Bioinformatics and Functional Genomics Research Group

Salamanca, Spain

Bioinformatics and Functional Genomics Research Group

Salamanca, Spain
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PubMed | La Paz University Hospital Idi, Grupo Espanol Multidisciplinar en Cancer Digestivo GEMCAD ., IMDEA Madrid Institute for Advanced Studies, Genomics Unit and Bioinformatics and Functional Genomics Research Group
Type: Journal Article | Journal: Oncotarget | Year: 2015

Lipid metabolism plays an essential role in carcinogenesis due to the requirements of tumoral cells to sustain increased structural, energetic and biosynthetic precursor demands for cell proliferation. We investigated the association between expression of lipid metabolism-related genes and clinical outcome in intermediate-stage colon cancer patients with the aim of identifying a metabolic profile associated with greater malignancy and increased risk of relapse. Expression profile of 70 lipid metabolism-related genes was determined in 77 patients with stage II colon cancer. Cox regression analyses using c-index methodology was applied to identify a metabolic-related signature associated to prognosis. The metabolic signature was further confirmed in two independent validation sets of 120 patients and additionally, in a group of 264 patients from a public database. The combined analysis of these 4 genes, ABCA1, ACSL1, AGPAT1 and SCD, constitutes a metabolic-signature (ColoLipidGene) able to accurately stratify stage II colon cancer patients with 5-fold higher risk of relapse with strong statistical power in the four independent groups of patients. The identification of a group of 4 genes that predict survival in intermediate-stage colon cancer patients allows delineation of a high-risk group that may benefit from adjuvant therapy, and avoids the toxic and unnecessary chemotherapy in patients classified as low-risk group.


Ferragud J.,Research Center Principe Felipe | Avivar-Valderas A.,Research Center Principe Felipe | Avivar-Valderas A.,Mount Sinai School of Medicine | Pla A.,Research Center Principe Felipe | And 2 more authors.
FEBS Letters | Year: 2011

Using transcriptomic gene expression profiling we found tumor suppressor DRO1 being repressed in AIB1 transgenic mice. In agreement, AIB1 represses DRO1 promoter and its expression levels inversely correlate with DRO1 in several cancer cell lines and in ectopic and silencing assays. Estrogen modulators treatment showed a regulation in an estrogen receptor-dependent fashion. Importantly, DRO1 overexpression resulted in BCLAF1 upregulation, a compelling concept given that BCLAF1 is a death-promoting transcriptional repressor. Additionally, DRO1 shuttles from Golgi to the endoplasmic reticulum upon apoptotic stimuli, where it is predicted to facilitate the apoptosis cascade. Finally, DRO1 repression is an important factor for AIB1-mediated inhibition of apoptosis. Collectively, our results reveal DRO1 as an AIB1-targeted tumor suppressor, providing a novel mechanism for AIB1-dependent inhibition of apoptosis. © 2011 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.


Souiai O.,French Institute of Health and Medical Research | Souiai O.,Aix - Marseille University | Souiai O.,Institute Pasteur | Becker E.,French Institute of Health and Medical Research | And 7 more authors.
PLoS ONE | Year: 2011

Interactome networks represent sets of possible physical interactions between proteins. They lack spatio-temporal information by construction. However, the specialized functions of the differentiated cell types which are assembled into tissues or organs depend on the combinatorial arrangements of proteins and their physical interactions. Is tissue-specificity, therefore, encoded within the interactome? In order to address this question, we combined protein-protein interactions, expression data, functional annotations and interactome topology. We first identified a subnetwork formed exclusively of proteins whose interactions were observed in all tested tissues. These are mainly involved in housekeeping functions and are located at the topological center of the interactome. This 'Largest Common Interactome Network' represents a 'functional interactome core'. Interestingly, two types of tissue-specific interactions are distinguished when considering function and network topology: tissue-specific interactions involved in regulatory and developmental functions are central whereas tissue-specific interactions involved in organ physiological functions are peripheral. Overall, the functional organization of the human interactome reflects several integrative levels of functions with housekeeping and regulatory tissue-specific functions at the center and physiological tissue-specific functions at the periphery. This gradient of functions recapitulates the organization of organs, from cells to organs. Given that several gradients have already been identified across interactomes, we propose that gradients may represent a general principle of protein-protein interaction network organization. © 2011 Souiai et al.


PubMed | Bioinformatics and Functional Genomics Research Group, Lund University, University of Navarra, Cancer Research Center USAL IBSAL and 2 more.
Type: Journal Article | Journal: Journal of proteome research | Year: 2015

A comprehensive study of the molecular active landscape of human cells can be undertaken to integrate two different but complementary perspectives: transcriptomics, and proteomics. After the genome era, proteomics has emerged as a powerful tool to simultaneously identify and characterize the compendium of thousands of different proteins active in a cell. Thus, the Chromosome-centric Human Proteome Project (C-HPP) is promoting a full characterization of the human proteome combining high-throughput proteomics with the data derived from genome-wide expression profiling of protein-coding genes. Here we present a full proteomic profiling of a human lymphoma B-cell line (Ramos) performed using a nanoUPLC-LTQ-Orbitrap Velos proteomic platform, combined to an in-depth transcriptomic profiling of the same cell type. Data are available via ProteomeXchange with identifier PXD001933. Integration of the proteomic and transcriptomic data sets revealed a 94% overlap in the proteins identified by both -omics approaches. Moreover, functional enrichment analysis of the proteomic profiles showed an enrichment of several functions directly related to the biological and morphological characteristics of B-cells. In turn, about 30% of all protein-coding genes present in the whole human genome were identified as being expressed by the Ramos cells (stable average of 30% genes along all the chromosomes), revealing the size of the protein expression-set present in one specific human cell type. Additionally, the identification of missing proteins in our data sets has been reported, highlighting the power of the approach. Also, a comparison between neXtProt and UniProt database searches has been performed. In summary, our transcriptomic and proteomic experimental profiling provided a high coverage report of the expressed proteome from a human lymphoma B-cell type with a clear insight into the biological processes that characterized these cells. In this way, we demonstrated the usefulness of combining -omics for a comprehensive characterization of specific biological systems.


Santos-Garcia G.,University of Salamanca | Santos-Garcia G.,Bioinformatics and Functional Genomics Research Group | Santos-Garcia G.,SRI International
Advances in Intelligent Systems and Computing | Year: 2014

Biological pathways define complex interaction networks where multiple molecular elements work in a series of reactions to produce a response to different biomolecular signals. These biological systems are dynamic and we need mathematical methods that can analyze symbolic elements and complex interactions between them to produce adequate readouts of such systems. Rewriting logic procedures are adequate tools to handle dynamic systems which are applied to the study of specific biological pathways behaviour. Pathway Logic is a rewriting logic development applied to symbolic systems biology. Rewriting logic language Maude allows us to define transition rules and to set up queries about the flow in the biological system. In this paper we describe the use of Pathway Logic to model and analyze the dynamics in a well-known signaling transduction pathway: epidermal growth factor (EGF) pathway. We also use Pathway Logic Assistant (PLA) tool to browse and query this system. © Springer International Publishing Switzerland 2014.


Risueno A.,Bioinformatics and Functional Genomics Research Group | Fontanillo C.,Bioinformatics and Functional Genomics Research Group | Dinger M.E.,University of Queensland | De Las Rivas J.,Bioinformatics and Functional Genomics Research Group
BMC Bioinformatics | Year: 2010

Background: Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. However, expression signals are typically calculated using the assignment of "probesets" to genes, without addressing the problem of "gene" definition or proper consideration of the location of the measuring probes in the context of the currently known genomes and transcriptomes. Moreover, as our knowledge of metazoan genomes improves, the number of both protein-coding and noncoding genes, as well as their associated isoforms, continues to increase. Consequently, there is a need for new databases that combine genomic and transcriptomic information and provide updated mapping of expression probes to current genomic annotations.Results: GATExplorer (Genomic and Transcriptomic Explorer) is a database and web platform that integrates a gene loci browser with nucleotide level mappings of oligo probes from expression microarrays. It allows interactive exploration of gene loci, transcripts and exons of human, mouse and rat genomes, and shows the specific location of all mappable Affymetrix microarray probes and their respective expression levels in a broad set of biological samples. The web site allows visualization of probes in their genomic context together with any associated protein-coding or noncoding transcripts. In the case of all-exon arrays, this provides a means by which the expression of the individual exons within a gene can be compared, thereby facilitating the identification and analysis of alternatively spliced exons. The application integrates data from four major source databases: Ensembl, RNAdb, Affymetrix and GeneAtlas; and it provides the users with a series of files and packages (R CDFs) to analyze particular query expression datasets. The maps cover both the widely used Affymetrix GeneChip microarrays based on 3' expression (e.g. human HG U133 series) and the all-exon expression microarrays (Gene 1.0 and Exon 1.0).Conclusions: GATExplorer is an integrated database that combines genomic/transcriptomic visualization with nucleotide-level probe mapping. By considering expression at the nucleotide level rather than the gene level, it shows that the arrays detect expression signals from entities that most researchers do not contemplate or discriminate. This approach provides the means to undertake a higher resolution analysis of microarray data and potentially extract considerably more detailed and biologically accurate information from existing and future microarray experiments. © 2010 Risueño et al; licensee BioMed Central Ltd.


De las rivas J.,Bioinformatics and Functional Genomics Research Group | Fontanillo C.,Bioinformatics and Functional Genomics Research Group
Briefings in Functional Genomics | Year: 2012

Mapping and understanding of the protein interaction networks with their key modules and hubs can provide deeper insights into the molecular machinery underlying complex phenotypes. In this article, we present the basic characteristics and definitions of protein networks, starting with a distinction of the different types of associations between proteins. We focus the review on protein-protein interactions (PPIs), a subset of associations defined as physical contacts between proteins that occur by selective molecular docking in a particular biological context. We present such definition as opposed to other types of protein associations derived from regulatory, genetic, structural or functional relations. To determine PPIs, a variety of binary and co-complex methods exist; however, not all the technologies provide the same information and data quality. A way of increasing confidence in a given protein interaction is to integrate orthogonal experimental evidences. The use of several complementary methods testing each single interaction assesses the accuracy of PPI data and tries to minimize the occurrence of false interactions. Following this approach there have been important efforts to unify primary databases of experimentally proven PPIs into integrated databases. These meta-databases provide a measure of the confidence of interactions based on the number of experimental proofs that report them. As a conclusion, we can state that integrated information allows the building of more reliable interaction networks. Identification of communities, cliques, modules and hubs by analysing the topological parameters and graph properties of the protein networks allows the discovery of central/critical nodes, which are candidates to regulate cellular flux and dynamics. © The Author 2012. Published by Oxford University Press.


Prieto C.,Bioinformatics and Functional Genomics Research Group | De Las Rivas J.,Bioinformatics and Functional Genomics Research Group
Proteins: Structure, Function and Bioinformatics | Year: 2010

Assessment and improvement of the reliability of protein-protein interaction (ppi) data is critical for the progress of the currently active research on interactomes. Some interesting questions in this respect are: How three-dimensional (3D) protein structural data is present in known ppi data?, and How this kind of information can be used to validate and improve the interactomes? To address this problem, analysis and unification of six structural domain-domain interaction (sddi) datasets is presented; followed by a comparative study of these sddi data in three ppi reference sets produced at different levels of confidence. The results show that protein structural and interactomic data are partially complementary and that a larger proportion of structural information is observed in more confident interactomes. We also present, focused on the human interactome, an analysis of the domains that are more frequently present in: (i) an interactome based on validation by at least two experimental methods versus (ii) another interactome based on validation by 3D structural interaction data. These results allow to distinguish between domain pairs associated to protein interactions supported by 3D structures and domain pairs that at present are not supported by structural information. The domain pairs exclusive of interactions without associated 3D data reveal interacting conserved modules that are probably flexible, disordered, and difficult to crystallize; and which are often found in proteins involved in signaling pathways and DNA processing. © 2009 Wiley-Liss, Inc.


De Las Rivas J.,Bioinformatics and Functional Genomics Research Group
BMC genomics | Year: 2014

BACKGROUND: Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic technologies widely used in many studies on human samples are the exon-specific expression microarray platforms.RESULTS: Since there are not many validated comparative analyses to identify specific splicing events using data derived from these types of platforms, we have developed an algorithm (called ESLiM) to detect significant changes in exon use, and applied it to a reference dataset of 270 human genes that show alternative expression in different tissues. We compared the results with three other methodological approaches and provided the R source code to be applied elsewhere. The genes positively detected by these analyses also provide a verified subset of human genes that present tissue-regulated isoforms. Furthermore, we performed a validation analysis on human patient samples comparing two different subtypes of acute myeloid leukemia (AML) and we experimentally validated the splicing in several selected genes that showed exons with highly significant signal change.CONCLUSIONS: The comparative analyses with other methods using a fair set of human genes that show alternative splicing and the validation on clinical samples demonstrate that the proposed novel algorithm is a reliable tool for detecting differential splicing in exon-level expression data.


PubMed | Bioinformatics and Functional Genomics Research Group
Type: | Journal: BMC genomics | Year: 2014

Accurate analysis of whole-gene expression and individual-exon expression is essential to characterize different transcript isoforms and identify alternative splicing events in human genes. One of the omic technologies widely used in many studies on human samples are the exon-specific expression microarray platforms.Since there are not many validated comparative analyses to identify specific splicing events using data derived from these types of platforms, we have developed an algorithm (called ESLiM) to detect significant changes in exon use, and applied it to a reference dataset of 270 human genes that show alternative expression in different tissues. We compared the results with three other methodological approaches and provided the R source code to be applied elsewhere. The genes positively detected by these analyses also provide a verified subset of human genes that present tissue-regulated isoforms. Furthermore, we performed a validation analysis on human patient samples comparing two different subtypes of acute myeloid leukemia (AML) and we experimentally validated the splicing in several selected genes that showed exons with highly significant signal change.The comparative analyses with other methods using a fair set of human genes that show alternative splicing and the validation on clinical samples demonstrate that the proposed novel algorithm is a reliable tool for detecting differential splicing in exon-level expression data.

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