Harmonizing Labeling and Analytical Strategies to Obtain Protein Turnover Rates in Intact Adult Animals

Chronic alcohol consumption reprograms hepatic metabolism through organelle-specific acetylation in mice

Post-translational acetylation of proteins by acetyl-CoA is a crucial regulator of proteostasis and substrate metabolism. Ethanol metabolism in the liver induces protein accumulation, acetylation and metabolic disruption. While acetylation impacts enzyme activity and stability, its role in ethanol-related protein accumulation and metabolic dysfunction remains unclear. Using stable isotope-based proteomics, acetylomics, and metabolic profiling in a mouse model of chronic ethanol-induced liver injury, we demonstrate that ethanol induces hepatic steatosis, inflammation, oxidative stress, and proteinopathy linked to altered protein turnover. Ethanol increased the cytosolic protein turnover related to oxidative stress and detoxification, while reducing turnover of mitochondrial metabolic enzymes. It also elevated the acetylation of mitochondrial enzymes and nuclear histones with minimal cytosolic changes, impairing mitochondrial protein degradation. These changes were associated with altered levels of acyl-CoAs and acyl-carnitines, amino acids, and tricarboxylic acid (TCA) cycle intermediates, reflecting impaired fatty acid oxidation, nitrogen disposal and TCA cycle activities. These results suggest that ethanol-induced acetylation contributes to liver injury and that targeting acetylation may offer treatment for alcohol-induced liver diseases.

Improved Method to Determine Protein Turnover Rates with Heavy Water Labeling by Mass Isotopomer Ratio Selection

The synthesis and degradation rates of proteins form an essential component of gene expression control. Heavy water labeling has been used in conjunction with mass spectrometry to measure protein turnover rates, but the optimal analytical approaches to derive turnover rates from the mass isotopomer patterns of deuterium-labeled peptides continue to be a subject of research. Here, we describe a method that comprises (1) a nearest lookup of numerically approximated peptide isotope envelopes, coupled to (2) the selection of optimal mass isotopomer pairs based on peptide sequence rules, to calculate the molar fraction of new peptide synthesis in heavy water labeling mass spectrometry experiments. We validated our approach using an experimental calibration standard comprising mixtures of fully unlabeled and fully labeled proteomes. We then reanalyzed 17 proteome-wide turnover experiments from four mouse organs across multiple data sets and showed that the combined nearest-lookup and rule-based mass isotopomer ratio selection method increases the coverage of well-fitted peptides in protein turnover experiments by up to 58 ± 13%. The workflow is implemented in the Riana software tool for protein turnover analysis and may avail ongoing efforts to study the synthesis and degradation kinetics of proteins in animals on a proteome-wide scale.

Quantifying protein kinetics in vivo: influence of precursor dynamics on product labeling

Protein kinetics can be quantified by coupling stable isotope tracer methods with mass spectrometry readouts; however, interconnected decision points in the experimental design affect the complexity of the workflow and impact data interpretations. For example, choosing between a single bolus (pulse-chase) or a continuous exposure protocol influences subsequent decisions regarding when to measure and how to model the temporal labeling of a target protein. Herein, we examine the merits of in vivo tracer protocols, and we direct attention toward stable isotope tracer experiments that rely on administering a single bolus since these are generally more practical to use as compared with continuous administration protocols. We demonstrate how the interplay between precursor and product kinetics impacts downstream analytics and calculations by contrasting fast versus slow turnover precursors (e.g., 13C-leucine vs. 2H-water, respectively). Although the data collected here underscore certain advantages of using longer-lived precursors (e.g., 2H- or 18O-water), the results also highlight the influence of tracer recycling on measures of protein turnover. We discuss the impact of tracer recycling and consider how the sampling interval is critical for interpreting studies. Finally, we demonstrate that tracer recycling does not limit the ability to perform back-to-back studies of protein kinetics. It is possible to run experiments in which subjects are used as their own controls even though the precursor and product remain labeled following an initial tracer dosing.NEW & NOTEWORTHY We demonstrate a simple and robust protocol for measuring protein synthesis, the work considers problems encountered in experimental design. The logic can enable biologists with limited resources and/or can facilitate scenarios where higher throughput experiments are needed.

Deuterium labeling enables proteome wide turnover kinetics analysis in cell culture

The half-life of proteins is tightly regulated and underlies many cellular processes. It remains unclear the extent to which proteins are dynamically synthesized and degraded in different cell types and cell states. We introduce an improved D2O labeling workflow and apply it to examine the landscape of protein turnover in pluripotent and differentiating human induced pluripotent stem cells (hiPSC). The majority of hiPSC proteins show minimal turnover beyond cell doubling rates, but we also identify over 100 new fast-turnover proteins not previously described as short-lived. These include proteins that function in cell division and cell cycle checkpoints, that are enriched in APC/C and SPOP degrons, and that are depleted upon pluripotency exit. Differentiation rapidly shifts the set of fast-turnover proteins toward including RNA binding and splicing proteins. The ability to identify fast-turnover proteins in different cell cultures also facilitates secretome analysis, as exemplified by studies of hiPSC-derived cardiac myocytes and primary human cardiac fibroblasts. The presented workflow is broadly applicable to protein turnover studies in diverse primary, pluripotent, and transformed cells.

Current approaches in CRISPR-Cas system for metabolic disorder

A new era in genomic medicine has been brought by the development of CRISPR-Cas technology, which presents hitherto unheard-of possibilities for the treatment of metabolic illnesses. The treatment approaches used in CRISPR/Cas9-mediated gene therapy, emphasize distribution techniques such as viral vectors and their use in preclinical models of metabolic diseases like hypercholesterolemia, glycogen storage diseases, and phenylketonuria. The relevance of high-throughput CRISPR screens for target identification in discovering new genes and pathways associated with metabolic dysfunctions is an important aspect of the discovery of new approaches. With cutting-edge options for genetic correction and cellular regeneration, the combination of CRISPR-Cas technology with stem cell therapy has opened new avenues for the treatment of metabolic illnesses. The integration of stem cell therapy and CRISPR-Cas technology is an important advance in the treatment of metabolic diseases, which are difficult to treat because of their intricate genetic foundations. This chapter addresses the most recent developments in the application of stem cell therapy and CRISPR-Cas systems to treat a variety of metabolic disorders, providing fresh hope for effective and maybe curative therapies. This chapter examines techniques and developments that have been made recently to address a variety of metabolic disorders using CRISPR-Cas systems. Our chapter focuses on the foundational workings of CRISPR-Cas technology and its potential uses in gene editing, gene knockout, and activation/repression-based gene modification.

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.

Improved determination of protein turnover rate with heavy water labeling by mass isotopomer ratio selection

The synthesis and degradation rates of proteins form an essential component of gene expression control. Heavy water labeling has been used in conjunction with mass spectrometry to measure protein turnover rates, but the optimal analytical approaches to derive turnover rates from the isotopomer patterns of deuterium labeled peptides continue to be a subject of research. Here we describe a method, which comprises a reverse lookup of numerically approximated peptide isotope envelopes, coupled to the selection of optimal isotopomer pairs based on peptide sequence, to calculate the molar fraction of new peptide synthesis in heavy water labeling mass spectrometry experiments. We validated this approach using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes. We then re-analyzed 17 proteome-wide turnover experiments from four mouse organs, and showed that the method increases the coverage of well-fitted peptides in protein turnover experiments by 25-82%. The method is implemented in the Riana software tool for protein turnover analysis, and may avail ongoing efforts to study the synthesis and degradation kinetics of proteins in animals on a proteome-wide scale.

What’s new

We describe a reverse lookup method to calculate the molar fraction of new synthesis from numerically approximated peptide isotopomer profiles in heavy water labeling mass spectrometry experiments. Using an experimental calibration curve comprising mixtures of fully unlabeled and fully labeled proteomes at various proportions, we show that this method provides a straightforward way to calculate the proportion of new proteins in a protein pool from arbitrarily chosen isotopomer ratios. We next analyzed which of the isotopomer pairs within the peptide isotope envelope yielded isotopomer time courses that fit most closely to kinetic models, and found that the identity of the isotopomer pair depends partially on the number of deuterium accessible labeling sites of the peptide. We next derived a strategy to automatically select the isotopomer pairs to calculate turnover rates based on peptide sequence, and showed that this increases the coverage of existing proteome-wide turnover experiments in multiple data sets of the mouse heart, liver, kidney, and skeletal muscle by up to 25-82%.

Exact Integral Formulas for False Discovery Rate and the Variance of False Discovery Proportion

Multiple hypothesis testing is an integral component of data analysis for large-scale technologies such as proteomics, transcriptomics, or metabolomics, for which the false discovery rate (FDR) and positive FDR (pFDR) have been accepted as error estimation and control measures. The pFDR is the expectation of false discovery proportion (FDP), which refers to the ratio of the number of null hypotheses to that of all rejected hypotheses. In practice, the expectation of ratio is approximated by the ratio of expectation; however, the conditions for transforming the former into the latter have not been investigated. This work derives exact integral expressions for the expectation (pFDR) and variance of FDP. The widely used approximation (ratio of expectations) is shown to be a particular case (in the limit of a large sample size) of the integral formula for pFDR. A recurrence formula is provided to compute the pFDR for a predefined number of null hypotheses. The variance of FDP was approximated for a practical application in peptide identification using forward and reversed protein sequences. The simulations demonstrate that the integral expression exhibits better accuracy than the approximate formula in the case of a small number of hypotheses. For large sample sizes, the pFDRs obtained by the integral expression and approximation do not differ substantially. Applications to proteomics data sets are included.

A Ratiometric Catalog of Protein Isoform Shifts in the Cardiac Fetal Gene Program

The fetal genetic program orchestrates cardiac development and the re-expression of fetal genes is thought to underlie cardiac disease and adaptation. Here, a proteomics ratio test using mass spectrometry is applied to find protein isoforms with statistically significant usage differences in the fetal vs. postnatal mouse heart. Changes in isoform usage ratios are pervasive at the protein level, with 104 significant events observed, including 88 paralog-derived isoform switching events and 16 splicing-derived isoform switching events between fetal and postnatal hearts. The ratiometric proteomic comparisons rediscovered hallmark fetal gene signatures including a postnatal switch from fetal β (MYH7) toward ɑ (MYH6) myosin heavy chains and from slow skeletal muscle (TNNI1) toward cardiac (TNNI3) troponin I. Altered usages in metabolic proteins are prominent, including a platelet to muscle phosphofructokinase (PFKP - PFKM), enolase 1 to 3 (ENO1 - ENO3), and alternative splicing of pyruvate kinase M2 toward M1 (PKM2 - PKM1) isoforms in glycolysis. The data also revealed a parallel change in mitochondrial proteins in cardiac development, suggesting the shift toward aerobic respiration involves also a remodeling of the mitochondrial protein isoform proportion. Finally, a number of glycolytic protein isoforms revert toward their fetal forms in adult hearts under pathological cardiac hypertrophy, suggesting their functional roles in adaptive or maladaptive response, but this reversal is partial. In summary, this work presents a catalog of ratiometric protein markers of the fetal genetic program of the mouse heart, including previously unreported splice isoform markers.

Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions

The spatial and temporal distributions of proteins are critical to protein function, but cannot be directly assessed by measuring protein bundance. Here we describe a mass spectrometry-based proteomics strategy, Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently protein turnover rates and subcellular localization in the same experiment. Applying the method, we find that unfolded protein response (UPR) has different effects on protein turnover dependent on their subcellular location in human AC16 cells, with proteome-wide slowdown but acceleration among stress response proteins in the ER and Golgi. In parallel, UPR triggers broad differential localization of proteins including RNA-binding proteins and amino acid transporters. Moreover, we observe newly synthesized proteins including EGFR that show a differential localization under stress than the existing protein pools, reminiscent of protein trafficking disruptions. We next applied SPLAT to an induced pluripotent stem cell derived cardiomyocyte (iPSC-CM) model of cancer drug cardiotoxicity upon treatment with the proteasome inhibitor carfilzomib. Paradoxically, carfilzomib has little effect on global average protein half-life, but may instead selectively disrupt sarcomere protein homeostasis. This study provides a view into the interactions of protein spatial and temporal dynamics and demonstrates a method to examine protein homeostasis regulations in stress and drug response.

In utero pulse injection of isotopic amino acids quantifies protein turnover rates during murine fetal development

Protein translational control is critical for ensuring that the fetus develops correctly and that necessary organs and tissues are formed and functional. We developed an in utero method to quantify tissue-specific protein dynamics by monitoring amino acid incorporation into the proteome after pulse injection. Fetuses of pregnant mice were injected with isotopically labeled lysine and arginine via the vitelline vein at various embyonic days, and organs and tissues were harvested. By analyzing the nascent proteome, unique signatures of each tissue were identified by hierarchical clustering. In addition, the quantified proteome-wide turnover rates were calculated between 3.81E-5 and 0.424 h-1. We observed similar protein turnover profiles for analyzed organs (e.g., liver vs. brain); however, their distributions of turnover rates vary significantly. The translational kinetic profiles of developing organs displayed differentially expressed protein pathways and synthesis rates, which correlated with known physiological changes during mouse development.

The 2023 Report on the Proteome from the HUPO Human Proteome Project

Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.

Simultaneous proteome localization and turnover analysis reveals spatiotemporal features of protein homeostasis disruptions

The functions of proteins depend on their spatial and temporal distributions, which are not directly measured by static protein abundance. Under endoplasmic reticulum (ER) stress, the unfolded protein response (UPR) pathway remediates proteostasis in part by altering the turnover kinetics and spatial distribution of proteins. A global view of these spatiotemporal changes has yet to emerge and it is unknown how they affect different cellular compartments and pathways. Here we describe a mass spectrometry-based proteomics strategy and data analysis pipeline, termed Simultaneous Proteome Localization and Turnover (SPLAT), to measure concurrently the changes in protein turnover and subcellular distribution in the same experiment. Investigating two common UPR models of thapsigargin and tunicamycin challenge in human AC16 cells, we find that the changes in protein turnover kinetics during UPR varies across subcellular localizations, with overall slowdown but an acceleration in endoplasmic reticulum and Golgi proteins involved in stress response. In parallel, the spatial proteomics component of the experiment revealed an externalization of amino acid transporters and ion channels under UPR, as well as the migration of RNA-binding proteins toward an endosome co-sedimenting compartment. The SPLAT experimental design classifies heavy and light SILAC labeled proteins separately, allowing the observation of differential localization of new and old protein pools and capturing a partition of newly synthesized EGFR and ITGAV to the ER under stress that suggests protein trafficking disruptions. Finally, application of SPLAT toward human induced pluripotent stem cell derived cardiomyocytes (iPSC-CM) exposed to the cancer drug carfilzomib, identified a selective disruption of proteostasis in sarcomeric proteins as a potential mechanism of carfilzomib-mediated cardiotoxicity. Taken together, this study provides a global view into the spatiotemporal dynamics of human cardiac cells and demonstrates a method for inferring the coordinations between spatial and temporal proteome regulations in stress and drug response.

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.

A large-scale LC-MS dataset of murine liver proteome from time course of heavy water metabolic labeling

Metabolic stable isotope labeling with heavy water followed by liquid chromatography coupled with mass spectrometry (LC-MS) is a powerful tool for in vivo protein turnover studies. Several algorithms and tools have been developed to determine the turnover rates of peptides and proteins from time-course stable isotope labeling experiments. The availability of benchmark mass spectrometry data is crucial to compare and validate the effectiveness of newly developed techniques and algorithms. In this work, we report a heavy water-labeled LC-MS dataset from the murine liver for protein turnover rate analysis. The dataset contains eighteen mass spectral data with their corresponding database search results from nine different labeling durations and quantification outputs from d2ome+ software. The dataset also contains eight mass spectral data from two-dimensional fractionation experiments on unlabeled samples.

Cryptic kin discrimination during communal lactation in mice favours cooperation between relatives

Breeding females can cooperate by rearing their offspring communally, sharing synergistic benefits of offspring care but risking exploitation by partners. In lactating mammals, communal rearing occurs mostly among close relatives. Inclusive fitness theory predicts enhanced cooperation between related partners and greater willingness to compensate for any partner under-investment, while females are less likely to bias investment towards own offspring. We use a dual isotopic tracer approach to track individual milk allocation when familiar pairs of sisters or unrelated house mice reared offspring communally. Closely related pairs show lower energy demand and pups experience better access to non-maternal milk. Lactational investment is more skewed between sister partners but females pay greater energetic costs per own offspring reared with an unrelated partner. The choice of close kin as cooperative partners is strongly favoured by these direct as well as indirect benefits, providing a driver to maintain female kin groups for communal breeding.

Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling

Heavy water metabolic labeling followed by liquid chromatography coupled with mass spectrometry is a powerful high throughput technique for measuring the turnover rates of individual proteins in vivo. The turnover rate is obtained from the exponential decay modeling of the depletion of the monoisotopic relative isotope abundance. We provide theoretical formulas for the time course dynamics of six mass isotopomers and use the formulas to introduce a method that utilizes partial isotope profiles, only two mass isotopomers, to compute protein turnover rate. The use of partial isotope profiles alleviates the interferences from co-eluting contaminants in complex proteome mixtures and improves the accuracy of the estimation of label enrichment. In five different datasets, the technique consistently doubles the number of peptides with high goodness-of-fit characteristics of the turnover rate model. We also introduce a software tool, d2ome+, which automates the protein turnover estimation from partial isotope profiles.

An Integrative Biology Approach to Quantify the Biodistribution of Azidohomoalanine In Vivo

Background

Identification and quantitation of newly synthesized proteins (NSPs) are critical to understanding protein dynamics in development and disease. Probing the nascent proteome can be achieved using non-canonical amino acids (ncAAs) to selectively label the NSPs utilizing endogenous translation machinery, which can then be quantitated with mass spectrometry. We have previously demonstrated that labeling the in vivo murine proteome is feasible via injection of azidohomoalanine (Aha), an ncAA and methionine (Met) analog, without the need for Met depletion. Aha labeling can address biological questions wherein temporal protein dynamics are significant. However, accessing this temporal resolution requires a more complete understanding of Aha distribution kinetics in tissues.

Results

To address these gaps, we created a deterministic, compartmental model of the kinetic transport and incorporation of Aha in mice. Model results demonstrate the ability to predict Aha distribution and protein labeling in a variety of tissues and dosing paradigms. To establish the suitability of the method for in vivo studies, we investigated the impact of Aha administration on normal physiology by analyzing plasma and liver metabolomes following various Aha dosing regimens. We show that Aha administration induces minimal metabolic alterations in mice.

Conclusions

Our results demonstrate that we can reproducibly predict protein labeling and that the administration of this analog does not significantly alter in vivo physiology over the course of our experimental study. We expect this model to be a useful tool to guide future experiments utilizing this technique to study proteomic responses to stimuli.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12195-023-00760-4.

Metabolic deuterium oxide (D2O) labeling in quantitative omics studies: A tutorial review

Mass spectrometry (MS) is an invaluable tool for sensitive detection and characterization of individual biomolecules in omics studies. MS combined with stable isotope labeling enables the accurate and precise determination of quantitative changes occurring in biological samples. Metabolic isotope labeling, wherein isotopes are introduced into biomolecules through biosynthetic metabolism, is one of the main labeling strategies. Among the precursors employed in metabolic isotope labeling, deuterium oxide (D₂O) is cost-effective and easy to implement in any biological systems. This tutorial review aims to explain the basic principle of D₂O labeling and its applications in omics research. D₂O labeling incorporates D into stable C-H bonds in various biomolecules, including nucleotides, proteins, lipids, and carbohydrates. Typically, D₂O labeling is performed at low enrichment of 1%-10% D₂O, which causes subtle changes in the isotopic distribution of a biomolecule, instead of the complete separation between labeled and unlabeled samples in a mass spectrum. D₂O labeling has been employed in various omics studies to determine the metabolic flux, turnover rate, and relative quantification. Moreover, the advantages and challenges of D₂O labeling and its future prospects in quantitative omics are discussed. The economy, versatility, and convenience of D₂O labeling will be beneficial for the long-term omics studies for higher organisms.

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.

Determining and interpreting protein lifetimes in mammalian tissues

The orchestration of protein production and degradation, and the regulation of protein lifetimes, play a central role in the majority of biological processes. Recent advances in proteomics have enabled the estimation of protein half-lives for thousands of proteins in vivo. What is the utility of these measurements, and how can they be leveraged to interpret the proteome changes occurring during development, aging, and disease? This opinion article summarizes leading technical approaches and highlights their strengths and weaknesses. We also disambiguate frequently used terminology, illustrate recent mechanistic insights, and provide guidance for interpreting and validating protein turnover measurements. Overall, protein lifetimes, coupled to estimates of protein levels, are essential for obtaining a deep understanding of mammalian biology and the basic processes defining life itself.

A bright future for proteomics of health and disease. Introduction to the US HUPO 2021 themed issue - proteomics from single cell to systems biology in health and disease

In this themed issue of Molecular Omics, in partnership with the U.S. Human Proteome Organization, we are proud to present the latest research featured at the 17th Annual US HUPO conference: Proteomics from Single Cell to Systems Biology in Health and Disease. This issue is a testament to the continuing contributions of proteomic research, particularly the application of modern mass spectrometry-based proteomic workflows, to the advancement of our understanding of the underlying human biology and mechanisms of disease.

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.

Utilizing Nonequilibrium Isotope Enrichments to Dramatically Increase Turnover Measurement Ranges in Single Biopsy Samples from Humans

The synthesis of new proteins and the degradation of old proteins in vivo can be quantified in serial samples using metabolic isotope labeling to measure turnover. Because serial biopsies in humans are impractical, we set out to develop a method to calculate the turnover rates of proteins from single human biopsies. This method involved a new metabolic labeling approach and adjustments to the calculations used in previous work to calculate protein turnover. We demonstrate that using a nonequilibrium isotope enrichment strategy avoids the time dependent bias caused by variable lag in label delivery to different tissues observed in traditional metabolic labeling methods. Turnover rates are consistent for the same subject in biopsies from different labeling periods, and turnover rates calculated in this study are consistent with previously reported values. We also demonstrate that by measuring protein turnover we can determine where proteins are synthesized. In human subjects a significant difference in turnover rates differentiated proteins synthesized in the salivary glands versus those imported from the serum. We also provide a data analysis tool, DeuteRater-H, to calculate protein turnover using this nonequilibrium metabolic 2H2O method.