Functional Protein Networks Research Articles

Functional Study of Mammalian Neph Proteins in Drosophila melanogaster

Neph molecules are highly conserved immunoglobulin superfamily proteins (IgSF) which are essential for multiple morphogenetic processes, including glomerular development in mammals and neuronal as well as nephrocyte development in D. melanogaster. While D. melanogaster expresses two Neph-like proteins (Kirre and IrreC/Rst), three Neph proteins (Neph1–3) are expressed in the mammalian system. However, although these molecules are highly abundant, their molecular functions are still poorly understood. Here we report on a fly system in which we overexpress and replace endogenous Neph homologs with mammalian Neph1–3 proteins to identify functional Neph protein networks required for neuronal and nephrocyte development. Misexpression of Neph1, but neither Neph2 nor Neph3, phenocopies the overexpression of endogenous Neph molecules suggesting a functional diversity of mammalian Neph family proteins. Moreover, structure-function analysis identified a conserved and specific Neph1 protein motif that appears to be required for the functional replacement of Kirre. Hereby, we establish D. melanogaster as a genetic system to specifically model molecular Neph1 functions in vivo and identify a conserved amino acid motif linking Neph1 to Drosophila Kirre function.

Detecting cancer outlier genes with potential rearrangement using gene expression data and biological networks.

Gene alterations are a major component of the landscape of tumor genomes. To assess the significance of these alterations in the development of prostate cancer, it is necessary to identify these alterations and analyze them from systems biology perspective. Here, we present a new method (EigFusion) for predicting outlier genes with potential gene rearrangement. EigFusion demonstrated excellent performance in identifying outlier genes with potential rearrangement by testing it to synthetic and real data to evaluate performance. EigFusion was able to identify previously unrecognized genes such as FABP5 and KCNH8 and confirmed their association with primary and metastatic prostate samples while confirmed the metastatic specificity for other genes such as PAH, TOP2A, and SPINK1. We performed protein network based approaches to analyze the network context of potential rearranged genes. Functional gene rearrangement Modules are constructed by integrating functional protein networks. Rearranged genes showed to be highly connected to well-known altered genes in cancer such as AR, RB1, MYC, and BRCA1. Finally, using clinical outcome data of prostate cancer patients, potential rearranged genes demonstrated significant association with prostate cancer specific death.

Functional Proteomics Establishes the Interaction of SIRT7 with Chromatin Remodeling Complexes and Expands Its Role in Regulation of RNA Polymerase I Transcription

Among mammalian sirtuins, SIRT7 is the only enzyme residing in nucleoli where ribosomal DNA is transcribed. Recent reports established that SIRT7 associates with RNA Pol I machinery and is required for rDNA transcription. Although defined by its homology to the yeast histone deacetylase Sir2, current knowledge suggests that SIRT7 itself has little to no deacetylase activity. Because only two SIRT7 interactions have been thus far described: RNA Pol I and upstream binding factor, identification of proteins and complexes associating with SIRT7 is critical to understanding its functions. Here, we present the first characterization of SIRT7 interaction networks. We have systematically investigated protein interactions of three EGFP-tagged SIRT7 constructs: wild type, a point mutation affecting rDNA transcription, and a deletion mutant lacking the predicted coiled-coil domain. A combinatorial proteomics and bioinformatics approach was used to integrate gene ontology classifications, functional protein networks, and normalized abundances of proteins co-isolated with SIRT7. The resulting refined proteomic data set confirmed SIRT7 interactions with RNA Pol I and upstream binding factor and highlighted association with factors involved in RNA Pol I- and II-dependent transcriptional processes and several nucleolus-localized chromatin remodeling complexes. Particularly enriched were members of the B-WICH complex, such as Mybbp1a, WSTF, and SNF2h. Prominent interactions were validated by a selected reaction monitoring-like approach using metabolic labeling with stable isotopes, confocal microscopy, reciprocal immunoaffinity precipitation, and co-isolation with endogenous SIRT7. To extend the current knowledge of mechanisms involved in SIRT7-dependent regulation of rDNA transcription, we showed that small interfering RNA-mediated SIRT7 knockdown leads to reduced levels of RNA Pol I protein, but not messenger RNA, which was confirmed in diverse cell types. The down-regulation of RNA Pol I protein levels placed in the context of SIRT7 interaction networks led us to propose that SIRT7 plays a crucial role in connecting the function of chromatin remodeling complexes to RNA Pol I machinery during transcription.

ANAP: An Integrated Knowledge Base for Arabidopsis Protein Interaction Network Analysis

Protein interactions are fundamental to the molecular processes occurring within an organism and can be utilized in network biology to help organize, simplify, and understand biological complexity. Currently, there are more than 10 publicly available Arabidopsis (Arabidopsis thaliana) protein interaction databases. However, there are limitations with these databases, including different types of interaction evidence, a lack of defined standards for protein identifiers, differing levels of information, and, critically, a lack of integration between them. In this paper, we present an interactive bioinformatics Web tool, ANAP (Arabidopsis Network Analysis Pipeline), which serves to effectively integrate the different data sets and maximize access to available data. ANAP has been developed for Arabidopsis protein interaction integration and network-based study to facilitate functional protein network analysis. ANAP integrates 11 Arabidopsis protein interaction databases, comprising 201,699 unique protein interaction pairs, 15,208 identifiers (including 11,931 The Arabidopsis Information Resource Arabidopsis Genome Initiative codes), 89 interaction detection methods, 73 species that interact with Arabidopsis, and 6,161 references. ANAP can be used as a knowledge base for constructing protein interaction networks based on user input and supports both direct and indirect interaction analysis. It has an intuitive graphical interface allowing easy network visualization and provides extensive detailed evidence for each interaction. In addition, ANAP displays the gene and protein annotation in the generated interactive network with links to The Arabidopsis Information Resource, the AtGenExpress Visualization Tool, the Arabidopsis 1,001 Genomes GBrowse, the Protein Knowledgebase, the Kyoto Encyclopedia of Genes and Genomes, and the Ensembl Genome Browser to significantly aid functional network analysis. The tool is available open access at http://gmdd.shgmo.org/Computational-Biology/ANAP.

RAMTaB: Robust Alignment of Multi-Tag Bioimages

BackgroundIn recent years, new microscopic imaging techniques have evolved to allow us to visualize several different proteins (or other biomolecules) in a visual field. Analysis of protein co-localization becomes viable because molecules can interact only when they are located close to each other. We present a novel approach to align images in a multi-tag fluorescence image stack. The proposed approach is applicable to multi-tag bioimaging systems which (a) acquire fluorescence images by sequential staining and (b) simultaneously capture a phase contrast image corresponding to each of the fluorescence images. To the best of our knowledge, there is no existing method in the literature, which addresses simultaneous registration of multi-tag bioimages and selection of the reference image in order to maximize the overall overlap between the images.Methodology/Principal FindingsWe employ a block-based method for registration, which yields a confidence measure to indicate the accuracy of our registration results. We derive a shift metric in order to select the Reference Image with Maximal Overlap (RIMO), in turn minimizing the total amount of non-overlapping signal for a given number of tags. Experimental results show that the Robust Alignment of Multi-Tag Bioimages (RAMTaB) framework is robust to variations in contrast and illumination, yields sub-pixel accuracy, and successfully selects the reference image resulting in maximum overlap. The registration results are also shown to significantly improve any follow-up protein co-localization studies.ConclusionsFor the discovery of protein complexes and of functional protein networks within a cell, alignment of the tag images in a multi-tag fluorescence image stack is a key pre-processing step. The proposed framework is shown to produce accurate alignment results on both real and synthetic data. Our future work will use the aligned multi-channel fluorescence image data for normal and diseased tissue specimens to analyze molecular co-expression patterns and functional protein networks.

Detecting microRNAs of high influence on protein functional interaction networks: a prostate cancer case study

BackgroundThe use of biological molecular network information for diagnostic and prognostic purposes and elucidation of molecular disease mechanism is a key objective in systems biomedicine. The network of regulatory miRNA-target and functional protein interactions is a rich source of information to elucidate the function and the prognostic value of miRNAs in cancer. The objective of this study is to identify miRNAs that have high influence on target protein complexes in prostate cancer as a case study. This could provide biomarkers or therapeutic targets relevant for prostate cancer treatment.ResultsOur findings demonstrate that a miRNA’s functional role can be explained by its target protein connectivity within a physical and functional interaction network. To detect miRNAs with high influence on target protein modules, we integrated miRNA and mRNA expression profiles with a sequence based miRNA-target network and human functional and physical protein interactions (FPI). miRNAs with high influence on target protein complexes play a role in prostate cancer progression and are promising diagnostic or prognostic biomarkers. We uncovered several miRNA-regulated protein modules which were enriched in focal adhesion and prostate cancer genes. Several miRNAs such as miR-96, miR-182, and miR-143 demonstrated high influence on their target protein complexes and could explain most of the gene expression changes in our analyzed prostate cancer data set.ConclusionsWe describe a novel method to identify active miRNA-target modules relevant to prostate cancer progression and outcome. miRNAs with high influence on protein networks are valuable biomarkers that can be used in clinical investigations for prostate cancer treatment.

Mapping the human phosphatome on growth pathways.

Large-scale siRNA screenings allow linking the function of poorly characterized genes to phenotypic readouts. According to this strategy, genes are associated with a function of interest if the alteration of their expression perturbs the phenotypic readouts. However, given the intricacy of the cell regulatory network, the mapping procedure is low resolution and the resulting models provide little mechanistic insights. We have developed a new strategy that combines multiparametric analysis of cell perturbation with logic modeling to achieve a more detailed functional mapping of human genes onto complex pathways. A literature-derived optimized model is used to infer the cell activation state following upregulation or downregulation of the model entities. By matching this signature with the experimental profile obtained in the high-throughput siRNA screening it is possible to infer the target of each protein, thus defining its 'entry point' in the network. By this novel approach, 41 phosphatases that affect key growth pathways were identified and mapped onto a human epithelial cell-specific growth model, thus providing insights into the mechanisms underlying their function.

Next-generation biomarkers based on 100-parameter functional super-resolution microscopy TIS

Functional super-resolution (fSR) microscopy is based on the automated toponome imaging system (TIS). fSR-TIS provides insight into the myriad of different cellular functionalities by direct imaging of large subcellular protein networks in morphologically intact cells and tissues, referred to as the toponome. By cyclical fluorescence imaging of at least 100 molecular cell components, fSR-TIS overcomes the spectral limitations of fluorescence microscopy, which is the essential condition for the detection of protein network structures in situ/in vivo. The resulting data sets precisely discriminate between cell types, subcellular structures, cell states and diseases (fSR). With up to 16 bits per protein, the power of combinatorial molecular discrimination (PCMD) is at least 2(100) per subcellular data point. It provides the dimensionality necessary to uncover thousands of distinct protein clusters including their subcellular hierarchies controlling protein network topology and function in the one cell or tissue section. Here we review the technology and findings showing that functional protein networks of the cell surface in different cancers encompass the same hierarchical and spatial coding principle, but express cancer-specific toponome codes within that scheme (referred to as TIS codes). Findings suggest that TIS codes, extracted from large-scale toponome data, have the potential to be next-generation biomarkers because of their cell type and disease specificity. This is functionally substantiated by the observation that blocking toponome-specific lead proteins results in disassembly of molecular networks and loss of function.

Functional Proteomics Establishes the Interaction of SIRT7 with Chromatin Remodeling Complexes and Expands Its Role in Regulation of RNA Polymerase I Transcription

Among mammalian sirtuins, SIRT7 is the only enzyme residing in nucleoli where ribosomal DNA is transcribed. Recent reports established that SIRT7 associates with RNA Pol I machinery and is required for rDNA transcription. Although defined by its homology to the yeast histone deacetylase Sir2, current knowledge suggests that SIRT7 itself has little to no deacetylase activity. Because only two SIRT7 interactions have been thus far described: RNA Pol I and upstream binding factor, identification of proteins and complexes associating with SIRT7 is critical to understanding its functions. Here, we present the first characterization of SIRT7 interaction networks. We have systematically investigated protein interactions of three EGFP-tagged SIRT7 constructs: wild type, a point mutation affecting rDNA transcription, and a deletion mutant lacking the predicted coiled-coil domain. A combinatorial proteomics and bioinformatics approach was used to integrate gene ontology classifications, functional protein networks, and normalized abundances of proteins co-isolated with SIRT7. The resulting refined proteomic data set confirmed SIRT7 interactions with RNA Pol I and upstream binding factor and highlighted association with factors involved in RNA Pol I- and II-dependent transcriptional processes and several nucleolus-localized chromatin remodeling complexes. Particularly enriched were members of the B-WICH complex, such as Mybbp1a, WSTF, and SNF2h. Prominent interactions were validated by a selected reaction monitoring-like approach using metabolic labeling with stable isotopes, confocal microscopy, reciprocal immunoaffinity precipitation, and co-isolation with endogenous SIRT7. To extend the current knowledge of mechanisms involved in SIRT7-dependent regulation of rDNA transcription, we showed that small interfering RNA-mediated SIRT7 knockdown leads to reduced levels of RNA Pol I protein, but not messenger RNA, which was confirmed in diverse cell types. The down-regulation of RNA Pol I protein levels placed in the context of SIRT7 interaction networks led us to propose that SIRT7 plays a crucial role in connecting the function of chromatin remodeling complexes to RNA Pol I machinery during transcription.

A Metaproteomic Approach to Study Human-Microbial Ecosystems at the Mucosal Luminal Interface

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI.

Using the underlying biological organization of the Mycobacterium tuberculosis functional network for protein function prediction

Despite ever-increasing amounts of sequence and functional genomics data, there is still a deficiency of functional annotation for many newly sequenced proteins. For Mycobacterium tuberculosis (MTB), more than half of its genome is still uncharacterized, which hampers the search for new drug targets within the bacterial pathogen and limits our understanding of its pathogenicity. As for many other genomes, the annotations of proteins in the MTB proteome were generally inferred from sequence homology, which is effective but its applicability has limitations. We have carried out large-scale biological data integration to produce an MTB protein functional interaction network. Protein functional relationships were extracted from the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, and additional functional interactions from microarray, sequence and protein signature data. The confidence level of protein relationships in the additional functional interaction data was evaluated using a dynamic data-driven scoring system. This functional network has been used to predict functions of uncharacterized proteins using Gene Ontology (GO) terms, and the semantic similarity between these terms measured using a state-of-the-art GO similarity metric. To achieve better trade-off between improvement of quality, genomic coverage and scalability, this prediction is done by observing the key principles driving the biological organization of the functional network. This study yields a new functionally characterized MTB strain CDC1551 proteome, consisting of 3804 and 3698 proteins out of 4195 with annotations in terms of the biological process and molecular function ontologies, respectively. These data can contribute to research into the Development of effective anti-tubercular drugs with novel biological mechanisms of action.

ANAT: A Tool for Constructing and Analyzing Functional Protein Networks

Genome-scale screening studies are gradually accumulating a wealth of data on the putative involvement of hundreds of genes in various cellular responses or functions. A fundamental challenge is to chart the molecular pathways that underlie these systems. ANAT is an interactive software tool, implemented as a Cytoscape plug-in, for elucidating functional networks of proteins. It encompasses a number of network inference algorithms and provides access to networks of physical associations in several organisms. In contrast to existing software tools, ANAT can be used to infer subnetworks that connect hundreds of proteins to each other or to a given set of "anchor" proteins, a fundamental step in reconstructing cellular subnetworks. The interactive component of ANAT provides an array of tools for evaluating and exploring the resulting subnetwork models and for iteratively refining them. We demonstrate the utility of ANAT by studying the crosstalk between the autophagic and apoptotic cell death modules in humans, using a network of physical interactions. Relative to published software tools, ANAT is more accurate and provides more features for comprehensive network analysis. The latest version of the software is available at http://www.cs.tau.ac.il/~bnet/ANAT_SI.

DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules

BackgroundIdentifying cellular subsystems that are involved in the expression of a target phenotype has been a very active research area for the past several years. In this paper, cellular subsystem refers to a group of genes (or proteins) that interact and carry out a common function in the cell. Most studies identify genes associated with a phenotype on the basis of some statistical bias, others have extended these statistical methods to analyze functional modules and biological pathways for phenotype-relatedness. However, a biologist might often have a specific question in mind while performing such analysis and most of the resulting subsystems obtained by the existing methods might be largely irrelevant to the question in hand. Arguably, it would be valuable to incorporate biologist's knowledge about the phenotype into the algorithm. This way, it is anticipated that the resulting subsytems would not only be related to the target phenotype but also contain information that the biologist is likely to be interested in.ResultsIn this paper we introduce a fast and theoretically guranteed method called DENSE (Dense and ENriched Subgraph Enumeration) that can take in as input a biologist's prior knowledge as a set of query proteins and identify all the dense functional modules in a biological network that contain some part of the query vertices. The density (in terms of the number of network egdes) and the enrichment (the number of query proteins in the resulting functional module) can be manipulated via two parameters γ and μ, respectively.ConclusionThis algorithm has been applied to the protein functional association network of Clostridium acetobutylicum ATCC 824, a hydrogen producing, acid-tolerant organism. The algorithm was able to verify relationships known to exist in literature and also some previously unknown relationships including those with regulatory and signaling functions. Additionally, we were also able to hypothesize that some uncharacterized proteins are likely associated with the target phenotype. The DENSE code can be downloaded from http://www.freescience.org/cs/DENSE/

Proteomics Mapping of Cord Blood Identifies Haptoglobin “Switch-On” Pattern as Biomarker of Early-Onset Neonatal Sepsis in Preterm Newborns

BackgroundIntra-amniotic infection and/or inflammation (IAI) are important causes of preterm birth and early-onset neonatal sepsis (EONS). A prompt and accurate diagnosis of EONS is critical for improved neonatal outcomes. We sought to explore the cord blood proteome and identify biomarkers and functional protein networks characterizing EONS in preterm newborns.Methodology/Principal FindingsWe studied a prospective cohort of 180 premature newborns delivered May 2004-September 2009. A proteomics discovery phase employing two-dimensional differential gel electrophoresis (2D-DIGE) and mass spectrometry identified 19 differentially-expressed proteins in cord blood of newborns with culture-confirmed EONS (n = 3) versus GA-matched controls (n = 3). Ontological classifications of the proteins included transfer/carrier, immunity/defense, protease/extracellular matrix. The 1st-level external validation conducted in the remaining 174 samples confirmed elevated haptoglobin and haptoglobin-related protein immunoreactivity (Hp&HpRP) in newborns with EONS (presumed and culture-confirmed) independent of GA at birth and birthweight (P<0.001). Western blot concurred in determining that EONS babies had conspicuous Hp&HpRP bands in cord blood (“switch-on pattern”) as opposed to non-EONS newborns who had near-absent “switch-off pattern” (P<0.001). Fetal Hp phenotype independently impacted Hp&HpRP. A Bayesian latent-class analysis (LCA) was further used for unbiased classification of all 180 cases based on probability of “antenatal IAI exposure” as latent variable. This was then subjected to 2nd-level validation against indicators of adverse short-term neonatal outcome. The optimal LCA algorithm combined Hp&HpRP switch pattern (most input), interleukin-6 and neonatal hematological indices yielding two non-overlapping newborn clusters with low (≤20%) versus high (≥70%) probability of IAI exposure. This approach reclassified ∼30% of clinical EONS diagnoses lowering the number needed to harm and increasing the odds ratios for several adverse outcomes including intra-ventricular hemorrhage.Conclusions/SignificanceAntenatal exposure to IAI results in precocious switch-on of Hp&HpRP expression. As EONS biomarker, cord blood Hp&HpRP has potential to improve the selection of newborns for prompt and targeted treatment at birth.

Proteomic strategy for the identification of critical actors in reorganization of the post-meiotic male genome

After meiosis, during the final stages of spermatogenesis, the haploid male genome undergoes major structural changes, resulting in a shift from a nucleosome-based genome organization to the sperm-specific, highly compacted nucleoprotamine structure. Recent data support the idea that region-specific programming of the haploid male genome is of high importance for the post-fertilization events and for successful embryo development. Although these events constitute a unique and essential step in reproduction, the mechanisms by which they occur have remained completely obscure and the factors involved have mostly remained uncharacterized. Here, we sought a strategy to significantly increase our understanding of proteins controlling the haploid male genome reprogramming, based on the identification of proteins in two specific pools: those with the potential to bind nucleic acids (basic proteins) and proteins capable of binding basic proteins (acidic proteins). For the identification of acidic proteins, we developed an approach involving a transition-protein (TP)-based chromatography, which has the advantage of retaining not only acidic proteins due to the charge interactions, but also potential TP-interacting factors. A second strategy, based on an in-depth bioinformatic analysis of the identified proteins, was then applied to pinpoint within the lists obtained, male germ cells expressed factors relevant to the post-meiotic genome organization. This approach reveals a functional network of DNA-packaging proteins and their putative chaperones and sheds a new light on the way the critical transitions in genome organizations could take place. This work also points to a new area of research in male infertility and sperm quality assessments.

Network Clustering Revealed the Systemic Alterations of Mitochondrial Protein Expression

The mitochondrial protein repertoire varies depending on the cellular state. Protein component modifications caused by mitochondrial DNA (mtDNA) depletion are related to a wide range of human diseases; however, little is known about how nuclear-encoded mitochondrial proteins (mt proteome) changes under such dysfunctional states. In this study, we investigated the systemic alterations of mtDNA-depleted (ρ0) mitochondria by using network analysis of gene expression data. By modularizing the quantified proteomics data into protein functional networks, systemic properties of mitochondrial dysfunction were analyzed. We discovered that up-regulated and down-regulated proteins were organized into two predominant subnetworks that exhibited distinct biological processes. The down-regulated network modules are involved in typical mitochondrial functions, while up-regulated proteins are responsible for mtDNA repair and regulation of mt protein expression and transport. Furthermore, comparisons of proteome and transcriptome data revealed that ρ0 cells attempted to compensate for mtDNA depletion by modulating the coordinated expression/transport of mt proteins. Our results demonstrate that mt protein composition changed to remodel the functional organization of mitochondrial protein networks in response to dysfunctional cellular states. Human mt protein functional networks provide a framework for understanding how cells respond to mitochondrial dysfunctions.

MicroRNA Expression Analysis: Clinical Advantage of Propranolol Reveals Key MicroRNAs in Myocardial Infarction

BackgroundAs playing important roles in gene regulation, microRNAs (miRNAs) are believed as indispensable involvers in the pathogenesis of myocardial infarction (MI) that causes significant morbidity and mortality. Working on a hypothesis that modulation of only some key members in the miRNA superfamily could benefit ischemic heart, we proposed a microarray based network biology approach to identify them with the recognized clinical effect of propranolol as a prompt.MethodsA long-term MI model of rat was established in this study. The microarray technology was applied to determine the global miRNA expression change intervened by propranolol. Multiple network analyses were sequentially applied to evaluate the regulatory capacity, efficiency and emphasis of the miRNAs which dysexpression in MI were significantly reversed by propranolol.ResultsMicroarray data analysis indicated that long-term propranolol administration caused 18 of the 31 dysregulated miRNAs in MI undergoing reversed expression, implying that intentional modulation of miRNA expression might show favorable effects for ischemic heart. Our network analysis identified that, among these miRNAs, the prime players in MI were miR-1, miR-29b and miR-98. Further finding revealed that miR-1 focused on regulation of myocyte growth, yet miR-29b and miR-98 stressed on fibrosis and inflammation, respectively.ConclusionOur study illustrates how a combination of microarray technology and functional protein network analysis can be used to identify disease-related key miRNAs.

Coordinated interaction of multifunctional members of the p53 family determines many key processes in multicellular organisms

First time p53 was found in the complex with viral large T-antigene in the cells transformed by small DNA virus SV40. The cloning of p53 cDNA was done in the beginning of eighties and soon after that the whole p53 gene was cloned. The p53 family is comprised of three genes: TP53,TP63 and TP73, each of which is expressed as a set of structurally and functionally different isoforms. All of them intensively interact with each other forming a united functional network of proteins. In this review we discuss evolution of the p53 family and significance of all its members in embryonic development, reproduction, regeneration, regulation of aging and life span, as well as in the body's defense against cancer. With special attention we review the role of less studied members of the p53 family: p63 and p73, in oncogenesis and tumor progression and show that different isoforms of these proteins might exert a contrary effect on these processes.

Structural and functional protein network analyses predict novel signaling functions for rhodopsin

Orchestration of signaling, photoreceptor structural integrity, and maintenance needed for mammalian vision remain enigmatic. By integrating three proteomic data sets, literature mining, computational analyses, and structural information, we have generated a multiscale signal transduction network linked to the visual G protein-coupled receptor (GPCR) rhodopsin, the major protein component of rod outer segments. This network was complemented by domain decomposition of protein-protein interactions and then qualified for mutually exclusive or mutually compatible interactions and ternary complex formation using structural data. The resulting information not only offers a comprehensive view of signal transduction induced by this GPCR but also suggests novel signaling routes to cytoskeleton dynamics and vesicular trafficking, predicting an important level of regulation through small GTPases. Further, it demonstrates a specific disease susceptibility of the core visual pathway due to the uniqueness of its components present mainly in the eye. As a comprehensive multiscale network, it can serve as a basis to elucidate the physiological principles of photoreceptor function, identify potential disease-associated genes and proteins, and guide the development of therapies that target specific branches of the signaling pathway.

Functional protein network activation mapping reveals new potential molecular drug targets for poor prognosis pediatric BCP-ALL.

BackgroundIn spite of leukemia therapy improvements obtained over the last decades, therapy is not yet effective in all cases. Current approaches in Acute Lymphoblastic Leukemia (ALL) research focus on identifying new molecular targets to improve outcome for patients with a dismal prognosis. In this light phosphoproteomics seems to hold great promise for the identification of proteins suitable for targeted therapy.Methodology/Principal FindingsWe employed Reverse Phase Protein Microarrays to identify aberrantly activated proteins in 118 pediatric B-cell precursor (BCP)-ALL patients. Signal transduction pathways were assayed for activation/expression status of 92 key signalling proteins. We observed an increased activation/expression of several pathways involved in cell proliferation in poor clinical prognosis patients. MLL-rearranged tumours revealed BCL-2 hyperphosphorylation through AMPK activation, which indicates that AMPK could provide a functional role in inhibiting apoptosis in MLL-rearranged patients, and could be considered as a new potential therapeutic target. Second, in patients with poor clinical response to prednisone we observed the up-modulation of LCK activity with respect to patients with good response. This tyrosine-kinase can be down-modulated with clinically used inhibitors, thus modulating LCK activity could be considered for further studies as a new additional therapy for prednisone-resistant patients. Further we also found an association between high levels of CYCLIN E and relapse incidence. Moreover, CYCLIN E is more expressed in early relapsed patients, who usually show an unfavourable prognosis.Conclusions/SignificanceWe conclude that functional protein pathway activation mapping revealed specific deranged signalling networks in BCP-ALL that could be potentially modulated to produce a better clinical outcome for patients resistant to standard-of-care therapies.