Stem cells present unique regenerative abilities, offering great potential for treatment of prevalent pathologies such as diabetes, neurodegenerative and heart diseases. Various research groups dedicated significant effort to identify sets of genes—so-called stemness signatures—considered essential to define stem cells. However, their usage has been hindered by the lack of comprehensive resources and easy-to-use tools. For this we developed StemChecker, a novel stemness analysis tool, based on the curation of nearly fifty published stemness signatures defined by gene expression, RNAi screens, Transcription Factor (TF) binding sites, literature reviews and computational approaches. StemChecker allows researchers to explore the presence of stemness signatures in user-defined gene sets, without carrying-out lengthy literature curation or data processing. To assist in exploring underlying regulatory mechanisms, we collected over 80 target gene sets of TFs associated with pluri- or multipotency. StemChecker presents an intuitive graphical display, as well as detailed statistical results in table format, which helps revealing transcriptionally regulatory programs, indicating the putative involvement of stemness-associated processes in diseases like cancer. Overall, StemChecker substantially expands the available repertoire of online tools, designed to assist the stem cell biology, developmental biology, regenerative medicine and human disease research community. StemChecker is freely accessible at http://stemchecker.sysbiolab.eu.

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.

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.

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. Circadian clocks are endogenous oscillators that drive daily rhythms in physiology, metabolism, and behavior. In mammals, circadian rhythms are generated within nearly every cell; and, although dysfunction of circadian clocks is associated with various diseases (including diabetes and cancer), the molecular mechanisms linking the clock machinery with output pathways are little understood. Since essentially all biological processes depend on protein–protein interactions, we investigated here on a systems-wide level how time-of-day-specific protein–protein interactions contribute to the temporal organization of cellular physiology. We constructed a circadian interactome using experimentally generated protein–protein interaction data and made this network dynamic by the incorporation of time-of-day-dependent expression data. Interestingly, systematic genetic network perturbation (RNAi and overexpression) suggests a crucial role for circadian components involved in dynamic interactions. Systems analysis of a global network revealed that interacting proteins are in the liver significantly more expressed at similar daytimes likely to restrict regulatory interactions to specific circadian phases within cells. Overall, circadian protein–protein interactions are predicted to dynamically connect important cellular processes (signal transduction, cell cycle, etc.) using—very often—protein modules with components co-expressed in time, shedding new light on the daily organization of cellular physiology.

Introduction: The activation of the phosphoinositide 3-kinase (PI3K)/AKT signalling pathway is one the most frequent genetic events in breast cancer, consequently the development of PI3K inhibitors has attracted much attention. Here we evaluate the effect of PI3K inhibition on global gene expression in breast cancer cells. Methods: We used a range of methodologies that include in silico compound analysis, in vitro kinase assays, cell invasion assays, proliferation assays, genome-wide transcription studies (Agilent Technologies full genome arrays), gene set enrichment analysis, quantitative real-time PCR, immunoblotting in addition to chromatin immunoprecipitation. Results: We defined the physico-chemical and the biological properties of ETP-45658, a novel potent PI3K inhibitor. We demonstrated that ETP-45658 potently inhibited cell proliferation within a broad range of human cancer cells, most potently suppressing the growth of breast cancer cells via inhibiting cell cycle. We show that this response is Forkhead box O (FOXO) protein dependent and p53 independent. Our genome-wide microarray analysis revealed that the cell cycle was the most affected biological process after exposure to ETP-45658 (or our control PI3K inhibitor PI-103), that despite the multiple transcription factors that are regulated by the PI3K/AKT signalling cascade, only the binding sites for FOXO transcription factors were significantly enriched and only a subset of all FOXO-dependent genes were induced. This disparity in gene transcription was not due to differential FOXO promoter recruitment. Conclusions: The constitutive activation of PI3Ks and thus the exclusion of FOXO transcription factors from the nucleus is a key feature of breast cancer. Our results presented here highlight that PI3K inhibition activates specific FOXO-dependent genes that mediate cell cycle arrest in breast cancer cells.

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.