CiteULike: jyuh's proteomics

Identification of dominant signaling pathways from proteomics expression data.

RA Zubarev — 2008-08-19T02:13:01-00:00

Journal of proteomics, Vol. 71, No. 1. (30 April 2008), pp. 89-96.

The availability of the results of high-throughput analyses coming from 'omic' technologies has been one of the major driving forces of pathway biology. Analytical pathway biology strives to design a 'pathway search engine', where the input is the 'omic' data and the output is the list of activated or dominant pathways in a given sample. Here we describe the first attempt to design and validate such a pathway search engine using as input expression proteomics data. The engine represents a specific workflow in computational tools developed originally for mRNA analysis (BMC Bioinformatics 2006, 7 (Suppl 2), S13). Using our own datasets as well as data from recent proteomics literature we demonstrate that different dominant pathways (EGF, TGF(beta), stress, and Fas pathways) can be correctly identified even from limited datasets. Pathway search engines can find application in a variety of proteomics-related fields, from fundamental molecular biology to search for novel types of disease biomarkers.

Overview of kidney and urine proteome databases.

Y Yoshida — 2008-08-19T02:13:25-00:00

Contributions to nephrology, Vol. 160 (2008), pp. 186-197.

With the completion or almost completion of genome sequences of many organisms in combination with the tremendous development of mass spectrometric analysis of proteins, several comprehensive proteomic studies, targeting whole organisms, body fluids, organs, tissues, cells, cellular organelles, or functional protein complexes, have produced valuable resources that can be shared and retrieved. In the present review, we provide current concept of construction of protein databases with special emphasis on high-throughput identification of protein using mass spectrometry, annotations, computational tools, and search engines to retrieve information of the identified proteins. We then update the current status of available protein databases of kidney and urine proteomes.

Targeted peptide-centric proteomics reveals caspase-7 as a substrate of the caspase-1 inflammasomes.

Mohamed Lamkanfi — 2008-08-16T23:40:18-00:00

Molecular & cellular proteomics : MCP (30 July 2008)

The aspartate-specific cysteine protease caspase-1 is activated by the inflammasomes and is responsible for the proteolytic maturation of the cytokines IL-1beta and IL-18 during infection and inflammation. To discover new caspase-1 substrates, we made use of a proteome-wide gel-free differential peptide sorting methodology that allows unambiguous localization of the processing site in addition to identification of the substrate. Out of the 1022 proteins that were identified, 20 were found to be specifically cleaved after Asp in the setup incubated with recombinant caspase-1. Interestingly, caspase-7 emerged as one of the identified caspase-1 substrates. Moreover, half of the other identified cleavage events occurred at sites closely resembling the consensus caspase-7 recognition sequence DEVD, suggesting caspase-1-mediated activation of endogenous caspase-7 in this setup. Consistently, recombinant caspase-1 cleaved caspase-7 at the canonical activation sites Asp23 and Asp198, and recombinant caspase-7 processed a subset of the identified substrates. In vivo, caspase-7 activation was observed in conditions known to induce activation of caspase-1, including Salmonella infection and microbial stimuli combined with ATP. Interestingly, Salmonella- and LPS+ATP-induced activation of caspase-7 was abolished in macrophages deficient in caspase-1, the pattern-recognition receptors Ipaf and Cryopyrin and the inflammasome adaptor ASC, demonstrating an upstream role for the caspase-1 inflammasomes in caspase-7 activation in vivo. In contrast, caspase-1 and the inflammasomes were not required for caspase-3 activation. In conclusion, we identified 20 new substrates activated downstream of caspase-1 and validated caspase-1 mediated caspase-7 activation in vitro and in knockout macrophages. These results demonstrate for the first time the existence of a NOD-like receptor/caspase-1/caspase-7 cascade and the existence of distinct activation mechanisms for caspase-3 and -7 in response to microbial stimuli and bacterial infection.

Bayesian biomarker identification based on marker-expression proteomics data.

M Bhattacharjee — 2008-08-16T05:39:42-00:00

Genomics (24 July 2008)

We are studying variable selection in multiple regression models in which molecular markers and/or gene-expression measurements as well as intensity measurements from protein spectra serve as predictors for the outcome variable (i.e., trait or disease state). Finding genetic biomarkers and searching genetic-epidemiological factors can be formulated as a statistical problem of variable selection, in which, from a large set of candidates, a small number of trait-associated predictors are identified. We illustrate our approach by analyzing the data available for chronic fatigue syndrome (CFS). CFS is a complex disease from several aspects, e.g., it is difficult to diagnose and difficult to quantify. To identify biomarkers we used microarray data and SELDI-TOF-based proteomics data. We also analyzed genetic marker information for a large number of SNPs for an overlapping set of individuals. The objectives of the analyses were to identify markers specific to fatigue that are also possibly exclusive to CFS. The use of such models can be motivated, for example, by the search for new biomarkers for the diagnosis and prognosis of cancer and measures of response to therapy. Generally, for this we use Bayesian hierarchical modeling and Markov Chain Monte Carlo computation.

Affinity separation and enrichment methods in proteomic analysis.

X Fang — 2008-08-16T05:38:06-00:00

Journal of proteomics, Vol. 71, No. 3. (21 August 2008), pp. 284-303.

Protein separation or enrichment is one of the rate-limiting steps in proteomic studies. Specific capture and removal of highly-abundant proteins (HAP) with large sample-handling capacities are in great demand for enabling detection and analysis of low-abundant proteins (LAP). How to grasp and enrich these specific proteins or LAP in complex protein mixtures is also an outstanding challenge for biomarker discovery and validation. In response to these needs, various approaches for removal of HAP or capture of LAP in biological fluids, particularly in plasma or serum, have been developed. Among them, immunoaffinity subtraction methods based upon polyclonal IgY or IgG antibodies have shown to possess unique advantages for proteomic analysis of plasma, serum and other biological samples. In addition, other affinity methods that use recombinant proteins, lectins, peptides, or chemical ligands have also been developed and applied to LAP capture or enrichment. This review discusses in detail the need to put technologies and methods in affinity subtraction or enrichment into a context of proteomic and systems biology as "Separomics" and provides a prospective of affinity-mediated proteomics. Specific products, along with their features, advantages, and disadvantages will also be discussed.

Urinary proteomics: towards biomarker discovery, diagnostics and prognostics.

V Thongboonkerd — 2008-08-16T05:03:45-00:00

Molecular bioSystems, Vol. 4, No. 8. (August 2008), pp. 810-815.

During recent years, the proteomics field has moved onward to clinical applications, particularly for biomarker discovery, diagnostics and prognostics of human diseases. The urine is one of the ideal clinical samples for such applications because it is readily available in almost all patients, and its collection is very simple and non-invasive. Urinary proteomics thus becomes one of the most interesting subdisciplines in the clinical proteomics area. This article highlights and updates recent progress in the urinary proteomics field for clinical applications.

Tagging and detection strategies for activity-based proteomics.

AM Sadaghiani — 2008-08-14T16:28:08-00:00

Current opinion in chemical biology, Vol. 11, No. 1. (February 2007), pp. 20-28.

The field of activity-based proteomics is a relatively new discipline that makes use of small molecules, termed activity-based probes (ABPs), to tag and monitor distinct sets of proteins within a complex proteome. These activity-dependant labels facilitate analysis of systems-wide changes at the level of enzyme activity rather than simple protein abundance. While the use of small molecule inhibitors to label enzyme targets is not a new concept, the past ten years have seen a rapid expansion in the diversity of probe families that have been developed. In addition to increasing the number and types of enzymes that can be targeted by this method, there has also been an increase in the number of methods used to visualize probes once they are bound to target enzymes. In particular, the use of small organic fluorophores has created a wealth of applications for ABPs that range from biochemical profiling of diverse proteomes to direct imaging of active enzymes in live cells and even whole animals. In addition, the advent of new bioorthogonal coupling chemistries now enables a diverse array of tags to be added after targets are labeled with an ABP. This strategy has opened the door to new in vivo applications for activity-based proteomic methods.

PatternLab for proteomics: a tool for differential shotgun proteomics

Paulo Carvalho — 2008-07-23T03:32:17-00:00

BMC Bioinformatics, Vol. 9 (21 July 2008), 316.

ProteoWizard: Open Source Software for Rapid Proteomics Tools Development

Darren Kessner — 2008-07-08T19:03:32-00:00

Bioinformatics (7 July 2008), btn323.

Summary: The ProteoWizard software project provides a modular and extensible set of open-source, cross-platform tools and libraries. The tools perform proteomics data analyses; the libraries enable rapid tool creation by providing a robust, pluggable development framework that simplifies and unifies data file access, and performs standard proteomics and LCMS dataset computations. The library contains readers and writers of the mzML data format, has been written using modern C++ techniques and design principles and supports a variety of platforms with native compilers. The software has been specifically released under the Apache v2 license to ensure it can be used in both academic and commercial projects. In addition to the library, we also introduce a rapidly growing set of companion tools whose implementation helps illustrate the simplicity of developing applications on top of the ProteoWizard library. Availability: Cross-platform software that compiles using native compilers (i.e. GCC on Linux, MSVC on Windows and XCode on OSX) is available for download free of charge, at http://proteowizard.sourceforge.net. This website also provides code examples, and documentation. It is our hope the ProteoWizard project will become a standard platform for proteomics development; consequently, code use, contribution and further development are strongly encouraged. Contact: darren@proteowizard.org 10.1093/bioinformatics/btn323

A high throughput proteomics screen identifies novel substrates of death-associated protein kinase.

S Bialik — 2008-08-09T04:45:10-00:00

Molecular & cellular proteomics : MCP, Vol. 7, No. 6. (June 2008), pp. 1089-1098.

Death-associated protein kinase (DAPk) is a Ser/Thr kinase whose activity is necessary for different cell death phenotypes. Although its contribution to cell death is well established, only a handful of direct substrates have been identified; these do not fully account for the multiple cellular effects of DAPk. To identify such substrates on a large scale, we developed an in vitro, unbiased, proteomics-based assay to search for novel DAPk substrates. Biochemical fractionation and mass spectrometric analysis were used to purify and identify several potential substrates from HeLa cell lysate. Here we report the identification of two such candidate substrates, the ribosomal protein L5 and MCM3, a replication licensing factor. Although L5 proved to be a weak substrate, MCM3 was efficiently and specifically phosphorylated by DAPk on a unique site, Ser160. Significantly DAPk phosphorylated this site in vivo upon overexpression in 293T cells. Activation of endogenous DAPk by increasing intracellular Ca2+ also led to increased phosphorylation of MCM3. Importantly short hairpin RNA-mediated knockdown of endogenous DAPk blocked both basal phosphorylation and Ca2+-induced phosphorylation, indicating that DAPk is both necessary and sufficient for MCM3 Ser160 phosphorylation in vivo. Identification of MCM3 as an in vivo DAPk substrate indicates the usefulness of this approach for identification of physiologically relevant substrates that may shed light on novel functions of the kinase.

Quantitative proteomics as a new piece of the systems biology puzzle.

Angela Bachi — 2008-08-09T04:38:51-00:00

Journal of proteomics (9 July 2008)

The definition of the role of each gene product in its cellular context is of outstanding importance in the post-genomics era. Recent technological innovations have driven research in proteomics from single protein characterization to global approaches, aiming to achieve a comprehensive qualitative and quantitative description of complex molecular mechanisms. In this review, we discuss the methodology of quantitative proteomics as it applies to the analysis of complex biological model systems. A special attention will be given to model systems that are suitable for functional genomic studies, where the potential of quantitative proteomics can be effectively demonstrated.

TandTRAQ: an open-source tool for integrated protein identification and quantitation.

T Laderas — 2008-08-08T14:21:03-00:00

Bioinformatics (Oxford, England), Vol. 23, No. 24. (15 December 2007), pp. 3394-3396.

Integrating qualitative protein identification with quantitative protein analysis is non-trivial, given incompatibility in output formats. We present TandTRAQ, a standalone utility that integrates results from i-Tracker, an open-source iTRAQ quantitation program with the search results from X?Tandem, an open-source proteome search engine. The utility runs from the command-line and can be easily integrated into a pipeline for automation. Availability: The TandTRAQ Perl scripts are freely available for download at http://www.ohsucancer.com/isrdev/tandtraq/

Biomarker discovery for arsenic exposure using functional data. Analysis and feature learning of mass spectrometry proteomic data.

J Harezlak — 2008-07-29T03:50:36-00:00

Journal of proteome research, Vol. 7, No. 1. (January 2008), pp. 217-224.

Plasma biomarkers of exposure to environmental contaminants play an important role in early detection of disease. The emerging field of proteomics presents an attractive opportunity for candidate biomarker discovery, as it simultaneously measures and analyzes a large number of proteins. This article presents a case study for measuring arsenic concentrations in a population residing in an As-endemic region of Bangladesh using plasma protein expressions measured by SELDI-TOF mass spectrometry. We analyze the data using a unified statistical method based on functional learning to preprocess mass spectra and extract mass spectrometry (MS) features and to associate the selected MS features with arsenic exposure measurements. The task is challenging due to several factors, the high dimensionality of mass spectrometry data, complicated error structures, and a multiple comparison problem. We use nonparametric functional regression techniques for MS modeling, peak detection based on the significant zero-downcrossing method, and peak alignment using a warping algorithm. Our results show significant associations of arsenic exposure to either under- or overexpressions of 20 proteins.

The Protein Information and Property Explorer: an easy-to-use, rich-client web application for the management and functional analysis of proteomic data.

H Ramos — 2008-07-25T14:58:08-00:00

Bioinformatics (Oxford, England) (16 July 2008)

MOTIVATION: Mass spectrometry experiments in the field of proteomics produce lists containing tens to thousands of identified proteins. With the Protein Information and Property Explorer (PIPE) the biologist can acquire functional annotations for these proteins and explore the enrichment of the list, or fraction thereof, with respect to functional classes. These protein lists may be saved for access at a later time or different location. The PIPE is interoperable with the Firegoose and the Gaggle, permitting wide-ranging data exploration and analysis. The PIPE is a rich-client web application which uses AJAX capabilities provided by the Google Web Toolkit, and server side data storage using Hibernate. AVAILABILITY: http://pipe.systemsbiology.net CONTACT: pshannon@systemsbiology.org.

Proteomic methodological recommendations for studies involving human plasma, platelets, and peripheral blood mononuclear cells.

B de Roos — 2008-07-24T09:22:31-00:00

Journal of proteome research, Vol. 7, No. 6. (June 2008), pp. 2280-2290.

This study was designed to develop, optimize and validate protocols for blood processing prior to proteomic analysis of plasma, platelets and peripheral blood mononuclear cells (PBMC) and to determine analytical variation of a single sample of depleted plasma, platelet and PBMC proteins within and between four laboratories each using their own standard operating protocols for 2D gel electrophoresis. Plasma depleted either using the Beckman Coulter IgY-12 proteome partitioning kit or the Amersham albumin and IgG depletion columns gave good quality gels, but reproducibility appeared better with the single-use immuno-affinity column. The use of the Millipore Filter Device for protein concentration gave a 16% ( p < 0.005) higher recovery of protein in flow-through sample compared with acetone precipitation. The use of OptiPrep gave the lowest level of platelet contamination (1:0.8) during the isolation of PBMC from blood. Several proteins (among which are alpha-tropomyosin, fibrinogen and coagulation factor XIII A) were identified that may be used as biomarkers of platelet contamination in future studies. When identifying preselected spots, at least three out of the four centers found similar identities for 10 out of the 10 plasma proteins, 8 out of the 10 platelet proteins and 8 out of the 10 PBMC proteins. The discrepancy in spot identifications has been described before and may be explained by the mis-selection of spots due to laboratory-to-laboratory variation in gel formats, low scores on the peptide analysis leading to no or only tentative identifications, or incomplete resolution of different proteins in what appears as a single abundant spot. The average within-laboratory coefficient of variation (CV) for each of the matched spots after automatic matching using either PDQuest or ProteomWeaver software ranged between 18 and 69% for depleted plasma proteins, between 21 and 55% for platelet proteins, and between 22 and 38% for PBMC proteins. Subsequent manual matching improved the CV with on average between 1 and 16%. The average between laboratory CV for each of the matched spots after automatic matching ranged between 4 and 54% for depleted plasma proteins, between 5 and 60% for platelet proteins, and between 18 and 70% for PBMC proteins. This variation must be considered when designing sufficiently powered studies that use proteomics tools for biomarker discovery. The use of tricine in the running buffer for the second dimension appears to enhance the resolution of proteins especially in the high molecular weight range.

MassTRIX: mass translator into pathways.

K Suhre — 2008-07-18T01:14:35-00:00

Nucleic acids research, Vol. 36, No. Web Server issue. (1 July 2008)

Recent technical advances in mass spectrometry (MS) have brought the field of metabolomics to a point where large numbers of metabolites from numerous prokaryotic and eukaryotic organisms can now be easily and precisely detected. The challenge today lies in the correct annotation of these metabolites on the basis of their accurate measured masses. Assignment of bulk chemical formula is generally possible, but without consideration of the biological and genomic context, concrete metabolite annotations remain difficult and uncertain. MassTRIX responds to this challenge by providing a hypothesis-driven approach to high precision MS data annotation. It presents the identified chemical compounds in their genomic context as differentially colored objects on KEGG pathway maps. Information on gene transcription or differences in the gene complement (e.g. samples from different bacterial strains) can be easily added. The user can thus interpret the metabolic state of the organism in the context of its potential and, in the case of submitted transcriptomics data, real enzymatic capacities. The MassTRIX web server is freely accessible at http://masstrix.org.

Exploiting the proteome to improve the genome-wide genetic analysis of epistasis in common human diseases.

KA Pattin — 2008-07-16T14:50:27-00:00

Human genetics, Vol. 124, No. 1. (August 2008), pp. 19-29.

One of the central goals of human genetics is the identification of loci with alleles or genotypes that confer increased susceptibility. The availability of dense maps of single-nucleotide polymorphisms (SNPs) along with high-throughput genotyping technologies has set the stage for routine genome-wide association studies that are expected to significantly improve our ability to identify susceptibility loci. Before this promise can be realized, there are some significant challenges that need to be addressed. We address here the challenge of detecting epistasis or gene-gene interactions in genome-wide association studies. Discovering epistatic interactions in high dimensional datasets remains a challenge due to the computational complexity resulting from the analysis of all possible combinations of SNPs. One potential way to overcome the computational burden of a genome-wide epistasis analysis would be to devise a logical way to prioritize the many SNPs in a dataset so that the data may be analyzed more efficiently and yet still retain important biological information. One of the strongest demonstrations of the functional relationship between genes is protein-protein interaction. Thus, it is plausible that the expert knowledge extracted from protein interaction databases may allow for a more efficient analysis of genome-wide studies as well as facilitate the biological interpretation of the data. In this review we will discuss the challenges of detecting epistasis in genome-wide genetic studies and the means by which we propose to apply expert knowledge extracted from protein interaction databases to facilitate this process. We explore some of the fundamentals of protein interactions and the databases that are publicly available.

iHOPerator: User-scripting a personalized bioinformatics Web, starting with the iHOP website

Benjamin Good — 2006-12-16T04:14:08-00:00

BMC Bioinformatics, Vol. 7 (15 December 2006), 534.

Statistical similarities between transcriptomics and quantitative shotgun proteomics data.

Norman Pavelka — 2008-02-03T22:07:40-00:00

Mol Cell Proteomics (19 November 2007)

If the large collection of microarray-specific statistical tools was applicable to the analysis of quantitative shotgun proteomics datasets, it would certainly foster an important advancement of proteomics research. Here, we analyze two large multi-dimensional protein identification technology (MudPIT) datasets - one containing 8 replicates of the soluble fraction of a yeast whole-cell lysate, one containing 9 replicates of a human immuno-precipitate - to test whether normalized spectral abundance factor (NSAF) values share substantially similar statistical properties with transcript abundance values from Affymetrix GeneChip data. First, we show similar dynamic range and distribution properties of these two types of numeric values. Next, we observe that the standard deviation (SD) of a protein's NSAF values is dependent on the average NSAF value of the protein itself, following a power law. This relationship can be modeled by a power law global error model (PLGEM), initially developed to describe the variance-versus-mean dependence that exists in GeneChip data. PLGEM parameters obtained from NSAF datasets prove to be surprisingly similar to the typical parameters observed in GeneChip datasets. The most important common feature identified by this approach is that, although in absolute terms the SD of replicated abundance values increases as a function of increasing average abundance, the coefficient of variation - a relative measure of variability - becomes progressively smaller under the same conditions. We next show that PLGEM parameters are reasonably stable to decreasing numbers of replicates. We finally illustrate one possible application of PLGEM in the identification of differentially abundant proteins, which might potentially outperform standard statistical tests. In summary, we believe that this body of work lays the foundation for the application of microarray-specific tools in the analysis of NSAF datasets.

Proteomic methods for drug target discovery.

L Sleno — 2008-07-08T08:10:23-00:00

Current opinion in chemical biology, Vol. 12, No. 1. (February 2008), pp. 46-54.

The field of drug target discovery is currently very popular with a great potential for advancing biomedical research and chemical genomics. Innovative strategies have been developed to aid the process of target identification, either by elucidating the primary mechanism-of-action of a drug, by understanding side effects involving unanticipated 'off-target' interactions, or by finding new potential therapeutic value for an established drug. Several promising proteomic methods have been introduced for directly isolating and identifying the protein targets of interest that are bound by active small molecules or for visualizing enzyme activities affected by drug treatment. Significant progress has been made in this rapidly advancing field, speeding the clinical validation of drug candidates and the discovery of the novel targets for lead compounds developed using cell-based phenotypic screens. Using these proteomic methods, further insight into drug activity and toxicity can be ascertained.

Human proteinpedia as a resource for clinical proteomics.

Suresh Mathivanan — 2008-07-05T00:25:13-00:00

Molecular & cellular proteomics : MCP (23 June 2008)

Clinical proteomics is an emerging field that deals with use of proteomic technologies for medical applications. With a major objective of identifying proteins involved in pathological processes and as potential biomarkers, this field is already gaining momentum. Consequently, clinical proteomic data are being generated at a rapid pace although mechanisms of sharing such data with the biomedical community lag far behind. Most of these data are either provided as supplementary information through journal websites or directly made available by the authors through their own web resources. Integration of these data within a single resource that displays information in the context of individual proteins is likely to enhance the use of proteomic data in biomedical research. Human Proteinpedia is one such portal that unifies human proteomic data under a single banner. The goal of this resource is to ultimately capture and integrate all proteomic data obtained from individual studies on normal and diseased tissues. We anticipate that harnessing of these data will help prioritize experiments related to protein targets and also permit meta-analyses to uncover molecular signatures of diseases. Finally, we encourage all biomedical investigators to maximize dissemination of their valuable proteomic data to rest of the community by active participation in existing repositories such as Human Proteinpedia.

Strategies for label-free optical detection.

G Gauglitz — 2008-07-04T23:26:57-00:00

Advances in biochemical engineering/biotechnology, Vol. 109 (2008), pp. 395-432.

A large number of methods using direct detection with label-free systems are known. They compete with the well-introduced fluorescence-based methods. However, recent applications take advantage of label-free detection in protein-protein interactions, high-throughput screening, and high-content screening. These new applications require new strategies for biosensors. It becomes more and more obvious that neither the transduction principle nor the recognition elements for the biomolecular interaction process alone determine the quality of the biosensor. Accordingly, the biosensor system has to be considered as a whole. This chapter focuses on strategies to optimize the detection platform and the biomolecular recognition layer. It concentrates on direct detection methods, with special focus on optical transduction. Since even this restriction still leaves a large number of methods, only microrefractometric and microreflectometric methods using planar transducers have been selected for a detailed description and a listing of applications. However, since many review articles on the physical principles exist, the description is kept short. Other methods are just mentioned in brief and for comparison. The outlook and the applications demonstrate the future perspectives of direct optical detection in bioanalytics.

Heteroatom(isotope)-tagged proteomics via ICP-MS: screening and quantification of proteins and their post-translational modifications.

A Sanz-Medel — 2008-07-03T08:48:57-00:00

Analytical and bioanalytical chemistry, Vol. 391, No. 3. (June 2008), pp. 885-894.

Analysis of environmental stress response on the proteome level.

Victor J Nesatyy — 2008-07-03T08:14:27-00:00

Mass spectrometry reviews (13 June 2008)

Thousands of man-made chemicals are annually released into the environment by agriculture, transport, industries, and other human activities. In general, chemical analysis of environmental samples used to assess the pollution status of a specific ecosystem is complicated by the complexity of the mixture, and in some cases by the very low toxicity thresholds of chemicals present. In that sense, a proteomics approach, capable of detecting subtle changes in the level and structure of individual proteins within the whole proteome in response to the altered surroundings, has obvious applications in the field of ecotoxicology. In addition to identifying new protein biomarkers, it can also help to provide an insight into underlying mechanisms of toxicity. Despite being a comparatively new field with a number of caveats, proteomics applications have spread from microorganisms and plants to invertebrates and vertebrates, gradually becoming an established technology used in environmental research. This review article highlights recent advances in the field of environmental proteomics, mainly focusing on experimental approaches with a potential to understand toxic modes of action and to identify novel ecotoxicological biomarkers. (c) 2008 Wiley Periodicals, Inc., Mass Spec Rev.

Experimental and computational approaches to quantitative proteomics: Status quo and outlook.

Alexandre Panchaud — 2008-07-03T08:15:13-00:00

Journal of proteomics, Vol. 71, No. 1. (30 April 2008), pp. 19-33.

Proteomics has come a long way from the initial qualitative analysis of proteins present in a given sample at a given time ("cataloguing") to large-scale characterization of proteomes, their interactions and dynamic behavior. Originally enabled by breakthroughs in protein separation and visualization (by two-dimensional gels) and protein identification (by mass spectrometry), the discipline now encompasses a large body of protein and peptide separation, labeling, detection and sequencing tools supported by computational data processing. The decisive mass spectrometric developments and most recent instrumentation news are briefly mentioned accompanied by a short review of gel and chromatographic techniques for protein/peptide separation, depletion and enrichment. Special emphasis is placed on quantification techniques: gel-based, and label-free techniques are briefly discussed whereas stable-isotope coding and internal peptide standards are extensively reviewed. Another special chapter is dedicated to software and computing tools for proteomic data processing and validation. A short assessment of the status quo and recommendations for future developments round up this journey through quantitative proteomics.

Interactive proteomics: what lies ahead?

B Suter — 2008-07-03T08:18:03-00:00

BioTechniques, Vol. 44, No. 5. (April 2008), pp. 681-691.

Interactive proteomics addresses the physical associations among proteins and establishes global, disease-, and pathway-specific protein interaction networks. The inherent chemical and structural diversity of proteins, their different expression levels, and their distinct subcellular localizations pose unique challenges for the exploration of these networks, necessitating the use of a variety of innovative and ingenious approaches. Consequently, recent years have seen exciting developments in protein interaction mapping and the establishment of very large interaction networks, especially in model organisms. In the near future, attention will shift to the establishment of interaction networks in humans and their application in drug discovery and understanding of diseases. In this review, we present an impressive toolbox of different technologies that we expect to be crucial for interactive proteomics in the coming years.

Protein purification using chromatography: selection of type, modelling and optimization of operating conditions.

J A Asenjo — 2008-07-03T08:28:49-00:00

Journal of molecular recognition : JMR (10 June 2008)

To achieve a high level of purity in the purification of recombinant proteins for therapeutic or analytical application, it is necessary to use several chromatographic steps. There is a range of techniques available including anion and cation exchange, which can be carried out at different pHs, hydrophobic interaction chromatography, gel filtration and affinity chromatography. In the case of a complex mixture of partially unknown proteins or a clarified cell extract, there are many different routes one can take in order to choose the minimum and most efficient number of purification steps to achieve a desired level of purity (e.g. 98%, 99.5% or 99.9%).This review shows how an initial 'proteomic' characterization of the complex mixture of target protein and protein contaminants can be used to select the most efficient chromatographic separation steps in order to achieve a specific level of purity with a minimum number of steps. The chosen methodology was implemented in a computer- based Expert System. Two algorithms were developed, the first algorithm was used to select the most efficient purification method to separate a protein from its contaminants based on the physicochemical properties of the protein product and the protein contaminants and the second algorithm was used to predict the number and concentration of contaminants after each separation as well as protein product purity.The application of the Expert System approach was experimentally tested and validated with a mixture of four proteins and the experimental validation was also carried out with a supernatant of Bacillus subtilis producing a recombinant beta-1,3-glucanase.Once the type of chromatography is chosen, optimization of the operating conditions is essential. Chromatographic elution curves for a three-protein mixture (alpha-lactoalbumin, ovalbumin and beta-lactoglobulin), carried out under different flow rates and ionic strength conditions, were simulated using two different mathematical models. These models were the Plate Model and the more fundamentally based Rate Model. Simulated elution curves were compared with experimental data not used for parameter identification. Deviation between experimental data and the simulated curves using the Plate Model was less than 0.0189 (absorbance units); a slightly higher deviation [0.0252 (absorbance units)] was obtained when the Rate Model was used. In order to optimize operating conditions, a cost function was built that included the effect of the different production stages, namely fermentation, purification and concentration. This cost function was also successfully used for the determination of the fraction of product to be collected (peak cutting) in chromatography. It can be used for protein products with different characteristics and qualities, such as purity and yield, by choosing the appropriate parameters. Copyright (c) 2008 John Wiley & Sons, Ltd.

A Framework for the Automated Analysis of Subcellular Patterns in Human Protein Atlas Images

Justin Newberg — 2008-04-26T04:07:50-00:00

J. Proteome Res. (25 April 2008)

Abstract: The systematic study of subcellular location patterns is required to fully characterize the human proteome, as subcellular location provides critical context necessary for understanding a proteins function. The analysis of tens of thousands of expressed proteins for the many cell types and cellular conditions under which they may be found creates a need for automated subcellular pattern analysis. We therefore describe the application of automated methods, previously developed and validated by our laboratory on fluorescence micrographs of cultured cell lines, to analyze subcellular patterns in tissue images from the Human Protein Atlas. The Atlas currently contains images of over 3000 protein patterns in various human tissues obtained using immunohistochemistry. We chose a 16 protein subset from the Atlas that reflects the major classes of subcellular location. We then separated DNA and protein staining in the images, extracted various features from each image, and trained a support vector machine classifier to recognize the protein patterns. Our results show that our system can distinguish the patterns with 83% accuracy in 45 different tissues, and when only the most confident classifications are considered, this rises to 97%. These results are encouraging given that the tissues contain many different cell types organized in different manners, and that the Atlas images are of moderate resolution. The approach described is an important starting point for automatically assigning subcellular locations on a proteome-wide basis for collections of tissue images such as the Atlas.

Toward a confocal subcellular atlas of the human proteome.

L Barbe — 2008-07-03T08:25:06-00:00

Molecular & cellular proteomics : MCP, Vol. 7, No. 3. (March 2008), pp. 499-508.

Information on protein localization on the subcellular level is important to map and characterize the proteome and to better understand cellular functions of proteins. Here we report on a pilot study of 466 proteins in three human cell lines aimed to allow large scale confocal microscopy analysis using protein-specific antibodies. Approximately 3000 high resolution images were generated, and more than 80% of the analyzed proteins could be classified in one or multiple subcellular compartment(s). The localizations of the proteins showed, in many cases, good agreement with the Gene Ontology localization prediction model. This is the first large scale antibody-based study to localize proteins into subcellular compartments using antibodies and confocal microscopy. The results suggest that this approach might be a valuable tool in conjunction with predictive models for protein localization.

Affinity as a tool in life science.

M Uhlén — 2008-06-19T09:06:45-00:00

BioTechniques, Vol. 44, No. 5. (April 2008), pp. 649-654.

The use of affinity-based tools has become invaluable as a platform for basic research and in the development of drugs and diagnostics. Applications include affinity chromatography and affinity tag fusions for efficient purification of proteins as well as methods to probe the protein network interactions on a whole-proteome level. A variety of selection systems has been described for in vitro evolution of affinity reagents using combinatorial libraries, which make it possible to create high-affinity reagents to virtually all biomolecules, as exemplified by generation of therapeutic antibodies and new protein scaffold binders. The strategies for high-throughput generation of affinity reagents have also opened up the possibility of generating specific protein probes on a whole-proteome level. Recently, such affinity proteomics have allowed the detailed analysis of human protein expression in a comprehensive manner both in normal and disease tissue using tissue microarrays and confocal microscopy.

Generation and validation of affinity reagents on a proteome-wide level.

Mathias Uhlén — 2008-07-03T08:12:45-00:00

Journal of molecular recognition : JMR (10 June 2008)

There is a need for protein-specific affinity reagents to explore the gene products encoded by the genome. Recently, systematic efforts to generate validated affinity reagents on a whole human proteome level have been initiated. There are several issues for such efforts, including choice of antigen, type of affinity reagent, and the subsequent validation of the generated protein-specific binders. The advantages and disadvantages with the different approaches are discussed and the problems related to quality assessment of antibodies to be used in multi-platform applications are addressed. This review also describes the efforts to create a virtual resource of validated antibodies using a community-based portal and summarizes the status and visions for the publicly available human protein atlas (http://www.proteinatlas.org) showing the human protein profiles in a large number of normal and cancer tissues as well as a large set of human cell lines. Copyright (c) 2008 John Wiley & Sons, Ltd.

ProteomeBinders: planning a European resource of affinity reagents for analysis of the human proteome

Michael Taussig — 2006-12-29T07:30:47-00:00

Nature Methods, Vol. 4, No. 1., pp. 13-17.

Antibody Suspension Bead Arrays within Serum Proteomics.

Jochen Schwenk — 2008-07-03T06:32:28-00:00

Journal of proteome research (28 June 2008)

Antibody microarrays offer a powerful tool to screen for target proteins in complex samples. Here, we describe an approach for systematic analysis of serum, based on antibodies and using color-coded beads for the creation of antibody arrays in suspension. This method, adapted from planar antibody arrays, offers a fast, flexible, and multiplexed procedure to screen larger numbers of serum samples, and no purification steps are required to remove excess labeling substance. The assay system detected proteins down to lower picomolar levels with dynamic ranges over 3 orders of magnitude. The feasibility of this workflow was shown in a study with more than 200 clinical serum samples tested for 20 serum proteins.

Suspension microarrays for the identification of the response patterns in hyperinflammatory diseases.

Hsin-Yun Hsu — 2008-07-03T06:35:39-00:00

Medical engineering & physics (28 February 2008)

Miniaturized and parallelized sandwich immunoassays allow the simultaneous analysis of a variety of parameters in a single experiment. Bead-based protein array systems or suspension microarrays are well-established multiplex sandwich immunoassay formats. To study inflammatory diseases, protein arrays can be used to analyze changes in plasma protein levels, such as cytokines, chemokines, soluble receptors, and matrix metalloproteinases. Using the bead-based Luminex system, multiplexed sandwich immunoassays have been developed to analyze the plasma concentrations of soluble receptors: sTNF-RI, sTNF-RII, sIL-2R, sgp130, sFas, sRAGE, sE-selectin, sICAM-1, sVCAM-1, sMIF-1 and sFasL. This newly established 11-plex soluble receptors assay demonstrated acceptable intra-assay and inter-assay precision, appropriate accuracy, and no crossreactivity between analytes. Using this assay, 100 plasma samples derived from 36 critically ill intensive care unit (ICU) patients with trauma or sepsis were analyzed for their soluble receptor plasma concentrations. Results obtained allowed grouping of patients' samples into a trauma and a sepsis group. Four candidate molecules: sFas, sICAM-1, sTNF-RI, and sTNF-RII had higher concentrations in patients with sepsis than in those with trauma, contributing the highest discriminatory values to define the nature of the inflammatory disease originating from pathogen-involved (sepsis) or pathogen-independent inflammation.

CE at the omics level: towards systems biology--an update.

EJ Song — 2008-07-03T06:05:01-00:00

Electrophoresis, Vol. 29, No. 1. (January 2008), pp. 129-142.

This review provides an updated overview of recent developments and applications of CE based on previously published reports in the field of omic research. The increased number of published articles on omics shows that the field is growing and attracting the attention of many life science researchers. Due to developments in the omics sciences, many researchers have been studying systems biology, in which biological events in organisms are systematically interpreted through the combination of complex measurements from various methods resulting in high-throughput data. Given the challenges of such complex forms of analysis, CE is a strong candidate for generating omics data useful for acquiring the qualitative and quantitative knowledge necessary for systems-level investigation. By emphasizing CE for systems biology, this review will discuss and focus on the applicability of CE to systems-based analytical data at the genomic, transcriptomic, proteomic, and metabolomic levels from 2005 to the present.

Disparate proteome reactivity profiles of carbon electrophiles.

E Weerapana — 2008-07-03T05:50:15-00:00

Nature chemical biology, Vol. 4, No. 7. (July 2008), pp. 405-407.

Insights into the proteome reactivity of electrophiles are crucial for designing activity-based probes for enzymes lacking cognate affinity labels. Here, we show that different classes of carbon electrophiles exhibit markedly distinct amino acid labeling profiles in proteomes, ranging from selective reactivity with cysteine to adducts with several amino acids. These data specify electrophilic chemotypes with restricted and permissive reactivity profiles to guide the design of next-generation functional proteomics probes.

Detection of heteromerization of more than two proteins by sequential BRET-FRET.

Paulina Carriba — 2008-07-02T06:30:14-00:00

Nature methods (29 June 2008)

Identification of higher-order oligomers in the plasma membrane is essential to decode the properties of molecular networks controlling intercellular communication. We combined bioluminescence resonance energy transfer (BRET) and fluorescence resonance energy transfer (FRET) in a technique called sequential BRET-FRET (SRET) that permits identification of heteromers formed by three different proteins. In SRET, the oxidation of a Renilla luciferase (Rluc) substrate by an Rluc fusion protein triggers acceptor excitation of a second fusion protein by BRET and subsequent FRET to a third fusion protein. We describe two variations of SRET that use different Rluc substrates with appropriately paired acceptor fluorescent proteins. Using SRET, we identified complexes of cannabinoid CB(1), dopamine D(2) and adenosine A(2A) receptors in living cells. SRET is an invaluable technique to identify heteromeric complexes of more than two neurotransmitter receptors, which will allow us to better understand how signals are integrated at the molecular level.

Activity-Based Protein Profiling: From Enzyme Chemistry to Proteomic Chemistry

Benjamin Cravatt — 2008-06-16T04:52:08-00:00

Annual Review of Biochemistry, Vol. 77, No. 1. (2008), pp. 383-414.

Genome sequencing projects have provided researchers with a complete inventory of the predicted proteins produced by eukaryotic and prokaryotic organisms. Assignment of functions to these proteins represents one of the principal challenges for the field of proteomics. Activity-based protein profiling (ABPP) has emerged as a powerful chemical proteomic strategy to characterize enzyme function directly in native biological systems on a global scale. Here, we review the basic technology of ABPP, the enzyme classes addressable by this method, and the biological discoveries attributable to its application.

From the Cover: A drug-controllable tag for visualizing newly synthesized proteins in cells and whole animals

Michael Lin — 2008-06-10T10:20:00-00:00

Proceedings of the National Academy of Sciences, Vol. 105, No. 22. (3 June 2008), pp. 7744-7749.

Research on basic cellular processes involving local production or delivery of proteins, such as activity-dependent synaptic modification in neurons, would benefit greatly from a robust, nontoxic method to visualize selectively newly synthesized copies of proteins of interest within cells, tissues, or animals. We report a technique for covalent labeling of newly synthesized proteins of interest based on drug-dependent preservation of epitope tags. Epitope tags are removed from proteins of interest immediately after translation by the activity of a sequence-specific protease until the time a protease inhibitor is added, after which newly synthesized protein copies retain their tags. This method, which we call TimeSTAMP for time-specific tagging for the age measurement of proteins, allows sensitive and nonperturbative visualization and quantification of newly synthesized proteins of interest with exceptionally tight temporal control. We demonstrate applications of TimeSTAMP in retrospectively identifying growing synapses in cultured neurons and in visualizing the distribution of recently synthesized proteins in intact fly brains. 10.1073/pnas.0803060105

Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT)

Daniela Dieterich — 2008-06-10T10:19:33-00:00

Proceedings of the National Academy of Sciences, Vol. 103, No. 25. (20 June 2006), pp. 9482-9487.

In both normal and pathological states, cells respond rapidly to environmental cues by synthesizing new proteins. The selective identification of a newly synthesized proteome has been hindered by the basic fact that all proteins, new and old, share the same pool of amino acids and thus are chemically indistinguishable. We describe here a technology, based on the cotranslational introduction of azide groups into proteins and the chemoselective tagging of azide-labeled proteins with an alkyne affinity tag, to separate and identify, specifically, the newly synthesized proteins in mammalian cells. Incorporation of the azide-bearing amino acid azidohomoalanine is unbiased, not toxic, and does not increase protein degradation. As a first demonstration of the method, we report the selective purification and identification of 195 metabolically labeled proteins with multidimensional liquid chromatography in-line with tandem MS. Furthermore, in combination with leucine-based mass tagging, candidates were immediately validated as newly synthesized proteins. The identified proteins, synthesized in a 2-h window, possess a broad range of biochemical properties and span most functional gene ontology categories. This technology makes it possible to address the temporal and spatial characteristics of newly synthesized proteomes in any cell type. 10.1073/pnas.0601637103

Enhanced nuclear proteomics.

M Barthéléry — 2008-06-08T02:47:31-00:00

Proteomics, Vol. 8, No. 9. (May 2008), pp. 1832-1838.

Nuclear proteomics provides an opportunity to examine protein effectors that contribute to cellular phenotype. Both the quality and sensitivity of gel-based nuclear proteomics are limited, however, by the over-representation of histones in the protein mixture. These highly charged proteins overshadow rare species and interfere with IEF. A nuclear isolation and protein extraction procedure, tested on human embryonic stem cells, is reported that effectively isolates intact nuclei and then depletes the sample of histones by taking advantage of their ability to form an insoluble complex with DNA at lower pH (even under denaturing conditions). Ubiquitous histones and abundant nuclear actin, are depleted up to 99 +/- 0.02 and 42 +/- 5%, respectively. This technique greatly improves electrofocusing efficacy and nearly doubles the number of detected protein spots. This approach to nuclear protein isolation for 2-D PAGE opens the door to better investigation of nuclear protein dynamics.

PEDRo: a database for storing, searching and disseminating experimental proteomics data.

K Garwood — 2004-11-22T00:17:30-00:00

BMC Genomics, Vol. 5, No. 1. (17 September 2004)

BACKGROUND: Proteomics is rapidly evolving into a high-throughput technology, in which substantial and systematic studies are conducted on samples from a wide range of physiological, developmental, or pathological conditions. Reference maps from 2D gels are widely circulated. However, there is, as yet, no formally accepted standard representation to support the sharing of proteomics data, and little systematic dissemination of comprehensive proteomic data sets. RESULTS: This paper describes the design, implementation and use of a Proteome Experimental Data Repository (PEDRo), which makes comprehensive proteomics data sets available for browsing, searching and downloading. It is also serves to extend the debate on the level of detail at which proteomics data should be captured, the sorts of facilities that should be provided by proteome data management systems, and the techniques by which such facilities can be made available. CONCLUSIONS: The PEDRo database provides access to a collection of comprehensive descriptions of experimental data sets in proteomics. Not only are these data sets interesting in and of themselves, they also provide a useful early validation of the PEDRo data model, which has served as a starting point for the ongoing standardisation activity through the Proteome Standards Initiative of the Human Proteome Organisation.

soaPDB: a web application for searching the Protein Data Bank, organizing results, and receiving automatic email alerts.

Charles A Lesburg — 2008-05-24T01:41:42-00:00

Nucleic acids research (16 May 2008)

soaPDB is a web application that allows generation and organization of saved PDB searches, and offers automatic email alerts. This tool is used from a web interface to store PDB searches and results in a backend relational database. Written using the Ruby on Rails open-source web framework, soaPDB is easy to deploy, maintain and customize. soaPDB is freely available upon request for local installation and is also available at http://soapdb.dyndns.org:3000.

Evaluation of proteomic strategies for analyzing ubiquitinated proteins.

J Peng — 2008-05-24T01:10:31-00:00

BMB reports, Vol. 41, No. 3. (31 March 2008), pp. 177-183.

Ubiquitin is an essential, highly-conserved small regulatory protein in eukaryotic cells. It covalently modifies a wide variety of targeted proteins in the forms of monomer and polymers, altering the conformation and binding properties of the proteins and thus regulating proteasomal delivery, protein activities and localization. Mass spectrometry has emerged as an indispensable tool for in-depth characterization of protein ubiquitination. Ubiquitinated proteins in cell lysates are usually enriched by affinity chromatography and subsequently analyzed by mass spectrometry for identification and quantification. Ubiquitin-conjugated amino acid residues can be determined by unique mass shift caused by the modification. Moreover, the complex structure of polyubiquitin chains on substrates can be dissected by bottom-up and middle-down mass spectrometric approaches, revealing potential novel functions of polyubiquitin linkages. Here I review the advances and caveats of these strategies, emphasizing caution in the validation of ubiquitinated proteins and in the interpretation of raw data.

Integrating forward and reverse proteomics to unravel protein function.

S Palcy — 2008-05-24T00:47:40-00:00

Proteomics, Vol. 6, No. 20. (October 2006), pp. 5467-5480.

To date, proteomics approaches have aimed to either identify novel proteins or change in protein expression/modification in various organisms under normal or disease conditions. One major aspect of functional proteomics is to identify protein biological properties in a given context, however, forward proteomics approaches alone cannot complete this goal. Indeed, with the increasing successes of such proteomics-based research strategies and the subsequent increasing amounts of proteins identified with unknown molecular functions, approaches allowing for systematic analyses of protein functions are desired. In this review, we propose to depict the complementarities of forward and reverse proteomics approaches in the definite understanding of protein functions. This dual strategy requires a data integration loop which allows for systematic characterization of protein function(s). The details of the integrative process combining both in silico and experimental resources and tools are presented. Altogether, we believe that the integration of forward and reverse proteomics approaches supported by bioinformatics will provide an efficient path towards systems biology.

From proteomics toward systems biology: integration of different types of proteomics data into network models.

S Rho — 2008-05-24T00:48:28-00:00

BMB reports, Vol. 41, No. 3. (31 March 2008), pp. 184-193.

Living organisms are comprised of various systems at different levels, i.e., organs, tissues, and cells. Each system carries out its diverse functions in response to environmental and genetic perturbations, by utilizing biological networks, in which nodal components, such as, DNA, mRNAs, proteins, and metabolites, closely interact with each other. Systems biology investigates such systems by producing comprehensive global data that represent different levels of biological information, i.e., at the DNA, mRNA, protein, or metabolite levels, and by integrating this data into network models that generate coherent hypotheses for given biological situations. This review presents a systems biology framework, called the 'Integrative Proteomics Data Analysis Pipeline' (IPDAP), which generates mechanistic hypotheses from network models reconstructed by integrating diverse types of proteomic data generated by mass spectrometry-based proteomic analyses. The devised framework includes a serial set of computational and network analysis tools. Here, we demonstrate its functionalities by applying these tools to several conceptual examples.

Urinary Proteomics in Diabetes and CKD.

Kasper Rossing — 2008-05-23T13:55:41-00:00

Journal of the American Society of Nephrology : JASN (30 April 2008)

Urinary biomarkers for diabetes, diabetic nephropathy, and nondiabetic proteinuric renal diseases were sought. For 305 individuals, biomarkers were defined and validated in blinded data sets using high-resolution capillary electrophoresis coupled with electrospray-ionization mass spectrometry. A panel of 40 biomarkers distinguished patients with diabetes from healthy individuals with 89% sensitivity and 91% specificity. Among patients with diabetes, 102 urinary biomarkers differed significantly between patients with normoalbuminuria and nephropathy, and a model that included 65 of these correctly identified diabetic nephropathy with 97% sensitivity and specificity. Furthermore, this panel of biomarkers identified patients who had microalbuminuria and diabetes and progressed toward overt diabetic nephropathy over 3 yr. Differentiation between diabetic nephropathy and other chronic renal diseases reached 81% sensitivity and 91% specificity. Many of the biomarkers were fragments of collagen type I, and quantities were reduced in patients with diabetes or diabetic nephropathy. In conclusion, this study shows that analysis of the urinary proteome may allow early detection of diabetic nephropathy and may provide prognostic information.

Challenges in Translating Plasma Proteomics from Bench to Bedside: Update from the NHLBI Clinical Proteomics Programs.

Robert E Gerszten — 2008-05-20T04:30:27-00:00

American journal of physiology. Lung cellular and molecular physiology (2 May 2008)

The emerging scientific field of proteomics encompasses the identification, characterization and quantification of the protein content or proteome of whole cells, tissues or body fluids. The potential for proteomic technologies to identify and quantify novel proteins in the plasma that can function as biomarkers of the presence or severity of clinical disease states holds great promise for clinical use. However, there are many challenges in translating plasma proteomics from bench to bedside and relatively few plasma biomarkers have successfully transitioned from proteomic discovery to routine clinical use. Key barriers to this translation include the need for "orthogonal" biomarkers (ie., uncorrelated with existing markers), the complexity of the proteome in biological samples, the presence of high abundance proteins such as albumin in biological samples that hinder detection of low abundance proteins, false positive associations that occur with analysis of high dimensional datasets and the limited understanding of the effects of growth, development and age on the normal plasma proteome. Strategies to overcome these challenges are discussed. Key words: proteomics, biomarkers, multiplex assays.

Comprehensive and quantitative proteome profiling of the mouse liver and plasma.

KK Lai — 2008-05-20T04:30:49-00:00

Hepatology (Baltimore, Md.), Vol. 47, No. 3. (March 2008), pp. 1043-1051.

We report a comprehensive and quantitative analysis of the mouse liver and plasma proteomes. The method used is based on extensive fractionation of intact proteins, further separation of proteins based on their abundance and size, and high-accuracy mass spectrometry. This analysis reached a depth in proteomic profiling not reported to date for a mammalian tissue or a biological fluid, with 7099 and 4727 proteins identified with high confidence in the liver and in the corresponding plasma, respectively. This method allowed for the identification in both compartments of low-abundance proteins such as cytokines, chemokines, and receptors and for the detection in plasma of proteins in the pg/mL concentration range. This method also allowed for semiquantitation of all identified proteins. The calculated abundance scores correlated with the abundance of the corresponding transcripts for the large majority of the proteins identified in the liver. Finally, comparison of the liver and plasma datasets demonstrated that a significant number of proteins identified in the liver can be detected in plasma. These included proteins involved in complement and coagulation, in fatty acid, purine and pyruvate metabolism, in gluconeogenesis and glycolysis, in protein ubiquitination, and in insulin, interleukin-4, epidermal growth factor, and platelet-derived growth factor signaling. CONCLUSION: This in-depth analysis of the mouse liver and corresponding plasma proteomes provides a strong basis for investigations of liver pathobiology and biology that employ mouse models of hepatic diseases in an effort to better understand, diagnose, treat, and prevent human hepatic diseases.

Performance of Combinatorial Peptide Libraries in Capturing the Low-Abundance Proteome of Red Blood Cells. 1. Behavior of Mono- to Hexapeptides

Carolina Simó — 2008-05-08T17:03:35-00:00

Anal. Chem. (10 April 2008)

Abstract: For a better understanding of the behavior of solid-phase combinatorial peptide ligands for capturing the red blood cell low-abundance soluble proteome, combinatorial peptides of different lengths from a single amino acid up to a hexapeptide were evaluated. A red blood cell lysate (6 g total protein) was loaded in a cascade fashion to the six columns, which were individually eluted with 8 M urea, 2% 3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate (v/w), and 50 mM citric acid. Each eluate was analyzed via sodium dodecyl sulfate polyacrylamide gel electrophoresis, two-dimensional maps, and nanoLC-MS/MS. The results: mixed beads with a single amino acid attached showed the capture of a non-negligible portion of the proteome. A progressive increasing of the length of the peptide bait enlarges the pool of captured proteins. Above a length of four amino acids, a plateau is progressively reached, suggesting that not much could be gained with baits longer than six amino acids. Interestingly, whereas the beads laden with a single amino acid seem to be able to capture large-size proteins (>40 kDa), beads with progressively longer peptides capture additional proteins in the smaller size range (1050 kDa). This suggests that interactions already begin with a single amino acid, but selectivity requires baits of proper length, at least above four amino acids. Plain beads, with a spacer arm carrying a primary amino terminal group for anchoring the baits, are essentially unable to capture proteins, suggesting that the peptide baits do not act by a mechanism of ion exchange but rather via a complex mixed mode, yielding a specific capture.