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accession-icon GSE21476
Comparison of Mineralizing Synchronous UMR106-01 cells with UMR106-01 cells treated with Mineralization Inhibitor AEBSF
  • organism-icon Rattus norvegicus
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Rat Genome 230 2.0 Array (rat2302)

Description

UMR106-01 osteoblastic cells are a model for studying bone mineralization. We have shown that mineralization is temporally synchronized within cultures grown under defined conditions . Cells are plated at time zero and differentiate into osteoblastic phenotype by 64 h later. If an exogenous phosphate source is added to the cultures, the cells form and deposit hydroxyapatite mineral within distinct extracellular supramolecular lipid protein complexes termed biomineralization foci (BMF) starting 12 h later. Mineralization is largely complete by 24 h later (88 h after plating). We have also shown that AEBSF, covalent serine protease inhibitor, blocks mineralization within BMF and inhibits the fragmentation of several proteins related to biomineralization. The present experiment was designed to test whether AEBSF treatment for 12 h has an effect on transcription by UMR106-01 osteoblastic cells. AEBSF is known to inactivate several serine proteases including SKI-1 (site 1, subtilisin kexin protease-1).SKI-1 functions intracellularly to activate transmembrane bound transcription factor precursors releasing the transcriptionally active N-terminal portions to imported into the nucleus. Thus, if AEBSF blocks transcription of mineralization related genes, it would support a role for SKI-1 in gene regulation in mineralizing UMR106-01 osteoblastic cells.

Publication Title

Inhibition of proprotein convertase SKI-1 blocks transcription of key extracellular matrix genes regulating osteoblastic mineralization.

Sample Metadata Fields

Cell line

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accession-icon GSE69975
Comparison of osteocyte-enriched bone from DMP1 cre Mbtps1 knockout mice with littermate controls.
  • organism-icon Mus musculus
  • sample-icon 7 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Purpose of arrays were to determine what the effect of deletion of Mbtps1 gene was on gene expression of osteocytes in bone in vivo. DMP1 cre driver was used to delete the Mbtps1 gene in osteocytes and osteoblasts in bone. We then isolated osteocyte enriched bone particles from 40 week old male mice to determine the effect of this deletion on gene expression. We have previously shown that Mbtps1 is needed for transcription of Phex, DMP1, and MEPE genes in osteoblasts in culture. Arrays showed these genes were reduced as expected in osteocytes in vivo. Controls represent osteocyte enriched bone from 40 week old littermates. Also, as expected, Mbtps1 expression was reduced in these knockout mice

Publication Title

Deletion of Mbtps1 (Pcsk8, S1p, Ski-1) Gene in Osteocytes Stimulates Soleus Muscle Regeneration and Increased Size and Contractile Force with Age.

Sample Metadata Fields

Sex

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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.

Sample Metadata Fields

No sample metadata fields

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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.

Sample Metadata Fields

No sample metadata fields

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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.

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.

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.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE54756
Expression data from TGFBR3 controls and TGFBR3 knockdown of SUM159 3D cultures
  • organism-icon Homo sapiens
  • sample-icon 6 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

The objective of this experiment was to determine global gene expression change in triple negative cell line upon knockdown of TGFBR3. Genotype specific differences in expression profiles have been evaluated using human HuGene1.0-ST affymetrix array. RNA was extracted from SUM159 controls and SUM159 TGFBR3KD cells cultured in 3-dimensional in vitro system.

Publication Title

Transforming growth factor beta receptor type III is a tumor promoter in mesenchymal-stem like triple negative breast cancer.

Sample Metadata Fields

Cell line

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accession-icon GSE19539
Identification of Novel Oncogene Loci in Ovarian Cancer through Integrated Copy Number and Expression Analysis
  • organism-icon Homo sapiens
  • sample-icon 68 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Use of expression data to analyse ovarian cancer often yields long lists of genes that do not agree across various studies. Copy number however is more stable and can reliable predict important regions of change. Using matched copy number and expressiion data helps accurately identify novel drivers of ovarian cancer.

Publication Title

Identification of candidate growth promoting genes in ovarian cancer through integrated copy number and expression analysis.

Sample Metadata Fields

Age, Disease stage

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accession-icon GSE59774
Key hub and bottleneck genes differentiate the macrophage response to virulent and attenuated Mycobacterium bovis
  • organism-icon Bos taurus
  • sample-icon 39 Downloadable Samples
  • Technology Badge Icon Affymetrix Bovine Genome Array (bovine)

Description

Mycobacterium bovis is an intracellular pathogen that causes tuberculosis in cattle. Following infection, the pathogen resides and persists inside host macrophages by subverting host immune responses via a diverse range of mechanisms. Here, a high-density bovine microarray platform was used to examine the bovine monocyte-derived macrophage transcriptome response to M. bovis infection relative to infection with the attenuated vaccine strain, M. bovis Bacille CalmetteGurin. Differentially expressed genes were identified (adjusted P-value 0.01) and interaction networks generated across an infection time course of 2, 6 and 24 h. The largest number of biological interactions was observed in the 24 h network, which exhibited small-worldscale-free network properties. The 24 h network featured a small number of key hub and bottleneck gene nodes, including IKBKE, MYC, NFKB1 and EGR1 that differentiated the macrophage response to virulent and attenuated M. bovis strains, possibly via the modulation of host cell death mechanisms. These hub and bottleneck genes represent possible targets for immunomodulation of host macrophages by virulent mycobacterial species that enable their survival within a hostile environment.

Publication Title

Key Hub and Bottleneck Genes Differentiate the Macrophage Response to Virulent and Attenuated Mycobacterium bovis.

Sample Metadata Fields

Sex, Age, Specimen part, Treatment, Time

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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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