Proteomics of calcium-signaling components in plants

Calcium functions as a versatile messenger in mediating responses to hormones, biotic/abiotic stress signals and a variety of developmental cues in plants. The Ca(2+)-signaling circuit consists of three major "nodes"--generation of a Ca(2+)-signature in response to a signal, recognition of the signature by Ca2+ sensors and transduction of the signature message to targets that participate in producing signal-specific responses. Molecular genetic and protein-protein interaction approaches together with bioinformatic analysis of the Arabidopsis genome have resulted in identification of a large number of proteins at each "node"--approximately 80 at Ca2+ signature, approximately 400 sensors and approximately 200 targets--that form a myriad of Ca2+ signaling networks in a "mix and match" fashion. In parallel, biochemical, cell biological, genetic and transgenic approaches have unraveled functions and regulatory mechanisms of a few of these components. The emerging paradigm from these studies is that plants have many unique Ca2+ signaling proteins. The presence of a large number of proteins, including several families, at each "node" and potential interaction of several targets by a sensor or vice versa are likely to generate highly complex networks that regulate Ca(2+)-mediated processes. Therefore, there is a great demand for high-throughput technologies for identification of signaling networks in the "Ca(2+)-signaling-grid" and their roles in cellular processes. Here we discuss the current status of Ca2+ signaling components, their known functions and potential of emerging high-throughput genomic and proteomic technologies in unraveling complex Ca2+ circuitry.     

Identification of additional proteins in differential proteomics using protein interaction networks

In this study, we developed a novel computational approach based on protein–protein interaction networks to identify a list of proteins that might have remained undetected in differential proteomic profiling experiments. We tested our computational approach on two sets of human smooth muscle cell protein extracts that were affected differently by DNase I treatment. Differential proteomic analysis by saturation DIGE resulted in the identification of 41 human proteins. The application of our approach to these 41 input proteins consisted of four steps: (i) Compilation of a human protein–protein interaction network from public databases; (ii) calculation of interaction scores based on functional similarity; (iii) determination of a set of candidate proteins that are needed to efficiently and confidently connect the 41 input proteins; and (iv) ranking of the resulting 25 candidate proteins. Two of the three highest‐ranked proteins, beta‐arrestin 1, and beta‐arrestin 2, were experimentally tested, revealing that their abundance levels in human smooth muscle cell samples were indeed affected by DNase I treatment. These proteins had not been detected during the experimental proteomic analysis. Our study suggests that our computational approach may represent a simple, universal, and cost‐effective means to identify additional proteins that remain elusive for current 2D gel‐based proteomic profiling techniques.     

Proteomic analysis of vascular endothelial cells in response to laminar shear stress

Isotope-coded affinity tags (cICAT) coupled with mass spectrometric analysis is one of the leading technologies for quantitative proteomic profiling and protein quantification. We performed proteomic analysis of bovine aortic endothelial cells (BAEC) in response to laminar shear stress using cICAT labeling coupled with LC-MS/MS. Protein expressions in BAEC under 15 dynes/cm2 of shear stress for 10 min, 3 h, and 6 h were compared with matched stationary controls. Analysis of each sample produced 1800-2400 proteins at >or=75% confidence level. We found 142, 213, and 186 candidate proteins that were up- or down-regulated by at least two-fold after 10 min, 3 h, and 6 h of shear stress, respectively. Some of these proteins have known cellular functions and they encompass many signaling pathways. The signaling pathways that respond to shear stress include those of integrins, G-protein-coupled receptors, glutamate receptors, PI3K/AKT, apoptosis, Notch and cAMP-mediated signaling pathways. The validity of the mass spectrometric analysis was also confirmed by Western blot and confocal immunofluorescence microscopy. The present quantitative proteomic analysis suggests novel potential regulatory mechanisms in vascular endothelial cells in response to shear stress. These results provide preliminary footprints for further studies on the signaling mechanisms induced by shear stress.     

Proteomics meets microbiology: technical advances in the global mapping of protein expression and function

The availability of complete genome sequences for a large number of pathogenic organisms has opened the door for large-scale proteomic studies to dissect both protein expression/regulation and function. This review highlights key proteomic methods including two-dimensional gel electrophoresis, reference mapping, protein expression profiling and recent advances in gel-free separation techniques that have made a significant impact on the resolution of complex proteomes. In addition, we highlight recent developments in the field of chemical proteomics, a branch of proteomics aimed at functionally profiling a proteome. These techniques include the development of activity-based probes and activity-based protein profiling methods as well as the use of synthetic small molecule libraries to screen for pharmacological tools to perturb basic biological processes. This review will focus on the applications of these technologies to the field of microbiology.     

PDZ domains and their binding partners: structure, specificity, and modification

PDZ domains are abundant protein interaction modules that often recognize short amino acid motifs at the C-termini of target proteins. They regulate multiple biological processes such as transport, ion channel signaling, and other signal transduction systems. This review discusses the structural characterization of PDZ domains and the use of recently emerging technologies such as proteomic arrays and peptide libraries to study the binding properties of PDZ-mediated interactions. Regulatory mechanisms responsible for PDZ-mediated interactions, such as phosphorylation in the PDZ ligands or PDZ domains, are also discussed. A better understanding of PDZ protein-protein interaction networks and regulatory mechanisms will improve our knowledge of many cellular and biological processes.     

Discovery of pathway biomarkers from coupled proteomics and systems biology methods

Background

Breast cancer is worldwide the second most common type of cancer after lung cancer. Plasma proteome profiling may have a higher chance to identify protein changes between plasma samples such as normal and breast cancer tissues. Breast cancer cell lines have long been used by researches as model system for identifying protein biomarkers. A comparison of the set of proteins which change in plasma with previously published findings from proteomic analysis of human breast cancer cell lines may identify with a higher confidence a subset of candidate protein biomarker.

Results

In this study, we analyzed a liquid chromatography (LC) coupled tandem mass spectrometry (MS/MS) proteomics dataset from plasma samples of 40 healthy women and 40 women diagnosed with breast cancer. Using a two-sample t-statistics and permutation procedure, we identified 254 statistically significant, differentially expressed proteins, among which 208 are over-expressed and 46 are under-expressed in breast cancer plasma. We validated this result against previously published proteomic results of human breast cancer cell lines and signaling pathways to derive 25 candidate protein biomarkers in a panel. Using the pathway analysis, we observed that the 25 “activated” plasma proteins were present in several cancer pathways, including ‘Complement and coagulation cascades’, ‘Regulation of actin cytoskeleton’, and ‘Focal adhesion’, and match well with previously reported studies. Additional gene ontology analysis of the 25 proteins also showed that cellular metabolic process and response to external stimulus (especially proteolysis and acute inflammatory response) were enriched functional annotations of the proteins identified in the breast cancer plasma samples. By cross-validation using two additional proteomics studies, we obtained 86% and 83% similarities in pathway-protein matrix between the first study and the two testing studies, which is much better than the similarity we measured with proteins.

Conclusions

We presented a ‘systems biology’ method to identify, characterize, analyze and validate panel biomarkers in breast cancer proteomics data, which includes 1) t statistics and permutation process, 2) network, pathway and function annotation analysis, and 3) cross-validation of multiple studies. Our results showed that the systems biology approach is essential to the understanding molecular mechanisms of panel protein biomarkers.

     

Identification and characterization of sORF-encoded polypeptides

Abstract

Molecular biology, genomics and proteomics methods have been utilized to reveal a non-annotated class of endogenous polypeptides (small proteins and peptides) encoded by short open reading frames (sORFs), or small open reading frames (smORFs). We refer to these polypeptides as s(m)ORF-encoded polypeptides or SEPs. The early SEPs were identified via genetic screens, and many of the RNAs that contain s(m)ORFs were originally considered to be non-coding; however, elegant work in bacteria and flies demonstrated that these s(m)ORFs code for functional polypeptides as small as 11-amino acids in length. The discovery of these initial SEPs led to search for these molecules using methods such as ribosome profiling and proteomics, which have revealed the existence of many SEPs, including novel human SEPs. Unlike screens, omics methods do not necessarily link a SEP to a cellular or biological function, but functional genomic and proteomic strategies have demonstrated that at least some of these newly discovered SEPs have biochemical and cellular functions. Here, we provide an overview of these results and discuss the future directions in this emerging field.     

Proteomic profiling of pancreatic cancer for biomarker discovery

Pancreatic cancer is a uniformly lethal disease that is difficult to diagnose at early stage and even more difficult to cure. In recent years, there has been a substantial interest in applying proteomics technologies to identify protein biomarkers for early detection of cancer. Quantitative proteomic profiling of body fluids, tissues, or other biological samples to identify differentially expressed proteins represents a very promising approach for improving the outcome of this disease. Proteins associated with pancreatic cancer identified through proteomic profiling technologies could be useful as biomarkers for the early diagnosis, therapeutic targets, and disease response markers. In this article, we discuss recent progress and challenges for applying quantitative proteomics technologies for biomarker discovery in pancreatic cancer.     

Molecular interaction networks for the analysis of human disease: Utility,limitations, and considerations

High‐throughput ‘‐omics’ data can be combined with large‐scale molecular interaction networks, for example, protein–protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative ‘‐omics’ methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and “network‐type.” The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high‐throughput studies versus a meta‐database of smaller literature‐curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large‐scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.     

Accurate prediction of protein–protein interactions from sequence alignments using a Bayesian method

Accurate and large‐scale prediction of protein–protein interactions directly from amino‐acid sequences is one of the great challenges in computational biology. Here we present a new Bayesian network method that predicts interaction partners using only multiple alignments of amino‐acid sequences of interacting protein domains, without tunable parameters, and without the need for any training examples. We first apply the method to bacterial two‐component systems and comprehensively reconstruct two‐component signaling networks across all sequenced bacteria. Comparisons of our predictions with known interactions show that our method infers interaction partners genome‐wide with high accuracy. To demonstrate the general applicability of our method we show that it also accurately predicts interaction partners in a recent dataset of polyketide synthases. Analysis of the predicted genome‐wide two‐component signaling networks shows that cognates (interacting kinase/regulator pairs, which lie adjacent on the genome) and orphans (which lie isolated) form two relatively independent components of the signaling network in each genome. In addition, while most genes are predicted to have only a small number of interaction partners, we find that 10% of orphans form a separate class of ‘hub’ nodes that distribute and integrate signals to and from up to tens of different interaction partners.     

Proteomic and Phospho-Proteomic Profile of Human Platelets in Basal,Resting State: Insights into Integrin Signaling

During atherogenesis and vascular inflammation quiescent platelets are activated to increase the surface expression and ligand affinity of the integrin αIIbβ3 via inside-out signaling. Diverse signals such as thrombin, ADP and epinephrine transduce signals through their respective GPCRs to activate protein kinases that ultimately lead to the phosphorylation of the cytoplasmic tail of the integrin αIIbβ3 and augment its function. The signaling pathways that transmit signals from the GPCR to the cytosolic domain of the integrin are not well defined. In an effort to better understand these pathways, we employed a combination of proteomic profiling and computational analyses of isolated human platelets. We analyzed ten independent human samples and identified a total of 1507 unique proteins in platelets. This is the most comprehensive platelet proteome assembled to date and includes 190 membrane-associated and 262 phosphorylated proteins, which were identified via independent proteomic and phospho-proteomic profiling. We used this proteomic dataset to create a platelet protein-protein interaction (PPI) network and applied novel contextual information about the phosphorylation step to introduce limited directionality in the PPI graph. This newly developed contextual PPI network computationally recapitulated an integrin signaling pathway. Most importantly, our approach not only provided insights into the mechanism of integrin αIIbβ3 activation in resting platelets but also provides an improved model for analysis and discovery of PPI dynamics and signaling pathways in the future.     

Perspectives and limitations of gene expression profiling in rheumatology: new molecular strategies

The deciphering of the sequence of the human genome has raised the expectation of unravelling the specific role of each gene in physiology and pathology. High-throughput technologies for gene expression profiling provide the first practical basis for applying this information. In rheumatology, with its many diseases of unknown pathogenesis and puzzling inflammatory aspects, these advances appear to promise a significant advance towards the identification of leading mechanisms of pathology. Expression patterns reflect the complexity of the molecular processes and are expected to provide the molecular basis for specific diagnosis, therapeutic stratification, long-term monitoring and prognostic evaluation. Identification of the molecular networks will help in the discovery of appropriate drug targets, and permit focusing on the most effective and least toxic compounds. Current limitations in screening technologies, experimental strategies and bioinformatic interpretation will shortly be overcome by the rapid development in this field. However, gene expression profiling, by its nature, will not provide biochemical information on functional activities of proteins and might only in part reflect underlying genetic dysfunction. Genomic and proteomic technologies will therefore be complementary in their scientific and clinical application.     

Identifying responsive functional modules from protein-protein interaction network

Proteins interact with each other within a cell, and those interactions give rise to the biological function and dynamical behavior of cellular systems. Generally, the protein interactions are temporal, spatial, or condition dependent in a specific cell, where only a small part of interactions usually take place under certain conditions. Recently, although a large amount of protein interaction data have been collected by high-throughput technologies, the interactions are recorded or summarized under various or different conditions and therefore cannot be directly used to identify signaling pathways or active networks, which are believed to work in specific cells under specific conditions. However, protein interactions activated under specific conditions may give hints to the biological process underlying corresponding phenotypes. In particular, responsive functional modules consist of protein interactions activated under specific conditions can provide insight into the mechanism underlying biological systems, e.g. protein interaction subnetworks found for certain diseases rather than normal conditions may help to discover potential biomarkers. From computational viewpoint, identifying responsive functional modules can be formulated as an optimization problem. Therefore, efficient computational methods for extracting responsive functional modules are strongly demanded due to the NP-hard nature of such a combinatorial problem. In this review, we first report recent advances in development of computational methods for extracting responsive functional modules or active pathways from protein interaction network and microarray data. Then from computational aspect, we discuss remaining obstacles and perspectives for this attractive and challenging topic in the area of systems biology.     

Influence of Protein Abundance on High-Throughput Protein-Protein Interaction Detection

Experimental protein-protein interaction (PPI) networks are increasingly being exploited in diverse ways for biological discovery. Accordingly, it is vital to discern their underlying natures by identifying and classifying the various types of deterministic (specific) and probabilistic (nonspecific) interactions detected. To this end, we have analyzed PPI networks determined using a range of high-throughput experimental techniques with the aim of systematically quantifying any biases that arise from the varying cellular abundances of the proteins. We confirm that PPI networks determined using affinity purification methods for yeast and Eschericia coli incorporate a correlation between protein degree, or number of interactions, and cellular abundance. The observed correlations are small but statistically significant and occur in both unprocessed (raw) and processed (high-confidence) data sets. In contrast, the yeast two-hybrid system yields networks that contain no such relationship. While previously commented based on mRNA abundance, our more extensive analysis based on protein abundance confirms a systematic difference between PPI networks determined from the two technologies. We additionally demonstrate that the centrality-lethality rule, which implies that higher-degree proteins are more likely to be essential, may be misleading, as protein abundance measurements identify essential proteins to be more prevalent than nonessential proteins. In fact, we generally find that when there is a degree/abundance correlation, the degree distributions of nonessential and essential proteins are also disparate. Conversely, when there is no degree/abundance correlation, the degree distributions of nonessential and essential proteins are not different. However, we show that essentiality manifests itself as a biological property in all of the yeast PPI networks investigated here via enrichments of interactions between essential proteins. These findings provide valuable insights into the underlying natures of the various high-throughput technologies utilized to detect PPIs and should lead to more effective strategies for the inference and analysis of high-quality PPI data sets.     

A proteomic-based approach to study underlying molecular responses of the small intestine of Wistar rats to genetically modified corn (MON810)

A genetically modified (GM) commercial corn variety, MON810, resistant to European corn borer, has been shown to be non-toxic to mammals in a number of rodent feeding studies carried out in accordance with OECD Guidelines. Insect resistance results from expression of the Cry1Ab gene encoding an insecticidal Bt protein that causes lysis and cell death in susceptible insect larvae by binding to midgut epithelial cells, which is a key determinant of Cry toxin species specificity. Whilst whole animal studies are still recognised as the ‘gold standard’ for safety assessment, they only provide indirect evidence for changes at the cellular/organ/tissue level. In contrast, omics-based technologies enable mechanistic understanding of toxicological or nutritional events at the cellular/receptor level. To address this important knowledge-gap and to gain insights into the underlying molecular responses in rat to MON810, differential gene expression in the epithelial cells of the small intestine of rats fed formulated diets containing MON810, its near isogenic line, two conventional corn varieties, and a commercial (Purina?) corn-based control diet were investigated using comparative proteomic profiling. Pairwise and five-way comparisons showed that the majority of proteins that were differentially expressed in the small intestine epithelial cells in response to consumption of the different diets in both 7-day and 28-day studies were related to lipid and carbohydrate metabolism and protein biosynthesis. Irrespective of the diet, a limited number of stress-related proteins were shown to be differentially expressed. However these stress-related proteins differed between diets. No adverse clinical or behavioural effects, or biomarkers of adverse health, were observed in rats fed GM corn compared to the other corn diets. These findings suggest that MON810 has negligible effects on the small intestine of rats at the cellular level compared with the well-documented toxicity observed in susceptible insects.