Biology subjects

Govender, I. S.

Publications and source records attributed to Govender, I. S..

2 recordsLinked to original sources

bioRxiv2023

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

BackgroundHypertension 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. MethodsThe 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. ResultsOverall, 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. ConclusionsThe 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.

biochemistry↗

bioRxiv2023

Urine-HILIC: Automated sample preparation for bottom-up urinary proteome profiling in clinical proteomics

Urine provides a diverse source of information related to a patients health status and is ideal for clinical proteomics because of its ease of collection. To date, there is no standard operating procedure for reproducible and robust urine sample preparation for mass spectrometry-based clinical proteomics. To this end, a novel workflow was developed based on an on-bead protein capture, clean up, and digestion without the requirement for processing steps such as precipitation or centrifugation. The workflow was applied to an acute kidney injury (AKI) pilot study. Urine from clinical samples and a pooled sample were subjected to automated sample preparation in a KingFisher Flex magnetic handling station using a novel urine-HILIC (uHLC) approach based on MagReSyn(R) HILIC microspheres. For benchmarking, the pooled sample was also prepared using a published protocol based on an on-membrane (OM) protein capture and digestion workflow. Peptides were analysed by LCMS in data independent acquisition (DIA) mode using a Dionex Ultimate 3000 UPLC coupled to a Sciex 5600 mass spectrometer. Data was searched in Spectronaut 17. Both workflows showed similar peptide and protein identifications in the pooled sample. The uHLC workflow was easier to set up and complete, having less hands-on time than the OM method, with fewer manual processing steps. Lower peptide and protein CV was observed in the uHLC technical replicates. Following statistical analysis, candidate protein markers were filtered, at [≥] 2-fold change in abundance, [≥] 2 unique peptides and [≤] 1% false discovery rate, and revealed many significant, differentially abundant kidney injury-associated urinary proteins. The pilot data derived using this novel workflow provides information on the urinary proteome of patients with AKI. Further exploration in a larger cohort using this novel high-throughput method is warranted.

biochemistry↗