Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles.
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Machine learning approaches offer the potential to systematically identify transcriptional regulatory interactions from a compendium of microarray expression profiles. However, experimental validation of the performance of these methods at the genome scale has remained elusive. Here we assess the global performance of four existing classes of inference algorithms using 445 Escherichia coli Affymetrix arrays and 3,216 known E. coli regulatory interactions from RegulonDB. We also developed and applied the context likelihood of relatedness (CLR) algorithm, a novel extension of the relevance networks class of algorithms. CLR demonstrates an average precision gain of 36% relative to the next-best performing algorithm. At a 60% true positive rate, CLR identifies 1,079 regulatory interactions, of which 338 were in the previously known network and 741 were novel predictions. We tested the predicted interactions for three transcription factors with chromatin immunoprecipitation, confirming 21 novel interactions and verifying our RegulonDB-based performance estimates. CLR also identified a regulatory link providing central metabolic control of iron transport, which we confirmed with real-time quantitative PCR. The compendium of expression data compiled in this study, coupled with RegulonDB, provides a valuable model system for further improvement of network inference algorithms using experimental data. (+info)
Qualitative network models and genome-wide expression data define carbon/nitrogen-responsive molecular machines in Arabidopsis.
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BACKGROUND: Carbon (C) and nitrogen (N) metabolites can regulate gene expression in Arabidopsis thaliana. Here, we use multi-network analysis of microarray data to identify molecular networks regulated by C and N in the Arabidopsis root system. RESULTS: We used the Arabidopsis whole genome Affymetrix gene chip to explore global gene expression responses in plants exposed transiently to a matrix of C and N treatments. We used ANOVA analysis to define quantitative models of regulation for all detected genes. Our results suggest that about half of the Arabidopsis transcriptome is regulated by C, N or CN interactions. We found ample evidence for interactions between C and N that include genes involved in metabolic pathways, protein degradation and auxin signaling. To provide a global, yet detailed, view of how the cell molecular network is adjusted in response to the CN treatments, we constructed a qualitative multi-network model of the Arabidopsis metabolic and regulatory molecular network, including 6,176 genes, 1,459 metabolites and 230,900 interactions among them. We integrated the quantitative models of CN gene regulation with the wiring diagram in the multi-network, and identified specific interacting genes in biological modules that respond to C, N or CN treatments. CONCLUSION: Our results indicate that CN regulation occurs at multiple levels, including potential post-transcriptional control by microRNAs. The network analysis of our systematic dataset of CN treatments indicates that CN sensing is a mechanism that coordinates the global and coordinated regulation of specific sets of molecular machines in the plant cell. (+info)
Ciona intestinalis as a model for cardiac development.
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The primitive chordate Ciona intestinalis has emerged as a significant model system for the study of heart development. The Ciona embryo employs a conserved heart gene network in the context of extremely low cell numbers and reduced genetic redundancy. Here, I review recent studies on the molecular genetics of Ciona cardiogenesis as well as classic work on heart anatomy and physiology. I also discuss the potential of employing Ciona to decipher a comprehensive chordate gene network and to determine how this network controls heart morphogenesis. (+info)
Identification of tightly regulated groups of genes during Drosophila melanogaster embryogenesis.
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Time-series analysis of whole-genome expression data during Drosophila melanogaster development indicates that up to 86% of its genes change their relative transcript level during embryogenesis. By applying conservative filtering criteria and requiring 'sharp' transcript changes, we identified 1534 maternal genes, 792 transient zygotic genes, and 1053 genes whose transcript levels increase during embryogenesis. Each of these three categories is dominated by groups of genes where all transcript levels increase and/or decrease at similar times, suggesting a common mode of regulation. For example, 34% of the transiently expressed genes fall into three groups, with increased transcript levels between 2.5-12, 11-20, and 15-20 h of development, respectively. We highlight common and distinctive functional features of these expression groups and identify a coupling between downregulation of transcript levels and targeted protein degradation. By mapping the groups to the protein network, we also predict and experimentally confirm new functional associations. (+info)
Reconstructing dynamic regulatory maps.
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Even simple organisms have the ability to respond to internal and external stimuli. This response is carried out by a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an input-output hidden Markov model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified temporal map. Applying our method to study yeast response to stress, we derive dynamic models that are able to recover many of the known aspects of these responses. Predictions made by our method have been experimentally validated leading to new roles for Ino4 and Gcn4 in controlling yeast response to stress. The temporal cascade of factors reveals common pathways and highlights differences between master and secondary factors in the utilization of network motifs and in condition-specific regulation. (+info)
Molecular analysis of human endometrium: short-term tibolone signaling differs significantly from estrogen and estrogen + progestagen signaling.
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Tibolone, a tissue-selective compound with a combination of estrogenic, progestagenic, and androgenic properties, is used as an alternative for estrogen or estrogen plus progesterone hormone therapy for the treatment of symptoms associated with menopause and osteoporosis. The current study compares the endometrial gene expression profiles after short-term (21 days) treatment with tibolone to the profiles after treatment with estradiol-only (E(2)) and E(2) + medroxyprogesterone acetate (E(2) + MPA) in healthy postmenopausal women undergoing hysterectomy for endometrial prolapse. The impact of E(2) treatment on endometrial gene expression (799 genes) was much higher than the effect of tibolone (173 genes) or E(2) + MPA treatment (174 genes). Furthermore, endometrial gene expression profiles after tibolone treatment show a weak similarity to the profiles after E(2) treatment (overlap 72 genes) and even less profile similarity to E(2) + MPA treatment (overlap 17 genes). Interestingly, 95 tibolone-specific genes were identified. Translation of profile similarity into biological processes and pathways showed that ER-mediated downstream processes, such as cell cycle and cell proliferation, are not affected by E2 + MPA, slightly by tibolone, but are significantly affected by E(2). In conclusion, tibolone treatment results in a tibolone-specific gene expression profile in the human endometrium, which shares only limited resemblance to E(2) and even less resemblance to E2 + MPA induced profiles. (+info)
EDGEdb: a transcription factor-DNA interaction database for the analysis of C. elegans differential gene expression.
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BACKGROUND: Transcription regulatory networks are composed of protein-DNA interactions between transcription factors and their target genes. A long-term goal in genome biology is to map protein-DNA interaction networks of all regulatory regions in a genome of interest. Both transcription factor -and gene-centered methods can be used to systematically identify such interactions. We use high-throughput yeast one-hybrid assays as a gene-centered method to identify protein-DNA interactions between regulatory sequences (e.g. gene promoters) and transcription factors in the nematode Caenorhabditis elegans. We have already mapped several hundred protein-DNA interactions and analyzed the transcriptional consequences of some by examining differential gene expression of targets in the presence or absence of an upstream regulator. The rapidly increasing amount of protein-DNA interaction data at a genome scale requires a database that facilitates efficient data storage, retrieval and integration. DESCRIPTION: Here, we report the implementation of a C. elegans differential gene expression database (EDGEdb). This database enables the storage and retrieval of protein-DNA interactions and other data that relate to differential gene expression. Specifically, EDGEdb contains: i) sequence information of regulatory elements, including gene promoters, ii) sequence information of all 934 predicted transcription factors, their DNA binding domains, and, where available, their dimerization partners and consensus DNA binding sites, iii) protein-DNA interactions between regulatory elements and transcription factors, and iv) expression patterns conferred by regulatory elements, and how such patterns are affected by interacting transcription factors. CONCLUSION: EDGEdb provides a protein-DNA -and protein-protein interaction resource for C. elegans transcription factors and a framework for similar databases for other organisms. The database is available at http://edgedb.umassmed.edu. (+info)
Discovering biological guilds through topological abstraction.
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High-throughput generation of new types of relational biological datasets is creating a demand for methods to provide insights into their complexity. Such networks are often too large to interpret visually and too complicated to be explained solely based on local topological properties. One way to try to make sense of such complex networks would be to transform them into discernable abstracts, or summaries, of the original networks. Then, important components could become more readily visible. This work presents such an approach for understanding networks via abstraction of global network connectivity using compression. This made possible the discovery of a new type of topological class, referred to herein as a guild, that captures global connectivity similarity. Lastly, the correspondence of these guilds to biological function is validated via an E. Coli gene regulation network. This resulted in biological findings that could not be derived from local topology of the original network. (+info)