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accession-icon GSE9488
Influence of CFTR on Lipid Metabolism Gene Expression in Marrow Derived Dendritic Cells infected with P. aeruginosa
  • organism-icon Mus musculus
  • sample-icon 20 Downloadable Samples
  • Technology Badge Icon Affymetrix Murine Genome U74A Version 2 Array (mgu74av2)

Description

Dysfunction of the cystic fibrosis transmembrane regulator (CFTR) in cystic fibrosis (CF) results in exaggerated and chronic inflammation as well as increased susceptibility to chronic pulmonary infections, in particular with Pseudomonas aeruginosa. Based on the concept that host immune responses do not seem to be adequate to eradicate P.aeruginosa from the lungs of CF patients and that dendritic cells (DC) play an important role in initiating and shaping adaptive immune responses, this study analyzed the role of CFTR in bone marrow-derived murine DC from CFTR knockout (CF) mice with and without exposure to P.aeruginosa. DC expressed CFTR mRNA and protein, although at much lower levels compared to whole lung. Microarray analysis of gene expression levels in DC generated from CF and wild type (WT) mice revealed significantly different expression of 16 genes in CF DC compared to WT DC. Among the genes with lower expression in CF DC was Caveolin-1, a membrane lipid raft protein. Messenger RNA and protein levels of Caveolin-1 were decreased in the CF DC compared to WT DC. Consistently, the active form of sterol-responsive element binding protein (SREBP), a negative regulator of Caveolin-1 expression, was increased in CF DC. Following exposure to P.aeruginosa, gene expression levels in CF and WT DC changed for 912 genes involved in inflammation, chemotaxis, signaling, cell cycling and apoptosis more than 1.5-fold. Among the genes that showed a different response between WT and CF DC infected with P.aeruginosa, were 3-hydroxysterol-7 reductase (Dhcr7) and stearoyl-CoA desaturase 2 (Scd2), two enzymes involved in the lipid metabolism that are also regulated by SREBP. These results suggest that CFTR dysfunction in non-epithelial cells results in changes in the expression of genes encoding factors involved in membrane structure and lipid-metabolism. These membrane alterations in immune cells may contribute to the abnormal inflammatory and immune response characteristic of CF.

Publication Title

Influence of the cystic fibrosis transmembrane conductance regulator on expression of lipid metabolism-related genes in dendritic cells.

Alternate Accession IDs

E-GEOD-9488

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE12814
Identifying the molecular signature of the interstitial del(7q) subgroup of uterine leiomyomata using a paired analysis
  • organism-icon Homo sapiens
  • sample-icon 33 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Uterine leiomyomata (UL), the most common neoplasm in reproductive-age women, have recurrent cytogenetic abnormalities including del(7)(q22q32). To develop a molecular signature, matched del(7q) and non-del(7q) tumors identified by FISH or karyotyping from 11 women were profiled with expression arrays. Our analysis using paired t-tests demonstrates this matched design is critical to eliminate confounding effects of genotype and environment that underlie patient variation. A gene list ordered by genome-wide significance showed enrichment for the 7q22 target region. Modification of the gene list by weighting each sample for percent of del(7q) cells to account for the mosaic nature of these tumors further enhanced the frequency of 7q22 genes. Pathway analysis revealed two of the 19 significant functional networks were associated with development and the most represented pathway was protein ubiquitination, which can influence tumor development by stabilizing oncoproteins and destabilizing tumor suppressor proteins. Array CGH (aCGH) studies determined the only consistent genomic imbalance was deletion of 9.5 megabases from 7q22-7q31.1. Combining the aCGH data with the del(7q) UL mosacism-weighted expression analysis resulted in a list of genes that are commonly deleted and whose copy number is correlated with significantly decreased expression. These genes include the proliferation inhibitor HPB1, the loss of expression of which has been associated with invasive breast cancer, as well as the mitosis integrity-maintenance tumor suppressor RINT1. This study provides a molecular signature of the del(7q) UL subgroup and will serve as a platform for future studies of tumor pathogenesis.

Publication Title

Identifying the molecular signature of the interstitial deletion 7q subgroup of uterine leiomyomata using a paired analysis.

Alternate Accession IDs

E-GEOD-12814

Sample Metadata Fields

Disease

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accession-icon GSE18096
Identifying the molecular signature of the t(12;14) subgroup of uterine leiomyomata using a paired analysis
  • organism-icon Homo sapiens
  • sample-icon 24 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

Uterine leiomyomata (UL), the most common neoplasm in reproductive age women, have recurrent cytogenetic abnormalities including t(12;14). To develop a molecular signature, matched t(12;14) and non-t(12;14) tumors identified by FISH or karyotyping from each of 9 women were profiled using Affymetrix GeneChip U133 Plus 2.0 oligonucleotide arrays. Model analysis demonstrated the necessity for a matched design to eliminate the confounding effect of genotype and environment that underlay patient to patient variation.

Publication Title

Expression profiling of uterine leiomyomata cytogenetic subgroups reveals distinct signatures in matched myometrium: transcriptional profilingof the t(12;14) and evidence in support of predisposing genetic heterogeneity.

Alternate Accession IDs

E-GEOD-18096

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE63051
Genome-wide analysis of gene expression in DJ-1 deficient muscle cells
  • organism-icon Mus musculus
  • sample-icon 6 Downloadable Samples
  • Technology Badge IconIllumina MouseWG-6 v2.0 expression beadchip

Description

Excessive reactive oxygen species (ROS) underlie the pathogenesis of multiple disorders. Nevertheless, physiological levels of ROS are required for intracellular signalling and maintenance of metabolic homeostasis. DJ-1, a Parkinsons disease-associated protein, is involved in the regulation of oxidative stress. Our aim in this study was to determine the effect of DJ-1 disruption on gene expression in muscle cells. To this end, we transfected a murine myoblast cell line, C2C12 cells with siRNA targeting DJ-1.

Publication Title

DJ-1 links muscle ROS production with metabolic reprogramming and systemic energy homeostasis in mice.

Alternate Accession IDs

E-GEOD-63051

Sample Metadata Fields

Specimen part

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accession-icon GSE124253
Characterization of an Immortalized Human Small Airway Basal Stem/Progenitor Cell Line with Airway Region-specific Differentiation Capacity [array]
  • organism-icon Homo sapiens
  • sample-icon 28 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The pathology of chronic obstructive pulmonary disease, idiopathic pulmonary fibrosis and the majority of lung cancers involve the small airway epithelium (SAE), the single continuous layer of cells lining the airways ?6th generations. The basal cells (BC) are the stem/progenitor cells of the SAE, responsible for the differentiation into intermediate cells and ciliated, club and mucous differentiated cells. To facilitate the study of the biology of the human SAE in health and disease, we immortalized and characterized a normal human SAE basal cell line.

Publication Title

Characterization of an immortalized human small airway basal stem/progenitor cell line with airway region-specific differentiation capacity.

Alternate Accession IDs

E-GEOD-124253

Sample Metadata Fields

Sex, Age, Specimen part, Race

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accession-icon GSE35713
Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes
  • organism-icon Homo sapiens
  • sample-icon 202 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

Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes.

Alternate Accession IDs

E-GEOD-35713

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE35725
Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes [T1D_114]
  • organism-icon Homo sapiens
  • sample-icon 114 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). Here, using an optimized cryopreserved PBMC-based protocol, we analyzed larger RO T1D and HC cohorts. In addition, we examined T1D progression by looking at longitudinal, pre-onset and longstanding T1D samples.

Publication Title

Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes.

Alternate Accession IDs

E-GEOD-35725

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE35711
Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes [CF_S1S3_5Auto_20CF_10HC]
  • organism-icon Homo sapiens
  • sample-icon 49 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). Here, using an optimized cryopreserved PBMC-based protocol, we compared the signature found between unrelated healthy controls and non-diabetic cystic fibrosis patients possessing Pseudomonas aeruginosa pulmonary tract infection.

Publication Title

Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes.

Alternate Accession IDs

E-GEOD-35711

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE35716
Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes [Pneu_S3S24_10Pneu_4HC]
  • organism-icon Homo sapiens
  • sample-icon 27 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously, we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). Here, using an optimized cryopreserved PBMC-based protocol, we compared the signature found between unrelated healthy controls and patients with bacterial pneumonia.

Publication Title

Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes.

Alternate Accession IDs

E-GEOD-35716

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE35712
Transcriptional Signatures as a Disease-Specific and Predictive Inflammatory Biomarker for Type 1 Diabetes [H1N1_S5_5Pre_5D0]
  • organism-icon Homo sapiens
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Description

The complex milieu of inflammatory mediators associated with many diseases is often too dilute to directly measure in the periphery, necessitating development of more sensitive measurements suitable for mechanistic studies, earlier diagnosis, guiding selection of therapy, and monitoring interventions. Previously we determined that plasma of recent-onset (RO) Type 1 diabetes (T1D) patients induce a proinflammatory transcriptional signature in fresh peripheral blood mononuclear cells (PBMC) relative to that of unrelated healthy controls (HC). Here, using an optimized cryopreserved PBMC-based protocol, we compared the signature found in pre H1N1 samples to the signature associated with active H1N1 flu.

Publication Title

Transcriptional signatures as a disease-specific and predictive inflammatory biomarker for type 1 diabetes.

Alternate Accession IDs

E-GEOD-35712

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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