A New in Vivo Cross-linking Mass Spectrometry Platform to Define Protein–Protein Interactions in Living Cells

Protein–protein interactions (PPIs) are fundamental to the structure and function of protein complexes. Resolving the physical contacts between proteins as they occur in cells is critical to uncovering the molecular details underlying various cellular activities. To advance the study of PPIs in living cells, we have developed a new in vivo cross-linking mass spectrometry platform that couples a novel membrane-permeable, enrichable, and MS-cleavable cross-linker with multistage tandem mass spectrometry. This strategy permits the effective capture, enrichment, and identification of in vivo cross-linked products from mammalian cells and thus enables the determination of protein interaction interfaces. The utility of the developed method has been demonstrated by profiling PPIs in mammalian cells at the proteome scale and the targeted protein complex level. Our work represents a general approach for studying in vivo PPIs and provides a solid foundation for future studies toward the complete mapping of PPI networks in living systems.Protein–protein interactions (PPIs)¹ play a key role in defining protein functions in biological systems. Aberrant PPIs can have drastic effects on biochemical activities essential to cell homeostasis, growth, and proliferation, and thereby lead to various human diseases (1). Consequently, PPI interfaces have been recognized as a new paradigm for drug development. Therefore, mapping PPIs and their interaction interfaces in living cells is critical not only for a comprehensive understanding of protein function and regulation, but also for describing the molecular mechanisms underlying human pathologies and identifying potential targets for better therapeutics.Several strategies exist for identifying and mapping PPIs, including yeast two-hybrid, protein microarray, and affinity purification mass spectrometry (AP-MS) (2–5). Thanks to new developments in sample preparation strategies, mass spectrometry technologies, and bioinformatics tools, AP-MS has become a powerful and preferred method for studying PPIs at the systems level (6–9). Unlike other approaches, AP-MS experiments allow the capture of protein interactions directly from their natural cellular environment, thus better retaining native protein structures and biologically relevant interactions. In addition, a broader scope of PPI networks can be obtained with greater sensitivity, accuracy, versatility, and speed. Despite the success of this very promising technique, AP-MS experiments can lead to the loss of weak/transient interactions and/or the reorganization of protein interactions during biochemical manipulation under native purification conditions. To circumvent these problems, in vivo chemical cross-linking has been successfully employed to stabilize protein interactions in native cells or tissues prior to cell lysis (10–16). The resulting covalent bonds formed between interacting partners allow affinity purification under stringent and fully denaturing conditions, consequently reducing nonspecific background while preserving stable and weak/transient interactions (12–16). Subsequent mass spectrometric analysis can reveal not only the identities of interacting proteins, but also cross-linked amino acid residues. The latter provides direct molecular evidence describing the physical contacts between and within proteins (17). This information can be used for computational modeling to establish structural topologies of proteins and protein complexes (17–22), as well as for generating experimentally derived protein interaction network topology maps (23, 24). Thus, cross-linking mass spectrometry (XL-MS) strategies represent a powerful and emergent technology that possesses unparalleled capabilities for studying PPIs.Despite their great potential, current XL-MS studies that have aimed to identify cross-linked peptides have been mostly limited to in vitro cross-linking experiments, with few successfully identifying protein interaction interfaces in living cells (24, 25). This is largely because XL-MS studies remain challenging due to the inherent difficulty in the effective MS detection and accurate identification of cross-linked peptides, as well as in unambiguous assignment of cross-linked residues. In general, cross-linked products are heterogeneous and low in abundance relative to non-cross-linked products. In addition, their MS fragmentation is too complex to be interpreted using conventional database searching tools (17, 26). It is noted that almost all of the current in vivo PPI studies utilize formaldehyde cross-linking because of its membrane permeability and fast kinetics (10–16). However, in comparison to the most commonly used amine reactive NHS ester cross-linkers, identification of formaldehyde cross-linked peptides is even more challenging because of its promiscuous nonspecific reactivity and extremely short spacer length (27). Therefore, further developments in reagents and methods are urgently needed to enable simple MS detection and effective identification of in vivo cross-linked products, and thus allow the mapping of authentic protein contact sites as established in cells, especially for protein complexes.Various efforts have been made to address the limitations of XL-MS studies, resulting in new developments in bioinformatics tools for improved data interpretation (28–32) and new designs of cross-linking reagents for enhanced MS analysis of cross-linked peptides (24, 33–39). Among these approaches, the development of new cross-linking reagents holds great promise for mapping PPIs on the systems level. One class of cross-linking reagents containing an enrichment handle have been shown to allow selective isolation of cross-linked products from complex mixtures, boosting their detectability by MS (33–35, 40–42). A second class of cross-linkers containing MS-cleavable bonds have proven to be effective in facilitating the unambiguous identification of cross-linked peptides (36–39, 43, 44), as the resulting cross-linked products can be identified based on their characteristic and simplified fragmentation behavior during MS analysis. Therefore, an ideal cross-linking reagent would possess the combined features of both classes of cross-linkers. To advance the study of in vivo PPIs, we have developed a new XL-MS platform based on a novel membrane-permeable, enrichable, and MS-cleavable cross-linker, Azide-A-DSBSO (azide-tagged, acid-cleavable disuccinimidyl bis-sulfoxide), and multistage tandem mass spectrometry (MSⁿ). This new XL-MS strategy has been successfully employed to map in vivo PPIs from mammalian cells at both the proteome scale and the targeted protein complex level.     

Unbiased Quantitation of Escherichia coli Membrane Proteome Using Phase Transfer Surfactants

We developed a sample preparation protocol for rapid and unbiased analysis of the membrane proteome using an alimentary canal-mimicking system in which proteases are activated in the presence of bile salts. In this rapid and unbiased protocol, immobilized trypsin is used in the presence of deoxycholate and lauroylsarcosine to increase digestion efficiency as well as to increase the solubility of the membrane proteins. Using 22.5 μg of Escherichia coli whole cell lysate, we quantitatively demonstrated that membrane proteins were extracted and digested at the same level as soluble proteins without any solubility-related bias. The recovery of membrane proteins was independent of the number of transmembrane domains per protein. In the analysis of the membrane-enriched fraction from 22.5 μg of E. coli cell lysate, the abundance distribution of the membrane proteins was in agreement with that of the membrane protein-coding genes when this protocol, coupled with strong cation exchange prefractionation prior to nano-LC-MS/MS analysis, was used. Because this protocol allows unbiased sample preparation, protein abundance estimation based on the number of observed peptides per protein was applied to both soluble and membrane proteins simultaneously, and the copy numbers per cell for 1,453 E. coli proteins, including 545 membrane proteins, were successfully obtained. Finally, this protocol was applied to quantitative analysis of guanosine tetra- and pentaphosphate-dependent signaling in E. coli wild-type and relA knock-out strains.Despite the importance of cell surface biology, the conventional shotgun proteomics strategy generally underrepresents the membrane proteome because of inadequate solubilization and protease digestion (1, 2). The ageless gel strategy, consisting of SDS-PAGE followed by in-gel digestion, can partially solve this problem (3–5), but the recovery from in-gel digestion is generally lower than that from in-solution digestion, and this approach is far from suitable for a rapid, simple, and high throughput automated system. Numerous approaches have been reported to overcome the difficulties in membrane proteome analysis, such as the use of surfactants (2, 6–11), organic solvents (6, 7, 12–15), or chaotropic reagents (2, 6, 16). Acid-labile surfactants, such as RapiGest SF, are among the most promising additives to enhance protein solubilization without interfering with LC-MS performance (6, 10, 17–19). However, the cleavage step at acidic pH causes loss of hydrophobic peptides because of coprecipitation with the hydrophobic part of RapiGest SF (20). Recently, we developed a new protocol to dissolve and digest membrane proteins with the aid of a removable phase transfer surfactant (PTS),1 such as sodium deoxycholate (SDC) (20). The solubility of membrane proteins with SDC was comparable to that with sodium dodecyl sulfate. In addition, the activity of trypsin was enhanced ∼5-fold in the presence of 1% SDC because this rapid PTS method mimics conditions in the alimentary canal in which bile salts such as cholate and deoxycholate are secreted together with trypsin. After tryptic digestion, SDC is removed prior to LC-MS/MS analysis by adding an organic solvent followed by pH-induced transfer of the surfactant to the organic phase, whereas tryptic peptides remain in the aqueous phase. This protocol offers a significant improvement in identifying membrane proteins by increasing the recovery of hydrophobic tryptic peptides compared with the protocols using urea and RapiGest SF.The goal of this study is to establish a membrane proteomics method that is unbiased with respect to protein solubility, hydrophobicity, and protein abundance; i.e. membrane proteins can be as efficiently extracted and digested as soluble proteins. So far, to our knowledge, little information about the recovery of the membrane proteome has been reported. Instead, the number of identified membrane proteins or the content of membrane proteins identified in the membrane-enriched fraction has been used as an indicator of the efficiency of procedures for membrane proteome analysis (4, 5, 21–23). However, these parameters usually depend on the experimental conditions, including the sample preparation procedure and LC-MS instrument used. Therefore, it is difficult to compare data obtained with these protocols except in the case of direct comparison. Furthermore, there has been no report quantitatively comparing the recovery of membrane proteome with that of soluble proteins.In this study, we used a modified version of our PTS protocol with immobilized trypsin columns to reduce the digestion time and evaluated its suitability for unbiased quantitation of the membrane proteome. In addition, we applied this protocol to estimate the copy numbers per cell of 1,453 proteins, including 545 membrane proteins, using the exponentially modified protein abundance index (emPAI). Finally, this rapid and unbiased PTS protocol was applied to the quantitative analysis of Escherichia coli BW25113 wild-type and relA knock-out (KO) strains.     

Splitting the BLOSUM Score into Numbers of Biological Significance

Mathematical tools developed in the context of Shannon information theory were used to analyze the meaning of the BLOSUM score, which was split into three components termed as the BLOSUM spectrum (or BLOSpectrum). These relate respectively to the sequence convergence (the stochastic similarity of the two protein sequences), to the background frequency divergence (typicality of the amino acid probability distribution in each sequence), and to the target frequency divergence (compliance of the amino acid variations between the two sequences to the protein model implicit in the BLOCKS database). This treatment sharpens the protein sequence comparison, providing a rationale for the biological significance of the obtained score, and helps to identify weakly related sequences. Moreover, the BLOSpectrum can guide the choice of the most appropriate scoring matrix, tailoring it to the evolutionary divergence associated with the two sequences, or indicate if a compositionally adjusted matrix could perform better.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29]     

Large Scale Comparative Proteomics of a Chloroplast Clp Protease Mutant Reveals Folding Stress, Altered Protein Homeostasis, and Feedback Regulation of Metabolism

The clpr2-1 mutant is delayed in development due to reduction of the chloroplast ClpPR protease complex. To understand the role of Clp proteases in plastid biogenesis and homeostasis, leaf proteomes of young seedlings of clpr2-1 and wild type were compared using large scale mass spectrometry-based quantification using an LTQ-Orbitrap and spectral counting with significance determined by G-tests. Virtually only chloroplast-localized proteins were significantly affected, indicating that the molecular phenotype was confined to the chloroplast. A comparative chloroplast stromal proteome analysis of fully developed plants was used to complement the data set. Chloroplast unfoldase ClpB3 was strongly up-regulated in both young and mature leaves, suggesting widespread and persistent protein folding stress. The importance of ClpB3 in the clp2-1 mutant was demonstrated by the observation that a CLPR2 and CLPB3 double mutant was seedling-lethal. The observed up-regulation of chloroplast chaperones and protein sorting components further illustrated destabilization of protein homeostasis. Delayed rRNA processing and up-regulation of a chloroplast DEAD box RNA helicase and polynucleotide phosphorylase, but no significant change in accumulation of ribosomal subunits, suggested a bottleneck in ribosome assembly or RNA metabolism. Strong up-regulation of a chloroplast translational regulator TypA/BipA GTPase suggested a specific response in plastid gene expression to the distorted homeostasis. The stromal proteases PreP1,2 were up-regulated, likely constituting compensation for reduced Clp protease activity and possibly shared substrates between the ClpP and PreP protease systems. The thylakoid photosynthetic apparatus was decreased in the seedlings, whereas several structural thylakoid-associated plastoglobular proteins were strongly up-regulated. Two thylakoid-associated reductases involved in isoprenoid and chlorophyll synthesis were up-regulated reflecting feedback from rate-limiting photosynthetic electron transport. We discuss the quantitative proteomics data and the role of Clp proteolysis using a “systems view” of chloroplast homeostasis and metabolism and provide testable hypotheses and putative substrates to further determine the significance of Clp-driven proteolysis.Intracellular proteolysis is important for regulation of metabolic and signaling pathways as well as protein homeostasis and viability of cells and organelles. Chloroplasts contain multiple soluble and membrane-bound proteases and processing peptidases (1) presumably with partially overlapping substrates. These include stromal processing peptidase (2) and stromal PreP1,2 involved in degradation of cleaved transit peptides (3); various amino peptidases (4, 5); the thylakoid processing peptidases cTPA (6), TPP (7), and thylakoid/envelope signal peptidase I (8); and thylakoid-bound proteases SppA (9) and Egy1 (10) as well as stromal and thylakoid members of the Deg, FtsH, and Clp families (11–13). Together with several chaperone systems, including CPN60/CPN10, HSP70/DnaJ, HSP90, and ClpB3 (14), these proteases are part of the chloroplast protein homeostasis network. Importantly the connectivity and overlap of proteins within this homeostasis network is poorly understood; in particular it is unclear how protease substrates are recognized by the different proteolytic systems. Several suppressors of variegated FtsH protease mutants in Arabidopsis have elegantly demonstrated that the balance between protein synthesis and degradation plays an important role in chloroplast homeostasis (15–17). Comparative proteome analysis of chloroplast homeostasis mutants will provide insights in this homeostasis network as we recently showed for a protein sorting mutant (18), and it will identify candidate protease substrates.The Clp proteins form the largest plastid localized protease family with five serine-type ClpP (P1,P3–6) proteases, four non-catalytic ClpR (R1–4) proteins, three Clp AAA⁺ chaperones (C1,C2, and D), and several additional members (ClpT1,T2,S) with unknown functions (1, 13, 19). We note that we renamed Arabidopsis ClpS1,S2 and ClpT as ClpT1,T2 and ClpS to be consistent with the Escherichia coli nomenclature for ClpS (20). The ClpR proteins lack the three catalytic amino acid residues that are conserved across ClpP proteins (21). All proteins of the Arabidopsis Clp proteolytic system have been identified by mass spectrometry (13), including a potential substrate affinity regulator, ClpS.¹Recent evidence shows that the Clp proteolytic system plays a critical role in plant growth, development, and protein homeostasis. ClpP1 is plastid-encoded and was shown to be essential for shoot development in tobacco (22, 23). Down-regulation of the plastid-encoded CLPP1 gene in the green algae Chlamydomonas reinhardtii suggested that ClpP1 is involved in the degradation of the thylakoid-bound subunits of cytochrome b₆f and photosystem II (PSII)² complex (24, 25). Arabidopsis mutant clpr1-1 carries a premature stop codon in the CLPR1 gene and showed a virescent phenotype and delayed chloroplast development and differentiation (26). Maturation of 23 and 4.5 S chloroplast ribosomal RNA (rRNA) is delayed in clpr1-1 (26), but it is not clear how this is related to the loss of ClpR1 protein. Phenotypes of Arabidopsis antisense lines against CLPP4 (27) and CLPP6 (28) also showed delayed chloroplast and plant development as well as reduced accumulation of other ClpP,R subunits. Based on two-dimensional gel analysis, several chloroplast proteins were suggested to be substrates of the Clp machinery (28–30), but direct evidence is lacking. A null mutant for the CLPC1 chaperone (also named HSP93-V) resulted in reduced plant growth, chloroplast development, and protein import rates, but homozygous plants are autotrophic and seeds are viable (31–33). A null mutant for chaperone CLPC2 has no visible phenotype, whereas lack of both CLPC1 and CLPC2 prevents embryogenesis (34). Interestingly ClpC1 is also involved in accumulation of chlorophyll a oxygenase, which is responsible for conversion of chlorophyll a to chlorophyll b (35).In a previous study, we identified and characterized a T-DNA-tagged Arabidopsis thaliana mutant with reduced expression of CLPR2; this mutant was named clpr2-1 (36). Accumulation of the assembled 325-kDa ClpPRT complex was 2–3-fold reduced and resulted in delayed chloroplast and plant development with small chloroplasts and a pale green phenotype. The clpr2-1 mutant shows the strongest visible phenotype when seedlings are young. To better understand the role of the Clp machinery in chloroplast biogenesis and homeostasis and to discover potential Clp substrates, a comprehensive proteome analysis at different points in leaf development of the clpr2-1 mutant is presented in the current study. The methods to quantitatively analyze differences in protein accumulation have greatly improved over the last decade and have shifted from gel image-based quantification to quantification within the mass spectrometer (37–39). Taking advantage of these new developments and opportunities, we compared the leaf proteome of clpr2-1 and wt seedlings early in development using spectral counting. This was complemented with a comparative analysis of the chloroplast soluble proteome of fully developed leaf rosettes. The seedling proteome analysis showed that the strongest effects occurred within the chloroplast. The functional significance of one of the most up-regulated proteins, ClpB3, was confirmed by additional mutant analysis. Putative substrates for the Clp system suggested in recent studies (28–30, 35) are reviewed in the context of our findings. This study provides testable hypotheses to further determine the significance of Clp-driven proteolysis and provides new insights in the plastid protein homeostasis network and how secondary metabolism is intertwined with photosynthetic capacity. We show that a systems view of chloroplast biogenesis and proteome homeostasis is needed to identify putative protease substrates and to understand the role of proteolysis in chloroplast biology. Finally we believe that the experimental setup described in this study provides an attractive template for comparative proteome analysis of other (chloroplast) mutants.     

Defining,Comparing, and Improving iTRAQ Quantification in Mass Spectrometry Proteomics Data

The purpose of this study was to generate a basis for the decision of what protein quantities are reliable and find a way for accurate and precise protein quantification. To investigate this we have used thousands of peptide measurements to estimate variance and bias for quantification by iTRAQ (isobaric tags for relative and absolute quantification) mass spectrometry in complex human samples. A549 cell lysate was mixed in the proportions 2:2:1:1:2:2:1:1, fractionated by high resolution isoelectric focusing and liquid chromatography and analyzed by three mass spectrometry platforms; LTQ Orbitrap Velos, 4800 MALDI-TOF/TOF and 6530 Q-TOF. We have investigated how variance and bias in the iTRAQ reporter ions data are affected by common experimental variables such as sample amount, sample fractionation, fragmentation energy, and instrument platform. Based on this, we have suggested a concept for experimental design and a methodology for protein quantification. By using duplicate samples in each run, each experiment is validated based on its internal experimental variation. The duplicates are used for calculating peptide weights, unique to the experiment, which is used in the protein quantification. By weighting the peptides depending on reporter ion intensity, we can decrease the relative error in quantification at the protein level and assign a total weight to each protein that reflects the protein quantitation confidence. We also demonstrate the usability of this methodology in a cancer cell line experiment as well as in a clinical data set of lung cancer tissue samples. In conclusion, we have in this study developed a methodology for improved protein quantification in shotgun proteomics and introduced a way to assess quantification for proteins with few peptides. The experimental design and developed algorithms decreased the relative protein quantification error in the analysis of complex biological samples.Recent developments in methods and instruments for mass spectrometry enable quantitative proteomics analysis of complex samples with good coverage (1–4). Several techniques for quantification by mass spectrometry exist, both using isotopic labeling and label free methods (5, 6). Quantification by isotopic labeling can be done on precursor ion level or by quantifying isobaric label fragments in fragment spectra. Isotope-coded affinity tag (7), isobaric tags for relative and absolute quantification (iTRAQ)¹ (8), and stable isotope labeling by amino acids in cell culture (SILAC) (9) are among the most commonly used labeling methods based on stable isotopes. iTRAQ allows for simultaneous relative quantification of up to eight samples within a single run. Quantification by mass spectrometry is however a challenge, and several factors contribute to the uncertainty in the quantitative estimate; differences in labeling efficiency, protein digestion, precursor mixing, ion suppression, peak detection, data preprocessing, and data analysis (10). The quality of quantitation methods can be measured in terms of precision and accuracy. Precision is affected by random errors, that is, random fluctuations around the true value (variance). Lack of accuracy is caused by systematic errors, that is, differences between true and observed values (bias).Several studies have shown that iTRAQ labeling is associated with bias; fold changes are compressed toward one (11–14). It has been suggested that this underestimation of fold change is caused by co-eluting peptides with similar m/z values that are isolated together, creating mixed iTRAQ intensities in complex samples (14). Concerning precision, iTRAQ data has been reported to exhibit variance heterogeneity. The coefficient of variance (CV) of the signal depends on the intensity, with larger CV for low intensity peaks (11, 12, 15, 16). Measurements of iTRAQ intensities for quantification are made in the MS/MS spectra of the peptides, and thereafter combined to calculate a summarized relative protein quantity. There are several different approaches for combining the iTRAQ peptide data to compute a reliable protein ratio. Methods to improve the protein quantification by addressing the variance heterogeneity have been based on excluding low intensity peptide data (17, 18), weighting the peptide data according to intensity (18–21) or stabilizing the variance (12).Quantitative studies of complex human samples are subject to even more challenges related to large biological variation, large and unknown complexity of the human proteome and a large concentration range of proteins. This in turn results in many peptides and a large variety of peptides that can cause interference and related problems in the mass spectrometry analysis. In, for example, biomarker discovery research the goal is to measure quantitative changes or differences in protein levels between two or more clinical conditions. It is therefore crucial to achieve as accurate and precise quantitative information from the data as possible as well as to correctly estimate the limitations of the quantification. Setting adequate standards for quantitative proteomics analysis is hence essential for being able to detect relevant changes in protein abundance, select important proteins, and further use those proteins to interpret the biological and clinical meaning (10, 22). Selecting a protein as significant and taking it to further validation in other clinical material using complementary techniques is time consuming and costly (23). For successful use of iTRAQ labeling in biomarker discovery, and to avoid false discoveries, it is hence essential to assess the accuracy and precision of the methodology.A common approach to study variance and bias in mass spectrometry based protein quantification is to spike a set of standard proteins into a sample and then measure the CV and bias of the intensities of those peptides. Spike-in of proteins has the benefit of looking at a small controlled set of peptides and how they behave in the studied system. This strategy has been used in several of the previously mentioned papers that address iTRAQ quantification (11–14). However, the number of data points studied may be unlikely to represent the complexity of a real biological sample, which often contains thousands of proteins (24). In the current study, all peptides detected in a complex human cell line sample (A549) are used to get an estimate of the quantitative accuracy and precision. This experimental setup is hence more similar to a real biomarker discovery study with high complex human proteome samples. The quality of the protein quantifications is compared among several different mass spectrometers in this work; also the influence of different loaded peptide amounts and the use of different methods for sample separation are examined. Factors such as variance and bias of peptide quantification by iTRAQ are systematically evaluated in those high complex samples. Further, methods for improving the protein quantification are investigated; by filtering on the peptide level to remove low quality intensities and by weighting the peptide values to account for the higher risk of errors at low intensities (20).We have described the factors contributing to bias and variance in protein quantification by iTRAQ labeling. This has generated guidelines for how to estimate the accuracy of protein quantities, which will be an essential tool in both biomarker discovery and studies of biological systems. Based on the results, we suggest an experimental design where each labeling set (e.g., iTRAQ) includes duplicate samples, and we describe how these duplicates are used for calculating peptide weights that can be used in addressing the accuracy of protein quantities. This novel approach is shown to improve protein quantification by iTRAQ in six data sets of A431 cell line samples treated with drug and a clinical data set of lung cancer tissue samples.     

N-Terminal-oriented Proteogenomics of the Marine Bacterium Roseobacter Denitrificans Och114 using N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP) Labeling and Diagonal Chromatography

Given the ease of whole genome sequencing with next-generation sequencers, structural and functional gene annotation is now purely based on automated prediction. However, errors in gene structure are frequent, the correct determination of start codons being one of the main concerns. Here, we combine protein N termini derivatization using (N-Succinimidyloxycarbonylmethyl)tris(2,4,6-trimethoxyphenyl)phosphonium bromide (TMPP Ac-OSu) as a labeling reagent with the COmbined FRActional DIagonal Chromatography (COFRADIC) sorting method to enrich labeled N-terminal peptides for mass spectrometry detection. Protein digestion was performed in parallel with three proteases to obtain a reliable automatic validation of protein N termini. The analysis of these N-terminal enriched fractions by high-resolution tandem mass spectrometry allowed the annotation refinement of 534 proteins of the model marine bacterium Roseobacter denitrificans OCh114. This study is especially efficient regarding mass spectrometry analytical time. From the 534 validated N termini, 480 confirmed existing gene annotations, 41 highlighted erroneous start codon annotations, five revealed totally new mis-annotated genes; the mass spectrometry data also suggested the existence of multiple start sites for eight different genes, a result that challenges the current view of protein translation initiation. Finally, we identified several proteins for which classical genome homology-driven annotation was inconsistent, questioning the validity of automatic annotation pipelines and emphasizing the need for complementary proteomic data. All data have been deposited to the ProteomeXchange with identifier PXD000337.Recent developments in mass spectrometry and bioinformatics have established proteomics as a common and powerful technique for identifying and quantifying proteins at a very broad scale, but also for characterizing their post-translational modifications and interaction networks (1, 2). In addition to the avalanche of proteomic data currently being reported, many genome sequences are established using next-generation sequencing, fostering proteomic investigations of new cellular models. Proteogenomics is a relatively recent field in which high-throughput proteomic data is used to verify coding regions within model genomes to refine the annotation of their sequences (2–8). Because genome annotation is now fully automated, the need for accurate annotation for model organisms with experimental data is crucial. Many projects related to genome re-annotation of microorganisms with the help of proteomics have been recently reported, such as for Mycoplasma pneumoniae (9), Rhodopseudomonas palustris (10), Shewanella oneidensis (11), Thermococcus gammatolerans (12), Deinococcus deserti (13), Salmonella thyphimurium (14), Mycobacterium tuberculosis (15, 16), Shigella flexneri (17), Ruegeria pomeroyi (18), and Candida glabrata (19), as well as for higher organisms such as Anopheles gambiae (20) and Arabidopsis thaliana (4, 5).The most frequently reported problem in automatic annotation systems is the correct identification of the translational start codon (21–23). The error rate depends on the primary annotation system, but also on the organism, as reported for Halobacterium salinarum and Natromonas pharaonis (24), Deinococcus deserti (21), and Ruegeria pomeroyi (18), where the error rate is estimated above 10%. Identification of a correct translational start site is essential for the genetic and biochemical analysis of a protein because errors can seriously impact subsequent biological studies. If the N terminus is not correctly identified, the protein will be considered in either a truncated or extended form, leading to errors in bioinformatic analyses (e.g. during the prediction of its molecular weight, isoelectric point, cellular localization) and major difficulties during its experimental characterization. For example, a truncated protein may be heterologously produced as an unfolded polypeptide recalcitrant to structure determination (25). Moreover, N-terminal modifications, which are poorly documented in annotation databases, may occur (26, 27).Unfortunately, the poor polypeptide sequence coverage obtained for the numerous low abundance proteins in current shotgun MS/MS proteomic studies implies that the overall detection of N-terminal peptides obtained in proteogenomic studies is relatively low. Different methods for establishing the most extensive list of protein N termini, grouped under the so-called “N-terminomics” theme, have been proposed to selectively enrich or improve the detection of these peptides (2, 28, 29). Large N-terminome studies have recently been reported based on resin-assisted enrichment of N-terminal peptides (30) or terminal amine isotopic labeling of substrates (TAILS) coupled to depletion of internal peptides with a water-soluble aldehyde-functionalized polymer (31–35). Among the numerous N-terminal-oriented methods (2), specific labeling of the N terminus of intact proteins with N-tris(2,4,6-trimethoxyphenyl)phosphonium acetyl succinamide (TMPP-Ac-OSu)¹ has proven reliable (21, 36–39). TMPP-derivatized N-terminal peptides have interesting properties for further LC-MS/MS mass spectrometry: (1) an increase in hydrophobicity because of the trimethoxyphenyl moiety added to the peptides, increasing their retention times in reverse phase chromatography, (2) improvement of their ionization because of the introduction of a positively charged group, and (3) a much simpler fragmentation pattern in tandem mass spectrometry. Other reported approaches rely on acetylation, followed by trypsin digestion, and then biotinylation of free amino groups (40); guanidination of lysine lateral chains followed by N-biotinylation of the N termini and trypsin digestion (41); or reductive amination of all free amino groups with formaldehyde preceeding trypsin digestion (42). Recently, we applied the TMPP method to the proteome of the Deinococcus deserti bacterium isolated from upper sand layers of the Sahara desert (13). This method enabled the detection of N-terminal peptides allowing the confirmation of 278 translation initiation codons, the correction of 73 translation starts, and the identification of non-canonical translation initiation codons (21). However, most TMPP-labeled N-terminal peptides are hidden among the more abundant internal peptides generated after proteolysis of a complex proteome, precluding their detection. This results in disproportionately fewer N-terminal validations, that is, 5 and 8% of total polypeptides coded in the theoretical proteomes of Mycobacterium smegmatis (37) and Deinococcus deserti (21) with a total of 342 and 278 validations, respectively.An interesting chromatographic method to fractionate peptide mixtures for gel-free high-throughput proteome analysis has been developed over the last years and applied to various topics (43, 44). This technique, known as COmbined FRActional DIagonal Chromatography (COFRADIC), uses a double chromatographic separation with a chemical reaction in between to change the physico-chemical properties of the extraneous peptides to be resolved from the peptides of interest. Its previous applications include the separation of methionine-containing peptides (43), N-terminal peptide enrichment (45, 46), sulfur amino acid-containing peptides (47), and phosphorylated peptides (48). COFRADIC was identified as the best method for identification of N-terminal peptides of two archaea, resulting in the identification of 240 polypeptides (9% of the theoretical proteome) for Halobacterium salinarum and 220 (8%) for Natronomonas pharaonis (24).Taking advantage of both the specificity of TMPP labeling, the resolving power of COFRADIC for enrichment, and the increase in information through the use of multiple proteases, we performed the proteogenomic analysis of a marine bacterium from the Roseobacter clade, namely Roseobacter denitrificans OCh114. This novel approach allowed us to validate and correct 534 unique proteins (13% of the theoretical proteome) with TMPP-labeled N-terminal signatures obtained using high-resolution tandem mass spectrometry. We corrected 41 annotations and detected five new open reading frames in the R. denitrificans genome. We further identified eight distinct proteins showing direct evidence for multiple start sites.     

Phosphoproteome Analysis Reveals Regulatory Sites in Major Pathways of Cardiac Mitochondria

Mitochondrial functions are dynamically regulated in the heart. In particular, protein phosphorylation has been shown to be a key mechanism modulating mitochondrial function in diverse cardiovascular phenotypes. However, site-specific phosphorylation information remains scarce for this organ. Accordingly, we performed a comprehensive characterization of murine cardiac mitochondrial phosphoproteome in the context of mitochondrial functional pathways. A platform using the complementary fragmentation technologies of collision-induced dissociation (CID) and electron transfer dissociation (ETD) demonstrated successful identification of a total of 236 phosphorylation sites in the murine heart; 210 of these sites were novel. These 236 sites were mapped to 181 phosphoproteins and 203 phosphopeptides. Among those identified, 45 phosphorylation sites were captured only by CID, whereas 185 phosphorylation sites, including a novel modification on ubiquinol-cytochrome c reductase protein 1 (Ser-212), were identified only by ETD, underscoring the advantage of a combined CID and ETD approach. The biological significance of the cardiac mitochondrial phosphoproteome was evaluated. Our investigations illustrated key regulatory sites in murine cardiac mitochondrial pathways as targets of phosphorylation regulation, including components of the electron transport chain (ETC) complexes and enzymes involved in metabolic pathways (e.g. tricarboxylic acid cycle). Furthermore, calcium overload injured cardiac mitochondrial ETC function, whereas enhanced phosphorylation of ETC via application of phosphatase inhibitors restored calcium-attenuated ETC complex I and complex III activities, demonstrating positive regulation of ETC function by phosphorylation. Moreover, in silico analyses of the identified phosphopeptide motifs illuminated the molecular nature of participating kinases, which included several known mitochondrial kinases (e.g. pyruvate dehydrogenase kinase) as well as kinases whose mitochondrial location was not previously appreciated (e.g. Src). In conclusion, the phosphorylation events defined herein advance our understanding of cardiac mitochondrial biology, facilitating the integration of the still fragmentary knowledge about mitochondrial signaling networks, metabolic pathways, and intrinsic mechanisms of functional regulation in the heart.Mitochondria are the source of energy to sustain life. In addition to their evolutionary origin as an energy-producing organelle, their functionality has integrated into every aspect of life, including the cell cycle, ROS¹ production, apoptosis, and ion balance (1, 2). Our understanding of mitochondrial biology is still growing. Several systems biology approaches have been dedicated to exploring the molecular infrastructure and dynamics of the functional versatility associated with this organelle (3–5).To meet tissue-specific functional demands, mitochondria acquire heterogeneous properties in individual organs, a first statement of their plasticity in function and proteome composition (1, 6). The heterogeneity is evident even in an individual cardiomyocyte (7). A catalogue of the cardiac mitochondrial proteome is emerging via a joint effort (3–5). The dynamics of the mitochondrial proteome manifest at multiple levels, including post-translational modifications, such as phosphorylation. Our investigative goal is to decode this organellar proteome and its post-translational modification in a biological and functional context. In cardiomyocytes, mitochondria are also constantly exposed to fluctuation in energy demands and in ionic conditions. The capacity of mitochondria to cope with such a dynamic environment is essential for the functional role of mitochondria in normal and disease phenotypes (8–10). Unique protein features enabling the mitochondrial proteome to adapt to these biological changes can be interrogated by proteomics tools (10–12). Protein phosphorylation as a rapid and reversible chemical event is an integral component of these protein features (12–14).It has been estimated that one-third of cellular proteins exist in a phosphorylated state at least one time in their lifetime (15). However, only a handful of phosphorylation events have been identified to tune mitochondrial functionality (13, 14, 16) despite the fact that the first demonstration of phosphorylation was reported on a mitochondrial protein more than 5 decades ago (17). Kinases and phosphatases comprise nearly 3% of the human genome (18, 19). In mitochondria, ∼30 kinases and phosphatases have been identified thus far within the expected organellar proteome of a few thousand (3–5, 16). The number of identified mitochondrial phosphoproteins is far below one-third of its proteome size (20). Thus, it appears that the current pool of reported phosphoproteins represents only a small fraction of the anticipated mitochondrial phosphoproteome. The seminal studies from several groups (12–14, 16) demonstrated the prevalence as well as the dynamic nature of phosphorylation in cardiac mitochondria, suggesting that obtaining a comprehensive map of the mitochondrial phosphoproteome is feasible.In this study, we took a systematic approach to tackle the phosphorylation of murine cardiac mitochondrial pathways. We applied the unique strengths of both electron transfer dissociation (ETD) and collision-induced dissociation (CID) LC-MS/MS to screen phosphorylation events in a site-specific fashion. A total of 236 phosphorylation sites in 203 unique phosphopeptides were identified and mapped to 181 phosphoproteins. Novel phosphorylation modifications were discovered in diverse pathways of mitochondrial biology, including ion balance, proteolysis, and apoptosis. Consistent with the role of mitochondria as the major source of energy production under delicate control, metabolic pathways claimed one-third of phosphorylation sites captured in this analysis. To study molecular players steering mitochondrial phosphorylation, we probed the effects of calcium loading on phosphorylation. In addition, a number of kinases with previously unappreciated mitochondrial residence are suggested as potential players modulating mitochondrial pathways. Taken together, the cohort of novel phosphorylation events discovered in this study constitutes an essential step toward the full delineation of the cardiac mitochondrial phosphoproteome.