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: (regulation), track (TxnFactorChIP), table: (weEncoderesTFbsCo7steredv3), region: (Genome), output format (all genes from selected table).</li>
		<li>Save the output from 1.1.2 as a .txt file and into a spreadsheet.</li>
		<li>Sort and filter for the transcription factor of interest (e.g., STAT2).</li>
	</ol>
	</li>
	<li>Use the list of microRNA promoters based on H3K4me3 epigenetics signature available at Baeer et al.9. Copy this list into a .txt file.</li>
	<li>To match according to coordinates on the human genome, map the data from 1.1 and 1.2 (e.g., STAT3 binding on putative microRNA promoters) and determine the median binding affinity using the code written in C sharp as outlined in supplemental coding file 1.</li>
</ol>
2. Use shRNA to Down-regulate the Expression of a Transcription Factor of Interest
<ol>
	<li>Plate 1.5 x 106 cells from 293 human embryonic kidney cell line in 10 cm plate at approximately 50% confluence (DMEM with 10% FBS).</li>
	<li>Transfect cells from 293 human embryonic kidney cell line with 5 &#181;g of green fluorescence protein (GFP) lentivirus containing shRNA directed to the transcription factor of interest and with 5 &#181;g of packaging vectors using transfection reagent for adherent cells according to manufacturer&#39;s protocol.</li>
	<li>As a control, transfect the cells from 293 human embryonic kidney cell line with scrambled shRNA and the packaging vectors according to manufacturer&#39;s protocol.</li>
	<li>Keep transfection mix on cells for 16 hr (at 37 &#176;C, CO2 incubator), then change media to 10 ml fresh 10% DMEM media with 10% FBS.</li>
	<li>Wait 48 hr post transfection, centrifuge the cell culture (300 x g, 5 min) and collect infectious supernatant. Filter the supernatant through a 0.45 &#181;m syringe filter (25-mm surfactant free cellulose acetate membrane) to remove any floating cells.</li>
	<li>Concentrate the supernatant and collect the lentivirus using an ultracentrifugal filter device with threshold of 100 kDa. Spin at 950 x g for 30 - 60 min until the volume has been concentrate to less than 250 &#181;l. Store the concentrated virus at -80 &#176;C.</li>
	<li>Transfect the cells with the lentivirus. Remove frozen lentivirus from -80 &#176;C freezer and thaw to room temperature. Transfer 100 &#181;l of viral supernatant to a fresh 1.5 ml microfuge tube.
	<ol>
		<li>Bring up the volume in the tube to 1 ml with reduced serum medium. Add hexadimethrine bromide to 1 ml virus suspension for final concentration of 10 ng/ml, mix gently and let the mixture stand for 5 min.</li>
	</ol>
	</li>
	<li>Centrifuge 5 x 106 cells for each transduction and gently resuspend the cell pellet in 0.5 ml of media containing virus. Let the cells stay in the incubator for 4 - 24 hr, then add 0.5 ml of medium with 20% FBS to a final concentration of 10% FBS.</li>
	<li>Wait 48 to 72 hr and stain the cells with propidium iodide (PI) and green florescent protein (GFP) according to the manufacturer&#39;s instructions. Protect the cells from light and use a FACS sorter to measure the rates of GFP+ / PI- cells. Since PI stains only dead cell, this rate is an estimate of transfection efficiency in living cells.</li>
	<li>Sort the positive cell population to GFP expression (GFP+) by FACS sorter as previously described 10.</li>
	<li>Use Western immune blotting as previously described 10 to determine the levels of a transcription factor of interest before and after infecting the cells of interest (for example, CLL cells) with designated shRNA.</li>
</ol>
3. Determine the Expression Level of MicroRNA Transcriptome in Cells Transfected with Transcription Factor-shRNA
<ol>
	<li>Isolate RNA using a commercial kit according to manufacturer&#39;s protocol.</li>
	<li>Label the RNA and hybridize it to microRNA microarray11.</li>
	<li>Determine the differentially expressed microRNA in cells transfected with transcription factor-shRNA or with empty vector controls5.</li>
	<li>Validate the microarray results for the most differentially expressed microRNAs using real-time PCR 5.</li>
</ol>
4. Determine the Overlap between the Bioinformatics and the shRNA Approach in Describing the Transcription Factor Dependent Transcriptome
<ol>
	<li>To determine the expected and observed ratios of microRNA genes that harbor transcription factor binding sites in their promoters and were downregulated in transcription factor-shRNA transfected cells, do the following:
	<ol>
		<li>Obtain the ratio of genes that harbor the transcription factor of interest in their promoter / total microRNA from the list generated in 1.3. This list is the expected ratio (e.g., microRNA genes with STAT3 binding sites / total microRNA genes tested = 0.25).</li>
		<li>Determine the number of genes that were downregulated in transcription factor-shRNA transfected cells from the list generated in 3.3 AND has transcription factor of interest binding sites from the list generated in 1.3. This number/Total number of downregulated genes is the observed ratio (e.g., microRNA genes with STAT3 binding sites/total number of downregulated microRNAs = 0.6).</li>
	</ol>
	</li>
	<li>Use &#967;2 statistics to compare the observed and expected ratios that were generated above and determine whether the list of genes that were downregulated in transcription factor-shRNA transfected cells are enriched with transcription factor binding sites in their promoter.</li>
</ol>

<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. A., Mahajan, S., Ritz, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=B+lymphocytes+from+patients+with+chronic+lymphocytic+leukemia+contain+signal+transducer+and+activator+of+transcription+(STAT)+1+and+STAT3+constitutively+phosphorylated+on+serine+residues.">B lymphocytes from patients with chronic lymphocytic leukemia contain signal transducer and activator of transcription (STAT) 1 and STAT3 constitutively phosphorylated on serine residues.</a> J Clin Invest. 100, 3140-3148 (1997).</li><li>Calin, G. 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=MicroRNA+profiling+reveals+distinct+signatures+in+B+cell+chronic+lymphocytic+leukemias.">MicroRNA profiling reveals distinct signatures in B cell chronic lymphocytic leukemias.</a> Proc Natl Acad Sci U S A. 101, 11755-11760 (2004).</li><li>Consortium, E. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+user's+guide+to+the+encyclopedia+of+DNA+elements+(ENCODE).">A user's guide to the encyclopedia of DNA elements (ENCODE).</a> PLoS biology. 9, e1001046 (2011).</li><li>Miranda, K. 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The authors declare no competing financial interests.

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 of some non-protein-coding genes including microRNAs 6, led us to develop a strategy that combine data from publically available resources, computational algorithms and in vitro studies to decipher the potential function of a transcription factor of interest in regulating transcription of microRNAs. The strategy suggested herein includes 2 critical steps. First we use publically available data to identify transcription factor binding in microRNA promoters. To that end we use Chip-seq data published by the ENCODE consortium to identify transcription factor binding and epigenetic signature that typifies promoter as an indirect marker of microRNA-promoters. Crossing the genetic coordinates from these datasets provides a gross estimation of how frequent a transcription factor of interest binds to microRNA-promoter. Second by using shRNA technology to silence the expression of a transcription factor and subjecting the cells to microRNA-array, it is possible to explore the functional significance of a transcription factor on the microRNA-transcriptome.
The variability in microRNA expression is only partially explained by transcription factor dependent regulation. Stoichiometric variability and other cellular or extracellular factors play an important role and are not simulated in the proposed algorithm. Other limitations include the following: The promoter analysis based on the H3K4me3 signature was done on 939 annotated microRNAs. Since then the genomic locations of many more microRNAs have been identified. However to the best of our knowledge a more comprehensive list that is based on an updated database has not been published yet. Of the 939 microRNA genes, the H3K4me3 which typifies the promoter region was identified in 781 microRNA genes (83%). Hence, while this analysis is clearly based on an incomplete dataset it captures a significant fraction of the microRNA-transcriptome.
Moreover, epigenetics signature is in part imprinted and in part cell-specific. Therefore, the generability of putative promoters that were defined by epigenetics markers may be questioned. Because H3K4me3 persists independent of transcription 18 it is generally considered a marker of imprinted promoters. The analytic algorithm we propose herein may therefore miss cell-specific microRNA promoters if these promoters were identified at a different cell-type. Finally, any conclusion should be tested and confirmed empirically. Most notably, the association between down regulation of a transcription factor (using shRNA approach) and microRNA expression (identified by microRNA array) can only suggest a direct transcriptional role that should be confirmed by acceptable assays such as chromatin immunoprecipitation (ChIP) or electrophoretic mobility shift assay (EMSA). Modifications to the method suggested herein include different ways of identifying microRNA promoter and different ways of knocking down the expression of a transcription factor, for example, small interfering RNA instead of shRNA. The transcription regulation of microRNAs may be substantially different for microRNAs that reside within protein coding genes (intragenic microRNAs) and those that do not (intergenic microRNAs). Because intragenic microRNAs are commonly transcribed in conjunction with their host genes 19 the promoter is usually found immediately upstream the transcription start site. However for many intergenic microRNAs the transcription start site is poorly annotated, and prediction tools that are commonly used for protein coding genes perform poorly 20.

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.

<table><tbody><tr><td>Lipofectamin 2000</td><td>Life Technologies</td><td>11668027</td><td></td></tr><tr><td>0.45 &amp;micro;m syringe filter</td><td>Thermo Scientific (Nalgene)</td><td>190-2545</td><td></td></tr><tr><td>Amicon ultracentrifugal filter device with threshold of 100&amp;nbsp;kDa</td><td>Merck Millipore</td><td></td><td></td></tr><tr><td>Polybrene</td><td>Merck Millipore</td><td>TR-1003-G</td><td></td></tr><tr><td>TRIzol reagent</td><td>Life Tachnologies (Invitrogen)</td><td>15596-026</td><td></td></tr><tr><td>293 Cell line human</td><td>Sigma-Aldrich</td><td>85120602</td><td></td></tr></tbody></table>

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 CLL is characterized by global deregulation of the microRNA network 14, we hypothesized that the presence of serine pSTAT3 affects the expression of microRNAs in CLL cells.
To test this hypothesis promoters of microRNAs that harbor STAT3 binding sites had to be identified. By crossing data generated by Baer et al. 9 of regions with the H2K4me3 histone modification which characterize promoter sites, with STAT3 binding sites identified by ChIP-seq experiment 15, putative STAT3 binding sites were identified. Using this approach 160 putative promoters were detected in nearly 25% of the microRNA genes examined (N=200) with binding scores ranging from 100 (the lowest score) to 1,000 (the highest score) (Table 1).
Subsequently CLL cells were transfected with STAT3-shRNA or with an empty vector and using microRNA array and identified 63 microRNAs that were down regulated following the transfection (Figure 1) suggesting that STAT3 promotes the transcription of these microRNAs. For 60% of the 63 downregulated microRNA genes (n = 38) ChIP-seq data confirmed STAT3 binding in a putative promoter upstream of the gene location, significantly more than expected by chance (p&#60;0.0001). Nine microRNAs that were down-regulated after the transfection, suggesting that STAT3 negatively regulate its levels of transcription.
<img alt="Figure 1" src="/files/ftp_upload/53300/53300fig1.jpg" /> 
Figure 1. Transfection of CLL cells with STAT3-shRNA reduces the expression levels of STAT3 protein and STAT3 mRNA. A: After transfection of CLL cells with STAT3-shRNA levels of STAT3 mRNA (left panel, detected by Quantitative RT-PCR) and levels of the STAT3 protein (right panel, detected by western immunoblotting) significantly decreased. B: microRNA array of CLL cells depicted 23 microRNAs whose expression differed significantly between CLL cells transfected with STAT3-shRNA and CLL cells transfected with an empty vector. P value of less than 0.01 was considered statistically significant. C: The mean expression quantified by RT-PCR of 7 microRNAs who had differential expression after STAT3-shRNA treatment using the microRNA array. Bars represent the standard error of the mean. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53300/53300fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Coding File 1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/53300/Supplemental_coding_file_1.docx" target="_blank">Please click here to download this file.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Micro RNA gene</td>
			<td>Chromosome</td>
			<td>Promoter start coordinates</td>
			<td>Promoter end coordinates</td>
			<td>Median (range) STAT3 binding score*</td>
		</tr>
		<tr>
			<td>miR-1205, miR-1206,miR-1207</td>
			<td>8 q24.21</td>
			<td>128961454</td>
			<td>128962791</td>
			<td>1000 (1000-1000)</td>
		</tr>
		<tr>
			<td>miR-1537</td>
			<td>1 q42.3</td>
			<td>236045425</td>
			<td>236047415</td>
			<td>1000 (1000-1000)</td>
		</tr>
		<tr>
			<td>miR-21</td>
			<td>17 q23.1</td>
			<td>57901872</td>
			<td>57921277</td>
			<td>1000 (112-1000)</td>
		</tr>
		<tr>
			<td>miR-3124</td>
			<td>1 q44</td>
			<td>249115404</td>
			<td>249123965</td>
			<td>1000 (1000-1000)</td>
		</tr>
		<tr>
			<td>miR-451</td>
			<td>17q11.2</td>
			<td>27222251</td>
			<td>27224114</td>
			<td>1000 (1000-1000)</td>
		</tr>
		<tr>
			<td>miR-92b</td>
			<td>1 q22</td>
			<td>155162340</td>
			<td>155168439</td>
			<td>1000 (1000-1000)</td>
		</tr>
		<tr>
			<td>miR-3197</td>
			<td>21 q22.2</td>
			<td>42537544</td>
			<td>42543023</td>
			<td>943 (943-943)</td>
		</tr>
		<tr>
			<td>miR-646</td>
			<td>20 q13.33</td>
			<td>58712550</td>
			<td>58715320</td>
			<td>789 (789-789)</td>
		</tr>
		<tr>
			<td>miR-629</td>
			<td>15 q23</td>
			<td>70383751</td>
			<td>70394586</td>
			<td>773 (661-885)</td>
		</tr>
		<tr>
			<td>miR-30e, miR-30c-1</td>
			<td>1 p34.2</td>
			<td>41173077</td>
			<td>41177703</td>
			<td>759 (759-759)</td>
		</tr>
		<tr>
			<td>miR-3125</td>
			<td>2 p24.3</td>
			<td>12855381</td>
			<td>12862915</td>
			<td>756 (756-756)</td>
		</tr>
		<tr>
			<td>miR-3145</td>
			<td>6 q23.3</td>
			<td>138776942</td>
			<td>138779365</td>
			<td>743 (487-1000)</td>
		</tr>
		<tr>
			<td>miR-645</td>
			<td>20 q13.13</td>
			<td>49199911</td>
			<td>49201187</td>
			<td>743 (743-743)</td>
		</tr>
		<tr>
			<td>miR-1256</td>
			<td>1 p36.12</td>
			<td>21346830</td>
			<td>21350211</td>
			<td>725 (725-725)</td>
		</tr>
		<tr>
			<td>miR-619</td>
			<td>12 q24.11</td>
			<td>109248263</td>
			<td>109253306</td>
			<td>719 (719-719)</td>
		</tr>
		<tr>
			<td>miR-181a-2, miR-181b-2</td>
			<td>9 q33.3</td>
			<td>127418928</td>
			<td>127426139</td>
			<td>710 (710-710)</td>
		</tr>
		<tr>
			<td>miR-29a, miR-29b-1</td>
			<td>7 q32.3</td>
			<td>130583383</td>
			<td>130597803</td>
			<td>697 (482-1000)</td>
		</tr>
		<tr>
			<td>miR-202</td>
			<td>10 q26.3</td>
			<td>135069499</td>
			<td>135077337</td>
			<td>696 (393-1000)</td>
		</tr>
		<tr>
			<td>miR-3142, miR-146a</td>
			<td>5 q34</td>
			<td>159890882</td>
			<td>159899475</td>
			<td>671 (671-671)</td>
		</tr>
		<tr>
			<td>miR-548c</td>
			<td>12 q14.2</td>
			<td>65000968</td>
			<td>65011503</td>
			<td>660 (660-660)</td>
		</tr>
		<tr>
			<td>miR-630</td>
			<td>15 q24.1</td>
			<td>72764289</td>
			<td>72769197</td>
			<td>627 (255-1000)</td>
		</tr>
		<tr>
			<td>miR-135b</td>
			<td>1 q32.1</td>
			<td>205416952</td>
			<td>205452990</td>
			<td>622 (245-1000)</td>
		</tr>
		<tr>
			<td>miR-29c, miR-29b-2</td>
			<td>1 q32.2</td>
			<td>207991044</td>
			<td>208002382</td>
			<td>608 (608-608)</td>
		</tr>
		<tr>
			<td>miR-1825</td>
			<td>20 q11.21</td>
			<td>30791020</td>
			<td>30798310</td>
			<td>604 (209-1000)</td>
		</tr>
		<tr>
			<td>miR-548h-1</td>
			<td>14 q23.2</td>
			<td>64578834</td>
			<td>64581657</td>
			<td>587 (174-1000)</td>
		</tr>
		<tr>
			<td>miR-612</td>
			<td>11 q13.1</td>
			<td>65183633</td>
			<td>65198528</td>
			<td>581 (157-1000)</td>
		</tr>
		<tr>
			<td>miR-148b</td>
			<td>12 q13.13</td>
			<td>54717640</td>
			<td>54721204</td>
			<td>578 (578-578)</td>
		</tr>
		<tr>
			<td>miR-3174</td>
			<td>15 q26.1</td>
			<td>90543381</td>
			<td>90549092</td>
			<td>576 (152-1000)</td>
		</tr>
		<tr>
			<td>let7a-3, let7b</td>
			<td>22</td>
			<td>46480680</td>
			<td>46481826</td>
			<td>573 (146-1000)</td>
		</tr>
		<tr>
			<td>miR-1255a</td>
			<td>4 q24</td>
			<td>102263848</td>
			<td>102272541</td>
			<td>557 (557-557)</td>
		</tr>
	</tbody>
</table>
Table 1. Putative microRNA&#39;s promoters with STAT3 binding sites. The protocol provides a method to identify transcription factor binding on putative promoters of microRNAs using data mining of published data. As an example, we present here a table depicting the binding of STAT3 to putative microRNA promoters. The binding score is given at a 0 to 1,000 scale from whole genome ChIP seq data published as part of the ENCODE project 15. The promoters are identified by the presence of the H3K4me3 epigenetic signature 9.

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 ...

describing-transcription-factor-dependent-regulation-microrna

Nanyang Technological University

Research

JoVE Journal

Bioengineering

8.3K Views.  Rabin Medical Center. 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. 

Video: Describing a Transcription Factor Dependent Regulation of the MicroRNA Transcriptome

Biotin-based Pulldown Assay to Validate mRNA Targets of Cellular miRNAs

MicroRNAs (miRNAs) are a class of small noncoding RNAs that post-transcriptionally regulate cellular gene expression. MiRNAs bind to the 3&#39; untranslated region (UTR) of target mRNA to inhibit protein translation or in some instances cause mRNA degradation. The binding of the miRNA to the 3&#39; UTR of the target mRNA is mediated by a 2&#8211;8 nucleotide seed sequence at the 5&#39; end of miRNA. While the role of miRNAs as cellular regulatory molecules is well established, identification of the target mRNAs with functional relevance remains a challenge. Bioinformatic tools have been employed to predict sequences within the 3&#39; UTR of mRNAs as potential targets for miRNA binding. These tools have also been utilized to determine the evolutionary conservation of such sequences among related species in an attempt to predict functional role. However, these computational methods often generate false positive results and are limited to predicting canonical interaction between miRNA and mRNA. Therefore, experimental procedures that measure direct binding of miRNA to its mRNA target are necessary to establish functional interaction. In this report, we describe a sensitive method for validating direct interaction between the cellular miRNA miR-125b and the 3&#39; UTR of PARP-1 mRNA. We elaborate a protocol in which synthetic biotinylated-miRNA mimics were transfected into mammalian cells and the miRNA-mRNA complex in the cellular lysate was pulled down with streptavidin-coated magnetic beads. Finally, the target mRNA in the pulled-down nucleic acid complex was quantified using a qPCR-based strategy.

This report describes a fast and reliable method for validating mRNA targets of cellular miRNAs. The method uses synthetic biotinylated Locked Nucleic Acid (LNA)-based miRNA mimics to capture target mRNA. Subsequently, streptavidin-coated magnetic beads are employed to pulldown the target mRNA for quantification by qPCR polymerase chain reaction.

MicroRNAs (miRNAs) are a class of small noncoding RNAs that post-transcriptionally regulate cellular gene expression. MiRNAs bind to the 3&#39; ...

Genetics

A Reporter Assay to Analyze Intronic microRNA Maturation in Mammalian Cells

MicroRNAs (miRNAs) are short RNA molecules that are widespread in eukaryotes. Most miRNAs are transcribed from introns, and their maturation involves different RNA-binding proteins in the nucleus. Mature miRNAs frequently mediate gene silencing, and this has become an important tool for comprehending post-transcriptional events. Besides that, it can be explored as a promising methodology for gene therapies. However, there is currently a lack of direct methods for assessing miRNA expression in mammalian cell cultures. Here, we describe an efficient and simple method that aids in determining miRNA biogenesis and maturation through confirmation of its interaction with target sequences. Also, this system allows the separation of exogenous miRNA maturation from its endogenous activity using a doxycycline-inducible promoter capable of controlling primary miRNA (pri-miRNA) transcription with high efficiency and low cost. This tool also allows modulation with RNA-binding proteins in a separate plasmid. In addition to its use with a variety of different miRNAs and their respective targets, it can be adapted to different cell lines, provided these are amenable to transfection.

We developed an intronic microRNA biogenesis reporter assay to be used in cells in vitro with four plasmids: one with intronic miRNA, one with the target, one to overexpress a regulatory protein, and one for Renilla luciferase. The miRNA was processed and could control luciferase expression by binding to the target sequence.

MicroRNAs (miRNAs) are short RNA molecules that are widespread in eukaryotes. Most miRNAs are transcribed from introns, and their maturation involves ...

Cancer Research

MicroRNA-based Regulation of Picornavirus Tropism

Cell-specific restriction of viral replication without concomitant attenuation can benefit vaccine development, gene therapy, oncolytic virotherapy, and understanding the biological properties of viruses. There are several mechanisms for regulating viral tropism, however they tend to be virus class specific and many result in virus attenuation. Additionally, many viruses, including picornaviruses, exhibit size constraints that do not allow for incorporation of large amounts of foreign genetic material required for some targeting methods. MicroRNAs are short, non-coding RNAs that regulate gene expression in eukaryotic cells by binding complementary target sequences in messenger RNAs, preventing their translation or accelerating their degradation. Different cells exhibit distinct microRNA signatures and many microRNAs serve as biomarkers. These differential expression patterns can be exploited for restricting gene expression in cells that express specific microRNAs while maintaining expression in cells that do not. In regards to regulating viral tropism, sequences complementary to specific microRNAs are incorporated into the viral genome, generally in the 3' non-coding regions, targeting them for destruction in the presence of the cognate microRNAs thus preventing viral gene expression and/or replication. MicroRNA-targeting is a technique that theoretically can be applied to all viral vectors without altering the potency of the virus in the absence of the corresponding microRNAs. Here we describe experimental methods associated with generating a microRNA-targeted picornavirus and evaluating the efficacy and specificity of that targeting in vitro. This protocol is designed for a rapidly replicating virus with a lytic replication cycle, however, modification of the time points analyzed and the specific virus titration readouts used will aid in the adaptation of this protocol to many different viruses.

We describe here a method for regulating picornavirus tropism by incorporating sequences complementary to specific microRNAs into the viral genome. This protocol can be adapted to all different classes of viruses with modifications based upon the length and nature of their life cycle.

Cell-specific restriction of viral replication without concomitant attenuation can benefit vaccine development, gene therapy, oncolytic virotherapy, and ...

Immunology and Infection

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, genome-wide cDNA library cloned into the 3'UTR downstream from the dual-selection fusion protein, thymidine kinase-zeocin (TKzeo). The first round of selection is for stable transformants, followed with introduction of a miRNA of interest, and finally, selecting for cDNAs containing the miRNA's target. Selected cDNAs are identified by sequencing (see Figure 1-3 for Target ID Library Workflow and details).
To ensure broad coverage of the human transcriptome, Target ID Library cDNAs were generated via oligo-dT priming using a pool of total RNA prepared from multiple human tissues and cell lines. Resulting cDNA range from 0.5 to 4 kb, with an average size of 1.2 kb, and were cloned into the p3&#900;TKzeo dual-selection plasmid (see Figure 4 for plasmid map). The gene targets represented in the library can be found on the Sigma-Aldrich webpage. Results from Illumina sequencing (Table 3), show that the library includes 16,922 of the 21,518 unique genes in UCSC RefGene (79%), or 14,000 genes with 10 or more reads (66%).

The Target ID Library is a plasmid-based, genome-wide collection of cloned cDNA used to identify miRNA targets. Here we demonstrate its use and application.

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, ...

Biology

Identifying Targets of Human microRNAs with the LightSwitch Luciferase Assay System using 3'UTR-reporter Constructs and a microRNA Mimic in Adherent Cells

MicroRNAs (miRNAs) are important regulators of gene expression and play a role in many biological processes. More than 700 human miRNAs have been identified so far with each having up to hundreds of unique target mRNAs. Computational tools, expression and proteomics assays, and chromatin-immunoprecipitation-based techniques provide important clues for identifying mRNAs that are direct targets of a particular miRNA. In addition, 3'UTR-reporter assays have become an important component of thorough miRNA target studies because they provide functional evidence for and quantitate the effects of specific miRNA-3'UTR interactions in a cell-based system. To enable more researchers to leverage 3'UTR-reporter assays and to support the scale-up of such assays to high-throughput levels, we have created a genome-wide collection of human 3'UTR luciferase reporters in the highly-optimized LightSwitch Luciferase Assay System. The system also includes synthetic miRNA target reporter constructs for use as positive controls, various endogenous 3'UTR reporter constructs, and a series of standardized experimental protocols.
Here we describe a method for co-transfection of individual 3'UTR-reporter constructs along with a miRNA mimic that is efficient, reproducible, and amenable to high-throughput analysis.

MicroRNAs (miRNAs) are important regulators of gene expression and have been shown to play a role in numerous biological processes. To better understand miRNA-UTR interactions, we have created a genome-wide collection of 3 UTR luciferase reporters paired with a novel luciferase gene and assay reagent, the LightSwitch system.

MicroRNAs (miRNAs) are important regulators of gene expression and play a role in many biological processes.  More than 700 human miRNAs have been ...

Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay

Luminescent Identification of Functional Elements in 3&#8217;UTRs (3&#8217;LIFE) allows the rapid identification of targets of specific miRNAs within an array of hundreds of queried 3&#8217;UTRs. Target identification is based on the dual-luciferase assay, which detects binding at the mRNA level by measuring translational output, giving a functional readout of miRNA targeting. 3&#8217;LIFE uses non-proprietary buffers and reagents, and publically available reporter libraries, making genome-wide screens feasible and cost-effective. 3&#8217;LIFE can be performed either in a standard lab setting or scaled up using liquid handling robots and other high-throughput instrumentation. We illustrate the approach using a dataset of human 3&#8217;UTRs cloned in 96-well plates, and two test miRNAs, let-7c and miR-10b. We demonstrate how to perform DNA preparation, transfection, cell culture and luciferase assays in 96-well format, and provide tools for data analysis. In conclusion 3&#39;LIFE is highly reproducible, rapid, systematic, and identifies high confidence targets.

Luminescent identification of functional elements in 3&#8217; untranslated regions (3&#8217;UTRs) (3&#8217;LIFE) is a technique to identify functional regulation in 3&#8217;UTRs by miRNAs or other regulatory factors. This protocol utilizes high-throughput methodology such as 96-well transfection and luciferase assays to screen hundreds of putative interactions for functional repression.

Luminescent Identification of Functional Elements in 3&#8217;UTRs (3&#8217;LIFE) allows the rapid identification of targets of specific miRNAs within an ...

The Ago2-miRNA-co-IP Assay to Study TGF- &#946;1  Mediated Recruitment of miRNA to the RISC in CFBE Cells

Micro(mi)RNAs are short, non-coding RNAs that mediate the RNA interference (RNAi) by post-transcriptional mechanisms. Specific miRNAs are recruited to the cytoplasmic RNA induced silencing complex (RISC). Argonaute2 (Ago2), an essential component of RISC, facilitates binding of miRNA to the target-site on mRNA, followed by cleaving the miRNA-mRNA duplex with its endonuclease activity. RNAi is mediated by a specific pool of miRNAs recruited to RISC, and thus is referred to as the functional pool. The cellular levels of many miRNAs are affected by the cytokine Transforming Growth Factor-&#946;1 (TGF-&#946;1). However, little is known about whether the TGF-&#946;1 affects the functional pools of these miRNAs. The Ago2-miRNA-co-IP assay, discussed in this manuscript, is designed to examine effects of TGF-&#946;1 on the recruitment of miRNAs to RISC and it helps to determine whether changes in the cellular miRNA levels correlate with changes in the RISC-associated, functional pools. The general principles of the assay are as follows. Cultured cells treated with TGF-&#946;1 or vehicle control are lysed and the endogenous Ago2 is immunoprecipitated with immobilized anti-Ago2 antibody, and the active miRNAs complexed with Ago2 are isolated with a RISC immunoprecipitation (RIP) assay kit. The miRNAs are identified with quantitative reverse-transcriptase polymerase chain reaction (qRT-PCR) using miRNA-specific stem-looped primers during reverse transcription, followed by PCR using miRNA-specific forward and reverse primers, and TaqMan hydrolysis probes.

Here, we describe the Ago2-miRNA-co-IP assay designed to quantify an active pool of specific miRNAs induced by TGF-&#946;1 in human bronchial epithelial CFBE41o- cells. This assay provides functional information on the recruitment of miRNA to the RNA induced silencing complex, quantified by qRT-PCR using specific miRNA primers and TaqMan hydrolysis probes.

Micro(mi)RNAs are short, non-coding RNAs that mediate the RNA interference (RNAi) by post-transcriptional mechanisms. Specific miRNAs are recruited to the ...

CRISPR Gene Editing Tool for MicroRNA Cluster Network Analysis

MicroRNAs (miRNAs) have emerged as important cellular regulators (tumor suppressors, pro-oncogenic factors) of cancer and metastasis. Most published studies focus on a single miRNA when characterizing the role of small RNAs in cancer. However, ~30%&#160;of human miRNA genes are organized in clustered units that are often co-expressed, indicating a complex and coordinated system of noncoding RNA regulation. A clearer understating of how clustered miRNA networks function cooperatively to regulate tumor growth, cancer aggressiveness, and drug resistance is required before translating noncoding small RNAs to the clinic.
The use of a high-throughput clustered regularly interspaced short palindromic repeats (CRISPR)-mediated gene editing procedure has been employed to study the oncogenic role of a genomic cluster of seven miRNA genes located within a locus spanning ~35,000 bp in length in the context of prostate cancer. For this approach, human cancer cell lines were infected with a lentivirus vector for doxycycline (DOX)-inducible Cas9 nuclease grown in DOX-containing medium for 48 h. The cells were subsequently co-transfected with synthetic trans-activating CRISPR RNA (tracrRNA) complexed with genomic site-specific CRISPR RNA (crRNA) oligonucleotides to allow the rapid generation of cancer cell lines carrying the entire miRNA cluster deletion and individual or combination miRNA gene cluster deletions within a single experiment.
The advantages of this high-throughput gene editing system are the ability to avoid time-consuming DNA vector subcloning, the flexibility in transfecting cells with unique guide RNA combinations in a 24-well format, and the lower-cost PCR genotyping using crude cell lysates. Studies using this streamlined approach promise to uncover functional redundancies and synergistic/antagonistic interactions between miRNA cluster members, which will aid in characterizing the complex small noncoding RNA networks involved in human disease and better inform future therapeutic design.

This protocol describes a high-throughput clustered regularly interspaced short palindromic repeats (CRISPR) gene editing workflow for microRNA cluster network analysis that allows the rapid generation of a panel of genetically modified cell lines carrying unique miRNA cluster member deletion combinations as large as 35 kb within a single experiment.

MicroRNAs (miRNAs) have emerged as important cellular regulators (tumor suppressors, pro-oncogenic factors) of cancer and metastasis. Most published ...

Regulation of Expression Occurs at Multiple Steps

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the binding of proteins to short regulatory sequences on the DNA. This association can either promote or inhibit the transcription of a gene associated with the respective sequence.
Transcription results in the generation of precursor (pre-mRNA) that consists of both exons and introns, which needs further processing before being translated to a protein. This happens through mRNA splicing, which involves the removal of non-coding regions and merging of the coding ones. mRNA processing can also be used as a regulatory mechanism through variation in splicing patterns such as skipping of certain exons, alternative splicing, and inclusion of introns.&#160;
The addition of a poly-A tail at the 3&#8217; end and a 5&#8217; cap to produce the mature mRNA are also regulatory points during RNA processing. Regulation occurs through variation in the polyadenylation signal, which determines where the poly-A tail will be added onto the mRNA.&#160; In some cases, more than one poly-A signal is present at the 3&#8217; end which will change the length of the 3&#8217; untranslated region, but the final protein product will be the same. However, the stability and the translation potential the variants mRNA may differ, which can alter the amount of protein produced. In other cases, an additional poly-A signal is present on the intron or exon within the gene sequence which may lead to variation in splicing sites for polyadenylation and result in different proteins from the same strand of pre-mRNA.The addition of the 5&#8217; cap, which is made up of methylated guanosine, is regulated by two mechanisms.&#160; One involves the regulation of the methyltransferases that add the methyl group to the guanosine, and the other is through regulation of the cellular signaling pathways that lead to methylation.
Next, the mature mRNA needs to be transported from the nucleus to the cytoplasm through nuclear pore complexes (NPCs) to be translated. This is regulated by the mRNA to forming a complex, known as the ribonucleoparticle, with RNA binding proteins. NPCs only allow mRNAs that are in the complex to pass into the cytoplasm. Once an mRNA enters the cytoplasm for translation, it can either be targetted individually or as a part of a group through specific regulations, or it can undergo a common regulation with all other mRNAs in the cytoplasm. In specific regulation, particular trans-acting elements, like proteins and different types of RNAs, regulate transcription. In general regulation, the proteins involved in the translation machinery are activated or inhibited, which in turn affects the translation of all transcripts. The most common translational regulatory mechanism is the modification of the translation initiation factor.
Gene expression can also be regulated through post-translational modifications, where an enzyme-catalyzed reversible modification can alter the function of a protein. A common post-translational modification is phosphorylation, which is carried out by enzymes known as kinases. Dephosphorylation of proteins, on the other hand, is carried out by proteins known as phosphatases. Phosphorylation of a protein can result in its activation or deactivation and alter its function.

Regulation of Expression during Transcription and Translation

Gene expression can be regulated at almost every step from gene to protein. Transcription is the step that is most commonly regulated. This involves the ...

Education

JoVE Core

Molecular Biology

Unit 1

Gene Expression

KEY TERMS AND CONCEPTS

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors&#8212;these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5&#39; capping, splicing, and the addition of a 3&#39; poly-A tail, forming the mature mRNA. Only those mature mRNAs that associate with RNA-binding proteins (RBPs) forming ribonucleoprotein particles are prevented from degradation and are selectively transported out of the nucleus for protein synthesis. mRNAs that do not associate with RBPs are not transported out of the nucleus and hence not translated into proteins. Alternatively, if a mature mRNA binds a complementary microRNA, it undergoes degradation, thereby inhibiting protein synthesis.
However, once the mRNA is translated, the newly synthesized protein undergoes modification that affects its activity. For example, adding functional groups, such as methyl groups, phosphate, or acetyl groups, may activate or inactivate the protein. In contrast, adding multiple ubiquitin proteins to a substrate protein marks them for degradation. Thus, ubiquitination regulates protein stability and its functional activity.

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. ...