaged for all cells at each timepoint to generate the pseudobulk miRa dataset. To validate miRa, we performed a miRNAseq experiment with identical conditions to the above dataset (n=5 per timepoint) and sequenced miR from bulk kidney tissue. RNA was extracted from snap frozen kidneys (Qiagen miRNeasy), library prepared (Qiaseq) and sequenced (10-15 million read depth/sample). FASTQs aligned to mm10 transcriptome using STAR. Read counts were TMM normalized, log-transformed (counts-per-million) using EdgeR, generating the bulk miR expression dataset. Pearson correlation was performed for each miR between pseudobulk miRa and bulk miR expression. Correlation scores with r >0.7 and p-value <0.05 were considered significantly correlated. Pathway analysis was performed with miRdb and Mienturnet.

Results

MiR activity analysis derived 1,588 miRa from the pseudobulk miRa data and 566 miRs identified in the bulk miR expression data, with 322 (of 566, 56%) miRs shared between pseudobulk miRa and bulk miR expression data. Of the shared miRs, 14 miRs were significantly correlated (of 322, 4%, p <0.05) with a bias towards -3p strands. Significantly correlated miRs (e.g., miR-1955-3p, r=0.99, miR-323-3p, r=0.98, miR-3069-3p, r=0.97) were related to inflammatory and immune response pathways.

Conclusion

In our murine kidney data, miRa was not well correlated with miR expression. The discordance of activity from expression warrants further exploration into the regulatory mechanisms of renal miR expression, which may improve computational predictions of miRa.

Funding

  • Other NIH Support