Simple Shared Motifs (SSM) in conserved region of promoters: a new approach to identify co-regulation patterns.

BACKGROUND: Regulation of gene expression plays a pivotal role in cellular functions. However, understanding the dynamics of transcription remains a challenging task. A host of computational approaches have been developed to identify regulatory motifs, mainly based on the recognition of DNA sequences for transcription factor binding sites. Recent integration of additional data from genomic analyses or phylogenetic footprinting has significantly improved these methods. RESULTS: Here, we propose a different approach based on the compilation of Simple Shared Motifs (SSM), groups of sequences defined by their length and similarity and present in conserved sequences of gene promoters. We developed an original algorithm to search and count SSM in pairs of genes. An exceptional number of SSM is considered as a common regulatory pattern. The SSM approach is applied to a sample set of genes and validated using functional gene-set enrichment analyses. We demonstrate that the SSM approach selects genes that are over-represented in specific biological categories (Ontology and Pathways) and are enriched in co-expressed genes. Finally we show that genes co-expressed in the same tissue or involved in the same biological pathway have increased SSM values. CONCLUSIONS: Using unbiased clustering of genes, Simple Shared Motifs analysis constitutes an original contribution to provide a clearer definition of expression networks.

Differential gene expression in benign prostate epithelium of men with and without prostate cancer: evidence for a prostate cancer field effect.

BACKGROUND: Several malignancies are known to exhibit a "field effect," whereby regions beyond tumor boundaries harbor histologic or molecular changes that are associated with cancer. We sought to determine if histologically benign prostate epithelium collected from men with prostate cancer exhibits features indicative of premalignancy or field effect. EXPERIMENTAL DESIGN: Prostate needle biopsies from 15 men with high-grade (Gleason 8-10) prostate cancer and 15 age- and body mass index-matched controls were identified from a biospecimen repository. Benign epithelia from each patient were isolated by laser capture microdissection. RNA was isolated, amplified, and used for microarray hybridization. Quantitative PCR was used to determine the expression of specific genes of interest. Alterations in protein expression were analyzed through immunohistochemistry. RESULTS: Overall patterns of gene expression in microdissected benign prostate-associated benign epithelium (BABE) and cancer-associated benign epithelium (CABE) were similar. Two genes previously associated with prostate cancer, PSMA and SSTR1, were significantly upregulated in the CABE group (false discovery rate <1%). Expression of other prostate cancer-associated genes, including ERG, HOXC4, HOXC5, and MME, were also increased in CABE by quantitative reverse transcription-PCR, although other genes commonly altered in prostate cancer were not different between the BABE and CABE samples. The expression of MME and PSMA proteins on immunohistochemistry coincided with their mRNA alterations. CONCLUSION: Gene expression profiles between benign epithelia of patients with and without prostate cancer are very similar. However, these tissues exhibit differences in the expression levels of several genes previously associated with prostate cancer development or progression. These differences may comprise a field effect and represent early events in carcinogenesis.

In silico investigation of ADAM12 effect on TGF-beta receptors trafficking.

BACKGROUND: The transforming growth factor beta is known to have pleiotropic effects, including differentiation, proliferation and apoptosis. However the underlying mechanisms remain poorly understood. The regulation and effect of TGF-beta signaling is complex and highly depends on specific protein context. In liver, we have recently showed that the disintegrin and metalloproteinase ADAM12 interacts with TGF-beta receptors and modulates their trafficking among membranes, a crucial point in TGF-beta signaling and development of fibrosis. The present study aims to better understand how ADAM12 impacts on TGF-beta receptors trafficking and TGF-beta signaling. FINDINGS: We extracted qualitative biological observations from experimental data and defined a family of models producing a behavior compatible with the presence of ADAM12. We computationally explored the properties of this family of models which allowed us to make novel predictions. We predict that ADAM12 increases TGF-beta receptors internalization rate between the cell surface and the endosomal membrane. It also appears that ADAM12 modifies TGF-beta signaling shape favoring a permanent response by removing the transient component observed under physiological conditions. CONCLUSION: In this work, confronting differential models with qualitative biological observations, we obtained predictions giving new insights into the role of ADAM12 in TGF-beta signaling and hepatic fibrosis process.

Knowledge based identification of essential signaling from genome-scale siRNA experiments.

BACKGROUND: A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits. RESULTS: We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK cytoskeletal remodeling pathway. Different branches of the FAK pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction. CONCLUSION: PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets.

flowCore: a Bioconductor package for high throughput flow cytometry.

BACKGROUND: Recent advances in automation technologies have enabled the use of flow cytometry for high throughput screening, generating large complex data sets often in clinical trials or drug discovery settings. However, data management and data analysis methods have not advanced sufficiently far from the initial small-scale studies to support modeling in the presence of multiple covariates. RESULTS: We developed a set of flexible open source computational tools in the R package flowCore to facilitate the analysis of these complex data. A key component of which is having suitable data structures that support the application of similar operations to a collection of samples or a clinical cohort. In addition, our software constitutes a shared and extensible research platform that enables collaboration between bioinformaticians, computer scientists, statisticians, biologists and clinicians. This platform will foster the development of novel analytic methods for flow cytometry. CONCLUSION: The software has been applied in the analysis of various data sets and its data structures have proven to be highly efficient in capturing and organizing the analytic work flow. Finally, a number of additional Bioconductor packages successfully build on the infrastructure provided by flowCore, open new avenues for flow data analysis.