Development of a Novel Cross-linking Strategy for Fast and Accurate Identification of Cross-linked Peptides of Protein Complexes

Knowledge of elaborate structures of protein complexes is fundamental for understanding their functions and regulations. Although cross-linking coupled with mass spectrometry (MS) has been presented as a feasible strategy for structural elucidation of large multisubunit protein complexes, this method has proven challenging because of technical difficulties in unambiguous identification of cross-linked peptides and determination of cross-linked sites by MS analysis. In this work, we developed a novel cross-linking strategy using a newly designed MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). DSSO contains two symmetric collision-induced dissociation (CID)-cleavable sites that allow effective identification of DSSO-cross-linked peptides based on their distinct fragmentation patterns unique to cross-linking types (i.e. interlink, intralink, and dead end). The CID-induced separation of interlinked peptides in MS/MS permits MS³ analysis of single peptide chain fragment ions with defined modifications (due to DSSO remnants) for easy interpretation and unambiguous identification using existing database searching tools. Integration of data analyses from three generated data sets (MS, MS/MS, and MS³) allows high confidence identification of DSSO cross-linked peptides. The efficacy of the newly developed DSSO-based cross-linking strategy was demonstrated using model peptides and proteins. In addition, this method was successfully used for structural characterization of the yeast 20 S proteasome complex. In total, 13 non-redundant interlinked peptides of the 20 S proteasome were identified, representing the first application of an MS-cleavable cross-linker for the characterization of a multisubunit protein complex. Given its effectiveness and simplicity, this cross-linking strategy can find a broad range of applications in elucidating the structural topology of proteins and protein complexes.Proteins form stable and dynamic multisubunit complexes under different physiological conditions to maintain cell viability and normal cell homeostasis. Detailed knowledge of protein interactions and protein complex structures is fundamental to understanding how individual proteins function within a complex and how the complex functions as a whole. However, structural elucidation of large multisubunit protein complexes has been difficult because of a lack of technologies that can effectively handle their dynamic and heterogeneous nature. Traditional methods such as nuclear magnetic resonance (NMR) analysis and x-ray crystallography can yield detailed information on protein structures; however, NMR spectroscopy requires large quantities of pure protein in a specific solvent, whereas x-ray crystallography is often limited by the crystallization process.In recent years, chemical cross-linking coupled with mass spectrometry (MS) has become a powerful method for studying protein interactions (1–3). Chemical cross-linking stabilizes protein interactions through the formation of covalent bonds and allows the detection of stable, weak, and/or transient protein-protein interactions in native cells or tissues (4–9). In addition to capturing protein interacting partners, many studies have shown that chemical cross-linking can yield low resolution structural information about the constraints within a molecule (2, 3, 10) or protein complex (11–13). The application of chemical cross-linking, enzymatic digestion, and subsequent mass spectrometric and computational analyses for the elucidation of three-dimensional protein structures offers distinct advantages over traditional methods because of its speed, sensitivity, and versatility. Identification of cross-linked peptides provides distance constraints that aid in constructing the structural topology of proteins and/or protein complexes. Although this approach has been successful, effective detection and accurate identification of cross-linked peptides as well as unambiguous assignment of cross-linked sites remain extremely challenging due to their low abundance and complicated fragmentation behavior in MS analysis (2, 3, 10, 14). Therefore, new reagents and methods are urgently needed to allow unambiguous identification of cross-linked products and to improve the speed and accuracy of data analysis to facilitate its application in structural elucidation of large protein complexes.A number of approaches have been developed to facilitate MS detection of low abundance cross-linked peptides from complex mixtures. These include selective enrichment using affinity purification with biotinylated cross-linkers (15–17) and click chemistry with alkyne-tagged (18) or azide-tagged (19, 20) cross-linkers. In addition, Staudinger ligation has recently been shown to be effective for selective enrichment of azide-tagged cross-linked peptides (21). Apart from enrichment, detection of cross-linked peptides can be achieved by isotope-labeled (22–24), fluorescently labeled (25), and mass tag-labeled cross-linking reagents (16, 26). These methods can identify cross-linked peptides with MS analysis, but interpretation of the data generated from interlinked peptides (two peptides connected with the cross-link) by automated database searching remains difficult. Several bioinformatics tools have thus been developed to interpret MS/MS data and determine interlinked peptide sequences from complex mixtures (12, 14, 27–32). Although promising, further developments are still needed to make such data analyses as robust and reliable as analyzing MS/MS data of single peptide sequences using existing database searching tools (e.g. Protein Prospector, Mascot, or SEQUEST).Various types of cleavable cross-linkers with distinct chemical properties have been developed to facilitate MS identification and characterization of cross-linked peptides. These include UV photocleavable (33), chemical cleavable (19), isotopically coded cleavable (24), and MS-cleavable reagents (16, 26, 34–38). MS-cleavable cross-linkers have received considerable attention because the resulting cross-linked products can be identified based on their characteristic fragmentation behavior observed during MS analysis. Gas-phase cleavage sites result in the detection of a “reporter” ion (26), single peptide chain fragment ions (35–38), or both reporter and fragment ions (16, 34). In each case, further structural characterization of the peptide product ions generated during the cleavage reaction can be accomplished by subsequent MSⁿ1 analysis. Among these linkers, the “fixed charge” sulfonium ion-containing cross-linker developed by Lu et al. (37) appears to be the most attractive as it allows specific and selective fragmentation of cross-linked peptides regardless of their charge and amino acid composition based on their studies with model peptides.Despite the availability of multiple types of cleavable cross-linkers, most of the applications have been limited to the study of model peptides and single proteins. Additionally, complicated synthesis and fragmentation patterns have impeded most of the known MS-cleavable cross-linkers from wide adaptation by the community. Here we describe the design and characterization of a novel and simple MS-cleavable cross-linker, DSSO, and its application to model peptides and proteins and the yeast 20 S proteasome complex. In combination with new software developed for data integration, we were able to identify DSSO-cross-linked peptides from complex peptide mixtures with speed and accuracy. Given its effectiveness and simplicity, we anticipate a broader application of this MS-cleavable cross-linker in the study of structural topology of other protein complexes using cross-linking and mass spectrometry.     

DeMix Workflow for Efficient Identification of Cofragmented Peptides in High Resolution Data-dependent Tandem Mass Spectrometry

Based on conventional data-dependent acquisition strategy of shotgun proteomics, we present a new workflow DeMix, which significantly increases the efficiency of peptide identification for in-depth shotgun analysis of complex proteomes. Capitalizing on the high resolution and mass accuracy of Orbitrap-based tandem mass spectrometry, we developed a simple deconvolution method of “cloning” chimeric tandem spectra for cofragmented peptides. Additional to a database search, a simple rescoring scheme utilizes mass accuracy and converts the unwanted cofragmenting events into a surprising advantage of multiplexing. With the combination of cloning and rescoring, we obtained on average nine peptide-spectrum matches per second on a Q-Exactive workbench, whereas the actual MS/MS acquisition rate was close to seven spectra per second. This efficiency boost to 1.24 identified peptides per MS/MS spectrum enabled analysis of over 5000 human proteins in single-dimensional LC-MS/MS shotgun experiments with an only two-hour gradient. These findings suggest a change in the dominant “one MS/MS spectrum - one peptide” paradigm for data acquisition and analysis in shotgun data-dependent proteomics. DeMix also demonstrated higher robustness than conventional approaches in terms of lower variation among the results of consecutive LC-MS/MS runs.Shotgun proteomics analysis based on a combination of high performance liquid chromatography and tandem mass spectrometry (MS/MS) (1) has achieved remarkable speed and efficiency (2–7). In a single four-hour long high performance liquid chromatography-MS/MS run, over 40,000 peptides and 5000 proteins can be identified using a high-resolution Orbitrap mass spectrometer with data-dependent acquisition (DDA)¹ (2, 3). However, in a typical LC-MS analysis of unfractionated human cell lysate, over 100,000 individual peptide isotopic patterns can be detected (4), which corresponds to simultaneous elution of hundreds of peptides. With this complexity, a mass spectrometer needs to achieve ≥25 Hz MS/MS acquisition rate to fully sample all the detectable peptides, and ≥17 Hz to cover reasonably abundant ones (4). Although this acquisition rate is reachable by modern time-of-flight (TOF) instruments, the reported DDA identification results do not encompass all expected peptides. Recently, the next-generation Orbitrap instrument, working at 20 Hz MS/MS acquisition rate, demonstrated nearly full profiling of yeast proteome using an 80 min gradient, which opened the way for comprehensive analysis of human proteome in a time efficient manner (5).During the high performance liquid chromatography-MS/MS DDA analysis of complex samples, high density of co-eluting peptides results in a high probability for two or more peptides to overlap within an MS/MS isolation window. With the commonly used ±1.0–2.0 Th isolation windows, most MS/MS spectra are chimeric (4, 8–10), with cofragmenting precursors being naturally multiplexed. However, as has been discussed previously (9, 10), the cofragmentation events are currently ignored in most of the conventional analysis workflows. According to the prevailing assumption of “one MS/MS spectrum–one peptide,” chimeric MS/MS spectra are generally unwelcome in DDA, because the product ions from different precursors may interfere with the assignment of MS/MS fragment identities, increasing the rate of false discoveries in database search (8, 9). In some studies, the precursor isolation width was set as narrow as ±0.35 Th to prevent unwanted ions from being coselected, fragmented or detected (4, 5).On the contrary, multiplexing by cofragmentation is considered to be one of the solid advantages in data-independent acquisition (DIA) (10–13). In several commonly used DIA methods, the precursor ion selection windows are set much wider than in DDA: from 25 Th as in SWATH (12), to extremely broad range as in AIF (13). In order to use the benefit of MS/MS multiplexing in DDA, several approaches have been proposed to deconvolute chimeric MS/MS spectra. In “alternative peptide identification” method implemented in Percolator (14), a machine learning algorithm reranks and rescores peptide-spectrum matches (PSMs) obtained from one or more MS/MS search engines. But the deconvolution in Percolator is limited to cofragmented peptides with masses differing from the target peptide by the tolerance of the database search, which can be as narrow as a few ppm. The “active demultiplexing” method proposed by Ledvina et al. (15) actively separates MS/MS data from several precursors using masses of complementary fragments. However, higher-energy collisional dissociation often produces MS/MS spectra with too few complementary pairs for reliable peptide identification. The “MixDB” method introduces a sophisticated new search engine, also with a machine learning algorithm (9). And the “second peptide identification” method implemented in Andromeda/MaxQuant workflow (16) submits the same dataset to the search engine several times based on the list of chromatographic peptide features, subtracting assigned MS/MS peaks after each identification round. This approach is similar to the ProbIDTree search engine that also performed iterative identification while removing assigned peaks after each round of identification (17).One important factor for spectral deconvolution that has not been fully utilized in most conventional workflows is the excellent mass accuracy achievable with modern high-resolution mass spectrometry (18). An Orbitrap Fourier-transform mass spectrometer can provide mass accuracy in the range of hundreds of ppb (parts per billion) for mass peaks with high signal-to-noise (S/N) ratio (19). However, the mass error of peaks with lower S/N ratios can be significantly higher and exceed 1 ppm. Despite this dependence of the mass accuracy from the S/N level, most MS and MS/MS search engines only allow users to set hard cut-off values for the mass error tolerances. Moreover, some search engines do not provide the option of choosing a relative error tolerance for MS/MS fragments. Such negligent treatment of mass accuracy reduces the analytical power of high accuracy experiments (18).Identification results coming from different MS/MS search engines are sometimes not consistent because of different statistical assumptions used in scoring PSMs. Introduction of tools integrating the results of different search engines (14, 20, 21) makes the data interpretation even more complex and opaque for the user. The opposite trend—simplification of MS/MS data interpretation—is therefore a welcome development. For example, an extremely straightforward algorithm recently proposed by Wenger et al. (22) demonstrated a surprisingly high performance in peptide identification, even though it is only marginally more complex than simply counting the number of matches of theoretical fragment peaks in high resolution MS/MS, without any a priori statistical assumption.In order to take advantage of natural multiplexing of MS/MS spectra in DDA, as well as properly utilize high accuracy of Orbitrap-based mass spectrometry, we developed a simple and robust data analysis workflow DeMix. It is presented in Fig. 1 as an expansion of the conventional workflow. Principles of some of the processes used by the workflow are borrowed from other approaches, including the custom-made mass peak centroiding (20), chromatographic feature detection (19, 20), and two-pass database search with the first limited pass to provide a “software lock mass” for mass scale recalibration (23).Open in a separate windowFig. 1.An overview of the DeMix workflow that expands the conventional workflow, shown by the dashed line. Processes are colored in purple for TOPP, red for search engine (Morpheus/Mascot/MS-GF+), and blue for in-house programs.In DeMix workflow, the deconvolution of chimeric MS/MS spectra consists of simply “cloning” an MS/MS spectrum if a potential cofragmented peptide is detected. The list of candidate peptide precursors is generated from chromatographic feature detection, as in the MaxQuant/Andromeda workflow (16, 19), but using The OpenMS Proteomics Pipeline (TOPP) (20, 24). During the cloning, the precursor is replaced by the new candidate, but no changes in the MS/MS fragment list are made, and therefore the cloned MS/MS spectra remain chimeric. Processing such spectra requires a search engine tolerant to the presence of unassigned peaks, as such peaks are always expected when multiple precursors cofragment. Thus, we chose Morpheus (22) as a search engine. Based on the original search algorithm, we implement a reformed scoring scheme: Morpheus-AS (advanced scoring). It inherits all the basic principles from Morpheus but deeper utilizes the high mass accuracy of the data. This kind of database search removes the necessity of spectral processing for physical separation of MS/MS data into multiple subspectra (15), or consecutive subtraction of peaks (16, 17).Despite the fact that DeMix workflow is largely a combination of known approaches, it provides remarkable improvement compared with the state-of-the-art. On our Orbitrap Q-Exactive workbench, testing on a benchmark dataset of two-hour single-dimension LC-MS/MS experiments from HeLa cell lysate, we identified on average 1.24 peptide per MS/MS spectrum, breaking the “one MS/MS spectrum–one peptide” paradigm on the level of whole data set. At 1% false discovery rate (FDR), we obtained on average nine PSMs per second (at the actual acquisition rate of ca. seven MS/MS spectra per second), and detected 40 human proteins per minute.     

JUMP: A Tag-based Database Search Tool for Peptide Identification with High Sensitivity and Accuracy

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from “mixture spectra” to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.Peptide identification by tandem mass spectra is a critical step in mass spectrometry (MS)-based¹ proteomics (1). Numerous computational algorithms and software tools have been developed for this purpose (2–6). These algorithms can be classified into three categories: (i) pattern-based database search, (ii) de novo sequencing, and (iii) hybrid search that combines database search and de novo sequencing. With the continuous development of high-performance liquid chromatography and high-resolution mass spectrometers, it is now possible to analyze almost all protein components in mammalian cells (7). In contrast to rapid data collection, it remains a challenge to extract accurate information from the raw data to identify peptides with low false positive rates (specificity) and minimal false negatives (sensitivity) (8).Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS² (19), and Morpheus (20). Some other programs, such as SpectraST (21) and Pepitome (22), utilize a spectral library composed of experimentally identified and validated MS/MS spectra. These methods use a variety of scoring algorithms to rank potential peptide spectrum matches (PSMs) and select the top hit as a putative PSM. However, not all PSMs are correctly assigned. For example, false peptides may be assigned to MS/MS spectra with numerous noisy peaks and poor fragmentation patterns. If the samples contain unknown protein modifications, mutations, and contaminants, the related MS/MS spectra also result in false positives, as their corresponding peptides are not in the database. Other false positives may be generated simply by random matches. Therefore, it is of importance to remove these false PSMs to improve dataset quality. One common approach is to filter putative PSMs to achieve a final list with a predefined false discovery rate (FDR) via a target-decoy strategy, in which decoy proteins are merged with target proteins in the same database for estimating false PSMs (23–26). However, the true and false PSMs are not always distinguishable based on matching scores. It is a problem to set up an appropriate score threshold to achieve maximal sensitivity and high specificity (13, 27, 28).De novo methods, including Lutefisk (29), PEAKS (30), NovoHMM (31), PepNovo (32), pNovo (33), Vonovo (34), and UniNovo (35), identify peptide sequences directly from MS/MS spectra. These methods can be used to derive novel peptides and post-translational modifications without a database, which is useful, especially when the related genome is not sequenced. High-resolution MS/MS spectra greatly facilitate the generation of peptide sequences in these de novo methods. However, because MS/MS fragmentation cannot always produce all predicted product ions, only a portion of collected MS/MS spectra have sufficient quality to extract partial or full peptide sequences, leading to lower sensitivity than achieved with the database search methods.To improve the sensitivity of the de novo methods, a hybrid approach has been proposed to integrate peptide sequence tags into PSM scoring during database searches (36). Numerous software packages have been developed, such as GutenTag (37), InsPecT (38), Byonic (39), DirecTag (40), and PEAKS DB (41). These methods use peptide tag sequences to filter a protein database, followed by error-tolerant database searching. One restriction in most of these algorithms is the requirement of a minimum tag length of three amino acids for matching protein sequences in the database. This restriction reduces the sensitivity of the database search, because it filters out some high-quality spectra in which consecutive tags cannot be generated.In this paper, we describe JUMP, a novel tag-based hybrid algorithm for peptide identification. The program is optimized to balance sensitivity and specificity during tag derivation and MS/MS pattern matching. JUMP can use all potential sequence tags, including tags consisting of only one amino acid. When we compared its performance to that of two widely used search algorithms, SEQUEST and Mascot, JUMP identified ∼30% more PSMs at the same FDR threshold. In addition, the program provides two additional features: (i) using tag sequences to improve modification site assignment, and (ii) analyzing co-fragmented peptides from mixture MS/MS spectra.     

Endogenous Peptide Discovery of the Rat Circadian Clock: A FOCUSED STUDY OF THE SUPRACHIASMATIC NUCLEUS BY ULTRAHIGH PERFORMANCE TANDEM MASS SPECTROMETRY*

Understanding how a small brain region, the suprachiasmatic nucleus (SCN), can synchronize the body''s circadian rhythms is an ongoing research area. This important time-keeping system requires a complex suite of peptide hormones and transmitters that remain incompletely characterized. Here, capillary liquid chromatography and FTMS have been coupled with tailored software for the analysis of endogenous peptides present in the SCN of the rat brain. After ex vivo processing of brain slices, peptide extraction, identification, and characterization from tandem FTMS data with <5-ppm mass accuracy produced a hyperconfident list of 102 endogenous peptides, including 33 previously unidentified peptides, and 12 peptides that were post-translationally modified with amidation, phosphorylation, pyroglutamylation, or acetylation. This characterization of endogenous peptides from the SCN will aid in understanding the molecular mechanisms that mediate rhythmic behaviors in mammals.Central nervous system neuropeptides function in cell-to-cell signaling and are involved in many physiological processes such as circadian rhythms, pain, hunger, feeding, and body weight regulation (1–4). Neuropeptides are produced from larger protein precursors by the selective action of endopeptidases, which cleave at mono- or dibasic sites and then remove the C-terminal basic residues (1, 2). Some neuropeptides undergo functionally important post-translational modifications (PTMs),1 including amidation, phosphorylation, pyroglutamylation, or acetylation. These aspects of peptide synthesis impact the properties of neuropeptides, further expanding their diverse physiological implications. Therefore, unveiling new peptides and unreported peptide properties is critical to advancing our understanding of nervous system function.Historically, the analysis of neuropeptides was performed by Edman degradation in which the N-terminal amino acid is sequentially removed. However, analysis by this method is slow and does not allow for sequencing of the peptides containing N-terminal PTMs (5). Immunological techniques, such as radioimmunoassay and immunohistochemistry, are used for measuring relative peptide levels and spatial localization, but these methods only detect peptide sequences with known structure (6). More direct, high throughput methods of analyzing brain regions can be used.Mass spectrometry, a rapid and sensitive method that has been used for the analysis of complex biological samples, can detect and identify the precise forms of neuropeptides without prior knowledge of peptide identity, with these approaches making up the field of peptidomics (7–12). The direct tissue and single neuron analysis by MALDI MS has enabled the discovery of hundreds of neuropeptides in the last decade, and the neuronal homogenate analysis by fractionation and subsequent ESI or MALDI MS has yielded an equivalent number of new brain peptides (5). Several recent peptidome studies, including the work by Dowell et al. (10), have used the specificity of FTMS for peptide discovery (10, 13–15). Here, we combine the ability to fragment ions at ultrahigh mass accuracy (16) with a software pipeline designed for neuropeptide discovery. We use nanocapillary reversed-phase LC coupled to 12 Tesla FTMS for the analysis of peptides present in the suprachiasmatic nucleus (SCN) of rat brain.A relatively small, paired brain nucleus located at the base of the hypothalamus directly above the optic chiasm, the SCN contains a biological clock that generates circadian rhythms in behaviors and homeostatic functions (17, 18). The SCN comprises ∼10,000 cellular clocks that are integrated as a tissue level clock which, in turn, orchestrates circadian rhythms throughout the brain and body. It is sensitive to incoming signals from the light-sensing retina and other brain regions, which cause temporal adjustments that align the SCN appropriately with changes in environmental or behavioral state. Previous physiological studies have implicated peptides as critical synchronizers of normal SCN function as well as mediators of SCN inputs, internal signal processing, and outputs; however, only a small number of peptides have been identified and explored in the SCN, leaving unresolved many circadian mechanisms that may involve peptide function.Most peptide expression in the SCN has only been studied through indirect antibody-based techniques (19–29), although we recently used MS approaches to characterize several peptides detected in SCN releasates (30). Previous studies indicate that the SCN expresses a rich diversity of peptides relative to other brain regions studied with the same techniques. Previously used immunohistochemical approaches are not only inadequate for comprehensively evaluating PTMs and alternate isoforms of known peptides but are also incapable of exhaustively examining the full peptide complement of this complex biological network of peptidergic inputs and intrinsic components. A comprehensive study of SCN peptidomics is required that utilizes high resolution strategies for directly analyzing the peptide content of the neuronal networks comprising the SCN.In our study, the SCN was obtained from ex vivo coronal brain slices via tissue punch and subjected to multistage peptide extraction. The SCN tissue extract was analyzed by FTMS/MS, and the high resolution MS and MS/MS data were processed using ProSightPC 2.0 (16), which allows the identification and characterization of peptides or proteins from high mass accuracy MS/MS data. In addition, the Sequence Gazer included in ProSightPC was used for manually determining PTMs (31, 32). As a result, a total of 102 endogenous peptides were identified, including 33 that were previously unidentified, and 12 PTMs (including amidation, phosphorylation, pyroglutamylation, and acetylation) were found. The present study is the first comprehensive peptidomics study for identifying peptides present within the mammalian SCN. In fact, this is one of the first peptidome studies to work with discrete brain nuclei as opposed to larger brain structures and follows up on our recent report using LC-ion trap for analysis of the peptides in the supraoptic nucleus (33); here, the use of FTMS allows a greater range of PTMs to be confirmed and allows higher confidence in the peptide assignments. This information on the peptides in the SCN will serve as a basis to more exhaustively explore the extent that previously unreported SCN neuropeptides may function in SCN regulation of mammalian circadian physiology.     

��-Defensins in Enteric Innate Immunity: FUNCTIONAL PANETH CELL ��-DEFENSINS IN MOUSE COLONIC LUMEN*

Paneth cells are a secretory epithelial lineage that release dense core granules rich in host defense peptides and proteins from the base of small intestinal crypts. Enteric α-defensins, termed cryptdins (Crps) in mice, are highly abundant in Paneth cell secretions and inherently resistant to proteolysis. Accordingly, we tested the hypothesis that enteric α-defensins of Paneth cell origin persist in a functional state in the mouse large bowel lumen. To test this idea, putative Crps purified from mouse distal colonic lumen were characterized biochemically and assayed in vitro for bactericidal peptide activities. The peptides comigrated with cryptdin control peptides in acid-urea-PAGE and SDS-PAGE, providing identification as putative Crps. Matrix-assisted laser desorption ionization time-of-flight mass spectrometry experiments showed that the molecular masses of the putative α-defensins matched those of the six most abundant known Crps, as well as N-terminally truncated forms of each, and that the peptides contain six Cys residues, consistent with identities as α-defensins. N-terminal sequencing definitively revealed peptides with N termini corresponding to full-length, (des-Leu)-truncated, and (des-Leu-Arg)-truncated N termini of Crps 1–4 and 6. Crps from mouse large bowel lumen were bactericidal in the low micromolar range. Thus, Paneth cell α-defensins secreted into the small intestinal lumen persist as intact and functional forms throughout the intestinal tract, suggesting that the peptides may mediate enteric innate immunity in the colonic lumen, far from their upstream point of secretion in small intestinal crypts.Antimicrobial peptides (AMPs)² are released by epithelial cells onto mucosal surfaces as effectors of innate immunity (1–5). In mammals, most AMPs derive from two major families, the cathelicidins and defensins (6). The defensins comprise the α-, β-, and θ-defensin subfamilies, which are defined by the presence of six cysteine residues paired in characteristic tridisulfide arrays (7). α-Defensins are highly abundant in two primary cell lineages: phagocytic leukocytes, primarily neutrophils, of myeloid origin and Paneth cells, which are secretory epithelial cells located at the base of the crypts of Lieberkühn in the small intestine (8–10). Neutrophil α-defensins are stored in azurophilic granules and contribute to non-oxidative microbial cell killing in phagolysosomes (11, 12), except in mice whose neutrophils lack defensins (13). In the small bowel, α-defensins and other host defense proteins (14–18) are released apically as components of Paneth cell secretory granules in response to cholinergic stimulation and after exposure to bacterial antigens (19). Therefore, the release of Paneth cell products into the crypt lumen is inferred to protect mitotically active crypt cells from colonization by potential pathogens and confer protection against enteric infection (7, 20, 21).Under normal, homeostatic conditions, Paneth cells are not found outside the small bowel, although they may appear ectopically in response to local inflammation throughout the gastrointestinal tract (22, 23). Paneth cell numbers increase progressively throughout the small intestine, occurring at highest numbers in the distal ileum (24). Mouse Paneth cells express numerous α-defensin isoforms, termed cryptdins (Crps) (25), that have broad spectrum antimicrobial activities (6, 26). Collectively, α-defensins constitute approximately seventy percent of the bactericidal peptide activity in mouse Paneth cell secretions (19), selectively killing bacteria by membrane-disruptive mechanisms (27–30). The role of Paneth cell α-defensins in gastrointestinal mucosal immunity is evident from studies of mice transgenic for human enteric α-defensin-5, HD-5, which are immune to infection by orally administered Salmonella enterica sv. typhimurium (S. typhimurium) (31).The biosynthesis of mature, bactericidal α-defensins from their inactive precursors requires activation by lineage-specific proteolytic convertases. In mouse Paneth cells, inactive ∼8.4-kDa Crp precursors are processed intracellularly into microbicidal ∼4-kDa Crps by specific cleavage events mediated by matrix metalloproteinase-7 (MMP-7) (32, 33). MMP-7 null mice exhibit increased susceptibility to systemic S. typhimurium infection and decreased clearance of orally administered non-invasive Escherichia coli (19, 32). Although the α-defensin proregions are sensitive to proteolysis, the mature, disulfide-stabilized peptides resist digestion by their converting enzymes in vitro, whether the convertase is MMP-7 (32), trypsin (34), or neutrophil serine proteinases (35). Because α-defensins resist proteolysis in vitro, we hypothesized that Paneth cell α-defensins resist degradation and remain in a functional state in the large bowel, a complex, hostile environment containing varied proteases of both host and microbial origin.Here, we report on the isolation and characterization of a population of enteric α-defensins from the mouse colonic lumen. Full-length and N-terminally truncated Paneth cell α-defensins were identified and are abundant in the distal large bowel lumen.     

High-definition De Novo Sequencing of Crustacean Hyperglycemic Hormone (CHH)-family Neuropeptides

A complete understanding of the biological functions of large signaling peptides (>4 kDa) requires comprehensive characterization of their amino acid sequences and post-translational modifications, which presents significant analytical challenges. In the past decade, there has been great success with mass spectrometry-based de novo sequencing of small neuropeptides. However, these approaches are less applicable to larger neuropeptides because of the inefficient fragmentation of peptides larger than 4 kDa and their lower endogenous abundance. The conventional proteomics approach focuses on large-scale determination of protein identities via database searching, lacking the ability for in-depth elucidation of individual amino acid residues. Here, we present a multifaceted MS approach for identification and characterization of large crustacean hyperglycemic hormone (CHH)-family neuropeptides, a class of peptide hormones that play central roles in the regulation of many important physiological processes of crustaceans. Six crustacean CHH-family neuropeptides (8–9.5 kDa), including two novel peptides with extensive disulfide linkages and PTMs, were fully sequenced without reference to genomic databases. High-definition de novo sequencing was achieved by a combination of bottom-up, off-line top-down, and on-line top-down tandem MS methods. Statistical evaluation indicated that these methods provided complementary information for sequence interpretation and increased the local identification confidence of each amino acid. Further investigations by MALDI imaging MS mapped the spatial distribution and colocalization patterns of various CHH-family neuropeptides in the neuroendocrine organs, revealing that two CHH-subfamilies are involved in distinct signaling pathways.Neuropeptides and hormones comprise a diverse class of signaling molecules involved in numerous essential physiological processes, including analgesia, reward, food intake, learning and memory (1). Disorders of the neurosecretory and neuroendocrine systems influence many pathological processes. For example, obesity results from failure of energy homeostasis in association with endocrine alterations (2, 3). Previous work from our lab used crustaceans as model organisms found that multiple neuropeptides were implicated in control of food intake, including RFamides, tachykinin related peptides, RYamides, and pyrokinins (4–6).Crustacean hyperglycemic hormone (CHH)¹ family neuropeptides play a central role in energy homeostasis of crustaceans (7–17). Hyperglycemic response of the CHHs was first reported after injection of crude eyestalk extract in crustaceans. Based on their preprohormone organization, the CHH family can be grouped into two sub-families: subfamily-I containing CHH, and subfamily-II containing molt-inhibiting hormone (MIH) and mandibular organ-inhibiting hormone (MOIH). The preprohormones of the subfamily-I have a CHH precursor related peptide (CPRP) that is cleaved off during processing; and preprohormones of the subfamily-II lack the CPRP (9). Uncovering their physiological functions will provide new insights into neuroendocrine regulation of energy homeostasis.Characterization of CHH-family neuropeptides is challenging. They are comprised of more than 70 amino acids and often contain multiple post-translational modifications (PTMs) and complex disulfide bridge connections (7). In addition, physiological concentrations of these peptide hormones are typically below picomolar level, and most crustacean species do not have available genome and proteome databases to assist MS-based sequencing.MS-based neuropeptidomics provides a powerful tool for rapid discovery and analysis of a large number of endogenous peptides from the brain and the central nervous system. Our group and others have greatly expanded the peptidomes of many model organisms (3, 18–33). For example, we have discovered more than 200 neuropeptides with several neuropeptide families consisting of as many as 20–40 members in a simple crustacean model system (5, 6, 25–31, 34). However, a majority of these neuropeptides are small peptides with 5–15 amino acid residues long, leaving a gap of identifying larger signaling peptides from organisms without sequenced genome. The observed lack of larger size peptide hormones can be attributed to the lack of effective de novo sequencing strategies for neuropeptides larger than 4 kDa, which are inherently more difficult to fragment using conventional techniques (34–37). Although classical proteomics studies examine larger proteins, these tools are limited to identification based on database searching with one or more peptides matching without complete amino acid sequence coverage (36, 38).Large populations of neuropeptides from 4–10 kDa exist in the nervous systems of both vertebrates and invertebrates (9, 39, 40). Understanding their functional roles requires sufficient molecular knowledge and a unique analytical approach. Therefore, developing effective and reliable methods for de novo sequencing of large neuropeptides at the individual amino acid residue level is an urgent gap to fill in neurobiology. In this study, we present a multifaceted MS strategy aimed at high-definition de novo sequencing and comprehensive characterization of the CHH-family neuropeptides in crustacean central nervous system. The high-definition de novo sequencing was achieved by a combination of three methods: (1) enzymatic digestion and LC-tandem mass spectrometry (MS/MS) bottom-up analysis to generate detailed sequences of proteolytic peptides; (2) off-line LC fractionation and subsequent top-down MS/MS to obtain high-quality fragmentation maps of intact peptides; and (3) on-line LC coupled to top-down MS/MS to allow rapid sequence analysis of low abundance peptides. Combining the three methods overcomes the limitations of each, and thus offers complementary and high-confidence determination of amino acid residues. We report the complete sequence analysis of six CHH-family neuropeptides including the discovery of two novel peptides. With the accurate molecular information, MALDI imaging and ion mobility MS were conducted for the first time to explore their anatomical distribution and biochemical properties.     

Proteomics on an Orbitrap Benchtop Mass Spectrometer Using All-ion Fragmentation

The orbitrap mass analyzer combines high sensitivity, high resolution, and high mass accuracy in a compact format. In proteomics applications, it is used in a hybrid configuration with a linear ion trap (LTQ-Orbitrap) where the linear trap quadrupole (LTQ) accumulates, isolates, and fragments peptide ions. Alternatively, isolated ions can be fragmented by higher energy collisional dissociation. A recently introduced stand-alone orbitrap analyzer (Exactive) also features a higher energy collisional dissociation cell but cannot isolate ions. Here we report that this instrument can efficiently characterize protein mixtures by alternating MS and “all-ion fragmentation” (AIF) MS/MS scans in a manner similar to that previously described for quadrupole time-of-flight instruments. We applied the peak recognition algorithms of the MaxQuant software at both the precursor and product ion levels. Assignment of fragment ions to co-eluting precursor ions was facilitated by high resolution (100,000 at m/z 200) and high mass accuracy. For efficient fragmentation of different mass precursors, we implemented a stepped collision energy procedure with cumulative MS readout. AIF on the Exactive identified 45 of 48 proteins in an equimolar protein standard mixture and all of them when using a small database. The technique also identified proteins with more than 100-fold abundance differences in a high dynamic range standard. When applied to protein identification in gel slices, AIF unambiguously characterized an immunoprecipitated protein that was barely visible by Coomassie staining and quantified it relative to contaminating proteins. AIF on a benchtop orbitrap instrument is therefore an attractive technology for a wide range of proteomics analyses.Mass spectrometry (MS)-based proteomics is commonly performed in a “shotgun” format where proteins are digested to peptides, which are separated and analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) (1, 2). Many peptides typically co-elute from the column and are selected for fragmentation on the basis of their abundance (“data dependent acquisition”). The precursor mass, which can be determined with high mass accuracy in most current instruments, together with a list of fragment ions, which are often determined at lower mass accuracy, are together used to identify the peptide in a sequence database. This scheme is the basis of most of current proteomics research from the identification of single protein bands to the comprehensive characterization of entire proteomes. To minimize stochastic effects from the selection of peptides for fragmentation and to maximize coverage in complex mixtures, very high sequencing speed is desirable. Although this is achievable, it requires complex instrumentation, and there is still no guarantee that all peptides in a mixture are fragmented and identified. Illustrating this challenge, when the Association of Biomolecular Resource Facilities (ABRF)¹ and the Human Proteome Organisation (HUPO) conducted studies of protein identification success in different laboratories, results were varying (4, 5).² Despite using state of the art proteomics workflows, often with extensive fractionation, only a few laboratories correctly identified all of the proteins in an equimolar 49-protein mixture (ABRF) or a 20-protein mixture (HUPO).As an alternative to data-dependent shotgun proteomics, the mass spectrometer can be operated to fragment the entire mass range of co-eluting analytes. This approach has its roots in precursor ion scanning techniques in which all precursors were fragmented simultaneously either in the source region or in the collision cell, and the appearance of specific “reporter ions” for a modification of interest was recorded (6–8). Several groups reported the identification of peptides from MS scans in conjunction with MS/MS scans without precursor ion selection (9–12). Yates and co-workers (13) pursued an intermediate strategy by cycling through the mass range in 10 m/z fragmentation windows. The major challenge of data-independent acquisition is that the direct relationship between precursor and fragments is lost. In most of the above studies, this problem was alleviated by making use of the fact that precursors and fragments have to “co-elute.”In recent years, data-independent proteomics has mainly been pursued on the quadrupole TOF platform where it has been termed MS^E in analogy to MS², MS³, and MSⁿ techniques used for fragmenting one peptide at a time. Geromanos and co-workers (14–16) applied MS^E to absolute quantification of proteins in mixtures. Another study showed excellent protein coverage of yeast enolase with data-independent peptide fragmentation where enolase peptide intensities varied over 2 orders of magnitude (17). In a recent comparison of data-dependent and -independent peptide fragmentation, the authors concluded that fragmentation information was highly comparable (18, 19).Recently, the orbitrap mass analyzer (20–23) has been introduced in a benchtop format without the linear ion trap that normally performs ion accumulation, fragmentation, and analysis of the fragments. This instrument, termed Exactive, was developed for small molecule applications such as metabolite analysis. It can be obtained with a higher energy collisional dissociation (HCD) cell (24), enabling efficient fragmentation but no precursor ion selection. This option is called “all-ion fragmentation” (AIF) by the manufacturer, and this is the term that we use below. We reasoned that the high resolution (100,000 compared with 10,000 in quadrupole TOF) and mass accuracy of this device in both the MS and MS/MS modes might facilitate the analysis of the complex fragmentation spectra generated by dissociating several precursors simultaneously. The simplicity and compactness of this instrumentation platform would then make it interesting for diverse proteomics applications.     

Proinsulin and the Genetics of Diabetes Mellitus

Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.Insulin plays a central role in the regulation of vertebrate metabolism. The hormone, the post-translational product of a single-chain precursor, is a globular protein containing two chains, A (21 residues) and B (30 residues). Recent advances in human genetics have identified dominant mutations in the insulin gene causing permanent neonatal-onset DM² (1–4). The mutations are predicted to block folding of the precursor in the ER of pancreatic β-cells. Although expression of the wild-type allele would in other circumstances be sufficient to maintain homeostasis, studies of a corresponding mouse model (5–7) suggest that the misfolded variant perturbs wild-type biosynthesis (8, 9). Impaired β-cell secretion is associated with ER stress, distorted organelle architecture, and cell death (10). These findings have renewed interest in insulin biosynthesis (11–13) and the structural basis of disulfide pairing (14–19). Protein evolution is constrained not only by structure and function but also by susceptibility to toxic misfolding.     

Online Peptide Fractionation Using a Multiphasic Microfluidic Liquid Chromatography Chip Improves Reproducibility and Detection Limits for Quantitation in Discovery and Targeted Proteomics

Comprehensive proteomic profiling of biological specimens usually requires multidimensional chromatographic peptide fractionation prior to mass spectrometry. However, this approach can suffer from poor reproducibility because of the lack of standardization and automation of the entire workflow, thus compromising performance of quantitative proteomic investigations. To address these variables we developed an online peptide fractionation system comprising a multiphasic liquid chromatography (LC) chip that integrates reversed phase and strong cation exchange chromatography upstream of the mass spectrometer (MS). We showed superiority of this system for standardizing discovery and targeted proteomic workflows using cancer cell lysates and nondepleted human plasma. Five-step multiphase chip LC MS/MS acquisition showed clear advantages over analyses of unfractionated samples by identifying more peptides, consuming less sample and often improving the lower limits of quantitation, all in highly reproducible, automated, online configuration. We further showed that multiphase chip LC fractionation provided a facile means to detect many N- and C-terminal peptides (including acetylated N terminus) that are challenging to identify in complex tryptic peptide matrices because of less favorable ionization characteristics. Given as much as 95% of peptides were detected in only a single salt fraction from cell lysates we exploited this high reproducibility and coupled it with multiple reaction monitoring on a high-resolution MS instrument (MRM-HR). This approach increased target analyte peak area and improved lower limits of quantitation without negatively influencing variance or bias. Further, we showed a strategy to use multiphase LC chip fractionation LC-MS/MS for ion library generation to integrate with SWATH^TM data-independent acquisition quantitative workflows. All MS data are available via ProteomeXchange with identifier PXD001464.Mass spectrometry based proteomic quantitation is an essential technique used for contemporary, integrative biological studies. Whether used in discovery experiments or for targeted biomarker applications, quantitative proteomic studies require high reproducibility at many levels. It requires reproducible run-to-run peptide detection, reproducible peptide quantitation, reproducible depth of proteome coverage, and ideally, a high degree of cross-laboratory analytical reproducibility. Mass spectrometry centered proteomics has evolved steadily over the past decade, now mature enough to derive extensive draft maps of the human proteome (1, 2). Nonetheless, a key requirement yet to be realized is to ensure that quantitative proteomics can be carried out in a timely manner while satisfying the aforementioned challenges associated with reproducibility. This is especially important for recent developments using data independent MS quantitation and multiple reaction monitoring on high-resolution MS (MRM-HR)¹ as they are both highly dependent on LC peptide retention time reproducibility and precursor detectability, while attempting to maximize proteome coverage (3). Strategies usually employed to increase the depth of proteome coverage utilize various sample fractionation methods including gel-based separation, affinity enrichment or depletion, protein or peptide chemical modification-based enrichment, and various peptide chromatography methods, particularly ion exchange chromatography (4–10). In comparison to an unfractionated “naive” sample, the trade-off in using these enrichments/fractionation approaches are higher risk of sample losses, introduction of undesired chemical modifications (e.g. oxidation, deamidation, N-terminal lactam formation), and the potential for result skewing and bias, as well as numerous time and human resources required to perform the sample preparation tasks. Online-coupled approaches aim to minimize those risks and address resource constraints. A widely practiced example of the benefits of online sample fractionation has been the decade long use of combining strong cation exchange chromatography (SCX) with C18 reversed-phase (RP) for peptide fractionation (known as MudPIT – multidimensional protein identification technology), where SCX and RP is performed under the same buffer conditions and the SCX elution performed with volatile organic cations compatible with reversed phase separation (11). This approach greatly increases analyte detection while avoiding sample handling losses. The MudPIT approach has been widely used for discovery proteomics (12–14), and we have previously shown that multiphasic separations also have utility for targeted proteomics when configured for selected reaction monitoring MS (SRM-MS). We showed substantial advantages of MudPIT-SRM-MS with reduced ion suppression, increased peak areas and lower limits of detection (LLOD) compared with conventional RP-SRM-MS (15).To improve the reproducibility of proteomic workflows, increase throughput and minimize sample loss, numerous microfluidic devices have been developed and integrated for proteomic applications (16, 17). These devices can broadly be classified into two groups: (1) microfluidic chips for peptide separation (18–25) and; (2) proteome reactors that combine enzymatic processing with peptide based fractionation (26–30). Because of the small dimension of these devices, they are readily able to integrate into nanoLC workflows. Various applications have been described including increasing proteome coverage (22, 27, 28) and targeting of phosphopeptides (24, 31, 32), glycopeptides and released glycans (29, 33, 34).In this work, we set out to take advantage of the benefits of multiphasic peptide separations and address the reproducibility needs required for high-throughput comparative proteomics using a variety of workflows. We integrated a multiphasic SCX and RP column in a “plug-and-play” microfluidic chip format for online fractionation, eliminating the need for users to make minimal dead volume connections between traps and columns. We show the flexibility of this format to provide robust peptide separation and reproducibility using conventional and topical mass spectrometry workflows. This was undertaken by coupling the multiphase liquid chromatography (LC) chip to a fast scanning Q-ToF mass spectrometer for data dependent MS/MS, data independent MS (SWATH) and for targeted proteomics using MRM-HR, showing clear advantages for repeatable analyses compared with conventional proteomic workflows.     

Maximizing Peptide Identification Events in Proteomic Workflows Using Data-Dependent Acquisition (DDA)

Current analytical strategies for collecting proteomic data using data-dependent acquisition (DDA) are limited by the low analytical reproducibility of the method. Proteomic discovery efforts that exploit the benefits of DDA, such as providing peptide sequence information, but that enable improved analytical reproducibility, represent an ideal scenario for maximizing measureable peptide identifications in “shotgun”-type proteomic studies. Therefore, we propose an analytical workflow combining DDA with retention time aligned extracted ion chromatogram (XIC) areas obtained from high mass accuracy MS1 data acquired in parallel. We applied this workflow to the analyses of sample matrixes prepared from mouse blood plasma and brain tissues and observed increases in peptide detection of up to 30.5% due to the comparison of peptide MS1 XIC areas following retention time alignment of co-identified peptides. Furthermore, we show that the approach is quantitative using peptide standards diluted into a complex matrix. These data revealed that peptide MS1 XIC areas provide linear response of over three orders of magnitude down to low femtomole (fmol) levels. These findings argue that augmenting “shotgun” proteomic workflows with retention time alignment of peptide identifications and comparative analyses of corresponding peptide MS1 XIC areas improve the analytical performance of global proteomic discovery methods using DDA.Label-free methods in mass spectrometry-based proteomics, such as those used in common “shotgun” proteomic studies, provide peptide sequence information as well as relative measurements of peptide abundance (1–3). A common data acquisition strategy is based on data-dependent acquisition (DDA)¹ where the most abundant precursor ions are selected for tandem mass spectrometry (MS/MS) analysis (1–2). DDA attempts to minimize redundant peptide precursor selection and maximize the depth of proteome coverage (2). However, the analytical reproducibility of peptide identifications obtained using DDA-based methods result in <75% overlap between technical replicates (3–4). Comparisons of peptide identifications between replicate analyses have shown that the rate of new peptide identifications increases sharply following two replicate sample injections and gradually tapers off after approximately five replicate injections (4). This phenomenon is due, in part, to the semirandom sampling of peptides in a DDA experiment (5).Alternate label-free methods focused on measuring the abundance of intact peptide ions, such as the accurate mass and time tag (AMT) approach (6–8, 42), are aimed at differential analyses of extracted ion chromatogram (XIC) areas integrated from high mass accuracy peptide precursor mass spectra (MS1 spectra) exhibiting discrete chromatographic elution times. This method is particularly powerful for the analysis of post-translationally modified (PTM) peptides as pairing the low abundance of PTM candidates with the variable nature of DDA complicates comparisons between samples (9, 43). However, label-free strategies focused on the analysis of peptide MS1 XIC areas are dependent on a priori knowledge of peptide ions and retention times (2–10). Thus, prospective analyses of samples are needed to assess peptides and their respective retention times. This prospective analysis may not be possible for reagent-limited samples. Further, the usage of previously established peptide features in the analysis of different sample types can be confounded by unknown matrix effects that can produce variable retention time characteristics and peptide ion suppression (2). Therefore, proteomic strategies that make use of DDA, to provide peptide sequence information and identify features within the sample, but that also use MS1 data for comparisons between samples, represent an ideal combination for maximizing measureable peptide identification events in “shotgun” proteomic discovery analyses.Here we describe an analytical workflow that combines traditional DDA methods with the analysis of retention time aligned XIC areas extracted from high mass accuracy peptide precursor MS1 spectra. This method resulted in a 25.1% (±6.6%) increase in measureable peptide identification events across samples of diverse composition because of the inferential extraction of peptide MS1 XIC areas in sample sets lacking corresponding MS/MS events. These findings were observed in measurements of peptide MS1 XIC abundances using sample types ranging from tryptic digests of olfactory bulb tissues dissected from Homer2 knockout and wild-type mice to mouse blood plasma exhibiting differential levels of hemolysis. We further establish that this method is quantitative using a dilution series of known bovine standard peptide concentrations spiked into mouse blood plasma. These data show that comparative analysis between samples should be performed using peptide MS1 data as opposed to semirandomly sampled peptide MS/MS data. This approach maximizes the number of peptides that can be compared between samples.     

Complex Lipid Requirements for SNARE- and SNARE Chaperone-dependent Membrane Fusion

Membrane fusion without lysis has been reconstituted with purified yeast vacuolar SNAREs (soluble N-ethylmaleimide-sensitive factor attachment protein receptors), the SNARE chaperones Sec17p/Sec18p and the multifunctional HOPS complex, which includes a subunit of the SNARE-interactive Sec1-Munc18 family, and vacuolar lipids: phosphatidylcholine (PC), phosphatidylethanolamine (PE), phosphatidylinositol (PI), phosphatidylserine (PS), phosphatidic acid (PA), cardiolipin (CL), ergosterol (ERG), diacylglycerol (DAG), and phosphatidylinositol 3-phosphate (PI3P). We now report that many of these lipids are required for rapid and efficient fusion of the reconstituted SNARE proteoliposomes in the presence of SNARE chaperones. Omission of either PE, PA, or PI3P from the complete set of lipids strongly reduces fusion, and PC, PE, PA, and PI3P constitute a minimal set of lipids for fusion. PA could neither be replaced by other lipids with small headgroups such as DAG or ERG nor by the acidic lipids PS or PI. PA is needed for full association of HOPS and Sec18p with proteoliposomes having a minimal set of lipids. Strikingly, PA and PE are as essential for SNARE complex assembly as for fusion, suggesting that these lipids facilitate functional interactions among SNAREs and SNARE chaperones.Biological membrane fusion is the regulated rearrangement of the lipids in two apposed sealed membranes to form one bilayer while mixing lumenal contents without leakage or lysis. It is fundamental for intracellular vesicular traffic, cell growth and division, regulated secretion of hormones and other blood proteins, and neurotransmission and thus has attracted wide and sustained study (1, 2). Its fundamental mechanisms are conserved and employ a Rab-family GTPase, proteins which bind to the GTP-bound form of a Rab, termed its “effectors” (3), and SNARE³ (soluble N-ethylmaleimide-sensitive factor attachment protein receptors) proteins (4) with their attendant chaperones. SNAREs are integral or peripheral membrane proteins with characteristic heptad-repeat domains, which can associate in 4-helical coiled-coils (5), termed “cis-SNARE complexes,” if they are all anchored to the same membrane bilayer, or “trans-SNARE complexes” if they are anchored to apposed membranes.Stable membrane proximity (docking) does not suffice for fusion. Studies in model systems have shown that fusion can be promoted by any of several agents, which promote bilayer rearrangement, such as diacylglycerol (6), high levels of calcium (7), viral-encoded fusion proteins (8, 9), or SNAREs (10, 11). These studies frequently employed liposomes or proteoliposomes of simple lipid composition, suggesting that fusion may not have stringent requirements of lipid head group species. However, each of these model fusion reactions is accompanied by substantial lysis (12–15), whereas the preservation of subcellular compartments is a hallmark of physiological membrane fusion.We have studied membrane fusion with the vacuole (lysosome) of Saccharomyces cerevisiae (reviewed in Ref. 16). The fusion of isolated vacuoles requires the Rab Ypt7p, 4 SNAREs (Vam3p, Vti1p, Vam7p, and Nyv1p), the SNARE chaperones Sec17p (α-soluble N-ethylmaleimide-sensitive factor attachment protein)/Sec18p (N-ethylmaleimide-sensitive factor) and the hexameric HOPS complex (17), and key “regulatory” lipids including ERG, phosphoinositides, and DAG (18). HOPS interacts physically or functionally with each component of this fusion system. HOPS stably associates with Ypt7p in its GTP-bound state (19). One HOPS subunit, Vps33p, is a member of the Sec1-Munc18 family of SNARE-binding proteins, and HOPS exhibits direct affinity for SNAREs (17, 20–22) and proofreads correct vacuolar SNARE pairing (23). HOPS also has direct affinity for phosphoinositides (17). The SNAREs on isolated vacuoles are in cis-complexes, which are disassembled by Sec17p, Sec18p, and ATP (24). Docking requires Ypt7p (25) and HOPS (17). During docking, vacuoles are drawn against each other until each has a substantial membrane domain tightly apposed to the other. Each of the proteins (26) and lipids (18) required for fusion becomes enriched in a ring-shaped microdomain, the “vertex ring,” which surrounds the two tightly apposed membrane domains. Not only do the proteins depend on each other, in a cascade fashion, for vertex ring enrichment, and the lipids depend on each other for their vertex ring enrichment as well, but the lipids and proteins are mutually interdependent for their enrichment at this ring-shaped microdomain (18, 27). Fusion occurs around the ring, joining the two organelles. The fusion of vacuoles bearing physiological fusion constituents does not cause measurable organelle lysis, although fusion supported exclusively by higher levels of SNARE proteins is accompanied by massive lysis (28), in accord with model liposome studies (14). Thus fusion microdomain assembly and the coordinate action of SNAREs with other proteins and lipids to promote fusion without lysis are central topics in membrane fusion studies.Reconstitution of fusion with pure components allows chemical definition of essential elements of this biologically important reaction. Although SNAREs can drive a slow fusion of PC/PS proteoliposomes (29), this was not stimulated by HOPS and Sec17p/Sec18p (30). SNARE proteoliposomes bearing all the vacuolar lipids (18, 31–33), PC, PE, PI, PS, CL, PA, ERG, DAG, PI3P, and phosphatidylinositol 4,5-bisphosphate (PI(4,5)P₂), showed rapid and efficient fusion that was fully dependent on Sec17p/Sec18p and HOPS (30). The omission of either DAG, ERG, or phosphoinositide from the liposomes caused a marked reduction in fusion (30). We now report that PE and PA are also necessary for rapid and efficient fusion, function in distinct manners, and are required for efficient assembly of newly formed SNARE complexes by the SNARE chaperones Sec17p/Sec18p and HOPS.     

In Vitro and in Vivo Protein-bound Tyrosine Nitration Characterized by Diagonal Chromatography

A new proteomics technique for analyzing 3-nitrotyrosine-containing peptides is presented here. This technique is based on the combined fractional diagonal chromatography peptide isolation procedures by which specific classes of peptides are isolated following a series of identical reverse-phase HPLC separation steps. Here dithionite is used to reduce 3-nitrotyrosine to 3-aminotyrosine peptides, which thereby become more hydrophilic. Our combined fractional diagonal chromatography technique was first applied to characterize tyrosine nitration in tetranitromethane-modified BSA and further led to a high quality list of 335 tyrosine nitration sites in 267 proteins in a peroxynitrite-treated lysate of human Jurkat cells. We then analyzed a serum sample of a C57BL6/J mouse in which septic shock was induced by intravenous Salmonella infection and identified six in vivo nitration events in four serum proteins, thereby illustrating that our technique is sufficiently sensitive to identify rare in vivo tyrosine nitration sites in a very complex background.Nitration of tyrosine to 3-nitrotyrosine is one of several protein modifications occurring during oxidative stress (1, 2). This modification is considered as a two-step process in which a tyrosine radical is first formed followed by the addition of ^•NO₂ yielding 3-nitrotyrosine. One of the mechanisms through which tyrosine can be nitrated is via the peroxynitrite radical (ONOO⁻); however, alternative pathways exist such as nitration by hemoperoxidases in the presence of hydrogen peroxide and nitrite (3) and reaction of the tyrosyl radical with nitric oxide yielding 3-nitrosotyrosine, which can be further oxidized to 3-nitrotyrosine.Nitration of protein-bound tyrosines can have important implications on the structure and activity of proteins (4–6) and is linked to a variety of pathological conditions such as Alzheimer disease (7) and atherosclerosis (8). Proteins containing 3-nitrotyrosine residues have mainly been identified by one- or two-dimensional PAGE followed by Western blotting using 3-nitrotyrosine-specific antibodies (9) or following affinity enrichment (10, 11). However, rather few in vivo tyrosine nitration sites have thus far been mapped onto proteins, and hence, the exact sites of in vivo nitration remain elusive. This is highly likely due to the overall low abundance of this modification as was recently illustrated by the identification of only 31 nitration sites in 29 proteins in a comprehensive analysis of mouse brain tissue covering 7,792 proteins (12). Furthermore, it was estimated that in diseased cells or organs the number of nitrated tyrosines should be as low as 0.00001–0.001% of all tyrosines (5), numbers that clearly indicate the need to enrich for 3-nitrotyrosine peptides prior to mass spectrometric analysis. In addition, several MS and MS/MS detection problems for 3-nitrotyrosine peptides were reported (13, 14) that also influence identification of such peptides.Recently, a procedure for enriching 3-nitrotyrosine peptides was described (10). In brief, reduced and alkylated proteins were digested after which primary amino groups were blocked by acetylation. Nitrotyrosines were then reduced to aminotyrosine using dithionite (15), unveiling novel primary amino groups on which sulfhydryl groups were coupled that allowed binding peptides to thiopropyl-Sepharose beads. In contrast to an earlier affinity-based isolation protocol (16), this improved enrichment procedure was more effective and led to the characterization of 150 tyrosine nitration sites in 102 proteins using a total of 3.1 mg of a mouse brain homogenate sample that was in vitro nitrated (10). However, this procedure requires many modification steps, which, when incomplete, will introduce artifacts (see “Results”). Among others, these explain the rather low number of identified nitrated tyrosines peptides using the high amount of starting material that was in vitro nitrated.Our laboratory adapted diagonal chromatography (17) for contemporary mass spectrometry-driven proteomics. Central in our combined fractional diagonal chromatography (COFRADIC1 (18)) approach is a chemical or enzymatic step that changes the reverse-phase column retention properties of a set of peptides such that these can be isolated. Among others, we developed COFRADIC protocols for isolating peptides carrying amino acid modifications such as phosphorylation (19), N-glycosylation (20), and sialylation (21) or peptides that are the result of protein processing (22–24). Here we describe a COFRADIC procedure for sorting peptides carrying nitrated tyrosines. Peptide sorting is based on a hydrophilic shift after reducing the nitro group to its amino counterpart. The applicability of COFRADIC for analyzing this modification is illustrated by characterization of four 3-nitrotyrosines in BSA treated with tetranitromethane, the mapping of 335 different nitration sites in 267 different proteins starting from 300 μg of an in vitro peroxynitrite (ONOO⁻)-treated Jurkat lysate, and the identification of six unique nitrated tyrosine residues in four serum proteins in a mouse sepsis model.     

Peptide Identification from Mixture Tandem Mass Spectra

The success of high-throughput proteomics hinges on the ability of computational methods to identify peptides from tandem mass spectra (MS/MS). However, a common limitation of most peptide identification approaches is the nearly ubiquitous assumption that each MS/MS spectrum is generated from a single peptide. We propose a new computational approach for the identification of mixture spectra generated from more than one peptide. Capitalizing on the growing availability of large libraries of single-peptide spectra (spectral libraries), our quantitative approach is able to identify up to 98% of all mixture spectra from equally abundant peptides and automatically adjust to varying abundance ratios of up to 10:1. Furthermore, we show how theoretical bounds on spectral similarity avoid the need to compare each experimental spectrum against all possible combinations of candidate peptides (achieving speedups of over five orders of magnitude) and demonstrate that mixture-spectra can be identified in a matter of seconds against proteome-scale spectral libraries. Although our approach was developed for and is demonstrated on peptide spectra, we argue that the generality of the methods allows for their direct application to other types of spectral libraries and mixture spectra.The success of tandem MS (MS/MS¹) approaches to peptide identification is partly due to advances in computational techniques allowing for the reliable interpretation of MS/MS spectra. Mainstream computational techniques mainly fall into two categories: database search approaches that score each spectrum against peptides in a sequence database (1–4) or de novo techniques that directly reconstruct the peptide sequence from each spectrum (5–8). The combination of these methods with advances in high-throughput MS/MS have promoted the accelerated growth of spectral libraries, collections of peptide MS/MS spectra the identification of which were validated by accepted statistical methods (9, 10) and often also manually confirmed by mass spectrometry experts. The similar concept of spectral archives was also recently proposed to denote spectral libraries including “interesting” nonidentified spectra (11) (i.e. recurring spectra with good de novo reconstructions but no database match). The growing availability of these large collections of MS/MS spectra has reignited the development of alternative peptide identification approaches based on spectral matching (12–14) and alignment (15–17) algorithms.However, mainstream approaches were developed under the (often unstated) assumption that each MS/MS spectrum is generated from a single peptide. Although chromatographic procedures greatly contribute to making this a reasonable assumption, there are several situations where it is difficult or even impossible to separate pairs of peptides. Examples include certain permutations of the peptide sequence or post-translational modifications (see (18) for examples of co-eluting histone modification variants). In addition, innovative experimental setups have demonstrated the potential for increased throughput in peptide identification using mixture spectra; examples include data-independent acquisition (19) ion-mobility MS (20), and MS^E strategies (21).To alleviate the algorithmic bottleneck in such scenarios, we describe a computational approach, M-SPLIT (mixture-spectrum partitioning using library of identified tandem mass spectra), that is able to reliably and efficiently identify peptides from mixture spectra, which are generated from a pair of peptides. In brief, a mixture spectrum is modeled as linear combination of two single-peptide spectra, and peptide identification is done by searching against a spectral library. We show that efficient filtration and accurate branch-and-bound strategies can be used to avoid the huge computational cost of searching all possible pairs. Thus equipped, our approach is able to identify the correct matches by considering only a minuscule fraction of all possible matches. Beyond potentially enhancing the identification capabilities of current MS/MS acquisition setups, we argue that the availability of methods to reliably identify MS/MS spectra from mixtures of peptides could enable the collection of MS/MS data using accelerated chromatography setups to obtain the same or better peptide identification results in a fraction of the experimental time currently required for exhaustive peptide separation.     

��ChopNSpice,�� a Mass Spectrometric Approach That Allows Identification of Endogenous Small Ubiquitin-like Modifier-conjugated Peptides

Conjugation of small ubiquitin-like modifier (SUMO) to substrates is involved in a large number of cellular processes. Typically, SUMO is conjugated to lysine residues within a SUMO consensus site; however, an increasing number of proteins are sumoylated on non-consensus sites. To appreciate the functional consequences of sumoylation, the identification of SUMO attachment sites is of critical importance. Discovery of SUMO acceptor sites is usually performed by a laborious mutagenesis approach or using MS. In MS, identification of SUMO acceptor sites in higher eukaryotes is hampered by the large tryptic fragments of SUMO1 and SUMO2/3. MS search engines in combination with known databases lack the possibility to search MSMS spectra for larger modifications, such as sumoylation. Therefore, we developed a simple and straightforward database search tool (“ChopNSpice”) that successfully allows identification of SUMO acceptor sites from proteins sumoylated in vivo and in vitro. By applying this approach we identified SUMO acceptor sites in, among others, endogenous SUMO1, SUMO2, RanBP2, and Ubc9.Post-translational modification with ubiquitin and ubiquitin-like modifiers (Ubls)1 such as SUMO plays an important role in most, if not all, cellular processes (1–6). Conjugation of Ubls to their targets involves an isopeptide bond between the carboxyl group of the modifier and the ε-amino group of a lysine residue within the targets. Attachment of Ubls to specific targets involves an enzymatic cascade. First the Ubls are processed to expose their C-terminal diglycine motif. The mature Ubl is then transferred to its target via a cascade of E1 (activating), E2 (conjugating), and E3 (ligase) enzymes. The conjugation system for SUMO consists of a heterodimeric activating enzyme, Aos1/Uba2; a conjugating enzyme, Ubc9; and E3 ligases, such as RanBP2 or members of the PIAS family. The conjugation status undergoes perpetual change and is governed by a small family of SUMO proteases that hydrolyze the isopeptide bond between SUMO and its target (7, 8). Although in lower eukaryotes only one SUMO is present, vertebrates express at least three different SUMO paralogs: SUMO1, SUMO2, and SUMO3. Mature SUMO2 and SUMO3 (referred to as SUMO2/3) are 97% identical but differ substantially from SUMO1 (∼50% identity).Although the list of known SUMO substrates is growing rapidly, our understanding of the functional consequences for many of these targets is lagging behind. At a molecular level, the functional consequences of SUMO conjugation can be explained by a gain or loss of interaction with other macromolecules (3, 4). SUMO-dependent intramolecular conformational changes have also been described (9, 10). Thus, to appreciate the role that SUMO plays in the regulation of specific substrates, identification of the acceptor site(s) for SUMO conjugation is of key importance.So far, identification of SUMO acceptor sites has relied largely on mutation of the SUMO consensus site, which consists of a short motif with the sequence ψKXE (ψ represents a bulky hydrophobic residue, and X represents any amino acid). This motif is recognized by Ubc9 if presented in an extended conformation (11–13). However, an increasing number of proteins, such as PCNA, E2-25K, Daxx, and USP25, turned out to be sumoylated on lysine residues that do not conform to the SUMO consensus site (14–17). For this category of proteins, as well as for proteins that contain a large number of SUMO consensus sites, the identification of acceptor lysines is a burdensome task that often involves mutagenesis of each lysine residue within the substrate in turn.MS is currently one of the state-of-the-art technologies to identify protein factors and their post-translational modifications in an unbiased and sensitive manner. Several groups have shown that, using overexpressed tagged SUMO, MS can be efficiently exploited to identify endogenous substrates for SUMO conjugation (18–20). However, the identification of SUMO acceptor lysines using MS has remained a more challenging task (18, 21, 23, 24). So far, using tagged SUMO, unbiased identification of acceptor lysines for endogenous substrates has only been observed in Saccharomyces cerevisiae (18). The identification of substrates in higher eukaryotes has been hampered by the large conjugated SUMO peptide that arises upon tryptic digestion (>2154 Da with human SUMO1 and >3568 Da with human SUMO2/3 compared with 484 Da for Smt3 in S. cerevisiae). Such large fragments, in addition to the mass of the conjugated peptide, can impede their in-gel digestion, extraction, detection, and sequencing in MS. To overcome some of these limitations, several different strategies have been developed: 1) mutation of the tryptic fragment of SUMO, yielding a smaller tryptic fragment (23), 2) development of an automated recognition pattern tool (SUMmOn) (24), and 3) identification of targets using an in vitro to in vivo approach (21). Although these approaches have been applied successfully for the identification of SUMO conjugates in vitro and in vivo, unbiased identification of SUMO conjugates in vivo has not been achieved in higher eukaryotes. Another hurdle to such identification of SUMO conjugates is the variety of masses that can theoretically arise for just one SUMO-conjugated lysine in a given protein because of tryptic miscleavages. Thus, the unambiguous identification of SUMO acceptor sites requires the mass of the modified peptide carrying the conjugated SUMO (fragment) to be measured with high accuracy, and most importantly, it requires sequence analysis of the modified peptides. Because available proteomics search engines lack the possibility to search MSMS spectra for larger modifications, e.g. those that occur upon sumoylation, we developed a novel, simple, and straightforward database search tool (“ChopNSpice”) that, in combination with current proteomics search engines (such as MASCOT (25) or SEQUEST (26)), allows one to identify SUMO1 and SUMO2/3 acceptor sites unambiguously. We confirmed this strategy in vitro on various substrates and demonstrate the power of this technique by the identification of acceptor lysines within several endogenous targets from HeLa cells.