This dataset details the time-dependent response of human Huh7 hepatoma cells to type I and type III IFN.
Dynamic expression profiling of type I and type III interferon-stimulated hepatocytes reveals a stable hierarchy of gene expression.
Cell line, Treatment, Time
View SamplesBasilar papillae (i.e.auditory epithelia) were isolated from 4-day-old chickens and sectioned into low, middle, and high frequency segments. RNA was isolated from each segment separately, amplified using a two-cycle approach, biotinylated, and hybridized to Affymetrix chicken whole-genome arrays.
Gene expression gradients along the tonotopic axis of the chicken auditory epithelium.
Specimen part
View SamplesWe used microarray to characterize interferon stimulated genes in dendritic cells
Comparative analysis of anti-viral transcriptomics reveals novel effects of influenza immune antagonism.
Specimen part
View SamplesThis SuperSeries is composed of the SubSeries listed below.
The blood transcriptional signature of chronic hepatitis C virus is consistent with an ongoing interferon-mediated antiviral response.
No sample metadata fields
View SamplesThis study characterizes the effects of chronic Hepatitis C virus (HCV) infection on gene expression by analyzing blood samples from 10 treatment-naive HCV patients and 6 healthy volunteers.
The blood transcriptional signature of chronic hepatitis C virus is consistent with an ongoing interferon-mediated antiviral response.
No sample metadata fields
View SamplesNP-reactive murine splenic memory B cells were sorted based on the expression of the surface markers CD80 and PD-L2
CD80 and PD-L2 define functionally distinct memory B cell subsets that are independent of antibody isotype.
Specimen part
View SamplesGene expressions of murine germinal center and naive B cells on Affymetrix platform
Multiple transcription factor binding sites predict AID targeting in non-Ig genes.
No sample metadata fields
View SamplesThe dendritic cell (DC) is a master regulator of immune responses. Pathogenic viruses subvert normal immune function in DCs through the expression of immune antagonists. Understanding how these antagonists interact with the host immune system requires knowledge of the underlying genetic regulatory network that operates during an uninhibited antiviral response. In order to isolate and identify this network, we studied DCs infected with Newcastle Disease Virus (NDV), which is able to stimulate innate immunity and DC maturation through activation of RIG-I signaling, but lacks the ability to evade the human interferon response. To analyze this experimental model, we developed a new approach integrating genome-wide expression kinetics and time-dependent promoter analysis. We found that the genetic program underlying the antiviral cell state transition during the first 18-hours post-infection could be explained by a single regulatory network. Gene expression changes were driven by a step-wise multi-factor cascading control mechanism, where the specific transcription factors controlling expression changed over time. Within this network, most individual genes are regulated by multiple factors, indicating robustness against virus-encoded immune evasion genes. In addition to effectively recapitulating current biological knowledge, we predicted, and validated experimentally, antiviral roles for several novel transcription factors. More generally, our results show how a genetic program can be temporally controlled through a single regulatory network to achieve the large-scale genetic reprogramming characteristic of cell state transitions.
Antiviral response dictated by choreographed cascade of transcription factors.
Specimen part, Subject
View SamplesTo examine gene expression in young and aged aortas with and without atherosclerosis
Age-associated vascular inflammation promotes monocytosis during atherogenesis.
Specimen part
View SamplesHigh-throughput transcriptomic (HTTr) technologies are increasingly being used to screen environmental chemicals in vitro to identify molecular targets and provide mechanistic context for regulatory testing. The androgen receptor (AR, NR3C4) regulates male sexual development, is involved in the pathogenesis of a number of cancers, and is often the target of endocrine disruptors. Here, we describe the development and validation of a novel gene expression biomarker to identify AR-modulating chemicals using a pattern matching method. AR biomarker genes were identified by their consistent expression after exposure to 4 AR agonists and opposite expression after exposure to 4 AR antagonists. A genetic filter was used to include only those genes that were regulated by AR. Most of the resulting 51 biomarker genes were shown to be directly regulated by AR as determined by ChIP-Seq analysis of AR-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm which compares the expression of AR biomarker genes under various treatment conditions. Using 163 comparisons from cells treated with 98 chemicals, the biomarker gave balanced accuracies for prediction of AR activation or AR suppression of 97% or 98%, respectively. The biomarker was able to correctly classify 16 out of 17 AR reference antagonists including those that are weak and very weak. Predictions based on comparisons from AR-positive LAPC-4 cells treated with 28 chemicals in antagonist mode were compared to those from an AR pathway model based on 11 in vitro high-throughput screening assays that queried different steps in AR signaling. The balanced accuracy was 93% for suppression. Using our approach, we identified conditions in which AR was modulated in a large collection of microarray profiles from prostate cancer cell lines including 1) AR constitutively active mutants or knockdown of AR, 2) depletion of androgens by castration or removal from media, and 3) modulators that work through indirect mechanisms including suppression of AR expression. These results demonstrate that the AR gene expression biomarker could be a useful tool in HTTr to identify AR modulators in large collections of microarray data derived from AR-positive prostate cancer cell lines.
Identification of Androgen Receptor Modulators in a Prostate Cancer Cell Line Microarray Compendium.
Specimen part, Cell line
View Samples