Human saliva is a protein-rich, easily accessible source of potential local and systemic biomarkers to monitor changes that occur under pathological conditions; however, little is known about the changes in abundance associated with normal aging. In this study, we performed a comprehensive proteomic profiling of pooled saliva collected from the parotid glands of healthy female subjects, divided into two age groups 1 and 2 (20−30 and 55−65 years old, respectively). Hydrophobic charge interaction chromatography was used to separate high- from low-abundance proteins prior to characterization of the parotid saliva using multidimensional protein identification technology (MudPIT). Collectively, 532 proteins were identified in the two age groups. Of these proteins, 266 were identified exclusively in one age group, while 266 proteins were common to both groups. The majority of the proteins identified in the two age groups belonged to the defense and immune response category. Of note, several defense related proteins (e.g., lysozyme, lactoferrin and histatin-1) were significantly more abundant in group 2 as determined by G-test. Selected representative mass spectrometric findings were validated by Western blot analysis. Our study reports the first quantitative analysis of differentially regulated proteins in ductal saliva collected from young and older female subjects. This study supports the use of high-throughput proteomics as a robust discovery tool. Such results provide a foundation for future studies to identify specific salivary proteins which may be linked to age-related diseases specific to women.Keywords: Hydrophobic Charge Interaction Chromatography; Mass Spectrometry; Parotid Saliva;

Conus species of marine snails deliver a potent collection of toxins from the venom duct via a long proboscis attached to a harpoon tooth. Conotoxins are known to possess powerful neurological effects and some have been developed for therapeutic uses. Using mass-spectrometry based proteomics, qualitative and quantitative differences in conotoxin components were found in the proximal, central and distal sections of the Conus textile venom duct suggesting specialization of duct sections for biosynthesis of particular conotoxins. Reversed phase HPLC followed by Orbitrap mass spectrometry and data analysis using SEQUEST and ProLuCID identified 31 conotoxin sequences and 25 post-translational modification (PTM) variants with King-Kong 2 peptide being the most abundant. Several previously unreported variants of known conopeptides were found and this is the first time that HyVal is reported for a disulfide rich Conus peptide. Differential expression along the venom duct, production of PTM variants, alternative proteolytic cleavage sites, and venom processing enroute to the proboscis all appear to contribute to enriching the combinatorial pool of conopeptides and producing the appropriate formulation for a particular hunting situation. The complementary tools of mass spectrometry-based proteomics and molecular biology can greatly accelerate the discovery of Conus peptides and provide insights on envenomation and other biological strategies of cone snails.Keywords: conopeptides; conotoxin; differential expression; Orbitrap; post-translational modification; proteomics;

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.Keywords: hydroxyapatite; mouse amygdala; MudPIT; phosphopeptide enrichment; phosphorylation cooperativity; whole cell lysate;

Phosphoproteomics, the targeted study of a subfraction of the proteome which is modified by phosphorylation, has become an indispensable tool to study cell signaling dynamics. We described a methodology that linked phosphoproteome and proteome analysis based on Ba2+ binding properties of amino acids. This technology selected motif-specific phosphopeptides independent of the system under analysis. MudPIT (Multidimensional Identification Technology) identified 1037 precipitated phosphopeptides from as little as 250 µg of proteins. To extend coverage of the phosphoproteome, we sampled the nuclear extract of HeLa cells with three values of Ba2+ ions molarity. The presence of more than 70% of identified phosphoproteins was further substantiated by their nonmodified peptides. Upon isoproterenol stimulation of HEK cells, we identified an increasing number of phosphoproteins from MAPK cascades and AKAP signaling hubs. We quantified changes in both protein and phosphorylation levels of 197 phosphoproteins including a critical kinase, MAPK1. Integration of differential phosphorylation of MAPK1 with knowledge bases constructed modules that correlated well with its role as node in cross-talk of canonical pathways.Keywords: Barium; Beta adrenergic; Phosphoproteome; Protein Quantification; Signal transduction;

Chemical labeling of peptides prior to shotgun proteomics allows relative quantification of proteins in biological samples independent of sample origin. Current strategies utilize isobaric labels that fragment into reporter ions. However, quantification of reporter ions results in distorted ratio measurements due to contaminating peptides that are co-selected in the same precursor isolation window. Here, we show that quantitation of isobaric peptide fragment isotopologues in tandem mass spectra reduces precursor interference. The method is based on the relative quantitation of isobaric isotopologues of dimethylated peptide fragments in tandem mass spectra following higher energy collisional dissociation (HCD). The approach enables precise quantification of a proteome down to single spectra per protein and quantifies >90% of proteins in a MudPIT experiment and accurately measures proteins in a model cell line for cystic fibrosis.Keywords: mass spectrometry; quantitative proteomics;

Here we describe a new strategy, HILAQ (Heavy Isotope Labeled Azidohomoalanine Quantification), to rapidly quantify the molecular vulnerability profile to oxytosis, which is an oxidative stress-induced programed cell death pathway that has been reported to be involved in aging and neurodegenerative diseases. HILAQ was able to quantify 1962 newly synthesized proteins (NSPs) after 1 h of pulse labeling in HEK293T cell line, while 353 proteins were quantified using the previously published QuaNCAT protocol. HILAQ was successfully applied to the HT22 oxytosis model. 226 proteins were found to have a two-fold change in abundance, and 108 proteins were enriched in the cell death pathway, demonstrating the utility of HT22 cells as a tool to study the molecular details of cell death involved in neurodegenerative diseases. The HILAQ strategy simplifies the analysis of newly synthesized proteomes through the use of isobaric labels and achieves higher sensitivity than previously published methods.Keywords: azidohomoalanine; heavy isotope; HT22; new synthesized proteins; oxytosis; proteomics;

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 15N-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.Keywords: mass spectrometry; phencyclidine; phosphorylation; quantification; stable isotope labeling;

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 ≤ log2 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 15N-labeled p45 rats as an internal standard.Keywords: isobaric tag; mass spectrometry; metabolic labeling; MudPIT; quantitative proteomics; SILAM; tandem mass tag; TMT;

Large-scale proteomics often employs two orthogonal separation methods to fractionate complex peptide mixtures. Fractionation can involve ion exchange separation coupled to reversed-phase separation or, more recently, two reversed-phase separations performed at different pH values. When multidimensional separations are combined with tandem mass spectrometry for protein identification, the strategy is often referred to as multidimensional protein identification technology (MudPIT). MudPIT has been used in either an automated (online) or manual (offline) format. In this study, we evaluated the performance of different MudPIT strategies by both label-free and tandem mass tag (TMT) isobaric tagging. Our findings revealed that online MudPIT provided more peptide/protein identifications and higher sequence coverage than offline platforms. When employing an off-line fractionation method with direct loading of samples onto the column from an eppendorf tube via a high-pressure device, a 5.3% loss in protein identifications is observed. When off-line fractionated samples are loaded via an autosampler, a 44.5% loss in protein identifications is observed compared with direct loading of samples onto a triphasic capillary column. Moreover, peptide recovery was significantly lower after offline fractionation than in online fractionation. Signal-to-noise (S/N) ratio, however, was not significantly altered between experimental groups. It is likely that offline sample collection results in stochastic peptide loss due to noncovalent adsorption to solid surfaces. Therefore, the use of the offline approaches should be considered carefully when processing minute quantities of valuable samples.Keywords: HEK293; isobaric; label-free; MudPIT; offline; online; TMT;

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 15N 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.Keywords: brain development; metabolic labeling; MudPIT; quantitation; SILAM;

Prepulse inhibition (PPI) is an example of sensorimotor gating and deficits in PPI have been demonstrated in schizophrenia patients. Phencyclidine (PCP) suppression of PPI in animals has been studied to elucidate the pathological elements of schizophrenia. However, the molecular mechanisms underlying PCP treatment or PPI in the brain are still poorly understood. In this study, quantitative phosphoproteomic analysis was performed on the prefrontal cortex from rats that were subjected to PPI after being systemically injected with PCP or saline. PCP downregulated phosphorylation events were significantly enriched in proteins associated with long-term potentiation (LTP). Importantly, this data set identifies functionally novel phosphorylation sites on known LTP-associated signaling molecules. In addition, mutagenesis of a significantly altered phosphorylation site on xCT (SLC7A11), the light chain of system xc-, the cystine/glutamate antiporter, suggests that PCP also regulates the activity of this protein. Finally, new insights were also derived on PPI signaling independent of PCP treatment. This is the first quantitative phosphorylation proteomic analysis providing new molecular insights into sensorimotor gating.

Extraction of data from the proprietary RAW files generated by Thermo Fisher mass spectrometers is the primary step for subsequent data analysis. High resolution and high mass accuracy data obtained by state-of-the-art mass spectrometers (e.g., Orbitraps) can significantly improve both peptide/protein identification and quantification. We developed RawConverter, a stand-alone software tool, to improve data extraction on RAW files from high-resolution Thermo Fisher mass spectrometers. RawConverter extracts full scan and MSn data from RAW files like its predecessor RawXtract; most importantly, it associates the accurate precursor mass-to-charge (m/z) value with the tandem mass spectrum. RawConverter accepts RAW data generated by either data-dependent acquisition (DDA) or data-independent acquisition (DIA). It generates output into MS1/MS2/MS3, MGF, or mzXML file formats, which fulfills the format requirements for most data identification and quantification tools. Using the tandem mass spectra extracted by RawConverter with corrected m/z values, 32.8%, 27.1%, and 84.1%, peptide spectra matches (PSMs) produce 17.4% (13.0%), 14.4% (11.5%), and 45.7% (36.2%) more peptide (protein) identifications than ProteoWizard, pXtract, and RawXtract, respectively. RawConverter is implemented in C# and is freely accessible at http://fields.scripps.edu/rawconv.

The purpose of this study is to determine the behavioral and proteomic consequences of shock-induced stress in zebrafish (Danio rerio) as a vertebrate model. Here we describe the behavioral effects of exposure to predictable and unpredictable electric shock, together with quantitative tandem mass tag isobaric labeling workflow to detect altered protein candidates in response to shock exposure. Behavioral results demonstrate a hyperactivity response to electric shock and a suppression of activity to a stimulus predicting shock. On the basis of the quantitative changes in protein abundance following shock exposure, eight proteins were significantly up-regulated (HADHB, hspa8, hspa5, actb1, mych4, atp2a1, zgc:86709, and zgc:86725). These proteins contribute crucially in catalytic activities, stress response, cation transport, and motor activities. This behavioral proteomic driven study clearly showed that besides the rapid induction of heat shock proteins, other catalytic enzymes and cation transporters were rapidly elevated as a mechanism to counteract oxidative stress conditions resulting from elevated fear/anxiety levels.

Shotgun proteomics generates valuable information from large-scale and target protein characterizations, including protein expression, protein quantification, protein post-translational modifications (PTMs), protein localization, and protein–protein interactions. Typically, peptides derived from proteolytic digestion, rather than intact proteins, are analyzed by mass spectrometers because peptides are more readily separated, ionized and fragmented. The amino acid sequences of peptides can be interpreted by matching the observed tandem mass spectra to theoretical spectra derived from a protein sequence database. Identified peptides serve as surrogates for their proteins and are often used to establish what proteins were present in the original mixture and to quantify protein abundance. Two major issues exist for assigning peptides to their originating protein. The first issue is maintaining a desired false discovery rate (FDR) when comparing or combining multiple large datasets generated by shotgun analysis and the second issue is properly assigning peptides to proteins when homologous proteins are present in the database. Herein we demonstrate a new computational tool, ProteinInferencer, which can be used for protein inference with both small- or large-scale data sets to produce a well-controlled protein FDR. In addition, ProteinInferencer introduces confidence scoring for individual proteins, which makes protein identifications evaluable.This article is part of a Special Issue entitled: Computational Proteomics.Figure optionsDownload full-size imageDownload high-quality image (128 K)Download as PowerPoint slide

ProLuCID, a new algorithm for peptide identification using tandem mass spectrometry and protein sequence databases has been developed. This algorithm uses a three tier scoring scheme. First, a binomial probability is used as a preliminary scoring scheme to select candidate peptides. The binomial probability scores generated by ProLuCID minimize molecular weight bias and are independent of database size. A modified cross-correlation score is calculated for each candidate peptide identified by the binomial probability. This cross-correlation scoring function models the isotopic distributions of fragment ions of candidate peptides which ultimately results in higher sensitivity and specificity than that obtained with the SEQUEST XCorr. Finally, ProLuCID uses the distribution of XCorr values for all of the selected candidate peptides to compute a Z score for the peptide hit with the highest XCorr. The ProLuCID Z score combines the discriminative power of XCorr and DeltaCN, the standard parameters for assessing the quality of the peptide identification using SEQUEST, and displays significant improvement in specificity over ProLuCID XCorr alone. ProLuCID is also able to take advantage of high resolution MS/MS spectra leading to further improvements in specificity when compared to low resolution tandem MS data. A comparison of filtered data searched with SEQUEST and ProLuCID using the same false discovery rate as estimated by a target-decoy database strategy, shows that ProLuCID was able to identify as many as 25% more proteins than SEQUEST. ProLuCID is implemented in Java and can be easily installed on a single computer or a computer cluster.This article is part of a Special Issue entitled: Computational Proteomics.Figure optionsDownload full-size imageDownload high-quality image (55 K)Download as PowerPoint slide

Quantification of proteomes by mass spectrometry has proven to be useful to study human pathology recapitulated in cellular or animal models of disease. Enriching and quantifying newly synthesized proteins (NSPs) at set time points by mass spectrometry has the potential to identify important early regulatory or expression changes associated with disease states or perturbations. NSP can be enriched from proteomes by employing pulsed introduction of the noncanonical amino acid, azidohomoalanine (AHA). We demonstrate that pulsed introduction of AHA in the feed of mice can label and identify NSP from multiple tissues. Furthermore, we quantitate differences in new protein expression resulting from CRE-LOX initiated knockout of LKB1 in mouse livers. Overall, the PALM strategy allows for the first time in vivo labeling of mouse tissues to differentiate protein synthesis rates at discrete time points.

In the last two decades, computational tools for mass spectrometry-based proteomics data analysis have evolved from a few stand-alone software solutions serving specific goals, such as the identification of amino acid sequences based on mass spectrometry spectra, to large-scale complex pipelines integrating multiple computer programs to solve a collection of problems. This software evolution has been mostly driven by the appearance of novel technologies that allowed the community to tackle complex biological problems, such as the identification of proteins that are differentially expressed in two samples under different conditions. The achievement of such objectives requires a large suite of programs to analyze the intricate mass spectrometry data. Our laboratory addresses complex proteomics questions by producing and using algorithms and software packages. Our current computational pipeline includes, among other things, tools for mass spectrometry raw data processing, peptide and protein identification and quantification, post-translational modification analysis, and protein functional enrichment analysis. In this paper, we describe a suite of software packages we have developed to process mass spectrometry-based proteomics data and we highlight some of the new features of previously published programs as well as tools currently under development.

Advances in computer technology and software have driven developments in mass spectrometry over the last 50 years. Computers and software have been impactful in three areas: the automation of difficult calculations to aid interpretation, the collection of data and control of instruments, and data interpretation. As the power of computers has grown, so too has the utility and impact on mass spectrometers and their capabilities. This has been particularly evident in the use of tandem mass spectrometry data to search protein and nucleotide sequence databases to identify peptide and protein sequences. This capability has driven the development of many new approaches to study biological systems, including the use of “bottom-up shotgun proteomics” to directly analyze protein mixtures.

Intact protein analysis via top-down mass spectrometry (MS) provides the unique capability of fully characterizing protein isoforms and combinatorial post-translational modifications (PTMs) compared to the bottom-up MS approach. Front-end protein separation poses a challenge for analyzing complex mixtures of intact proteins on a proteomic scale. Here we applied capillary electrophoresis (CE) through a sheathless capillary electrophoresis-electrospray ionization (CESI) interface coupled to an Orbitrap Elite mass spectrometer to profile the proteome from Pyrococcus furiosus. CESI-top-down MS analysis of Pyrococcus furiosus cell lysate identified 134 proteins and 291 proteoforms with a total sample consumption of 270 ng in 120 min of total analysis time. Truncations and various PTMs were detected, including acetylation, disulfide bonds, oxidation, glycosylation, and hypusine. This is the largest scale analysis of intact proteins by CE-top-down MS to date.

Cryptococcus neoformans, a pathogenic yeast, causes meningoencephalitis, especially in immunocompromised patients, leading in some cases to death. Microbes in biofilms can cause persistent infections, which are harder to treat. Cryptococcal biofilms are becoming common due to the growing use of brain valves and other medical devices. Using shotgun proteomics we determine the differences in protein abundance between biofilm and planktonic cells. Applying bioinformatic tools, we also evaluated the metabolic pathways involved in biofilm maintenance and protein interactions. Our proteomic data suggest general changes in metabolism, protein turnover, and global stress responses. Biofilm cells show an increase in proteins related to oxidation–reduction, proteolysis, and response to stress and a reduction in proteins related to metabolic process, transport, and translation. An increase in pyruvate-utilizing enzymes was detected, suggesting a shift from the TCA cycle to fermentation-derived energy acquisition. Additionally, we assign putative roles to 33 proteins previously categorized as hypothetical. Many changes in metabolic enzymes were identified in studies of bacterial biofilm, potentially revealing a conserved strategy in biofilm lifestyle.

For more than 30 years, the study of learning and memory in Drosophila melanogaster (fruit fly) has used an olfactory learning paradigm and has resulted in the discovery of many genes involved in memory formation. By varying learning programs, the creation of different memory types can be achieved, from short-term memory formation to long-term. Previous studies in the fruit fly used gene mutation methods to identify genes involved in memory formation. Presumably, memory creation involves a combination of genes, pathways, and neural circuits. To examine memory formation at the protein level, a quantitative proteomic analysis was performed using olfactory learning and 15N-labeled fruit flies. Differences were observed in protein expression and relevant pathways between different learning programs. Our data showed major protein expression changes occurred between short-term memory (STM) and long-lasting memory, and only minor changes were found between long-term memory (LTM) and anesthesia-resistant memory (ARM).

The most prevalent cause of cystic fibrosis (CF) is the deletion of a phenylalanine residue at position 508 in CFTR (ΔF508-CFTR) protein. The mutated protein fails to fold properly, is retained in the endoplasmic reticulum via the action of molecular chaperones, and is tagged for degradation. In this study, the differences in protein expression levels in CF cell models were assessed using a systems biology approach aided by the sensitivity of MudPIT proteomics. Analysis of the differential proteome modulation without a priori hypotheses has the potential to identify markers that have not yet been documented. These may also serve as the basis for developing new diagnostic and treatment modalities for CF. Several novel differentially expressed proteins observed in our study are likely to play important roles in the pathogenesis of CF and may serve as a useful resource for the CF scientific community.

Members of the 2-aminobenzamide class of histone deacetylase (HDAC) inhibitors show promise as therapeutics for the neurodegenerative diseases Friedreich’s ataxia (FRDA) and Huntington’s disease (HD). While it is clear that HDAC3 is one of the important targets of the 2-aminobenzamide HDAC inhibitors, inhibition of other class I HDACs (HDACs 1 and 2) may also be involved in the beneficial effects of these compounds in FRDA and HD, and other HDAC interacting proteins may be impacted by the compound. To this end, we synthesized activity-based profiling probe (ABPP) versions of one of our HDAC inhibitors (compound 106), and in the present study we used a quantitative proteomic method coupled with multidimensional protein identification technology (MudPIT) to identify the proteins captured by the ABPP 106 probe. Nuclear proteins were extracted from FRDA patient iPSC-derived neural stem cells, and then were reacted with control and ABPP 106 probe. After reaction, the bound proteins were digested on the beads, and the peptides were modified using stable isotope-labeled formaldehyde to form dimethyl amine. The selectively bound proteins determined by mass spectrometry were subjected to functional and pathway analysis. Our findings suggest that the targets of compound 106 are involved not only in transcriptional regulation but also in posttranscriptional processing of mRNA.

Intact protein analysis via top-down mass spectrometry (MS) provides a bird’s eye view over the protein complexes and complex protein mixtures with the unique capability of characterizing protein variants, splice isoforms, and combinatorial post-translational modifications (PTMs). Here we applied capillary electrophoresis (CE) through a sheathless CE–electrospray ionization interface coupled to an LTQ Velos Orbitrap Elite mass spectrometer to analyze the Dam1 complex from Saccharomyces cerevisiae. We achieved a 100-fold increase in sensitivity compared to a reversed-phase liquid chromatography coupled MS analysis of recombinant Dam1 complex with a total loading of 2.5 ng (12 amol). N-terminal processing forms of individual subunits of the Dam1 complex were observed as well as their phosphorylation stoichiometry upon Mps1p kinase treatment.

The majority of large-scale proteomics quantification methods yield long lists of quantified proteins that are often difficult to interpret and poorly reproduced. Computational approaches are required to analyze such intricate quantitative proteomics data sets. We propose a statistical approach to computationally identify protein sets (e.g., Gene Ontology (GO) terms) that are significantly enriched with abundant proteins with reproducible quantification measurements across a set of replicates. To this end, we developed PSEA-Quant, a protein set enrichment analysis algorithm for label-free and label-based protein quantification data sets. It offers an alternative approach to classic GO analyses, models protein annotation biases, and allows the analysis of samples originating from a single condition, unlike analogous approaches such as GSEA and PSEA. We demonstrate that PSEA-Quant produces results complementary to GO analyses. We also show that PSEA-Quant provides valuable information about the biological processes involved in cystic fibrosis using label-free protein quantification of a cell line expressing a CFTR mutant. Finally, PSEA-Quant highlights the differences in the mechanisms taking place in the human, rat, and mouse brain frontal cortices based on tandem mass tag quantification. Our approach, which is available online, will thus improve the analysis of proteomics quantification data sets by providing meaningful biological insights.

Mass spectrometry plays a key role in relative quantitative comparisons of proteins in order to understand their functional role in biological systems upon perturbation. In this review, we review studies that examine different aspects of isobaric labeling-based relative quantification for shotgun proteomic analysis. In particular, we focus on different types of isobaric reagents and their reaction chemistry (e.g., amine-, carbonyl-, and sulfhydryl-reactive). Various factors, such as ratio compression, reporter ion dynamic range, and others, cause an underestimation of changes in relative abundance of proteins across samples, undermining the ability of the isobaric labeling approach to be truly quantitative. These factors that affect quantification and the suggested combinations of experimental design and optimal data acquisition methods to increase the precision and accuracy of the measurements will be discussed. Finally, the extended application of isobaric labeling-based approach in hyperplexing strategy, targeted quantification, and phosphopeptide analysis are also examined.

Lysosomal storage diseases (LSDs) are often caused by mutations compromising lysosomal enzyme folding in the endoplasmic reticulum (ER), leading to degradation and loss of function. Mass spectrometry analysis of Gaucher fibroblasts treated with mechanistically distinct molecules that increase LSD enzyme folding, trafficking, and function resulted in the identification of nine commonly downregulated and two jointly upregulated proteins, which we hypothesized would be critical proteostasis network components for ameliorating loss-of-function diseases. LIMP-2 and FK506 binding protein 10 (FKBP10) were validated as such herein. Increased FKBP10 levels accelerated mutant glucocerebrosidase degradation over folding and trafficking, whereas decreased ER FKBP10 concentration led to more LSD enzyme partitioning into the calnexin profolding pathway, enhancing folding and activity to levels thought to ameliorate LSDs. Thus, targeting FKBP10 appears to be a heretofore unrecognized therapeutic strategy to ameliorate LSDs.Highlights► Whole-cell proteomics identifies FKBP10 as key to lysosomal enzyme proteostasis ► FKBP10 knockdown increases mutant lysosomal enzyme folding, trafficking, and function ► FKBP10 overexpression accelerates ERAD of mutant lysosomal enzymes ► FKBP10 appears to act as an ER degradation versus folding partitioning factor

Pyrococcus furiosus (Pfu) is an excellent organism to generate reference samples for proteomics laboratories because of its moderately sized genome and very little sequence duplication within the genome. We demonstrated a stable and consistent method to prepare proteins in bulk that eliminates growth and preparation as a source of uncertainty in the standard. We performed several proteomic studies in different laboratories using each laboratory’s specific workflow as well as separate and integrated data analysis. This study demonstrated that a Pfu whole cell lysate provides suitable protein sample complexity to not only validate proteomic methods, work flows, and benchmark new instruments but also to facilitate comparison of experimental data generated over time and across instruments or laboratories.

A modified multidimensional protein identification technology (MudPIT) separation was coupled to an LTQ Orbitrap Velos mass spectrometer and used to rapidly identify the near-complete yeast proteome from a whole cell tryptic digest. This modified online two-dimensional liquid chromatography separation consists of 39 strong cation exchange steps followed by a short 18.5 min reversed-phase (RP) gradient. A total of 4269 protein identifications were made from 4189 distinguishable protein families from yeast during log phase growth. The “Micro” MudPIT separation performed as well as a standard MudPIT separation in 40% less gradient time. The majority of the yeast proteome can now be routinely covered in less than a days’ time with high reproducibility and sensitivity. The newly devised separation method was used to detect changes in protein expression during cellular quiescence in yeast. An enrichment in the GO annotations “oxidation reduction”, “catabolic processing” and “cellular response to oxidative stress” was seen in the quiescent cellular fraction, consistent with their long-lived stress resistant phenotypes. Heterogeneity was observed in the stationary phase fraction with a less dense cell population showing reductions in KEGG pathway categories of “Ribosome” and “Proteasome”, further defining the complex nature of yeast populations present during stationary phase growth. In total, 4488 distinguishable protein families were identified in all cellular conditions tested.

Sarcopenia describes an age-related decline in skeletal muscle mass, strength, and function that ultimately impairs metabolism and leads to poor balance, frequent falling, limited mobility, and a reduction in quality of life. Here we investigate the pathogenesis of sarcopenia through a proteomic shotgun approach. In brief, we employed tandem mass tags to quantitate and compare the protein profiles obtained from young versus old rat slow-twitch type of muscle (soleus) and a fast-twitch type of muscle (extensor digitorum longus, EDL). Our results disclose 3452 and 1848 proteins identified from soleus and EDL muscles samples, of which 78 and 174 were found to be differentially expressed, respectively. In general, most of the proteins were structural related and involved in energy metabolism, oxidative stress, detoxification, or transport. Aging affected soleus and EDL muscles differently, and several proteins were regulated in opposite ways. For example, pyruvate kinase had its expression and activity different in both soleus and EDL muscles. We were able to verify with existing literature many of our differentially expressed proteins as candidate aging biomarkers and, most importantly, disclose several new candidate biomarkers such as the glioblastoma amplified sequence, zero β-globin, and prolargin.

γ-Amino butyric acid type C (GABAC) receptors inhibit neuronal firing primarily in retina. Maintenance of GABAC receptor protein homeostasis in cells is essential for its function. However, a systematic study of GABAC receptor protein homeostasis (proteostasis) network components is absent. Here coimmunoprecipitation of human GABAC-ρ1-receptor complexes was performed in HEK293 cells overexpressing ρ1 receptors. To enhance the coverage and reliability of identified proteins, immunoisolated ρ1-receptor complexes were subjected to three tandem mass spectrometry (MS)-based proteomic analyses, namely, gel-based tandem MS (GeLC–MS/MS), solution-based tandem MS (SoLC–MS/MS), and multidimensional protein identification technology (MudPIT). From the 107 identified proteins, we assembled GABAC-ρ1-receptor proteostasis network components, including proteins with protein folding, degradation, and trafficking functions. We studied representative individual ρ1-receptor-interacting proteins, including calnexin, a lectin chaperone that facilitates glycoprotein folding, and LMAN1, a glycoprotein trafficking receptor, and global effectors that regulate protein folding in cells based on bioinformatics analysis, including HSF1, a master regulator of the heat shock response, and XBP1, a key transcription factor of the unfolded protein response. Manipulating selected GABAC receptor proteostasis network components is a promising strategy to regulate GABAC receptor folding, trafficking, degradation and thus function to ameliorate related retinal diseases.

An understanding of the process of peptide fragmentation and what parameters are best to obtain the most useful information is important. This is especially true for large-scale proteomics where data collection and data analysis are most often automated, and manual interpretation of spectra is rare because of the vast amounts of data generated. We show herein that collisional cell peptide fragmentation, in this case higher collisional dissociation (HCD) in the Q Exactive, is significantly affected by the normalized energy applied. Both peptide sequence and energy applied determine what ion fragments are observed. However, by applying a stepped normalized collisional energy scheme and combining ions from low, medium, and high collision energies, we are able to increase the diversity of fragmentation ions generated. Application of stepped collision energy to HEK293T lysate demonstrated a minimal effect on peptide and protein identification in a large-scale proteomics dataset, but improved phospho site localization through increased sequence coverage. Stepped HCD is also beneficial for tandem mass tagged (TMT) experiments, increasing intensity of TMT reporters used for quantitation without adversely effecting peptide identification.

Postnatal bilateral whisker trimming was used as a model system to test how synaptic proteomes are altered in barrel cortex by sensory deprivation during synaptogenesis. Using quantitative mass spectrometry, we quantified more than 7,000 synaptic proteins and identified 89 significantly reduced and 161 significantly elevated proteins in sensory-deprived synapses, 22 of which were validated by immunoblotting. More than 95% of quantified proteins, including abundant synaptic proteins such as PSD-95 and gephyrin, exhibited no significant difference under high- and low-activity rearing conditions, suggesting no tissue-wide changes in excitatory or inhibitory synaptic density. In contrast, several proteins that promote mature spine morphology and synaptic strength, such as excitatory glutamate receptors and known accessory factors, were reduced significantly in deprived synapses. Immunohistochemistry revealed that the reduction in SynGAP1, a postsynaptic scaffolding protein, was restricted largely to layer I of barrel cortex in sensory-deprived rats. In addition, protein-degradation machinery such as proteasome subunits, E2 ligases, and E3 ligases, accumulated significantly in deprived synapses, suggesting targeted synaptic protein degradation under sensory deprivation. Importantly, this screen identified synaptic proteins whose levels were affected by sensory deprivation but whose synaptic roles have not yet been characterized in mammalian neurons. These data demonstrate the feasibility of defining synaptic proteomes under different sensory rearing conditions and could be applied to elucidate further molecular mechanisms of sensory development.

We describe a solid phase microextraction (SPME), multistep elution, transient isotachophoresis (tITP) capillary electrophoresis–tandem mass spectrometry (CE–MS/MS) procedure which employs a high sensitivity porous electrospray ionization (ESI) sprayer for the proteomic analysis of a moderately complex protein mixture. In order to improve comprehensiveness and sensitivity over a previously reported proteomic application of the ESI sprayer, we evaluated preconcentration with SPME and multistep elution prior to tITP stacking and CE separation. To maximize separation efficiency, we primarily employed electrokinetic methods for elution and separation after loading the sample by application of pressure. Conditions were developed for optimum simultaneous electrokinetic elution and sample stacking using a tryptic digest of 16 proteins to maximize peptide identifications and minimize band broadening. We performed comparative proteomic analysis of a dilution series using CE and nanoflow liquid chromatography (nLC). We found complementary peptide and protein identifications with larger quantities (100 ng) of a Pyrococcus furiosus tryptic digest, but with mass-limited amounts (5 ng) CE was 3 times more effective at identifying proteins. We attribute these gains in sensitivity to lower noise levels with the porous CE sprayer, illustrated by better signal-to-noise ratios of peptide precursor ions and associated higher XCorr values of identified peptides when compared directly to nLC. From comparative analysis of SPME-tITP-CE with direct injection CE, the SPME-tITP process improved comprehensiveness and sensitivity.

In-depth analysis of the salivary proteome is fundamental to understanding the functions of salivary proteins in the oral cavity and to reveal disease biomarkers involved in different pathophysiological conditions, with the ultimate goal of improving patient diagnosis and prognosis. Submandibular and sublingual glands contribute saliva rich in glycoproteins to the total saliva output, making them valuable sources for glycoproteomic analysis. Lectin-affinity chromatography coupled to mass spectrometry-based shotgun proteomics was used to explore the submandibular/sublingual (SM/SL) saliva glycoproteome. A total of 262 N- and O-linked glycoproteins were identified by multidimensional protein identification technology (MudPIT). Only 38 were previously described in SM and SL salivas from the human salivary N-linked glycoproteome, while 224 were unique. Further comparison analysis with SM/SL saliva of the human saliva proteome, revealed 125 glycoproteins not formerly reported in this secretion. KEGG pathway analyses demonstrated that many of these glycoproteins are involved in processes such as complement and coagulation cascades, cell communication, glycosphingolipid biosynthesis neo-lactoseries, O-glycan biosynthesis, glycan structures-biosynthesis 2, starch and sucrose metabolism, peptidoglycan biosynthesis or others pathways. In summary, lectin-affinity chromatography coupled to MudPIT mass spectrometry identified many novel glycoproteins in SM/SL saliva. These new additions to the salivary proteome may prove to be a critical step for providing reliable biomarkers in the diagnosis of a myriad of oral and systemic diseases.

Ideally, shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors that facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.

Here we report the use of capillary isoelectric focusing under native conditions for the separation of protein complex isoforms and subcomplexes. Using biologically relevant HIS-tag and FLAG-tag purified protein complexes, we demonstrate the separations of protein complex isoforms of the mammalian target of rapamycin complex (mTORC1 and 2) and the subcomplexes and different phosphorylation states of the Dam1 complex. The high efficiency capillary isoelectric focusing separation allowed for resolution of protein complexes and subcomplexes similar in size and biochemical composition. By performing separations with native buffers and reduced temperature (15 °C) we were able to maintain the complex integrity of the more thermolabile mTORC2 during isoelectric focusing and detection (<45 min). Increasing the separation temperature allowed us to monitor dissociation of the Dam1 complex into its subcomplexes (25 °C) and eventually its individual protein components (30 °C). The separation of two different phosphorylation states of the Dam1 complex, generated from an in vitro kinase assay with Mps1 kinase, was straightforward due to the large pI shift upon multiple phosphorylation events. The separation of the protein complex isoforms of mTORC, on the other hand, required the addition of a small pI range (4−6.5) of ampholytes to improve resolution and stability of the complexes. We show that native capillary isoelectric focusing is a powerful method for the difficult separations of large, similar, unstable protein complexes. This method shows potential for differentiation of protein complex isoform and subcomplex compositions, post-translational modifications, architectures, stabilities, equilibria, and relative abundances under biologically relevant conditions.

Mass spectrometry-based proteomics is rapidly becoming an essential tool for biologists. One of the most common applications is identifying the components of protein complexes isolated by co-immunoprecipitation. In this review, we discuss the co-immunoprecipitation, mass spectrometry and data analysis techniques that have been used successfully to define protein complexes in C. elegans research. In this discussion, two strategies emerged. One approach is to use stringent biochemical purification methods and attempt to identify a small number of complex components with a high degree of certainty based on MS data. A second approach is to use less stringent purification and identification parameters, and ultimately test a longer list of potential binding partners in biological validation assays. This should provide a useful guide for biologists planning proteomic experiments.

Perillyl alcohol (POH) is a naturally occurring terpene and a promising chemotherapeutic agent for glioblastoma multiform; yet, little is known about its molecular effects. Here we present results of a semi-quantitative proteomic analysis of A172 cells exposed to POH for different time-periods (1′, 10′, 30′, 60′, 4 h, and 24 h). The analysis identified more than 4000 proteins; which were clustered using PatternLab for proteomics and then linked to Ras signaling, tissue homeostasis, induction of apoptosis, metallopeptidase activity, and ubiquitin-protein ligase activity. Our results make available one of the most complete protein repositories for the A172. Moreover, we detected the phosphorylation of GSK3β (Glycogen synthase kinase) and the inhibition of ERK's (extracellular signal regulated kinase) phosphorylation after 10′, which suggests a new mechanism of POH's activation for apoptosis.

The saliva proteome includes host defense factors and specific bacterial-binding proteins that modulate microbial growth and colonization of the tooth surface in the oral cavity. A multidimensional mass spectrometry approach identified the major host-derived salivary proteins that interacted with Streptococcus mutans (strain UA159), the primary microorganism associated with the pathogenesis of dental caries. Two abundant host proteins were found to tightly bind to S. mutans cells, common salivary protein-1 (CSP-1) and deleted in malignant brain tumor 1 (DMBT1, also known as salivary agglutinin or gp340). In contrast to gp340, limited functional information is available on CSP-1. The sequence of CSP-1 shares 38.1% similarity with rat CSP-1. Recombinant CSP-1 (rCSP-1) protein did not cause aggregation of S. mutans cells and was devoid of any significant biocidal activity (2.5 to 10 μg/mL). However, S. mutans cells exposed to rCSP-1 (10 μg/mL) in saliva displayed enhanced adherence to experimental salivary pellicle and to glucans in the pellicle formed on hydroxyapatite surfaces. Thus, our data demonstrate that the host salivary protein CSP-1 binds to S. mutans cells and may influence the initial colonization of this pathogenic bacterium onto the tooth surface.

Human ductal saliva contributes over a thousand unique proteins to whole oral fluids. The mechanism by which most of these proteins are secreted by salivary glands remains to be determined. The present study used a mass spectrometry-based, shotgun proteomics approach to explore the possibility that a subset of the proteins found in saliva are derived from exosomes, membrane-bound vesicles of endosomal origin within multivesicular endosomes. Using MudPIT (multidimensional protein identification technology) mass spectrometry, we catalogued 491 proteins in the exosome fraction of human parotid saliva. Many of these proteins were previously observed in ductal saliva from parotid glands (265 proteins). Furthermore, 72 of the proteins in parotid exosomes overlap with those previously identified as urinary exosome proteins, proteins which are also frequently associated with exosomes from other tissues and cell types. Gene Ontology (GO) and KEGG pathway analyses found that cytosolic proteins comprise the largest category of proteins in parotid exosomes (43%), involved in such processes as phosphatidylinositol signaling system, calcium signaling pathway, inositol metabolism, protein export, and signal transduction, among others; whereas the integral plasma membrane proteins and associated/peripheral plasma membrane proteins (26%) were associated with extracellular matrix-receptor interaction, epithelial cell signaling, T-cell and B-cell receptor signaling, cytokine receptor interaction, and antigen processing and presentation, among other biological functions. In addition, these putative saliva exosomal proteins were linked to specific diseases (e.g., neurodegenerative disorders, prion disease, cancers, type I and II diabetes). Consequently, parotid glands secrete exosomes that reflect the metabolic and functional status of the gland and may also carry informative protein markers useful in the diagnosis and treatment of systemic diseases.

We evaluate the effect of ion-abundance threshold settings for data-dependent acquisition on a hybrid LTQ-Orbitrap mass spectrometer, analyzing features such as the total number of spectra collected, the signal to noise ratio of the full MS scans, the spectral quality of the tandem mass spectra acquired, and the number of peptides and proteins identified from a complex mixture. We find that increasing the threshold for data-dependent acquisition generally decreases the quantity but increases the quality of the spectra acquired. This is especially true when the threshold setting is set above the noise level of the full MS scan. We compare two distinct experimental configurations: one where full MS scans are acquired in the Orbitrap analyzer while tandem MS scans are acquired in the LTQ analyzer, and one where both full MS and tandem MS scans are acquired in the LTQ analyzer. We examine the number of spectra, peptides, and proteins identified under various threshold conditions, and we find that the optimal threshold setting is at or below the respective noise level of the instrument regardless of whether the full MS scan is performed in the Orbitrap or in the LTQ analyzer. When comparing the high-throughput identification performance of the two analyzers, we conclude that, used at optimal threshold levels, the LTQ and the Orbitrap identify similar numbers of peptides and proteins. The higher scan speed of the LTQ, which results in more spectra being collected, is roughly compensated by the higher mass accuracy of the Orbitrap, which results in improved database searching and peptide validation software performance.

Mass spectrometry has been widely used to analyze biological samples and has evolved into an indispensable tool for proteomics research. Our desire to understand the proteome has led to new technologies that push the boundary of mass spectrometry capabilities, which in return has allowed mass spectrometry to address an ever-increasing array of biological questions. The recent development of a novel mass spectrometer (Orbitrap) and new dissociation methods such as electron-transfer dissociation has made possible the exciting new areas of proteomic application. Although bottom-up proteomics (analysis of proteolytic peptide mixtures) remains the workhorse for proteomic analysis, middle-down and top-down strategies (analysis of longer peptides and intact proteins, respectively) should allow more complete characterization of protein isoforms and post-translational modifications. Finally, stable isotope labeling strategies have transformed mass spectrometry from merely descriptive to a tool for measuring dynamic changes in protein expression, interaction, and modification.

Methods for the global analysis of protein expression offer an approach to study the molecular basis of disease. Studies of protein expression in tissue, such as brain, are complicated by the need for efficient and unbiased digestion of proteins that permit identification of peptides by shotgun proteomic methods. In particular, identification and characterization of less abundant membrane proteins has been of great interest for studies of brain physiology, but often proteins of interest are of low abundance or exist in multiple isoforms. Parsing protein isoforms as a function of disease will be essential. In this study, we develop a digestion scheme using detergents compatible with mass spectrometry that improves membrane protein identification from brain tissue. We show the modified procedure yields close to 5,000 protein identifications from 1.8 mg of rat brain homogenate with an average of 25% protein sequence coverage. This procedure achieves a remarkable reduction in the amount of starting material required to observe a broad spectrum of membrane proteins. Among the proteins identified from a mammalian brain homogenate, 1897 (35%) proteins are annotated by Gene Ontology as membrane proteins, and 1225 (22.6%) proteins are predicted to contain at least one transmembrane domain. Membrane proteins identified included neurotransmitter receptors and ion channels implicated in important physiological functions and disease.

Phosphoproteomics, the targeted study of a subfraction of the proteome which is modified by phosphorylation, has become an indispensable tool to study cell signaling dynamics. We described a methodology that linked phosphoproteome and proteome analysis based on Ba2+ binding properties of amino acids. This technology selected motif-specific phosphopeptides independent of the system under analysis. MudPIT (Multidimensional Identification Technology) identified 1037 precipitated phosphopeptides from as little as 250 µg of proteins. To extend coverage of the phosphoproteome, we sampled the nuclear extract of HeLa cells with three values of Ba2+ ions molarity. The presence of more than 70% of identified phosphoproteins was further substantiated by their nonmodified peptides. Upon isoproterenol stimulation of HEK cells, we identified an increasing number of phosphoproteins from MAPK cascades and AKAP signaling hubs. We quantified changes in both protein and phosphorylation levels of 197 phosphoproteins including a critical kinase, MAPK1. Integration of differential phosphorylation of MAPK1 with knowledge bases constructed modules that correlated well with its role as node in cross-talk of canonical pathways.

We developed a probability-based machine-learning program, Colander, to identify tandem mass spectra that are highly likely to represent phosphopeptides prior to database search. We identified statistically significant diagnostic features of phosphopeptide tandem mass spectra based on ion trap CID MS/MS experiments. Statistics for the features are calculated from 376 validated phosphopeptide spectra and 376 nonphosphopeptide spectra. A probability-based support vector machine (SVM) program, Colander, was then trained on five selected features. Data sets were assembled both from LC/LC-MS/MS analyses of large-scale phosphopeptide enrichments from proteolyzed cells, tissues and synthetic phosphopeptides. These data sets were used to evaluate the capability of Colander to select pS/pT-containing phosphopeptide tandem mass spectra. When applied to unknown tandem mass spectra, Colander can routinely remove 80% of tandem mass spectra while retaining 95% of phosphopeptide tandem mass spectra. The program significantly reduced computational time spent on database search by 60−90%. Furthermore, prefiltering tandem mass spectra representing phosphopeptides can increase the number of phosphopeptide identifications under a predefined false positive rate.

Protein phosphorylation is a globally adopted and tightly controlled post-translational modification, and represents one of the most important molecular switching mechanisms that govern the entire spectrum of biological processes. In the central nervous system, it has been demonstrated that phosphorylation of key proteins mediating chromatin remodeling and gene transcription plays an important role controlling brain development, synaptogenesis, learning and memory. Many studies have focused on large scale identification of phosphopeptides in brain tissue. These studies have identified phosphorylation site specific motifs useful for predicting protein kinase substrates. In this study, we applied a previously developed quantitative approach, stable isotope labeling of amino acids in mammals (SILAM), to quantify changes in the phosphorylation of nuclear proteins between a postnatal day one (p1) and a p45 rat brain cortex. Using a 15N labeled rat brain as an internal standard, we quantified 705 phosphopeptides in the p1 cortex and 1477 phosphopeptides in the p45 cortex, which translates to 380 and 585 phosphoproteins in p1 and p45 cortex, respectively. Bioinformatic analysis of the differentially modified phosphoproteins revealed that phosphorylation is upregulated on multiple components of chromatin remodeling complexes in the p1 cortex. Taken together, we demonstrated for the first time the usefulness of employing stable isotope labeled rat tissue for global quantitative phosphorylation analysis.

Immobilized metal affinity chromatography (IMAC) is a common strategy used for the enrichment of phosphopeptides from digested protein mixtures. However, this strategy by itself is inefficient when analyzing complex protein mixtures. Here, we assess the effectiveness of using protein-based IMAC as a pre-enrichment step prior to peptide-based IMAC. Ultimately, we couple the two IMAC-based enrichments and MudPIT in a quantitative phosphoproteomic analysis of the epidermal growth factor pathway in mammalian cells identifying 4470 unique phosphopeptides containing 4729 phosphorylation sites.

The quantitative proteomic analysis of complex protein mixtures is emerging as a technically challenging but viable systems-level approach for studying cellular function. This study presents a large-scale comparative analysis of protein abundances from yeast protein lysates derived from both wild-type yeast and yeast strains lacking key components of the Snf1 kinase complex. Four different strains were grown under well-controlled chemostat conditions. Multidimensional protein identification technology followed by quantitation using either spectral counting or stable isotope labeling approaches was used to identify relative changes in the protein expression levels between the strains. A total of 2388 proteins were relatively quantified, and more than 350 proteins were found to have significantly different expression levels between the two strains of comparison when using the stable isotope labeling strategy. The stable isotope labeling based quantitative approach was found to be highly reproducible among biological replicates when complex protein mixtures containing small expression changes were analyzed. Where poor correlation between stable isotope labeling and spectral counting was found, the major reason behind the discrepancy was the lack of reproducible sampling for proteins with low spectral counts. The functional categorization of the relative protein expression differences that occur in Snf1-deficient strains uncovers a wide range of biological processes regulated by this important cellular kinase.

DNA-bound transcription factors recruit an array of coregulatory proteins that influence gene expression. We previously demonstrated that DNA functions as an allosteric modulator of estrogen receptor α (ERα) conformation, alters the recruitment of regulatory proteins, and influences estrogen-responsive gene expression and reasoned that it would be useful to develop a method of isolating proteins associated with the DNA-bound ERα using full-length receptor and endogenously-expressed nuclear proteins.We have developed a novel approach to isolate large complexes of proteins associated with the DNA-bound ERα. Purified ERα and HeLa nuclear extracts were combined with oligos containing ERα binding sites and fractionated on agarose gels. The protein-DNA complexes were isolated and mass spectrometry analysis was used to identify proteins associated with the DNA-bound receptor. Rather than simply identifying individual proteins that interact with ERα, we identified interconnected networks of proteins with a variety of enzymatic and catalytic activities that interact not only with ERα, but also with each other. Characterization of a number of these proteins has demonstrated that, in addition to their previously identified functions, they also influence ERα activity and expression of estrogen-responsive genes.The agarose gel fractionation method we have developed would be useful in identifying proteins that interact with DNA-bound transcription factors and should be easily adapted for use with a variety of cultured cell lines, DNA sequences, and transcription factors.

Fragile X syndrome (FXS) is a common inherited form of mental retardation that is caused, in the vast majority of cases, by the transcriptional silencing of a single gene, fmr1. The encoded protein, FMRP, regulates mRNA translation in neuronal dendrites, and it is thought that changes in translation-dependent forms of synaptic plasticity lead to many symptoms of FXS. However, little is known about the potentially extensive changes in synaptic protein content that accompany loss of FMRP. Here, we describe the development of a high-throughput quantitative proteomic method to identify differences in synaptic protein expression between wild-type and fmr1−/− mouse cortical neurons. The method is based on stable isotope labeling by amino acids in cell culture (SILAC), which has been used to characterize differentially expressed proteins in dividing cells, but not in terminally differentiated cells because of reduced labeling efficiency. To address the issue of incomplete labeling, we developed a mathematical method to normalize protein ratios relative to a reference based on the labeling efficiency. Using this approach, in conjunction with multidimensional protein identification technology (MudPIT), we identified >100 proteins that are up- or down-regulated. These proteins fall into a variety of functional categories, including those regulating synaptic structure, neurotransmission, dendritic mRNA transport, and several proteins implicated in epilepsy and autism, two endophenotypes of FXS. These studies provide insights into the potential origins of synaptic abnormalities in FXS and a demonstration of a methodology that can be used to explore neuronal protein changes in neurological disorders.

In this paper, we show that ion trap mass spectrometers can differentiate acetylation and carbamylation modifications based on database search results for a lens protein sample. These types of modifications are difficult to distinguish on ion trap instruments because of their lower resolution and mass accuracy. The results were corroborated by using accurate mass information derived from MALDI TOF MS analysis of eluted peptides from a duplicate capillary RPLC separation. Tandem mass spectra of lysine carbamylated peptides were further verified by manual assignments of fragment ions and by the presence of characteristic fragment ions of carbamylated peptides. It was also observed that carbamylated peptides show a strong neutral loss of the carbamyl group in collision induced dissociation (CID), a feature that can be prognostic for carbamylation. In a lens tissue sample of a 67-year-old patient, 12 in vivo carbamylation sites were detected on 7 different lens proteins and 4 lysine acetylation sites were detected on 3 different lens proteins. Among the 12 in vivo carbamylation sites, 9 are novel in vivo carbamylation modification sites. Notably, in vivo carbamylation of γS crystallin, βA4 crystallin, βB1 crystallin, and βB2 crystallin observed in this study have never been reported before.

The mass-spectrometry-based identification of proteins has created opportunities for the study of organelles, transport intermediates and large subcellular structures. Traditional cell-biology techniques are used to enrich these structures for proteomics analyses, and such analyses provide insights into the biology and functions of these structures. Here, we review the state-of-the-art proteomics techniques for the analysis of subcellular structures and discuss the biological insights that have been derived from such studies.

Abstract

To comprehensively identify integral membrane proteins of the nuclear envelope (NE), we prepared separately NEs and organelles known to cofractionate with them from liver. Proteins detected by multidimensional protein identification technology in the cofractionating organelles were subtracted from the NE data set. In addition to all 13 known NE integral proteins, 67 uncharacterized open reading frames with predicted membrane-spanning regions were identified. All of the eight proteins tested targeted to the NE, indicating that there are substantially more integral proteins of the NE than previously thought. Furthermore, 23 of these mapped within chromosome regions linked to a variety of dystrophies.

A comparative analysis of protein identification for a total of 162 protein spots separated by two-dimensional gel electrophoresis from two fully sequenced archaea, Methanococcus jannaschii and Pyrococcus furiosus, using MALDI-TOF peptide mass mapping (PMM) and μLC-MS/MS is presented. 100% of the gel spots analyzed were successfully matched to the predicted proteins in the two corresponding open reading frame databases by μLC-MS/MS while 97% of them were identified by MALDI-TOF PMM. The high success rate from the PMM resulted from sample desalting/concentrating with ZipTipC18 and optimization of several PMM search parameters including a 25 ppm average mass tolerance and the application of two different protein molecular weight search windows. By using this strategy, low-molecular weight (<23 kDa) proteins could be identified unambiguously with less than 5 peptide matches. Nine percent of spots were identified as containing multiple proteins. By using μLC-MS/MS, 50% of the spots analyzed were identified as containing multiple proteins. μLC-MS/MS demonstrated better protein sequence coverage than MALDI-TOF PMM over the entire mass range of proteins identified. MALDI-TOF and PMM produced unique peptide molecular weight matches that were not identified by μLC-MS/MS. By incorporating amino acid sequence modifications into database searches, combined sequence coverage obtained from these two complimentary ionization methods exceeded 50% for ∼70% of the 162 spots analyzed. This improved sequence coverage in combination with enzymatic digestions of different specificity is proposed as a method for analysis of post-translational modification from 2D-gel separated proteins.

Recent achievements in genomics have created an infrastructure of biological information. The enormous success of genomics promptly induced a subsequent explosion in proteomics technology, the emerging science for systematic study of proteins in complexes, organelles, and cells. Proteomics is developing powerful technologies to identify proteins, to map proteomes in cells, to quantify the differential expression of proteins under different states, and to study aspects of protein–protein interaction. The dynamic nature of protein expression, protein interactions, and protein modifications requires measurement as a function of time and cellular state. These types of studies require many measurements and thus high throughput protein identification is essential. This review will discuss aspects of mass spectrometry with emphasis on methods and applications for large-scale protein identification, a fundamental tool for proteomics.

The completion of the Plasmodium falciparum clone 3D7 genome provides a basis on which to conduct comparative proteomics studies of this human pathogen. Here, we applied a high-throughput proteomics approach to identify new potential drug and vaccine targets and to better understand the biology of this complex protozoan parasite. We characterized four stages of the parasite life cycle (sporozoites, merozoites, trophozoites and gametocytes) by multidimensional protein identification technology. Functional profiling of over 2,400 proteins agreed with the physiology of each stage. Unexpectedly, the antigenically variant proteins of var and rif genes, defined as molecules on the surface of infected erythrocytes, were also largely expressed in sporozoites. The detection of chromosomal clusters encoding co-expressed proteins suggested a potential mechanism for controlling gene expression.

Large-scale genomics has enabled proteomics by creating sequence infrastructures that can be used with mass spectrometry data to identify proteins. Although protein sequences can be deduced from nucleotide sequences, posttranslational modifications to proteins, in general, cannot. We describe a process for the analysis of posttranslational modifications that is simple, robust, general, and can be applied to complicated protein mixtures. A protein or protein mixture is digested by using three different enzymes: one that cleaves in a site-specific manner and two others that cleave nonspecifically. The mixture of peptides is separated by multidimensional liquid chromatography and analyzed by a tandem mass spectrometer. This approach has been applied to modification analyses of proteins in a simple protein mixture, Cdc2p protein complexes isolated through the use of an affinity tag, and lens tissue from a patient with congenital cataracts. Phosphorylation sites have been detected with known stoichiometry of as low as 10%. Eighteen sites of four different types of modification have been detected on three of the five proteins in a simple mixture, three of which were previously unreported. Three proteins from Cdc2p isolated complexes yielded eight sites containing three different types of modifications. In the lens tissue, 270 proteins were identified, and 11 different crystallins were found to contain a total of 73 sites of modification. Modifications identified in the crystallin proteins included Ser, Thr, and Tyr phosphorylation, Arg and Lys methylation, Lys acetylation, and Met, Tyr, and Trp oxidations. The method presented will be useful in discovering co- and posttranslational modifications of proteins.

A major scientific challenge at the present time for cancer research is the determination of the underlying biological basis for cancer development. It is further complicated by the heterogeneity of cancer's origin. Understanding the molecular basis of cancer requires studying the dynamic and spatial interactions among proteins in cells, signaling events among cancer cells, and interactions between the cancer cells and the tumor microenvironment. Recently, it has been proposed that large-scale protein expression analysis of cancer cell proteomes promises to be valuable for investigating mechanisms of cancer transformation. Advances in mass spectrometry technologies and bioinformatics tools provide a tremendous opportunity to qualitatively and quantitatively interrogate dynamic protein–protein interactions and differential regulation of cellular signaling pathways associated with tumor development. In this review, progress in shotgun proteomics technologies for examining the molecular basis of cancer development is presented and discussed.

We evaluate the effect of ion-abundance threshold settings for data-dependent acquisition on a hybrid LTQ-Orbitrap mass spectrometer, analyzing features such as the total number of spectra collected, the signal to noise ratio of the full MS scans, the spectral quality of the tandem mass spectra acquired, and the number of peptides and proteins identified from a complex mixture. We find that increasing the threshold for data-dependent acquisition generally decreases the quantity but increases the quality of the spectra acquired. This is especially true when the threshold setting is set above the noise level of the full MS scan. We compare two distinct experimental configurations: one where full MS scans are acquired in the Orbitrap analyzer while tandem MS scans are acquired in the LTQ analyzer, and one where both full MS and tandem MS scans are acquired in the LTQ analyzer. We examine the number of spectra, peptides, and proteins identified under various threshold conditions, and we find that the optimal threshold setting is at or below the respective noise level of the instrument regardless of whether the full MS scan is performed in the Orbitrap or in the LTQ analyzer. When comparing the high-throughput identification performance of the two analyzers, we conclude that, used at optimal threshold levels, the LTQ and the Orbitrap identify similar numbers of peptides and proteins. The higher scan speed of the LTQ, which results in more spectra being collected, is roughly compensated by the higher mass accuracy of the Orbitrap, which results in improved database searching and peptide validation software performance.We investigate the effects of Orbitrap and LTQ signal threshold levels in shotgun proteomics.Download high-res image (275KB)Download full-size image

•Proteomics identifies neurexins and AMPA receptors as key proteins sorted by SorCS1•SorCS1 regulates surface levels of neurexin and AMPA receptors•SorCS1 maintains synaptic abundance of adhesion proteins and AMPA receptors in vivo•Impaired AMPA receptor trafficking in the absence of SorCS1 reduces synaptic transmissionThe formation, function, and plasticity of synapses require dynamic changes in synaptic receptor composition. Here, we identify the sorting receptor SorCS1 as a key regulator of synaptic receptor trafficking. Four independent proteomic analyses identify the synaptic adhesion molecule neurexin and the AMPA glutamate receptor (AMPAR) as major proteins sorted by SorCS1. SorCS1 localizes to early and recycling endosomes and regulates neurexin and AMPAR surface trafficking. Surface proteome analysis of SorCS1-deficient neurons shows decreased surface levels of these, and additional, receptors. Quantitative in vivo analysis of SorCS1-knockout synaptic proteomes identifies SorCS1 as a global trafficking regulator and reveals decreased levels of receptors regulating adhesion and neurotransmission, including neurexins and AMPARs. Consequently, glutamatergic transmission at SorCS1–deficient synapses is reduced due to impaired AMPAR surface expression. SORCS1 mutations have been associated with autism and Alzheimer disease, suggesting that perturbed receptor trafficking contributes to synaptic-composition and -function defects underlying synaptopathies.

Mass spectrometry-based proteomics is increasingly used to address basic and clinical questions in biomedical research through studies of differential protein expression, protein-protein interactions, and posttranslational modifications. The complex structural and functional organization of the human brain warrants the application of high-throughput, systematic approaches to understand the functional alterations under normal physiological conditions and the perturbations of neurological diseases. This primer focuses on shotgun-proteomics-based tandem mass spectrometry for the identification of proteins in a complex mixture. It describes the basic concepts of protein differential expression analysis and posttranslational modification analysis and discusses several strategies to improve the coverage of the proteome.

Metabolic labeling of rodent proteins with 15N, a heavy stable isotope of nitrogen, provides an efficient way for relative quantitation of differentially expressed proteins. Here we describe a protocol for metabolic labeling of rats with an 15N-enriched spirulina diet. As a case study, we also demonstrate the application of 15N-enriched tissue as a common internal standard in quantitative analysis of differentially expressed proteins in neurodevelopment in rats at two different time points, postnatal day 1 and 45. We briefly discuss the bioinformatics tools, ProLucid and Census, which can easily be used in a sequential manner to identify and quantitate relative protein levels on a proteomic scale.