Linagliptin treatment is associated with altered cobalamin (VitB12) homeostasis in mice and humans.

Linagliptin is a dipeptidyl peptidase-4 (DPP-4) inhibitor used for the treatment of type 2 diabetes, with additional beneficial effects for the kidney. Treatment of mice with linagliptin revealed increased storage of cobalamin (Cbl, Vitamin B12) in organs if a standard Cbl diet (30 µg Cbl/kg chow) is given. In order to translate these findings to humans, we determined methylmalonic acid (MMA), a surrogate marker of functional Cbl homeostasis, in human plasma and urine samples (n = 1092) from baseline and end of trial (6 months after baseline) of the previously completed MARLINA-T2D clinical trial.

Urinary dipeptidyl peptidase-4 protein is increased by linagliptin and is a potential predictive marker of urine albumin-to-creatinine ratio reduction in patients with type 2 diabetes.

Results of a post hoc analysis of urinary dipeptidyl peptidase-4 (DPP-4) protein as a predictor of urine albumin-to-creatinine ratio (UACR) response to linagliptin treatment based on MARLINA-T2D trial data are described. MARLINA was a 24-week, phase 3b, multinational, placebo-controlled clinical trial, in which patients with type 2 diabetes (T2D), HbA1c 6.5%-10.

Characterization of combined linagliptin and Y2R agonist treatment in diet-induced obese mice.

Dipeptidyl peptidase IV (DPP-IV) inhibitors improve glycemic control by prolonging the action of glucagon-like peptide-1 (GLP-1). In contrast to GLP-1 analogues, DPP-IV inhibitors are weight-neutral. DPP-IV cleavage of PYY and NPY gives rise to PYY and NPY which exert potent anorectic action by stimulating Y2 receptor (Y2R) function.

Mechanisms of GLP-1 receptor-independent renoprotective effects of the dipeptidyl peptidase type 4 inhibitor linagliptin in GLP-1 receptor knockout mice with 5/6 nephrectomy.

Dipeptidyl peptidase type 4 (DPP-4) inhibitors were reported to have beneficial effects in experimental models of chronic kidney disease. The underlying mechanisms are not completely understood. However, these effects could be mediated via the glucagon-like peptide-1 (GLP-1)/GLP-1 receptor (GLP1R) pathway.

The effect of DPP-4 inhibition to improve functional outcome after stroke is mediated by the SDF-1α/CXCR4 pathway.

Background: Dipeptidyl peptidase-4 (DPP-4) inhibitors (gliptins) are approved drugs for the treatment of hyperglycemia in patients with type 2 diabetes. These effects are mainly mediated by inhibiting endogenous glucagon-like peptide-1 (GLP-1) cleavage. Interestingly, gliptins can also improve stroke outcome in rodents independently from GLP1.

Data Preprocessing, Visualization, and Statistical Analyses of Nontargeted Peptidomics Data from MALDI-MS.

Mass spectrometric (MS) comparative analysis of peptides in biological specimens (nontargeted peptidomics) can result in large amounts of data due to chromatographic separation of a multitude of samples and subsequent MS analysis of numerous chromatographic fractions. Efficient yet effective strategies are needed to obtain relevant information. Combining visual and numerical data analysis offers a suitable approach to retrieve information and to filter data for significant differences as targets for succeeding MS/MS identifications.

The dipeptidyl peptidase inhibitor linagliptin and the angiotensin II receptor blocker telmisartan show renal benefit by different pathways in rats with 5/6 nephrectomy.

Dipeptidyl peptidase (DPP)-4 inhibitors delay chronic kidney disease (CKD) progression in experimental diabetic nephropathy in a glucose-independent manner. Here we compared the effects of the DPP-4 inhibitor linagliptin versus telmisartan in preventing CKD progression in non-diabetic rats with 5/6 nephrectomy. Animals were allocated to 1 of 4 groups: sham operated plus placebo; 5/6 nephrectomy plus placebo; 5/6 nephrectomy plus linagliptin; and 5/6 nephrectomy plus telmisartan.

Collection and handling of blood specimens for peptidomics.

Pre-analytical variables can alter the analysis of blood-derived samples. In particular sample collection and specimen preparation can alter the validity of results obtained by modern multiplex assays (e.g.

Collection and handling of blood specimens for peptidomics.

Pre-analytical variables can alter the analysis of blood-derived samples. In particular, sample collection and specimen preparation can alter the validity of results obtained by modern multiplex assays (e.g.

In vivo profiling of DPP4 inhibitors reveals alterations in collagen metabolism and accumulation of an amyloid peptide in rat plasma.

Dipeptidyl peptidase 4 (DPP4) inhibitors represent a novel class of oral anti-hyperglycemic agents. The complete pharmacological profile of these protease inhibitors remains unclear. In order to gain deeper insight into the in vivo effects caused by DPP4 inhibition, two different DPP4 inhibitors (vildagliptin and AB192) were analyzed using differential peptide display.

Peptidomic analysis of blood plasma after in vivo treatment with protease inhibitors--a proof of concept study.

Native peptides can be regarded as surrogate markers for protease activity in biological samples. Analysis of peptides by peptidomics allows to monitor protease activity in vivo and to describe the influence of protease inhibition. To elucidate the potential of peptides as markers for in vivo protease inhibition we analyzed plasma samples from animals treated with either the indirect FXa inhibitor FONDAPARINUX or the dipeptidylpeptidase IV inhibitor AB192.

Specimen collection and handling: standardization of blood sample collection.

Preanalytical variables can alter the analysis of blood-derived samples. Prior to the analysis of a blood sample, multiple steps are necessary to generate the desired specimen. The choice of blood specimens, its collection, handling, processing, and storage are important aspects since these characteristics can have a tremendous impact on the results of the analysis.

Peptidomics analysis of human blood specimens for biomarker discovery.

This review addresses the concepts, limitations and perspectives for the application of peptidomics science and technologies to discover putative biomarkers in blood specimens. Peptidomics can be defined as the comprehensive multiplex analysis of endogenous peptides contained within a biological sample under defined conditions to describe the multitude of native peptides in a biological compartment. In addition to the discovery of disease associated biomarkers, an emerging field in peptidomics is the analysis of peptides to describe in vivo effects of protease inhibitors.

Metrological sharp shooting for plasma proteins and peptides: The need for reference materials for accurate measurements in clinical proteomics and in vitro diagnostics to generate reliable results.

Reliable study results are necessary for the assessment of discoveries, including those from proteomics. Reliable study results are also crucial to increase the likelihood of making a successful choice of biomarker candidates for verification and subsequent validation studies, a current bottleneck for the transition to in vitro diagnostic (IVD). In this respect, a major need for improvement in proteomics appears to be accuracy of measurements, including both trueness and precision of measurement.

Oncopeptidomics--a commentary on opportunities and limitations.

Cancer cells exhibit specific changes in protein expression and alterations in proteolytic activities. Peptides are capable of reflecting these pathological changes and are educible by dedicated analytical technologies. Oncopeptidomics can be defined as the comprehensive multiplexed analysis of endogenous peptides from a biological sample, under defined conditions, to discover probable valid peptide tumor biomarker.

Peptidomic analysis of human peripheral monocytes persistently infected by Chlamydia trachomatis.

Peptidomic analysis using Differential Peptide Display (DPD) of human peripheral blood mononuclear cells (PBMC) mock-infected or persistently infected by Chlamydia trachomatis (CT) revealed 10 peptides, expressed upon CT infection. Analysis of these 10 candidates by tandem mass spectrometry enabled the determination of seven candidates as fragments from the precursors (I) ferritin heavy chain subunit, (II) HLA class II histocompatibility antigen, (III) vimentin, (IV) indoleamine 2,3-dioxygenase, (V and VI) pre-B cell enhancing factor (PBEF), and (VII) Interleukin-8 (CXCL8). The identified candidates proved the presence of anti-bacterial and immunologically active monocytic proteins after CT infection.

Peptidomic analysis of breast cancer reveals a putative surrogate marker for estrogen receptor-negative carcinomas.

Estrogen-receptor status provides a major biomarker in breast cancer classification and has an important impact on prognosis and treatment options. The aim of this study was to investigate peptide profiles of invasive breast cancer with positive (n=39) and negative receptor status (n=41). Peptide profiles were generated by 'Differential Peptide Display', which is an offline-coupled combination of reversed-phase-HPLC and MALDI mass spectrometry.

Identification of Peptide tumor markers in a tumor graft model in immunodeficient mice.

The medical demand for useful biomarkers is large and still increasing. This is especially true for cancer, because for this disease adequate diagnostic markers with high specificity and sensitivity are still lacking. Despite advances in imaging technologies for early detection of cancer, peptidomic multiplex techniques evolved in recent years will provide new opportunities for detection of low molecular weight (LMW) proteome biomarker (peptides) by mass spectrometry.

Peptidomics biomarker discovery in mouse models of obesity and type 2 diabetes.

Type 2 diabetes mellitus (T2DM) is caused by the failure of the pancreatic beta-cell to secrete sufficient insulin to compensate a decreased response of peripheral tissues to insulin action. The pathological events causing beta-cell dysfunctions are only poorly understood and early markers that would predict islet function are missing. In contrast to immunoassays, unbiased proteomic technologies provide the opportunity to screen for novel marker protein and peptides of T2DM.

The concept of functional peptidomics for the discovery of bioactive peptides in cell culture models.

Detection and purification of novel bioactive peptides from biological sources is a scientific task that led to a substantial number of important discoveries. One major laborious approach used is the repetitive stepwise separation of the test sample into several fractions followed by the determination of their bioactivity, until purity allows for sequence identification. We tested whether functional peptidomics, a combination of biological read-outs with differential peptide display (DPD) is a suitable strategy to isolate bioactive peptides at lower workload and with improved success.

Towards characterization of the human urinary peptidome.

Biomarker discovery in human urine has become an evolving and potentially valuable topic in relation to renal function and diseases of the urinary tract. In order to deliver on the promises and to facilitate the development of validated biomarkers or biomarker panels, protein and peptide profiling techniques need high sample throughput, speed of analysis, and reproducibility of results. Here, we outline the performance characteristics of the liquid chromatography/MALDI-TOF-MS based differential peptide display (DPD(1)) approach for separating, detecting, abundance profiling and identification of native peptides derived from human urine.

Prerequisites for peptidomic analysis of blood samples: II. Analysis of human plasma after oral glucose challenge -- a proof of concept.

Mass spectrometric plasma analysis for biomarker discovery has become an exploratory focus in proteomic research: the challenges of analyzing plasma samples by mass spectrometry have become apparent not only since the human proteome organization (HUPO) has put much emphasis on the human plasma proteome. This work demonstrates fundamental proteomic research to reveal sensitivity and quantification capabilities of our Peptidomics technologies by detecting distinct changes in plasma peptide composition in samples after challenging healthy volunteers with orally administered glucose. Differential Peptide Display (DPD) is a technique for peptidomics studies to compare peptides from distinct biological samples.

Prerequisites for peptidomic analysis of blood samples: I. Evaluation of blood specimen qualities and determination of technical performance characteristics.

Proteomics studies aiming at a detailed analysis of proteins, and peptidomics, aiming at the analysis of the low molecular weight proteome (peptidome) offer a promising approach to discover novel biomarkers valuable for different crucial steps in detection, prevention and treatment of disease. Much emphasis has been given to the analysis of blood, since this source would by far offer the largest number of meaningful biomarker applications. Blood is a complex liquid tissue that comprises cells and extra-cellular fluid.

Datamining methodology for LC-MALDI-MS based peptide profiling.

This report will provide a brief overview of the application of data mining in proteomic peptide profiling used for medical biomarker research. Mass spectrometry based profiling of peptides and proteins is frequently used to distinguish disease from non-disease groups and to monitor and predict drug effects. It has the promising potential to enter clinical laboratories as a general purpose diagnostic tool.

Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.