The novel urinary proteomic classifier HF1 has similar diagnostic and prognostic utility to BNP in heart failure.

AIMS: Measurement of B-type natriuretic peptide (BNP) or N-terminal pro-BNP is recommended as part of the diagnostic workup of patients with suspected heart failure (HF). We evaluated the diagnostic and prognostic utility of the novel urinary proteomic classifier HF1, compared with BNP, in HF. HF1 consists of 85 unique urinary peptide fragments thought, mainly, to reflect collagen turnover. METHODS AND RESULTS: We performed urinary proteome analysis using capillary electrophoresis coupled with mass spectrometry in 829 participants. Of these, 622 had HF (504 had chronic HF and 118 acute HF) and 207 were controls (62 coronary heart disease patients without HF and 145 healthy controls). The area under the receiver operating characteristic (ROC) curve (AUC) using HF1 for the diagnosis of HF (cases vs. controls) was 0.94 (95% CI, 0.92-0.96). This compared with an AUC for BNP of 0.98 (95% CI, 0.97-0.99). Adding HF1 to BNP increased the AUC to 0.99 (0.98-0.99), P < 0.001, and led to a net reclassification improvement of 0.67 (95% CI, 0.54-0.77), P < 0.001. Among 433 HF patients followed up for a median of 989 days, we observed 186 deaths. HF1 had poorer predictive value to BNP for all-cause mortality and did not add prognostic information when combined with BNP. CONCLUSIONS: The urinary proteomic classifier HF1 performed as well, diagnostically, as BNP and provided incremental diagnostic information when added to BNP. HF1 had less prognostic utility than BNP.

A novel urinary biomarker predicts 1-year mortality after discharge from intensive care.

RATIONALE: The urinary proteome reflects molecular drivers of disease. OBJECTIVES: To construct a urinary proteomic biomarker predicting 1-year post-ICU mortality. METHODS: In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis coupled with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL), and urinary albumin. Methods included support vector modeling to construct the classifier, Cox regression, the integrated discrimination (IDI), and net reclassification (NRI) improvement, and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. MEASUREMENTS AND MAIN RESULTS: In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708-0.798) and 0.688 (0.656-0.719), respectively. While accounting for study site and clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00-2.91) for ACM128 (+ 1 SD), 1.24 (1.16-1.32) for the Charlson Comorbidity Index (+ 1 point), and ≥ 1.19 (P ≤ 0.022) for other biomarkers (+ 1 SD). ACM128 improved (P ≤ 0.0001) IDI (≥ + 0.50), NRI (≥ + 53.7), and AUC (≥ + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity, and apoptosis. CONCLUSIONS: The urinary proteomic classifier ACM128 predicts the 1-year post-ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.

Apelin affects the mouse aging urinary peptidome with minimal effects on kidney.

Kidney function is altered by age together with a declined filtration capacity of 5-10% per decade after 35 years. Renal aging shares many characteristics with chronic kidney disease. Plasma levels of the bioactive peptide apelin also decline with age and apelin has been shown to be protective in chronic kidney disease. Therefore we evaluated whether apelin could also improve aging-induced renal lesions and function in mice. Since urine is for the major part composed of proteins and peptides originating from the kidney, we first studied apelin-induced changes, in the aging urinary peptidome. Despite the recently published age-associated plasma decrease of apelin, expression of the peptide and its receptor was increased in the kidneys of 24 months old mice. Twenty-eight days treatment with apelin significantly modified the urinary peptidome of 3 and 24 months old mice towards a signature suggesting more advanced age at 3 months, and a younger age at 24 months. The latter was accompanied by a decreased staining of collagen (Sirius red staining) in 24 months old apelin-treated mice, without changing aging-induced glomerular hypertrophy. In addition, apelin was without effect on aging-induced renal autophagy, apoptosis, inflammation and reduced renal function. In conclusion, treatment of aged mice with apelin had a limited effect on kidney lesions although modifying the urinary peptidome towards a younger signature. This supports evidence of apelin inducing more general beneficial effects on other aging organs, muscles in particular, as recently shown for sarcopenia, markers of which end up via the glomerular filtration in urine.

Urinary Peptidomic Biomarker for Personalized Prevention and Treatment of Diastolic Left Ventricular Dysfunction.

Diastolic heart failure (DHF) is characterized by slow left ventricular (LV) relaxation, increased LV stiffness, interstitial deposition of collagen, and a modified extracellular matrix proteins. Among Europeans, the frequency of asymptomatic diastolic LV dysfunction (DD) is 25%. This constitutes a large pool of people at high risk of DHF. The goal of this review was to describe the discovery and the initial validation of new multidimensional urinary peptidomic biomarkers (UPB) indicative of DD, mainly consisting of collagen fragments, and to describe a roadmap for their introduction into clinical practice. The availability of new drugs creates a window of opportunity for mounting a randomized clinical trial consolidating the clinical applicability of UPB to screen for DD. If successfully completed, such trial will benefit ≈25% of all people older than 50 years and open a large market for a UPB diagnostic tool and the drug tested. Moreover, sequenced peptides making up UPB will generate novel insights in the pathophysiology of DD and facilitate personalized treatment of patients with DHF for whom prevention came too late. If proven cost-effective, the clinical application of UPB will contribute to the sustainability of health care in aging population in epidemiologic transition.

Urinary peptidomic biomarkers of renal function in heart transplant recipients.

BACKGROUND: Chronic kidney disease (CKD) is common in patients after heart transplantation (HTx). We assessed whether in HTx recipients the proteomic urinary classifier CKD273 or sequenced urinary peptides revealing the parental proteins correlated with the estimated glomerular filtration rate (eGFR). METHODS: In 368 HTx patients, we measured the urinary peptidome and analysed CKD273 and 48 urinary peptides with a detectable signal in >95% of participants. After 9.1 months (median), eGFR and the urinary biomarkers were reassessed. RESULTS: In multivariable Bonferroni-corrected analyses of the baseline data, a 1-SD increase in CKD273 was associated with a 11.4 [95% confidence interval (CI) 7.25-15.5] mL/min/1.73 m2 lower eGFR and an odds ratio of 2.63 (1.56-4.46) for having eGFR <60 mL/min/1.73 m2. While relating eGFR category at follow-up to baseline urinary biomarkers, CKD273 had higher (P = 0.007) area under the curve (0.75; 95% CI 0.70-0.80) than 24-h proteinuria (0.64; 95% CI 0.58-0.69), but additional adjustment for baseline eGFR removed significance of both biomarkers. In partial least squares analysis, the strongest correlates of the multivariable-adjusted baseline eGFR were fragments of collagen I (positive) and the mucin-1 subunit α (inverse). Associations between the changes in eGFR and the urinary markers were inverse for CKD273 and mucin-1 and positive for urinary collagen I. CONCLUSIONS: With the exception of baseline eGFR, CKD273 was more closer associated with imminent renal dysfunction than 24-h proteinuria. Fragments of collagen I and mucin-1-respectively, positively and inversely associated with eGFR and change in eGFR-are single-peptide markers associated with renal dysfunction.

Urinary proteomic signatures associated with ß-blockade and heart rate in heart transplant recipients.

OBJECTIVES: Heart transplant (HTx) recipients have a high heart rate (HR), because of graft denervation and are frequently started on ß-blockade (BB). We assessed whether BB and HR post HTx are associated with a specific urinary proteomic signature. METHODS: In 336 HTx patients (mean age, 56.8 years; 22.3% women), we analyzed cross-sectional data obtained 7.3 years (median) after HTx. We recorded medication use, measured HR during right heart catheterization, and applied capillary electrophoresis coupled with mass spectrometry to determine the multidimensional urinary classifiers HF1 and HF2 (known to be associated with left ventricular dysfunction), ACSP75 (acute coronary syndrome) and CKD273 (renal dysfunction) and 48 sequenced urinary peptides revealing the parental proteins. RESULTS: In adjusted analyses, HF1, HF2 and CKD273 (p ≤ 0.024) were higher in BB users than non-users with a similar trend for ACSP75 (p = 0.06). Patients started on BB within 1 year after HTx and non-users had similar HF1 and HF2 levels (p ≥ 0.098), whereas starting BB later was associated with higher HF1 and HF2 compared with non-users (p ≤ 0.014). There were no differences in the urinary biomarkers (p ≥ 0.27) according to HR. BB use was associated with higher urinary levels of collagen II and III fragments and non-use with higher levels of collagen I fragments. CONCLUSIONS: BB use, but not HR, is associated with a urinary proteomic signature that is usually associated with worse outcome, because unhealthier conditions probably lead to initiation of BB. Starting BB early after HTx surgery might be beneficial.

Biomarkers to Assess Right Heart Pressures in Recipients of a Heart Transplant: A Proof-of-Concept Study.

BACKGROUND: This proof-of-concept study investigated the feasibility of using biomarkers to monitor right heart pressures (RHP) in heart transplanted (HTx) patients. METHODS: In 298 patients, we measured 7.6 years post-HTx mean pressures in the right atrium (mRAP) and pulmonary artery (mPAP) and capillaries (mPCWP) along with plasma high-sensitivity troponin T (hsTnT), a marker of cardiomyocyte injury, and the multidimensional urinary classifiers HF1 and HF2, mainly consisting of dysregulated collagen fragments. RESULTS: In multivariable models, mRAP and mPAP increased with hsTnT (per 1-SD, +0.91 and +1.26 mm Hg; P < 0.0001) and with HF2 (+0.42 and +0.62 mm Hg; P ≤ 0.035), but not with HF1. mPCWP increased with hsTnT (+1.16 mm Hg; P < 0.0001), but not with HF1 or HF2. The adjusted odds ratios for having elevated RHP (mRAP, mPAP or mPCWP ≥10, ≥24, ≥17 mm Hg, respectively) were 1.99 for hsTnT and 1.56 for HF2 (P ≤ 0.005). In detecting elevated RHPs, areas under the curve were similar for hsTnT and HF2 (0.63 vs 0.65; P = 0.66). Adding hsTnT continuous or per threshold or HF2 continuous to a basic model including all covariables did not increase diagnostic accuracy (P ≥ 0.11), whereas adding HF2 per optimized threshold increased both the integrated discrimination (+1.92%; P = 0.023) and net reclassification (+30.3%; P = 0.010) improvement. CONCLUSIONS: Correlating RHPs with noninvasive biomarkers in HTx patients is feasible. However, further refinement and validation of such biomarkers is required before their clinical application can be considered.

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.

Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET)-Rationale and database description.

OBJECTIVES: Urinary Proteomics in Predicting Heart Transplantation Outcomes (uPROPHET; NCT03152422) aims: (i) to construct new multidimensional urinary proteomic (UP) classifiers that after heart transplantation (HTx) help in detecting graft vasculopathy, monitoring immune system activity and graft performance, and in adjusting immunosuppression; (ii) to sequence UP peptide fragments and to identify key proteins mediating HTx-related complications; (iii) to validate UP classifiers by demonstrating analogy between UP profiles and tissue proteomic signatures (TP) in diseased explanted hearts, to be compared with normal donor hearts; (iv) and to identify new drug targets. This article describes the uPROPHET database construction, follow-up strategies and baseline characteristics of the HTx patients. METHODS: HTx patients enrolled at the University Hospital Gasthuisberg (Leuven) collected mid-morning urine samples. Cardiac biopsies were obtained at HTx. UP and TP methods and the statistical work flow in pursuit of the research objectives are described in detail in the Data supplement. RESULTS: Of 352 participants in the UP study (24.4% women), 38.9%, 40.3%, 5.7% and 15.1% had ischemic, dilated, hypertrophic or other cardiomyopathy. The median interval between HTx and first UP assessment (baseline) was 7.8 years. At baseline, mean values were 56.5 years for age, 25.2 kg/m2 for body mass index, 142.3/84.8 mm Hg and 124.2/79.8 mm Hg for office and 24-h ambulatory systolic/diastolic pressure, and 58.6 mL/min/1.73 m2 for the estimated glomerular filtration rate. Of all patients, 37.2% and 6.5% had a history of mild (grade = 1B) or severe (grade ≥ 2) cellular rejection. Anti-body mediated rejection had occurred in 6.2% patients. The number of follow-up urine samples available for future analyses totals over 950. The TP study currently includes biopsies from 7 healthy donors and 15, 14, and 3 patients with ischemic, dilated, and hypertrophic cardiomyopathy. CONCLUSIONS: uPROPHET constitutes a solid resources for UP and TP research in the field of HTx and has the ambition to lay the foundation for the clinical application of UP in risk stratification in HTx patients.

Novel Urinary Peptidomic Classifier Predicts Incident Heart Failure.

BACKGROUND: Detection of preclinical cardiac dysfunction and prognosis of left ventricular heart failure (HF) would allow targeted intervention, and appears to be the most promising approach in its management. Novel biomarker panels may support this approach and provide new insights into the pathophysiology. METHODS AND RESULTS: A retrospective comparison of urinary proteomic profiles generated by mass spectrometric analysis from 49 HF patients, 36 patients who progressed to HF within 2.6±1.6 years, and 192 sex- and age-matched controls who did not progress to HF enabled identification of 96 potentially HF-specific peptide biomarkers. Based on these 96 peptides, the classifier called Heart Failure Predictor (HFP) was established by support vector machine modeling. The incremental prognostic value of HFP was subsequently evaluated in urine samples from 175 individuals with asymptomatic diastolic dysfunction from an independent population cohort. Within 4.8 years, 17 of these individuals progressed to overt HF. The area under receiver-operating characteristic curve was 0.70 (95% CI, 0.56-0.82); P=0.0047 for HFP and 0.57 (0.42-0.72; P=0.62) for N-terminal pro b-type natriuretic peptide. Hazard ratios were 1.63 (CI, 1.04-2.55; P=0.032) per 1-SD increment in HFP and 0.70 (CI, 0.35-1.41; P=0.32) for a doubling of the logarithmically transformed N-terminal pro b-type natriuretic peptide. CONCLUSIONS: HFP is a novel biomarker derived from the urinary proteome and might serve as a sensitive tool to improve risk stratification, patient management, and understanding of the pathophysiology of HF.

Prediction of acute coronary syndromes by urinary proteome analysis.

Identification of individuals who are at risk of suffering from acute coronary syndromes (ACS) may allow to introduce preventative measures. We aimed to identify ACS-related urinary peptides, that combined as a pattern can be used as prognostic biomarker. Proteomic data of 252 individuals enrolled in four prospective studies from Australia, Europe and North America were analyzed. 126 of these had suffered from ACS within a period of up to 5 years post urine sampling (cases). Proteomic analysis of 84 cases and 84 matched controls resulted in the discovery of 75 ACS-related urinary peptides. Combining these to a peptide pattern, we established a prognostic biomarker named Acute Coronary Syndrome Predictor 75 (ACSP75). ACSP75 demonstrated reasonable prognostic discrimination (c-statistic = 0.664), which was similar to Framingham risk scoring (c-statistics = 0.644) in a validation cohort of 42 cases and 42 controls. However, generating by a composite algorithm named Acute Coronary Syndrome Composite Predictor (ACSCP), combining the biomarker pattern ACSP75 with the previously established urinary proteomic biomarker CAD238 characterizing coronary artery disease as the underlying aetiology, and age as a risk factor, further improved discrimination (c-statistic = 0.751) resulting in an added prognostic value over Framingham risk scoring expressed by an integrated discrimination improvement of 0.273 ± 0.048 (P < 0.0001) and net reclassification improvement of 0.405 ± 0.113 (P = 0.0007). In conclusion, we demonstrate that urinary peptide biomarkers have the potential to predict future ACS events in asymptomatic patients. Further large scale studies are warranted to determine the role of urinary biomarkers in clinical practice.

The use of urinary proteomics in the assessment of suitability of mouse models for ageing.

Ageing is a complex process characterised by a systemic and progressive deterioration of biological functions. As ageing is associated with an increased prevalence of age-related chronic disorders, understanding its underlying molecular mechanisms can pave the way for therapeutic interventions and managing complications. Animal models such as mice are commonly used in ageing research as they have a shorter lifespan in comparison to humans and are also genetically close to humans. To assess the translatability of mouse ageing to human ageing, the urinary proteome in 89 wild-type (C57BL/6) mice aged between 8-96 weeks was investigated using capillary electrophoresis coupled to mass spectrometry (CE-MS). Using age as a continuous variable, 295 peptides significantly correlated with age in mice were identified. To investigate the relevance of using mouse models in human ageing studies, a comparison was performed with a previous correlation analysis using 1227 healthy subjects. In mice and humans, a decrease in urinary excretion of fibrillar collagens and an increase of uromodulin fragments was observed with advanced age. Of the 295 peptides correlating with age, 49 had a strong homology to the respective human age-related peptides. These ortholog peptides including several collagen (N = 44) and uromodulin (N = 5) fragments were used to generate an ageing classifier that was able to discriminate the age among both wild-type mice and healthy subjects. Additionally, the ageing classifier depicted that telomerase knock-out mice were older than their chronological age. Hence, with a focus on ortholog urinary peptides mouse ageing can be translated to human ageing.

The long path towards implementation of clinical proteomics: Exemplified based on CKD273.

Clinical proteomics aims at the development and the implementation of novel biomarkers that demonstrate a clear clinical benefit in the management of diseases. However, though the attention in the field is increasing and multiple articles on biomarker research are published, clinical implementation of these biomarkers is scarce. In this paper, we aim towards identifying the hurdles on the path towards implementation, and present one successful approach, based on capillary electrophoresis coupled with mass spectrometry. A panel of biomarkers identified and assessed using this approach, termed CKD273, has recently received a Letter-of-Support from the US-Food and Drug Administration (FDA), and is now implemented in the (early) management of chronic kidney disease. Based on this experience in the process towards implementation of CKD273, issues associated with implementation and suggestions how to meet these challenges are given.

Comparison of different statistical approaches for urinary peptide biomarker detection in the context of coronary artery disease.

BACKGROUND: When combined with a clinical outcome variable, the size, complexity and nature of mass-spectrometry proteomics data impose great statistical challenges in the discovery of potential disease-associated biomarkers. The purpose of this study was thus to evaluate the effectiveness of different statistical methods applied for urinary proteomic biomarker discovery and different methods of classifier modelling in respect of the diagnosis of coronary artery disease in 197 study subjects and the prognostication of acute coronary syndromes in 368 study subjects. RESULTS: Computing the discovery sub-cohorts comprising [Formula: see text] of the study subjects based on the Wilcoxon rank sum test, t-score, cat-score, binary discriminant analysis and random forests provided largely different numbers (ranging from 2 to 398) of potential peptide biomarkers. Moreover, these biomarker patterns showed very little overlap limited to fragments of type I and III collagens as the common denominator. However, these differences in biomarker patterns did mostly not translate into significant differently performing diagnostic or prognostic classifiers modelled by support vector machine, diagonal discriminant analysis, linear discriminant analysis, binary discriminant analysis and random forest. This was even true when different biomarker patterns were combined into master-patterns. CONCLUSION: In conclusion, our study revealed a very considerable dependence of peptide biomarker discovery on statistical computing of urinary peptide profiles while the observed diagnostic and/or prognostic reliability of classifiers was widely independent of the modelling approach. This may however be due to the limited statistical power in classifier testing. Nonetheless, our study showed that urinary proteome analysis has the potential to provide valuable biomarkers for coronary artery disease mirroring especially alterations in the extracellular matrix. It further showed that for a comprehensive discovery of biomarkers and thus of pathological information, the results of different statistical methods may best be combined into a master pattern that then can be used for classifier modelling.

Urinary Proteomics Pilot Study for Biomarker Discovery and Diagnosis in Heart Failure with Reduced Ejection Fraction.

BACKGROUND: Biomarker discovery and new insights into the pathophysiology of heart failure with reduced ejection fraction (HFrEF) may emerge from recent advances in high-throughput urinary proteomics. This could lead to improved diagnosis, risk stratification and management of HFrEF. METHODS AND RESULTS: Urine samples were analyzed by on-line capillary electrophoresis coupled to electrospray ionization micro time-of-flight mass spectrometry (CE-MS) to generate individual urinary proteome profiles. In an initial biomarker discovery cohort, analysis of urinary proteome profiles from 33 HFrEF patients and 29 age- and sex-matched individuals without HFrEF resulted in identification of 103 peptides that were significantly differentially excreted in HFrEF. These 103 peptides were used to establish the support vector machine-based HFrEF classifier HFrEF103. In a subsequent validation cohort, HFrEF103 very accurately (area under the curve, AUC = 0.972) discriminated between HFrEF patients (N = 94, sensitivity = 93.6%) and control individuals with and without impaired renal function and hypertension (N = 552, specificity = 92.9%). Interestingly, HFrEF103 showed low sensitivity (12.6%) in individuals with diastolic left ventricular dysfunction (N = 176). The HFrEF-related peptide biomarkers mainly included fragments of fibrillar type I and III collagen but also, e.g., of fibrinogen beta and alpha-1-antitrypsin. CONCLUSION: CE-MS based urine proteome analysis served as a sensitive tool to determine a vast array of HFrEF-related urinary peptide biomarkers which might help improving our understanding and diagnosis of heart failure.

Identification of ageing-associated naturally occurring peptides in human urine.

To assess normal and pathological peptidomic changes that may lead to an improved understanding of molecular mechanisms underlying ageing, urinarypeptidomes of 1227 healthy and 10333 diseased individuals between 20 and 86 years of age were investigated. The diseases thereby comprised diabetes mellitus, renal and cardiovascular diseases. Using age as a continuous variable, 116 peptides were identified that significantly (p < 0.05; |ρ|≥0.2) correlated with age in the healthy cohort. The same approach was applied to the diseased cohort. Upon comparison of the peptide patterns of the two cohorts 112 common age-correlated peptides were identified. These 112 peptides predominantly originated from collagen, uromodulin and fibrinogen. While most fibrillar and basement membrane collagen fragments showed a decreased age-related excretion, uromodulin, beta-2-microglobulin and fibrinogen fragments showed an increase. Peptide-based in silico protease analysis was performed and 32 proteases, including matrix metalloproteinases and cathepsins, were predicted to be involved in ageing. Identified peptides, predicted proteases and patient information were combined in a systems biology pathway analysis to identify molecular pathways associated with normal and/or pathological ageing. While perturbations in collagen homeostasis, trafficking of toll-like receptors and endosomal pathways were commonly identified, degradation of insulin-like growth factor-binding proteins was uniquely identified in pathological ageing.

Proteome analysis in the assessment of ageing.

Based on demographic trends, the societies in many developed countries are facing an increasing number and proportion of people over the age of 65. The raise in elderly populations along with improved health-care will be concomitant with an increased prevalence of ageing-associated chronic conditions like cardiovascular, renal, and respiratory diseases, arthritis, dementia, and diabetes mellitus. This is expected to pose unprecedented challenges both for individuals and societies and their health care systems. An ultimate goal of ageing research is therefore the understanding of physiological ageing and the achievement of 'healthy' ageing by decreasing age-related pathologies. However, on a molecular level, ageing is a complex multi-mechanistic process whose contributing factors may vary individually, partly overlap with pathological alterations, and are often poorly understood. Proteome analysis potentially allows modelling of these multifactorial processes. This review summarises recent proteomic research on age-related changes identified in animal models and human studies. We combined this information with pathway analysis to identify molecular mechanisms associated with ageing. We identified some molecular pathways that are affected in most or even all organs and others that are organ-specific. However, appropriately powered studies are needed to confirm these findings based in in silico evaluation.

Assessment of metabolomic and proteomic biomarkers in detection and prognosis of progression of renal function in chronic kidney disease.

Chronic kidney disease (CKD) is part of a number of systemic and renal diseases and may reach epidemic proportions over the next decade. Efforts have been made to improve diagnosis and management of CKD. We hypothesised that combining metabolomic and proteomic approaches could generate a more systemic and complete view of the disease mechanisms. To test this approach, we examined samples from a cohort of 49 patients representing different stages of CKD. Urine samples were analysed for proteomic changes using capillary electrophoresis-mass spectrometry and urine and plasma samples for metabolomic changes using different mass spectrometry-based techniques. The training set included 20 CKD patients selected according to their estimated glomerular filtration rate (eGFR) at mild (59.9±16.5 mL/min/1.73 m2; n = 10) or advanced (8.9±4.5 mL/min/1.73 m2; n = 10) CKD and the remaining 29 patients left for the test set. We identified a panel of 76 statistically significant metabolites and peptides that correlated with CKD in the training set. We combined these biomarkers in different classifiers and then performed correlation analyses with eGFR at baseline and follow-up after 2.8±0.8 years in the test set. A solely plasma metabolite biomarker-based classifier significantly correlated with the loss of kidney function in the test set at baseline and follow-up (ρ = -0.8031; p<0.0001 and ρ = -0.6009; p = 0.0019, respectively). Similarly, a urinary metabolite biomarker-based classifier did reveal significant association to kidney function (ρ = -0.6557; p = 0.0001 and ρ = -0.6574; p = 0.0005). A classifier utilising 46 identified urinary peptide biomarkers performed statistically equivalent to the urinary and plasma metabolite classifier (ρ = -0.7752; p<0.0001 and ρ = -0.8400; p<0.0001). The combination of both urinary proteomic and urinary and plasma metabolic biomarkers did not improve the correlation with eGFR. In conclusion, we found excellent association of plasma and urinary metabolites and urinary peptides with kidney function, and disease progression, but no added value in combining the different biomarkers data.

Improving efficiency of viability-PCR for selective detection of live cells.

Viability PCR (v-PCR) as a method to selectively detect intact live cells has gained considerable interest over the last years with an increasing number of applications. The principle is based on treatment of microbiological samples with a viability dye prior to extraction of genomic DNA and its amplification. The dye is selectively taken up by membrane-compromised dead cells resulting in the degradation of their DNA upon light exposure and therefore inhibition of amplification. Although the treatment greatly helps to generate more meaningful data, one of the main drawbacks of the technique is currently that the exclusion of dead cell signals can be incomplete leading to false-positive signals. The resulting overestimation of live cell population is especially problematic for the detection of pathogens. We assessed in this study different conditions to increase the penetration of propidium monoazide (PMA) into dead cells of Salmonella Typhimurium and Listeria monocytogenes as representatives of gram-negative and gram-positive bacteria. When working with a low dye concentration of 10µM, a strong relationship of PMA treatment efficiency with temperature and incubation time was observed. Exposing cells to PMA at a temperature exceeding the growth temperature by 10°C for 30min proved greatly beneficial. Co-incubation of cells with PMA and deoxycholate on the other hand was only beneficial for Salmonella, but resulted in a strong undesired uptake of PMA by live Listeria cells. This difference is in agreement with the gram-specific effect of the bile salt during growth.