Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios

Background: There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. Methods: Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. Results: In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17–1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47–1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39–1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). Conclusion: The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death

Epidemiologic observations guiding clinical application of a urinary peptidomic marker of diastolic left ventricular dysfunction

Hypertension, obesity, and old age are major risk factors for left ventricular (LV) diastolic dysfunction (LVDD), but easily applicable screening tools for people at risk are lacking. We investigated whether HF1, a urinary biomarker consisting of 85 peptides, can predict over a 5-year time span mildly impaired diastolic LV function as assessed by echocardiography. In 645 white Flemish (50.5% women; 50.9 years [mean]), we measured HF1 by capillary electrophoresis coupled with mass spectrometry in 2005-2010. We measured early (E) and late (A) peak velocities of the transmitral blood flow and early (e') and late (a') mitral annular peak velocities and their ratios in 2009-2013. In multivariable-adjusted analyses, per 1-standard deviation increment in HF1, e' was -0.193 cm/s lower (95% confidence interval: -0.352 to -0.033; P = .018) and E/e' 0.174 units higher (0.005-0.342; P = .043). Of 645 participants, 179 (27.8%) had LVDD at follow-up, based on impaired relaxation in 69 patients (38.5%) or an elevated filling pressure in the presence of a normal (74 [43.8%]) or low (36 [20.1%]) age-specific E/A ratio. For a 1-standard deviation increment in HF1, the adjusted odds ratio was 1.37 (confidence interval, 1.07-1.76; P = .013). The integrated discrimination (+1.14%) and net reclassification (+31.7%) improvement of the optimized HF1 threshold (-0.350) in discriminating normal from abnormal diastolic LV function at follow-up over and beyond other risk factors was significant (P ≤ .024). In conclusion, HF1 may allow screening for LVDD over a 5-year horizon in asymptomatic people

Multicentre prospective validation of a urinary peptidome-based classifier for the diagnosis of type 2 diabetic nephropathy

Background Diabetic nephropathy (DN) is one of the major late complications of diabetes. Treatment aimed at slowing down the progression of DN is available but methods for early and definitive detection of DN progression are currently lacking. The ‘Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention Of early diabetic nephRopathy In TYpe 2 diabetic patients with normoalbuminuria trial' (PRIORITY) aims to evaluate the early detection of DN in patients with type 2 diabetes (T2D) using a urinary proteome-based classifier (CKD273). Methods In this ancillary study of the recently initiated PRIORITY trial we aimed to validate for the first time the CKD273 classifier in a multicentre (9 different institutions providing samples from 165 T2D patients) prospective setting. In addition we also investigated the influence of sample containers, age and gender on the CKD273 classifier. Results We observed a high consistency of the CKD273 classification scores across the different centres with areas under the curves ranging from 0.95 to 1.00. The classifier was independent of age (range tested 16-89 years) and gender. Furthermore, the use of different urine storage containers did not affect the classification scores. Analysis of the distribution of the individual peptides of the classifier over the nine different centres showed that fragments of blood-derived and extracellular matrix proteins were the most consistently found. Conclusion We provide for the first time validation of this urinary proteome-based classifier in a multicentre prospective setting and show the suitability of the CKD273 classifier to be used in the PRIORITY tria

On the Relation between a Nonlinear Elliptic Equation and Its Uniform Approximation

AbstractThe qualitative behavior of the solution set of nonlinear elliptic boundary value problems has in some instances been studied by reducing the partial differential equation to a related algebraic equation. Although this procedure often gives a good picture of the bifurcation diagram, it can be quite wrong. In this paper some relationships between the solutions of the two problems are investigated

A database of naturally occurring human urinary peptides and proteins for use in clinical applications

Owing to its availability, ease of collection and correlation with (patho-) physiology, urine is an attractive source for clinical proteomics. However, the lack of comparable datasets from large cohorts has greatly hindered development in this field. Here we report the establishment of a high resolution proteome database of naturally occurring human urinary peptides and proteins - ranging from 800-17,000 Da - from over 3,600 individual samples using capillary electrophoresis coupled to mass spectrometry, yielding an average of 1,500 peptides per sample. All processed data were deposited in an SQL database, currently containing 5,010 relevant unique urinary peptides that serve as classifiers for diagnosis and monitoring of diseases, including kidney and vascular diseases. Of these, 352 have been sequenced to date. To demonstrate the applicability of this database, two examples of disease diagnosis were provided: For renal damage diagnosis, patients with a specific renal disease were identified with high specificity and sensitivity in a blinded cohort of 131 individuals. We further show definition of biomarkers specific for immunosuppression and complications after transplantation (Kaposi's sarcoma). Due to its high information content, this database will be a powerful tool for the validation of biomarkers for both renal and non-renal diseases

The ANTENATAL multicentre study to predict postnatal renal outcome in fetuses with posterior urethral valves: objectives and design

Abstract Background Posterior urethral valves (PUV) account for 17% of paediatric end-stage renal disease. A major issue in the management of PUV is prenatal prediction of postnatal renal function. Fetal ultrasound and fetal urine biochemistry are currently employed for this prediction, but clearly lack precision. We previously developed a fetal urine peptide signature that predicted in utero with high precision postnatal renal function in fetuses with PUV. We describe here the objectives and design of the prospective international multicentre ANTENATAL (multicentre validation of a fetal urine peptidome-based classifier to predict postnatal renal function in posterior urethral valves) study, set up to validate this fetal urine peptide signature. Methods Participants will be PUV pregnancies enrolled from 2017 to 2021 and followed up until 2023 in >30 European centres endorsed and supported by European reference networks for rare urological disorders (ERN eUROGEN) and rare kidney diseases (ERN ERKNet). The endpoint will be renal/patient survival at 2 years postnatally. Assuming α = 0.05, 1–β = 0.8 and a mean prevalence of severe renal outcome in PUV individuals of 0.35, 400 patients need to be enrolled to validate the previously reported sensitivity and specificity of the peptide signature. Results In this largest multicentre study of antenatally detected PUV, we anticipate bringing a novel tool to the clinic. Based on urinary peptides and potentially amended in the future with additional omics traits, this tool will be able to precisely quantify postnatal renal survival in PUV pregnancies. The main limitation of the employed approach is the need for specialized equipment. Conclusions Accurate risk assessment in the prenatal period should strongly improve the management of fetuses with PUV

Holoalkane dehalogenase engineering: kenitics and specificity.

Halkoalkane dehalogenase from the baterium Xanthobacter autotrophicus GJ10 converts haloalkanes tot their corresponding alcohols (Keuning et al., 1985). The Dehalogenase is the first enzyme in the degradation route of 1,2-dichloroethane , and is essential for growth of bateria on this substrate as the sole source of carbon and energy (Janssen et al., 1985; Van den Wijngaard, 1992). ... Zie: Summary and concluding remarks