Absolute quantification of microbial proteomes at different states by directed mass spectrometry

Phosphoproteomics data classify hematological cancer cell lines according to tumor type and sensitivity to kinase inhibitors

Background

Tumor classification based on their predicted responses to kinase inhibitors is a major goal for advancing targeted personalized therapies. Here, we used a phosphoproteomic approach to investigate biological heterogeneity across hematological cancer cell lines including acute myeloid leukemia, lymphoma, and multiple myeloma.

Results

Mass spectrometry was used to quantify 2,000 phosphorylation sites across three acute myeloid leukemia, three lymphoma, and three multiple myeloma cell lines in six biological replicates. The intensities of the phosphorylation sites grouped these cancer cell lines according to their tumor type. In addition, a phosphoproteomic analysis of seven acute myeloid leukemia cell lines revealed a battery of phosphorylation sites whose combined intensities correlated with the growth-inhibitory responses to three kinase inhibitors with remarkable correlation coefficients and fold changes (> 100 between the most resistant and sensitive cells). Modeling based on regression analysis indicated that a subset of phosphorylation sites could be used to predict response to the tested drugs. Quantitative analysis of phosphorylation motifs indicated that resistant and sensitive cells differed in their patterns of kinase activities, but, interestingly, phosphorylations correlating with responses were not on members of the pathway being targeted; instead, these mainly were on parallel kinase pathways.

Conclusion

This study reveals that the information on kinase activation encoded in phosphoproteomics data correlates remarkably well with the phenotypic responses of cancer cells to compounds that target kinase signaling and could be useful for the identification of novel markers of resistance or sensitivity to drugs that target the signaling network.

Parallel exploitation of diverse host nutrients enhances Salmonella virulence

Pathogen access to host nutrients in infected tissues is fundamental for pathogen growth and virulence, disease progression, and infection control. However, our understanding of this crucial process is still rather limited because of experimental and conceptual challenges. Here, we used proteomics, microbial genetics, competitive infections, and computational approaches to obtain a comprehensive overview of Salmonella nutrition and growth in a mouse typhoid fever model. The data revealed that Salmonella accessed an unexpectedly diverse set of at least 31 different host nutrients in infected tissues but the individual nutrients were available in only scarce amounts. Salmonella adapted to this situation by expressing versatile catabolic pathways to simultaneously exploit multiple host nutrients. A genome-scale computational model of Salmonella in vivo metabolism based on these data was fully consistent with independent large-scale experimental data on Salmonella enzyme quantities, and correctly predicted 92% of 738 reported experimental mutant virulence phenotypes, suggesting that our analysis provided a comprehensive overview of host nutrient supply, Salmonella metabolism, and Salmonella growth during infection. Comparison of metabolic networks of other pathogens suggested that complex host/pathogen nutritional interfaces are a common feature underlying many infectious diseases.

Genotype-phenotype relationships in light of a modular protein interaction landscape

Recent progress in genomic sequencing has revealed genotype-phenotype information of enormous complexity and challenges earlier hypotheses on how phenotypes emerge from altered gene structures. The field of proteomics has advanced in parallel and offers promising new concepts for a modern interpretation of complex and nonlinear genotype-phenotype relationships. We are beginning to decipher global proteome organization with increasing throughput and accuracy. These efforts revealed a highly modular organization of the protein landscape. Here we discuss the challenges and implications emerging from a modular protein landscape for a better understanding of complex genotype-phenotype patterns.

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.

Systematic proteomic analysis identifies β-site amyloid precursor protein cleaving enzyme 2 and 1 (BACE2 and BACE1) substrates in pancreatic β-cells

Expansion of functional islet β-cell mass is a physiological process to compensate for increased insulin demand. Deficiency or pharmacological inhibition of the plasma membrane protease BACE2 enhances pancreatic β-cell function and proliferation, and therefore BACE2 is a putative target for the therapeutic intervention under conditions of β-cell loss and dysfunction. To gain a molecular understanding of BACE2 function, we performed a systematic and quantitative proteomic analysis to map the natural substrate repertoire of BACE2 and its homologue BACE1 in β-cells. Loss- and gain-of-function studies of in vitro and in vivo models identified specific and functionally heterogeneous targets. Our analysis revealed non-redundant roles of BACE1/2 in ectodomain shedding with BACE1 regulating a broader and BACE2 a more distinct set of β-cell-enriched substrates including two proteins of the seizure 6 protein family (SEZ6L and SEZ6L2). Lastly, our study provides insights into the global β-cell sheddome and secretome, an important prerequisite to uncover novel mechanisms contributing to β-cell homeostasis and a resource for therapeutic target and biomarker discoveries.

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.

Quantitative analysis of fission yeast transcriptomes and proteomes in proliferating and quiescent cells

Data on absolute molecule numbers will empower the modeling, understanding, and comparison of cellular functions and biological systems. We quantified transcriptomes and proteomes in fission yeast during cellular proliferation and quiescence. This rich resource provides the first comprehensive reference for all RNA and most protein concentrations in a eukaryote under two key physiological conditions. The integrated data set supports quantitative biology and affords unique insights into cell regulation. Although mRNAs are typically expressed in a narrow range above 1 copy/cell, most long, noncoding RNAs, except for a distinct subset, are tightly repressed below 1 copy/cell. Cell-cycle-regulated transcription tunes mRNA numbers to phase-specific requirements but can also bring about more switch-like expression. Proteins greatly exceed mRNAs in abundance and dynamic range, and concentrations are regulated to functional demands. Upon transition to quiescence, the proteome changes substantially, but, in stark contrast to mRNAs, proteins do not uniformly decrease but scale with cell volume.

Determining protein concentrations of the human ventricular proteome

Proteomics is mostly used for measurements of relative differences in protein concentrations. Although such analyses are meaningful for comparing differences between two and more conditions, they do not directly provide details on the absolute protein concentrations within a system. Now, proteomics is heading more towards absolute quantitative strategies with results being expressed in copies/cell or ng/mg tissue. In the cardiac context, such quantitative information is crucial for (1) evaluating the feasibility of selecting a certain protein as potential novel drug target, (2) the expected concentration excreted into the circulation when selecting a biomarker, and (3) to build a model of cardiac function at the molecular level. At the same time, by mass spectrometry-based proteomics, a wealth of spectral information is gathered that can be used to evaluate protein levels of a select set of novel disease-altered proteins using, for instance, single reaction monitoring. Here we describe how to build a quantitative map of the human left ventricular proteome using a simple yet effective mass spectrometry-based spectral count method.

Immunity to intracellular Salmonella depends on surface-associated antigens

Invasive Salmonella infection is an important health problem that is worsening because of rising antimicrobial resistance and changing Salmonella serovar spectrum. Novel vaccines with broad serovar coverage are needed, but suitable protective antigens remain largely unknown. Here, we tested 37 broadly conserved Salmonella antigens in a mouse typhoid fever model, and identified antigen candidates that conferred partial protection against lethal disease. Antigen properties such as high in vivo abundance or immunodominance in convalescent individuals were not required for protectivity, but all promising antigen candidates were associated with the Salmonella surface. Surprisingly, this was not due to superior immunogenicity of surface antigens compared to internal antigens as had been suggested by previous studies and novel findings for CD4 T cell responses to model antigens. Confocal microscopy of infected tissues revealed that many live Salmonella resided alone in infected host macrophages with no damaged Salmonella releasing internal antigens in their vicinity. In the absence of accessible internal antigens, detection of these infected cells might require CD4 T cell recognition of Salmonella surface-associated antigens that could be processed and presented even from intact Salmonella. In conclusion, our findings might pave the way for development of an efficacious Salmonella vaccine with broad serovar coverage, and suggest a similar crucial role of surface antigens for immunity to both extracellular and intracellular pathogens.

Salmonella infections cause extensive morbidity and mortality worldwide. A vaccine that prevents systemic Salmonella infections is urgently needed but suitable antigens remain largely unknown. In this study we identified several antigen candidates that mediated protective immunity to Salmonella in a mouse typhoid fever model. Interestingly, all these antigens were associated with the Salmonella surface. This suggested that similar antigen properties might be relevant for CD4 T cell dependent immunity to intracellular pathogens like Salmonella, as for antibody-dependent immunity to extracellular pathogens. Detailed analysis revealed that Salmonella surface antigens were not generally more immunogenic compared to internal antigens. However, internal antigens were inaccessible for CD4 T cell recognition of a substantial number of infected host cells that contained exclusively live intact Salmonella. Together, these results might pave the way for development of an efficacious Salmonella vaccine, and provide a basis to facilitate antigen identification for Salmonella and possibly other intracellular pathogens.

Large-scale quantitative assessment of different in-solution protein digestion protocols reveals superior cleavage efficiency of tandem Lys-C/trypsin proteolysis over trypsin digestion

The complete and specific proteolytic cleavage of protein samples into peptides is crucial for the success of every shotgun LC-MS/MS experiment. In particular, popular peptide-based label-free and targeted mass spectrometry approaches rely on efficient generation of fully cleaved peptides to ensure accurate and sensitive protein quantification. In contrast to previous studies, we globally and quantitatively assessed the efficiency of different digestion strategies using a yeast cell lysate, label-free quantification, and statistical analysis. Digestion conditions include double tryptic, surfactant-assisted, and tandem-combinatorial Lys-C/trypsin digestion. In comparison to tryptic digests, Lys-C/trypsin digests were found most efficient to yield fully cleaved peptides while reducing the abundance of miscleaved peptides. Subsequent sequence context analysis revealed improved digestion performances of Lys-C/trypsin for miscleaved sequence stretches flanked by charged basic and particulary acidic residues. Furthermore, targeted MS analysis demonstrated a more comprehensive protein cleavage only after Lys-C/trypsin digestion, resulting in a more accurrate absolute protein quantification and extending the number of peptides suitable for SRM assay development. Therefore, we conclude that a serial Lys-C/trypsin digestion is highly attractive for most applications in quantitative MS-based proteomics building on in-solution digestion schemes.

The identification of global patterns and unique signatures of proteins across 14 environments using outer membrane proteomics of bacteria

We test the hypothesis that organisms sourced from different environments exhibit unique fingerprints in macromolecular composition. Experimentally, we followed proteomic changes with 14 different sub-lethal environmental stimuli in Escherichia coli at controlled growth rates. The focus was on the outer membrane sub-proteome, which is known to be extremely sensitive to environmental controls. The analyses surprisingly revealed that pairs of proteins belonging to very different regulons, such as Slp and OmpX or FadL and OmpF, have the closest patterns of change with the 14 conditions. Fe-limited and cold-cultured bacteria have the most distinct global patterns of spot changes, but the patterns with fast growth and oxygen limitation are the closest amongst the 14 environments. These unexpected but statistically robust results suggest that we have an incomplete picture of bacterial regulation across different stress responses; baseline choices and growth-rate influences are probably underestimated factors in such systems-level analysis. In terms of our aim of getting a unique profile for each of the 14 investigated environments, we find that it is unnecessary to compare all the proteins in a proteome and that a panel of five proteins is sufficient for identification of environmental fingerprints. This demonstrates the future feasibility of tracing the history of contaminating bacteria in hospitals, foods or industrial settings as well as for released organisms and biosecurity purposes.

Mass spectrometry-based proteomics and network biology

In the life sciences, a new paradigm is emerging that places networks of interacting molecules between genotype and phenotype. These networks are dynamically modulated by a multitude of factors, and the properties emerging from the network as a whole determine observable phenotypes. This paradigm is usually referred to as systems biology, network biology, or integrative biology. Mass spectrometry (MS)-based proteomics is a central life science technology that has realized great progress toward the identification, quantification, and characterization of the proteins that constitute a proteome. Here, we review how MS-based proteomics has been applied to network biology to identify the nodes and edges of biological networks, to detect and quantify perturbation-induced network changes, and to correlate dynamic network rewiring with the cellular phenotype. We discuss future directions for MS-based proteomics within the network biology paradigm.

OmpL1 is an extracellular matrix- and plasminogen-interacting protein of Leptospira spp

Leptospirosis is a zoonosis with multisystem involvement caused by pathogenic strains of the genus Leptospira. OmpL1 is an outer membrane protein of Leptospira spp. that is expressed during infection. In this work, we investigated novel features of this protein. We describe that OmpL1 is a novel leptospiral extracellular matrix (ECM)-binding protein and a plasminogen (PLG) receptor. The recombinant protein was expressed in Escherichia coli BL21(DE3) Star/pLysS as inclusion bodies, refolded, and purified by metal-chelating chromatography. The protein presented a typical β-strand secondary structure, as evaluated by circular dichroism spectroscopy. The recombinant protein reacted with antibodies in serum samples from convalescent leptospirosis patients with a high specificity compared to serum samples from individuals with unrelated diseases. These data strengthen the usefulness of OmpL1 as a diagnostic marker of leptospirosis. The characterization of the immunogenicity of recombinant OmpL1 in inoculated BALB/c mice showed that the protein has the capacity to elicit humoral and cellular immune responses, as denoted by high antibody titers and the proliferation of lymphocytes. We demonstrate that OmpL1 has the ability to mediate attachment to laminin and plasma fibronectin, with K(D) (equilibrium dissociation constant) values of 2,099.93 ± 871.03 nM and 1,239.23 ± 506.85 nM, respectively. OmpL1 is also a PLG receptor, with a K(D) of 368.63 ± 121.23 nM, capable of generating enzymatically active plasmin. This is the first report that shows and characterizes OmpL1 as an ECM-interacting and a PLG-binding protein of Leptospira spp. that may play a role in bacterial pathogenesis when expressed during infection.

Quantitative mass spectrometry in proteomics: critical review update from 2007 to the present

Mass-spectrometry-based proteomics is continuing to make major contributions to the discovery of fundamental biological processes and, more recently, has also developed into an assay platform capable of measuring hundreds to thousands of proteins in any biological system. The field has progressed at an amazing rate over the past five years in terms of technology as well as the breadth and depth of applications in all areas of the life sciences. Some of the technical approaches that were at an experimental stage back then are considered the gold standard today, and the community is learning to come to grips with the volume and complexity of the data generated. The revolution in DNA/RNA sequencing technology extends the reach of proteomic research to practically any species, and the notion that mass spectrometry has the potential to eventually retire the western blot is no longer in the realm of science fiction. In this review, we focus on the major technical and conceptual developments since 2007 and illustrate these by important recent applications.

Comparison and applications of label-free absolute proteome quantification methods on Escherichia coli

Three different label-free proteome quantification methods--APEX, emPAI and iBAQ--were evaluated to measure proteome-wide protein concentrations in the cell. All the methods were applied to a sample from Escherichia coli chemostat culture. A Pearson squared correlation of approximately 0.6 among the three quantification methods was demonstrated. Importantly, the sum of quantified proteins by iBAQ and emPAI corresponded with the Lowry total protein quantification, demonstrating applicability of label-free methods for an accurate calculation of protein concentrations at the proteome level. The iBAQ method showed the best correlation between biological replicates, a normal distribution among all protein abundances, and the lowest variation among ribosomal protein abundances, which are expected to have equal amounts. Absolute quantitative proteome data enabled us to evaluate metabolic cost for protein synthesis and apparent catalytic activities of enzymes by integration with flux analysis. All the methods demonstrated similar ATP costs for protein synthesis for different cellular processes and that costs for expressing biomass synthesis related proteins were higher than those for energy generation. Importantly, catalytic activities of energy metabolism enzymes were an order or two higher than those of monomer synthesis. Interestingly, a staircase-like protein expression was demonstrated for most of the transcription units.

Quantitative analysis of energy metabolic pathways in MCF-7 breast cancer cells by selected reaction monitoring assay

To investigate the quantitative response of energy metabolic pathways in human MCF-7 breast cancer cells to hypoxia, glucose deprivation, and estradiol stimulation, we developed a targeted proteomics assay for accurate quantification of protein expression in glycolysis/gluconeogenesis, TCA cycle, and pentose phosphate pathways. Cell growth conditions were selected to roughly mimic the exposure of cells in the cancer tissue to the intermittent hypoxia, glucose deprivation, and hormonal stimulation. Targeted proteomics assay allowed for reproducible quantification of 76 proteins in four different growth conditions after 24 and 48 h of perturbation. Differential expression of a number of control and metabolic pathway proteins in response to the change of growth conditions was found. Elevated expression of the majority of glycolytic enzymes was observed in hypoxia. Cancer cells, as opposed to near-normal MCF-10A cells, exhibited significantly increased expression of key energy metabolic pathway enzymes (FBP1, IDH2, and G6PD) that are known to redirect cellular metabolism and increase carbon flux through the pentose phosphate pathway. Our quantitative proteomic protocol is based on a mass spectrometry-compatible acid-labile detergent and is described in detail. Optimized parameters of a multiplex selected reaction monitoring (SRM) assay for 76 proteins, 134 proteotypic peptides, and 401 transitions are included and can be downloaded and used with any SRM-compatible mass spectrometer. The presented workflow is an integrated tool for hypothesis-driven studies of mammalian cells as well as functional studies of proteins, and can greatly complement experimental methods in systems biology, metabolic engineering, and metabolic transformation of cancer cells.

Global relative and absolute quantitation in microbial proteomics

Proteomic studies are designed to yield either qualitative information on proteins (identification, distribution, posttranslational modifications, interactions, structure and function) or quantitative information (abundance, distribution within different localizations, temporal changes in abundance due to synthesis and degradation or both). To this end these studies can draw upon a wide range of qualitative and quantitative gel-based and gel-free techniques. This review summarizes current proteomic workflows for global relative or absolute protein quantitation and their application in microbial physiology.

PTMScan direct: identification and quantification of peptides from critical signaling proteins by immunoaffinity enrichment coupled with LC-MS/MS

Proteomic studies of post-translational modifications by metal affinity or antibody-based methods often employ data-dependent analysis, providing rich data sets that consist of randomly sampled identified peptides because of the dynamic response of the mass spectrometer. This can complicate the primary goal of programs for drug development, mutational analysis, and kinase profiling studies, which is to monitor how multiple nodes of known, critical signaling pathways are affected by a variety of treatment conditions. Cell Signaling Technology has developed an immunoaffinity-based LC-MS/MS method called PTMScan Direct for multiplexed analysis of these important signaling proteins. PTMScan Direct enables the identification and quantification of hundreds of peptides derived from specific proteins in signaling pathways or specific protein types. Cell lines, tissues, or xenografts can be used as starting material. PTMScan Direct is compatible with both SILAC and label-free quantification. Current PTMScan Direct reagents target key nodes of many signaling pathways (PTMScan Direct: Multipathway), serine/threonine kinases, tyrosine kinases, and the Akt/PI3K pathway. Validation of each reagent includes score filtering of MS/MS assignments, filtering by identification of peptides derived from expected targets, identification of peptides homologous to expected targets, minimum signal intensity of peptide ions, and dependence upon the presence of the reagent itself compared with a negative control. The Multipathway reagent was used to study sensitivity of human cancer cell lines to receptor tyrosine kinase inhibitors and showed consistent results with previously published studies. The Ser/Thr kinase reagent was used to compare relative levels of kinase-derived phosphopeptides in mouse liver, brain, and embryo, showing tissue-specific activity of many kinases including Akt and PKC family members. PTMScan Direct will be a powerful quantitative method for elucidation of changes in signaling in a wide array of experimental systems, combining the specificity of traditional biochemical methods with the high number of data points and dynamic range of proteomic methods.

Comprehensive description of the N-glycoproteome of mouse pancreatic β-cells and human islets

Cell surface N-glycoproteins provide a key interface of cells to their environment and therapeutic entry points for drug and biomarker discovery. Their comprehensive description denotes therefore a formidable challenge. The β-cells of the pancreas play a crucial role in blood glucose homeostasis, and disruption of their function contributes to diabetes. By combining cell surface and whole cell capturing technologies with high-throughput quantitative proteomic analysis, we report on the identification of a total of 956 unique N-glycoproteins from mouse MIN6 β-cells and human islets. Three-hundred-forty-nine of these proteins encompass potential surface N-glycoproteins and include orphan G-protein-coupled receptors, novel proteases, receptor protein kinases, and phosphatases. Interestingly, stimulation of MIN6 β-cells with glucose and the hormone GLP1, known stimulators of insulin secretion, causes significant changes in surface N-glycoproteome expression. Taken together, this β-cell N-glycoproteome resource provides a comprehensive view on the composition of β-cell surface proteins and expands the scope of signaling systems potentially involved in mediating responses of β-cells to various forms of (patho)physiologic stress and the extent of dynamic remodeling of surface N-glycoprotein expression associated with metabolic and hormonal stimulation. Moreover, it provides a foundation for the development of diabetes medicines that target or are derived from the β-cell surface N-glycoproteome.

Adenylylation control by intra- or intermolecular active-site obstruction in Fic proteins

Fic proteins that are defined by the ubiquitous FIC (filamentation induced by cyclic AMP) domain are known to catalyse adenylylation (also called AMPylation); that is, the transfer of AMP onto a target protein. In mammalian cells, adenylylation of small GTPases through Fic proteins injected by pathogenic bacteria can cause collapse of the actin cytoskeleton and cell death. It is unknown how this potentially deleterious adenylylation activity is regulated in the widespread Fic proteins that are found in all domains of life and that are thought to have critical roles in intrinsic signalling processes. Here we show that FIC-domain-mediated adenylylation is controlled by a conserved mechanism of ATP-binding-site obstruction that involves an inhibitory α-helix (α(inh)) with a conserved (S/T)XXXE(G/N) motif, and that in this mechanism the invariable glutamate competes with ATP γ-phosphate binding. Consistent with this, FIC-domain-mediated growth arrest of bacteria by the VbhT toxin of Bartonella schoenbuchensis is intermolecularly repressed by the VbhA antitoxin through tight binding of its α(inh) to the FIC domain of VbhT, as shown by structure and function analysis. Furthermore, structural comparisons with other bacterial Fic proteins, such as Fic of Neisseria meningitidis and of Shewanella oneidensis, show that α(inh) frequently constitutes an amino-terminal or carboxy-terminal extension to the FIC domain, respectively, partially obstructing the ATP binding site in an intramolecular manner. After mutation of the inhibitory motif in various Fic proteins, including the human homologue FICD (also known as HYPE), adenylylation activity is considerably boosted, consistent with the anticipated relief of inhibition. Structural homology modelling of all annotated Fic proteins indicates that inhibition by α(inh) is universal and conserved through evolution, as the inhibitory motif is present in ∼90% of all putatively adenylylation-active FIC domains, including examples from all domains of life and from viruses. Future studies should reveal how intrinsic or extrinsic factors modulate adenylylation activity by weakening the interaction of α(inh) with the FIC active site.

Current status of the plant phosphorylation site database PhosPhAt and its use as a resource for molecular plant physiology

As the most studied post-translational modification, protein phosphorylation is analyzed in a growing number of proteomic experiments. These high-throughput approaches generate large datasets, from which specific spectrum-based information can be hard to find. In 2007, the PhosPhAt database was launched to collect and present Arabidopsis phosphorylation sites identified by mass spectrometry from and for the scientific community. At present, PhosPhAt 3.0 consolidates phosphoproteomics data from 19 published proteomic studies. Out of 5460 listed unique phosphoproteins, about 25% have been identified in at least two independent experimental setups. This is especially important when considering issues of false positive and false negative identification rates and data quality (Durek etal., 2010). This valuable data set encompasses over 13205 unique phosphopeptides, with unambiguous mapping to serine (77%), threonine (17%), and tyrosine (6%). Sorting the functional annotations of experimentally found phosphorylated proteins in PhosPhAt using Gene Ontology terms shows an over-representation of proteins in regulatory pathways and signaling processes. A similar distribution is found when the PhosPhAt predictor, trained on experimentally obtained plant phosphorylation sites, is used to predict phosphorylation sites for the Arabidopsis genome. Finally, the possibility to insert a protein sequence into the PhosPhAt predictor allows species independent use of the prediction resource. In practice, PhosPhAt also allows easy exploitation of proteomic data for design of further targeted 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.

Estimation of absolute protein quantities of unlabeled samples by selected reaction monitoring mass spectrometry

For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the “best flyer” hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R² of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.

The quantitative proteome of a human cell line

The majority of all proteins expressed in the human osteosarcoma cell line U2OS were absolutely quantified by mass spectrometry. The quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell.

The generation of mathematical models of biological processes, the simulation of these processes under different conditions, and the comparison and integration of multiple data sets are explicit goals of systems biology that require the knowledge of the absolute quantity of the system's components. To date, systematic estimates of cellular protein concentrations have been exceptionally scarce. Here, we provide a quantitative description of the proteome of a commonly used human cell line in two functional states, interphase and mitosis. We show that these human cultured cells express at least ∼10 000 proteins and that the quantified proteins span a concentration range of seven orders of magnitude up to 20 000 000 copies per cell. We discuss how protein abundance is linked to function and evolution.

Generic comparison of protein inference engines

Protein identifications, instead of peptide-spectrum matches, constitute the biologically relevant result of shotgun proteomics studies. How to appropriately infer and report protein identifications has triggered a still ongoing debate. This debate has so far suffered from the lack of appropriate performance measures that allow us to objectively assess protein inference approaches. This study describes an intuitive, generic and yet formal performance measure and demonstrates how it enables experimentalists to select an optimal protein inference strategy for a given collection of fragment ion spectra. We applied the performance measure to systematically explore the benefit of excluding possibly unreliable protein identifications, such as single-hit wonders. Therefore, we defined a family of protein inference engines by extending a simple inference engine by thousands of pruning variants, each excluding a different specified set of possibly unreliable identifications. We benchmarked these protein inference engines on several data sets representing different proteomes and mass spectrometry platforms. Optimally performing inference engines retained all high confidence spectral evidence, without posterior exclusion of any type of protein identifications. Despite the diversity of studied data sets consistently supporting this rule, other data sets might behave differently. In order to ensure maximal reliable proteome coverage for data sets arising in other studies we advocate abstaining from rigid protein inference rules, such as exclusion of single-hit wonders, and instead consider several protein inference approaches and assess these with respect to the presented performance measure in the specific application context.