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accession-icon GSE20174
Mouse Lung Response to Stainless Steel and Mild Steel Welding Fume
  • organism-icon Mus musculus
  • sample-icon 48 Downloadable Samples
  • Technology Badge IconIllumina mouseRef-8 v1.1 expression beadchip

Description

A/J mice are genetically predisposed to spontaneous and/or chemically-induced lung tumors while C57BL/6J (B6) mice are resistant. This genetic disparity provides a unique scenario to identify molecular mechanisms associated with the lung response to welding fume at the transcriptome level.

Publication Title

Response of the mouse lung transcriptome to welding fume: effects of stainless and mild steel fumes on lung gene expression in A/J and C57BL/6J mice.

Sample Metadata Fields

Treatment

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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
accession-icon GSE45544
Ewing sarcoma compared to a normal body map
  • organism-icon Homo sapiens
  • sample-icon 44 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Primary pediatric Ewing sarcoma (ES), one uncharacterized sarcoma as well as primary and well established ES cell lines were compared to probes of different normal tissues

Publication Title

Distinct transcriptional signature and immunoprofile of CIC-DUX4 fusion-positive round cell tumors compared to EWSR1-rearranged Ewing sarcomas: further evidence toward distinct pathologic entities.

Sample Metadata Fields

Specimen part, Cell line, Subject

View Samples
accession-icon GSE64334
PW1/Peg3 expression regulates the key properties that determine mesoangioblast stem cell competence
  • organism-icon Mus musculus
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Mesoangioblasts are vessel-associated progenitor cells that show therapeutic promise for the treatment of muscular dystrophy. Mesoangioblasts have the ability to undergo skeletal muscle differentiation and cross the blood vessel wall regardless of the developmental stage at which they are isolated. Here we show that PW1/Peg3 is expressed at high levels in mesoangioblasts obtained from mouse, dog and human tissues and its level of expression correlates with their myogenic competence. Silencing PW1/Peg3 markedly inhibits myogenic potential of mesoangioblasts in vitro through MyoD degradation. Moreover, lack of PW1/Peg3 abrogates mesoangioblast ability to cross the vessel wall and to engraft into damaged myofibers through the modulation of the junctional adhesion molecule-A. We conclude that PW1/Peg3 function is essential for conferring proper mesoangioblast competence and that the determination of PW1/Peg3 levels in human mesoangioblasts may serve as a biomarker to identify the best donor populations for therapeutic application in muscular dystrophies.

Publication Title

PW1/Peg3 expression regulates key properties that determine mesoangioblast stem cell competence.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE73888
Transcriptome analysis of liver and kidneys of rats chronically fed a NK603 Roundup-tolerant genetically modified maize
  • organism-icon Rattus norvegicus
  • sample-icon 55 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Gene 2.0 ST Array (ragene20st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Transcriptome and metabolome analysis of liver and kidneys of rats chronically fed NK603 Roundup-tolerant genetically modified maize.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon SRP075490
RNA-Seq experiments performed on rat Schwann (S16) cells
  • organism-icon Rattus norvegicus
  • sample-icon 52 Downloadable Samples
  • Technology Badge IconIlluminaHiSeq2000

Description

We utilized RNA-Seq on rat Schwann (S16) cells to determine global gene expression. This information was generated as part of a larger effort to characterize cis-regulatory elements and global gene expression within Schwann cells. To achieve this, we generated RPKM values across two independent biological replicates. This dataset was also used to predict cis-regulatory element function on genes following CRISPR knockout studies. Overall design: Performed two technical replicates of RNA-Seq on two independent biological replicates of S16 cells

Publication Title

A genome-wide assessment of conserved SNP alleles reveals a panel of regulatory SNPs relevant to the peripheral nerve.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE73886
Transcriptome analysis of liver and kidneys of rats chronically fed a NK603 Roundup-tolerant genetically modified maize [liver]
  • organism-icon Rattus norvegicus
  • sample-icon 27 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Gene 2.0 ST Array (ragene20st)

Description

There is an ongoing debate on the potential toxicity of genetically modified food. The ability of rodent feeding trials to assess the potential toxicity of these products is highly debated since a 2-year study in rats fed NK603 Roundup-tolerant genetically modified maize, treated or not with Roundup during the cultivation, resulted in anatomorphological and blood/urine biochemical changes indicative of liver and kidney structure and functional pathology.

Publication Title

Transcriptome and metabolome analysis of liver and kidneys of rats chronically fed NK603 Roundup-tolerant genetically modified maize.

Sample Metadata Fields

Sex, Specimen part

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)

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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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