A Stringent Systems Approach Uncovers Gene-Specific Mechanisms Regulating Inflammation [ChIP-seq]
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE67343 | GEO | 2016/01/18
SECONDARY ACCESSION(S): PRJNA279552
REPOSITORIES: GEO
Similar Datasets
Project description:Much has been learned about transcriptional cascades and networks from large-scale systems analyses of high-throughput data sets. However, analysis methods that optimize statistical power through simultaneous evaluation of thousands of ChIP-seq peaks or differentially expressed genes possess substantial limitations in their ability to uncover mechanistic principles of transcriptional control. By examining nascent transcript RNA-seq, ChIP-seq, and binding motif data sets from lipid A-stimulated macrophages with increased attention to the quantitative distribution of signals, we identified unexpected relationships between the in vivo binding properties of inducible transcription factors, motif strength, and transcription. Furthermore, rather than emphasizing common features of large clusters of co-regulated genes, our results highlight the extent to which unique mechanisms regulate individual genes with key biological functions. Our findings demonstrate the mechanistic value of stringent interrogation of well- defined sets of genes as a complement to broader systems analyses of transcriptional cascades and networks. Bone marrow-derived macrophages were stimulated with lipid A for 0, 15, 30, 60, and 120 minutes. Chromatin was immunoprecipitated with antibodies against RelA, SRF, or IRF3.
2016-01-18 | E-GEOD-67343 | biostudies-arrayexpress
Project description:Much has been learned about transcriptional cascades and networks from large-scale systems analyses of high-throughput data sets. However, analysis methods that optimize statistical power through simultaneous evaluation of thousands of ChIP-seq peaks or differentially expressed genes possess substantial limitations in their ability to uncover mechanistic principles of transcriptional control. By examining nascent transcript RNA-seq, ChIP-seq, and binding motif data sets from lipid A-stimulated macrophages with increased attention to the quantitative distribution of signals, we identified unexpected relationships between the in vivo binding properties of inducible transcription factors, motif strength, and transcription. Furthermore, rather than emphasizing common features of large clusters of co-regulated genes, our results highlight the extent to which unique mechanisms regulate individual genes with key biological functions. Our findings demonstrate the mechanistic value of stringent interrogation of well-defined sets of genes as a complement to broader systems analyses of transcriptional cascades and networks. Bone marrow-derived macrophages were stimulated with lipid A for 0 or 120 minutes. Chromatin was treated with Tn5 transposase to measure DNA accessibility
2016-01-18 | E-GEOD-74191 | biostudies-arrayexpress
Project description:Much has been learned about transcriptional cascades and networks from large-scale systems analyses of high-throughput data sets. However, analysis methods that optimize statistical power through simultaneous evaluation of thousands of ChIP-seq peaks or differentially expressed genes possess substantial limitations in their ability to uncover mechanistic principles of transcriptional control. By examining nascent transcript RNA-seq, ChIP-seq, and binding motif data sets from lipid A-stimulated macrophages with increased attention to the quantitative distribution of signals, we identified unexpected relationships between the in vivo binding properties of inducible transcription factors, motif strength, and transcription. Furthermore, rather than emphasizing common features of large clusters of co-regulated genes, our results highlight the extent to which unique mechanisms regulate individual genes with key biological functions. Our findings demonstrate the mechanistic value of stringent interrogation of well- defined sets of genes as a complement to broader systems analyses of transcriptional cascades and networks.
2016-01-18 | GSE67355 | GEO
Project description:Much has been learned about transcriptional cascades and networks from large-scale systems analyses of high-throughput data sets. However, analysis methods that optimize statistical power through simultaneous evaluation of thousands of ChIP-seq peaks or differentially expressed genes possess substantial limitations in their ability to uncover mechanistic principles of transcriptional control. By examining nascent transcript RNA-seq, ChIP-seq, and binding motif data sets from lipid A-stimulated macrophages with increased attention to the quantitative distribution of signals, we identified unexpected relationships between the in vivo binding properties of inducible transcription factors, motif strength, and transcription. Furthermore, rather than emphasizing common features of large clusters of co-regulated genes, our results highlight the extent to which unique mechanisms regulate individual genes with key biological functions. Our findings demonstrate the mechanistic value of stringent interrogation of well-defined sets of genes as a complement to broader systems analyses of transcriptional cascades and networks.
2016-01-18 | GSE74191 | GEO
Project description:Much has been learned about transcriptional cascades and networks from large-scale systems analyses of high-throughput data sets. However, analysis methods that optimize statistical power through simultaneous evaluation of thousands of ChIP-seq peaks or differentially expressed genes possess substantial limitations in their ability to uncover mechanistic principles of transcriptional control. By examining nascent transcript RNA-seq, ChIP-seq, and binding motif data sets from lipid A-stimulated macrophages with increased attention to the quantitative distribution of signals, we identified unexpected relationships between the in vivo binding properties of inducible transcription factors, motif strength, and transcription. Furthermore, rather than emphasizing common features of large clusters of co-regulated genes, our results highlight the extent to which unique mechanisms regulate individual genes with key biological functions. Our findings demonstrate the mechanistic value of stringent interrogation of well- defined sets of genes as a complement to broader systems analyses of transcriptional cascades and networks. Bone marrow-derived macrophages derived from C57Bl/6, Myd88-/-, Trif-/-, Irf3-/-, Ifnar-/-, and RelA-/- mice were stimulated with lipid A; C57Bl/6 macrophages were stimulated with lipid A in the presence of MAPK inhibitors or cycloheximide, or stimulated with PAM3CSK4 for 0, 15, 30, 60, and 120 minutes, or stimulated with lipid A for 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60 minutes. Two biological replicates were generated for each time point for each treatment type.
2016-01-18 | E-GEOD-67355 | biostudies-arrayexpress
Project description:Toll-like receptors (TLRs) are responsible for sensing pathogen components, initiating cascades of signaling pathways, transcription factors, chromatin regulators, and activating thousands of genes contributing to diverse immune functions in macrophages. Conventional system approaches have been used to analyze TLR-induced transcriptional cascades. However, commonly used approaches possess a number of limitations, including a bias toward weakly induced genes, which are far more prevalent than strongly induced genes, and an inability to distinguish functionally important protein-DNA interactions identified by ChIP-seq from interactions that may be functionally insignificant. To develop a better understanding of a TLR-induced transcriptional cascade, we used more stringent criteria to analyze RNA-seq, ChIP-seq, and transcription factor binding motif data sets. Our analysis provided a number of insights into the regulation of an inducible transcriptional cascade. This stringent and quantitative strategy can effectively reveal gene-specific regulatory mechanisms from genome-wide studies.
2017-06-11 | GSE99895 | GEO
Project description:This mechanistic model describes the activation of immediate early genes such as cFos after EGF or heregulin (HRG) stimulation of the MAPK pathway. Phosphorylated cFos is a key transcription factor triggering downstream cascades of cell fate determination. The model can explain how the switch-like response of p-cFos emerges from the spatiotemporal dynamics. This mechanistic model comprises the explicit reaction kinetics of the signal transduction pathway, the transcriptional and the posttranslational feedback and feedforward loops. In the below article, two different mechanistic models have been studied, the first one based on previously known interactions but failing to account for the experimental data and the second one including additional interactions which were discovered and confirmed by new experiments. The mechanistic model encoded here is the second one, the extended and at the time of creation most complete model of cell fate decision making in response to different doses of EGF or HRG stimulation. The encoded parameter set corresponds to 10mM HRG stimulation as shown in Fig.1 of the article. The Supplementary Methods of the article provide further parameter sets that allow simulations for different ligands and different doses. A corresponding core model is available from http://www.ebi.ac.uk/biomodels/ as MODEL1003170000.
Ligand-specific c-Fos expression emerges from the spatiotemporal control of ErbB network dynamics.
Takashi Nakakuki(1), Marc R. Birtwistle(2,3,4), Yuko Saeki(1,5), Noriko Yumoto(1,5), Kaori Ide(1), Takeshi Nagashima(1,5), Lutz Brusch(6), Babatunde A. Ogunnaike(3), Mariko Hatakeyama(1,5), and Boris N. Kholodenko(2,4); Cell In Press, online 20 May 2010
, doi: 10.1016/j.cell.2010.03.054
(1) RIKEN Advanced Science Institute, Computational Systems Biology Research Group, Advanced Computational Sciences Department, 1-7-22 Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
(2) Systems Biology Ireland, University College Dublin, Belfield, Dublin 4, Ireland
(3) University of Delaware, Department of Chemical Engineering, 150 Academy St., Newark, DE 19716, USA
(4) Thomas Jefferson University, Department of Pathology, Anatomy, and Cell Biology, 1020 Locust Street, Philadelphia, PA 19107, USA
(5) RIKEN Research Center for Allergy and Immunology, Laboratory for Cellular Systems Modeling, 1-7-22 Tsurumi-ku, Yokohama, 230-0045, Japan
(6) Dresden University of Technology, Center for Information Services and High Performance Computing, 01062 Dresden, Germany
2024-09-02 | BIOMD0000000250 | BioModels
Project description:Interventions: Case series:None
Primary outcome(s): exon genes;transcriptional expression;proteome;protein phosphorylation group
Study Design: Sequential
| 2739647 | ecrin-mdr-crc
Project description:Synthetic biology holds immense promise to tackle key problems we are facing, for instance in resource use, environmental health, and human health care. However, comprehensive safety measures are needed to deploy genetically engineered microorganisms in open-environment applications. Intrinsic, genetically encoded biocontainment systems, which control cell survival based on environmental cues, can solve this issue. Here, we describe a genetic biocontainment system based on conditional stability of essential proteins. We used a yeast-adapted destabilizing domain degron, which can be stabilized by estradiol addition (ERdd). Leveraging the yeast GFP collection and lab automation platforms, we ERdd-tagged 775 essential genes and screened for strains with estradiol dependent growth. Three genes, SPC110, DIS3 and RRP46, were found to be particularly suitable targets. Respective strains showed no growth defect in the presence of estradiol and strong growth inhibition in its absence. SPC110-ERdd offered the most stringent containment, with an escape frequency of 7.0x10-8, and full growth restoration at 100 nM estradiol. By systematically analyzing the containment escapees, we identified the non-essential C-terminal region of SPC110 as target for escape mutations. Its removal decreased the escape frequency with a single ERdd tag further to 4.9x10-9. Combining SPC110-ERdd with a second ERdd tag on either DIS3 or RRP46 resulted in escape frequencies below the detection limit of the used assay (<2x10-10). Being based on conditional protein stability, this approach is mechanistically orthogonal to previously reported intrinsic biocontainment systems. It thus can be readily combined with other systems, for instance ones based on transcriptional or translational control of essential gene expression, to achieve multiplexed, extremely stringent control over the survival of engineered organisms.
2024-02-05 | PXD042326 | Pride
Project description:Human neonates and older adults frequently exhibit a reduced capacity to control microbial infections. A variety of mechanisms involving both the innate and adaptive immune systems have been proposed to contribute to these deficiencies. The emergence of RNA sequencing (RNA-seq) as an accurate and quantitative method for examining mRNA levels provides an opportunity to compare transcriptional responses to a stimulus at a global scale in neonates, adults, and older adults. An examination of ex vivo monocyte responses to lipopolysaccharide stimulation or Listeria monocytogenes infection (with cord blood monocytes representing neonatal monocytes) revealed extensive similarities between all three age groups, with only a small number of genes exhibiting statistically significant differences. Using transcription factor motif analyses and RNA-seq data sets from a variety of mouse mutants, the most significant neonatal deficiencies corresponded to genes that require interferon response factor-3 or type 1 interferon signaling for their activation. In older adults, the most striking difference was broad, low-level activation of inflammatory genes prior to stimulation, consistent with prior evidence of a chronic inflammatory state in older adults. These results demonstrate the value of quantitative RNA-seq analyses and the feasibility of cross-species comparisons between well-defined mouse networks and human data sets.
2014-08-09 | GSE60216 | GEO