Understanding glomerular diseases through proteomics

Minimal Change Disease: Pathogenetic Insights from Glomerular Proteomics

The mechanism underlying podocyte dysfunction in minimal change disease (MCD) remains unknown. This study aimed to shed light on the potential pathophysiology of MCD using glomerular proteomic analysis. Shotgun proteomics using label-free quantitative mass spectrometry was performed on formalin-fixed, paraffin-embedded (FFPE) renal biopsies from two groups of samples: control (CTR) and MCD. Glomeruli were excised from FFPE renal biopsies using laser capture microdissection (LCM), and a single-pot solid-phase-enhanced sample preparation (SP3) digestion method was used to improve yield and protein identifications. Principal component analysis (PCA) revealed a distinct separation between the CTR and MCD groups. Forty-eight proteins with different abundance between the two groups (p-value ≤ 0.05 and |FC| ≥ 1.5) were identified. These may represent differences in podocyte structure, as well as changes in endothelial or mesangial cells and extracellular matrix, and some were indeed found in several of these structures. However, most differentially expressed proteins were linked to the podocyte cytoskeleton and its dynamics. Some of these proteins are known to be involved in focal adhesion (NID1 and ITGA3) or slit diaphragm signaling (ANXA2, TJP1 and MYO1C), while others are structural components of the actin and microtubule cytoskeleton of podocytes (ACTR3 and NES). This study suggests the potential of mass spectrometry-based shotgun proteomic analysis with LCM glomeruli to yield valuable insights into the pathogenesis of podocytopathies like MCD. The most significantly dysregulated proteins in MCD could be attributable to cytoskeleton dysfunction or may be a compensatory response to cytoskeleton malfunction caused by various triggers.

Urinary peptidomic liquid biopsy for non-invasive differential diagnosis of chronic kidney disease

BACKGROUND AND HYPOTHESIS

Specific urinary peptides hold information on disease pathophysiology, which, in combination with artificial intelligence, could enable non-invasive assessment of chronic kidney disease (CKD) aetiology. Existing approaches are generally specific for the diagnosis of single aetiologies. We present the development of models able to simultaneously distinguish and spatially visualize multiple CKD aetiologies.

METHODS

The urinary peptide data of 1850 healthy control (HC) and CKD [diabetic kidney disease (DKD), immunoglobulin A nephropathy (IgAN) and vasculitis] participants were extracted from the Human Urinary Proteome Database. Uniform manifold approximation and projection (UMAP) coupled to a support vector machine algorithm was used to generate multi-peptide models to perform binary (DKD, HC) and multiclass (DKD, HC, IgAN, vasculitis) classifications. This pipeline was compared with the current state-of-the-art single-aetiology CKD urinary peptide models.

RESULTS

In an independent test set, the developed models achieved 90.35% and 70.13% overall predictive accuracies, respectively, for the binary and the multiclass classifications. Omitting the UMAP step led to improved predictive accuracies (96.14% and 85.06%, respectively). As expected, the HC class was distinguished with the highest accuracy. The different classes displayed a tendency to form distinct clusters in the 3D space based on their disease state.

CONCLUSION

Urinary peptide data present an effective basis for CKD aetiology differentiation using machine learning models. Although adding the UMAP step to the models did not improve prediction accuracy, it may provide a unique visualization advantage. Additional studies are warranted to further validate the pipeline's clinical potential as well as to expand it to other CKD aetiologies and also other diseases.

Gestational Age-Related Urinary Peptidome Changes in Preterm and Term Born Infants

INTRODUCTION

Preterm infants are at risk for a variety of somatic and neurological disorders. In recent years, biofluid proteomics has emerged as a potential diagnostic tool for biomarker analysis. The aim of this study was to determine gestational age (GA)-related patterns of the urinary peptidome in preterm infants for researching potential novel prognostic biomarkers.

METHODS

We performed urinary peptidomics in longitudinal samples of 24 preterm (mean GA weeks 28 + 1 [24+1-31 + 6]) and 27 term born controls (mean GA weeks 39 + 2 [37+0-41 + 1]) using capillary electrophoresis combined with mass spectrometry (CE-MS). Peptides were sequenced using CE-MS/MS or LC-MS/MS analysis and were deposited, matched, and annotated in a Microsoft SQL database for statistical analysis. We compared their abundance in urine of preterm and term born infants and performed a validation analysis as well as correlations to GA and clinical risk scores.

RESULTS

Our results confirmed significant differences in the abundance of peptides and the hypothesis of age-dependent urinary peptidome changes in preterm and term infants. In preterm infants, SLC38A10 (solute carrier family 38 member 10) is one of the most abundant peptides. Combined urinary peptides correlated with clinical risk scores (p < 0.05).

CONCLUSION

This is the first study reporting GA-related urinary peptidome changes of preterm infants detected by CE-MS and a modulation of the peptidome with GA. Further research is required to locate peptidome clusters correlated with specific clinical complications and long-term outcome. This may identify preterm infants at higher risk for adverse outcome who would benefit from early intervention.

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.

Comparative proteomic analysis of children FSGS FFPE tissues

BACKGROUND

In children, focal segmental glomerulosclerosis (FSGS) is the main cause of steroid resistant nephrotic syndrome (SRNS). To identify specific candidates and the mechanism of steroid resistance, we examined the formalin-fixed paraffin embedded (FFPE) renal tissue protein profiles via liquid chromatography tandem mass spectrometry (LC-MS/MS).

METHODS

Renal biopsies from seven steroid-sensitive (SS) and eleven steroid-resistant (SR) children FSGS patients were obtained. We examined the formalin-fixed paraffin embedded (FFPE) renal tissue protein profiles via liquid chromatography tandem mass spectrometry (LC-MS/MS). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment and Gene Ontology (GO) analysis, as well as the construction of protein-protein interaction (PPI) network were performed. Two proteins were further valiadated by immunohistochemistry staining in FSGS patients and mice models.

RESULTS

In total, we quantified more than 4000 proteins, of which 325 were found to be differentially expressed proteins (DEPs) between the SS and SR group (foldchange ≥2, P<0.05). The results of GO revealed that the most significant up-regulated proteins were primarily related to protein transportation, regulation of the complement activation process and cytolysis. Moreover, clustering analysis showed differences in the pathways (lysosome, terminal pathway of complement) between the two groups. Among these potential candidates, validation analyses for LAMP1 and ACSL4 were conducted. LAMP1 was observed to have a higher expression in glomerulus, while ACSL4 was expressed more in tubular epithelial cells.

CONCLUSIONS

In this study, the potential mechanism and candidates related to steroid resistance in children FSGS patients were identified. It could be helpful in identifying potential therapeutic targets and predicting outcomes with these proteomic changes for children FSGS patients.

Collagen-Derived Peptides in CKD: A Link to Fibrosis

Collagen is a major component of the extracellular matrix (ECM) and has an imminent role in fibrosis, in, among others, chronic kidney disease (CKD). Collagen alpha-1(I) (col1a1) is the most abundant collagen type and has previously been underlined for its contribution to the disease phenotype. Here, we examined 5000 urinary peptidomic datasets randomly selected from healthy participants or patients with CKD to identify urinary col1a1 fragments and study their abundance, position in the main protein, as well as their correlation with renal function. We identified 707 col1a1 peptides that differed in their amino acid sequence and/or post-translational modifications (hydroxyprolines). Well-correlated peptides with the same amino acid sequence, but a different number of hydroxyprolines, were combined into a final list of 503 peptides. These 503 col1a1 peptides covered 69% of the full col1a1 sequence. Sixty-three col1a1 peptides were significantly and highly positively associated (rho > +0.3) with the estimated glomerular filtration rate (eGFR), while only six peptides showed a significant and strong, negative association (rho < −0.3). A similar tendency was observed for col1a1 peptides associated with ageing, where the abundance of most col1a1 peptides decreased with increasing age. Collectively the results show a strong association between collagen peptides and loss of kidney function and suggest that fibrosis, potentially also of other organs, may be the main consequence of an attenuation of collagen degradation, and not increased synthesis.

Reproducibility Evaluation of Urinary Peptide Detection Using CE-MS

In recent years, capillary electrophoresis coupled to mass spectrometry (CE-MS) has been increasingly applied in clinical research especially in the context of chronic and age-associated diseases, such as chronic kidney disease, heart failure and cancer. Biomarkers identified using this technique are already used for diagnosis, prognosis and monitoring of these complex diseases, as well as patient stratification in clinical trials. CE-MS allows for a comprehensive assessment of small molecular weight proteins and peptides (<20 kDa) through the combination of the high resolution and reproducibility of CE and the distinct sensitivity of MS, in a high-throughput system. In this study we assessed CE-MS analytical performance with regards to its inter- and intra-day reproducibility, variability and efficiency in peptide detection, along with a characterization of the urinary peptidome content. To this end, CE-MS performance was evaluated based on 72 measurements of a standard urine sample (60 for inter- and 12 for intra-day assessment) analyzed during the second quarter of 2021. Analysis was performed per run, per peptide, as well as at the level of biomarker panels. The obtained datasets showed high correlation between the different runs, low variation of the ten highest average individual log2 signal intensities (coefficient of variation, CV < 10%) and very low variation of biomarker panels applied (CV close to 1%). The findings of the study support the analytical performance of CE-MS, underlining its value for clinical application.