Urine provides a diverse source of information related to a patient’s health status and is ideal for clinical proteomics due to its ease of collection. To date, most methods for the preparation of urine samples lack the throughput required to analyze large clinical cohorts. To this end, we developed a novel workflow, urine-HILIC (uHLC), based on an on-bead protein capture, clean-up, and digestion without the need for bottleneck processing steps such as protein precipitation or centrifugation. The workflow was applied to an acute kidney injury (AKI) pilot study. Urine from clinical samples and a pooled sample was subjected to automated sample preparation in a KingFisher™ Flex magnetic handling station using the novel approach based on MagReSyn^® 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 analyzed by LCMS in data-independent acquisition (DIA) mode using a Dionex Ultimate 3000 UPLC coupled to a Sciex 5600 mass spectrometer. The data were 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 coefficient of variation was observed in the uHLC technical replicates. Following statistical analysis, candidate protein markers were filtered, at ≥8.35-fold change in abundance, ≥2 unique peptides and ≤1% false discovery rate, and revealed 121 significant, differentially abundant proteins, some of which have known associations with kidney injury. The pilot data derived using this novel workflow provide information on the urinary proteome of patients with AKI. Further exploration in a larger cohort using this novel high-throughput method is warranted.

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 were 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] = 1.4 × 10^− 45), innate immune system (q = 1.1 × 10^− 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 chronic kidney disease 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-associated albuminuria.

Kidney function parameters including estimated glomerular filtration rate (eGFR) and urine albumin excretion are commonly used to diagnose chronic kidney disease (CKD). However, these parameters are relatively insensitive, limiting their utility for screening and early detection of kidney disease. Studies have suggested that urinary proteomic profiles differ by eGFR stage, offering potential insights into kidney disease pathogenesis alongside opportunities to increase the sensitivity of current testing strategies. In this study, we characterized and compared the urinary proteome across different eGFR stages in a Black African cohort from rural Mpumalanga Province, South Africa. We stratified 81 urine samples by eGFR stage (mL/min/1.73 m²): Stage G1 (eGFR ≥ 90; n = 36), Stage G2 (eGFR 60–89; n = 35), and Stage G3–G5 (eGFR < 60; n = 10). Urine proteomic analysis was performed using an Evosep One liquid chromatography system coupled to a Sciex 5600 TripleTOF in data-independent acquisition mode. Nonparametric multivariate analysis and receiver operating characteristic (ROC) curves were used to assess the performance of differentially abundant proteins (DAPs). Pathway analysis was performed on DAPs. Creatinine-based eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. In this study, thirty-eight urinary proteins were differentially abundant for eGFR Stages 3–5 when compared to Stages G1 (AUC = 0.95; CI: 0.86–1) and G2 (AUC = 0.84; CI: 0.64–0.98). Notably, only six urinary proteins (Cystatin M (CST6), glutathione hydrolase 6 (GGT6), sushi domain containing 2 (SUSD2), insulin-like growth factor binding protein 6 (IGFBP6), heat shock protein 90 beta family member 1 (HSP90B1), and mannosidase alpha class 1A member 1 (MAN1A1)) were differentially abundant when comparing Stage G1 and Stage G2 with a modest AUC = 0.81 (CI: 0.67–0.92). Pathway analysis indicated that DAPs were associated with haemostasis and fibrin clot formation. In a rural cohort from South Africa, the urinary proteome differed by eGFR stage, and we identified six differentially abundant proteins which, in combination, could help to differentiate earlier eGFR stages with higher predictive accuracy than the currently available tests.

Phosphopeptide enrichment is an essential step in large-scale, quantitative phosphoproteomics studies by mass spectrometry. Several phosphopeptide affinity enrichment techniques exist, such as Immobilized Metal ion Affinity Chromatography (IMAC) and Metal Oxide Affinity Chromatography (MOAC). We compared Zirconium (IV) IMAC (Zr-IMAC) magnetic microparticles to more commonly used Titanium (IV) IMAC (Ti-IMAC) and TiO ₂ magnetic microparticles for phosphopeptide enrichment from simple and complex protein samples prior phosphopeptide sequencing and characterization by mass spectrometry (LC-MS/MS). We optimized sample-loading conditions to increase phosphopeptide recovery for Zr-IMAC, Ti-IMAC and TiO ₂ based workflows. The performance of Zr-IMAC was enhanced by 19-22% to recover up to 5173 phosphopeptides from 200 µg of protein extract from HepG2/C3A cells, making Zr-IMAC the preferred method for phosphopeptide enrichment in this study. Ti-IMAC and TiO ₂ performance were also optimized to improve phosphopeptide numbers by 28% and 35%, respectively. Furthermore, Zr-IMAC based phosphoproteomics in the magnetic microsphere format identified 23% more phosphopeptides than HPLC-based Fe(III)-IMAC for same sample amount (200 µg), thereby adding 37% more uniquely identified phosphopeptides. We conclude that Zr-IMAC improves phosphoproteome coverage and recommend that this affinity enrichment method should be more widely used in biological and biomedical studies of cell signalling and in the search for disease-biomarkers.

Albuminuria may precede decreases in the 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. This case-controlled study compared the 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 (Evosep Biosystems, Odense, Denmark) coupled to a Sciex 5600 Triple-TOF (Sciex, Framingham, MA, USA) in data-independent acquisition mode. The data were searched on Spectronaut^TM 15. Differentially abundant proteins (DAPs) were filtered to include those with a ≥2.25-fold change and a false discovery rate ≤ 1%. Receiver–operating characteristic curves were used to assess the discriminating abilities of proteins of interest. Pathway analysis was performed using Enrichr software. As expected, the albuminuric group had higher uACR (7.9 vs. 0.55 mg/mmol, p < 0.001). The median eGFR (mL/min/1.73 m²) showed no difference between the groups (111 vs. 114, p = 0.707). We identified 80 DAPs in the albuminuria group compared to the normoalbuminuria group, of which 59 proteins were increased while 21 proteins were decreased in abundance. We found 12 urinary proteins with an AUC > 0.8 and a p < 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 found that the DAPs were involved in insulin growth factor (IGF) functions, innate immunity, platelet degranulation, and extracellular matrix organization. In albuminuric individuals with a 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.

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 Spectronaut ^TM 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 0.8, and p -value 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.