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Doñinos de Salamanca, Spain

De Las Rivas J.,Bioinformatics and Functional Genomics Group | Prieto C.,Biotechnology Institute of Leon INBIOTEC
Methods in Molecular Biology | Year: 2012

Proteins are biomolecular structures that build the microscopic working machinery of any living system. Proteins within the cells and biological systems do not act alone, but rather team up into macromolecular structures enclosing intricate physicochemical dynamic connections to undertake biological functions. A critical step towards unraveling the complex molecular relationships in living systems is the mapping of protein-to-protein physical "interactions". The complete map of protein interactions that can occur in a living organism is called the "interactome". Achieving an adequate atlas of all the protein interactions within a living system should allow to build its interaction network and to identity the "central nodes" that can be critical for the function, the homeostasis, and the movement of such system. Focusing on human studies, the data about the human interactome are most relevant for current biomedical research, because it is clear that the location of the proteins in the interactome network will allow to evaluate their centrality and to redefine the potential value of each protein as a drug target. This chapter presents our current knowledge on the human protein-protein interactome and explains how such knowledge can help us to select adequate targets for drugs. © 2012 Springer Science+Business Media New York. Source


Eduati F.,University of Padua | Eduati F.,European Bioinformatics Institute | De Las Rivas J.,Bioinformatics and Functional Genomics Group | Di Camillo B.,University of Padua | And 2 more authors.
Bioinformatics | Year: 2012

Motivation: Recent developments in experimental methods facilitate increasingly larger signal transduction datasets. Two main approaches can be taken to derive a mathematical model from these data: training a network (obtained, e.g., from literature) to the data, or inferring the network from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, whereas literature-constrained methods cannot deal with incomplete networks. Results: We present an efficient approach, implemented in the R package CNORfeeder, to integrate literature-constrained and datadriven methods to infer signalling networks from perturbation experiments. Our method extends a given network with links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. We apply CNORfeeder to a network of growth and inflammatory signalling. We obtain a model with superior data fit in the human liver cancer HepG2 and propose potential missing pathways. © The Author(s) 2012. Published by Oxford University Press. Source


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

BACKGROUND: Human Mesenchymal Stromal/Stem Cells (MSCs) are adult multipotent cells that behave in a highly plastic manner, inhabiting the stroma of several tissues. The potential utility of MSCs is nowadays strongly investigated in the field of regenerative medicine and cell therapy, although many questions about their molecular identity remain uncertain.RESULTS: MSC primary cultures from human bone marrow (BM) and placenta (PL) were derived and verified by their immunophenotype standard pattern and trilineage differentiation potential. Then, a broad characterization of the transcriptome of these MSCs was performed using RNA deep sequencing (RNA-Seq). Quantitative analysis of these data rendered an extensive expression footprint that includes 5,271 protein-coding genes. Flow cytometry assays of canonical MSC CD-markers were congruent with their expression levels detected by the RNA-Seq. Expression of other recently proposed MSC markers (CD146, Nestin and CD271) was tested in the placenta samples, finding only CD146 and Nestin. Functional analysis revealed enrichment in stem cell related genes and mesenchymal regulatory transcription factors (TFs). Analysis of TF binding sites (TFBSs) identified 11 meta-regulators, including factors KLF4 and MYC among them. Epigenetically, hypomethylated promoter patterns supported the active expression of the MSC TFs found. An interaction network of these TFs was built to show up their links and relations. Assessment of dissimilarities between cell origins (BM versus PL) disclosed two hundred differentially expressed genes enrolled in microenvironment processes related to the cellular niche, as regulation of bone formation and blood vessel morphogenesis for the case of BM-MSCs. By contrast genes overexpressed in PL-MSCs showed functional enrichment on mitosis, negative regulation of cell-death and embryonic morphogenesis that supported the higher growth rates observed in the cultures of these fetal cells and their closer links with development processes.CONCLUSIONS: The results present a transcriptomic portrait of the human MSCs isolated from bone marrow and placenta. The data are released as a cell-specific resource, providing a comprehensive expression footprint of the MSCs useful to better understand their cellular and molecular biology and for further investigations on the isolation and biomedical use of these multipotent cells. Source


Roson-Burgo B.,Bioinformatics and Functional Genomics Group | Roson-Burgo B.,University of Salamanca | Sanchez-Guijo F.,University of Salamanca | Del Canizo C.,University of Salamanca | De Las Rivas J.,Bioinformatics and Functional Genomics Group
BMC Genomics | Year: 2015

Background: Human Mesenchymal Stromal/Stem Cells (MSCs) are adult multipotent cells that behave in a highly plastic manner, inhabiting the stroma of several tissues. The potential utility of MSCs is nowadays strongly investigated in the field of regenerative medicine and cell therapy, although many questions about their molecular identity remain uncertain. Results: MSC primary cultures from human bone marrow (BM) and placenta (PL) were derived and verified by their immunophenotype standard pattern and trilineage differentiation potential. Then, a broad characterization of the transcriptome of these MSCs was performed using RNA deep sequencing (RNA-Seq). Quantitative analysis of these data rendered an extensive expression footprint that includes 5,271 protein-coding genes. Flow cytometry assays of canonical MSC CD-markers were congruent with their expression levels detected by the RNA-Seq. Expression of other recently proposed MSC markers (CD146, Nestin and CD271) was tested in the placenta samples, finding only CD146 and Nestin. Functional analysis revealed enrichment in stem cell related genes and mesenchymal regulatory transcription factors (TFs). Analysis of TF binding sites (TFBSs) identified 11 meta-regulators, including factors KLF4 and MYC among them. Epigenetically, hypomethylated promoter patterns supported the active expression of the MSC TFs found. An interaction network of these TFs was built to show up their links and relations. Assessment of dissimilarities between cell origins (BM versus PL) disclosed two hundred differentially expressed genes enrolled in microenvironment processes related to the cellular niche, as regulation of bone formation and blood vessel morphogenesis for the case of BM-MSCs. By contrast genes overexpressed in PL-MSCs showed functional enrichment on mitosis, negative regulation of cell-death and embryonic morphogenesis that supported the higher growth rates observed in the cultures of these fetal cells and their closer links with development processes. Conclusions: The results present a transcriptomic portrait of the human MSCs isolated from bone marrow and placenta. The data are released as a cell-specific resource, providing a comprehensive expression footprint of the MSCs useful to better understand their cellular and molecular biology and for further investigations on the isolation and biomedical use of these multipotent cells. © 2014 Roson-Burgo et al.; licensee BioMed Central Ltd. Source


De Las Rivas J.,Bioinformatics and Functional Genomics Group | Aibar S.,Bioinformatics and Functional Genomics Group | Roson B.,Bioinformatics and Functional Genomics Group
Comprehensive Analytical Chemistry | Year: 2014

Current genome-wide studies of gene expression are achieved using two major omic technologies: high-density oligonucleotide microarrays and deep RNA sequencing. These high-throughput experimental techniques allow the detection of most known genes and are providing global gene expression profiles and gene signatures for normal and pathological states of multiple biological systems, including many human samples and cell types. At present, microarrays technology is still better established and more widely used than RNA sequencing and has provided the most gene expression data. Most analyses of the human transcriptome focus on the identification and characterization of protein-coding genes; however, the complexity of the human transcriptomic system has been found to be much more than expected, and we still do not have a clear genome-wide compendium of the genes that are active in each human tissue and cell type. Development and application of adequate bioinformatic methods is the only way to achieve a proper use of the omic-wide gene expression datasets. Thorough analysis and integration of omic studies is essential to achieve an unbiased global characterization of the active human transcriptome. In this chapter we present and describe several important concepts in modern transcriptomics and bioinformatic methods to analyze genome-wide data mainly derived from microarrays technology but also from deep-sequencing technology, in both cases applied to gene expression measurements. © 2014 Elsevier B.V. Source

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