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accession-icon SRP009220
Extracellular vesicles from neural stem cells transfer IFN-? via Ifngr1 to activate Stat1 signalling in target cells
  • organism-icon Mus musculus
  • sample-icon 9 Downloadable Samples
  • Technology Badge IconIllumina Genome Analyzer IIx

Description

The idea that stem cell therapies work only via cell replacement is challenged by the observation of consistent intercellular molecule exchange between the graft and the host. Here we defined a mechanism of cellular signaling by which neural stem/precursor cells (NPCs) communicate with the microenvironment via extracellular vesicles (EVs), and we elucidated its molecular signature and function. We observed cytokine-regulated pathways that sort proteins and mRNAs into EVs. We described induction of interferon gamma (IFN-?) pathway in NPCs exposed to proinflammatory cytokines that is mirrored in EVs. We showed that IFN-? bound to EVs through Ifngr1 activates Stat1 in target cells. Finally, we demonstrated that endogenous Stat1 and Ifngr1 in target cells are indispensable to sustain the activation of Stat1 signaling by EV-associated IFN-?/Ifngr1 complexes. Our study identifies a mechanism of cellular signaling regulated by EV-associated IFN-?/Ifngr1 complexes, which grafted stem cells may use to communicate with the host immune system. Overall design: polyA RNA profiling of Neural Stem/Progenitor cells (NPCs) cultured in basal/Th1/Th2 conditions, of Exosomes derived from NPCs cultured in basal/Th1/Th2 conditions and of EVs derived from NPCs cultured in Basal/Th1/Th2 conditions. Total RNA was purified using Trizol. Purity and integrity were confirmed by BioAnalyser (Agilent). Paired End library construction and poly-A selection were performed by EASIH (The Eastern Sequence and Informatics Hub, University of Cambridge, Cambridge) according to the Illumina standard protocol. Sequencing was performed by EASIH using Illumina GAII.

Publication Title

Extracellular vesicles from neural stem cells transfer IFN-γ via Ifngr1 to activate Stat1 signaling in target cells.

Sample Metadata Fields

Specimen part, Cell line, Subject

View Samples
accession-icon GSE6116
Transcriptional Biomarkers to Predict Female Mouse Lung Tumors in Rodent Cancer Bioassays - A 13 Chemical Training Set
  • organism-icon Mus musculus
  • sample-icon 70 Downloadable Samples
  • Technology Badge Icon 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

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accession-icon GSE34779
A cross platform genome wide comparison of the relationship of promoter DNA methylation to gene expression
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon 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

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accession-icon GSE34776
Expression data from human lymphoblasts [Affymetrix]
  • organism-icon Homo sapiens
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon 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
accession-icon GSE5127
Gene Expression Biomarkers for Predicting Lung Tumors in Two-Year Rodent Bioassays
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon 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
accession-icon GSE5128
Gene Expression Biomarkers for Predicting Liver Tumors in Two-Year Rodent Bioassays
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon 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
accession-icon GSE68110
Trancriptional profiling of rat liver after short-term (up tp 14 days) administration of carcinogenic and non-carcinogenic chemicals
  • organism-icon Rattus norvegicus
  • sample-icon 418 Downloadable Samples
  • Technology Badge Icon 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

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accession-icon GSE53085
Cross-platform toxicogenomics for the prediction of nongenotoxic hepatocarcinogenesis in rat
  • organism-icon Rattus norvegicus
  • sample-icon 63 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Expression 230A Array (rae230a)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE53082
Cross-platform toxicogenomics for the prediction of nongenotoxic hepatocarcinogenesis in rat (mRNA)
  • organism-icon Rattus norvegicus
  • sample-icon 63 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Expression 230A Array (rae230a)

Description

In this study we performed microarray-based molecular profiling of liver samples from Wistar rats exposed to genotoxic carcinogens (GC), nongenotoxic carcinogens (NGC) or non-hepatocarcinogens (NC) for up to 14 days. In contrast to previous toxicogenomics studies aimed at the inference of molecular signatures for assessing the potential and mode of compound carcinogenicity, we considered multi-level omics data. Besides evaluating the predictive power of signatures observed on individual biological levels, such as mRNA, miRNA and protein expression, we also introduced novel feature representations which capture putative molecular interactions or pathway alterations by integrating expression profiles across platforms interrogating different biological levels.

Publication Title

Cross-platform toxicogenomics for the prediction of non-genotoxic hepatocarcinogenesis in rat.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE3697
Treatment of heat shocked HeLa cells with siRNA (siHSF1#1)
  • organism-icon Homo sapiens
  • sample-icon 42 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

Although HSF1 is known to play an important role in regulating the cellular response to proteotoxic stressors, little is known about the structure and function of the HSF1 signaling network under both stressed and unstressed conditions. In this study, we used a combination of chromatin immunoprecipitation (ChIP) microarray analysis and time course gene expression microarray analysis with and without siRNA-mediated inhibition of HSF1 comprehensively identify genes directly and indirectly regulated by HSF1 and examine the structure of the extended HSF1 signaling network. Correlation between promoter binding and gene expression was not significant for all genes bound by HSF1 suggesting that HSF1 binding per se is not sufficient for expression. However, the correlation with promoter binding was significant for genes identified as HSF1-regulated following siRNA knockdown allowing the identification of direct transcriptional targets of HSF1. Among promoters bound by HSF1 following heat shock, a gene ontology (GO) analysis showed significant enrichment only in categories related to protein folding. In contrast, analysis of the extended HSF1 signaling network showed enrichment in a variety of categories related to protein folding, anti-apoptosis, RNA splicing, ubiquitination and others, highlighting a complex transcriptional program directly and indirectly regulated by HSF1.

Publication Title

Genome-wide analysis of human HSF1 signaling reveals a transcriptional program linked to cellular adaptation and survival.

Sample Metadata Fields

No sample metadata fields

View Samples
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refine.bio is a repository of uniformly processed and normalized, ready-to-use transcriptome data from publicly available sources. refine.bio is a project of the Childhood Cancer Data Lab (CCDL)

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Cite refine.bio

Casey S. Greene, Dongbo Hu, Richard W. W. Jones, Stephanie Liu, David S. Mejia, Rob Patro, Stephen R. Piccolo, Ariel Rodriguez Romero, Hirak Sarkar, Candace L. Savonen, Jaclyn N. Taroni, William E. Vauclain, Deepashree Venkatesh Prasad, Kurt G. Wheeler. refine.bio: a resource of uniformly processed publicly available gene expression datasets.
URL: https://www.refine.bio

Note that the contributor list is in alphabetical order as we prepare a manuscript for submission.

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