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accession-icon GSE43970
Reconstruction of the dynamic regulatory network that controls Th17 cell differentiation by systematic perturbation in primary cells
  • organism-icon Mus musculus
  • sample-icon 86 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Dynamic regulatory network controlling TH17 cell differentiation.

Sample Metadata Fields

Specimen part, Treatment

View Samples
accession-icon SRP018336
Reconstruction of the dynamic regulatory network that controls Th17 cell differentiation by systematic perturbation in primary cells (RNA-Seq)
  • organism-icon Mus musculus
  • sample-icon 61 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer

Description

Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy – combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells – to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells. Overall design: RNA-seq of knockdown of 12 genes in Th17 cell differentiation

Publication Title

Dynamic regulatory network controlling TH17 cell differentiation.

Sample Metadata Fields

Specimen part, Cell line, Treatment, Subject

View Samples
accession-icon GSE43955
Reconstruction of the dynamic regulatory network that controls Th17 cell differentiation by systematic perturbation in primary cells (Th17 differentiation timecourse)
  • organism-icon Mus musculus
  • sample-icon 58 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells.

Publication Title

Dynamic regulatory network controlling TH17 cell differentiation.

Sample Metadata Fields

Specimen part, Treatment

View Samples
accession-icon GSE43969
Reconstruction of the dynamic regulatory network that controls Th17 cell differentiation by systematic perturbation in primary cells (Affymetrix timecourse IL23 KO)
  • organism-icon Mus musculus
  • sample-icon 20 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Despite their enormous importance, the molecular circuits that control the differentiation of Th17 cells remain largely unknown. Recent studies have reconstructed regulatory networks in mammalian cells, but have focused on short-term responses and relied on perturbation approaches that cannot be applied to primary T cells. Here, we develop a systematic strategy combining transcriptional profiling at high temporal resolution, novel computational algorithms, and innovative nanowire-based tools for performing gene perturbations in primary T cells to derive and experimentally validate a temporal model of the dynamic regulatory network that controls Th17 differentiation. The network is arranged into two self-reinforcing and mutually antagonistic modules that either suppress or promote Th17 differentiation. The two modules contain 12 novel regulators with no previous implication in Th17 differentiation, which may be essential to maintain the appropriate balance of Th17 and other CD4+ T cell subsets. Overall, our study identifies and validates 39 regulatory factors that are embedded within a comprehensive temporal network and identifies novel drug targets and organizational principles for the differentiation of Th17 cells.

Publication Title

Dynamic regulatory network controlling TH17 cell differentiation.

Sample Metadata Fields

Specimen part, Treatment

View Samples
accession-icon GSE89131
Preferential Epigenetic Programming of Estrogen Response after in utero xenoestrogen (bisphenol-A) exposure
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconIllumina MouseWG-6 v2.0 expression beadchip

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Preferential epigenetic programming of estrogen response after in utero xenoestrogen (bisphenol-A) exposure.

Sample Metadata Fields

Age, Specimen part

View Samples
accession-icon GSE86923
Preferential Epigenetic Programming of Estrogen Response after in utero xenoestrogen (bisphenol-A) exposure [Illumina]
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge IconIllumina MouseWG-6 v2.0 expression beadchip

Description

Bisphenol-A (BPA) is an environmentally ubiquitous estrogen-like endocrine-disrupting compound. Exposure toBPAin utero hasbeen linked to female reproductive disorders, including endometrial hyperplasia and breast cancer. Estrogens are an etiological factor in many of these conditions. We sought to determine whether in utero exposure to BPA altered the global CpG methylation pattern of the uterine genome, subsequent gene expression, and estrogen response. Pregnant mice were exposed to an environmentally relevant dose of BPA or DMSO control. Uterine DNA and RNA were examined by using methylated DNA immunoprecipitation methylation microarray, expression microarray, and quantitative PCR. In utero BPA exposure altered the global CpG methylation profile of the uterine genome and subsequent gene expression. The effect on gene expression was not apparent until sexual maturation, which suggested that estrogen response was the primary alteration. Indeed, prenatal BPA exposure preferentially altered adult estrogen-responsive gene expression. Changes in estrogen response were accompanied by altered methylation that preferentially affected estrogen receptor-a (ERa)binding genes. The majority of genes that demonstrated both altered expression and ERa binding had decreased methylation. BPA selectively altered the normal developmental programming of estrogen-responsive genes via modification of the genes that bind ERa. Gene environment interactions driven by early life xenoestrogen exposure likely contributes to increased risk of estrogen related disease in adults.Jorgensen, E. M.,Alderman,M.H., III,Taylor, H. S. Preferential epigenetic programmingof estrogen response after in utero xenoestrogen (bisphenol-A) exposure.

Publication Title

Preferential epigenetic programming of estrogen response after in utero xenoestrogen (bisphenol-A) exposure.

Sample Metadata Fields

Age, Specimen part

View Samples
accession-icon GSE21070
Expression profile of contrasting maize genotypes grown on acid and control soil (root tips)
  • organism-icon Zea mays
  • sample-icon 19 Downloadable Samples
  • Technology Badge Icon Affymetrix Maize Genome Array (maize)

Description

Aluminum toxicity is one of the major limiting factors for many crops worldwide. The primary symptom of Al toxicity syndrome is the inhibition of root growth, leading to poor water and nutrient absorption. The causes of this inhibition are still elusive, with several biochemical pathways being affected and with a significant variation between species. Most of the work done so far to investigate the genes responsible for Al tolerance used hydroponic culture. Here we evaluated plant responses using soil as substrate, which is a condition closer to the field reality.

Publication Title

Transcriptional profile of maize roots under acid soil growth.

Sample Metadata Fields

Specimen part

View Samples
accession-icon GSE10700
Time course of NHBE cells exposed to whole cigarette smoke
  • organism-icon Homo sapiens
  • sample-icon 49 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Gene expression patterns were assessed in normal human bronchial epithelial (NHBE) cells exposed to cigarette smoke from a reference cigarette (2R4F, University of Kentucky) and a typical American brand of "light" cigarettes ("Lights") in order to develop a better understanding of the genomic impact of tobacco exposure, which can ultimately define biomarkers that discriminate tobacco-related effects and outcomes in a clinical setting. NHBE cells were treated with whole cigarette smoke for 15 minutes and alterations to the transcriptome assessed at 2, 4, 8 and 24 hours post-exposure using high-density oligonucleotide microarrays.

Publication Title

Cigarette smoke induces endoplasmic reticulum stress and the unfolded protein response in normal and malignant human lung cells.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE10718
Time course of NHBE cells exposed to whole cigarette smoke (full flavor)
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Gene expression patterns were assessed in normal human bronchial epithelial (NHBE) cells exposed to cigarette smoke (CS) from a typical "full flavor" American brand of cigarettes in order to develop a better understanding of the genomic impact of tobacco exposure, which can ultimately define biomarkers that discriminate tobacco-related effects and outcomes in a clinical setting. NHBE cells were treated with CS for 15 minutes and alterations to the transcriptome assessed at 1,2,4 and 24 hours post-CS-exposure using high-density oligonucleotide microarrays.

Publication Title

Cigarette smoke induces endoplasmic reticulum stress and the unfolded protein response in normal and malignant human lung cells.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE7150
Activin-deficient granulosa cells
  • organism-icon Mus musculus
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Compares activin deficient granulosa cells (Inhba flox/-; Inhbb-/-; Amhr2cre/+) to wild type granulosa cells

Publication Title

Intraovarian activins are required for female fertility.

Sample Metadata Fields

No sample metadata fields

View Samples
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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

Developed by the Childhood Cancer Data Lab

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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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