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accession-icon GSE56520
Effect of somatostatin knockout on sexually dimorphic hepatic gene expression
  • organism-icon Mus musculus
  • sample-icon 12 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

The liver is one of the most sexually dimorphic organs as measured by gene expression differences. About 80% of the sexually dimorphic genes are known to be regulated by growth hormone (GH). Somatostatin (SST) inhibits the release of GH.

Publication Title

Somatostatin is essential for the sexual dimorphism of GH secretion, corticosteroid-binding globulin production, and corticosterone levels in mice.

Sample Metadata Fields

Sex, Specimen part

View Samples
accession-icon GSE84900
Differential gene expression on islet transplantation with or without the presence of autologous fibroblasts
  • organism-icon Mus musculus
  • sample-icon 10 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302)

Description

Pancreatic islet transplantation was performed in the subcutaneous space of diabetic nude mice. In order to promote long survival and function of transplanted islets a plasma-based scaffold was developed in combination with fibroblasts as graft-supporting accesory cells. Gene expression analysis was carried out to evaluate expression differences due to the presence of fibroblast which could explain the long-term glycemic control observed under these circumstances.

Publication Title

Fibroblasts accelerate islet revascularization and improve long-term graft survival in a mouse model of subcutaneous islet transplantation.

Sample Metadata Fields

Disease, Time

View Samples
accession-icon GSE94914
MYCN induces neuroblastoma in primary neural crest cells
  • organism-icon Mus musculus
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 2.0 ST Array (mogene20st)

Description

Neuroblastoma (NBL) is an embryonal cancer of the sympathetic nervous system (SNS) that causes 15% of pediatric cancer deaths. High-risk neuroblastoma is characterized by N-Myc amplification and segmental chromosomal gains and losses. Due to limited disease models, the etiology of neuroblastoma is largely unknown, including both the cell of origin and the majority of oncogenic drivers. We have established a novel system for studying neuroblastoma based on the transformation of neural crest cells (NCCs), the progenitor cells of the SNS, isolated from mouse embryonic day 9.5 trunk neural tube explants. Based on pathology and gene expression analysis, we report the first successful transformation of wild-type NCCs into NBL by enforced expression of N-Myc to generate phenotypically and molecularly accurate tumors that closely model human MYCN-amplified NBL. Using comparative genomic hybridization, we found that NCC-derived neuroblastoma tumors acquired copy number gains and losses that are syntenic to those observed in human MYCN-amplified neuroblastoma including 17q gain, 2p gain and loss of 1p36. When p53-compromised NCCs were transformed with N-Myc we generated primitive neuroectodermal tumors with divergent differentiation including osteosarcoma. These subcutaneous tumors were metastatic to regional lymph nodes, liver and lung. Our novel experimental approach accurately models human neuroblastoma and establishes a new system with potential to study early stages of neuroblastoma oncogenesis, to functionally assess neuroblastoma oncogenic drivers, and to characterize neuroblastoma metastasis.

Publication Title

MYCN induces neuroblastoma in primary neural crest cells.

Sample Metadata Fields

Specimen part

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accession-icon SRP049105
B cell survival and development is dependent on the coordination of NFkappaB family members RelB and cRel
  • organism-icon Mus musculus
  • sample-icon 14 Downloadable Samples
  • Technology Badge IconIllumina HiSeq 2000

Description

Identify genes which are induced in wild type, crel ko, and relbcrle dbko B cells under BAFF stimulation, and find the differential expressed genes which are distinct from wildtype controls. Overall design: RNA-seq analysis of wild type, crelko, relbcrel dbko follicular B cells stimulated with BAFF ligand for 6 hours and wildtype only for 27 hours

Publication Title

B-cell survival and development controlled by the coordination of NF-κB family members RelB and cRel.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE21479
Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications
  • organism-icon Saccharomyces cerevisiae
  • sample-icon 8 Downloadable Samples
  • Technology Badge Icon Affymetrix Yeast Genome 2.0 Array (yeast2)

Description

The needs for rapid and efficient microbial cell factory design and construction are possible through the enabling technology, metabolic engineering, which is now being facilitated by systems biology approaches. Metabolic engineering is often complimented by directed evolution, where selective pressure is applied to a partially genetically engineered strain to confer a desirable phenotype. The exact genetic modification or resulting genotype that leads to the improved phenotype is often not identified or understood to enable further metabolic engineering. In this work we establish proof-of-concept that whole genome high-throughput sequencing and annotation can be used to identify single nucleotide polymorphisms (SNPs) between Saccharomyces cerevisiae strains S288c and CEN.PK113-7D. The yeast strain S288c was the first eukaryote sequenced, serving as the reference genome for the Saccharomyces Genome Database, while CEN.PK113-7D is a preferred laboratory strain for industrial biotechnology research. A total of 13,787 high-quality SNPs were detected between both strains (reference strain: S288c). Considering only metabolic genes (782 of 5,873 annotated genes), a total of 219 metabolism specific SNPs are distributed across 158 metabolic genes, with 85 of the SNPs being non-silent (e.g., encoding amino acid modifications). Amongst metabolic SNPs detected, there was pathway enrichment in the galactose uptake pathway (GAL1, GAL10) and ergosterol biosynthetic pathway (ERG8, ERG9). Physiological characterization confirmed a strong deficiency in galactose uptake and metabolism in S288c compared to CEN.PK113-7D, and similarly, ergosterol content in CEN.PK113-7D was significantly higher in both glucose and galactose supplemented cultivations compared to S288c. Furthermore, DNA microarray profiling of S288c and CEN.PK113-7D in both glucose and galactose batch cultures did not provide a clear hypothesis for major phenotypes observed, suggesting that genotype to phenotype correlations are manifested post-transcriptionally or post-translationally either through protein concentration and/or function. With an intensifying need for microbial cell factories that produce a wide array of target compounds, whole genome high-throughput sequencing and annotation for SNP detection can aid in better reducing and defining the metabolic landscape. This work demonstrates direct correlations between genotype and phenotype that provides clear and high-probability of success metabolic engineering targets. The genome sequence, annotation, and a SNP viewer of CEN.PK113-7D are deposited at www.sysbio.se/cenpk.

Publication Title

Whole genome sequencing of Saccharomyces cerevisiae: from genotype to phenotype for improved metabolic engineering applications.

Sample Metadata Fields

No sample metadata fields

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accession-icon GSE42955
Expression data from human heart
  • organism-icon Homo sapiens
  • sample-icon 29 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Gene 1.0 ST Array (hugene10st)

Description

Global gene expression is altered in heart failure. This syndrome can be caused by cardiovascular diseases, including dilated cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), hypertrophic cardiomyopathy, viral or toxic myocarditis, hypertension, and valvular diseases.

Publication Title

Differential gene expression of cardiac ion channels in human dilated cardiomyopathy.

Sample Metadata Fields

Sex, Specimen part

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accession-icon GSE70475
TREM2 regulates microglial cell activation in response to demyelination in vivo
  • organism-icon Mus musculus
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Gene 1.0 ST Array (mogene10st)

Description

Microglia are phagocytic cells that survey the brain and perform neuroprotective functions in response to tissue damage, but their activating receptors are largely unknown. Triggering receptor expressed on myeloid cells 2 (TREM2) is a microglial immunoreceptor whose loss-of-function mutations in humans cause presenile dementia, while genetic variants are associated with increased risk of neurodegenerative diseases. In myeloid cells, TREM2 has been involved in the regulation of phagocytosis, cell proliferation and inflammatory responses in vitro. However, it is unknown how TREM2 contributes to microglia function in vivo. Here, we identify a critical role for TREM2 in the activation and function of microglia during cuprizone (CPZ)-induced demyelination. TREM2-deficient (TREM2(-/-)) mice had defective clearance of myelin debris and more axonal pathology, resulting in impaired clinical performances compared to wild-type (WT) mice. TREM2(-/-) microglia proliferated less in areas of demyelination and were less activated, displaying a more resting morphology and decreased expression of the activation markers MHC II and inducible nitric oxide synthase as compared to WT. Mechanistically, gene expression and ultrastructural analysis of microglia suggested a defect in myelin degradation and phagosome processing during CPZ intoxication in TREM2(-/-) microglia. These findings place TREM2 as a key regulator of microglia activation in vivo in response to tissue damage.

Publication Title

TREM2 regulates microglial cell activation in response to demyelination in vivo.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE33341
Gene Expression-Based Classifiers Identify Staphylococcus aureus Infection in Mice and Humans
  • organism-icon Mus musculus, Homo sapiens
  • sample-icon 321 Downloadable Samples
  • Technology Badge Icon Affymetrix Mouse Genome 430 2.0 Array (mouse4302), Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

Staphylococcus aureus causes a spectrum of human infection. Diagnostic delays and uncertainty lead to treatment delays and inappropriate antibiotic use. A growing literature suggests the hosts inflammatory response to the pathogen represents a potential tool to improve upon current diagnostics. The hypothesis of this study is that the host responds differently to S. aureus than to E. coli infection in a quantifiable way, providing a new diagnostic avenue. This study uses Bayesian sparse factor modeling and penalized binary regression to define peripheral blood gene-expression classifiers of murine and human S. aureus infection. The murine-derived classifier distinguished S. aureus infection from healthy controls and Escherichia coli-infected mice across a range of conditions (mouse and bacterial strain, time post infection) and was validated in outbred mice (AUC>0.97). A S. aureus classifier derived from a cohort of 95 human subjects distinguished S. aureus blood stream infection (BSI) from healthy subjects (AUC 0.99) and E. coli BSI (AUC 0.82). Murine and human responses to S. aureus infection share common biological pathways, allowing the murine model to classify S. aureus BSI in humans (AUC 0.84). Both murine and human S. aureus classifiers were validated in an independent human cohort (AUC 0.95 and 0.94, respectively). The approach described here lends insight into the conserved and disparate pathways utilized by mice and humans in response to these infections. Furthermore, this study advances our understanding of S. aureus infection; the host response to it; and identifies new diagnostic and therapeutic avenues.

Publication Title

Gene expression-based classifiers identify Staphylococcus aureus infection in mice and humans.

Sample Metadata Fields

Race

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accession-icon GSE63990
Profiling of bacterial respiratory infection, viral respiratory infection, and non-infectious illness
  • organism-icon Homo sapiens
  • sample-icon 277 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Genome U133A 2.0 Array (hgu133a2)

Description

A pressing clinical challenge is identifying the etiologic basis of acute respiratory illness. Without reliable diagnostics, the uncertainty associated with this clinical entity leads to a significant, inappropriate use of antibacterials. Use of host peripheral blood gene expression data to classify individuals with bacterial infection, viral infection, or non-infection represents a complementary diagnostic approach.

Publication Title

Host gene expression classifiers diagnose acute respiratory illness etiology.

Sample Metadata Fields

No sample metadata fields

View Samples
accession-icon GSE105402
Cdk4-inhibitor induces tumor regression of Bladder cancer in vivo
  • organism-icon Homo sapiens
  • sample-icon 18 Downloadable Samples
  • Technology Badge Icon Affymetrix Human Transcriptome Array 2.0 (hta20)

Description

Cdk4/6 inhibitors have shown to increase overall survival in hormone-positive breast tumors, but whether other solid tumors could respond to these inhibitors has not yet defined. Here we show that Palbociclib (a Cdk4/6 specific inhibitor in clinic use) treatment exerts antiproliferative effects in vivo using a bladder cancer cell lines.

Publication Title

CDK4/6 Inhibitor as a Novel Therapeutic Approach for Advanced Bladder Cancer Independently of <i>RB1</i> Status.

Sample Metadata Fields

Specimen part, Cell line

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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)

fund-icon Fund the CCDL

Developed by the Childhood Cancer Data Lab

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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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