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C. elegans: an invaluable model organism for the proteomics studies of the cholesterol-mediated signaling pathway.

With the availability of its complete genome sequence and unique biological features relevant to human disease, Caenorhabditis elegans has become an invaluable model organism for the studies of proteomics, leading to the elucidation of nematode gene function. A journey from the genome to proteome of C. elegans may begin with preparation of expressed proteins, which enables a large-scale analysis of all possible proteins expressed under specific physiological conditions. Although various techniques have been used for proteomic analysis of C. elegans, systematic high-throughput analysis is still to come in order to accommodate studies of post-translational modification and quantitative analysis. Given that no integrated C. elegans protein expression database is available, it is about time that a global C. elegans proteome project is launched through which datasets of transcriptomes, protein-protein interaction and functional annotation can be integrated. As an initial target of a pilot project of the C. elegans proteome project, the cholesterol-mediated signaling pathway will be an excellent example since, like in other organisms, it is one of the key controlling pathways in cell growth and development in C. elegans. As this field tends to broaden to functional proteomics, there is a high demand to develop the versatile proteome informatics tools that can mange many different data in an integrative manner.

Animals↗

Proteome response of Escherichia coli fed-batch culture to temperature downshift.

During fed-batch cultivation of Escherichia coli K-12, the proteomic response to a temperature downshift from 37 to 20 degrees C was quantitatively monitored and analyzed by using two-dimensional electrophoresis. When the temperature of exponentially growing E. coli K-12 culture was downshifted to 20 degrees C, the synthesis level of 57 intracellular proteins showed significant changes for a prolonged period of time, compared to the fed-batch culture controlled at 37 degrees C. Thus, these proteins are regarded as important stress proteins responsive to cold shock, which were analyzed by using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and identified using the E. coli SWISS-2DPAGE database. Most of the identified proteins were shown to be involved in energy metabolism, several cellular molecule biosynthetic pathways and catabolism, cell processes, flagellar biosynthesis and motility, and protein translation and folding. The systematic approach to the monitoring of proteomic responses and the detailed analysis results reported in this article would be useful in understanding the metabolic adaptation to lowered culture temperature and designing efficient fermentation strategies for the production of recombinant proteins and metabolites using E. coli strains.

Cold Temperature↗

Strategies for the physiome project.

The physiome is the quantitative description of the functioning organism in normal and pathophysiological states. The human physiome can be regarded as the virtual human. It is built upon the morphome, the quantitative description of anatomical structure, chemical and biochemical composition, and material properties of an intact organism, including its genome, proteome, cell, tissue, and organ structures up to those of the whole intact being. The Physiome Project is a multicentric integrated program to design, develop, implement, test and document, archive and disseminate quantitative information, and integrative models of the functional behavior of molecules, organelles, cells, tissues, organs, and intact organisms from bacteria to man. A fundamental and major feature of the project is the databasing of experimental observations for retrieval and evaluation. Technologies allowing many groups to work together are being rapidly developed. Internet II will facilitate this immensely. When problems are huge and complex, a particular working group can be expert in only a small part of the overall project. The strategies to be worked out must therefore include how to pull models composed of many submodules together even when the expertise in each is scattered amongst diverse institutions. The technologies of bioinformatics will contribute greatly to this effort. Developing and implementing code for large-scale systems has many problems. Most of the submodules are complex, requiring consideration of spatial and temporal events and processes. Submodules have to be linked to one another in a way that preserves mass balance and gives an accurate representation of variables in nonlinear complex biochemical networks with many signaling and controlling pathways. Microcompartmentalization vitiates the use of simplified model structures. The stiffness of the systems of equations is computationally costly. Faster computation is needed when using models as thinking tools and for iterative data analysis. Perhaps the most serious problem is the current lack of definitive information on kinetics and dynamics of systems, due in part to the almost total lack of databased observations, but also because, though we are nearly drowning in new information being published each day, either the information required for the modeling cannot be found or has never been obtained. "Simple" things like tissue composition, material properties, and mechanical behavior of cells and tissues are not generally available. The development of comprehensive models of biological systems is a key to pharmaceutics and drug design, for the models will become gradually better predictors of the results of interventions, both genomic and pharmaceutic. Good models will be useful in predicting the side effects and long term effects of drugs and toxins, and when the models are really good, to predict where genomic intervention will be effective and where the multiple redundancies in our biological systems will render a proposed intervention useless. The Physiome Project will provide the integrating scientific basis for the Genes to Health initiative, and make physiological genomics a reality applicable to whole organisms, from bacteria to man.

Computer Simulation↗

18O labeling: a tool for proteomics.

An evaluation of the proteolytic labeling and quantification of proteins for diagnostic purposes using trypsin and 18O-enriched H2O is presented. We demonstrate that comparative or relative quantitation can be performed effectively with this approach. We have developed a protocol that allows the conservation of the labeled peptides in natural abundance water without fear of back-exchange providing that pH is sufficiently low to quench the catalytic activity of trypsin, but not so low as to promote chemical back-exchange. Because the labeling efficiency depends on the nature of the peptide, a simple linear relationship between the relative 16O/18O digest buffer mixture content (x) and labeling efficiency (y) does not exist; rather it follows a probability based y = x(2) relationship. As such, the extent of peptide labeling using 16O/18O digest buffer mixture ratios may deviate significantly from that expected based on a linear relationship. The evaluation of the relative Ziptip efficiency indicated a loss in sample recovery as the peptide concentration was reduced using normal conditions, suggesting that there is a limit below which there are diminishing returns. In addition, the adsorptive losses due to Speedvac dry down and recovery indicated modest (20%) losses that may vary widely (0-50%) from peptide to peptide. The in-solution digestion efficiency of standard protein mixtures as a function of concentration revealed a linear decrease with decreasing concentration. This is consistent with enzyme kinetic effects and emphasizes a potential quantitation error that could arise when evaluating differential expression based on peptide detection. The results from our studies demonstrate the power of 18O labeling as an optimization tool for proteomics process development.

Hydrogen-Ion Concentration↗

Proteome analysis of human metaphase chromosomes.

DNA is packaged as chromatin in the interphase nucleus. During mitosis, chromatin fibers are highly condensed to form metaphase chromosomes, which ensure equal segregation of replicated chromosomal DNA into the daughter cells. Despite >1 century of research on metaphase chromosomes, information regarding the higher order structure of metaphase chromosomes is limited, and it is still not clear which proteins are involved in further folding of the chromatin fiber into metaphase chromosomes. To obtain a global view of the chromosomal proteins, we performed proteome analyses on three types of isolated human metaphase chromosomes. We first show the results from comparative proteome analyses of two types of isolated human metaphase chromosomes that have been frequently used in biochemical and morphological analyses. 209 proteins were quantitatively identified and classified into six groups on the basis of their known interphase localization. Furthermore, a list of 107 proteins was obtained from the proteome analyses of highly purified metaphase chromosomes, the majority of which are essential for chromosome structure and function. Based on the information obtained on these proteins and on their localizations during mitosis as assessed by immunostaining, we present a four-layer model of metaphase chromosomes. According to this model, the chromosomal proteins have been newly classified into each of four groups: chromosome coating proteins, chromosome peripheral proteins, chromosome structural proteins, and chromosome fibrous proteins. This analysis represents the first compositional view of human metaphase chromosomes and provides a protein framework for future research on this topic.

Chromatin↗

Laser-assisted microdissection: applications in molecular pathology.

Tissue microdissection is potentially one of the most useful techniques in molecular pathology. Laser-assisted microdissection has been developed to procure precisely the cells of interest in a tissue specimen, in a rapid and practical manner. Together with multiplex molecular approaches, it is now feasible to study genetic alterations and isolate genes and proteins in defined cell populations from complex normal and diseased tissues. The fundamental advantage of this technique is the possibility of capturing single cells from which high-quality DNA and mRNA can be isolated for analysis of sequence and quantitation of expression. Moreover, the integration of laser-assisted microdissection and proteomic analysis could identify novel protein markers for disease. The advent of laser-assisted microdissection is likely to have a profound impact on molecular pathology.

Cell Separation↗

Genomics to fluxomics and physiomics - pathway engineering.

Developments in microanalytical methods are enabling quantitative measurement of multiple metabolic fluxes and, in conjunction with transcript and proteomic profiling, are revolutionizing the ability of researchers to manipulate metabolism through pathway engineering in a variety of species. We review recent literature on the advances in genomics, proteomics, fluxomics and computational modeling focused on metabolic pathway engineering applications.

Bacteria↗

The CyberCell Database (CCDB): a comprehensive, self-updating, relational database to coordinate and facilitate in silico modeling of Escherichia coli.

The CyberCell Database (CCDB: http://redpoll. pharmacy.ualberta.ca/CCDB) is a comprehensive, web-accessible database designed to support and coordinate international efforts in modeling an Escherichia coli cell on a computer. The CCDB brings together both observed and derived quantitative data from numerous independent sources covering many aspects of the genomic, proteomic and metabolomic character of E.coli (strain K12). The database is self-updating but also supports 'community' annotation, and provides an extensive array of viewing, querying and search options including a powerful, easy-to-use relational data extraction system.

Computational Biology↗

Unveiling microbial risks in Chinese household dust: a comprehensive analysis from absolute abundance to virulence unit.

BACKGROUND: People spend the majority of their lives indoors, yet the risk and virulence potential of household microbiota remain largely unexplored, particularly in developing countries. RESULTS: Here, we conducted a nationwide survey on both dust samples and health information across 118 Chinese households. The microbiota composition and its functional units were analyzed using absolute 16S rRNA/ITS sequencing, metagenomics, and metaproteomics. Cross-domain network analysis of the core microbial communities revealed robust co-occurrence patterns in household dust. The mean absolute abundance of potentially pathogenic bacteria and fungi in households was 2.39 × 105 and 2.83 × 106 DNA copies/g dust. The potentially pathogenic community was primarily influenced by latitude, relative humidity, and average temperature. Although total absolute abundance was substantially lower in urban areas, the relative abundance of potentially pathogenic bacteria was markedly higher compared to rural environments. While urban-rural differences existed, the underlying statistical drivers were the environmental variables. The absolute abundance of potential pathogens was significantly associated with the prevalence of rhinitis, wheeze, and dermatitis in 266 participants. Children were identified as the highest-risk group from inhalation exposure of average daily dose. A total of 170 bacterial, 223 fungal virulence factors (VFs), and 370 antibiotic resistance genes (ARGs) were detected in dust and dust extracellular vesicle (EV)-associated DNA. EV-associated cargoes contributed 47.13% to the bacterial VF profiles, 11.90% to fungal VF profiles, and 44.45% to ARG profiles. Metaproteomic analysis confirmed the presence of VF profiles in dust EVs, which was further verified by curated proteomics data from 35 household pathogens. CONCLUSIONS: This study provides a comprehensive, quantitative framework linking indoor microbial exposure to health risks, highlighting EVs as a non-negligible, novel, extracellular mechanistic pathway for health impact in household environments. Video Abstract.

Child↗

Proteomic characterization of the human centrosome by protein correlation profiling.

The centrosome is the major microtubule-organizing centre of animal cells and through its influence on the cytoskeleton is involved in cell shape, polarity and motility. It also has a crucial function in cell division because it determines the poles of the mitotic spindle that segregates duplicated chromosomes between dividing cells. Despite the importance of this organelle to cell biology and more than 100 years of study, many aspects of its function remain enigmatic and its structure and composition are still largely unknown. We performed a mass-spectrometry-based proteomic analysis of human centrosomes in the interphase of the cell cycle by quantitatively profiling hundreds of proteins across several centrifugation fractions. True centrosomal proteins were revealed by both correlation with already known centrosomal proteins and in vivo localization. We identified and validated 23 novel components and identified 41 likely candidates as well as the vast majority of the known centrosomal proteins in a large background of nonspecific proteins. Protein correlation profiling permits the analysis of any multiprotein complex that can be enriched by fractionation but not purified to homogeneity.

Amino Acid Sequence↗

Quantitation with virtual expert mass spectrometrist.

Quantitative analysis of proteins and peptides by mass spectrometry has been greatly advanced by the development of proteomic technologies within recent years. Particularly, labeling of peptides and proteins with stable isotopes such as 2H, 15N, and 13C facilitated the unbiased comparison of protein amounts in distinct samples in a single mass spectrometric experiment. These methods can be applied to detect quantitative changes in protein amounts and posttranslational modifications such as phosphorylation. Quantitation of mass spectra requires accurate and efficient bioinformatics tools, which can match corresponding peptides, determine peak intensities, and calculate relative amounts. In this chapter, we describe the use of virtual expert mass spectrometrist for the quantitation of mass spectra from samples with peptides and proteins encoded with stable isotopes.

Computational Biology↗

High-throughput antibody isolation.

To define the proteome of an organism, there is a need for robust and reproducible methods for the quantitative detection of all the polypeptides in a cell. High-density arrays of receptors specific for each of the polypeptides in a complex sample hold great promise for the analysis of complex protein mixtures. Because of their high affinity, specificity and their ability to bind to virtually any protein, antibodies appear particularly promising as the receptor element in protein-detection arrays. For proteomic-scale analyses, the ability to isolate and produce antibodies en masse to large numbers of target molecules is critical. A variety of systems for the high-throughput isolation of antibodies from combinatorial libraries are being developed and are outlined in this review. However, there are several other important considerations to be borne in mind before such systems can realistically be applied on a large scale.

Antibodies↗

Bioinformatic methods to exploit mass spectrometric data for proteomic applications.

The new technologies in mass spectrometric analysis of peptides and proteins necessary to accommodate proteomics-scale analyses require, in turn, concomitant development of informatics technologies suitable for very large-scale data handling and analysis. This chapter focuses on the data analysis tools available to the community for analysis of mass spectrometric proteomics data. Different database searching strategies are discussed for peptide and protein identification, and approaches and tools available for comparative quantitative analysis of samples are outlined.

Animals↗

Contribution of mass spectrometry-based proteomics to immunology.

Antigen processing forwards various information about the cellular status and the proteome to the cell surface for scrutiny by the cellular immune system. Thus the repertoire of major histocompatibility complex (MHC)-bound peptides and the MHC ligandome, indirectly mirrors the proteome in order to make alterations instantly detectable and, if necessary, to oppose them. Mass spectrometry is the core technology for analysis of both proteome and MHC ligandome and has evoked several strategies to gain qualitative and quantitative insight into the MHC-presented peptide repertoire. After immunoaffinity purification of detergent-solubilized peptide-MHC complexes followed by acid elution of peptides, liquid chromatography-mass spectrometry is applied to determine individual peptide sequences and, thus, allow qualitative characterization of the MHC-bound repertoire. Differential quantification based on stable isotope labeling enables the relative comparison of two samples, such as diseased and healthy tissue. Targeted searches for certain natural ligands, such as the 'predict-calibrate-detect' strategy, include motif-based epitope prediction and calibration with reference peptides. Thus, various approaches are now available for exposing and understanding the intricacies of the MHC ligand repertoire. Analysis of differences in the MHC ligandome under distinct conditions contributes to our understanding of basic cellular processes, but also enables the formulation of immunodiagnostic or immunotherapeutic strategies.

Animals↗

Assessing reproducibility of a protein dynamics study using in vivo labeling and liquid chromatography tandem mass spectrometry.

Measuring dynamics of proteins abundance in cells in response to stimuli such as growth factors or drugs requires analysis of more than one time point. Proteomic approaches have traditionally been used to measure only one state at a time because quantitation is difficult, especially when mass spectrometry is used as a readout. Isotopically labeled reagents have recently been introduced that allow comparison of two or three different states by mass spectrometry. Here, we evaluate the reproducibility of an experiment that measures three states simultaneously through stable isotope labeling of cells with amino acids in cell culture (SILAC) using light, medium, and heavy versions of amino acids. The major goal of this study was to assess the reproducibility of such experiments in combination with liquid chromatography tandem mass spectrometry (LC-MS/MS). Our results show that it is possible to obtain reproducible quantitative data to study protein dynamics based on our analysis of more than 220 peptide sets derived from 20 proteins from 3 different LC-MS/MS runs.

Arginine↗

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis, and the application of statistical tools for data analysis and interpretation.

Proteomic approaches to biological research that will prove the most useful and productive require robust, sensitive, and reproducible technologies for both the qualitative and quantitative analysis of complex protein mixtures. Here we applied the isotope-coded affinity tag (ICAT) approach to quantitative protein profiling, in this case proteins that copurified with lipid raft plasma membrane domains isolated from control and stimulated Jurkat human T cells. With the ICAT approach, cysteine residues of the two related protein isolates were covalently labeled with isotopically normal and heavy versions of the same reagent, respectively. Following proteolytic cleavage of combined labeled proteins, peptides were fractionated by multidimensional chromatography and subsequently analyzed via automated tandem mass spectrometry. Individual tandem mass spectrometry spectra were searched against a human sequence database, and a variety of recently developed, publicly available software applications were used to sort, filter, analyze, and compare the results of two repetitions of the same experiment. In particular, robust statistical modeling algorithms were used to assign measures of confidence to both peptide sequences and the proteins from which they were likely derived, identified via the database searches. We show that by applying such statistical tools to the identification of T cell lipid raft-associated proteins, we were able to estimate the accuracy of peptide and protein identifications made. These tools also allow for determination of the false positive rate as a function of user-defined data filtering parameters, thus giving the user significant control over and information about the final output of large-scale proteomic experiments. With the ability to assign probabilities to all identifications, the need for manual verification of results is substantially reduced, thus making the rapid evaluation of large proteomic datasets possible. Finally, by repeating the experiment, information relating to the general reproducibility and validity of this approach to large-scale proteomic analyses was also obtained.

Amino Acid Sequence↗

Ultra-high sensitivity multi-photon detection imaging in proteomics analyses.

We report on the use of 125I and 131I labeling and of new, multicolor, multi-photon detection (MPD) methods to routinely and quantitatively detect protein spots on two-dimensional gel electrophoresis plates in the zeptomole to attomole range. We demonstrate that the MPD methodology can be used to detect radioactive labels on two-dimensional gels and has several characteristics that are advantageous for functional proteomics. First, by using single particle detectors, the sensitivity for detection of radiolabels can be improved dramatically. Second, because single particle detectors can differentiate the particle energies produced by different decay processes, it is possible to choose combinations of radioisotopes that can be detected and quantified individually on the same 2-D gel. Third, the MPD technology is essentially linear over six to seven orders of magnitude, i.e., it is possible to accurately quantify radiolabeled proteins over a range from at least 60 zeptomoles to 60 femtomoles. Finally for radionuclides that decay by electron capture, e.g., with emission of both beta and gamma rays, co-incident detection of two particles/photons can be used to detect such radionuclides well below background radiation levels. These methods are used to monitor acidic/phosphorylated proteins in as little as 60 ng of HeLa cells proteins.

Color↗

The role of mass spectrometry in biomarker discovery and measurement.

Recent advances in the biological and analytical sciences have led to unprecedented interest in the discovery and quantitation of endogenous molecules that serve as indicators of drug safety, mechanism of action, efficacy, and disease state progression. By allowing for improved decision-making, these indicators, referred to as biomarkers, can dramatically improve the efficiency of drug discovery and development. Mass spectrometry has been a key part of biomarker discovery and evaluation owing to several important attributes, which include sensitive and selective detection, multi-analyte analysis, and the ability to provide structural information. Because of these capabilities, mass spectrometry has been widely deployed in search for new markers both through the analysis of large molecules (proteomics) and small molecules (metabonomics). In addition, mass spectrometry is increasingly being used to support quantitative measurement to assist in the evaluation and validation of biomarker leads. In this review, the dual role of mass spectrometry for biomarker discovery and measurement is explored for both large and small molecules by examining the key technologies and methods used along the continuum from drug discovery through clinical development.

Amino Acid Sequence↗