Gene Regulatory Modules Research Articles

Biclustering reveals breast cancer tumour subgroups with common clinical features and improves prediction of disease recurrence.

BackgroundMany studies have revealed correlations between breast tumour phenotypes, variations in gene expression, and patient survival outcomes. The molecular heterogeneity between breast tumours revealed by these studies has allowed prediction of prognosis and has underpinned stratified therapy, where groups of patients with particular tumour types receive specific treatments. The molecular tests used to predict prognosis and stratify treatment usually utilise fixed sets of genomic biomarkers, with the same biomarker sets being used to test all patients. In this paper we suggest that instead of fixed sets of genomic biomarkers, it may be more effective to use a stratified biomarker approach, where optimal biomarker sets are automatically chosen for particular patient groups, analogous to the choice of optimal treatments for groups of similar patients in stratified therapy. We illustrate the effectiveness of a biclustering approach to select optimal gene sets for determining the prognosis of specific strata of patients, based on potentially overlapping, non-discrete molecular characteristics of tumours.ResultsBiclustering identified tightly co-expressed gene sets in the tumours of restricted subgroups of breast cancer patients. The co-expressed genes in these biclusters were significantly enriched for particular biological annotations and gene regulatory modules associated with breast cancer biology. Tumours identified within the same bicluster were more likely to present with similar clinical features. Bicluster membership combined with clinical information could predict patient prognosis in conditional inference tree and ridge regression class prediction models.ConclusionsThe increasing clinical use of genomic profiling demands identification of more effective methods to segregate patients into prognostic and treatment groups. We have shown that biclustering can be used to select optimal gene sets for determining the prognosis of specific strata of patients.

An Integrative Approach to Inferring Gene Regulatory Module Networks

BackgroundGene regulatory networks (GRNs) provide insight into the mechanisms of differential gene expression at a system level. However, the methods for inference, functional analysis and visualization of gene regulatory modules and GRNs require the user to collect heterogeneous data from many sources using numerous bioinformatics tools. This makes the analysis expensive and time-consuming.ResultsIn this work, the BiologicalNetworks application–the data integration and network based research environment–was extended with tools for inference and analysis of gene regulatory modules and networks. The backend database of the application integrates public data on gene expression, pathways, transcription factor binding sites, gene and protein sequences, and functional annotations. Thus, all data essential for the gene regulation analysis can be mined publicly. In addition, the user’s data can either be integrated in the database and become public, or kept private within the application. The capabilities to analyze multiple gene expression experiments are also provided.ConclusionThe generated modular networks, regulatory modules and binding sites can be visualized and further analyzed within this same application. The developed tools were applied to the mouse model of asthma and the OCT4 regulatory network in embryonic stem cells. Developed methods and data are available through the Java application from BiologicalNetworks program at http://www.biologicalnetworks.org.

Targeting gene expression to the female larval fat body of transgenic Aedes aegypti mosquitoes

As the fat body is a critical tissue for mosquito development, metamorphosis, immune and reproductive system function, the characterization of regulatory modules targeting gene expression to the female mosquito fat body at distinct life stages is much needed for multiple, varied strategies for controlling vector-borne diseases such as dengue and malaria. The hexameric storage protein, Hexamerin-1.2, of the mosquito Aedes atropalpus is female-specific and uniquely expressed in the fat body of fourth instar larvae and young adults. We have identified in the Hex-1.2 gene, a short regulatory module that directs female-, tissue-, and stage-specific lacZ reporter gene expression using a heterologous promoter in transgenic lines of the dengue vector Aedes aegypti. Male transgenic larvae and pupae of one line expressed no Escherichia coli β-galactosidase or transgene product; in two other lines reporter gene activity was highly female-biased. All transgenic lines expressed the reporter only in the fat body; however, lacZ mRNA levels were no different in males and females at any stage examined, suggesting that the gene regulatory module drives female-specific expression by post-transcriptional regulation in the heterologous mosquito. This regulatory element from the Hex-1.2 gene thus provides a new molecular tool for transgenic mosquito control as well as functional genetic analysis in aedine mosquitoes.

P118 IFN-A transcriptional response in endothelial cells: Key modulators and gene regulatory modules

Introduction Inference of gene regulation from expression data may help to unravel regulatory mechanisms involved in complex diseases or in the action of specific drugs. Interferon-α (IFN-α) is a pleiotropic cytokine endowed with potent biological activities, achieved through the up-regulation of hundreds of interferon-stimulated genes (ISGs). Aim of this study was to extend previous work on IFN-α transcriptional response in endothelial cells, by identifying IFN-α modulators and inferring putative gene regulatory modules involving them. Methods Primary endothelial cells (HUVEC) were pulsed in vitro with human recombinant IFN-α under different perturbation conditions: siRNA inactivation of target genes. A panel of 96 transcripts, including ISGs belonging to IFN-α top signature and genes related to the IFN-α signalling pathway, were screened by custom TaqMan Low-Density Arrays. Stealth siRNA were used for RNAi-mediated knockdown of seven candidate IFN-α modulators: STAT1, IRF1, IRF7, GBP1, OAS2, IFIH1 and USP18. The effects of each siRNA were evaluated with respect to a calibrator siRNA using the comparative threshold cycle method (ΔΔCT method). A selection procedure, testing the null hypothesis of no difference in the effects of a siRNA targeting a candidate modulator and the calibrator siRNA, was used to assign a significance value (p-value) to each regulation. Significant regulations were defined fixing a cut-off of 0.05 on the Bonferroni corrected p-values. Regulatory modules, built by different feed-forward loops (FFLs), the basic three-node building blocks of biological networks, were then inferred by combining the significant regulations, induced by different couples of IFN-α modulators. Results Our analysis showed both mainly positive (STAT1, IFIH1) and mainly negative (USP18, GBP1, IRF1) modulators of IFN-α transcriptional response, while IRF7 and OAS2 were found to act equally in both directions. STAT1 was confirmed as the primary positive modulator of IFN-α; following its silencing several genes were significantly down-regulated and only few genes up-regulated. Conversely, USP18 was found to be the strongest negative modulator and its silencing caused a massive up-regulation of genes, including some involved in cell-to-cell adhesion. An interesting regulatory module in which STAT1 activates IFIH1 and they both regulate six genes, including IFNα-R1, was reconstructed by our inference method. Some of the inferred FFLs are currently being validated. Conclusion The investigation of IFN-α transcriptional modulators through our method has led to a clear picture, depicting the transcriptional impact of each considered modulator in terms of sign of regulation, strength and target genes. The inferred gene regulatory modules, involving key modulators, may help to unravel the mechanisms of action of IFN-α and discover future target of intervention in knock-out experiments.

MULTIDIMENSIONAL ADAPTIVE EVOLUTION OF A FEED-FORWARD NETWORK AND THE ILLUSION OF COMPENSATION

When multiple substitutions affect a trait in opposing ways, they are often assumed to be compensatory, not only with respect to the trait, but also with respect to fitness. This type of compensatory evolution has been suggested to underlie the evolution of protein structures and interactions, RNA secondary structures, and gene regulatory modules and networks. The possibility for compensatory evolution results from epistasis. Yet if epistasis is widespread, then it is also possible that the opposing substitutions are individually adaptive. I term this possibility an adaptive reversal. Although possible for arbitrary phenotype-fitness mappings, it has not yet been investigated whether such epistasis is prevalent in a biologically realistic setting. I investigate a particular regulatory circuit, the type I coherent feed-forward loop, which is ubiquitous in natural systems and is accurately described by a simple mathematical model. I show that such reversals are common during adaptive evolution, can result solely from the topology of the fitness landscape, and can occur even when adaptation follows a modest environmental change and the network was well adapted to the original environment. The possibility of adaptive reversals warrants a systems perspective when interpreting substitution patterns in gene regulatory networks.

Identifying multi-layer gene regulatory modules from multi-dimensional genomic data

Motivation: Eukaryotic gene expression (GE) is subjected to precisely coordinated multi-layer controls, across the levels of epigenetic, transcriptional and post-transcriptional regulations. Recently, the emerging multi-dimensional genomic dataset has provided unprecedented opportunities to study the cross-layer regulatory interplay. In these datasets, the same set of samples is profiled on several layers of genomic activities, e.g. copy number variation (CNV), DNA methylation (DM), GE and microRNA expression (ME). However, suitable analysis methods for such data are currently sparse.Results: In this article, we introduced a sparse Multi-Block Partial Least Squares (sMBPLS) regression method to identify multi-dimensional regulatory modules from this new type of data. A multi-dimensional regulatory module contains sets of regulatory factors from different layers that are likely to jointly contribute to a local ‘gene expression factory’. We demonstrated the performance of our method on the simulated data as well as on The Cancer Genomic Atlas Ovarian Cancer datasets including the CNV, DM, ME and GE data measured on 230 samples. We showed that majority of identified modules have significant functional and transcriptional enrichment, higher than that observed in modules identified using only a single type of genomic data. Our network analysis of the modules revealed that the CNV, DM and microRNA can have coupled impact on expression of important oncogenes and tumor suppressor genes.Availability and implementation: The source code implemented by MATLAB is freely available at: http://zhoulab.usc.edu/sMBPLS/.Contact: xjzhou@usc.eduSupplementary information: Supplementary material are available at Bioinformatics online.

Targeting Tetramer-Forming GABPβ Isoforms Impairs Self-Renewal of Hematopoietic and Leukemic Stem Cells

Hematopoietic stem cells (HSCs) and leukemic stem cells (LSCs) are both capable of self-renewal, with HSCs sustaining multiple blood lineage differentiation and LSCs indefinitely propagating leukemia. The GABP complex, consisting of DNA binding GABPα subunit and transactivation GABPβ subunit, critically regulates HSC multipotency and self-renewal via controlling an essential gene regulatory module. Two GABPβ isoforms, GABPβ1L and GABPβ2, contribute to assembly of GABPα(2)β(2) tetramer. We demonstrate that GABPβ1L/β2 deficiency specifically impairs HSC quiescence and survival, with little impact on cell cycle or apoptosis in differentiated blood cells. The HSC-specific effect is mechanistically ascribed to perturbed integrity of the GABP-controlled gene regulatory module in HSCs. Targeting GABPβ1L/β2 also impairs LSC self-renewal in p210(BCR-ABL)-induced chronic myelogenous leukemia (CML) and exhibits synergistic effects with tyrosine kinase inhibitor imatinib therapy in inhibiting CML propagation. These findings identify the tetramer-forming GABPβ isoforms as specific HSC regulators and potential therapeutic targets in treating LSC-based hematological malignancy.

Additive Functions in Boolean Models of Gene Regulatory Network Modules

Gene-on-gene regulations are key components of every living organism. Dynamical abstract models of genetic regulatory networks help explain the genome's evolvability and robustness. These properties can be attributed to the structural topology of the graph formed by genes, as vertices, and regulatory interactions, as edges. Moreover, the actual gene interaction of each gene is believed to play a key role in the stability of the structure. With advances in biology, some effort was deployed to develop update functions in Boolean models that include recent knowledge. We combine real-life gene interaction networks with novel update functions in a Boolean model. We use two sub-networks of biological organisms, the yeast cell-cycle and the mouse embryonic stem cell, as topological support for our system. On these structures, we substitute the original random update functions by a novel threshold-based dynamic function in which the promoting and repressing effect of each interaction is considered. We use a third real-life regulatory network, along with its inferred Boolean update functions to validate the proposed update function. Results of this validation hint to increased biological plausibility of the threshold-based function. To investigate the dynamical behavior of this new model, we visualized the phase transition between order and chaos into the critical regime using Derrida plots. We complement the qualitative nature of Derrida plots with an alternative measure, the criticality distance, that also allows to discriminate between regimes in a quantitative way. Simulation on both real-life genetic regulatory networks show that there exists a set of parameters that allows the systems to operate in the critical region. This new model includes experimentally derived biological information and recent discoveries, which makes it potentially useful to guide experimental research. The update function confers additional realism to the model, while reducing the complexity and solution space, thus making it easier to investigate.

Motif-guided sparse decomposition of gene expression data for regulatory module identification

BackgroundGenes work coordinately as gene modules or gene networks. Various computational approaches have been proposed to find gene modules based on gene expression data; for example, gene clustering is a popular method for grouping genes with similar gene expression patterns. However, traditional gene clustering often yields unsatisfactory results for regulatory module identification because the resulting gene clusters are co-expressed but not necessarily co-regulated.ResultsWe propose a novel approach, motif-guided sparse decomposition (mSD), to identify gene regulatory modules by integrating gene expression data and DNA sequence motif information. The mSD approach is implemented as a two-step algorithm comprising estimates of (1) transcription factor activity and (2) the strength of the predicted gene regulation event(s). Specifically, a motif-guided clustering method is first developed to estimate the transcription factor activity of a gene module; sparse component analysis is then applied to estimate the regulation strength, and so predict the target genes of the transcription factors. The mSD approach was first tested for its improved performance in finding regulatory modules using simulated and real yeast data, revealing functionally distinct gene modules enriched with biologically validated transcription factors. We then demonstrated the efficacy of the mSD approach on breast cancer cell line data and uncovered several important gene regulatory modules related to endocrine therapy of breast cancer.ConclusionWe have developed a new integrated strategy, namely motif-guided sparse decomposition (mSD) of gene expression data, for regulatory module identification. The mSD method features a novel motif-guided clustering method for transcription factor activity estimation by finding a balance between co-regulation and co-expression. The mSD method further utilizes a sparse decomposition method for regulation strength estimation. The experimental results show that such a motif-guided strategy can provide context-specific regulatory modules in both yeast and breast cancer studies.

GABP controls a critical transcription regulatory module that is essential for maintenance and differentiation of hematopoietic stem/progenitor cells

AbstractMaintaining a steady pool of self-renewing hematopoietic stem cells (HSCs) is critical for sustained production of multiple blood lineages. Many transcription factors and molecules involved in chromatin and epigenetic modifications have been found to be critical for HSC self-renewal and differentiation; however, their interplay is less understood. The transcription factor GA binding protein (GABP), consisting of DNA-binding subunit GABPα and transactivating subunit GABPβ, is essential for lymphopoiesis as shown in our previous studies. Here we demonstrate cell-intrinsic, absolute dependence on GABPα for maintenance and differentiation of hematopoietic stem/progenitor cells. Through genome-wide mapping of GABPα binding and transcriptomic analysis of GABPα-deficient HSCs, we identified Zfx and Etv6 transcription factors and prosurvival Bcl-2 family members including Bcl-2, Bcl-XL, and Mcl-1 as direct GABP target genes, underlying its pivotal role in HSC survival. GABP also directly regulates Foxo3 and Pten and hence sustains HSC quiescence. Furthermore, GABP activates transcription of DNA methyltransferases and histone acetylases including p300, contributing to regulation of HSC self-renewal and differentiation. These systematic analyses revealed a GABP-controlled gene regulatory module that programs multiple aspects of HSC biology. Our studies thus constitute a critical first step in decoding how transcription factors are orchestrated to regulate maintenance and multipotency of HSCs.

Bacterial artificial chromosomes as analytical basis for gene transcriptional machineries

Bacterial Artificial Chromosomes (BACs) had been minimal components of various genome-sequencing projects, constituting perfect analytical basis for functional genomics. Here we describe an enhancer screening strategy in which BAC clones that cover any genomic segments of interest are modified to harbor a reporter cassette by transposon tagging, then processed to carry selected combinations of gene regulatory modules by homologous recombination mediated systematic deletions. Such engineered BAC-reporter constructs in bacterial cells are ready for efficient transgenesis in mice to evaluate activities of gene regulatory modules intact or absent in the constructs. By utilizing the strategy, we could speedily identify a critical genomic fragment for spatio-temporally regulated expression of a mouse cadherin gene whose structure is extraordinarily huge and intricate. This BAC-based methodology would hence provide a novel screening platform for gene transcriptional machineries that dynamically fluctuate during development, pathogenesis and/or evolution.

Chromosomal organization at the level of gene complexes

Metazoan genomes primarily consist of non-coding DNA in comparison to coding regions. Non-coding fraction of the genome contains cis-regulatory elements, which ensure that the genetic code is read properly at the right time and space during development. Regulatory elements and their target genes define functional landscapes within the genome, and some developmentally important genes evolve by keeping the genes involved in specification of common organs/tissues in clusters and are termed gene complex. The clustering of genes involved in a common function may help in robust spatio-temporal gene expression. Gene complexes are often found to be evolutionarily conserved, and the classic example is the hox complex. The evolutionary constraints seen among gene complexes provide an ideal model system to understand cis and trans-regulation of gene function. This review will discuss the various characteristics of gene regulatory modules found within gene complexes and how they can be characterized.

MicroR159 regulation of most conserved targets in Arabidopsis has negligible phenotypic effects

BackgroundA current challenge of microRNA (miRNA) research is the identification of biologically relevant miRNA:target gene relationships. In plants, high miRNA:target gene complementarity has enabled accurate target predictions, and slicing of target mRNAs has facilitated target validation through rapid amplification of 5' cDNA ends (5'-RACE) analysis. Together, these approaches have identified more than 20 targets potentially regulated by the deeply conserved miR159 family in Arabidopsis, including eight MYB genes with highly conserved miR159 target sites. However, genetic analysis has revealed the functional specificity of the major family members, miR159a and miR159b is limited to only two targets, MYB33 and MYB65. Here, we examine the functional role of miR159 regulation for the other potential MYB target genes.ResultsFor these target genes, functional analysis failed to identify miR159 regulation that resulted in any major phenotypic impact, either at the morphological or molecular level. This appears to be mainly due to the quiescent nature of the remaining family member, MIR159c. Although its expression overlaps in a temporal and spatial cell-specific manner with a subset of these targets in anthers, the abundance of miR159c is extremely low and concomitantly a mir159c mutant displays no anther defects. Examination of potential miR159c targets with conserved miR159 binding sites found neither their spatial or temporal expression domains appeared miR159 regulated, despite the detection of miR159-guided cleavage products by 5'-RACE. Moreover, expression of a miR159-resistant target (mMYB101) resulted predominantly in plants that are indistinguishable from wild type. Plants that displayed altered morphological phenotypes were found to be ectopically expressing the mMYB101 transgene, and hence were misrepresentative of the in vivo functional role of miR159.ConclusionsThis study presents a novel explanation for a paradox common to plant and animal miRNA systems, where among many potential miRNA-target relationships usually only a few appear physiologically relevant. The identification of a quiescent miR159c:target gene regulatory module in anthers provides a likely rationale for the presence of conserved miR159 binding sites in many targets for which miR159 regulation has no obvious functional role. Remnants from the demise of such modules may lead to an overestimation of miRNA regulatory complexity when investigated using bioinformatic, 5'-RACE or transgenic approaches.

Smoke and miRrors: pseudogenes tricking miRNAs

Smoke and miRrors: pseudogenes tricking miRNAs

Reconstruction of Gene Regulatory Modules in Cancer Cell Cycle by Multi-Source Data Integration

BackgroundPrecise regulation of the cell cycle is crucial to the growth and development of all organisms. Understanding the regulatory mechanism of the cell cycle is crucial to unraveling many complicated diseases, most notably cancer. Multiple sources of biological data are available to study the dynamic interactions among many genes that are related to the cancer cell cycle. Integrating these informative and complementary data sources can help to infer a mutually consistent gene transcriptional regulatory network with strong similarity to the underlying gene regulatory relationships in cancer cells.Results and Principal FindingsWe propose an integrative framework that infers gene regulatory modules from the cell cycle of cancer cells by incorporating multiple sources of biological data, including gene expression profiles, gene ontology, and molecular interaction. Among 846 human genes with putative roles in cell cycle regulation, we identified 46 transcription factors and 39 gene ontology groups. We reconstructed regulatory modules to infer the underlying regulatory relationships. Four regulatory network motifs were identified from the interaction network. The relationship between each transcription factor and predicted target gene groups was examined by training a recurrent neural network whose topology mimics the network motif(s) to which the transcription factor was assigned. Inferred network motifs related to eight well-known cell cycle genes were confirmed by gene set enrichment analysis, binding site enrichment analysis, and comparison with previously published experimental results.ConclusionsWe established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data. Our method could also be beneficial to biologists for predicting the components of regulatory modules in which any candidate gene is involved. Such predictions can then be used to design a more streamlined experimental approach for biological validation. Understanding the dynamics of these modules will shed light on the processes that occur in cancer cells resulting from errors in cell cycle regulation.

Multilevel support vector regression analysis to identify condition-specific regulatory networks.

The identification of gene regulatory modules is an important yet challenging problem in computational biology. While many computational methods have been proposed to identify regulatory modules, their initial success is largely compromised by a high rate of false positives, especially when applied to human cancer studies. New strategies are needed for reliable regulatory module identification. We present a new approach, namely multilevel support vector regression (ml-SVR), to systematically identify condition-specific regulatory modules. The approach is built upon a multilevel analysis strategy designed for suppressing false positive predictions. With this strategy, a regulatory module becomes ever more significant as more relevant gene sets are formed at finer levels. At each level, a two-stage support vector regression (SVR) method is utilized to help reduce false positive predictions by integrating binding motif information and gene expression data; a significant analysis procedure is followed to assess the significance of each regulatory module. To evaluate the effectiveness of the proposed strategy, we first compared the ml-SVR approach with other existing methods on simulation data and yeast cell cycle data. The resulting performance shows that the ml-SVR approach outperforms other methods in the identification of both regulators and their target genes. We then applied our method to breast cancer cell line data to identify condition-specific regulatory modules associated with estrogen treatment. Experimental results show that our method can identify biologically meaningful regulatory modules related to estrogen signaling and action in breast cancer. The ml-SVR MATLAB package can be downloaded at http://www.cbil.ece.vt.edu/software.htm.

Modeling Co-Expression across Species for Complex Traits: Insights to the Difference of Human and Mouse Embryonic Stem Cells

Complex interactions between genes or proteins contribute substantially to phenotypic evolution. We present a probabilistic model and a maximum likelihood approach for cross-species clustering analysis and for identification of conserved as well as species-specific co-expression modules. This model enables a “soft” cross-species clustering (SCSC) approach by encouraging but not enforcing orthologous genes to be grouped into the same cluster. SCSC is therefore robust to obscure orthologous relationships and can reflect different functional roles of orthologous genes in different species. We generated a time-course gene expression dataset for differentiating mouse embryonic stem (ES) cells, and compiled a dataset of published gene expression data on differentiating human ES cells. Applying SCSC to analyze these datasets, we identified conserved and species-specific gene regulatory modules. Together with protein-DNA binding data, an SCSC cluster specifically induced in murine ES cells indicated that the KLF2/4/5 transcription factors, although critical to maintaining the pluripotent phenotype in mouse ES cells, were decoupled from the OCT4/SOX2/NANOG regulatory module in human ES cells. Two of the target genes of murine KLF2/4/5, LIN28 and NODAL, were rewired to be targets of OCT4/SOX2/NANOG in human ES cells. Moreover, by mapping SCSC clusters onto KEGG signaling pathways, we identified the signal transduction components that were induced in pluripotent ES cells in either a conserved or a species-specific manner. These results suggest that the pluripotent cell identity can be established and maintained through more than one gene regulatory network.

Computational discovery of miR-TF regulatory modules in human genome.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression at the post-transcriptional level. They play an important role in several biological processes such as cell development and differentiation. Similar to transcription factors (TFs), miRNAs regulate gene expression in a combinatorial fashion, i.e., an individual miRNA can regulate multiple genes, and an individual gene can be regulated by multiple miRNAs. The functions of TFs in biological regulatory networks have been well explored. And, recently, a few studies have explored miRNA functions in the context of gene regulation networks. However, how TFs and miRNAs function together in the gene regulatory network has not yet been examined. In this paper, we propose a new computational method to discover the gene regulatory modules that consist of miRNAs, TFs, and genes regulated by them. We analyzed the regulatory associations among the sets of predicted miRNAs and sets of TFs on the sets of genes regulated by them in the human genome. We found 182 gene regulatory modules of combinatorial regulation by miRNAs and TFs (miR-TF modules). By validating these modules with the Gene Ontology (GO) and the literature, it was found that our method allows us to detect functionally-correlated gene regulatory modules involved in specific biological processes. Moreover, our miR-TF modules provide a global view of coordinated regulation of target genes by miRNAs and TFs.

Flower development.

Flowers are the most complex structures of plants. Studies of Arabidopsis thaliana, which has typical eudicot flowers, have been fundamental in advancing the structural and molecular understanding of flower development. The main processes and stages of Arabidopsis flower development are summarized to provide a framework in which to interpret the detailed molecular genetic studies of genes assigned functions during flower development and is extended to recent genomics studies uncovering the key regulatory modules involved. Computational models have been used to study the concerted action and dynamics of the gene regulatory module that underlies patterning of the Arabidopsis inflorescence meristem and specification of the primordial cell types during early stages of flower development. This includes the gene combinations that specify sepal, petal, stamen and carpel identity, and genes that interact with them. As a dynamic gene regulatory network this module has been shown to converge to stable multigenic profiles that depend upon the overall network topology and are thus robust, which can explain the canalization of flower organ determination and the overall conservation of the basic flower plan among eudicots. Comparative and evolutionary approaches derived from Arabidopsis studies pave the way to studying the molecular basis of diverse floral morphologies.

Genome-Wide Profiling of Histone H3 Lysine 4 and Lysine 27 Trimethylation Reveals an Epigenetic Signature in Prostate Carcinogenesis

BackgroundIncreasing evidence implicates the critical roles of epigenetic regulation in cancer. Very recent reports indicate that global gene silencing in cancer is associated with specific epigenetic modifications. However, the relationship between epigenetic switches and more dynamic patterns of gene activation and repression has remained largely unknown.Methodology/Principal FindingsGenome-wide profiling of the trimethylation of histone H3 lysine 4 (H3K4me3) and lysine 27 (H3K27me3) was performed using chromatin immunoprecipitation coupled with whole genome promoter microarray (ChIP-chip) techniques. Comparison of the ChIP-chip data and microarray gene expression data revealed that loss and/or gain of H3K4me3 and/or H3K27me3 were strongly associated with differential gene expression, including microRNA expression, between prostate cancer and primary cells. The most common switches were gain or loss of H3K27me3 coupled with low effect on gene expression. The least prevalent switches were between H3K4me3 and H3K27me3 coupled with much higher fractions of activated and silenced genes. Promoter patterns of H3K4me3 and H3K27me3 corresponded strongly with coordinated expression changes of regulatory gene modules, such as HOX and microRNA genes, and structural gene modules, such as desmosome and gap junction genes. A number of epigenetically switched oncogenes and tumor suppressor genes were found overexpressed and underexpressed accordingly in prostate cancer cells.Conclusions/SignificanceThis work offers a dynamic picture of epigenetic switches in carcinogenesis and contributes to an overall understanding of coordinated regulation of gene expression in cancer. Our data indicate an H3K4me3/H3K27me3 epigenetic signature of prostate carcinogenesis.