Gene Promoter Evolution Targets the Center of the Human Protein Interaction Network

Assessing the contribution of promoters and coding sequences to gene evolution is an important step toward discovering the major genetic determinants of human evolution. Many specific examples have revealed the evolutionary importance of cis-regulatory regions. However, the relative contribution of regulatory and coding regions to the evolutionary process and whether systemic factors differentially influence their evolution remains unclear. To address these questions, we carried out an analysis at the genome scale to identify signatures of positive selection in human proximal promoters. Next, we examined whether genes with positively selected promoters (Prom+ genes) show systemic differences with respect to a set of genes with positively selected protein-coding regions (Cod+ genes). We found that the number of genes in each set was not significantly different (8.1% and 8.5%, respectively). Furthermore, a functional analysis showed that, in both cases, positive selection affects almost all biological processes and only a few genes of each group are located in enriched categories, indicating that promoters and coding regions are not evolutionarily specialized with respect to gene function. On the other hand, we show that the topology of the human protein network has a different influence on the molecular evolution of proximal promoters and coding regions. Notably, Prom+ genes have an unexpectedly high centrality when compared with a reference distribution (P = 0.008, for Eigenvalue centrality). Moreover, the frequency of Prom+ genes increases from the periphery to the center of the protein network (P = 0.02, for the logistic regression coefficient). This means that gene centrality does not constrain the evolution of proximal promoters, unlike the case with coding regions, and further indicates that the evolution of proximal promoters is more efficient in the center of the protein network than in the periphery. These results show that proximal promoters have had a systemic contribution to human evolution by increasing the participation of central genes in the evolutionary process.

Protein interaction studies point to new functions for Escherichia coli glyceraldehyde-3-phosphate dehydrogenase

Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) is considered a multifunctional protein with defined functions in numerous mammalian cellular processes. GAPDH functional diversity depends on various factors such as covalent modifications, subcellular localization, oligomeric state and intracellular concentration of substrates or ligands, as well as protein-protein interactions. In bacteria, alternative GAPDH functions have been associated with its extracellular location in pathogens or probiotics. In this study, new intracellular functions of E. coli GAPDH were investigated following a proteomic approach aimed at identifying interacting partners using in vivo formaldehyde cross-linking followed by mass spectrometry. The identified proteins were involved in metabolic processes, protein synthesis and folding or DNA repair. Some interacting proteins were also identified in immunopurification experiments in the absence of cross-linking. Pull-down experiments and overlay immunoblotting were performed to further characterize the interaction with phosphoglycolate phosphatase (Gph). This enzyme is involved in the metabolism of 2-phosphoglycolate formed in the DNA repair of 3"-phosphoglycolate ends generated by bleomycin damage. We show that interaction between Gph and GAPDH increases in cells challenged with bleomycin, suggesting involvement of GAPDH in cellular processes linked to DNA repair mechanisms.

Prediction of enzyme function by combining sequence similarity and protein interactions

Background: A number of studies have used protein interaction data alone for protein function prediction. Here, we introduce a computational approach for annotation of enzymes, based on the observation that similar protein sequences are more likely to perform the same function if they share similar interacting partners. Results: The method has been tested against the PSI-BLAST program using a set of 3,890 protein sequences from which interaction data was available. For protein sequences that align with at least 40% sequence identity to a known enzyme, the specificity of our method in predicting the first three EC digits increased from 80% to 90% at 80% coverage when compared to PSI-BLAST. Conclusion: Our method can also be used in proteins for which homologous sequences with known interacting partners can be detected. Thus, our method could increase 10% the specificity of genome-wide enzyme predictions based on sequence matching by PSI-BLAST alone.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

A targeted association study of immunity genes and networks suggests novel associations with placental malaria infection

A large proportion of the death toll associated with malaria is a consequence of malaria infection during pregnancy, causing up to 200,000 infant deaths annually. We previously published the first extensive genetic association study of placental malaria infection, and here we extend this analysis considerably, investigating genetic variation in over 9,000 SNPs in more than 1,000 genes involved in immunity and inflammation for their involvement in susceptibility to placental malaria infection. We applied a new approach incorporating results from both single gene analysis as well as gene-gene interactionson a protein-protein interaction network. We found suggestive associations of variants in the gene KLRK1 in the single geneanalysis, as well as evidence for associations of multiple members of the IL-7/IL-7R signalling cascade in the combined analysis. To our knowledge, this is the first large-scale genetic study on placental malaria infection to date, opening the door for follow-up studies trying to elucidate the genetic basis of this neglected form of malaria.

From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways

Background: Single nucleotide polymorphisms (SNPs) are the most frequent type of sequence variation between individuals, and represent a promising tool for finding genetic determinants of complex diseases and understanding the differences in drug response. In this regard, it is of particular interest to study the effect of non-synonymous SNPs in the context of biological networks such as cell signalling pathways. UniProt provides curated information about the functional and phenotypic effects of sequence variation, including SNPs, as well as on mutations of protein sequences. However, no strategy has been developed to integrate this information with biological networks, with the ultimate goal of studying the impact of the functional effect of SNPs in the structure and dynamics of biological networks. Results: First, we identified the different challenges posed by the integration of the phenotypic effect of sequence variants and mutations with biological networks. Second, we developed a strategy for the combination of data extracted from public resources, such as UniProt, NCBI dbSNP, Reactome and BioModels. We generated attribute files containing phenotypic and genotypic annotations to the nodes of biological networks, which can be imported into network visualization tools such as Cytoscape. These resources allow the mapping and visualization of mutations and natural variations of human proteins and their phenotypic effect on biological networks (e.g. signalling pathways, protein-protein interaction networks, dynamic models). Finally, an example on the use of the sequence variation data in the dynamics of a network model is presented. Conclusion: In this paper we present a general strategy for the integration of pathway and sequence variation data for visualization, analysis and modelling purposes, including the study of the functional impact of protein sequence variations on the dynamics of signalling pathways. This is of particular interest when the SNP or mutation is known to be associated to disease. We expect that this approach will help in the study of the functional impact of disease-associated SNPs on the behaviour of cell signalling pathways, which ultimately will lead to a better understanding of the mechanisms underlying complex diseases.

Topological comparison of methods for predicting transcriptional cooperativity in yeast

Background: The cooperative interaction between transcription factors has a decisive role in the control of the fate of the eukaryotic cell. Computational approaches for characterizing cooperative transcription factors in yeast, however, are based on different rationales and provide a low overlap between their results. Because the wealth of information contained in protein interaction networks and regulatory networks has proven highly effective in elucidating functional relationships between proteins, we compared different sets of cooperative transcription factor pairs (predicted by four different computational methods) within the frame of those networks. Results: Our results show that the overlap between the sets of cooperative transcription factors predicted by the different methods is low yet significant. Cooperative transcription factors predicted by all methods are closer and more clustered in the protein interaction network than expected by chance. On the other hand, members of a cooperative transcription factor pair neither seemed to regulate each other nor shared similar regulatory inputs, although they do regulate similar groups of target genes. Conclusion: Despite the different definitions of transcriptional cooperativity and the different computational approaches used to characterize cooperativity between transcription factors, the analysis of their roles in the framework of the protein interaction network and the regulatory network indicates a common denominator for the predictions under study. The knowledge of the shared topological properties of cooperative transcription factor pairs in both networks can be useful not only for designing better prediction methods but also for better understanding the complexities of transcriptional control in eukaryotes.

Predicting cancer involvement of genes from heterogeneous data

Background: Systematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data. Results: We implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature. Conclusion: Our approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.

A system-level, molecular evolutionary analysis of mammalian phototransduction

Visual perception is initiated in the photoreceptor cells of the retina via the phototransduction system.This system has shown marked evolution during mammalian divergence in such complex attributes as activation time and recovery time. We have performed a molecular evolutionary analysis of proteins involved in mammalianphototransduction in order to unravel how the action of natural selection has been distributed throughout thesystem to evolve such traits. We found selective pressures to be non-randomly distributed according to both a simple protein classification scheme and a protein-interaction network representation of the signaling pathway. Proteins which are topologically central in the signaling pathway, such as the G proteins, as well as retinoid cycle chaperones and proteins involved in photoreceptor cell-type determination, were found to be more constrained in their evolution. Proteins peripheral to the pathway, such as ion channels and exchangers, as well as the retinoid cycle enzymes, have experienced a relaxation of selective pressures. Furthermore, signals of positive selection were detected in two genes: the short-wave (blue) opsin (OPN1SW) in hominids and the rod-specific Na+/Ca2+,K+ ion exchanger (SLC24A1) in rodents. The functions of the proteins involved in phototransduction and the topology of the interactions between them have imposed non-random constraints on their evolution. Thus, in shaping or conserving system-level phototransduction traits, natural selection has targeted the underlying proteins in a concerted manner.

Lipid binding by the unique and SH3 domains of c-Src suggests a new regulation mechanism

c-Src is a non-receptor tyrosine kinase involved in numerous signal transduction pathways. The kinase,SH3 and SH2 domains of c-Src are attached to the membrane-anchoring SH4 domain through the flexible Unique domain. Here we show intra- and intermolecular interactions involving the Unique and SH3 domains suggesting the presence of a previously unrecognized additional regulation layer in c-Src. We have characterized lipid binding by the Unique and SH3 domains, their intramolecular interaction and its allosteric modulation by a SH3-binding peptide or by Calcium-loaded calmodulin binding to the Unique domain. We also show reduced lipid binding following phosphorylation at conserved sites of the Unique domain. Finally, we show that injection of full-length c-Src with mutations that abolish lipid binding by the Unique domain causes a strong in vivo phenotype distinct from that of wild-type c-Src in a Xenopus oocyte model system, confirming the functional role of the Unique domain in c-Src regulation.

Plant-derived phenolics inhibit the accrual of structurally characterised protein and lipid oxidative modifications

Epidemiological data suggest that plant-derived phenolics beneficial effects include an inhibition of LDL oxidation. After applying a screening method based on 2,4-dinitrophenyl hydrazine- protein carbonyl reaction to 21 different plant-derived phenolic acids, we selected the most antioxidant ones. Their effect was assessed in 5 different oxidation systems, as well as in other model proteins. Mass-spectrometry was then used, evidencing a heterogeneous effect on the accumulation of the structurally characterized protein carbonyl glutamic and aminoadipic semialdehydes as well as for malondialdehyde-lysine in LDL apoprotein. After TOF based lipidomics, we identified the most abundant differential lipids in Cu++-incubated LDL as 1-palmitoyllysophosphatidylcholine and 1-stearoyl-sn-glycero-3-phosphocholine. Most of selected phenolic compounds prevented the accumulation of those phospholipids and the cellular impairment induced by oxidized LDL. Finally, to validate these effects in vivo, we evaluated the effect of the intake of a phenolic-enriched extract in plasma protein and lipid modifications in a well-established model of atherosclerosis (diet-induced hypercholesterolemia in hamsters). This showed that a dietary supplement with a phenolic-enriched extract diminished plasma protein oxidative and lipid damage. Globally, these data show structural basis of antioxidant properties of plant-derived phenolic acids in protein oxidation that may be relevant for the health-promoting effects of its dietary intake. that a dietary supplement with a phenolic-enriched extract diminished plasma protein oxidative and lipid damage. Globally, these data show structural basis of antioxidant properties of plant-derived phenolic acids in protein oxidation that may be relevant for the health-promoting effects of its dietary intake.

Grape epicatechin conjugates prevent erythrocyte membrane protein oxidation

Epicatechin conjugates obtained from grape have shown antioxidant activity in various systems. However, how these conjugates exert their antioxidant benefits has not been widely studied. We assessed the activity of epicatechin and epicatechin conjugates on the erythrocyte membrane in the presence and absence of a peroxyl radical initiator, to increase our understanding of their mechanisms. Thus, we studied cell membrane fluidity by fluorescence anisotropy measurements, morphology of erythrocytes by scanning electron microscopy, and finally, red cell membrane proteins by sodium dodecyl sulfate-polyacrylamide gel electrophoresis. Our data showed that incubation of red cells in the presence of epicatechin derivatives altered membrane fluidity and erythrocyte morphology but not the membrane protein pattern. The presence in the medium of the peroxyl radical initiator 2,2′-azobis(amidinopropane) dihydrochloride (AAPH) resulted in membrane disruptions at all levels analyzed, causing changes in membrane fluidity, cell morphology, and protein degradation. The presence of antioxidants avoided protein oxidation, indicating that the interaction of epicatechin conjugates with the lipid bilayer might reduce the accessibility of AAPH to membranes, which could explain in part the inhibitory ability of these compounds against hemolysis induced by peroxidative insult.

Lipid phosphate phosphatase 3 participates in transport carrier formation and protein trafficking in the early secretory pathway

The inhibition of phosphatidic acid phosphatase (PAP) activity by propanolol indicates that diacylglycerol (DAG) is required for the formation of transport carriers at the Golgi and for retrograde trafficking to the ER. Here we report that the PAP2 family member lipid phosphate phosphatase 3 (LPP3, also known as PAP2b) localizes in compartments of the secretory pathway from ER export sites to the Golgi complex. The depletion of human LPP3: (i) reduces the number of tubules generated from the ER-Golgi intermediate compartment and the Golgi, with those formed from the Golgi being longer in LPP3-silenced cells than in control cells; (ii) impairs the Rab6-dependent retrograde transport of Shiga toxin subunit B from the Golgi to the ER, but not the anterograde transport of VSV-G or ssDsRed; and (iii) induces a high accumulation of Golgi-associated membrane buds. LPP3 depletion also reduces levels of de novo synthesized DAG and the Golgi-associated DAG contents. Remarkably, overexpression of a catalytically inactive form of LPP3 mimics the effects of LPP3 knockdown on Rab6-dependent retrograde transport. We conclude that LPP3 participates in the formation of retrograde transport carriers at the ER-Golgi interface, where it transitorily cycles, and during its route to the plasma membrane.

Multidrug resistance protein 1 localization in lipid raft domains and prostasomes in prostate cancer cell lines

Background: One of the problems in prostate cancer (CaP) treatment is the appearance of the multidrug resistance phenotype, in which ATP-binding cassette transporters such as multidrug resistance protein 1 (MRP1) play a role. Different localizations of the transporter have been reported, some of them related to the chemoresistant phenotype. Aim: This study aimed to compare the localization of MRP1 in three prostate cell lines (normal, androgen-sensitive, and androgen-independent) in order to understand its possible role in CaP chemoresistance. Methods: MRP1 and caveolae protein markers were detected using confocal microscopy, performing colocalization techniques. Lipid raft isolation made it possible to detect these proteins by Western blot analysis. Caveolae and prostasomes were identified by electron microscopy. Results: We show that MRP1 is found in lipid raft fractions of tumor cells and that the number of caveolae increases with malignancy acquisition. MRP1 is found not only in the plasma membrane associated with lipid rafts but also in cytoplasmic accumulations colocalizing with the prostasome markers Caveolin-1 and CD59, suggesting that in CaP cells, MRP1 is localized in prostasomes. Conclusion: We hypothesize that the presence of MRP1 in prostasomes could serve as a reservoir of MRP1; thus, taking advantage of the release of their content, MRP1 could be translocated to the plasma membrane contributing to the chemoresistant phenotype. The presence of MRP1 in prostasomes could serve as a predictor of malignancy in CaP

Multidrug resistance protein 1 localization in lipid raft domains and prostasomes in prostate cancer cell lines

Background: One of the problems in prostate cancer (CaP) treatment is the appearance of the multidrug resistance phenotype, in which ATP-binding cassette transporters such as multidrug resistance protein 1 (MRP1) play a role. Different localizations of the transporter have been reported, some of them related to the chemoresistant phenotype. Aim: This study aimed to compare the localization of MRP1 in three prostate cell lines (normal, androgen-sensitive, and androgen-independent) in order to understand its possible role in CaP chemoresistance. Methods: MRP1 and caveolae protein markers were detected using confocal microscopy, performing colocalization techniques. Lipid raft isolation made it possible to detect these proteins by Western blot analysis. Caveolae and prostasomes were identified by electron microscopy. Results: We show that MRP1 is found in lipid raft fractions of tumor cells and that the number of caveolae increases with malignancy acquisition. MRP1 is found not only in the plasma membrane associated with lipid rafts but also in cytoplasmic accumulations colocalizing with the prostasome markers Caveolin-1 and CD59, suggesting that in CaP cells, MRP1 is localized in prostasomes. Conclusion: We hypothesize that the presence of MRP1 in prostasomes could serve as a reservoir of MRP1; thus, taking advantage of the release of their content, MRP1 could be translocated to the plasma membrane contributing to the chemoresistant phenotype. The presence of MRP1 in prostasomes could serve as a predictor of malignancy in CaP