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.     

Analysis of Free Energy Signals Arising from Nucleotide Hybridization Between rRNA and mRNA Sequences during Translation in Eubacteria

A decoding algorithm is tested that mechanistically models the progressive alignments that arise as the mRNA moves past the rRNA tail during translation elongation. Each of these alignments provides an opportunity for hybridization between the single-stranded, -terminal nucleotides of the 16S rRNA and the spatially accessible window of mRNA sequence, from which a free energy value can be calculated. Using this algorithm we show that a periodic, energetic pattern of frequency 1/3 is revealed. This periodic signal exists in the majority of coding regions of eubacterial genes, but not in the non-coding regions encoding the 16S and 23S rRNAs. Signal analysis reveals that the population of coding regions of each bacterial species has a mean phase that is correlated in a statistically significant way with species () content. These results suggest that the periodic signal could function as a synchronization signal for the maintenance of reading frame and that codon usage provides a mechanism for manipulation of signal phase.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

Algorithms for Finding Small Attractors in Boolean Networks

A Boolean network is a model used to study the interactions between different genes in genetic regulatory networks. In this paper, we present several algorithms using gene ordering and feedback vertex sets to identify singleton attractors and small attractors in Boolean networks. We analyze the average case time complexities of some of the proposed algorithms. For instance, it is shown that the outdegree-based ordering algorithm for finding singleton attractors works in time for , which is much faster than the naive time algorithm, where is the number of genes and is the maximum indegree. We performed extensive computational experiments on these algorithms, which resulted in good agreement with theoretical results. In contrast, we give a simple and complete proof for showing that finding an attractor with the shortest period is NP-hard.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32]     

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.     

The Wavelet-Based Cluster Analysis for Temporal Gene Expression Data

A variety of high-throughput methods have made it possible to generate detailed temporal expression data for a single gene or large numbers of genes. Common methods for analysis of these large data sets can be problematic. One challenge is the comparison of temporal expression data obtained from different growth conditions where the patterns of expression may be shifted in time. We propose the use of wavelet analysis to transform the data obtained under different growth conditions to permit comparison of expression patterns from experiments that have time shifts or delays. We demonstrate this approach using detailed temporal data for a single bacterial gene obtained under 72 different growth conditions. This general strategy can be applied in the analysis of data sets of thousands of genes under different conditions.[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]     

The Effect of Proteasome Inhibition on the Generation of the Human Leukocyte Antigen (HLA) Peptidome

The Major histocompatibility complex (MHC) class I peptidome is thought to be generated mostly through proteasomal degradation of cellular proteins, a notion that is based on the alterations in presentation of selected peptides following proteasome inhibition. We evaluated the effects of proteasome inhibitors, epoxomicin and bortezomib, on human cultured cancer cells. Because the inhibitors did not reduce the level of presentation of the cell surface human leukocyte antigen (HLA) molecules, we followed their effects on the rates of synthesis of both HLA peptidome and proteome of the cells, using dynamic stable isotope labeling in tissue culture (dynamic-SILAC). The inhibitors reduced the rates of synthesis of most cellular proteins and HLA peptides, yet the synthesis rates of some of the proteins and HLA peptides was not decreased by the inhibitors and of some even increased. Therefore, we concluded that the inhibitors affected the production of the HLA peptidome in a complex manner, including modulation of the synthesis rates of the source proteins of the HLA peptides, in addition to their effect on their degradation. The collected data may suggest that the current reliance on proteasome inhibition may overestimate the centrality of the proteasome in the generation of the MHC peptidome. It is therefore suggested that the relative contribution of the proteasomal and nonproteasomal pathways to the production of the MHC peptidome should be revaluated in accordance with the inhibitors effects on the synthesis rates of the source proteins of the MHC peptides.The repertoires and levels of peptides, presented by the major histocompatibility complex (MHC)¹ class I molecules at the cells'' surface, are modulated by multiple factors. These include the rates of synthesis and degradation of their source proteins, the transport efficacy of the peptides through the transporter associated with antigen processing (TAP) into the endoplasmic reticulum (ER), their subsequent processing and loading onto the MHC molecules within the ER, and the rates of transport of the MHC molecules with their peptide cargo to the cell surface. The off-rates of the presented peptides, the residence time of the MHC complexes at the cell surface, and their retrograde transport back into the cytoplasm, influence, as well, the presented peptidomes (reviewed in (1)). Even though significant portions of the MHC class I peptidomes are thought to be derived from newly synthesized proteins, including misfolded proteins, defective ribosome products (DRiPs), and short lived proteins (SLiPs), most of the MHC peptidome is assumed to originate from long-lived proteins, which completed their functional cellular roles or became defective (retirees), (reviewed in (2)).The main protease, supplying the MHC peptidome production pipeline, is thought to be the proteasome (3). It is also responsible for generation of the final carboxyl termini of the MHC peptides (4), (reviewed in (5)). The final trimming of the n-termini of the peptides, within the endoplasmic reticulum (ER), is thought to be performed by amino peptidases, such as ERAP1/ERAAP, which fit the peptides into their binding groove on the MHC molecules (6) (reviewed in (7)). Nonproteasomal proteolytic pathways were also suggested as possible contributors to the MHC peptidome, including proteolysis by the ER resident Signal peptide peptidase (8, 9), the cytoplasmic proteases Insulin degrading enzyme (10), Tripeptidyl peptidase (11–13), and a number of proteases within the endolysosome pathway (reviewed recently in (14–17)). In contrast to the mostly cytoplasmic and ER production of the MHC class I peptidome, the class II peptidome is produced in a special compartment, associated with the endolysosome pathway (18–20). This pathway is also thought to participate in the cross presentation of class I peptides, derived from proteins up-taken by professional antigen presenting cells (21), (reviewed in (15–17, 22)).The centrality of the proteasomes in the generation of the MHC peptidome was deduced mostly from the observed change in presentation levels of small numbers of selected peptides, following proteasome inhibition (3, 23). Even the location of some of the genes encoding the catalytic subunits of the immunoproteasome (LMP2 and LMP7) (24) within the MHC class II genomic locus, was suggested to support the involvement of the proteasome in the generation of the MHC class I peptidome (25). Similar conclusions were deduced from alterations in peptide presentation, following expression of the catalytic subunits of the immunoproteasome (26), (reviewed in (5)). Yet, although most of the reports indicated reductions in presentation of selected peptides by proteasome inhibition (3, 27–29), others have observed only limited, and sometimes even opposite effects (23, 30–32).The matter is further complicated by the indirect effects of proteasome inhibition used for such studies on the arrest of protein synthesis by the cells (33–35), on the transport rates of the MHC molecules to the cell surface, and on their retrograde transport back to the vesicular system (36) (reviewed in (37)). Proteasome inhibition likely causes shortage of free ubiquitin, reduced supply of free amino acids, and induces an ER unfolded protein response (UPR), which signals the cells to block most (but not all) cellular protein synthesis (reviewed in (38)). Because a significant portion of the MHC peptidome originates from degradation of DRiPs and SLiPs (reviewed in (2)), arrest of new protein synthesis should influence the presentation of their derived MHC peptides. Taken together, these arguments may suggest that merely following the changes in the presentation levels of the MHC molecules, or even of specific MHC peptides, after proteasome inhibition, does not provide the full picture for deducing the relative contribution of the proteasomal pathway to the production of the MHC peptidome (reviewed in (7)).MHC peptidome analysis can be performed relatively easily by modern capillary chromatography combined with mass spectrometry (reviewed in (39)). The peptides are recovered from immunoaffinity purified MHC molecules after detergent solubilization of the cells (40, 41), from soluble MHC molecules secreted to the cells'' growth medium (42, 43) or from patients'' plasma (44). The purified peptides pools are resolved by capillary chromatography and the individual peptides are identified and quantified by tandem mass spectrometry (40), (reviewed in (45–47)). In cultured cells, quantitative analysis can also be followed by metabolic incorporation of stable isotope labeled amino acids (SILAC) (48). Furthermore, the rates of de novo synthesis of both MHC peptides and their proteins of origin can be followed using the dynamic-SILAC proteomics approach (49) with its further adaptation to HLA peptidomics (50–52).This study attempts to define the relative contribution of the proteasomes to the production of HLA class I peptidome by simultaneously following the effects of proteasome inhibitors, epoxomicin and bortezomib (Velcade), on the rates of de novo synthesis of both the HLA class I peptidome and the cellular proteome of the same MCF-7 human breast cancer cultured cells. The proteasome inhibitors did not reduce the levels of HLA presentations, yet affected the rates of production of both the HLA peptidome and cellular proteome, mostly decreasing, but also increasing some of the synthesis rates of the HLA peptides and cellular proteins. Thus, we suggest that the degree of contribution of the proteasomal pathway to the production of the HLA-I peptidome should be re-evaluated in accordance with their effects on the entire HLA class-I peptidome, while taking into consideration the inhibitors'' effects on the synthesis (and degradation) rates of the source proteins of each of the studied HLA peptides.     

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]     

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.[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]     

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.     

��-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.     

Combinatorial Approach for Large-scale Identification of Linked Peptides from Tandem Mass Spectrometry Spectra

The combination of chemical cross-linking and mass spectrometry has recently been shown to constitute a powerful tool for studying protein–protein interactions and elucidating the structure of large protein complexes. However, computational methods for interpreting the complex MS/MS spectra from linked peptides are still in their infancy, making the high-throughput application of this approach largely impractical. Because of the lack of large annotated datasets, most current approaches do not capture the specific fragmentation patterns of linked peptides and therefore are not optimal for the identification of cross-linked peptides. Here we propose a generic approach to address this problem and demonstrate it using disulfide-bridged peptide libraries to (i) efficiently generate large mass spectral reference data for linked peptides at a low cost and (ii) automatically train an algorithm that can efficiently and accurately identify linked peptides from MS/MS spectra. We show that using this approach we were able to identify thousands of MS/MS spectra from disulfide-bridged peptides through comparison with proteome-scale sequence databases and significantly improve the sensitivity of cross-linked peptide identification. This allowed us to identify 60% more direct pairwise interactions between the protein subunits in the 20S proteasome complex than existing tools on cross-linking studies of the proteasome complexes. The basic framework of this approach and the MS/MS reference dataset generated should be valuable resources for the future development of new tools for the identification of linked peptides.The study of protein–protein interactions is crucial to understanding how cellular systems function because proteins act in concert through a highly organized set of interactions. Most cellular processes are carried out by large macromolecular assemblies and regulated through complex cascades of transient protein–protein interactions (1). In the past several years numerous high-throughput studies have pioneered the systematic characterization of protein–protein interactions in model organisms (2–4). Such studies mainly utilize two techniques: the yeast two-hybrid system, which aims at identifying binary interactions (5), and affinity purification combined with tandem mass spectrometry analysis for the identification of multi-protein assemblies (6–8). Together these led to a rapid expansion of known protein–protein interactions in human and other model organisms. Patche and Aloy recently estimated that there are more than one million interactions catalogued to date (9).But despite rapid progress, most current techniques allow one to determine only whether proteins interact, which is only the first step toward understanding how proteins interact. A more complete picture comes from characterizing the three-dimensional structures of protein complexes, which provide mechanistic insights that govern how interactions occur and the high specificity observed inside the cell. Traditionally the gold-standard methods used to solve protein structures are x-ray crystallography and NMR, and there have been several efforts similar to structural genomics (10) aiming to comprehensively solve the structures of protein complexes (11, 12). Although there has been accelerated growth of structures for protein monomers in the Protein Data Bank in recent years (11), the growth of structures for protein complexes has remained relatively small (9). Many factors, including their large size, transient nature, and dynamics of interactions, have prevented many complexes from being solved via traditional approaches in structural biology. Thus, the development of complementary analytical techniques with which to probe the structure of large protein complexes continues to evolve (13–18).Recent developments have advanced the analysis of protein structures and interaction by combining cross-linking and tandem mass spectrometry (17, 19–24). The basic idea behind this technique is to capture and identify pairs of amino acid residues that are spatially close to each other. When these linked pairs of residues are from the same protein (intraprotein cross-links), they provide distance constraints that help one infer the possible conformations of protein structures. Conversely, when pairs of residues come from different proteins (interprotein cross-links), they provide information about how proteins interact with one another. Although cross-linking strategies date back almost a decade (25, 26), difficulty in analyzing the complex MS/MS spectrum generated from linked peptides made this approach challenging, and therefore it was not widely used. With recent advances in mass spectrometry instrumentation, there has been renewed interest in employing this strategy to determine protein structures and identify protein–protein interactions. However, most studies thus far have been focused on purified protein complexes. With today''s mass spectrometers being capable of analyzing tens of thousands of spectra in a single experiment, it is now potentially feasible to extend this approach to the analysis of complex biological samples. Researchers have tried to realize this goal using both experimental and computational approaches. Indeed, a plethora of chemical cross-linking reagents are now available for stabilizing these complexes, and some are designed to allow for easier peptide identification when employed in concert with MS analysis (20, 27, 28). There have also been several recent efforts to develop computational methods for the automatic identification of linked peptides from MS/MS spectra (29–36). However, because of the lack of large annotated training data, most approaches to date either borrow fragmentation models learned from unlinked, linear peptides or learn the fragmentation statistics from training data of limited size (30, 37), which might not generalize well across different samples. In some cases it is possible to generate relatively large training data, but it is often very labor intensive and involves hundreds of separate LC-MS/MS runs (36). Here, employing disulfide-bridged peptides as an example, we propose a novel method that uses a combinatorial peptide library to (a) efficiently generate a large mass spectral reference dataset for linked peptides and (b) use these data to automatically train our new algorithm, MXDB, which can efficiently and accurately identify linked peptides from MS/MS spectra.     

Question Processing and Clustering in INDOC: A Biomedical Question Answering System

The exponential growth in the volume of publications in the biomedical domain has made it impossible for an individual to keep pace with the advances. Even though evidence-based medicine has gained wide acceptance, the physicians are unable to access the relevant information in the required time, leaving most of the questions unanswered. This accentuates the need for fast and accurate biomedical question answering systems. In this paper we introduce INDOC—a biomedical question answering system based on novel ideas of indexing and extracting the answer to the questions posed. INDOC displays the results in clusters to help the user arrive the most relevant set of documents quickly. Evaluation was done against the standard OHSUMED test collection. Our system achieves high accuracy and minimizes user effort.[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24]