Expression profiling of genes regulated by TGF-beta: Differential regulation in normal and tumour cells

BACKGROUND: TGF-beta is one of the key cytokines implicated in various disease processes including cancer. TGF-beta inhibits growth and promotes apoptosis in normal epithelial cells and in contrast, acts as a pro-tumour cytokine by promoting tumour angiogenesis, immune-escape and metastasis. It is not clear if various actions of TGF-beta on normal and tumour cells are due to differential gene regulations. Hence we studied the regulation of gene expression by TGF-beta in normal and cancer cells. RESULTS: Using human 19 K cDNA microarrays, we show that 1757 genes are exclusively regulated by TGF-beta in A549 cells in contrast to 733 genes exclusively regulated in HPL1D cells. In addition, 267 genes are commonly regulated in both the cell-lines. Semi-quantitative and real-time qRT-PCR analysis of some genes agrees with the microarray data. In order to identify the signalling pathways that influence TGF-beta mediated gene regulation, we used specific inhibitors of p38 MAP kinase, ERK kinase, JNK kinase and integrin signalling pathways. The data suggest that regulation of majority of the selected genes is dependent on at least one of these pathways and this dependence is cell-type specific. Interestingly, an integrin pathway inhibitor, RGD peptide, significantly affected TGF-beta regulation of Thrombospondin 1 in A549 cells. CONCLUSION: These data suggest major differences with respect to TGF-beta mediated gene regulation in normal and transformed cells and significant role of non-canonical TGF-beta pathways in the regulation of many genes by TGF-beta

RETINOBASE: a web database, data mining and analysis platform for gene expression data on retina

<p>Abstract</p> <p>Background</p> <p>The retina is a multi-layered sensory tissue that lines the back of the eye and acts at the interface of input light and visual perception. Its main function is to capture photons and convert them into electrical impulses that travel along the optic nerve to the brain where they are turned into images. It consists of neurons, nourishing blood vessels and different cell types, of which neural cells predominate. Defects in any of these cells can lead to a variety of retinal diseases, including age-related macular degeneration, retinitis pigmentosa, Leber congenital amaurosis and glaucoma. Recent progress in genomics and microarray technology provides extensive opportunities to examine alterations in retinal gene expression profiles during development and diseases. However, there is no specific database that deals with retinal gene expression profiling. In this context we have built RETINOBASE, a dedicated microarray database for retina.</p> <p>Description</p> <p>RETINOBASE is a microarray relational database, analysis and visualization system that allows simple yet powerful queries to retrieve information about gene expression in retina. It provides access to gene expression meta-data and offers significant insights into gene networks in retina, resulting in better hypothesis framing for biological problems that can subsequently be tested in the laboratory. Public and proprietary data are automatically analyzed with 3 distinct methods, RMA, dChip and MAS5, then clustered using 2 different K-means and 1 mixture models method. Thus, RETINOBASE provides a framework to compare these methods and to optimize the retinal data analysis. RETINOBASE has three different modules, "Gene Information", "Raw Data System Analysis" and "Fold change system Analysis" that are interconnected in a relational schema, allowing efficient retrieval and cross comparison of data. Currently, RETINOBASE contains datasets from 28 different microarray experiments performed in 5 different model systems: drosophila, zebrafish, rat, mouse and human. The database is supported by a platform that is designed to easily integrate new functionalities and is also frequently updated.</p> <p>Conclusion</p> <p>The results obtained from various biological scenarios can be visualized, compared and downloaded. The results of a case study are presented that highlight the utility of RETINOBASE. Overall, RETINOBASE provides efficient access to the global expression profiling of retinal genes from different organisms under various conditions.</p

UniHI 7: an enhanced database for retrieval and interactive analysis of human molecular interaction networks.

Unified Human Interactome (UniHI) (http://www.unihi.org) is a database for retrieval, analysis and visualization of human molecular interaction networks. Its primary aim is to provide a comprehensive and easy-to-use platform for network-based investigations to a wide community of researchers in biology and medicine. Here, we describe a major update (version 7) of the database previously featured in NAR Database Issue. UniHI 7 currently includes almost 350,000 molecular interactions between genes, proteins and drugs, as well as numerous other types of data such as gene expression and functional annotation. Multiple options for interactive filtering and highlighting of proteins can be employed to obtain more reliable and specific network structures. Expression and other genomic data can be uploaded by the user to examine local network structures. Additional built-in tools enable ready identification of known drug targets, as well as of biological processes, phenotypes and pathways enriched with network proteins. A distinctive feature of UniHI 7 is its user-friendly interface designed to be utilized in an intuitive manner, enabling researchers less acquainted with network analysis to perform state-of-the-art network-based investigations

A chemogenomic screening identifies CK2 as a target for pro-senescence therapy in PTEN-deficient tumours

Enhancement of cellular senescence in tumours triggers a stable cell growth arrest and activation of an antitumour immune response that can be exploited for cancer therapy. Currently, there are only a limited number of targeted therapies that act by increasing senescence in cancers, but the majority of them are not selective and also target healthy cells. Here we developed a chemogenomic screening to identify compounds that enhance senescence in PTEN-deficient cells without affecting normal cells. By using this approach, we identified casein kinase 2 (CK2) as a pro-senescent target. Mechanistically, we show that Pten loss increases CK2 levels by activating STAT3. CK2 upregulation in Pten null tumours affects the stability of Pml, an essential regulator of senescence. However, CK2 inhibition stabilizes Pml levels enhancing senescence in Pten null tumours. Taken together, our screening strategy has identified a novel STAT3-CK2-PML network that can be targeted for pro-senescence therapy for cancer

StemCellNet: an interactive platform for network-oriented investigations in stem cell biology.

Stem cells are characterized by their potential for self-renewal and their capacity to differentiate into mature cells. These two key features emerge through the interplay of various factors within complex molecular networks. To provide researchers with a dedicated tool to investigate these networks, we have developed StemCellNet, a versatile web server for interactive network analysis and visualization. It rapidly generates focused networks based on a large collection of physical and regulatory interactions identified in human and murine stem cells. The StemCellNet web-interface has various easy-to-use tools for selection and prioritization of network components, as well as for integration of expression data provided by the user. As a unique feature, the networks generated can be screened against a compendium of stemness-associated genes. StemCellNet can also indicate novel candidate genes by evaluating their connectivity patterns. Finally, an optional dataset of generic interactions, which provides large coverage of the human and mouse proteome, extends the versatility of StemCellNet to other biomedical research areas in which stem cells play important roles, such as in degenerative diseases or cancer. The StemCellNet web server is freely accessible at http://stemcellnet.sysbiolab.eu

TRIB2 as a biomarker for diagnosis and progression of melanoma

Malignant melanoma is the most deadly form of skin cancer. There is a critical need to identify the patients that could be successfully treated by surgery alone and those that require adjuvant treatment. In this study, we demonstrate that the expression of tribbles2 (TRIB2) strongly correlates with both the presence and progression of melanocyte-derived malignancies. We examined the expression of TRIB2 in addition to 12 previously described melanoma biomarkers across three independent full genome microarray studies. TRIB2 expression was consistently and significantly increased in benign nevi and melanoma, and was highest in samples from patients with metastatic melanoma. The expression profiles for the 12 biomarkers were poorly conserved throughout these studies with only TYR, S100B and SPP1 showing consistently elevated expression in metastatic melanoma versus normal skin. Strikingly we confirmed these findings in 20 freshly obtained primary melanoma tissue samples from metastatic lesions where the expression of these biomarkers were evaluated revealing that TRIB2 expression correlated with disease stage and clinical prognosis. Our results suggest that TRIB2 is a meaningful biomarker reflecting diagnosis and progression of melanoma, as well as predicting clinical response to chemotherapy.Fundacao para a Ciencia e a Tecnologia (FCT) [PEst-OE/EQB/LA0023/2012, SFRH/BPD/84634/2012, SFRH/BPD/70718/2010]info:eu-repo/semantics/publishedVersio

Dynamic circadian protein-protein interaction networks predict temporal organization of cellular functions.

Essentially all biological processes depend on protein-protein interactions (PPIs). Timing of such interactions is crucial for regulatory function. Although circadian (~24-hour) clocks constitute fundamental cellular timing mechanisms regulating important physiological processes, PPI dynamics on this timescale are largely unknown. Here, we identified 109 novel PPIs among circadian clock proteins via a yeast-two-hybrid approach. Among them, the interaction of protein phosphatase 1 and CLOCK/BMAL1 was found to result in BMAL1 destabilization. We constructed a dynamic circadian PPI network predicting the PPI timing using circadian expression data. Systematic circadian phenotyping (RNAi and overexpression) suggests a crucial role for components involved in dynamic interactions. Systems analysis of a global dynamic network in liver revealed that interacting proteins are expressed at similar times likely to restrict regulatory interactions to specific phases. Moreover, we predict that circadian PPIs dynamically connect many important cellular processes (signal transduction, cell cycle, etc.) contributing to temporal organization of cellular physiology in an unprecedented manner

Huntington's disease and its therapeutic target genes: a global functional profile based on the HD Research Crossroads database.

BACKGROUND: Huntington's disease (HD) is a fatal progressive neurodegenerative disorder caused by the expansion of the polyglutamine repeat region in the huntingtin gene. Although the disease is triggered by the mutation of a single gene, intensive research has linked numerous other genes to its pathogenesis. To obtain a systematic overview of these genes, which may serve as therapeutic targets, CHDI Foundation has recently established the HD Research Crossroads database. With currently over 800 cataloged genes, this web-based resource constitutes the most extensive curation of genes relevant to HD. It provides us with an unprecedented opportunity to survey molecular mechanisms involved in HD in a holistic manner. METHODS: To gain a synoptic view of therapeutic targets for HD, we have carried out a variety of bioinformatical and statistical analyses to scrutinize the functional association of genes curated in the HD Research Crossroads database. In particular, enrichment analyses were performed with respect to Gene Ontology categories, KEGG signaling pathways, and Pfam protein families. For selected processes, we also analyzed differential expression, using published microarray data. Additionally, we generated a candidate set of novel genetic modifiers of HD by combining information from the HD Research Crossroads database with previous genome-wide linkage studies. RESULTS: Our analyses led to a comprehensive identification of molecular mechanisms associated with HD. Remarkably, we not only recovered processes and pathways, which have frequently been linked to HD (such as cytotoxicity, apoptosis, and calcium signaling), but also found strong indications for other potentially disease-relevant mechanisms that have been less intensively studied in the context of HD (such as the cell cycle and RNA splicing, as well as Wnt and ErbB signaling). For follow-up studies, we provide a regularly updated compendium of molecular mechanism, that are associated with HD, at http://hdtt.sysbiolab.eu Additionally, we derived a candidate set of 24 novel genetic modifiers, including histone deacetylase 3 (HDAC3), metabotropic glutamate receptor 1 (GRM1), CDK5 regulatory subunit 2 (CDK5R2), and coactivator 1ß of the peroxisome proliferator-activated receptor gamma (PPARGC1B). CONCLUSIONS: The results of our study give us an intriguing picture of the molecular complexity of HD. Our analyses can be seen as a first step towards a comprehensive list of biological processes, molecular functions, and pathways involved in HD, and may provide a basis for the development of more holistic disease models and new therapeutics

An integrated systematic approach for storage, analysis and visualization of gene expression data from neuronal tissues acquired through high-throughput techniques

Le travail présenté dans ce manuscrit concerne différents aspects de l'analyse des données d'expression de gènes, qui englobe l'utilisation de méthodes statistiques et de systèmes de stockage et de visualisation, pour exploiter et extraire des informations pertinentes à partir de grands volumes de données. Durant ma thèse j'ai eu l'opportunité de travailler sur ces différents aspects, en contribuant en premier lieu aux tests de nouvelles approches de classification et de méta-analyses à travers la conception d'applications biologiques, puis dans le développement de RETINOBASE (http://alnitak.u-strasbg.fr/RetinoBase/), une base de données relationnelle qui permet le stockage et l'interrogation performante de données de transcriptomique et qui représente la partie majeure de mon travail.The work presented in this manuscript concerns different aspects of gene expression data analysis, encompassing statistical methods and storage and visualization systems used to exploit and mine pertinent information from large volumes of data. Overall, I had the opportunity during my thesis to work on these various aspects firstly, by contributing to the tests through the design of biological applications for new clustering and meta-analysis approaches developed in our laboratory and secondly, by the development of RETINOBASE (http://alnitak.u-strasbg.fr/RetinoBase/.) , a relational database for storage and efficient querying of transcriptomic data which represents my major project

Approche systématique et intégrative pour le stockage, l'analyse et la visualisation des données d'expression génique acquises par des techniques à haut débit, dans les tissus neuronaux

The work presented in this manuscript concerns different aspects of gene expression data analysis, encompassing statistical methods and storage and visualization systems used to exploit and mine pertinent information from large volumes of data. Overall, I had the opportunity during my thesis to work on these various aspects firstly, by contributing to the tests through the design of biological applications for new clustering and meta-analysis approaches developed in our laboratory and secondly, by the development of RETINOBASE (http://alnitak.u-strasbg.fr/RetinoBase/.) , a relational database for storage and efficient querying of transcriptomic data which represents my major project.Le travail présenté dans ce manuscrit concerne différents aspects de l'analyse des données d'expression de gènes, qui englobe l'utilisation de méthodes statistiques et de systèmes de stockage et de visualisation, pour exploiter et extraire des informations pertinentes à partir de grands volumes de données. Durant ma thèse j'ai eu l'opportunité de travailler sur ces différents aspects, en contribuant en premier lieu aux tests de nouvelles approches de classification et de méta-analyses à travers la conception d'applications biologiques, puis dans le développement de RETINOBASE (http://alnitak.u-strasbg.fr/RetinoBase/), une base de données relationnelle qui permet le stockage et l'interrogation performante de données de transcriptomique et qui représente la partie majeure de mon travail.STRASBOURG-Sc. et Techniques (674822102) / SudocSudocFranceF