Biology subjects

Govender, I. S.

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

4 recordsLinked to original sources

bioRxiv2024

Plasma proteome profiling for AKI biomarker candidates associated with first-line ART in people living with HIV in South Africa

With the highest global burden of HIV, South Africa initiates HIV treatment using first-line antiretroviral therapy (ART) regimens in newly diagnosed patients. Antiretroviral (ARV)-associated nephrotoxicity has been observed in -10% of South African patients newly receiving first-line ART, as well as in patients who have extensively received TDF-based ART regimens, and this can progress to acute kidney injury (AKI). To identify potential biomarkers that will improve the detection of ARV-associated nephrotoxicity, proteomic analysis was performed using the sequential window acquisition of all theoretical mass spectra (SWATH-MS) data acquisition method on the plasma of the case group (AKI) and the control group (non-AKI). Data are available via ProteomeXchange with identifier PXD054218. Evaluation of the results identified thirty-four proteins that showed a significant change in abundance, with three proteins showing increased abundance, while thirty-one proteins showed decreased abundance between the AKI and non-AKI groups. Machine learning was also used to evaluate the results and showed twenty ranked proteins that contributed to distinguishing between the AKI and non-AKI groups. The majority of the proteins of significant differential abundance and those ranked by the machine learning model participate in enriched biological processes that correspond to known pathophysiological and cellular mechanisms that contribute to AKI, suggesting that those proteins can serve as potential biomarkers to be further verified and validated for AKI diagnosis. Comparison of the plasma and previously generated urinary proteome profiles showed five proteins with significant differences in abundance that overlapped both proteomes providing evidence for the renal physiological dynamics of these proteins in renal injury and disease. Significance: Improving the detection of ARV-associated nephrotoxicity is challenging because current serum creatinine (srCr) based-equations used to estimate the glomerular filtration rate (eGFR) for assessing kidney function have been reported to overestimate the eGFR in the South African population and changes in srCr measurements also reflect a delayed response to the initial structural injury of the kidney. This hinders the timeous detection of ARV-associated nephrotoxicity which can lead to irreversible renal damage and can compromise the continuation of treatment in PLHIV, thus emphasising the need to introduce novel AKI biomarkers to mitigate ARV-associated nephrotoxicity in at risk PLHIV in South Africa. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=99 SRC="FIGDIR/small/605118v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@18743eaorg.highwire.dtl.DTLVardef@17bba7borg.highwire.dtl.DTLVardef@101c78org.highwire.dtl.DTLVardef@1bd2b23_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIIdentified thirty-four proteins with significant changes in abundance between study groups. C_LIO_LIMachine learning showed fourteen proteins associated with renal disease biology that differentiate the study groups. C_LIO_LIFirst study to analyse plasma proteome of this cohort of PLHIV in South Africa C_LIO_LIFive proteins overlap the plasma and urinary proteome profiles of the study groups. C_LI

biochemistry↗

bioRxiv2024

Proteomic analysis identifies dysregulated proteins in albuminuria: a South African pilot study

Albuminuria may precede decreases in glomerular filtration rate (GFR) and both tests are insensitive predictors of early stages of kidney disease. Our aim was to characterise the urinary proteome in black African individuals with albuminuria and well-preserved GFR from South Africa. A case-controlled study that compared urinary proteomes of 52 normoalbuminuric (urine albumin: creatinine ratio (uACR) <3 mg/mmol) and 56 albuminuric (uACR [≥] 3 mg/mmol) adults of Black African ethnicity. Urine proteins were precipitated, reduced, alkylated, digested, and analysed using an Evosep One LC coupled to a Sciex 5600 Triple-TOF in data-independent acquisition mode. Data were searched on SpectronautTM 15. Differentially abundant proteins (DAPs) were filtered [≥] 2.25-fold change and false discovery rate [≤] 1%. Receiver operating characteristic curves were used to assess the discriminating ability of proteins of interest. Pathway analysis was performed using Enrichr software. The albuminuric group had a higher uACR (7.9 vs 0.55 mg/mmol, p <0.001). The median eGFR (mL/min/1.73m2) showed no difference between the groups (111 vs 114, p=0.707). We identified 80 DAPs in the albuminuria group compared to normoalbuminuria, of which 59 proteins increased while 21 proteins decreased in abundance. We found 12 urinary proteins with AUC > 0.8, and p-value <0.001 in the multivariate analysis. Furthermore, an 80-protein model was developed that showed a high AUC >0.907 and a predictive accuracy of 91.3% between the two groups. Pathway analysis associated with DAPs were involved in insulin growth factor (IGF) functions, innate immunity, platelet degranulation, and extracellular matrix organization. In albuminuric individuals with well-preserved eGFR, pathways involved in preventing the release and uptake of IGF by insulin growth factor binding protein were significantly enriched. These proteins are indicative of a homeostatic imbalance in a variety of cellular processes underlying renal dysfunction and are implicated in chronic kidney disease.

biochemistry↗

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↗