Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry

Abstract

Background Halting progression of chronic kidney disease (CKD) to established end stage kidney disease is a major goal of global health research. The mechanism of CKD progression involves pro-inflammatory, pro-fibrotic, and vascular pathways, but pathophysiological differentiation is currently lacking. Methods Plasma samples of 414 non-dialysis CKD patients, 170 fast progressors (with ∂ eGFR-3 ml/min/1.73 m2/year or worse) and 244 stable patients (∂ eGFR of − 0.5 to + 1 ml/min/1.73 m2/year) with a broad range of kidney disease aetiologies, were obtained and interrogated for proteomic signals with SWATH-MS. We applied a machine learning approach to feature selection of proteins quantifiable in at least 20% of the samples, using the Boruta algorithm. Biological pathways enriched by these proteins were identified using ClueGo pathway analyses. Results The resulting digitised proteomic maps inclusive of 626 proteins were investigated in tandem with available clinical data to identify biomarkers of progression. The machine learning model using Boruta Feature Selection identified 25 biomarkers as being important to progression type classification (Area Under the Curve = 0.81, Accuracy = 0.72). Our functional enrichment analysis revealed associations with the complement cascade pathway, which is relevant to CKD as the kidney is particularly vulnerable to complement overactivation. This provides further evidence to target complement inhibition as a potential approach to modulating the progression of diabetic nephropathy. Proteins involved in the ubiquitin–proteasome pathway, a crucial protein degradation system, were also found to be significantly enriched. Conclusions The in-depth proteomic characterisation of this large-scale CKD cohort is a step toward generating mechanism-based hypotheses that might lend themselves to future drug targeting. Candidate biomarkers will be validated in samples from selected patients in other large non-dialysis CKD cohorts using a targeted mass spectrometric analysis.

Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort

“…Specifically, these pathways included the “RHO GTPases activate PAKs”, “Regulation of actin cytoskeleton”, and “Tight junctions”. Importantly, among the proteins identified within these enriched pathways were Myosin light chain kinase (MYLK) and Complement component (C6), proteins that previous research has identified as significant biomarkers in CKD progression [ 8 ]. This convergence of findings underscores the relevance of these pathways and proteins in the context of CKD and kidney failure.…”

“…This method allowed estimation of the GFR using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) Equation [ 10 ], a prerequisite for the new UK-calibrated KFRE. CKD-EPI eGFR values were used to calculate GFR slopes (ΔGFR) [ 8 ]. The ΔGFR for each patient was computed using ordinary least-squares linear regression based on all outpatient eGFR values during the follow-up period of the study.…”

“…Patients derived from the two ΔGFR groups: rapid decline (> 3 ml/min/year) and stable (- 0.5 to + 1 ml/min/year) CKD. This study encompasses a diverse group of patients with various renal diseases, including diabetic nephropathy, hypertensive nephropathy, autosomal dominant polycystic kidney disease (ADPKD), glomerulonephritis, as well as individuals with 'other' CKD (multiple less frequent diagnoses) or those with unknown cause of CKD [ 8 ]. The urine albumin-to-creatinine ratio (uACR) was calculated from urine protein-to-creatinine ratio for all patients using a validated online conversion tool [ 11 ].…”

“…Plasma samples were processed and then analysed using SWATH-MS according to our previously published methods [ 8 ]. The SWATH-MS analysis was performed using defined mass spectrometry parameters, including isolation window size, overlap and total cycle time, enabled protein-relative quantification of more than 900 proteins, as previously reported [ 8 , 14 ]. The resulting SWATH map was investigated with reference to clinical data to identify potential blood-borne biomarkers of renal disease.…”

“…Previously, our research team proposed to investigate whether proteomic signatures of rapidly progressive CKD could be derived [ 8 ]. Our findings supported the complement cascade and coagulation pathway playing a role in the development and progression of renal disease.…”