Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

Quantitative proteomics reveals a dynamic interactome and phase-specific phosphorylation in the Neurospora circadian clock

Circadian systems are comprised of multiple proteins functioning together to produce feedback loops driving robust, approximately 24 hr rhythms. In all circadian systems, proteins in these loops are regulated through myriad physically and temporally distinct posttranslational modifications (PTMs). To better understand how PTMs impact a circadian oscillator, we implemented a proteomics-based approach by combining purification of endogenous FREQUENCY (FRQ) and its interacting partners with quantitative mass spectrometry (MS). We identify and quantify time-of-day-specific protein-protein interactions in the clock and show how these provide a platform for temporal and physical separation between the dual roles of FRQ. Additionally, by unambiguously identifying over 75 phosphorylated residues, following their quantitative change over a circadian cycle, and examining the phenotypes of strains that have lost these sites, we demonstrate how spatially and temporally regulated phosphorylation has opposing effects directly on overt circadian rhythms and FRQ stability.

HDL antielastase activity prevents smooth muscle cell anoikis, a potential new antiatherogenic property

Various studies using proteomic approaches have shown that HDL can carry many proteins other than its constitutive apolipoprotein A-I (apoA-I). Using mass spectrometry and Western blotting, we showed the presence of alpha(1)-antitrypsin (AAT) (SERPINA1, serpin peptidase inhibitor, clade A, an elastase inhibitor) in HDL, isolated either by ultracentrifugation or by selected-affinity immunosorption using an anti-apoA-I column. Furthermore, we report that HDL possesses potent antielastase activity. We further showed that only HDL but not LDL is able to bind AAT. HDL-associated AAT was able to inhibit extracellular matrix degradation, cell detachment, and apoptosis induced by elastase in human vascular smooth muscle cells (VSMCs) and in mammary artery cultured ex vivo. Degradation of fibronectin by elastase used as a marker of pericellular proteolysis was prevented by addition of HDL. Elastase present in aortic abdominal aneurysm (AAA) thrombus samples was also able to induce apoptosis of VSMCs in culture. This phenomenon was prevented by addition of HDL but not of LDL. Finally, we report that the proportion of AAT in HDL isolated from patients with an AAA is decreased relative to that from matched control subjects, suggesting a reduced capacity of HDL to inhibit elastase in these patients. In conclusion, our data provide evidence of a new potential antiatherogenic property of HDL attributable to AAT and its antielastase activity.

Predicting intensity ranks of peptide fragment ions

Accurate modeling of peptide fragmentation is necessary for the development of robust scoring functions for peptide-spectrum matches, which are the cornerstone of MS/MS-based identification algorithms. Unfortunately, peptide fragmentation is a complex process that can involve several competing chemical pathways, which makes it difficult to develop generative probabilistic models that describe it accurately. However, the vast amounts of MS/MS data being generated now make it possible to use data-driven machine learning methods to develop discriminative ranking-based models that predict the intensity ranks of a peptide's fragment ions. We use simple sequence-based features that get combined by a boosting algorithm into models that make peak rank predictions with high accuracy. In an accompanying manuscript, we demonstrate how these prediction models are used to significantly improve the performance of peptide identification algorithms. The models can also be useful in the design of optimal multiple reaction monitoring (MRM) transitions, in cases where there is insufficient experimental data to guide the peak selection process. The prediction algorithm can also be run independently through PepNovo+, which is available for download from http://bix.ucsd.edu/Software/PepNovo.html.

Proteomics analysis of perilymph and cerebrospinal fluid in mouse

OBJECTIVES

Proteins in perilymph may alter the delivery profile of implantable intracochlear drug delivery systems through biofouling. Knowledge of protein composition will help anticipate interactions with delivered agents.

STUDY DESIGN

Analysis of mouse perilymph.

METHODS

Protein composition of perilymph and cerebrospinal fluid (CSF) was analyzed using a capillary liquid chromatography-mass spectrometry-based iTRAQ quantitative proteomics approach. We searched against a mouse subset of the Uniprot FASTA protein database. We sampled perilymph from the apex of the mouse cochlea to minimize CSF contamination.

RESULTS

More than 50 explicit protein isoforms were identified with very high confidence. iTRAQ reporter ions allowed determination of relative molar amounts of proteins between perilymph and CSF. Protein in perilymph was almost three times more concentrated than in CSF. More than one-third of the proteins in perilymph comprised protease inhibitors, with serpins being the predominant group. Apolipoproteins constituted 16%. Fifteen percent of the proteins were enzymes. Albumin was the most abundant single protein (14%). Proteins with relatively high perilymph/CSF ratios included broad-spectrum protease inhibitors and apolipoproteins.

DISCUSSION

Some proteins found in perilymph, such as albumin and HMW kininogen, have been implicated in biofouling through adsorption to device materials. The relatively large quantities of apolipoprotein and albumin may serve as a reservoir for acidic and lipophilic drugs. Alpha-2-glycoprotein can bind basic drugs.

CONCLUSIONS

Perilymph is similar in protein composition to CSF, though amounts are 2.8 times higher. Protease inhibitors comprise the largest category of proteins.

A ranking-based scoring function for peptide-spectrum matches

The analysis of the large volume of tandem mass spectrometry (MS/MS) proteomics data that is generated these days relies on automated algorithms that identify peptides from their mass spectra. An essential component of these algorithms is the scoring function used to evaluate the quality of peptide-spectrum matches (PSMs). In this paper, we present new approach to scoring of PSMs. We argue that since this problem is at its core a ranking task (especially in the case of de novo sequencing), it can be solved effectively using machine learning ranking algorithms. We developed a new discriminative boosting-based approach to scoring. Our scoring models draw upon a large set of diverse feature functions that measure different qualities of PSMs. Our method improves the performance of our de novo sequencing algorithm beyond the current state-of-the-art, and also greatly enhances the performance of database search programs. Furthermore, by increasing the efficiency of tag filtration and improving the sensitivity of PSM scoring, we make it practical to perform large-scale MS/MS analysis, such as proteogenomic search of a six-frame translation of the human genome (in which we achieve a reduction of the running time by a factor of 15 and a 60% increase in the number of identified peptides, compared to the InsPecT database search tool). Our scoring function is incorporated into PepNovo+ which is available for download or can be run online at http://bix.ucsd.edu.

Impact of pretreated Switchgrass and biomass carbohydrates on Clostridium thermocellum ATCC 27405 cellulosome composition: a quantitative proteomic analysis

Background

Economic feasibility and sustainability of lignocellulosic ethanol production requires the development of robust microorganisms that can efficiently degrade and convert plant biomass to ethanol. The anaerobic thermophilic bacterium Clostridium thermocellum is a candidate microorganism as it is capable of hydrolyzing cellulose and fermenting the hydrolysis products to ethanol and other metabolites. C. thermocellum achieves efficient cellulose hydrolysis using multiprotein extracellular enzymatic complexes, termed cellulosomes.

Methodology/Principal Findings

In this study, we used quantitative proteomics (multidimensional LC-MS/MS and ¹⁵N-metabolic labeling) to measure relative changes in levels of cellulosomal subunit proteins (per CipA scaffoldin basis) when C. thermocellum ATCC 27405 was grown on a variety of carbon sources [dilute-acid pretreated switchgrass, cellobiose, amorphous cellulose, crystalline cellulose (Avicel) and combinations of crystalline cellulose with pectin or xylan or both]. Cellulosome samples isolated from cultures grown on these carbon sources were compared to ¹⁵N labeled cellulosome samples isolated from crystalline cellulose-grown cultures. In total from all samples, proteomic analysis identified 59 dockerin- and 8 cohesin-module containing components, including 16 previously undetected cellulosomal subunits. Many cellulosomal components showed differential protein abundance in the presence of non-cellulose substrates in the growth medium. Cellulosome samples from amorphous cellulose, cellobiose and pretreated switchgrass-grown cultures displayed the most distinct differences in composition as compared to cellulosome samples from crystalline cellulose-grown cultures. While Glycoside Hydrolase Family 9 enzymes showed increased levels in the presence of crystalline cellulose, and pretreated switchgrass, in particular, GH5 enzymes showed increased levels in response to the presence of cellulose in general, amorphous or crystalline.

Conclusions/Significance

Overall, the quantitative results suggest a coordinated substrate-specific regulation of cellulosomal subunit composition in C. thermocellum to better suit the organism's needs for growth under different conditions. To date, this study provides the most comprehensive comparison of cellulosomal compositional changes in C. thermocellum in response to different carbon sources. Such studies are vital to engineering a strain that is best suited to grow on specific substrates of interest and provide the building blocks for constructing designer cellulosomes with tailored enzyme composition for industrial ethanol production.

Comprehensive proteomic analysis of human cervical-vaginal fluid using colposcopy samples

Background

Cervical-vaginal fluid (CVF) plays an important role in the prevention of gynecological infections, although little is known about the contribution of CVF proteins to the immunity of the lower female genital tract. In order to analyze the protein composition of human CVF, we used CVF samples that are routinely collected during colposcopy, but are usually discarded. Since these samples are available in large quantities we aimed to analyze their usefulness for proteomics experiments. The samples were analyzed using different prefractionation techniques (ultrafiltration and C₄(RP)-LC protein separation) followed by C₁₈(RP)-LC peptide separation and identification by MALDI-TOF-TOF mass spectrometry. To determine the reproducibility of this proteomics platform we analyzed three technical replicates. Using spectral counting, protein abundances were estimated in a semiquantitative way. We also compared the results obtained in this study with those from previous studies derived from patients with different physiological conditions in order to determine an overlapping protein set.

Results

In total, we were able to identify 339 proteins in human CVF of which 151 proteins were not identified in any other proteomics study on human CVF so far. Those included antimicrobial peptides, such as human beta-defensin 2 and cathelicidin, which were known to be present in CVF, and endometrial proteins such as glycodelin and ribonucleoprotein A. Comparison of our results with previously published data led to the identification of a common protein set of 136 proteins. This overlapping protein set shows increased fractions of immunological and extracellular proteins, confirming the extracellular immunological role of CVF.

Conclusion

We demonstrated here that CVF colposcopy samples can be used in proteomics experiments and hence are applicable for biomarker discovery experiments. The delineation of an overlapping set of proteins that is identified in most proteomics studies on CVF may help in the description of a reference proteome when performing proteomics studies on human CVF.

Novel breast cancer metastasis-associated proteins

With the use of the breast cancer metastatic model, which comprises four isogenic cell lines, iTRAQ-based ESI-LC/MS/MS proteomics was employed to catalog protein expression changes as cancer cells acquire increasing metastatic potential. From more than 1000 proteins detected, 197 proteins, including drug-targetable kinases, phosphatases, proteases and transcription factors, displayed differential expression when cancer cells becomes more metastatic. Overall, the number of protein expression changes was evenly distributed across mildly ( approximately 30%), moderately ( approximately 40%) and aggressively ( approximately 30%) metastatic cancer cells. Some changes were found to be specific to one while others were required for two or more phenotypes. KEGG Orthology suggests major reprogramming in cell metabolism and to smaller extents in genetic and environmental information processing. Ten novel metastasis-associated proteins were identified and the iTRAQ-based expression profiles of 7 proteins were verified to be congruent with antibody-based methods. With the use of tissue microarrays comprising 50 matched cases of invasive and metastatic lesions, the expression profiles of SH3GLB1 and SUB1, SND1, TRIM28 were validated to be down- and up-regulated, respectively, during clinical progression of carcinoma in situ to invasive and metastatic carcinomas. Our study has unraveled proteome-wide molecular aberrations and potentially new players in breast cancer metastasis.

Quantitative proteomics reveals the function of unconventional ubiquitin chains in proteasomal degradation

All seven lysine residues in ubiquitin contribute to the synthesis of polyubiquitin chains on protein substrates. Whereas K48-linked chains are well established as mediators of proteasomal degradation, and K63-linked chains act in nonproteolytic events, the roles of unconventional polyubiquitin chains linked through K6, K11, K27, K29, or K33 are not well understood. Here, we report that the unconventional linkages are abundant in vivo and that all non-K63 linkages may target proteins for degradation. Ubiquitin with K48 as the single lysine cannot support yeast viability, and different linkages have partially redundant functions. By profiling both the entire yeast proteome and ubiquitinated proteins in wild-type and ubiquitin K11R mutant strains using mass spectrometry, we identified K11 linkage-specific substrates, including Ubc6, a ubiquitin-conjugating enzyme involved in endoplasmic reticulum-associated degradation (ERAD). Ubc6 primarily synthesizes K11-linked chains, and K11 linkages function in the ERAD pathway. Thus, unconventional polyubiquitin chains are critical for ubiquitin-proteasome system function.

Insights into the physiology of Methylotenera mobilis as revealed by metagenome-based shotgun proteomic analysis

While the shotgun proteomics approach is gaining momentum in understanding microbial physiology, it remains limited by the paucity of high-quality genomic data, especially when it comes to poorly characterized newly identified phyla. At the same time, large-scale metagenomic sequencing projects produce datasets representing genomes of a variety of environmental microbes, although with lower sequence coverage and sequence quality. In this work we tested the utility of a metagenomic dataset enriched in sequences of environmental strains of Methylotenera mobilis, to assess the protein profile of a laboratory-cultivated strain, M. mobilis JLW8, as a proof of principle. We demonstrate that a large portion of the proteome predicted from the metagenomic sequence (approx. 20 %) could be identified with high confidence (three or more peptide sequences), thus gaining insights into the physiology of this bacterium, which represents a new genus within the family Methylophilaceae.

Proteomic analysis for process development and control of therapeutic protein separation from human plasma

The use of proteomics technology during the development of a new process for plasma protein separation was demonstrated. In a two-step process, the two most abundant proteins, HSA and IgG, were removed in a first step of anion-exchange chromatography using a gel with very high capacity. Subsequently, two fractions containing medium and low abundance proteins were re-chromatographed on a smaller column with the same type of gel. Collected fractions were separated by SDS-PAGE and 2-D electrophoresis, and excised proteins were digested with trypsin and identified by LC-ESI-MS/MS. This proteomic analysis proved to be a useful method for detection of low abundance therapeutic proteins and potential harmful contaminants during process development. Based on this method, low abundance therapeutic proteins, such as vitamin-K-dependent clotting factors and inhibitors, could be identified as present in target fractions after chromatographic separation. In addition, the tracking of potentially dangerous impurities and designing proper steps for their removal are important outcomes when developing, refining or controlling a new fractionation schema. For the purpose of in-process control, in-solution digestion of complete fractions followed by protein identification with LC-ESI-MS/MS was demonstrated as a rapid and simple alternative to the entire analysis including 1-D or 2-D electrophoretic steps.

A simulated MS/MS library for spectrum-to-spectrum searching in large scale identification of proteins

Identifying peptides from mass spectrometric fragmentation data (MS/MS spectra) using search strategies that map protein sequences to spectra is computationally expensive. An alternative strategy uses direct spectrum-to-spectrum matching against a reference library of previously observed MS/MS that has the advantage of evaluating matches using fragment ion intensities and other ion types than the simple set normally used. However, this approach is limited by the small sizes of the available peptide MS/MS libraries and the inability to evaluate the rate of false assignments. In this study, we observed good performance of simulated spectra generated by the kinetic model implemented in MassAnalyzer (Zhang, Z. (2004) Prediction of low-energy collision-induced dissociation spectra of peptides. Anal. Chem. 76, 3908-3922; Zhang, Z. (2005) Prediction of low-energy collision-induced dissociation spectra of peptides with three or more charges. Anal. Chem. 77, 6364-6373) as a substitute for the reference libraries used by the spectrum-to-spectrum search programs X!Hunter and BiblioSpec and similar results in comparison with the spectrum-to-sequence program Mascot. We also demonstrate the use of simulated spectra for searching against decoy sequences to estimate false discovery rates. Although we found lower score discrimination with spectrum-to-spectrum searches than with Mascot, particularly for higher charge forms, comparable peptide assignments with low false discovery rate were achieved by examining consensus between X!Hunter and Mascot, filtering results by mass accuracy, and ignoring score thresholds. Protein identification results are comparable to those achieved when evaluating consensus between Sequest and Mascot. Run times with large scale data sets using X!Hunter with the simulated spectral library are 7 times faster than Mascot and 80 times faster than Sequest with the human International Protein Index (IPI) database. We conclude that simulated spectral libraries greatly expand the search space available for spectrum-to-spectrum searching while enabling principled analyses and that the approach can be used in consensus strategies for large scale studies while reducing search times.

p90 ribosomal S6 kinase and p70 ribosomal S6 kinase link phosphorylation of the eukaryotic chaperonin containing TCP-1 to growth factor, insulin, and nutrient signaling

Chaperonin containing TCP-1 (CCT) is a large multisubunit complex that mediates protein folding in eukaryotic cells. CCT participates in the folding of newly synthesized polypeptides, including actin, tubulin, and several cell cycle regulators; therefore, CCT plays an important role in cytoskeletal organization and cell division. Here we identify the chaperonin CCT as a novel physiological substrate for p90 ribosomal S6 kinase (RSK) and p70 ribosomal S6 kinase (S6K). RSK phosphorylates the beta subunit of CCT in response to tumor promoters or growth factors that activate the Ras-mitogen-activated protein kinase (MAPK) pathway. CCTbeta Ser-260 was identified as the RSK site by mass spectrometry and confirmed by site-directed mutagenesis. RSK-dependent Ser-260 phosphorylation was sensitive to the MEK inhibitor UO126 and the RSK inhibitor BID-1870. Insulin weakly activates RSK but strongly activates the phosphoinositide 3-kinase (PI3K)-mammalian target of rapamycin (mTOR) pathway and utilizes S6K to regulate CCTbeta phosphorylation. Thus, the Ras-MAPK and PI3K-mTOR pathways converge on CCTbeta Ser-260 phosphorylation in response to multiple agonists in various mammalian cells. We also show that RNA interference-mediated knockdown of endogenous CCTbeta causes impaired cell proliferation that can be rescued with ectopically expressed murine CCTbeta wild-type or phosphomimetic mutant S260D, but not the phosphorylation-deficient mutant S260A. Although the molecular mechanism of CCTbeta regulation remains unclear, our findings demonstrate a link between oncogene and growth factor signaling and chaperonin CCT-mediated cellular activities.

On the acquisition of +1 charge states during high-throughput proteomics: Implications on reproducibility, number and confidence of protein identifications

Modern high-throughput methods for the proteome analysis are gradually replacing more traditional 2D gel-based techniques. Almost immediately after the introduction of high-throughput proteomics techniques in 2001, reproducibility of the results became an issue. Extensive discussion in the literature led to the conclusion that certain "undersampling" exhibited during measurements could be due to the stochastic nature of the data-dependent sampling, routinely used with current mass spectrometry equipment. At the same time, the effect of the acquisition of different charge states on the reproducibility and confidence of protein identifications, to the best of our knowledge, has never been properly evaluated. There exists the frequently voiced yet hardly documented opinion that +1 charge states should be rejected during data-dependent acquisition. The work presented here shows that inclusion of the +1 charge state in the data-dependent acquisition protocols can indeed lead to improved proteome coverage, reproducibility, and the confidence of protein identifications by high-throughput proteomics. It was also shown that contrary to the established opinion, gas-phase dissociation of singly charged peptide species results in rich fragmentation patterns containing both b- and y-ions allowing for successful and confident peptide identification.

The thioredoxin-like protein rod-derived cone viability factor (RdCVFL) interacts with TAU and inhibits its phosphorylation in the retina

Rod-derived cone viability factor (RdCVF) is produced by the Nxnl1 gene that codes for a second polypeptide, RdCVFL, by alternative splicing. Although the role of RdCVF in promoting cone survival has been described, the implication of RdCVFL, a putative thioredoxin enzyme, in the protection of photoreceptors is presently unknown. Using a proteomics approach we identified 90 proteins interacting with RdCVFL including the microtubule-binding protein TAU. We demonstrate that the level of phosphorylation of TAU is increased in the retina of the Nxnl1(-/-) mice as it is hyperphosphorylated in the brain of patients suffering from Alzheimer disease, presumably in some cases through oxidative stress. Using a cell-based assay, we show that RdCVFL inhibits TAU phosphorylation. In vitro, RdCVFL protects TAU from oxidative damage. Photooxidative stress is implicated in retinal degeneration, particularly in retinitis pigmentosa, where it is considered to be a contributor to secondary cone death. The functional interaction between RdCVFL and TAU described here is the first characterization of the RdCVFL signaling pathway involved in neuronal cell death mediated by oxidative stress.

Multidimensional protein identification technology analysis highlights mitoxantrone-induced expression modulations in the primary prostate cancer cell proteome

Chemotherapeutic agents as they are used today have limited effectiveness against prostate cancer, but may potentially be used in new combinations with more efficacious results. Mitoxantrone, used for palliation of prostate cancer, has recently been found by our group to improve the susceptibility of primary prostate cancer cells to killing through the Fas-mediated death pathway. Here we used a shotgun proteomics approach to first profile the entire prostate cancer proteome and then identify specific factors involved in this mitoxantrone response. Peptides derived from primary prostate cancer cells treated with or without 100 nM mitoxantrone were analyzed by multidimensional protein identification technology (MudPIT). Strict limits and data filtering hierarchies were applied to identify proteins with high confidence. We identified 1498 proteins belonging to the prostate cancer proteome, 83 of which were significantly upregulated and 27 of which were markedly downregulated following mitoxantrone treatment. These proteins perform diverse functions, including ceramide production, tumour suppression, and oxidative reduction. Detailed proteomic analyses of prostate cancer cells and their response to mitoxantrone will further our understanding of its mechanisms of action. Identification of proteins influenced by treatment with mitoxantrone or other compounds may lead to the development of more effective drug combinations against prostate cancer.

A method for investigating protein-protein interactions related to salmonella typhimurium pathogenesis

We successfully modified an existing method to investigate protein-protein interactions in the pathogenic bacterium Salmonella enterica serovar Typhimurium (Salmonella Typhimurium). This method includes (i) addition of a histidine-biotin-histidine tag to the bait proteins via recombinant DNA techniques, (ii) in vivo cross-linking with formaldehyde, (iii) tandem affinity purification of bait proteins under fully denaturing conditions, and (iv) identification of the proteins cross-linked to the bait proteins by liquid-chromatography in conjunction with tandem mass-spectrometry. In vivo cross-linking stabilized protein interactions and permitted the subsequent two-step purification step conducted under denaturing conditions. The two-step purification greatly reduced nonspecific binding of noncross-linked proteins to bait proteins. Two different negative controls were employed to eliminate the possibility of identifying background and nonspecific proteins as interacting partners, especially those caused by nonspecific binding to the stationary phase used for protein purification. In an initial demonstration of this approach, we tagged three Salmonella proteinsHimD, PduB and PhoPwith known binding partners that ranged from stable (e.g., HimD) to transient (i.e., PhoP). Distinct sets of interacting proteins were identified for each bait protein, including the known binding partners such as HimA for HimD, as well as unexpected binding partners. Our results suggest that novel protein-protein interactions identified may be critical to pathogenesis by Salmonella.

Improving sensitivity in proteome studies by analysis of false discovery rates for multiple search engines

LC-MS experiments can generate large quantities of data, for which a variety of database search engines are available to make peptide and protein identifications. Decoy databases are becoming widely used to place statistical confidence in result sets, allowing the false discovery rate (FDR) to be estimated. Different search engines produce different identification sets so employing more than one search engine could result in an increased number of peptides (and proteins) being identified, if an appropriate mechanism for combining data can be defined. We have developed a search engine independent score, based on FDR, which allows peptide identifications from different search engines to be combined, called the FDR Score. The results demonstrate that the observed FDR is significantly different when analysing the set of identifications made by all three search engines, by each pair of search engines or by a single search engine. Our algorithm assigns identifications to groups according to the set of search engines that have made the identification, and re-assigns the score (combined FDR Score). The combined FDR Score can differentiate between correct and incorrect peptide identifications with high accuracy, allowing on average 35% more peptide identifications to be made at a fixed FDR than using a single search engine.

MassMatrix: a database search program for rapid characterization of proteins and peptides from tandem mass spectrometry data

MassMatrix is a program that matches tandem mass spectra with theoretical peptide sequences derived from a protein database. The program uses a mass accuracy sensitive probabilistic score model to rank peptide matches. The MS/MS search software was evaluated by use of a high mass accuracy dataset and its results compared with those from MASCOT, SEQUEST, X!Tandem, and OMSSA. For the high mass accuracy data, MassMatrix provided better sensitivity than MASCOT, SEQUEST, X!Tandem, and OMSSA for a given specificity and the percentage of false positives was 2%. More importantly all manually validated true positives corresponded to a unique peptide/spectrum match. The presence of decoy sequence and additional variable PTMs did not significantly affect the results from the high mass accuracy search. MassMatrix performs well when compared with MASCOT, SEQUEST, X!Tandem, and OMSSA with regard to search time. MassMatrix was also run on a distributed memory clusters and achieved search speeds of approximately 100,000 spectra per hour when searching against a complete human database with eight variable modifications. The algorithm is available for public searches at (http://www.massmatrix.net).

Quantitative overview of N2 fixation in Nostoc punctiforme ATCC 29133 through cellular enrichments and iTRAQ shotgun proteomics

Nostoc punctiforme ATCC 29133 is a photoautotrophic cyanobacterium with the capacity to fix atmospheric N 2. Its ability to mediate this process is similar to that described for Nostoc sp. PCC 7120, where vegetative cells differentiate into heterocysts. Quantitative proteomic investigations at both the filament level and the heterocyst level are presented using isobaric tagging technology (iTRAQ), with 721 proteins at the 95% confidence interval quantified across both studies. Observations from both experiments yielded findings confirmatory of both transcriptional studies, and published Nostoc sp. PCC 7120 iTRAQ data. N. punctiforme exhibits similar metabolic trends, though changes in a number of metabolic pathways are less pronounced than in Nostoc sp. PCC 7120. Results also suggest a number of proteins that may benefit from future investigations. These include ATP dependent Zn-proteases, N-reserve degraders and also redox balance proteins. Complementary proteomic data sets from both organisms present key precursor knowledge that is important for future cyanobacterial biohydrogen research.

Two chromogranin a-derived peptides induce calcium entry in human neutrophils by calmodulin-regulated calcium independent phospholipase A2

Background

Antimicrobial peptides derived from the natural processing of chromogranin A (CgA) are co-secreted with catecholamines upon stimulation of chromaffin cells. Since PMNs play a central role in innate immunity, we examine responses by PMNs following stimulation by two antimicrobial CgA-derived peptides.

Methodology/Principal Findings

PMNs were treated with different concentrations of CgA-derived peptides in presence of several drugs. Calcium mobilization was observed by using flow cytometry and calcium imaging experiments. Immunocytochemistry and confocal microscopy have shown the intracellular localization of the peptides. The calmodulin-binding and iPLA2 activating properties of the peptides were shown by Surface Plasmon Resonance and iPLA2 activity assays. Finally, a proteomic analysis of the material released after PMNs treatment with CgA-derived peptides was performed by using HPLC and Nano-LC MS-MS. By using flow cytometry we first observed that after 15 s, in presence of extracellular calcium, Chromofungin (CHR) or Catestatin (CAT) induce a concentration-dependent transient increase of intracellular calcium. In contrast, in absence of extra cellular calcium the peptides are unable to induce calcium depletion from the stores after 10 minutes exposure. Treatment with 2-APB (2-aminoethoxydiphenyl borate), a store operated channels (SOCs) blocker, inhibits completely the calcium entry, as shown by calcium imaging. We also showed that they activate iPLA2 as the two CaM-binding factors (W7 and CMZ) and that the two sequences can be aligned with the two CaM-binding domains reported for iPLA2. We finally analyzed by HPLC and Nano-LC MS-MS the material released by PMNs following stimulation by CHR and CAT. We characterized several factors important for inflammation and innate immunity.

Conclusions/Significance

For the first time, we demonstrate that CHR and CAT, penetrate into PMNs, inducing extracellular calcium entry by a CaM-regulated iPLA2 pathway. Our study highlights the role of two CgA-derived peptides in the active communication between neuroendocrine and immune systems.

Global analysis of cellular protein translation by pulsed SILAC

Current methods for system-wide gene expression analysis detect changes in mRNA abundance, but neglect regulation at the level of translation. Pulse labeling with stable isotopes has been used to measure protein turnover rates, but this does not directly provide information about translation rates. Here, we developed pulsed stable isotope labeling by amino acids in cell culture (pSILAC) with two heavy isotope labels to directly quantify protein translation on a proteome-wide scale. We applied the method to cellular iron homeostasis as a model system and demonstrate that it can confidently identify proteins that are translationally regulated by iron availability.

Mass spectrometry: from proteomics to metabolomics and lipidomics

Mass spectrometry (MS) in combination with liquid chromatography (LC), i.e. LC-MS, is the key analytical technique on which the emerging "omics" technologies of proteomics, metabolomics and lipidomics are based. It provides both structural and quantitative data and can be used in a "global" or "targeted" manner allowing on the one hand the identification of thousands of proteins from a tissue, or on the other the detection of biologically active metabolites at levels of a few parts-per-billion. It can be expected that the continued incremental developments in LC-MS along with data-handling routines will soon bear fruit in the quest for a greater understanding of human disease, leading to new drug targets and therapies. This tutorial review on proteomics, metabolomics and lipidomics will be of interest to the biochemical, biomedical and pharmaceutical communities, as well as those working in other branches of analytical bioscience.

Comparison of MS(2)-only, MSA, and MS(2)/MS(3) methodologies for phosphopeptide identification

Current mass spectrometers provide a number of alternative methodologies for producing tandem mass spectra specifically for phosphopeptide analysis. In particular, generation of MS(3) spectra in a data-dependent manner upon detection of the neutral loss of a phosphoric acid in MS(2) spectra is a popular technique for circumventing the problem of poor phosphopeptide backbone fragmentation. The newer Multistage Activation method provides another option. Both these strategies require additional cycle time on the instrument and therefore reduce the number of spectra that can be measured in the same amount of time. Additional informatics is often required to make most efficient use of the additional information provided by these spectra as well. This work presents a comparison of several commonly used mass spectrometry methods for the study of phosphopeptide-enriched samples: an MS(2)-only method, a Multistage Activation method, and an MS(2)/MS(3) data-dependent neutral loss method. Several strategies for dealing effectively with the resulting MS(3) data in the latter approach are also presented and compared. The overall goal is to infer whether any one methodology performs significantly better than another for identifying phosphopeptides. On data presented here, the Multistage Activation methodology is demonstrated to perform optimally and does not result in significant loss of unique peptide identifications.

Analysis of the trypanosome flagellar proteome using a combined electron transfer/collisionally activated dissociation strategy

The use of electron-transfer dissociation as an alternative peptide ion activation method for generation of protein sequence information is examined here in comparison with the conventional method of choice, collisionally activated dissociation, using a linear ion trapping instrument. Direct comparability between collisionally and electron-transfer-activated product ion data were ensured by employing an activation-switching method during acquisition, sequentially activating precisely the same precursor ion species with each fragmentation method in turn. Sequest (Thermo Fisher Scientific, San Jose, CA) searching of product ion data generated an overlapping yet distinct pool of polypeptide identifications from the products of collisional and electron-transfer-mediated activation products. To provide a highly confident set of protein recognitions, identification data were filtered using parameters that achieved a peptide false discovery rate of 1%, with two or more independent peptide assignments required for each protein. The use of electron transfer dissociation (ETD) has allowed us to identify additional peptides where the quality of product ion data generated by collisionally activated dissociation (CAD) was insufficient to infer peptide sequence. Thus, a combined ETD/CAD approach leads to the recognition of more peptides and proteins than are achieved using peptide analysis by CAD- or ETD-based tandem mass spectrometry alone.

Evaluation of archival time on shotgun proteomics of formalin-fixed and paraffin-embedded tissues

There is increasing acceptance of the critical importance of correlating the morphologic features of tissue with the data obtained from various molecular analytic techniques. Access to archived formalin-fixed and paraffin-embedded (FFPE) tissue specimens via shotgun-based proteomic analyses may, therefore, open new avenues for both prospective and retrospective translational research. However, one of the remaining issues in performing comparative proteomic measurements among FFPE tissues relates to potential variability in protein composition and retrieval based on length of storage periods. Optimized protein extraction and digestion procedures for handling FFPE tissues are coupled with the capillary isotachophoresis-based proteome technology to evaluate the effects of length of storage period on archival tissue proteome analysis across 10 archived uterine mesenchymal tumor tissue blocks, including 9 uterine leiomyomas dating from 1990 to 2002 and a single case of alveolar soft part sarcoma (ASPS) from 1980. Several statistical measures, including the Pearson correlation coefficient, coefficient of variance, k-means clustering, and ANOVA, are employed to evaluate the possibility of an archival effect on individual proteins or groups of proteins within nine leiomyomas. Low abundance proteins may be more susceptible to the long-term storage as these proteins are more difficult to be retrieved and extracted as the tissue block ages in paraffin. Despite using tissue blocks stored for as many as 28 years, high confidence and comparative proteome analysis between the leiomyomas and the sarcoma is achieved. Though sharing over 1800 common proteins in a core set, a total of 80 proteins unique to the sarcoma are identified distinguishing the ASPS from the leiomyomas. Vacuolar proton translocating ATPase 116 kDa subunit isoform a3, one of the unique proteins expressed in the ASPS, is further validated by immunohistochemistry (IHC). Although IHC is highly sensitive and provides the subcellular resolution, mass spectrometry-based proteome profiling enables global identification and quantification of thousands of proteins without a priori knowledge of individual proteins being analyzed or the need of validated antibodies.

Large-scale profiling of protein palmitoylation in mammalian cells

S-palmitoylation is a pervasive post-translational modification required for the trafficking, compartmentalization and membrane tethering of many proteins. We demonstrate that the commercially available compound 17-octadecynoic acid (17-ODYA) can serve as a bioorthogonal, click chemistry probe for in situ labeling, identification and verification of palmitoylated proteins in human cells. We identified approximately 125 predicted palmitoylated proteins, including G proteins, receptors and a family of uncharacterized hydrolases whose plasma membrane localization depends on palmitoylation.

Efficient discovery of abundant post-translational modifications and spectral pairs using peptide mass and retention time differences

Background

Peptide identification via tandem mass spectrometry is the basic task of current proteomics research. Due to the complexity of mass spectra, the majority of mass spectra cannot be interpreted at present. The existence of unexpected or unknown protein post-translational modifications is a major reason.

Results

This paper describes an efficient and sequence database-independent approach to detecting abundant post-translational modifications in high-accuracy peptide mass spectra. The approach is based on the observation that the spectra of a modified peptide and its unmodified counterpart are correlated with each other in their peptide masses and retention time. Frequently occurring peptide mass differences in a data set imply possible modifications, while small and consistent retention time differences provide orthogonal supporting evidence. We propose to use a bivariate Gaussian mixture model to discriminate modification-related spectral pairs from random ones. Due to the use of two-dimensional information, accurate modification masses and confident spectral pairs can be determined as well as the quantitative influences of modifications on peptide retention time.

Conclusion

Experiments on two glycoprotein data sets demonstrate that our method can effectively detect abundant modifications and spectral pairs. By including the discovered modifications into database search or by propagating peptide assignments between paired spectra, an average of 10% more spectra are interpreted.

Human embryonic stem cell phosphoproteome revealed by electron transfer dissociation tandem mass spectrometry

Protein phosphorylation is central to the understanding of cellular signaling, and cellular signaling is suggested to play a major role in the regulation of human embryonic stem (ES) cell pluripotency. Here, we describe the use of conventional tandem mass spectrometry-based sequencing technology--collision-activated dissociation (CAD)--and the more recently developed method electron transfer dissociation (ETD) to characterize the human ES cell phosphoproteome. In total, these experiments resulted in the identification of 11,995 unique phosphopeptides, corresponding to 10,844 nonredundant phosphorylation sites, at a 1% false discovery rate (FDR). Among these phosphorylation sites are 5 localized to 2 pluripotency critical transcription factors--OCT4 and SOX2. From these experiments, we conclude that ETD identifies a larger number of unique phosphopeptides than CAD (8,087 to 3,868), more frequently localizes the phosphorylation site to a specific residue (49.8% compared with 29.6%), and sequences whole classes of phosphopeptides previously unobserved.

From complementarity to comprehensiveness--targeting the membrane proteome of growing Bacillus subtilis by divergent approaches

The analysis of integral membrane proteins (IMPs) with mass spectrometry-centered technologies has undergone great progress during the past few years, allowing for the analysis of several hundreds of IMPs. In this study, we investigated three promising shotgun approaches for the identification of IMPs of the model organism Bacillus subtilis. One comprises a classical membrane preparation procedure with carbonate and high-ionic-strength buffers, followed by SDS-PAGE and LC-MS/MS analysis. The two others are based on enzymatic trimming of the crude membrane fraction either with trypsin or proteinase K and subsequent gel-free analysis. As a result, we observed the highest degree of complementarity between the gel-based and the proteinase K approach, since the first exclusively addresses soluble loops and domains of IMPs and gave rise to 8709 unique peptides, whereas the latter contributed 1180 unique peptide identifications from otherwise inaccessible transmembrane helices (TMHs). All three methods contribute significant numbers (381, 284, and 276, respectively) to the total of 527 IMP identifications from the membrane fraction of exponentially growing B. subtilis cells, thus representing approximately 69% of all transcribed IMPs.

Proteome-wide identification of novel binding partners to the oncogenic fusion gene protein, NPM-ALK, using tandem affinity purification and mass spectrometry

Nucleophosmin-anaplastic lymphoma kinase (NPM-ALK), an oncogenic fusion gene protein that is characteristically found in a subset of anaplastic large cell lymphomas, promotes tumorigenesis through its functional and physical interactions with various biologically important proteins. The identification of these interacting proteins has proven to be useful to further our understanding of NPM-ALK-mediated tumorigenesis. For the first time, we performed a proteome-wide identification of NPM-ALK-binding proteins using tandem affinity purification and a highly sensitive mass spectrometric technique. Tandem affinity purification is a recently developed method that carries a lower background and higher sensitivity compared with the conventional immunoprecipitation-based protein purification protocols. The NPM-ALK gene was cloned into an HB-tagged vector and expressed in GP293 cells. Three independent experiments were performed and the reproducibility of the data was 68%. The vast majority of the previously reported NPM-ALK-binding proteins were detected. We also identified proteins that are involved in various cellular processes that were not previously described in association with NPM-ALK, such as MCM6 and MSH2 (DNA repair), Nup98 and importin 8 (subcellular protein transport), Stim1 (calcium signaling), 82Fip (RNA regulation), and BAG2 (proteosome degradation). We believe that these data highlight the functional diversity of NPM-ALK and provide new research directions for the study of the biology of this oncoprotein.

Proteomic profiling of gamma-secretase substrates and mapping of substrate requirements

The presenilin/γ-secretase complex, an unusual intramembrane aspartyl protease, plays an essential role in cellular signaling and membrane protein turnover. Its ability to liberate numerous intracellular signaling proteins from the membrane and also mediate the secretion of amyloid-β protein (Aβ) has made modulation of γ-secretase activity a therapeutic goal for cancer and Alzheimer disease. Although the proteolysis of the prototypical substrates Notch and β-amyloid precursor protein (APP) has been intensely studied, the full spectrum of substrates and the determinants that make a transmembrane protein a substrate remain unclear. Using an unbiased approach to substrate identification, we surveyed the proteome of a human cell line for targets of γ-secretase and found a relatively small population of new substrates, all of which are type I transmembrane proteins but have diverse biological roles. By comparing these substrates to type I proteins not regulated by γ-secretase, we determined that besides a short ectodomain, γ-secretase requires permissive transmembrane and cytoplasmic domains to bind and cleave its substrates. In addition, we provide evidence for at least two mechanisms that can target a substrate for γ cleavage: one in which a substrate with a short ectodomain is directly cleaved independent of sheddase association, and a second where a substrate requires ectodomain shedding to instruct subsequent γ-secretase processing. These findings expand our understanding of the mechanisms of substrate selection as well as the diverse cellular processes to which γ-secretase contributes.

All cells face the challenge of removing transmembrane proteins from the lipid bilayer for the purpose of signaling or degradation. One molecular solution to this problem is the multiprotein enzyme complex γ-secretase, which is able to hydrolyze several known transmembrane proteins within the hydrophobic lipid environment. Due to its central role in the pathogenesis of Alzheimer disease, modulation of γ-secretase activity has become a therapeutic goal. However, the number and diversity of proteins that can be cleaved by this protease remain unknown, and the attributes that target these proteins to γ-secretase are unclear. In this study, we used an unbiased approach to substrate identification and surveyed the proteome for targets of γ-secretase. Of the thousands of proteins detectable, only a relative few were substrates of γ-secretase, all of which were type I transmembrane proteins. In addition to validating several of these novel substrates, we compared them to other proteins that we identified as nonsubstrates and determined that there are specific domains that can activate or inhibit γ-secretase processing. These findings should advance our understanding of the many cellular processes regulated by γ-secretase and may offer insights into how γ-secretase can be exploited for therapeutic purposes.

Using an unbiased quantitative proteomics approach, novel substrate targets for the protease γ-secretase are identified and analyzed to determine which domains enable their cleavage.

Hydrophilic interaction chromatography for fractionation and enrichment of the phosphoproteome

Mass spectrometry-based protein phosphorylation analysis on a proteome-wide scale remains a formidable challenge, hampered by the complexity and dynamic range of protein expression on the global level and multi-site phosphorylation at substoichiometric ratios at the individual protein level. It is recognized that reduction of sample complexity or enrichment of the phosphopeptide pool is a necessary prerequisite for global phospho-proteomics. Immobilized metal affinity chromatography (IMAC) and strong cation exchange chromatography, either alone or in tandem, have emerged as the most widely used chromatographic-based enrichment strategies. However, each is not without shortcomings. Both techniques provide little fractionation of phosphorylated species and are compromised by competition and co-elution of highly acidic peptides. Here, we describe a phosphopeptide prefractionation scheme using hydrophilic interaction chromatography, which both enriches the phosphopeptide pool and efficiently fractionates the remaining peptides. When used in front of IMAC, the selectivity of the metal affinity resin is improved to greater than 95%. The lack of significant numbers of nonphosphorylated peptides also allows for more efficient use of the mass spectrometer duty cycle in that the instrument spends nearly all of its time in sequencing the phosphopeptides.

Characterization of leaf apoplastic peroxidases and metabolites in Vigna unguiculata in response to toxic manganese supply and silicon

Previous work suggested that the apoplastic phenol composition and its interaction with apoplastic class III peroxidases (PODs) are decisive in the development or avoidance of manganese (Mn) toxicity in cowpea (Vigna unguiculata L.). This study characterizes apoplastic PODs with particular emphasis on the activities of specific isoenzymes and their modulation by phenols in the Mn-sensitive cowpea cultivar TVu 91 as affected by Mn and silicon (Si) supply. Si reduced Mn-induced toxicity symptoms without affecting the Mn uptake. Blue Native-PAGE combined with Nano-LC-MS/MS allowed identification of a range of POD isoenzymes in the apoplastic washing fluid (AWF). In Si-treated plants Mn-mediated induction of POD activity was delayed. Four POD isoenzymes eluted from the BN gels catalysed both H(2)O(2)-consuming and H(2)O(2)-producing activity with pH optima at 6.5 and 5.5, respectively. Four phenols enhanced NADH-peroxidase activity of these isoenzymes in the presence of Mn(2+) (p-coumaric=vanillic>>benzoic>ferulic acid). p-Coumaric acid-enhanced NADH-peroxidase activity was inhibited by ferulic acid (50%) and five other phenols (50-90%). An independent component analysis (ICA) of the total and apoplastic GC-MS-based metabolome profile showed that Mn, Si supply, and the AWF fraction (AWF(H(2)O), AWF(NaCl)) significantly changed the metabolite composition. Extracting non-polar metabolites from the AWF allowed the identification of phenols. Predominantly NADH-peroxidase activity-inhibiting ferulic acid appeared to be down-regulated in Mn-sensitive (+Mn, -Si) and up-regulated in Mn-tolerant (+Si) leaf tissue. The results presented here support the previously hypothesized role of apoplastic NADH-peroxidase and its activity-modulating phenols in Mn toxicity and Si-enhanced Mn tolerance.

Decoy methods for assessing false positives and false discovery rates in shotgun proteomics

The potential of getting a significant number of false positives (FPs) in peptide-spectrum matches (PSMs) obtained by proteomic database search has been well-recognized. Among the attempts to assess FPs, the concomitant use of target and decoy databases is widely practiced. By adjusting filtering criteria, FPs and false discovery rate (FDR) can be controlled at a desired level. Although the target-decoy approach is gaining in popularity, subtle differences in decoy construction (e.g., reversing vs stochastic methods), rate calculation (e.g., total vs unique PSMs), or searching (separate vs composite) do exist among various implementations. In the present study, we evaluated the effects of these differences on FP and FDR estimations using a rat kidney protein sample and the SEQUEST search engine as an example. On the effects of decoy construction, we found that, when a single scoring filter (XCorr) was used, stochastic methods generated a higher estimation of FPs and FDR than sequence reversing methods, likely due to an increase in unique peptides. This higher estimation could largely be attenuated by creating decoy databases similar in effective size but not by a simple normalization with a unique-peptide coefficient. When multiple filters were applied, the differences seen between reversing and stochastic methods significantly diminished, suggesting multiple filterings reduce the dependency on how a decoy is constructed. For a fixed set of filtering criteria, FDR and FPs estimated by using unique PSMs were almost twice those using total PSMs. The higher estimation seemed to be dependent on data acquisition setup. As to the differences between performing separate or composite searches, in general, FDR estimated from the separate search was about three times that from the composite search. The degree of difference gradually decreased as the filtering criteria became more stringent. Paradoxically, the estimated true positives in separate search were higher when multiple filters were used. By analyzing a standard protein mixture, we demonstrated that the higher estimation of FDR and FPs in the separate search likely reflected an overestimation, which could be corrected with a simple merging procedure. Our study illustrates the relative merits of different implementations of the target-decoy strategy, which should be worth contemplating when large-scale proteomic biomarker discovery is to be attempted.

Oscore: a combined score to reduce false negative rates for peptide identification in tandem mass spectrometry analysis

Tandem mass spectrometry (MS/MS) has been widely used in proteomics studies. Multiple algorithms have been developed for assessing matches between MS/MS spectra and peptide sequences in databases. However, it is still a challenge to reduce false negative rates without compromising the high confidence of peptide identification. In this study, we developed the score, Oscore, by logistic regression using SEQUEST and AMASS variables to identify fully tryptic peptides. Since these variables showed complicated association with each other, combining them together rather than applying them to a threshold model improved the classification of correct and incorrect peptide identifications. Oscore achieved both a lower false negative rate and a lower false positive rate than PeptideProphet on datasets from 18 known protein mixtures and several proteome-scale samples of different complexity, database size and separation methods. By a three-way comparison among Oscore, PeptideProphet and another logistic regression model which made use of PeptideProphet's variables, the main contributor for the improvement made by Oscore is discussed.

On the estimation of false positives in peptide identifications using decoy search strategy

False positive control/estimate in peptide identifications by MS is of critical importance for reliable inference at the protein level and downstream bioinformatics analysis. Approaches based on search against decoy databases have become popular for its conceptual simplicity and easy implementation. Although various decoy search strategies have been proposed, few studies have investigated their difference in performance. With datasets collected on a mixture of model proteins, we demonstrate that a single search against the target database coupled with its reversed version offers a good balance between performance and simplicity. In particular, both the accuracy of the estimate of the number of false positives and sensitivity is at least comparable to other procedures examined in this study. It is also shown that scrambling while preserving frequency of amino acid words can potentially improve the accuracy of false positive estimate, though more studies are needed to investigate the optimal scrambling procedure for specific condition and the variation of the estimate across repeated scrambling.