Proteome Dynamics from Heavy Water Metabolic Labeling and Peptide Tandem Mass Spectrometry

Retention Time Alignment for Protein Turnover Studies Using Heavy Water Metabolic Labeling

Retention time (RT) alignment has been important for robust protein identification and quantification in proteomics. In data-dependent acquisition mode, whereby the precursor ions are semistochastically chosen for fragmentation in MS/MS, the alignment is used in an approach termed matched between runs (MBR). MBR transfers peptides, which were fragmented and identified in one experiment, to a replicate experiment where they were not identified. Before the MBR transfer, the RTs of experiments are aligned to reduce the chance of erroneous transfers. Despite its widespread use in other areas of quantitative proteomics, RT alignment has not been applied in data analyses for protein turnover using an atom-based stable isotope-labeling agent such as metabolic labeling with deuterium oxide, D2O. Deuterium incorporation changes isotope profiles of intact peptides in full scans and their fragment ions in tandem mass spectra. It reduces the peptide identification rates in current database search engines. Therefore, the MBR becomes more important. Here, we report on an approach to incorporate RT alignment with peptide quantification in studies of proteome turnover using heavy water metabolic labeling and LC-MS. The RT alignment uses correlation-optimized time warping. The alignment, followed by the MBR, improves labeling time point coverage, especially for long labeling durations.

Protein turnover models for LC-MS data of heavy water metabolic labeling

Protein turnover is vital for cellular functioning and is often associated with the pathophysiology of a variety of diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled to mass spectrometry is a powerful tool to study in vivo protein turnover in high throughput and large scale. Heavy water is a cost-effective and easy to use labeling agent. It labels all nonessential amino acids. Due to its toxicity in high concentrations (20% or higher), small enrichments (8% or smaller) of heavy water are used with most organisms. The low concentration results in incomplete labeling of peptides/proteins. Therefore, the data processing is more challenging and requires accurate quantification of labeled and unlabeled forms of a peptide from overlapping mass isotopomer distributions. The work describes the bioinformatics aspects of the analysis of heavy water labeled mass spectral data, available software tools and current challenges and opportunities.

Day and night isotope labelling reveal metabolic pathway specific regulation of protein synthesis rates in Arabidopsis

Plants have a diurnal separation of metabolic fluxes and a need for differential maintenance of protein machinery in the day and night. To directly assess the output of the translation process and to estimate the ATP investment involved, the individual rates of protein synthesis and degradation of hundreds of different proteins need to be measured simultaneously. We quantified protein synthesis and degradation through pulse labelling with heavy hydrogen in Arabidopsis thaliana rosettes to allow such an assessment of ATP investment in leaf proteome homeostasis on a gene-by-gene basis. Light-harvesting complex proteins were synthesised and degraded much faster in the day (approximately 10:1), while carbon metabolism and vesicle trafficking components were translated at similar rates day or night. Few leaf proteins changed in abundance between the day and the night despite reduced protein synthesis rates at night, indicating that protein degradation rates are tightly coordinated. The data reveal how the pausing of photosystem synthesis and degradation at night allows the redirection of a decreased energy budget to a selective night-time maintenance schedule.

Influence of Subcellular Localization and Functional State on Protein Turnover

Protein homeostasis is an equilibrium of paramount importance that maintains cellular performance by preserving an efficient proteome. This equilibrium avoids the accumulation of potentially toxic proteins, which could lead to cellular stress and death. While the regulators of proteostasis are the machineries controlling protein production, folding and degradation, several other factors can influence this process. Here, we have considered two factors influencing protein turnover: the subcellular localization of a protein and its functional state. For this purpose, we used an imaging approach based on the pulse-labeling of 17 representative SNAP-tag constructs for measuring protein lifetimes. With this approach, we obtained precise measurements of protein turnover rates in several subcellular compartments. We also tested a selection of mutants modulating the function of three extensively studied proteins, the Ca²⁺ sensor calmodulin, the small GTPase Rab5a and the brain creatine kinase (CKB). Finally, we followed up on the increased lifetime observed for the constitutively active Rab5a (Q79L), and we found that its stabilization correlates with enlarged endosomes and increased interaction with membranes. Overall, our data reveal that both changes in protein localization and functional state are key modulators of protein turnover, and protein lifetime fluctuations can be considered to infer changes in cellular behavior.

Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy

Simple light isotope metabolic labeling (SLIM labeling) is an innovative method to quantify variations in the proteome based on an original in vivo labeling strategy. Heterotrophic cells grown in U-[12C] as the sole source of carbon synthesize U-[12C]-amino acids, which are incorporated into proteins, giving rise to U-[12C]-proteins. This results in a large increase in the intensity of the monoisotope ion of peptides and proteins, thus allowing higher identification scores and protein sequence coverage in mass spectrometry experiments. This method, initially developed for signal processing and quantification of the incorporation rate of 12C into peptides, was based on a multistep process that was difficult to implement for many laboratories. To overcome these limitations, we developed a new theoretical background to analyze bottom-up proteomics data using SLIM-labeling (bSLIM) and established simple procedures based on open-source software, using dedicated OpenMS modules, and embedded R scripts to process the bSLIM experimental data. These new tools allow computation of both the 12C abundance in peptides to follow the kinetics of protein labeling and the molar fraction of unlabeled and 12C-labeled peptides in multiplexing experiments to determine the relative abundance of proteins extracted under different biological conditions. They also make it possible to consider incomplete 12C labeling, such as that observed in cells with nutritional requirements for nonlabeled amino acids. These tools were validated on an experimental dataset produced using various yeast strains of Saccharomyces cerevisiae and growth conditions. The workflows are built on the implementation of appropriate calculation modules in a KNIME working environment. These new integrated tools provide a convenient framework for the wider use of the SLIM-labeling strategy.

Isotope Fractionation during Gas Chromatography Can Enhance Mass Spectrometry-Based Measures of 2H-Labeling of Small Molecules

Stable isotope tracers can be used to quantify the activity of metabolic pathways. Specifically, 2H-water is quite versatile, and its incorporation into various products can enable measurements of carbohydrate, lipid, protein and nucleic acid kinetics. However, since there are limits on how much 2H-water can be administered and since some metabolic processes may be slow, it is possible that one may be challenged with measuring small changes in isotopic enrichment. We demonstrate an advantage of the isotope fractionation that occurs during gas chromatography, namely, setting tightly bounded integration regions yields a powerful approach for determining isotope ratios. We determined how the degree of isotope fractionation, chromatographic peak width and mass spectrometer dwell time can increase the apparent isotope labeling. Relatively simple changes in the logic surrounding data acquisition and processing can enhance gas chromatography-mass spectrometry measures of low levels of 2H-labeling, this is especially useful when asymmetrical peaks are recorded at low signal:background. Although we have largely focused attention on alanine (which is of interest in studies of protein synthesis), it should be possible to extend the concepts to other analytes and/or hardware configurations.

Partial Isotope Profiles Are Sufficient for Protein Turnover Analysis Using Closed-Form Equations of Mass Isotopomer Dynamics

Metabolic labeling with atom-based heavy isotopes, followed by liquid chromatography coupled with mass spectrometry (LC-MS), has been a powerful technique for studies of proteome and metabolome. In proteomics, the protein turnover of thousands of proteins can be estimated from the gradual incorporation of ²H or ¹⁵N in the diet. Software tools have been developed to automate the estimation of protein turnover. Traditionally, the turnover has been estimated using the time course of the depletion of the normalized abundance of monoisotopes. While the bioinformatic aspects of peak detection and integration, time course modeling, and uncertainty estimation have progressed, mass isotopomer dynamics during label incorporation has only been modeled from approximate approaches or numerical simulations. We derive closed-form equations that describe the dynamics of mass isotopomers during metabolic labeling with an atom-based stable isotope. The derived equations create an alternative method for estimating label incorporation. They also provide opportunities for estimation of precursor-product relationships in species or systems where they are unknown. The equations are useful in bioinformatic tools for analyzing mass spectral data from metabolic labeling.

Timepoint Selection Strategy for In Vivo Proteome Dynamics from Heavy Water Metabolic Labeling and LC-MS

Protein homeostasis, proteostasis, is essential for healthy cell functioning and is dysregulated in many diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled online to mass spectrometry (LC-MS) is a powerful high-throughput technique to study proteome dynamics in vivo. Longer labeling duration and dense timepoint sampling (TPS) of tissues provide accurate proteome dynamics estimations. However, the experiments are expensive, and they require animal housing and care, as well as labeling with stable isotopes. Often, the animals are sacrificed at selected timepoints to collect tissues. Therefore, it is necessary to optimize TPS for a given number of sampling points and labeling duration and target a specific tissue of study. Currently, such techniques are missing in proteomics. Here, we report on a formula-based stochastic simulation strategy for TPS for in vivo studies with heavy water metabolic labeling and LC-MS. We model the rate constant (lognormal), measurement error (Laplace), peptide length (gamma), relative abundance of the monoisotopic peak (beta regression), and the number of exchangeable hydrogens (gamma regression). The parameters of the distributions are determined using the corresponding empirical probability density functions from a large-scale dataset of murine heart proteome. The models are used in the simulations of the rate constant to minimize the root-mean-square error (rmse). The rmse for different TPSs shows structured patterns. They are analyzed to elucidate common features in the patterns.