Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression profiling of over-expression of conspecific and heterspecific miR-310 cluster in Drosophila (cDNA array)

ABSTRACT: The miRNA 310-311-312-313 cluster from D. melanogaster (referred to as Dm310s) and the homolog from D. pseudoobscura (Dp310s) were used to transform D. melanogaster. The over-expression of UAS-miR310s in the Dm310s transgenic line M1-7 and Dp310s transgenic line P4-18 was driven by GAL4 line NP5941 with native miR-310 expression pattern. We used DGRC-1 cDNA arrays to assess the effects of miR310s over-expression on the whole transcriptome. Direct two-colour design involving six arrays in three dye-swap pairs, with each pair used for the pairwise comparison of Dm310s, Dp310s and w1118 (control).

ORGANISM(S): Drosophila melanogaster

SUBMITTER: TIAN TANG

PROVIDER: E-GEOD-15847 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Adverse interactions between micro-RNAs and target genes from different species.

Tang Tian T Kumar Supriya S Shen Yang Y Lu Jian J Wu Mao-Lien ML Shi Suhua S Li Wen-Hsiung WH Li Wen-Hsiung WH Wu Chung-I CI

Proceedings of the National Academy of Sciences of the United States of America 20100706 29

It is commonly assumed but not proven that microRNAs (miRNAs) and their targets coevolve. Under this assumption, miRNAs and targets from different species may interact adversely, resulting in reduced fitness. However, the strength of the adverse interactions may not be detectable because even outright deletions of miRNAs often manifest only subtle fitness effects. We tested and measured the strength of heterospecific interactions by carrying out transgenic experiments across Drosophila species b ...[more]

PMID: 20615957

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Project description:Cell-type specific transcriptional profiling is key to understanding cell fate specification and function. In order to achieve this it has been necessary, to date, to isolate specific cell types from complex tissues. We have developed 'TaDa', a technique that enables cell-specific profiling without cell isolation. TaDa permits genome-wide profiling of DNA- or chromatin-binding proteins without cell sorting, fixation or affinity purification. The method is simple, sensitive, highly reproducible and is in principle transferable to any model system. Here we show that TaDa can be used to identify transcribed genes in a cell-type specific manner. We profile the genome-wide binding of RNA polymerase II (Pol II) in adjacent, clonally related neural stem cells in intact Drosophila brains. Our data reveal the activity of non-canonical metabolic pathways in proliferating neuroepithelial cells, and highlight a possible role for the retinal determination gene regulatory network in patterning neural stem cell fates. We also identify temporal differences in the activity of signalling pathways that control neuroepithelial cell fate by profiling Pol II occupancy at two different stages of brain development. Using RNA Pol II TaDa to profile, in a cell-type specific manner, the transcriptional state of neuroepithelial cells at two stages of larval brain development. Closely related asymmetrically dividing neural stem cells (neuroblasts) were also profiled, in order to compare the transcriptomes of two different types of neural stem cells. 3 biological relicates were performed for 3rd instar neuroepithelial cells (with one dye-swap). 2 biological relicates were performed for 3rd instar neuroblasts (with dye-swap). 2 biological relicates were performed for 1st instar neuroepithelial cells (with dye-swap). As additional supporting evidence for the Pol II TaDa technique, 2 biological relicates were performed for 3rd instar salivary glands (with dye-swap) in order to compare with previous Pol II-ChIP data for this tissue [PMID 22821985].

Dataset Information

Expression profiling of over-expression of conspecific and heterspecific miR-310 cluster in Drosophila (cDNA array)

Publications

Adverse interactions between micro-RNAs and target genes from different species.

Similar Datasets

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