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accession-icon GSE84568
Immuno-genomic effects of JAK blockade
  • organism-icon Mus musculus
  • sample-icon 332 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Network pharmacology of JAK inhibitors.

Sample Metadata Fields

Sex, Age, Specimen part, Compound

View Samples
accession-icon GSE84853
Immuno-genomic effects of JAK blockade in vivo
  • organism-icon Mus musculus
  • sample-icon 238 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Small molecule inhibitors of JAK kinases have shown clinical effcacy in the treatment of certain autoimmune diseases. While these are known to block upstream JAK signalling events, their broader impact on the transcriptional footprint in immunocytes are unknown. Here we explore the effects of pan- and isoform-specific JAK blockade on the immuno-genomic network by genomic profiling.

Publication Title

Network pharmacology of JAK inhibitors.

Sample Metadata Fields

Sex, Age, Specimen part, Compound

View Samples
accession-icon GSE84560
Effect of JAK blockade on IFNa response in B cells in vitro
  • organism-icon Mus musculus
  • sample-icon 32 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

B cells respond robustly to type 1 interferons which signal through JAK1 and TYK2. Here we analyzed the effects of a panel of JAK inhibitors on the IFNa transcriptional response in activated B cells in vitro.

Publication Title

Network pharmacology of JAK inhibitors.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE84562
Effect of JAK1/3 blockade on IL2 response in NK cells
  • organism-icon Mus musculus
  • sample-icon 19 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

IL2 signals are transmitted through JAK1 and JAK3, but the transcriptomic consequences of each to the overall response is unclear. Here we analyzed the relative contribution of JAK1 and JAK3 to the NK cell IL2 response in vitro using titrated doses of isoform specific JAK inhibitors. Blockade of JAK1 and JAK3 have unequal effects on IL2-induced transcripts at pharmacologically relevant doses.

Publication Title

Network pharmacology of JAK inhibitors.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE84565
Effect of JAK blockade on IFNa response in CD4+ T cells in vitro
  • organism-icon Mus musculus
  • sample-icon 14 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

CD4+ T cells respond robustly to type 1 interferons which signal through JAK1 and TYK2. Here we analyzed the effects of a panel of JAK inhibitors on the IFNa transcriptional response in activated CD4+ T cells in vitro.

Publication Title

Network pharmacology of JAK inhibitors.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE84564
NK cell response to IL2 in vitro
  • organism-icon Mus musculus
  • sample-icon 7 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Here we analyzed the transcriptional response to IL2 in NK cells in vitro.

Publication Title

Network pharmacology of JAK inhibitors.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE84561
B cell response to IFNa in vitro
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Here we analyzed thetranscriptional response to IFNa in activated B cells in vitro. We found robust induction of ISGs as early as 2hrs.

Publication Title

Network pharmacology of JAK inhibitors.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE84566
CD4+ T cell response to IFNa in vitro
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Here we analyzed the transcriptional response to IFNa in activated CD4+ T cells in vitro. We found robust induction of ISGs as early as 2hrs.

Publication Title

Network pharmacology of JAK inhibitors.

Sample Metadata Fields

Sex, Age, Specimen part

View Samples
accession-icon GSE72788
PKC regulates the transformed growth of K-ras dependent lung cancer cells through regulation of integrin V3 expression
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

To explore how PKC regulates tumorigenesis, we performed mRNA expression analysis of four KRAS mutant NSCLC cell lines that stably express scrambled shRNA or PKC targeted shRNA

Publication Title

PKCδ regulates integrin αVβ3 expression and transformed growth of K-ras dependent lung cancer cells.

Sample Metadata Fields

Disease, Cell line, Treatment

View Samples
accession-icon GSE33726
The circadian clock coordinates ribosome biogenesis.
  • organism-icon Mus musculus
  • sample-icon 48 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Evolutionary conserved biological rhythms play a fundamental role in the physiology and behavior of all light-sensitive organisms. Generation of rhythmic expression of clock-controlled genes is orchestrated by a molecular circadian clock constitutes by interconnected negative feedback loops of transcription factors. In this study, we want to characterize gene which also present a rhythmic translation through the characterization of genes with a rhythmic polysomal/total RNA ratio.

Publication Title

The circadian clock coordinates ribosome biogenesis.

Sample Metadata Fields

Sex, Age, Specimen part, Disease, Time

View Samples

refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

fund-icon Fund the CCDL

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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