PRISM, a generic large scale proteomic investigation strategy for mammals

Proteomics and mass spectrometry: what have we learned about the heart?

The emergence of new platforms for the discovery of innovative therapeutics has provided a means for diagnosing cardiac disease in its early stages. Taking into consideration the global health burden of cardiac disease, clinicians require innovations in medical diagnostics that can be used for risk stratification. Proteomic based studies offer an avenue for the discovery of proteins that are differentially regulated during disease; such proteins could serve as novel biomarkers of the disease state. For instance, in clinical practice, the abundance of such biomarkers in blood could be correlated with the severity of the disease state. As such, early detection of biomarkers would enable an improvement in patient prognosis. In this review, we outline advancements in various proteomic platforms used to study the disease proteome and their applications to the field of clinical medicine. Specifically, we highlight the contributions of proteomic-based profiling experiments to the analysis of cardiovascular diseases.

A systematic characterization of Cwc21, the yeast ortholog of the human spliceosomal protein SRm300

Cwc21 (complexed with Cef1 protein 21) is a 135 amino acid yeast protein that shares homology with the N-terminal domain of human SRm300/SRRM2, a large serine/arginine-repeat protein shown previously to associate with the splicing coactivator and 3'-end processing stimulatory factor, SRm160. Proteomic analysis of spliceosomal complexes has suggested a role for Cwc21 and SRm300 at the core of the spliceosome. However, specific functions for these proteins have remained elusive. In this report, we employ quantitative genetic interaction mapping, mass spectrometry of tandem affinity-purified complexes, and microarray profiling to investigate genetic, physical, and functional interactions involving Cwc21. Combined data from these assays support multiple roles for Cwc21 in the formation and function of splicing complexes. Consistent with a role for Cwc21 at the core of the spliceosome, we observe strong genetic, physical, and functional interactions with Isy1, a protein previously implicated in the first catalytic step of splicing and splicing fidelity. Together, the results suggest multiple functions for Cwc21/SRm300 in the splicing process, including an important role in the activation of splicing in association with Isy1.

Sequential peptide affinity purification system for the systematic isolation and identification of protein complexes from Escherichia coli

Biochemical purification of affinity-tagged proteins in combination with mass spectrometry methods is increasingly seen as a cornerstone of systems biology, as it allows for the systematic genome-scale characterization of macromolecular protein complexes, representing demarcated sets of stably interacting protein partners. Accurate and sensitive identification of both the specific and shared polypeptide components of distinct complexes requires purification to near homogeneity. To this end, a sequential peptide affinity (SPA) purification system was developed to enable the rapid and efficient isolation of native Escherichia coli protein complexes (J Proteome Res 3:463-468, 2004). SPA purification makes use of a dual-affinity tag, consisting of three modified FLAG sequences (3X FLAG) and a calmodulin binding peptide (CBP), spaced by a cleavage site for tobacco etch virus (TEV) protease (J Proteome Res 3:463-468, 2004). Using the lambda-phage Red homologous recombination system (PNAS 97:5978-5983, 2000), a DNA cassette, encoding the SPA-tag and a selectable marker flanked by gene-specific targeting sequences, is introduced into a selected locus in the E. coli chromosome so as to create a C-terminal fusion with the protein of interest. This procedure aims for near-endogenous levels of tagged protein production in the recombinant bacteria to avoid spurious, non-specific protein associations (J Proteome Res 3:463-468, 2004). In this chapter, we describe a detailed, optimized protocol for the tagging, purification, and subsequent mass spectrometry-based identification of the subunits of even low-abundance bacterial protein complexes isolated as part of an ongoing large-scale proteomic study in E. coli (Nature 433:531-537, 2005).

Global quantitative proteomic profiling through 18O-labeling in combination with MS/MS spectra analysis

Several stable-isotope-based peptide labeling methods have been developed to support large-scale relative quantitation, through mass spectrometry, of proteins present in two different biological samples. In one of these, trypsin-catalyzed 18O-based labeling, quantitation is typically performed at the full scan (MS) level by comparing the peak intensities of sister precursor ions corresponding to the labeled and unlabeled forms of an intact peptide as they co-elute during liquid chromatography (LC) separations. We show here that measuring relative abundance at the product ion (MS/MS) level after fragmentation provides excellent accuracy, sensitivity and signal-to-noise, while combining quantitation with global shotgun protein identification. To facilitate routine data analysis using this approach, we have developed two specialized software programs, ySelect and yRatios, which draw upon database search results for 18O-based data sets and combine fragmentation spectra peak lists to (1) accurately determine protein ratios between two samples while applying a correction for incomplete labeling and (2) tabulate these results in both intuitive summary reports and in formats amenable to systematic pathway level analysis. To validate our process, we subjected simple and complex test protein mixtures to single-step and multistep LC-MS/MS profiling experiments. Ratio distributions approached the expected means, allowing empirical derivation of confidence level cutoffs for determining statistically significant fold-changes in protein abundance. A set of stringent criteria for detecting spurious ratios based on consistency checking between unlabeled and labeled y-ion pairs was found to highlight putative false positive identifications. In summary, this toolkit facilitates comparative proteomic quantitation under conditions that are optimized for making reliable protein inferences.

Systematic characterization of the protein interaction network and protein complexes in Saccharomyces cerevisiae using tandem affinity purification and mass spectrometry

Defining protein complexes is a vital aspect of cell biology because cellular processes are often carried out by stable protein complexes and their characterization often provides insights into their function. Accurate identification of the interacting proteins in macromolecular complexes is easiest after purification to near homogeneity. To this end, the tandem affinity purification (TAP) system with subsequent protein identification by high-throughput mass spectrometry was developed (1, 2) to systematically characterize native protein complexes and transient protein interactions under near-physiological conditions. The TAP tag containing two adjacent affinity purification tags (calmodulin-binding peptide and Staphylococcus aureus protein A) separated by a tobacco etch virus (TEV) protease cleavage site is fused with the open reading frame of interest. Using homologous recombination, a fusion library was constructed for the yeast Saccharomyces cerevisiae (3) in which the carboxy-terminal end of each predicted open reading frame is individually tagged in the chromosome so that the resulting fusion proteins are expressed under the control of their natural promoters (3). In this chapter, an optimized protocol for systematic protein purification and subsequent mass spectrometry-based protein identification is described in detail for the protein complexes of S. cerevisiae (4-6).

Automated 2D peptide separation on a 1D nano-LC-MS system

Given the complexity of the mammalian proteome, high-resolution separation technologies are required to achieve comprehensive proteome coverage and to enhance the detection of low-abundance proteins. Among several technologies, Multidimensional Protein Identification Technology (MudPIT) enables the on-line separation of highly complex peptide mixtures directly coupled with mass spectrometry-based identification. Here, we present a variation of the traditional MudPIT protocol, combining highly sensitive chromatography using a nanoflow liquid chromatography system (nano-LC) with a two-dimensional precolumn in a vented column setup. When compared to the traditional MudPIT approach, this nanoflow variation demonstrated better first-phase separation leading to more proteins being characterized while using rather simple instrumentation and a protocol that requires less time and very little technical expertise to perform.

Comparative systems biology of human and mouse as a tool to guide the modeling of human placental pathology

Placental abnormalities are associated with two of the most common and serious complications of human pregnancy, maternal preeclampsia (PE) and fetal intrauterine growth restriction (IUGR), each disorder affecting ∼5% of all pregnancies. An important question for the use of the mouse as a model for studying human disease is the degree of functional conservation of genetic control pathways from human to mouse. The human and mouse placenta show structural similarities, but there have been no systematic attempts to assess their molecular similarities or differences. We collected protein and mRNA expression data through shot-gun proteomics and microarray expression analysis of the highly vascular exchange region, microdissected from the human and mouse near-term placenta. Over 7000 ortholog genes were detected with 70% co-expressed in both species. Close to 90% agreement was found between our human proteomic results and 1649 genes assayed by immunohistochemistry for expression in the human placenta in the Human Protein Atlas. Interestingly, over 80% of genes known to cause placental phenotypes in mouse are co-expressed in human. Several of these phenotype-associated proteins form a tight protein–protein interaction network involving 15 known and 34 novel candidate proteins also likely important in placental structure and/or function. The entire data are available as a web-accessible database to guide the informed development of mouse models to study human disease.

Improved results in proteomics by use of local and peptide-class specific false discovery rates

BACKGROUND

Proteomic protein identification results need to be compared across laboratories and platforms, and thus a reliable method is needed to estimate false discovery rates. The target-decoy strategy is a platform-independent and thus a prime candidate for standardized reporting of data. In its current usage based on global population parameters, the method does not utilize individual peptide scores optimally.

RESULTS

Here we show that proteomic analyses largely benefit from using separate treatment of peptides matching to proteins alone or in groups based on locally estimated false discovery rates. Our implementation reduces the number of false positives and simultaneously increases the number of proteins identified. Importantly, single peptide identifications achieve defined confidence and the sequence coverage of proteins is optimized. As a result, we improve the number of proteins identified in a human serum analysis by 58% without compromising identification confidence.

CONCLUSION

We show that proteins can reliably be identified with a single peptide and the sequence coverage for multi-peptide proteins can be increased when using an improved estimation of false discovery rates.

Multidimensional protein identification technology analysis highlights mitoxantrone-induced expression modulations in the primary prostate cancer cell proteome

Chemotherapeutic agents as they are used today have limited effectiveness against prostate cancer, but may potentially be used in new combinations with more efficacious results. Mitoxantrone, used for palliation of prostate cancer, has recently been found by our group to improve the susceptibility of primary prostate cancer cells to killing through the Fas-mediated death pathway. Here we used a shotgun proteomics approach to first profile the entire prostate cancer proteome and then identify specific factors involved in this mitoxantrone response. Peptides derived from primary prostate cancer cells treated with or without 100 nM mitoxantrone were analyzed by multidimensional protein identification technology (MudPIT). Strict limits and data filtering hierarchies were applied to identify proteins with high confidence. We identified 1498 proteins belonging to the prostate cancer proteome, 83 of which were significantly upregulated and 27 of which were markedly downregulated following mitoxantrone treatment. These proteins perform diverse functions, including ceramide production, tumour suppression, and oxidative reduction. Detailed proteomic analyses of prostate cancer cells and their response to mitoxantrone will further our understanding of its mechanisms of action. Identification of proteins influenced by treatment with mitoxantrone or other compounds may lead to the development of more effective drug combinations against prostate cancer.

Quantitative peptide and protein profiling by mass spectrometry

Proteomics may be defined as the systematic analysis of proteins expressed in a given organism (Electrophoresis 16:1090-1094, 1995). Important technical innovations in mass spectrometry (MS), protein identification methods, and database annotation, over the past decade, now make it possible to routinely identify thousands of proteins in complex biological samples (Nature 422:198-207, 2003). However, to gain new insights regarding fundamental biological questions, accurate protein quantification is also required. In this chapter, we present methods for the biochemical separation of different cellular compartments, two-dimensional chromatographic separation of the constituent peptide populations, and the recently published Spectral Counting Strategy, a label-free MS-based protein quantification technology (Cell 125:173-186, 2006; Anal Chem 76:4193-4201, 2004; Mol Cell Proteomics 4:1487-1502, 2005; Cell 125:1003-1013, 2006; Methods 40:135-142, 2006; Anal Chem 77:6218-6224, 2005; J Proteome Res 5:2339-2347, 2006). Additionally, highly accurate protein quantification based on isotope dilution, describing the isotope coded protein label (ICPL) -- method shall be explained in detail (Mol Cell Proteomics 5:1543-1558, 2006; Proteomics 5:4-15, 2005).

Improvements to cardiovascular gene ontology

Gene Ontology (GO) provides a controlled vocabulary to describe the attributes of genes and gene products in any organism. Although one might initially wonder what relevance a ‘controlled vocabulary’ might have for cardiovascular science, such a resource is proving highly useful for researchers investigating complex cardiovascular disease phenotypes as well as those interpreting results from high-throughput methodologies. GO enables the current functional knowledge of individual genes to be used to annotate genomic or proteomic datasets. In this way, the GO data provides a very effective way of linking biological knowledge with the analysis of the large datasets of post-genomics research. Consequently, users of high-throughput methodologies such as expression arrays or proteomics will be the main beneficiaries of such annotation sets. However, as GO annotations increase in quality and quantity, groups using small-scale approaches will gradually begin to benefit too. For example, genome wide association scans for coronary heart disease are identifying novel genes, with previously unknown connections to cardiovascular processes, and the comprehensive annotation of these novel genes might provide clues to their cardiovascular link. At least 4000 genes, to date, have been implicated in cardiovascular processes and an initiative is underway to focus on annotating these genes for the benefit of the cardiovascular community. In this article we review the current uses of Gene Ontology annotation to highlight why Gene Ontology should be of interest to all those involved in cardiovascular research.

Sequential interval motif search: unrestricted database surveys of global MS/MS data sets for detection of putative post-translational modifications

Tandem mass spectrometry is the prevailing approach for large-scale peptide sequencing in high-throughput proteomic profiling studies. Effective database search engines have been developed to identify peptide sequences from MS/MS fragmentation spectra. Since proteins are polymorphic and subject to post-translational modifications (PTM), however, computational methods for detecting unanticipated variants are also needed to achieve true proteome-wide coverage. Different from existing "unrestrictive" search tools, we present a novel algorithm, termed SIMS (for Sequential Motif Interval Search), that interprets pairs of product ion peaks, representing potential amino acid residues or "intervals", as a means of mapping PTMs or substitutions in a blind database search mode. An effective heuristic software program was likewise developed to evaluate, rank, and filter optimal combinations of relevant intervals to identify candidate sequences, and any associated PTM or polymorphism, from large collections of MS/MS spectra. The prediction performance of SIMS was benchmarked extensively against annotated reference spectral data sets and compared favorably with, and was complementary to, current state-of-the-art methods. An exhaustive discovery screen using SIMS also revealed thousands of previously overlooked putative PTMs in a compendium of yeast protein complexes and in a proteome-wide map of adult mouse cardiomyocytes. We demonstrate that SIMS, freely accessible for academic research use, addresses gaps in current proteomic data interpretation pipelines, improving overall detection coverage, and facilitating comprehensive investigations of the fundamental multiplicity of the expressed proteome.

Toward high-throughput and reliable peptide identification via MS/MS spectra

One fundamental problem in proteomics study is to identify proteins and determine their expression levels in cells. Coupled with advanced liquid chromatography, tandem mass spectrometry has become the standard tool for peptide sequencing. In the past decade, many different algorithms and software packages have been developed to support high-throughput proteomics studies. This chapter reviews and compares the computational methods and software for the interpretation of tandem mass spectra. We also present techniques to assess the reliability of peptide identification. Finally, future directions and new research paradigms in tandem mass spectrometry are discussed.

The Glc7 phosphatase subunit of the cleavage and polyadenylation factor is essential for transcription termination on snoRNA genes

Glc7, the yeast protein phosphatase 1, is a component of the cleavage and polyadenylation factor (CPF). Here we show that downregulation of Glc7, or its dissociation from CPF in the absence of CPF subunits Ref2 or Swd2, results in similar snoRNA termination defects. Overexpressing a C-terminal fragment of Sen1, a superfamily I helicase required for snoRNA termination, suppresses the growth and termination defects associated with loss of Swd2 or Ref2, but not Glc7. Suppression by Sen1 requires nuclear localization and direct interaction with Glc7, which can dephosphorylate Sen1 in vitro. The suppressing fragment, and in a similar manner full-length Sen1, copurifies with the snoRNA termination factors Nrd1 and Nab3, suggesting loss of Glc7 from CPF can be compensated by recruiting Glc7 to Nrd1-Nab3 through Sen1. Swd2 is also a subunit of the Set1c histone H3K4 methyltransferase complex and is required for its stability and optimal methyltransferase activity.

Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT)

Proteomic profiling has emerged as a useful tool for identifying tissue alterations in disease states including malignant transformation. The aim of this study was to reveal expression profiles associated with the highly motile/invasive ovarian cancer cell phenotype. Six ovarian cancer cell lines were subjected to proteomic characterization using multidimensional protein identification technology (MudPIT), and evaluated for their motile/invasive behavior, so that these parameters could be compared. Within whole cell extracts of the ovarian cancer cells, MudPIT identified proteins that mapped to 2245 unique genes. Western blot analysis for selected proteins confirmed the expression profiles revealed by MudPIT, demonstrating the fidelity of this high-throughput analysis. Unsupervised cluster analysis partitioned the cell lines in a manner that reflected their motile/invasive capacity. A comparison of protein expression profiles between cell lines of high (group 1) versus low (group 2) motile/invasive capacity revealed 300 proteins that were differentially expressed, of which 196 proteins were significantly upregulated in group 1. Protein network and KEGG pathway analysis indicated a functional interplay between proteins up-regulated in group 1 cells, with increased expression of several key members of the actin cytoskeleton, extracellular matrix (ECM) and focal adhesion pathways. These proteomic expression profiles can be utilized to distinguish highly motile, aggressive ovarian cancer cells from lesser invasive ones, and could prove to be essential in the development of more effective strategies that target pivotal cell signaling pathways used by cancer cells during local invasion and distant metastasis.

Alignment of LC-MS images, with applications to biomarker discovery and protein identification

LC-MS-based approaches have gained considerable interest for the analysis of complex peptide or protein mixtures, due to their potential for full automation and high sampling rates. Advances in resolution and accuracy of modern mass spectrometers allow new analytical LC-MS-based applications, such as biomarker discovery and cross-sample protein identification. Many of these applications compare multiple LC-MS experiments, each of which can be represented as a 2-D image. In this article, we survey current approaches to LC-MS image alignment. LC-MS image alignment corrects for experimental variations in the chromatography and represents a computational key technology for the comparison of LC-MS experiments. It is a required processing step for its two major applications: biomarker discovery and protein identification. Along with descriptions of the computational analysis approaches, we discuss their relative merits and potential pitfalls.

Properties of average score distributions of SEQUEST: the probability ratio method

High throughput identification of peptides in databases from tandem mass spectrometry data is a key technique in modern proteomics. Common approaches to interpret large scale peptide identification results are based on the statistical analysis of average score distributions, which are constructed from the set of best scores produced by large collections of MS/MS spectra by using searching engines such as SEQUEST. Other approaches calculate individual peptide identification probabilities on the basis of theoretical models or from single-spectrum score distributions constructed by the set of scores produced by each MS/MS spectrum. In this work, we study the mathematical properties of average SEQUEST score distributions by introducing the concept of spectrum quality and expressing these average distributions as compositions of single-spectrum distributions. We predict and demonstrate in the practice that average score distributions are dominated by the quality distribution in the spectra collection, except in the low probability region, where it is possible to predict the dependence of average probability on database size. Our analysis leads to a novel indicator, the probability ratio, which takes optimally into account the statistical information provided by the first and second best scores. The probability ratio is a non-parametric and robust indicator that makes spectra classification according to parameters such as charge state unnecessary and allows a peptide identification performance, on the basis of false discovery rates, that is better than that obtained by other empirical statistical approaches. The probability ratio also compares favorably with statistical probability indicators obtained by the construction of single-spectrum SEQUEST score distributions. These results make the robustness, conceptual simplicity, and ease of automation of the probability ratio algorithm a very attractive alternative to determine peptide identification confidences and error rates in high throughput experiments.

Interactions of elongation factor EF-P with the Escherichia coli ribosome

EF-P (eubacterial elongation factor P) is a highly conserved protein essential for protein synthesis. We report that EF-P protects 16S rRNA near the G526 streptomycin and the S12 and mRNA binding sites (30S T-site). EF-P also protects domain V of the 23S rRNA proximal to the A-site (50S T-site) and more strongly the A-site of 70S ribosomes. We suggest that EF-P: (a) may play a role in translational fidelity and (b) prevents entry of fMet-tRNA into the A-site enabling it to bind to the 50S P-site. We also report that EF-P promotes a ribosome-dependent accommodation of fMet-tRNA into the 70S P-site.

A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers

Epithelial ovarian cancer is the most lethal gynecological malignancy, and disease-specific biomarkers are urgently needed to improve diagnosis, prognosis, and to predict and monitor treatment efficiency. We present an in-depth proteomic analysis of selected biochemical fractions of human ovarian cancer ascites, resulting in the stringent and confident identification of over 2500 proteins. Rigorous filter schemes were applied to objectively minimize the number of false-positive identifications, and we only report proteins with substantial peptide evidence. Integrated computational analysis of the ascites proteome combined with several recently published proteomic data sets of human plasma, urine, 59 ovarian cancer related microarray data sets, and protein-protein interactions from the Interologous Interaction Database I (2)D ( http://ophid.utoronto.ca/i2d) resulted in a short-list of 80 putative biomarkers. The presented proteomics analysis provides a significant resource for ovarian cancer research, and a framework for biomarker discovery.

Comparative proteomics profiling of a phospholamban mutant mouse model of dilated cardiomyopathy reveals progressive intracellular stress responses

Defective mobilization of Ca2+ by cardiomyocytes can lead to cardiac insufficiency, but the causative mechanisms leading to congestive heart failure (HF) remain unclear. In the present study we performed exhaustive global proteomics surveys of cardiac ventricle isolated from a mouse model of cardiomyopathy overexpressing a phospholamban mutant, R9C (PLN-R9C), and exhibiting impaired Ca2+ handling and death at 24 weeks and compared them with normal control littermates. The relative expression patterns of 6190 high confidence proteins were monitored by shotgun tandem mass spectrometry at 8, 16, and 24 weeks of disease progression. Significant differential abundance of 593 proteins was detected. These proteins mapped to select biological pathways such as endoplasmic reticulum stress response, cytoskeletal remodeling, and apoptosis and included known biomarkers of HF (e.g. brain natriuretic peptide/atrial natriuretic factor and angiotensin-converting enzyme) and other indicators of presymptomatic functional impairment. These altered proteomic profiles were concordant with cognate mRNA patterns recorded in parallel using high density mRNA microarrays, and top candidates were validated by RT-PCR and Western blotting. Mapping of our highest ranked proteins against a human diseased explant and to available data sets indicated that many of these proteins could serve as markers of disease. Indeed we showed that several of these proteins are detectable in mouse and human plasma and display differential abundance in the plasma of diseased mice and affected patients. These results offer a systems-wide perspective of the dynamic maladaptions associated with impaired Ca2+ homeostasis that perturb myocyte function and ultimately converge to cause HF.

Lost in translation: five grand challenges for proteomic biomarker discovery

The biomedical community has the imperative to develop reliable, clinically relevant and generalizable bioassays that can be used to accurately recognize those individuals with early-stage disease or those patients who will respond to therapy, with the ultimate aim of achieving individualized medicine. In recent years, increasingly sophisticated proteomic screening technologies have been introduced, providing the biomedical community with a valuable new approach for the systematic discovery and validation of novel diagnostic, prognostic and therapeutic tools. Nevertheless, the complexity of the cellular milieu wherein a variety of macromolecules interact in dynamic fashion, combined with the complex clinical manifestation of chronic pathologies and widespread diversity of patient populations, mean that universal biomarkers will not be easily developed. In this review, the five key challenges that must be surmounted in order to advance the clinical impact of this nascent field are described, and plausible solutions based on the authors' own ongoing proteomic profiling of cardiovascular disease is outlined.

Tissue subcellular fractionation and protein extraction for use in mass-spectrometry-based proteomics

We have shown that sample fractionation is an effective method for increasing the detection coverage of the proteome of complex samples, such as organs, by mass-spectrometric techniques. Further fractionating a sample based on subcellular compartments can generate molecular information on the state of a tissue and the distribution of its protein components. Although many methods exist for fractionating proteins, the method described here can capture the majority of subcellular fractions simultaneously at reasonable purity. The scalability of this method makes it amenable to small samples, such as embryonic tissues, in addition to larger tissues. The protocol described is for the general fractionation and extraction of proteins from organs or tissues for subsequent analysis by mass spectrometry. It uses differential centrifugation in density gradients to isolate nuclear, cytosolic, mitochondrial and mixed microsomal (Golgi, endoplasmic reticulum, other vesicles and plasma membrane) fractions. Once the fractions are isolated, they are extracted for protein and the samples can then be frozen for processing and analysis at a later date. The procedure can typically be completed in 5 h.

Recent advances in protein profiling of tissues and tissue fluids

Creating protein profiles of tissues and tissue fluids, which contain secreted proteins and peptides released from various cells, is critical for biomarker discovery as well as drug and vaccine target selection. It is extremely difficult to obtain pure samples from tissues or tissue fluids, however, and identification of complex protein mixtures is still a challenge for mass spectrometry analysis. Here, we summarize recent advances in techniques for extracting proteins from tissues for mass spectrometry profiling and imaging. We also introduce a novel technique using a capillary ultrafiltration (CUF) probe to enable in vivo collection of proteins from the tissue microenvironment. The CUF probe technique is compared with existing sampling techniques, including perfusion, saline wash, fine-needle aspiration and microdialysis. In this review, we also highlight quantitative mass spectrometric proteomic approaches with, and without, stable-isotope labels. Advances in quantitative proteomics will significantly improve protein profiling of tissue and tissue fluid samples collected by CUF probes.

Analysis and validation of proteomic data generated by tandem mass spectrometry

The analysis of the large amount of data generated in mass spectrometry-based proteomics experiments represents a significant challenge and is currently a bottleneck in many proteomics projects. In this review we discuss critical issues related to data processing and analysis in proteomics and describe available methods and tools. We place special emphasis on the elaboration of results that are supported by sound statistical arguments.

Suction blister fluid as potential body fluid for biomarker proteins

Early diagnosis is important for effective disease management. Measurement of biomarkers present at the local level of the skin could be advantageous in facilitating the diagnostic process. The analysis of the proteome of suction blister fluid, representative for the interstitial fluid of the skin, is therefore a desirable first step in the search for potential biomarkers involved in biological pathways of particular diseases. Here, we describe a global analysis of the suction blister fluid proteome as potential body fluid for biomarker proteins. The suction blister fluid proteome was compared with a serum proteome analyzed using identical protocols. By using stringent criteria allowing less than 1% false positive identifications, we were able to detect, using identical experimental conditions and amount of starting material, 401 proteins in suction blister fluid and 240 proteins in serum. As a major result of our analysis we construct a prejudiced list of 34 proteins, relatively highly and uniquely detected in suction blister fluid as compared to serum, with established and putative characteristics as biomarkers. We conclude that suction blister fluid might potentially serve as a good alternative biomarker body fluid for diseases that involve the skin.

Assessing enzyme activities using stable isotope labeling and mass spectrometry

Activity-based protein profiling has emerged as a valuable technology for labeling, enriching, and assessing protein activities from complex mixtures. This is primarily accomplished via a two-step identification and quantification process. Here we show a highly quantitative and streamlined method, termed catch-and-release activity profiling of enzymes (CAPE), which reduces this procedure to a single step. Furthermore the CAPE approach has the ability to detect small quantitative changes that may have been missed by alternative mass spectrometry-based techniques.

MASPECTRAS: a platform for management and analysis of proteomics LC-MS/MS data

BACKGROUND

The advancements of proteomics technologies have led to a rapid increase in the number, size and rate at which datasets are generated. Managing and extracting valuable information from such datasets requires the use of data management platforms and computational approaches.

RESULTS

We have developed the MAss SPECTRometry Analysis System (MASPECTRAS), a platform for management and analysis of proteomics LC-MS/MS data. MASPECTRAS is based on the Proteome Experimental Data Repository (PEDRo) relational database schema and follows the guidelines of the Proteomics Standards Initiative (PSI). Analysis modules include: 1) import and parsing of the results from the search engines SEQUEST, Mascot, Spectrum Mill, X! Tandem, and OMSSA; 2) peptide validation, 3) clustering of proteins based on Markov Clustering and multiple alignments; and 4) quantification using the Automated Statistical Analysis of Protein Abundance Ratios algorithm (ASAPRatio). The system provides customizable data retrieval and visualization tools, as well as export to PRoteomics IDEntifications public repository (PRIDE). MASPECTRAS is freely available at http://genome.tugraz.at/maspectras

CONCLUSION

Given the unique features and the flexibility due to the use of standard software technology, our platform represents significant advance and could be of great interest to the proteomics community.

Integrated proteomic and transcriptomic profiling of mouse lung development and Nmyc target genes

Although microarray analysis has provided information regarding the dynamics of gene expression during development of the mouse lung, no extensive correlations have been made to the levels of corresponding protein products. Here, we present a global survey of protein expression during mouse lung organogenesis from embryonic day E13.5 until adulthood using gel-free two-dimensional liquid chromatography coupled to shotgun tandem mass spectrometry (MudPIT). Mathematical modeling of the proteomic profiles with parallel DNA microarray data identified large groups of gene products with statistically significant correlation or divergence in coregulation of protein and transcript levels during lung development. We also present an integrative analysis of mRNA and protein expression in Nmyc loss- and gain-of-function mutants. This revealed a set of 90 positively and negatively regulated putative target genes. These targets are evidence that Nmyc is a regulator of genes involved in mRNA processing and a repressor of the imprinted gene Igf2r in the developing lung.

hnRNP E1 and E2 have distinct roles in modulating HIV-1 gene expression

Pre-mRNA processing, including 5' end capping, splicing, and 3' end cleavage/polyadenylation, are events coordinated by transcription that can influence the subsequent export and translation of mRNAs. Coordination of RNA processing is crucial in retroviruses such as HIV-1, where inefficient splicing and the export of intron-containing RNAs are required for expression of the full complement of viral proteins. RNA processing can be affected by both viral and cellular proteins, and in this study we demonstrate that a member of the hnRNP E family of proteins can modulate HIV-1 RNA metabolism and expression. We show that hnRNP E1/E2 are able to interact with the ESS3a element of the bipartite ESS in tat/rev exon 3 of HIV-1 and that modulation of hnRNP E1 expression alters HIV-1 structural protein synthesis. Overexpression of hnRNP E1 leads to a reduction in Rev, achieved in part through a decrease in rev mRNA levels. However, the reduction in Rev levels cannot fully account for the effect of hnRNP E1, suggesting that hmRNP E1 might also act to suppress viral RNA translation. Deletion mutagenesis determined that the C-terminal end of hnRNP E1 was required for the reduction in Rev expression and that replacing this portion of hnRNP E1 with that of hnRNP E2, despite the high degree of conservation, could not rescue the loss of function.

beta-Subunit appendages promote 20S proteasome assembly by overcoming an Ump1-dependent checkpoint

Proteasomes are responsible for most intracellular protein degradation in eukaryotes. The 20S proteasome comprises a dyad-symmetric stack of four heptameric rings made from 14 distinct subunits. How it assembles is not understood. Most subunits in the central pair of beta-subunit rings are synthesized in precursor form. Normally, the beta5 (Doa3) propeptide is essential for yeast proteasome biogenesis, but overproduction of beta7 (Pre4) bypasses this requirement. Bypass depends on a unique beta7 extension, which contacts the opposing beta ring. The resulting proteasomes appear normal but assemble inefficiently, facilitating identification of assembly intermediates. Assembly occurs stepwise into precursor dimers, and intermediates contain the Ump1 assembly factor and a novel complex, Pba1-Pba2. beta7 incorporation occurs late and is closely linked to the association of two half-proteasomes. We propose that dimerization is normally driven by the beta5 propeptide, an intramolecular chaperone, but beta7 addition overcomes an Ump1-dependent assembly checkpoint and stabilizes the precursor dimer.

Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry

Liquid chromatography and tandem mass spectrometry (LC-MS/MS) has become the preferred method for conducting large-scale surveys of proteomes. Automated interpretation of tandem mass spectrometry (MS/MS) spectra can be problematic, however, for a variety of reasons. As most sequence search engines return results even for 'unmatchable' spectra, proteome researchers must devise ways to distinguish correct from incorrect peptide identifications. The target-decoy search strategy represents a straightforward and effective way to manage this effort. Despite the apparent simplicity of this method, some controversy surrounds its successful application. Here we clarify our preferred methodology by addressing four issues based on observed decoy hit frequencies: (i) the major assumptions made with this database search strategy are reasonable; (ii) concatenated target-decoy database searches are preferable to separate target and decoy database searches; (iii) the theoretical error associated with target-decoy false positive (FP) rate measurements can be estimated; and (iv) alternate methods for constructing decoy databases are similarly effective once certain considerations are taken into account.

Catch-and-Release Reagents for Broadscale Quantitative Proteomics Analyses

The relative quantification of protein expression levels in different cell samples through the utilization of stable isotope dilution has become a standard method in the field of proteomics. We describe here the development of a new reductively cleavable reagent which facilitates the relative quantification of thousands of proteins from only tens of micrograms of starting protein. The ligand features a novel disulfide moiety that links biotin and a thiol-reactive entity. The disulfide is stable to reductive conditions employed during sample labeling but is readily cleaved under mild conditions using tris-(2-carboxyethyl) phosphine (TCEP). This unique chemical property allows for the facile use of immobilized avidin in a manner equivalent to the use of conventional reversible-binding affinity resins. Target peptides are bound to avidin resin, washed rigorously, then cleaved directly from the resin, resulting in simplified sample handling procedures and reduced nonspecific interactions. Here we demonstrate the stability of the linker under two different reducing conditions and show how this "catch-and-release (CAR)" reagent can be used to quantitatively compare protein abundances from two distinct cellular lysates. Starting with only 40 microg protein from each sample, 1840 individual proteins were identified in a single experiment. Using in-house software for automated peak integration, 1620 of these proteins were quantified for differential expression.

Peptidomics: a logical sequel to proteomics

Rapid progress of separation techniques as well as methods of structural analysis provided conditions in the past decade for total screening of complex biologic mixtures for any given class of biomolecules. The present review updates the reader with the modern state of peptidomics, a chapter of chemical biology that deals with structure and biologic properties of sets of peptides present in biologic tissues, cells or fluids. Scope and limitations of currently employed experimental techniques are considered and the main results are outlined. Considerable attention will be afforded to the biologic role of peptides formed in vivo by proteolysis of nonspecialized precursor proteins with other well-defined functions. In conclusion, the connection is discussed between peptidomics and the much more mature and still closely related field of proteomics.

Improved proteomic discovery by sample pre-fractionation using dual-column ion-exchange high performance liquid chromatography

Clinically relevant biomarkers are urgently needed for improving patient diagnosis, risk stratification, prognosis and therapeutic treatments. There is a particularly compelling motivation for identifying protein-based indicators of early-stage disease for more effective interventions. Despite recent progress, the proteomic discovery process remains a daunting challenge due to the sheer heterogeneity and skewed protein abundances in biofluids. Even the most advanced mass spectrometry systems exhibit limiting overall dynamic ranges and sensitivities relative to the needs of modern biomedical applications. To this end, we report the development of a robust, rapid, and reproducible high performance ion-exchange liquid chromatography pre-fractionation method that allows for improved proteomic detection coverage of complex biological specimens using basic tandem mass spectrometry screening procedures. This form of sample simplification prior to global proteomic profiling, which we refer to collectively as 'fractionomics', increases the number and diversity of proteins that can be confidently identified in tissue and cell lysates as compared to the straight analysis of unfractionated crude extracts.

Cardiovascular proteomics: tools to develop novel biomarkers and potential applications

Proteomics is the new systems biological approach to the study of proteins and protein variation on a large scale as a result of biological processes and perturbations. The field is undergoing a dramatic transformation, owing to the completion and annotation of the human genome as well as technological advances to study proteins on a large scale. The new science of proteomics can potentially yield novel biomarkers reflecting cardiovascular disease, establish earlier detection strategies, and monitor responses to therapy. Technological advances permit the unprecedented large-scale identification of peptide sequences in a biological sample with mass spectrometry, whereas gel-based techniques provide further refinement on the status of post-translational modification. The application of high throughput protein evaluation with a subset of predefined targets, identified through proteomics, microarray profiling, and pathway analysis in animal models and human tissues, is gaining momentum in research and clinical applications. Proteomic analysis has provided important insights into ischemic heart disease, heart failure, and cardiovascular pathophysiology. The combination of proteomic biomarkers with clinical phenotypes and genetic haplotype information can lead to a more precise diagnosis and therapy on an individual basis--the fundamental premise of "personalized medicine."

Analysis of Complex Protein Mixtures with Improved Sequence Coverage Using (CE−MS/MS)n

Identification of proteins, in a complex protein mixture, using one-dimensional high-performance liquid chromatography tandem mass spectrometry (HPLC-MS/MS) analysis of its digest, usually suffers from low sequence coverage. There are several reasons for the low coverage including undersampling, wide concentration dynamic range of the proteins in a complex protein mixture, and wide range of electrospray ionization efficiency of peptides under each mobile-phase composition. To address this low sequence coverage, we introduce a novel technique, (CE-MS/MS)n, which utilizes the most significant advantages of CE-MS/MS, including economy of sample size, fast analysis time, and high separation efficiency, to increase the sequence coverage of a complex protein mixture. Based on these characteristics, (CE-MS/MS)n can be performed in which multiple CE-MS/MS subanalyses (injections followed by analyses) are analyzed and experimental variables are manipulated during each CE-MS/MS subanalysis in order to maximize sequence coverage. (CE-MS/MS)n is a practical technique since each CE-MS/MS subanalysis consumes <10 nL, and each CE-MS/MS subanalysis takes approximately 10 min; therefore, several subanalyses can be performed in approximately 1 h consuming only nanoliters of the sample. Two techniques have been introduced to address the undersampling: (1) (CE-MS/MS)n using dynamic exclusion. In this technique, several CE-MS/MS analyses (injection followed by separation) were performed in one run using the dynamic exclusion capability of the mass spectrometer until all peptide peaks were analyzed by MS/MS. (2) Gas-phase fractionation. In this technique, (CE-MS/MS)n is performed by scanning a narrow mass range (every approximately 100 m/z) during each CE-MS/MS subanalysis without using dynamic exclusion. Under this condition, in each subanalysis, the number of peptides available for MS/MS analysis is significantly reduced, and peptides with the same nominal masses are analyzed, thereby increasing sequence coverage. Additionally, to address the lack of detection of low-level peptides in a mixture containing a wide concentration dynamic range, the concentration of the sample was systematically increased in each subanalysis (while utilizing dynamic exclusion) so that low-intensity peptides would rise above the mass spectrometer threshold and, consequently, undergo MS/MS analysis. Moreover, to alter the ionization efficiency of peptides with low electrospray ionization efficiency, and to change the migration behavior of comigrating peptides under a specific liquid composition, the CE background electrolyte was modified in several subanalyses to further improve sequence coverage. The combination of the above-mentioned techniques was applied to the analysis of the tryptic digests of three well-characterized protein mixtures: a six-protein mixture with average MW of approximately 26,000 (standard I), a six-protein mixture with an average MW approximately 49,000 (standard II), and a more complex protein mixture containing 55 proteins (E. coli ribosomal proteins). In approximately 1 h, when the MS/MS of the peptides were manually checked, all peptides that produced peaks under electrospray ionization in the scanned range of the analysis (500-2000 m/z) and within the practical fragmentation capability of the MS (peptides with MW <3500) were identified for standard I by consuming only 200 fmol of each protein. When searched against a Swissprot database, the average sequence coverage for the standard I, II, and E. coli's ribosomal proteins were 57, 34, and 15%, respectively.

Different immunoaffinity fractionation strategies to characterize the human plasma proteome

Plasma proteins may often serve as indicators of disease and are a rich source for biomarker discovery. However, the intrinsic large dynamic range of plasma proteins makes the analysis very challenging because a large number of low abundance proteins are often masked by a few high abundance proteins. The use of prefractionation methods, such as depletion of higher abundance proteins before protein profiling, can assist in the discovery and detection of less abundant proteins that may ultimately prove to be informative biomarkers. But there are few studies on comprehensive investigation of the proteins both in the fractions depleted and remainder. In the present study, two different immunoaffinity fractionation columns for the top-6 or the top-12 proteins in plasma were investigated and both the proteins in column-bound and flow-through fractions were subsequently analyzed. A two-dimensional peptide separation strategy, utilizing chromatographic separation techniques, combined with tandem mass spectrometry (MS/MS) was employed for proteomic analysis of the four fractions. Using the established HUPO PPP criteria, a total of 2401 unique plasma proteins were identified. The Multiple Affinity Removal System yielded 921 and 725 unique proteins from the flow-through and bound fractions, respectively, whereas the Seppro MIXED 12 column yielded identification of 897 and 730 unique proteins from the flow-through and bound fractions, respectively. When more stringent criteria, based on searching against the reversed database, were implemented, 529 unique proteins were identified from the four fractions with the confidence in peptide identification increased from 73.6% to 99%. To determine whether the presence of nontarget proteins in the immunoaffinity-bound fraction could be attributed to their interaction with high abundance proteins, co-immunoprecipitation analysis with an antibody to human plasma albumin was performed, which resulted in an identification of 40 unique proteins from the coimmunoprecipitate with the more stringent criteria. This study illustrated that combining the column-bound and flow-through fractions from immunoaffinity separation affords more extensive profiling of the protein content of human plasma. The presence of nontarget proteins in the column-bound fractions may be induced by their binding to the higher abundance proteins targeted by the immunoaffinity column.

Cluster analysis of protein array results via similarity of Gene Ontology annotation

BACKGROUND

With the advent of high-throughput proteomic experiments such as arrays of purified proteins comes the need to analyse sets of proteins as an ensemble, as opposed to the traditional one-protein-at-a-time approach. Although there are several publicly available tools that facilitate the analysis of protein sets, they do not display integrated results in an easily-interpreted image or do not allow the user to specify the proteins to be analysed.

RESULTS

We developed a novel computational approach to analyse the annotation of sets of molecules. As proof of principle, we analysed two sets of proteins identified in published protein array screens. The distance between any two proteins was measured as the graph similarity between their Gene Ontology (GO) annotations. These distances were then clustered to highlight subsets of proteins sharing related GO annotation. In the first set of proteins found to bind small molecule inhibitors of rapamycin, we identified three subsets containing four or five proteins each that may help to elucidate how rapamycin affects cell growth whereas the original authors chose only one novel protein from the array results for further study. In a set of phosphoinositide-binding proteins, we identified subsets of proteins associated with different intracellular structures that were not highlighted by the analysis performed in the original publication.

CONCLUSION

By determining the distances between annotations, our methodology reveals trends and enrichment of proteins of particular functions within high-throughput datasets at a higher sensitivity than perusal of end-point annotations. In an era of increasingly complex datasets, such tools will help in the formulation of new, testable hypotheses from high-throughput experimental data.

Comparative proteomics analysis of Barrett metaplasia and esophageal adenocarcinoma using two-dimensional liquid mass mapping

Esophageal adenocarcinoma, currently the seventh leading cause of cancer-related death, has been associated with the presence of Barrett metaplasia. The malignant potential of Barrett metaplasia is evidenced by ultimate progression of this condition to invasive adenocarcinoma. We utilized liquid phase separation of proteins with chromatofocusing in the first dimension and nonporous reverse phase HPLC in the second dimension followed by ESI-TOF mass spectrometry to identify proteins differentially expressed in six Barrett metaplasia samples as compared with six esophageal adenocarcinoma samples; all six Barrett samples were obtained from the identical six patients from whom we obtained the esophageal adenocarcinoma tissue. Approximately 300 protein bands were detected by mass mappings, and 38 differentially expressed proteins were identified by microLC-MS/MS. The false positive rates of the peptide identifications were evaluated by reversed database searching. Among the proteins that were identified, Rho GDP dissociation inhibitor 2, alpha-enolase, Lamin A/C, and nucleoside-diphosphate kinase A were demonstrated to be up-regulated in both mRNA and protein expression in esophageal adenocarcinomas relative to Barrett metaplasia. Candidate proteins were examined at the mRNA level using high density oligonucleotide microarrays. The cellular expression patterns were verified in both esophageal adenocarcinomas and in Barrett metaplasia by immunohistochemistry. These differentially expressed proteins may have utility as useful candidate markers of esophageal adenocarcinoma.

Label-free Semiquantitative Peptide Feature Profiling of Human Breast Cancer and Breast Disease Sera via Two-dimensional Liquid Chromatography-Mass Spectrometry

A label-free semiquantitative peptide feature profiling method was developed in response to challenges associated with analysis of two-dimensional liquid chromatography-tandem mass spectrometry data. One hundred twenty human sera (49 from invasive breast carcinoma patients, 26 from non-invasive breast carcinoma patients, 35 from benign breast disease patients, and 10 from normal controls) were repeatedly analyzed using a standardized two-dimensional liquid chromatography-mass spectrometry method. Data were extracted using the novel semiquantitative peptide feature profiling method, which is based on comparisons of normalized relative ion intensities. Hierarchical cluster analyses and principle component analyses were used to evaluate the predicative capability of the extracted data, and results were promising. Extracted data were also randomly assigned to either a training group (65%) or to a test group (35%) for artificial neural network modeling. Models best identified invasive breast carcinomas (212 predictions, 94% accurate) and benign non-neoplastic breast disease (96 predictions, 81.3% accurate). These results suggest that, after further development, the novel method may be useful for large scale clinical proteomic profiling.

Global survey of organ and organelle protein expression in mouse: combined proteomic and transcriptomic profiling

Organs and organelles represent core biological systems in mammals, but the diversity in protein composition remains unclear. Here, we combine subcellular fractionation with exhaustive tandem mass spectrometry-based shotgun sequencing to examine the protein content of four major organellar compartments (cytosol, membranes [microsomes], mitochondria, and nuclei) in six organs (brain, heart, kidney, liver, lung, and placenta) of the laboratory mouse, Mus musculus. Using rigorous statistical filtering and machine-learning methods, the subcellular localization of 3274 of the 4768 proteins identified was determined with high confidence, including 1503 previously uncharacterized factors, while tissue selectivity was evaluated by comparison to previously reported mRNA expression patterns. This molecular compendium, fully accessible via a searchable web-browser interface, serves as a reliable reference of the expressed tissue and organelle proteomes of a leading model mammal.