Identification of novel ER-alpha target genes in breast cancer cells: Gene- and cell-selective co-regulator recruitment at target promoters determines the response to 17beta-estradiol and tamoxifen

International audienceTamoxifen and 17β-estradiol are capable of up-regulating the expression of some genes and down-regulate the expression of others simultaneously in the same cell. In addition, tamoxifen shows distinct transcriptional activities in different target tissues.To elucidate whether these events are determined by differences in the recruitment of co-regulators by activated estrogen receptor-α (ER-α) at target promoters, we applied chromatin-immunoprecipitation (ChIP) with promoter microarray hybridisation in breast cancer T47D cells and identified 904 ER-α targets genome-wide. On a selection of newly identified targets, we show that 17β-estradiol and tamoxifen stimulated up- or down-regulation of transcription correlates with the selective recruitment of co-activators or co-repressors, respectively. This is shown for both breast (T47D) and endometrial carcinoma cells (ECC1). Moreover, differential co-regulator recruitment also explains that tamoxifen regulates a number of genes in opposite direction in breast and endometrial cancer cells. Over-expression of co-activator SRC-1 or co-repressor SMRT is sufficient to alter the transcriptional action of tamoxifen on a number of targets. Our findings support the notion that recruitment of co-regulator at target gene promoters and their expression levels determine the effect of ER-α on gene expression to a large extent

Understanding regulation of gene transcription through epigenomics and cistromics : unfolding ones and zeros into (un)folding chromatin

Modern measuring technologies, such as microarray and sequencing technology, have resulted in an explosion of information in biomedical research. The big challenge for a bioinformatician is to develop computer procedures to distil biology from this bunch of ones and zeros. This dissertation crossed bioinformatics with epigenetics, the field that studies the form, not the content, of the genetic material we carry with us, and the influence this form has on the way the genetic material is expressed. The results have among others led to new methods of analysis and contributed to new therapeutic insights in the fight against cancer

Molecular pathways involved in prostate carcinogenesis: insights from public microarray datasets

BACKGROUND: Prostate cancer is currently the most frequently diagnosed malignancy in men and the second leading cause of cancer-related deaths in industrialized countries. Worldwide, an increase in prostate cancer incidence is expected due to an increased life-expectancy, aging of the population and improved diagnosis. Although the specific underlying mechanisms of prostate carcinogenesis remain unknown, prostate cancer is thought to result from a combination of genetic and environmental factors altering key cellular processes. To elucidate these complex interactions and to contribute to the understanding of prostate cancer progression and metastasis, analysis of large scale gene expression studies using bioinformatics approaches is used to decipher regulation of core processes. METHODOLOGY/PRINCIPAL FINDINGS: In this study, a standardized quality control procedure and statistical analysis (http://www.arrayanalysis.org/) were applied to multiple prostate cancer datasets retrieved from the ArrayExpress data repository and pathway analysis using PathVisio (http://www.pathvisio.org/) was performed. The results led to the identification of three core biological processes that are strongly affected during prostate carcinogenesis: cholesterol biosynthesis, the process of epithelial-to-mesenchymal transition and an increased metabolic activity. CONCLUSIONS: This study illustrates how a standardized bioinformatics evaluation of existing microarray data and subsequent pathway analysis can quickly and cost-effectively provide essential information about important molecular pathways and cellular processes involved in prostate cancer development and disease progression. The presented results may assist in biomarker profiling and the development of novel treatment approaches

The public road to high-quality curated biological pathways

Biological pathways are abstract and functional visual representations of existing biological knowledge. By mapping high-throughput data on these representations, changes and patterns in biological systems on the genetic, metabolic and protein level are instantly assessable. Many public domain repositories exist for storing biological pathways, each applying its own conventions and storage format. A pathway-based content review of these repositories reveals that none of them are comprehensive. To address this issue, we apply a general workflow to create curated biological pathways, in which we combine three content sources: public domain databases, literature and experts. In this workflow all content of a particular biological pathway is manually retrieved from biological pathway databases and literature, after which this content is compared, combined and subsequently curated by experts. From the curated content, new biological pathways can be created for a pathway analysis tool of choice and distributed among its user base. We applied this procedure to construct high-quality curated biological pathways involved in human fatty acid metabolism

The association between vitamin D receptor polymorphisms and tissue-specific insulin resistance in human obesity

Background/objectives To investigate (1) the association of four VDR polymorphisms (TaqI/rs731236, ApaI/rs7975232, FokI/rs10735810, and Bsml/rs1544410) with markers of adiposity and tissue-specific insulin resistance at baseline, after weight loss and weight maintenance; (2) the effect of the VDR polymorphisms in the SAT transcriptome in overweight/obese Caucasians of the DiOGenes cohort. Methods We included 553 adult obese individuals (mean BMI 34.8 kg/m(2)), men (n = 197) and women (n = 356) at baseline, following an 8-week weight loss intervention and 26 weeks weight maintenance. Genotyping was performed using an Illumina 660W-Quad SNP chip on the Illumina iScan Genotyping System. Tissue-specific IR was determined using Hepatic Insulin Resistance Index (HIRI), Muscle Insulin Sensitivity Index (MISI), and Adipose Tissue Insulin Resistance Index (Adipo-IR). Expression quantitative trait loci (eQTL) analysis was performed to determine the effect of SNPs on SAT gene expression. Results None of the VDR polymorphisms were associated with HIRI or MISI. Interestingly, carriers of the G allele of VDR FokI showed higher Adipo-IR (GG + GA 7.8 +/- 0.4 vs. AA 5.6 +/- 0.5, P = 0.010) and higher systemic FFA (GG + GA: 637.8 +/- 13.4 vs. AA: 547.9 +/- 24.7 mu mol/L, P = 0.011), even after adjustment with age, sex, center, and FM. However, eQTL analysis showed minor to no effect of these genotypes on the transcriptional level in SAT. Also, VDR polymorphisms were not related to changes in body weight and IR as result of dietary intervention (P > 0.05 for all parameters). Conclusions The VDR Fokl variant is associated with elevated circulating FFA and Adipo-IR at baseline. Nevertheless, minor to no effect of VDR SNPs on the transcriptional level in SAT, indicating that putative mechanisms of action remain to be determined. Finally, VDR SNPs did not affect dietary intervention outcome in the present cohort

Elucidating the Corneal Endothelial Cell Proliferation Capacity through an Interspecies Transcriptome Comparison

The regenerative capacity of corneal endothelial cells (CECs) differs between species; in bigger mammals, CECs are arrested in a non-proliferative state. Damage to these cells can compromise their function causing corneal opacity. Corneal transplantation is the current treatment for the recovery of clear eyesight, but the donor tissue demand is higher than the availability and there is a need to develop novel treatments. Interestingly, rabbit CECs retain a high proliferative profile and can repopulate the endothelium. There is a lack of fundamental knowledge to explain these differences. Gaining information on their transcriptomic variances could allow the identification of CEC proliferation drivers. In this study, human, sheep, and rabbit CECs are analyzed at the transcriptomic level. To understand the differences across each species, a pipeline for the analysis of pathways with different activities is generated. The results reveal that 52 pathways have different activity when comparing species with non-proliferative CECs (human and sheep) to species with proliferative CECs (rabbit). The results show that Notch and TGF-beta pathways have increased activity in species with non-proliferative CECs, which might be associated with their low proliferation. Overall, this study illustrates transcriptomic pathway-level differences that can provide leads to develop novel therapies to regenerate the corneal endothelium

User-friendly solutions for microarray quality control and pre-processing on ArrayAnalysis.org.

Quality control (QC) is crucial for any scientific method producing data. Applying adequate QC introduces new challenges in the genomics field where large amounts of data are produced with complex technologies. For DNA microarrays, specific algorithms for QC and pre-processing including normalization have been developed by the scientific community, especially for expression chips of the Affymetrix platform. Many of these have been implemented in the statistical scripting language R and are available from the Bioconductor repository. However, application is hampered by lack of integrative tools that can be used by users of any experience level. To fill this gap, we developed a freely available tool for QC and pre-processing of Affymetrix gene expression results, extending, integrating and harmonizing functionality of Bioconductor packages. The tool can be easily accessed through a wizard-like web portal at http://www.arrayanalysis.org or downloaded for local use in R. The portal provides extensive documentation, including user guides, interpretation help with real output illustrations and detailed technical documentation. It assists newcomers to the field in performing state-of-the-art QC and pre-processing while offering data analysts an integral open-source package. Providing the scientific community with this easily accessible tool will allow improving data quality and reuse and adoption of standards

Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA

The intricate dependency structure of biological “omics” data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.</p