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Homo sapiens
9 Downloadable Samples
NextSeq 500
Description
We report here on G4RP-seq, which comprises of a cross-linking step, followed by chemical-affinity capture with the G4-specific small-molecule, BioTASQ and target identification using sequencing. This allows for capturing global snapshots of relative average levels of transiently folded G4-RNAs. We observed widespread G4-RNA targets indicative of transient G4 formation in several RNA entities in living human cells. G4RP-seq has also demonstrated that G4-stabilizing ligands (BRACO-19 and RHPS4) can change the G4 transcriptomic landscape, most notably in long non-coding RNAs. G4RP-seq thus provides a proof-of-principle for studying the G4-RNA landscape, as well as new ways of considering the mechanisms underlying G4-RNA formation and the activity of G4-stabilizing ligands. Overall design: Two BioTASQ-enriched samples and one input control for three different conditions (Untreated, BRACO-19-treated, and RHPS4-treated) in MCF7 cells
Publication Title
Transcriptome-wide identification of transient RNA G-quadruplexes in human cells.
Sample Metadata Fields
Specimen part, Cell line, Treatment, Subject
View Samples- Mus musculus
- 30 Downloadable Samples
Affymetrix HT Mouse Genome 430A Array (htmg430a)
Description
Individual genetic variation affects gene expression and cell phenotype by acting within complex molecular circuits, but this relationship is still largely unknown. Here, we combine genomic and meso-scale profiling with novel computational methods to detect genetic variants that affect the responsiveness of gene expression to stimulus (responsiveness QTLs) and position them in circuit diagrams. We apply this approach to study individual variation in transcriptional responsiveness to three different pathogen components in the model response of primary bone marrow dendritic cells (DCs) from recombinant inbred mice strains. We show that reQTLs are common both in cis (affecting a single target gene) and in trans (pleiotropically affecting co-regulated gene modules) and are specific to some stimuli but not others. Leveraging the stimulus-specific activity of reQTLs and the differential responsiveness of their associated targets, we show how to position reQTLs within the context of known pathways in this regulatory circuit. For example, we find that a pleiotropic trans-acting genetic factor in chr1:129-165Mb affects the responsiveness of 35 anti-viral genes only during an anti-viral like stimulus. Using RNAi we uncover RGS16 the likely causal gene in this interval, and an activator of the antiviral response. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in other complex circuits in primary mammalian cells.
Publication Title
Deciphering molecular circuits from genetic variation underlying transcriptional responsiveness to stimuli.
Sample Metadata Fields
Age, Specimen part
View Samples- Mus musculus
- 18 Downloadable Samples
- Illumina HiSeq 2000
Description
Tfh and B cells were cultured together with or without Tfr cells. After 4 days Tfh and B cells were sorted and prepared for 3'' targeted RNA-seq. Overall design: Examination of transcriptional changes upon suppression of Tfh and B cells.
Publication Title
Suppression by T<sub>FR</sub> cells leads to durable and selective inhibition of B cell effector function.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Mus musculus
- 12 Downloadable Samples
- NextSeq 500
Description
Tfh and B cells were cultured together with or without Tfr cells and IL-21. After 4 days Tfh and B cells were sorted and prepared for 3'' targeted RNA-seq. Overall design: Examination of transcriptional changes upon IL-21 rescue of B cell suppression
Publication Title
Suppression by T<sub>FR</sub> cells leads to durable and selective inhibition of B cell effector function.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Mus musculus
- 28 Downloadable Samples
- Illumina HiSeq 2500
Description
Protein expression is regulated by production and degradation of mRNAs and proteins, but their specific relationships remain unknown. We combine measurements of protein production and degradation and mRNA dynamics to build a quantitative genomic model of the differential regulation of gene expression in LPS stimulated mouse dendritic cells. Changes in mRNA abundance play a dominant role in determining most dynamic fold changes in protein levels. Conversely, the preexisting proteome of proteins performing basic cellular functions is remodeled primarily through changes in protein production or degradation, accounting for over half of the absolute change in protein molecules in the cell. Thus, the proteome is regulated by transcriptional induction of novel cellular functions and remodeling of preexisting functions through the protein life cycle. Overall design: Mouse primary dendritic cells were treated with LPS or mock stimulus and profiled over a 12-hour time course. Cells were grown in M-labeled SILAC media, which was replaced with H-labeled SILAC media at time 0. Aliquots were taken at 0, 0.5, 1, 2, 3, 4, 5, 6, 9, and 12 hours post-stimulation and added to equal volumes of a master mix of unlabeled (L) cells for the purpose of normalization. RNA-Seq was performed at 0, 1, 2, 4, 6, 9, and 12 hours post-stimulation.
Publication Title
Immunogenetics. Dynamic profiling of the protein life cycle in response to pathogens.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 9 Downloadable Samples
- Illumina Genome Analyzer IIx
Description
Dynamic binding of transcription factors to DNA elements specifies gene expression and cell fate, in both normal physiology and disease. To date, our understanding of mammalian gene regulation has been hampered by the difficulty of directly measuring in vivo binding of large numbers of transcription factors to DNA. Here, we develop a high-throughput indexed Chromatin ImmunoPrecipitation (iChIP) method coupled to massively parallel sequencing to systematically map protein-DNA interactions. We apply iChIP to reconstruct the physical regulatory landscape of a mammalian cell, by building genome-wide binding maps for 29 transcription factors (TFs) and chromatin marks at four time points following stimulation of primary dendritic cells (DCs) with pathogen components. Using over 180,000 TF-DNA interactions in these maps, we derive an initial dynamic physical model of a mammalian cell regulatory network. Our data demonstrates that transcription factors vary substantially in their binding dynamics, genomic localization, number of binding events, and degree of interaction with other factors. Further, many of the TF-DNA interactions at stimulus-activated genes are established during differentiation and maintained in a poised state. Functionally, the TFs are organized in a hierarchy of different types: Cell differentiation factors bind most of the genes and remain largely unchanged during the stimulation. A second set of TFs bind already in the un-stimulated and preferentially target induced genes. A third set consists of TF that bind mainly after the stimuli and target specific gene functions. Together these factors determine the magnitude and timing of stimulus induced gene expression. Our method, which allowed us to map routinely temporal binding profiles of dozens of TFs, provides a foundation for future understanding of the mammalian regulatory code. Overall design: A study of dynamic binding of transcription factors in an immune cell following pathogen stimulation
Publication Title
A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals.
Sample Metadata Fields
No sample metadata fields
View Samples
GSE75306- Mus musculus
- 154 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Parsing the Interferon Transcriptional Network and Its Disease Associations.
Sample Metadata Fields
Sex, Age, Specimen part, Treatment, Time
View Samples- Mus musculus
- 26 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
We analyze the expression profile of ISGs in the context of IFNAR1-KO primary murine B cells and macrophages. These analses were used to define ISG gene sets that are under tonic control. Furthermore, these analyses enabled the definition of ISGs that are dependent on Tyk2 signaling.
Publication Title
Parsing the Interferon Transcriptional Network and Its Disease Associations.
Sample Metadata Fields
Sex, Age
View Samples GSE112876- Mus musculus
- 22 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
We report cell specific responses to IFNg in 11 different peripheral immunocyte populations in the mouse. These cells represent the core ImmGen immunocyte lineage panel. Profiles from these were used to analyze cell specific responses to IFNg. In general a core set of ISG transcripts are induced in all cells. Smaller sets of ISGs were induced in a cell specific manner. In particular, splenic granulocytes and dendritic cells show restriced indcution of sets of ISGs.
Publication Title
Parsing the Interferon Transcriptional Network and Its Disease Associations.
Sample Metadata Fields
Sex, Age
View Samples- Mus musculus
- 17 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
B cells respond robustly to IFNa. Here we analyze gene expression profiles of primary murine splenic B cells treated with 10 fold serially diluted IFNa in vitro. We explore sensitivity to ISGs to IFNa as they relate to dose. Generally ISGs do not cluster significantly in a dose dependent manner. However there are notable spreads in sensitivity to IFNa.
Publication Title
Parsing the Interferon Transcriptional Network and Its Disease Associations.
Sample Metadata Fields
Sex, Age
View Samples