Urinary Protein and Peptide Markers in Chronic Kidney Disease

Urinary peptide analysis to predict the response to blood pressure medication

BACKGROUND

The risk of diabetic kidney disease (DKD) progression is significant despite treatment with renin-angiotensin system (RAS) blocking agents. Current clinical tools cannot predict whether or not patients will respond to treatment with RAS inhibitors (RASi). We aimed to investigate whether proteome analysis could identify urinary peptides as biomarkers that could predict the response to angiotensin-converting enzyme inhibitor and angiotensin-receptor blockers treatment to avoid DKD progression. Furthermore, we investigated the comparability of the estimated glomerular filtration rate (eGFR), calculated using four different GFR equations, for DKD progression.

METHODS

We evaluated urine samples from a discovery cohort of 199 diabetic patients treated with RASi. DKD progression was defined based on eGFR percentage slope results between visits (∼1 year) and for the entire period (∼3 years) based on the eGFR values of each GFR equation. Urine samples were analysed using capillary electrophoresis-coupled mass spectrometry. Statistical analysis was performed between the uncontrolled (patients who did not respond to RASi treatment) and controlled kidney function groups (patients who responded to the RASi treatment). Peptides were combined in a support vector machine-based model. The area under the receiver operating characteristic curve was used to evaluate the risk prediction models in two independent validation cohorts treated with RASi.

RESULTS

The classification of patients into uncontrolled and controlled kidney function varies depending on the GFR equation used, despite the same sample set. We identified 227 peptides showing nominal significant difference and consistent fold changes between uncontrolled and controlled patients in at least three methods of eGFR calculation. These included fragments of collagens, alpha-1-antitrypsin, antithrombin-III, CD99 antigen and uromodulin. A model based on 189 of 227 peptides (DKDp189) showed a significant prediction of non-response to the treatment/DKD progression in two independent cohorts.

CONCLUSIONS

The DKDp189 model demonstrates potential as a predictive tool for guiding treatment with RASi in diabetic patients.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

The Strategy for Peptidomic LC-MS/MS Data Analysis: The Case of Urinary Peptidome Study

The study of urinary peptidome is an important area of research, which concerns the characterization of endogenous peptides, as well as the identification of biomarkers for a wide range of socially significant diseases. First of all, this relates to renal and genitourinary pathologies and/or pathologies associated with proteinuria, such as kidney diseases, bladder, prostate and ovarian cancers, diabetic nephropathy, and pre-eclampsia. Unlike proteins, peptides do not require proteolytic hydrolysis, can be analyzed in their native form and can provide certain information about occurring (patho)physiological processes. Mass spectrometry (MS)-based approaches are the most unbiased and sensitive instruments with high multiplexing capacity and provided most of the current information about endogenous urine peptides. However, despite the large number of urine peptidomic studies, there are certain issues related to the insufficient comparability of their results due to the lack of consistent approaches to their interpretation. Also the development of a custom project-specific protein library for endogenous peptides search and identification is another important point that should be noted in the context of high-throughput peptidomic analysis. Here we propose the custom-specific urinary protein database and the grouping of endogenous urinary peptides with overlapping sequences as useful tools, which can facilitate the acquisition and analysis of LC-MS peptidomic data, as well as the comparison of results of different studies, which should facilitate their more efficient further application.

Pregnancy Zone Protein as an Emerging Biomarker for Cardiovascular Risk in Pediatric Chronic Kidney Disease

Cardiovascular disease (CVD) is a significant cause of mortality and morbidity among children with chronic kidney disease (CKD). The causes of pediatric CKD differ from those in adults, as congenital anomalies in the kidney and urinary tract (CAKUT) are the leading causes in childhood. Identifying ideal markers of CVD risk early is crucial for CKD children to improve their care. Previously, we screened differentially expressed proteins in CKD children with or without blood pressure (BP) abnormalities and identified pregnancy zone protein (PZP). In 106 children and adolescents with CKD stages G1-G4, we analyzed plasma PZP concentration. The associations between PZP and ambulatory BP monitoring (ABPM) profile, parameters of cardiac and carotid ultrasounds, indices of arterial stiffness, and nitric oxide (NO) parameters were determined. We observed that PZP positively correlated with arterial stiffness indices, beta index, and pulse wave velocity in CAKUT. CKD children with abnormalities in ABPM and night dipping displayed a higher PZP concentration than those without. Additionally, the PZP level was positively correlated with NO bioavailability. In conclusion, our results suggest PZP has differential influences on cardiovascular risk in CAKUT and non-CAKUT children. Identification of this relationship is novel in the pediatric CKD literature.

APOC1 exacerbates renal fibrosis through the activation of the NF-κB signaling pathway in IgAN

Introduction: IgA nephropathy (IgAN) is the most common disease leading to end-stage renal disease, and tubular fibrosis represents an important risk factor for disease progression. However, research on early molecular diagnostic indicators of tubular fibrosis and the mechanisms underlying disease progression is still lacking. Methods: The GSE93798 dataset was downloaded from the GEO database. DEGs were screened and analyzed for GO and KEGG enrichment in IgAN. The least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms were applied to screen for hub secretory genes. The expression and diagnostic efficacy of hub genes were confirmed by the GSE35487 dataset. ELISA was applied to detect the expression of APOC1 in serum. The expression and localization of hub genes in IgAN were verified by the expression of IHC and IF in human kidney tissues, and the correlation of expression with clinical data was verified in the Nephroseq database. Finally, cellular experiments clarified the role of hub genes in the signaling pathway. Results: A total of 339 DEGs were identified in IgAN, of which 237 were upregulated and 102 downregulated. The KEGG signaling pathway is enriched in the ECM-receptor interaction and AGE-RAGE signaling pathway. APOC1, ALB, CCL8, CXCL2, SRPX2, and TGFBI identified six hub secretory genes using the LASSO and SVM-RFE algorithms. In vivo and in vitro experiments demonstrated that APOC1 expression was elevated in IgAN. The serum concentration of APOC1 was 1.232 ± 0.1812 μg/ml in IgAN patients, whereas it was 0.3956 ± 0.1233 μg/ml in healthy individuals. APOC1 exhibited high diagnostic efficacy for IgAN (AUC of 99.091%, specificity of 95.455%, and sensitivity of 99.141%) in the GSE93798 dataset. APOC1 expression negatively correlated with eGFR (R ² = 0.2285, p = 0.0385) and positively correlated with serum creatinine (R ² = 0.41, p = 0.000567) in IgAN. APOC1 exacerbated renal fibrosis, possibly in part by activating the NF-κB pathway in IgAN. Conclusion: APOC1 was identified as the core secretory gene of IgAN, which was closely associated with blood creatinine and eGFR and had significant efficacy in the diagnosis of IgAN. Mechanistic studies revealed that the knockdown of APOC1 could improve IgAN renal fibrosis by inhibiting the NF pathway, which may be a potential therapeutic target for improving renal fibrosis in IgAN.

Integration of metabolomics and peptidomics reveals distinct molecular landscape of human diabetic kidney disease

Diabetic kidney disease (DKD) is the most common microvascular complication of diabetes, and there is an urgent need to discover reliable biomarkers for early diagnosis. Here, we established an effective urine multi-omics platform and integrated metabolomics and peptidomics to investigate the biological changes during DKD pathogenesis. Methods: Totally 766 volunteers (221 HC, 198 T2DM, 175 early DKD, 125 overt DKD, and 47 grey-zone T2DM patients with abnormal urinary mALB concentration) were included in this study. Non-targeted metabolic fingerprints of urine samples were acquired on matrix-free LDI-MS platform by the tip-contact extraction method using fluorinated ethylene propylene coated silicon nanowires chips (FEP@SiNWs), while peptide profiles hidden in urine samples were uncovered by MALDI-TOF MS after capturing urine peptides by porous silicon microparticles. Results: After multivariate analysis, ten metabolites and six peptides were verified to be stepwise regulated in different DKD stages. The altered metabolic pathways and biological processes associated with the DKD pathogenesis were concentrated in amino acid metabolism and cellular protein metabolic process, which were supported by renal transcriptomics. Interestingly, multi-omics significantly increased the diagnostic accuracy for both early DKD diagnosis and DKD status discrimination. Combined with machine learning, a stepwise prediction model was constructed and 89.9% of HC, 75.5% of T2DM, 69.6% of early DKD and 75.7% of overt DKD subjects in the external validation cohort were correctly classified. In addition, 87.5% of grey-zone patients were successfully distinguished from T2DM patients. Conclusion: This multi-omics platform displayed a satisfactory ability to explore molecular information and provided a new insight for establishing effective DKD management.

Targeted MRM Quantification of Urinary Proteins in Chronic Kidney Disease Caused by Glomerulopathies

Glomerulopathies with nephrotic syndrome that are resistant to therapy often progress to end-stage chronic kidney disease (CKD) and require timely and accurate diagnosis. Targeted quantitative urine proteome analysis by mass spectrometry (MS) with multiple-reaction monitoring (MRM) is a promising tool for early CKD diagnostics that could replace the invasive biopsy procedure. However, there are few studies regarding the development of highly multiplexed MRM assays for urine proteome analysis, and the two MRM assays for urine proteomics described so far demonstrate very low consistency. Thus, the further development of targeted urine proteome assays for CKD is actual task. Herein, a BAK270 MRM assay previously validated for blood plasma protein analysis was adapted for urine-targeted proteomics. Because proteinuria associated with renal impairment is usually associated with an increased diversity of plasma proteins being present in urine, the use of this panel was appropriate. Another advantage of the BAK270 MRM assay is that it includes 35 potential CKD markers described previously. Targeted LC-MRM MS analysis was performed for 69 urine samples from 46 CKD patients and 23 healthy controls, revealing 138 proteins that were found in ≥2/3 of the samples from at least one of the groups. The results obtained confirm 31 previously proposed CKD markers. Combination of MRM analysis with machine learning for data processing was performed. As a result, a highly accurate classifier was developed (AUC = 0.99) that enables distinguishing between mild and severe glomerulopathies based on the assessment of only three urine proteins (GPX3, PLMN, and A1AT or SHBG).

Discovery of Fibrinogen γ-chain as a potential urinary biomarker for renal interstitial fibrosis in IgA nephropathy

BACKGROUND

IgA nephropathy (IgAN) is a major cause of chronic kidney disease (CKD). Renal interstitial fibrosis is a hallmark of CKD progression. Non-invasive biomarkers are needed to dynamically evaluate renal fibrosis. Data independent acquisition (DIA)-based liquid chromatography-mass spectrometry (DIA-MS) was used to identify candidate urinary biomarkers in IgAN patients with different renal interstitial fibrosis degrees.

METHODS

Eighteen biopsy-proven IgAN patients and six healthy controls were recruited in a discovery cohort. Interstitial fibrosis changes were evaluated according to Oxford MEST-C scores. Urinary samples were analyzed with DIA-MS to identify hub proteins. Hub proteins were then confirmed by enzyme-linked immunosorbent assay (ELISA) in a validation cohort and the associated gene mRNA expression was analyzed using public gene expression omnibus (GEO) datasets.

RESULTS

Complement and coagulation cascades pathway was the main KEGG pathway related to the over-expressed proteins. Fibrinogen γ-Chain (FGG) was selected as the potential urinary marker for further validation. Urinary FGG to creatinine ratio (uFGG/Cr) levels were higher in both disease controls and IgAN group than in healthy controls, but were not significantly different between IgAN and disease groups. uFGG/Cr was confirmed to be increased with the extent of renal fibrosis and presented moderate correlations with T score (r = 0.614, p < 0.01) and eGFR (r = -0.682, p < 0.01), and a mild correlation with UTP (r = 0.497, p < 0.01) in IgAN group. In disease control group, uFGG/Cr was higher in patients with T1 + 2 compared to those with T0. uFGG/Cr had a good discriminatory power to distinguish different fibrosis stages in IgAN: interstitial fibrosis ≤ 5% (minimal fibrosis) vs. interstitial fibrosis (mild fibrosis) > 5%, AUC 0.743; T0 vs. T1 + 2, AUC 0.839; T0 + 1 vs. T2, AUC 0.854. In disease control group, uFGG/Cr showed better performance of AUC than UTP between minimal and mild fibrosis (p = 0.038 for Delong's test). Moreover, GSE104954 dataset showed that FGG mRNA expression was up-regulated (fold change 1.20, p = 0.009) in tubulointerstitium of IgAN patients when compared to healthy living kidney donors.

CONCLUSION

Urinary FGG is associated with renal interstitial fibrosis and could be used as a noninvasive biomarker for renal fibrosis in IgAN.

Progress in understanding primary glomerular disease: insights from urinary proteomics and in-depth analyses of potential biomarkers based on bioinformatics

Chronic kidney disease (CKD) has become a global public health challenge. While primary glomerular disease (PGD) is one of the leading causes of CKD, the specific pathogenesis of PGD is still unclear. Accurate diagnosis relies largely on invasive renal biopsy, which carries risks of bleeding, pain, infection and kidney vein thrombosis. Problems with the biopsy procedure include lack of glomeruli in the tissue obtained, and the sampling site not being reflective of the overall lesion in the kidney. Repeated renal biopsies to monitor disease progression cannot be performed because of the significant risks of bleeding and kidney vein thrombosis. On the other hand, urine collection, a noninvasive method, can be performed repeatedly, and urinary proteins can reflect pathological changes in the urinary system. Advancements in proteomics technologies, especially mass spectrometry, have facilitated the identification of candidate biomarkers in different pathological types of PGD. Such biomarkers not only provide insights into the pathogenesis of PGD but also are important for diagnosis, monitoring treatment, and prognosis. In this review, we summarize the findings from studies that have used urinary proteomics, among other omics screens, to identify potential biomarkers for different types of PGD. Moreover, we performed an in-depth bioinformatic analysis to gain a deeper understanding of the biological processes and protein-protein interaction networks in which these candidate biomarkers may participate. This review, including a description of an integrated analysis method, is intended to provide insights into the pathogenesis, noninvasive diagnosis, and personalized treatment efforts of PGD and other associated diseases.

Cardiovascular Risks of Hypertension: Lessons from Children with Chronic Kidney Disease

Hypertension is the most common complication of chronic kidney disease (CKD) in children, having a strong association with subsequential cardiovascular disease (CVD). In pediatric CKD, a considerable percentage of children with hypertension are undiagnosed or undertreated. Prior research has evaluated structural and functional markers of subclinical CVD and biomarkers in adults with CKD, while ideal biomarkers in pediatrics are still insufficiently studied. The ultimate goal of this review is to summarize what is currently known about state of hypertension, cardiovascular risk factors, and potential CVD markers/biomarkers in children with pre-dialysis CKD. We discuss omics-related biomarkers and the pathophysiologic processes of endothelial dysfunction, kidney injury, oxidative stress and inflammation that are classified by specific biomarkers. Moreover, we illustrate the existing challenges and highlight the paucity of pediatric CKD research to evaluate these CVD biomarkers for future clinical pediatric practice. Thus, achieving clinical utility of CVD biomarkers for use in pediatric CKD remains a significant challenge requiring additional efforts.

Modulation of diabetic kidney disease markers by an antagonist of p75NTR in streptozotocin-treated mice

Two therapeutic agents targeting p75NTR pathways have been recently developed to alleviate retinopathy and bladder dysfunction in diabetes mellitus (DM), namely the small molecule p75NTR antagonist THX-B and a monoclonal antibody (mAb) that neutralizes the receptor ligand proNGF. We herein explore these two components in the context of diabetic kidney disease (DKD). Streptozotocin-injected mice were treated for 4 weeks with THX-B or anti-proNGF mAb. Kidneys were taken for quantification of microRNAs and mRNAs by RT-qPCR and for detection of proteins by immunohistochemistry, immunoblotting and ELISA. Blood was sampled to measure plasma levels of urea, creatinine, and albumin. DM led to increases in plasma concentrations of urea and creatinine and decreases in plasma albumin. Receptor p75NTR was expressed in kidneys and its expression was decreased by DM. All these changes were reversed by THX-B treatment while the effect of mAb was less pronounced. MicroRNAs tightly linked to DKD (miR-21-5p, miR-214-3p and miR-342-3p) were highly expressed in diabetic kidneys compared to healthy ones. Also, miR-146a, a marker of kidney inflammation, and mRNA levels of Fn-1 and Nphs, two markers of fibrosis and inflammation, were elevated in DM. Treatments with THX-B or mAb partially or completely reduced the expression of the aforementioned microRNAs and mRNAs. P75NTR antagonism and proNGF mAb might constitute new therapeutic tools to treat or slow down the progression of kidney disease in DM, along with other diabetic related complications. The translational potential of these strategies is currently being investigated.

OUP accepted manuscript

Podocytopathies are glomerular disorders in which podocyte injury drives proteinuria and progressive kidney disease. They encompass a broad spectrum of aetiologies, resulting in pathological pictures of minimal-changes, focal segmental glomerulosclerosis, diffuse mesangial sclerosis or collapsing glomerulopathy. Despite improvement in classifying podocytopathies as a distinct group of disorders, the histological definition fails to capture the relevant biological heterogeneity underlying each case, manifesting as extensive variability in disease progression and response to therapies. Increasing evidence suggests that podocytopathies can result from a single causative factor or a combination of multiple genetic and/or environmental risk factors with different relative contributions, identifying complex physiopathological mechanisms. Consequently, the diagnosis can still be challenging. In recent years, significant advances in genetic, microscopy and biological techniques revolutionized our understanding of the molecular mechanisms underlying podocytopathies, pushing nephrologists to integrate innovative information with more conventional data obtained from kidney biopsy in the diagnostic workflow. In this review, we will summarize current approaches in the diagnosis of podocytopathies, focusing on strategies aimed at elucidating the aetiology underlying the histological picture. We will provide several examples of an integrative view of traditional concepts and new data in patients with suspected podocytopathies, along with a perspective on how a reclassification could help to improve not only diagnostic pathways and therapeutic strategies, but also the management of disease recurrence after kidney transplantation. In the future, the advantages of precision medicine will probably allow diagnostic trajectories to be increasingly focused, maximizing therapeutic results and long-term prognosis.