Transcriptional override: a regulatory network model of indirect responses to modulations in microRNA expression
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE52460 | GEO | 2013/11/19
SECONDARY ACCESSION(S): PRJNA228982
REPOSITORIES: GEO
Similar Datasets
Project description:BackgroundDocumented changes in levels of microRNAs (miRNA) in a variety of diseases including cancer are leading to their development as early indicators of disease, and as a potential new class of therapeutic agents. A significant hurdle to the rational application of miRNAs as therapeutics is our current inability to reliably predict the range of molecular and cellular consequences of perturbations in the levels of specific miRNAs on targeted cells. While the direct gene (mRNA) targets of individual miRNAs can be computationally predicted with reasonable degrees of accuracy, reliable predictions of the indirect molecular effects of perturbations in miRNA levels remain a major challenge in molecular systems biology.ResultsChanges in gene (mRNA) and miRNA expression levels between normal precursor and ovarian cancer cells isolated from patient tissue samples were measured by microarray. Expression of 31 miRNAs was significantly elevated in the cancer samples. Consistent with previous reports, the expected decrease in expression of the mRNA targets of upregulated miRNAs was observed in only 20-30% of the cancer samples. We present and provide experimental support for a network model (The Transcriptional Override Model; TOM) to account for the unexpected regulatory consequences of modulations in the expression of miRNAs on expression levels of their target mRNAs in ovarian cancer.ConclusionsThe direct and indirect regulatory effects of changes in miRNA expression levels in vivo are interactive and complex but amenable to systems level modeling. Although TOM has been developed and validated within the context of ovarian cancer, it may be applicable in other biological contexts as well, including of potential future use in the rational design of miRNA-based strategies for the treatment of cancers and other diseases.
| S-EPMC3987680 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2013-11-19 | E-GEOD-52460 | biostudies-arrayexpress
Project description:MicroRNAs are small non-coding molecules that have been shown to repress the translation of thousands of genes. Changes in microRNA expression in a variety of diseases, including cancer, are leading to the development of microRNAs as early indicators of disease, and to their potential use as therapeutic agents. A significant hurdle to the use of microRNAs as therapeutics is our inability to predict the molecular and cellular consequences of perturbations in the levels of specific microRNAs on targeted cells. While the direct gene (mRNA) targets of individual microRNAs can be computationally predicted and are often experimentally validated, assessing the indirect effects of microRNA variation remains a major challenge in molecular systems biology. We present experimental evidence for a computational model that quantifies the extent to which down-regulated transcriptional repressors contribute to the unanticipated upregulation of putative microRNA targets. An appreciation of the effects of these repressors may provide a more complete understanding of the indirect effects of microRNA dysregulation in diseases such as cancer, and to their successful clinical application. mRNA were collected from the surface epithelial cells of 10 normal ovaries and from laser capture microdisection of 10 ovarian tumors. mRNA expression was captured on Affymetrix U133 Plus 2 chips. Present/absent calls were generated using MAS5, while signals were calculated using GCRMA. All signals were then log2 normalized. Expression was compared between miRanda-mirSVR predicted target genes of upregulated microRNAs, non-predicted target genes, and genes putatively targeted by down-regulated transcriptional repressors.
2013-11-05 | E-GEOD-52037 | biostudies-arrayexpress
Project description:MicroRNAs are small non-coding molecules that have been shown to repress the translation of thousands of genes. Changes in microRNA expression in a variety of diseases, including cancer, are leading to the development of microRNAs as early indicators of disease, and to their potential use as therapeutic agents. A significant hurdle to the use of microRNAs as therapeutics is our inability to predict the molecular and cellular consequences of perturbations in the levels of specific microRNAs on targeted cells. While the direct gene (mRNA) targets of individual microRNAs can be computationally predicted and are often experimentally validated, assessing the indirect effects of microRNA variation remains a major challenge in molecular systems biology. We present experimental evidence for a computational model that quantifies the extent to which down-regulated transcriptional repressors contribute to the unanticipated upregulation of putative microRNA targets. An appreciation of the effects of these repressors may provide a more complete understanding of the indirect effects of microRNA dysregulation in diseases such as cancer, and to their successful clinical application. This is a re-analysis of GSE23383, as described below in “overall design”.
2013-11-19 | GSE52459 | GEO
Project description:MicroRNAs are small non-coding molecules that have been shown to repress the translation of thousands of genes. Changes in microRNA expression in a variety of diseases, including cancer, are leading to the development of microRNAs as early indicators of disease, and to their potential use as therapeutic agents. A significant hurdle to the use of microRNAs as therapeutics is our inability to predict the molecular and cellular consequences of perturbations in the levels of specific microRNAs on targeted cells. While the direct gene (mRNA) targets of individual microRNAs can be computationally predicted and are often experimentally validated, assessing the indirect effects of microRNA variation remains a major challenge in molecular systems biology. We present experimental evidence for a computational model that quantifies the extent to which down-regulated transcriptional repressors contribute to the unanticipated upregulation of putative microRNA targets. An appreciation of the effects of these repressors may provide a more complete understanding of the indirect effects of microRNA dysregulation in diseases such as cancer, and to their successful clinical application.
2013-11-05 | GSE52037 | GEO
Project description:The innate inflammatory response must be tightly regulated to ensure effective immune protection. NF-?B is a key mediator of the inflammatory response, and its dysregulation has been associated with immune-related malignancies. Here, we describe a miRNA-based regulatory network that enables precise NF-?B activity in mouse macrophages. Elevated miR-155 expression potentiates NF-?B activity in miR-146a-deficient mice, leading to both an overactive acute inflammatory response and chronic inflammation. Enforced miR-155 expression overrides miR-146a-mediated repression of NF-?B activation, thus emphasizing the dominant function of miR-155 in promoting inflammation. Moreover, miR-155-deficient macrophages exhibit a suboptimal inflammatory response when exposed to low levels of inflammatory stimuli. Importantly, we demonstrate a temporal asymmetry between miR-155 and miR-146a expression during macrophage activation, which creates a combined positive and negative feedback network controlling NF-?B activity. This miRNA-based regulatory network enables a robust yet time-limited inflammatory response essential for functional immunity.MicroRNAs (miR) are important regulators of gene transcription, with miR-155 and miR-146a both implicated in macrophage activation. Here the authors show that NF-?B signalling, miR-155 and miR-146a form a complex network of cross-regulations to control gene transcription in macrophages for modulating inflammatory responses.
| S-EPMC5636846 | biostudies-literature
Project description:Organisms from all domains of life use gene regulation networks to control cell growth, identity, function, and responses to environmental challenges. Although accurate global regulatory models would provide critical evolutionary and functional insights, they remain incomplete, even for the best studied organisms. Efforts to build comprehensive networks are confounded by challenges including network scale, degree of connectivity, complexity of organism-environment interactions, and difficulty of estimating the activity of regulatory factors. Taking advantage of the large number of known regulatory interactions in Bacillus subtilis and two transcriptomics datasets (including one with 38 separate experiments collected specifically for this study), we use a new combination of network component analysis and model selection to simultaneously estimate transcription factor activities and learn a substantially expanded transcriptional regulatory network for this bacterium. In total, we predict 2,258 novel regulatory interactions and recall 74% of the previously known interactions. We obtained experimental support for 391 (out of 635 evaluated) novel regulatory edges (62% accuracy), thus significantly increasing our understanding of various cell processes, such as spore formation.
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Project description:Information about the extra- or intracellular environment is often captured as biochemical signals that propagate through regulatory networks. These signals eventually drive phenotypic changes, typically by altering gene expression programmes in the cell. Reconstruction of transcriptional regulatory networks has given a compelling picture of bacterial physiology, but transcriptional network maps alone often fail to describe phenotypes. Cellular response dynamics are ultimately determined by interactions between transcriptional and non-transcriptional networks, with dramatic implications for physiology and evolution. Here, we provide an overview of non-transcriptional interactions that can affect the performance of natural and synthetic bacterial regulatory networks.
| S-EPMC3755963 | biostudies-literature
Project description:Transcriptional regulation of gene expression is an essential cellular process that is arranged by transcription factors (TFs), microRNAs (miRNA) and their target genes through a variety of mechanisms. Here, we set out to reconstruct a comprehensive transcriptional regulatory network of Homo sapiens consisting of experimentally verified regulatory information on miRNAs, TFs and their target genes. We have performed topological analyses to elucidate the transcriptional regulatory roles of miRNAs and TFs. When we thoroughly investigated the network motifs, different gene regulatory scenarios were observed; whereas, mutual TF-miRNA regulation (interactive cooperation) and hierarchical operation where miRNAs were the upstream regulators of TFs came into prominence. Otherwise, biological process specific subnetworks were also constructed and integration of gene and miRNA expression data on ovarian cancer was achieved as a case study to observe dynamic patterns of the gene expression. Meanwhile, both co-operation and hierarchical operation types were determined in active ovarian cancer and process-specific subnetworks. In addition, the analysis showed that multiple signals from miRNAs were integrated by TFs. Our results demonstrate new insights on the architecture of the human transcriptional regulatory network, and here we present some lessons we gained from deciphering the reciprocal interplay between miRNAs, TFs and their target genes.
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Project description:Nasopharyngeal carcinoma (NPC) is a prevalent malignancy in Southeast Asia among the Chinese population. Aberrant regulation of transcripts has been implicated in many types of cancers including NPC. Herein, we characterized mRNA and miRNA transcriptomes by RNA sequencing (RNASeq) of NPC model systems. Matched total mRNA and small RNA of undifferentiated Epstein-Barr virus (EBV)-positive NPC xenograft X666 and its derived cell line C666, well-differentiated NPC cell line HK1, and the immortalized nasopharyngeal epithelial cell line NP460 were sequenced by Solexa technology. We found 2812 genes and 149 miRNAs (human and EBV) to be differentially expressed in NP460, HK1, C666 and X666 with RNASeq; 533 miRNA-mRNA target pairs were inversely regulated in the three NPC cell lines compared to NP460. Integrated mRNA/miRNA expression profiling and pathway analysis show extracellular matrix organization, Beta-1 integrin cell surface interactions, and the PI3K/AKT, EGFR, ErbB, and Wnt pathways were potentially deregulated in NPC. Real-time quantitative PCR was performed on selected mRNA/miRNAs in order to validate their expression. Transcript sequence variants such as short insertions and deletions (INDEL), single nucleotide variant (SNV), and isomiRs were characterized in the NPC model systems. A novel TP53 transcript variant was identified in NP460, HK1, and C666. Detection of three previously reported novel EBV-encoded BART miRNAs and their isomiRs were also observed. Meta-analysis of a model system to a clinical system aids the choice of different cell lines in NPC studies. This comprehensive characterization of mRNA and miRNA transcriptomes in NPC cell lines and the xenograft provides insights on miRNA regulation of mRNA and valuable resources on transcript variation and regulation in NPC, which are potentially useful for mechanistic and preclinical studies.
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