Assigning Significance to Peptides Identified by Tandem Mass Spectrometry Using Decoy Databases

An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer

Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis.

Beyond the E-Value: Stratified Statistics for Protein Domain Prediction

E-values have been the dominant statistic for protein sequence analysis for the past two decades: from identifying statistically significant local sequence alignments to evaluating matches to hidden Markov models describing protein domain families. Here we formally show that for “stratified” multiple hypothesis testing problems—that is, those in which statistical tests can be partitioned naturally—controlling the local False Discovery Rate (lFDR) per stratum, or partition, yields the most predictions across the data at any given threshold on the FDR or E-value over all strata combined. For the important problem of protein domain prediction, a key step in characterizing protein structure, function and evolution, we show that stratifying statistical tests by domain family yields excellent results. We develop the first FDR-estimating algorithms for domain prediction, and evaluate how well thresholds based on q-values, E-values and lFDRs perform in domain prediction using five complementary approaches for estimating empirical FDRs in this context. We show that stratified q-value thresholds substantially outperform E-values. Contradicting our theoretical results, q-values also outperform lFDRs; however, our tests reveal a small but coherent subset of domain families, biased towards models for specific repetitive patterns, for which weaknesses in random sequence models yield notably inaccurate statistical significance measures. Usage of lFDR thresholds outperform q-values for the remaining families, which have as-expected noise, suggesting that further improvements in domain predictions can be achieved with improved modeling of random sequences. Overall, our theoretical and empirical findings suggest that the use of stratified q-values and lFDRs could result in improvements in a host of structured multiple hypothesis testing problems arising in bioinformatics, including genome-wide association studies, orthology prediction, and motif scanning.

Despite decades of research, it remains a challenge to distinguish homologous relationships between proteins from sequence similarities arising due to chance alone. This is an increasingly important problem as sequence database sizes continue to grow, and even today many computational analyses require that the statistics of billions of sequence comparisons be assessed automatically. Here we explore statistical significance evaluation on data that is stratified—that is, naturally partitioned into subsets that may differ in their amount of signal—and find a theoretically optimal criterion for automatically setting thresholds of significance for each stratum. For the task of domain prediction, an important component of efforts to annotate protein sequences and identify remote sequence homologs, we empirically show that our stratified analysis of statistical significance greatly improves upon a combined analysis. Further, we identify weaknesses in the prevailing random sequence model for assessing statistical significance for a small subset of domain families with repetitive sequence patterns and known biological, structural, and evolutionary properties. Our theoretical findings in statistics are relevant not only for identifying protein domains, but for arbitrary stratified problems in genomics and beyond.

An adaptive classification model for peptide identification

Background

Peptide sequence assignment is the central task in protein identification with MS/MS-based strategies. Although a number of post-database search algorithms for filtering target peptide spectrum matches (PSMs) have been developed, the discrepancy among the output PSMs is usually significant, remaining a few disputable PSMs. Current studies show that a number of target PSMs which are close to decoy PSMs can hardly be separated from those decoys by only using the discrimination function.

Results

In this paper, we assign each target PSM a weight showing its possibility of being correct. We employ a SVM-based learning model to search the optimal weight for each target PSM and develop a new score system, CRanker, to rank all target PSMs. Due to the large PSM datasets generated in routine database searches, we use the Cholesky factorization technique for storing a kernel matrix to reduce the memory requirement.

Conclusions

Compared with PeptideProphet and Percolator, CRanker has identified more PSMs under similar false discover rates over different datasets. CRanker has shown consistent performance on different test sets, validated the reasonability the proposed model.

Life Stage-specific Proteomes of Legionella pneumophila Reveal a Highly Differential Abundance of Virulence-associated Dot/Icm effectors

Major differences in the transcriptional program underlying the phenotypic switch between exponential and post-exponential growth of Legionella pneumophila were formerly described characterizing important alterations in infection capacity. Additionally, a third state is known where the bacteria transform in a viable but nonculturable state under stress, such as starvation. We here describe phase-related proteomic changes in exponential phase (E), postexponential phase (PE) bacteria, and unculturable microcosms (UNC) containing viable but nonculturable state cells, and identify phase-specific proteins. We present data on different bacterial subproteomes of E and PE, such as soluble whole cell proteins, outer membrane-associated proteins, and extracellular proteins. In total, 1368 different proteins were identified, 922 were quantified and 397 showed differential abundance in E/PE. The quantified subproteomes of soluble whole cell proteins, outer membrane-associated proteins, and extracellular proteins; 841, 55, and 77 proteins, respectively, were visualized in Voronoi treemaps. 95 proteins were quantified exclusively in E, such as cell division proteins MreC, FtsN, FtsA, and ZipA; 33 exclusively in PE, such as motility-related proteins of flagellum biogenesis FlgE, FlgK, and FliA; and 9 exclusively in unculturable microcosms soluble whole cell proteins, such as hypothetical, as well as transport/binding-, and metabolism-related proteins. A high frequency of differentially abundant or phase-exclusive proteins was observed among the 91 quantified effectors of the major virulence-associated protein secretion system Dot/Icm (> 60%). 24 were E-exclusive, such as LepA/B, YlfA, MavG, Lpg2271, and 13 were PE-exclusive, such as RalF, VipD, Lem10. The growth phase-related specific abundance of a subset of Dot/Icm virulence effectors was confirmed by means of Western blotting. We therefore conclude that many effectors are predominantly abundant at either E or PE which suggests their phase specific function. The distinct temporal or spatial presence of such proteins might have important implications for functional assignments in the future or for use as life-stage specific markers for pathogen analysis.

Novor: real-time peptide de novo sequencing software

De novo sequencing software has been widely used in proteomics to sequence new peptides from tandem mass spectrometry data. This study presents a new software tool, Novor, to greatly improve both the speed and accuracy of today's peptide de novo sequencing analyses. To improve the accuracy, Novor's scoring functions are based on two large decision trees built from a peptide spectral library with more than 300,000 spectra with machine learning. Important knowledge about peptide fragmentation is extracted automatically from the library and incorporated into the scoring functions. The decision tree model also enables efficient score calculation and contributes to the speed improvement. To further improve the speed, a two-stage algorithmic approach, namely dynamic programming and refinement, is used. The software program was also carefully optimized. On the testing datasets, Novor sequenced 7%-37% more correct residues than the state-of-the-art de novo sequencing tool, PEAKS, while being an order of magnitude faster. Novor can de novo sequence more than 300 MS/MS spectra per second on a laptop computer. The speed surpasses the acquisition speed of today's mass spectrometer and, therefore, opens a new possibility to de novo sequence in real time while the spectrometer is acquiring the spectral data. Graphical Abstract ᅟ.

Abundant toxin-related genes in the genomes of beneficial symbionts from deep-sea hydrothermal vent mussels

Bathymodiolus mussels live in symbiosis with intracellular sulfur-oxidizing (SOX) bacteria that provide them with nutrition. We sequenced the SOX symbiont genomes from two Bathymodiolus species. Comparison of these symbiont genomes with those of their closest relatives revealed that the symbionts have undergone genome rearrangements, and up to 35% of their genes may have been acquired by horizontal gene transfer. Many of the genes specific to the symbionts were homologs of virulence genes. We discovered an abundant and diverse array of genes similar to insecticidal toxins of nematode and aphid symbionts, and toxins of pathogens such as Yersinia and Vibrio. Transcriptomics and proteomics revealed that the SOX symbionts express the toxin-related genes (TRGs) in their hosts. We hypothesize that the symbionts use these TRGs in beneficial interactions with their host, including protection against parasites. This would explain why a mutualistic symbiont would contain such a remarkable 'arsenal' of TRGs.

Comparative plasma proteomic studies of pulmonary TiO2 nanoparticle exposure in rats using liquid chromatography tandem mass spectrometry

Mounting evidence suggests that pulmonary exposure to nanoparticles (NPs) has a toxic effect on biological systems. A number of studies have shown that exposure to NPs result in systemic inflammatory response, oxidative stress, and leukocyte adhesion. However, significant knowledge gaps exist for understanding the key molecular mechanisms responsible for altered microvasculature function. Utilizing comprehensive LC-MS/MS and comparative proteomic analysis strategies, important proteins related to TiO2 NP exposure in rat plasma have been identified. Molecular pathway analysis of these proteins revealed 13 canonical pathways as being significant (p ≤ 0.05), but none were found to be significantly up or down-regulated (z>|2|). This work lays the foundation for future research that will monitor relative changes in protein abundance in plasma and tissue as a function of post-exposure time and TiO2 NP dosage to further elucidate mechanisms of pathway activation as well as to decipher other affected pathways.

Recent advances in the use of mass spectrometry to examine structure/function relationships in photosystem II

Tandem mass spectrometry often coupled with chemical modification techniques, is developing into increasingly important tool in structural biology. These methods can provide important supplementary information concerning the structural organization and subunit make-up of membrane protein complexes, identification of conformational changes occurring during enzymatic reactions, identification of the location of posttranslational modifications, and elucidation of the structure of assembly and repair complexes. In this review, we will present a brief introduction to Photosystem II, tandem mass spectrometry and protein modification techniques that have been used to examine the photosystem. We will then discuss a number of recent case studies that have used these techniques to address open questions concerning PS II. These include the nature of subunit-subunit interactions within the phycobilisome, the interaction of phycobilisomes with Photosystem I and the Orange Carotenoid Protein, the location of CyanoQ, PsbQ and PsbP within Photosystem II, and the identification of phosphorylation and oxidative modification sites within the photosystem. Finally, we will discuss some of the future prospects for the use of these methods in examining other open questions in PS II structural biochemistry.

Beads-free protein immunoprecipitation for a mass spectrometry-based interactome and posttranslational modifications analysis

BACKGROUND

Protein immunoprecipitation (IP) coupled with MS provides means to interrogate protein complexes and their posttranslational modifications (PTMs). In a typical protein IP assay antibodies are conjugated to protein A/G beads requiring large amounts of antibodies, tube transfers and centrifugations.

RESULTS

As an alternative, we present Matrix-IP, beads-free microplate-based platform with surface-immobilized antibodies. Assay utilizes standard 96-well polypropylene PCR plates that are laboratory-fabricated with UV-C light and then protein A/G coated prior to IP reaction. We demonstrate application of Matrix-IP platform in MS analysis of heterogeneous nuclear ribonucleoprotein K (hnRNP K) interactome and PTMs.

CONCLUSION

Matrix-IP is time-saving, easy to use high throughput method adaptable for low sample amounts and automation.

Protein adsorption steers blood contact activation on engineered cobalt chromium alloy oxide layers

Biomaterials upon implantation are immediately covered by blood proteins which direct the subsequent blood activation. These early events determine the following cascade of biological reactions and consequently the long-term success of implants. The ability to modulate surface properties of biomaterials is therefore of considerable clinical significance. Goal of this study was an in-depth understanding of the biological response to cobalt chromium stent alloys with engineered surface oxide layers, which showed altered body reactions in vivo. We analyzed in vitro the biological events following initial blood contact on engineered cobalt chromium surfaces featuring said oxide layers. Surface-specific blood reactions were confirmed by scanning electron microscopy and the adsorbed protein layers were characterized by mass spectrometry. This powerful proteomics tool allowed the identification and quantification of over hundred surface-adhering proteins. Proteins associated with the coagulation cascade, platelet adhesion and neutrophil function correlated with the various blood surface activations observed. Furthermore, results of pre-coated surfaces with defined fibrinogen-albumin mixtures suggest that neutrophil adhesion was controlled by fibrinogen orientation and conformation rather than quantity. This study highlights the importance of controlling the biological response in the complex protein-implant surface interactions and the potential of the surface modifications to improve the clinical performance of medical implants.

STATEMENT OF SIGNIFICANCE

The blood contact activation of CoCr alloys is determined by their surface oxide layer properties. Modifications of the oxide layer affected the total amount of adsorbed proteins and the composition of the adsorbed protein layer. Additionally fibrinogen coatings mediated the surface-dependent neutrophil adhesion in a concentration-independent manner, indicating the influence of conformation and/or orientation of the adsorbed protein. Despite the complexity of protein-implant interactions, this study highlights the importance of understanding and controlling mechanisms of protein adhesion in order to improve and steer the performance of medical implants. It shows that modification of the surface oxide layer is a very attractive strategy to directly functionalize metallic implant surfaces and optimize their blood interaction for the desired orthopedic or cardiovascular applications.

Proteome Characterization of Leaves in Common Bean

Dry edible bean (Phaseolus vulgaris L.) is a globally relevant food crop. The bean genome was recently sequenced and annotated allowing for proteomics investigations aimed at characterization of leaf phenotypes important to agriculture. The objective of this study was to utilize a shotgun proteomics approach to characterize the leaf proteome and to identify protein abundance differences between two bean lines with known variation in their physiological resistance to biotic stresses. Overall, 640 proteins were confidently identified. Among these are proteins known to be involved in a variety of molecular functions including oxidoreductase activity, binding peroxidase activity, and hydrolase activity. Twenty nine proteins were found to significantly vary in abundance (p-value < 0.05) between the two bean lines, including proteins associated with biotic stress. To our knowledge, this work represents the first large scale shotgun proteomic analysis of beans and our results lay the groundwork for future studies designed to investigate the molecular mechanisms involved in pathogen resistance.

mIMT-visHTS: A novel method for multiplexing isobaric mass tagged datasets with an accompanying visualization high throughput screening tool for protein profiling

Isobaric mass tagging (IMT) methods enable the analysis of thousands of proteins simultaneously. We used tandem mass tagging reagents (TMT™) to monitor the relative changes in the proteome of the mouse macrophage cell line RAW264.7 at the same six time points after no stimulation (baseline phenotype), stimulation with interferon gamma (pro-inflammatory phenotype) or stimulation with interleukin-4 (anti-inflammatory phenotype). The combined TMT datasets yielded nearly 12,000 protein profiles for comparison. To facilitate this large analysis, we developed a novel method that combines or multiplexes the separate IMT (mIMT) datasets into a single super dataset for subsequent model-based clustering and co-regulation analysis. Specially designed visual High Throughput Screening (visHTS) software screened co-regulated proteins. visHTS generates an interactive and visually intuitive color-coded bullseye plot that enables users to browse the cluster outputs and identify co-regulated proteins.

Improved False Discovery Rate Estimation Procedure for Shotgun Proteomics

Interpreting the potentially vast number of hypotheses generated by a shotgun proteomics experiment requires a valid and accurate procedure for assigning statistical confidence estimates to identified tandem mass spectra. Despite the crucial role such procedures play in most high-throughput proteomics experiments, the scientific literature has not reached a consensus about the best confidence estimation methodology. In this work, we evaluate, using theoretical and empirical analysis, four previously proposed protocols for estimating the false discovery rate (FDR) associated with a set of identified tandem mass spectra: two variants of the target-decoy competition protocol (TDC) of Elias and Gygi and two variants of the separate target-decoy search protocol of Käll et al. Our analysis reveals significant biases in the two separate target-decoy search protocols. Moreover, the one TDC protocol that provides an unbiased FDR estimate among the target PSMs does so at the cost of forfeiting a random subset of high-scoring spectrum identifications. We therefore propose the mix-max procedure to provide unbiased, accurate FDR estimates in the presence of well-calibrated scores. The method avoids biases associated with the two separate target-decoy search protocols and also avoids the propensity for target-decoy competition to discard a random subset of high-scoring target identifications.

New CETP inhibitor K-312 reduces PCSK9 expression: a potential effect on LDL cholesterol metabolism

Despite significant reduction of cardiovascular events by statin treatment, substantial residual risk persists, driving emerging needs for the development of new therapies. We identified a novel cholesteryl ester transfer protein (CETP) inhibitor, K-312, that raises HDL and lowers LDL cholesterol levels in animals. K-312 also suppresses hepatocyte expression of proprotein convertase subtilisin/kexin 9 (PCSK9), a molecule that increases LDL cholesterol. We explored the underlying mechanism for the reduction of PCSK9 expression by K-312. K-312 inhibited in vitro human plasma CETP activity (IC50; 0.06 μM). Administration of K-312 to cholesterol-fed New Zealand White rabbits for 18 wk raised HDL cholesterol, decreased LDL cholesterol, and attenuated aortic atherosclerosis. Our search for additional beneficial characteristics of this compound revealed that K-312 decreases PCSK9 expression in human primary hepatocytes and in the human hepatoma cell line HepG2. siRNA silencing of CETP in HepG2 did not compromise the suppression of PCSK9 by K-312, suggesting a mechanism independent of CETP. In HepG2 cells, K-312 treatment decreased the active forms of sterol regulatory element-binding proteins (SREBP-1 and -2) that regulate promoter activity of PCSK9. Chromatin immunoprecipitation assays demonstrated that K-312 decreased the occupancy of SREBP-1 and SREBP-2 on the sterol regulatory element of the PCSK9 promoter. PCSK9 protein levels decreased by K-312 treatment in the circulating blood of cholesterol-fed rabbits, as determined by two independent mass spectrometry approaches, including the recently developed, highly sensitive parallel reaction monitoring method. New CETP inhibitor K-312 decreases LDL cholesterol and PCSK9 levels, serving as a new therapy for dyslipidemia and cardiovascular disease.

A Thermophilic Phage Endolysin Fusion to a Clostridium perfringens-Specific Cell Wall Binding Domain Creates an Anti-Clostridium Antimicrobial with Improved Thermostability

Clostridium perfringens is the third leading cause of human foodborne bacterial disease and is the presumptive etiologic agent of necrotic enteritis among chickens. Treatment of poultry with antibiotics is becoming less acceptable. Endolysin enzymes are potential replacements for antibiotics. Many enzymes are added to animal feed during production and are subjected to high-heat stress during feed processing. To produce a thermostabile endolysin for treating poultry, an E. coli codon-optimized gene was synthesized that fused the N-acetylmuramoyl-l-alanine amidase domain from the endolysin of the thermophilic bacteriophage ΦGVE2 to the cell-wall binding domain (CWB) from the endolysin of the C. perfringens-specific bacteriophage ΦCP26F. The resulting protein, PlyGVE2CpCWB, lysed C. perfringens in liquid and solid cultures. PlyGVE2CpCWB was most active at pH 8, had peak activity at 10 mM NaCl, 40% activity at 150 mM NaCl and was still 16% active at 600 mM NaCl. The protein was able to withstand temperatures up to 50 °C and still lyse C. perfringens. Herein, we report the construction and characterization of a thermostable chimeric endolysin that could potentially be utilized as a feed additive to control the bacterium during poultry production.

UV-induced retinal proteome changes in the rat model of age-related macular degeneration

Age-related macular degeneration (AMD) is characterized by irreversible damage of photoreceptors in the central posterior part of the retina, called the macula and is the most common cause of vision loss in those aged over 50. A growing body of evidence shows that cumulative long-term exposure to UV radiation may be harmful to the retina and possibly leads to AMD irrespective of age. In spite of many research efforts, cellular and molecular mechanisms leading to UV-induced retinal damage and possibly retinal diseases such as AMD are not completely understood. In the present study we explored damage mechanisms accounting for UV-induced retinal phototoxicity in the rats exposed to UVA and UVB irradiation using a proteomics approach. Our study showed that UV irradiation induces profound changes in the retinal proteomes of the rats associated with the disruption of energy homeostasis, oxidative stress, DNA damage response and structural and functional impairments of the interphotoreceptor matrix components and their cell surface receptors such as galectins. Two small leucine-rich proteoglycans, biglycan and lumican, were identified as phototoxicity biomarkers associated with UV-induced disruption of interphotoreceptor matrix (IPM). In addition, UVB induced activation of Src kinase, which could account for cytoskeletal rearrangements in the retina was observed at the proteomics level. Pharmacological intervention either to target Src kinase with the aim of preventing cytoskeletal rearrangements in the retinal pigment epithelium (RPE) and neuronal retina or to help rebuild damaged IPM may provide fresh avenues of treatment for patients suffering from AMD.

Target of Rapamycin Complex 2 Regulates Actin Polarization and Endocytosis via Multiple Pathways

Target of rapamycin is a Ser/Thr kinase that operates in two conserved multiprotein complexes, TORC1 and TORC2. Unlike TORC1, TORC2 is insensitive to rapamycin, and its functional characterization is less advanced. Previous genetic studies demonstrated that TORC2 depletion leads to loss of actin polarization and loss of endocytosis. To determine how TORC2 regulates these readouts, we engineered a yeast strain in which TORC2 can be specifically and acutely inhibited by the imidazoquinoline NVP-BHS345. Kinetic analyses following inhibition of TORC2, supported with quantitative phosphoproteomics, revealed that TORC2 regulates these readouts via distinct pathways as follows: rapidly through direct protein phosphorylation cascades and slowly through indirect changes in the tensile properties of the plasma membrane. The rapid signaling events are mediated in large part through the phospholipid flippase kinases Fpk1 and Fpk2, whereas the slow signaling pathway involves increased plasma membrane tension resulting from a gradual depletion of sphingolipids. Additional hits in our phosphoproteomic screens highlight the intricate control TORC2 exerts over diverse aspects of eukaryote cell physiology.

Secretome of gingival epithelium in response to subgingival biofilms

Periodontitis is the chronic inflammatory destruction of periodontal tissues as a result of bacterial biofilm formation on the tooth surface. Proteins secreted by the gingival epithelium challenged by subgingival biofilms represent an important initial response for periodontal inflammation. The aim of this in vitro study was to characterize the whole secreted proteome of gingival epithelial tissue challenged by subgingival biofilms, and to evaluate the differential effects of the presence of the red-complex species in the biofilm. Multi-layered human gingival epithelial cultures were challenged with a 10-species in vitro biofilm model or its seven-species variant excluding the red complex. Liquid chromatography-tandem mass spectrometry for label-free quantitative proteomics was applied to identify and quantify the secreted epithelial proteins in the culture supernatant. A total of 192 proteins were identified and quantified. The biofilm challenge resulted in more secreted proteins being downregulated than upregulated. Even so, presence of the red complex in the biofilm was responsible for much of this downregulatory effect. Over 24 h, the upregulated biological processes were associated with inflammation and apoptosis, whereas the downregulated processes were associated with the disruption of epithelial tissue integrity and impairment of tissue turnover. Over 48 h, negative regulation of several metabolic processes and degradation of various molecular complexes was further intensified. Again, many of these biological regulations were attributed to the presence of the red complex. In conclusion, the present study provides the secreted proteome profile of gingival epithelial tissue to subgingival biofilms, and identifies a significant role for the red-complex species in the observed effects.

Mass spectrometric analysis of the cell surface N-glycoproteome by combining metabolic labeling and click chemistry

Cell surface N-glycoproteins play extraordinarily important roles in cell-cell communication, cell-matrix interactions, and cellular response to environmental cues. Global analysis is exceptionally challenging because many N-glycoproteins are present at low abundances and effective separation is difficult to achieve. Here, we have developed a novel strategy integrating metabolic labeling, copper-free click chemistry, and mass spectrometry (MS)-based proteomics methods to analyze cell surface N-glycoproteins comprehensively and site-specifically. A sugar analog containing an azido group, N-azidoacetylgalactosamine, was fed to cells to label glycoproteins. Glycoproteins with the functional group on the cell surface were then bound to dibenzocyclooctyne-sulfo-biotin via copper-free click chemistry under physiological conditions. After protein extraction and digestion, glycopeptides with the biotin tag were enriched by NeutrAvidin conjugated beads. Enriched glycopeptides were deglycosylated with peptide-N-glycosidase F in heavy-oxygen water, and in the process of glycan removal, asparagine was converted to aspartic acid and tagged with 18O for MS analysis. With this strategy, 144 unique N-glycopeptides containing 152 N-glycosylation sites were identified in 110 proteins in HEK293T cells. As expected, 95% of identified glycoproteins were membrane proteins, which were highly enriched. Many sites were located on important receptors, transporters, and cluster of differentiation proteins. The experimental results demonstrated that the current method is very effective for the comprehensive and site-specific identification of the cell surface N-glycoproteome and can be extensively applied to other cell surface protein studies.

A paired ions scoring algorithm based on Morpheus for simultaneous identification and quantification of proteome samples prepared by isobaric peptide termini labeling strategies

The isobaric peptide termini labeling (IPTL) method is a promising strategy in quantitative proteomics for its high accuracy, while the increased complexity of MS2 spectra originated from the paired b, y ions has adverse effect on the identification and the coverage of quantification. Here, a paired ions scoring algorithm (PISA) based on Morpheus, a database searching algorithm specifically designed for high-resolution MS2 spectra, was proposed to address this issue. PISA was first tested on two 1:1 mixed IPTL datasets, and increases in peptide to spectrum matchings, distinct peptides and protein groups compared to Morpheus itself and MASCOT were shown. Furthermore, the quantification is simultaneously performed and 100% quantification coverage is achieved by PISA since each of the identified peptide to spectrum matchings has several pairs of fragment ions which could be used for quantification. Then the PISA was applied to the relative quantification of human hepatocellular carcinoma cell lines with high and low metastatic potentials prepared by an IPTL strategy.

The proteome and lipidome of Synechocystis sp. PCC 6803 cells grown under light-activated heterotrophic conditions

Cyanobacteria are photoautotrophic prokaryotes with a plant-like photosynthetic machinery. Because of their short generation times, the ease of their genetic manipulation, and the limited size of their genome and proteome, cyanobacteria are popular model organisms for photosynthetic research. Although the principal mechanisms of photosynthesis are well-known, much less is known about the biogenesis of the thylakoid membrane, hosting the components of the photosynthetic, and respiratory electron transport chain in cyanobacteria. Here we present a detailed proteome analysis of the important model and host organism Synechocystis sp. PCC 6803 under light-activated heterotrophic growth conditions. Because of the mechanistic importance and severe changes in thylakoid membrane morphology under light-activated heterotrophic growth conditions, a focus was put on the analysis of the membrane proteome, which was supported by a targeted lipidome analysis. In total, 1528 proteins (24.5% membrane integral) were identified in our analysis. For 641 of these proteins quantitative information was obtained by spectral counting. Prominent changes were observed for proteins associated with oxidative stress response and protein folding. Because of the heterotrophic growth conditions, also proteins involved in carbon metabolism and C/N-balance were severely affected. Although intracellular thylakoid membranes were significantly reduced, only minor changes were observed in their protein composition. The increased proportion of the membrane-stabilizing sulfoqinovosyl diacyl lipids found in the lipidome analysis, as well as the increased content of lipids with more saturated acyl chains, are clear indications for a coordinated synthesis of proteins and lipids, resulting in stabilization of intracellular thylakoid membranes under stress conditions.

iTRAQ quantitative proteomics-based identification of cell adhesion as a dominant phenotypic modulation in thrombin-stimulated human aortic endothelial cells

INTRODUCTION

The phenotypic changes in thrombin-stimulated endothelial cells include alterations in permeability, cell shape, vasomotor tone, leukocyte trafficking, migration, proliferation, and angiogenesis. Previous studies regarding the pleotropic effects of thrombin on the endothelium used human umbilical vein endothelial cells (HUVECs)-cells derived from fetal tissue that does not exist in adults. Only a few groups have used screening approaches such as microarrays to profile the global effects of thrombin on endothelial cells. Moreover, the proteomic changes of thrombin-stimulated human aortic endothelial cells (HAECs) have not been elucidated.

MATERIALS AND METHODS

HAECs were stimulated with 2 units/mL thrombin for 5h and their proteome was investigated using isobaric tags for the relative and absolute quantification (iTRAQ) and the MetaCore(TM) software.

RESULTS

A total of 627 (experiment A) and 622 proteins (experiment B) were quantified in the duplicated iTRAQ analyses. MetaCore(TM) pathway analysis identified cell adhesion as a dominant phenotype in thrombin-stimulated HAECs. Replicated iTRAQ data revealed that "Cell adhesion_Chemokines and adhesion," "Cell adhesion_Histamine H1 receptor signaling in the interruption of cell barrier integrity," and "Cell adhesion_Integrin-mediated cell adhesion and migration" were among the top 10 statistically significant pathways. The cell adhesion phenotype was verified by increased THP-1 adhesion to thrombin-stimulated HAECs. In addition, the expression of ICAM-1, VCAM-1, and SELE was significantly upregulated in thrombin-stimulated HAECs.

CONCLUSIONS

Several regulatory pathways are altered in thrombin-stimulated HAECs, with cell adhesion being the dominant altered phenotype. Our findings show the feasibility of the iTRAQ technique for evaluating cellular responses to acute stimulation.

Protein Methionine Sulfoxide Dynamics in Arabidopsis thaliana under Oxidative Stress

Reactive oxygen species such as hydrogen peroxide can modify proteins via direct oxidation of their sulfur-containing amino acids, cysteine and methionine. Methionine oxidation, studied here, is a reversible posttranslational modification that is emerging as a mechanism by which proteins perceive oxidative stress and function in redox signaling. Identification of proteins with oxidized methionines is the first prerequisite toward understanding the functional effect of methionine oxidation on proteins and the biological processes in which they are involved. Here, we describe a proteome-wide study of in vivo protein-bound methionine oxidation in plants upon oxidative stress using Arabidopsis thaliana catalase 2 knock-out plants as a model system. We identified over 500 sites of oxidation in about 400 proteins and quantified the differences in oxidation between wild-type and catalase 2 knock-out plants. We show that the activity of two plant-specific glutathione S-transferases, GSTF9 and GSTT23, is significantly reduced upon oxidation. And, by sampling over time, we mapped the dynamics of methionine oxidation and gained new insights into this complex and dynamic landscape of a part of the plant proteome that is sculpted by oxidative stress.

PhosphoHunter: An Efficient Software Tool for Phosphopeptide Identification

Phosphorylation is a protein posttranslational modification. It is responsible of the activation/inactivation of disease-related pathways, thanks to its role of “molecular switch.” The study of phosphorylated proteins becomes a key point for the proteomic analyses focused on the identification of diagnostic/therapeutic targets. Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) is the most widely used analytical approach. Although unmodified peptides are automatically identified by consolidated algorithms, phosphopeptides still require automated tools to avoid time-consuming manual interpretation. To improve phosphopeptide identification efficiency, a novel procedure was developed and implemented in a Perl/C tool called PhosphoHunter, here proposed and evaluated. It includes a preliminary heuristic step for filtering out the MS/MS spectra produced by nonphosphorylated peptides before sequence identification. A method to assess the statistical significance of identified phosphopeptides was also formulated. PhosphoHunter performance was tested on a dataset of 1500 MS/MS spectra and it was compared with two other tools: Mascot and Inspect. Comparisons demonstrated that a strong point of PhosphoHunter is sensitivity, suggesting that it is able to identify real phosphopeptides with superior performance. Performance indexes depend on a single parameter (intensity threshold) that users can tune according to the study aim. All the three tools localized >90% of phosphosites.

Comparative proteomic analysis of hyphae and germinating cysts of Phytophthora pisi and Phytophthora sojae

The recently described oomycete pathogen Phytophthora pisi causes root rot on pea and faba bean, while the closely related Phytophthora sojae is the causal agent of soybean root and stem rot. Differences in the pathogenicity factor repertoires that enable the two species to have distinct host specificity towards pea and soybean, were studied using tandem mass spectrometry in a global proteome study of hyphae and germinating cysts in P. pisi and P. sojae. In total 2775 proteins from P. pisi and 2891 proteins from P. sojae were identified. Fifty-eight orthologous proteins were more abundant in germinated cysts of both pathogens and thus identified as candidate proteins for the infective stage. Several of these proteins were associated with lipid transport and metabolism, and energy production. Twenty-three orthologous proteins were more abundant in hyphae of both pathogens and thus identified as candidate proteins for vegetative growth. Proteins uniquely present in germinating cysts of either P. pisi or P. sojae were considered as candidates for species-specific pathogenicity factors that may be involved in host specificity. Among these proteins were serine proteases, membrane transporters and a berberine-like protein. These results significantly expand the knowledge of the expressed proteome in P. pisi and P. sojae.

BIOLOGICAL SIGNIFICANCE

P. sojae and P. pisi are closely related species that specifically cause root rot on soybean and pea, respectively. The pathogenicity factors contributing to their host specificity remained unknown. We carried out a comparative large-scale proteome analysis of vegetative (hyphae) and infective (germinating cysts) life stages in P. pisi and P. sojae. This study provides knowledge of the common factors and mechanism involved in initiation of infection and species-specific proteins that may contribute to the host specificity of these pathogens. This knowledge will lead to a better understanding of the infection biology of these pathogens, allowing new possibilities towards developing alternative and effective plant protection measures.

Proteomic characterization of the nucleolar linker histone H1 interaction network

To investigate the relationship between linker histone H1 and protein-protein interactions in the nucleolus, we used biochemical and proteomics approaches to characterize nucleoli purified from cultured human and mouse cells. Mass spectrometry identified 175 proteins in human T cell nucleolar extracts that bound to Sepharose-immobilized H1 in vitro. Gene ontology analysis found significant enrichment for H1 binding proteins with functions related to nucleolar chromatin structure and RNA polymerase I transcription regulation, rRNA processing, and mRNA splicing. Consistent with the affinity binding results, H1 existed in large (400 to >650kDa) macromolecular complexes in human T cell nucleolar extracts. To complement the biochemical experiments, we investigated the effects of in vivo H1 depletion on protein content and structural integrity of the nucleolus using the H1 triple isoform knockout (H1ΔTKO) mouse embryonic stem cell (mESC) model system. Proteomic profiling of purified wild-type mESC nucleoli identified a total of 613 proteins, only ~60% of which were detected in the H1 mutant nucleoli. Within the affected group, spectral counting analysis quantitated 135 specific nucleolar proteins whose levels were significantly altered in H1ΔTKO mESC. Importantly, the functions of the affected proteins in mESC closely overlapped with those of the human T cell nucleolar H1 binding proteins. Immunofluorescence microscopy of intact H1ΔTKO mESC demonstrated both a loss of nucleolar RNA content and altered nucleolar morphology resulting from in vivo H1 depletion. We conclude that H1 organizes and maintains an extensive protein-protein interaction network in the nucleolus required for nucleolar structure and integrity.

Autonomous metabolomics for rapid metabolite identification in global profiling

An autonomous metabolomic workflow combining mass spectrometry analysis with tandem mass spectrometry data acquisition was designed to allow for simultaneous data processing and metabolite characterization. Although previously tandem mass spectrometry data have been generated on the fly, the experiments described herein combine this technology with the bioinformatic resources of XCMS and METLIN. As a result of this unique integration, we can analyze large profiling datasets and simultaneously obtain structural identifications. Validation of the workflow on bacterial samples allowed the profiling on the order of a thousand metabolite features with simultaneous tandem mass spectra data acquisition. The tandem mass spectrometry data acquisition enabled automatic search and matching against the METLIN tandem mass spectrometry database, shortening the current workflow from days to hours. Overall, the autonomous approach to untargeted metabolomics provides an efficient means of metabolomic profiling, and will ultimately allow the more rapid integration of comparative analyses, metabolite identification, and data analysis at a systems biology level.

Revisiting peptide identification by high-accuracy mass spectrometry: problems associated with the use of narrow mass precursor windows

Peptide identification is increasingly achieved through database searches in which mass precursor tolerance is set in the ppm range. This trend is driven by the high resolution and accuracy of modern mass spectrometers and the belief that the quality of peptide identification is fully controlled by estimating the false discovery rate (FDR) using the decoy-target approach. However, narrowing mass tolerance decreases the number of sequence candidates, and several authors have raised concerns that these search conditions can introduce inaccuracies. Here, we demonstrate that when scores that only depend on one sequence candidate are used, decoy-based estimates of the number of false positive identifications are accurate even with an average number of candidates of just 200, to the point that remarkably accurate FDR predictions can be made in completely different search conditions. However, when scores that are constructed taking information from additional sequence candidates are used together with low precursor mass tolerances, the proportion of peptides incorrectly identified may become significantly higher than the FDR estimated by the target-decoy approach. Our results suggest that with this kind of score the high mass accuracy of modern mass spectrometers should be exploited by using wide mass windows followed by postscoring mass filtering algorithms.

On the importance of well-calibrated scores for identifying shotgun proteomics spectra

Identifying the peptide responsible for generating an observed fragmentation spectrum requires scoring a collection of candidate peptides and then identifying the peptide that achieves the highest score. However, analysis of a large collection of such spectra requires that the score assigned to one spectrum be well-calibrated with respect to the scores assigned to other spectra. In this work, we define the notion of calibration in the context of shotgun proteomics spectrum identification, and we introduce a simple, albeit computationally intensive, technique to calibrate an arbitrary score function. We demonstrate that this calibration procedure yields an increased number of identified spectra at a fixed false discovery rate (FDR) threshold. We also show that proper calibration of scores has a surprising effect on a previously described FDR estimation procedure, making the procedure less conservative. Finally, we provide empirical results suggesting that even partial calibration, which is much less computationally demanding, can yield significant increases in spectrum identification. Overall, we argue that accurate shotgun proteomics analysis requires careful attention to score calibration.

Data set for the mass spectrometry based exoproteome analysis of Aspergillus flavus isolates

Aspergillus flavus is one of the predominant causative organisms of mycotic keratitis in tropical parts of the world. Extracellular proteins are the earliest proteins that come in contact with the host and have a role in the infection process. Exoproteins of A. flavus isolated from infected cornea, sputum and a saprophyte were pooled and identified using high resolution mass spectrometry in order to get the total exoproteome from cultures isolated from different sources. A total of 637 proteins was identified from the pooled A. flavus exoproteome. Analysis based on GO annotations of the 637 identified proteins revealed that hydrolases form the predominant class of proteins in the exoproteome. Interestingly, a greater proportion of the exoproteins seem to be secreted through the non-classical pathways. This data represent the first in-depth analysis of the representative A. flavus exoproteome of a large set of isolates from distinct sources. This data have been deposited to the ProteomeXchange with identifier PXD001296.

Exoproteome of Aspergillus flavus corneal isolates and saprophytes: identification of proteoforms of an oversecreted alkaline protease

Aspergillus flavus infects the human eye leading to keratitis. Extracellular proteins, the earliest proteins that come in contact with the host and virulence related exoproteins, were identified in the fungus isolated from infected cornea. Virulence of the corneal isolates was tested in the Galleria mellonella larvae model and those isolates showing higher virulence were taken for subsequent exoproteome analysis. High resolution two-dimensional electrophoresis and mass spectrometry were used to generate A. flavus exoproteome reference map as well as to profile most of the exoproteins. Analysis of the identified proteins clearly shows the major biological processes that they are involved in. Nearly 50% of the exoproteins possess catalytic activity and one of these, an alkaline serine protease (Alp1) is present in high abundance as well as multiple proteoforms. Many proteins in the A. flavus exoproteome have been shown to be virulence factors in other pathogens indicating the probable role for these proteins in the corneal infection as well. Interestingly, the majority of the exoproteins do not have secretory signal indicating that they are secreted through the non-classical pathway. Thus, this study provides a clue to the early strategies employed by the pathogen to establish an infection in an immunocompetent host.

BIOLOGICAL SIGNIFICANCE

The outcome of a fungal infection in an immunocompetent human eye depends on the ability of the fungus to overcome the host defense and propagate itself. In this process, the earliest events with respect to the fungal proteins involved include the secretory proteins of the invading organism. As a first step towards understanding the role of the extracellular proteins, exoproteome profile of the fungal isolates was generated. The fungal isolates from cornea showed a distinct pattern of the exoproteome when compared to the saprophyte. Since corneal isolates also showed higher virulence in the insect larval model, presumably the proteins elaborated by the corneal isolates are virulence related. One of the abundant proteins is an alkaline serine protease and this protein exists as multiple proteoforms. This study reports the comprehensive profile of exoproteome and reveals proteins that are potential virulence factors.

Phosphoproteomic analyses reveal novel cross-modulation mechanisms between two signaling pathways in yeast

Cells respond to environmental stimuli via specialized signaling pathways. Concurrent stimuli trigger multiple pathways that integrate information, predominantly via protein phosphorylation. Budding yeast responds to NaCl and pheromone via two mitogen-activated protein kinase cascades, the high osmolarity, and the mating pathways, respectively. To investigate signal integration between these pathways, we quantified the time-resolved phosphorylation site dynamics after pathway co-stimulation. Using shotgun mass spectrometry, we quantified 2,536 phosphopeptides across 36 conditions. Our data indicate that NaCl and pheromone affect phosphorylation events within both pathways, which thus affect each other at more levels than anticipated, allowing for information exchange and signal integration. We observed a pheromone-induced down-regulation of Hog1 phosphorylation due to Gpd1, Ste20, Ptp2, Pbs2, and Ptc1. Distinct Ste20 and Pbs2 phosphosites responded differently to the two stimuli, suggesting these proteins as key mediators of the information exchange. A set of logic models was then used to assess the role of measured phosphopeptides in the crosstalk. Our results show that the integration of the response to different stimuli requires complex interconnections between signaling pathways.

The bacterial proteogenomic pipeline

Background

Proteogenomics combines the cutting-edge methods from genomics and proteomics. While it has become cheap to sequence whole genomes, the correct annotation of protein coding regions in the genome is still tedious and error prone. Mass spectrometry on the other hand relies on good characterizations of proteins derived from the genome, but can also be used to help improving the annotation of genomes or find species specific peptides. Additionally, proteomics is widely used to find evidence for differential expression of proteins under different conditions, e.g. growth conditions for bacteria. The concept of proteogenomics is not altogether new, in-house scripts are used by different labs and some special tools for eukaryotic and human analyses are available.

Results

The Bacterial Proteogenomic Pipeline, which is completely written in Java, alleviates the conducting of proteogenomic analyses of bacteria. From a given genome sequence, a naïve six frame translation is performed and, if desired, a decoy database generated. This database is used to identify MS/MS spectra by common peptide identification algorithms. After combination of the search results and optional flagging for different experimental conditions, the results can be browsed and further inspected. In particular, for each peptide the number of identifications for each condition and the positions in the corresponding protein sequences are shown. Intermediate and final results can be exported into GFF3 format for visualization in common genome browsers.

Conclusions

To facilitate proteogenomics analyses the Bacterial Proteogenomic Pipeline is a set of comprehensive tools running on common desktop computers, written in Java and thus platform independent. The pipeline allows integrating peptide identifications from various algorithms and emphasizes the visualization of spectral counts from different experimental conditions.

Enrichment of calcifying extracellular vesicles using density-based ultracentrifugation protocol

Calcifying extracellular vesicles (EVs) released from cells within atherosclerotic plaques have received increased attention for their role in mediating vascular calcification, a major predictor of cardiovascular morbidity and mortality. However, little is known about the difference between this pathologic vesicle population and other EVs that contribute to physiological cellular processes. One major challenge that hinders research into these differences is the inability to selectively isolate calcifying EVs from other vesicle populations. In this study, we hypothesized that the formation of mineral within calcifying EVs would increase the density of the vesicles such that they would pellet at a faster rate during ultracentrifugation. We show that after 10 min of ultracentrifugation at 100,000×g, calcifying EVs are depleted from the conditioned media of calcifying coronary artery smooth muscle cells and are enriched in the pelleted portion. We utilized mass spectrometry to establish functional proteomic differences between the calcifying EVs enriched in the 10 min ultracentrifugation compared to other vesicle populations preferentially pelleted by longer ultracentrifugation times. The procedures established in this study will allow us to enrich the vesicle population of interest and perform advanced proteomic analyses to find subtle differences between calcifying EVs and other vesicle populations that may be translated into therapeutic targets for vascular calcification. Finally, we will show that the differences in ultracentrifugation times required to pellet the vesicle populations can also be used to estimate physical differences between the vesicles.

C-terminomics screen for natural substrates of cytosolic carboxypeptidase 1 reveals processing of acidic protein C termini

Cytosolic carboxypeptidases (CCPs) constitute a new subfamily of M14 metallocarboxypeptidases associated to axonal regeneration and neuronal degeneration, among others. CCPs are deglutamylating enzymes, able to catalyze the shortening of polyglutamate side-chains and the gene-encoded C termini of tubulin, telokin, and myosin light chain kinase. The functions of these enzymes are not entirely understood, in part because of the lack of information about C-terminal protein processing in the cell and its functional implications. By means of C-terminal COFRADIC, a positional proteomics approach, we searched for cellular substrates targets of CCP1, the most relevant member of this family. We here identified seven new putative CCP1 protein substrates, including ribosomal proteins, translation factors, and high mobility group proteins. Furthermore, we showed for the first time that CCP1 processes both glutamates as well as C-terminal aspartates. The implication of these C termini in molecular interactions furthermore suggests that CCP1-mediated shortening of acidic protein tails might regulate protein-protein and protein-DNA interactions.

The pea seedling mitochondrial Nε-lysine acetylome

Posttranslational lysine acetylation is believed to occur in all taxa and to affect thousands of proteins. In contrast to the hundreds of mitochondrial proteins reported to be lysine-acetylated in non-plant species, only a handful have been reported from the plant taxa previously examined. To investigate whether this reflects a biologically significant difference or merely a peculiarity of the samples thus far examined, we immunoenriched and analyzed acetylated peptides from highly purified pea seedling mitochondria using mass spectrometry. Our results indicate that a multitude of mitochondrial proteins, involved in a variety of processes, are acetylated in pea seedlings.

DIANA--algorithmic improvements for analysis of data-independent acquisition MS data

MOTIVATION

Data independent acquisition mass spectrometry has emerged as a reproducible and sensitive alternative in quantitative proteomics, where parsing the highly complex tandem mass spectra requires dedicated algorithms. Recently, targeted data extraction was proposed as a novel analysis strategy for this type of data, but it is important to further develop these concepts to provide quality-controlled, interference-adjusted and sensitive peptide quantification.

RESULTS

We here present the algorithm DIANA and the classifier PyProphet, which are based on new probabilistic sub-scores to classify the chromatographic peaks in targeted data-independent acquisition data analysis. The algorithm is capable of providing accurate quantitative values and increased recall at a controlled false discovery rate, in a complex gold standard dataset. Importantly, we further demonstrate increased confidence gained by the use of two complementary data-independent acquisition targeted analysis algorithms, as well as increased numbers of quantified peptide precursors in complex biological samples.

AVAILABILITY AND IMPLEMENTATION

DIANA is implemented in scala and python and available as open source (Apache 2.0 license) or pre-compiled binaries from http://quantitativeproteomics.org/diana. PyProphet can be installed from PyPi (https://pypi.python.org/pypi/pyprophet).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application of clinical proteomics in acute respiratory distress syndrome

Acute Respiratory Distress Syndrome (ARDS) is a devastating cause of hypoxic respiratory failure, which continues to have high mortality. It is expected that a comprehensive systems- level approach will identify global and complex changes that contribute to the development of ARDS and subsequent repair of the damaged lung. In the last decade, powerful genome-wide analytical and informatics tools have been developed, that have provided valuable insights into the mechanisms of complex diseases such as ARDS. These tools include the rapid and precise measure of gene expression at the proteomic level. This article reviews the contemporary proteomics platforms that are available for comprehensive studies in ARDS. The challenges of various biofluids that could be investigated and some of the studies performed are also discussed.

JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from "mixture spectra" to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs.

New glycoproteomics software, GlycoPep Evaluator, generates decoy glycopeptides de novo and enables accurate false discovery rate analysis for small data sets

Glycoproteins are biologically significant large molecules that participate in numerous cellular activities. In order to obtain site-specific protein glycosylation information, intact glycopeptides, with the glycan attached to the peptide sequence, are characterized by tandem mass spectrometry (MS/MS) methods such as collision-induced dissociation (CID) and electron transfer dissociation (ETD). While several emerging automated tools are developed, no consensus is present in the field about the best way to determine the reliability of the tools and/or provide the false discovery rate (FDR). A common approach to calculate FDRs for glycopeptide analysis, adopted from the target-decoy strategy in proteomics, employs a decoy database that is created based on the target protein sequence database. Nonetheless, this approach is not optimal in measuring the confidence of N-linked glycopeptide matches, because the glycopeptide data set is considerably smaller compared to that of peptides, and the requirement of a consensus sequence for N-glycosylation further limits the number of possible decoy glycopeptides tested in a database search. To address the need to accurately determine FDRs for automated glycopeptide assignments, we developed GlycoPep Evaluator (GPE), a tool that helps to measure FDRs in identifying glycopeptides without using a decoy database. GPE generates decoy glycopeptides de novo for every target glycopeptide, in a 1:20 target-to-decoy ratio. The decoys, along with target glycopeptides, are scored against the ETD data, from which FDRs can be calculated accurately based on the number of decoy matches and the ratio of the number of targets to decoys, for small data sets. GPE is freely accessible for download and can work with any search engine that interprets ETD data of N-linked glycopeptides. The software is provided at https://desairegroup.ku.edu/research.

Proteomics for systems toxicology

Current toxicology studies frequently lack measurements at molecular resolution to enable a more mechanism-based and predictive toxicological assessment. Recently, a systems toxicology assessment framework has been proposed, which combines conventional toxicological assessment strategies with system-wide measurement methods and computational analysis approaches from the field of systems biology. Proteomic measurements are an integral component of this integrative strategy because protein alterations closely mirror biological effects, such as biological stress responses or global tissue alterations. Here, we provide an overview of the technical foundations and highlight select applications of proteomics for systems toxicology studies. With a focus on mass spectrometry-based proteomics, we summarize the experimental methods for quantitative proteomics and describe the computational approaches used to derive biological/mechanistic insights from these datasets. To illustrate how proteomics has been successfully employed to address mechanistic questions in toxicology, we summarized several case studies. Overall, we provide the technical and conceptual foundation for the integration of proteomic measurements in a more comprehensive systems toxicology assessment framework. We conclude that, owing to the critical importance of protein-level measurements and recent technological advances, proteomics will be an integral part of integrative systems toxicology approaches in the future.

Annotation of the zebrafish genome through an integrated transcriptomic and proteomic analysis

Accurate annotation of protein-coding genes is one of the primary tasks upon the completion of whole genome sequencing of any organism. In this study, we used an integrated transcriptomic and proteomic strategy to validate and improve the existing zebrafish genome annotation. We undertook high-resolution mass-spectrometry-based proteomic profiling of 10 adult organs, whole adult fish body, and two developmental stages of zebrafish (SAT line), in addition to transcriptomic profiling of six organs. More than 7,000 proteins were identified from proteomic analyses, and ∼ 69,000 high-confidence transcripts were assembled from the RNA sequencing data. Approximately 15% of the transcripts mapped to intergenic regions, the majority of which are likely long non-coding RNAs. These high-quality transcriptomic and proteomic data were used to manually reannotate the zebrafish genome. We report the identification of 157 novel protein-coding genes. In addition, our data led to modification of existing gene structures including novel exons, changes in exon coordinates, changes in frame of translation, translation in annotated UTRs, and joining of genes. Finally, we discovered four instances of genome assembly errors that were supported by both proteomic and transcriptomic data. Our study shows how an integrative analysis of the transcriptome and the proteome can extend our understanding of even well-annotated genomes.

Proteome Changes Induced by Imatinib and Novel Imatinib Derivatives in K562 Human Chronic Myeloid Leukemia Cells

Imatinib mesylate is the leading compound to treat chronic myeloid leukemia (CML) and other cancers, through its inhibition of Bcr-Abl tyrosine kinases. However, resistance to imatinib develops frequently, particularly in late-stage disease and has necessitated the development of new Bcr-Abl inhibitors. The synthesis of a new series of phenylaminopyrimidines, structurally related to imatinib, showed large interest since the introduction of nilotinib. Here, we compare the protein levels in K562 cells treated with either imatinib or with novel imatinib derivates. Our results revealed that among the 986 quantified proteins, 35 had significantly altered levels of expression by imatinib or its derivates. In a second series of experiments, we directly compared the proteomes of imatinib treated K562 cells with those K562 cells treated with any of the four imatinib derivates. More than 1029 protein were quantified, 80 of which had altered levels of expression. Both experiments pointed to changes in the expression of the ATP-dependent RNA helicase DDX3X and of two mitochondrial coiled-coil-helix-coiled-coil-helix domain-containing proteins.

Histone H3 lysine 27 acetylation is altered in colon cancer

Background

Histone post-translational modifications (PTMs) play an important role in the regulation of the expression of genes, including those involved in cancer development and progression. However, our knowledge of PTM patterns in human tumours is limited.

Methods

MS-based analyses were used to quantify global alterations of histone PTMs in colorectal cancer (CRC) samples. Histones isolated from 12 CRCs and their corresponding normal mucosa by acidic extraction were separated by SDS-PAGE and analysed by liquid chromatography-mass spectrometry.

Results

Among 96 modified peptides, 41 distinct PTM sites were identified, of which 7, 13, 11, and 10 were located within the H2A, H2B, H3, and H4 sequences, respectively, and distributed among the amino-terminal tails and the globular domain of the four histones. Modification intensities were quantified for 33 sites, of which 4 showed significant (p-value ≤ 0.05) differences between CRC tissues and healthy mucosa samples. We identified histone H3 lysine 27 acetylation (H3K27Ac) as a modification upregulated in CRC, which had not been shown previously.

Conclusions

The present results indicate the usefulness of a bottom-up proteomic approach for the detection of histone modifications at a global scale. The differential abundance of H3K27Ac mark in CRC, a PTM associated with active enhancers, suggests its role in regulating genes whose expression changes in CRC.

Computing exact p-values for a cross-correlation shotgun proteomics score function

The core of every protein mass spectrometry analysis pipeline is a function that assesses the quality of a match between an observed spectrum and a candidate peptide. We describe a procedure for computing exact p-values for the oldest and still widely used score function, SEQUEST XCorr. The procedure uses dynamic programming to enumerate efficiently the full distribution of scores for all possible peptides whose masses are close to that of the spectrum precursor mass. Ranking identified spectra by p-value rather than XCorr significantly reduces variance because of spectrum-specific effects on the score. In combination with the Percolator postprocessor, the XCorr p-value yields more spectrum and peptide identifications at a fixed false discovery rate than Mascot, X!Tandem, Comet, and MS-GF+ across a variety of data sets.