Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Multiple platform assessment and isomir analysis of the EGF dependent microRNA-ome by microarray and deep sequencing analysis

ABSTRACT: This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

ORGANISM(S): Homo sapiens

SUBMITTER: Lauro Sumoy

PROVIDER: E-GEOD-41360 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Microarray and deep sequencing cross-platform analysis of the mirRNome and isomiR variation in response to epidermal growth factor.

Llorens Franc F Hummel Manuela M Pantano Lorena L Pastor Xavier X Vivancos Ana A Castillo Ester E Mattlin Heidi H Ferrer Anna A Ingham Matthew M Noguera Marc M Kofler Robert R Dohm Juliane C JC Pluvinet Raquel R Bayés Mònica M Himmelbauer Heinz H del Rio José Antonio JA Martí Eulàlia E Sumoy Lauro L

BMC genomics 20130601

<h4>Background</h4>Epidermal Growth Factor (EGF) plays an important function in the regulation of cell growth, proliferation, and differentiation by binding to its receptor (EGFR) and providing cancer cells with increased survival responsiveness. Signal transduction carried out by EGF has been extensively studied at both transcriptional and post-transcriptional levels. Little is known about the involvement of microRNAs (miRNAs) in the EGF signaling pathway. miRNAs have emerged as major players i ...[more]

PMID: 23724959

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Project description:Epidermal growth factor (EGF) is a key regulatory growth factor activating a myriad of processes affecting cell proliferation and survival that are relevant to normal development and disease. Here we have used a combined approach to study the EGF dependent transcriptome of HeLa cells. We obtained mRNA expression profiles using multiple long oligonucleotide based microarray platforms (from Agilent, Operon, Febit, and Illumina) in combination with digital gene expression profiling (DGE) with the Illumina Genome Analyzer I (GA-I). By applying a procedure for cross-platform data meta-analysis based on rank product and global ancova tests, we establish a well validated gene set with transcript levels altered after EGF treatment. We used this robust gene list to build higher order networks of gene interaction by interconnecting associated networks, supporting and extending the important role of the EGF signaling pathway in cancer. In addition, we found a whole new set of genes previously unrelated to the currently accepted EGF associated cellular functions, among which are metallothionein genes. We propose the use of global genomic cross-validation to generate more reliable datasets derived from high content technologies (microarrays or deep sequencing). This approach should help to improve the confidence of downstream in silico functional inference analyses based on high content data. Keywords: treated vs. untreated comparison, time course Time course experiment comparing HeLa gene expression in response to EGF analyzed on different microarray platforms (Agilent, IMPPC, Illumina, and Operon) and by digital gene expression using short read high throughput tag sequencing. Three independent experiments were performed where HeLa cells were serum deprived for 24 hours and were either left untreated or treated with EGF for 6, and 24 h and harvested for RNA extraction. Technical dye swap duplicates were performed for each of the three biological replicates in both time points. Comparative genomic hybridization of HeLa cell genomic DNA versus poooled genomic DNA from blood obtained from human females conducted on commercial oligonucleotide microarrays (Human Genome CGH Microarray Kit 244A, Agilent Technologies) in order to assess DNA dosage dependence of gene expression levels and response to EGF. Digital gene expression using short read high throughput tag sequencing data submitted to NCBI's SRA

Project description:Since the worldwide increase in obesity represents a growing challenge for the health care systems, new approaches are needed to treat obesity and associated disease effectively. The food intake is primarily stored in the adipose tissue and therefore this organ is in focus to develop new anti-obesity treatments. To provide a systematic analysis of genes that regulate adipose tissue biology and to establish a target-oriented compound screening, we performed a high throughput siRNA screen with primary (pre)adipocytes, using a druggable siRNA library targeting 7,784 human genes. Beside well known regulators of adipogenesis and neutral lipid storage (like PPAR?, RXR, Perilipin A) the screening revealed a large number of genes which were not previously described in the context of fatty tissue biology. An enrichment of genes was observed for axonemal dyneins. Five out of ten anxonemal dyneins were identified in our primary screen and retested positive with independent siRNAs and cell-donors. Quantitative RT-PCR- and immunoblot analysis revealed that axonemal dyneins are expressed in preadipocytes and maturing adipocytes. Using microarray analysis, we further characterize the remaining genes identified in our primary screen to determine their expression pattern during adipogenesis. In the course of fat cell differentiation, 149 among the 459 positive genes were regulated on the gene expression level. Assuming that an adipogenesis-specific expression pattern is another independent hind, that these genes are involved in neutral lipid storage and/or adipocyte differentiation, we retested this gene-pool with independent siRNAs and cell donors. Finally, to show that the genes identified are per se druggable we performed a proof of principle experiment using an antagonist for one randomly chosen gene. The results showed a very similar phenotype compared to knock-down experiments proofing the druggability. Thus, we identified new adipogenesis-associated genes and those involved in neutral lipid storage. Moreover, by using a druggable siRNA library the screen data provides a very attractive starting point, to identify anti-obesity compounds targeting the adipose tissue. For microarray analysis, RNA was isolated at 3 different points in time during adipogenesis (day 0, day 3 and day 7 after induction of differentiation). This experiment was carried out with cells obtained from three different donors. No replicates are included for each point in time.

Dataset Information

Multiple platform assessment and isomir analysis of the EGF dependent microRNA-ome by microarray and deep sequencing analysis

Publications

Microarray and deep sequencing cross-platform analysis of the mirRNome and isomiR variation in response to epidermal growth factor.

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