We used next generation sequencing to analyze the gene expression changes in U2OS osteosarcoma cells expressing shRNA targeting the promyelocytic leukemia (PML) gene transcripts Overall design: cDNA libraries of U2OS cells expressing control shRNA or shRNA targeting PML were generated from one biological replicate
PML nuclear bodies contribute to the basal expression of the mTOR inhibitor DDIT4.
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View SamplesHuman SH-SY5Y neuroblastoma cells are widely utilized in in vitro studies to dissect out pathogenetic mechanisms of neurodegenerative disorders. These cells are considered as neuronal precursors and differentiate into more mature neuronal phenotypes under selected growth conditions. In this study, we performed systematic transcriptomic (RNA-seq) and bioinformatic analysis to pinpoint pathways and cellular processes underlying neuronal differentiation of SH-SY5Y cells according to a two-step paradigm: retinoic acid treatment followed by enriched neurobasal medium. Categorization of 1989 differentially expressed genes (DEGs) identified in differentiated cells outlined meaningful biological functions associated with changes in cell morphology including remodelling of plasma membrane and cytoskeleton, neuritogenesis. Seventy-three DEGs were assigned to Axonal Guidance Signalling pathway, and the expression of selected gene products such as neurotrophin receptors, the functionally related SLITRK6, and semaphorins, was validated by immunoblotting. Along with these findings, the differentiated cells exhibited the ability to elongate longer axonal process as assessed by the morphometric evaluation. Recognition of molecular events occurring in differentiated SH-SY5Y cells is necessary to accurately interpret the cellular responses to specific stimuli in studies on disease pathogenesis. Overall design: Comparison of cell line SH-SY5Y differentiated and undifferentiated.
Transcriptomic Profiling Discloses Molecular and Cellular Events Related to Neuronal Differentiation in SH-SY5Y Neuroblastoma Cells.
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View SamplesGene expression analysis identified a MLL translocation-specific signature of differentially expressed genes discriminating ALL and AML with and without MLL rearrangements.
MLL rearrangements in pediatric acute lymphoblastic and myeloblastic leukemias: MLL specific and lineage specific signatures.
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View SamplesTocopherols (vitamin E) are lipid-soluble antioxidants produced by all plants and algae, and many cyanobacteria, yet their functions in these photosynthetic organisms are still not fully understood. We have previously reported that the vitamin E deficient 2 (vte2) mutant of Arabidopsis thaliana is sensitive to low temperature (LT) due to impaired transfer cell wall (TCW) development and photoassimilate export, associated with massive callose deposition in transfer cells of the phloem. To further understand the roles of tocopherols in LT induced TCW development we compared the global transcript profiles of vte2 and wild type leaves during LT treatment.
Role of callose synthases in transfer cell wall development in tocopherol deficient Arabidopsis mutants.
Specimen part
View SamplesComparison of rosette leaves of two different RAP2.2 overexpressing lines with wild type leaves. The AP2/EREBP transcription factor RAP2.2 was shown to bind to a cis-acting motif within the phytoene synthase promoter from Arabidopsis. To investigate effects of increased RAP2.2 levels, two RAP2.2 overexpressing Arabidopsis thaliana (ecotype Wassilewskija) lines were generated: one line, nosr2, carried the nos promoter and showed a two-fold increase in RAP2.2 transcript level, the second line, cmr-5, carried four copies of the CaMV-35S enhancer and showed a 12-fold increase. However, neither weak nor strong increase in RAP2.2 transcript amounts had any effect on RAP2.2 protein levels as shown by Western blot analysis. The strong robustness of RAP2.2 protein levels towards transcriptional changes can be explained by specific protein degradation which includes SINAT2, an E3 ubiquitin ligase which was isolated using a two-hybrid approach. Accordingly, global gene expression analysis using both RAP2.2 overexpressing lines showed only minor transcriptional changes which are probably due to minor growth variation than to mechanisms involved in the down-regulation of RAP2.2 protein amounts.
Transcription factor RAP2.2 and its interacting partner SINAT2: stable elements in the carotenogenesis of Arabidopsis leaves.
Specimen part
View SamplesTocopherols (Vitamin E) are lipophilic antioxidants that are synthesized by all plants and are particularly abundant in seeds. Two tocopherol deficient mutant loci were used to examine how tocopherol deficiency impacts global gene expression during the critical peroid of germination and early seedling development when tocopherols are essential. vte1 lacks all tocopherols, but accumulates the tocopherol pathway intermediate DMPBQ,. vte2 which lacks all tocopherols and pathway intermediates.
Nonenzymatic lipid peroxidation reprograms gene expression and activates defense markers in Arabidopsis tocopherol-deficient mutants.
Age
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.
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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 Samples