Filters
Technology
Platform
SRP054971
Homo sapiens
7 Downloadable Samples
IlluminaHiSeq2000
Description
We applied ribosome profiling and RNA sequencing to examine gene expression regulation during oncogenic cell transformation. One model involves normal mammary epithelial cells (MCF10A) containing ER-Src. Treatment of such cells with tamoxifen rapidly induces Src, thereby making it possible to kinetically follow the transition between normal and transformed cells. The other model consists of three isogenic cell lines derived from primary fibroblasts in a serial manner (Hahn et al., 1999). EH cell is immortalized by overexpression of telomerase (hTERT), and exhibits normal fibroblast morphology. EL cell expresses hTERT along with both large and small T antigens of Simian virus 40, and it displays an altered morphology but is not transformed. ELR cell expresses hTERT, T antigens, and an oncogenic derivative of Ras (H-RasV12). Overall design: Ribosome profiling and RNA sequencing in two cancer cell models
Publication Title
Many lncRNAs, 5'UTRs, and pseudogenes are translated and some are likely to express functional proteins.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 10 Downloadable Samples
- Illumina HiSeq 2000
Description
We performed RNA-seq to examine RNA expression profiles during MCF10A-ER-Src cell transformation and upon knockdowns of transcription factors Overall design: RNA-seq before and after MCF10A-ER-Src cell transformation, and RNA-seq upon factor knockdowns after inducing cell transformation
Publication Title
Genome-scale identification of transcription factors that mediate an inflammatory network during breast cellular transformation.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 22 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
siSTAT3 knockdown of a tamoxifen initiated, transformation inducible, breast cancer model system (MCF10A-ER-Src), with associated controls of EtOH and siNEG treatments.
Publication Title
STAT3 acts through pre-existing nucleosome-depleted regions bound by FOS during an epigenetic switch linking inflammation to cancer.
Sample Metadata Fields
Cell line, Treatment
View Samples- Homo sapiens
- 6 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
NF-Y, a trimeric transcription factor (TF) composed of two histone-like subunits (NF-YB (NFYB) and NF-YC (NFYC)) and a sequence-specific subunit (NF-YA), binds to the CCAAT motif, a common promoter element. Genome-wide mapping reveals 5,000-15,000 NF-Y binding sites depending on the cell type, with the NF-YA and NF-YB subunits binding asymmetrically with respect to the CCAAT motif. Despite being characterized as a proximal promoter TF, only 25% of NF-Y sites map to promoters. A comparable number of NF-Y sites are located at enhancers, many of which are tissue specific, and nearly half of NF-Y sites are in select subclasses of HERV LTR repeats. Unlike most TFs, NF-Y can access its target DNA motif in inactive (non-modified) or polycomb-repressed chromatin domains. Unexpectedly, NF-Y extensively co-localizes with FOS in all genomic contexts, and at promoters and enhancers this often occurs in the absence of JUN and the AP-1 motif. NF-Y also co-associates with a select cluster of growth-controlling and oncogenic TFs, consistent with the abundance of CCAAT motifs in the promoters of genes overexpressed in cancer. Interestingly, NF-Y and several growth-controlling TFs bind in a stereo-specific manner, suggesting a mechanism for cooperative action at promoters and enhancers. Our results indicate that NF-Y is not merely a commonly-used, proximal promoter TF, but rather performs a more diverse set of biological functions, many of which are likely to involve co-association with FOS.
Publication Title
NF-Y coassociates with FOS at promoters, enhancers, repetitive elements, and inactive chromatin regions, and is stereo-positioned with growth-controlling transcription factors.
Sample Metadata Fields
Cell line, Treatment
View Samples- Homo sapiens
- 6 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We investigated the transcriptional effects of p63 binding by analyzing ME180 cells depleted for all p63 isoforms via expression of a small hairpin RNA (shRNA) targeting the p63 oligomerization domain.
Publication Title
Relationships between p63 binding, DNA sequence, transcription activity, and biological function in human cells.
Sample Metadata Fields
Age
View Samples- Homo sapiens
- 49 Downloadable Samples
- Affymetrix Human Genome U133A 2.0 Array (hgu133a2)
Description
Epidemiological studies have revealed concurrence of specific cancers with other disease states such as metabolic syndrome, inflammatory disease and autoimmune disease. Patients with these chronic conditions have a higher incidence of various cancers, more aggressive tumors, and a higher mortality rate. It has been proposed that obesity, inflammation and chronic disease should be correlated with cancer at the molecular level, but common gene signatures or networks have yet to be described. Here, we identify genes regulated during the process of cellular transformation in both a breast epithelial cell line and a set of isogenic fibroblastic cell lines.
Publication Title
A transcriptional signature and common gene networks link cancer with lipid metabolism and diverse human diseases.
Sample Metadata Fields
Cell line, Time
View Samples- Homo sapiens
- 4 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Differential gene expression profiling in KMT2D-depleted MIA PaCa-2 cells was performed using Human Genome U133 Plus 2.0 Array
Publication Title
Lysine methyltransferase 2D regulates pancreatic carcinogenesis through metabolic reprogramming.
Sample Metadata Fields
Treatment
View Samples- Mus musculus
- 12 Downloadable Samples
- Illumina HiSeq 2000
Description
Purpose: The goal of the present study is to provide an independent assessment of the retinal transcriptome signatures of the C57BL/6J (B6) and DBA/2J (D2) mice and to enhance existing microarray datasets for accurately defining the allelic differences in the BXD recombinant inbred strains. Methods: Retinas from both B6 and D2 mice (3 of each) were used for the RNA-seq analysis. Transcriptome features were examined for both strains. Differentially expressed genes between the 2 strains were identified and bioinformatic analysis was performed to analyze the transcriptome differences between B6 and D2 strains, including Gene ontology (GO) analysis, Phenotype and Reactome enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The RNA-seq data were then directly compared with one of the microarray datasets (DoD Retina Normal Affy MoGene 2.0 ST RMA Gene Level Microarray Database) hosted on GeneNetwork (www.genenetwork.org). Results: RNA-seq provided an in-depth analysis of the transcriptome of the B6 and D2 retina with a total of more than 30,000,000 reads per sample. Over 70% of the reads were uniquely mapped, resulting in a total of 18,100 gene counts for all 6 samples. 1,665 genes were differentially expressed, with 858 of these more highly expressed in B6 and 807 more highly expressed in D2. Several molecular pathways were differentially active between the two strains, including the retinoic acid metabolic process, endoplasmic reticulum lumen, extracellular matrix organization, and PI3K-Akt signaling pathway. The most enriched KEGG pathways were the pentose and glucuronate interconversions pathway, the cytochrome P450 pathway, protein digestion and absorption pathway and the ECM-receptor interaction pathway. Each of these pathways had a more than 4-fold enrichment. The DoD normal retina microarray database provided expression profiling for 26,191 annotated transcripts for B6 mouse, D2 mouse and 53 BXD strains. A total of 13,793 genes in this microarray dataset were comparable to the RNA-seq dataset. For both B6 and D2, the RNA-seq data and microarray data were highly correlated with each other (Pearson's r = 0.780 for B6 and 0.784 for D2). Our results suggest that the microarray dataset can reliably detect differentially expressed genes between the B6 and D2 retinas, with a positive predictive value of 45.6%, and a negative predictive value of 93.6%. Examples of true positive and false positive genes are provided. Conclusions: Retinal transcriptome features of B6 and D2 mouse strains provide a useful reference for a better understanding of the mouse retina. Generally, the microarray database presented on GeneNetwork shows good agreement with the RNA-seq data, while we note that any allelic difference between B6 and D2 should be verified with the latter. Overall design: Retinal mRNA profiles of 2 strains of mice, C57BL/6J and DBA/2J, were generated by deep sequencing, in triplicate, using Illumina TruSeq Stranded Total RNA kit.
Publication Title
RNA sequencing profiling of the retina in C57BL/6J and DBA/2J mice: Enhancing the retinal microarray data sets from GeneNetwork.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Homo sapiens
- 28 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Background: The first step in SARS-CoV-2 infection is binding of the virus to angiotensin converting enzyme 2 (ACE2) on the airway epithelium. Asthma affects over 300 million people world-wide, many of whom may encounter SARS-CoV-2. Epidemiologic data suggests that asthmatics who get infected may be at increased risk of more severe disease. Our objective was to assess whether maintenance inhaled corticosteroids (ICS), a major treatment for asthma, is associated with airway ACE2 expression in asthmatics.
Publication Title
Up-regulation of ACE2, the SARS-CoV-2 receptor, in asthmatics on maintenance inhaled corticosteroids.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Mus musculus
- 15 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
How secondary CD4 T cell effectors, derived from resting memory cells, differ from primary cells, derived from nave precursors, and how such differences impact recall responses to pathogens is unknown.
Publication Title
Memory CD4+ T-cell-mediated protection depends on secondary effectors that are distinct from and superior to primary effectors.
Sample Metadata Fields
Specimen part
View Samples