A Comparative Peptidomic Characterization of Cultured Skeletal Muscle Tissues Derived From db/db Mice

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.

Peptidome analysis reveals critical roles for peptides in a rat model of intestinal ischemia/reperfusion injury

Intestinal ischemia/reperfusion injury (IIRI) has the potential to be life threatening and is associated with significant morbidity and serious damage to distant sites in the body on account of disruption of the intestinal mucosal barrier. In the present study, we have explored this line of research by comparing and identifying peptides that originated from the intestinal segments of IIRI model rats by using liquid chromatography-mass spectrometry (LC-MS). We also analyzed the basic characteristics, cleavage patterns, and functional domains of differentially expressed peptides (DEPs) between the IIRI model rats and control (sham-operated) rats and identified bioactive peptides that are potentially associated with ischemia reperfusion injury. We also performed bioinformatics analyses in order to identify the biological roles of the DEPs based on their precursor proteins. Enrichment analysis demonstrated the role of several DEPs in impairment of the intestinal mucosal barrier caused by IIRI. Based on the results of comprehensive ingenuity pathway analysis, we identified the DEPs that were significantly correlated with IIRI. We identified a candidate precursor protein (Actg2) and seven of its peptides, and we found that Actg2-6 had a more significant difference in its expression, a longer half-life, and better lipophilicity, hydrophobicity, and stability than the other candidate Actg2 peptides examined. Furthermore, we observed that Actg2-6 might play critical roles in the protection of the intestinal mucosal barrier during IIRI. In summary, our study provides a better understanding of the peptidomics profile of IIRI, and the results indicate that Actg2-6 could be a useful target in the treatment of IIRI.

TMT-Based Comparative Peptidomics Analysis of Rice Seedlings under Salt Stress: An Accessible Method to Explore Plant Stress-Tolerance Processing

Rice (Oryza sativa L.) is an important staple crop, particularly in Asia, and abiotic stress conditions easily reduce its yields. Salt stress is one of the critical factors affecting rice growth and yield. In this study, a tandem mass tag (TMT)-based comparative peptidomics analysis of rice seedlings under salt stress was conducted. Rice seedlings were exposed to 50 and 150 mM NaCl for 24 and 72 h, respectively, and the root and shoot tissues of different treatment groups were collected separately for peptidomics analysis. A total of 911 and 1263 nonredundant peptides were identified in two pooled shoot tissue samples, while there were 770 and 672 nonredundant peptides in two pooled root tissue samples, respectively. Compared with the control groups, dozens to hundreds of differentially expressed peptides (DEPs) were characterized in all treatment groups. To explore the potential functions of these DEPs, we analyzed the basic characteristics of DEPs and further analyzed the annotated Gene Ontology terms according to their precursor proteins. Several DEP precursor proteins were closely related to the response to salt stress, and some were derived from the functional domains of their corresponding precursors. The germination rate and cotyledon greening rate of transgenic Arabidopsis expressing two DEPs, OsSTPE2 and OsSTPE3, were significantly enhanced under salt stress. The described workflow enables the discovery of a functional pipeline for the characterization of the plant peptidome and reveals two new plant peptides that confer salinity tolerance to plants. Data are available via ProteomeXchange with identifier PXD037574.

Combining mass spectrometry and machine learning to discover bioactive peptides

Peptides play important roles in regulating biological processes and form the basis of a multiplicity of therapeutic drugs. To date, only about 300 peptides in human have confirmed bioactivity, although tens of thousands have been reported in the literature. The majority of these are inactive degradation products of endogenous proteins and peptides, presenting a needle-in-a-haystack problem of identifying the most promising candidate peptides from large-scale peptidomics experiments to test for bioactivity. To address this challenge, we conducted a comprehensive analysis of the mammalian peptidome across seven tissues in four different mouse strains and used the data to train a machine learning model that predicts hundreds of peptide candidates based on patterns in the mass spectrometry data. We provide in silico validation examples and experimental confirmation of bioactivity for two peptides, demonstrating the utility of this resource for discovering lead peptides for further characterization and therapeutic development.