Analysis of gene expression data from non-small celllung carcinoma cell lines reveals distinct sub-classesfrom those identified at the phenotype level

Microarray data from cell lines of Non-Small Cell Lung Carcinoma (NSCLC) can be used to look for differences in gene expression between the cell lines derived from different tumour samples, and to investigate if these differences can be used to cluster the cell lines into distinct groups. Dividing the cell lines into classes can help to improve diagnosis and the development of screens for new drug candidates. The micro-array data is first subjected to quality control analysis and then subsequently normalised using three alternate methods to reduce the chances of differences being artefacts resulting from the normalisation process. The final clustering into sub-classes was carried out in a conservative manner such that subclasses were consistent across all three normalisation methods. If there is structure in the cell line population it was expected that this would agree with histological classifications, but this was not found to be the case. To check the biological consistency of the sub-classes the set of most strongly differentially expressed genes was be identified for each pair of clusters to check if the genes that most strongly define sub-classes have biological functions consistent with NSCLC

COLOMBOS: Access Port for Cross-Platform Bacterial Expression Compendia

Background: Microarrays are the main technology for large-scale transcriptional gene expression profiling, but the large bodies of data available in public databases are not useful due to the large heterogeneity. There are several initiatives that attempt to bundle these data into expression compendia, but such resources for bacterial organisms are scarce and limited to integration of experiments from the same platform or to indirect integration of per experiment analysis results. Methodology/Principal Findings: We have constructed comprehensive organism-specific cross-platform expression compendia for three bacterial model organisms (Escherichia coli, Bacillus subtilis, and Salmonella enterica serovar Typhimurium) together with an access portal, dubbed COLOMBOS, that not only provides easy access to the compendia, but also includes a suite of tools for exploring, analyzing, and visualizing the data within these compendia. It is freely available at http://bioi.biw.kuleuven.be/colombos. The compendia are unique in directly combining expression information from different microarray platforms and experiments, and we illustrate the potential benefits of this direct integration with a case study: extending the known regulon of the Fur transcription factor of E. coli. The compendia also incorporate extensive annotations for both genes and experimental conditions; these heterogeneous data are functionally integrated in the COLOMBOS analysis tools to interactively browse and query the compendia not only for specific genes or experiments, but also metabolic pathways, transcriptional regulation mechanisms, experimental conditions, biological processes, etc. Conclusions/Significance: We have created cross-platform expression compendia for several bacterial organisms and developed a complementary access port COLOMBOS, that also serves as a convenient expression analysis tool to extract useful biological information. This work is relevant to a large community of microbiologists by facilitating the use of publicly available microarray experiments to support their research

Using Functional Signatures to Identify Repositioned Drugs for Breast, Myelogenous Leukemia and Prostate Cancer

The cost and time to develop a drug continues to be a major barrier to widespread distribution of medication. Although the genomic revolution appears to have had little impact on this problem, and might even have exacerbated it because of the flood of additional and usually ineffective leads, the emergence of high throughput resources promises the possibility of rapid, reliable and systematic identification of approved drugs for originally unintended uses. In this paper we develop and apply a method for identifying such repositioned drug candidates against breast cancer, myelogenous leukemia and prostate cancer by looking for inverse correlations between the most perturbed gene expression levels in human cancer tissue and the most perturbed expression levels induced by bioactive compounds. The method uses variable gene signatures to identify bioactive compounds that modulate a given disease. This is in contrast to previous methods that use small and fixed signatures. This strategy is based on the observation that diseases stem from failed/modified cellular functions, irrespective of the particular genes that contribute to the function, i.e., this strategy targets the functional signatures for a given cancer. This function-based strategy broadens the search space for the effective drugs with an impressive hit rate. Among the 79, 94 and 88 candidate drugs for breast cancer, myelogenous leukemia and prostate cancer, 32%, 13% and 17% respectively are either FDA-approved/in-clinical-trial drugs, or drugs with suggestive literature evidences, with an FDR of 0.01. These findings indicate that the method presented here could lead to a substantial increase in efficiency in drug discovery and development, and has potential application for the personalized medicine

Differential expression of THOC1 and ALY mRNP biogenesis/export factors in human cancers

<p>Abstract</p> <p>Background</p> <p>One key step in gene expression is the biogenesis of mRNA ribonucleoparticle complexes (mRNPs). Formation of the mRNP requires the participation of a number of conserved factors such as the THO complex. THO interacts physically and functionally with the Sub2/UAP56 RNA-dependent ATPase, and the Yra1/REF1/ALY RNA-binding protein linking transcription, mRNA export and genome integrity. Given the link between genome instability and cancer, we have performed a comparative analysis of the expression patterns of THOC1, a THO complex subunit, and ALY in tumor samples.</p> <p>Methods</p> <p>The mRNA levels were measured by quantitative real-time PCR and hybridization of a tumor tissue cDNA array; and the protein levels and distribution by immunostaining of a custom tissue array containing a set of paraffin-embedded samples of different tumor and normal tissues followed by statistical analysis.</p> <p>Results</p> <p>We show that the expression of two mRNP factors, THOC1 and ALY are altered in several tumor tissues. THOC1 mRNA and protein levels are up-regulated in ovarian and lung tumors and down-regulated in those of testis and skin, whereas ALY is altered in a wide variety of tumors. In contrast to THOC1, ALY protein is highly detected in normal proliferative cells, but poorly in high-grade cancers.</p> <p>Conclusions</p> <p>These results suggest a differential connection between tumorogenesis and the expression levels of human THO and ALY. This study opens the possibility of defining mRNP biogenesis factors as putative players in cell proliferation that could contribute to tumor development.</p

Methods for visual mining of genomic and proteomic data atlases

<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p

A gene expression atlas of the domestic pig

<p>Abstract</p> <p>Background</p> <p>This work describes the first genome-wide analysis of the transcriptional landscape of the pig. A new porcine Affymetrix expression array was designed in order to provide comprehensive coverage of the known pig transcriptome. The new array was used to generate a genome-wide expression atlas of pig tissues derived from 62 tissue/cell types. These data were subjected to network correlation analysis and clustering.</p> <p>Results</p> <p>The analysis presented here provides a detailed functional clustering of the pig transcriptome where transcripts are grouped according to their expression pattern, so one can infer the function of an uncharacterized gene from the company it keeps and the locations in which it is expressed. We describe the overall transcriptional signatures present in the tissue atlas, where possible assigning those signatures to specific cell populations or pathways. In particular, we discuss the expression signatures associated with the gastrointestinal tract, an organ that was sampled at 15 sites along its length and whose biology in the pig is similar to human. We identify sets of genes that define specialized cellular compartments and region-specific digestive functions. Finally, we performed a network analysis of the transcription factors expressed in the gastrointestinal tract and demonstrate how they sub-divide into functional groups that may control cellular gastrointestinal development.</p> <p>Conclusions</p> <p>As an important livestock animal with a physiology that is more similar than mouse to man, we provide a major new resource for understanding gene expression with respect to the known physiology of mammalian tissues and cells. The data and analyses are available on the websites <url>http://biogps.org and http://www.macrophages.com/pig-atlas</url>.</p

CD44 Expression in Oro-Pharyngeal Carcinoma Tissues and Cell Lines

Expression of CD44, a transmembrane hyaluronan-binding glycoprotein, is variably considered to have prognostic significance for different cancers, including oral squamous cell carcinoma. Although unclear at present, tissue-specific expression of particular isoforms of CD44 might underlie the different outcomes in currently available studies. We mined public transcriptomics databases for gene expression data on CD44, and analyzed normal, immortalized and tumour-derived human cell lines for splice variants of CD44 at both the transcript and protein levels. Bioinformatics readouts, from a total of more than 15,000 analyses, implied an increased CD44 expression in head and neck cancer, including increased expression levels relative to many normal and tumor tissue types. Also, meta-analysis of over 260 cell lines and over 4,000 tissue specimens of diverse origins indicated lower CD44 expression levels in cell lines compared to tissue. With minor exceptions, reverse transcribed polymerase chain reaction identified expression of the four main isoforms of CD44 in normal oral keratinocytes, transformed lines termed DT and HaCaT, and a series of paired primary and metastasis-derived cell lines from oral or pharyngeal carcinomas termed HN4/HN12, HN22/HN8 and HN30/HN31. Immunocytochemistry, Western blotting and flow cytometric assessments all confirmed the isoform expression pattern at the protein level. Overall, bioinformatic processing of large numbers of global gene expression analyses demonstrated elevated CD44 expression in head and neck cancer relative to other cancer types, and that the application of standard cell culture protocols might decrease CD44 expression. Additionally, the results show that the many variant CD44 exons are not fundamentally deregulated in a diverse range of cultured normal and transformed keratinocyte lines

A user's guide to the Encyclopedia of DNA elements (ENCODE)

The mission of the Encyclopedia of DNA Elements (ENCODE) Project is to enable the scientific and medical communities to interpret the human genome sequence and apply it to understand human biology and improve health. The ENCODE Consortium is integrating multiple technologies and approaches in a collective effort to discover and define the functional elements encoded in the human genome, including genes, transcripts, and transcriptional regulatory regions, together with their attendant chromatin states and DNA methylation patterns. In the process, standards to ensure high-quality data have been implemented, and novel algorithms have been developed to facilitate analysis. Data and derived results are made available through a freely accessible database. Here we provide an overview of the project and the resources it is generating and illustrate the application of ENCODE data to interpret the human genome

The enterococcal cytolysin synthetase has an unanticipated lipid kinase fold

Abstract The enterococcal cytolysin is a virulence factor consisting of two post-translationally modified peptides that synergistically kill human immune cells. Both peptides are made by CylM, a member of the LanM lanthipeptide synthetases. CylM catalyzes seven dehydrations of Ser and Thr residues and three cyclization reactions during the biosynthesis of the cytolysin large subunit. We present here the 2.2Å resolution structure of CylM, the first structural information on a LanM. Unexpectedly, the structure reveals that the dehydratase domain of CylM resembles the catalytic core of eukaryotic lipid kinases, despite the absence of clear sequence homology. The kinase and phosphate elimination active sites that affect net dehydration are immediately adjacent to each other. Characterization of mutants provided insights into the mechanism of the dehydration process. The structure is also of interest because of the interactions of human homologs of lanthipeptide cyclases with kinases such as mammalian target of rapamycin