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accession-icon GSE79623
Gene expression analysis in the aorta from non-diabetic or STZ-induced diabetic apolipoprotein E deficient (ApoE-/-) mice fed with high fat diet in the presence or absence of PKC inhibitor, ruboxistaurin (RBX, or LY333531)
  • organism-icon Mus musculus
  • sample-icon 16 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

We found that hyperglycemia and elevated fatty acids in diabetes could activate protein kinase C- isoforms and selectively induce insulin resistance via inhibiting vascular insulin signaling.

Publication Title

Insulin decreases atherosclerosis by inducing endothelin receptor B expression.

Sample Metadata Fields

Age, Specimen part, Disease, Disease stage, Treatment

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accession-icon GSE7439
Escherichia coli strain 8624 and Escherichia coli strain VS94 with signaling molecules
  • organism-icon Escherichia coli
  • sample-icon 5 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

These E. coli strains were grown with various signaling molecules and the expression profiles were determined.

Publication Title

Global effects of the cell-to-cell signaling molecules autoinducer-2, autoinducer-3, and epinephrine in a luxS mutant of enterohemorrhagic Escherichia coli.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE18118
QseA regulation of virulence factors in EHEC
  • organism-icon Escherichia coli
  • sample-icon 2 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

Enterohemorrhagic E. coli (EHEC) colonizes the large intestine and causes attaching and effacing lesions (AE). Most of the genes involved in the formation of AE lesions are encoded within a chromosomal pathogenicity island termed the Locus of Enterocyte Effacement (LEE). The LysR-like transcriptional factor QseA regulates the LEE by binding directly to the regulatory region of ler. Here, we performed transcriptome analyses comparing WT EHEC and the isogenic qseA mutant in order to elucidate the extent of QseAs role in gene regulation in EHEC. The following results compare genes that were up-regulated and down-regulated ! 2-fold in the qseA mutant strain compared to the WT strain. At mid-exponential growth, 222 genes were up-regulated and 1874 were downregulated. At late-exponential growth, a total of 55 genes were up-regulated and 605 genes were down-regulated. During mid-exponential growth, QseA represses its own transcription, whereas during late-logarithmic growth, QseA activates expression of the LEE genes as well as non-LEE encoded effector proteins. During both growth phases, several genes carried in O-islands, were activated by QseA, whereas genes involved in cell metabolism were repressed. We also performed electrophoretic mobility shift assays, competition experiments, and DNAseI footprints, and the results suggested that QseA directly binds both the ler proximal and distal promoters, its own promoter, as well as promoters of genes encoded in EHEC-specific O-islands. Additionally, we mapped the transcriptional start site of qseA, leading to the identification of two promoter sequences. Taken together, these results indicate that QseA acts as a global regulator in EHEC, coordinating expression of virulence genes.

Publication Title

The LysR-type regulator QseA regulates both characterized and putative virulence genes in enterohaemorrhagic Escherichia coli O157:H7.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE12831
The role of qseE, qseF and qseG in the regulation of EHEC virulence
  • organism-icon Escherichia coli
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

Escherichia coli 8624 and the isogenic mutants in qseE, qseF and qseG are compared to determine the role that each of the genes play in regulation of the transcriptome. These results are verified by qRT-PCR and reveal the important role of this three-component signaling system.

Publication Title

The two-component system QseEF and the membrane protein QseG link adrenergic and stress sensing to bacterial pathogenesis.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE15050
Bacterial Adrenergic signaling
  • organism-icon Escherichia coli
  • sample-icon 11 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

The ability to respond to stress is at the core of an organisms survival. The hormones epinephrine and norepinephrine play a central role in stress responses in mammals, which require the synchronized interaction of the whole neuroendocrine system. Bacteria also sense and respond to epinephrine and norepinephrine as a means to gauge the metabolic and immune state of the host. Mammalian adrenergic receptors are G-coupled protein receptors (GPCRs), bacteria, however, sense these hormones through histidine sensor kinases (HKs). HKs autophosphorylate in response to multiple signals and transfer this phosphate to response regulators (RRs). Two bacterial adrenergic receptors have been identified in EHEC, QseC and QseE, with QseE being downstream of QseC in this signaling cascade. We mapped the QseC signaling cascade in the deadly pathogen enterohemorrhagic E. coli (EHEC), which exploits this signaling system to promote disease. Through QseC, EHEC activates expression of metabolic, virulence and stress response genes, synchronizing the cell response to these stress hormones. Coordination of these responses is achieved by QseC phosphorylating three of the thirty two EHEC RRs. The QseB RR, which is QseCs cognate RR, activates the flagella regulon which controls bacteria motility and chemotaxis. The QseF RR, which is phosphorylated by the QseE adrenergic sensor, coordinates expression of virulence genes involved in formation of lesions in the intestinal epithelia by EHEC, and the bacterial SOS stress response. The third RR, KdpE, controls potassium uptake, osmolarity response, and also the formation of lesions in the intestine. Adrenergic regulation of bacterial gene expression shares several parallels with mammalian adrenergic signaling having profound effects in the whole organism. Understanding adrenergic regulation of a bacterial cell is a powerful approach to study the underlying mechanisms of stress and cellular survival.

Publication Title

The QseC adrenergic signaling cascade in Enterohemorrhagic E. coli (EHEC).

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE57624
Identification of nuclear-enriched miRNAs during mouse granulopoiesis
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

This SuperSeries is composed of the SubSeries listed below.

Publication Title

Identification of nuclear-enriched miRNAs during mouse granulopoiesis.

Sample Metadata Fields

Specimen part

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accession-icon GSE57622
Gene expression data from mouse hemopoietic stem cells (LSKs), promyelocytes, myelocytes and granulocytes.
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Differentiation of hemopoietic stem cells into granulocytes is characterized by distinct changes in the transcriptome.

Publication Title

Identification of nuclear-enriched miRNAs during mouse granulopoiesis.

Sample Metadata Fields

Specimen part

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accession-icon GSE15605
Transcriptome profiling identifies HMGA2 as a novel gene in melanoma progression
  • organism-icon Homo sapiens
  • sample-icon 69 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The identification of novel tumor-specific markers may improve understanding of melanoma progression and prognostic accuracy. Whole genome expression profiling of 46 primary melanomas, 12 metastases, and 16 normal skin samples using Affymetrix U133 PLUS 2.0 array generated gene lists including both known and new melanoma genes.

Publication Title

Transcriptome profiling identifies HMGA2 as a biomarker of melanoma progression and prognosis.

Sample Metadata Fields

Sex, Age, Disease

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accession-icon GSE13562
Effects of sidA and AHL on EHEC virulence
  • organism-icon Escherichia coli
  • sample-icon 4 Downloadable Samples
  • Technology Badge Icon Affymetrix E. coli Genome 2.0 Array (ecoli2)

Description

A study on the effects of an sdiA mutant and the AHL molecule on the virulence of EHEC

Publication Title

Chemical sensing in mammalian host-bacterial commensal associations.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE26682
MRE11 Deficiency Increases Sensitivity to Poly(ADP-ribose) Polymerase Inhibition in Microsatellite Unstable Colorectal Cancers.
  • organism-icon Homo sapiens
  • sample-icon 331 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A Array (hgu133a)

Description

We have performed bioinformatic approaches to identify the level of enrichment between gene expression profiles characterizing MSI tumors and gene changes induced in vitro by the PARP-1 inhibitor Phenanthridinone and others using the Connectivity Map tool. In a first step, we have anyzed the expression of 300 colorectal cancers from the MECC study and generated a gene expression signature by microsatellite status. The criteria followed for selection of probe sets and detailed lists to be submitted subsequently to the Connectivity Map have been published previously by us in Clinical Cancer Research in 2009. In a second step, once we observed that deficiency in MRE11 exist among MSI tumors, our interest was focused on assessing if the homologous recombination pathway showed evidence of deregulation in MSI tumors. Therefore, we examined the expression levels of those genes integrated in the KEGG pathway hsa03440 using the previously generated gene expression data set.

Publication Title

MRE11 deficiency increases sensitivity to poly(ADP-ribose) polymerase inhibition in microsatellite unstable colorectal cancers.

Sample Metadata Fields

Sex, Age

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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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