Proteomics in radiation research: present status and future perspectives

Rapidly developing postgenome research has made proteins an attractive target for biological analysis. The well-established term of proteome is defined as the complete set of proteins expressed in a given cell, tissue or organism. Unlike the genome, a proteome is rapidly changing as it tends to adapt to microenvironmental signals. The systematic analysis of the proteome at a given time and state is referred to as proteomics. This technique provides information on the molecular and cellular mechanisms that regulate physiology and pathophysiology of the cell. Applications of proteome profiling in radiation research are increasing. However, the large-scale proteomics data sets generated need to be integrated into other fields of radiation biology to facilitate the interpretation of radiation-induced cellular and tissue effects. The aim of this review is to introduce the most recent developments in the field of radiation proteomics.     

Development and application of proteomics technologies in Saccharomyces cerevisiae

Proteomics research focuses on the identification and quantification of "all" proteins present in cells, organisms or tissue. Proteomics is technically complicated because it encompasses the characterization and functional analysis of all proteins that are expressed by a genome. Moreover, because the expression levels of proteins strongly depend on complex regulatory systems, the proteome is highly dynamic. This review focuses on the two major proteomics methodologies, one based on 2D gel electrophoresis and the other based on liquid chromatography coupled to mass spectrometry. The recent developments of these methodologies and their application to quantitative proteomics are described. The model system Saccharomyces cerevisiae is considered to be the optimal vehicle for proteomics and we review studies investigating yeast adaptation to changes in (nutritional) environment.     

Large-scale identification of Caenorhabditis elegans proteins by multidimensional liquid chromatography-tandem mass spectrometry

A proteome of a model organism, Caenorhabditis elegans, was analyzed by an integrated liquid chromatography (LC)-based protein identification system, which was constructed by microscale two-dimensional liquid chromatography (2DLC) coupled with electrospray ionization (ESI) tandem mass spectrometry (MS/MS) on a high-resolution hybrid mass spectrometer with an automated data analysis system. Soluble and insoluble protein fractions were prepared from a mixed growth phase culture of the worm C. elegans, digested with trypsin, and fractionated separately on the 2DLC system. The separated peptides were directly analyzed by on-line ESI-MS/MS in a data-dependent mode, and the resultant spectral data were automatically processed to search a genome sequence database, wormpep 66, for protein identification. The total number of proteins of the composite proteome identified in this method was 1,616, including 110 secreted/targeted proteins and 242 transmembrane proteins. The codon adaptation indices of the identified proteins suggested that the system could identify proteins of relatively low abundance, which are difficult to identify by conventional 2D-gel electrophoresis (GE) followed by an offline mass spectrometric analysis such as peptide mass fingerprinting. Among the approximately 5,400 peptides assigned in this study, many peptides with post-translational modifications, such as N-terminal acetylation and phosphorylation, were detected. This expression profile of C. elegans, containing 571 hypothetical gene products, will serve as the basic data of a major proteome set expressed in the worm.     

High-throughput expression,purification, and characterization of recombinant Caenorhabditis elegans proteins

Modern proteomics approaches include techniques to examine the expression, localization, modifications, and complex formation of proteins in cells. In order to address issues of protein function in vitro using classical biochemical and biophysical approaches, high-throughput methods of cloning the appropriate reading frames, and expressing and purifying proteins efficiently are an important goal of modern proteomics approaches. This process becomes more difficult as functional proteomics efforts focus on the proteins from higher organisms, since issues of correctly identifying intron-exon boundaries and efficiently expressing and solubilizing the (often) multi-domain proteins from higher eukaryotes are challenging. Recently, 12,000 open-reading-frame (ORF) sequences from Caenorhabditis elegans have become available for functional proteomics studies [Nat. Gen. 34 (2003) 35]. We have implemented a high-throughput screening procedure to express, purify, and analyze by mass spectrometry hexa-histidine-tagged C. elegans ORFs in Escherichia coli using metal affinity ZipTips. We find that over 65% of the expressed proteins are of the correct mass as analyzed by matrix-assisted laser desorption MS. Many of the remaining proteins indicated to be "incorrect" can be explained by high-throughput cloning or genome database annotation errors. This provides a general understanding of the expected error rates in such high-throughput cloning projects. The ZipTip purified proteins can be further analyzed under both native and denaturing conditions for functional proteomics efforts.     

The Drosophila melanogaster sperm proteome-II (DmSP-II)

Advances in mass spectrometry technology, high-throughput proteomics and genome annotations have resulted in significant increases in our molecular understanding of sperm composition. Using improved separation and detection methods and an updated genome annotation, a re-analysis of the Drosophila melanogaster sperm proteome (DmSP) has resulted in the identification of 956 sperm proteins. Comparative analysis with our previous proteomic dataset revealed 766 new proteins and an updated sperm proteome containing a total of 1108 proteins, termed the DmSP-II. This expanded dataset includes additional proteins with predicted sperm functions and confirms previous findings concerning the genomic organization of sperm loci. Bioinformatic and protein network analyses demonstrated high quality and reproducibility of proteome coverage across three replicate mass spectrometry runs. The use of whole-cell proteomics in conjunction with characterized phenotypes, functional annotations and pathway information has advanced our systems level understanding of sperm proteome functional networks.     

Applications of cell-based phage display panning to proteomic analysis

In the post-genomic era, proteomics together with genomic tools have led to powerful new strategies in basic and clinical research. These combined “omics” technologies are being integrated into the drug target discovery process. Unlike the genome, the proteome is a highly dynamic entity that requires techniques capable of analyzing on selected populations of proteins in specific biological conditions that reflect the proteins’ functional characteristics. Antibodies have become one of the most important reagents for the analysis of selected populations of proteins, and the application of phage-display antibody libraries to high-throughput antibody generation against large numbers of various antigens provides a tool for proteome-wide protein expression analysis. In this review, we will discuss the utility of phage-display antibodies in proteomics applications, specifically for the discovery of novel disease markers and therapeutic targets.     

Proteome analysis of Epstein-Barr virus-transformed B-lymphoblasts and the proteome database

The proteome is the entire protein complement of the genome expressed in a particular cell, tissue, or organism at a given time under a specific set of environmental conditions. Proteomics is a combinatorial methodology to comprehensively analyze the proteome. The general protocol of the expression proteomics consists of advanced methods of high-resolution protein separation, high-quality image analysis and high-throughput protein identification. Although Epstein-Barr virus-transformed B-lymphoblastoid cell lines (LCLs) have long been believed to be immortalized, recent studies have provided ample evidence that a large proportion of LCLs have limited life spans due to shortening of telomeres, and that part of them are truly immortalized by developing strong telomerase activity to maintain telomeres. Differential proteome analysis of pre- and post-immortal LCLs would provide a powerful tool to analyze proteins participating in the process of immortalization. We focus in this review on cumulative data of proteomic information on pre- and post-immortal LCLs.     

Proteomics: quantitative and physical mapping of cellular proteins

Genome sequencing provides a wealth of information on predicted gene products (mostly proteins), but the majority of these have no known function. Two-dimensional gel electrophoresis and mass spectrometry have, coupled with searches in protein and EST databases, transformed the protein-identification process. The proteome is the expressed protein complement of a genome and proteomics is functional genomics at the protein level. Proteomics can be divided into expression proteomics, the study of global changes in protein expression, and cell-map proteomics, the systematic study of protein-protein interactions through the isolation of protein complexes.     

Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry

A crucial aim upon the completion of the human genome is the verification and functional annotation of all predicted genes and their protein products. Here we describe the mapping of peptides derived from accurate interpretations of protein tandem mass spectrometry (MS) data to eukaryotic genomes and the generation of an expandable resource for integration of data from many diverse proteomics experiments. Furthermore, we demonstrate that peptide identifications obtained from high-throughput proteomics can be integrated on a large scale with the human genome. This resource could serve as an expandable repository for MS-derived proteome information.     

The state of proteome profiling in the fungal genus Aspergillus.

Aspergilli are an important genus of filamentous fungi that contribute to a multibillion dollar industry. Since many fungal genome sequencing were recently completed, it would be advantageous to profile their proteome to better understand the fungal cell factory. Here, we review proteomic data generated for the Aspergilli in recent years. Thus far, a combined total of 28 cell surface, 102 secreted and 139 intracellular proteins have been identified based on 10 different studies on Aspergillus proteomics. A summary proteome map highlighting identified proteins in major metabolic pathway is presented.     

A high-quality catalog of the Drosophila melanogaster proteome

Understanding how proteins and their complex interaction networks convert the genomic information into a dynamic living organism is a fundamental challenge in biological sciences. As an important step towards understanding the systems biology of a complex eukaryote, we cataloged 63% of the predicted Drosophila melanogaster proteome by detecting 9,124 proteins from 498,000 redundant and 72,281 distinct peptide identifications. This unprecedented high proteome coverage for a complex eukaryote was achieved by combining sample diversity, multidimensional biochemical fractionation and analysis-driven experimentation feedback loops, whereby data collection is guided by statistical analysis of prior data. We show that high-quality proteomics data provide crucial information to amend genome annotation and to confirm many predicted gene models. We also present experimentally identified proteotypic peptides matching approximately 50% of D. melanogaster gene models. This library of proteotypic peptides should enable fast, targeted and quantitative proteomic studies to elucidate the systems biology of this model organism.     

Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.     

Differential detergent fractionation for non-electrophoretic eukaryote cell proteomics

Differential detergent fractionation (DDF), which relies on detergents to sequentially extract proteins from eukaryotic cells, has been used to increase proteome coverage of 2D-PAGE. Here, we used DDF extraction in conjunction with the nonelectrophoretic proteomics method of liquid chromatography and electrospray ionization tandem mass spectrometry. We demonstrate that DDF can be used with 2D-LC ESI MS2 for comprehensive cellular proteomics, including a large proportion of membrane proteins. Compared to some published methods designed to isolate membrane proteins specifically, DDF extraction yields comprehensive proteomes which include twice as many membrane proteins. Two-thirds of these membrane proteins have more than one trans-membrane domain. Since DDF separates proteins based upon their physicochemistry and subcellular localization, this method also provides data useful for functional genome annotation. As more genome sequences are completed, methods which can aid in functional annotation will become increasingly important.     

Protein-protein interaction networks: from interactions to networks

The goal of interaction proteomics that studies the protein-protein interactions of all expressed proteins is to understand biological processes that are strictly regulated by these interactions. The availability of entire genome sequences of many organisms and high-throughput analysis tools has led scientists to study the entire proteome (Pandey and Mann, 2000). There are various high-throughput methods for detecting protein interactions such as yeast two-hybrid approach and mass spectrometry to produce vast amounts of data that can be utilized to decipher protein functions in complicated biological networks. In this review, we discuss recent developments in analytical methods for large-scale protein interactions and the future direction of interaction proteomics.