A modulated empirical Bayes model for identifying topological and temporal estrogen receptor α regulatory networks in breast cancer.

BACKGROUND: Estrogens regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. Dynamic gene expression changes have been shown to characterize the breast cancer cell response to estrogens, the every molecular mechanism of which is still not well understood. RESULTS: We developed a modulated empirical Bayes model, and constructed a novel topological and temporal transcription factor (TF) regulatory network in MCF7 breast cancer cell line upon stimulation by 17β-estradiol stimulation. In the network, significant TF genomic hubs were identified including ER-alpha and AP-1; significant non-genomic hubs include ZFP161, TFDP1, NRF1, TFAP2A, EGR1, E2F1, and PITX2. Although the early and late networks were distinct (<5% overlap of ERα target genes between the 4 and 24 h time points), all nine hubs were significantly represented in both networks. In MCF7 cells with acquired resistance to tamoxifen, the ERα regulatory network was unresponsive to 17β-estradiol stimulation. The significant loss of hormone responsiveness was associated with marked epigenomic changes, including hyper- or hypo-methylation of promoter CpG islands and repressive histone methylations. CONCLUSIONS: We identified a number of estrogen regulated target genes and established estrogen-regulated network that distinguishes the genomic and non-genomic actions of estrogen receptor. Many gene targets of this network were not active anymore in anti-estrogen resistant cell lines, possibly because their DNA methylation and histone acetylation patterns have changed

Gene Regulation, Modulation, and Their Applications in Gene Expression Data Analysis

Common microarray and next-generation sequencing data analysis concentrate on tumor subtype classification, marker detection, and transcriptional regulation discovery during biological processes by exploring the correlated gene expression patterns and their shared functions. Genetic regulatory network (GRN) based approaches have been employed in many large studies in order to scrutinize for dysregulation and potential treatment controls. In addition to gene regulation and network construction, the concept of the network modulator that has significant systemic impact has been proposed, and detection algorithms have been developed in past years. Here we provide a unified mathematic description of these methods, followed with a brief survey of these modulator identification algorithms. As an early attempt to extend the concept to new RNA regulation mechanism, competitive endogenous RNA (ceRNA), into a modulator framework, we provide two applications to illustrate the network construction, modulation effect, and the preliminary finding from these networks. Those methods we surveyed and developed are used to dissect the regulated network under different modulators. Not limit to these, the concept of &quot;modulation&quot; can adapt to various biological mechanisms to discover the novel gene regulation mechanisms

Predicting new molecular targets for rhein using network pharmacology

<p>Abstract</p> <p>Background</p> <p>Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.</p> <p>Methods</p> <p>In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.</p> <p>Results</p> <p>Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.</p> <p>Conclusions</p> <p>Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.</p

Молекулярно-генетические предикторы развития доброкачественных заболеваний молочных желез

This article presents the current data of world literature, highlighting the role of estrogen metabolites and regulation of this process in the pathogenesis of breast diseases.В данной статье представлены современные данные мировой литературы, освещающие роль метаболизма эстрогенов и регуляции этого процесса в патогенезе заболеваний молочной железы (МЖ)

The integration of gene and miRNA expression using pathway topology: a case study on Epithelial Ovarian Cancer

Pathways are formal descriptions of the biological processes involving finely regulated structures by which a cell converts molecules or processes signals. The study of gene expression in terms of pathways is defined as pathway analysis and aims at identifying groups of functionally related genes that show coordinated expression changes. Recently, pathway analysis moved from algorithms using merely gene list to ones exploiting the topology that define gene connections. A crucial, and unfortunately limiting step for these novel methods are the availability of the pathways as gene networks in which nodes are genes and edges are the relations between two elements. To this aim, we develop a pathway data interpreter, called graphite, able to uniformly store, process and convert pathway information into gene networks. graphite has been made publicly available as R package within the Bioconductor platform. In the field of the topological pathway analysis, graphite fills the existing gap lying between technical and methodological aspects. graphite i) allows performing more informative analysis on omics data and ii) allows developing new methods based on the increased accessibil- ity of biological knowledge. However, the pathways of the four main public resources integrated into graphite (KEGG, Reactome, Biocarta and PID), still lack of crucial interactors: the microRNAs. The microRNAs are small non-coding RNAs that post-transcriptionally regulate gene expression, their function on the messenger target is repressive but their effect on the transcription is dependent of the topology of the pathway in which the miRNA is involved. In the last decade, many targets have been discovered and experimentally validated, dedicated databases are available providing these information. Thus, I worked on an extension of graphite package able to integrate microRNAs in pathway topology, i) linking the non-coding RNAs to their validated target genes, ii) providing integrated networks suitable for the topological pathway analyses. The feasibility of this approach has been validated on a specific biological context, the early stage of Epithelial Ovarian Cancer (EOC). EOC has long been considered as a single disease. The emerging opinion, however, sees ovarian cancer as a general term that encloses a group of histo-pathological subtypes sharing a common anatomic location. In collaboration with the Mario Negri institute, 257 stage I EOC tumour biopsies were collected and stratified into training and validation sets. miRNA microarray data was used to generate the most highly reproducible signatures for each histotype through a dedicated resampling inferential strategy. qRT- PCR was used to validate the results in both the training and validation set. The results indicate that the clear cell histotype is characterized by high expression levels of miR- 30a and miR-30a*, while mucinous patients by high levels of miR-192 and miR-194, interestingly as well as mucinous non-ovarian tissues. Then, the integrative approach that combines mRNA and miRNA profiles using graphite has been applied to identify the mucinous specific regulatory circuits. Taken together our findings demonstrate that EOC histotypes have discriminant regulatory circuits that drive the differentiation of the tumour environment. Our approach successfully guides us towards important biological results with interesting therapeutic implications in EOC

Analysis of protein-protein interaction networks by means of annotated graph mining algorithms

This thesis discusses solutions to several open problems in Protein-Protein Interaction (PPI) networks with the aid of Knowledge Discovery. PPI networks are usually represented as undirected graphs, with nodes corresponding to proteins and edges representing interactions among protein pairs. A large amount of available PPI data and noise within it has made the knowledge discovery process a necessary central part for the network analysis. We define Knowledge Discovery as a process of extracting informative knowledge from the huge amount of data. Much success has been achieved when the input data is represented as a set of independent instances and their attributes. But, in the context of PPI networks, there is interesting knowledge to be mined from the relationships between instances (proteins). The resulting research area is called ``Graph Mining''. Here, the input data is modeled as a graph and the output could be any type of knowledge. In this thesis, we propose several graph mining algorithms to examine structural characteristics of PPI networks and link them to the information useful for biologists, such as function or disease.LEI Universiteit LeidenThis research is supported by the Dutch Science Foundation (NWO) through a VIDI grant.Algorithms and the Foundations of Software technolog