Urine peptidome analysis in cardiorenal syndrome reflects molecular processes

Urine Peptidome Analysis Identifies Common and Stage-Specific Markers in Early Versus Advanced CKD

Given the pathophysiological continuum of chronic kidney disease (CKD), different molecular determinants affecting progression may be associated with distinct disease phases; thus, identification of these players are crucial for guiding therapeutic decisions, ideally in a non-invasive, repeatable setting. Analyzing the urinary peptidome has been proven an efficient method for biomarker determination in CKD, among other diseases. In this work, after applying several selection criteria, urine samples from 317 early (stage 2) and advanced (stage 3b-5) CKD patients were analyzed using capillary electrophoresis coupled to mass spectrometry (CE-MS). The entire two groups were initially compared to highlight the respective pathophysiology between initial and late disease phases. Subsequently, slow and fast progressors were compared within each group in an attempt to distinguish phase-specific disease progression molecules. The early vs. late-stage CKD comparison revealed 929 significantly different peptides, most of which were downregulated and 268 with collagen origins. When comparing slow vs. fast progressors in early stage CKD, 42 peptides were significantly altered, 30 of which were collagen peptide fragments. This association suggests the development of structural changes may be reversible at an early stage. The study confirms previous findings, based on its multivariable-matched progression groups derived from a large initial cohort. However, only four peptide fragments differed between slow vs. fast progressors in late-stage CKD, indicating different pathogenic processes occur in fast and slow progressors in different stages of CKD. The defined peptides associated with CKD progression at early stage might potentially constitute a non-invasive approach to improve patient management by guiding (personalized) intervention.

A comprehensive review of acute cardio-renal syndrome: need for novel biomarkers

Cardiorenal syndrome represents a wide-spectrum disorder involving the heart and kidneys as the primary affected organs. India has an increasingly high burden of acute CRS, coinciding with the rise in global statistics. Up to 2022, approximately 46.1% of all cardiorenal patients have been diagnosed with acute CRS in India. Acute CRS involves a sudden deterioration of kidney functionalities, referred to as acute kidney injury (AKI) in acute heart failure patients. The pathophysiology of CRS involves hyperactivation of the sympathetic nervous system (SNS) and the renin-angiotensin-aldosterone system (RAAS) following acute myocardial stress. The pathological phenotype of acute CRS is associated with perturbed inflammatory, cellular, and neurohormonal markers in circulation. These complications increase the risk of mortality in clinically diagnosed acute CRS patients, making it a worldwide healthcare burden. Hence, effective diagnosis and early prevention are crucial to prevent the progression of CRS in AHF patients. Present biomarkers, such as serum creatinine (sCr), cystatin C (CysC), glomerular filtration rate (GFR), blood urea nitrogen (BUN), serum and/or urine neutrophil gelatinase-associated lipocalin (NGAL), B-type natriuretic peptide (BNP), and NT-proBNP, are clinically used to diagnose AKI stages in CRS patients but are limitedly sensitive to the early detection of the pathology. Therefore, the need for protein biomarkers is emerging for early intervention in CRS progression. Here, we summarized the cardio-renal nexus in acute CRS, with an emphasis on the present clinicopathological biomarkers and their limitations. The objective of this review is to highlight the need for novel proteomic biomarkers that will curb the burgeoning concern and direct future research trials.

Use of Computation Ecosystems to Analyze the Kidney-Heart Crosstalk

The identification of mediators for physiologic processes, correlation of molecular processes, or even pathophysiological processes within a single organ such as the kidney or heart has been extensively studied to answer specific research questions using organ-centered approaches in the past 50 years. However, it has become evident that these approaches do not adequately complement each other and display a distorted single-disease progression, lacking holistic multilevel/multidimensional correlations. Holistic approaches have become increasingly significant in understanding and uncovering high dimensional interactions and molecular overlaps between different organ systems in the pathophysiology of multimorbid and systemic diseases like cardiorenal syndrome because of pathological heart-kidney crosstalk. Holistic approaches to unraveling multimorbid diseases are based on the integration, merging, and correlation of extensive, heterogeneous, and multidimensional data from different data sources, both -omics and nonomics databases. These approaches aimed at generating viable and translatable disease models using mathematical, statistical, and computational tools, thereby creating first computational ecosystems. As part of these computational ecosystems, systems medicine solutions focus on the analysis of -omics data in single-organ diseases. However, the data-scientific requirements to address the complexity of multimodality and multimorbidity reach far beyond what is currently available and require multiphased and cross-sectional approaches. These approaches break down complexity into small and comprehensible challenges. Such holistic computational ecosystems encompass data, methods, processes, and interdisciplinary knowledge to manage the complexity of multiorgan crosstalk. Therefore, this review summarizes the current knowledge of kidney-heart crosstalk, along with methods and opportunities that arise from the novel application of computational ecosystems providing a holistic analysis on the example of kidney-heart crosstalk.

Glycosylation Analysis of Urinary Peptidome Highlights IGF2 Glycopeptides in Association with CKD

Chronic kidney disease (CKD) is prevalent in 10% of world’s adult population. The role of protein glycosylation in causal mechanisms of CKD progression is largely unknown. The aim of this study was to identify urinary O-linked glycopeptides in association to CKD for better characterization of CKD molecular manifestations. Urine samples from eight CKD and two healthy subjects were analyzed by CE-MS/MS and glycopeptides were identified by a specific software followed by manual inspection of the spectra. Distribution of the identified glycopeptides and their correlation with Age, eGFR and Albuminuria were evaluated in 3810 existing datasets. In total, 17 O-linked glycopeptides from 7 different proteins were identified, derived primarily from Insulin-like growth factor-II (IGF2). Glycosylation occurred at the surface exposed IGF2 Threonine 96 position. Three glycopeptides (DVStPPTVLPDNFPRYPVGKF, DVStPPTVLPDNFPRYPVG and DVStPPTVLPDNFPRYP) exhibited positive correlation with Age. The IGF2 glycopeptide (tPPTVLPDNFPRYP) showed a strong negative association with eGFR. These results suggest that with aging and deteriorating kidney function, alterations in IGF2 proteoforms take place, which may reflect changes in mature IGF2 protein. Further experiments corroborated this hypothesis as IGF2 increased plasma levels were observed in CKD patients. Protease predictions, considering also available transcriptomics data, suggest activation of cathepsin S with CKD, meriting further investigation.

Salivary peptidome analysis and protease prediction during orthodontic treatment with fixed appliances

Orthodontic tooth movement (OTM) occurs through proteolytic remodelling within the periodontium following the application of external force to the tooth. This study describes the first characterization of the salivary peptidome and protease profile during the alignment stage of fixed appliance orthodontic treatment. Unstimulated whole mouth saliva from 16 orthodontic patients (10 males, 6 females, mean (SD) age 15.2 (1.6) years) was collected prior to fixed appliance placement (T1), 1-h (T2), 1-week (T3) following fixed appliance placement and on completion of mandibular arch alignment (T4). Salivary peptides were extracted using filtration followed by mass spectrometry to identify amino acid sequences. Protease prediction was carried out in silico using Proteasix and validated with gelatin zymography and enzyme-linked immunosorbent assay. A total of 2852 naturally-occurring peptides were detected, originating from 436 different proteins. Both collagen and statherin-derived peptide levels were increased at T2. Proteasix predicted 73 proteases potentially involved in generating these peptides, including metalloproteinases, calpains and cathepsins. Changes in predicted activity of proteases over time were also observed, with most metalloproteinases showing increased predicted activity at T2-T3. Increased gelatinolytic activity and MMP8/MMP9 levels were detected at T3. Collectively, multiple protein targets and changes in protease-predicted activity during OTM have been identified.

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Urinary Proteomic Signature in Acute Decompensated Heart Failure: Advances into Molecular Pathophysiology

Acute decompensated heart failure (ADHF) is a life-threatening clinical syndrome involving multi-organ function deterioration. ADHF results from multifaceted, dysregulated pathways that remain poorly understood. Better characterization of proteins associated with heart failure decompensation is needed to gain understanding of the disease pathophysiology and support a more accurate disease phenotyping. In this study, we used an untargeted mass spectrometry (MS) proteomic approach to identify the differential urine protein signature in ADHF patients and examine its pathophysiological link to disease evolution. Urine samples were collected at hospital admission and compared with a group of healthy subjects by two-dimensional electrophoresis coupled to MALDI-TOF/TOF mass spectrometry. A differential pattern of 26 proteins (>1.5-fold change, p < 0.005), mostly of hepatic origin, was identified. The top four biological pathways (p < 0.0001; in silico analysis) were associated to the differential ADHF proteome including retinol metabolism and transport, immune response/inflammation, extracellular matrix organization, and platelet degranulation. Transthyretin (TTR) was the protein most widely represented among them. Quantitative analysis by ELISA of TTR and its binding protein, retinol-binding protein 4 (RBP4), validated the proteomic results. ROC analysis evidenced that combining RBP4 and TTR urine levels highly discriminated ADHF patients with renal dysfunction (AUC: 0.826, p < 0.001) and significantly predicted poor disease evolution over 18-month follow-up. In conclusion, the MS proteomic approach enabled identification of a specific urine protein signature in ADHF at hospitalization, highlighting changes in hepatic proteins such as TTR and RBP4.