Proteomic insights into the pathophysiology of hypertension-associated albuminuria: Pilot study in a South African cohort

Author:

Govender Melanie A.1,Stoychev Stoyan H.2,Brandenburg Jean-Tristan3,Ramsay Michèle1,Fabian June4,Govender Ireshyn S.2

Affiliation:

1. Division of Human Genetics, National Health Laboratory Service and School of Pathology, Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand

2. Council for Scientific and Industrial Research, NextGen Health, and ReSyn Biosciences

3. Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand

4. Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand

Abstract

Abstract Background: Hypertension is an important public health priority with a high prevalence in Africa. It is also an independent risk factor for kidney outcomes. We aimed to identify potential proteins and pathways involved in hypertension-associated albuminuria by assessing urinary proteomic profiles in black South African participants with combined hypertension and albuminuria compared to those who have neither condition. Methods: The study included 24 South African cases with both hypertension and albuminuria and 49 control participants who had neither condition. Protein was extracted from urine samples and analysed using ultra-high-performance liquid chromatography coupled with mass spectrometry. Data was generated using data-independent acquisition (DIA) and processed using Spectronaut™ 15. Statistical and functional data annotation were performed on Perseus and Cytoscape to identify and annotate differentially abundant proteins. Machine learning was applied to the dataset using the OmicLearn platform. Results: Overall, a mean of 1,225 and 915 proteins were quantified in the control and case groups, respectively. Three hundred and thirty-two differentially abundant proteins were constructed into a network. Pathways associated with these differentially abundant proteins included the immune system (q-value [false discovery rate]=1.4x10-45), innate immune system (q=1.1x10-32), extracellular matrix (ECM) organisation (q=0.03) and activation of matrix metalloproteinases (q=0.04). Proteins with high disease scores (76–100% confidence) for both hypertension and CKD included angiotensinogen (AGT), albumin (ALB), apolipoprotein L1 (APOL1), and uromodulin (UMOD). A machine learning approach was able to identify a set of 20 proteins, differentiating between cases and controls. Conclusions: The urinary proteomic data combined with the machine learning approach was able to classify disease status and identify proteins and pathways associated with hypertension and albuminuria.

Publisher

Research Square Platform LLC

Reference60 articles.

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