Metabolic Labeling of Mammalian Organisms with Stable Isotopes for Quantitative Proteomic Analysis

Proteomic and metabolomic profiling reveals time-dependent changes in hippocampal metabolism upon paroxetine treatment and biomarker candidates

Most of the commonly used antidepressants block monoamine reuptake transporters to enhance serotonergic or noradrenergic neurotransmission. Effects besides or downstream of monoamine reuptake inhibition are poorly understood and yet presumably important for the drugs' mode of action. In the present study we aimed at identifying hippocampal cellular pathway alterations in DBA/2 mice using paroxetine as a representative Selective Serotonin Reuptake Inhibitor (SSRI). Furthermore we identified biomarker candidates for the assessment of antidepressant treatment effects in plasma. Hippocampal protein levels were compared between chronic paroxetine- and vehicle-treated animals using in vivo(15)N metabolic labeling combined with mass spectrometry. We also studied the time course of metabolite level changes in hippocampus and plasma using a targeted polar metabolomics profiling platform. In silico pathway analyses revealed profound alterations related to hippocampal energy metabolism. Glycolytic metabolite levels acutely increased while Krebs cycle metabolite levels decreased upon chronic treatment. Changes in energy metabolism were influenced by altered glycogen metabolism rather than by altered glycolytic or Krebs cycle enzyme levels. Increased energy levels were reflected by an increased ATP/ADP ratio and by increased ratios of high-to-low energy purines and pyrimidines. In the course of our analyses we also identified myo-inositol as a biomarker candidate for the assessment of antidepressant treatment effects in the periphery. This study defines the cellular response to paroxetine treatment at the proteome and metabolome levels in the hippocampus of DBA/2 mice and suggests novel SSRI modes of action that warrant consideration in antidepressant development efforts.

Quantification of histone modifications using ¹⁵N metabolic labeling

Mass spectrometry has made major contributions to recent discoveries in the field of epigenetics, particularly in the characterization of the myriad post-translational modifications (PTMs) of histones which are technically challenging to analyze. These new developments have further aroused great interest in development of robust, new mass spectrometric methods to quantitatively study the dynamics of histone modifications. This review covers quantitative analysis of histone PTMs and discuss an ¹⁵N metabolic labeling procedure for quantifying histone PTMs applied to the analysis of methyltransferase knockouts in the model organism, Tetrahymena thermophila.

The revolution and evolution of shotgun proteomics for large-scale proteome analysis

Mass spectrometry has evolved at an exponential rate over the last 100 years. Innovations in the development of mass spectrometers have created powerful instruments capable of analyzing a wide range of targets, from rare atoms and molecules to very large molecules, such as a proteins, protein complexes, and DNA. These performance gains have been driven by sustaining innovations, punctuated by the occasional disruptive innovation. The use of mass spectrometry for proteome analysis was driven by disruptive innovations that created a capability for large-scale analysis of proteins and modifications.

A proteomics view of the molecular mechanisms and biomarkers of glaucomatous neurodegeneration

Despite improving understanding of glaucoma, key molecular players of neurodegeneration that can be targeted for treatment of glaucoma, or molecular biomarkers that can be useful for clinical testing, remain unclear. Proteomics technology offers a powerful toolbox to accomplish these important goals of the glaucoma research and is increasingly being applied to identify molecular mechanisms and biomarkers of glaucoma. Recent studies of glaucoma using proteomics analysis techniques have resulted in the lists of differentially expressed proteins in human glaucoma and animal models. The global analysis of protein expression in glaucoma has been followed by cell-specific proteome analysis of retinal ganglion cells and astrocytes. The proteomics data have also guided targeted studies to identify post-translational modifications and protein-protein interactions during glaucomatous neurodegeneration. In addition, recent applications of proteomics have provided a number of potential biomarker candidates. Proteomics technology holds great promise to move glaucoma research forward toward new treatment strategies and biomarker discovery. By reviewing the major proteomics approaches and their applications in the field of glaucoma, this article highlights the power of proteomics in translational and clinical research related to glaucoma and also provides a framework for future research to functionally test the importance of specific molecular pathways and validate candidate biomarkers.

The application of quantification techniques in proteomics for biomedical research

The systematic analysis of biological processes requires an understanding of the quantitative expression patterns of proteins, their interacting partners and their subcellular localization. This information was formerly difficult to accrue as the relative quantification of proteins relied on antibody-based methods and other approaches with low throughput. The advent of soft ionization techniques in mass spectrometry plus advances in separation technologies has aligned protein systems biology with messenger RNA, DNA, and microarray technologies to provide data on systems as opposed to singular protein entities. Another aspect of quantitative proteomics that increases its importance for the coming few years is the significant technical developments underway both for high pressure liquid chromatography and mass spectrum devices. Hence, robustness, reproducibility and mass accuracy are still improving with every new generation of instruments. Nonetheless, the methods employed require validation and comparison to design fit for purpose experiments in advanced protein analyses. This review considers the newly developed systematic protein investigation methods and their value from the standpoint that relative or absolute protein quantification is required de rigueur in biomedical research.

Comparison of protein expression ratios observed by sixplex and duplex TMT labeling method

Stable isotope labeling via isobaric derivatization of peptides is a universally applicable approach that enables concurrent identification and quantification of proteins in different samples using tandem mass spectrometry. In this study, we evaluated the performance of amine-reactive isobaric tandem mass tag (TMT), available as duplex and sixplex sets, with regard to their ability to elucidate protein expression changes. Using rat brain tissue from two different developmental time points, postnatal day 1 (p1) and 45 (p45), as a model system, we compared the protein expression ratios (p45/p1) observed using duplex TMT tags in triplicate measurements versus sixplex tag in a single LC-MS/MS analysis. A correlation of 0.79 in relative protein abundance was observed in the proteins quantified by these two sets of reagents. However, more proteins passed the criteria for significant fold change (-1.0 ≤ log(2) ratio (p45/p1) ≥ +1.0 and p < 0.05) in the sixplex analysis. Nevertheless, in both methods most proteins showing significant fold change were identified by multiple spectra, increasing their quantification precision. Additionally, the fold change in p45 rats against p1, observed in TMT experiments, was corroborated by a metabolic labeling strategy where relative quantification of differentially expressed proteins was obtained using (15)N-labeled p45 rats as an internal standard.

The effect of long term calorie restriction on in vivo hepatic proteostatis: a novel combination of dynamic and quantitative proteomics

Calorie restriction (CR) promotes longevity. A prevalent mechanistic hypothesis explaining this effect suggests that protein degradation, including mitochondrial autophagy, is increased with CR, removing damaged proteins and improving cellular fitness. At steady state, increased catabolism must be balanced by increasing mitochondrial biogenesis and protein synthesis, resulting in faster protein replacement rates. To test this hypothesis, we measured replacement kinetics and relative concentrations of hundreds of proteins in vivo in long-term CR and ad libitum-fed mice using metabolic (2)H(2)O-labeling combined with the Stable Isotope Labeling in Mammals protocol and LC-MS/MS analysis of mass isotopomer abundances in tryptic peptides. CR reduced absolute synthesis and breakdown rates of almost all measured hepatic proteins and prolonged the half-lives of most (≈ 80%), particularly mitochondrial proteins (but not ribosomal subunits). Proteins with related functions exhibited coordinated changes in relative concentration and replacement rates. In silico expression pathway interrogation allowed the testing of potential regulators of altered network dynamics (e.g. peroxisome proliferator-activated receptor gamma coactivator 1-alpha). In summary, our combination of dynamic and quantitative proteomics suggests that long-term CR reduces mitochondrial biogenesis and mitophagy. Our findings contradict the theory that CR increases mitochondrial protein turnover and provide compelling evidence that cellular fitness is accompanied by reduced global protein synthetic burden.

Quantitative proteomics reveals that the specific methyltransferases Txr1p and Ezl2p differentially affect the mono-, di- and trimethylation states of histone H3 lysine 27 (H3K27)

Nuclear DNA in eukaryotic cells is assembled into the hierarchical chromatin structure via a process that is dynamically affected by the combinatorial set of post-translational modifications (PTMs) of histones in a dynamic manner responsive to physiological and environmental changes. The precise quantification of these complex modifications is challenging. Here we present a robust MS-based quantitative proteomics method for studying histone PTMs using (15)N metabolically labeled histones as the internal reference. Using this approach, we identified Tetrahymena trithorax related 1 (Txr1p) as a histone methyltransferase in Tetrahymena thermophila and characterized the relationships of the Txr1p and Ezl2p methyltransferases to histone H3 modification. We identified 32 PTMs in more than 60 tryptic peptides from histone H3 of the ciliate model organism Tetrahymena thermophila, and we quantified them (average coefficient of variation: 13%). We examined perturbations to histone modification patterns in two knockout strains of SET-domain-containing histone methyltransferases (HMT). Knockout of TXR1 led to progressively decreased mono-, di-, and tri-methylation of H3K27 and apparent reduced monomethylation of H3K36 in vivo. In contrast, EZL2 knockout resulted in dramatic reductions in both di- and tri-methylation of H3K27 in vivo, whereas the levels of monomethylation of H3K27 increased significantly. This buildup of monomethyl H3K27 is consistent with its role as a substrate for Ezl2p. These results were validated via immunoblotting using modification site-specific antibodies. Taken together, our studies define Txr1p as an H3K27 monomethylation-specific HMT that facilitates the buildup of H3K27 di- and trimethylation by the canonical H3K27-specific HMT, Ezl2p. Our studies also delineate some of the interdependences between various H3 modifications, as compensatory increases in monomethylation at H3K4, H3K23, and H3K56 were also observed for both TXR1 and ELZ2 mutants.

Proteome dynamics: revisiting turnover with a global perspective

Although bulk protein turnover has been measured with the use of stable isotope labeled tracers for over half a century, it is only recently that the same approach has become applicable to the level of the proteome, permitting analysis of the turnover of many proteins instead of single proteins or an aggregated protein pool. The optimal experimental design for turnover studies is dependent on the nature of the biological system under study, which dictates the choice of precursor label, protein pool sampling strategy, and treatment of data. In this review we discuss different approaches and, in particular, explore how complexity in experimental design and data processing increases as we shift from unicellular to multicellular systems, in particular animals.

Benchmarking stable isotope labeling based quantitative proteomics

Several quantitative mass spectrometry based technologies have recently evolved to interrogate the complexity, interconnectivity and dynamic nature of proteomes. Currently, the most popular methods use either metabolic or chemical isotope labeling with MS based quantification or chemical labeling using isobaric tags with MS/MS based quantification. Here, we assess the performance of three of the most popular approaches through systematic independent large scale quantitative proteomics experiments, comparing SILAC, dimethyl and TMT labeling strategies. Although all three methods have their strengths and weaknesses, our data indicate that all three can reach a similar depth in number of identified proteins using a classical (MS2 based) shotgun approach. TMT quantification using only MS2 is heavily affected by co-isolation leading to compromised precision and accuracy. This issue may be partly resolved by using an MS3 based acquisition; however, at the cost of a significant reduction in number of proteins quantified. Interestingly, SILAC and chemical labeling with MS based quantification produce almost indistinguishable results, independent of which database search algorithm used.

The 15N isotope effect in Escherichia 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.

Parallel reaction monitoring for high resolution and high mass accuracy quantitative, targeted proteomics

Selected reaction monitoring on a triple quadrupole mass spectrometer is currently experiencing a renaissance within the proteomics community for its, as yet, unparalleled ability to characterize and quantify a set of proteins reproducibly, completely, and with high sensitivity. Given the immense benefit that high resolution and accurate mass instruments have brought to the discovery proteomics field, we wondered if highly accurate mass measurement capabilities could be leveraged to provide benefits in the targeted proteomics domain as well. Here, we propose a new targeted proteomics paradigm centered on the use of next generation, quadrupole-equipped high resolution and accurate mass instruments: parallel reaction monitoring (PRM). In PRM, the third quadrupole of a triple quadrupole is substituted with a high resolution and accurate mass mass analyzer to permit the parallel detection of all target product ions in one, concerted high resolution mass analysis. We detail the analytical performance of the PRM method, using a quadrupole-equipped bench-top Orbitrap MS, and draw a performance comparison to selected reaction monitoring in terms of run-to-run reproducibility, dynamic range, and measurement accuracy. In addition to requiring minimal upfront method development and facilitating automated data analysis, PRM yielded quantitative data over a wider dynamic range than selected reaction monitoring in the presence of a yeast background matrix because of PRM's high selectivity in the mass-to-charge domain. With achievable linearity over the quantifiable dynamic range found to be statistically equal between the two methods, our investigation suggests that PRM will be a promising new addition to the quantitative proteomics toolbox.

Plasma proteome dynamics: analysis of lipoproteins and acute phase response proteins with 2H2O metabolic labeling

Understanding the pathologies related to the regulation of protein metabolism requires methods for studying the kinetics of individual proteins. We developed a (2)H(2)O metabolic labeling technique and software for protein kinetic studies in free living organisms. This approach for proteome dynamic studies requires the measurement of total body water enrichments by GC-MS, isotopic distribution of the tryptic peptide by LC-MS/MS, and estimation of the asymptotical number of deuterium incorporated into a peptide by software. We applied this technique to measure the synthesis rates of several plasma lipoproteins and acute phase response proteins in rats. Samples were collected at different time points, and proteins were separated by a gradient gel electrophoresis. (2)H labeling of tryptic peptides was analyzed by ion trap tandem mass spectrometry (LTQ MS/MS) for measurement of the fractional synthesis rates of plasma proteins. The high sensitivity of LTQ MS in zoom scan mode in combination with (2)H label amplification in proteolytic peptides allows detection of the changes in plasma protein synthesis related to animal nutritional status. Our results demonstrate that fasting has divergent effects on the rate of synthesis of plasma proteins, increasing synthesis of ApoB 100 but decreasing formation of albumin and fibrinogen. We conclude that this technique can effectively measure the synthesis of plasma proteins and can be used to study the regulation of protein homeostasis under physiological and pathological conditions.

Quantitative assessment of the impact of the gut microbiota on lysine epsilon-acetylation of host proteins using gnotobiotic mice

The gut microbiota influences numerous aspects of human biology. One facet that has not been thoroughly explored is its impact on the host proteome. We hypothesized that the microbiota may produce certain of its effects through covalent modification of host proteins. We focused on protein lysine ε-acetylation because of its recently discovered roles in regulation of cell metabolism, and the potential for products of microbial fermentation to interact with the lysine acetylation machinery of host cells. Germ-free mice, fed a (15)N-labeled diet for two generations, were colonized as adults with a microbiota harvested from conventionally raised mouse donors. Using high-resolution mass spectrometry, we quantified 3,891 liver and proximal colonic proteins, 558 of which contained 1,602 sites of lysine acetylation, 43% not previously described. Multiple proteins from multiple subcellular compartments underwent microbiota-associated increases in their levels of lysine acetylation at one or more residues, in one or both tissues. Acetylated proteins were enriched in functions related to energy production, respiration, and primary metabolism. A number of the acetylation events affect lysine residues at or near the active sites of enzymes, whereas others occur at locations that may affect other facets of protein function. One of these modifications, affecting Lys292 in mouse α-1-antitrypsin, was detected in the corresponding lysine of the human serum protein. Methods described in this report can be applied to other co- or posttranslational modifications, and add quantitation of protein expression and covalent modification to the arsenal of techniques for characterizing the dynamic, important interactions between gut symbionts and their hosts.

The Lgr5 intestinal stem cell signature: robust expression of proposed quiescent '+4' cell markers

Two types of stem cells are currently defined in small intestinal crypts: cycling crypt base columnar (CBC) cells and quiescent '+4' cells. Here, we combine transcriptomics with proteomics to define a definitive molecular signature for Lgr5(+) CBC cells. Transcriptional profiling of FACS-sorted Lgr5(+) stem cells and their daughters using two microarray platforms revealed an mRNA stem cell signature of 384 unique genes. Quantitative mass spectrometry on the same cell populations identified 278 proteins enriched in intestinal stem cells. The mRNA and protein data sets showed a high level of correlation and a combined signature of 510 stem cell-enriched genes was defined. Spatial expression patterns were further characterized by mRNA in-situ hybridization, revealing that approximately half of the genes were expressed in a gradient with highest levels at the crypt bottom, while the other half was expressed uniquely in Lgr5(+)stem cells. Lineage tracing using a newly established knock-in mouse for one of the signature genes, Smoc2, confirmed its stem cell specificity. Using this resource, we find-and confirm by independent approaches-that the proposed quiescent/'+4' stem cell markers Bmi1, Tert, Hopx and Lrig1 are robustly expressed in CBC cells.

Recent advances on multidimensional liquid chromatography-mass spectrometry for proteomics: from qualitative to quantitative analysis--a review

With the acceleration of proteome research, increasing attention has been paid to multidimensional liquid chromatography-mass spectrometry (MDLC-MS) due to its high peak capacity and separation efficiency. Recently, many efforts have been put to improve MDLC-based strategies including "top-down" and "bottom-up" to enable highly sensitive qualitative and quantitative analysis of proteins, as well as accelerate the whole analytical procedure. Integrated platforms with combination of sample pretreatment, multidimensional separations and identification were also developed to achieve high throughput and sensitive detection of proteomes, facilitating highly accurate and reproducible quantification. This review summarized the recent advances of such techniques and their applications in qualitative and quantitative analysis of proteomes.

Single-step inline hydroxyapatite enrichment facilitates identification and quantitation of phosphopeptides from mass-limited proteomes with MudPIT

Herein we report the characterization and optimization of single-step inline enrichment of phosphopeptides directly from small amounts of whole cell and tissue lysates (100-500 μg) using a hydroxyapatite (HAP) microcolumn and Multidimensional Protein Identification Technology (MudPIT). In comparison to a triplicate HILIC-IMAC phosphopeptide enrichment study, ∼80% of the phosphopeptides identified using HAP-MudPIT were unique. Similarly, analysis of the consensus phosphorylation motifs between the two enrichment methods illustrates the complementarity of calcium- and iron-based enrichment methods and the higher sensitivity and selectivity of HAP-MudPIT for acidic motifs. We demonstrate how the identification of more multiply phosphorylated peptides from HAP-MudPIT can be used to quantify phosphorylation cooperativity. Through optimization of HAP-MudPIT on a whole cell lysate we routinely achieved identification and quantification of ca. 1000 phosphopeptides from a ∼1 h enrichment and 12 h MudPIT analysis on small quantities of material. Finally, we applied this optimized method to identify phosphorylation sites from a mass-limited mouse brain region, the amygdala (200-500 μg), identifying up to 4000 phosphopeptides per run.

Dynamics of subcellular proteomes during brain development

Many neurological disorders are caused by perturbations during brain development, but these perturbations cannot be readily identified until there is comprehensive description of the development process. In this study, we performed mass spectrometry analysis of the synaptosomal and mitochondrial fractions from three rat brain regions at four postnatal time points. To quantitate our analysis, we employed (15)N labeled rat brains using a technique called SILAM (stable isotope labeling in mammals). We quantified 167429 peptides and identified over 5000 statistically significant changes during development including known disease-associated proteins. Global analysis revealed distinct trends between the synaptic and nonsynaptic mitochondrial proteomes and common protein networks between regions each consisting of a unique array of expression patterns. Finally, we identified novel regulators of neurodevelopment that possess the identical temporal pattern of known regulators of neurodevelopment. Overall, this study is the most comprehensive quantitative analysis of the developing brain proteome to date, providing an important resource for neurobiologists.

An exclusion list based label-free proteome quantification approach using an LTQ Orbitrap

RATIONALE

Label-based mass spectrometry is a powerful tool for large-scale protein identification and quantification. However, it requires the chemical or metabolic incorporation of the labeled compound(s) which can be difficult to attain, e.g. for non-cultivable organisms or scarce sample, such as biopsies. Therefore, we set out to develop and validate an efficient label-free liquid chromatography/tandem mass spectrometry (LC/MS/MS) workflow based on optimized instrument settings and incremental exclusion lists.

METHODS

To increase the number of quantified peptides an incremental exclusion list was incorporated along with optimized instrument settings for the used LTQ Orbitrap. As a proof of concept, label-free quantification data from this optimized approach were compared to the results of control measurements without exclusion lists and of an in vivo metabolic labeling GeLC/MS/MS experiment. The data were drawn from Staphylococcus aureus whole cell lysates of non-stressed and nitric oxide (NO)-stressed cells.

RESULTS

Compared to MS analysis without exclusion lists the new approach resulted in an increased number of identified peptides, enabling label-free quantification of more than 990 S. aureus proteins. With respect to the number of quantified proteins and differences in protein levels between the control and NO-treated samples the results of the new method were consistent with those of the GeLC/MS/MS experiment.

CONCLUSIONS

The application of exclusion lists and optimized instrument settings in LC/MS/MS analysis significantly enhances the sensitivity and resolution of label-free protein identification and quantification. Therefore, the new workflow is a powerful alternative to label-based quantification methods.

The use of heavy nitrogen in quantitative proteomics experiments in plants

In the growing field of plant systems biology, there is an undisputed need for methods allowing accurate quantitation of proteins and metabolites. As autotrophic organisms, plants can easily metabolize different nitrogen isotopes, resulting in proteins and metabolites with distinct molecular mass that can be separated on a mass spectrometer. In comparative quantitative experiments, treated and untreated samples are differentially labeled by nitrogen isotopes and jointly processed, thereby minimizing sample-to-sample variation. In recent years, heavy nitrogen labeling has become a widely used strategy in quantitative proteomics and novel approaches have been developed for metabolite identification. Here, we present an overview of currently used experimental strategies in heavy nitrogen labeling in plants and provide background on the history and function of this quantitation technique.

Transcriptome and proteome research in veterinary science: what is possible and what questions can be asked?

In recent years several technologies for the complete analysis of the transcriptome and proteome have reached a technological level which allows their routine application as scientific tools. The principle of these methods is the identification and quantification of up to ten thousands of RNA and proteins species in a tissue, in contrast to the sequential analysis of conventional methods such as PCR and Western blotting. Due to their technical progress transcriptome and proteome analyses are becoming increasingly relevant in all fields of biological research. They are mainly used for the explorative identification of disease associated complex gene expression patterns and thereby set the stage for hypothesis-driven studies. This review gives an overview on the methods currently available for transcriptome analysis, that is, microarrays, Ref-Seq, quantitative PCR arrays and discusses their potentials and limitations. Second, the most powerful current approaches to proteome analysis are introduced, that is, 2D-gel electrophoresis, shotgun proteomics, MudPIT and the diverse technological concepts are reviewed. Finally, experimental strategies for biomarker discovery, experimental settings for the identification of prognostic gene sets and explorative versus hypothesis driven approaches for the elucidation of diseases associated genes and molecular pathways are described and their potential for studies in veterinary research is highlighted.

Quantitative mass spectrometry-based proteomics: an overview

In recent years, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review the strategies and problems of the methods currently in use for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics.

Psychiatric disorder biomarker discovery using quantitative proteomics

To date, no molecular biomarker exists for any psychiatric disorder. To identify phenotype-specific biomarkers and investigate the molecular underpinnings of anxiety pathophysiology, we interrogated the well-established mouse model of high (HAB), normal (NAB) and low (LAB) anxiety-related behavior by in vivo (15)N metabolic labeling and quantitative proteomics. The (15)N metabolic labeling approach enables accurate quantification due to the early mixing of the labeled and unlabeled samples under comparison, thus avoiding the biased experimental error introduction during handling. Differentially expressed proteins between HAB and LAB mice can be validated with non-mass-spectrometry-based methods. In silico pathway analysis enables identification of protein networks implicated in anxiety neural circuits. The presented workflow provides a precise and non-hypothesis-driven tool for identifying candidate biomarkers using animal models of psychiatric disorders.

SILAM for quantitative proteomics of liver Akt1/PKBα after burn injury

Akt1/protein kinase Bα (Akt1/PKBα) is a downstream mediator of the insulin signaling system. In this study we explored mechanism(s) for its role in burn injury. Akt1/PKBα in liver extracts from mice with burn injury fed with (²H₇)-L-Leu was immunoprecipitated and isolated with SDS-PAGE. Two tryptic peptides, one in the kinase loop and a control peptide just outside of the loop were sequenced via nano-LC interfaced with quadruple time-of-flight tandem mass spectrometry (Q-TOF tandem MS). Their relative isotopologue abundances were determined by stable isotope labeling by amino acids in mammalians (SILAM). Relative quantifications based on paired heavy/light peptides were obtained in 3 steps. The first step included homogenization of mixtures of equal amounts of tissue from burned and sham-treated animals (i.e., isotope dilution) and acquisition of uncorrected data based on parent monoisotopic MS ion ratios. The second step included determination of isotopic enrichment of the kinase from burned mice on Day 7 and the third step enrichment correction of partially labeled heavy and light monoisotopic MS ion ratios for relative quantification of bioactivity (loop peptide) and expression level (control peptide). Protein synthesis and enrichment after injury were found to be dependent on tissue and turnover of individual proteins. Three heavy and light monoisotopic ion ratios for albumin peptides from burned mice indicated ~55% enrichment and ~16.7-fold downregulation. In contract, serum amyloid P had ~66% enrichment and was significantly upregulated. Akt1/PKBα had ~56% enrichment and kinase level in response to the burn injury was upregulated compared with the control peptide. However, kinase bioactivity, represented by the Cys²⁹⁶ peptide, was significantly reduced. Overall, we demonstrated that i) quantitative proteomics can be performed without completely labeled mice; ii) measurement of enrichment of acyl-tRNAs is unnecessary and iii) Cys²⁹⁶ plays an important role in kinase activity after burn injury.

15N-labeled brain enables quantification of proteome and phosphoproteome in cultured primary neurons

Terminally differentiated primary cells represent a valuable in vitro model to study signaling events associated within a specific tissue. Quantitative proteomic methods using metabolic labeling in primary cells encounter labeling efficiency issues hindering the use of these cells. Here we developed a method to quantify the proteome and phosphoproteome of cultured neurons using (15)N-labeled brain tissue as an internal standard and applied this method to determine how an inhibitor of an excitatory neural transmitter receptor, phencyclidine (PCP), affects the global phosphoproteome of cortical neurons. We identified over 10,000 phosphopeptides and made accurate quantitative measurements of the neuronal phosphoproteome after neuronal inhibition. We show that short PCP treatments lead to changes in phosphorylation for 7% of neuronal phosphopeptides and that prolonged PCP treatment alters the total levels of several proteins essential for synaptic transmission and plasticity and leads to a massive reduction in the synaptic strength of inhibitory synapses. The results provide valuable insights into the dynamics of molecular networks implicated in PCP-mediated NMDA receptor inhibition and sensorimotor deficits.

18O-labeled proteome reference as global internal standards for targeted quantification by selected reaction monitoring-mass spectrometry

Selected reaction monitoring (SRM)-MS is an emerging technology for high throughput targeted protein quantification and verification in biomarker discovery studies; however, the cost associated with the application of stable isotope-labeled synthetic peptides as internal standards can be prohibitive for screening a large number of candidate proteins as often required in the preverification phase of discovery studies. Herein we present a proof of concept study using an (18)O-labeled proteome reference as global internal standards (GIS) for SRM-based relative quantification. The (18)O-labeled proteome reference (or GIS) can be readily prepared and contains a heavy isotope ((18)O)-labeled internal standard for every possible tryptic peptide. Our results showed that the percentage of heavy isotope ((18)O) incorporation applying an improved protocol was >99.5% for most peptides investigated. The accuracy, reproducibility, and linear dynamic range of quantification were further assessed based on known ratios of standard proteins spiked into the labeled mouse plasma reference. Reliable quantification was observed with high reproducibility (i.e. coefficient of variance <10%) for analyte concentrations that were set at 100-fold higher or lower than those of the GIS based on the light ((16)O)/heavy ((18)O) peak area ratios. The utility of (18)O-labeled GIS was further illustrated by accurate relative quantification of 45 major human plasma proteins. Moreover, quantification of the concentrations of C-reactive protein and prostate-specific antigen was illustrated by coupling the GIS with standard additions of purified protein standards. Collectively, our results demonstrated that the use of (18)O-labeled proteome reference as GIS provides a convenient, low cost, and effective strategy for relative quantification of a large number of candidate proteins in biological or clinical samples using SRM.

Quantitative, high-resolution proteomics for data-driven systems biology

Systems biology requires comprehensive data at all molecular levels. Mass spectrometry (MS)-based proteomics has emerged as a powerful and universal method for the global measurement of proteins. In the most widespread format, it uses liquid chromatography (LC) coupled to high-resolution tandem mass spectrometry (MS/MS) to identify and quantify peptides at a large scale. This peptide intensity information is the basic quantitative proteomic data type. It is used to quantify proteins between different proteome states, including the temporal variation of the proteome, to determine the complete primary structure of proteins including posttranslational modifications, to localize proteins to organelles, and to determine protein interactions. Here, we describe the principles of analysis and the areas of biology where proteomics can make unique contributions. The large-scale nature of proteomics data and its high accuracy pose special opportunities as well as challenges in systems biology that have been largely untapped so far.

In-depth quantitative cardiac proteomics combining electron transfer dissociation and the metalloendopeptidase Lys-N with the SILAC mouse

In quantitative proteomics stable isotope labeling has progressed from cultured cells toward the total incorporation of labeled atoms or amino acids into whole multicellular organisms. For instance, the recently introduced (13)C(6)-lysine labeled SILAC mouse allows accurate comparison of protein expression directly in tissue. In this model, only lysine, but not arginine, residues are isotope labeled, as the latter may cause complications to the quantification by in vivo conversion of arginine to proline. The sole labeling of lysines discourages the use of trypsin, as not all peptides will be quantifiable. Therefore, in the initial work Lys-C was used for digestion. Here, we demonstrate that the lysine-directed protease metalloendopeptidase Lys-N is an excellent alternative. As lysine directed peptides generally yield longer and higher charged peptides, alongside the more traditional collision induced dissociation we also implemented electron transfer dissociation in a quantitative stable isotope labeling with amino acid in cell culture workflow for the first time. The utility of these two complementary approaches is highlighted by investigating the differences in protein expression between the left and right ventricle of a mouse heart. Using Lys-N and electron transfer dissociation yielded coverage to a depth of 3749 proteins, which is similar as earlier investigations into the murine heart proteome. In addition, this strategy yields quantitative information on ∼ 2000 proteins with a median coverage of four peptides per protein in a single strong cation exchange-liquid chromatography-MS experiment, revealing that the left and right ventricle proteomes are very similar qualitatively as well as quantitatively.

A data processing pipeline for mammalian proteome dynamics studies using stable isotope metabolic labeling

In a recent study, in vivo metabolic labeling using (15)N traced the rate of label incorporation among more than 1700 proteins simultaneously and enabled the determination of individual protein turnover rate constants over a dynamic range of three orders of magnitude (Price, J. C., Guan, S., Burlingame, A., Prusiner, S. B., and Ghaemmaghami, S. (2010) Analysis of proteome dynamics in the mouse brain. Proc. Natl. Acad. Sci. U. S. A. 107, 14508-14513). These studies of protein dynamics provide a deeper understanding of healthy development and well-being of complex organisms, as well as the possible causes and progression of disease. In addition to a fully labeled food source and appropriate mass spectrometry platform, an essential and enabling component of such large scale investigations is a robust data processing and analysis pipeline, which is capable of the reduction of large sets of liquid chromatography tandem MS raw data files into the desired protein turnover rate constants. The data processing pipeline described in this contribution is comprised of a suite of software modules required for the workflow that fulfills such requirements. This software platform includes established software tools such as a mass spectrometry database search engine together with several additional, novel data processing modules specifically developed for (15)N metabolic labeling. These fulfill the following functions: (1) cross-extraction of (15)N-containing ion intensities from raw data files at varying biosynthetic incorporation times, (2) computation of peptide (15)N isotopic incorporation distributions, and (3) aggregation of relative isotope abundance curves for multiple peptides into single protein curves. In addition, processing parameter optimization and noise reduction procedures were found to be necessary in the processing modules in order to reduce propagation of errors in the long chain of the processing steps of the entire workflow.

Proteomic and metabolomic profiling of a trait anxiety mouse model implicate affected pathways

Depression and anxiety disorders affect a great number of people worldwide. Whereas singular factors have been associated with the pathogenesis of psychiatric disorders, growing evidence emphasizes the significance of dysfunctional neural circuits and signaling pathways. Hence, a systems biology approach is required to get a better understanding of psychiatric phenotypes such as depression and anxiety. Furthermore, the availability of biomarkers for these disorders is critical for improved diagnosis and monitoring treatment response. In the present study, a mouse model presenting with robust high versus low anxiety phenotypes was subjected to thorough molecular biomarker and pathway discovery analyses. Reference animals were metabolically labeled with the stable (15)N isotope allowing an accurate comparison of protein expression levels between the high anxiety-related behavior versus low anxiety-related behavior mouse lines using quantitative mass spectrometry. Plasma metabolomic analyses identified a number of small molecule biomarkers characteristic for the anxiety phenotype with particular focus on myo-inositol and glutamate as well as the intermediates involved in the tricarboxylic acid cycle. In silico analyses suggested pathways and subnetworks as relevant for the anxiety phenotype. Our data demonstrate that the high anxiety-related behavior and low anxiety-related behavior mouse model is a valuable tool for anxiety disorder drug discovery efforts.

Mass spectrometry-based approaches toward absolute quantitative proteomics

Mass spectrometry has served as a major tool for the discipline of proteomics to catalogue proteins in an unprecedented scale. With chemical and metabolic techniques for stable isotope labeling developed over the past decade, it is now routinely used as a method for relative quantification to provide valuable information on alteration of protein abundance in a proteome-wide scale. More recently, absolute or stoichiometric quantification of proteome is becoming feasible, in particular, with the development of strategies with isotope-labeled standards composed of concatenated peptides. On the other hand, remarkable progress has been also made in label-free quantification methods based on the number of identified peptides. Here we review these mass spectrometry-based approaches for absolute quantification of proteome and discuss their implications.

Frozen tissue can provide reproducible proteomic results of subcellular fractionation

Differential detergent fractionation (DDF) is frequently used to partition fresh cells and tissues into distinct compartments. We have tested whether DDF can reproducibly extract and fractionate cellular protein components from frozen tissues. Frozen kidneys were sequentially extracted with three different buffer systems. Analysis of the three fractions with liquid chromatography-tandem mass spectrometry (LC-MS/MS) identified 1693 proteins, some of which were common to all fractions and others of which were unique to specific fractions. Normalized spectral index (SI(N)) values obtained from these data were compared to evaluate both the reproducibility of the method and the efficiency of enrichment. SI(N) values between replicate fractions demonstrated a high correlation, confirming the reproducibility of the method. Correlation coefficients across the three fractions were significantly lower than those for the replicates, supporting the capability of DDF to differentially fractionate proteins into separate compartments. Subcellular annotation of the proteins identified in each fraction demonstrated a significant enrichment of cytoplasmic, cell membrane, and nuclear proteins in the three respective buffer system fractions. We conclude that DDF can be applied to frozen tissue to generate reproducible proteome coverage discriminating subcellular compartments. This demonstrates the feasibility of analyzing cellular compartment-specific proteins in archived tissue samples with the simple DDF method.

Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

Fully automated software solution for protein quantitation by global metabolic labeling with stable isotopes

Metabolic stable isotope labeling is increasingly employed for accurate protein (and metabolite) quantitation using mass spectrometry (MS). It provides sample-specific isotopologues that can be used to facilitate comparative analysis of two or more samples. Stable Isotope Labeling by Amino acids in Cell culture (SILAC) has been used for almost a decade in proteomic research and analytical software solutions have been established that provide an easy and integrated workflow for elucidating sample abundance ratios for most MS data formats. While SILAC is a discrete labeling method using specific amino acids, global metabolic stable isotope labeling using isotopes such as (15)N labels the entire element content of the sample, i.e. for (15)N the entire peptide backbone in addition to all nitrogen-containing side chains. Although global metabolic labeling can deliver advantages with regard to isotope incorporation and costs, the requirements for data analysis are more demanding because, for instance for polypeptides, the mass difference introduced by the label depends on the amino acid composition. Consequently, there has been less progress on the automation of the data processing and mining steps for this type of protein quantitation. Here, we present a new integrated software solution for the quantitative analysis of protein expression in differential samples and show the benefits of high-resolution MS data in quantitative proteomic analyses.

Identification of ejaculated proteins in the house mouse (Mus domesticus) via isotopic labeling

BACKGROUND

Seminal fluid plays an important role in successful fertilization, but knowledge of the full suite of proteins transferred from males to females during copulation is incomplete. The list of ejaculated proteins remains particularly scant in one of the best-studied mammalian systems, the house mouse (Mus domesticus), where artificial ejaculation techniques have proven inadequate. Here we investigate an alternative method for identifying ejaculated proteins, by isotopically labeling females with 15N and then mating them to unlabeled, vasectomized males. Proteins were then isolated from mated females and identified using mass spectrometry. In addition to gaining insights into possible functions and fates of ejaculated proteins, our study serves as proof of concept that isotopic labeling is a powerful means to study reproductive proteins.

RESULTS

We identified 69 male-derived proteins from the female reproductive tract following copulation. More than a third of all spectra detected mapped to just seven genes known to be structurally important in the formation of the copulatory plug, a hard coagulum that forms shortly after mating. Seminal fluid is significantly enriched for proteins that function in protection from oxidative stress and endopeptidase inhibition. Females, on the other hand, produce endopeptidases in response to mating. The 69 ejaculated proteins evolve significantly more rapidly than other proteins that we previously identified directly from dissection of the male reproductive tract.

CONCLUSION

Our study attempts to comprehensively identify the proteins transferred from males to females during mating, expanding the application of isotopic labeling to mammalian reproductive genomics. This technique opens the way to the targeted monitoring of the fate of ejaculated proteins as they incubate in the female reproductive tract.

Quantitative phospho-proteomic profiling of hepatocyte growth factor (HGF)-MET signaling in colorectal cancer

Colorectal cancer (CRC) is the second leading cause of death from cancer. The MET receptor tyrosine kinase and/or its ligand HGF are frequently amplified or overexpressed in CRC. It is known that tyrosine phosphorylated proteins are involved in progression and metastasis of colorectal cancer; however, little is known about the MET phospho-proteome in CRC. High resolution mass spectrometry was used to characterize immunoaffinity-purified, phosphotyrosine (pY)-containing tryptic peptides of the MET-expressing CRC cell model, DLD1. A total of 266 unambiguously identified pY sites spanning 168 proteins were identified. Quantification of mass spectrometry ion currents identified 161 pY sites, including many not previously linked to MET signaling, that were modulated in abundance by HGF stimulation. Overlay of these data with protein-protein interaction data sets suggested that many of the identified HGF-modulated phospho-proteins may be directly or indirectly associated with MET. Analysis of pY sequence motifs indicated a prevalence of Src family kinase consensus sequences, and reciprocal signaling between Src and MET was confirmed by using selective small molecule inhibitors of these kinases. Therefore, using quantitative phospho-proteomics profiling, kinase modulation by ligand and inhibitors, and data integration, an outline of the MET signaling network was generated for the CRC model.

Quantitative shotgun proteomics using a uniform ¹⁵N-labeled standard to monitor proteome dynamics in time course experiments reveals new insights into the heat stress response of Chlamydomonas reinhardtii

Crop-plant-yield safety is jeopardized by temperature stress caused by the global climate change. To take countermeasures by breeding and/or transgenic approaches it is essential to understand the mechanisms underlying plant acclimation to heat stress. To this end proteomics approaches are most promising, as acclimation is largely mediated by proteins. Accordingly, several proteomics studies, mainly based on two-dimensional gel-tandem MS approaches, were conducted in the past. However, results often were inconsistent, presumably attributable to artifacts inherent to the display of complex proteomes via two-dimensional-gels. We describe here a new approach to monitor proteome dynamics in time course experiments. This approach involves full ¹⁵N metabolic labeling and mass spectrometry based quantitative shotgun proteomics using a uniform ¹⁵N standard over all time points. It comprises a software framework, IOMIQS, that features batch job mediated automated peptide identification by four parallelized search engines, peptide quantification and data assembly for the processing of large numbers of samples. We have applied this approach to monitor proteome dynamics in a heat stress time course using the unicellular green alga Chlamydomonas reinhardtii as model system. We were able to identify 3433 Chlamydomonas proteins, of which 1116 were quantified in at least three of five time points of the time course. Statistical analyses revealed that levels of 38 proteins significantly increased, whereas levels of 206 proteins significantly decreased during heat stress. The increasing proteins comprise 25 (co-)chaperones and 13 proteins involved in chromatin remodeling, signal transduction, apoptosis, photosynthetic light reactions, and yet unknown functions. Proteins decreasing during heat stress were significantly enriched in functional categories that mediate carbon flux from CO₂ and external acetate into protein biosynthesis, which also correlated with a rapid, but fully reversible cell cycle arrest after onset of stress. Our approach opens up new perspectives for plant systems biology and provides novel insights into plant stress acclimation.

Elementary research of the formation mechanism of sex-related fluorescent cocoon of silkworm, Bombyx mori

To understand mechanisms for the difference of uptaking and transporting the pigments between the male and female in the silkworm, Bombyx mori strain of sex-related fluorescent cocoon, the fluorescent pigments in the midgut lumen, midgut, blood, silk glands and cocoon were analyzed with thin-layer chromatography, and showed that fluorescent colors of cocoons consisted with that of blood and silk glands. The different fluorescent colors of cocoons between the male and female may be mainly caused by the difference of accumulation and transportation for fluorescent pigments in the midgut and in the silk glands. Furthermore the midgut proteins were separated with Native-PAGE, and the proteins respectively recovered from three fluorescent regions presenting on a Native-PAGE gel for the female silkworms were determined using shotgun proteomics and mass spectrometry sequencing, of which 60, 40 and 18 proteins respectively from the region 1, 2 and 3 were identified. It was found that the several kinds of low molecular mass 30 kDa lipoproteins and the actins could be detected in all three regions, troponin, 30 kDa lipoprotein and 27 kDa glycoprotein precursor could be detected in the region 2 and 3, suggesting these proteins may be fluorescent pigments binding candidates proteins. Analysis of gene ontology indicated that the identified proteins in the three regions linked to the cellular component, molecular function, and biological process categories. These results provide a new clew to understand the formation mechanism of sex-related fluorescent cocoon of silkworm.

Global quantification of mammalian gene expression control

Gene expression is a multistep process that involves the transcription, translation and turnover of messenger RNAs and proteins. Although it is one of the most fundamental processes of life, the entire cascade has never been quantified on a genome-wide scale. Here we simultaneously measured absolute mRNA and protein abundance and turnover by parallel metabolic pulse labelling for more than 5,000 genes in mammalian cells. Whereas mRNA and protein levels correlated better than previously thought, corresponding half-lives showed no correlation. Using a quantitative model we have obtained the first genome-scale prediction of synthesis rates of mRNAs and proteins. We find that the cellular abundance of proteins is predominantly controlled at the level of translation. Genes with similar combinations of mRNA and protein stability shared functional properties, indicating that half-lives evolved under energetic and dynamic constraints. Quantitative information about all stages of gene expression provides a rich resource and helps to provide a greater understanding of the underlying design principles.

Click-generated triazole ureas as ultrapotent in vivo-active serine hydrolase inhibitors

Serine hydrolases are a diverse enzyme class representing ∼1% of all human proteins. The biological functions of most serine hydrolases remain poorly characterized owing to a lack of selective inhibitors to probe their activity in living systems. Here we show that a substantial number of serine hydrolases can be irreversibly inactivated by 1,2,3-triazole ureas, which show negligible cross-reactivity with other protein classes. Rapid lead optimization by click chemistry-enabled synthesis and competitive activity-based profiling identified 1,2,3-triazole ureas that selectively inhibit enzymes from diverse branches of the serine hydrolase class, including peptidases (acyl-peptide hydrolase, or APEH), lipases (platelet-activating factor acetylhydrolase-2, or PAFAH2) and uncharacterized hydrolases (α,β-hydrolase-11, or ABHD11), with exceptional potency in cells (sub-nanomolar) and mice (<1 mg kg(-1)). We show that APEH inhibition leads to accumulation of N-acetylated proteins and promotes proliferation in T cells. These data indicate 1,2,3-triazole ureas are a pharmacologically privileged chemotype for serine hydrolase inhibition, combining broad activity across the serine hydrolase class with tunable selectivity for individual enzymes.

Seasonal liver protein differences in a hibernator revealed by quantitative proteomics using whole animal isotopic labeling

Hibernation is an energy-saving strategy used by diverse species of mammals to survive winter. It is characterized by cycles between multi-day periods of torpor with low body temperature (T(b)), and short periods of rapid, spontaneous rewarming. The ability to retain cellular integrity and function throughout torpor and rewarming is a key attribute of hibernation. Livers from winter hibernators are resistant to cellular damage induced by cold storage followed by warm reperfusion. Identifying proteins that differ between the summer-sensitive and winter-protected phenotypic states is one useful approach that may elucidate the molecular mechanisms that underlie this protection. Here we employ a novel quantitative proteomics screening strategy whereby a newly-weaned 13-lined ground squirrel was metabolically labeled by ingesting heavy-isotope substituted ((15)N) Spirulina. The liver protein extract from this animal provided a common reference for quantitative evaluation of protein differences by its addition to extracts from pooled samples of summer active (SA) or winter entrance (Ent) phase hibernating ground squirrels. We identified 61 significantly different proteins between the two groups and compared them to proteins identified previously in the same samples using 2D gels. Of the 20 proteins common to the two datasets, the direction and magnitude of their differences were perfectly concordant for 18, providing confidence that both sets of altered proteins reflect bona fide differences between the two physiological states. Furthermore, the 41 novel proteins recovered in this study included many new enzymes in pathways identified previously: specifically, additional enzymes belonging to the urea cycle, amino acid and carbohydrate degradation, and lipid biosynthetic pathways were decreased, whereas enzymes involved in ketone body synthesis, fatty acid utilization, protein synthesis and gluconeogenesis were increased in the samples from entrance hibernators compared to summer active animals, providing additional specific evidence for the importance of these pathways in the hibernating phenotype.