Metabolic Stable Isotope Labeling Research Articles

Derailed protein turnover in the aging mammalian brain

Efficient protein turnover is essential for cellular homeostasis and organ function. Loss of proteostasis is a hallmark of aging culminating in severe dysfunction of protein turnover. To investigate protein turnover dynamics as a function of age, we performed continuous in vivo metabolic stable isotope labeling in mice along the aging continuum. First, we discovered that the brain proteome uniquely undergoes dynamic turnover fluctuations during aging compared to heart and liver tissue. Second, trends in protein turnover in the brain proteome during aging showed sex-specific differences that were tightly tied to cellular compartments. Next, parallel analyses of the insoluble proteome revealed that several cellular compartments experience hampered turnover, in part due to misfolding. Finally, we found that age-associated fluctuations in proteasome activity were associated with the turnover of core proteolytic subunits, which was recapitulated by pharmacological suppression of proteasome activity. Taken together, our study provides a proteome-wide atlas of protein turnover across the aging continuum and reveals a link between the turnover of individual proteasome subunits and the age-associated decline in proteasome activity.

Glycan/Protein-Stable Isotope Labeling in Cell Culture for Enabling Concurrent Quantitative Glycomics/Proteomics/Glycoproteomics.

The complexity and heterogeneity of protein glycosylation present an analytical challenge to the studies of characterization and quantitation. Various LC-MS-based quantitation strategies have emerged in recent decades. Metabolic stable isotope labeling has been developed to enhance the accurate LC/MS-based quantitation between different cell lines. Stable isotope labeling by amino acids in a cell culture (SILAC) is the most widely used metabolic labeling method in proteomic analysis. However, it can only label the peptide backbone and is thus limited in glycomic studies. Here, we present a metabolic isotope labeling strategy, named GlyProSILC (Glycan Protein Stable Isotope Labeling in Cell Culture), that can label both the glycan motif and peptide backbone from the same batch of cells. It was performed by feeding cells with a heavy medium containing amide-15N-glutamine, 13C6-arginine (Arg6), and 13C6-15N2-lysine (Lys8). No significant change of cell line metabolism after GlyProSILC labeling was observed based on transcriptomic, glycomic, and proteomic data. The labeling conditions, labeling efficiency, and quantitation accuracy were investigated. After quantitation correction, we simultaneously quantified 62 N-glycans, 574 proteins, and 344 glycopeptides using the same batch of mixed 231BR/231 cell lines. So far, GlyProSILC provides an accurate and effective quantitation approach for glycomics, proteomics, and glycoproteomics in a cell culture system.

Analysis of proteome-wide degradation dynamics in ALS SOD1 iPSC-derived patient neurons reveals disrupted VCP homeostasis

Mutations in SOD1 cause amyotrophic lateral sclerosis (ALS) through gain-of-function effects, yet the mechanisms by which misfolded mutant SOD1 (mutSOD1) protein impairs human motor neurons (MNs) remain unclear. Here, we use induced-pluripotent-stem-cell-derived MNs coupled to metabolic stable isotope labeling and mass spectrometry to investigate proteome-wide degradation dynamics. We find several proteins, including the ALS-causal valosin-containing protein (VCP), which predominantly acts in proteasome degradation and autophagy, that degrade slower in mutSOD1 relative to isogenic control MNs. The interactome of VCP is altered in mutSOD1 MNs invitro, while VCP selectively accumulates in the affected motor cortex of ALS-SOD1 patients. Overexpression of VCP rescues mutSOD1 toxicity in MNs invitro and in a C. elegans model invivo, in part due to its ability to modulate the degradation of insoluble mutSOD1. Our results demonstrate that VCP contributes to mutSOD1-dependent degeneration, link two distinct ALS-causal genes, and highlight selective protein degradation impairment in ALS pathophysiology.

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.

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.

Metabolic Adaptation to Tyrosine Kinase Inhibition in Chronic Myelogenous Leukemia Stem Cells

Objectives: BCR-ABL tyrosine kinase inhibitors (TKI) are highly effective in treatment of chronic myelogenous leukemia (CML) but fail to eliminate leukemia stem cells, which persist as a source of disease recurrence. Previous studies have suggested that primitive CML cells rely on upregulated oxidative metabolism for survival. However, there is limited knowledge about the effects of TKI treatment on CML stem cell metabolism and the role of metabolic adaptations in stem cell persistence after TKI treatment. Here we evaluated the metabolic response to TKI treatment in CML stem and progenitor cells using a representative SCL-tTA/BCR-ABL mouse model of CML. Methods: CML mice were treated with the TKI Nilotinib or vehicle for 2 days, to evaluate initial effects of treatment, and for 2 weeks, to evaluate response to continued treatment. Bioenergetics, metabolite profiling, and stable isotope metabolic labeling was performed on BM c-Kit+ progenitor cells obtained from vehicle and TKI treated mice. The effects of TKI treatment on metabolism-related gene expression in CML hematopoietic stem cells (HSC) was studied using single cell RNA-Seq (scRNA) analysis of BM Lin- c-Kit+ Sca-1+ (LSK) cells from CML mice treated with vehicle or TKI for 2 days or 2 weeks. Results: Extracellular flux analysis revealed that TKI treatment initially inhibited oxygen consumption rate (OCR), representing mitochondrial respiration, and extracellular acidification rate (ECAR), representing glycolysis, in CML progenitor cells, but that these were restored with continued TKI treatment. Metabolite profiling using high-resolution mass spectrometry (HRMS) identified significant initial reduction in glycolytic, glutaminolytic and tricarboxylic acid cycle (TCA) intermediates after 2 days of TKI treatment. However, metabolite levels were restored to levels similar to controls with continued TKI treatment. Steady-state metabolic labeling with [U-13C6]-glucose or [U-13C5]-Glutamine showed reduced glycolytic flux, glutaminolysis and TCA intermediates after treatment with TKI for 2 days, but restoration of glycolytic activity and increased glucose-mediated lactate generation, and increased glutaminolytic and TCA cycle activity with continued treatment. The above studies indicate initial inhibition of energy metabolism in CML progenitors with TKI treatment, but metabolic adaptation to continued TKI exposure. scRNA analysis of CML HSC populations showed that TKI exposure led to selective enrichment of primitive HSC subsets characterized by reduced OXPHOS, glycolysis, nucleotide metabolism, and MYC gene signatures. Treatment with TKI for 2 days further reduced metabolic gene signatures, but continued treatment led to restoration of MYC and OXPHOS gene signature expression coupled with enrichment of HSC and quiescence signatures in persistent HSC. TKI-treated stem cells showed increased levels of Hif1a protein and enhanced activity of HIF-1, a key transcriptional regulator of energy metabolism. Treatment of CML mice with a HIF-1 inhibitor, Echinomycin, in combination with TKI treatment, resulted in significant depletion of CML stem cells compared to TKI alone. The combination of HIF-1 inhibitor with TKI treatment also depleted human CML stem cells in xenografted mice. Conclusions: Our results demonstrate metabolic adaptation in CML and progenitor stem cells following TKI treatment, and identify HIF-1 activation as a targetable mechanism to enhance elimination of CML stem cell that persist after TKI treatment.

Antidepressant treatment effects on hippocampal protein turnover: Molecular and spatial insights from mass spectrometry.

A major challenge in managing depression is that antidepressant drugs take a long time to exert their therapeutic effects. For the development of faster-acting treatments, it is crucial to get an improved understanding of the molecular mechanisms underlying antidepressant mode of action. Here, we used a novel mass spectrometry-based workflow to investigate how antidepressant treatment affects brain protein turnover at single-cell and subcellular resolution. We combined stable isotope metabolic labeling, quantitative Tandem Mass Spectrometry (qTMS) and Multi-isotope Imaging Mass Spectrometry (MIMS) to simultaneously quantify and image protein synthesis and turnover in hippocampi of mice treated with the antidepressant paroxetine. We identified changes in turnover of individual hippocampal proteins that reveal altered metabolism-mitochondrial processes and report on subregion-specific turnover patterns upon paroxetine treatment. This workflow can be used to investigate brain protein turnover changes in vivo upon pharmacological interventions at a resolution and precision that has not been possible with other methods to date. Our results reveal acute paroxetine effects on brain protein turnover and shed light on antidepressant mode of action.

Strategies to Avoid Artifacts in Mass Spectrometry-Based Epitranscriptome Analyses.

In this report, we perform structure validation of recently reported RNA phosphorothioate (PT) modifications, a new set of epitranscriptome marks found in bacteria and eukaryotes including humans. By comparing synthetic PT‐containing diribonucleotides with native species in RNA hydrolysates by high‐resolution mass spectrometry (MS), metabolic stable isotope labeling, and PT‐specific iodine‐desulfurization, we disprove the existence of PTs in RNA from E. coli, S. cerevisiae, human cell lines, and mouse brain. Furthermore, we discuss how an MS artifact led to the initial misidentification of 2′‐O‐methylated diribonucleotides as RNA phosphorothioates. To aid structure validation of new nucleic acid modifications, we present a detailed guideline for MS analysis of RNA hydrolysates, emphasizing how the chosen RNA hydrolysis protocol can be a decisive factor in discovering and quantifying RNA modifications in biological samples.

Long-lived mitochondrial cristae proteins in mouse heart and brain.

Long-lived proteins (LLPs) have recently emerged as vital components of intracellular structures whose function is coupled to long-term stability. Mitochondria are multifaceted organelles, and their function hinges on efficient proteome renewal and replacement. Here, using metabolic stable isotope labeling of mice combined with mass spectrometry (MS)-based proteomic analysis, we demonstrate remarkable longevity for a subset of the mitochondrial proteome. We discovered that mitochondrial LLPs (mt-LLPs) can persist for months in tissues harboring long-lived cells, such as brain and heart. Our analysis revealed enrichment of mt-LLPs within the inner mitochondrial membrane, specifically in the cristae subcompartment, and demonstrates that the mitochondrial proteome is not turned over in bulk. Pioneering cross-linking experiments revealed that mt-LLPs are spatially restricted and copreserved within protein OXPHOS complexes, with limited subunit exchange throughout their lifetimes. This study provides an explanation for the exceptional mitochondrial protein lifetimes and supports the concept that LLPs provide key structural stability to multiple large and dynamic intracellular structures.

Multidimensional Proteomics Identifies Declines in Protein Homeostasis and Mitochondria as Early Signals for Normal Aging and Age-associated Disease in Drosophila*[S

Aging is characterized by a gradual deterioration in proteome. However, how protein dynamics that changes with normal aging and in disease is less well understood. Here, we profiled the snapshots of aging proteome in Drosophila, from head and muscle tissues of post-mitotic somatic cells, and the testis of mitotically-active cells. Our data demonstrated that dysregulation of proteome homeostasis, or proteostasis, might be a common feature associated with age. We further used pulsed metabolic stable isotope labeling analysis to characterize protein synthesis. Interestingly, this study determined an age-modulated decline in protein synthesis with age, particularly in the pathways related to mitochondria, neurotransmission, and proteostasis. Importantly, this decline became dramatically accelerated in Pink1 mutants, a Drosophila model of human age-related Parkinson's disease. Taken together, our multidimensional proteomic study revealed tissue-specific protein dynamics with age, highlighting mitochondrial and proteostasis-related proteins. We suggest that declines in proteostasis and mitochondria early in life are critical signals prior to the onset of aging and aging-associated diseases.

Improved Sensitivity for Protein Turnover Quantification by Monitoring Immonium Ion Isotopologue Abundance.

We describe an analytical strategy allowing for the direct quantification of stable isotope label incorporation in newly synthesized proteins following administration of the stable isotope tracer deuterium oxide. We present a demonstration of coupling high-resolution mass spectrometry, metabolic stable isotope labeling, and MS/MS-based isotopologue quantification for the measurement of protein turnover. Stable isotope labeling with deuterium oxide, followed by immonium ion isotopologue quantification, is a more sensitive strategy for determining protein fractional synthesis rates compared to peptide centric mass isotopomer distribution analysis approaches when labeling time and/or stable isotope tracer exposure is limited and, as such, offers a great advantage for human studies.

Development of methionine methylation profiling and relative quantification in human breast cancer cells based on metabolic stable isotope labeling.

Methylation of components involved in one-carbon metabolism is extremely important in cancer; comprehensive studies on methylation are essential and may provide us with a better understanding of tumorigenesis, and lead to the discovery of potential biomarkers. Here, we present an improved methodology for methylated metabolite profiling and its relative quantification in breast cancer cell lines by isotope dilution mass spectrometry based on 13CD3-methionine metabolic labeling using ultra-high-performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UPLC-HRMS/MS). First, all the methylated metabolites related to methionine were first screened and profiled by introducing 13CD3-methionine as the only medium into breast cancer cell growth cultures for both cellular polar metabolites and lipids. In total, we successfully found 20 labeled methylated metabolites and most of them were identified, some of which have not been reported before. We also developed a relative quantification method for all identified methylated metabolites based on isotope dilution mass spectrometry assays. Finally, the developed method was used for different breast cancer cells and mammary epithelial cells. Most methylated metabolites were disrupted in cancer cells. 1-Methyl-nicotinamide was decreased significantly, while trimethylglycine-glutamic acid-lysine and trimethyl-lysine were increased more than five times. This method offers a new insight into the methylation process, with several key pathways and important new metabolites being identified. Further investigation with biological assays should help to reveal the overall methylation metabolic network.

Structural Biological Approach to Biopharmaceuticals

Detailed structural characterization of protein biopharmaceuticals is a critical step in research and development; however, this step is often hampered by the structural complexities associated with glycosylation. Most protein biopharmaceuticals are modified with structurally heterogeneous and dynamic oligosaccharides which govern the physicochemical properties, functionality, pharmacokinetics, and potential pathogenicity of these glycoproteins. Considering this, we have developed a structural biological approach to describe the dynamic three-dimensional structures and interactions of glycoproteins as biopharmaceuticals. We developed an NMR technique assisted by metabolic stable-isotope labeling that can provide useful atomic-level probes for detecting and characterizing structural perturbations of glycoproteins caused by alterations in solution conditions and production protocols, as well as by mutagenesis. We have applied this method in conjunction with X-ray crystallography to investigate the structural impacts of varying glycoforms of the Fc region of immunoglobulin G (IgG), thereby elucidating the functional roles of the Fc glycans. In particular, we have successfully elucidated the structural mechanisms by which defucosylation of the IgG-Fc region increases its affinity for Fcγ receptor IIIa, leading to an improvement in ameliorating antibody-dependent cell-mediated cytotoxicity. In addition, we applied our stable-isotope-assisted NMR method to analyzing biomolecular interactions in serum environments, which are characterized by molecular crowding and promiscuous intermolecular interactions. An integrative structural biological approach combining NMR spectroscopy, X-ray crystallography, neutron scattering, atomic force microscopy, and molecular dynamics simulation will provide new research tools that will enable the visualization of dynamic structures and interactions of glycoproteins of pharmaceutical interest, thereby providing valuable insights for the development of biopharmaceuticals.

Quantitative analysis of newly synthesized proteins

Measuring proteome response to perturbations is critical for understanding the underlying mechanisms involved. Traditional quantitative proteomic methods are limited by the large numbers of proteins in the proteome and the mass spectrometer's dynamic range. A previous method uses the biorthogonal reagent azidohomoalanine (AHA), a methionine analog, for labeling, enrichment and detection of newly synthesized proteins (NSPs). Newly synthesized AHA proteins can be coupled to biotin via CuAAC-mediated click chemistry and enriched using avidin-based affinity purification. The combination of AHA-mediated NSP labeling with metabolic stable isotope labeling allows quantitation of low-abundant, newly secreted proteins by mass spectrometry (MS). However, the resulting multiplicity of labeling complicates NSP analysis. We developed a new NSP quantification strategy, called HILAQ (heavy isotope-labeled azidohomoalanine quantification), that uses a heavy isotope-labeled AHA molecule to enable NSP labeling, enrichment, identification and quantification. In addition, the AHA-peptide enrichment used in HILAQ improves both the identification and quantification of NSPs over AHA-protein enrichment. Here, we provide a description of the HILAQ method that includes procedures for (i) pulse-labeling and harvesting NSPs; (ii) addition of biotin by click reaction; (iii) protein precipitation; (iv) protein digestion; (v) enrichment of AHA-biotin peptides by NeutrAvidin beads and four-step elution; (vi) MS analysis; and (vii) data analysis for the identification and quantification of NSPs by ProLuCID and pQuant. We demonstrate our HILAQ approach by identifying NSPs from cell cultures, but we anticipate that it can be adapted for applications in animal models. The whole protocol takes ~6 d to complete.

Quantitative proteomics in plant protease substrate identification

Contents Summary 936 I. Introduction 936 II. The quest for plant protease substrates - proteomics to the rescue? 937 III. Quantitative proteome comparison reveals candidate substrates 938 IV. Dynamic metabolic stable isotope labeling to measure protein turnover invivo 938 V. Terminomics - large-scale identification of protease cleavage sites 939 VI. Substrate or not substrate, that is the question 940 VII. Concluding remarks 941 Acknowledgements 941 References 941 SUMMARY: Proteolysis is a central regulatory mechanism of protein homeostasis and protein function that affects all aspects of plant life. Higher plants encode for hundreds of proteases, but their physiological substrates and hence their molecular functions remain mostly unknown. Current quantitative mass spectrometry-based proteomics enables unbiased large-scale interrogation of the proteome and its modifications. Here we provide an overview of proteomics techniques that allow profiling of changes in protein abundance, measurement of proteome turnover rates, identification of protease cleavage sites invivo and invitro and determination of protease sequence specificity. We discuss how these techniques can help to reveal protease substrates and determine plant protease function, illustrated by recent studies on selected plant proteases.

Stable isotope metabolic labeling suggests differential turnover of the DPYSL protein family.

In this work, we discuss how in vivo 15 N metabolic labeling in combination with MS simultaneously provides information on protein expression and protein turnover. We metabolically labeled mice with the stable nitrogen isotope 15 N using a 15 N-enriched diet and analyzed unlabeled (14 N) versus 15 N-labeled brain tissue with LC-MS/MS. We then compared the 14 N versus 15 N peptide isotopologue clusters of 14 N and 15 N-labeled dihydropyrimidinase-related (DPYSL) proteins. We present a workflow assessing protein expression and turnover at different time points of mouse brain development. Our data demonstrate distinct protein turnover patterns of DPYSL3 and DPYSL5 compared to other quantified proteins. We report the presence of two DPYSL3 and DPYSL5 populations with different 15 N incorporation rates, indicating altered protein turnover during development. In vivo 15 N metabolic labeling allows the simultaneous investigation of protein expression and turnover, enabling detailed protein dynamics studies. We report for the first time protein turnover data for the DPYSL2, DPYSL3, and DPYSL5 protein family members. As DPYSL proteins have important functions for nervous system maturation, our data provide useful information on their molecular fate during brain development.

Discovery of Specific Metastasis-Related N-Glycan Alterations in Epithelial Ovarian Cancer Based on Quantitative Glycomics

Generally, most of ovarian cancer cannot be detected until large scale and remote metastasis occurs, which is the major cause of high mortality in ovarian cancer. Therefore, it is urgent to discover metastasis-related biomarkers for the detection of ovarian cancer in its occult metastasis stage. Altered glycosylation is a universal feature of malignancy and certain types of glycan structures are well-known markers for tumor progressions. Thus, this study aimed to reveal specific changes of N-glycans in the secretome of the metastatic ovarian cancer. We employed a quantitative glycomics approach based on metabolic stable isotope labeling to compare the differential N-glycosylation of secretome between an ovarian cancer cell line SKOV3 and its high metastatic derivative SKOV3-ip. Intriguingly, among total 17 N-glycans identified, the N-glycans with bisecting GlcNAc were all significantly decreased in SKOV3-ip in comparison to SKOV3. This alteration in bisecting GlcNAc glycoforms as well as its corresponding association with ovarian cancer metastatic behavior was further validated at the glycotransferase level with multiple techniques including real-time PCR, western blotting, transwell assay, lectin blotting and immunohistochemistry analysis. This study illustrated metastasis-related N-glycan alterations in ovarian cancer secretome in vitro for the first time, which is a valuable source for biomarker discovery as well. Moreover, N-glycans with bisecting GlcNAc shed light on the detection of ovarian cancer in early peritoneal metastasis stage which may accordingly improve the prognosis of ovarian cancer patients.

Colonization resistance and microbial ecophysiology: using gnotobiotic mouse models and single-cell technology to explore the intestinal jungle

The highly diverse intestinal microbiota forms a structured community engaged in constant communication with itself and its host and is characterized by extensive ecological interactions. A key benefit that the microbiota affords its host is its ability to protect against infections in a process termed colonization resistance (CR), which remains insufficiently understood. In this review, we connect basic concepts of CR with new insights from recent years and highlight key technological advances in the field of microbial ecology. We present a selection of statistical and bioinformatics tools used to generate hypotheses about synergistic and antagonistic interactions in microbial ecosystems from metagenomic datasets. We emphasize the importance of experimentally testing these hypotheses and discuss the value of gnotobiotic mouse models for investigating specific aspects related to microbiota-host-pathogen interactions in a well-defined experimental system. We further introduce new developments in the area of single-cell analysis using fluorescence in situ hybridization in combination with metabolic stable isotope labeling technologies for studying the in vivo activities of complex community members. These approaches promise to yield novel insights into the mechanisms of CR and intestinal ecophysiology in general, and give researchers the means to experimentally test hypotheses in vivo at varying levels of biological and ecological complexity.

The15N isotope effect inEscherichia coli: A neutron can make the difference

Several techniques based on stable isotope labeling are used for quantitative MS. These include stable isotope metabolic labeling methods for cells in culture as well as live organisms with the assumption that the stable isotope has no effect on the proteome. Here, we investigate the (15) N isotope effect on Escherichia coli cultures that were grown in either unlabeled ((14) N) or (15) N-labeled media by LC-ESI-MS/MS-based relative protein quantification. Consistent protein expression level differences and altered growth rates were observed between (14) N and (15) N-labeled cultures. Furthermore, targeted metabolite analyses revealed altered metabolite levels between (14) N and (15) N-labeled bacteria. Our data demonstrate for the first time that the introduction of the (15) N isotope affects protein and metabolite levels in E. coli and underline the importance of implementing controls for unbiased protein quantification using stable isotope labeling techniques.

Compartment Modeling for Mammalian Protein Turnover Studies by Stable Isotope Metabolic Labeling

Protein turnover studies on a proteome scale based on metabolic isotopic labeling can provide a systematic understanding of mechanisms for regulation of protein abundances and their transient behaviors. At this time, these large-scale studies typically utilize a simple kinetic model to extract protein dynamic information. Although many high-quality, protein isotope incorporation data are available from those experiments, accurate and additionally useful protein dynamic information cannot be extracted from the experimental data by use of the simple kinetic models. In this paper, we describe a formal connection between data obtained from elemental isotope labeling experiments and the well-known compartment modeling, and we demonstrate that an appropriate application of a compartment model to turnover of proteins from mammalian tissues can indeed lead to a better fitting of the experimental data.