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GSE10565
Mus musculus
28 Downloadable Samples
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- Mus musculus
- 13 Downloadable Samples
- 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- Mus musculus
- 8 Downloadable Samples
- 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- Mus musculus
- 7 Downloadable Samples
- 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- Homo sapiens
- 6 Downloadable Samples
IlluminaHiSeq2000
Description
We identify perhexiline, a small molecule inhibitor of mitochondrial carnitine palmitoyltransferase-1, as a HES1-signature antagonist drug with robust antileukemic activity against NOTCH1 induced leukemias in vitro and in vivo. Overall design: RNA-Seq from CUTLL1 cell lines treated with Perhexiline or vehicle for 3 days
Publication Title
Therapeutic targeting of HES1 transcriptional programs in T-ALL.
Sample Metadata Fields
No sample metadata fields
View Samples- Rattus norvegicus
- 24 Downloadable Samples
- Illumina Genome Analyzer II
Description
The study pursued dual goals: To advance mRNA-seq bioinformatics towards unbiased transcriptome capture and to demonstrate its potential for discovery in neuroscience by applying the approach to an in vivo model of neurological disease. We found that 12.4% of known genes were induced and 7% were suppressed in the dysfunctional (but anatomically intact) L4 dorsal root ganglion (DRG) 2 weeks after L5 spinal Nerve Ligation (SNL). A new algorithm for agnostic mapping of pre-mRNA splice junctions (SJ) achieved a precision of 97%. Overall design: mRNA-seq of L4 DRG 2 weeks and 2 months after L5 spinal nerve ligation. CONTROL and SNL were used to identify differential gene expression between chronic pain and standard conditions in Rattus norvegicus. CONTROL and SNL and PILOT were used to perform 'agnostic splice site discovery' in the nervous system transcriptome in Rattus norvegicus
Publication Title
mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 12 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Reexpression of microRNAs miR-15a/16-1 in a cell line deficient for these miRs (homozygous deletion of chromosomal region 13q14) results in the downregulation of certain mRNAs.
Publication Title
The DLEU2/miR-15a/16-1 cluster controls B cell proliferation and its deletion leads to chronic lymphocytic leukemia.
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 7 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
T-cell acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic cancer frequently associated with activating mutations in NOTCH1. Early studies identified NOTCH1 as an attractive therapeutic target for the treatment of T-ALL through the use of gamma-secretase inhibitors (GSIs). Here, we characterized the interaction between PF-03084014, a clinically-relevant GSI, and dexamethasone in preclinical models of glucocorticoid-resistant T-ALL. Combination treatment of the GSI PF-03084014 with glucocorticoids induced a synergistic antileukemic effect in human T-ALL cell lines and primary human T-ALL patient samples. Molecular characterization of the response to PF-03084014 plus glucocorticoids through gene expression profiling revealed transcriptional upregulation of the glucocorticoid receptor as the mechanism mediating the enhanced glucocorticoid response. Moreover, treatment with PF-03084014 and glucocorticoids in combination was highly efficacious in vivo, with enhanced reduction of tumor burden in a xenograft model of T-ALL. Finally, glucocorticoid treatment was highly effective at reversing PF-03084014-induced gastrointestinal toxicity via inhibition of goblet cell metaplasia. These results suggest that combination of PF-03084014 treatment with glucocorticoids may be well-tolerated and highly active for the treatment of glucorticoid-resistant T-ALL.
Publication Title
Preclinical analysis of the γ-secretase inhibitor PF-03084014 in combination with glucocorticoids in T-cell acute lymphoblastic leukemia.
Sample Metadata Fields
Cell line, Treatment
View Samples- Mus musculus
- 2756 Downloadable Samples
- Illumina HiSeq 2500
Description
During cortical development, distinct subtypes of glutamatergic neurons are sequentially born and differentiate from dynamic populations of progenitors. How progenitors and their daughter cells are temporally patterned remains unknown. Here, we trace the transcriptional trajectories of successive generations of apical progenitors (APs) and isochronic cohorts of their daughter neurons in the developing mouse neocortex using high temporal resolution parallel single-cell RNA sequencing. We identify and functionally characterize a core set of evolutionarily-conserved temporally patterned genes which drive APs from internally-driven states to more exteroceptive states, revealing a progressively increasing role for extracellular signals as corticogenesis unfolds. These embryonic age-dependent AP molecular states are reflected in their neuronal progeny as successive ground states, onto which essentially conserved early post-mitotic differentiation programs are applied. Thus, temporally unfolding molecular birthmarks present in progenitors act in their post-mitotic progeny as seeds for adult neuronal diversity. Overall design: Investigation of the transcriptional dynamics in time-locked cohorts of cortical cells across embryonic neurogenesis. Flashtag is injected at 4 ages (E12, E13, E14, E15), and cells collected 1H, 24H, 96H after birth (= a total of 12 conditions) and analyzed by single cell transcriptomics.
Publication Title
Temporal patterning of apical progenitors and their daughter neurons in the developing neocortex.
Sample Metadata Fields
Subject
View Samples- Homo sapiens
- 12 Downloadable Samples
- Affymetrix Human Gene 2.0 ST Array (hugene20st)
Description
To better understand the scale of gene expression changes that occur during the formation of mature adipocytes from preadipocytes, we compared and characterised the transcriptome profile of mesenchymal stromal cells derived from human adipose tissue, otherwise known as adipose-derived stromal cells (ASCs), undergoing adipocyte differentiation on day 1, 7, 14 and 21 (representing the early to late stage process of adipogenesis). Microarray technique was systematically employed to study gene expression in adipose-derived stromal cells during adipogenic differentiation over a 21 day period to identify genes that are important in driving adipogenesis in humans.
Publication Title
Genome-wide analysis of gene expression during adipogenesis in human adipose-derived stromal cells reveals novel patterns of gene expression during adipocyte differentiation.
Sample Metadata Fields
Sex, Age, Specimen part, Subject
View Samples