Mass Spectrometry-based Proteomic Technologies Research Articles

Development of LysargiNase, a mirror trypsin and its application in proteomics

Proteomics is a fast-growing discipline that aims at systematic identification, quantification of proteins and their post-translational modifications in cells. Mass spectrometry-based shotgun proteomics technology is currently one of the mainstream methods for proteomics research. With this method, proteins need to be digested to peptides by site-specific proteases before they can be detected with mass spectrometry. Therefore, site-specific proteases played key roles in this process and so far, a variety of specific proteases have been developed and used in proteomics study. Particularly, the identification, characterization and development of proteases that cleave at the N-termini of corresponding amino acid residues, which are just mirrors to those of typical C-termini proteases, provide novel tools for proteomics analysis. In this review, we summarized the proprieties of LysargiNase, a most recently identified mirror trypsin, and its applications in proteomics research to promote its more widespread usage.

Identification of key transcription factors in endometrial cancer by systems bioinformatics analysis.

Endometrial cancer (EC) is one of the most common malignant diseases worldwide. Although many studies have been performed on EC, a systems analysis between transcription factors (TFs) and EC relationship remains poorly characterized. Here, we present a systems bioinformatics analysis of TFs in EC patient samples to identify key TFs in EC. First, dysregulated and survival-related TFs were identified in EC using data from The Cancer Genome Atlas database and Gene Expression Omnibus. Second, we investigated the mechanisms of dysregulated TFs and tested whether their expression is correlated with prognosis of EC. Finally, we addressed new perspectives in EC biomarker research, including comprehensive knowledge of previously suggested candidate biomarkers in conjunction with novel mass spectrometry-based proteomic technologies with enhanced sensitivity and specificity not yet applied to EC studies, enabling a directed clinical perspective of the study design. Our study identified three promising TFs, E2F1, HMGA1, and PGR, which closely correlate with EC. Although treatments targeting TFs are not always efficient, these TFs may be useful as biomarkers for the diagnosis and prognosis of EC. Furthermore, we found that these dysregulated TFs and their target genes are primarily involved in the cell cycle and may promote endometrial carcinoma occurrence and development. Using integrated bioinformatic analysis, we identified candidate genes and pathways in EC, which could improve our understanding of the etiology and underlying molecular events of EC. Furthermore, these candidate genes and pathways could be therapeutic targets for EC.

Proteomics Analysis of Monocyte-Derived Hepatocyte-Like Cells Identifies Integrin Beta 3 as a Specific Biomarker for Drug-Induced Liver Injury by Diclofenac.

Idiosyncratic drug-induced liver injury (iDILI) is a major cause of acute liver failure resulting in liver transplantation or death. Prediction and diagnosis of iDILI remain a great challenge, as current models provide unsatisfying results in terms of sensitivity, specificity, and prognostic value. The absence of appropriate tools for iDILI detection also impairs the development of reliable biomarkers. Here, we report on a new method for identification of drug-specific biomarkers. We combined the advantages of monocyte-derived hepatocyte-like (MH) cells, able to mimic individual characteristics, with those of a novel mass spectrometry-based proteomics technology to assess potential biomarkers for Diclofenac-induced DILI. We found over 2,700 proteins differentially regulated in MH cells derived from individual patients. Herefrom, we identified integrin beta 3 (ITGB3) to be specifically upregulated in Diclofenac-treated MH cells from Diclofenac-DILI patients compared to control groups. Finally, we validated ITGB3 by flow cytometry analysis of whole blood and histological staining of liver biopsies derived from patients diagnosed with Diclofenac-DILI. In summary, our results show that biomarker candidates can be identified by proteomics analysis of MH cells. Application of this method to a broader range of drugs in the future will exploit its full potential for the development of drug-specific biomarkers. Data are available via ProteomeXchange with identifier PXD008918.

Proteomic Analysis of the Downstream Signaling Network of PARP1.

Poly-ADP-ribosylation (PARylation) is a protein posttranslational modification (PTM) that is critically involved in many biological processes that are linked to cell stress responses. It is catalyzed by a class of enzymes known as poly-ADP-ribose polymerases (PARPs). In particular, PARP1 is a nuclear protein that is activated upon sensing nicked DNA. Once activated, PARP1 is responsible for the synthesis of a large number of PARylated proteins and initiation of the DNA damage response mechanisms. This observation provided the rationale for developing PARP1 inhibitors for the treatment of human malignancies. Indeed, three PARP1 inhibitors (Olaparib, Rucaparib, and Niraparib) have recently been approved by the Food and Drug Administration for the treatment of ovarian cancer. Moreover, in 2017, both Olaparib and Niraparib have also been approved for the treatment of fallopian tube cancer and primary peritoneal cancer. Despite this very exciting progress in the clinic, the basic signaling mechanism that connects PARP1 to a diverse array of biological processes is still poorly understood. This is, in large part, due to the inherent technical difficulty associated with the analysis of protein PARylation, which is a low-abundance, labile, and heterogeneous PTM. The study of PARylation has been greatly facilitated by the recent advances in mass spectrometry-based proteomic technologies tailored to the analysis of this modification. In this Perspective, we discuss these breakthroughs, including their technical development, and applications that provide a global view of the many biological processes regulated by this important protein modification.

Advancement of mass spectrometry-based proteomics technologies to explore triple negative breast cancer.

Understanding the complexity of cancer biology requires extensive information about the cancer proteome over the course of the disease. The recent advances in mass spectrometry-based proteomics technologies have led to the accumulation of an incredible amount of such proteomic information. This information allows us to identify protein signatures or protein biomarkers, which can be used to improve cancer diagnosis, prognosis and treatment. For example, mass spectrometry-based proteomics has been used in breast cancer research for over two decades to elucidate protein function. Breast cancer is a heterogeneous group of diseases with distinct molecular features that are reflected in tumour characteristics and clinical outcomes. Compared with all other subtypes of breast cancer, triple-negative breast cancer is perhaps the most distinct in nature and heterogeneity. In this review, we provide an introductory overview of the application of advanced proteomic technologies to triple-negative breast cancer research.

Proteome Profiling Outperforms Transcriptome Profiling for Coexpression Based Gene Function Prediction

Coexpression of mRNAs under multiple conditions is commonly used to infer cofunctionality of their gene products despite well-known limitations of this “guilt-by-association” (GBA) approach. Recent advancements in mass spectrometry-based proteomic technologies have enabled global expression profiling at the protein level; however, whether proteome profiling data can outperform transcriptome profiling data for coexpression based gene function prediction has not been systematically investigated. Here, we address this question by constructing and analyzing mRNA and protein coexpression networks for three cancer types with matched mRNA and protein profiling data from The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Our analyses revealed a marked difference in wiring between the mRNA and protein coexpression networks. Whereas protein coexpression was driven primarily by functional similarity between coexpressed genes, mRNA coexpression was driven by both cofunction and chromosomal colocalization of the genes. Functionally coherent mRNA modules were more likely to have their edges preserved in corresponding protein networks than functionally incoherent mRNA modules. Proteomic data strengthened the link between gene expression and function for at least 75% of Gene Ontology (GO) biological processes and 90% of KEGG pathways. A web application Gene2Net (http://cptac.gene2net.org) developed based on the three protein coexpression networks revealed novel gene-function relationships, such as linking ERBB2 (HER2) to lipid biosynthetic process in breast cancer, identifying PLG as a new gene involved in complement activation, and identifying AEBP1 as a new epithelial-mesenchymal transition (EMT) marker. Our results demonstrate that proteome profiling outperforms transcriptome profiling for coexpression based gene function prediction. Proteomics should be integrated if not preferred in gene function and human disease studies.

Enhanced Detection of Low-Abundance Human Plasma Proteins by Integrating Polyethylene Glycol Fractionation and Immunoaffinity Depletion.

The enormous depth complexity of the human plasma proteome poses a significant challenge for current mass spectrometry-based proteomic technologies in terms of detecting low-level proteins in plasma, which is essential for successful biomarker discovery efforts. Typically, a single-step analytical approach cannot reduce this intrinsic complexity. Current simplex immunodepletion techniques offer limited capacity for detecting low-abundance proteins, and integrated strategies are thus desirable. In this respect, we developed an improved strategy for analyzing the human plasma proteome by integrating polyethylene glycol (PEG) fractionation with immunoaffinity depletion. PEG fractionation of plasma proteins is simple, rapid, efficient, and compatible with a downstream immunodepletion step. Compared with immunodepletion alone, our integrated strategy substantially improved the proteome coverage afforded by PEG fractionation. Coupling this new protocol with liquid chromatography-tandem mass spectrometry, 135 proteins with reported normal concentrations below 100 ng/mL were confidently identified as common low-abundance proteins. A side-by-side comparison indicated that our integrated strategy was increased by average 43.0% in the identification rate of low-abundance proteins, relying on an average 65.8% increase of the corresponding unique peptides. Further investigation demonstrated that this combined strategy could effectively alleviate the signal-suppressive effects of the major high-abundance proteins by affinity depletion, especially with moderate-abundance proteins after incorporating PEG fractionation, thereby greatly enhancing the detection of low-abundance proteins. In sum, the newly developed strategy of incorporating PEG fractionation to immunodepletion methods can potentially aid in the discovery of plasma biomarkers of therapeutic and clinical interest.

Dementia-linked amyloidosis is associated with brain protein deamidation as revealed by proteomic profiling of human brain tissues.

BackgroundAggregation of malformed proteins is a key feature of many neurodegenerative diseases, but the mechanisms that drive proteinopathy in the brain are poorly understood. We aimed to characterize aggregated proteins in human brain tissues affected by dementia.ResultsTo characterize amyloidal plaque purified from post-mortem brain tissue of dementia patient, we applied ultracentrifugation-electrostatic repulsion hydrophilic interaction chromatography (UC-ERLIC) coupled mass spectrometry-based proteomics technologies. Proteomics profiling of both soluble and aggregated amyloidal plaque demonstrated significant enrichment and deamidation of S100A9, ferritin, hemoglobin subunits, creatine kinase and collagen protein among the aggregated brain proteins. Amyloidal plaques were enriched in the deamidated variant of protein S100A9, and structural analysis indicated that both the low- and high-affinity calcium binding motifs of S100A9 were deamidated exclusively in the aggregated fraction, suggesting altered charge state and function of this protein in brain tissues affected by dementia. The multiple deamidated residues of S100A9 predicts introduction of negative charge that alter Ca++ binding, suggesting increased capacity to form pathological aggregates in the brain.ConclusionUC-coupled proteomics revealed that brain amyloidal plaques are enriched in deamidated proteins, and suggested that altered charge state and calcium-binding capacity of S100A9 may enhance protein aggregation and promote neurodegeneration in the human brain.Electronic supplementary materialThe online version of this article (doi:10.1186/s13041-016-0200-z) contains supplementary material, which is available to authorized users.

Cellular Transcriptional Coactivator RanBP10 and Herpes Simplex Virus 1 ICP0 Interact and Synergistically Promote Viral Gene Expression and Replication.

To investigate the molecular mechanism(s) by which herpes simplex virus 1 (HSV-1) regulatory protein ICP0 promotes viral gene expression and replication, we screened cells overexpressing ICP0 for ICP0-binding host cell proteins. Tandem affinity purification of transiently expressed ICP0 coupled with mass spectrometry-based proteomics technology and subsequent analyses showed that ICP0 interacted with cell protein RanBP10, a known transcriptional coactivator, in HSV-1-infected cells. Knockdown of RanBP10 in infected HEp-2 cells resulted in a phenotype similar to that observed with the ICP0-null mutation, including reduction in viral replication and in the accumulation of viral immediate early (ICP27), early (ICP8), and late (VP16) mRNAs and proteins. In addition, RanBP10 knockdown or the ICP0-null mutation increased the level of histone H3 association with the promoters of these viral genes, which is known to repress transcription. These effects observed in wild-type HSV-1-infected HEp-2 RanBP10 knockdown cells or those observed in ICP0-null mutant virus-infected control HEp-2 cells were remarkably increased in ICP0-null mutant virus-infected HEp-2 RanBP10 knockdown cells. Our results suggested that ICP0 and RanBP10 redundantly and synergistically promoted viral gene expression by regulating chromatin remodeling of the HSV-1 genome for efficient viral replication. Upon entry of herpesviruses into a cell, viral gene expression is restricted by heterochromatinization of the viral genome. Therefore, HSV-1 has evolved multiple mechanisms to counteract this epigenetic silencing for efficient viral gene expression and replication. HSV-1 ICP0 is one of the viral proteins involved in counteracting epigenetic silencing. Here, we identified RanBP10 as a novel cellular ICP0-binding protein and showed that RanBP10 and ICP0 appeared to act synergistically to promote viral gene expression and replication by modulating viral chromatin remodeling. Our results provide insight into the mechanisms by which HSV-1 regulates viral chromatin remodeling for efficient viral gene expression and replication.

Network-Based Protein Biomarker Discovery Platforms.

The advances in mass spectrometry-based proteomics technologies have enabled the generation of global proteome data from tissue or body fluid samples collected from a broad spectrum of human diseases. Comparative proteomic analysis of global proteome data identifies and prioritizes the proteins showing altered abundances, called differentially expressed proteins (DEPs), in disease samples, compared to control samples. Protein biomarker candidates that can serve as indicators of disease states are then selected as key molecules among these proteins. Recently, it has been addressed that cellular pathways can provide better indications of disease states than individual molecules and also network analysis of the DEPs enables effective identification of cellular pathways altered in disease conditions and key molecules representing the altered cellular pathways. Accordingly, a number of network-based approaches to identify disease-related pathways and representative molecules of such pathways have been developed. In this review, we summarize analytical platforms for network-based protein biomarker discovery and key components in the platforms.

Identifying Predictors of Taxane-Induced Peripheral Neuropathy Using Mass Spectrometry-Based Proteomics Technology.

Major advances in early detection and therapy have significantly increased the survival of breast cancer patients. Unfortunately, most cancer therapies are known to carry a substantial risk of adverse long-term treatment-related effects. Little is known about patient susceptibility to severe side effects after chemotherapy. Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of taxanes. Recent advances in genome-wide genotyping and sequencing technologies have supported the discoveries of a number of pharmacogenetic markers that predict response to chemotherapy. However, effectively implementing these pharmacogenetic markers in the clinic remains a major challenge. On the other hand, recent advances in proteomic technologies incorporating mass spectrometry (MS) for biomarker discovery show great promise to provide clinically relevant protein biomarkers. In this study, we evaluated the association between protein content in serum exosomes and severity of CIPN. Women with early stage breast cancer receiving adjuvant taxane chemotherapy were assessed with the FACT-Ntx score and serum was collected before and after the taxane treatment. Based on the change in FACT-Ntx score from baseline to 12 month follow-up, we separated patients into two groups: those who had no change (Group 1, N = 9) and those who had a ≥20% worsening (Group 1, N = 8). MS-based proteomics technology was used to identify proteins present in serum exosomes to determine potential biomarkers. Mann–Whitney–Wilcoxon analysis was applied and maximum FDR was controlled at 20%. From the serum exosomes derived from this cohort, we identified over 700 proteins known to be in different subcellular locations and have different functions. Statistical analysis revealed a 12-protein signature that resulted in a distinct separation between baseline serum samples of both groups (q<0.2) suggesting that the baseline samples can predict subsequent neurotoxicity. These toxicity-associated biomarkers can be further validated in larger retrospective cohorts for their utility in identifying patients at high risk for CIPN.

Role of Host Cell p32 in Herpes Simplex Virus 1 De-Envelopment during Viral Nuclear Egress.

To clarify the function(s) of the herpes simplex virus 1 (HSV-1) major virion structural protein UL47 (also designated VP13/14), we screened cells overexpressing UL47 for UL47-binding cellular proteins. Tandem affinity purification of transiently expressed UL47 coupled with mass spectrometry-based proteomics technology and subsequent analyses showed that UL47 interacted with cell protein p32 in HSV-1-infected cells. Unlike in mock-infected cells, p32 accumulated at the nuclear rim in HSV-1-infected cells, and this p32 recruitment to the nuclear rim required UL47. p32 formed a complex(es) with HSV-1 proteins UL31, UL34, Us3, UL47, and/or ICP22 in HSV-1-infected cells. All these HSV-1 proteins were previously reported to be important for HSV-1 nuclear egress, in which nucleocapsids bud through the inner nuclear membrane (primary envelopment) and the enveloped nucleocapsids then fuse with the outer nuclear membrane (de-envelopment). Like viral proteins UL31, UL34, Us3, and UL47, p32 was detected in primary enveloped virions. p32 knockdown reduced viral replication and induced membranous invaginations adjacent to the nuclear rim containing primary enveloped virions and aberrant localization of UL31 and UL34 in punctate structures at the nuclear rim. These effects of p32 knockdown were reduced in the absence of UL47. Therefore, the effects of p32 knockdown in HSV-1 nuclear egress were similar to those of the previously reported mutation(s) in HSV-1 regulatory proteins for HSV-1 de-envelopment during viral nuclear egress. Collectively, these results suggested that p32 regulated HSV-1 de-envelopment and replication in a UL47-dependent manner. IMPORTANCE In this study, we have obtained data suggesting that (i) the HSV-1 major virion structural protein UL47 interacted with host cell protein p32 and mediated the recruitment of p32 to the nuclear rim in HSV-1-infected cells; (ii) p32 was a component of the HSV-1 nuclear egress complex (NEC), whose core components were UL31 and UL34; and (iii) p32 regulated HSV-1 de-envelopment during viral nuclear egress. It has been reported that p32 was a component of human cytomegalovirus NEC and was required for efficient disintegration of nuclear lamina, which has been thought to facilitate HSV-1 primary envelopment during viral nuclear egress. Thus, p32 appeared to be a core component of herpesvirus NECs, like UL31 and UL34 homologs in other herpesviruses, and to play multiple roles in herpesvirus nuclear egress.

Automated Extraction of Proteotypic Peptides by Shotgun Proteomic Experiments: A New Computational Tool and Two Actual Cases

The rapid development of mass spectrometry-based proteomic technologies has allowed the quantification and validation of protein biomarkers toward detection of signature molecules, called proteotypic peptides. To facilitate their extraction from experimental protein and peptide lists, we present here a friendly computational tool called Experimental Proteotypic Peptides Investigator (EPPI). In this study, it was used for extracting proteotypic peptides from two collections of experimental data obtained by MudPIT analysis of adipose and gut human tissue. In particular, EPPI allows the selection of peptides presenting higher occurrence, evaluates their uniqueness by molecular weight and amino-acid sequence, and takes into consideration combinations of multiple proteotypic peptides (proteotypic peptide sets) for evaluating their capacity to target a single protein. In fact, in combination with high-resolution MS instruments, it could be a starting point for targeting proteins by following only precursor ions in full MS scan mode. The software is available under the permissive Apache 2.0 open-source license, and the code can be accessed from https://github.com/ITB-ProtMet/eppi.git. Keywords: Computational tool, MudPIT, python, proteotypic peptide, targeted proteomics.

The application of mass spectrometry for protein markers discovery in cancer detection

Protein is involved in a variety of biological activities and associated with disease development, such as cancer.With the development of mass spectrometry-based proteomic technologies, qualitative and quantitative analysis of protein in clinical specimen using mass spectrometry is rapidly progressing in cancer biomarker study.Novel cancer biomarker for early diagnosis and prognosis evaluation is the key to reducing death rate and improving survival rate.This review summarizes mass spectrometry technologies and proteomic studies for cancer biomarker discovery. Key words: Mass spectrometry; Proteomics; Cancer biomarkers

Plasma proteomics to identify biomarkers – application to cardiovascular diseases

There is an unmet need for new cardiovascular biomarkers. Despite this only few biomarkers for the diagnosis or screening of cardiovascular diseases have been implemented in the clinic. Thousands of proteins can be analysed in plasma by mass spectrometry-based proteomics technologies. Therefore, this technology may therefore identify new biomarkers that previously have not been associated with cardiovascular diseases. In this review, we summarize the key challenges and considerations, including strategies, recent discoveries and clinical applications in cardiovascular proteomics that may lead to the discovery of novel cardiovascular biomarkers.

Uncovering Neurodegenerative Protein Modifications via Proteomic Profiling.

Degenerative protein modifications (DPMs) are caused by nonenzymatic chemical reactions that induce changes in protein structure and function which promote disease initiation, pathological progression and also natural aging. These undesirable DPMs include oxidation, carbonylation, carbamylation, glycation, deamidation, isomerization, nitration, and racemization, which impart deleterious structural and functional changes on extracellular matrix proteins and long-lived cell types such as cardiomyocytes and neurons, leading to impaired overall organ function. Despite the obvious clinical importance of understanding DPM biology, the molecular mechanisms that mediate these modifications remain poorly understood largely due to the technical challenges associated with their study. However, recent advances in mass spectrometry-based proteomics technologies now permit global quantitative proteomic profiling of cell lines, animal models, and human clinical samples from a variety of different patient types. These new methods have not only uncovered changes in global protein expression levels but have also identified specific modifications of particular amino acid residues in protein backbones that are associated with disease progression. The nonenzymatic induction of DPMs as revealed by proteomic profiling can help us to better understand the underlying molecular pathology of protein dysfunction in human diseases and natural aging. This chapter discusses recent progress in understanding how proteomic profiling of patient samples derived from the central nervous system can elucidate the DPM biology of human neurodegenerative diseases.

Insights into the Cellular Response Triggered by Silver Nanoparticles Using Quantitative Proteomics

The use of nanoparticles in foods, materials, and clinical treatments has increased dramatically in the past decade. Because of the possibility of human exposure to nanoparticles, there is an urgent need to investigate the molecular mechanisms underlying the cellular responses that might be triggered. Such information is necessary to assess potential health risks arising from the use of nanoparticles, and for developing new formulations of next generation nanoparticles for clinical treatments. Using mass spectrometry-based proteomic technologies and complementary techniques (e.g., Western blotting and confocal laser scanning microscopy), we present insights into the silver nanoparticle-protein interaction in the human LoVo cell line. Our data indicate that some unique cellular processes are driven by the size. The 100 nm nanoparticles exerted indirect effects via serine/threonine protein kinase (PAK), mitogen-activated protein kinase (MAPK), and phosphatase 2A pathways, and the 20 nm nanoparticles induced direct effects on cellular stress, including generation of reactive oxygen species and protein carbonylation. In addition, we report that proteins involved in SUMOylation were up-regulated after exposure to 20 nm silver nanoparticles. These results were further substantiated by the observation of silver nanoparticles entering the cells; however, data indicate that this was determined by the size of the nanoparticles, since 20 nm particles entered the cells while 100 nm particles did not.

Derivative component analysis for mass spectral serum proteomic profiles.

BackgroundAs a promising way to transform medicine, mass spectrometry based proteomics technologies have seen a great progress in identifying disease biomarkers for clinical diagnosis and prognosis. However, there is a lack of effective feature selection methods that are able to capture essential data behaviors to achieve clinical level disease diagnosis. Moreover, it faces a challenge from data reproducibility, which means that no two independent studies have been found to produce same proteomic patterns. Such reproducibility issue causes the identified biomarker patterns to lose repeatability and prevents it from real clinical usage.MethodsIn this work, we propose a novel machine-learning algorithm: derivative component analysis (DCA) for high-dimensional mass spectral proteomic profiles. As an implicit feature selection algorithm, derivative component analysis examines input proteomics data in a multi-resolution approach by seeking its derivatives to capture latent data characteristics and conduct de-noising. We further demonstrate DCA's advantages in disease diagnosis by viewing input proteomics data as a profile biomarker via integrating it with support vector machines to tackle the reproducibility issue, besides comparing it with state-of-the-art peers.ResultsOur results show that high-dimensional proteomics data are actually linearly separable under proposed derivative component analysis (DCA). As a novel multi-resolution feature selection algorithm, DCA not only overcomes the weakness of the traditional methods in subtle data behavior discovery, but also suggests an effective resolution to overcoming proteomics data's reproducibility problem and provides new techniques and insights in translational bioinformatics and machine learning. The DCA-based profile biomarker diagnosis makes clinical level diagnostic performances reproducible across different proteomic data, which is more robust and systematic than the existing biomarker discovery based diagnosis.ConclusionsOur findings demonstrate the feasibility and power of the proposed DCA-based profile biomarker diagnosis in achieving high sensitivity and conquering the data reproducibility issue in serum proteomics. Furthermore, our proposed derivative component analysis suggests the subtle data characteristics gleaning and de-noising are essential in separating true signals from red herrings for high-dimensional proteomic profiles, which can be more important than the conventional feature selection or dimension reduction. In particular, our profile biomarker diagnosis can be generalized to other omics data for derivative component analysis (DCA)'s nature of generic data analysis.

Roles of p53 in Herpes Simplex Virus 1 Replication

p53 is a critical factor in the cellular response to a broad range of stress factors through its ability to regulate various cellular pathways. In this study, tandem affinity purification of transiently expressed herpes simplex virus 1 (HSV-1) regulatory protein ICP22 coupled with mass spectrometry-based proteomics technology and subsequent analyses showed that ICP22 interacted with p53 in HSV-1-infected cells. In p53(-/-) cells, replication of wild-type HSV-1 was reduced compared to that in parental p53(+/+) cells, indicating that p53 had a positive effect on HSV-1 replication. In contrast, the levels of viral replication of an ICP22-null mutant virus were similar in both p53(-/-) and p53(+/+) cells. At 2 h postinfection, the level of expression of ICP27, an essential viral regulatory protein, in p53(-/-) cells infected with wild-type HSV-1 or the ICP22-null mutant virus was lower than in p53(+/+) cells. In contrast, at 18 h postinfection, the level of expression of ICP0, a critical viral regulatory protein, in p53(-/-) cells infected with the ICP22-null mutant virus was higher than in p53(+/+) cells, although the levels of ICP0 expression in p53(-/-) and p53(+/+) cells infected with wild-type HSV-1 were almost identical. These results suggested that p53 overall promoted HSV-1 replication and that p53 played both positive and negative roles in HSV-1 replication: upregulating ICP27 expression very early in infection and downregulating ICP0 expression later in infection, which was antagonized by ICP22.

Computer aided manual validation of mass spectrometry-based proteomic data

Advances in mass spectrometry-based proteomic technologies have increased the speed of analysis and the depth provided by a single analysis. Computational tools to evaluate the accuracy of peptide identifications from these high-throughput analyses have not kept pace with technological advances; currently the most common quality evaluation methods are based on statistical analysis of the likelihood of false positive identifications in large-scale data sets. While helpful, these calculations do not consider the accuracy of each identification, thus creating a precarious situation for biologists relying on the data to inform experimental design. Manual validation is the gold standard approach to confirm accuracy of database identifications, but is extremely time-intensive. To palliate the increasing time required to manually validate large proteomic datasets, we provide computer aided manual validation software (CAMV) to expedite the process. Relevant spectra are collected, catalogued, and pre-labeled, allowing users to efficiently judge the quality of each identification and summarize applicable quantitative information. CAMV significantly reduces the burden associated with manual validation and will hopefully encourage broader adoption of manual validation in mass spectrometry-based proteomics.