Charting protein complexes, signaling pathways, and networks in the immune system

A comprehensive strategy to clarify the pharmacodynamic constituents and mechanism of Wu-tou decoction based on the constituents migrating to blood and their in vivo process under pathological state

ETHNOPHARMACOLOGICAL RELEVANCE

As a traditional Chinese medicine (TCM) formula, Wu-tou decoction has been used for treating rheumatoid arthritis (RA) for more than a thousand years. Identifying pharmacodynamic constituents (PCs) of WTD and exploring their in vivo process are very meaningful for promoting the modernization of TCM. However, the pathological state might change this process.

AIM OF THE STUDY

Hence, it is necessary and significant to compare the process in vivo of drugs both in normal and disease state and clarify their action mechanism.

MATERIALS AND METHODS

Taking Wu-tou decoction (WTD) as the research object, a comprehensive strategy based on liquid chromatography coupled with mass spectrometry (LC-MS) was developed to identify PCs, clarify and compare their absorption and distribution in normal and model rats, and then explore the potential mechanism of TCM. Firstly, the PCs in WTD were identified. Then, the pharmacokinetics (PK) and tissue distribution of these ingredients were studied. Finally, the constituents with the difference between normal and model rats were selected for target network pharmacological analysis to clarify the mechanism.

RESULTS

A total of 27 PCs of WTD were identified. The absorption and distribution of 20 PCs were successfully analyzed. In the disease state, the absorption and distribution of all these components were improved to have better treatment effects. The results of target network pharmacological analysis indicated that PTGS1, PTGS2, ABCB1, SLC6A4, CHRM2, ESR1, ESR2, CDK2, TNF and IL-6 are 10 key targets for WTD against RA. The regulatory effects of WTD on the expression of PTGS2 and TNF were further verified. Pathway enrichment analysis showed that the key mechanism of WTD against RA is to reduce inflammation and regulate the immune response.

CONCLUSION

These results indicated that this strategy could better understand the in vivo process and mechanism of WTD under the pathological state. Furthermore, this strategy is also appropriate for other TCM.

A network pharmacology approach to determine the synergetic mechanisms of herb couple for treating rheumatic arthritis

Purpose

The purpose of this study was to investigate the therapeutic mechanism(s) of Clematis chinensis Osbeck/Notopterygium incisum K.C. Ting ex H.T (CN).

Methods

A network pharmacology approach integrating prediction of ingredients, target exploration, network construction, module partition and pathway analysis was used.

Results

This approach successfully helped to identify 12 active ingredients of CN, interacting with 13 key targets (Akt1, STAT3, TNFsf13, TP53, EPHB2, IL-10, IL-6, TNF, MAPK8, IL-8, RELA, ROS1 and STAT4). Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that CN-regulated pathways were mainly classified into signal transduction and immune system.

Conclusion

The present work may help to illustrate the mechanism(s) of action of CN, and it may provide a better understanding of antirheumatic effects.

Predictive and Experimental Approaches for Elucidating Protein-Protein Interactions and Quaternary Structures

The elucidation of protein-protein interactions is vital for determining the function and action of quaternary protein structures. Here, we discuss the difficulty and importance of establishing protein quaternary structure and review in vitro and in silico methods for doing so. Determining the interacting partner proteins of predicted protein structures is very time-consuming when using in vitro methods, this can be somewhat alleviated by use of predictive methods. However, developing reliably accurate predictive tools has proved to be difficult. We review the current state of the art in predictive protein interaction software and discuss the problem of scoring and therefore ranking predictions. Current community-based predictive exercises are discussed in relation to the growth of protein interaction prediction as an area within these exercises. We suggest a fusion of experimental and predictive methods that make use of sparse experimental data to determine higher resolution predicted protein interactions as being necessary to drive forward development.

In vitro detection of NEMO-ubiquitin binding using DELFIA and microscale thermophoresis assays

Canonical NF-κB signaling in response to various stimuli converges at the level of the IκB kinase (IKK) complex to ultimately activate NF-κB. To achieve this, the IKK complex uses one of its regulatory subunit (IKKγ/NEMO) to sense ubiquitin chains formed by upstream complexes. Various studies have shown that different Ubiquitin chains are involved in the binding of NEMO and thereby the activation of NF-κB. We have utilized two distinct biochemical methods, i.e., Dissociation-Enhanced Lanthanide Fluorescence Immunoassay (DELFIA) and Microscale Thermophoresis (MST), to detect the interaction of NEMO to linear and K63-linked Ubiquitin chains, respectively. Here, we describe the brief basis of the methods and a detailed underlying protocol.

Detecting protein association at the T cell plasma membrane

At the moment, many models on T cell signaling rely on results obtained via rather indirect methodologies, which makes direct comparison and conclusions to the in vivo situation difficult. Recently, a variety of new imaging methods were developed, which have the potential to directly shed light onto the mysteries of protein association at the T cell membrane. While the new modalities are extremely promising, for a broad readership it may be difficult to judge the results, since technological shortcomings are not always obvious. In this review article, we put key questions on the mechanism of protein interactions in the T cell plasma membrane into relation with techniques that allow to address such questions. We discuss applicability of the techniques, their strengths and weaknesses. This article is part of a Special Issue entitled: Nanoscale membrane organisation and signalling.

From proteomes to complexomes in the era of systems biology

Protein complexes carry out almost the entire signaling and functional processes in the cell. The protein complex complement of a cell, and its network of complex-complex interactions, is referred to here as the complexome. Computational methods to predict protein complexes from proteomics data, resulting in network representations of complexomes, have recently being developed. In addition, key advances have been made toward understanding the network and structural organization of complexomes. We review these bioinformatics advances, and their discovery-potential, as well as the merits of integrating proteomics data with emerging methods in systems biology to study protein complex signaling. It is envisioned that improved integration of proteomics and systems biology, incorporating the dynamics of protein complexes in space and time, may lead to more predictive models of cell signaling networks for effective modulation.

Application of quantitative proteomics technologies to the biomarker discovery pipeline for multiple sclerosis

Multiple sclerosis is an inflammatory-mediated demyelinating disorder most prevalent in young Caucasian adults. The various clinical manifestations of the disease present several challenges in the clinic in terms of diagnosis, monitoring disease progression and response to treatment. Advances in MS-based proteomic technologies have revolutionized the field of biomarker research and paved the way for the identification and validation of disease-specific markers. This review focuses on the novel candidates discovered by the application of quantitative proteomics to relevant disease-affected tissues in both the human context and within the animal model of the disease known as experimental autoimmune encephalomyelitis. The role of targeted MS approaches for biomarker validation studies, such as multiple reaction monitoring will also be discussed.

Bioinformatics analysis for the antirheumatic effects of huang-lian-jie-du-tang from a network perspective

Huang-Lian-Jie-Du-Tang (HLJDT) is a classic TCM formula to clear “heat” and “poison” that exhibits antirheumatic activity. Here we investigated the therapeutic mechanisms of HLJDT at protein network level using bioinformatics approach. It was found that HLJDT shares 5 target proteins with 3 types of anti-RA drugs, and several pathways in immune system and bone formation are significantly regulated by HLJDT's components, suggesting the therapeutic effect of HLJDT on RA. By defining an antirheumatic effect score to quantitatively measure the therapeutic effect, we found that the score of each HLJDT's component is very low, while the whole HLJDT achieves a much higher effect score, suggesting a synergistic effect of HLJDT achieved by its multiple components acting on multiple targets. At last, topological analysis on the RA-associated PPI network was conducted to illustrate key roles of HLJDT's target proteins on this network. Integrating our findings with TCM theory suggests that HLJDT targets on hub nodes and main pathway in the Hot ZENG network, and thus it could be applied as adjuvant treatment for Hot-ZENG-related RA. This study may facilitate our understanding of antirheumatic effect of HLJDT and it may suggest new approach for the study of TCM pharmacology.

Proteomics and NF-κB: an update

The transcription factor NF-κB was discovered in 1986 and since then has been extensively studied in relation to cancer research and inflammatory or autoimmune diseases due to its important roles in the regulation of apoptosis and inflammation as well as innate and adaptive immunity. Although much is known about NF-κB signaling, novel NF-κB functions in different diseases are still being uncovered, together with its target proteins, interaction partners and regulators of its activation cascade. Proteomic approaches are particularly suited to the discovery of new proteins involved in distinct signal transduction cascades. This review provides an update on and extension of a recent review that summarized a number of proteomic approaches to NF-κB signaling. The studies discussed here utilized innovative techniques and offer several new hypotheses on the role of NF-κB in physiological and pathophysiological processes, which open new avenues for research on NF-κB in the future.

Characterization of molecules binding to the 70K N-terminal region of fibronectin by IFAST purification coupled with mass spectrometry

Fibronectin (Fn) is a large glycoprotein present in plasma and extracellular matrix and is important for many processes. Within Fn the 70 kDa N-terminal region (70k-Fn) is involved in cell-mediated Fn assembly, a process that contributes to embryogenesis, development, and platelet thrombus formation. In addition, major human pathogens including Staphlycoccus aureus and Streptococcus pyogenes bind the 70k-Fn region by a novel form of protein-protein interaction called β-zipper formation, facilitating bacterial spread and colonization. Knowledge of blood plasma and platelet proteins that interact with 70k-Fn by β-zipper formation is incomplete. In the current study, we aimed to characterize these proteins through affinity purification. For this affinity purification, we used a novel purification technique termed immiscible filtration assisted by surface tension (IFAST). The foundation of this technology is immiscible phase filtration, using a magnet to draw paramagnetic particle (PMP)-bound analyte through an immiscible barrier (oil or organic solvent) that separates an aqueous sample from an aqueous eluting buffer. The immiscible barrier functions to remove unbound proteins via exclusion rather than dilutive washing used in traditional isolation methods. We identified 31 interactors from plasma, of which only seven were previously known to interact with Fn. Furthermore, five proteins were identified to interact with 70k-Fn from platelet lysate, of which one was previously known. These results demonstrate that IFAST offers advantages for proteomic studies of interacting molecules in that the technique requires small sample volumes, can be done with high enough throughput to sample multiple interaction conditions, and is amenable to exploratory mass spectrometric and confirmatory immuno-blotting read-outs.

Analysis and prediction of pathways in HeLa cells by integrating biological levels of organization with systems-biology approaches

It has recently begun to be considered that cancer is a systemic disease and that it must be studied at every level of complexity using many of the currently available approaches, including high-throughput technologies and bioinformatics. To achieve such understanding in cervical cancer, we collected information on gene, protein and phosphoprotein expression of the HeLa cell line and performed a comprehensive analysis of the different signaling pathways, transcription networks and metabolic events in which they participate. A total expression analysis by RNA-Seq of the HeLa cell line showed that 19,974 genes were transcribed. Of these, 3,360 were over-expressed, and 2,129 under-expressed when compared to the NHEK cell line. A protein-protein interaction network was derived from the over-expressed genes and used to identify central elements and, together with the analysis of over-represented transcription factor motifs, to predict active signaling and regulatory pathways. This was further validated by Metal-Oxide Affinity Chromatography (MOAC) and Tandem Mass Spectrometry (MS/MS) assays which retrieved phosphorylated proteins. The 14-3-3 family members emerge as important regulators in carcinogenesis and as possible clinical targets. We observed that the different over- and under-regulated pathways in cervical cancer could be interrelated through elements that participate in crosstalks, therefore belong to what we term "meta-pathways". Additionally, we highlighted the relations of each one of the differentially represented pathways to one or more of the ten hallmarks of cancer. These features could be maintained in many other types of cancer, regardless of mutations or genomic rearrangements, and favor their robustness, adaptations and the evasion of tissue control. Probably, this could explain why cancer cells are not eliminated by selective pressure and why therapy trials directed against molecular targets are not as effective as expected.

Insights into the pathogenesis of axial spondyloarthropathy from network and pathway analysis

Background

Complex chronic diseases are usually not caused by changes in a single causal gene but by an unbalanced regulating network resulting from the dysfunctions of multiple genes or their products. Therefore, network based systems approach can be helpful for the identification of candidate genes related to complex diseases and their relationships. Axial spondyloarthropathy (SpA) is a group of chronic inflammatory joint diseases that mainly affect the spine and the sacroiliac joints. The pathogenesis of SpA remains largely unknown.

Results

In this paper, we conducted a network study of the pathogenesis of SpA. We integrated data related to SpA, from the OMIM database, proteomics and microarray experiments of SpA, to prioritize SpA candidate disease genes in the context of human protein interactome. Based on the top ranked SpA related genes, we constructed a SpA specific PPI network, identified potential pathways associated with SpA, and finally sketched an overview of biological processes involved in the development of SpA.

Conclusions

The protein-protein interaction (PPI) network and pathways reflect the link between the two pathological processes of SpA, i.e., immune mediated inflammation, as well as imbalanced bone modelling caused new boneformation and bone loss. We found that some known disease causative genes, such as TNFand ILs, play pivotal roles in this interaction.

The Use of High-Throughput Technologies to Investigate Vascular Inflammation and Atherosclerosis

The greatest challenge of scientific research is to understand the causes and consequences of disease. In recent years, great efforts have been devoted to unraveling the basic mechanisms of atherosclerosis (the underlying pathology of cardiovascular disease), which remains a major cause of morbidity and mortality worldwide. Because of the complex and multifactorial pathophysiology of cardiovascular disease, different research techniques have increasingly been combined to unravel genetic aspects, molecular pathways, and cellular functions involved in atherogenesis, vascular inflammation, and dyslipidemia to gain a multifaceted picture addressing this complexity. Thanks to the rapid evolution of high-throughput technologies, we are now able to generate large-scale data on the DNA, RNA, and protein levels. With the help of sophisticated computational tools, these data sets are integrated to enhance information extraction and are being increasingly used in a systems biology approach to model biological processes as interconnected and regulated networks. This review exemplifies the use of high-throughput technologies—such as genomics, transcriptomics, proteomics, and epigenomics—and systems biology to explore pathomechanisms of vascular inflammation and atherosclerosis.

Differential gene expression and translational approaches to identify biomarkers of interferon beta activity in multiple sclerosis

More than 16 years ago human interferon-β-1b (IFN-β-1β) was shown to be effective in the treatment of the relapsing-remitting form of multiple sclerosis (MS). Over time, IFN-β has been demonstrated to be both a safe and effective treatment. However, the mechanism of action of IFN-β in MS remains unknown. To better understand the mechanism of action of IFN-β, considerable effort has been made in transcriptional profiling of peripheral blood mononuclear cells collected from MS patients. IFN-β is known to induce a large number of genes that play an important role in regulating responses to viral infection, immune modulation, and cell proliferation. Identifying differentially induced genes that are linked to the beneficial effects observed during treatment is under active investigation. IFN biomarkers in MS patients have been proposed but have not been clearly confirmed in independent studies or consistently correlated with clinical measures of disease progression. Organizing single genes or gene signatures grouped according to molecular mechanisms meaningful in MS may help to link IFN activity measurements to clinical outcomes. In this review, IFN activity measurements will be discussed with a specific emphasis on what is known about differential gene expression and treatment effects in MS.

Detection of protein-protein interactions in the live cell plasma membrane by quantifying prey redistribution upon bait micropatterning

Our understanding of complex biological systems is based on high-quality proteomics tools for the parallelized detection and quantification of protein interactions. Current screening platforms, however, rely on measuring protein interactions in rather artificial systems, rendering the results difficult to confer on the in vivo situation. We describe here a detailed protocol for the design and the construction of a system to detect and quantify interactions between a fluorophore-labeled protein ("prey") and a membrane protein ("bait") in living cells. Cells are plated on micropatterned surfaces functionalized with antibodies to the bait exoplasmic domain. Bait-prey interactions are assayed via the redistribution of the fluorescent prey. The method is characterized by high sensitivity down to the level of single molecules, the capability to detect weak interactions, and high throughput, making it applicable as a screening tool. The proof-of-concept is demonstrated for the interaction between CD4, a major coreceptor in T-cell signaling, and Lck, a protein tyrosine kinase essential for early T-cell signaling.

Analysis of NF-kappaB signaling pathways by proteomic approaches

NF-kappaB is a transcription factor that plays important roles in the regulation of apoptosis and inflammation as well as innate and adaptive immunity. Consequently, dysregulations in the NF-kappaB activation cascade have been associated with the pathogenesis of several diseases such as cancer, atherosclerosis and rheumatoid arthritis. Although NF-kappaB signaling pathways have been extensively investigated in this context, its varying components and targets are far from being completely elucidated. There is still an urgent need for the detection of novel NF-kappaB target proteins, novel interaction partners and novel regulators in the activation cascade, in particular with regard to its role in the aforementioned diseases. Therefore, several groups have performed different proteomic approaches to further investigate NF-kappaB signal transduction pathways. Most of these studies have been carried out in the area of cancer research; however, there are also several analyses in the field of inflammatory or autoimmune diseases. Furthermore, there have been a number of basic investigations that principally examined binding partners or so far unknown target proteins of NF-kappaB-related proteins. With these approaches, a number of novel and interesting proteins have been found that interfere with NF-kappaB signal transduction and might have an impact on NF-kappaB-related diseases. The results of these studies are summarized and discussed in this review.

Long-term genome-wide blood RNA expression profiles yield novel molecular response candidates for IFN-β-1b treatment in relapsing remitting MS

Aims: In multiple sclerosis patients, treatment with recombinant IFN-β (rIFN-β) is partially efficient in reducing clinical exacerbations. However, its molecular mechanism of action is still under scrutiny. Materials & methods: We used DNA microarrays (Affymetrix, CA, USA) and peripheral mononuclear blood cells from 25 relapsing remitting multiple sclerosis patients to analyze the longitudinal transcriptional profile within 2 years of rIFN-β administration. Sets of differentially expressed genes were attained by applying a combination of independent criteria, thereby providing efficient data curation and gene filtering that accounted for technical and biological noise. Gene ontology term-association analysis and scientific literature text mining were used to explore evidence of gene interaction. Results: Post-therapy initiation, we identified 42 (day 2), 175 (month 1), 103 (month 12) and 108 (month 24) differentially expressed genes. Increased expression of established IFN-β marker genes, as well as differential expression of circulating IFN-β-responsive candidate genes, were observed. MS4A1 (CD20), a known target of B-cell depletion therapy, was significantly downregulated after one month. CMPK2, FCER1A, and FFAR2 appeared as hitherto unrecognized multiple sclerosis treatment-related differentially expressed genes that were consistently modulated over time. Overall, 84 interactions between 54 genes were attained, of which two major gene networks were identified at an earlier stage of therapy: the first (n = 15 genes) consisted of mostly known IFN-β-activated genes, whereas the second (n = 12) mainly contained downregulated genes that to date have not been associated with IFN-β effects in multiple sclerosis array research. Conclusion: We achieved both a broadening of the knowledge of IFN-β mechanism-of-action-related constituents and the identification of time-dependent interactions between IFN-β regulated genes.

Monitoring of multiple sclerosis immunotherapy: from single candidates to biomarker networks

Applying microarray technology to identify new diagnostic and prognostic markers in peripheral blood cells (PBC) after therapeutic intervention opens great perspectives regarding patient subclassification. Three recombinant products of the pleiotropic agent interferon beta (rIFN-beta) are available for disease modifying therapy of relapsing remitting multiple sclerosis (RRMS), a complex inflammatory autoimmune disease that targets the central nervous system. They differ according to formulation, route of administration and dosage regimens. The currently, only partially understood mechanism of action of injected rIFN-beta into human organisms needs provision with accessory key molecules; in addition, the significance of established clinical IFN-beta response criteria that distinguish responding from non-responding patients remain unclear.With respect to these major questions, we discuss promising candidates on the gene transcription level, attained from scientific MS literature that included a longitudinal aspect. Reviewed studies were in part carried out with distinct gene interrogating platforms (GeneArrays; RT-PCR), settings (in vitro; ex vivo), and study designs (drug formulations and regimen; inclusion criteria and clinical endpoints), hampering meaningful meta-analysis. Nevertheless, PBC from therapy-naïve MS patients, rIFN-beta treated MS patients, and healthy controls served to characterize facets of both the disease and its treatment. Hence, the field of MS transcriptomics in immunomodulatory therapy is (by far) not adequately understood and should be embedded into systems biology disciplines, yielding multi-layer analyses that deliver timely identification of MS subjects who will profit from applied rIFN-beta therapy.

Systems biology approaches for the study of multiple sclerosis

Multiple sclerosis (MS) is a progressive neurological disease caused by an autoimmune attack to the central nervous system (CNS). MS is thought to result from a complex interaction between genetic and environmental factors. In this review we analyze the contribution of genomics, trancriptomics and proteomics in delineating these factors, as well as their utility for the monitoring of disease progression, the identification of new targets for therapeutic intervention and the early detection of individuals at risk.

Protein networking: insights into global functional organization of proteomes

The formulation of network models from global protein studies is essential to understand the functioning of organisms. Network models of the proteome enable the application of Complex Network Analysis, a quantitative framework to investigate large complex networks using techniques from graph theory, statistical physics, dynamical systems and other fields. This approach has provided many insights into the functional organization of the proteome so far and will likely continue to do so. Currently, several network concepts have emerged in the field of proteomics. It is important to highlight the differences between these concepts, since different representations allow different insights into functional organization. One such concept is the protein interaction network, which contains proteins as nodes and undirected edges representing the occurrence of binding in large-scale protein-protein interaction studies. A second concept is the protein-signaling network, in which the nodes correspond to levels of post-translationally modified forms of proteins and directed edges to causal effects through post-translational modification, such as phosphorylation. Several other network concepts were introduced for proteomics. Although all formulated as networks, the concepts represent widely different physical systems. Therefore caution should be taken when applying relevant topological analysis. We review recent literature formulating and analyzing such networks.

Mass spectrometry-based functional proteomics: from molecular machines to protein networks

The study of protein-protein interactions by mass spectrometry is an increasingly important part of post-genomics strategies to understand protein function. A variety of mass spectrometry-based approaches allow characterization of cellular protein assemblies under near-physiological conditions and subsequent assignment of individual proteins to specific molecular machines, pathways and networks, according to an increasing level of organizational complexity. An appropriate analytical strategy can be individually tailored--from an in-depth analysis of single complexes to a large-scale characterization of entire molecular pathways or even an analysis of the molecular organization of entire expressed proteomes. Here we review different options regarding protein-complex purification strategies, mass spectrometry analysis and bioinformatic methods according to the specific question that is being addressed.

Contribution of IL-12R mediated feedback loop to Th1 cell differentiation

T helper 1 (Th1) cell fate is induced by overlapping signaling pathways, whose kinetic principles and regulatory motifs are largely unknown. We identified a simple positive feedback loop in the STAT4 signaling pathway, whereby activation by IL-12 leads to the increased expression in IL-12 receptor. A computational analysis shows that this feedback loop synergizes with the one mediated by the IFN-gamma secreted by differentiating cells, when the induction of Th1 cell fate is weak. Positive feedback loops are often utilized to enhance phenotypic differentiation. This effect was confirmed by experiments showing that stochastic fluctuations in the expression of IL-12 receptor gene were amplified, leading to two discrete levels of expression in a cell population.

Methods for the detection and analysis of protein-protein interactions

A large number of methods have been developed over the years to study protein-protein interactions. Many of these techniques are now available to the nonspecialist researcher thanks to new affordable instruments and/or resource centres. A typical protein-protein interaction study usually starts with an initial screen for novel binding partners. We start this review by describing three techniques that can be used for this purpose: (i) affinity-tagged proteins (ii) the two-hybrid system and (iii) some quantitative proteomic techniques that can be used in combination with, e.g., affinity chromatography and coimmunoprecipitation for screening of protein-protein interactions. We then describe some public protein-protein interaction databases that can be searched to identify previously reported interactions for a given bait protein. Four strategies for validation of protein-protein interactions are presented: confocal microscopy for intracellular colocalization of proteins, coimmunoprecipitation, surface plasmon resonance (SPR) and spectroscopic studies. Throughout the review we focus particularly on the advantages and limitations of each method.

Deterministic and stochastic models of NFkappaB pathway

In the article, we discuss the state of art and perspectives in deterministic and stochastic models of NFkappaB regulatory module. The NFkappaB is a transcription factor controlling various immune responses including inflammation and apoptosis. It is tightly regulated by at least two negative feedback loops involving IkappaBalpha and A20. This mode of regulation results in nucleus-to-cytoplasm oscillations in NFkappaB localization, which induce subsequent waves of NFkappaB responsive genes. Single cell experiments carried by several groups provided comprehensive evidence that stochastic effects play an important role in NFkappaB regulation. From modeling point of view, living cells might be considered noisy or stochastic biochemical reactors. In eukaryotic cells, in which the number of protein or mRNA molecules is relatively large, stochastic effects primarily originate in regulation of gene activity. Transcriptional activity of a gene can be initiated by trans-activator molecules binding to the specific regulatory site(s) in the target gene. The stochastic event of gene activation is amplified by transcription and translation, since it results in a burst of mRNA molecules, and each copy of mRNA then serves as a template for numerous protein molecules. Another potential source of variability can be receptors activation. At low-dose stimulation, important in cell-to-cell signaling, the number of active receptors can be low enough to introduce substantial noise to downstream signaling. Stochastic modeling confirms the large variability in cell responses and shows that no cell behaves like an "average" cell. This high cell-to-cell variability can be one of the weapons of the immune defense. Such non-deterministic defense may be harder to overcome by relatively simple programs coded in viruses and other pathogens.

Amino acid-coded tagging approaches in quantitative proteomics

To improve the efficiency, accuracy, reproducibility, throughput and proteome coverage of mass spectrometry-based quantitative approaches, both in vitro and in vivo tagging of particular amino acid residues of cellular proteins have been introduced to assist mass spectrometry for global-scale comparative studies of differentially expressed proteins/modifications between different biologically relevant cell states or cells at different pathological states. The basic features of these methods introduce pair-wise isotope signals of each individual peptide containing a particular type of tagged amino acid (amino acid-coded mass tagging) that originated from different cell states. In this review, the applications of major amino acid-coded mass tagging-based quantitative proteomics approaches, including isotope-coded affinity tag, isobaric tags for relative and absolute quantification (iTRAQ) and stable isotope labeling by amino acids in cell culture are summarized in the context of their respective strengths/weakness in identifying those differentially expressed or post-translational modified proteins regulated by particular cellular stress on a genomic scale in a high-throughput manner. Importantly, these gel-free, in-spectra quantitative mechanisms have been further explored to identify/characterize large-scale protein-protein interactions involving various functional pathways. Taken together, the information about quantitative proteome changes, including multiple regulated proteins and their interconnected relationships, will provide an important insight into the molecular mechanisms, where novel targets for diagnosis and therapeutic intervention will be identified.

Can pharmacology possibly have a role for bioinformatics?

In today's information-driven culture, there is virtually no walk of life that is not impacted on by computing. As a bridging discipline in the health sciences with activities that span both basic science and clinical interests, modern pharmacology is no exception. As the plethora of data and databases spawned by the 'omics' generation expand in number and complexity, bioinformatics is necessary to manage, integrate and exploit this cohort of data so that the appropriate links to molecular pathology and therapeutic response can be made. Bioinformatics is now an integral part of drug discovery and development. This article reviews the use of bioinformatics in this process, from target identification and validation, to pharmacogenomics, toxicogenomics and systems biology.

An efficient tandem affinity purification procedure for interaction proteomics in mammalian cells

Tandem affinity purification (TAP) is a generic two-step affinity purification protocol that enables the isolation of protein complexes under close-to-physiological conditions for subsequent analysis by mass spectrometry. Although TAP was instrumental in elucidating the yeast cellular machinery, in mammalian cells the method suffers from a low overall yield. We designed several dual-affinity tags optimized for use in mammalian cells and compared the efficiency of each tag to the conventional TAP tag. A tag based on protein G and the streptavidin-binding peptide (GS-TAP) resulted in a tenfold increase in protein-complex yield and improved the specificity of the procedure. This allows purification of protein complexes that were hitherto not amenable to TAP and use of less starting material, leading to higher success rates and enabling systematic interaction proteomics projects. Using the well-characterized Ku70-Ku80 protein complex as an example, we identified both core elements as well as new candidate effectors.

Keeping up NF-κB appearances: Epigenetic control of immunity or inflammation-triggered epigenetics

Controlled expression of cytokine genes is an essential component of an immune response and is crucial for homeostasis. In order to generate an appropriate response to an infectious condition, the type of cytokine, as well as the cell type, dose range and the kinetics of its expression are of critical importance. The nuclear factor-kappaB (NF-kappaB) family of transcription factors has a crucial role in rapid responses to stress and pathogens (innate immunity), as well as in development and differentiation of immune cells (acquired immunity). Although quite a number of genes contain NF-kappaB-responsive elements in their regulatory regions, their expression pattern can significantly vary from both a kinetic and quantitative point of view, reflecting the impact of environmental and differentiative cues. At the transcription level, selectivity is conferred by the expression of specific NF-kappaB subunits and their respective posttranslational modifications, and by combinatorial interactions between NF-kappaB and other transcription factors and coactivators, that form specific enhanceosome complexes in association with particular promoters. These enhanceosome complexes represent another level of signaling integration, whereby the activities of multiple upstream pathways converge to impress a distinct pattern of gene expression upon the NF-kappaB-dependent transcriptional network. Today, several pieces of evidence suggest that the chromatin structure and epigenetic settings are the ultimate integration sites of both environmental and differentiative inputs, determining proper expression of each NF-kappaB-dependent gene. We will therefore discuss in this review the multilayered interplay of NF-kappaB signaling and epigenome dynamics, in achieving appropriate gene expression responses and transcriptional activity.