Host-pathogen interactions: a proteomic view

Host-pathogen interactions reflect the balance of host defenses and pathogen virulence mechanisms. Advances in proteomic technologies now afford opportunities to compare protein content between complex biologic systems ranging from cells to animals and clinical samples. Thus, it is now possible to characterize host-pathogen interactions from a global proteomic view. Most reports to date focus on cataloging protein content of pathogens and identifying virulence-associated proteins or proteomic alterations in host response. A more in-depth understanding of host-pathogen interactions has the potential to improve our mechanistic understanding of pathogenicity and virulence, thereby defining novel therapeutic and vaccine targets. In addition, proteomic characterization of the host response can provide pathogen-specific host biomarkers for rapid pathogen detection and characterization, as well as for early and specific detection of infectious diseases. A review of host-pathogen interactions focusing on proteomic analyses of both pathogen and host will be presented. Relevant genomic studies and host model systems will be also be discussed.     

Host–pathogen interactions: a proteomic view

Host–pathogen interactions reflect the balance of host defenses and pathogen virulence mechanisms. Advances in proteomic technologies now afford opportunities to compare protein content between complex biologic systems ranging from cells to animals and clinical samples. Thus, it is now possible to characterize host–pathogen interactions from a global proteomic view. Most reports to date focus on cataloging protein content of pathogens and identifying virulence-associated proteins or proteomic alterations in host response. A more in-depth understanding of host–pathogen interactions has the potential to improve our mechanistic understanding of pathogenicity and virulence, thereby defining novel therapeutic and vaccine targets. In addition, proteomic characterization of the host response can provide pathogen-specific host biomarkers for rapid pathogen detection and characterization, as well as for early and specific detection of infectious diseases. A review of host–pathogen interactions focusing on proteomic analyses of both pathogen and host will be presented. Relevant genomic studies and host model systems will be also be discussed.     

Quantitative proteomic analysis of the budding yeast cell cycle using acid-cleavable isotope-coded affinity tag reagents

Quantitative profiling of proteins, the direct effectors of nearly all biological functions, will undoubtedly complement technologies for the measurement of mRNA. Systematic proteomic measurement of the cell cycle is now possible by using stable isotopic labeling with isotope-coded affinity tag reagents and software tools for high-throughput analysis of LC-MS/MS data. We provide here the first such study achieving quantitative, global proteomic measurement of a time-course gene expression experiment in a model eukaryote, the budding yeast Saccharomyces cerevisiae, during the cell cycle. We sampled 48% of all predicted ORFs, and provide the data, including identifications, quantitations, and statistical measures of certainty, to the community in a sortable matrix. We do not detect significant concordance in the dynamics of the system over the time-course tested between our proteomic measurements and microarray measures collected from similarly treated yeast cultures. Our proteomic dataset therefore provides a necessary and complementary measure of eukaryotic gene expression, establishes a rich database for the functional analysis of S. cerevisiae proteins, and will enable further development of technologies for global proteomic analysis of higher eukaryotes.     

MCAM: multiple clustering analysis methodology for deriving hypotheses and insights from high-throughput proteomic datasets

Advances in proteomic technologies continue to substantially accelerate capability for generating experimental data on protein levels, states, and activities in biological samples. For example, studies on receptor tyrosine kinase signaling networks can now capture the phosphorylation state of hundreds to thousands of proteins across multiple conditions. However, little is known about the function of many of these protein modifications, or the enzymes responsible for modifying them. To address this challenge, we have developed an approach that enhances the power of clustering techniques to infer functional and regulatory meaning of protein states in cell signaling networks. We have created a new computational framework for applying clustering to biological data in order to overcome the typical dependence on specific a priori assumptions and expert knowledge concerning the technical aspects of clustering. Multiple clustering analysis methodology ('MCAM') employs an array of diverse data transformations, distance metrics, set sizes, and clustering algorithms, in a combinatorial fashion, to create a suite of clustering sets. These sets are then evaluated based on their ability to produce biological insights through statistical enrichment of metadata relating to knowledge concerning protein functions, kinase substrates, and sequence motifs. We applied MCAM to a set of dynamic phosphorylation measurements of the ERRB network to explore the relationships between algorithmic parameters and the biological meaning that could be inferred and report on interesting biological predictions. Further, we applied MCAM to multiple phosphoproteomic datasets for the ERBB network, which allowed us to compare independent and incomplete overlapping measurements of phosphorylation sites in the network. We report specific and global differences of the ERBB network stimulated with different ligands and with changes in HER2 expression. Overall, we offer MCAM as a broadly-applicable approach for analysis of proteomic data which may help increase the current understanding of molecular networks in a variety of biological problems.     

From protein-protein interactions to protein co-expression networks: a new perspective to evaluate large-scale proteomic data

The reductionist approach of dissecting biological systems into their constituents has been successful in the first stage of the molecular biology to elucidate the chemical basis of several biological processes. This knowledge helped biologists to understand the complexity of the biological systems evidencing that most biological functions do not arise from individual molecules; thus, realizing that the emergent properties of the biological systems cannot be explained or be predicted by investigating individual molecules without taking into consideration their relations. Thanks to the improvement of the current -omics technologies and the increasing understanding of the molecular relationships, even more studies are evaluating the biological systems through approaches based on graph theory. Genomic and proteomic data are often combined with protein-protein interaction (PPI) networks whose structure is routinely analyzed by algorithms and tools to characterize hubs/bottlenecks and topological, functional, and disease modules. On the other hand, co-expression networks represent a complementary procedure that give the opportunity to evaluate at system level including organisms that lack information on PPIs. Based on these premises, we introduce the reader to the PPI and to the co-expression networks, including aspects of reconstruction and analysis. In particular, the new idea to evaluate large-scale proteomic data by means of co-expression networks will be discussed presenting some examples of application. Their use to infer biological knowledge will be shown, and a special attention will be devoted to the topological and module analysis.     

A biological approach to computational models of proteomic networks

Computational modeling is useful as a means to assemble and test what we know about proteins and networks. Models can help address key questions about the measurement, definition and function of proteomic networks. Here, we place these biological questions at the forefront in reviewing the computational strategies that are available to analyze proteomic networks. Recent examples illustrate how models can extract more information from proteomic data, test possible interactions between network proteins and link networks to cellular behavior. No single model can achieve all these goals, however, which is why it is critical to prioritize biological questions before specifying a particular modeling approach.     

Comparative proteomic analysis provides new insights into the fiber elongating process in cotton

A comparative proteomic analysis was performed to explore the mechanism of cell elongation in developing cotton fibers. The temporal changes of global proteomes at five representative development stages (5-25 days post-anthesis [dpa]) were examined using 2-D electrophoresis. Among approximately 1800 stained protein spots reproducibly detected on each gel, 235 spots were differentially expressed with significant dynamics in elongating fibers. Of these, 120 spots showed a more than 2-fold change in at least one stage point, and 21 spots appeared to be specific to developmental stages. Furthermore, 106 differentially expressed proteins were identified from mass spectrometry to match 66 unique protein species. These proteins involve different cellular and metabolic processes with obvious functional tendencies toward energy/carbohydrate metabolism, protein turnover, cytoskeleton dynamics, cellular responses and redox homeostasis, indicating a good correlation between development-dependent proteins and fiber biochemical processes, as well as morphogenesis. Newly identified proteins such as phospholipase D alpha, vf14-3-3 protein, small ras-related protein, and GDP dissociation inhibitor will advance our knowledge of the complicated regulatory network. Identification of these proteins, combined with their changes in abundance, provides a global view of the development-dependent protein changes in cotton fibers, and offers a framework for further functional research of target proteins associated with fiber development.     

Rapid proteomic remodeling of cardiac tissue caused by total body ionizing radiation

Accidental nuclear scenarios lead to environmental contamination of unknown level. Immediate radiation‐induced biological responses that trigger processes leading to adverse health effects decades later are not well understood. A comprehensive proteomic analysis provides a promising means to identify and quantify the initial damage after radiation exposure. Early changes in the cardiac tissue of C57BL/6 mice exposed to total body irradiation were studied, using a dose relevant to both intentional and accidental exposure (3 Gy gamma ray). Heart tissue protein lysates were analyzed 5 and 24 h after the exposure using isotope‐coded protein labeling (ICPL) and 2‐dimensional difference‐in‐gel‐electrophoresis (2‐D DIGE) proteomics approaches. The differentially expressed proteins were identified by LC‐ESI‐MS‐MS. Both techniques showed similar functional groups of proteins to be involved in the initial injury. Pathway analyses indicated that total body irradiation immediately induced biological responses such as inflammation, antioxidative defense, and reorganization of structural proteins. Mitochondrial proteins represented the protein class most sensitive to ionizing radiation. The proteins involved in the initial damage processes map to several functional categories involving cardiotoxicity. This prompts us to propose that these early changes are indicative of the processes that lead to an increased risk of cardiovascular disease after radiation exposure.     

Analytical and biological variances associated with proteomic studies of Medicago truncatula by two-dimensional polyacrylamide gel electrophoresis

Two-dimensional polyacrylamide gel electrophoresis (2-DE) and mass spectrometry are being used as proteomic tools in an integrated functional genomics program focused on the model legume Medicago truncatula. Due to the perceived high levels of indeterminate error associated with 2-DE we deemed it necessary to quantify the coefficient of variance (or relative standard deviation) for both analytical and biological sources associated with 2-DE of Medicago truncatula leaf protein extracts. Leaf protein extracts were chosen because of their biological significance and due to the more challenging nature of green tissues. Analytical variance was calculated for fifty proteins from ten replicate 2-DE gels of the same protein extract. Biological variance was calculated for the same fifty proteins from ten independent 2-DE gel analyses of ten independent but similar plants grown under identical conditions. Average analytical and biological variances were calculated for both data sets and represent the average variance of approximately 500 independent measurements of protein concentration. Analytical variance was determined to be 16.2% and biological variance was determined to be 24.2%. These average variances provide a quantified and statistical basis for evaluation of protein expression changes in future comparative proteomic investigations. It is proposed that 2-DE measured protein expression levels should differ by a minimum of 3.92sigma (i.e. /+/-2sigma/ and sigma = standard deviation), or 94.7% based on our measured variances, for the difference to be significant at the 95% confidence level.     

Affinity separation and enrichment methods in proteomic analysis

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.     

The gain in brain: novel imaging techniques and multiplexed proteomic imaging of brain tissue ultrastructure

The rapid accumulation of neuroproteomics data in recent years has prompted the emergence of novel antibody-based imaging methods that aim to understand the anatomical and functional context of the multitude of identified proteins. The pioneering field of ultrastructural multiplexed proteomic imaging now includes a number of high resolution methods, such as array tomography, stimulated emission depletion microscopy, stochastic optical reconstruction microscopy and automated transmission electron microscopy, which allow a detailed molecular characterization of individual synapses and subsynaptic structures within brain tissues for the first time. While all of these methods still face considerable limitations, a combined complementary approach building on the respective strengths of each method is possible and will enable fascinating research into the proteomic diversity of the nervous system.     

Proteomic maps of the cancer-associated infectious agents

The number of infectious agents associated with cancer is increasing. There is a need to develop approaches for the early detection of the infected host which might lead to tumor development. Recent advances in proteomic approaches provide that opportunity, and it is now possible to generate proteomic maps of cancer-associated infectious agents. Protein arrays, interaction maps, data archives, and biological assays are being developed to enable efficient and reliable protein identification and functional analysis. Herein, we discuss the current technologies and challenges in the field, and application of protein signatures in cancer detection and prevention.     

The emerging role of ICP-MS in proteomic analysis

Quantitative proteomics and absolute determination of proteins are topics of fast growing interest, since only the quantity of proteins or changes in their abundance reflect the status and extent of changes of a given biological system. Quantification of the desired proteins has been carried out by molecule specific MS techniques, but relative quantifications are commonplace so far even resorting to stable isotope labelling techniques such as ICAT and SILAC. In the last decade the idea of using element-selective mass spectrometric detection (e.g. ICP-MS instruments) to achieve absolute quantification has been realised and ICP-MS stands now as a new tool in the field of quantitative proteomics.In this review the emerging role of ICP-MS in protein and proteomic analysis is highlighted. The potential of ICP-MS methods and strategies for screening multiple heteroatoms (e.g. S, P, Se, metals) in proteins and their mixtures and extraordinary capabilities to tackle the problem of absolute protein quantifications, via heteroatom determinations, are discussed and illustrated. New avenues are also open derived from the use of ICP-MS for precise isotope abundance measurements in polyisotopic heteroatoms. The “heteroatom (isotope)-tagged proteomics” concept is focused on the use of naturally present element tags and also extended to any protein by resorting to bioconjugation reactions (i.e. labelling sought proteins and peptides with ICP-MS detectable heteroatoms). A major point of this review is displaying the possibilities of using a “hard” ion source, the ICP, to complement well-established “soft” ion sources for mass spectrometry to tackle present proteomic analysis.     

Advanced proteomic technologies for cancer biomarker discovery

Proteomic technologies have experienced major improvements in recent years. Such advances have facilitated the discovery of potential tumor markers with improved sensitivities and specificities for the diagnosis, prognosis and treatment monitoring of cancer patients. This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about and examples of their applications in cancer biomarker discovery will be presented in this review, so that readers can appreciate the immense progress in proteomic technologies from 1997 to 2008. Finally, a summary will be presented that discusses current hurdles faced by proteomic researchers, such as the wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems will be provided.