HITS-CLIP of control and transfected cells to find direct targetting of miR-200 family to mRNA
Genome-wide identification of miR-200 targets reveals a regulatory network controlling cell invasion.
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View SamplesHENMT1 is required for the 2’ O-methylation of mammalian piRNAs. The absence of such methylation leads to a reduction in both piRNA bulk and length, and ultimately male infertility.
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View SamplesMetformin and aspirin have been studied extensively as cancer preventative and therapeutic agents. However, the underlying molecular mechanisms for the inhibitory effects of pancreatic cancer development remain largely unknown. To gain further insight into their biological function in pancreatic cancer, we conducted a transcriptomic analysis using high throughput RNA sequencing to assess the differential gene expression induced by metformin (5 mM) and aspirin (2 mM), alone or in combination, after treatment of PANC-1 cells for 48 hours. Compared to untreated control, metformin alone down-regulated 58 genes, and up-regulated 91 genes, aspirin alone down-regulated 12 genes only, while the combination of metformin and aspirin down-regulated 656 genes, and down-regulated 449 genes (fold-change > 2, P value < 10-5). Of the top 10 genes (fold-change > 10, P value < 10-10) regulated by the combination of metformin and aspirin, PCDH18, CCL2, RASL11A, FAM111B, and BMP5, were down-regulated more than 20-fold, while NGFR, NPTX1, C7orf57, MRPL23AS1 and UNC5B were up-regulated more than 10-fold. The ingenuity pathway analysis (IPA) was applied to explore the top signaling pathways regulated by metformin and aspirin. The top canonical pathways, “cholesterol biosynthesis”, “cell cycle: G1/S checkpoint regulation”, and “axonal guidance signaling” were the most statistical significant pathways that were modulated by the combination of metformin and aspirin. Although the results need further functional validation, these data provide, for the first time, a transcriptional profile of pancreatic cancer cells in response to metformin and aspirin.
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View SamplesNo description.
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Sex, Specimen part, Cell line, Treatment
View SamplesRNAseq of YAC128 mice treated with pridopidine
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Sex, Age, Specimen part, Cell line, Treatment
View SamplesFACS RNAseq of transgenic lines pWUS and pYAB
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Specimen part
View SamplesRaw sequence reads are provided for RNA-seq of parental and transgenerational worms in which the P0 were treated with OP50 (control) or PA14.
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Sex, Specimen part, Cell line
View SamplesIdentifying transcriptional changes in adults, whose biology and behavior differsubstantially from developing animals, is important when evaluating adult phenotypes.Moreover, cell- and tissue-specific information is critical for understanding the biologyof multicellular animals. We used adult cell-specific isolation to identify thetranscriptomes of C. elegans'' major adult tissues (muscle, intestine, epidermis, andneurons).
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Sex, Specimen part, Cell line
View SamplesWe evaluated the therapeutic activity of the modified U1 particles in a mouse model affected by severe spinal muscular atrophy. ExSpeU1 introduced by germline transgenesis efficiently rescued the phenotype increasing SMN2 exon 7 splicing, SMN protein production and radically extending the life span.
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Sex, Age, Specimen part, Disease, Cell line
View SamplesRegulatory T-cells (Treg) play an essential role in the negative regulation of immune answers by developing an attenuated cytokine response that allows suppressing proliferation and effector function of T-cells (CD4+ Th). The transcription factor FoxP3 is responsible for the regulation of many genes involved in the Treg gene signature. Its ablation leads to severe immune deficiencies in human and mice. Recent developments in sequencing technologies have revolutionized the possibilities to gain insights into transcription factor binding by ChiP-Seq and into transcriptome analysis by mRNA-Seq. We combine FoxP3 ChiP-Seq and mRNA-Seq in order to understand the transcriptional differences between primary human CD4+ T helper and regulatory T-cells, as well as to study the role of FoxP3 in generating those differences. We show, that mRNA-Seq allows analyzing the transcriptomal landscape of T-cells including the expression of specific splice variants at much greater depth than previous approaches, whereas 50% of transcriptional regulation events have not been described before by using diverse array technologies.
Next-generation insights into regulatory T cells: expression profiling and FoxP3 occupancy in Human.
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