Deciphering Master Gene Regulators and Associated Networks of Human Mesenchymal Stromal Cells.

Mesenchymal Stromal Cells (MSC) are multipotent cells characterized by self-renewal, multilineage differentiation, and immunomodulatory properties. To obtain a gene regulatory profile of human MSCs, we generated a compendium of more than two hundred cell samples with genome-wide expression data, including a homogeneous set of 93 samples of five related primary cell types: bone marrow mesenchymal stem cells (BM-MSC), hematopoietic stem cells (HSC), lymphocytes (LYM), fibroblasts (FIB), and osteoblasts (OSTB). All these samples were integrated to generate a regulatory gene network using the algorithm ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks; based on mutual information), that finds regulons (groups of target genes regulated by transcription factors) and regulators (i.e., transcription factors, TFs). Furtherly, the algorithm VIPER (Algorithm for Virtual Inference of Protein-activity by Enriched Regulon analysis) was used to inference protein activity and to identify the most significant TF regulators, which control the expression profile of the studied cells. Applying these algorithms, a footprint of candidate master regulators of BM-MSCs was defined, including the genes EPAS1, NFE2L1, SNAI2, STAB2, TEAD1, and TULP3, that presented consistent upregulation and hypomethylation in BM-MSCs. These TFs regulate the activation of the genes in the bone marrow MSC lineage and are involved in development, morphogenesis, cell differentiation, regulation of cell adhesion, and cell structure

Stroma-Mediated Resistance to S63845 and Venetoclax through MCL-1 and BCL-2 Expression Changes Induced by miR-193b-3p and miR-21-5p Dysregulation in Multiple Myeloma.

BH3-mimetics targeting anti-apoptotic proteins such as MCL-1 (S63845) or BCL-2 (venetoclax) are currently being evaluated as effective therapies for the treatment of multiple myeloma (MM). Interleukin 6, produced by mesenchymal stromal cells (MSCs), has been shown to modify the expression of anti-apoptotic proteins and their interaction with the pro-apoptotic BIM protein in MM cells. In this study, we assess the efficacy of S63845 and venetoclax in MM cells in direct co-culture with MSCs derived from MM patients (pMSCs) to identify additional mechanisms involved in the stroma-induced resistance to these agents. MicroRNAs miR-193b-3p and miR-21-5p emerged among the top deregulated miRNAs in myeloma cells when directly co-cultured with pMSCs, and we show their contribution to changes in MCL-1 and BCL-2 protein expression and in the activity of S63845 and venetoclax. Additionally, direct contact with pMSCs under S63845 and/or venetoclax treatment modifies myeloma cell dependence on different BCL-2 family anti-apoptotic proteins in relation to BIM, making myeloma cells more dependent on the non-targeted anti-apoptotic protein or BCL-XL. Finally, we show a potent effect of the combination of S63845 and venetoclax even in the presence of pMSCs, which supports this combinatorial approach for the treatment of MM

A reference map of the human binary protein interactome.

Global insights into cellular organization and genome function require comprehensive understanding of the interactome networks that mediate genotype-phenotype relationships(1,2). Here we present a human 'all-by-all' reference interactome map of human binary protein interactions, or 'HuRI'. With approximately 53,000 protein-protein interactions, HuRI has approximately four times as many such interactions as there are high-quality curated interactions from small-scale studies. The integration of HuRI with genome(3), transcriptome(4) and proteome(5) data enables cellular function to be studied within most physiological or pathological cellular contexts. We demonstrate the utility of HuRI in identifying the specific subcellular roles of protein-protein interactions. Inferred tissue-specific networks reveal general principles for the formation of cellular context-specific functions and elucidate potential molecular mechanisms that might underlie tissue-specific phenotypes of Mendelian diseases. HuRI is a systematic proteome-wide reference that links genomic variation to phenotypic outcomes

Analysis of breast cancer genomic data to identify master gene regulators in a network context using Transcription Factor Enriched Regulons (TFERs) and Topologically Associating Domains (TADs)

Resumen del póster presentado al 1st Joint Meeting of the French-Portuguese-Spanish Biochemical and Molecular Biology Societies y al XL Spanish Society of Biochemistry and Molecular Biology (SEBBM) Congress, celebrado en Barcelona (España) del 23 al 26 de octubre de 2017.Tumorigenesis and tumor progression is a complex pathological process that occurs in cells that undergo a progressive alteration in which multiple genes and gene products are modified to change the normal state of a native cell in an transformed state. This progressive alteration is not always the same in every cell, and we currently know that there are –at least in humans– several hundred “onco-genes” that can drive such transformation and are different in different types of cancer. Despite this complexity, all cancer cells acquire or gain some common features such as: (i) reversion towards less differentiated cellular states; (ii) stimulation of cell cycle with enhanced proliferation; (iii) increased basal metabolic rate; etc. These common functional features allow to postulate that cancer formation and growth relies on the existence of some master and critical genes that regulate and drive tumorigenesis. This idea is in line with the “tumor bottleneck hypothesis” postulated by Dr. Andrea Califano (http://califano.c2b2.columbia.edu/cancer-systems-biology), which holds that if different genetic events contribute to a relatively uniform disease phenotype, their effect must eventually converge to a single gene or a small number of genes within the context of the tumor-driving cellular network. Based on this approach we performed a network-based analysis of the genome-wide expression profiles of a large dataset of breast cancer (BCC) samples (including major subtypes). Within this data set, we applied an algorithm to look for “master regulators” based on two complementary strategies: (i) search and mapping of Transcription Factor Enriched Regulons (TFERs) to identify co-regulated genes under the control of specific TFs; (ii) search and mapping of Topologically Associating Domains (TADs) to identify modular units of coordinated gene expression. We developed an algorithm to implement these analyses and we discovered genes postulated as “master regulators” that are different in different BCC subtypes (luminal, basal, etc.).Peer reviewe

Classification of protein motifs based on subcellular localization uncovers evolutionary relationships at both sequence and functional levels

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.[Background]: Most proteins have evolved in specific cellular compartments that limit their functions and potential interactions. On the other hand, motifs define amino acid arrangements conserved between protein family members and represent powerful tools for assigning function to protein sequences. The ideal motif would identify all members of a protein family but in practice many motifs identify both family members and unrelated proteins, referred to as True Positive (TP) and False Positive (FP) sequences, respectively. [Results]: To address the relationship between protein motifs, protein function and cellular localization, we systematically assigned subcellular localization data to motif sequences from the comprehensive PROSITE sequence motif database. Using this data we analyzed relationships between localization and function. We find that TPs and FPs have a strong tendency to localize in different compartments. When multiple localizations are considered, TPs are usually distributed between related cellular compartments. We also identified cases where FPs are concentrated in particular subcellular regions, indicating possible functional or evolutionary relationships with TP sequences of the same motif. [Conclusions]: Our findings suggest that the systematic examination of subcellular localization has the potential to uncover evolutionary and functional relationships between motif-containing sequences. We believe that this type of analysis complements existing motif annotations and could aid in their interpretation. Our results shed light on the evolution of cellular organelles and potentially establish the basis for new subcellular localization and function prediction algorithms.We acknowledge institutional support from the Junta de Andalucía to the CABD, and to the Unit of Information Resources for Research at the “Consejo Superior de Investigaciones Científicas” (CSIC) for the article-processing charge contribution.Peer Reviewe

Bioinformatics in Latin America and SoIBio impact, a tale of spin-off and expansion around genomes and protein structures

Owing to the emerging impact of bioinformatics and computational biology, in this article, we present an overview of the history and current state of the research on this field in Latin America (LA). It will be difficult to cover without inequality all the efforts, initiatives and works that have happened for the past two decades in this vast region (that includes >19 million km2 and >600 million people). Despite the difficulty, we have done an analytical search looking for publications in the field made by researchers from 19 LA countries in the past 25 years. In this way, we find that research in bioinformatics in this region should develop twice to approach the average world scientific production in the field. We also found some of the pioneering scientists who initiated and led bioinformatics in the region and were promoters of this new scientific field. Our analysis also reveals that spin-off began around some specific areas within the biomolecular sciences: studies on genomes (anchored in the new generation of deep sequencing technologies, followed by developments in proteomics) and studies on protein structures (supported by three-dimensional structural determination technologies and their computational advancement). Finally, we show that the contribution to this endeavour of the Iberoamerican Society for Bioinformatics, founded in Mexico in 2009, has been significant, as it is a leading forum to join efforts of many scientists from LA interested in promoting research, training and education in bioinformatics.The publication charges for this article were funded by the research grant PI15/00328, given by the Instituto de Salud Carlos III (ISCiii, MINECO, Spain) and co-funded by the Fondo Europeo de Desarrollo Regional (FEDER, Europe)

Decomposing variation in heterogeneous clinical omic data

Resumen del póster presentado al XXXIX Congreso de la Sociedad Española de Bioquímica y Biología Molecular, celebrado en Salamanca del 5 al 8 de septiembre de 2016.Individual diversity is one of the most complex issues to deal with in omic studies of large populations. Most of the current approaches to detect differences using new generation omic-wide data are based on the analyses of significant mean or median changes that reflect average alterations in the whole population studied versus their controls or references. In this situation the biomarkers that are only related to a subset of samples are difficult to detect and often wrongly assigned, but in many occasions such sample subsets are quite relevant for the biological study performed. Considering that technical and batch-associated variations are mostly treated and corrected by robust normalization methods developed in recent years (RUV, SVA) (PMIDs: 25150836, 22257669), we explore different methodologies to understand the frequent heterogeneity in disease sample sets that come from specific clinical or biological features only associated to a subset of the population. Moreover, the classical normalization approaches often apply ways to reduce or remove unwanted variation (PMID: 26286812), but our scope is not to decrease or alter the sample signals but to identify omic biomarkers that are modified only in a sub-population of the clinical cohorts studied. The first method proposing the identification of disease outliers using genomic data was COPA (PMID: 16895932), and several other more recent approaches have tried to tackle this problem. One efficient approach to identify features associated to subsets of a population can be to explore in a recursive way the existing dependent relationships between features and samples provided by large-scale omic datasets. This type of comprehensive omic data (genomic, transcriptomic, etc) can facilitate the finding of new significant biomarkers related to hidden or not clear phenotypic conditions. The stratification of patients according to their omic profiles achieved using recursive heuristic methods is a way that we propose to assign new biomarker features between closely related states and to subset samples depending on their omic patterns.Peer reviewe

Deciphering master gene regulators and associated networks of human Mesenchymal Stromal Cells

This article belongs to the Special Issue Bioinformatics and Precision Computational Biology: Selected Papers from the X International Conference on Bioinformatics #SoIBio+10.Mesenchymal Stromal Cells (MSC) are multipotent cells characterized by self-renewal, multilineage differentiation, and immunomodulatory properties. To obtain a gene regulatory profile of human MSCs, we generated a compendium of more than two hundred cell samples with genome-wide expression data, including a homogeneous set of 93 samples of five related primary cell types: bone marrow mesenchymal stem cells (BM-MSC), hematopoietic stem cells (HSC), lymphocytes (LYM), fibroblasts (FIB), and osteoblasts (OSTB). All these samples were integrated to generate a regulatory gene network using the algorithm ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks; based on mutual information), that finds regulons (groups of target genes regulated by transcription factors) and regulators (i.e., transcription factors, TFs). Furtherly, the algorithm VIPER (Algorithm for Virtual Inference of Protein-activity by Enriched Regulon analysis) was used to inference protein activity and to identify the most significant TF regulators, which control the expression profile of the studied cells. Applying these algorithms, a footprint of candidate master regulators of BM-MSCs was defined, including the genes EPAS1, NFE2L1, SNAI2, STAB2, TEAD1, and TULP3, that presented consistent upregulation and hypomethylation in BM-MSCs. These TFs regulate the activation of the genes in the bone marrow MSC lineage and are involved in development, morphogenesis, cell differentiation, regulation of cell adhesion, and cell structure.This research was funded by Fondo de Investigación Sanitaria—Instituto de Salud Carlos III (FIS—ISCIII, Spanish Ministry of Health, project reference PI18/00591) where J.D.L.R. was the PI. The research was also partially funded by Fondo de Investigación Sanitaria (FIS—ISCIII, project reference PI16/01407). The work within these projects was also cofunded by the FEDER program of the European Union. J.D.L.R. lab acknowledges also the support given by the European Project H2020 ArrestAD (Project ID: 737390, H2020-FETOPEN-1-2016-2017).Peer reviewe

Evolutionary hallmarks of the human proteome: Chasing the age and coregulation of protein-coding genes

[Background]: 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. [Results]: 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. [Conclusions]: 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.We acknowledge the funding provided to Dr. J. De Las Rivas group by the Local Government, “Junta de Castilla y Leon” (JCyL, Valladolid, Spain, grant number BIO/SA08/14); and by the Spanish Government, “Ministerio de Economia y Competitividad” (MINECO) with grants of the ISCiii co-funded by FEDER (grant references PI12/00624 and PI15/00328). We also acknowledge a research grant to K.P. Lopes as visiting PhD student at the CiC-IMBCC (from January 2015 to January 2016) provided by the Brazilian National Council of Technological and Scientific Development (CNPq). We also acknowledge a PhD research grant to F.J. Campos-Laborie (“Ayudas a la contratación de Personal Investigador”) provided by the JCyL with the support of the “Fondo Social Europeo” (FSE).Peer Reviewe

Identification of expression patterns in the progression of disease stages by integration of transcriptomic data

From Statistical Methods for Omics Data Integration and Analysis 2015 - Valencia, Spain. 14-16 September 2015.[Background]: In the study of complex diseases using genome-wide expression data from clinical samples, a difficult case is the identification and mapping of the gene signatures associated to the stages that occur in the progression of a disease. The stages usually correspond to different subtypes or classes of the disease, and the difficulty to identify them often comes from patient heterogeneity and sample variability that can hide the biomedical relevant changes that characterize each stage, making standard differential analysis inadequate or inefficient. [Results]: We propose a methodology to study diseases or disease stages ordered in a sequential manner (e.g. from early stages with good prognosis to more acute or serious stages associated to poor prognosis). The methodology is applied to diseases that have been studied obtaining genome-wide expression profiling of cohorts of patients at different stages. The approach allows searching for consistent expression patterns along the progression of the disease through two major steps: (i) identifying genes with increasing or decreasing trends in the progression of the disease; (ii) clustering the increasing/decreasing gene expression patterns using an unsupervised approach to reveal whether there are consistent patterns and find genes altered at specific disease stages. The first step is carried out using Gamma rank correlation to identify genes whose expression correlates with a categorical variable that represents the stages of the disease. The second step is done using a Self Organizing Map (SOM) to cluster the genes according to their progressive profiles and identify specific patterns. Both steps are done after normalization of the genomic data to allow the integration of multiple independent datasets. In order to validate the results and evaluate their consistency and biological relevance, the methodology is applied to datasets of three different diseases: myelodysplastic syndrome, colorectal cancer and Alzheimer's disease. A software script written in R, named genediseasePatterns, is provided to allow the use and application of the methodology. [Conclusion]: The method presented allows the analysis of the progression of complex and heterogeneous diseases that can be divided in pathological stages. It identifies gene groups whose expression patterns change along the advance of the disease, and it can be applied to different types of genomic data studying cohorts of patients in different states.SA and FJCL were supported by a Research Grant to young scientists given by the Consejeria de Educacion (Junta de Castilla y León, Spain) and co-funded by the European Social Fund (ESF). MA was supported by a Research Grant for the PhD of the Spanish National Research Council (Junta para Ampliación de Estudios, Consejo Superior de Investigaciones Cientificas, CSIC; ref. 09-02402) also co-funded by the European Social Fund (ESF). We also acknowledge the funding provided to Dr. J. De Las Rivas research group by the Consejeria de Sanidad (Junta de Castilla y León, Spain) with project grant on Biomedicine: BIO/SA08/14; and by the Spanish Ministry of Economy and Competitiveness (MINECO) through the National Institute of Health Carlos III (ISCiii) with project grants co-funded by FEDER: PI12/00624 and PI15/00328.Peer Reviewe