EzArray: A web-based highly automated Affymetrix expression array data management and analysis system

<p>Abstract</p> <p>Background</p> <p>Though microarray experiments are very popular in life science research, managing and analyzing microarray data are still challenging tasks for many biologists. Most microarray programs require users to have sophisticated knowledge of mathematics, statistics and computer skills for usage. With accumulating microarray data deposited in public databases, easy-to-use programs to re-analyze previously published microarray data are in high demand.</p> <p>Results</p> <p>EzArray is a web-based Affymetrix expression array data management and analysis system for researchers who need to organize microarray data efficiently and get data analyzed instantly. EzArray organizes microarray data into projects that can be analyzed online with predefined or custom procedures. EzArray performs data preprocessing and detection of differentially expressed genes with statistical methods. All analysis procedures are optimized and highly automated so that even novice users with limited pre-knowledge of microarray data analysis can complete initial analysis quickly. Since all input files, analysis parameters, and executed scripts can be downloaded, EzArray provides maximum reproducibility for each analysis. In addition, EzArray integrates with Gene Expression Omnibus (GEO) and allows instantaneous re-analysis of published array data.</p> <p>Conclusion</p> <p>EzArray is a novel Affymetrix expression array data analysis and sharing system. EzArray provides easy-to-use tools for re-analyzing published microarray data and will help both novice and experienced users perform initial analysis of their microarray data from the location of data storage. We believe EzArray will be a useful system for facilities with microarray services and laboratories with multiple members involved in microarray data analysis. EzArray is freely available from <url>http://www.ezarray.com/</url>.</p

The Annotation, Mapping, Expression and Network (AMEN) suite of tools for molecular systems biology

<p>Abstract</p> <p>Background</p> <p>High-throughput genome biological experiments yield large and multifaceted datasets that require flexible and user-friendly analysis tools to facilitate their interpretation by life scientists. Many solutions currently exist, but they are often limited to specific steps in the complex process of data management and analysis and some require extensive informatics skills to be installed and run efficiently.</p> <p>Results</p> <p>We developed the Annotation, Mapping, Expression and Network (AMEN) software as a stand-alone, unified suite of tools that enables biological and medical researchers with basic bioinformatics training to manage and explore genome annotation, chromosomal mapping, protein-protein interaction, expression profiling and proteomics data. The current version provides modules for (i) uploading and pre-processing data from microarray expression profiling experiments, (ii) detecting groups of significantly co-expressed genes, and (iii) searching for enrichment of functional annotations within those groups. Moreover, the user interface is designed to simultaneously visualize several types of data such as protein-protein interaction networks in conjunction with expression profiles and cellular co-localization patterns. We have successfully applied the program to interpret expression profiling data from budding yeast, rodents and human.</p> <p>Conclusion</p> <p>AMEN is an innovative solution for molecular systems biological data analysis freely available under the GNU license. The program is available via a website at the Sourceforge portal which includes a user guide with concrete examples, links to external databases and helpful comments to implement additional functionalities. We emphasize that AMEN will continue to be developed and maintained by our laboratory because it has proven to be extremely useful for our genome biological research program.</p

Discovery and Validation of Molecular Biomarkers for Colorectal Adenomas and Cancer with Application to Blood Testing

BACKGROUND & AIMS: Colorectal cancer incidence and deaths are reduced by the detection and removal of early-stage, treatable neoplasia but we lack proven biomarkers sensitive for both cancer and pre-invasive adenomas. The aims of this study were to determine if adenomas and cancers exhibit characteristic patterns of biomarker expression and to explore whether a tissue-discovered (and validated) biomarker is differentially expressed in the plasma of patients with colorectal adenomas or cancer. METHODS: Candidate RNA biomarkers were identified by oligonucleotide microarray analysis of colorectal specimens (222 normal, 29 adenoma, 161 adenocarcinoma and 50 colitis) and validated in a previously untested cohort of 68 colorectal specimens using a custom-designed oligonucleotide microarray. One validated biomarker, KIAA1199, was assayed using qRT-PCR on plasma extracted RNA from 20 colonoscopy-confirmed healthy controls, 20 patients with adenoma, and 20 with cancer. RESULTS: Genome-wide analysis uncovered reproducible gene expression signatures for both adenomas and cancers compared to controls. 386/489 (79%) of the adenoma and 439/529 (83%) of the adenocarcinoma biomarkers were validated in independent tissues. We also identified genes differentially expressed in adenomas compared to cancer. KIAA1199 was selected for further analysis based on consistent up-regulation in neoplasia, previous studies and its interest as an uncharacterized gene. Plasma KIAA1199 RNA levels were significantly higher in patients with either cancer or adenoma (31/40) compared to neoplasia-free controls (6/20). CONCLUSIONS: Colorectal neoplasia exhibits characteristic patterns of gene expression. KIAA1199 is differentially expressed in neoplastic tissues and KIAA1199 transcripts are more abundant in the plasma of patients with either cancer or adenoma compared to controls

Covert Genetic Selections to Optimize Phenotypes

In many high complexity systems (cells, organisms, institutions, societies, economies, etc.), it is unclear which components should be regulated to affect overall performance. To identify and prioritize molecular targets which impact cellular phenotypes, we have developed a selection procedure (“SPI”–single promoting/inhibiting target identification) which monitors the abundance of ectopic cDNAs. We have used this approach to identify growth regulators. For this purpose, complex pools of S. cerevisiae cDNA transformants were established and we quantitated the evolution of the spectrum of cDNAs which was initially present. These data emphasized the importance of translation initiation and ER-Golgi traffic for growth. SPI provides functional insight into the stability of cellular phenotypes under circumstances in which established genetic approaches cannot be implemented. It provides a functional “synthetic genetic signature” for each state of the cell (i.e. genotype and environment) by surveying complex genetic libraries, and does not require specialized arrays of cDNAs/shRNAs, deletion strains, direct assessment of clonal growth or even a conditional phenotype. Moreover, it establishes a hierarchy of importance of those targets which can contribute, either positively or negatively, to modify the prevailing phenotype. Extensions of these proof-of-principle experiments to other cell types should provide a novel and powerful approach to analyze multiple aspects of the basic biology of yeast and animal cells as well as clinically-relevant issues

The Organophosphate Chlorpyrifos Interferes with the Responses to 17β-Estradiol in the Digestive Gland of the Marine Mussel Mytilus galloprovincialis

BACKGROUND: Many pesticides have been shown to act as endocrine disrupters. Although the potencies of currently used pesticides as hormone agonists/antagonists are low compared with those of natural ligands, their ability to act via multiple mechanisms might enhance the biological effect. The organophosphate Chlorpyrifos (CHP) has been shown to be weakly estrogenic and cause adverse neurodevelopmental effects in mammals. However, no information is available on the endocrine effects of CHP in aquatic organisms. In the digestive gland of the bivalve Mytilus galloprovincialis, a target tissue of both estrogens and pesticides, the possible effects of CHP on the responses to the natural estrogen 17β-estradiol (E(2)) were investigated. METHODOLOGY/PRINCIPAL FINDINGS: Mussels were exposed to CHP (4.5 mg/l, 72 hrs) and subsequently injected with E(2) (6.75 ng/g dw). Responses were evaluated in CHP, E(2) and CHP/E(2) treatment groups at 24 h p.i. by a biomarker/transcriptomic approach. CHP and E(2) induced additive, synergistic, and antagonistic effects on lysosomal biomarkers (lysosomal membrane stability, lysosome/cytoplasm volume ratio, lipofuscin and neutral lipid accumulation). Additive and synergistic effects were also observed on the expression of estrogen-responsive genes (GSTπ, catalase, 5-HTR) evaluated by RT-Q-PCR. The use of a 1.7K cDNA Mytilus microarray showed that CHP, E(2) and CHP/E(2), induced 81, 44, and 65 Differentially Expressed Genes (DEGs), respectively. 24 genes were exclusively shared between CHP and CHP/E(2), only 2 genes between E(2) and CHP/E(2). Moreover, 36 genes were uniquely modulated by CHP/E(2). Gene ontology annotation was used to elucidate the putative mechanisms involved in the responses elicited by different treatments. CONCLUSIONS: The results show complex interactions between CHP and E(2) in the digestive gland, indicating that the combination of certain pesticides and hormones may give rise to unexpected effects at the molecular/cellular level. Overall, these data demonstrate that CHP can interfere with the mussel responses to natural estrogens

Mitochondrial Physiology and Gene Expression Analyses Reveal Metabolic and Translational Dysregulation in Oocyte-Induced Somatic Nuclear Reprogramming

While reprogramming a foreign nucleus after somatic cell nuclear transfer (SCNT), the enucleated oocyte (ooplasm) must signal that biomass and cellular requirements changed compared to the nucleus donor cell. Using cells expressing nuclear-encoded but mitochondria-targeted EGFP, a strategy was developed to directly distinguish maternal and embryonic products, testing ooplasm demands on transcriptional and post-transcriptional activity during reprogramming. Specifically, we compared transcript and protein levels for EGFP and other products in pre-implantation SCNT embryos, side-by-side to fertilized controls (embryos produced from the same oocyte pool, by intracytoplasmic injection of sperm containing the EGFP transgene). We observed that while EGFP transcript abundance is not different, protein levels are significantly lower in SCNT compared to fertilized blastocysts. This was not observed for Gapdh and Actb, whose protein reflected mRNA. This transcript-protein relationship indicates that the somatic nucleus can keep up with ooplasm transcript demands, whilst transcription and translation mismatch occurs after SCNT for certain mRNAs. We further detected metabolic disturbances after SCNT, suggesting a place among forces regulating post-transcriptional changes during reprogramming. Our observations ascribe oocyte-induced reprogramming with previously unsuspected regulatory dimensions, in that presence of functional proteins may no longer be inferred from mRNA, but rather depend on post-transcriptional regulation possibly modulated through metabolism

A origem das parcerias público-privada na governança global da educação

Durante a última década, a globalização da governança educacional por meio de parcerias público-privadas (PPP) tem gerado considerável debate quanto ao seu significado, propósito, status e resultados. Este debate é particularmente aquecido no setor da educação por causa da ampla aceitação da educação como atividade complexa, social e política que deve permanecer, em grande parte, se não totalmente, no setor público, servindo a interesses públicos. O artigo analisa a rápida expansão das parcerias público-privadas em educação (PPPE) articulada à introdução de regras de mercado no setor. Neste estudo nos concentramos sobre o papel de uma rede de desenvolvimento global, fundamental na globalização de um tipo particular de PPPE, indicando que a ideia de PPP encaixa-se em um projeto mais amplo de reconstituição da educação pública no âmbito do setor de serviços, a ser governada como parte da construção de uma sociedade de mercado.Over the past decade, the globalization and governing of education through Public Private Partnerships (PPPs) have generated considerable debate as to their meaning, purpose, status and outcomes. This debate is particularly heated in the education sector because of the widely-held view that education is a complex social and political activity that should remain largely, if not wholly, in the public sector serving public interests. The article analyses the rapid expansion of Education Public Private Partnerships (EPPPs) and the associated introduction of market rules into the education sector. We focus on the role of a key global development network in globalizing a particular kind of ePPPs, and show that the EPPP idea fi ts into a wider project of reconstituting public education as an education services industry to be governed as part of the construction of a market society

Beta-carotene affects gene expression in lungs of male and female Bcmo1−/− mice in opposite directions

Molecular mechanisms triggered by high dietary beta-carotene (BC) intake in lung are largely unknown. We performed microarray gene expression analysis on lung tissue of BC supplemented beta-carotene 15,15′-monooxygenase 1 knockout (Bcmo1−/−) mice, which are—like humans—able to accumulate BC. Our main observation was that the genes were regulated in an opposite direction in male and female Bcmo1−/− mice by BC. The steroid biosynthetic pathway was overrepresented in BC-supplemented male Bcmo1−/− mice. Testosterone levels were higher after BC supplementation only in Bcmo1−/− mice, which had, unlike wild-type (Bcmo1+/+) mice, large variations. We hypothesize that BC possibly affects hormone synthesis or metabolism. Since sex hormones influence lung cancer risk, these data might contribute to an explanation for the previously found increased lung cancer risk after BC supplementation (ATBC and CARET studies). Moreover, effects of BC may depend on the presence of frequent human BCMO1 polymorphisms, since these effects were not found in wild-type mice

GENE-Counter: A Computational Pipeline for the Analysis of RNA-Seq Data for Gene Expression Differences

GENE-counter is a complete Perl-based computational pipeline for analyzing RNA-Sequencing (RNA-Seq) data for differential gene expression. In addition to its use in studying transcriptomes of eukaryotic model organisms, GENE-counter is applicable for prokaryotes and non-model organisms without an available genome reference sequence. For alignments, GENE-counter is configured for CASHX, Bowtie, and BWA, but an end user can use any Sequence Alignment/Map (SAM)-compliant program of preference. To analyze data for differential gene expression, GENE-counter can be run with any one of three statistics packages that are based on variations of the negative binomial distribution. The default method is a new and simple statistical test we developed based on an over-parameterized version of the negative binomial distribution. GENE-counter also includes three different methods for assessing differentially expressed features for enriched gene ontology (GO) terms. Results are transparent and data are systematically stored in a MySQL relational database to facilitate additional analyses as well as quality assessment. We used next generation sequencing to generate a small-scale RNA-Seq dataset derived from the heavily studied defense response of Arabidopsis thaliana and used GENE-counter to process the data. Collectively, the support from analysis of microarrays as well as the observed and substantial overlap in results from each of the three statistics packages demonstrates that GENE-counter is well suited for handling the unique characteristics of small sample sizes and high variability in gene counts