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%-10.0% and UACR 30-3000 mg/g (n = 360) were treated with linagliptin or placebo. After 24 weeks of treatment, linagliptin significantly inhibited urinary DPP-4 activity and increased urinary DPP-4 protein. Furthermore, medium urinary DPP-4 protein levels (between 5.5 and 7.5 natural logarithmic [ln] µg/g creatinine) at baseline allowed for prediction of improved UACR in linagliptin-treated individuals. In patients with lower or higher levels of urinary DPP-4 protein at baseline, no association between linagliptin treatment and improved UACR was present. This might suggest a varying degree of importance of DPP-4 as a pathophysiological factor in T2D-associated kidney disease. In summary, urinary DPP-4 might be a useful predictive biomarker for UACR improvement by linagliptin.

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. Here we investigated the renal effects of the DPP-4 inhibitor linagliptin in Glp1r-/- knock out and wild-type mice with 5/6 nephrectomy (5/6Nx). Mice were allocated to groups: sham+wild type+placebo; 5/6Nx+ wild type+placebo; 5/6Nx+wild type+linagliptin; sham+knock out+placebo; 5/6Nx+knock out+ placebo; 5/6Nx+knock out+linagliptin. 5/6Nx caused the development of renal interstitial fibrosis, significantly increased plasma cystatin C and creatinine levels and suppressed renal gelatinase/collagenase, matrix metalloproteinase-1 and -13 activities; effects counteracted by linagliptin treatment in wildtype and Glp1r-/- mice. Two hundred ninety-eight proteomics signals were differentially regulated in kidneys among the groups, with 150 signals specific to linagliptin treatment as shown by mass spectrometry. Treatment significantly upregulated three peptides derived from collagen alpha-1(I), thymosin ß4 and heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) and significantly downregulated one peptide derived from Y box binding protein-1 (YB-1). The proteomics results were further confirmed using western blot and immunofluorescence microscopy. Also, 5/6Nx led to significant up-regulation of renal transforming growth factor-ß1 and pSMAD3 expression in wild type mice and linagliptin significantly counteracted this up-regulation in wild type and Glp1r-/- mice. Thus, the renoprotective effects of linagliptin cannot solely be attributed to the GLP-1/GLP1R pathway, highlighting the importance of other signaling pathways (collagen I homeostasis, HNRNPA1, YB-1, thymosin ß4 and TGF-ß1) influenced by DPP-4 inhibition.

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. However, the underlying mechanisms are unknown. Stromal cell-derived factor-1α (SDF-1α) is a DPP-4 substrate and CXCR4 agonist promoting beneficial effects in injured brains. However, SDF-1α involvement in gliptin-mediated neuroprotection after ischemic injury is unproven. We aimed to determine whether the gliptin linagliptin improves stroke outcome via the SDF-1α/CXCR4 pathway, and identify additional effectors behind the efficacy. METHODS: Mice were subjected to stroke by transient middle cerebral artery occlusion (MCAO). linagliptin was administered for 3 days or 3 weeks from stroke onset. The CXCR4-antagonist AMD3100 was administered 1 day before MCAO until 3 days thereafter. Stroke outcome was assessed by measuring upper-limb function, infarct volume and neuronal survival. The plasma and brain levels of active GLP-1, GIP and SDF-1α were quantified by ELISA. To identify additional gliptin-mediated molecular effectors, brain samples were analyzed by mass spectrometry. RESULTS: Linagliptin specifically increased active SDF-1α but not glucose-dependent insulinotropic peptide (GIP) or GLP-1 brain levels. Blocking of SDF-1α/CXCR4 pathway abolished the positive effects of linagliptin on upper-limb function and histological outcome after stroke. Moreover, linagliptin treatment after stroke decreased the presence of peptides derived from neurogranin and from an isoform of the myelin basic protein. CONCLUSIONS: We showed that linagliptin improves functional stroke outcome in a SDF-1α/CXCR4-dependent manner. Considering that Calpain activity and intracellular Ca2+ regulate neurogranin and myelin basic protein detection, our data suggest a gliptin-mediated neuroprotective mechanism via the SDF-1α/CXCR4 pathway that could involve the regulation of Ca2+ homeostasis and the reduction of Calpain activity. These results provide new insights into restorative gliptin-mediated effects against stroke.

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. Interstitial fibrosis was significantly decreased by 48% with linagliptin but a non-significant 24% with telmisartan versus placebo. The urine albumin-to-creatinine ratio was significantly decreased by 66% with linagliptin and 92% with telmisartan versus placebo. Blood pressure was significantly lowered by telmisartan, but it was not affected by linagliptin. As shown by mass spectrometry, the number of altered peptide signals for linagliptin in plasma was 552 and 320 in the kidney. For telmisartan, there were 108 peptide changes in plasma and 363 in the kidney versus placebo. Linagliptin up-regulated peptides derived from collagen type I, apolipoprotein C1, and heterogeneous nuclear ribonucleoproteins A2/B1, a potential downstream target of atrial natriuretic peptide, whereas telmisartan up-regulated angiotensin II. A second study was conducted to confirm these findings in 5/6 nephrectomy wild-type and genetically deficient DPP-4 rats treated with linagliptin or placebo. Linagliptin therapy in wild-type rats was as effective as DPP-4 genetic deficiency in terms of albuminuria reduction. Thus, linagliptin showed comparable efficacy to telmisartan in preventing CKD progression in non-diabetic rats with 5/6 nephrectomy. However, the underlying pathways seem to be different.

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. We found that individuals with medium Cbl levels (MMA between 50 and 270 nmol/L for plasma, 0.4 and 3.5 µmol/mmol creatinine for urine, at baseline and end of trial) exhibited higher MMA values at the end of study in placebo compared with linagliptin. Linagliptin might inhibit the N-terminal degradation of the transcobalamin receptor CD320, which is necessary for uptake of Cbl into endothelial cells. Because we demonstrate that linagliptin led to increased organ levels of Cbl in mice, sustained constant medium MMA levels in humans, and inhibited CD320 processing by DPP-4 in-vitro, we speculate that linagliptin promotes intra-cellular uptake of Cbl by prolonging half-life of CD320.

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 PYY3-36 and NPY3-36 which exert potent anorectic action by stimulating Y2 receptor (Y2R) function. This invites the possibility that DPP-IV inhibitors could be weight-neutral by preventing conversion of PYY/NPY to Y2R-selective peptide agonists. We therefore investigated whether co-administration of an Y2R-selective agonist could unmask potential weight lowering effects of the DDP-IV inhibitor linagliptin. Male diet-induced obese (DIO) mice received once daily subcutaneous treatment with linagliptin (3 mg/kg), a Y2R-selective PYY3-36 analogue (3 or 30 nmol/kg) or combination therapy for 14 days. While linagliptin promoted marginal weight loss without influencing food intake, the PYY3-36 analogue induced significant weight loss and transient suppression of food intake. Both compounds significantly improved oral glucose tolerance. Because combination treatment did not further improve weight loss and glucose tolerance in DIO mice, this suggests that potential negative modulatory effects of DPP-IV inhibitors on endogenous Y2R peptide agonist activity is likely insufficient to influence weight homeostasis. Weight-neutrality of DPP-IV inhibitors may therefore not be explained by counter-regulatory effects on PYY/NPY responses.

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. Signals correlating with the treatment were subsequently identified and assessed with respect to protease-dependent consensus cleavage motifs and occurrence of downstream targets. It could be shown that regulated peptides were either substrates, products or downstream targets of the inhibited protease. The results from the present study demonstrate that the in vivo analysis of peptides by peptidomics has the potential to broaden the knowledge of inhibitor related effects in vivo and that this method may pave the way to develop predictive biomarkers.

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. The awareness of clinical practices in medical laboratories and the current knowledge allow for identification of specific variables that affect the results of a proteomic study. The knowledge of preanalytical variables is a prerequisite to understand and control their impact.

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.Visual analysis allows assessing features within a spatial context. Specific patterns are easily recognizable by the human eye. For example, derivatives representing modified forms of signals present are easily identifiable due to an apparent shift in mass and chromatographic retention times. On the other hand numerical data analysis offers the possibility to optimize spectra and to perform high-throughput calculations. A useful tool for such calculations is R, a freely available language and environment for statistical computing. R can be extended via packages to enable functionalities like mzML (open mass spectrometric data format) import and processing. R is capable of parallel processing enabling faster computation using the power of multicore systems.The combination and interplay of both approaches allows evaluating the data in a holistic way, thus helping the researcher to better understand data and experimental outcomes.

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. Here, mass spectrometry has shown its potential as a comprehensive peptide profiling tool. The efforts to arrive at diagnostically relevant biomarkers may have been underestimated. The establishment of novel cancer biomarkers will necessitate a multidisciplinary effort and presumably require a duration comparable to the drug development process. This review will address current concepts, new perspectives and the developmental process leading to clinically useful peptide tumor markers.

Peptides in body fluids and tissues as markers of disease.

The general awareness of the importance of peptides in physiology and pathophysiology has increased strongly over the last few years. With worldwide progress in the analysis of whole genomes, the knowledge base in gene sequence and expression data useful for protein and peptide analysis has drastically increased. The medical need for relevant biomarkers is enormous. This is particularly true for the many types of cancer, but other diseases such as Type 2 diabetes also lack useful and adequate diagnostic markers with high specificity and sensitivity. Despite advances in imaging technologies for early detection of diseases, proteomic and peptidomic multiplex techniques have evolved in recent years. This review focuses on the application of peptidomics technologies to peptides in health and disease. Peptidomics technologies provide new opportunities for the detection of low-molecular-weight proteome biomarkers (peptides) by mass spectrometry. Improvements in peptidomics research are based on separation of peptides and/or proteins by their physicochemical properties in combination with mass spectrometric detection, identification and sophisticated bioinformatics tools for data analysis. Therefore, peptidomics technologies offer an opportunity to discover novel biomarkers for diagnosis and management of disease (e.g., prognosis, treatment decision and monitoring response to therapy).

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. Data mining methodologies support biomedical science to manage the vast data sets obtained from these instrumentations. Here we will review the typical workflow of peptide profiling, together with typical data mining methodology. Mass spectrometric experiments in peptidomics raise numerous questions in the fields of signal processing, statistics, experimental design and discriminant analysis.

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. The choice of suitable specimen collection is crucial to minimize artificial occurring processes during specimen collection and preparation (e.g. cell lysis, proteolysis). After specimen collection, sample preparation for peptidomics is carried out by physical methods (filtration, gel-chromatography, precipitation) which allow for separation based on molecular size, with and without immunodepletion of major abundant proteins. Differential Peptide Display (DPD) is an offline-coupled combination of Reversed-Phase-HPLC and MALDI mass spectrometry in combination with in-house developed data display and analysis tools. Identifications of peptides are carried out by additional mass spectrometric methods (e.g. online LC-ESI-MS/MS). In the work presented here, insights into semi-quantitative mass spectrometric profiling of plasma peptides by DPD are given. This includes proper specimen selection (plasma vs. serum), sample preparation, especially peptide extraction, the determination of sensitivity (i.e. by establishing detection limits of exogenously spiked peptides), the reproducibility for individual as well as for all peptides (Coefficient of Variation calculations) and quantification (correlation between signal intensity and concentration). Finally, the implications for clinical peptidomics are discussed.

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. The typical complexity of peptides in human urine (resolution of the technique with respect to detectable number of peptides), the reproducibility (coefficient of variation for abundance profiles of all peptides detected in biological samples) and dynamic range of the technique as well as the lower limit of detection were characterized. A substantial number of peptides present in normal human urine were identified and compared to findings in four published proteome studies. In an explorative approach, pathological urines from patients suffering from post-renal-filtration diseases were qualitatively compared to normal urine. In conclusion, the peptidomics technology as shown here has a great potential for high throughput and high resolution urine peptide profiling analyses. It is a promising tool to study not only renal physiology and pathophysiology and to determine new biomarkers of renal diseases; it also has the potential to study remotely localized or systemic aberrations within human biology.

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. Additionally, we evaluated the use of DPD to monitor the processing status of proinsulin by inhibition of the insulin processing pathway. The rat insulinoma cell line INS-1 stimulated either with 2 mmol/l or 10 mmol/l glucose was used as model to generate differential peptide displays. In parallel, the bioactivity of the supernatants from the INS-1 cells was measured by glucose uptake and lipolysis assays using the adipocyte cell line 3T3-L1. We were able to quickly and elegantly trace the known activity of insulin to increase glucose uptake and inhibit lipolysis. Following re-chromatography of selected fractions, relevant peptides were identified by DPD and bioassays: the rat insulin-1 precursor and two different insulin peptides. We demonstrated in a semi-quantitative fashion that inhibition of proinsulin processing leads to accumulation of the insulin precursor, and reduced secretion of insulin-1. Thus, we conclude that DPD is an attractive support technology in peptide purification strategies aiming to identify bioactive compounds, and is superior to ELISA in discriminating between the processing status of insulin and its precursor.

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. An important subset of the proteome, peptides and peptide hormones secreted by the pancreas are deregulated in T2DM. The mass range of peptides and small proteins (1-20 kDa) is only sufficiently targeted by peptidomics, a combination of liquid chromatographic and mass spectrometric (MS) peptide analysis. Here, we describe the application of isotope label-free quantitative peptidomics to display and quantify relevant changes in the level of pancreatic peptides and peptide hormones in a preclinical model of T2DM, the Lep(ob)/Lep(ob) mouse. The amino acid sequence of statistical relevant top candidates was determined by MS/MS fragmentation or Edman degradation. The comparison of lean versus obese mice revealed increased levels of islet-specific peptides that can be divided into the following categories 1) the major islet peptide hormones insulin, amylin and glucagon; 2) proinsulin and C-peptide and 3) novel processing products of secretogranin, glucagon and amylin. Furthermore, we found increased levels of proteins and peptides implicated in zymogen granule maturation (syncollin) and nutritional digestion. In summary, our findings demonstrate that peptidomics is a valid approach to screen for novel peptide biomarkers.

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. Improvements in peptidomics research were made based on separation of peptides and/or proteins by their physico-chemical properties in combination with mass spectrometric detection, respectively identification, and sophisticated bioinformatic tools for data analysis. To evaluate the potential of serological tumor marker detection by differential peptide display (DPD) we analyzed plasma samples from a tumor graft model. After subcutaneous injection of HCT-116 cells in immunodeficient mice and their growth to a palpable tumor, plasma samples were analyzed by DPD. The comparison of obtained mass spectrometric data allows discovery of tumor specific peptides which fit well into the biological context of cancer pathogenesis and show a strong correlation to tumor growth. The identified peptides indicate events associated with hyper-proliferation and dedifferentiation of cells from an epithelial origin, which are typical characteristics of human carcinomas. We conclude that these findings are a "proof of principle" to detect differentially expressed, tumor-related peptides in plasma of tumor-bearing mice.

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. Mass spectrometry (MS) is used as a qualitative and quantitative analysis tool without previous trypsin digestion or labeling of the samples. Circulating peptides (< 15 kDa) were extracted from 1.3 mL plasma samples and the extracts separated by liquid chromatography into 96 fractions. Each fraction was subjected to MALDI MS, and mass spectra of all fractions were combined resulting in a 2D-display of > 2,000 peptides from each sample. Endogenous peptides that responded to oral glucose challenge were detected by DPD of pre-and post-challenge plasma samples from 16 healthy volunteers and subsequently identified by nESI-qTOF MS. Two of the 15 MS peaks that were significantly modulated by glucose challenge were subsequently identified as insulin and C-peptide. These results were validated by using immunoassays for insulin and C-peptide. This paper serves as a proof of principle for proteomic biomarker discovery down to the pM concentration range by using small amounts of human plasma.

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., LC-MS). Low-molecular-weight proteins (peptides) as products of proteolytic cleavage events exhibit a close connection to protease activity. Increased or altered activity of proteases during sample collection, specimen generation, sample storage, and processing is mirrored by alterations in abundance of specific peptides. Awareness of clinical practices in medical laboratories and the current knowledge allow for identification of specific variables that affect the results of a peptidomic study. Knowledge of pre-analytical variables is a prerequisite to understand and control their impact.

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., LC-MS). Low-molecular weight proteins (peptides) as products of proteolytic cleavage events exhibit a close connection to protease activity and function. Altered proteolytic activity during sample collection, preparation, handling, and storage is mirrored by alterations in abundance of specific peptides. Awareness of clinical practices in medical laboratories allows for the identification of specific variables that may affect the results of a peptidomic study. Knowledge of pre-analytical variables is a prerequisite to understand and control their impact.