A predictive model for identifying proteins by a single peptide match

A multi-platform analysis of human gingival crevicular fluid reveals ferroptosis as a relevant regulated cell death mechanism during the clinical progression of periodontitis

Ferroptosis is implicated in the pathogenesis of numerous chronic-inflammatory diseases, yet its association with progressive periodontitis remains unexplored. To investigate the involvement and significance of ferroptosis in periodontitis progression, we assessed sixteen periodontitis-diagnosed patients. Disease progression was clinically monitored over twelve weeks via weekly clinical evaluations and gingival crevicular fluid (GCF) collection was performed for further analyses. Clinical metrics, proteomic data, in silico methods, and bioinformatics tools were combined to identify protein profiles linked to periodontitis progression and to explore their potential connection with ferroptosis. Subsequent western blot analyses validated key findings. Finally, a single-cell RNA sequencing (scRNA-seq) dataset (GSE164241) for gingival tissues was analyzed to elucidate cellular dynamics during periodontitis progression. Periodontitis progression was identified as occurring at a faster rate than traditionally thought. GCF samples from progressing and non-progressing periodontal sites showed quantitative and qualitatively distinct proteomic profiles. In addition, specific biological processes and molecular functions during progressive periodontitis were revealed and a set of hub proteins, including SNCA, CA1, HBB, SLC4A1, and ANK1 was strongly associated with the clinical progression status of periodontitis. Moreover, we found specific proteins - drivers or suppressors - associated with ferroptosis (SNCA, FTH1, HSPB1, CD44, and GCLC), revealing the co-occurrence of this specific type of regulated cell death during the clinical progression of periodontitis. Additionally, the integration of quantitative proteomic data with scRNA-seq analysis suggested the susceptibility of fibroblasts to ferroptosis. Our analyses reveal proteins and processes linked to ferroptosis for the first time in periodontal patients, which offer new insights into the molecular mechanisms of progressive periodontal disease. These findings may lead to novel diagnostic and therapeutic strategies.

Activity-based protein profiling of serine hydrolases and penicillin-binding proteins in Enterococcus faecium

Enterococcus faecium is a gut commensal bacterium which is gaining increasing relevance as an opportunistic, nosocomial pathogen. Its high level of intrinsic and acquired antimicrobial resistance is causing a lack of treatment options, particularly for infections with vancomycin-resistant strains, and prioritizes the identification and functional validation of novel druggable targets. Here, we use activity-based protein profiling (ABPP), a chemoproteomics approach using functionalized covalent inhibitors, to detect active serine hydrolases across 11 E. faecium and Enterococcus lactis strains. Serine hydrolases are a big and diverse enzyme family, that includes known drug targets such as penicillin-binding proteins (PBPs), whereas other subfamilies are underexplored. Comparative gel-based ABPP using Bocillin-FL revealed strain- and growth condition-dependent variations in PBP activities. Profiling with the broadly serine hydrolase-reactive fluorescent probe fluorophosphonate-TMR showed a high similarity across E. faecium clade A1 strains, but higher variation across A2 and E. lactis strains. To identify these serine hydrolases, we used a biotinylated probe analog allowing for enrichment and identification via liquid chromatography-mass spectrometry. We identified 11 largely uncharacterized targets (α,β-hydrolases, SGNH-hydrolases, phospholipases, and amidases, peptidases) that are druggable and accessible in live vancomycin-resistant E. faecium E745 and may possess vital functions that are to be characterized in future studies.

Biotransformation of Trastuzumab and Pertuzumab in Breast Cancer Patients Assessed by Affinity Enrichment and Ion-Exchange Chromatography

Therapeutic proteins (TPs) are known to be heterogeneous due to modifications that occur during the production process and storage. Modifications may also occur in TPs after their administration to patients due to in vivo biotransformation. Ligand binding assays, which are widely used in the bioanalysis of TPs in body fluids, are typically unable to distinguish such modifications. Liquid chromatography coupled to mass spectrometry is being increasingly used to study modifications in TPs, but its use to study in vivo biotransformation has been limited until now. We present a novel approach that combines affinity enrichment using Affimer reagents with ion-exchange chromatography (IEX) to analyze charge variants of the TPs trastuzumab and pertuzumab in plasma of patients undergoing therapy for HER2-positive breast cancer. Affimer reagents were immobilized via engineered Cys tags to maleimide beads, and the TPs were eluted under acidic conditions followed by rapid neutralization. The enriched TPs were analyzed by cation-exchange chromatography (IEX) using pH-gradient elution, resulting in the separation of about 20 charge variants for trastuzumab and about five charge variants for pertuzumab. A comparison between in vitro stressed TPs spiked into plasma, and TPs enriched from patient plasma showed that the observed profiles were highly similar. This indicates that in vitro stress testing in plasma can mimic the situation in patient plasma, as far as the generation of charge variants is concerned. SIGNIFICANCE STATEMENT: This research attempts to elucidate the modifications that occur in therapeutic proteins (TPs) after they have been administered to patients. This is important because there is little knowledge about the fate of TPs in this regard, and certain modifications could affect their efficiency. Our results show that the modifications discovered are most likely due to a chemical process and are not patient specific.

Influence of endplate avulsion and Modic changes on the inflammation profile of herniated discs: a proteomic and bioinformatic approach

PURPOSE

The aim of this observational radiographic and proteomic study is to explore the influence of both Modic change (MC) and endplate avulsion (EPA) on the inflammation profile of herniated discs using a proteomic and bioinformatics approach.

METHODS

Fifteen nucleus pulposus (NP) harvested from surgery underwent LC-MS/MC analysis, the proteome was subsequently scanned for inflammatory pathways using a bioinformatics approach. All proteins that were identified in inflammatory pathways and Gene Ontology and present in > 7 samples were integrated in a multiple regression analysis with MC and EPA as predictors. Significant proteins were imputed in an interaction and pathway analysis.

RESULTS

Compared to annulus fibrosus tear (AFT), six proteins were significantly altered in EPA: catalase, Fibrinogen beta chain, protein disulfide-isomerase, pigment epithelium-derived factor, osteoprotegerin and lower expression of antithrombin-III, all of which corresponded to an upregulation of pathways involved in coagulation and detoxification of reactive oxygen species (ROS). Moreover, the presence of MC resulted in a significant alteration of nine proteins compared to patients without MC. Patients with MC showed a significantly higher expression of clusterin and lumican, and lower expression of catalase, complement factor B, Fibrinogen beta chain, protein disulfide-isomerase, periostin, Alpha-1-antitrypsin and pigment epithelium-derived factor. Together these altered protein expressions resulted in a downregulation of pathways involved in detoxification of ROS, complement system and immune system. Results were verified by Immunohistochemistry with CD68 cell counts.

CONCLUSION

Both EPA and MC status significantly influence disc inflammation. The beneficial inflammatory signature of EPA illustrates that endplate pathology does not necessarily have to worsen the outcome, but the pathological inflammatory state is dependent on the presence of MC.

Profiling extra cellular matrix associated proteome of human fetal nucleus pulposus in search for regenerative targets

Degeneration of the intervertebral disc is associated with a decrease in extra-cellular matrix (ECM) content due to an imbalance in anabolic and catabolic signaling. Our previous study profiled the core matrisome of fetal NP’s and identified various proteins with anabolic potential for regenerative therapies. This study aims to complement those results by exploring ECM regulators, associated proteins and secreted factors of the fetal nucleus pulposus (NP). Proteomic data of 9 fetal, 7 healthy adults (age 22–79), and 11 degenerated NP’s was analyzed. Based on the selection criteria, a total of 45 proteins were identified, of which 14 were uniquely expressed or upregulated in fetus compared to adult NP’s. Pathway analysis with these proteins revealed a significant upregulation of one pathway and two biological processes, in which 12 proteins were involved. Prolyl 4 hydroxylase (P4HA) 1 and 2, Procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD) 1, and Heat shock protein 47 (SERPINH1) were involved in ‘collagen biosynthesis’ pathway. In addition, PLOD 1, SERPINH1, Annexin A1 and A4, CD109 and Galectin 3 (LGALS3) were all involved in biological process of ‘tissue development’. Furthermore Annexin A1, A4 and A5, LGALS-3 and SERPINF1 were featured in ‘negative regulation of cell death’. In conclusion, additionally to core ECM proteome, this study reveals ECM regulators and ECM affiliated proteins of interest to study for regenerative therapies, and their potential should be validated in future mechanistic experiments.

The skeletome of the red coral Corallium rubrum indicates an independent evolution of biomineralization process in octocorals

Background

The process of calcium carbonate biomineralization has arisen multiple times during metazoan evolution. In the phylum Cnidaria, biomineralization has mostly been studied in the subclass Hexacorallia (i.e. stony corals) in comparison to the subclass Octocorallia (i.e. red corals); the two diverged approximately 600 million years ago. The precious Mediterranean red coral, Corallium rubrum, is an octocorallian species, which produces two distinct high-magnesium calcite biominerals, the axial skeleton and the sclerites. In order to gain insight into the red coral biomineralization process and cnidarian biomineralization evolution, we studied the protein repertoire forming the organic matrix (OM) of its two biominerals.

Results

We combined High-Resolution Mass Spectrometry and transcriptome analysis to study the OM composition of the axial skeleton and the sclerites. We identified a total of 102 OM proteins, 52 are found in the two red coral biominerals with scleritin being the most abundant protein in each fraction. Contrary to reef building corals, the red coral organic matrix possesses a large number of collagen-like proteins. Agrin-like glycoproteins and proteins with sugar-binding domains are also predominant. Twenty-seven and 23 proteins were uniquely assigned to the axial skeleton and the sclerites, respectively. The inferred regulatory function of these OM proteins suggests that the difference between the two biominerals is due to the modeling of the matrix network, rather than the presence of specific structural components. At least one OM component could have been horizontally transferred from prokaryotes early during Octocorallia evolution.

Conclusion

Our results suggest that calcification of the red coral axial skeleton likely represents a secondary calcification of an ancestral gorgonian horny axis. In addition, the comparison with stony coral skeletomes highlighted the low proportion of similar proteins between the biomineral OMs of hexacorallian and octocorallian corals, suggesting an independent acquisition of calcification in anthozoans.

Part 1: profiling extra cellular matrix core proteome of human fetal nucleus pulposus in search for regenerative targets

Intervertebral disc degeneration is accompanied by a loss of Extra-cellular matrix (ECM) due to an imbalance in anabolic and catabolic pathways. Identifying ECM proteins with anabolic and/or regenerative potential could be the key to developing regenerative therapies. Since human fetal discs grow and develop rapidly, studying these discs may provide valuable insights on proteins with regenerative potential. This study compares core matrisome of 9 fetal and 7 healthy adult (age 22-79) nucleus pulposus (NP), using a proteomic and bioinformatic approach. Of the 33 upregulated proteins in fetus NP's, 20 of which were involved in ECM assembly pathways: fibromodulin, biglycan, heparan sulfate proteoglycan 2, chondroitin sulfate proteoglycan 4, procollagen C-endopeptidase enhancer and Collagen-type 1a1, 1a2, 6a1, 6a3, 11a1, 11a2, 12a1, 14a1 and 15a1. Moreover, 10 of the upregulated proteins were involved in growth pathways 'PI3L-Akt signaling' and 'regulation of insulin like growth factor transport and uptake.' Thrombospondin 1,3 and 4, tenascin C, matrilin-3, and collagen- type 1a1, 1a2, 6a1, 6a3 and 9a1. Additionally, matrillin-2 and 'Collagen triple helix repeat containing 1' were identified as possible regenerative proteins due to their involvement in 'Regeneration' and 'tissue development' respectively. In conclusion, the consistency of human fetal NP's differs greatly from that of healthy adults. In view of these outcomes, the core matrisome of human fetal discs contains an abundant number of proteins that could potentially show regenerative properties, and their potential should be explored in future machinal experiments.

Characterization of the Burkholderia cenocepacia J2315 Surface-Exposed Immunoproteome

Infections by the Burkholderia cepacia complex (Bcc) remain seriously life threatening to cystic fibrosis (CF) patients, and no effective eradication is available. A vaccine to protect patients against Bcc infections is a highly attractive therapeutic option, but none is available. A strategy combining the bioinformatics identification of putative surface-exposed proteins with an experimental approach encompassing the “shaving” of surface-exposed proteins with trypsin followed by peptide identification by liquid chromatography and mass spectrometry is here reported. The methodology allowed the bioinformatics identification of 263 potentially surface-exposed proteins, 16 of them also experimentally identified by the “shaving” approach. Of the proteins identified, 143 have a high probability of containing B-cell epitopes that are surface-exposed. The immunogenicity of three of these proteins was demonstrated using serum samples from Bcc-infected CF patients and Western blotting, validating the usefulness of this methodology in identifying potentially immunogenic surface-exposed proteins that might be used for the development of Bcc-protective vaccines.

Soil and leaf litter metaproteomics-a brief guideline from sampling to understanding

The increasing application of soil metaproteomics is providing unprecedented, in-depth characterization of the composition and functionality of in situ microbial communities. Despite recent advances in high-resolution mass spectrometry, soil metaproteomics still suffers from a lack of effective and reproducible protein extraction protocols and standardized data analyses. This review discusses the opportunities and limitations of selected techniques in soil-, and leaf litter metaproteomics, and presents a step-by-step guideline on their application, covering sampling, sample preparation, extraction and data evaluation strategies. In addition, we present recent applications of soil metaproteomics and discuss how such approaches, linking phylogenetics and functionality, can help gain deeper insights into terrestrial microbial ecology. Finally, we strongly recommend that to maximize the insights environmental metaproteomics may provide, such methods should be employed within a holistic experimental approach considering relevant aboveground and belowground ecosystem parameters.

The promise of multi-omics and clinical data integration to identify and target personalized healthcare approaches in autism spectrum disorders

Complex diseases are caused by a combination of genetic and environmental factors, creating a difficult challenge for diagnosis and defining subtypes. This review article describes how distinct disease subtypes can be identified through integration and analysis of clinical and multi-omics data. A broad shift toward molecular subtyping of disease using genetic and omics data has yielded successful results in cancer and other complex diseases. To determine molecular subtypes, patients are first classified by applying clustering methods to different types of omics data, then these results are integrated with clinical data to characterize distinct disease subtypes. An example of this molecular-data-first approach is in research on Autism Spectrum Disorder (ASD), a spectrum of social communication disorders marked by tremendous etiological and phenotypic heterogeneity. In the case of ASD, omics data such as exome sequences and gene and protein expression data are combined with clinical data such as psychometric testing and imaging to enable subtype identification. Novel ASD subtypes have been proposed, such as CHD8, using this molecular subtyping approach. Broader use of molecular subtyping in complex disease research is impeded by data heterogeneity, diversity of standards, and ineffective analysis tools. The future of molecular subtyping for ASD and other complex diseases calls for an integrated resource to identify disease mechanisms, classify new patients, and inform effective treatment options. This in turn will empower and accelerate precision medicine and personalized healthcare.

Can "normal" protein expression ranges be estimated with high-throughput proteomics?

Although biological science discovery often involves comparing conditions to a normal state, in proteomics little is actually known about normal. Two Human Proteome studies featured in Nature offer new insights into protein expression and an opportunity to assess how high-throughput proteomics measures normal protein ranges. We use data from these studies to estimate technical and biological variability in protein expression and compare them to other expression data sets from normal tissue. Results show that measured protein expression across same-tissue replicates vary by ±4- to 10-fold for most proteins. Coefficients of variation (CV) for protein expression measurements range from 62% to 117% across different tissue experiments; however, adjusting for technical variation reduced this variability by as much as 50%. In addition, the CV could also be reduced by limiting comparisons to proteins with at least 3 or more unique peptide identifications as the CV was on average 33% lower than for proteins with 2 or fewer peptide identifications. We also selected 13 housekeeping proteins and genes that were expressed across all tissues with low variability to determine their utility as a reference set for normalization and comparative purposes. These results present the first step toward estimating normal protein ranges by determining the variability in expression measurements through combining publicly available data. They support an approach that combines standard protocols with replicates of normal tissues to estimate normal protein ranges for large numbers of proteins and tissues. This would be a tremendous resource for normal cellular physiology and comparisons of proteomics studies.

Identification of proteins involved in starch and polygalacturonic acid degradation using LC/MS

Plant biomass in the form of cheap wastes, such as straw, corn stalks, wood chips, sawdust, bagasse, pomace, etc., is abundant throughout the world. To convert these wastes into the useful value-added compounds microbial enzymes are the preferred choice. In this paper, we identify enzymes involved in the degradation of starch and polygalacturonic acid using liquid chromatography/mass spectrometry based analysis. We analysed total protein from soil and compost samples. Extracellular proteins from enrichment cultures were analysed in parallel and used as controls in the sample preparation and identification of proteins. In general, both protein sequence coverage and the number of identified peptides were higher in the samples obtained from the enrichment cultures than from the total protein from soil and compost. The influence of the nature of gel (zymography vs. SDS/polyacrylamide) was negligible. Thus, starch and polygalacturonic acid degradation associated proteins can be directly excised from the zymograms without the need to align zymograms with the SDS/polyacrylamide gels. A range of starch and polygalacturonic acid degradation associated enzymes were identified in both total protein samples and extracellular proteins from the enrichment cultures. Our results show that proteins involved in starch and polygalacturonic acid degradation can be identified by liquid chromatography/mass spectrometry from the complex protein mixtures both with and without cultivation of microorganism

MOPED enables discoveries through consistently processed proteomics data

The Model Organism Protein Expression Database (MOPED, http://moped.proteinspire.org) is an expanding proteomics resource to enable biological and biomedical discoveries. MOPED aggregates simple, standardized and consistently processed summaries of protein expression and metadata from proteomics (mass spectrometry) experiments from human and model organisms (mouse, worm, and yeast). The latest version of MOPED adds new estimates of protein abundance and concentration as well as relative (differential) expression data. MOPED provides a new updated query interface that allows users to explore information by organism, tissue, localization, condition, experiment, or keyword. MOPED supports the Human Proteome Project's efforts to generate chromosome- and diseases-specific proteomes by providing links from proteins to chromosome and disease information as well as many complementary resources. MOPED supports a new omics metadata checklist to harmonize data integration, analysis, and use. MOPED's development is driven by the user community, which spans 90 countries and guides future development that will transform MOPED into a multiomics resource. MOPED encourages users to submit data in a simple format. They can use the metadata checklist to generate a data publication for this submission. As a result, MOPED will provide even greater insights into complex biological processes and systems and enable deeper and more comprehensive biological and biomedical discoveries.

Purification of a NAD(P) reductase-like protein from the thermogenic appendix of the Sauromatum guttatum inflorescence

A NAD(P) reductase-like protein with a molecular mass of 34.146 ± 34 Da was purified to homogeneity from the appendix of the inflorescence of the Sauromatum guttatum. On-line liquid chromatography/electrospray ionization-mass spectrometry was used to isolate and quantify the protein. For the identification of the protein, liquid chromatography/electrospray ionization-tandem mass spectrometry analysis of tryptic digests of the protein was carried out. The acquired mass spectra were used for database searching, which led to the identification of a single tryptic peptide. The 12 amino acid tryptic peptide (FLPSEFGNDVDR) was found to be identical to amino acid residues at the positions 108-120 of isoflavone reductase in the Arabidopsis genome. A BLAST search identified this sequence region as unique and specific to a class of NAD(P)-dependent reductases involved in phenylpropanoid biosynthesis. Edman degradation revealed that the protein was N-terminally blocked. The amount of the protein (termed RL, NAD(P) reductase-like protein) increased 60-fold from D-4 (4 days before inflorescence-opening, designated as D-day) to D-Day, and declined the following day, when heat-production ceased. When salicylic acid, the endogenous trigger of heat-production in the Sauromatum appendix, was applied to premature appendices, a fivefold decrease in the amount of RL was detected in the treated section relative to the non-treated section. About 40 % of RL was found in the cytoplasm. Another 30 % was detected in Percoll-purified mitochondria and the rest, about 30 % was associated with a low speed centrifugation pellet due to nuclei and amyloplast localization. RL was also found in other thermogenic plants and detected in Arabidopsis leaves. The function of RL in thermogenic and non-thermogenic plants requires further investigation.

Protective role of the PG1036-PG1037-PG1038 operon in oxidative stress in Porphyromonas gingivalis W83

As an anaerobe, Porphyromonas gingivalis is significantly affected by the harsh inflammatory environment of the periodontal pocket during initial colonization and active periodontal disease. We reported previously that the repair of oxidative stress-induced DNA damage involving 8-oxo-7,8-dihydroguanine (8-oxoG) may occur by an undescribed mechanism in P. gingivalis. DNA affinity fractionation identified PG1037, a conserved hypothetical protein, among other proteins, that were bound to the 8-oxoG lesion. PG1037 is part of the uvrA-PG1037-pcrA operon in P. gingivalis which is known to be upregulated under H2O2 induced stress. A PCR-based linear transformation method was used to inactivate the uvrA and pcrA genes by allelic exchange mutagenesis. Several attempts to inactivate PG1037 were unsuccessful. Similar to the wild-type when plated on Brucella blood agar, the uvrA and pcrA-defective mutants were black-pigmented and beta-hemolytic. These isogenic mutants also had reduced gingipain activities and were more sensitive to H2O2 and UV irradiation compared to the parent strain. Additionally, glycosylase assays revealed that 8-oxoG repair activities were similar in both wild-type and mutant P. gingivalis strains. Several proteins, some of which are known to have oxidoreducatse activity, were shown to interact with PG1037. The purified recombinant PG1037 protein could protect DNA from H2O2-induced damage. Collectively, these findings suggest that the uvrA-PG1037-pcrA operon may play an important role in hydrogen peroxide stress-induced resistance in P. gingivalis.

Unraveling the Complexities of Life Sciences Data

The life sciences have entered into the realm of big data and data-enabled science, where data can either empower or overwhelm. These data bring the challenges of the 5 Vs of big data: volume, veracity, velocity, variety, and value. Both independently and through our involvement with DELSA Global (Data-Enabled Life Sciences Alliance, DELSAglobal.org), the Kolker Lab ( kolkerlab.org ) is creating partnerships that identify data challenges and solve community needs. We specialize in solutions to complex biological data challenges, as exemplified by the community resource of MOPED (Model Organism Protein Expression Database, MOPED.proteinspire.org ) and the analysis pipeline of SPIRE (Systematic Protein Investigative Research Environment, PROTEINSPIRE.org ). Our collaborative work extends into the computationally intensive tasks of analysis and visualization of millions of protein sequences through innovative implementations of sequence alignment algorithms and creation of the Protein Sequence Universe tool (PSU). Pushing into the future together with our collaborators, our lab is pursuing integration of multi-omics data and exploration of biological pathways, as well as assigning function to proteins and porting solutions to the cloud. Big data have come to the life sciences; discovering the knowledge in the data will bring breakthroughs and benefits.

Paramecium bursaria chlorella virus 1 proteome reveals novel architectural and regulatory features of a giant virus

The 331-kbp chlorovirus Paramecium bursaria chlorella virus 1 (PBCV-1) genome was resequenced and annotated to correct errors in the original 15-year-old sequence; 40 codons was considered the minimum protein size of an open reading frame. PBCV-1 has 416 predicted protein-encoding sequences and 11 tRNAs. A proteome analysis was also conducted on highly purified PBCV-1 virions using two mass spectrometry-based protocols. The mass spectrometry-derived data were compared to PBCV-1 and its host Chlorella variabilis NC64A predicted proteomes. Combined, these analyses revealed 148 unique virus-encoded proteins associated with the virion (about 35% of the coding capacity of the virus) and 1 host protein. Some of these proteins appear to be structural/architectural, whereas others have enzymatic, chromatin modification, and signal transduction functions. Most (106) of the proteins have no known function or homologs in the existing gene databases except as orthologs with proteins of other chloroviruses, phycodnaviruses, and nuclear-cytoplasmic large DNA viruses. The genes encoding these proteins are dispersed throughout the virus genome, and most are transcribed late or early-late in the infection cycle, which is consistent with virion morphogenesis.

Comparative omics-driven genome annotation refinement: application across Yersiniae

Genome sequencing continues to be a rapidly evolving technology, yet most downstream aspects of genome annotation pipelines remain relatively stable or are even being abandoned. The annotation process is now performed almost exclusively in an automated fashion to balance the large number of sequences generated. One possible way of reducing errors inherent to automated computational annotations is to apply data from omics measurements (i.e. transcriptional and proteomic) to the un-annotated genome with a proteogenomic-based approach. Here, the concept of annotation refinement has been extended to include a comparative assessment of genomes across closely related species. Transcriptomic and proteomic data derived from highly similar pathogenic Yersiniae (Y. pestis CO92, Y. pestis Pestoides F, and Y. pseudotuberculosis PB1/+) was used to demonstrate a comprehensive comparative omic-based annotation methodology. Peptide and oligo measurements experimentally validated the expression of nearly 40% of each strain's predicted proteome and revealed the identification of 28 novel and 68 incorrect (i.e., observed frameshifts, extended start sites, and translated pseudogenes) protein-coding sequences within the three current genome annotations. Gene loss is presumed to play a major role in Y. pestis acquiring its niche as a virulent pathogen, thus the discovery of many translated pseudogenes, including the insertion-ablated argD, underscores a need for functional analyses to investigate hypotheses related to divergence. Refinements included the discovery of a seemingly essential ribosomal protein, several virulence-associated factors, a transcriptional regulator, and many hypothetical proteins that were missed during annotation.

Identifying and quantifying proteolytic events and the natural N terminome by terminal amine isotopic labeling of substrates

Analysis of the sequence and nature of protein N termini has many applications. Defining the termini of proteins for proteome annotation in the Human Proteome Project is of increasing importance. Terminomics analysis of protease cleavage sites in degradomics for substrate discovery is a key new application. Here we describe the step-by-step procedures for performing terminal amine isotopic labeling of substrates (TAILS), a 2- to 3-d (depending on method of labeling) high-throughput method to identify and distinguish protease-generated neo-N termini from mature protein N termini with all natural modifications with high confidence. TAILS uses negative selection to enrich for all N-terminal peptides and uses primary amine labeling-based quantification as the discriminating factor. Labeling is versatile and suited to many applications, including biochemical and cell culture analyses in vitro; in vivo analyses using tissue samples from animal and human sources can also be readily performed. At the protein level, N-terminal and lysine amines are blocked by dimethylation (formaldehyde/sodium cyanoborohydride) and isotopically labeled by incorporating heavy and light dimethylation reagents or stable isotope labeling with amino acids in cell culture labels. Alternatively, easy multiplex sample analysis can be achieved using amine blocking and labeling with isobaric tags for relative and absolute quantification, also known as iTRAQ. After tryptic digestion, N-terminal peptide separation is achieved using a high-molecular-weight dendritic polyglycerol aldehyde polymer that binds internal tryptic and C-terminal peptides that now have N-terminal alpha amines. The unbound naturally blocked (acetylation, cyclization, methylation and so on) or labeled mature N-terminal and neo-N-terminal peptides are recovered by ultrafiltration and analyzed by tandem mass spectrometry (MS/MS). Hierarchical substrate winnowing discriminates substrates from the background proteolysis products and non-cleaved proteins by peptide isotope quantification and bioinformatics search criteria.

IPM: An integrated protein model for false discovery rate estimation and identification in high-throughput proteomics

In high-throughput mass spectrometry proteomics, peptides and proteins are not simply identified as present or not present in a sample, rather the identifications are associated with differing levels of confidence. The false discovery rate (FDR) has emerged as an accepted means for measuring the confidence associated with identifications. We have developed the Systematic Protein Investigative Research Environment (SPIRE) for the purpose of integrating the best available proteomics methods. Two successful approaches to estimating the FDR for MS protein identifications are the MAYU and our current SPIRE methods. We present here a method to combine these two approaches to estimating the FDR for MS protein identifications into an integrated protein model (IPM). We illustrate the high quality performance of this IPM approach through testing on two large publicly available proteomics datasets. MAYU and SPIRE show remarkable consistency in identifying proteins in these datasets. Still, IPM results in a more robust FDR estimation approach and additional identifications, particularly among low abundance proteins. IPM is now implemented as a part of the SPIRE system.

SPIRE: Systematic protein investigative research environment

The SPIRE (Systematic Protein Investigative Research Environment) provides web-based experiment-specific mass spectrometry (MS) proteomics analysis (https://www.proteinspire.org). Its emphasis is on usability and integration of the best analytic tools. SPIRE provides an easy to use web-interface and generates results in both interactive and simple data formats. In contrast to run-based approaches, SPIRE conducts the analysis based on the experimental design. It employs novel methods to generate false discovery rates and local false discovery rates (FDR, LFDR) and integrates the best and complementary open-source search and data analysis methods. The SPIRE approach of integrating X!Tandem, OMSSA and SpectraST can produce an increase in protein IDs (52-88%) over current combinations of scoring and single search engines while also providing accurate multi-faceted error estimation. One of SPIRE's primary assets is combining the results with data on protein function, pathways and protein expression from model organisms. We demonstrate some of SPIRE's capabilities by analyzing mitochondrial proteins from the wild type and 3 mutants of C. elegans. SPIRE also connects results to publically available proteomics data through its Model Organism Protein Expression Database (MOPED). SPIRE can also provide analysis and annotation for user supplied protein ID and expression data.

Cysteine tagging for MS-based proteomics

Amino acid-tagging strategies are widespread in proteomics. Because of the central role of mass spectrometry (MS) as a detection technique in protein sciences, the term "mass tagging" was coined to describe the attachment of a label, which serves MS analysis and/or adds analytical value to the measurements. These so-called mass tags can be used for separation, enrichment, detection, and quantitation of peptides and proteins. In this context, cysteine is a frequent target for modifications because the thiol function can react specifically by nucleophilic substitution or addition. Furthermore, cysteines present natural modifications of biological importance and a low occurrence in the proteome that justify the development of strategies to specifically target them in peptides or proteins. In the present review, the mass-tagging methods directed to cysteine residues are comprehensively discussed, and the advantages and drawbacks of these strategies are addressed. Some concrete applications are given to underline the relevance of cysteine-tagging techniques for MS-based proteomics.

Identification of outer membrane proteins from an Antarctic bacterium Pseudomonas syringae Lz4W

Subcellular fractionation of proteins is a preferred method of choice for detection and identification of proteins from complex mixtures such as bacterial cells. To characterize the membrane proteins of the Antarctic bacterium Pseudomonas syringae Lz4W, the membrane fractions were prepared using three different methods, namely Triton X-100 solubilization, sucrose density gradient, and carbonate extraction methods. The proteins were separated on one-dimensional polyacrylamide gels and analyzed using a combination of liquid chromatography-coupled electrospray ionization-MS. The membrane proteins that were prepared by carbonate extraction were separated on two-dimensional PAGE in different pI ranges using the detergent 2% amidosulfobetaine (ASB). The proteins were then subjected to matrix-assisted laser desorption ionization-time-of-flight/time-of-flight for analysis and identification. Because the genome sequence of P. syringae Lz4W is not known, the proteins were identified by using the relevant sequence databases of the Pseudomonas sp available at National Centre for Biotechnology Information (NCBI). The sequence identification of some tryptic peptides were validated by de novo sequencing and others by chemical modification and mass spectrometry. The peptide sequences of P. syringae Lz4W were then matched with the sequences of the peptides from different Pseudomonas sp. by similarity search of the proteins from different species using clustal W2 program. Thus by using a combination of the methods, we have been able to identify large number of proteins of this bacterial strain, which include most of the outer membrane proteins.

Cross-sample validation provides enhanced proteome coverage in rat vocal fold mucosa

The vocal fold mucosa is a biomechanically unique tissue comprised of a densely cellular epithelium, superficial to an extracellular matrix (ECM)-rich lamina propria. Such ECM-rich tissues are challenging to analyze using proteomic assays, primarily due to extensive crosslinking and glycosylation of the majority of high M_r ECM proteins. In this study, we implemented an LC-MS/MS-based strategy to characterize the rat vocal fold mucosa proteome. Our sample preparation protocol successfully solubilized both proteins and certain high M_r glycoconjugates and resulted in the identification of hundreds of mucosal proteins. A straightforward approach to the treatment of protein identifications attributed to single peptide hits allowed the retention of potentially important low abundance identifications (validated by a cross-sample match and de novo interpretation of relevant spectra) while still eliminating potentially spurious identifications (global single peptide hits with no cross-sample match). The resulting vocal fold mucosa proteome was characterized by a wide range of cellular and extracellular proteins spanning 12 functional categories.

Design and initial characterization of the SC-200 proteomics standard mixture

High-throughput (HTP) proteomics studies generate large amounts of data. Interpretation of these data requires effective approaches to distinguish noise from biological signal, particularly as instrument and computational capacity increase and studies become more complex. Resolving this issue requires validated and reproducible methods and models, which in turn requires complex experimental and computational standards. The absence of appropriate standards and data sets for validating experimental and computational workflows hinders the development of HTP proteomics methods. Most protein standards are simple mixtures of proteins or peptides, or undercharacterized reference standards in which the identity and concentration of the constituent proteins is unknown. The Seattle Children's 200 (SC-200) proposed proteomics standard mixture is the next step toward developing realistic, fully characterized HTP proteomics standards. The SC-200 exhibits a unique modular design to extend its functionality, and consists of 200 proteins of known identities and molar concentrations from 6 microbial genomes, distributed into 10 molar concentration tiers spanning a 1,000-fold range. We describe the SC-200's design, potential uses, and initial characterization. We identified 84% of SC-200 proteins with an LTQ-Orbitrap and 65% with an LTQ-Velos (false discovery rate = 1% for both). There were obvious trends in success rate, sequence coverage, and spectral counts with protein concentration; however, protein identification, sequence coverage, and spectral counts vary greatly within concentration levels.

Competition between PARP-1 and Ku70 control the decision between high-fidelity and mutagenic DNA repair

Affinity maturation of antibodies requires a unique process of targeted mutation that allows changes to accumulate in the antibody genes while the rest of the genome is protected from off-target mutations that can be oncogenic. This targeting requires that the same deamination event be repaired either by a mutagenic or a high-fidelity pathway depending on the genomic location. We have previously shown that the BRCT domain of the DNA-damage sensor PARP-1 is required for mutagenic repair occurring in the context of IgH and IgL diversification in the chicken B cell line DT40. Here we show that immunoprecipitation of the BRCT domain of PARP-1 pulls down Ku70 and the DNA-PK complex although the BRCT domain of PARP-1 does not bind DNA, suggesting that this interaction is not DNA dependent. Through sequencing the IgL variable region in PARP-1(-/-) cells that also lack Ku70 or Lig4, we show that Ku70 or Lig4 deficiency restores GCV to PARP-1(-/-) cells and conclude that the mechanism by which PARP-1 is promoting mutagenic repair is by inhibiting high-fidelity repair which would otherwise be mediated by Ku70 and Lig4.

The necessity of adjusting tests of protein category enrichment in discovery proteomics

MOTIVATION

Enrichment tests are used in high-throughput experimentation to measure the association between gene or protein expression and membership in groups or pathways. The Fisher's exact test is commonly used. We specifically examined the associations produced by the Fisher test between protein identification by mass spectrometry discovery proteomics, and their Gene Ontology (GO) term assignments in a large yeast dataset. We found that direct application of the Fisher test is misleading in proteomics due to the bias in mass spectrometry to preferentially identify proteins based on their biochemical properties. False inference about associations can be made if this bias is not corrected. Our method adjusts Fisher tests for these biases and produces associations more directly attributable to protein expression rather than experimental bias.

RESULTS

Using logistic regression, we modeled the association between protein identification and GO term assignments while adjusting for identification bias in mass spectrometry. The model accounts for five biochemical properties of peptides: (i) hydrophobicity, (ii) molecular weight, (iii) transfer energy, (iv) beta turn frequency and (v) isoelectric point. The model was fit on 181 060 peptides from 2678 proteins identified in 24 yeast proteomics datasets with a 1% false discovery rate. In analyzing the association between protein identification and their GO term assignments, we found that 25% (134 out of 544) of Fisher tests that showed significant association (q-value ≤0.05) were non-significant after adjustment using our model. Simulations generating yeast protein sets enriched for identification propensity show that unadjusted enrichment tests were biased while our approach worked well.

Proteomics of dense core secretory vesicles reveal distinct protein categories for secretion of neuroeffectors for cell-cell communication

Regulated secretion of neurotransmitters and neurohumoral factors from dense core secretory vesicles provides essential neuroeffectors for cell-cell communication in the nervous and endocrine systems. This study provides comprehensive proteomic characterization of the categories of proteins in chromaffin dense core secretory vesicles that participate in cell-cell communication from the adrenal medulla. Proteomic studies were conducted by nano-HPLC Chip MS/MS tandem mass spectrometry. Results demonstrate that these secretory vesicles contain proteins of distinct functional categories consisting of neuropeptides and neurohumoral factors, protease systems, neurotransmitter enzymes and transporters, receptors, enzymes for biochemical processes, reduction/oxidation regulation, ATPases, protein folding, lipid biochemistry, signal transduction, exocytosis, calcium regulation, as well as structural and cell adhesion proteins. The secretory vesicle proteomic data identified 371 proteins in the soluble fraction and 384 membrane proteins, for a total of 686 distinct secretory vesicle proteins. Notably, these proteomic analyses illustrate the presence of several neurological disease-related proteins in these secretory vesicles, including huntingtin interacting protein, cystatin C, ataxin 7, and prion protein. Overall, these findings demonstrate that multiple protein categories participate in dense core secretory vesicles for production, storage, and secretion of bioactive neuroeffectors for cell-cell communication in health and disease.

Estimating false discovery rates for peptide and protein identification using randomized databases

MS-based proteomics characterizes protein contents of biological samples. The most common approach is to first match observed MS/MS peptide spectra against theoretical spectra from a protein sequence database and then to score these matches. The false discovery rate (FDR) can be estimated as a function of the score by searching together the protein sequence database and its randomized version and comparing the score distributions of the randomized versus nonrandomized matches. This work introduces a straightforward isotonic regression-based method to estimate the cumulative FDRs and local FDRs (LFDRs) of peptide identification. Our isotonic method not only performed as well as other methods used for comparison, but also has the advantages of being: (i) monotonic in the score, (ii) computationally simple, and (iii) not dependent on assumptions about score distributions. We demonstrate the flexibility of our approach by using it to estimate FDRs and LFDRs for protein identification using summaries of the peptide spectra scores. We reconfirmed that several of these methods were superior to a two-peptide rule. Finally, by estimating both the FDRs and LFDRs, we showed for both peptide and protein identification, moderate FDR values (5%) corresponded to large LFDR values (53 and 60%).

Meta-analysis for protein identification: a case study on yeast data

Large amounts of mass spectrometry (MS) proteomics data are now publicly available; however, little attention has been given to how to best combine these data and assess the error rates for protein identification. The objective of this article is to show how variation in the type and amount of data included with each study impacts coverage of the yeast proteome and estimation of the false discovery rate (FDR). Our analysis of a subset of the publicly available yeast data showed that failure to reevaluate the FDR when combining protein IDs from different experiments resulted in an underestimation of the FDR by approximately threefold. A worst-case approximation of the FDR was only slightly larger than estimating the FDR by randomized database matches. The use of a weighted model to emphasize the most informative experimental data provided an increase in the number of IDs at a 1% FDR when compared to other meta-analysis approaches. Also, using an FDR higher than 1% results in a very high rate of false discoveries for IDs above the 1% threshold. Ideally, raw MS data will be made publicly available for complete and consistent reanalysis. In the circumstance that raw data is not available, determining a combined FDR on the basis of the worst-case estimation provides a reasonable approximation of the FDR. When combining experimental results, adding additional experiments results in diminishing and in some cases negative returns on protein identifications. It may be beneficial to include only those experiments generating the most unique identifications due to solid experimental design and sensitive instrumentation.

MS-driven protease substrate degradomics

Proteolytic processing has recently received increased attention in the field of signal propagation and cellular differentiation. Because of its irreversible nature, protein cleavage has been associated with committed steps in cell function. One aspect of protease biology that boomed the past few years is the detailed characterization of protease substrates by both shotgun as well as targeted MS-driven proteomics techniques. The most promising techniques are discussed in this review and we further elaborate on the bioinformatics challenges that accompany mainly qualitative, MS-driven protease substrate degradome studies.

A new cross-linking strategy: protein interaction reporter (PIR) technology for protein-protein interaction studies

Chemical cross-linking coupled with mass spectrometry, an emerging approach for protein topology and interaction studies, has gained increasing interest in the past few years. A number of recent proof-of-principle studies on model proteins or protein complex systems with improved cross-linking strategies have shown great promise. However, the heterogeneity and low abundance of the cross-linked products as well as data complexity continue to pose enormous challenges for large-scale application of cross-linking approaches. A novel mass spectrometry-cleavable cross-linking strategy embodied in Protein Interaction Reporter (PIR) technology, first reported in 2005, was recently successfully applied for in vivo identification of protein-protein interactions as well as actual regions of the interacting proteins that share close proximity while present within cells. PIR technology holds great promise for achieving the ultimate goal of mapping protein interaction network at systems level using chemical cross-linking. In this review, we will briefly describe the recent progress in the field of chemical cross-linking development with an emphasis on the PIR concepts, its applications and future directions.

False discovery rates of protein identifications: a strike against the two-peptide rule

Most proteomics studies attempt to maximize the number of peptide identifications and subsequently infer proteins containing two or more peptides as reliable protein identifications. In this study, we evaluate the effect of this "two-peptide" rule on protein identifications, using multiple search tools and data sets. Contrary to the intuition, the "two-peptide" rule reduces the number of protein identifications in the target database more significantly than in the decoy database and results in increased false discovery rates, compared to the case when single-hit proteins are not discarded. We therefore recommend that the "two-peptide" rule should be abandoned, and instead, protein identifications should be subject to the estimation of error rates, as is the case with peptide identifications. We further extend the generating function approach (originally proposed for evaluating matches between a peptide and a single spectrum) to evaluating matches between a protein and an entire spectral data set.

Network-assisted protein identification and data interpretation in shotgun proteomics

Protein assembly and biological interpretation of the assembled protein lists are critical steps in shotgun proteomics data analysis. Although most biological functions arise from interactions among proteins, current protein assembly pipelines treat proteins as independent entities. Usually, only individual proteins with strong experimental evidence, that is, confident proteins, are reported, whereas many possible proteins of biological interest are eliminated. We have developed a clique-enrichment approach (CEA) to rescue eliminated proteins by incorporating the relationship among proteins as embedded in a protein interaction network. In several data sets tested, CEA increased protein identification by 8–23% with an estimated accuracy of 85%. Rescued proteins were supported by existing literature or transcriptome profiling studies at similar levels as confident proteins and at a significantly higher level than abandoned ones. Applying CEA on a breast cancer data set, rescued proteins coded by well-known breast cancer genes. In addition, CEA generated a network view of the proteins and helped show the modular organization of proteins that may underpin the molecular mechanisms of the disease.

Deciphering diatom biochemical pathways via whole-cell proteomics

Diatoms play a critical role in the oceans' carbon and silicon cycles; however, a mechanistic understanding of the biochemical processes that contribute to their ecological success remains elusive. Completion of the Thalassiosira pseudonana genome provided 'blueprints' for the potential biochemical machinery of diatoms, but offers only a limited insight into their biology under various environmental conditions. Using high-throughput shotgun proteomics, we identified a total of 1928 proteins expressed by T. pseudonana cultured under optimal growth conditions, enabling us to analyze this diatom's primary metabolic and biosynthetic pathways. Of the proteins identified, 70% are involved in cellular metabolism, while 11% are involved in the transport of molecules. We identified all of the enzymes involved in the urea cycle, thereby describing the complete pathway to convert ammonia to urea, along with urea transporters, and the urea-degrading enzyme urease. Although metabolic exchange between these pathways remains ambiguous, their constitutive presence suggests complex intracellular nitrogen recycling. In addition, all C(4) related enzymes for carbon fixation have been identified to be in abundance, with high protein sequence coverage. Quantification of mass spectra acquisitions demonstrated that the 20 most abundant proteins included an unexpectedly high expression of clathrin, which is the primary structural protein involved in endocytic transport. This result highlights a previously overlooked mechanism for the inter- and intra-cellular transport of nutrients and macromolecules in diatoms, potentially providing a missing link to organelle communication and metabolite exchange. Our results demonstrate the power of proteomics, and lay the groundwork for future comparative proteomic studies and directed analyses of specifically expressed proteins and biochemical pathways of oceanic diatoms.

Staphylococcus aureus elicits marked alterations in the airway proteome during early pneumonia

Pneumonia caused by Staphylococcus aureus is a growing concern in the health care community. We hypothesized that characterization of the early innate immune response to bacteria in the lungs would provide insight into the mechanisms used by the host to protect itself from infection. An adult mouse model of Staphylococcus aureus pneumonia was utilized to define the early events in the innate immune response and to assess the changes in the airway proteome during the first 6 h of pneumonia. S. aureus actively replicated in the lungs of mice inoculated intranasally under anesthesia to cause significant morbidity and mortality. By 6 h postinoculation, the release of proinflammatory cytokines caused effective recruitment of neutrophils to the airway. Neutrophil influx, loss of alveolar architecture, and consolidated pneumonia were observed histologically 6 h postinoculation. Bronchoalveolar lavage fluids from mice inoculated with phosphate-buffered saline (PBS) or S. aureus were depleted of overabundant proteins and subjected to strong cation exchange fractionation followed by liquid chromatography and tandem mass spectrometry to identify the proteins present in the airway. No significant changes in response to PBS inoculation or 30 min following S. aureus inoculation were observed. However, a dramatic increase in extracellular proteins was observed 6 h postinoculation with S. aureus, with the increase dominated by inflammatory and coagulation proteins. The data presented here provide a comprehensive evaluation of the rapid and vigorous innate immune response mounted in the host airway during the earliest stages of S. aureus pneumonia.

DNA repair of 8-oxo-7,8-dihydroguanine lesions in Porphyromonas gingivalis

The persistence of Porphyromonas gingivalis in the inflammatory environment of the periodontal pocket requires an ability to overcome oxidative stress. DNA damage is a major consequence of oxidative stress. Unlike the case for other organisms, our previous report suggests a role for a non-base excision repair mechanism for the removal of 8-oxo-7,8-dihydroguanine (8-oxo-G) in P. gingivalis. Because the uvrB gene is known to be important in nucleotide excision repair, the role of this gene in the repair of oxidative stress-induced DNA damage was investigated. A 3.1-kb fragment containing the uvrB gene was PCR amplified from the chromosomal DNA of P. gingivalis W83. This gene was insertionally inactivated using the ermF-ermAM antibiotic cassette and used to create a uvrB-deficient mutant by allelic exchange. When plated on brucella blood agar, the mutant strain, designated P. gingivalis FLL144, was similar in black pigmentation and beta-hemolysis to the parent strain. In addition, P. gingivalis FLL144 demonstrated no significant difference in growth rate, proteolytic activity, or sensitivity to hydrogen peroxide from that of the parent strain. However, in contrast to the wild type, P. gingivalis FLL144 was significantly sensitive to UV irradiation. The enzymatic removal of 8-oxo-G from duplex DNA was unaffected by the inactivation of the uvrB gene. DNA affinity fractionation identified unique proteins that preferentially bound to the oligonucleotide fragment carrying the 8-oxo-G lesion. Collectively, these results suggest that the repair of oxidative stress-induced DNA damage involving 8-oxo-G may occur by a still undescribed mechanism in P. gingivalis.

Vaccinia peptides eluted from HLA-DR1 isolated from virus-infected cells are recognized by CD4+ T cells from a vaccinated donor

Class II MHC proteins bind peptides and present them to CD4 (+) T cells as part of the immune system's surveillance of bodily tissues for foreign and pathogenic material. Antigen processing and presentation pathways have been characterized in detail in normal cells, but there is little known about the actual viral peptides that are presented to CD4 (+) T cells that signal infection. In this study, two-dimensional LC-MS/MS was used to identify vaccinia virus-derived peptides among the hundreds to thousands of peptide antigens bound to the human class II MHC protein HLA-DR1 on the surface of vaccinia virus-infected cells. The peptides, derived from the I6L, D6R, and A10L viral proteins, were 15 residues in length, bound efficiently to HLA-DR1 as synthetic peptides, and were recognized by vaccinia-specific CD4 (+) T cells obtained from an immunized donor.

Experiment-specific estimation of peptide identification probabilities using a randomized database

Determining the error rate for peptide and protein identification accurately and reliably is necessary to enable evaluation and crosscomparisons of high throughput proteomics experiments. Currently, peptide identification is based either on preset scoring thresholds or on probabilistic models trained on datasets that are often dissimilar to experimental results. The false discovery rates (FDR) and peptide identification probabilities for these preset thresholds or models often vary greatly across different experimental treatments, organisms, or instruments used in specific experiments. To overcome these difficulties, randomized databases have been used to estimate the FDR. However, the cumulative FDR may include low probability identifications when there are a large number of peptide identifications and exclude high probability identifications when there are few. To overcome this logical inconsistency, this study expands the use of randomized databases to generate experiment-specific estimates of peptide identification probabilities. These experiment-specific probabilities are generated by logistic and Loess regression models of the peptide scores obtained from original and reshuffled database matches. These experiment-specific probabilities are shown to very well approximate "true" probabilities based on known standard protein mixtures across different experiments. Probabilities generated by the earlier Peptide_Prophet and more recent LIPS models are shown to differ significantly from this study's experiment-specific probabilities, especially for unknown samples. The experiment-specific probabilities reliably estimate the accuracy of peptide identifications and overcome potential logical inconsistencies of the cumulative FDR. This estimation method is demonstrated using a Sequest database search, LIPS model, and a reshuffled database. However, this approach is generally applicable to any search algorithm, peptide scoring, and statistical model when using a randomized database.

Automatic validation of phosphopeptide identifications by the MS2/MS3 target-decoy search strategy

Manual checking is commonly employed to validate the phosphopeptide identifications from database searching of tandem mass spectra. It is very time-consuming and labor intensive as the number of phosphopeptide identifications increases greatly. In this study, a simple automatic validation approach was developed for phosphopeptide identification by combining consecutive stage mass spectrometry data and the target-decoy database searching strategy. Only phosphopeptides identified from both MS2 and its corresponding MS3 were accepted for further filtering, which greatly improved the reliability in phosphopeptide identification. Before database searching, the spectra were validated for charge state and neutral loss peak intensity, and then the invalid MS2/MS3 spectra were removed, which greatly reduced the database searching time. It was found that the sensitivity was significantly improved in MS2/MS3 strategy as the number of identified phosphopeptides was 2.5 times that obtained by the conventional filter-based MS2 approach. Because of the use of the target-decoy database, the false-discovery rate (FDR) of the identified phosphopeptides could be easily determined, and it was demonstrated that the determined FDR can precisely reflect the actual FDR without any manual validation stage.

Host airway proteins interact with Staphylococcus aureus during early pneumonia

Staphylococcus aureus is a major cause of hospital-acquired pneumonia and is emerging as an important etiological agent of community-acquired pneumonia. Little is known about the specific host-pathogen interactions that occur when S. aureus first enters the airway. A shotgun proteomics approach was utilized to identify the airway proteins associated with S. aureus during the first 6 h of infection. Host proteins eluted from bacteria recovered from the airways of mice 30 min or 6 h following intranasal inoculation under anesthesia were subjected to liquid chromatography and tandem mass spectrometry. A total of 513 host proteins were associated with S. aureus 30 min and/or 6 h postinoculation. A majority of the identified proteins were host cytosolic proteins, suggesting that S. aureus was rapidly internalized by phagocytes in the airway and that significant host cell lysis occurred during early infection. In addition, extracellular matrix and secreted proteins, including fibronectin, antimicrobial peptides, and complement components, were associated with S. aureus at both time points. The interaction of 12 host proteins shown to bind to S. aureus in vitro was demonstrated in vivo for the first time. The association of hemoglobin, which is thought to be the primary staphylococcal iron source during infection, with S. aureus in the airway was validated by immunoblotting. Thus, we used our recently developed S. aureus pneumonia model and shotgun proteomics to validate previous in vitro findings and to identify nearly 500 other proteins that interact with S. aureus in vivo. The data presented here provide novel insights into the host-pathogen interactions that occur when S. aureus enters the airway.

Experimental-confirmation and functional-annotation of predicted proteins in the chicken genome

Background

The chicken genome was sequenced because of its phylogenetic position as a non-mammalian vertebrate, its use as a biomedical model especially to study embryology and development, its role as a source of human disease organisms and its importance as the major source of animal derived food protein. However, genomic sequence data is, in itself, of limited value; generally it is not equivalent to understanding biological function. The benefit of having a genome sequence is that it provides a basis for functional genomics. However, the sequence data currently available is poorly structurally and functionally annotated and many genes do not have standard nomenclature assigned.

Results

We analysed eight chicken tissues and improved the chicken genome structural annotation by providing experimental support for the in vivo expression of 7,809 computationally predicted proteins, including 30 chicken proteins that were only electronically predicted or hypothetical translations in human. To improve functional annotation (based on Gene Ontology), we mapped these identified proteins to their human and mouse orthologs and used this orthology to transfer Gene Ontology (GO) functional annotations to the chicken proteins. The 8,213 orthology-based GO annotations that we produced represent an 8% increase in currently available chicken GO annotations. Orthologous chicken products were also assigned standardized nomenclature based on current chicken nomenclature guidelines.

Conclusion

We demonstrate the utility of high-throughput expression proteomics for rapid experimental structural annotation of a newly sequenced eukaryote genome. These experimentally-supported predicted proteins were further annotated by assigning the proteins with standardized nomenclature and functional annotation. This method is widely applicable to a diverse range of species. Moreover, information from one genome can be used to improve the annotation of other genomes and inform gene prediction algorithms.

Optimization of filtering criterion for SEQUEST database searching to improve proteome coverage in shotgun proteomics

Background

In proteomic analysis, MS/MS spectra acquired by mass spectrometer are assigned to peptides by database searching algorithms such as SEQUEST. The assignations of peptides to MS/MS spectra by SEQUEST searching algorithm are defined by several scores including Xcorr, ΔCn, Sp, Rsp, matched ion count and so on. Filtering criterion using several above scores is used to isolate correct identifications from random assignments. However, the filtering criterion was not favorably optimized up to now.

Results

In this study, we implemented a machine learning approach known as predictive genetic algorithm (GA) for the optimization of filtering criteria to maximize the number of identified peptides at fixed false-discovery rate (FDR) for SEQUEST database searching. As the FDR was directly determined by decoy database search scheme, the GA based optimization approach did not require any pre-knowledge on the characteristics of the data set, which represented significant advantages over statistical approaches such as PeptideProphet. Compared with PeptideProphet, the GA based approach can achieve similar performance in distinguishing true from false assignment with only 1/10 of the processing time. Moreover, the GA based approach can be easily extended to process other database search results as it did not rely on any assumption on the data.

Conclusion

Our results indicated that filtering criteria should be optimized individually for different samples. The new developed software using GA provides a convenient and fast way to create tailored optimal criteria for different proteome samples to improve proteome coverage.

Proteome informatics for cancer research: from molecules to clinic

Proteomics offers the most direct approach to understand disease and its molecular biomarkers. Biomarkers denote the biological states of tissues, cells, or body fluids that are useful for disease detection and classification. Clinical proteomics is used for early disease detection, molecular diagnosis of disease, identification and formulation of therapies, and disease monitoring and prognostics. Bioinformatics tools are essential for converting raw proteomics data into knowledge and subsequently into useful applications. These tools are used for the collection, processing, analysis, and interpretation of the vast amounts of proteomics data. Management, analysis, and interpretation of large quantities of raw and processed data require a combination of various informatics technologies such as databases, sequence comparison, predictive models, and statistical tools. We have demonstrated the utility of bioinformatics in clinical proteomics through the analysis of the cancer antigen survivin and its suitability as a target for cancer immunotherapy.