Mass Spectrometry-based Quantitative Proteomics Research Articles

Androgen-Sensitive Microsomal Signaling Networks Coupled to the Proliferation and Differentiation of Human Prostate Cancer Cells

Increasing evidence suggests that the disruption of androgen-mediated cellular processes, such as cell proliferation and cell differentiation, contributes to the development of early-stage androgen-dependent prostate cancers. Large-scale mRNA profiling experiments have paved the way in identifying androgen-regulated gene networks that control the proliferation, survival, and differentiation of prostate cancer cells. Despite these extensive research efforts, it remains to be determined whether all androgen-mediated mRNA changes faithfully translate into changes in protein abundance that influence prostate tumorigenesis. Here, we report on a mass spectrometry-based quantitative proteomics analysis that identified known androgen signaling pathways and also novel, androgen-sensitive microsome-associated proteins and protein networks that had not been discovered by gene network studies in human LNCaP prostate cancer cells. Androgen-sensitive microsome-associated proteins encoded components of the insulin growth factor-1 (IGF-1), phosphoinositide 3-kinase (PI3K)/AKT, and extracellular signal-regulated kinase (ERK)/mitogen-activated protein kinase (MAPK) signaling pathways. Further bioinformatic analyses showed most of the androgen-sensitive microsome-associated protein networks play roles in cell proliferation and differentiation. Functional validation experiments showed that the androgen-sensitive microsome-associated proteins Janus kinase 2 (JAK2) and I-kappa B kinase complex-associated protein (IKAP) modulated the expression of prostate epithelial and neuronal markers, attenuated proliferation through an androgen receptor-dependent mechanism, and co-regulated androgen receptor-mediated transcription in LNCaP cells. Further biochemical analyses showed that the increased proliferation in JAK2 knockdown cells was mediated by activation of the mammalian target of rapamycin (mTOR), as determined by increased phosphorylation of several downstream targets (p70 S6 kinase, translational repressor 4E-BP1, and 40S ribosomal S6 protein). We conclude that the expression of microsome-associated proteins that were previously implicated in the tumorigenesis of prostate epithelial cells is strongly influenced by androgens. These findings provide a molecular framework for exploring the mechanisms underlying prostate tumorigenesis and how these protein networks might be attenuated or potentiated in disrupting the growth and survival of human prostate cancers.

Absolute SILAC-Compatible Expression Strain Allows Sumo-2 Copy Number Determination in Clinical Samples

Quantitative mass spectrometry-based proteomics is a vital tool in modern life science research. In contrast to the popularity of approaches for relative protein quantitation, the widespread use of absolute quantitation has been hampered by inefficient and expensive production of labeled protein standards. To optimize production of isotopically labeled standards, we genetically modified a commonly employed protein expression Escherichia coli strain, BL21 (DE3), to construct an auxotroph for arginine and lysine. This bacterial strain allows low-cost, high-level expression of fully labeled proteins with no conversion of labeled arginine to proline. In combination with a fluorescence-based quantitation of standards and nontargeted LC–MS/MS analysis of unfractionated total cell lysates, this strain was used to determine the copy number of a post-translational modifier, small ubiquitin-like modifier (SUMO-2), in HeLa, human sperm, and chronic lymphocytic leukemia cells. By streamlining and improving the generation of labeled standards, this production system increases the breadth of absolute quantitation by mass spectrometry and will facilitate a far wider uptake of this important technique than previously possible.

Development of Algorithms for Mass Spectrometry-based Label-free Quantitative Proteomics*

Development of Algorithms for Mass Spectrometry-based Label-free Quantitative Proteomics*

Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

Viral proteomics: The emerging cutting-edge of virus research

Viruses replicate and proliferate in host cells while continuously adjusting to and modulating the host environment. They encode a wide spectrum of multifunctional proteins, which interplay with and modify proteins in host cells. Viral genomes were chronologically the first to be sequenced. However, the corresponding viral proteomes, the alterations of host proteomes upon viral infection, and the dynamic nature of proteins, such as post-translational modifications, enzymatic cleavage, and activation or destruction by proteolysis, remain largely unknown. Emerging high-throughput techniques, in particular quantitative or semi-quantitative mass spectrometry-based proteomics analysis of viral and cellular proteomes, have been applied to define viruses and their interactions with their hosts. Here, we review the major areas of viral proteomics, including virion proteomics, structural proteomics, viral protein interactomics, and changes to the host cell proteome upon viral infection.

Towards cracking the epigenetic code using a combination of high‐throughput epigenomics and quantitative mass spectrometry‐based proteomics

High-throughput genomic sequencing and quantitative mass spectrometry (MS)-based proteomics technology have recently emerged as powerful tools, increasing our understanding of chromatin structure and function. Both of these approaches require substantial investments and expertise in terms of instrumentation, experimental methodology, bioinformatics, and data interpretation and are, therefore, usually applied independently from each other by dedicated research groups. However, when applied reiteratively in the context of epigenetics research these approaches are strongly synergistic in nature.

Unambiguous Characterization of Site-specific Phosphorylation of Leucine-rich Repeat Fli-I-interacting Protein 2 (LRRFIP2) in Toll-like Receptor 4 (TLR4)-mediated Signaling

In the TLR4 signaling pathways, we previously characterized a signal regulator, LRRFIP2, that modulates the time course-dependent changes in NF-κB activity through its dynamic interaction with the TLR adaptor protein, MyD88. However, little is known about the driving force behind the LPS-inducible dynamics between LRRFIP2 and MyD88. We have therefore designed a multiplex label-free quantitative proteomics method to investigate dynamic changes of LRRFIP2 phosphorylation upon LPS stimulation. Given our observation that LRRFIP2 binds to MyD88 through its serine-rich domain in which most of serine residues have the propensity to be phosphorylated, we used collision-activated dissociation- and electron transfer dissociation-based methods in a complementary manner to unambiguously localize phosphorylation sites in the peptides constituting the serine-rich domain. Among 23 phosphorylation sites identified and first quantified by the label-free approach and then verified by the AACT/SILAC (amino acid-coded tagging/stable isotope labeling in cell culture)-based quantitation method, phosphorylation at serine 202 showed a significant LPS-induced dynamic change during the full-course cellular response to LPS stimulation. The substitution of serine 202 with nonphosphorylated residues by site-directed mutagenesis resulted in a weakened LRRFIP2-MyD88 interaction and a concurrently reduced activity in downstream NF-κB. Taking these results together, phosphorylation at serine 202 was found to regulate the dynamics of the LRRFIP2-MyD88 interaction, which in turn modulated the strength and duration of TLR4 signaling. Strategically, we have demonstrated the importance of precise identification of the biologically relevant phosphorylation site(s) using comprehensive mass spectrometry-based quantitative proteomics approaches in guiding downstream biological characterization experiments, which could otherwise be both time- and cost-consuming for a large number of phosphorylation possibilities.

Accurate proteome-wide protein quantification from high-resolution 15N mass spectra

In quantitative mass spectrometry-based proteomics, the metabolic incorporation of a single source of 15N-labeled nitrogen has many advantages over using stable isotope-labeled amino acids. However, the lack of a robust computational framework for analyzing the resulting spectra has impeded wide use of this approach. We have addressed this challenge by introducing a new computational methodology for analyzing 15N spectra in which quantification is integrated with identification. Application of this method to an Escherichia coli growth transition reveals significant improvement in quantification accuracy over previous methods.

A guide through the computational analysis of isotope-labeled mass spectrometry-based quantitative proteomics data: an application study.

BackgroundMass spectrometry-based proteomics has reached a stage where it is possible to comprehensively analyze the whole proteome of a cell in one experiment. Here, the employment of stable isotopes has become a standard technique to yield relative abundance values of proteins. In recent times, more and more experiments are conducted that depict not only a static image of the up- or down-regulated proteins at a distinct time point but instead compare developmental stages of an organism or varying experimental conditions.ResultsAlthough the scientific questions behind these experiments are of course manifold, there are, nevertheless, two questions that commonly arise: 1) which proteins are differentially regulated regarding the selected experimental conditions, and 2) are there groups of proteins that show similar abundance ratios, indicating that they have a similar turnover? We give advice on how these two questions can be answered and comprehensively compare a variety of commonly applied computational methods and their outcomes.ConclusionsThis work provides guidance through the jungle of computational methods to analyze mass spectrometry-based isotope-labeled datasets and recommends an effective and easy-to-use evaluation strategy. We demonstrate our approach with three recently published datasets on Bacillus subtilis [1,2] and Corynebacterium glutamicum [3]. Special focus is placed on the application and validation of cluster analysis methods. All applied methods were implemented within the rich internet application QuPE [4]. Results can be found at http://qupe.cebitec.uni-bielefeld.de.

Global turnover of histone post-translational modifications and variants in human cells

BackgroundPost-translational modifications (PTMs) on the N-terminal tails of histones and histone variants regulate distinct transcriptional states and nuclear events. Whereas the functional effects of specific PTMs are the current subject of intense investigation, most studies characterize histone PTMs/variants in a non-temporal fashion and very few studies have reported kinetic information about these histone forms. Previous studies have used radiolabeling, fluorescence microscopy and chromatin immunoprecipitation to determine rates of histone turnover, and have found interesting correlations between increased turnover and increased gene expression. Therefore, histone turnover is an understudied yet potentially important parameter that may contribute to epigenetic regulation. Understanding turnover in the context of histone modifications and sequence variants could provide valuable additional insight into the function of histone replacement.ResultsIn this study, we measured the metabolic rate of labeled isotope incorporation into the histone proteins of HeLa cells by combining stable isotope labeling of amino acids in cell culture (SILAC) pulse experiments with quantitative mass spectrometry-based proteomics. In general, we found that most core histones have similar turnover rates, with the exception of the H2A variants, which exhibit a wider range of rates, potentially consistent with their epigenetic function. In addition, acetylated histones have a significantly faster turnover compared with general histone protein and methylated histones, although these rates vary considerably, depending on the site and overall degree of methylation. Histones containing transcriptionally active marks have been consistently found to have faster turnover rates than histones containing silent marks. Interestingly, the presence of both active and silent marks on the same peptide resulted in a slower turnover rate than either mark alone on that same peptide. Lastly, we observed little difference in the turnover between nearly all modified forms of the H3.1, H3.2 and H3.3 variants, with the notable exception that H3.2K36me2 has a faster turnover than this mark on the other H3 variants.ConclusionsQuantitative proteomics provides complementary insight to previous work aimed at quantitatively measuring histone turnover, and our results suggest that turnover rates are dependent upon site-specific post-translational modifications and sequence variants.

Aneuploidy confers quantitative proteome changes and phenotypic variation in budding yeast

Aneuploidy, referring here to genome contents characterized by abnormal numbers of chromosomes, has been associated with developmental defects, cancer, and adaptive evolution in experimental organisms1–9. However, it remains unresolved how aneuploidy impacts gene expression and whether aneuploidy could directly bring phenotypic variation and improved fitness over that of euploid counterparts. In this work, we designed a novel scheme to generate, through random meiotic segregation, 38 stable and fully isogenic aneuploid yeast strains with distinct karyotypes and genome contents between 1N and 3N without involving any genetic selection. Through phenotypic profiling under various growth conditions or in the presence of a panel of chemotherapeutic or antifungal drugs, we found that aneuploid strains exhibited diverse growth phenotypes, and some aneuploid strains grew better than euploid control strains under conditions suboptimal for the latter. Using quantitative mass spectrometry-based proteomics, we show that the levels of protein expression largely scale with chromosome copy numbers, following the same trend observed for the transcriptome. These results provide strong evidence that aneuploidy directly impacts gene expression at both the transcriptome and proteome levels and can generate significant phenotypic variation that could bring about fitness gains under diverse conditions. Our findings suggest that the fitness ranking between euploid and aneuploid cells is context- and karyotype-dependent, providing the basis for the notion that aneuploidy can directly underlie phenotypic evolution and cellular adaptation.

Quantitative Proteomics Reveals Myosin and Actin as Promising Saliva Biomarkers for Distinguishing Pre-Malignant and Malignant Oral Lesions

BackgroundOral cancer survival rates increase significantly when it is detected and treated early. Unfortunately, clinicians now lack tests which easily and reliably distinguish pre-malignant oral lesions from those already transitioned to malignancy. A test for proteins, ones found in non-invasively-collected whole saliva and whose abundances distinguish these lesion types, would meet this critical need.Methodology/Principal FindingsTo discover such proteins, in a first-of-its-kind study we used advanced mass spectrometry-based quantitative proteomics analysis of the pooled soluble fraction of whole saliva from four subjects with pre-malignant lesions and four with malignant lesions. We prioritized candidate biomarkers via bioinformatics and validated selected proteins by western blotting. Bioinformatic analysis of differentially abundant proteins and initial western blotting revealed increased abundance of myosin and actin in patients with malignant lesions. We validated those results by additional western blotting of individual whole saliva samples from twelve other subjects with pre-malignant oral lesions and twelve with malignant oral lesions. Sensitivity/specificity values for distinguishing between different lesion types were 100%/75% (p = 0.002) for actin, and 67%/83% (p<0.00001) for myosin in soluble saliva. Exfoliated epithelial cells from subjects' saliva also showed increased myosin and actin abundance in those with malignant lesions, linking our observations in soluble saliva to abundance differences between pre-malignant and malignant cells.Conclusions/SignificanceSalivary actin and myosin abundances distinguish oral lesion types with sensitivity and specificity rivaling other non-invasive oral cancer tests. Our findings provide a promising starting point for the development of non-invasive and inexpensive salivary tests to reliably detect oral cancer early.

In Situ trans Ligands of CD22 Identified by Glycan-Protein Photocross-linking-enabled Proteomics

CD22, a regulator of B-cell signaling, is a siglec that recognizes the sequence NeuAcalpha2-6Gal on glycoprotein glycans as ligands. CD22 interactions with glycoproteins on the same cell (in cis) and apposing cells (in trans) modulate its activity in B-cell receptor signaling. Although CD22 predominantly recognizes neighboring CD22 molecules as cis ligands on B-cells, little is known about the trans ligands on apposing cells. We conducted a proteomics scale study to identify candidate trans ligands of CD22 on B-cells by UV photocross-linking CD22-Fc chimera bound to B-cell glycoproteins engineered to carry sialic acids with a 9-aryl azide moiety. Using mass spectrometry-based quantitative proteomics to analyze the cross-linked products, 27 glycoproteins were identified as candidate trans ligands. Next, CD22 expressed on the surface of one cell was photocross-linked to glycoproteins on apposing B-cells followed by immunochemical analysis of the products with antibodies to the candidate ligands. Of the many candidate ligands, only the B-cell receptor IgM was found to be a major in situ trans ligand of CD22 that is selectively redistributed to the site of cell contact upon interaction with CD22 on the apposing cell.

Site-specific Phosphorylation Dynamics of the Nuclear Proteome during the DNA Damage Response

To investigate the temporal regulation of the DNA damage response, we applied quantitative mass spectrometry-based proteomics to measure site-specific phosphorylation changes of nuclear proteins after ionizing radiation. We profiled 5204 phosphorylation sites at five time points following DNA damage of which 594 sites on 209 proteins were observed to be regulated more than 2-fold. Of the 594 sites, 372 are novel phosphorylation sites primarily of nuclear origin. The 594 sites could be classified to distinct temporal profiles. Sites regulated shortly after radiation were enriched in the ataxia telangiectasia mutated (ATM) kinase SQ consensus sequence motif and a novel SXXQ motif. Importantly, in addition to induced phosphorylation, we identified a considerable group of sites that undergo DNA damage-induced dephosphorylation. Together, our data extend the number of known phosphorylation sites regulated by DNA damage, provides so far unprecedented temporal dissection of DNA damage-modified phosphorylation events, and elucidate the cross-talk between different types of post-translational modifications in the dynamic regulation of a multifaceted DNA damage response.

Quantitative mass spectrometry-based proteomics reveals the dynamic range of primary mouse astrocyte protein secretion.

Growing appreciation for astrocytes as active participants in nervous system development, neurovascular metabolic coupling, and neurological disease progression has stimulated recent investigation into specific astrocyte-secreted proteins that may mediate these functions. The current work utilized SILAC-generated isotope reference proteomes to quantify relative protein abundances between the astrocyte proteome and secretome. Multidimensional GeLC-MS/MS analysis of astrocyte conditioned media and cell lysates resulted in the relative quantification of 516 proteins, 92 of which were greater than 1.5-fold enriched in astrocyte-conditioned media (ACM). Eighty of the ACM-enriched proteins had N-terminal signal peptides, comprising well-known classically secreted proteins, such as apolipoprotein E and SPARC, and several cathepsins that localize to endosomal/lysosomal compartments. The remaining twelve ACM-enriched proteins, such as vimentin, ferritins, and histones, lacked N-terminal signal peptides. Also, 47 proteins contained predicted N-terminal signal peptides but were not enriched in ACM (<1.5-fold), 25 of which were localized to ER, Golgi, or mitochondria membrane-bound compartments. Overall, by combining quantitative proteomics with subcellular localization prediction, an informative description of protein distribution can be obtained, providing insights into protein secretion.

New Developments of Quantitative Mass Spectrometry-based Proteomics

Abstract Mass spectrometry has become a powerful technology for proteome analysis. It would be highly desirable to obtain an exact quantitative value and the dynamic data of each protein in a system for providing important information to study protein function, to reveal the biological process and to discover protein biomarker and drug target. In this paper, the developments of quantitative mass spectrometry-based proteomics on strategy, method and application in recent years with further addressed characteristics and potentialities in protein analysis are discussed.

MSQuant, an Open Source Platform for Mass Spectrometry-Based Quantitative Proteomics

Mass spectrometry-based proteomics critically depends on algorithms for data interpretation. A current bottleneck in the rapid advance of proteomics technology is the closed nature and slow development cycle of vendor-supplied software solutions. We have created an open source software environment, called MSQuant, which allows visualization and validation of peptide identification results directly on the raw mass spectrometric data. MSQuant iteratively recalibrates MS data thereby significantly increasing mass accuracy leading to fewer false positive peptide identifications. Algorithms to increase data quality include an MS(3) score for peptide identification and a post-translational modification (PTM) score that determines the probability that a modification such as phosphorylation is placed at a specific residue in an identified peptide. MSQuant supports relative protein quantitation based on precursor ion intensities, including element labels (e.g., (15)N), residue labels (e.g., SILAC and ICAT), termini labels (e.g., (18)O), functional group labels (e.g., mTRAQ), and label-free ion intensity approaches. MSQuant is available, including an installer and supporting scripts, at http://msquant.sourceforge.net .

Computational protein profile similarity screening for quantitative mass spectrometry experiments

The qualitative and quantitative characterization of protein abundance profiles over a series of time points or a set of environmental conditions is becoming increasingly important. Using isobaric mass tagging experiments, mass spectrometry-based quantitative proteomics deliver accurate peptide abundance profiles for relative quantitation. Associated data analysis workflows need to provide tailored statistical treatment that (i) takes the correlation structure of the normalized peptide abundance profiles into account and (ii) allows inference of protein-level similarity. We introduce a suitable distance measure for relative abundance profiles, derive a statistical test for equality and propose a protein-level representation of peptide-level measurements. This yields a workflow that delivers a similarity ranking of protein abundance profiles with respect to a defined reference. All procedures have in common that they operate based on the true correlation structure that underlies the measurements. This optimizes power and delivers more intuitive and efficient results than existing methods that do not take these circumstances into account. We use protein profile similarity screening to identify candidate proteins whose abundances are post-transcriptionally controlled by the Anaphase Promoting Complex/Cyclosome (APC/C), a specific E3 ubiquitin ligase that is a master regulator of the cell cycle. Results are compared with an established protein correlation profiling method. The proposed procedure yields a 50.9-fold enrichment of co-regulated protein candidates and a 2.5-fold improvement over the previous method. A MATLAB toolbox is available from http://hci.iwr.uni-heidelberg.de/mip/proteomics.

Qupe—a Rich Internet Application to take a step forward in the analysis of mass spectrometry-based quantitative proteomics experiments

The goal of present -omics sciences is to understand biological systems as a whole in terms of interactions of the individual cellular components. One of the main building blocks in this field of study is proteomics where tandem mass spectrometry (LC-MS/MS) in combination with isotopic labelling techniques provides a common way to obtain a direct insight into regulation at the protein level. Methods to identify and quantify the peptides contained in a sample are well established, and their output usually results in lists of identified proteins and calculated relative abundance values. The next step is to move ahead from these abstract lists and apply statistical inference methods to compare measurements, to identify genes that are significantly up- or down-regulated, or to detect clusters of proteins with similar expression profiles. We introduce the Rich Internet Application (RIA) Qupe providing comprehensive data management and analysis functions for LC-MS/MS experiments. Starting with the import of mass spectra data the system guides the experimenter through the process of protein identification by database search, the calculation of protein abundance ratios, and in particular, the statistical evaluation of the quantification results including multivariate analysis methods such as analysis of variance or hierarchical cluster analysis. While a data model to store these results has been developed, a well-defined programming interface facilitates the integration of novel approaches. A compute cluster is utilized to distribute computationally intensive calculations, and a web service allows to interchange information with other -omics software applications. To demonstrate that Qupe represents a step forward in quantitative proteomics analysis an application study on Corynebacterium glutamicum has been carried out. Qupe is implemented in Java utilizing Hibernate, Echo2, R and the Spring framework. We encourage the usage of the RIA in the sense of the 'software as a service' concept, maintained on our servers and accessible at the following location: http://qupe.cebitec.uni-bielefeld.de. Supplementary data are available at Bioinformatics online.

La protéomique quantitative par la méthode SILAC

Mass spectrometry-based quantitative proteomics strategies are ideally adapted to the detection of global protein changes between different biological samples. Among these, SILAC (stable isotope labelling by amino acids in cell culture) has demonstrated a great potential. This method is extremely accurate and relatively easy to apply for the quantification of proteins extracted from cultured cells. SILAC involves cell culture either in regular culture media ("light" protein synthesis) or in media where amino acids have been replaced by their isotopically labelled counterparts ("heavy" protein synthesis). Cell populations to be compared can be mixed and treated as a single sample, which allows downstream sample preparation without the risk of introducing quantification errors. During mass spectrometry analysis, the relative protein abundance between biological samples can be calculated from the intensities of heavy and light peptides. As shown by numerous applications in biological and clinical studies, SILAC represents a promising method for the elucidation of cellular and physio-pathological mechanisms and for the identification of disease biomarkers. The restriction of SILAC to the quantification of proteins from cultured cells has just been overcome with the description in a recent paper of a SILAC mouse, which will allow this technology to be applied to the differential study of -tissues and biological fluids from model animals.