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SRP033410
Caenorhabditis elegans
52 Downloadable Samples
Illumina HiSeq 2000
Description
We report the presence of extensive, transcriptionally controlled oscillations in the C. elegans, developmental transcriptome. Furthermore, using ribosome profiling, we show that these oscillating transcripts are actively translated. Overall design: Examination of three timecourses that were collected over C. elegans development and analyzed by RNA-seq of mRNA libraries
Publication Title
Extensive oscillatory gene expression during C. elegans larval development.
Sample Metadata Fields
Cell line, Subject
View Samples- Caenorhabditis elegans
- 34 Downloadable Samples
- Illumina HiSeq 2000
Description
We report the presence of extensive, transcriptionally controlled oscillations in the C. elegans, developmental transcriptome. Furthermore, using ribosome profiling, we show that these oscillating transcripts are actively translated. Overall design: Examination of two timecourses that were collected over C. elegans development and analyzed by RNA-seq of "RiboMinus" libraries
Publication Title
Extensive oscillatory gene expression during C. elegans larval development.
Sample Metadata Fields
Cell line, Subject
View Samples- Mus musculus
- 30 Downloadable Samples
Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Background: MicroRNAs (miRNAs) are a family of small, non-coding single-stranded RNA molecules involved in post-transcriptional regulation of gene expression. As such, they are believed to play a role in regulating the step-wise changes in gene expression patterns that occur during cell fate specification of multipotent stem cells. Here, we have studied whether terminal differentiation of C2C12 myoblasts is indeed controlled by lineage-specific changes in miRNA expression.
Publication Title
MicroRNA miR-378 promotes BMP2-induced osteogenic differentiation of mesenchymal progenitor cells.
Sample Metadata Fields
Cell line
View Samples- Homo sapiens
- 48 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Gene Expression analysis of a differentiation timeseries of human Mesenchymal Stem Cells (hMSCs) in the presence of adipogenic/osteogenic factors. hMSCs differentiate into fat cells when treated with dexamethasone (10^-6 M), insulin (10 ug/ml), rosiglitazone (10^-7 M) and IBMX (250 uM). TGFbeta (5 ng/ml) inhibits this process and redirects these cells to differentiate into bone cells.
Publication Title
TGFβ-induced switch from adipogenic to osteogenic differentiation of human mesenchymal stem cells: identification of drug targets for prevention of fat cell differentiation.
Sample Metadata Fields
Specimen part, Treatment, Time
View Samples- Drosophila melanogaster
- 10 Downloadable Samples
- Affymetrix Drosophila Genome 2.0 Array (drosophila2)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
The exoribonuclease Nibbler controls 3' end processing of microRNAs in Drosophila.
Sample Metadata Fields
Sex, Specimen part
View Samples- Drosophila melanogaster
- 10 Downloadable Samples
- Affymetrix Drosophila Genome 2.0 Array (drosophila2)
Description
nbr/CG9247 gene function regulates the length of the 3'end of miRNAs.
Publication Title
The exoribonuclease Nibbler controls 3' end processing of microRNAs in Drosophila.
Sample Metadata Fields
No sample metadata fields
View Samples- Drosophila melanogaster
- 4 Downloadable Samples
- Illumina HiSeq 2000
Description
Drosophila miRNAs show distinct change in isoform distribution pattern with age. Some miRNAs show accumulation of the short isoforms, while other miRNAs show the accumulation of the long isoforms with age. The increase of the long isoforms of some miRNAs reflects increased 2''-O-methylated miRNA isoforms with age. The increase in 2''-O-methylated miRNA isoforms reflected increased Ago2-loading, but not Ago1-loading of specific miRNA isoforms with age. This raised a question on whether there is global shift in small RNA loading pattern between Ago1 and Ago2 with age. To investigate change in small RNA loading pattern between Ago1 and Ago2 with age, we performed small RNA deep-sequencing of Ago1 vs Ago2-IP small RNAs at 3d and 30d in Drosophila. This analysis revealed global increase of miRNA loading into Ago2, but not into Ago1 with age. Overall design: 3d and 30d FLAG-HA-Ago2 male flies were collected. Ago1 and Ago2 were immunoprecipitated by anti-Ago1 and anti-FLAG M2 beads respectively. RNA was purified from the beads, P32-labeled, and small RNA fraction was gel-purififed. Small RNA libraries were prepared using Illumina''s TruSeq small RNA sample preparation kit (#RS-200-0012, Illumina, Inc. San Diego, CA), following the manufacturer''s protocol. The libraries were multiplexed and sequenced on HiSeq2000 platform (Illumina).
Publication Title
Impact of age-associated increase in 2'-O-methylation of miRNAs on aging and neurodegeneration in Drosophila.
Sample Metadata Fields
Sex, Specimen part, Subject
View Samples- Drosophila melanogaster
- 2 Downloadable Samples
- Illumina Genome Analyzer
Description
nbr/CG9247 gene regulates 3''end heterogeneity of a subset of miRNAs. It is not clear how broad this effect is on small RNA population. To address this, we compared small RNA population in wild-type and tmr[f02257] mutants. This approach identified more miRNAs whose 3''end heterogeneity was affected in nbr[f02257] mutants. Overall design: 2-3 day old control (w homogeneous strain Bloomington stock center 5905) and nbr[f02257] null mutant flies were collected. nbr[f02257] line was in the homogenous (Bloomington stock center stock 5905) background through a minimum of 5 backcrosses. Total RNA from whole flies was extracted using TRIzol reagent (Invitrogen). 40ug of total RNA from each genotype was used for small RNA library preparation with Small RNA Sample Prep kit (v1.5) (Illumina).
Publication Title
The exoribonuclease Nibbler controls 3' end processing of microRNAs in Drosophila.
Sample Metadata Fields
Sex, Specimen part, Subject
View Samples- Homo sapiens
- 127 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Compounds with direct immunotoxic properties, including metals, mycotoxins, agricultural pesticides and industrial chemicals, form potential human health risks due to exposure through food, drinking water, and the environment. Insights into the mechanisms of action are currently lacking for the majority of these direct immunotoxicants. Therefore, the present work aimed to gain insights into the molecular mechanisms underlying direct immunotoxicity. To this end, we assessed in vitro the effects of 31 test compounds on the transcriptome of the human Jurkat T cell line. These compounds included direct immunotoxicants, immunosuppressive drugs with different mode of actions, and non-immunotoxic control chemicals. Pathway analysis of the microarray data allowed us to identify canonical pathways and Gene Ontology processes that were transcriptionally regulated in common by immunotoxicants (i) with structural similarities, such as the tributyltins TBTC and TBTO that activated the retinoic acid / X receptor (RAR / RXR) signaling pathway, and (ii) without structural similarities, such as As2O3, DBTC, diazinon, MeHg, ochratoxin A, S9 treated ochratoxin A, S9 treated cyclophosphamide, and S9 treated benzo[a]pyrene, that activated unfolded protein response, and FTY720, lindane, and propanil, that activated the cholesterol biosynthesis pathway. In addition, processes uniquely affected by individual immunotoxicants were identified, such as the induction of Notch receptor signaling and the down regulation of acute phase response genes by ochratoxin A. These findings were validated by quantitative Real-Time PCR (Q-RT-PCR) analysis of genes involved in these processes. Our study indicated that diverse modes of action are involved in direct immunotoxicity and that a set of pathways or genes, rather than one single gene can be used to screen compounds for direct immunotoxicity.
Publication Title
Toxicogenomics-based identification of mechanisms for direct immunotoxicity.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Rattus norvegicus
- 90 Downloadable Samples
- Affymetrix Rat Expression 230A Array (rae230a)
Description
The present research aimed to study the interaction of three chemicals, methyl mercury, benzene and trichloroethylene, on mRNA expression alterations in rat liver and kidney measured by microarray analysis. These compounds were selected on presumed different modes of action. The chemicals were administered daily for 14 days at the Lowest-Observed-Adverse-Effect-Level (LOAEL) or at a two- or three-fold lower concentration individually or in binary or ternary mixtures. The compounds had strong antagonistic effects on each others gene expression changes, which included several genes encoding Phase I and II metabolizing enzymes. On the other hand, the mixtures affected the expression of “novel” genes that were not or little affected by the individual compounds. Based on gene expression changes, the three compounds exhibited a synergistic interaction at the LOAEL in the liver and both at the sub-LOAEL and LOAEL in the kidney. Many of the genes induced by mixtures but not by single compounds, such as Id2, Nr2f6, Tnfrsf1a, Ccng1, Mdm2 and Nfkb1 in the liver, are known to affect cellular proliferation, apoptosis and function. This indicates a shift from compound specific response on exposure to individual compounds to a more generic stress response to mixtures. Most of the effects on cell viability as concluded from transcriptomics were not detected by classical toxicological research illustrating the difference in sensitivity of these techniques. These results emphasize the benefit of applying toxicogenomics in mixture interaction studies, which yields biomarkers for joint toxicity and eventually can result in an interaction model for most known toxins.
Publication Title
Transcriptomics analysis of interactive effects of benzene, trichloroethylene and methyl mercury within binary and ternary mixtures on the liver and kidney following subchronic exposure in the rat.
Sample Metadata Fields
Sex, Age, Specimen part, Treatment, Compound
View Samples