Bioinformatics and Functional Genomics Group

Salamanca, Spain

Bioinformatics and Functional Genomics Group

Salamanca, Spain

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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.


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.


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.


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.


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.


Droste C.,Bioinformatics and Functional Genomics Group | De Las Rivas J.,Bioinformatics and Functional Genomics Group
BMC Genomics | Year: 2016

Background: Biological pathways are subsets of the complex biomolecular wiring that occur in living cells. They are usually rationalized and depicted in cartoon maps or charts to show them in a friendly visible way. Despite these efforts to present biological pathways, the current progress of bioinformatics indicates that translation of pathways in networks can be a very useful approach to achieve a computer-based view of the complex processes and interactions that occurr in a living system. Results: We have developed a bioinformatic tool called Path2enet that provides a translation of biological pathways in protein networks integrating several layers of information about the biomolecular nodes in a multiplex view. Path2enet is an R package that reads the relations and links between proteins stored in a comprehensive database of biological pathways, KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg/ ), and integrates them with expression data from various resources and with data on protein-protein physical interactions. Path2enet tool uses the expression data to determine if a given protein in a network (i.e., a node) is active (ON) or inactive (OFF) in a specific cellular context or sample type. In this way, Path2enet reduces the complexity of the networks and reveals the proteins that are active (expressed) under specific conditions. As a proof of concept, this work presents a practical "case of use" generating the pathway-expression-networks corresponding to the NOTCH Signaling Pathway in human B- and T-lymphocytes. This case is produced by the analysis and integration in Path2enet of an experimental dataset of genome-wide expression microarrays produced with these cell types (i.e., B cells and T cells). Conclusions:Path2enet is an open source and open access tool that allows the construction of pathway-expression-networks, reading and integrating the information from biological pathways, protein interactions and gene expression cell specific data. The development of this type of tools aims to provide a more integrative and global view of the links and associations that exist between the proteins working in specific cellular systems. © 2016 The Author(s).


PubMed | Ibsal Hospital Universitario Of Salamanca and Bioinformatics and Functional Genomics Group
Type: Journal Article | Journal: BMC genomics | Year: 2016

Mesenchymal Stromal/Stem Cells (MSCs), isolated under the criteria established by the ISCT, still have a poorly characterized phenotype that is difficult to distinguish from similar cell populations. Although the field of transcriptomics and functional genomics has quickly grown in the last decade, a deep comparative analysis of human MSCs expression profiles in a meaningful cellular context has not been yet performed. There is also a need to find a well-defined MSCs gene-signature because many recent biomedical studies show that key cellular interaction processes (i.e. inmuno-modulation, cellular cross-talk, cellular maintenance, differentiation, epithelial-mesenchymal transition) are dependent on the mesenchymal stem cells within the stromal niche.In this work we define a core mesenchymal lineage signature of 489 genes based on a deep comparative analysis of multiple transcriptomic expression data series that comprise: (i) MSCs of different tissue origins; (ii) MSCs in different states of commitment; (iii) other related non-mesenchymal human cell types. The work integrates several public datasets, as well as de-novo produced microarray and RNA-Seq datasets. The results present tissue-specific signatures for adipose tissue, chorionic placenta, and bone marrow MSCs, as well as for dermal fibroblasts; providing a better definition of the relationship between fibroblasts and MSCs. Finally, novel CD marker patterns and cytokine-receptor profiles are unravelled, especially for BM-MSCs; with MCAM (CD146) revealed as a prevalent marker in this subtype of MSCs.The improved biomolecular characterization and the released genome-wide expression signatures of human MSCs provide a comprehensive new resource that can drive further functional studies and redesigned cell therapy applications.


PubMed | Bioinformatics and Functional Genomics Group and Federal University of Minas Gerais
Type: Journal Article | Journal: BMC genomics | Year: 2016

The development of large-scale technologies for quantitative transcriptomics has enabled comprehensive analysis of the gene expression profiles in complete genomes. RNA-Seq allows the measurement of gene expression levels in a manner far more precise and global than previous methods. Studies using this technology are altering our view about the extent and complexity of the eukaryotic transcriptomes. In this respect, multiple efforts have been done to determine and analyse the gene expression patterns of human cell types in different conditions, either in normal or pathological states. However, until recently, little has been reported about the evolutionary marks present in human protein-coding genes, particularly from the combined perspective of gene expression and protein evolution.We present a combined analysis of human protein-coding gene expression profiling and time-scale ancestry mapping, that places the genes in taxonomy clades and reveals eight evolutionary major steps (hallmarks), that include clusters of functionally coherent proteins. The human expressed genes are analysed using a RNA-Seq dataset of 116 samples from 32 tissues. The evolutionary analysis of the human proteins is performed combining the information from: (i) a database of orthologous proteins (OMA), (ii) the taxonomy mapping of genes to lineage clades (from NCBI Taxonomy) and (iii) the evolution time-scale mapping provided by TimeTree (Timescale of Life). The human protein-coding genes are also placed in a relational context based in the construction of a robust gene coexpression network, that reveals tighter links between age-related protein-coding genes and finds functionally coherent gene modules.Understanding the relational landscape of the human protein-coding genes is essential for interpreting the functional elements and modules of our active genome. Moreover, decoding the evolutionary history of the human genes can provide very valuable information to reveal or uncover their origin and function.


PubMed | Bioinformatics and Functional Genomics Group
Type: Journal Article | Journal: BMC genomics | Year: 2016

Biological pathways are subsets of the complex biomolecular wiring that occur in living cells. They are usually rationalized and depicted in cartoon maps or charts to show them in a friendly visible way. Despite these efforts to present biological pathways, the current progress of bioinformatics indicates that translation of pathways in networks can be a very useful approach to achieve a computer-based view of the complex processes and interactions that occurr in a living system.We have developed a bioinformatic tool called Path2enet that provides a translation of biological pathways in protein networks integrating several layers of information about the biomolecular nodes in a multiplex view. Path2enet is an R package that reads the relations and links between proteins stored in a comprehensive database of biological pathways, KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg/ ), and integrates them with expression data from various resources and with data on protein-protein physical interactions. Path2enet tool uses the expression data to determine if a given protein in a network (i.e., a node) is active (ON) or inactive (OFF) in a specific cellular context or sample type. In this way, Path2enet reduces the complexity of the networks and reveals the proteins that are active (expressed) under specific conditions. As a proof of concept, this work presents a practical case of use generating the pathway-expression-networks corresponding to the NOTCH Signaling Pathway in human B- and T-lymphocytes. This case is produced by the analysis and integration in Path2enet of an experimental dataset of genome-wide expression microarrays produced with these cell types (i.e., B cells and T cells).Path2enet is an open source and open access tool that allows the construction of pathway-expression-networks, reading and integrating the information from biological pathways, protein interactions and gene expression cell specific data. The development of this type of tools aims to provide a more integrative and global view of the links and associations that exist between the proteins working in specific cellular systems.


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

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.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.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.

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