Adenovirus VA RNA-derived miRNAs target cellular genes involved in cell growth, gene expression and DNA repair

Adenovirus virus-associated (VA) RNAs are processed to functional viral miRNAs or mivaRNAs. mivaRNAs are important for virus production, suggesting that they may target cellular or viral genes that affect the virus cell cycle. To look for cellular targets of mivaRNAs, we first identified genes downregulated in the presence of VA RNAs by microarray analysis. These genes were then screened for mivaRNA target sites using several bioinformatic tools. The combination of microarray analysis and bioinformatics allowed us to select the splicing and translation regulator TIA-1 as a putative mivaRNA target. We show that TIA-1 is downregulated at mRNA and protein levels in infected cells expressing functional mivaRNAs and in transfected cells that express mivaRNAI-138, one of the most abundant adenoviral miRNAs. Also, reporter assays show that TIA-1 is downregulated directly by mivaRNAI-138. To determine whether mivaRNAs could target other cellular genes we analyzed 50 additional putative targets. Thirty of them were downregulated in infected or transfected cells expressing mivaRNAs. Some of these genes are important for cell growth, transcription, RNA metabolism and DNA repair. We believe that a mivaRNA-mediated fine tune of the expression of some of these genes could be important in adenovirus cell cycle

BCR-ABL1-induced expression of HSPA8 promotes cell survival in chronic myeloid leukaemia

In order to determine new signal transduction pathways implicated in chronic myeloid leukaemia (CML), we performed a gene expression profile comparison between CD34+ cells from CML patients and healthy donors. Functional studies were performed using the Mo7e and Mo7e-p210 cell lines. Expression of CCND1 (Cyclin D1), as well as the chaperone HSPA8, which is important for regulation of CCND1, were significantly upregulated in CD34+ CML cells. Upregulation of HSPA8 was dependent, at least in part, on STAT5 (signal transducer and activator of transcrition 5)-dependent transcriptional activation, as demonstrated by chromatin immunoprecipitation. The presence of HSPA8 in the nuclear protein fraction as well as its binding to CCND1 suggests that it may contribute to stabilization of the CCND1/CDK4 complex, which, in turn, may participate in proliferation of CML cells. Treatment of CML cells with the specific HSPA8 inhibitor 15-deoxyspergualin induced inhibition of CML cell viability but did not induce apoptosis. In conclusion, our studies suggest that STAT5-mediated activation of HSPA8 induces nuclear translocation and activation of the CCND1/CDK4 complex leading to increased proliferation of CML cells, deciphering a new pathway implicated in CML and supporting a potential role of chaperone inhibitors in the treatment of CML

IL-10 expression defines an immunosuppressive dendritic cell population induced by antitumor therapeutic vaccination

Vaccination induces immunostimulatory signals that are often accompanied by regulatory mechanisms such as IL-10, which control T-cell activation and inhibit vaccine-dependent antitumor therapeutic effect. Here we characterized IL- 10-producing cells in different tumor models treated with therapeutic vaccines. Although several cell subsets produced IL-10 irrespective of treatment, an early vaccine-dependent induction of IL-10 was detected in dendritic cells (DC). IL-10 production defined a DC population characterized by a poorly mature phenotype, lower expression of T-cell stimulating molecules and upregulation of PD-L1. These IL-10+ DC showed impaired in vitro T-cell stimulatory capacity, which was rescued by incubation with IL-10R and PD-L1-inhibiting antibodies. In vivo IL-10 blockade during vaccination decreased the proportion of IL-10+ DC and improved their maturation, without modifying PD-L1 expression. Similarly, PD-L1 blockade did not affect IL-10 expression. Interestingly, vaccination combined with simultaneous blockade of IL-10 and PD-L1 induced stronger immune responses, resulting in a higher therapeutic efficacy in tumor-bearing mice. These results show that vaccine-induced immunoregulatory IL-10+ DC impair priming of antitumor immunity, suggesting that therapeutic vaccination protocols may benefit from combined targeting of inhibitory molecules expressed by this DC subset

Identification of novel synthetic lethal vulnerability in non small cell lung cancer by co targeting TMPRSS4 and DDR1

Finding novel targets in non-small cell lung cancer (NSCLC) is highly needed and identification of synthetic lethality between two genes is a new approach to target NSCLC. We previously found that TMPRSS4 promotes NSCLC growth and constitutes a prognostic biomarker. Here, through large-scale analyses across 5 public databases we identified consistent co-expression between TMPRSS4 and DDR1. Similar to TMPRSS4, DDR1 promoter was hypomethylated in NSCLC in 3 independent cohorts and hypomethylation was an independent prognostic factor of disease-free survival. Treatment with 5-azacitidine increased DDR1 levels in cell lines, suggesting an epigenetic regulation. Cells lacking TMPRSS4 were highly sensitive to the cytotoxic effect of the DDR1 inhibitor dasatinib. TMPRSS4/DDR1 double knock-down (KD) cells, but not single KD cells suffered a G0/G1 cell cycle arrest with loss of E2F1 and cyclins A and B, increased p21 levels and a larger number of cells in apoptosis. Moreover, double KD cells were highly sensitized to cisplatin, which caused massive apoptosis (~40%). In vivo studies demonstrated tumor regression in double KD-injected mice. In conclusion, we have identified a novel vulnerability in NSCLC resulting from a synthetic lethal interaction between DDR1 and TMPRSS4

Involvement of miRNAs in the differentiation of human glioblastoma multiforme stem-like cells

Glioblastoma multiforme (GBM)-initiating cells (GICs) represent a tumor subpopulation with neural stem cell-like properties that is responsible for the development, progression and therapeutic resistance of human GBM. We have recently shown that blockade of NFκB pathway promotes terminal differentiation and senescence of GICs both in vitro and in vivo, indicating that induction of differentiation may be a potential therapeutic strategy for GBM. MicroRNAs have been implicated in the pathogenesis of GBM, but a high-throughput analysis of their role in GIC differentiation has not been reported. We have established human GIC cell lines that can be efficiently differentiated into cells expressing astrocytic and neuronal lineage markers. Using this in vitro system, a microarray-based high-throughput analysis to determine global expression changes of microRNAs during differentiation of GICs was performed. A number of changes in the levels of microRNAs were detected in differentiating GICs, including over-expression of hsa-miR-21, hsa-miR-29a, hsa-miR-29b, hsa-miR-221 and hsa-miR-222, and down-regulation of hsa-miR-93 and hsa-miR-106a. Functional studies showed that miR-21 over-expression in GICs induced comparable cell differentiation features and targeted SPRY1 mRNA, which encodes for a negative regulator of neural stem-cell differentiation. In addition, miR-221 and miR-222 inhibition in differentiated cells restored the expression of stem cell markers while reducing differentiation markers. Finally, miR-29a and miR-29b targeted MCL1 mRNA in GICs and increased apoptosis. Our study uncovers the microRNA dynamic expression changes occurring during differentiation of GICs, and identifies miR-21 and miR-221/222 as key regulators of this process

From transcriptomics to proteomics: Unraveling biological knowledge via Machine Learning

We start by highlighting basic concepts of both molecular biology and machine learning. This overview focuses on the key ideas that are required to comprehend the rest of the work, and thus, it does not attempt at providing a comprehensive review. We start with the basis of DNA and RNA, the genetic building bricks, until the formation of the proteins, the final actors of the genetic machinery. We also explore state-of-the-art technologies to measure those processes along with their limitations. After introducing the basic biological concepts, we will discuss the basics of machine learning methodologies and some of the most important models used in recent years to solve many biological problems

Proteogenómica computacional para la detección y caracterización de proteínas en el contexto del Proyecto Proteoma Humano

En los últimos años, la consolidación de la tecnología de secuenciación masiva (NGS) en el área de la Genómica está ampliando la información que es posible obtener a partir de este tipo de experimentos (Trapnell, 2012). La posibilidad de obtener la secuencia completa de ADN de un genoma, o de ARN de un transcriptoma mediante un único experimento ha supuesto grandes avances en los estudios de genotipado y en la identificación de nuevas alteraciones cromosómicas y mutaciones puntuales. Tras los avances y resultados obtenidos en Genómica, el estudio de las proteínas codificadas por el genoma (proteoma) es el siguiente paso para comprender la fisiología humana, tanto en individuos sanos como enfermos. El estudio del proteoma, a pesar de presentar similitudes con el estudio del genoma, supone un gran desafío que la comunidad científica debe acometer. Los avances en espectrometría de masas, actualmente la tecnología más potente disponible en Proteómica (Nilsson, 2010), están generando un volumen de datos enorme que hacen imprescindible la participación de otras áreas como la Estadística, la Minería de datos y la Computación de Altas Prestaciones para poder analizarlos. Sin embargo, el análisis preciso de estos datos es un reto para el cual se requiere el desarrollo de nuevas herramientas. Otro de los desafíos pendientes dentro del campo de la Proteómica es la caracterización de proteínas detectadas experimentalmente de las que se desconoce su función, localización celular ó su implicación en determinadas enfermedades, para los que desde la comunidad científica se han propuesto distintos retos. La presente tesis describe varios flujos de trabajo, implementados para la identificación de proteínas de las que no existe evidencia experimental y para la caracterización funcional de proteínas de las que no se conoce su función, localización e implicación en enfermedades. Además, se hace una incursión en el campo del procesado de señal aplicando la Transformada Wavelet a distintos tipos de señales, tanto de experimentos de secuenciación de nueva generación (NGS) como de experimentos de espectrometría de masas, desarrollados con el objetivo de mejorar el análisis de este tipo de señales

FactorY, a bioinformatic resource for genome-wide promoter analysis

The interpretation of the complex molecular descriptions generated by high-throughput gene expression technologies is still challenging. The development of new tools to identify common regulatory mechanisms involved in the control of the expression of a set of co-expressed genes, might enhance our capacity to extract functional information from genomic data sets. Here we present FactorY, a website that allows identification of enriched transcription factor binding sites (TFBSs) in the proximal promoter of a cluster of genes, as well as functional interpretation, and intuitive visualization of the results

FactorY, a bioinformatic resource for genome-wide promoter analysis

The interpretation of the complex molecular descriptions generated by high-throughput gene expression technologies is still challenging. The development of new tools to identify common regulatory mechanisms involved in the control of the expression of a set of co-expressed genes, might enhance our capacity to extract functional information from genomic data sets. Here we present FactorY, a website that allows identification of enriched transcription factor binding sites (TFBSs) in the proximal promoter of a cluster of genes, as well as functional interpretation, and intuitive visualization of the results