Kidney Week

Abstract: SA-PO087

The Human Sepsis Blood Transcriptome Correlates with Molecular Signatures of Murine Sepsis-Associated AKI

Session Information

• AKI: Inflammation and Cell Cycle
  October 26, 2024 | Location: Exhibit Hall, Convention Center
  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

Danielle Janosevic, DO, MS, FASN

• Mccabe, Sean D., Indiana University School of Medicine, Indianapolis, Indiana, United States

Sean D. Mccabe, MS, PhD

• De Luca, Thomas, Indiana University School of Medicine, Indianapolis, Indiana, United States

Thomas De Luca, PhD

• Eadon, Michael T., Indiana University School of Medicine, Indianapolis, Indiana, United States

Michael T. Eadon, MD, FASN

• Hato, Takashi, Indiana University School of Medicine, Indianapolis, Indiana, United States

Takashi Hato, MD

• Dagher, Pierre C., Indiana University School of Medicine, Indianapolis, Indiana, United States

Pierre C. Dagher, MD, FASN

Background

Sepsis is a common cause of acute kidney injury for which we lack predictive biomarkers. In murine models of sAKI, renal tissue injury progresses through precise tissue gene expression stages that are mirrored in the blood transcriptome. We hypothesize that blood-based murine sAKI biomarkers can help diagnose and stratify human subjects with sepsis.

Methods

Whole blood RNAseq (25-30 million read depth/sample, mm10 transcriptome) was performed in a murine model of sAKI at 0 and 1,16 hr after endotoxin injection. Counts were TMM normalized and log-transformed (counts-per-million, EdgeR). Human data was derived from GSE 65682 (Scicluna et al., MARS consortium) and “abdominal-sepsis” (sepsis) and “control-GI” (non-sepsis) groups of blood RNA were compared. A within-species analysis was used to calculate gene log fold changes (LFC). Spearman correlations were calculated using a filtered gene set (> +/- 0.25, human, > +/- 1.5, murine samples) to determine sample relatedness.

Results

Murine blood-based sAKI biomarkers were able to discriminate between septic and non-septic patients. Correlations of gene changes between human non-sepsis with murine controls (0 hr) were as high as 0.43 and as high as 0.44 between human sepsis and murine late sAKI samples (16 hr, Figure). Correlated genes upregulated in sepsis were related to immune function (Bcl11b, Hvcn1, Cx3cr1) and metabolism (Pask).

Conclusion

Correlation of blood-based RNA biomarkers between human and murine sepsis data revealed shared alterations in critical processes related to sepsis- immune system regulation and metabolism. Defining such biomarkers may aid in the identification of subjects at risk for sAKI and afford early interventions to attenuate or reverse sAKI.

Funding

• NIDDK Support