Filters
Technology
Platform
SRP016004
Homo sapiens
6 Downloadable Samples
Illumina Genome Analyzer II
Description
We have used an integrative high content analysis approach to identify the specific miRNAs implicated in EGF signaling in HeLa cells as potential mediators of cancer mediated functions. We have used microarray and deep-sequencing technologies in order to obtain a global view of the EGF miRNA transcriptome with a robust experimental cross-validation. By applying a procedure based on Rankprod tests, we have delimited a solid set of EGF-regulated miRNAs. After validating regulated miRNAs by RT-qPCR, we have derived protein networks and biological functions from the predicted targets of the regulated miRNAs to gain insight into the potential role of miRNAs in EGF-treated cells. In addition, we have analyzed sequence heterogeneity due to editing relative to the reference sequence (isomirs) among regulated miRNAs. Overall design: Time course experiment comparing HeLa gene expression in response to EGF analyzed by small RNA-seq using Illumina 36-bp read massively parallel sequencing. Three independent experiments were performed where HeLa cells were serum deprived for 24 hours and were either left untreated or treated with EGF for 6h and harvested for RNA extraction. Thus, a total of 6 samples were analyzed, 3 controls and the 3 respective treated counterparts. These same samples were also analyzed in parallel on two different microarray platforms.
Publication Title
Microarray and deep sequencing cross-platform analysis of the mirRNome and isomiR variation in response to epidermal growth factor.
Sample Metadata Fields
Cell line, Subject
View Samples- Homo sapiens
- 6 Downloadable Samples
- IlluminaGenomeAnalyzer
Description
Epidermal growth factor (EGF) is a key regulatory growth factor activating a myriad of processes affecting cell proliferation and survival that are relevant to normal development and disease. Here we have used a combined approach to study the EGF dependent transcriptome of HeLa cells. We obtained mRNA expression profiles using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, Febit, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer I (GA-I). By applying a procedure for cross-platform data meta-analysis based on rank product and global ancova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We used this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we found a whole new set of genes previously unrelated to the currently accepted EGF associated cellular functions, among which are metallothionein genes. We propose the use of global genomic cross-validation to generate more reliable datasets derived from high content technologies (microarrays or deep sequencing). This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data. Keywords: treated vs. untreated comparison, time course Overall design: Time course experiment comparing HeLa gene expression in response to EGF analyzed on different microarray platforms (Agilent, IMPPC, Illumina, and Operon) and by digital gene expression using short read high throughput tag sequencing. Three independent experiments were performed where HeLa cells were serum deprived for 24 hours and were either left untreated or treated with EGF for 6, and 24 h and harvested for RNA extraction. Technical dye swap duplicates were performed for each of the three biological replicates in both time points. Comparative genomic hybridization of HeLa cell genomic DNA versus poooled genomic DNA from blood obtained from human females conducted on commercial oligonucleotide microarrays (Human Genome CGH Microarray Kit 244A, Agilent Technologies) in order to assess DNA dosage dependence of gene expression levels and response to EGF. Digital gene expression using short read high throughput tag sequencing data submitted to NCBI''s SRA
Publication Title
Multiple platform assessment of the EGF dependent transcriptome by microarray and deep tag sequencing analysis.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 19 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
A role for reduced ribosomal protein gene dosage in both DBA and 5q- MDS suggests that other forms of MDS might also involve altered expression of ribosomal protein genes.
Publication Title
Reduced ribosomal protein gene dosage and p53 activation in low-risk myelodysplastic syndrome.
Sample Metadata Fields
Age, Specimen part
View Samples- Mus musculus
- 14 Downloadable Samples
- Illumina HiSeq 4000
Description
Sequencing data related to the manuscript entitled, "CD22 blockade restores homeostatic microglial phagocytosis in the aging brain." Overall design: To assess the transcriptional effects of CD22 blockade, we implanted aged mice with osmotic pumps to continuously infuse a CD22 blocking antibody or an IgG control antibody directly into the cerebrospinal fluid for one month. Following one month of continuous infusion, we performed RNA-seq on purified microglia from the hemi-brains of these mice contralateral to the cannulation site to minimize injury-induced confounding factors. Primary mouse microglia were isolated by gentle dounce homogenization of the brain, magentic myelin removal, and FACS-purification of ~20,000 live CD11b+CD45lo cells. Microglia were sorted into RLT Plus buffer (Qiagen) containing beta-mercaptoethanol. RNA was extracted using a RNeasy Micro Plus kit (Qiagen) according the manufacturer's protocol. RNA integrity was assessed on a Bioanalyzer (Agilent), and high quality samples were used for library preparation. cDNA synthesis and amplification was performed using the SmartSeq v4 Ultra-low input kit (Takara), and libraries were tagmented, adaptor ligated, and indexed using the Nextera XT kit (Illumina). After normalization and pooling, libraries were sequenced on a Hiseq 4000 (Illumina) using paired-end 100bp reads. Raw sequencing files were demultiplexed with bcl2fastq, reads were aligned using STAR, the count matrix was generated using SummarizedExperiment, and differential expression analysis was performed using DESeq2 with standard settings.
Publication Title
CD22 blockade restores homeostatic microglial phagocytosis in ageing brains.
Sample Metadata Fields
Age, Cell line, Subject
View Samples- Mus musculus
- 70 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The primary goal of toxicology and safety testing is to identify agents that have the potential to cause adverse effects in humans. Unfortunately, many of these tests have not changed significantly in the past 30 years and most are inefficient, costly, and rely heavily on the use of animals. The rodent cancer bioassay is one of these safety tests and was originally established as a screen to identify potential carcinogens that would be further analyzed in human epidemiological studies. Today, the rodent cancer bioassay has evolved into the primary means to determine the carcinogenic potential of a chemical and generate quantitative information on dose-response behavior in chemical risk assessments. Due to the resource-intensive nature of these studies, each bioassay costs $2 to $4 million and takes over three years to complete. Over the past 30 years, only 1,468 chemicals have been tested in a rodent cancer bioassay. By comparison, approximately 9,000 chemicals are used by industry in quantities greater than 10,000 lbs and nearly 90,000 chemicals have been inventoried by the U.S. Environmental Protection Agency as part of the Toxic Substances Control Act. Given the disparity between the number of chemicals tested in a rodent cancer bioassay and the number of chemicals used by industry, a more efficient and economical system of identifying chemical carcinogens needs to be developed.
Publication Title
Application of genomic biomarkers to predict increased lung tumor incidence in 2-year rodent cancer bioassays.
Sample Metadata Fields
Sex, Age, Subject
View Samples- Homo sapiens
- 8 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
A cross-platform genome-wide comparison of the relationship of promoter DNA methylation to gene expression.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 8 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Transcriptional profiling of IAS subjects
Publication Title
A cross-platform genome-wide comparison of the relationship of promoter DNA methylation to gene expression.
Sample Metadata Fields
Specimen part, Subject
View Samples- Mus musculus
- 18 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Two-year rodent bioassays play a central role in evaluating both the carcinogenic potential of a chemical and generating quantitative information on the dose-response behavior for chemical risk assessments. The bioassays involved are expensive and time-consuming, requiring nearly lifetime exposures (two years) in mice and rats and costing $2 to $4 million per chemical. Since there are approximately 80,000 chemicals registered for commercial use in the United States and 2,000 more are added each year, applying animal bioassays to all chemicals of concern is clearly impossible. To efficiently and economically identify carcinogens prior to widespread use and human exposure, alternatives to the two-year rodent bioassay must be developed. In this study, animals were exposed for 13 weeks to two chemicals that were positive for lung tumors in the two-year rodent bioassay, two chemicals that were negative for tumors, and two vehicle controls. Gene expression analysis was performed on the lungs of the animals to assess the potential for identifying gene expression biomarkers that can predict tumor formation in a two-year bioassay following a 13 week exposure.
Publication Title
A comparison of transcriptomic and metabonomic technologies for identifying biomarkers predictive of two-year rodent cancer bioassays.
Sample Metadata Fields
Sex, Age, Subject
View Samples- Mus musculus
- 18 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Two-year rodent bioassays play a central role in evaluating both the carcinogenic potential of a chemical and generating quantitative information on the dose-response behavior for chemical risk assessments. The bioassays involved are expensive and time-consuming, requiring nearly lifetime exposures (two years) in mice and rats and costing $2 to $4 million per chemical. Since there are approximately 80,000 chemicals registered for commercial use in the United States and 2,000 more are added each year, applying animal bioassays to all chemicals of concern is clearly impossible. To efficiently and economically identify carcinogens prior to widespread use and human exposure, alternatives to the two-year rodent bioassay must be developed. In this study, animals were exposed for 13 weeks to two chemicals that were positive for liver tumors in the two-year rodent bioassay, two chemicals that were negative for liver tumors, and two vehicle controls. Gene expression analysis was performed on the livers of the animals to assess the potential for identifying gene expression biomarkers that can predict tumor formation in a two-year bioassay following a 13 week exposure.
Publication Title
A comparison of transcriptomic and metabonomic technologies for identifying biomarkers predictive of two-year rodent cancer bioassays.
Sample Metadata Fields
Sex, Age, Subject
View Samples- Rattus norvegicus
- 418 Downloadable Samples
- Affymetrix Rat Expression 230A Array (rae230a)
Description
The carcinogenic potential of chemicals is currently evaluated with rodent life-time bioassays, which are time consuming, and expensive with respect to cost, number of animals and amount of compound required. Since the results of these 2-year bioassays are not known until quite late during development of new chemical entities, and since the short-term test battery to test for genotoxicity, a characteristic of genotoxic carcinogens, is hampered by low specificity, the identification of early biomarkers for carcinogenicity would be a big step forward. Using gene expression profiles from the livers of rats treated up to 14 days with genotoxic and non-genotoxic carcinogens we previously identified characteristic gene expression profiles for these two groups of carcinogens. We have now added expression profiles from further hepatocarcinogens and from non-carcinogens the latter serving as control profiles. We used these profiles to extract biomarkers discriminating genotoxic from non-genotoxic carcinogens and to calculate classifiers based on the support vector machine (SVM) algorithm. These classifiers then predicted a set of independent validation compound profiles with up to 88% accuracy, depending on the marker gene set. We would like to present this study as proof of the concept that a classification of carcinogens based on short-term studies may be feasible.
Publication Title
Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.
Sample Metadata Fields
Specimen part
View Samples