Assessment of Fibrinogen-like 2 (FGL2) in Human Chronic Kidney Disease through Transcriptomics Data Analysis.
Sara DenicolòViji NairJohannes LeiererMichael RudnickiMatthias KretzlerGert MayerWenjun JuPaul PercoPublished in: Biomolecules (2022)
Fibrinogen-like 2 (FGL2) was recently found to be associated with fibrosis in a mouse model of kidney damage and was proposed as a potential therapeutic target in chronic kidney disease (CKD). We assessed the association of renal FGL2 mRNA expression with the disease outcome in two independent CKD cohorts (NEPTUNE and Innsbruck CKD cohort) using Kaplan Meier survival analysis. The regulation of FGL2 in kidney biopsies of CKD patients as compared to healthy controls was further assessed in 13 human CKD transcriptomics datasets. The FGL2 protein expression in human renal tissue sections was determined via immunohistochemistry. The regulators of FGL2 mRNA expression in renal tissue were identified in the co-expression and upstream regulator analysis of FGL2-positive renal cells via the use of single-cell RNA sequencing data from the kidney precision medicine project (KPMP). Higher renal FGL2 mRNA expression was positively associated with kidney fibrosis and negatively associated with eGFR. Renal FGL2 mRNA expression was upregulated in CKD as compared with healthy controls and associated with CKD progression in the Innsbruck CKD cohort ( p -value = 0.0036) and NEPTUNE cohort ( p -value = 0.0048). The highest abundance of FGL2 protein in renal tissue was detected in the thick ascending limb of the loop of Henle and macula densa, proximal tubular cells, as well as in glomerular endothelial cells. The upstream regulator analysis identified TNF, IL1B, IFNG, NFKB1, and SP1 as factors potentially inducing FGL2-co-expressed genes, whereas factors counterbalancing FGL2-co-expressed genes included GLI1, HNF1B, or PPARGC1A. In conclusion, renal FGL2 mRNA expression is elevated in human CKD, and higher FGL2 levels are associated with fibrosis and worse outcomes.
Keyphrases
- chronic kidney disease
- end stage renal disease
- endothelial cells
- single cell
- data analysis
- mouse model
- high glucose
- small cell lung cancer
- rna seq
- transcription factor
- pluripotent stem cells
- rheumatoid arthritis
- poor prognosis
- genome wide
- inflammatory response
- machine learning
- binding protein
- oxidative stress
- prognostic factors
- metabolic syndrome
- peritoneal dialysis
- dna methylation
- vascular endothelial growth factor
- risk assessment
- free survival
- big data