In order to understand the underlying mechanisms, which ensure that disease progression is prevented in EC, a comprehensive analysis of clinical phenotypes coupled to genetics and biomolecular mechanisms is required. The rapidly increasing accessibility of genetic and biomolecular expression data from new high-throughput technologies is the foundation to shift the traditional phenotype-first approach to explorative genetic or molecular data-first approaches. In this study, we aimed to explore a comprehensive analysis of host transcriptomics and proteomics data coupled to clinical phenotypes in a well-defined Swedish EC cohort with up to 20 years of clinical follow-up data.
Transcriptomics and Targeted Proteomics Analysis to Gain Insights Into the Immune-control Mechanisms of HIV-1 Infected Elite Controllers.
Sex, Age, Specimen part, Disease, Treatment, Race
View SamplesThis SuperSeries is composed of the SubSeries listed below.
Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.
No sample metadata fields
View SamplesGenome-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.
Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.
No sample metadata fields
View SamplesGenome-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.
Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.
No sample metadata fields
View SamplesGenome-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.
Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering.
No sample metadata fields
View SamplesWe 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
Therapeutic targeting of HES1 transcriptional programs in T-ALL.
No sample metadata fields
View SamplesReexpression 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.
The DLEU2/miR-15a/16-1 cluster controls B cell proliferation and its deletion leads to chronic lymphocytic leukemia.
Cell line
View SamplesT-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.
Preclinical analysis of the γ-secretase inhibitor PF-03084014 in combination with glucocorticoids in T-cell acute lymphoblastic leukemia.
Cell line, Treatment
View SamplesTo 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.
Genome-wide analysis of gene expression during adipogenesis in human adipose-derived stromal cells reveals novel patterns of gene expression during adipocyte differentiation.
Sex, Age, Specimen part, Subject
View SamplesAngioimmunoblastic T-cell lymphoma (AITL) is an aggressive lymphoid tumor derived from malignant transformation of T follicular helper (Tfh) cells. Genetically, AITL is characterized by loss of function mutations in the Ten-Eleven Translocation 2 (TET2) epigenetic tumor suppressor and a highly recurrent mutation (p.Gly17Val, G17V) in the RHOA small GTPase gene Moreover, RHOA G17V expression in Tet2 deficient hematopoietic progenitors resulted in the specific development of lymphoid tumors resembling human AITL. Notably, inhibition of ICOS signaling impaired the growth of RHOA G17V-induced mouse lymphomas in vivo, thus providing a potential new rational approach for the treatment of AITL. Overall design: We analyzed mRNA expression profiles of primary tumor cells expressing Rhoa G17V or Rhoa wild type.
RHOA G17V Induces T Follicular Helper Cell Specification and Promotes Lymphomagenesis.
Specimen part, Subject
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