Methyl-selective isotope labeling using α-ketoisovalerate for the yeast <Emphasis Type="Italic">Pichia pastoris</Emphasis> recombinant protein expression system

Methyl-detected NMR spectroscopy is a useful tool for investigating the structures and interactions of large macromolecules such as membrane proteins. The procedures for preparation of methyl-specific isotopically-labeled proteins were established for the Escherichia coli (E. coli) expression system, but typically it is not feasible to express eukaryotic proteins using E. coli. The Pichia pastoris (P. pastoris) expression system is the most common yeast expression system, and is known to be superior to the E. coli system for the expression of mammalian proteins, including secretory and membrane proteins. However, this system has not yet been optimized for methyl-specific isotope labeling, especially for Val/Leu-methyl specific isotope incorporation. To overcome this difficulty, we explored various culture conditions for the yeast cells to efficiently uptake Val/Leu precursors. Among the searched conditions, we found that the cultivation pH has a critical effect on Val/Leu precursor uptake. At an acidic cultivation pH, the uptake of the Val/Leu precursor was increased, and methyl groups of Val and Leu in the synthesized recombinant protein yielded intense ¹H–¹³C correlation signals. Based on these results, we present optimized protocols for the Val/Leu-methyl-selective ¹³C incorporation by the P. pastoris expression system.     

Quantitative,Time-Resolved Proteomic Analysis by Combining Bioorthogonal Noncanonical Amino Acid Tagging and Pulsed Stable Isotope Labeling by Amino Acids in Cell Culture

An approach to proteomic analysis that combines bioorthogonal noncanonical amino acid tagging (BONCAT) and pulsed stable isotope labeling with amino acids in cell culture (pSILAC) provides accurate quantitative information about rates of cellular protein synthesis on time scales of minutes. The method is capable of quantifying 1400 proteins produced by HeLa cells during a 30 min interval, a time scale that is inaccessible to isotope labeling techniques alone. Potential artifacts in protein quantification can be reduced to insignificant levels by limiting the extent of noncanonical amino acid tagging. We find no evidence for artifacts in protein identification in experiments that combine the BONCAT and pSILAC methods.Methods for the analysis of cellular protein synthesis should be quantitative and fast. In 2006, Dieterich and coworkers introduced a proteomics discovery tool called bioorthogonal noncanonical amino acid tagging (BONCAT),¹ in which noncanonical amino acids (ncAAs) with bioorthogonal functional groups (e.g. azides or alkynes) are used as metabolic labels to distinguish new proteins from old (1, 2). Labeled proteins can be conjugated to fluorescent reporters for visualization or affinity tags for purification and subsequent identification by mass spectrometry (3). Because the ncAA probe can be introduced to cells in a well-defined “pulse,” affinity purification removes pre-existing proteins and provides both reduced sample complexity and excellent time resolution.The methionine (Met) surrogate l-azidohomoalanine (Aha) has become standard in the application of BONCAT methodologies. Using Aha and fluorescent tagging, Tcherkezian et al. observed co-localization of the DCC receptor with sites of protein synthesis, providing support for the role of netrin as a stimulant of extranuclear protein production in neurons (4). Combining Aha labeling and 2D gel electrophoresis, Yoon et al. discovered that the protein lamin B2 is synthesized in axons and crucial to mitochondrial function and axon maintenance in Xenopus retinal glial cells (5). Aha has also been used to study histone turnover (6), protein palmitoylation (7), pathogen amino acid uptake (8), inflammatory response (9), and local translation in neuronal dendrites and axons (10). These labeling techniques have been expanded to tissue and animal culture, where Aha has been used to profile protein synthesis in rat hippocampal brain slices (11, 12) and zebrafish embryos (13).The development of fast, reliable, quantitative BONCAT methods will enable new insights into proteome dynamics in response to biological stimuli. Recent work by Eichelbaum et al. combined Aha labeling with stable isotope labeling to measure lipopolysaccharide-stimulated protein secretion by macrophages (14). Using similar approaches, Somasekharan et al. identified a set of proteins that are translationally regulated by the Y-box binding protein-1 (YB-1) in TC-32 Ewing sarcoma cells (15), and Howden et al. monitored changes in protein expression following stimulation of primary T cells with phorbol 12-myristate 13-acetate and ionomycin (16).A concern that arises in the use of Aha (as it does for all chemical probes of biological processes) is that the protocols used for Aha labeling might perturb cellular protein synthesis. The development of ncAAs as reliable analytic tools hinges on our ability to understand and minimize such unintended effects. For Aha, previous work has shown that protein labeling does not visibly alter cellular morphology in dissociated hippocampal neurons or HEK293 cells, and 1D gels reveal no discrepancies between the proteomes of Aha- and Met-treated cells (1). These experiments, however, offer only coarse measures of effects on protein synthesis, and as Aha labeling is frequently coupled to mass spectrometry-based proteomic analysis, the biological effects of Aha treatment must be investigated with equivalent sensitivity and resolution.Here we report sound methods for fast, reliable measurement of proteome dynamics via noncanonical amino acid tagging. First, we use the quantitative proteomics technique pulsed stable isotope labeling with amino acids in cell culture (pSILAC) to investigate potential unintended effects of Aha labeling on protein abundance in HeLa cell cultures, and we develop a strategy for minimizing these effects. Second, we show that a combined BONCAT-pSILAC approach, capable of both enriching and quantifying newly synthesized proteins, yields detailed proteomic information on time scales that are inaccessible to isotope labeling techniques alone.     

Affordable uniform isotope labeling with <Superscript>2</Superscript>H, <Superscript>13</Superscript>C and <Superscript>15</Superscript>N in insect cells

For a wide range of proteins of high interest, the major obstacle for NMR studies is the lack of an affordable eukaryotic expression system for isotope labeling. Here, a simple and affordable protocol is presented to produce uniform labeled proteins in the most prevalent eukaryotic expression system for structural biology, namely Spodoptera frugiperda insect cells. Incorporation levels of 80 % can be achieved for ¹⁵N and ¹³C with yields comparable to expression in full media. For ²H,¹⁵N and ²H,¹³C,¹⁵N labeling, incorporation is only slightly lower with 75 and 73 %, respectively, and yields are typically twofold reduced. The media were optimized for isotope incorporation, reproducibility, simplicity and cost. High isotope incorporation levels for all labeling patterns are achieved by using labeled algal amino acid extracts and exploiting well-known biochemical pathways. The final formulation consists of just five commercially available components, at costs 12-fold lower than labeling media from vendors. The approach was applied to several cytosolic and secreted target proteins.     

The SILAC Fly Allows for Accurate Protein Quantification in Vivo

Stable isotope labeling by amino acids in cell culture (SILAC) is widely used to quantify protein abundance in tissue culture cells. Until now, the only multicellular organism completely labeled at the amino acid level was the laboratory mouse. The fruit fly Drosophila melanogaster is one of the most widely used small animal models in biology. Here, we show that feeding flies with SILAC-labeled yeast leads to almost complete labeling in the first filial generation. We used these “SILAC flies” to investigate sexual dimorphism of protein abundance in D. melanogaster. Quantitative proteome comparison of adult male and female flies revealed distinct biological processes specific for each sex. Using a tudor mutant that is defective for germ cell generation allowed us to differentiate between sex-specific protein expression in the germ line and somatic tissue. We identified many proteins with known sex-specific expression bias. In addition, several new proteins with a potential role in sexual dimorphism were identified. Collectively, our data show that the SILAC fly can be used to accurately quantify protein abundance in vivo. The approach is simple, fast, and cost-effective, making SILAC flies an attractive model system for the emerging field of in vivo quantitative proteomics.Mass spectrometry-based quantitative proteomics has emerged as a highly successful approach to study biological processes in health and disease (1–3). Most studies have so far been limited to in vitro systems such as cell culture models. Although tremendously useful, these models cannot appropriately reflect relevant regulatory mechanisms of multicellular eukaryotes in vivo. This is particularly relevant for complex processes involving interactions between different cell types such as differentiation and development (4).Relative changes in protein abundance are most accurately measured by comparing the natural form of a peptide with its stable isotope-labeled analog. Several different approaches enable stable isotope labeling of peptides either by chemical reactions or metabolic incorporation of the label (5, 6). Metabolic labeling has several advantages such as high labeling efficiency and intrinsically higher precision. For example, metabolically labeled samples can be combined before further processing steps so that protein quantification is not affected by differences in sample preparation. Labeling of organisms with stable isotope tracers was pioneered by Rudolf Schoenheimer 75 years ago (7, 8). Since then, several model organisms ranging from prokaryotes to mammals have been labeled metabolically (for an excellent review, see Ref. 9). For example, Caenorhabditis elegans and Drosophila melanogaster have successfully been labeled with ¹⁵N (10), and ¹⁵N-labeled flies were recently used to study maternal-to-zygotic transition (11) and seminal fluid proteins (sfps)¹ transferred at mating (12). ¹⁵N has also been used to label entire rats, particularly for quantitative brain proteomics (13, 14). Despite its usefulness, ¹⁵N labeling also has several disadvantages. Because most peptides contain dozens of nitrogen atoms, labeling with highly enriched ¹⁵N still results in only partial peptide labeling and therefore complex isotope clusters. In addition, the mass shift between the labeled (i.e. heavy) and unlabeled (i.e. light) forms of a peptide depends on the number of nitrogen atoms and therefore varies depending on the peptide sequence. This leads to an increase in the number of candidate masses that need to be considered and therefore complicates peptide identification by search algorithms. Both problems result in smaller identification rates and less accurate quantification that can partially be overcome by computational correction (15, 16).Stable isotope labeling by amino acids in cell culture (SILAC) is another metabolic labeling approach with several unique advantages (17): because the label is introduced at the amino acid level, mass spectra can easily be interpreted, and peptides can be quantified with high precision. These features have made SILAC a very popular approach for cell culture-based quantitative and functional proteomics (18). As a potential disadvantage, SILAC is generally thought to be restricted to in vitro cell culture experiments. The only SILAC experiments in the fly model were carried out using cell lines cultivated in vitro (19, 20). However, in 2005, Hayter et al. (21) demonstrated that chicken can be partially labeled at the amino acid level by feeding them with a diet containing stable isotope-labeled valine. Three years later, Krüger et al. (22) achieved essentially complete labeling of the laboratory mouse. Until now, this so-called “SILAC mouse” was the only multicellular organism that has been completely labeled with the SILAC approach, and partial labeling was recently achieved in newts (21, 23).Here, we introduce the fruit fly D. melanogaster in the SILAC zoo. We refer to these animals as SILAC flies because they are obtained by feeding flies on SILAC-labeled yeast. D. melanogaster is one of the best characterized model organisms and has been used to address many fundamental questions in biology (24). Until now, most studies in D. melanogaster have focused on genetic aspects (25). However, proteins are the key actors in most biological processes. It is therefore highly desirable to obtain quantitative information at the protein level in D. melanogaster. We demonstrate in the present study that raising fly larvae on a diet of heavy lysine-labeled yeast cells results in virtually complete heavy labeling in the first filial (F₁) generation. Furthermore, we show that the SILAC fly enables proteome-wide quantification with higher precision than a label-free method. In a series of proof-of-principle experiments, we used the SILAC fly to investigate sexually dimorphic protein expression in D. melanogaster, thus providing the first systematic comparison of male and female flies at the protein level.     

Methyl labeling and TROSY NMR spectroscopy of proteins expressed in the eukaryote <Emphasis Type="Italic">Pichia pastoris</Emphasis>

¹³C Methyl TROSY NMR spectroscopy has emerged as a powerful method for studying the dynamics of large systems such as macromolecular assemblies and membrane proteins. Specific ¹³C labeling of aliphatic methyl groups and perdeuteration has been limited primarily to proteins expressed in E. coli, preventing studies of many eukaryotic proteins of physiological and biomedical significance. We demonstrate the feasibility of efficient ¹³C isoleucine δ1-methyl labeling in a deuterated background in an established eukaryotic expression host, Pichia pastoris, and show that this method can be used to label the eukaryotic protein actin, which cannot be expressed in bacteria. This approach will enable NMR studies of previously intractable targets.     

Metabolic Labeling Reveals Proteome Dynamics of Mouse Mitochondria

Mitochondrial dysfunction is associated with many human diseases. Mitochondrial damage is exacerbated by inadequate protein quality control and often further contributes to pathogenesis. The maintenance of mitochondrial functions requires a delicate balance of continuous protein synthesis and degradation, i.e. protein turnover. To understand mitochondrial protein dynamics in vivo, we designed a metabolic heavy water (²H₂O) labeling strategy customized to examine individual protein turnover in the mitochondria in a systematic fashion. Mice were fed with ²H₂O at a minimal level (<5% body water) without physiological impacts. Mitochondrial proteins were analyzed from 9 mice at each of the 13 time points between 0 and 90 days (d) of labeling. A novel multiparameter fitting approach computationally determined the normalized peak areas of peptide mass isotopomers at initial and steady-state time points and permitted the protein half-life to be determined without plateau-level ²H incorporation. We characterized the turnover rates of 458 proteins in mouse cardiac and hepatic mitochondria and found median turnover rates of 0.0402 d⁻¹ and 0.163 d⁻¹, respectively, corresponding to median half-lives of 17.2 d and 4.26 d. Mitochondria in the heart and those in the liver exhibited distinct turnover kinetics, with limited synchronization within functional clusters. We observed considerable interprotein differences in turnover rates in both organs, with half-lives spanning from hours to months (∼60 d). Our proteomics platform demonstrates the first large-scale analysis of mitochondrial protein turnover rates in vivo, with potential applications in translational research.Mitochondrial dysfunctions are observed in disorders such as neurodegeneration, cardiovascular diseases, and aging (1–3). It is postulated that the failure to contain or replenish mitochondrial proteins damaged by reactive oxygen species directly underlies many pathological phenotypes (4). The development of effective treatments for these diseases therefore relies on understanding the molecular basis of protein dynamics. Outstanding questions are how the processes of mitochondrial proteome dynamics are regulated in different systems, and how their perturbations could progress to pathological remodeling of the organelle. Thus far, quantitative proteomics efforts have been predominated by steady-state measurements, which often provide fragmentary snapshots of the proteome that are difficult to comprehend in the context of other cellular events.To further understand mitochondrial dynamics in vivo, we examined the turnover rates of individual heart and liver mitochondrial proteins on a proteome scale. Both the liver and the heart contain large numbers of mitochondria, but cardiac and hepatic mitochondria differ in their protein composition, oxygen consumption, substrate utilization, and disease manifestation. However, these differences are often interpreted only by protein compositions and steady-state abundance, without the consideration of protein kinetics in the temporal dimension. Abnormal protein kinetics may indicate dysfunctions in protein quality control, the accumulation of damaged proteins, misfolding, or other proteinopathies. Protein dynamics itself is an important intrinsic property of the proteome, the disruption of which could be causal of cellular etiologies.At minimum, a kinetic definition of the proteome requires knowledge of the rate at which individual proteins are being replaced. Isotope tracers are particularly useful for tracking such continual renewal of the proteome in living systems, because they allow differentiation between preexisting and newly synthesized proteins (5). Among the available stable isotope precursors, heavy water (²H₂O) labeling offers several advantages with respect to safety, labeling kinetics, and cost (6, 7). First, ²H₂O administration to animals and humans at low enrichment levels is safe for months or even years (8). Second, maintaining constant ²H enrichment levels in body water following the initial intake of ²H₂O is easily achieved, because administrated ²H₂O rapidly equilibrates over all tissues but decays slowly (9, 10). Third, ²H₂O labeling is more cost effective than other stable isotope labeling methods. Importantly, ²H₂O intake induces universal ²H incorporation into biomolecules. Systematic insights into protein turnover in vivo could therefore be correlated to that of nucleic acids, carbohydrates, or lipids, enabling broad applications for this technology in studying mammalian systems, including humans.A variety of methodologies have been developed to analyze the extent of ²H incorporation in proteins following ²H₂O labeling, including GC-MS measurements of hydrolyzed target proteins (11–14) and peptide analysis in MALDI-TOF MS (15) and LC-MS (16, 17). More recently, Price et al. described an approach for measuring protein turnover by calculating the theoretical number of ²H-labeling sites on a peptide sequence (18) and reported the turnover rates of ∼100 human plasma proteins. Here we describe another novel strategy to determine protein turnover rates on a proteomic scale using ²H₂O labeling. By computing the parameters needed to deduce fractional protein synthesis using software we developed, we were able to obtain protein half-life data without relying on the asymptotic isotopic abundance of peptide ions. Our approach also has the unique benefit of automating all steps of isotopomer quantification and postcollection data analysis, and it does not require knowledge of the exact precursor enrichment or labeling sites of peptides. We observed diverse kinetics from 458 liver and heart mitochondrial proteins that inform essential characteristics of mitochondrial dynamics and intragenomic differences between the two organs.     

A simple protocol for amino acid type selective isotope labeling in insect cells with improved yields and high reproducibility

An easy to use and robust approach for amino acid type selective isotope labeling in insect cells is presented. It relies on inexpensive commercial media and can be implemented in laboratories without sophisticated infrastructure. In contrast to previous protocols, where either high protein amounts or high incorporation ratios were obtained, here we achieve both at the same time. By supplementing media with a well considered amount of yeast extract, similar protein amounts as with full media are obtained, without compromising on isotope incorporation. In single and dual amino acid labeling experiments incorporation ratios are consistently ≥90% for all amino acids tested. This enables NMR studies of eukaryotic proteins and their interactions even for proteins with low expression levels. We show applications with human kinases, where protein–ligand interactions are characterized by 2D [¹⁵N, ¹H]- and [¹³C, ¹H]-HSQC spectra.     

Topograph,a Software Platform for Precursor Enrichment Corrected Global Protein Turnover Measurements

Defects in protein turnover have been implicated in a broad range of diseases, but current proteomics methods of measuring protein turnover are limited by the software tools available. Conventional methods require indirect approaches to differentiate newly synthesized protein when synthesized from partially labeled precursor pools. To address this, we have developed Topograph, a software platform which calculates the fraction of peptides that are from newly synthesized proteins and their turnover rates. A unique feature of Topograph is the ability to calculate amino acid precursor pool enrichment levels which allows for accurate calculations when the precursor pool is not fully labeled, and the approach used by Topograph is applicable regardless of the stable isotope label used. We validate the Topograph algorithms using data acquired from a mouse labeling experiment and demonstrate the influence that precursor pool corrections can have on protein turnover measurements.Methods of measuring protein synthesis and degradation using stable or radioactive isotope labels have existed for decades. The isotope label is introduced in the form of a labeled amino acid or amino acid precursor, and the incorporation or removal of that label from protein is used to estimate average protein turnover rates (1, 2). Historically, the amount of stable isotope label incorporated into a protein is measured by enriching for the protein (e.g. affinity chromatography, gel electrophoresis, and other biochemical methods), hydrolyzing the protein to amino acids, derivatizing the amino acids, and measuring the labeled amino acid by gas chromatography-mass spectrometry or gas chromatography-combustion-isotope ratio mass spectrometry (3, 4). More recently, proteomics methods have been developed that measure the labeled amino acid on the peptide level, eliminating the need for a protein enrichment step and enabling the monitoring of many proteins in a single experiment (5).Proteomics approaches to measuring protein turnover rates in mice have been accomplished by the introduction of a ¹⁵N stable isotope label. The labeled diets were created by supplementing a protein-free diet with a ¹⁵N enriched protein source. Price et al. (6) generated ¹⁵N-labeled protein from the alga, Spirulina platensis and Zhang et al. (7) introduced ¹⁵N-label in the form of lysate from the bacterium, Ralstonia eutropha. An advantage of using complete ¹⁵N labeling is the rapid incorporation of ¹⁵N and separation of isotope distributions between labeled and natural isotope abundance peptides, which reduces the need to deconvolute the two distributions. However, current methods require that the dietary protein content be derived from bacterial or alga lysate, a diet that is not normally fed to laboratory mice. As a result, measurements of protein turnover may not reflect conventional mouse model systems because of effects of diet on protein and amino acid metabolism. A more recent work by Claydon et al. (8) demonstrated a stable isotope labeling method by supplementing labeled valine into a standard mouse diet.The complex data generated from these analyses creates a data processing and analysis challenge; exemplified by recent software platforms that have been developed. Guan et al. (9) and Hoopmann et al. (10) demonstrated data analysis pipelines for ¹⁵N labeled SILAM and SILAC experiments. Here we describe the software platform, Topograph, we have developed for the analysis of liquid chromatography-tandem MS (LC-MS/MS) data from samples with isotopic labels. Topograph is able to deconvolute the complex spectra that may result from overlapping isotope distributions, regardless of the isotope label used. More uniquely, Topograph is able to calculate the relative isotope abundance (RIA)¹ of the amino acid precursor pool, which is necessary to correctly determine the amount of newly synthesized peptide and to subsequently calculate peptide and protein turnover rates.     

Efficient uniform isotope labeling of Abl kinase expressed in Baculovirus-infected insect cells

This report shows for the first time the efficient uniform isotope labeling of a recombinant protein expressed using Baculovirus-infected insect cells. The recent availability of suitable media for ¹⁵N- and ¹³C/¹⁵N-labeling in insect cells, the high expression of Abl kinase in these labeling media and a suitable labeling protocol made it possible to obtain a ¹H–¹⁵N-HSQC spectrum for the catalytic domain of Abl kinase of good quality and with label incorporation rates > 90%. The presented isotope labeling method should be applicable also to further proteins where successful expression is restricted to the Baculovirus expression system.     

A Quantitative Proteomics Analysis of Subcellular Proteome Localization and Changes Induced by DNA Damage

A major challenge in cell biology is to identify the subcellular distribution of proteins within cells and to characterize how protein localization changes under different cell growth conditions and in response to stress and other external signals. Protein localization is usually determined either by microscopy or by using cell fractionation combined with protein blotting techniques. Both these approaches are intrinsically low throughput and limited to the analysis of known components. Here we use mass spectrometry-based proteomics to provide an unbiased, quantitative, and high throughput approach for measuring the subcellular distribution of the proteome, termed “spatial proteomics.” The spatial proteomics method analyzes a whole cell extract created by recombining differentially labeled subcellular fractions derived from cells in which proteins have been mass-labeled with heavy isotopes. This was used here to measure the relative distribution between cytoplasm, nucleus, and nucleolus of over 2,000 proteins in HCT116 cells. The data show that, at steady state, the proteome is predominantly partitioned into specific subcellular locations with only a minor subset of proteins equally distributed between two or more compartments. Spatial proteomics also facilitates a proteome-wide comparison of changes in protein localization in response to a wide range of physiological and experimental perturbations, shown here by characterizing dynamic changes in protein localization elicited during the cellular response to DNA damage following treatment of HCT116 cells with etoposide. DNA damage was found to cause dissociation of the proteasome from inhibitory proteins and assembly chaperones in the cytoplasm and relocation to associate with proteasome activators in the nucleus.Many previous studies on organelle proteomics have provided a detailed list of the protein contents of organelles, substructures, or compartments isolated from cells (1–5). Such studies have also used quantitative proteomics in the high throughput assignment of proteins to subcellular compartments using methods such as protein correlation profiling (3, 6), recording the number of ions detected per protein (1, 2), or localization of organelle proteins by isotope tagging (7, 8). However, interpretation of the resulting protein inventory is complicated by the dynamic nature of organelle proteomes and by the fact that many proteins are not exclusive to one compartment but instead partition between separate subcellular locations (9, 10). This is illustrated by our previous studies of the human nucleolar proteome that have identified over 4,000 proteins that can co-purify reproducibly with nucleoli isolated from human cells but many of which are either present in low abundance in nucleoli and/or also have functions in other cellular locations (11). This highlights the importance of not only identifying the presence of a protein in any specific cellular organelle or structure but also measuring its relative abundance in different locations and assessing how this subcellular localization can change between different compartments under different cell growth and physiological conditions.Stable isotope labeling with amino acids in cell culture (SILAC)1 is the use of stable isotopic atoms along with mass spectrometry for quantitative mass spectrometry analysis (12, 13). This method allows quantitative analyses of proteins by comparison of the mass of light and heavier forms of the same peptide from a given protein, arising from the presence of heavier, stable isotopes such as ¹³C, ²H, and ¹⁵N. These stable isotopes are incorporated in proteins by in vivo labeling, i.e. growing the cells in specialized media where specific amino acids, typically arginine and lysine, are replaced with corresponding heavy isotope-substituted forms in which either all carbons or carbons, hydrogens, or nitrogens are isotope-labeled (14). Cleavage at the substituted arginine or lysine by trypsin generates a peptide with a shift in mass relative to the control (i.e. unsubstituted) peptide, and this can easily be resolved by mass spectrometry. The ratio of intensities of the “light” and “heavy” peptide signals identified by mass spectrometry directly correlates with the relative amount of the cognate protein from each sample. This method has been widely used for both relative quantification of protein levels after exposure of cells to drugs and inhibitors and for the identification of specific protein interaction partners (15–18).Here we used a quantitative and high throughput MS-based approach we term “spatial proteomics,” which both measures the relative intracellular localization of proteins and facilitates a comparison of changes in their subcellular localization under different conditions. This approach allows the rapid assignment of the cellular localization of proteins using common fractionation techniques. The major advantage of such a technique over other MS-based localization techniques such as protein correlation profiling or localization of organelle proteins by isotope tagging is that it provides a direct quantitative measurement of what fraction of each protein is localized to each cellular compartment, whereas the other techniques associate proteins showing similar profiles in a density centrifugation gradient while not describing the relative fraction of proteins in all locations. The spatial proteomics approach thus facilitates the comparison of protein localization under different conditions. We applied this spatial proteomics technique to determine the subcellular localization of over 2,000 proteins in HCT116 cells and then compared changes in localization following exposure to the topoisomerase inhibitor etoposide.     

DeltaMS: a tool to track isotopologues in GC- and LC-MS data

Introduction

Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing.

Objective

To introduce a software tool for the identification of isotopologues from mass spectrometry data.

Methods

DeltaMS relies on XCMS peak detection and X¹³CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described in X¹³CMS.

Results

To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures.

Conclusion

DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.

Graphical Abstract

Open image in new window

     

Measuring Proteome Dynamics in Vivo: AS EASY AS ADDING WATER?

Proteomics investigations typically yield information regarding static gene expression profiles. The central issues that limit the study of proteome dynamics include how to (i) administer a labeled amino acid in vivo, (ii) measure the isotopic labeling of a protein(s) (which may be low), and (iii) reliably interpret the precursor/product labeling relationships. In this study, we demonstrate the potential of quantifying proteome dynamics by coupling the administration of stable isotopes with mass spectrometric assays. Although the direct administration of a labeled amino acid(s) is typically used to measure protein synthesis, we explain the application of labeled water, comparing ²H₂O versus H₂¹⁸O for measuring albumin biosynthesis in vivo. This application emphasizes two distinct advantages of using labeled water over a labeled amino acid(s). First, in long term studies (e.g. days or weeks), it is not practical to continuously administer a labeled amino acid(s); however, in the presence of labeled water, organisms will generate labeled amino acids. Second, to calculate rates of protein synthesis in short term studies (e.g. hours), one must utilize a precursor/product labeling ratio; when using labeled water it is possible to reliably identify and easily measure the precursor labeling (i.e. water). We demonstrate that labeled water permits studies of protein synthesis (e.g. albumin synthesis in mice) during metabolic “steady-state” or “non-steady-state” conditions, i.e. integrating transitions between the fed and fasted state or during an acute perturbation (e.g. following a meal), respectively. We expect that the use of labeled water is applicable to wide scale investigations of proteome dynamics and can therein be used to obtain a functional image of gene expression in vivo.Proteomics investigations typically yield information regarding static gene expression profiles; i.e. current “state-of-the-art” research programs lack measurements of proteome dynamics (1–3). This deficiency is unfortunate because the ability to measure rates of protein synthesis and breakdown will likely facilitate the identification of biomarkers of disease and yield novel insight regarding underlying homeostatic abnormalities (3, 4). For example, by measuring the concentration of circulating aminotransferase and the synthesis/secretion of albumin, one might be able to determine the degree of liver damage and assess whether hepatic function is compromised, respectively (5). Also, it should be possible to determine the influence of specific factors on the regulation of protein synthesis; e.g. does a therapeutic agent stimulate insulin biosynthesis?Classic studies of protein biosynthesis have measured the incorporation of a labeled amino acid(s) into a protein(s) of interest and estimated a synthesis rate by using a “precursor/product labeling ratio” (6). Because modern proteomics technologies can rapidly separate and quantify individual proteins from complex mixtures, investigators have started to exploit the use of stable isotope tracers in mass spectrometry-based studies of proteome kinetics. However, the ability to study protein dynamics in vivo presents unique challenges (3, 4, 7–13); e.g. how does one (i) administer an isotope (typically a labeled amino acid) over a prolonged period and (ii) determine the true precursor labeling (because the amino acid will be rapidly turned over and its labeling will be diluted)? We have demonstrated how to quantify protein synthesis using ²H₂O in vivo (10, 11); the advantages are that the tracer can be given orally, body water is a homogeneous pool with a relatively slow turnover, and the organism will continuously generate ²H-labeled amino acids (consequently one can study free living subjects, including humans (9, 11, 14)). The assumption of the method is that the equilibration between ²H in body water and a free amino acid(s) is faster than the rate of incorporation of an amino acid(s) into a newly made protein(s); preferably, the labeling of a free amino acid(s) should remain constant regardless of the metabolic status. We have validated that assumption by measuring the time-dependent labeling of alanine in vivo during the administration of ²H₂O and by measuring the incorporation of ²H-labeled alanine into plasma albumin and total tissue proteins using gas chromatography-mass spectrometry methods (10, 11, 15). Subsequent reports support our observations (12, 13).In this study, we demonstrate (as a model example) the application of our ²H₂O-based approach for measuring albumin biosynthesis in vivo in mice during long term and short term investigations. Namely, we recently demonstrated how to obtain relatively precise measurements of mass isotopomer profiles of peptides and other relatively large molecules by developing a novel approach for integrating the data (16, 17). Our method allowed us to detect shifts in the isotope distribution profile of albumin-derived peptides from mice given ²H₂O (17). In the current report, parallel studies examined the use of H₂¹⁸O because it offers potential advantages over ²H₂O, especially during acute studies that involve perturbations such as consumption of a meal. For example, the cleavage of a protein will immediately add a labeled oxygen atom into the carboxyl group of a free amino acid; resonance effects will distribute the label over both carboxyl oxygens. Although repeated cleavage is required to achieve maximal labeling of both oxygens, cleavage of tRNA-bound amino acids will also contribute to the labeling of the carboxyl oxygen (18–21). The synthesis of a new protein(s) then results in the stable incorporation of ¹⁸O into the peptide bond; indeed, the oxygen in peptide bonds accounts for a majority of the total oxygen in a protein (18, 19), making it potentially easier to describe precursor/product labeling relationships (6). Finally, during the development of this work pitfalls were identified; thus we discuss strategies to circumvent potential problems.     

Decimal Place Slope,A Fast and Precise Method for Quantifying 13C Incorporation Levels for Detecting the Metabolic Activity of Microbial Species

The metabolic incorporation of stable isotopes such as ¹³C or ¹⁵N into proteins has become a powerful tool for qualitative and quantitative proteome studies. We recently introduced a method that monitors heavy isotope incorporation into proteins and presented data revealing the metabolic activity of various species in a microbial consortium using this technique. To further develop our method using an liquid chromatography (LC)-mass spectrometry (MS)-based approach, we present here a novel approach for calculating the incorporation level of ¹³C into peptides by using the information given in the decimal places of peptide masses obtained by modern high-resolution MS. In the present study, the applicability of this approach is demonstrated using Pseudomonas putida ML2 proteins uniformly labeled via the consumption of [¹³C₆]benzene present in the medium at concentrations of 0, 10, 25, 50, and 100 atom %. The incorporation of ¹³C was calculated on the basis of several labeled peptides derived from one band on an SDS-PAGE gel. The accuracy of the calculated incorporation level depended upon the number of peptide masses included in the analysis, and it was observed that at least 100 peptide masses were required to reduce the deviation below 4 atom %. This accuracy was comparable with calculations of incorporation based on the isotope envelope. Furthermore, this method can be extended to the calculation of the labeling efficiency for a wide range of biomolecules, including RNA and DNA. The technique will therefore allow a highly accurate determination of the carbon flux in microbial consortia with a direct approach based solely on LC-MS.The metabolic incorporation of stable isotopes such as ¹³C or ¹⁵N into proteins has become a powerful component of qualitative and quantitative proteome studies (1). Incorporation of heavy isotopes can be used to analyze microbial processes such as turnover rates and also to help to establish structure-function relationships within microbial communities. Stable isotope probing (SIP¹) techniques based on DNA-SIP (2) and RNA-SIP (3) have been used for this purpose previously. With the introduction of protein-SIP (4), the need for an accurate alternative method for calculating label incorporation into biomolecules arose. Protein-SIP has several advantages compared with DNA/RNA-SIP, the most important being its capacity to detect dynamic levels of incorporation, whereas only labeled or unlabeled states can be categorized by means of DNA/RNA-SIP because of the need to separate ¹³C-DNA/RNA by density gradient centrifugation. Quantitative analysis of ¹³C incorporation levels is of the utmost importance, especially when unraveling carbon fluxes through either microbial communities or food webs with different trophic levels.In contrast to the incorporation of isotopically labeled amino acids, which is often used in quantitative proteomics (5), metabolic labeling by growth substrates and nutrients (e.g. salts) is often imperfect and makes the processing of mass spectrometry (MS) data difficult. For example, when the incorporation of ¹³C exceeds ∼2 atom %, common database search algorithms fail to identify peptides and proteins. The problem can only be managed successfully if a stable, known degree of ¹³C incorporation can be achieved during the experiment (6). Using a low labeling efficiency of roughly 5 atom %, Huttlin et al. (6) chose the altered envelope chain for calculating the incorporation and simultaneously used the signal intensity for a quantitative comparison with the sample that had a natural abundance of ¹³C. Database approaches for peptide identification can cope only with the natural abundance of carbon isotopes; they fail if the incorporation of ¹³C significantly exceeds the natural isotope abundance or if incorporation patterns occur in unpredictable ways (7).The simplest method for determining the incorporation level is to compare the unlabeled average mass of the monoisotopic peptide with the mass of the labeled protein, as estimated by matrix-assisted laser desorption/ionization or electrospray ionization MS (8, 9). A more advanced approach for determining the isotopic mass distribution of peptides is based on the isotopic distribution of the peaks of a peptide envelope (10, 11). Here, for a given isotopomer, the incorporation efficiency is defined as the percentage of incorporated ¹³C atoms with relation to the total number of carbon atoms with the natural isotope abundance (approximately 1.01 atom % ¹³C). As a reference, the theoretical isotopic distribution of a peptide is calculated based upon an algorithm described elsewhere (12). The isotope distribution of both unlabeled and labeled peptides can subsequently be used to calculate the incorporation level. For this method, an Excel spreadsheet (ProSIPQuant.xls) was developed (4). A similar approach, also based on the calculation of isotopic distributions, has been used in other studies (7). In these studies, however, the identification of the peptides is limited to those that have unlabeled counterparts; in addition, an exact calculation can be hampered by overlapping signals coming from additional peaks with similar masses.In the present study, we describe a new way of determining the isotope incorporation level. Our method makes use of characteristic patterns in the digits after the decimal point of the peptide masses generated by high-accuracy instruments such as the linear ion trap LTQ-Orbitrap (Thermo Fisher Scientific, Bremen, Germany). For tryptic peptides, typical regularities in the decimal places of the monoisotopic masses have been observed (13, 14). These observations have been explored in detail for theoretical and experimental data of proteins originating from Helicobacter pylori (15). As a result, a rule called the “half decimal place rule” (HDPR) was defined; it states that the decimal place is nearly half of the first digit for tryptic peptides with masses in the range of 500–1,000 Da. In other words, the exact mass of a peptide is equal to its nominal mass times ∼1.005. Because the difference between ¹²C and ¹³C is slightly greater than 1 Da, exactly 1.0033548378, the decimal places of a tryptic peptide''s mass are shifted in a regular manner by the incorporation level and lead to a significantly increased slope for the digits in the third and fourth place after the decimal point. This shift can be used to estimate the incorporation level of heavy isotopes into the protein. Detecting such shifts requires the highly accurate measurement possible with modern mass spectrometers such as the LTQ-Orbitrap, the Fourier transform ion cyclotron resonance, or the quadrupole time of flight. In this communication, we demonstrate the applicability of this approach using Pseudomonas putida ML2 proteins labeled uniformly via the consumption of [¹³C₆]benzene with five different substrate concentrations (0, 10, 25, 50, and 100 atom % of ¹³C). The ¹³C incorporation was calculated based on several labeled peptides derived from different proteins in one SDS-PAGE band. By these means, we have established a method that allows the determination of ¹³C incorporation into proteins and can be used to assess the metabolic activity of a given species within a mixed community.