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.

The quest for a message: budding yeast, a model organism to study the control of pre-mRNA splicing

Removal of introns during pre-mRNA splicing is a critical process in gene expression, and understanding its control at both single-gene and genomic levels is one of the great challenges in Biology. Splicing takes place in a dynamic, large ribonucleoprotein complex known as the spliceosome. Combining Genetics and Biochemistry, Saccharomyces cerevisiae provides insights into its mechanisms, including its regulation by RNA-protein interactions. Recent genome-wide analyses indicate that regulated splicing is broad and biologically relevant even in organisms with a relatively simple intronic structure, such as yeast. Furthermore, the possibility of coordination in splicing regulation at genomic level is becoming clear in this model organism. This should provide a valuable system to approach the complex problem of the role of regulated splicing in genomic expression.

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.

The structure of human tRNALys3 anticodon bound to HIV genome is stabilized by modified nucleosides and adjacent mismatch base pairs

Replication of human immunodeficiency virus (HIV) requires base pairing of the reverse transcriptase primer, human tRNA(Lys3), to the viral RNA. Although the major complementary base pairing occurs between the HIV primer binding sequence (PBS) and the tRNA's 3'-terminus, an important discriminatory, secondary contact occurs between the viral A-rich Loop I, 5'-adjacent to the PBS, and the modified, U-rich anticodon domain of tRNA(Lys3). The importance of individual and combined anticodon modifications to the tRNA/HIV-1 Loop I RNA's interaction was determined. The thermal stabilities of variously modified tRNA anticodon region sequences bound to the Loop I of viral sub(sero)types G and B were analyzed and the structure of one duplex containing two modified nucleosides was determined using NMR spectroscopy and restrained molecular dynamics. The modifications 2-thiouridine, s(2)U(34), and pseudouridine, Psi(39), appreciably stabilized the interaction of the anticodon region with the viral subtype G and B RNAs. The structure of the duplex results in two coaxially stacked A-form RNA stems separated by two mismatched base pairs, U(162)*Psi(39) and G(163)*A(38), that maintained a reasonable A-form helix diameter. The tRNA's s(2)U(34) stabilized the interaction between the A-rich HIV Loop I sequence and the U-rich anticodon, whereas the tRNA's Psi(39) stabilized the adjacent mismatched pairs.

A short motif in Drosophila SECIS Binding Protein 2 provides differential binding affinity to SECIS RNA hairpins

Selenoproteins contain the amino acid selenocysteine which is encoded by a UGA Sec codon. Recoding UGA Sec requires a complex mechanism, comprising the cis-acting SECIS RNA hairpin in the 3′UTR of selenoprotein mRNAs, and trans-acting factors. Among these, the SECIS Binding Protein 2 (SBP2) is central to the mechanism. SBP2 has been so far functionally characterized only in rats and humans. In this work, we report the characterization of the Drosophila melanogaster SBP2 (dSBP2). Despite its shorter length, it retained the same selenoprotein synthesis-promoting capabilities as the mammalian counterpart. However, a major difference resides in the SECIS recognition pattern: while human SBP2 (hSBP2) binds the distinct form 1 and 2 SECIS RNAs with similar affinities, dSBP2 exhibits high affinity toward form 2 only. In addition, we report the identification of a K (lysine)-rich domain in all SBP2s, essential for SECIS and 60S ribosomal subunit binding, differing from the well-characterized L7Ae RNA-binding domain. Swapping only five amino acids between dSBP2 and hSBP2 in the K-rich domain conferred reversed SECIS-binding properties to the proteins, thus unveiling an important sequence for form 1 binding.

T-Coffee: a web server for the multiple sequence alignment of protein and RNA sequences using structural information and homology extension

This article introduces a new interface for T-Coffee, a consistency-based multiple sequence alignment program. This interface provides an easy and intuitive access to the most popular functionality of the package. These include the default T-Coffee mode for protein and nucleic acid sequences, the M-Coffee mode that allows combining the output of any other aligners, and template-based modes of T-Coffee that deliver high accuracy alignments while using structural or homology derived templates. These three available template modes are Expresso for the alignment of protein with a known 3D-Structure, R-Coffee to align RNA sequences with conserved secondary structures and PSI-Coffee to accurately align distantly related sequences using homology extension. The new server benefits from recent improvements of the T-Coffee algorithm and can align up to 150 sequences as long as 10 000 residues and is available from both http://www.tcoffee.org and its main mirror http://tcoffee.crg.cat.

Selección de péptidos sintéticos del Virus de la Hepatitis G (GBV-C/HGV) inhibidores del Péptido de Fusión HIV-I

El GB virus C (GBV-C) o virus de l'hepatitis G (HGV) es un virus format per una única cadena de RNA que pertany a la familia Flaviviridae. En els últims anys, s'han publicat nombrosos treballs en els quals s'associa la coinfecció del GBV-C i del virus de la immunodeficiència humana (VIH) amb una menor progressió de l'esmentada malaltia així com amb una major supervivència dels pacients una vegada que la SIDA s'ha desenvolupat. El mecanisme pel qual el virus GBV-C/HGV exerceix un “efecte protector” en els pacients amb VIH encara no està descrit. L’estudi de la interacció entre els virus GBVC/HGV i VIH podria donar lloc al desenvolupament de nous agents terapèutics per al tractament de la SIDA.Treballs recents mostren com la capacitat inhibitòria del virus del GBV-C/HGV és deguda a la seva glicoproteina estructural E2. S’ha vist que aquesta proteina seria capaç d’inhibir la primera fase de replicació de VIH, així com la unió i la fusió amb les membranes cel•lulars. Sobre la base d’aquests estudis, l’objectiu d’aquest treball ha estat seleccionar inhibidors del pèptid de fusió del VIH utilitzant pèptids sintètics de la proteina E2 del GBV-C/HGV. El treball realitzat ha consistit en estudiar, utilitzant assajos biofísics de leakage i de lipid mixing, la capacitat dels pèptids de la proteina estructural del virus del GBV-C/HGV per inhibir la interacció i el procés de desestabilització de membranes induïdes pel pèptid de fusió de la glicoproteina de l’embolcall, GP41, del VIH. Aquests assajos, com es descriu en treballs anteriors, han resultat útils per a la selecció i la identificació de compostos amb activitat específica anti-GP41. Es pot afirmar que efectivament els pèptids seleccionats de la proteina E2 del virus del GBV-C/HGV inhibeixen l’activitat del pèptid de fusió del VIH probablement com a consequència d’un canvi conformacional en aquest darrer.

Cirp function and characterisation in RNA and microRNAs

In order to search for novel genes involved in cell proliferation, the hypothesis was that by infecting primary cells with a cDNA library of immortal cells would render immortalizing genes. Consequently it has been discovered CIRP (Cold inducible RNA-binding protein). Mammalian cells exposed to mild hypothermia show a general inhibition of protein synthesis and a concomitant increase in the expression of a small number of cold-shock mRNAs and proteins. Rbm3, another RNA binding protein belonging to the same family, has been postulated to facilitate protein synthesis at mild cold shock. To investigate if the same occurs for CIRP, CIRP was overexpressed in primary cells and protein sintesis was measured. Interestingly, CIRP increased protein synthesis, however, such increase did not involve an increase in the polysome fraction or affected the ribosome profile. In addition, the effect caused by CIRP inhibition or knockdown was also analyzed. Different siRNAs against CIRP were tested. Once checked their efficiency by decreasing CIRP at mRNA and protein levels, proliferation was tested by BrdU, cell number (DAPI) and proliferation curves were performed. Interestingly, CIRP provoke a decreased proliferation in primary cells: MEFs, HMEC; and cancer cells: TERA2 and HeLa. In conclusion, we describe for the first time that CIRP bypasses replicative senescence when over-expressed at physiological temperature (37ºC) by increasing a general protein synthesis. This effect is achieved through ERK1/2 activation in MEFs.The decrease in growth rate found in mammalian cells treated with mild cold stress is not entirely attributable to arrested metabolism. This decrease may also involve an active process in which CIRP and other stress-responsive proteins play a fundamental role in stimulating proliferation. Although most cell proteins are down-regulated or inhibited with cold stress, CIRP is activated to maintain cells in an active proliferative status and its overexpression at 37°C might be potentially oncogenic.