Peptidomics analysis reveals changes in small urinary peptides in patients with interstitial cystitis/bladder pain syndrome

Peptidomic analysis of endogenous and bacterial protease activity in human plasma and wound fluids

Endogenous and bacterial proteases play important roles in wound healing and infection. Analysis of alterations in the low-molecular-weight peptidome by individual enzymes could therefore provide insight into proteolytic events occurring in wounds and may aid in the discovery of biomarkers. Using liquid chromatography with tandem mass spectrometry, we characterized the peptidome of plasma and acute wound fluids digested ex vivo with human (neutrophil elastase and cathepsin G) and bacterial proteases (Pseudomonas aeruginosa LasB and Staphyloccocus aureus V8). We identified over 100 protein targets for each enzyme and characterized enzyme specific peptides and cleavage patterns. Moreover, we found unique peptide regions in V8 digested samples that were also present in dressing extracts from S. aureus infected wounds. Finally, the work indicates that peptidomic analysis of qualitative differences of proteolytic activity of individual enzymes may aid in the discovery of potential diagnostic biomarkers for wound healing status.

Origins, Technological Advancement, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources instead of heading for a few single peptides in former peptide research. Mass spectrometry allows to detect a multitude of peptides in complex mixtures and thus enables new strategies leading to peptidomics. The term was established in the year 2001, and up to now, this new field has grown to over 3000 publications. Analytical techniques originally developed for fast and comprehensive analysis of peptides in proteomics were specifically adjusted for peptidomics. Although it is thus closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. Fundamental technological advancements of peptidomics since have occurred in mass spectrometry and data processing, including quantification, and more slightly in separation technology. Different strategies and diverse sources of peptidomes are mentioned by numerous applications, such as discovery of neuropeptides and other bioactive peptides, including the use of biochemical assays. Furthermore, food and plant peptidomics are introduced similarly. Additionally, applications with a clinical focus are included, comprising biomarker discovery as well as immunopeptidomics. This overview extensively reviews recent methods, strategies, and applications including links to all other chapters of this book.

Label-Free Quantitation of Endogenous Peptides

Liquid chromatography-mass spectrometry (LC-MS)-based peptidomics methods allow for the detection and identification of many peptides in a complex biological mixture in an untargeted manner. Quantitative peptidomics approaches allow for comparisons of peptide abundance between different samples, allowing one to draw conclusions about peptide differences as a function of experimental treatment or physiology. While stable isotope labeling is a powerful approach for quantitative proteomics and peptidomics, advances in mass spectrometry instrumentation and analysis tools have allowed label-free methods to gain popularity in recent years. In a general label-free quantitative peptidomics experiment, peak intensity information for each peptide is compared across multiple LC-MS runs. Here, we outline a general approach for label-free quantitative peptidomics experiments, including steps for sample preparation, LC-MS data acquisition, data processing, and statistical analysis. Special attention is paid to address run-to-run variability, which can lead to several major problems in label-free experiments. Overall, our method provides researchers with a framework for the development of their own quantitative peptidomics workflows applicable to quantitation of peptides from a wide variety of different biological sources.

Peptidomic Analysis Reveals Seasonal Neuropeptide and Peptide Hormone Changes in the Hypothalamus and Pituitary of a Hibernating Mammal

During the winter, hibernating mammals undergo extreme changes in physiology, which allow them to survive several months without access to food. These animals enter a state of torpor, which is characterized by decreased metabolism, near-freezing body temperatures, and a dramatically reduced heart rate. The neurochemical basis of this regulation is largely unknown. Based on prior evidence suggesting that the peptide-rich hypothalamus plays critical roles in hibernation, we hypothesized that changes in specific cell-cell signaling peptides (neuropeptides and peptide hormones) underlie physiological changes during torpor/arousal cycles. To test this hypothesis, we used a mass spectrometry-based peptidomics approach to examine seasonal changes of endogenous peptides that occur in the hypothalamus and pituitary of a model hibernating mammal, the thirteen-lined ground squirrel (Ictidomys tridecemlineatus). In the pituitary, we observed changes in several distinct peptide hormones as animals prepared for torpor in October, exited torpor in March, and progressed from spring (March) to fall (August). In the hypothalamus, we observed an overall increase in neuropeptides in October (pre-torpor), a decrease as the animal entered torpor, and an increase in a subset of neuropeptides during normothermic interbout arousals. Notable changes were observed for feeding regulatory peptides, opioid peptides, and several peptides without well-established functions. Overall, our study provides critical insight into changes in endogenous peptides in the hypothalamus and pituitary during mammalian hibernation that were not available from transcriptomic measurements. Understanding the molecular basis of the hibernation phenotype may pave the way for future efforts to employ hibernation-like strategies for organ preservation, combating obesity, and treatment of stroke.

Untargeted Metabolomics Pilot Study Using UHPLC-qTOF MS Profile in Sows' Urine Reveals Metabolites of Bladder Inflammation

Urinary tract infections (UTI) of sows (characterized by ascending infections of the urinary bladder (cyst), ureters, and renal pelvis), are major health issues with a significant economic impact to the swine industry. The current detection of UTI incidents lacks sensitivity; thus, UTIs remain largely under-diagnosed. The value of metabolomics in unraveling the mechanisms of sow UTI has not yet been established. This study aims to investigate the urine metabolome of sows for UTI biomarkers. Urine samples were collected from 58 culled sows from a farrow-to-finish herd in Greece. Urine metabolomic profiles in 31 healthy controls and in 27 inflammatory ones were evaluated. UHPLC-qTOF MS/MS was applied for the analysis with a combination of multivariate and univariate statistical analysis. Eighteen potential markers were found. The changes in several urine metabolites classes (nucleosides, indoles, isoflavones, and dipeptides), as well as amino-acids allowed for an adequate discrimination between the study groups. Identified metabolites were involved in purine metabolism; phenylalanine; tyrosine and tryptophan biosynthesis; and phenylalanine metabolism. Through ROC analysis it was shown that the 18 identified metabolite biomarkers exhibited good predictive accuracy. In summary, our study provided new information on the potential targets for predicting early and accurate diagnosis of UTI. Further, this information also sheds light on how it could be applied in live animals.