Search

Early detection of kidney transplant (KT) rejection remains a challenge in patient care. Non-invasive biomarkers hold high potential to detect rejection, adjust immunosuppression, and monitor KT patients. So far, no approach has fully satisfied requirements to innovate routine monitoring of KT patients. In this two-center study we analyzed a total of 380 urine samples. T cells and tubular epithelial cells were quantified in KT patients with graft deterioration using flow cytometry. Epigenetic urine cell quantification was used to confirm flow cytometric results. Moreover, a cohort of KT patients was followed up during the first year after transplantation, tracking cell subsets over time. Abundance of urinary cell counts differed in patients with and without rejection. Most strikingly, various T cell subsets were enriched in patients with T cell-mediated rejection (TCMR) compared to patients without TCMR. Among T cell subsets, CD8+HLA-DR+ T cells were most distinctive (AUC = 0.91, Spec.: 95.9%, Sens.: 76.5%). Epigenetic analysis confirmed T cell and tubular epithelial cell quantities as determined by flow cytometry. Urinary T cell abundance in new KT patients decreased during their first year after transplantation. In conclusion urinary T cells reflect intrarenal inflammation in TCMR. T cell subsets yield high potential to monitor KT patients and detect rejection. Hereby we present a promising biomarker to non-invasively diagnose TCMR.

Lupus nephritis (LN) is a common and severe organ manifestation of systemic lupus erythematosus (SLE) and is a major cause of SLE related deaths. Early diagnosis is essential to improve the prognosis of patients with LN. To screen the potential biomarkers associated with LN, we downloaded the gene expression profile of GSE99967 from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was utilized to construct a gene co-expression network and identify gene modules associated with LN. Gene Ontology (GO) analysis was also applied to explore the biological function of genes and identify the key module. Differentially expressed genes (DEGs) were identified and Maximal Clique Centrality (MCC) values were calculated to screen hub genes. Furthermore, we selected promising biomarkers for real-time PCR (qRT-PCR) and enzyme-linked immunosorbent assay (ELISA) validation in independent cohorts. Our results indicated that five hub genes, including IFI44, IFIT3, HERC5, RSAD2, and DDX60 play vital roles in the pathogenesis of LN. Importantly, IFI44 may considered as a key biomarker in LN for its diagnostic capabilities, which is also a promising therapeutic target in the future.