Name: describing a transcription factor dependent regulation of the microrna transcriptome
Uploaded: 2016-06-15T13:00:00
Description: Watch this Scientific Journal Video about Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome at JoVE.com

This study was supported by a grant from the CLL Global Research Foundation. The University of Texas MD Anderson Cancer Center is supported in part by the National Institutes of Health through a Cancer Center Support Grant (P30CA16672).

1. Identify Transcription Factor Binding Sites in the Promoter of MicroRNA Genes Using Data Mining Approach
<ol>
	<li>Use the University of California Santa Cruz (UCSC) genome browser to extract chromatin immunoprecipitation (ChIP) sequencing data generated as part of the Encyclopedia of DNA element (ENCODE) project.
	<ol>
		<li>Open the table browser in the UCSC genome browser.</li>
		<li>Use the following specifications to extract the table: Clade: (Mammals), genome: (Human), Assembly: (Feb2009(GRch37/hg18)), group: ...

<ol>
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A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+TARBP2+mutation+in+human+cancer+impairs+microRNA+processing+and+DICER1+function.">A TARBP2 mutation in human cancer impairs microRNA processing and DICER1 function.</a> Nat Genet. 41, 365-370 (2009).</li><li>Akira, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+roles+of+STAT+family+proteins:+lessons+from+knockout+mice.">Functional roles of STAT family proteins: lessons from knockout mice.</a> Stem Cells. 17, 138-146 (1999).</li><li>Frank, D. 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The mechanism underlying the RNA polymerase II- dependent transcription of protein coding genes has been extensively studied. While these elements make up only 1% - 2% of the human genome, evidence from the ENCODE project suggest that over 80% of the human genome may undergo transcription 17 and what regulates the transcription of the non-coding DNA elements remains largely unknown 6.
Several studies, which indicated that Pol II is also responsible for the transcription o...

MicroRNAs are endogenous small non coding regulatory RNAs that typically function as negative regulators of mRNA expression at the posttranscriptional level. Approximately 1,000 non-coding 20 to 25 nucleotide long microRNAs are found in the human genome 1,2. MicroRNAs regulate gene expression through canonical base pairing between the seed sequence of the microRNA and its complementary seed match sequence, which is commonly located at the 3&#39; untranslated region (UTR) of the target mRNAs. Collectively, microRNAs regulate more than 30% of protein coding genes 3, but only little is known about the transcription from DNA of microRNAs. It has been suggested that the regulation of microRNA transcription is similar to that of mRNA 4,5. In particular, similar to its activity in promoting transcription of protein coding genes, transcription factors are thought to activate transcription of microRNAs 6. Transcription factor-microRNA interplay has been reported as a modulator factor of gene expression 7, and may also form feed-back and feed-forward loops. For example, Yamakuchi et al. reported a feedback loop in which p53 induces the expression of microRNA34a, which in turn inhibits translation of the p53 repressor SIRT and thereby increasing p53 activity 8.
Whereas specific examples of transcription factor dependent expression of microRNAs have been reported, an accepted method which provides information on how a transcription factor of interest regulates the expression of the microRNA-transcriptome is lacking. The purpose of the protocol suggested herein is to provide an in-depth description of transcription factor-dependent regulation of the microRNA-transcriptome. By combining publically available data, bioinformatics tools and using microarray technology, researchers who follow this algorithm would be able to capture on a genomic scale how any transcription factor in any cell type of interest regulates the expression of the microRNA-transcriptome and to explore a putative contribution of the transcription factor-mRNA in regulating microRNA expression.

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			<td height="21" style="height:21px;width:363px;">Lipofectamin 2000</td>
			<td style="width:204px;">Life Technologies</td>
			<td style="width:119px;">11668027</td>
			<td style="width:167px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">0.45 um syringe filter&#160;</td>
			<td style="width:204px;">Thermo Scientific (Nalgene)</td>
			<td>190-2545</td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Amicon ultracentrifugal filter device with threshold of 100kDa&#160;</td>
			<td style="width:204px;">Merck Millipore</td>
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			<td height="21" style="height:21px;">Polybrene&#160;</td>
			<td style="width:204px;">Merck Millipore</td>
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			<td height="21" style="height:21px;">TRIzol reagent&#160;</td>
			<td style="width:204px;">Life Tachnologies (Invitrogen)</td>
			<td>15596-026</td>
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			<td height="21" style="height:21px;width:363px;">293 Cell line human</td>
			<td style="width:204px;">Sigma-Aldrich</td>
			<td style="width:119px;">85120602</td>
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describing a transcription factor dependent regulation of the microrna transcriptome

While the transcription regulation of protein coding genes was extensively studied, little is known on how transcription factors are involved in transcription of non-coding RNAs, specifically of microRNAs. Here, we propose a strategy to study the potential role of transcription factor in regulating transcription of microRNAs using publically available data, computational resources and high throughput data. We use the H3K4me3 epigenetic signature to identify microRNA promoters and chromatin immunoprecipitation (ChIP)-sequencing data from the ENCODE project to identify microRNA promoters that are enriched with transcription factor binding sites. By transfecting cells of interest with shRNA targeting a transcription factor of interest and subjecting the cells to microRNA array, we study the effect of this transcription factor on the microRNA transcriptome. As an illustrative example we use our study on the effect of STAT3 on the microRNA transcriptome of chronic lymphocytic leukemia (CLL) cells.

STAT3 is a transcription factor which typically induces the transcription of genes that have anti apoptotic and proliferative effects 12 . Whether STAT3 also affect the non-coding RNA transcriptome is currently unknown. In all CLL cells STAT3 is constitutively phosphorylated on serine 707 residues 10,13. Phosphoserine STAT3 shuttles to the nucleus, binds to DNA, and activates genes known to be activated by tyrosine pSTAT3 in other cell types 10. Because CL...

Watch this Scientific Journal Video about Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome at JoVE.com

Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Herein we propose a strategy to study the effect of a transcription factor of interest on the microRNA transcriptome using publically available data, computational resources and high throughput data from microRNA arrays after transfecting cells with small hairpin (sh)RNA targeting a transcription factor of interest.

While the transcription regulation of protein coding genes was extensively studied, little is known on how transcription factors are involved in ...

The overall goal of this methodology is to describe how a transcription factor of interest regulates the microRNA transcriptome. This method can help answer key questions on whether transcription factors that modulate transcription of protein coding genes are also involved in transcription of microRNAs. The main advantage of this methodology is that it integrates data mining, laboratory data, and bioinformatic analysis to provide a comprehensive description of microRNA genes transcriptions that is dependent on the activity of transcription factor of interest.Though this method can provide insight into the interaction of microRNA gene transcription and activity of the transcription factor status, it is important to remember that this is a descriptive method and confirmation of such an association should be done using additional assays. To begin, open a genome browser, such as the University of California Santa Cruz genome browser, in order to extract chromatin immunoprecipitation sequencing data. Once on the site, go to the top menu, select Tools and open the Table Browser.Enter the specifications, as shown here, into their respective boxes, and then select Get Output to extract the table. Save the output as a text file. Then, import the file into a spreadsheet.Once in the spreadsheet program, sort the name column and filter the results for a transcription factor of interest, such as STAT2. Then, copy the list of microRNA promoters based on their H3K4me3 epigenetic signature and paste the list into a txt file. Then, use the supplemental code written in SQL and provided in the supplementary data files to map the collected data and determine the median binding affinity.First, add 1.5 million HEK293 cells to a 10 centimeter dish and add DMEM supplemented with 10%FBS. Culture the cells for 24 hours until they reach about 50%confluency. Next, replace the media.Then, add a transvection agent combined with five micrograms of a green fluorescence protein Lentivirus containing the targeted shRNA and five micrograms of packaging vectors in accordance with the manufacturers instructions. As a control, transvect an identical plate of cells using scrambled shRNA and the packaging vectors. Incubate the transvection mix on the cells for 16 hours and then change the media to 10 milliliters of fresh DMEM media supplemented with 10%FBS.48 hours post-transvection, remove the media from the cells. Then, centrifuge for five minutes in order to collect infectious supernatant. Filter the supernatant through a 0.45 micron syringe filter to remove any floating cells.Next, add the media into an ultracentrifigal filter device with a threshold of 100 kilodaltons. Centrifuge the media to concentrate the Lentivirus until the volume is less than 250 microliters. Transfer the remaining liquid into a cryotube and store the concentrated virus at minus 80 degrees Celsius until it is needed.When ready to continue, remove the frozen Lentivirus from the minus 80 freezer and bring the vial to room temperature. Transfer 100 microliters of viral supernatant to a fresh 1.5 milliliter microfuge tube and add 900 microliters of medium without serum. Next, add 10 nanograms per milliliter hexadimethrine bromide to the one milliliter virus suspension.Mix the tube gently, and let the mixture stand for five minutes. Harvest five million cells for each planned transduction and then gently re-suspend the cell pellet in 0.5 milliliters of media containing virus. Transfer the cells to a 12-well plate and incubate them for four to 24 hours.Then, add an additional 0.5 milliliters of medium containing 20%FBS. At 48 to 72 hours post-transduction, harvest the cells and stain them with propidium iodide according to the manufacturer's instructions. After staining, protect the cells from light and use facts to estimate transvection efficiency by measuring the rates of GFP positive, propidium iodide negative cells.Then, collect the sorted GFP positive cell population and extract their proteins. Finally, use western blotting to determine levels of the targeted transcription factor before and after infecting the cells with the designated shRNA. After the transvection of CLL cells with STAT3 shRNA, the levels of STAT3 mRNA and STAT3 protein both significantly decreased.In addition, a microRNA array of the CLL cells depicted 23 microRNAs whose expression differed significantly between CLL cells that were trasnfected with STAT3 shRNA and CLL cells that were transvected with an empty vector. The results of the microRNA array were validated for seven of the genes, shown here, using quantitative RT-PCR. Following this procedure, other methods like chromatin immunoprecipitation and electromobility shift assay and a difference assay can be performed in order to validate that this transcription factor binds and activates the promoter of a specific mirror gene.After watching this video, you should have a good understanding of how to integrate bio informatics analysis, publicly available data and silencing a transcription factor using shRNA approach to find how this transcription factor regulates the expression of microRNA genes.

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