Combined Plasma Olink Proteomics and Transcriptomics Identifies CXCL1 and TNFRSF12A as Potential Predictive and Diagnostic Inflammatory Markers for Acute Kidney Injury.

Abstract

Acute kidney injury (AKI) poses a significant global public health challenge. Current methods for detecting AKI rely on monitoring changes in serum creatinine (Scr), blood urea nitrogen (BUN), urinary output and some commonly employed biomarkers. However, these indicators are usually neither specific nor sensitive to AKI, especially in cases of mild kidney injury. AKI is accompanied by severe inflammatory reactions, resulting in the upregulation of numerous inflammation-associated proteins in the plasma. Plasma biomarkers are a noninvasive method for detecting kidney injury, and to date, plasma inflammation-associated cytokines have not been adequately studied in AKI patients. The objective of our research was to identify novel inflammatory biomarkers for AKI. We utilized Olink proteomics to analyze the alterations in plasma inflammation-related proteins in the serum of healthy mice (n = 2) or mice treated with cisplatin (n = 6). Additionally, transcriptome datasets for the lipopolysaccharide (LPS), cisplatin, and ischemia‒reperfusion injury (IRI) groups were obtained from the National Center of Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. We calculated the intersection of differentially expressed proteins (DEPs) and genes (DEGs) from both datasets. In the Olink proteomics analysis, the AKI group had significantly greater levels of 11 DEPs than did the control group. In addition, 56 common upregulated DEGs were obtained from the transcriptome dataset. The expression of CXCL1 and TNFRSF12A overlapped across all the datasets. The transcription and protein expression levels of CXCL1 and TNFRSF12A were detected in vivo. The gene and protein levels of CXCL1 and TNFRSF12A were significantly increased in different AKI mouse models and clinical patients, suggesting that these genes and proteins could be potential specific biomarkers for the identification of AKI.