Ultrafast Proteomics.

Current stage of proteomic research in the field of biology, medicine, development of new drugs, population screening, or personalized approaches to therapy dictates the need to analyze large sets of samples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics were characterized as unique in identifying and quantifying cellular protein composition, but low throughput, requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed of the whole proteome analysis has become the main factor limiting developments in functional proteomics, where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over a long period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles. Development of mass spectrometry technologies providing high resolution and mass measurement accuracy, predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics. Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hundred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago. The review examines background of these developments, as well as modern methods and approaches that implement ultrafast analysis of the entire proteome.

On the utility of ultrafast MS1-only proteomics in drug target discovery studies based on thermal proteome profiling method.

Advances in high-throughput high-resolution mass spectrometry and the development of thermal proteome profiling approach (TPP) have made it possible to accelerate a drug target search. Since its introduction in 2014, TPP quickly became a method of choice in chemical proteomics for identifying drug-to-protein interactions on a proteome-wide scale and mapping the pathways of these interactions, thus further elucidating the unknown mechanisms of action of a drug under study. However, the current TPP implementations based on tandem mass spectrometry (MS/MS), associated with employing lengthy peptide separation protocols and expensive labeling techniques for sample multiplexing, limit the scaling of this approach for the ever growing variety of drug-to-proteomes. A variety of ultrafast proteomics methods have been developed in the last couple of years. Among them, DirectMS1 provides MS/MS-free quantitative proteome-wide analysis in 5-min time scale, thus opening the way for sample-hungry applications, such as TPP. In this work, we demonstrate the first implementation of the TPP approach using the ultrafast proteome-wide analysis based on DirectMS1. Using a drug topotecan, which is a known topoisomerase I (TOP1) inhibitor, the feasibility of the method for identifying drug targets at the whole proteome level was demonstrated for an ovarian cancer cell line.

Ultra-Fast Mass Spectrometry in Plant Biochemistry: Response of Winter Wheat Proteomics to Pre-Sowing Treatment with Iron Compounds.

In recent years, ultrafast liquid chromatography/mass spectrometry methods have been extensively developed for the use in proteome profiling in biochemical studies. These methods are intended for express monitoring of cell response to biotic stimuli and elucidation of correlation of molecular changes with biological processes and phenotypical changes. New technologies, including the use of nanomaterials, are actively introduced to increase agricultural production. However, this requires complex approbation of new fertilizers and investigation of mechanisms underlying the biotic effects on the germination, growth, and development of plants. The aim of this work was to adapt the method of ultrafast chromatography/mass spectrometry for rapid quantitative profiling of molecular changes in 7-day-old wheat seedlings in response to pre-sowing seed treatment with iron compounds. The used method allows to analyze up to 200 samples per day; its practical value lies in the possibility of express proteomic diagnostics of the biotic action of new treatments, including those intended for agricultural needs. Changes in the regulation of photosynthesis, biosynthesis of chlorophyll and porphyrin- and tetrapyrrole-containing compounds, glycolysis (in shoot tissues), and polysaccharide metabolism (in root tissues) were shown after seed treatment with suspensions containing film-forming polymers (PEG 400, Na-CMC, Na2-EDTA), iron (II, III) nanoparticles, or iron (II) sulfate. Observations at the protein levels were consistent with the results of morphometry, superoxide dismutase activity assay, and microelement analysis of 3-day-old germinated seeds and shoots and roots of 7-day-old seedlings. A characteristic molecular signature involving proteins participating in the regulation of photosynthesis and glycolytic process was suggested as a potential marker of the biotic effects of seed treatment with iron compounds, which will be confirmed in further studies.

Massive Proteogenomic Reanalysis of Publicly Available Proteomic Datasets of Human Tissues in Search for Protein Recoding via Adenosine-to-Inosine RNA Editing.

The proteogenomic search pipeline developed in this work has been applied for reanalysis of 40 publicly available shotgun proteomic datasets from various human tissues comprising more than 8000 individual LC-MS/MS runs, of which 5442 .raw data files were processed in total. This reanalysis was focused on searching for ADAR-mediated RNA editing events, their clustering across samples of different origins, and classification. In total, 33 recoded protein sites were identified in 21 datasets. Of those, 18 sites were detected in at least two datasets, representing the core human protein editome. In agreement with prior artworks, neural and cancer tissues were found to be enriched with recoded proteins. Quantitative analysis indicated that recoding the rate of specific sites did not directly depend on the levels of ADAR enzymes or targeted proteins themselves, rather it was governed by differential and yet undescribed regulation of interaction of enzymes with mRNA. Nine recoding sites conservative between humans and rodents were validated by targeted proteomics using stable isotope standards in the murine brain cortex and cerebellum, and an additional one was validated in human cerebrospinal fluid. In addition to previous data of the same type from cancer proteomes, we provide a comprehensive catalog of recoding events caused by ADAR RNA editing in the human proteome.

Yeast Ribonucleotide Reductase Is a Direct Target of the Proteasome and Provides Hyper Resistance to the Carcinogen 4-NQO.

Various external and internal factors damaging DNA constantly disrupt the stability of the genome. Cells use numerous dedicated DNA repair systems to detect damage and restore genomic integrity in a timely manner. Ribonucleotide reductase (RNR) is a key enzyme providing dNTPs for DNA repair. Molecular mechanisms of indirect regulation of yeast RNR activity are well understood, whereas little is known about its direct regulation. The study was aimed at elucidation of the proteasome-dependent mechanism of direct regulation of RNR subunits in Saccharomyces cerevisiae. Proteome analysis followed by Western blot, RT-PCR, and yeast plating analysis showed that upregulation of RNR by proteasome deregulation is associated with yeast hyper resistance to 4-nitroquinoline-1-oxide (4-NQO), a UV-mimetic DNA-damaging drug used in animal models to study oncogenesis. Inhibition of RNR or deletion of RNR regulatory proteins reverses the phenotype of yeast hyper resistance to 4-NQO. We have shown for the first time that the yeast Rnr1 subunit is a substrate of the proteasome, which suggests a common mechanism of RNR regulation in yeast and mammals.

Proteomics-based scoring of cellular response to stimuli for improved characterization of signaling pathway activity.

Omics technologies focus on uncovering the complex nature of molecular mechanisms in cells and organisms, including biomarkers and drug targets discovery. Aiming at these tasks, we see that information extracted from omics data is still underused. In particular, characteristics of differentially regulated molecules can be combined in a single score to quantify the signaling pathway activity. Such a metric can be useful for comprehensive data interpretation to follow: (1) developing molecular responses in time; (2) potency of a drug on a certain cell culture; (3) ranking the signaling pathway activity in stimulated cells; and (4) integration of the omics data and assay-based measurements of cell viability, cytotoxicity, and proliferation. With recent advances in ultrafast mass spectrometry for quantitative proteomics allowing data collection in a few minutes, proteomics score for cellular response to stimuli can become a fast, accurate, and informative complement to bioassays. Here, we utilized an interquartile-based selection of differentially regulated features and a variety of schemes for quantifying cellular responses to come up with the quantitative metric for total cellular response and pathway activity. Validation was performed using antiproliferative and virus assays and label-free proteomics data collected for cancer cells subjected to drug stimulation.

On the Feasibility of Using an Ultra-Fast DirectMS1 Method of Proteome-Wide Analysis for Searching Drug Targets in Chemical Proteomics.

Protein quantitation in tissue cells or physiological fluids based on liquid chromatography/mass spectrometry is one of the key sources of information on the mechanisms of cell functioning during chemotherapeutic treatment. Information on significant changes in protein expression upon treatment can be obtained by chemical proteomics and requires analysis of the cellular proteomes, as well as development of experimental and bioinformatic methods for identification of the drug targets. Low throughput of whole proteome analysis based on liquid chromatography and tandem mass spectrometry is one of the main factors limiting the scale of these studies. The method of direct mass spectrometric identification of proteins, DirectMS1, is one of the approaches developed in recent years allowing ultrafast proteome-wide analyses employing minute-scale gradients for separation of proteolytic mixtures. Aim of this work was evaluation of both possibilities and limitations of the method for identification of drug targets at the level of whole proteome and for revealing cellular processes activated by the treatment. Particularly, the available literature data on chemical proteomics obtained earlier for a large set of onco-pharmaceuticals using multiplex quantitative proteome profiling were analyzed. The results obtained were further compared with the proteome-wide data acquired by the DirectMS1 method using ultrashort separation gradients to evaluate efficiency of the method in identifying known drug targets. Using ovarian cancer cell line A2780 as an example, a whole-proteome comparison of two cell lysis techniques was performed, including the freeze-thaw lysis commonly employed in chemical proteomics and the one based on ultrasonication for cell disruption, which is the widely accepted as a standard in proteomic studies. Also, the proteome-wide profiling was performed using ultrafast DirectMS1 method for A2780 cell line treated with lonidamine, followed by gene ontology analyses to evaluate capabilities of the method in revealing regulation of proteins in the cellular processes associated with drug treatment.

Mass Spectrometry-Based Chemical Proteomics for Drug Target Discoveries.

Chemical proteomics, emerging rapidly in recent years, has become a main approach to identifying interactions between the small molecules and proteins in the cells on a proteome scale and mapping the signaling and/or metabolic pathways activated and regulated by these interactions. The methods of chemical proteomics allow not only identifying proteins targeted by drugs, characterizing their toxicity and discovering possible off-target proteins, but also elucidation of the fundamental mechanisms of cell functioning under conditions of drug exposure or due to the changes in physiological state of the organism itself. Solving these problems is essential for both basic research in biology and clinical practice, including approaches to early diagnosis of various forms of serious diseases or prediction of the effectiveness of therapeutic treatment. At the same time, recent developments in high-resolution mass spectrometry have provided the technology for searching the drug targets across the whole cell proteomes. This review provides a concise description of the main objectives and problems of mass spectrometry-based chemical proteomics, the methods and approaches to their solution, and examples of implementation of these methods in biomedical research.

DirectMS1Quant: Ultrafast Quantitative Proteomics with MS/MS-Free Mass Spectrometry.

Recently, we presented the DirectMS1 method of ultrafast proteome-wide analysis based on minute-long LC gradients and MS1-only mass spectra acquisition. Currently, the method provides the depth of human cell proteome coverage of 2500 proteins at a 1% false discovery rate (FDR) when using 5 min LC gradients and 7.3 min runtime in total. While the standard MS/MS approaches provide 4000-5000 protein identifications within a couple of hours of instrumentation time, we advocate here that the higher number of identified proteins does not always translate into better quantitation quality of the proteome analysis. To further elaborate on this issue, we performed a one-on-one comparison of quantitation results obtained using DirectMS1 with three popular MS/MS-based quantitation methods: label-free (LFQ) and tandem mass tag quantitation (TMT), both based on data-dependent acquisition (DDA) and data-independent acquisition (DIA). For comparison, we performed a series of proteome-wide analyses of well-characterized (ground truth) and biologically relevant samples, including a mix of UPS1 proteins spiked at different concentrations into an Echerichia coli digest used as a background and a set of glioblastoma cell lines. MS1-only data was analyzed using a novel quantitation workflow called DirectMS1Quant developed in this work. The results obtained in this study demonstrated comparable quantitation efficiency of 5 min DirectMS1 with both TMT and DIA methods, yet the latter two utilized a 10-20-fold longer instrumentation time.

Multiomic Profiling Identified EGF Receptor Signaling as a Potential Inhibitor of Type I Interferon Response in Models of Oncolytic Therapy by Vesicular Stomatitis Virus.

Cancer cell lines responded differentially to type I interferon treatment in models of oncolytic therapy using vesicular stomatitis virus (VSV). Two opposite cases were considered in this study, glioblastoma DBTRG-05MG and osteosarcoma HOS cell lines exhibiting resistance and sensitivity to VSV after the treatment, respectively. Type I interferon responses were compared for these cell lines by integrative analysis of the transcriptome, proteome, and RNA editome to identify molecular factors determining differential effects observed. Adenosine-to-inosine RNA editing was equally induced in both cell lines. However, transcriptome analysis showed that the number of differentially expressed genes was much higher in DBTRG-05MG with a specific enrichment in inflammatory proteins. Further, it was found that two genes, EGFR and HER2, were overexpressed in HOS cells compared with DBTRG-05MG, supporting recent reports that EGF receptor signaling attenuates interferon responses via HER2 co-receptor activity. Accordingly, combined treatment of cells with EGF receptor inhibitors such as gefitinib and type I interferon increases the resistance of sensitive cell lines to VSV. Moreover, sensitive cell lines had increased levels of HER2 protein compared with non-sensitive DBTRG-05MG. Presumably, the level of this protein expression in tumor cells might be a predictive biomarker of their resistance to oncolytic viral therapy.

Multi-Omics Analysis of Glioblastoma Cells' Sensitivity to Oncolytic Viruses.

Oncolytic viruses have gained momentum in the last decades as a promising tool for cancer treatment. Despite the progress, only a fraction of patients show a positive response to viral therapy. One of the key variable factors contributing to therapy outcomes is interferon-dependent antiviral mechanisms in tumor cells. Here, we evaluated this factor using patient-derived glioblastoma multiforme (GBM) cultures. Cell response to the type I interferons' (IFNs) stimulation was characterized at mRNA and protein levels. Omics analysis revealed that GBM cells overexpress interferon-stimulated genes (ISGs) and upregulate their proteins, similar to the normal cells. A conserved molecular pattern unambiguously differentiates between the preserved and defective responses. Comparing ISGs' portraits with titration-based measurements of cell sensitivity to a panel of viruses, the "strength" of IFN-induced resistance acquired by GBM cells was ranked. The study demonstrates that suppressing a single ISG and encoding an essential antiviral protein, does not necessarily increase sensitivity to viruses. Conversely, silencing IFIT3 and PLSCR1 genes in tumor cells can negatively affect the internalization of vesicular stomatitis and Newcastle disease viruses. We present evidence of a complex relationship between the interferon response genes and other factors affecting the sensitivity of tumor cells to viruses.

AA_stat: Intelligent profiling of in vivo and in vitro modifications from open search results.

Characterization of post-translational modifications is among the most challenging tasks in tandem mass spectrometry-based proteomics which has yet to find an efficient solution. The ultra-tolerant (open) database search attempts to meet this challenge. However, interpretation of the mass shifts observed in open search still requires an effective and automated solution. We have previously introduced the AA_stat tool for analysis of amino acid frequencies at different mass shifts and generation of hypotheses on unaccounted in vitro modifications. Here, we report on the new version of AA_stat, which now complements amino acid frequency statistics with a number of new features: (1) MS/MS-based localization of mass shifts and localization scoring, including shifts which are the sum of modifications; (2) inferring fixed modifications to increase method sensitivity; (3) inferring monoisotopic peak assignment errors and variable modifications based on abundant mass shift localizations to increase the yield of closed search; (4) new mass calibration algorithm to account for partial systematic shifts; (5) interactive integration of all results and a rated list of possible mass shift interpretations. With these options, we improve interpretation of open search results and demonstrate the utility of AA_stat for profiling of abundant and rare amino acid modifications. AA_stat is implemented in Python as an open-source tool available at https://github.com/SimpleNumber/aa_stat. SIGNIFICANCE: Mass spectrometry-based PTM characterization has a long history, yet most of the methods rely on a priori knowledge of modifications of interest and do not provide a whole proteome modification landscape in a blind manner. The open database search is an efficient attempt to address this challenge by identifying peptides with mass shifts corresponding to possible modifications. Then, interpreting these mass shifts is required. Therefore, development of bioinformatics software for post-processing of the open search results, which is capable of detection and accurate annotation of new or unexpected modifications, from characterization of sample preparation efficiency and quality control to discovery of rare post-translational modifications, is of high importance.

Proteomics of Cellular Response to Stress: Taking Control of False Positive Results.

One of the main goals of quantitative proteomics is molecular profiling of cellular response to stress at the protein level. To perform this profiling, statistical analysis of experimental data involves multiple testing of a hypothesis about the equality of protein concentrations between the cells under normal and stress conditions. This analysis is then associated with the multiple testing problem dealing with the increased chance of obtaining false positive results. A number of solutions to this problem are known, yet, they may lead to the loss of potentially important biological information when applied with commonly accepted thresholds of statistical significance. Using the proteomic data obtained earlier for the yeast samples containing proteins at known concentrations and the biological models of early and late cellular responses to stress, we analyzed dependences of distributions of false positive and false negative rates on the protein fold changes and thresholds of statistical significance. Based on the analysis of the density of data points in the volcano plots, Benjamini-Hochberg method, and gene ontology analysis, visual approach for optimization of the statistical threshold and selection of the differentially regulated proteins has been suggested, which could be useful for researchers working in the field of quantitative proteomics.

C-reactive protein as marker of post-operative analgesic quality after primary total knee arthroplasty.

OBJECTIVE: To study the correlation between the level of C-reactive protein (CRP) and the severity of pain in the post-operative period with primary total knee arthroplasty and to conduct a comparative assessment of these indicators with various methods of pain relief. The primary hypothesis of the investigation was that post-operative CRP level is likely to be correlated with the severity of post-operative pain after total knee arthroplasty. The secondary points were the evaluation of CRP and pain syndrome in the groups, as well as the identification of the correlation between the level of CRP and the method of analgesia. MATERIALS AND METHODS: Peri-operative levels of CRP and pain syndrome (10-point visual analogue scale) studied 160 patients with grade III gonarthrosis who have underwent primary total knee arthroplasty under conditions of subarachnoid anaesthesia in the period from years 2017 to 2019. Depending on the method of post-operative analgesia, patients were divided into five groups: group 1 had only systemic multimodal analgesia (SMA, n = 56), group 2 were treated with the epidural analgesia (EDA, n = 20), group 3 had local high-volume infiltration anaesthesia (LHVIA, n = 20), group 4 were getting LHVIA with a wound catheter (LHVIAc, n = 48), and group 5 had a single blockade of the femoral nerve (FNB, n = 16). RESULTS: A direct strong correlation was obtained between the level of CRP and the severity of pain syndrome in the knee joint during movement in four to six hours after surgery (n = 160, Kendall coefficient τ = 0.230, p = 0,000) and on the first post-operative day (n = 160, τ = 0.21, p = 0.001). The increase in CRP (the difference between pre-operative and post-operative CRP levels) also was positively correlated with the severity of pain in the post-operative period (n = 160, τ = 0.257, p = 0.000 and τ = 0.187, p = 0.001, respectively). CRP level significantly has increased in the post-operative period (p = 0,000). The lowest CRP indicators in the first post-operative day were recorded during the infiltrative anaesthesia (3rd and 4th groups);, the highest were during the administration of SMA group (1st group). CONCLUSION: The results confirm the correlation between the level of CRP and the severity of pain syndrome in the early post-operative period after total knee arthroplasty, its dependence on the method of analgesia, and allow to use it as a criterion for evaluating of the effectiveness of analgesia.

Rpn4 and proteasome-mediated yeast resistance to ethanol includes regulation of autophagy.

Distilled spirits production using Saccharomyces cerevisiae requires understanding of the mechanisms of yeast cell response to alcohol stress. Reportedly, specific mutations in genes of the ubiquitin-proteasome system, e.g., RPN4, may result in strains exhibiting hyper-resistance to different alcohols. To study the Rpn4-dependent yeast response to short-term ethanol exposure, we performed a comparative analysis of the wild-type (WT) strain, strain with RPN4 gene deletion (rpn4-Δ), and a mutant strain with decreased proteasome activity and consequent Rpn4 accumulation due to PRE1 deregulation (YPL). The stress resistance tests demonstrated an increased sensitivity of mutant strains to ethanol compared with WT. Comparative proteomics analysis revealed significant differences in molecular responses to ethanol between these strains. GO analysis of proteins upregulated in WT showed enrichments represented by oxidative and heat responses, protein folding/unfolding, and protein degradation. Enrichment of at least one of these responses was not observed in the mutant strains. Moreover, activity of autophagy was not increased in the RPN4 deletion strain upon ethanol stress which agrees with changes in mRNA levels of ATG7 and PRB1 genes of the autophagy system. Activity of the autophagic system was clearly induced and accompanied with PRB1 overexpression in the YPL strain upon ethanol stress. We demonstrated that Rpn4 stabilization contributes to the PRB1 upregulation. CRISPR-Cas9-mediated repression of PACE-core Rpn4 binding sites in the PRB1 promoter inhibits PRB1 induction in the YPL strain upon ethanol treatment and results in YPL hypersensitivity to ethanol. Our data suggest that Rpn4 affects the autophagic system activity upon ethanol stress through the PRB1 regulation. These findings can be a basis for creating genetically modified yeast strains resistant to high levels of alcohol, being further used for fermentation in ethanol production.

DirectMS1: MS/MS-Free Identification of 1000 Proteins of Cellular Proteomes in 5 Minutes.

Proteome characterization relies heavily on tandem mass spectrometry (MS/MS) and is thus associated with instrumentation complexity, lengthy analysis time, and limited duty cycle. It was always tempting to implement approaches that do not require MS/MS, yet they were constantly failing to achieve a meaningful depth of quantitative proteome coverage within short experimental times, which is particularly important for clinical or biomarker-discovery applications. Here, we report on the first successful attempt to develop a truly MS/MS-free method, DirectMS1, for bottom-up proteomics. The method is compared with the standard MS/MS-based data-dependent acquisition approach for proteome-wide analysis using 5 min LC gradients. Specifically, we demonstrate identification of 1 000 protein groups for a standard HeLa cell line digest. The amount of loaded sample was varied in a range from 1 to 500 ng, and the method demonstrated 10-fold higher sensitivity. Combined with the recently introduced Diffacto approach for relative protein quantification, DirectMS1 outperforms most popular MS/MS-based label-free quantitation approaches because of significantly higher protein sequence coverage.

Profiling modifications for glioblastoma proteome using ultra-tolerant database search: Are the peptide mass shifts biologically relevant or chemically induced?

Peptide mass shifts were profiled using ultra-tolerant database search strategy for shotgun proteomics data sets of human glioblastoma cell lines demonstrating strong response to the type I interferon (IFNα-2b) treatment. The main objective of this profiling was revealing the cell response to IFN treatment at the level of protein modifications. To achieve this objective, statistically significant changes in peptide mass shift profiles between IFN treated and untreated glioblastoma samples were analyzed. Detailed analysis of MS/MS spectra allowed further interpretation of the observed mass shifts and differentiation between post-translational and artifact modifications. SIGNIFICANCE: Malignant cells typically acquire increased sensitivity to viruses due to the deregulated antiviral mechanisms. Therefore, a viral therapy is considered as one of the promising approaches to treat cancer. However, recent studies have demonstrated that malignant cells can preserve intact antiviral mechanisms, e.g. interferon signaling, and develop resistance to virus infection in response to interferon treatment. Post translational modifications, e.g. tyrosine phosphorylation, are the interferon signaling drivers. Thus, comprehensive characterization of modifications is crucially important, yet, most challenging problem in cancer proteomics. Here, we report on the application of the recently introduced ultra-tolerant search strategy for profiling peptide modifications in the human glioblastoma cell lines demonstrating strong response to the type I interferon (IFNα-2b) treatment. The specific aim of the study was identification of statistically significant changes in peptide mass shift profiles between IFN treated and untreated glioblastoma samples, as well as determination of whether these shifts represent the biologically relevant modification.

IdentiPy: An Extensible Search Engine for Protein Identification in Shotgun Proteomics.

We present an open-source, extensible search engine for shotgun proteomics. Implemented in Python programming language, IdentiPy shows competitive processing speed and sensitivity compared with the state-of-the-art search engines. It is equipped with a user-friendly web interface, IdentiPy Server, enabling the use of a single server installation accessed from multiple workstations. Using a simplified version of X!Tandem scoring algorithm and its novel "autotune" feature, IdentiPy outperforms the popular alternatives on high-resolution data sets. Autotune adjusts the search parameters for the particular data set, resulting in improved search efficiency and simplifying the user experience. IdentiPy with the autotune feature shows higher sensitivity compared with the evaluated search engines. IdentiPy Server has built-in postprocessing and protein inference procedures and provides graphic visualization of the statistical properties of the data set and the search results. It is open-source and can be freely extended to use third-party scoring functions or processing algorithms and allows customization of the search workflow for specialized applications.

Comparative proteomics as a tool for identifying specific alterations within interferon response pathways in human glioblastoma multiforme cells.

An acquisition of increased sensitivity of cancer cells to viruses is a common outcome of malignant progression that justifies the development of oncolytic viruses as anticancer therapeutics. Studying molecular changes that underlie the sensitivity to viruses would help to identify cases where oncolytic virus therapy would be most effective. We quantified changes in protein abundances in two glioblastoma multiforme (GBM) cell lines that differ in the ability to induce resistance to vesicular stomatitis virus (VSV) infection in response to type I interferon (IFN) treatment. In IFN-treated samples we observed an up-regulation of protein products of some IFN-regulated genes (IRGs). In total, the proteome analysis revealed up to 20% more proteins encoded by IRGs in the glioblastoma cell line, which develops resistance to VSV infection after pre-treatment with IFN. In both cell lines protein-protein interaction and signaling pathway analyses have revealed a significant stimulation of processes related to type I IFN signaling and defense responses to viruses. However, we observed a deficiency in STAT2 protein in the VSV-sensitive cell line that suggests a de-regulation of the JAK/STAT/IRF9 signaling. The study has shown that the up-regulation of IRG proteins induced by the IFNα treatment of GBM cells can be detected at the proteome level. Similar analyses could be applied for revealing functional alterations within the antiviral mechanisms in glioblastoma samples, accompanying by acquisition of sensitivity to oncolytic viruses. The approach can be useful for discovering the biomarkers that predict a potential sensitivity of individual glioblastoma tumors to oncolytic virus therapy.

MS/MS-Free Protein Identification in Complex Mixtures Using Multiple Enzymes with Complementary Specificity.

In this work, we present the results of evaluation of a workflow that employs a multienzyme digestion strategy for MS1-based protein identification in "shotgun" proteomic applications. In the proposed strategy, several cleavage reagents of different specificity were used for parallel digestion of the protein sample followed by MS1 and retention time (RT) based search. Proof of principle for the proposed strategy was performed using experimental data obtained for the annotated 48-protein standard. By using the developed approach, up to 90% of proteins from the standard were unambiguously identified. The approach was further applied to HeLa proteome data. For the sample of this complexity, the proposed MS1-only strategy determined correctly up to 34% of all proteins identified using standard MS/MS-based database search. It was also found that the results of MS1-only search were independent of the chromatographic gradient time in a wide range of gradients from 15-120 min. Potentially, rapid MS1-only proteome characterization can be an alternative or complementary to the MS/MS-based "shotgun" analyses in the studies, in which the experimental time is more important than the depth of the proteome coverage.