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GSE769
Mus musculus
6 Downloadable Samples
Affymetrix Murine Genome U74A Version 2 Array (mgu74av2)
Description
Total RNA was prepared using TRIzol reagent from the pancreata of eight week old male mice. The genotypes were Control: gastrin+/-, CFTR+/+; and CF: gastrin+/-, CFTR-/-. All mice were on 95% black6, 5% 129Sv background. Mice were fed Peptamen from age 10 days to prevent intestinal obstruction.
Publication Title
Acidic duodenal pH alters gene expression in the cystic fibrosis mouse pancreas.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus
- 47 Downloadable Samples
- Affymetrix Mouse Gene 1.0 ST Array (mogene10st)
Description
Vaccine research today is focused on using safer, highly purified or recombinant antigens with poor immunogenicity, which has created a need for potent adjuvants. Rational design of effective and safe mucosal adjuvants for human use necessitates a thorough understanding of the mode of action of successful candidate adjuvants.
Publication Title
Unraveling molecular signatures of immunostimulatory adjuvants in the female genital tract through systems biology.
Sample Metadata Fields
Sex, Treatment
View Samples- Mus musculus
- 38 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Selective serotonin reuptake inhibitors (SSRIs) such as fluoxetine are the most common treatment for major depression. However, approximately 50% of depressed patients fail to achieve an effective treatment response. Understanding how gene expression systems relate to treatment responses may be critical for understanding antidepressant resistance. Transcriptome profiling allows for the simultaneous measurement of expression levels for thousands of genes and the opportunity to utilize this information to determine mechanisms underlying antidepressant treatment responses. However, the best way to relate this immense amount of information to treatment resistance remains unclear. We take a novel approach to this question by examining dentate gyrus transcriptomes from the perspective of a stereotyped fluoxetine-induced gene expression program. Expression programs usually represent stereotyped changes in expression levels that occur as cells transition phenotypes. Fluoxetine will shift transcriptomes so they lie somewhere between a baseline state and a full-response at the end of the program. The position along this fluoxetine-induced gene expression program (program status) was measured using principal components analysis (PCA). The same expression program was initiated in treatment-responsive and resistant mice but treatment response was associated with further progression along the fluoxetine-induced gene expression program. The study of treatment-related differences in gene expression program status represents a novel way to conceptualize differences in treatment responses at a transcriptome level. Understanding how antidepressant-induced gene expression program progression is modulated represents an important area for future research and could guide efforts to develop novel augmentation strategies for antidepressant treatment resistant individuals.
Publication Title
Global state measures of the dentate gyrus gene expression system predict antidepressant-sensitive behaviors.
Sample Metadata Fields
Sex, Specimen part, Treatment
View Samples- Mus musculus
- 12 Downloadable Samples
Illumina HiSeq 2000
Description
The most recurrently mutated oncogene in T-cell acute lymphoblastic leukemia (T-ALL) is NOTCH1. The core Notch complex consists of an ICN protein, a Maml cofactor, and the DNA binding factor Rbpj. The known direct cofactors of Notch appear to act nonselectively, homogeneously driving Notch gene expression functions. It is unclear whether there are direct cofactors of Notch that act selectively and heterogeneously regulate ICN. We discovered that Zmiz1, a Protein Inhibitor of Activated STAT (PIAS)-like coactivator, directly bound ICN1. ChIP-Seq showed that Zmiz1 selectively co-bound only a subset of Notch-regulated enhancers. This led to hypothesize that Zmiz1 regulates only a subset of Notch1 target genes. To investigate this, we performed RNA-Seq on four 8946 cell linesin which L1601P (activated Notch1) or Zmiz1 were expressed alone or in combination. Zmiz1 induced ~10% of Notch target genes. The Notch target gene that was most strongly induced by Zmiz1 was Myc. Our data suggest that Zmiz1 selectively amplifies a subset of Notch target genes with strong amplification of Myc. Overall design: RNA-Seq in a murine T-ALL cell line
Publication Title
The PIAS-like Coactivator Zmiz1 Is a Direct and Selective Cofactor of Notch1 in T Cell Development and Leukemia.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 9 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Complement receptor 2negative (CR2/CD21) B cells have been found enriched in patients with autoimmune diseases and in common variable immunodeficiency (CVID) patients who are prone to autoimmunity. However, the physiology of CD21/lo B cells remains poorly characterized. We found that some rheumatoid arthritis (RA) patients also display an increased frequency of CD21/lo B cells in their blood. A majority of CD21/lo B cells from RA and CVID patients expressed germline autoreactive antibodies, which recognized nuclear and cytoplasmic structures. In addition, these B cells were unable to induce calcium flux, become activated, or proliferate in response to B-cell receptor and/or CD40 triggering, suggesting that these autoreactive B cells may be anergic. Moreover, gene array analyses of CD21/lo B cells revealed molecules specifically expressed in these B cells and that are likely to induce their unresponsive stage. Thus, CD21/lo B cells contain mostly autoreactive unresponsive clones, which express a specific set of molecules that may represent new biomarkers to identify anergic B cells in humans.
Publication Title
Complement receptor 2/CD21- human naive B cells contain mostly autoreactive unresponsive clones.
Sample Metadata Fields
No sample metadata fields
View Samples- Caenorhabditis elegans
- 12 Downloadable Samples
- Affymetrix C. elegans Genome Array (celegans)
Description
The roundworm Caenorhabditis elegans is a heme auxotroph that requires the coordinated actions of HRG-1 heme permeases to transport environmental heme into the intestine and HRG-3, a secreted protein, to deliver intestinal heme to other tissues including the embryo. Here we show that heme homeostasis in the extraintestinal hypodermal tissue is facilitated by the transmembrane protein HRG-2. Systemic heme deficiency upregulates hrg-2 mRNA expression over 200-fold in the main body hypodermal syncytium hyp 7. HRG-2 is a type I membrane protein which binds heme and localizes to the endoplasmic reticulum and apical plasma membrane. Cytochrome heme profiles are aberrant in HRG-2 deficient worms, a phenotype that is partially suppressed by heme supplementation. Heme-deficient yeast strain, ectopically expressing worm HRG-2, reveal significantly improved growth at submicromolar concentrations of exogenous heme. Taken together, our results implicate HRG-2 as a facilitator of heme utilization in the C. elegans hypodermis and provide a mechanism for regulation of heme homeostasis in an extraintestinal tissue.
Publication Title
Heme utilization in the Caenorhabditis elegans hypodermal cells is facilitated by heme-responsive gene-2.
Sample Metadata Fields
No sample metadata fields
View Samples- Mus musculus, Homo sapiens, Rattus norvegicus
- 1314 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Rat Genome 230 2.0 Array (rat2302), Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
The MAQC-II Project: A comprehensive study of common practices for the development and validation of microarray-based predictive models
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Age, Specimen part, Race, Compound
View Samples- Homo sapiens
- 549 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a), Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
The multiple myeloma (MM) data set (endpoints F, G, H, and I) was contributed by the Myeloma Institute for Research and Therapy at the University of Arkansas for Medical Sciences (UAMS, Little Rock, AR, USA). Gene expression profiling of highly purified bone marrow plasma cells was performed in newly diagnosed patients with MM. The training set consisted of 340 cases enrolled on total therapy 2 (TT2) and the validation set comprised 214 patients enrolled in total therapy 3 (TT3). Plasma cells were enriched by anti-CD138 immunomagnetic bead selection of mononuclear cell fractions of bone marrow aspirates in a central laboratory. All samples applied to the microarray contained more than 85% plasma cells as determined by 2-color flow cytometry (CD38+ and CD45-/dim) performed after selection. Dichotomized overall survival (OS) and eventfree survival (EFS) were determined based on a two-year milestone cutoff. A gene expression model of high-risk multiple myeloma was developed and validated by the data provider and later on validated in three additional independent data sets.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Age
View Samples GSE24363- Rattus norvegicus
- 410 Downloadable Samples
- Affymetrix Rat Genome 230 2.0 Array (rat2302), Affymetrix Human Genome U133A Array (hgu133a)
Description
The NIEHS data set (endpoint C) was provided by the National Institute of Environmental Health Sciences (NIEHS) of the National Institutes of Health (Research Triangle Park, NC, USA). The study objective was to use microarray gene expression data acquired from the liver of rats exposed to hepatotoxicants to build classifiers for prediction of liver necrosis. The gene expression compendium data set was collected from 418 rats exposed to one of eight compounds (1,2-dichlorobenzene, 1,4-dichlorobenzene, bromobenzene, monocrotaline, N-nitrosomorpholine, thioacetamide, galactosamine, and diquat dibromide). All eight compounds were studied using standardized procedures, i.e. a common array platform (Affymetrix Rat 230 2.0 microarray), experimental procedures and data retrieving and analysis processes.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Sex, Specimen part, Compound
View Samples- Homo sapiens
- 267 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
The human breast cancer (BR) data set (endpoints D and E) was contributed by the University of Texas M. D. Anderson Cancer Center (MDACC, Houston, TX, USA). Gene expression data from 230 stage I-III breast cancers were generated from fine needle aspiration specimens of newly diagnosed breast cancers before any therapy. The biopsy specimens were collected sequentially during a prospective pharmacogenomic marker discovery study between 2000 and 2008. These specimens represent 70-90% pure neoplastic cells with minimal stromal contamination. Patients received 6 months of preoperative (neoadjuvant) chemotherapy including paclitaxel, 5-fluorouracil, cyclophosphamide and doxorubicin followed by surgical resection of the cancer. Response to preoperative chemotherapy was categorized as a pathological complete response (pCR = no residual invasive cancer in the breast or lymph nodes) or residual invasive cancer (RD), and used as endpoint D for prediction. Endpoint E is the clinical estrogen-receptor status as established by immunohistochemistry. RNA extraction and gene expression profiling were performed in multiple batches over time using Affymetrix U133A microarrays. Genomic analysis of a subset of this sequentially accrued patient population were reported previously. For each endpoint, the first 130 cases were used as a training set and the next 100 cases were used as an independent validation set.
Publication Title
Effect of training-sample size and classification difficulty on the accuracy of genomic predictors.
Sample Metadata Fields
Age, Specimen part, Race
View Samples