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accession-icon GSE71194
Muscle expression of SOD1G93A modulates microRNA and mRNA expression pattern associated with the myelination process in the spinal cord of transgenic mice.
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Muscle Expression of SOD1(G93A) Modulates microRNA and mRNA Transcription Pattern Associated with the Myelination Process in the Spinal Cord of Transgenic Mice.

Sample Metadata Fields

Age, Specimen part

View Samples
accession-icon SRP019249
CLIP-seq of S. cerevisiae Drn1-TAP and Dbr1-TAP
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 4 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000, Illumina Genome Analyzer IIx

Description

Data for replicate Drn1-TAP and Dbr1-TAP CLIP-seq experiments to identify RNA-protein interactions Overall design: Drn1-TAP and Dbr1-TAP CLIP-seq

Publication Title

A homolog of lariat-debranching enzyme modulates turnover of branched RNA.

Sample Metadata Fields

Disease, Subject

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accession-icon SRP058647
Mastermind-like 3 controls proliferation and differentiation in neuroblastoma (RNA-seq)
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

Neuroblastoma cell lines can differentiate upon retinoic acid (RA) treatment, a finding that provided the basis for the clinical use of RA to treat neuroblastoma. However, resistance to RA is often observed, which limits its clinical utility. Using a gain-of-function genetic screen we identify the transcriptional coactivator Mastermind-like 3 (MAML3) as a gene whose ectopic expression confers resistance to RA. We find that MAML3 expression leads to loss of activation of a subset of RA target genes, which hampers RA-induced differentiation. The regulatory DNA elements of this subset of RA target genes show overlap in binding of MAML3 and the retinoic acid receptor, suggesting a role for MAML3 in the regulation of these genes. In addition, MAML3 has RA independent functions, including the activation of IGF1R and downstream AKT signaling via upregulation of IGF2, resulting in increased proliferation. Our results indicate an important role for MAML3 in differentiation and proliferation of neuroblastomas. Overall design: RNA-seq of SK-N-SH control and MAML3 overexpressing (SD3.23) cells, either untreated (UT) or treated with 1 µM RA (RA).

Publication Title

Mastermind-Like 3 Controls Proliferation and Differentiation in Neuroblastoma.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP068229
miRNAs affected by antagomiR-17 treatment
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconNextSeq 500

Description

To understand the the effect of antagomir-17 treatment on human endothelial cells derived from human umbilical cord blood (UCB) CD34+ hematopoietic stem cells, we have employed mRNA sequencing. The antagomiR-17 used in this study was purchased from Dharmacon and cell transfection was performed using Lipofectamine RNAiMAx from Life Technologies. Scramble antagomiR from Ambion was used as control. Cells were transfected with antagomiR-17 or scrambled antagomiR for 48 hours. After 48 h, the cells were collected, RNA was isolated and RNA samples were shipped to Exiqon Services, Denmark for mRNA sequencing. All sequencing experiments (RNA integrity measurements, library preparation and next generation sequencing) were conducted at Exiqon Services, Denmark. Overall design: CD34+ endothelial cells differentiated from umbilical cord blood hematopoietic stem cells (CD34+) were treated with 50 nM antagomiR-17 (Dharmacon) or scrambled antagomiR (Ambion) using Lipofectamine RNAiMAx (Life Technologies) for 48 h. Three replicates were used for each condition (i.e. antagomiR-17 and scramble antagomiR conditions).

Publication Title

Synthetic microparticles conjugated with VEGF<sub>165</sub> improve the survival of endothelial progenitor cells via microRNA-17 inhibition.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon SRP073181
Single cell sequencing of Dgcr8 knockout embryonic stem cells plus/minus miR-294 or let-7
  • organism-icon Mus musculus
  • sample-icon 232 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2500

Description

We performed Fluidigm C1 single cell sequencing analysis of wild-type and microRNA deficient (Dgcr8 knockout) mouse embryonic stem cells mock treated or transfected with either miR-294 or let-7. Overall design: Wild-type and Dgcr8 knockout cells grown in naïve culture conditions were mock transfected or transfected with miRNA mimics for let-7b or miR-294, single cells were captured on Fluidigm C1 24 hours post-transfection and then prepared for sequencing on Illumina HiSeq1000 following manufacturer''s protocol.

Publication Title

The impact of microRNAs on transcriptional heterogeneity and gene co-expression across single embryonic stem cells.

Sample Metadata Fields

Specimen part, Subject

View Samples
accession-icon GSE30531
Expression data of A375 melanoma cells after DMSO or MLN4924 treatment from 1 hour to 24 hour
  • organism-icon Homo sapiens
  • sample-icon 30 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Microarrays were used to determine the change in gene expression of genes involved in the CDT1/NAE pathway

Publication Title

Quantitative proteomic analysis of cellular protein modulation upon inhibition of the NEDD8-activating enzyme by MLN4924.

Sample Metadata Fields

Cell line, Treatment, Time

View Samples
accession-icon GSE10565
Identification of targets of transcription factor Trp63: primary keratinocytes
  • organism-icon Mus musculus
  • sample-icon 28 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE10562
Induction of ERDNp63a via Tamoxifen in primary keratinocytes
  • organism-icon Mus musculus
  • sample-icon 13 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

Genome-wide identification of bona fide targets of transcription factors in mammalian cells is still a challenge. We present a novel integrated computational and experimental approach to identify direct targets of a transcription factor. This consists in measuring time-course (dynamic) gene expression profiles upon perturbation of the transcription factor under study, and in applying a novel reverse-engineering algorithm (TSNI) to rank genes according to their probability of being direct targets. Using primary keratinocytes as a model system, we identified novel transcriptional target genes of Trp63, a crucial regulator of skin development. TSNI-predicted Trp63 target genes were validated by Trp63 knockdown and by ChIP-chip to identify Trp63-bound regions in vivo. Our study revealed that short sampling times, in the order of minutes, are needed to capture the dynamics of gene expression in mammalian cells. We show that Trp63 transiently regulates a subset of its direct targets, thus highlighting the importance of considering temporal dynamics when identifying transcriptional targets. Using this approach, we uncovered a previously unsuspected transient regulation of the AP-1 complex by Trp63, through direct regulation of a subset of AP-1 components. The integrated experimental and computational approach described here is readily applicable to other transcription factors in mammalian systems and is complementary to genome-wide identification of transcription factor binding sites.

Publication Title

Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE10563
Primary keratinocytes treated with Tamoxifen
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

Genome-wide identification of bona fide targets of transcription factors in mammalian cells is still a challenge. We present a novel integrated computational and experimental approach to identify direct targets of a transcription factor. This consists in measuring time-course (dynamic) gene expression profiles upon perturbation of the transcription factor under study, and in applying a novel reverse-engineering algorithm (TSNI) to rank genes according to their probability of being direct targets. Using primary keratinocytes as a model system, we identified novel transcriptional target genes of Trp63, a crucial regulator of skin development. TSNI-predicted Trp63 target genes were validated by Trp63 knockdown and by ChIP-chip to identify Trp63-bound regions in vivo. Our study revealed that short sampling times, in the order of minutes, are needed to capture the dynamics of gene expression in mammalian cells. We show that Trp63 transiently regulates a subset of its direct targets, thus highlighting the importance of considering temporal dynamics when identifying transcriptional targets. Using this approach, we uncovered a previously unsuspected transient regulation of the AP-1 complex by Trp63, through direct regulation of a subset of AP-1 components. The integrated experimental and computational approach described here is readily applicable to other transcription factors in mammalian systems and is complementary to genome-wide identification of transcription factor binding sites.

Publication Title

Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE10564
Silencing of p63 (trp63) in primary keratinocytes via siRNA oligo transfection.
  • organism-icon Mus musculus
  • sample-icon 7 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430A 2.0 Array (mouse430a2)

Description

Genome-wide identification of bona fide targets of transcription factors in mammalian cells is still a challenge. We present a novel integrated computational and experimental approach to identify direct targets of a transcription factor. This consists in measuring time-course (dynamic) gene expression profiles upon perturbation of the transcription factor under study, and in applying a novel reverse-engineering algorithm (TSNI) to rank genes according to their probability of being direct targets. Using primary keratinocytes as a model system, we identified novel transcriptional target genes of Trp63, a crucial regulator of skin development. TSNI-predicted Trp63 target genes were validated by Trp63 knockdown and by ChIP-chip to identify Trp63-bound regions in vivo. Our study revealed that short sampling times, in the order of minutes, are needed to capture the dynamics of gene expression in mammalian cells. We show that Trp63 transiently regulates a subset of its direct targets, thus highlighting the importance of considering temporal dynamics when identifying transcriptional targets. Using this approach, we uncovered a previously unsuspected transient regulation of the AP-1 complex by Trp63, through direct regulation of a subset of AP-1 components. The integrated experimental and computational approach described here is readily applicable to other transcription factors in mammalian systems and is complementary to genome-wide identification of transcription factor binding sites.

Publication Title

Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.

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)

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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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