Filters
Technology
Platform
GSE60003
Rattus norvegicus
6 Downloadable Samples
Affymetrix Rat Genome 230 2.0 Array (rat2302)
Description
Polycomb-group proteins form multimeric protein complexes involved in transcriptional silencing. The Polycomb Repressive complex 2 (PRC2) contains the Suppressor of Zeste-12 protein (Suz12) and the histone methyltransferase Enhancer of Zeste protein-2 (Ezh2). This complex, catalyzing the di- and tri-methylation of histone H3 lysine 27, is essential for embryonic development and stem cell renewal. However, the role of Polycomb-group protein complexes in the control of the intestinal epithelial cell (IEC) phenotype is not known. We investigated the impact of Suz 12 depletion on gene expression in IEC-6 cells.
Publication Title
The histone H3K27 methylation mark regulates intestinal epithelial cell density-dependent proliferation and the inflammatory response.
Sample Metadata Fields
Cell line
View Samples- Mus musculus
- 18 Downloadable Samples
Illumina HiSeq 2000
Description
Mouse lung epithelial subpopulations (alveolar type 2, basal and airway luminal cells) freshly dissociated from mouse lung and trachea were isolated by FACS. RNA-seq gene expression profiling was used to determine gene signature from each population. Overall design: Cells were isolated from the small airway (SA) and large airway (LA) of 6 mouse lungs
Publication Title
Lung Basal Stem Cells Rapidly Repair DNA Damage Using the Error-Prone Nonhomologous End-Joining Pathway.
Sample Metadata Fields
Specimen part, Cell line, Subject
View Samples- Mus musculus
- 6 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Histone deacetylases (Hdac) remove acetyl groups from proteins, influencing global and specific gene expression. Hdacs control inflammation, as shown by Hdac inhibitor-dependent protection from DSS-induced murine colitis. While tissue-specific Hdac knockouts show redundant and specific functions, little is known of their intestinal epithelial cell (IEC) role. We have shown previously that dual Hdac1/Hdac2 IEC-specific loss disrupts cell proliferation and determination, with decreased secretory cell numbers and altered barrier function. We thus investigated how compound Hdac1/Hdac2 or Hdac2 IEC-specific deficiency alters the inflammatory response. Floxed Hdac1 and Hdac2 and villin-Cre mice were interbred. Compound Hdac1/Hdac2 IEC-deficient mice showed chronic basal inflammation, with increased basal Disease Activity Index (DAI) and deregulated Reg gene colonic expression. DSS-treated dual Hdac1/Hdac2 IEC-deficient mice displayed increased DAI, histological score, intestinal permeability and inflammatory gene expression. In contrast to double knockouts, Hdac2 IEC-specific loss did not affect IEC determination and growth, nor result in chronic inflammation. However, Hdac2 disruption protected against DSS colitis, as shown by decreased DAI, intestinal permeability and caspase-3 cleavage. Hdac2 IEC-specific deficient mice displayed increased expression of IEC gene subsets, such as colonic antimicrobial Reg3b and Reg3g mRNAs, and decreased expression of immune cell function-related genes. Our data show that Hdac1 and Hdac2 are essential IEC homeostasis regulators. IEC-specific Hdac1 and Hdac2 may act as epigenetic sensors and transmitters of environmental cues and regulate IEC-mediated mucosal homeostatic and inflammatory responses. Different levels of IEC Hdac activity may lead to positive or negative outcomes on intestinal homeostasis during inflammation
Publication Title
The acetylome regulators Hdac1 and Hdac2 differently modulate intestinal epithelial cell dependent homeostatic responses in experimental colitis.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 6 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Acetylation and deacetylation of histones and other proteins depend on the opposing activities of histone acetyltransferases and histone deacetylases (HDACs), leading to either positive or negative gene expression changes. The use of HDAC inhibitors (HDACi) has uncovered a role for HDACs in the control of proliferation, apoptosis and inflammation. However, little is known of the roles of specific HDACs in intestinal epithelial cells (IEC). We investigated the consequences of ablating both Hdac1 and Hdac2 in murine IECs gene expression.
Publication Title
HDAC1 and HDAC2 restrain the intestinal inflammatory response by regulating intestinal epithelial cell differentiation.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 160 Downloadable Samples
- IlluminaHiSeq2500
Description
Purpose: The aim of this study is to compare different RNA extraction methods using a mixture design that allows the relative changes of the majority of genes profiled to be estimated. A number of samples were degraded to allow us to compare methods for dealing with more variable samples. Methods - Cell Culture: Lung adenocarcinoma cell lines NCI-H1975 and HCC827 from a range of passages (2-4) were grown on 3 separate occasions in RPMI media (Gibco) supplemented with Glutamax and 10% fetal calf serum to a 70% confluence. To replicate common experimental conditions cell lines were treated with 0.01% Dimethyl sulfoxide (Sigma), which is commonly used as a vehicle in drug treatment experiments. After 6 hours of treatment, cells were collected, snap-frozen on dry ice and stored at -80 degrees C until required. Methods - RNA preparation: Total RNA was extracted from between half a million and million cells using Total RNA Purification Kit (Norgen Biotek) with on-column DNAse treatment accorting to the kit instructions. RNA concentration for each pair of samples to be mixed was equalised to ~100 ng/µl using Qubit RNA BR Assay Kit (Life Technologies). Replicates were pooled in known proportions to obtain mixtures ranging from pure NCI-H1975 (100:0) to pure HCC827 (0:100) and intermediate mixtures ranging from 75:25 to 50:50 to 25:75 NCI-H1975:HCC827. All mixtures corresponding to the second replicate were split into two equal aliquots. One aliquot was left intact (we refer to this as the ''good'' replicate), while the second aliquot was degraded to produce known outlier samples by incubation at 37 degrees C for 7 days in a thermal cycler with a heated lid. 10 µl from each replicated mixture (both good and degraded) were used for Next Generation Sequencing library preparation using two kits: Illumina TruSeq Total Stranded RNA with Ribozero (TotalRNA) and Illumina TruSeq RNA v2 (mRNA) according to the manufacturer''s instructions. Completed libraries were sequenced on HiSeq 2500 with TruSeq SBS Kit v4- HS reagents (Illumina) as 100 bp single-end reads at the Australian Genome Research Facility (AGRF), Melbourne. Approximately 30 million 100 bp single-end reads were obtained for each sample. Reads were aligned to the human reference genome hg19 and mapped to known genomic features at the gene level using the Rsubread package (version 1.16.1) (Liao et al. 2013). Single reads were then summarized into gene-level counts using FeatureCounts (Liao et al. 2014). Overall design: Total RNA was extracted from lung adenocarcinoma cell lines NCI-H1975 and HCC827 (3 independent samples for each cell line) and mixed in known ratios. Both mRNA and Total RNA transcriptomes from these mixtures were profiled by RNA-Seq.
Publication Title
RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 10 Downloadable Samples
- Illumina HiSeq 2000
Description
Purpose: The aim of this study is to compare the transcriptome profiles of a limited number of lung cancer cell lines with the intention of selecting the two most similar cell lines for a mixture experiment (GSE64098). Methods - Cell Culture: Five lung adenocarcinoma cell lines (H2228, NCI-H1975, HCC827, H838 and A549) from a range of passages (2-4) were grown on 2 separate occasions in RPMI media (Gibco) supplemented with Glutamax and 10\% fetal calf serum to a 70\% confluence. To replicate common experimental conditions cell lines were treated with 0.01\% Dimethyl sulfoxide (Sigma), which is commonly used as a vehicle in drug treatment experiments. After 6 hours of treatment, cells were collected, snap-frozen on dry ice and stored at -80 degree C until required. Methods - RNA preparation: Total RNA was extracted from between half a million and million cells using Total RNA Purification Kit (Norgen Biotek) according to the kit instructions. RNA quality and concentration were assessed using Nanodrop and Tapestation RNA ScreenTape (Agilent) respectively. Methods - RNA-seq: 1 ug of total RNA from each sample were used for RNA-seq library preparation using TruSeq Total Stranded RNA with Ribozero (Illumina) according to manufacturer''s instructions. Completed libraries were sequenced on HiSeq 2000 with TruSeq SBS Kit v3- HS reagents (Illumina) as 100 bp single end reads at the Australian Genome Research Facility (AGRF), Melbourne. We obtained on average 28 million for each sample (range from 25 to 29 million). Reads were aligned to the human reference genome hg19 using the Rsubread package (version 1.16.1) (Liao et al. 2013). Single reads were then summarized into gene-level counts using FeatureCounts in the reverse-stranded mode (Liao et al. 2014). Overall design: Total RNA was extracted from lung adenocarcinoma cell lines H2228, NCI-H1975, HCC827, H838 and A549 (2 independent samples for each cell line). Total RNA transcriptome from these samples was profiled by RNA-seq.
Publication Title
RNA-seq mixology: designing realistic control experiments to compare protocols and analysis methods.
Sample Metadata Fields
Cell line, Subject
View Samples- Mus musculus
- 6 Downloadable Samples
- Illumina HiSeq 2500
Description
Epithelial (CD31-CD45-EpCAM+) cells were isolated by FACS from Grhl2-deficient (Shh-Cre;Grhl2f/-) and control (Shh-Cre;Grhl2f/+) embryonic lungs at day E16.5 (3 biological replicates/genotype). Total RNA extracted from the samples was subjected to next-generation sequencing (NGS) library preparation using standard Illumina protocols. Completed libraries from individual samples were sequenced on a HiSeq2500 at the Australian Genome Research Facility. Overall design: RNA-seq was performed on Grhl2-deficient and control epithelium isolated from the lungs of E16.5 embryos (n=3 replicates/genotype/cell population).
Publication Title
Lung morphogenesis is orchestrated through Grainyhead-like 2 (Grhl2) transcriptional programs.
Sample Metadata Fields
Sex, Specimen part, Subject
View Samples- Homo sapiens
- 77 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Human Genome U133A Array (hgu133a)
Description
The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial.
Publication Title
Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 35 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2), Affymetrix Human Genome U133A Array (hgu133a)
Description
The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial.
Publication Title
Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 42 Downloadable Samples
Affymetrix Human Genome U133A Array (hgu133a)
Description
The clinical and cytogenetic features associated with T-cell acute lymphoblastic leukemia (T-ALL) are not predictive of early treatment failure. Based on the hypothesis that microarrays might identify patients who fail therapy, we used the Affymetrix U133 Plus 2.0 chip and prediction analysis of microarrays (PAM) to profile 50 newly diagnosed patients who were treated in the Children's Oncology Group (COG) T-ALL Study 9404. We identified a 116-member genomic classifier that could accurately distinguish all 6 induction failure (IF) cases from 44 patients who achieved remission; network analyses suggest a prominent role for genes mediating cellular quiescence. Seven genes were similarly upregulated in both the genomic classifier for IF patients and T-ALL cell lines having acquired resistance to neoplastic agents, identifying potential target genes for further study in drug resistance. We tested whether our classifier could predict IF within 42 patient samples obtained from COG 8704 and, using PAM to define a smaller classifier for the U133A chip, correctly identified the single IF case and patients with persistently circulating blasts. Genetic profiling may identify T-ALL patients who are likely to fail induction and for whom alternate treatment strategies might be beneficial.
Publication Title
Identification of genomic classifiers that distinguish induction failure in T-lineage acute lymphoblastic leukemia: a report from the Children's Oncology Group.
Sample Metadata Fields
No sample metadata fields
View Samples