A Stringent Systems Approach Uncovers Gene-Specific Mechanisms Regulating Inflammation
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ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE67357 | GEO | 2016/01/18
SECONDARY ACCESSION(S): PRJNA279581
REPOSITORIES: GEO
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2016-01-18 | E-GEOD-67357 | biostudies-arrayexpress
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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. 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: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 | GSE67343 | 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
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