TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies

Numbers of Exchangeable Hydrogens from LC-MS Data of Heavy Water Metabolically Labeled Samples

Labeling with deuterium oxide (D2O) has emerged as one of the preferred approaches for measuring the synthesis of individual proteins in vivo. In these experiments, the synthesis rates of proteins are determined by modeling mass shifts in peptides during the labeling period. This modeling depends on a theoretical maximum enrichment determined by the number of labeling sites (NEH) of each amino acid in the peptide sequence. Currently, NEH is determined from one set of published values. However, it has been demonstrated that NEH can differ between species and potentially tissues. The goal of this work was to determine the number of NEH for each amino acid within a given experiment to capture the conditions unique to that experiment. We used four methods to compute the NEH values. To test these approaches, we used two publicly available data sets. In a de novo approach, we compute NEH values and the label enrichment from the abundances of three mass isotopomers. The other three methods use the complete isotope profiles and body water enrichment in deuterium as an input parameter. They determine the NEH values by (1) minimizing the residual sum of squares, (2) from the mole percent excess of labeling, and (3) the time course profile of the depletion of the relative isotope abundance of monoisotope. In the test samples, the method using residual sum of squares performed the best. The methods are implemented in a tool for determining the NEH for each amino acid within a given experiment to use in the determination of protein synthesis rates using D2O.

Identification and functional analysis of senescent cells in the cardiovascular system using omics approaches

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality worldwide, and senescent cells have emerged as key contributors to its pathogenesis. Senescent cells exhibit cell cycle arrest and secrete a range of proinflammatory factors, termed the senescence-associated secretory phenotype (SASP), which promotes tissue dysfunction and exacerbates CVD progression. Omics technologies, specifically transcriptomics and proteomics, offer powerful tools to uncover and define the molecular signatures of senescent cells in cardiovascular tissue. By analyzing the comprehensive molecular profiles of senescent cells, omics approaches can identify specific genetic alterations, gene expression patterns, protein abundances, and metabolite levels associated with senescence in CVD. These omics-based discoveries provide insights into the mechanisms underlying senescence-induced cardiovascular damage, facilitating the development of novel diagnostic biomarkers and therapeutic targets. Furthermore, integration of multiple omics data sets enables a systems-level understanding of senescence in CVD, paving the way for precision medicine approaches to prevent or treat cardiovascular aging and its associated complications.

Flexible Quality Control for Protein Turnover Rates Using d2ome

Bioinformatics tools are used to estimate in vivo protein turnover rates from the LC-MS data of heavy water labeled samples in high throughput. The quantification includes peak detection and integration in the LC-MS domain of complex input data of the mammalian proteome, which requires the integration of results from different experiments. The existing software tools for the estimation of turnover rate use predefined, built-in, stringent filtering criteria to select well-fitted peptides and determine turnover rates for proteins. The flexible control of filtering and quality measures will help to reduce the effects of fluctuations and interferences to the signals from target peptides while retaining an adequate number of peptides. This work describes an approach for flexible error control and filtering measures implemented in the computational tool d2ome for automating protein turnover rates. The error control measures (based on spectral properties and signal features) reduced the standard deviation and tightened the confidence intervals of the estimated turnover rates.

Software Tool for Visualization and Validation of Protein Turnover Rates Using Heavy Water Metabolic Labeling and LC-MS

Metabolic stable isotope labeling followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies of individual proteins on a large scale and with high throughput. Turnover rates of thousands of proteins from dozens of time course experiments are determined by data processing tools, which are essential components of the workflows for automated extraction of turnover rates. The development of sophisticated algorithms for estimating protein turnover has been emphasized. However, the visualization and annotation of the time series data are no less important. The visualization tools help to validate the quality of the model fits, their goodness-of-fit characteristics, mass spectral features of peptides, and consistency of peptide identifications, among others. Here, we describe a graphical user interface (GUI) to visualize the results from the protein turnover analysis tool, d2ome, which determines protein turnover rates from metabolic D₂O labeling followed by LC-MS. We emphasize the specific features of the time series data and their visualization in the GUI. The time series data visualized by the GUI can be saved in JPEG format for storage and further dissemination.