Most of the plasma peptides are not detectable in urine, possibly due to tubular reabsorption. The majority of urinary peptides may in fact originate in the kidney. The notable exception is collagen fragments, which indicates potential selective exclusion of these peptides from tubular reabsorption. Experimental verification of this hypothesis is warranted.

Chronic kidney disease (CKD) is a prevalent cause of morbidity and mortality worldwide. A hallmark of CKD progression is renal fibrosis characterized by excessive accumulation of extracellular matrix (ECM) proteins. In this study, we aimed to investigate the correlation of the urinary proteome classifier CKD273 and individual urinary peptides with the degree of fibrosis. In total, 42 kidney biopsies and urine samples were examined. The percentage of fibrosis per total tissue area was assessed in Masson trichrome stained kidney tissues. The urinary proteome was analysed by capillary electrophoresis coupled to mass spectrometry. CKD273 displayed a significant and positive correlation with the degree of fibrosis (Rho = 0.430, P = 0.0044), while the routinely used parameters (glomerular filtration rate, urine albumin-to-creatinine ratio and urine protein-to-creatinine ratio) did not (Rho = −0.222; −0.137; −0.070 and P = 0.16; 0.39; 0.66, respectively). We identified seven fibrosis-associated peptides displaying a significant and negative correlation with the degree of fibrosis. All peptides were collagen fragments, suggesting that these may be causally related to the observed accumulation of ECM in the kidneys. CKD273 and specific peptides are significantly associated with kidney fibrosis; such an association could not be detected by other biomarkers for CKD. These non-invasive fibrosis-related biomarkers can potentially be implemented in future trials.

Parkinson’s disease (PD), the second most common progressive neurodegenerative disease, develops and progresses for 10–15 years before the clinical diagnostic symptoms of the disease are manifested. Furthermore, several aspects of PD pathology overlap with other neurodegenerative diseases (NDDs) linked to alpha-synuclein (aSyn) aggregation, also called synucleinopathies. Therefore, there is an urgent need to discover and validate early diagnostic and prognostic markers that reflect disease pathophysiology, progression, severity, and potential differences in disease mechanisms between PD and other NDDs. The close association between aSyn and the development of pathology in synucleinopathies, along with the identification of aSyn species in biological fluids, has led to increasing interest in aSyn species as potential biomarkers for early diagnosis of PD and differentiate it from other synucleinopathies. In this review, we (1) provide an overview of the progress toward mapping the distribution of aSyn species in the brain, peripheral tissues, and biological fluids; (2) present comparative and critical analysis of previous studies that measured total aSyn as well as other species such as modified and aggregated forms of aSyn in different biological fluids; and (3) highlight conceptual and technical gaps and challenges that could hinder the development and validation of reliable aSyn biomarkers; and (4) outline a series of recommendations to address these challenges. Finally, we propose a combined biomarker approach based on integrating biochemical, aggregation and structure features of aSyn, in addition to other biomarkers of neurodegeneration. We believe that capturing the diversity of aSyn species is essential to develop robust assays and diagnostics for early detection, patient stratification, monitoring of disease progression, and differentiation between synucleinopathies. This could transform clinical trial design and implementation, accelerate the development of new therapies, and improve clinical decisions and treatment strategies.

Antibodies against phosphorylated alpha-synuclein (aSyn) at S129 have emerged as the primary tools to investigate, monitor, and quantify aSyn pathology in the brain and peripheral tissues of patients with Parkinson’s disease and other neurodegenerative diseases. Herein, we demonstrate that the co-occurrence of multiple pathology-associated C-terminal post-translational modifications (PTMs) (e.g., phosphorylation at Tyrosine 125 or truncation at residue 133 or 135) differentially influences the detection of pS129-aSyn species by pS129-aSyn antibodies. These observations prompted us to systematically reassess the specificity of the most commonly used pS129 antibodies against monomeric and aggregated forms of pS129-aSyn in mouse brain slices, primary neurons, mammalian cells and seeding models of aSyn pathology formation. We identified two antibodies that are insensitive to pS129 neighboring PTMs. Although most pS129 antibodies showed good performance in detecting aSyn aggregates in cells, neurons and mouse brain tissue containing abundant aSyn pathology, they also showed cross-reactivity towards other proteins and often detected non-specific low and high molecular weight bands in aSyn knock-out samples that could be easily mistaken for monomeric or high molecular weight aSyn species. Our observations suggest that not all pS129 antibodies capture the biochemical and morphological diversity of aSyn pathology, and all should be used with the appropriate protein standards and controls when investigating aSyn under physiological conditions. Finally, our work underscores the need for more pS129 antibodies that are not sensitive to neighboring PTMs and more thorough characterization and validation of existing and new antibodies.

Background The urinary proteomic classifier chronic kidney disease 273 (CKD273) is predictive for the development and progression of chronic kidney disease (CKD) and/or albuminuria in type 2 diabetes. This study evaluates its role in the prediction of cardiovascular (CV) events in patients with CKD Stages G1–G5. Methods We applied the CKD273 classifier in a cohort of 451 patients with CKD Stages G1–G5 followed prospectively for a median of 5.5 years. Primary endpoints were all-cause mortality, CV mortality and the composite of non-fatal and fatal CV events (CVEs). Results In multivariate Cox regression models adjusting for age, sex, prevalent diabetes and CV history, the CKD273 classifier at baseline was significantly associated with total mortality and time to fatal or non-fatal CVE, but not CV mortality. Because of a significant interaction between CKD273 and CV history (P = 0.018) and CKD stages (P = 0.002), a stratified analysis was performed. In the fully adjusted models, CKD273 classifier was a strong and independent predictor of fatal or non-fatal CVE only in the subgroup of patients with CKD Stages G1–G3b and without a history of CV disease. In those patients, the highest tertile of CKD273 was associated with a >10-fold increased risk as compared with the lowest tertile. Conclusions The urinary CKD273 classifier provides additional independent information regarding the CV risk in patients with early CKD stage and a blank CV history. Determination of CKD273 scores on a random urine sample may improve the efficacy of intensified surveillance and preventive strategies by selecting patients who potentially will benefit most from early risk management.