iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system

Protein complexes and protein interaction networks are essential mediators of most biological functions. Complexes supporting transient functions such as signal transduction processes are frequently subject to dynamic remodeling. Currently, the majority of studies on the composition of protein complexes are carried out by affinity purification and mass spectrometry (AP-MS) and present a static view of the system. For a better understanding of inherently dynamic biological processes, methods to reliably quantify temporal changes of protein interaction networks are essential. Here we used affinity purification combined with sequential window acquisition of all theoretical spectra (AP-SWATH) mass spectrometry to study the dynamics of the 14-3-3β scaffold protein interactome after stimulation of the insulin-PI3K-AKT pathway. The consistent and reproducible quantification of 1,967 proteins across all stimulation time points provided insights into the 14-3-3β interactome and its dynamic changes following IGF1 stimulation. We therefore establish AP-SWATH as a tool to quantify dynamic changes in protein-complex interaction networks.

Proteomic protease specificity profiling of clostridial collagenases reveals their intrinsic nature as dedicated degraders of collagen

Clostridial collagenases are among the most efficient degraders of collagen. Most clostridia are saprophytes and secrete proteases to utilize proteins in their environment as carbon sources; during anaerobic infections, collagenases play a crucial role in host colonization. Several medical and biotechnological applications have emerged utilizing their high collagenolytic efficiency. However, the contribution of the functionally most important peptidase domain to substrate specificity remains unresolved. We investigated the active site sequence specificity of the peptidase domains of collagenase G and H from Clostridium histolyticum and collagenase T from Clostridium tetani. Both prime and non-prime cleavage site specificity were simultaneously profiled using Proteomic Identification of protease Cleavage Sites (PICS), a mass spectrometry-based method utilizing database searchable proteome-derived peptide libraries. For each enzyme we identified > 100 unique-cleaved peptides, resulting in robust cleavage logos revealing collagen-like specificity patterns: a strong preference for glycine in P3 and P1′, proline at P2 and P2′, and a slightly looser specificity at P1, which in collagen is typically occupied by hydroxyproline. This specificity for the classic collagen motifs Gly-Pro-X and Gly-X-Hyp represents a remarkable adaptation considering the complex requirements for substrate unfolding and presentation that need to be fulfilled before a single collagen strand becomes accessible for cleavage.

Biological significance

We demonstrate the striking sequence specificity of a family of clostridial collagenases using proteome derived peptide libraries and PICS, Proteomic Identification of protease Cleavage Sites. In combination with the previously published crystal structures of these proteases, our results represent an important piece of the puzzle in understanding the complex mechanism underlying collagen hydrolysis, and pave the way for the rational design of specific test substrates and selective inhibitors.

This article is part of a Special Issue entitled: Can Proteomics Fill the Gap Between Genomics and Phenotypes?

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Active site specificity profiling of 3 clostridial collagenases—ColG and H from C. histolyticum, and ColT from C. tetani.

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Their high sequence specificity to collagen-like sequence points towards a co-evolution with the mammalian substrate.

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Significant differences to MMPs and a more promiscuous cleavage mechanism facilitating rapid collagenolysis were revealed.

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Human proteome-derived peptide libraries & PICS are suitable for active site specificity profiling of pathogenic proteases.

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Results pave the way for rational design of test substrates and selective inhibitors.

Quantitative proteomics reveals that plasma membrane microdomains from poplar cell suspension cultures are enriched in markers of signal transduction, molecular transport, and callose biosynthesis

The plasma membrane (PM) is a highly dynamic interface that contains detergent-resistant microdomains (DRMs). The aim of this work was to determine the main functions of such microdomains in poplar through a proteomic analysis using gel-based and solution (iTRAQ) approaches. A total of 80 proteins from a limited number of functional classes were found to be significantly enriched in DRM relative to PM. The enriched proteins are markers of signal transduction, molecular transport at the PM, or cell wall biosynthesis. Their intrinsic properties are presented and discussed together with the biological significance of their enrichment in DRM. Of particular importance is the significant and specific enrichment of several callose [(1 → 3)-β-glucan] synthase isoforms, whose catalytic activity represents a final response to stress, leading to the deposition of callose plugs at the surface of the PM. An integrated functional model that connects all DRM-enriched proteins identified is proposed. This report is the only quantitative analysis available to date of the protein composition of membrane microdomains from a tree species.

Longitudinal study of differential protein expression in an Alzheimer's mouse model lacking inducible nitric oxide synthase

Alzheimer's disease (AD) is a complex neurodegenerative process that involves altered brain immune, neuronal and metabolic functions. Understanding the underlying mechanisms has relied on mouse models that mimic components of AD pathology. We used gel-free, label-free LC-MS/MS to quantify protein and phosphopeptide levels in brains of APPSwDI/NOS2-/- (CVN-AD) mice. CVN-AD mice show a full spectrum of AD-like pathology, including amyloid deposition, hyperphosphorylated and aggregated tau, and neuronal loss that worsens with age. Tryptic digests, with or without phosphopeptide enrichment on an automated titanium dioxide LC system, were separated by online two-dimensional LC and analyzed on a Waters Synapt G2 HDMS, yielding relative expression for >950 proteins and >1100 phosphopeptides. Among differentially expressed proteins were known markers found in humans with AD, including GFAP and C1Q. Phosphorylation of connexin 43, not previously described in AD, was increased at 42 weeks, consistent with dysregulation of gap junctions and activation of astrocytes. Additional alterations in phosphoproteins suggests dysregulation of mitochondria, synaptic transmission, vesicle trafficking, and innate immune pathways. These data validate the CVN-AD mouse model of AD, identify novel disease and age-related changes in the brain during disease progression, and demonstrate the utility of integrating unbiased and phosphoproteomics for understanding disease processes in AD.

Searching for a needle in a stack of needles: challenges in metaproteomics data analysis

In the past years the integral study of microbial communities of varying complexity has gained increasing research interest. Mass spectrometry-driven metaproteomics enables the analysis of such communities on the functional level, but this fledgling field still faces various technical and semantic challenges regarding experimental data analysis and interpretation. In the present review, we outline the hurdles involved and attempt to cover the most valuable methods and software implementations available to researchers in the field today. Beyond merely focusing on protein identification, we provide an overview on different data pre- and post-processing steps, such as metabolic pathway analysis, that can be useful in a typical metaproteomics workflow. Finally, we briefly discuss directions for future work.

Graph-based peak alignment algorithms for multiple liquid chromatography-mass spectrometry datasets

Liquid chromatography coupled to mass spectrometry (LC-MS) is the dominant technological platform for proteomics. An LC-MS analysis of a complex biological sample can be visualized as a 'map' of which the positional coordinates are the mass-to-charge ratio (m/z) and chromatographic retention time (RT) of the chemical species profiled. Label-free quantitative proteomics requires the alignment and comparison of multiple LC-MS maps to ascertain the reproducibility of experiments or reveal proteome changes under different conditions. The main challenge in this task lies in correcting inevitable RT shifts. Similar, but not identical, LC instruments and settings can cause peptides to elute at very different times and sometimes in a different order, violating the assumptions of many state-of-the-art alignment tools. To meet this challenge, we developed LWBMatch, a new algorithm based on weighted bipartite matching. Unlike existing tools, which search for accurate warping functions to correct RT shifts, we directly seek a peak-to-peak mapping by maximizing a global similarity function between two LC-MS maps. For alignment tasks with large RT shifts (>500 s), an approximate warping function is determined by locally weighted scatterplot smoothing of potential matched features, detected using a novel voting scheme based on co-elution. For validation, we defined the ground truth for alignment success based on tandem mass spectrometry identifications from sequence searching. We showed that our method outperforms several existing tools in terms of precision and recall, and is capable of aligning maps from different instruments and settings.

AVAILABILITY

Available at https://sourceforge.net/projects/rt-alignment/.

tRNA tKUUU, tQUUG, and tEUUC wobble position modifications fine-tune protein translation by promoting ribosome A-site binding

tRNA modifications are crucial to ensure translation efficiency and fidelity. In eukaryotes, the URM1 and ELP pathways increase cellular resistance to various stress conditions, such as nutrient starvation and oxidative agents, by promoting thiolation and methoxycarbonylmethylation, respectively, of the wobble uridine of cytoplasmic (tK(UUU)), (tQ(UUG)), and (tE(UUC)). Although in vitro experiments have implicated these tRNA modifications in modulating wobbling capacity and translation efficiency, their exact in vivo biological roles remain largely unexplored. Using a combination of quantitative proteomics and codon-specific translation reporters, we find that translation of a specific gene subset enriched for AAA, CAA, and GAA codons is impaired in the absence of URM1- and ELP-dependent tRNA modifications. Moreover, in vitro experiments using native tRNAs demonstrate that both modifications enhance binding of tK(UUU) to the ribosomal A-site. Taken together, our data suggest that tRNA thiolation and methoxycarbonylmethylation regulate translation of genes with specific codon content.

A Candida albicans PeptideAtlas

Candida albicans public proteomic datasets, though growing steadily in the last few years, still have a very limited presence in online repositories. We report here the creation of a C. albicans PeptideAtlas comprising near 22,000 distinct peptides at a 0.24% False Discovery Rate (FDR) that account for over 2500 canonical proteins at a 1.2% FDR. Based on data from 16 experiments, we attained coverage of 41% of the C. albicans open reading frame sequences (ORFs) in the database used for the searches. This PeptideAtlas provides several useful features, including comprehensive protein and peptide-centered search capabilities and visualization tools that establish a solid basis for the study of basic biological mechanisms key to virulence and pathogenesis such as dimorphism, adherence, and apoptosis. Further, it is a valuable resource for the selection of candidate proteotypic peptides for targeted proteomic experiments via Selected Reaction Monitoring (SRM) or SWATH-MS.

BIOLOGICAL SIGNIFICANCE

This C. albicans PeptideAtlas resolves the previous absence of fungal pathogens in the PeptideAtlas project. It represents the most extensive characterization of the proteome of this fungus that exists up to the current date, including evidence for uncharacterized ORFs. Through its web interface, PeptideAtlas supports the study of interesting proteins related to basic biological mechanisms key to virulence such as apoptosis, dimorphism and adherence. It also provides a valuable resource to select candidate proteotypic peptides for future (SRM) targeted proteomic experiments. This article is part of a Special Issue entitled: Trends in Microbial Proteomics.

Interleukin-2 signalling is modulated by a labile disulfide bond in the CD132 chain of its receptor

Certain disulfide bonds present in leucocyte membrane proteins are labile and can be reduced in inflammation. This can cause structural changes that result in downstream functional effects, for example, in integrin activation. Recent studies have shown that a wide range of membrane proteins have labile disulfide bonds including CD132, the common gamma chain of the receptors for several cytokines including interleukin-2 and interleukin-4 (IL-2 and IL-4). The Cys(183)-Cys(232) disulfide bond in mouse CD132 is susceptible to reduction by enzymes such as thioredoxin (TRX), gamma interferon-inducible lysosomal thiolreductase and protein disulfide isomerase, which are commonly secreted during immune activation. The Cys(183)-Cys(232) disulfide bond is also reduced in an in vivo lipopolysaccharide (LPS)-induced acute model of inflammation. Conditions that lead to the reduction of the Cys(183)-Cys(232) disulfide bond in CD132 inhibit proliferation of an IL-2-dependent T cell clone and concomitant inhibition of the STAT-5 signalling pathway. The same reducing conditions had no effect on the proliferation of an IL-2-independent T cell clone, nor did they reduce disulfide bonds in IL-2 itself. We postulate that reduction of the Cys(183)-Cys(232) disulfide in CD132 inhibits IL-2 binding to the receptor complex. Published data show that the Cys(183)-Cys(232) disulfide bond is exposed at the surface of CD132 and in close contact with IL-2 and IL-4 in their respective receptor complexes. In addition, mutants in these Cys residues in human CD132 lead to immunodeficiency and loss of IL-2 binding. These results have wider implications for the regulation of cytokine receptors in general, as their activity can be modulated by a 'redox regulator' mechanism caused by the changes in the redox environment that occur during inflammation and activation of the immune system.

Use of expressed sequence tags as an alternative approach for the identification of Taenia solium metacestode excretion/secretion proteins

Background

Taenia solium taeniasis/cysticercosis is a zoonotic helminth infection mainly found in rural regions of Africa, Asia and Latin America. In endemic areas, diagnosis of cysticercosis largely depends on serology, but these methods have their drawbacks and require improvement. This implies better knowledge of the proteins secreted and excreted by the parasite. In a previous study, we used a custom protein database containing protein sequences from related helminths to identify T. solium metacestode excretion/secretion proteins. An alternative or complementary approach would be to use expressed sequence tags combined with BLAST and protein mapping to supercontigs of Echinococcus granulosus, a closely related cestode. In this study, we evaluate this approach and compare the results to those obtained in the previous study.

Findings

We report 297 proteins organized in 106 protein groups based on homology. Additional classification was done using Gene Ontology information on biological process and molecular function. Of the 106 protein groups, 58 groups were newly identified, while 48 groups confirmed previous findings. Blast2GO analysis revealed that the majority of the proteins were involved in catalytic activities and binding.

Conclusions

In this study, we used translated expressed sequence tags combined with BLAST and mapping strategies to both confirm and complement previous research. Our findings are comparable to recent studies on other helminth genera like Echinococcus, Schistosoma and Clonorchis, indicating similarities between helminth excretion/secretion proteomes.

Combining results of multiple search engines in proteomics

A crucial component of the analysis of shotgun proteomics datasets is the search engine, an algorithm that attempts to identify the peptide sequence from the parent molecular ion that produced each fragment ion spectrum in the dataset. There are many different search engines, both commercial and open source, each employing a somewhat different technique for spectrum identification. The set of high-scoring peptide-spectrum matches for a defined set of input spectra differs markedly among the various search engine results; individual engines each provide unique correct identifications among a core set of correlative identifications. This has led to the approach of combining the results from multiple search engines to achieve improved analysis of each dataset. Here we review the techniques and available software for combining the results of multiple search engines and briefly compare the relative performance of these techniques.

Current methods for global proteome identification

In a time frame of a few decades, protein identification went from laborious single protein identification to automated identification of entire proteomes. This shift was enabled by the emergence of peptide-centric, gel-free analyses, in particular the so-called shotgun approaches, which not only rely on extensive experiments, but also on cutting-edge data processing methods. The present review therefore provides an overview of a shotgun proteomics identification workflow, listing the state-of-the-art methods involved and software that implement these. The authors focus on freely available tools where possible. Finally, data analysis in the context of emerging across-omics studies will also be discussed briefly, where proteomics goes beyond merely delivering a list of protein accession numbers.

Phosphoinositide-specific phospholipase C β 1b (PI-PLCβ1b) interactome: affinity purification-mass spectrometry analysis of PI-PLCβ1b with nuclear protein

Two isoforms of inositide-dependent phospholipase C β1 (PI-PLCβ1) are generated by alternative splicing (PLCβ1a and PLCβ1b). Both isoforms are present within the nucleus, but in contrast to PLCβ1a, the vast majority of PLCβ1b is nuclear. In mouse erythroid leukemia cells, PI-PLCβ1 is involved in the regulation of cell division and the balance between cell proliferation and differentiation. It has been demonstrated that nuclear localization is crucial for the enzymatic function of PI-PLCβ1, although the mechanism by which this nuclear import occurs has never been fully characterized. The aim of this study was to characterize both the mechanism of nuclear localization and the molecular function of nuclear PI-PLCβ1 by identifying its interactome in Friend's erythroleukemia isolated nuclei, utilizing a procedure that coupled immuno-affinity purification with tandem mass spectrometry analysis. Using this procedure, 160 proteins were demonstrated to be in association with PI-PLCβ1b, some of which have been previously characterized, such as the splicing factor SRp20 (Srsf3) and Lamin B (Lmnb1). Co-immunoprecipitation analysis of selected proteins confirmed the data obtained via mass spectrometry. Of particular interest was the identification of the nuclear import proteins Kpna2, Kpna4, Kpnb1, Ran, and Rangap1, as well as factors involved in hematological malignancies and several anti-apoptotic proteins. These data give new insight into possible mechanisms of nuclear trafficking and functioning of this critical signaling molecule.

Data processing methods and quality control strategies for label-free LC-MS protein quantification

Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Extensive mass spectrometry-based analysis of the fission yeast proteome: the Schizosaccharomyces pombe PeptideAtlas

We report a high quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high resolution Orbitrap mass spectrometry, and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from r(s) >0.80 for proteins involved in translation to r(s) <0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and publicly available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth, and quantitative proteomic studies on this microorganism.

An automated pipeline for high-throughput label-free quantitative proteomics

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.

A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis

Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) and the other supporting hypothesis-driven (targeted) proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

Total and putative surface proteomics of malaria parasite salivary gland sporozoites

Malaria infections of mammals are initiated by the transmission of Plasmodium salivary gland sporozoites during an Anopheles mosquito vector bite. Sporozoites make their way through the skin and eventually to the liver, where they infect hepatocytes. Blocking this initial stage of infection is a promising malaria vaccine strategy. Therefore, comprehensively elucidating the protein composition of sporozoites will be invaluable in identifying novel targets for blocking infection. Previous efforts to identify the proteins expressed in Plasmodium mosquito stages were hampered by the technical difficulty of separating the parasite from its vector; without effective purifications, the large majority of proteins identified were of vector origin. Here we describe the proteomic profiling of highly purified salivary gland sporozoites from two Plasmodium species: human-infective Plasmodium falciparum and rodent-infective Plasmodium yoelii. The combination of improved sample purification and high mass accuracy mass spectrometry has facilitated the most complete proteome coverage to date for a pre-erythrocytic stage of the parasite. A total of 1991 P. falciparum sporozoite proteins and 1876 P. yoelii sporozoite proteins were identified, with >86% identified with high sequence coverage. The proteomic data were used to confirm the presence of components of three features critical for sporozoite infection of the mammalian host: the sporozoite motility and invasion apparatus (glideosome), sporozoite signaling pathways, and the contents of the apical secretory organelles. Furthermore, chemical labeling and identification of proteins on live sporozoites revealed previously uncharacterized complexity of the putative sporozoite surface-exposed proteome. Taken together, the data constitute the most comprehensive analysis to date of the protein expression of salivary gland sporozoites and reveal novel potential surface-exposed proteins that might be valuable targets for antibody blockage of infection.

Hydrophilic strong anion exchange (hSAX) chromatography for highly orthogonal peptide separation of complex proteomes

Due to its compatibility and orthogonality to reversed phase (RP) liquid chromatography (LC) separation, ion exchange chromatography, and mainly strong cation exchange (SCX), has often been the first choice in multidimensional LC experiments in proteomics. Here, we have tested the ability of three strong anion exchanger (SAX) columns differing in their hydrophobicity to fractionate RAW264.7 macrophage cell lysate. IonPac AS24, a strong anion exchange material with ultralow hydrophobicity, demonstrated to be superior to other materials by fractionation and separation of tryptic peptides from both a mixture of 6 proteins as well as mouse cell lysate. The chromatography displayed very high orthogonality and high robustness depending on the hydrophilicity of column chemistry, which we termed hydrophilic strong anion exchange (hSAX). Mass spectrometry analysis of 34 SAX fractions from RAW264.7 macrophage cell lysate digest resulted in an identification of 9469 unique proteins and 126318 distinct peptides in one week of instrument time. Moreover, when compared to an optimized high pH/low pH RP separation approach, the method presented here raised the identification of proteins and peptides by 10 and 28%, respectively. This novel hSAX approach provides robust, reproducible, and highly orthogonal separation of complex protein digest samples for deep coverage proteome analysis.

A non-parametric cutout index for robust evaluation of identified proteins

This paper proposes a novel, automated method for evaluating sets of proteins identified using mass spectrometry. The remaining peptide-spectrum match score distributions of protein sets are compared to an empirical absent peptide-spectrum match score distribution, and a Bayesian non-parametric method reminiscent of the Dirichlet process is presented to accurately perform this comparison. Thus, for a given protein set, the process computes the likelihood that the proteins identified are correctly identified. First, the method is used to evaluate protein sets chosen using different protein-level false discovery rate (FDR) thresholds, assigning each protein set a likelihood. The protein set assigned the highest likelihood is used to choose a non-arbitrary protein-level FDR threshold. Because the method can be used to evaluate any protein identification strategy (and is not limited to mere comparisons of different FDR thresholds), we subsequently use the method to compare and evaluate multiple simple methods for merging peptide evidence over replicate experiments. The general statistical approach can be applied to other types of data (e.g. RNA sequencing) and generalizes to multivariate problems.

Determining the calibration of confidence estimation procedures for unique peptides in shotgun proteomics

The analysis of a shotgun proteomics experiment results in a list of peptide-spectrum matches (PSMs) in which each fragmentation spectrum has been matched to a peptide in a database. Subsequently, most protein inference algorithms rank peptides according to the best-scoring PSM for each peptide. However, there is disagreement in the scientific literature on the best method to assess the statistical significance of the resulting peptide identifications. Here, we use a previously described calibration protocol to evaluate the accuracy of three different peptide-level statistical confidence estimation procedures: the classical Fisher's method, and two complementary procedures that estimate significance, respectively, before and after selecting the top-scoring PSM for each spectrum. Our experiments show that the latter method, which is employed by MaxQuant and Percolator, produces the most accurate, well-calibrated results.

Proteomic analysis of Taenia solium metacestode excretion-secretion proteins

The metacestode larval stage of Taenia solium is the causal agent of a zoonotic disease called cysticercosis. The disease has an important impact on pork trade (due to porcine cysticercosis) and public health (due to human neurocysticercosis). In order to improve the current diagnostic tools and to get a better understanding of the interaction between T. solium metacestodes and their host, there is a need for more information about the proteins that are released by the parasite. In this study, we used protein sequences from different helminths, 1DE, reversed-phase LC, and MS/MS to analyze the excretion-secretion proteins produced by T. solium metacestodes from infected pigs. This is the first report of the T. solium metacestode excretion-secretion proteome. We report 76 proteins including 27 already described T. solium proteins, 17 host proteins and 32 proteins likely to be of T. solium origin, but identified using sequences from other helminths.

Recent progress using systems biology approaches to better understand molecular mechanisms of immunity

The immune system is composed of multiple dynamic molecular and cellular networks, the complexity of which has been revealed by decades of exacting reductionist research. However, understanding of the immune system sufficient to anticipate its response to novel perturbations requires a more integrative or systems approach to immunology. While methods for unbiased high-throughput data acquisition and computational integration of the resulting datasets are still relatively new, they have begun to substantially enhance our understanding of immunological phenomena. Such approaches have expanded our view of interconnected signaling and transcriptional networks and have highlighted the function of non-linear processes such as spatial regulation and feedback loops. In addition, advances in single cell measurement technology have demonstrated potential sources and functions of response heterogeneity in system behavior. The success of the studies reviewed here often depended upon integration of one or more systems biology approaches with more traditional methods. We hope these examples will inspire a broader range of immunologists to probe questions in a quantitative and integrated manner, advancing collective efforts to understand the immune "system".

Mapping the phosphoproteome of influenza A and B viruses by mass spectrometry

Protein phosphorylation is a common post-translational modification in eukaryotic cells and has a wide range of functional effects. Here, we used mass spectrometry to search for phosphorylated residues in all the proteins of influenza A and B viruses--to the best of our knowledge, the first time such a comprehensive approach has been applied to a virus. We identified 36 novel phosphorylation sites, as well as confirming 3 previously-identified sites. N-terminal processing and ubiquitination of viral proteins was also detected. Phosphorylation was detected in the polymerase proteins (PB2, PB1 and PA), glycoproteins (HA and NA), nucleoprotein (NP), matrix protein (M1), ion channel (M2), non-structural protein (NS1) and nuclear export protein (NEP). Many of the phosphorylation sites detected were conserved between influenza virus genera, indicating the fundamental importance of phosphorylation for all influenza viruses. Their structural context indicates roles for phosphorylation in regulating viral entry and exit (HA and NA); nuclear localisation (PB2, M1, NP, NS1 and, through NP and NEP, of the viral RNA genome); and protein multimerisation (NS1 dimers, M2 tetramers and NP oligomers). Using reverse genetics we show that for NP of influenza A viruses phosphorylation sites in the N-terminal NLS are important for viral growth, whereas mutating sites in the C-terminus has little or no effect. Mutating phosphorylation sites in the oligomerisation domains of NP inhibits viral growth and in some cases transcription and replication of the viral RNA genome. However, constitutive phosphorylation of these sites is not optimal. Taken together, the conservation, structural context and functional significance of phosphorylation sites implies a key role for phosphorylation in influenza biology. By identifying phosphorylation sites throughout the proteomes of influenza A and B viruses we provide a framework for further study of phosphorylation events in the viral life cycle and suggest a range of potential antiviral targets.

Current algorithmic solutions for peptide-based proteomics data generation and identification

Peptide-based proteomic data sets are ever increasing in size and complexity. These data sets provide computational challenges when attempting to quickly analyze spectra and obtain correct protein identifications. Database search and de novo algorithms must consider high-resolution MS/MS spectra and alternative fragmentation methods. Protein inference is a tricky problem when analyzing large data sets of degenerate peptide identifications. Combining multiple algorithms for improved peptide identification puts significant strain on computational systems when investigating large data sets. This review highlights some of the recent developments in peptide and protein identification algorithms for analyzing shotgun mass spectrometry data when encountering the aforementioned hurdles. Also explored are the roles that analytical pipelines, public spectral libraries, and cloud computing play in the evolution of peptide-based proteomics.

Characterization of a distinct population of circulating human non-adherent endothelial forming cells and their recruitment via intercellular adhesion molecule-3

Circulating vascular progenitor cells contribute to the pathological vasculogenesis of cancer whilst on the other hand offer much promise in therapeutic revascularization in post-occlusion intervention in cardiovascular disease. However, their characterization has been hampered by the many variables to produce them as well as their described phenotypic and functional heterogeneity. Herein we have isolated, enriched for and then characterized a human umbilical cord blood derived CD133⁺ population of non-adherent endothelial forming cells (naEFCs) which expressed the hematopoietic progenitor cell markers (CD133, CD34, CD117, CD90 and CD38) together with mature endothelial cell markers (VEGFR2, CD144 and CD31). These cells also expressed low levels of CD45 but did not express the lymphoid markers (CD3, CD4, CD8) or myeloid markers (CD11b and CD14) which distinguishes them from ‘early’ endothelial progenitor cells (EPCs). Functional studies demonstrated that these naEFCs (i) bound Ulex europaeus lectin, (ii) demonstrated acetylated-low density lipoprotein uptake, (iii) increased vascular cell adhesion molecule (VCAM-1) surface expression in response to tumor necrosis factor and (iv) in co-culture with mature endothelial cells increased the number of tubes, tubule branching and loops in a 3-dimensional in vitro matrix. More importantly, naEFCs placed in vivo generated new lumen containing vasculature lined by CD144 expressing human endothelial cells (ECs). Extensive genomic and proteomic analyses of the naEFCs showed that intercellular adhesion molecule (ICAM)-3 is expressed on their cell surface but not on mature endothelial cells. Furthermore, functional analysis demonstrated that ICAM-3 mediated the rolling and adhesive events of the naEFCs under shear stress. We suggest that the distinct population of naEFCs identified and characterized here represents a new valuable therapeutic target to control aberrant vasculogenesis.

Computational approaches to protein inference in shotgun proteomics

Shotgun proteomics has recently emerged as a powerful approach to characterizing proteomes in biological samples. Its overall objective is to identify the form and quantity of each protein in a high-throughput manner by coupling liquid chromatography with tandem mass spectrometry. As a consequence of its high throughput nature, shotgun proteomics faces challenges with respect to the analysis and interpretation of experimental data. Among such challenges, the identification of proteins present in a sample has been recognized as an important computational task. This task generally consists of (1) assigning experimental tandem mass spectra to peptides derived from a protein database, and (2) mapping assigned peptides to proteins and quantifying the confidence of identified proteins. Protein identification is fundamentally a statistical inference problem with a number of methods proposed to address its challenges. In this review we categorize current approaches into rule-based, combinatorial optimization and probabilistic inference techniques, and present them using integer programming and Bayesian inference frameworks. We also discuss the main challenges of protein identification and propose potential solutions with the goal of spurring innovative research in this area.

Cloud CPFP: a shotgun proteomics data analysis pipeline using cloud and high performance computing

We have extended the functionality of the Central Proteomics Facilities Pipeline (CPFP) to allow use of remote cloud and high performance computing (HPC) resources for shotgun proteomics data processing. CPFP has been modified to include modular local and remote scheduling for data processing jobs. The pipeline can now be run on a single PC or server, a local cluster, a remote HPC cluster, and/or the Amazon Web Services (AWS) cloud. We provide public images that allow easy deployment of CPFP in its entirety in the AWS cloud. This significantly reduces the effort necessary to use the software, and allows proteomics laboratories to pay for compute time ad hoc, rather than obtaining and maintaining expensive local server clusters. Alternatively the Amazon cloud can be used to increase the throughput of a local installation of CPFP as necessary. We demonstrate that cloud CPFP allows users to process data at higher speed than local installations but with similar cost and lower staff requirements. In addition to the computational improvements, the web interface to CPFP is simplified, and other functionalities are enhanced. The software is under active development at two leading institutions and continues to be released under an open-source license at http://cpfp.sourceforge.net.

Addressing statistical biases in nucleotide-derived protein databases for proteogenomic search strategies

Proteogenomics has the potential to advance genome annotation through high quality peptide identifications derived from mass spectrometry experiments, which demonstrate a given gene or isoform is expressed and translated at the protein level. This can advance our understanding of genome function, discovering novel genes and gene structure that have not yet been identified or validated. Because of the high-throughput shotgun nature of most proteomics experiments, it is essential to carefully control for false positives and prevent any potential misannotation. A number of statistical procedures to deal with this are in wide use in proteomics, calculating false discovery rate (FDR) and posterior error probability (PEP) values for groups and individual peptide spectrum matches (PSMs). These methods control for multiple testing and exploit decoy databases to estimate statistical significance. Here, we show that database choice has a major effect on these confidence estimates leading to significant differences in the number of PSMs reported. We note that standard target:decoy approaches using six-frame translations of nucleotide sequences, such as assembled transcriptome data, apparently underestimate the confidence assigned to the PSMs. The source of this error stems from the inflated and unusual nature of the six-frame database, where for every target sequence there exists five "incorrect" targets that are unlikely to code for protein. The attendant FDR and PEP estimates lead to fewer accepted PSMs at fixed thresholds, and we show that this effect is a product of the database and statistical modeling and not the search engine. A variety of approaches to limit database size and remove noncoding target sequences are examined and discussed in terms of the altered statistical estimates generated and PSMs reported. These results are of importance to groups carrying out proteogenomics, aiming to maximize the validation and discovery of gene structure in sequenced genomes, while still controlling for false positives.

Longitudinal analysis of maternal plasma apolipoproteins in pregnancy: a targeted proteomics approach

Minimally invasive diagnostic tests are needed in obstetrics to identify women at risk for complications during delivery. The apolipoproteins fluctuate in complexity and abundance in maternal plasma during pregnancy and could be incorporated into a blood test to evaluate this risk. The objective of this study was to examine the relative plasma concentrations of apolipoproteins and their biochemically modified subtypes (i.e. proteolytically processed, sialylated, cysteinylated, dimerized) over gestational time using a targeted mass spectrometry approach. Relative abundance of modified and unmodified apolipoproteins A-I, A-II, C-I, C-II, and C-III was determined by surface-enhanced laser desorption/ionization-time of flight-mass spectrometry in plasma prospectively collected from 11 gravidas with uncomplicated pregnancies at 4-5 gestational time points per patient. Apolipoproteins were readily identifiable by spectral pattern. Apo C-III(2) and Apo C-III(1) (doubly and singly sialylated Apo C-III subtypes) increased with gestational age (r(2)>0.8). Unmodified Apo A-II, Apo C-I, and Apo C-III(0) showed no correlation (r(2) = 0.01-0.1). Pro-Apo C-II did not increase significantly until third trimester (140 ± 13% of first trimester), but proteolytically cleaved, mature Apo C-II increased in late pregnancy (702 ± 130% of first trimester). Mature Apo C-II represented 6.7 ± 0.9% of total Apo C-II in early gestation and increased to 33 ± 4.5% in third trimester. A label-free, semiquantitative targeted proteomics approach was developed using LTQ-Orbitrap mass spectrometry to confirm the relative quantitative differences observed by surface-enhanced laser desorption/ionization-time of flight-mass spectrometry in Apo C-III and Apo C-II isoforms between first and third trimesters. Targeted apolipoprotein screening was applied to a cohort of term and preterm patients. Modified Apo A-II isoforms were significantly elevated in plasma from mothers who delivered prematurely relative to term controls (p = 0.02). These results support a role for targeted proteomics profiling approaches in monitoring healthy pregnancies and assessing risk of adverse obstetric outcomes.

Peptide identification by tandem mass spectrometry with alternate fragmentation modes

The high-throughput nature of proteomics mass spectrometry is enabled by a productive combination of data acquisition protocols and the computational tools used to interpret the resulting spectra. One of the key components in mainstream protocols is the generation of tandem mass (MS/MS) spectra by peptide fragmentation using collision induced dissociation, the approach currently used in the large majority of proteomics experiments to routinely identify hundreds to thousands of proteins from single mass spectrometry runs. Complementary to these, alternative peptide fragmentation methods such as electron capture/transfer dissociation and higher-energy collision dissociation have consistently achieved significant improvements in the identification of certain classes of peptides, proteins, and post-translational modifications. Recognizing these advantages, mass spectrometry instruments now conveniently support fine-tuned methods that automatically alternate between peptide fragmentation modes for either different types of peptides or for acquisition of multiple MS/MS spectra from each peptide. But although these developments have the potential to substantially improve peptide identification, their routine application requires corresponding adjustments to the software tools and procedures used for automated downstream processing. This review discusses the computational implications of alternative and alternate modes of MS/MS peptide fragmentation and addresses some practical aspects of using such protocols for identification of peptides and post-translational modifications.

Transcriptome and proteome dynamics of a light-dark synchronized bacterial cell cycle

BACKGROUND

Growth of the ocean's most abundant primary producer, the cyanobacterium Prochlorococcus, is tightly synchronized to the natural 24-hour light-dark cycle. We sought to quantify the relationship between transcriptome and proteome dynamics that underlie this obligate photoautotroph's highly choreographed response to the daily oscillation in energy supply.

METHODOLOGY/PRINCIPAL FINDINGS

Using RNA-sequencing transcriptomics and mass spectrometry-based quantitative proteomics, we measured timecourses of paired mRNA-protein abundances for 312 genes every 2 hours over a light-dark cycle. These temporal expression patterns reveal strong oscillations in transcript abundance that are broadly damped at the protein level, with mRNA levels varying on average 2.3 times more than the corresponding protein. The single strongest observed protein-level oscillation is in a ribonucleotide reductase, which may reflect a defense strategy against phage infection. The peak in abundance of most proteins also lags that of their transcript by 2-8 hours, and the two are completely antiphase for some genes. While abundant antisense RNA was detected, it apparently does not account for the observed divergences between expression levels. The redirection of flux through central carbon metabolism from daytime carbon fixation to nighttime respiration is associated with quite small changes in relative enzyme abundances.

CONCLUSIONS/SIGNIFICANCE

Our results indicate that expression responses to periodic stimuli that are common in natural ecosystems (such as the diel cycle) can diverge significantly between the mRNA and protein levels. Protein expression patterns that are distinct from those of cognate mRNA have implications for the interpretation of transcriptome and metatranscriptome data in terms of cellular metabolism and its biogeochemical impact.

Inference and validation of protein identifications

Discovery or shotgun proteomics has emerged as the most powerful technique to comprehensively map out a proteome. Reconstruction of protein identities from the raw mass spectrometric data constitutes a cornerstone of any shotgun proteomics workflow. The inherent uncertainty of mass spectrometric data and the complexity of a proteome render protein inference and the statistical validation of protein identifications a non-trivial task, still being a subject of ongoing research. This review aims to survey the different conceptual approaches to the different tasks of inferring and statistically validating protein identifications and to discuss their implications on the scope of proteome exploration.

Mass Spectrometry-based Proteomics and Peptidomics for Systems Biology and Biomarker Discovery

The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology. Proteomics technologies have evolved to produce large datasets of proteins or peptides involved in various biological and disease progression processes producing testable hypothesis for complex biological questions. This review provides an introduction and insight to relevant topics in proteomics and peptidomics including biological material selection, sample preparation, separation techniques, peptide fragmentation, post-translation modifications, quantification, bioinformatics, and biomarker discovery and validation. In addition, current literature and remaining challenges and emerging technologies for proteomics and peptidomics are presented.

The (15)N isotope effect as a means for correlating phenotypic alterations and affected pathways in a trait anxiety mouse model

Stable isotope labeling techniques hold great potential for accurate quantitative proteomics comparisons by MS. To investigate the effect of stable isotopes in vivo, we metabolically labeled high anxiety-related behavior (HAB) mice with the heavy nitrogen isotope (15)N. (15)N-labeled HAB mice exhibited behavioral alterations compared to unlabeled ((14)N) HAB mice in their depression-like phenotype. To correlate behavioral alterations with changes on the molecular level, we explored the (15)N isotope effect on the brain proteome by comparing protein expression levels between (15)N-labeled and (14)N HAB mouse brains using quantitative MS. By implementing two complementary in silico pathway analysis approaches, we were able to identify altered networks in (15)N-labeled HAB mice, including major metabolic pathways such as the tricarboxylic acid (TCA) cycle and oxidative phosphorylation. Here, we discuss the affected pathways with regard to their relevance for the behavioral phenotype and critically assess the utility of exploiting the (15)N isotope effect for correlating phenotypic and molecular alterations.

Prediction of missed proteolytic cleavages for the selection of surrogate peptides for quantitative proteomics

Quantitative proteomics experiments are usually performed using proteolytic peptides as surrogates for their parent proteins, inferring protein amounts from peptide-level quantitation. This process is frequently dependent on complete digestion of the parent protein to its limit peptides so that their signal is truly representative. Unfortunately, proteolysis is often incomplete, and missed cleavage peptides are frequently produced that are unlikely to be optimal surrogates for quantitation, particularly for label-mediated approaches seeking to derive absolute values. We have generated a predictive computational tool that is able to predict which candidate proteolytic peptide bonds are likely to be missed by the standard enzyme trypsin. Our cross-validated prediction tool uses support vector machines and achieves high accuracy in excess of 0.94 precision (PPV), with attendant high sensitivity of 0.79, across multiple proteomes. We believe this is a useful tool for selecting candidate quantotypic peptides, seeking to minimize likely loss owing to missed cleavage, which will be a boon for quantitative proteomic pipelines as well as other areas of proteomics. Our results are discussed in the context of recent results examining the kinetics of missed cleavages in proteomic digestion protocols, and show agreement with observed experimental trends. The software has been made available at http://king.smith.man.ac.uk/mcpred .

Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer

Kinase inhibitors have limited success in cancer treatment because tumors circumvent their action. Using a quantitative proteomics approach, we assessed kinome activity in response to MEK inhibition in triple-negative breast cancer (TNBC) cells and genetically engineered mice (GEMMs). MEK inhibition caused acute ERK activity loss, resulting in rapid c-Myc degradation that induced expression and activation of several receptor tyrosine kinases (RTKs). RNAi knockdown of ERK or c-Myc mimicked RTK induction by MEK inhibitors, and prevention of proteasomal c-Myc degradation blocked kinome reprogramming. MEK inhibitor-induced RTK stimulation overcame MEK2 inhibition, but not MEK1 inhibition, reactivating ERK and producing drug resistance. The C3Tag GEMM for TNBC similarly induced RTKs in response to MEK inhibition. The inhibitor-induced RTK profile suggested a kinase inhibitor combination therapy that produced GEMM tumor apoptosis and regression where single agents were ineffective. This approach defines mechanisms of drug resistance, allowing rational design of combination therapies for cancer.

Targeted proteome investigation via selected reaction monitoring mass spectrometry

Due to the enormous complexity of proteomes which constitute the entirety of protein species expressed by a certain cell or tissue, proteome-wide studies performed in discovery mode are still limited in their ability to reproducibly identify and quantify all proteins present in complex biological samples. Therefore, the targeted analysis of informative subsets of the proteome has been beneficial to generate reproducible data sets across multiple samples. Here we review the repertoire of antibody- and mass spectrometry (MS) -based analytical tools which is currently available for the directed analysis of predefined sets of proteins. The topics of emphasis for this review are Selected Reaction Monitoring (SRM) mass spectrometry, emerging tools to control error rates in targeted proteomic experiments, and some representative examples of applications. The ability to cost- and time-efficiently generate specific and quantitative assays for large numbers of proteins and posttranslational modifications has the potential to greatly expand the range of targeted proteomic coverage in biological studies. This article is part of a Special Section entitled: Understanding genome regulation and genetic diversity by mass spectrometry.

Analysis of high accuracy, quantitative proteomics data in the MaxQB database

MS-based proteomics generates rapidly increasing amounts of precise and quantitative information. Analysis of individual proteomic experiments has made great strides, but the crucial ability to compare and store information across different proteome measurements still presents many challenges. For example, it has been difficult to avoid contamination of databases with low quality peptide identifications, to control for the inflation in false positive identifications when combining data sets, and to integrate quantitative data. Although, for example, the contamination with low quality identifications has been addressed by joint analysis of deposited raw data in some public repositories, we reasoned that there should be a role for a database specifically designed for high resolution and quantitative data. Here we describe a novel database termed MaxQB that stores and displays collections of large proteomics projects and allows joint analysis and comparison. We demonstrate the analysis tools of MaxQB using proteome data of 11 different human cell lines and 28 mouse tissues. The database-wide false discovery rate is controlled by adjusting the project specific cutoff scores for the combined data sets. The 11 cell line proteomes together identify proteins expressed from more than half of all human genes. For each protein of interest, expression levels estimated by label-free quantification can be visualized across the cell lines. Similarly, the expression rank order and estimated amount of each protein within each proteome are plotted. We used MaxQB to calculate the signal reproducibility of the detected peptides for the same proteins across different proteomes. Spearman rank correlation between peptide intensity and detection probability of identified proteins was greater than 0.8 for 64% of the proteome, whereas a minority of proteins have negative correlation. This information can be used to pinpoint false protein identifications, independently of peptide database scores. The information contained in MaxQB, including high resolution fragment spectra, is accessible to the community via a user-friendly web interface at http://www.biochem.mpg.de/maxqb.

Profiling protease activities by dynamic proteomics workflows

Proteases play prominent roles in many physiological processes and the pathogenesis of various diseases, which makes them interesting drug targets. To fully understand the functional role of proteases in these processes, it is necessary to characterize the target specificity of the enzymes, identify endogenous substrates and cleavage products as well as protease activators and inhibitors. The complexity of these proteolytic networks presents a considerable analytic challenge. To comprehensively characterize these systems, quantitative methods that capture the spatial and temporal distributions of the network members are needed. Recently, activity-based workflows have come to the forefront to tackle the dynamic aspects of proteolytic processing networks in vitro, ex vivo and in vivo. In this review, we will discuss how mass spectrometry-based approaches can be used to gain new insights into protease biology by determining substrate specificities, profiling the activity-states of proteases, monitoring proteolysis in vivo, measuring reaction kinetics and defining in vitro and in vivo proteolytic events. In addition, examples of future aspects of protease research that go beyond mass spectrometry-based applications are given.

Nanospray FAIMS fractionation provides significant increases in proteome coverage of unfractionated complex protein digests

High-field asymmetric waveform ion mobility spectrometry (FAIMS) is an atmospheric pressure ion mobility technique that can be used to reduce sample complexity and increase dynamic range in tandem mass spectrometry experiments. FAIMS fractionates ions in the gas-phase according to characteristic differences in mobilities in electric fields of different strengths. Undesired ion species such as solvated clusters and singly charged chemical background ions can be prevented from reaching the mass analyzer, thus decreasing chemical noise. To date, there has been limited success using the commercially available Thermo Fisher FAIMS device with both standard ESI and nanoLC-MS. We have modified a Thermo Fisher electrospray source to accommodate a fused silica pulled tip capillary column for nanospray ionization, which will enable standard laboratories access to FAIMS technology. Our modified source allows easily obtainable stable spray at flow rates of 300 nL/min when coupled with FAIMS. The modified electrospray source allows the use of sheath gas, which provides a fivefold increase in signal obtained when nanoLC is coupled to FAIMS. In this work, nanoLC-FAIMS-MS and nanoLC-MS were compared by analyzing a tryptic digest of a 1:1 mixture of SILAC-labeled haploid and diploid yeast to demonstrate the performance of nanoLC-FAIMS-MS, at different compensation voltages, for post-column fractionation of complex protein digests. The effective dynamic range more than doubled when FAIMS was used. In total, 10,377 unique stripped peptides and 1649 unique proteins with SILAC ratios were identified from the combined nanoLC-FAIMS-MS experiments, compared with 6908 unique stripped peptides and 1003 unique proteins with SILAC ratios identified from the combined nanoLC-MS experiments. This work demonstrates how a commercially available FAIMS device can be combined with nanoLC to improve proteome coverage in shotgun and targeted type proteomics experiments.

Mass spectrometry-based proteomics for systems biology

Mass spectrometry (MS)-based proteomics has significantly contributed to the development of systems biology, a new paradigm for the life sciences in which biological processes are addressed in terms of dynamic networks of interacting molecules. Because of its advanced analytical capabilities, MS-based proteomics has been used extensively to identify the components of biological systems, and it is the method of choice to consistently quantify the effects of network perturbation in time and space. Herein, we review recent contributions of MS to systems biology and discuss several examples that illustrate the importance of mass spectrometry to elucidate the components and interactions of molecular networks.

Labile disulfide bonds are common at the leucocyte cell surface

Redox conditions change in events such as immune and platelet activation, and during viral infection, but the biochemical consequences are not well characterized. There is evidence that some disulfide bonds in membrane proteins are labile while others that are probably structurally important are not exposed at the protein surface. We have developed a proteomic/mass spectrometry method to screen for and identify non-structural, redox-labile disulfide bonds in leucocyte cell-surface proteins. These labile disulfide bonds are common, with several classes of proteins being identified and around 30 membrane proteins regularly identified under different reducing conditions including using enzymes such as thioredoxin. The proteins identified include integrins, receptors, transporters and cell-cell recognition proteins. In many cases, at least one cysteine residue was identified by mass spectrometry as being modified by the reduction process. In some cases, functional changes are predicted (e.g. in integrins and cytokine receptors) but the scale of molecular changes in membrane proteins observed suggests that widespread effects are likely on many different types of proteins including enzymes, adhesion proteins and transporters. The results imply that membrane protein activity is being modulated by a 'redox regulator' mechanism.