Extracellular Vesicles from Pancreatic Cancer Stem Cells Lead an Intratumor Communication Network (EVNet) to fuel tumour progression.

OBJECTIVE: Intratumor heterogeneity drives cancer progression and therapy resistance. However, it has yet to be determined whether and how subpopulations of cancer cells interact and how this interaction affects the tumour. DESIGN: We have studied the spontaneous flow of extracellular vesicles (EVs) between subpopulations of cancer cells: cancer stem cells (CSC) and non-stem cancer cells (NSCC). To determine the biological significance of the most frequent communication route, we used pancreatic ductal adenocarcinoma (PDAC) orthotopic models, patient-derived xenografts (PDXs) and genetically engineered mouse models (GEMMs). RESULTS: We demonstrate that PDAC tumours establish an organised communication network between subpopulations of cancer cells using EVs called the EVNet). The EVNet is plastic and reshapes in response to its environment. Communication within the EVNet occurs preferentially from CSC to NSCC. Inhibition of this communication route by impairing Rab27a function in orthotopic xenographs, GEMMs and PDXs is sufficient to hamper tumour growth and phenocopies the inhibition of communication in the whole tumour. Mechanistically, we provide evidence that CSC EVs use agrin protein to promote Yes1 associated transcriptional regulator (YAP) activation via LDL receptor related protein 4 (LRP-4). Ex vivo treatment of PDXs with antiagrin significantly impairs proliferation and decreases the levels of activated YAP.Patients with high levels of agrin and low inactive YAP show worse disease-free survival. In addition, patients with a higher number of circulating agrin+ EVs show a significant increased risk of disease progression. CONCLUSION: PDAC tumours establish a cooperation network mediated by EVs that is led by CSC and agrin, which allows tumours to adapt and thrive. Targeting agrin could make targeted therapy possible for patients with PDAC and has a significant impact on CSC that feeds the tumour and is at the centre of therapy resistance.

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 relationships1,2. 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 genome3, transcriptome4 and proteome5 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.

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.

APID database: redefining protein-protein interaction experimental evidences and binary interactomes.

The collection and integration of all the known protein-protein physical interactions within a proteome framework are critical to allow proper exploration of the protein interaction networks that drive biological processes in cells at molecular level. APID Interactomes is a public resource of biological data (http://apid.dep.usal.es) that provides a comprehensive and curated collection of `protein interactomes' for more than 1100 organisms, including 30 species with more than 500 interactions, derived from the integration of experimentally detected protein-to-protein physical interactions (PPIs). We have performed an update of APID database including a redefinition of several key properties of the PPIs to provide a more precise data integration and to avoid false duplicated records. This includes the unification of all the PPIs from five primary databases of molecular interactions (BioGRID, DIP, HPRD, IntAct and MINT), plus the information from two original systematic sources of human data and from experimentally resolved 3D structures (i.e. PDBs, Protein Data Bank files, where more than two distinct proteins have been identified). Thus, APID provides PPIs reported in published research articles (with traceable PMIDs) and detected by valid experimental interaction methods that give evidences about such protein interactions (following the `ontology and controlled vocabulary': www.ebi.ac.uk/ols/ontologies/mi; developed by `HUPO PSI-MI'). Within this data mining framework, all interaction detection methods have been grouped into two main types: (i) `binary' physical direct detection methods and (ii) `indirect' methods. As a result of these redefinitions, APID provides unified protein interactomes including the specific `experimental evidences' that support each PPI, indicating whether the interactions can be considered `binary' (i.e. supported by at least one binary detection method) or not.

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.

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.

Matrix-Gla protein promotes osteosarcoma lung metastasis and associates with poor prognosis.

Osteosarcoma (OS) is the most prevalent osseous tumour in children and adolescents and, within this, lung metastases remain one of the factors associated with a dismal prognosis. At present, the genetic determinants driving pulmonary metastasis are poorly understood. We adopted a novel strategy using robust filtering analysis of transcriptomic profiling in tumour osteoblastic cell populations derived from human chemo-naive primary tumours displaying extreme phenotypes (indolent versus metastatic) to uncover predictors associated with metastasis and poor survival. We identified MGP, encoding matrix-Gla protein (MGP), a non-collagenous matrix protein previously associated with the inhibition of arterial calcification. Using different orthotopic models, we found that ectopic expression of Mgp in murine and human OS cells led to a marked increase in lung metastasis. This effect was independent of the carboxylation of glutamic acid residues required for its physiological role. Abrogation of Mgp prevented lung metastatic activity, an effect that was rescued by forced expression. Mgp levels dramatically altered endothelial adhesion, trans-endothelial migration in vitro and tumour cell extravasation ability in vivo. Furthermore, Mgp modulated metalloproteinase activities and TGFß-induced Smad2/3 phosphorylation. In the clinical setting, OS patients who developed lung metastases had high serum levels of MGP at diagnosis. Thus, MGP represents a novel adverse prognostic factor and a potential therapeutic target in OS. Microarray datasets may be found at: http://bioinfow.dep.usal.es/osteosarcoma/ Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

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

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.

Analyse multiple disease subtypes and build associated gene networks using genome-wide expression profiles.

BACKGROUND: Despite the large increase of transcriptomic studies that look for gene signatures on diseases, there is still a need for integrative approaches that obtain separation of multiple pathological states providing robust selection of gene markers for each disease subtype and information about the possible links or relations between those genes. RESULTS: We present a network-oriented and data-driven bioinformatic approach that searches for association of genes and diseases based on the analysis of genome-wide expression data derived from microarrays or RNA-Seq studies. The approach aims to (i) identify gene sets associated to different pathological states analysed together; (ii) identify a minimum subset within these genes that unequivocally differentiates and classifies the compared disease subtypes; (iii) provide a measurement of the discriminant power of these genes and (iv) identify links between the genes that characterise each of the disease subtypes. This bioinformatic approach is implemented in an R package, named geNetClassifier, available as an open access tool in Bioconductor. To illustrate the performance of the tool, we applied it to two independent datasets: 250 samples from patients with four major leukemia subtypes analysed using expression arrays; another leukemia dataset analysed with RNA-Seq that includes a subtype also present in the previous set. The results show the selection of key deregulated genes recently reported in the literature and assigned to the leukemia subtypes studied. We also show, using these independent datasets, the selection of similar genes in a network built for the same disease subtype. CONCLUSIONS: The construction of gene networks related to specific disease subtypes that include parameters such as gene-to-gene association, gene disease specificity and gene discriminant power can be very useful to draw gene-disease maps and to unravel the molecular features that characterize specific pathological states. The application of the bioinformatic tool here presented shows a neat way to achieve such molecular characterization of the diseases using genome-wide expression data.

Deregulation of genes related to iron and mitochondrial metabolism in refractory anemia with ring sideroblasts.

The presence of SF3B1 gene mutations is a hallmark of refractory anemia with ring sideroblasts (RARS). However, the mechanisms responsible for iron accumulation that characterize the Myelodysplastic Syndrome with ring sideroblasts (MDS-RS) are not completely understood. In order to gain insight in the molecular basis of MDS-RS, an integrative study of the expression and mutational status of genes related to iron and mitochondrial metabolism was carried out. A total of 231 low-risk MDS patients and 81 controls were studied. Gene expression analysis revealed that iron metabolism and mitochondrial function had the highest number of genes deregulated in RARS patients compared to controls and the refractory cytopenias with unilineage dysplasia (RCUD). Thus mitochondrial transporters SLC25 (SLC25A37 and SLC25A38) and ALAD genes were over-expressed in RARS. Moreover, significant differences were observed between patients with SF3B1 mutations and patients without the mutations. The deregulation of genes involved in iron and mitochondrial metabolism provides new insights in our knowledge of MDS-RS. New variants that could be involved in the pathogenesis of these diseases have been identified.

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

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.