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

Carlos R Ramírez Medina; Ibrahim Ali; Ivona Baricevic-Jones; Aghogho Odudu; Moin A Saleem; Anthony D Whetton; Philip A Kalra; Nophar Geifman

doi:10.1186/s12014-023-09405-0

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

Clin Proteomics. 2023 Apr 20;20(1):19. doi: 10.1186/s12014-023-09405-0.

Authors

Carlos R Ramírez Medina¹, Ibrahim Ali², Ivona Baricevic-Jones^{3

2}, Aghogho Odudu⁴, Moin A Saleem⁵, Anthony D Whetton^{3

6}, Philip A Kalra^#², Nophar Geifman^#⁷

Affiliations

¹ Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK. carlos.ramirezmedina@manchester.ac.uk.
² Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK.
³ Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK.
⁴ Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK.
⁵ Bristol Renal and Children's Renal Unit, Bristol Medical School, University of Bristol, Bristol, UK.
⁶ School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK.
⁷ School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK.

^# Contributed equally.

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.

Keywords: Biomarkers; Chronic kidney disease (CKD) Progression; Complement cascade pathway; Proteasome pathway; Proteomics; SWATH-MS.

Abstract

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