We thank Stephen Blazie, Karen Anderson, Josh LaBaer for advice and discussion. Karen Anderson, John Chaput, and Josh LaBaer for sharing reagents and instrumentation, Michael Gaskin and Andrea Throop for technical advise and protocols. Justin Wolter is a Maher scholar and thanks the Maher family for their generous support.

1. Cell Culture (24-48 hr&#160;prior to transfection)
<ol>
	<li>24-48 hr prior to transfection seed a sufficient quantity of HEK293T cells based on the number of 96-well plates being transfected. 
	NOTE: For consistent transfections, plate cells at a sufficient density to favor rapid division, yet not be at more than 70-90% confluency at the time of transfection.</li>
	<li>Each 96-well plate requires 9 x 106 cells (75,000 cells per well, and 120 wells per plate to account for use of reservoir and multichannel pipette). Calculate the doubling time of HEK293 cells (typically ~20 hr), and seed the appropriate number of cells to obtain at least 9 x 106 cells at the time of transfection. A 145 mm circular culture plate is typically sufficient for 3 96-well transfections when grown to ~90% confluency, with ~10% of cells remaining to reseed a new plate.</li>
</ol>
2. Preparation Prior to Transfection
NOTE: The preparation of the buffers and plasmid DNA in step 2.0-2.2 should be performed in the days prior to transfection since the preparation of these reagents may be time consuming.
<ol>
	<li>Human 3&#39;UTR clones that are compatible with the 3&#39;LIFE assay are available through a public plasmid repository 30. Purify DNA plasmid manually or with liquid handling robots. Use a transfection grade alkaline lysis mini-prep 96-well kit and follow the manufacturer&#8217;s instructions. Resuspend the purified vectors to ~100 ng/&#956;l per well. 
	NOTE: Insufficient luciferase signal will result if plasmid concentration falls below 40 ng/&#956;l.</li>
	<li>Obtain the pLIFE-miRNA vectors 30 or clone using the pipeline in Figure 1B 24. Resuspend the vectors at a concentration of 500 ng/&#956;l for each miRNA and Blank control plasmids.</li>
	<li>Owing to the sensitivity of the nucleofection buffer conditions, ensure that the total volume of transfected materials (including cells and plasmids) does not exceed 10% of the total liquid in each well of the 96-well transfection plate. To achieve this, concentrate the pLIFE-miRNA plasmid stock at a concentration of least 500 ng/&#956;l.</li>
	<li>Prepare 10x firefly luciferase buffer reagents (Table 1), and the 1x Renilla luciferase buffer reagents (Table 2), which can be stored for up to 6 months. 
	NOTE: The DTT in the firefly luciferase buffer must to be stored in solution at -20&#160;oC in single use aliquots.</li>
</ol>
3. Prepare Following Items Immediately Prior to Transfection
<ol>
	<li>Prepare transfection buffer containing PBS, 1.5% HEPES, pH 7.0. Prepare this fresh, although it may be stored for up to 1 month at 4 &#176;C without noticeable decreases in transfection efficiency. In formulating buffer and plasmid DNA volumes, assume 120 reactions for each 96-well plate to sufficiently account for errors in pipetting and volume lost using liquid reservoirs and multichannel pipettes. Aliquot 18 &#956;l per well transfection buffer (120 wells/96-well plate = 2.16 ml per plate), and set aside. 
	NOTE: The cell-electroporation device is extremely sensitive to the buffer conditions used to transfect cells. Accuracy when preparing buffers will ensure consistent performance of the equipment. Extra care must be taken when performing the assay to prevent evaporation of buffers, specifically by minimizing the time that buffer is left uncovered in microcentrifuge tubes, 96-well plates, reservoirs, and electrode plates.</li>
	<li>Reserve four wells for the following controls, no pLIFE-3&#8217;UTR (to measure background of luciferase assay), pLIFE-SV40 3&#8217;UTR (negative target control), positive control for miRNA #1, positive control for miRNA #2. 
	NOTE: These vectors are publically available 30. Alternatively, any previously validated target can be used as a positive control.</li>
	<li>Warm media, trypsin (0.25%) to 37 &#176;C.</li>
	<li>To each well of 96-well cell culture plate, add 200 &#956;l of DMEM supplemented with 10% FBS, 1% Pen/Strep, and placed in a 37 &#176;C incubator for use following transfection.</li>
	<li>Turn on all cell electroporation devices followed by the supporting software. Use Pulse code FF120 for HEK293T cells and PBS/HEPES buffer.</li>
</ol>
4. Preparation of Plasmid DNA and Cell Mixture
NOTE: The following protocol assumes transfecting three 96-well plates in one experiment for a screen with two miRNAs (miRNA-#1 and miRNA-#2). Each plate will correspond to the same 96-well plate of pLIFE-3&#8217;UTR plasmids, and be treated three times with pLIFE-miRNA-blank, pLIFE-miRNA-#1, or pLIFE-miRNA-#2.
<ol>
	<li>Prepare 3 stocks of pLIFE-miRNA + transfection buffer for each miRNA. This stock should account for 50% (10 &#956;l) of total volume of each well, multiplied by 120 wells. Thus, each stock should contain 1.08 ml buffer + 120 &#956;l plasmid DNA (pLIFE-miRNA).</li>
	<li>Remove cells from 145 mm culture plate by eluting media, washing gently with PBS, and treating with ~5 ml 0.25% trypsin for 5 min at 37 &#176;C. Neutralize trypsin with an equal volume of media, and pellet cells at 300 x g for 5 min.</li>
	<li>Remove trypsin/media, and resuspend pellet in ~5-10 ml media (depending on cell density and accurate range of cell counter).</li>
	<li>Count cells using a cell counter. Ensure that cells are &#62;95% viable and within accurate range of machine. 
	NOTE: An inaccurate cell count can result from extremely high cell concentrations (&#62;6.0x&#160; 106/ml). Transfecting too many cells can drastically reduce efficiency of miRNA targeting by reducing plasmid:cell ratio and/or decreasing transfection efficiency.</li>
	<li>Aliquot three tubes each containing 9 x 106 cells, corresponding to the cells required for transfection of one 96-well plate. Spin cells at 300 x g for 3 min.</li>
	<li>Remove media. Be sure to remove as much media as possible with minimal disturbance of the pellet as excess media can impact transfection efficiency.</li>
	<li>Resuspend cells in 1.2 ml transfection buffer/miRNA plasmid mixture, and set aside.</li>
	<li>The following steps detail resuspension of pLIFE-3&#8217;UTR plasmid in transfection buffer. As this occurs in 96 well plates, take care to avoid evaporation of buffer by covering plates at all times.
	<ol>
		<li>Using a multichannel pipette, move 32.4 &#956;l transfection buffer into each well of a 96 well PCR plate (9 &#956;l [per transfection] * 3 [plates] * 1.2 [to account for pipette error]).</li>
		<li>Add 3.6 &#956;l (~100 ng/&#956;l) of mini-prepped pLIFE-3&#8217;UTR plasmid to each well and mix thoroughly.</li>
		<li>Pipette 10 &#956;l of this mixture into each well of the 96-well transfection plate and cover.</li>
	</ol>
	</li>
</ol>
5. Transfection
<ol>
	<li>Move 1.2 ml of the first cell/buffer/pLIFE-miRNA plasmid mixture into reservoir. Mix well.</li>
	<li>Add 10 &#956;l of this mixture into the first 96-well transfection plate already containing 10 &#956;l transfection buffer/pLIFE-3&#8217;UTR. Mix well by pipetting up and down several times. 
	NOTE: Equal suspension of cells in the buffer will ensure even and thorough passage of the electrical current through the cuvette and maximize transfection efficiency.</li>
	<li>Place 96-well transfection plate on the cell electroporation device and initiate transfection.</li>
	<li>Once transfection is complete, add 100 &#956;l of pre-warmed media from 96-well culture plate to each well of the 96-well transfection plate and mix well. Move 100 &#956;l from each well into the 96-well culture plate.</li>
	<li>Mix cells in culture plate with pipette positioned vertically in the center of the well, as cells will tend to aggregate on the sides of the well unless mixed properly.</li>
	<li>Repeat 5.1-5.5 for the remaining two plates.</li>
	<li>Cleaning 96-well transfection plate
	<ol>
		<li>The 96-well transfection plates can be recycled by washing with 70% EtOH to ensure no carry over of nucleic acids between experiments. Perform two 70% EtOH washes using a spray bottle to completely fill each well, followed by wiping down excess EtOH on the electrode strips (bottom side) and allowing the transfection plates to completely dry in the culture hood. 
		NOTE: We have tested for carry-over DNA contamination by transfecting 12 wells with 2 &#956;g pmaxGFP plasmid each into HEK293T cells, followed by a single wash with 70% EtOH, and a second transfection with no plasmid DNA. With this extremely high plasmid concentration, extremely bright reporter, and a single wash, there was no observable fluorescence in any of the 12 replicate transfections.</li>
	</ol>
	</li>
	<li>Culture cells for 48-72 hr at 37 &#176;C, followed by the dual luciferase assay.</li>
</ol>
6. Cell Lysate Preparation for Luciferase Assay
<ol>
	<li>Dilute lysis buffer with 4 parts water, 1 part 5x passive lysis buffer in a reservoir. Calculate 26 &#956;l/well, adding ~20 extra volumes to account for loss in the reservoir. 
	NOTE: Buffer is stored at -20 &#186;C and can be extremely viscous, thus prior to allowing the 5x buffer to approach room temperature will improve pipetting accuracy.</li>
	<li>Analyze each well for transfection efficiency using fluorescence microscopy. Note any inconsistencies in wells that did not transfect efficiently (&#62;90% transfection efficiency), or are expressing low levels of RFP indicative of overcrowding or media exhaustion. Remove these wells from the analysis.</li>
	<li>Completely remove the media from the cells, being careful not to elute too quickly which will cause cells to detach. 
	NOTE: Remaining media will dilute the lysate and cause fluctuations in values across experiments.</li>
	<li>Add 26 &#956;l of lysis buffer to each well, and place on a plate shaker/rocker at low/moderate speed for ~20-30 min. Use this time to prepare luciferase buffers, wash and prime the luminometer(s), and transfer lysate to opaque measurement plates.</li>
</ol>
7. Dual Luciferase Assay
NOTE: If multiple plates are being measured sequentially on one luminometer, create buffer master mixes with everything except ATP and substrates, adding these reagents followed by pH adjustment immediately before use with each plate. ATP and substrates may degrade over time; consistency in the amount of time these reagents are in the buffer will improve consistency across multiple plates.
<ol>
	<li>Prepare 1x luciferase buffers (Table 1):
	<ol>
		<li>Wrap two tubes (typically 15/50 ml centrifuge tubes) containing the firefly and Renilla buffers with aluminum foil, as substrates may be light sensitive.</li>
		<li>Prepare 1x Firefly luciferase buffer. Add 1 ml of each the five 10x firefly luciferase reagents, adding EGTA last, to 5 ml H20 to a final 1x concentration.
		<ol>
			<li>Add 0.025 g ATP to 10 ml 1x firefly buffer. Mix by inverting several times. Keep ATP on ice at all times. ATP will degrade, so if measuring more than one plate sequentially, buffer must be made fresh beginning at this step for each additional plate.</li>
			<li>Add 100 &#956;l of 100 beetle luciferin (substrate) (Table 1) Buffer should change to yellowish color based on pH.</li>
		</ol>
		</li>
		<li>1x Renilla luciferase buffer reconstitution: Per 96-well plate, aliquot 10 ml of 1 &#8220;Renilla buffer&#8221;.
		<ol>
			<li>Add 100 &#956;l of BSA (44 mg/ml stock).</li>
			<li>If screening more than one plate, separate master mix into 10 ml &#160;aliquots.</li>
			<li>Add 100 &#956;l of coelenterazine to buffer (previously aliquoted and stored at 100x conc.)</li>
		</ol>
		</li>
		<li>Adjust pH of 1x firefly buffer to 8.0, followed by the 1x Renilla buffer to 5.0 using NaOH and HCl. 
		NOTE: The activity of each buffer, and the ability of the Renilla buffer to quench the Firefly luciferase activity is highly dependent on pH. For consistent results be extremely accurate in this step.</li>
		<li>Bring volume of each buffer (corresponding to 1 96-well plate) to 10.5 ml to accommodate for luminometer priming.</li>
	</ol>
	</li>
	<li>Transfer lysate to opaque white plates: At this point the cells should be in lysis buffer for ~20 min. Take 25 &#956;l from each well using a multichannel pipette, be sure to pipet up and down thoroughly to break up the clumps of cells and homogenize the lysate.</li>
	<li>Prepare the luminometer. Turn on the luminometer and select the protocol in the DLR folder, called &#8220;DLR with two injections&#8221;. Other formats are not compatible with the data analysis pipeline (below).
	<ol>
		<li>Select the wells to be tested (all wells is the default).</li>
		<li>Extend the &#39;Delay before measurement&#39; setting to 5 seconds, with a 10 sec measurement time (see 29 for explanation).</li>
		<li>Capillary wash steps: water 3x, EtOH 3x, water 3x, dry 3x. Prime buffers once into the waste, and then prime a second time back into the buffer tubes to ensure mixing. Inject the firefly buffer first and prime it in the left capillary, followed by Renilla in the right capillary.</li>
	</ol>
	</li>
	<li>Initiate the luciferase assay. Each plate should take ~48 min to read. After completion save the file first, and then repeat the wash steps and shut off the luminometer. 
	NOTE: Multiple plates can be read and data stored on the same excel file, however issues may be encountered with multiple plate reads where the luminometer program will crash. Be sure to save all data between measurements and take screenshots if program crashes before save is possible.
	<ol>
		<li>Replace old buffers with new, being sure to prime at least twice with new buffers before starting the new plate.</li>
	</ol>
	</li>
</ol>
8. Data Analysis
<ol>
	<li>Utilize excel tables &#8220;3&#8217;LIFE - single plate analysis&#8221; and &#8220;3&#8217;LIFE &#8211; multiplate analysis&#8221; available from <a href="http://www.mangonelab.com">www.mangonelab.com</a>. Copy raw data for firefly and Renilla luciferase measurements from luminometer output file into locations corresponding to the negative condition, miRNA #1, and miRNA #2 into the &#8220;3&#8217;LIFE &#8211; single plate analysis&#8221; spreadsheet.</li>
	<li>The spreadsheet will automatically calculate firefly/Renilla ratio and normalize each miRNA to the appropriate negative control, and normalize repression values across each plate. 
	NOTE: This spreadsheet will automatically identify wells with low luciferase signal, highlight significantly repressed wells, and provide measures of repression across the entire plate. See 24 for detailed explanation of statistical analysis.
	<ol>
		<li>Copy the values from the &#8220;Normalized RI Index&#8221; box, into corresponding cells in the &#8220;3&#8217;LIFE &#8211; multiplate analysis&#8221; spreadsheet at the position corresponding to replicate #1 for each miRNA tested. Repeat for all biological replicates performed on different days.</li>
	</ol>
	</li>
	<li>If desired, insert the gene names and target prediction status in columns B and D, respectively.
	<ol>
		<li>Allow the spreadsheet to automatically calculate the average of all plate. Observe the data in 96 well format as a heat map (Figure 2). The file also arranges the data in list format (Figure 2). 
		NOTE: The repression index is the measure used to identify putative miRNA targets. Different stringency parameters can be used, based on individual preferences. On average we consider likely hits below repression index of 0.8 and statistically significant by t test (p-value &#60;0.05). As shown in Figure 3 potential targets based on these criteria will be highlighted in red.</li>
	</ol>
	</li>
</ol>

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Y., 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=DNASU+plasmid+and+PSI:Biology-Materials+repositories:+resources+to+accelerate+biological+research.">DNASU plasmid and PSI:Biology-Materials repositories: resources to accelerate biological research.</a> Nucleic Acids Res. , (2013).</li><li>Baek, D., 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=The+impact+of+microRNAs+on+protein+output.">The impact of microRNAs on protein output.</a> Nature. 455 (7209), 64-71 (2008).</li><li>Ebert, M. S., Sharp, P. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Roles+for+MicroRNAs+in+Conferring+Robustness+to+Biological+Processes.">Roles for MicroRNAs in Conferring Robustness to Biological Processes.</a> Cell. 149 (3), 515-524 (2012).</li><li>Pelaez, N., Carthew, R. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Biological+robustness+and+the+role+of+microRNAs:+a+network+perspective.">Biological robustness and the role of microRNAs: a network perspective.</a> Current Topics in Developmental Biology. 99, 237-255 (2012).</li></ol>

This work is supported by funds from the College of Liberal Arts and Science and the Biodesign Institute at Arizona State University and NIH Exploratory/Developmental Research Grant 1R21CA179144-01A1.

The 3&#8217;LIFE assay identifies functional miRNA targets in 3&#8217;UTRs in high-throughput. This assay is useful for researchers who wish to experimentally identify a large number of putative targets for their miRNA of interest. The 3&#8217;LIFE assay is a powerful approach to query for 3&#8217;UTR driven regulation, in that the assay provides a functional measure of miRNA targeting, and the binary testing of a single reporter::3&#8217;UTR against a single miRNA can confidently address the targeting status of individual genes. To validate this approach, we screened a panel of 275 3&#8217;UTRs and against two miRNAs, let-7c and miR-10b, and included 10 previously validated target genes in this library. Eight of these ten genes exhibited repression 24. We also observed a significant enrichment of unvalidated bioinformatically predicted targets, and unpredicted 3&#8217;UTRs that contain canonical seed elements among our top hits, suggesting that 3&#8217;LIFE is capable of identifying bona fide miRNA targets.
A key indicator of the sensitivity of high-throughput screens is the false positive and false negative rates. While the false positive rate of this assay needs to be evaluated using additional alternative approaches to validate hits, eight of the ten positive controls included in our proof-of-principle screen exhibited repression, suggesting a false negative rate of 20%. However, many techniques are used to identify miRNA targets in different cellular contexts, and 3&#8217;UTR processing and regulation by trans-acting factors is known to be highly tissue specific. For example, the majority of 3&#8217;UTRs contain multiple polyadenylation sites, which control the length of the 3&#8217;UTR in the mature mRNA. In many cases the use of proximal polyadenylation sites is tissue specific, and may exclude miRNA target sites. Additionally, cooperative miRNA targeting, competition with RNA-binding proteins, and mRNA secondary structure may all impact the ability of 3&#8217;LIFE to detect miRNA targets in specific tissues. Because of this, the detection of targets by the 3&#8217;LIFE assay may vary based on the cellular context in which the assay is performed, complicating the evaluation of absolute error rates. This protocol is optimized for HEK293T cells, but alternative cell lines can be used if the researcher wishes to perform the assay in a specific biological context. However, the optimization of transfection efficiency and cell survival with each cell line will have to be optimized using multiple buffer conditions, pulse codes, and number of cells. An example of an optimization scheme can be found at Wolter et al. 24.
This protocol has been optimized in 96-well format and specifies the use of certain high-throughput instrumentation. In the case that the institution does not possess the equipment required for 96-well Nucleofection, alternative transfection reagents could be used to perform the 3&#8217;LIFE assay, as long as the transfection efficiency remains high. Additionally, the luciferase assay is the most time consuming aspect of the 3&#8217;LIFE assay. As such, the use of multiple luminometers is recommended for high-throughput screens.

The overall goal of this method is to detect and precisely map microRNA (miRNA) targets in high-throughput. MiRNAs are endogenous non-coding RNAs ~22 nucleotides in length. Following transcription and processing, mature miRNAs are incorporated in a protein complex called the RNA induced silencing complex (RISC). Each miRNA guides the RISC to target elements located primarily in the 3&#8217;untranslated regions (3&#8217;UTRs) of messenger RNAs (mRNAs), resulting in either translation repression or mRNA cleavage 1. MiRNA recognize target sites based on standard Watson-Crick and G:U wobble base pairing, and are degenerate in nature, containing multiple mismatched base pairs and bulged regions. Many miRNAs are broadly conserved from plants to humans 2,3, where they play a diverse range of biological roles. In metazoans miRNAs can influence multiple biological processes including cell fate decisions 4, developmental timing 5, and frequently exhibit tissue specific expression patterns 6,7. MiRNA misexpression can also result in aberrant gene regulation, which can have substantial influence on cell behaviour based solely on the function of target genes. As such, miRNAs are linked to a wide range of diseases, including neurodegeneration 8,9, diabetes 10 and cancer 11. Bioinformatic and wet-bench approaches suggest that each miRNA may be capable of targeting hundreds to thousands of distinct mRNAs 12-14, indicating that high-throughput or genome wide approaches are required to probe this large pool of potential interactions.
Identifying target genes is a critical component of mechanistically defining miRNA function, and to do so researchers must be able to reveal targets on a large scale. Several approaches have been developed to identify miRNA targets, including bioinformatic prediction algorithms, high-throughput sequencing of targeted mRNAs, and reporter based assays. Each of these approaches has inherent strengths and weaknesses. Given that miRNA targeting is guided by sequence specificity, most notably of nucleotides 2-6 of the miRNA (termed the seed region), several algorithms have been developed to predict miRNA targets throughout the genome of many organisms. These algorithms are trained using the observed base-pairing motifs of validated miRNA targets, and frequently utilize parameters such as stringent seed pairing, site conservation, and/or thermodynamic stability 15. While these filters refine the large number of putative targets with sufficient complementarity to only high confidence targets, they may exclude species specific and non-canonical miRNA target sites, which recent evidence suggests are widespread 16-24. Furthermore, these predictions do not take into account mechanisms of mRNA processing that exclude miRNA target sites, such as alternative polyadenylation 25, RNA editing 26, RNA methylation 27, and cooperative binding. As such, high false positive and false negative rates have been reported for many algorithms 22,24,28. While these algorithms are useful to identify candidate miRNA targets for subsequent experimental validation, these high error rates limit the efficacy of bioinformatic approaches for systematic miRNA target detection.
To systematically probe for interactions between a given miRNA and potentially targeted 3&#8217;UTRs we have developed a high-throughput assay called Luminescent Identification of Functional Elements in 3&#8217;UTRs (3&#8217;LIFE) 24. This assay measures direct interactions and translational repression of the test 3&#8217;UTR by a query miRNA using a dual luciferase reporter system. In this system, the 3&#8217;UTR of a gene of interest is cloned downstream of the firefly luciferase (fluc) reporter reading frame. The reporter construct is cotransfected with a query miRNA in HEK293T cells. MiRNA targeting is determined by measuring the relative change between the test fluc::3&#8217;UTR reporter and a second non-specific Renilla luciferase reporter. Importantly, luciferase assays detect functional miRNA/mRNA interactions that influence the translational output of the reporter. This is a key advantage over traditional methods to detect miRNA regulation, such as RT-qPCR and Western blots, in that this bypasses differences in mRNA degradation and translational repression, as well as changes in protein abundance independent of 3&#8217;UTR based regulation.
Luciferase assays are widely utilized to validate direct miRNA targets because of their relative simplicity and sensitivity, yet their use in high-throughput screens is limited by high costs associated with consumable reagents, the lack of 3&#8217;UTR libraries from public sources, and the absence of standardized luciferase protocols, leading to difficulties in comparing functional repression across multiple datasets. To facilitate the use of the 3&#8217;LIFE assay, we have placed emphasis on simplification of experimental design, utilization of non-commercial transfection 24 and luciferase reagents 29, creating a 3&#8217;UTR library which is regularly updated and expanded, and is available through a public plasmid repository 30.
The scalability of the 3&#8217;LIFE assay allows screening of a large 3&#8217;UTR library for targeting by a given miRNA without biasing the screen towards bioinformatically identified genes. In addition to testing canonical and predicted interactions, this systematic approach allows the identification of novel targets driven via non-canonical and/or species-specific interactions. Importantly, the effect of miRNA targeting on protein production is generally understood to result in modest translational repression 15,31, suggesting that a primary role of miRNA regulation is to fine-tune protein output, protect against aberrant levels of gene expression, and provide robustness to cell specific programs 32,33. The sensitivity of the luciferase assay combined with the inherently large number of negative miRNA/mRNA interactions in the 3&#8217;LIFE screen allows the detection of subtle effects of miRNA targeting on a large number of genes, and the identification of multiple components of gene networks that are regulated by a given miRNA 24.
Here we describe the 3&#39;LIFE protocol, and demonstrate it&#8217;s feasibility by screening two well characterized miRNAs, miR-10b and let-7c against a panel of 275 human 3&#39;UTRs (Figure 1).

<table><tbody><tr><td>Reagents</td><td></td><td></td><td></td></tr><tr><td>glycylglycine</td><td>Sigma</td><td>G1127-25G</td><td></td></tr><tr><td>Kx PO4</td><td>Sigma</td><td>P2222</td><td></td></tr><tr><td>EGTA</td><td>Sigma</td><td>E3889</td><td></td></tr><tr><td>ATP</td><td>Sigma</td><td>FLAAS</td><td></td></tr><tr><td>DTT</td><td>Sigma</td><td>D0632</td><td></td></tr><tr><td>MgSO4</td><td>Sigma</td><td>M7506</td><td></td></tr><tr><td>CoA</td><td>Sigma</td><td>C4282</td><td></td></tr><tr><td>luciferin</td><td>Sigma</td><td>L9504</td><td></td></tr><tr><td>NaCl</td><td>Sigma</td><td>S7653</td><td></td></tr><tr><td>Na2EDTA</td><td>Sigma</td><td>E0399</td><td></td></tr><tr><td>K H2 P O4</td><td>Sigma</td><td>1551139</td><td></td></tr><tr><td>BSA</td><td>Sigma</td><td>A2153</td><td></td></tr><tr><td>NaN3</td><td>Sigma</td><td>S2002</td><td></td></tr><tr><td>Coelenterazine</td><td>Sigma</td><td>C3230</td><td></td></tr><tr><td>PBS/HEPES</td><td>Corning</td><td>21-040-CV</td><td></td></tr><tr><td>DMEM</td><td>Sigma</td><td>D5546</td><td></td></tr><tr><td>FBS</td><td>Sigma</td><td>F2442</td><td></td></tr><tr><td>Pennicilin/Streptomicin</td><td>Sigma</td><td>P4333</td><td></td></tr><tr><td>Trypsin</td><td></td><td>T2600000</td><td></td></tr><tr><td>Consumables</td><td></td><td></td><td></td></tr><tr><td>MaxiPrep Kit</td><td>Promega</td><td>A2392</td><td></td></tr><tr><td>96-well miniprep plate</td><td>Pall</td><td>8032</td><td></td></tr><tr><td>96-well shuttle plates</td><td>Lonza</td><td>V4SP-2096</td><td></td></tr><tr><td>5x Lysis Buffer</td><td>Promega</td><td>E1941&amp;nbsp;</td><td></td></tr><tr><td>Instruments</td><td></td><td></td><td></td></tr><tr><td>96-well GloMax Plate Reader</td><td>Promega</td><td>E9032</td><td></td></tr><tr><td>Biomech FX Liquid Handler Robot</td><td>Beckmann</td><td>A31842</td><td></td></tr><tr><td>4D-Nucleofector Core Unit</td><td>Lonza</td><td>AAF-1001</td><td></td></tr><tr><td>96-well Shuttle System</td><td>Lonza</td><td>AAM-1001</td><td></td></tr><tr><td>Cell Counter Countess</td><td>Invitrogen</td><td>C10227</td><td></td></tr></tbody></table>

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.

The luminometer output file contains raw measurements for both firefly and Renilla luciferase proteins. This raw format is compatible with the &#8220;3&#8217;LIFE &#8211; single plate analysis&#8221; and &#8220;3&#8217;LIFE &#8211; multiplate analysis&#8221; spreadsheets available from the Mangone lab website (<a href="http://www.mangonelab.com">www.mangonelab.com</a>). The single plate analysis spreadsheet automatically calculates firefly/Renilla ratio, normalizes each miRNA to the appropriate negative control, and normalizes repression values across each plate. This spreadsheet automatically identifies wells with low Renilla luciferase signal, highlights wells that exhibit repression compared to the negative control, and provides measures of repression across the entire plate (Figure 2). See 24 for detailed explanation of statistical analysis.
Multiple replicates can be analysed using the &#8220;multiplate analysis&#8221; spreadsheet. Each replicate is compared side by side, and statistical measures of the data are automatically calculated (Figure 3). In addition to comparing replicates with the &#8220;Normalized Repression&#8221; columns, the user can compare repression between the two miRNAs under the &#8220;miRNA#1/miRNA#2&#8221; columns. This measure divides the repression index for each miRNA for each replicate. This measure can indicate erroneous values from the luciferase assay (for example abnormally high or low readings with the negative control, see Figure 3, row A9, Rep #3), and wells where the repression index may not indicate substantial repression, but that do exhibit significant differences between the miRNAs. While this measure may not be used directly to indicate a miRNA target, it is useful for identifying outliers, problematic wells, or patterns in the data that are not solely attributed to direct miRNA regulation.
The Repression Index (RI) is used to call a putative miRNA target, with lower values corresponding to higher relative repression. The threshold for calling putative targets is based on the level of stringency required by the researcher, but combining the RI with 3&#8217;UTRs that display statistically significant p-values (p &#60;0.05) will indicate high confidence targets (see Figure 2 Rows B8 and B12).
<img alt="Figure 1" src="/files/ftp_upload/52647/52647fig1.jpg" /> 
Figure 1: 3&#8217;LIFE Assay. (A) Gateway-compatible vectors used in the 3&#8217;LIFE assay. Top: The luciferase gene (FLuc) is fused to the test 3&#8217;UTR, while the Renilla luciferase gene (RLuc) is fused to the unspecific SV40 pA 3&#8217;UTR as control. Bottom: The RFP- miRNA-intron vector - The probe pre-miRNA, plus ~400 nucleotides within its genomic locus (to recapitulate endogenous miRNA processing), is cloned within an intron to allow its co-expression with DSRed2 fluorochrome. Both vectors are publically available (Seiler et al., 2013). (B) Flow chart of the 3&#8217;LIFE Assay. (C) 3&#8217;LIFE Pipeline: The dual-luciferase vector containing the test 3&#8217;UTR with or without the miRNA vectors are co-transfected into HEK293 cells in 96-well plates. The interaction between the miRNA and a bona fide 3&#8217;UTR target will lower the relative luminescence in specific wells (exemplified by the orange spot in the experimental plate).
<img alt="Figure 2" src="/files/ftp_upload/52647/52647fig2.jpg" /> 
Figure 2: Sample of data produced with 3&#8217;LIFE assay. Each probe miRNA is tested in quadruplicate (replicates 1-4). Colors represent repression levels, with red colors indicating strong miRNA/3&#8217;UTR interaction. All replicates are averaged to produce high-quality putative targets shown in the summary plate below the yellow arrow. White box represent controls, failed transfections or wells with low transfection efficiency.
<img alt="Figure 3" src="/files/ftp_upload/52647/52647fig3.jpg" /> 
Figure 3: Table representing summary data of a subset of interactions produced using the 3&#8217;LIFE spreadsheet. The repression values are as in Figure 2. The software calculates standard deviation, standard error and z-score for each interaction. Statistically significant interactions are marked in red. The last four rows show relative repression of one miRNA to the other, and is used as secondary indicator to compare repression between two different miRNAs. The spreadsheet can be downloaded from <a href="http://www.mangonelab.com">www.mangonelab.com</a>
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Firefly luciferase buffer reagents</td>
			<td>Final concentration (1x)</td>
		</tr>
		<tr>
			<td>Glycylglycine</td>
			<td>25 mM</td>
		</tr>
		<tr>
			<td>KxPO4 (pH 7.8)</td>
			<td>15 mM</td>
		</tr>
		<tr>
			<td>MgSO4</td>
			<td>15 mM</td>
		</tr>
		<tr>
			<td>DTT (store at 4&#186;)</td>
			<td>1 mM</td>
		</tr>
		<tr>
			<td>EGTA</td>
			<td>4 mM</td>
		</tr>
		<tr>
			<td>ATP*</td>
			<td>2 mM</td>
		</tr>
		<tr>
			<td>Beetle luciferin*</td>
			<td>250 &#956;M</td>
		</tr>
	</tbody>
</table>
Table 1: Stock firefly luciferase reagents: 10x Stock solutions of Glycylglycine, KxPO4, MgSO4, DTT and EGTA can be prepared separately and stored prior to buffer reconstitution. 100x Beetle Luciferin (firefly luciferase substrate) can be stored by dissolving 50 mg luciferin in 7.134 ml H20 (25 mM). Aliquot 105 &#956;l/plate of dissolved Beetle luciferin into tubes and store at -80 &#186;C. Per Promega technical support, this should be stable for &#62;6 months, but may be light sensitive. NOTE: EGTA will not go into solution at neutral pH. Slowly add NaOH to EGTA until it dissolves completely.*Reagents added to final buffer immediately prior to the luciferase assay
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Renilla luciferase buffer reagents</td>
			<td>Final concentration (1x)</td>
		</tr>
		<tr>
			<td>NaCl</td>
			<td>1.1 M</td>
		</tr>
		<tr>
			<td>Na2EDTA</td>
			<td>2.2 mM</td>
		</tr>
		<tr>
			<td>KH2PO4</td>
			<td>.22 M</td>
		</tr>
		<tr>
			<td>NaN3</td>
			<td>1.3 mM</td>
		</tr>
		<tr>
			<td>BSA*</td>
			<td>.44 mg/ml</td>
		</tr>
		<tr>
			<td>Coelenterazine*</td>
			<td>2.5 &#956;M</td>
		</tr>
	</tbody>
</table>
Table 2: Stock Renilla luciferase buffer reagents All the reagents except BSA and Coelenterazine can be mixed at a 1x concentration and stored at room temperature. Coelenterazine can be dissolved in acidified methanol and aliquoted per plate. Acidify methanol by adding HCl to final concentration of 5 mM (&#60;3 pH). Dissolve 250 &#956;g coelenterazine in 2.36 ml acidified methanol (250 &#956;M) aliquot 105 ul/plate. The mix is stable for at least 6 months but may be light sensitive. *Reagents added to buffer immediately before luciferase assay.

Watch this Scientific Journal Video about Detection of miRNA Targets in High-throughput Using the 3'LIFE Assay at JoVE.com

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

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 ...

detection-of-mirna-targets-in-high-throughput-using-the-3-life-assay

Nanyang Technological University

Research

JoVE Journal

Biology

10.0K Views.  Arizona State University. Luminescent identification of functional elements in 3’ untranslated regions (3’UTRs) (3’LIFE) is a technique to identify functional regulation in 3’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.

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

High Throughput MicroRNA Profiling: Optimized Multiplex qRT-PCR at Nanoliter Scale on the Fluidigm Dynamic ArrayTM IFCs

The broad involvement of miRNAs in critical processes underlying development, tissue homoeostasis and disease has led to a surging interest among the research and pharmaceutical communities. To study miRNAs, it is essential that the quantification of microRNA levels is accurate and robust. By comparing wild-type to small RNA deficient mouse embryonic stem cells (mESC), we revealed a lack of accuracy and robustness in previous published multiplex qRT-PCR techniques. Here, we describe an optimized method, including purifying away excessive primers from previous multiplex steps before singleplex real time detection, which dramatically increases the accuracy and robustness of the technique. Furthermore, we explain how performing the technique on a microfluidic chip at nanoliter volumes significantly reduces reagent costs and permits time effective high throughput miRNA expression profiling.

Here we describe an optimized multiplex reverse transcriptase quantitative PCR (qRT-PCR) protocol in combination with a microfluidic platform as a cost and time effective high-throughput screening tool for microRNA (miRNA) expression levels, especially when working with limited amounts of sample.

The broad involvement of miRNAs in critical processes underlying development, tissue homoeostasis and disease has led to a surging interest among the ...

Bioengineering

Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in a single reaction. The miRNA assay counts transcripts by directly imaging and digitally counting miRNA molecules that are labeled with color-coded fluorescent barcoded probe sets (a reporter probe and a capture probe). Barcodes are hybridized directly to mature miRNAs that have been elongated by ligating a unique oligonucleotide tag (miRtag) to the 3&#39; end. Reverse transcription and amplification of the transcripts are not required. Reporter probes contain a sequence of six color positions populated using a combination of four fluorescent colors. The four colors over six positions are used to construct a gene-specific color barcode sequence. Post-hybridization processing is automated on a robotic prep station. After hybridization, the excess probes are washed away, and the tripartite structures (capture probe-miRNA-reporter probe) are fixed to a streptavidin-coated slide via the biotin-labeled capture probe. Imaging and barcode counting is done using a digital analyzer. The immobilized barcoded miRNAs are visualized and imaged using a microscope and camera, and the unique barcodes are decoded and counted. Data quality control (QC), normalization, and analysis are facilitated by a custom-designed data processing and analysis software that accompanies the assay software. The assay demonstrates high linearity over a broad range of expression, as well as high sensitivity. Sample and assay preparation does not involve enzymatic reactions, reverse transcription, or amplification; has few steps; and is largely automated, reducing investigator effects and resulting in high consistency and technical reproducibility. Here, we describe the application of this technology to identifying circulating miRNA perturbations in irritable bowel syndrome.

We describe the use of a multiplexed high-throughput gene expression platform that quantitates gene expression by barcoding and counting molecules in biological substrates without the need for amplification. We used the platform to quantitate microRNA (miRNA) expression in whole blood in subjects with and without irritable bowel syndrome.

The gene expression platform assay allows for robust and highly reproducible quantification of the expression of up to 800 transcripts (mRNA or miRNAs) in ...

Genetics

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; ...

Detection of a Circulating MicroRNA Custom Panel in Patients with Metastatic Colorectal Cancer

There is increasing interest in liquid biopsy for cancer diagnosis, prognosis and therapeutic monitoring, creating the need for reliable and useful biomarkers for clinical practice. Here, we present a protocol to extract, reverse transcribe and evaluate the expression levels of circulating miRNAs from plasma samples of patients with colorectal cancer (CRC). microRNAs (miRNAs) are a class of non-coding RNAs of 18-25 nucleotides in length that regulate the expression of target genes at the translational level and play an important role, including that of pro- and anti-angiogenic function, in the physiopathology of various organs. miRNAs are stable in biological fluids such as serum and plasma, which renders them ideal circulating biomarkers for cancer diagnosis, prognosis and treatment decision-making and monitoring. Circulating miRNA extraction was performed using a rapid and effective method that involves both organic and column-based methodologies. For miRNA retrotranscription, we used a multi-step procedure that considers polyadenylation at 3&#39; and the ligation of an adapter at 5&#39; of the mature miRNAs, followed by random miRNA pre-amplification. We selected a 24-miRNA custom panel to be tested by quantitative real-time polymerase chain reaction (qRT-PCR) and spotted the miRNA probes on array custom plates. We performed qRT-PCR plate runs on a real-time PCR System. Housekeeping miRNAs for normalization were selected using GeNorm software (v. 3.2). Data were analyzed using Expression suite software (v 1.1) and statistical analyses were performed. The method proved to be reliable and technically robust and could be useful to evaluate biomarker levels in liquid samples such as plasma and/or serum.

We present a protocol to evaluate the expression levels of circulating microRNA (miRNAs) in plasma samples from cancer patients. In particular, we used a commercially available kit for circulating miRNA extraction and reverse transcription. Finally, we analyzed a panel of 24 selected miRNAs using real-time pre-spotted probe custom plates.

There is increasing interest in liquid biopsy for cancer diagnosis, prognosis and therapeutic monitoring, creating the need for reliable and useful ...

Cancer Research

MicroRNA Detection in Prostate Tumors by Quantitative Real-time PCR (qPCR)

MicroRNAs (miRNAs) are single-stranded, 18&#x2013;24 nucleotide long, non-coding RNA molecules. They are involved in virtually every cellular process including development1, apoptosis2, and cell cycle regulation3. MiRNAs are estimated to regulate the expression of 30% to 90% of human genes4 by binding to their target messenger RNAs (mRNAs)5. Widespread dysregulation of miRNAs has been reported in various diseases and cancer subtypes6. Due to their prevalence and unique structure, these small molecules are likely to be the next generation of biomarkers, therapeutic agents and/or targets.
Methods used to investigate miRNA expression include SYBR green I dye- based as well as Taqman-probe based qPCR. If miRNAs are to be effectively used in the clinical setting, it is imperative that their detection in fresh and/or archived clinical samples be accurate, reproducible, and specific. qPCR has been widely used for validating expression of miRNAs in whole genome analyses such as microarray studies7. The samples used in this protocol were from patients who underwent radical prostatectomy for clinically localized prostate cancer; however other tissues and cell lines can be substituted in. Prostate specimens were snap-frozen in liquid nitrogen after resection. Clinical variables and follow-up information for each patient were collected for subsequent analysis8.
Quantification of miRNA levels in prostate tumor samples. The main steps in qPCR analysis of tumors are: Total RNA extraction, cDNA synthesis, and detection of qPCR products using miRNA-specific primers. Total RNA, which includes mRNA, miRNA, and other small RNAs were extracted from specimens using TRIzol reagent. Qiagen's miScript System was used to synthesize cDNA and perform qPCR (Figure 1). Endogenous miRNAs are not polyadenylated, therefore during the reverse transcription process, a poly(A) polymerase polyadenylates the miRNA. The miRNA is used as a template to synthesize cDNA using oligo-dT and Reverse Transcriptase. A universal tag sequence on the 5' end of oligo-dT primers facilitates the amplification of cDNA in the PCR step. PCR product amplification is detected by the level of fluorescence emitted by SYBR Green, a dye which intercalates into double stranded DNA. Specific miRNA primers, along with a Universal Primer that binds to the universal tag sequence will amplify specific miRNA sequences. 
The miScript Primer Assays are available for over a thousand human-specific miRNAs, and hundreds of murine-specific miRNAs. Relative quantification method was used here to quantify the expression of miRNAs. To correct for variability amongst different samples, expression levels of a target miRNA is normalized to the expression levels of a reference gene. The choice of a gene on which to normalize the expression of targets is critical in relative quantification method of analysis. Examples of reference genes typically used in this capacity are the small RNAs RNU6B, RNU44, and RNU48 as they are considered to be stably expressed across most samples. In this protocol, RNU6B is used as the reference gene.

MicroRNA Detection in Prostate Tumors by Quantitative Real-time PCR qPCR

Quantitative Real Time polymerase chain reaction (qPCR) is a rapid and sensitive method to investigate the expression levels of various microRNA (miRNA) molecules in tumor samples. Using this method expression of hundreds of different miRNA molecules can be amplified, quantified, and analyzed from the same cDNA template.

MicroRNAs (miRNAs) are single-stranded, 18&#x2013;24 nucleotide long, non-coding RNA molecules. They are involved in virtually every cellular process ...

Medicine

An In Vitro Protocol for Evaluating MicroRNA Levels, Functions, and Associated Target Genes in Tumor Cells

MicroRNAs (miRNAs) are small regulatory RNAs which are recognized to modulate numerous intracellular signaling pathways in several diseases including cancers. These small regulatory RNAs mainly interact with the 3&#8217; untranslated regions (3&#8217; UTR) of their target messenger RNAs (mRNAs) ultimately resulting in the inhibition of decoding processes of mRNAs and the augmentation of target mRNA degradations. Based on the expression levels and intracellular functions, miRNAs are able to serve as regulatory factors of oncogenic and tumor-suppressive mRNAs. Identification of bona fide target genes of a miRNA among hundreds or even thousands of computationally predicted targets is a crucial step to discern the roles and basic molecular mechanisms of a miRNA of interest. Various miRNA target prediction programs are available to search possible miRNA-mRNA interactions. However, the most challenging question is how to validate direct target genes of a miRNA of interest. This protocol describes a reproducible strategy of key methods on how to identify miRNA targets related to the function of a miRNA. This protocol presents a practical guide on step-by-step procedures to uncover miRNA levels, functions, and related target mRNAs using the probe-based real-time polymerase chain reaction (PCR), sulforhodamine B (SRB) assay following a miRNA mimic transfection, dose-response curve generation, and luciferase assay along with the cloning of 3&#8217; UTR of a gene, which is necessary for proper understanding of the roles of individual miRNAs.

This protocol uses a probe-based real-time polymerase chain reaction (PCR), a sulforhodamine B (SRB) assay, 3&#8217; untranslated regions (3&#8217; UTR) cloning, and a luciferase assay to verify the target genes of a miRNA of interest and to understand the functions of miRNAs.

MicroRNAs (miRNAs) are small regulatory RNAs which are recognized to modulate numerous intracellular signaling pathways in several diseases including ...

Profiling of Estrogen-regulated MicroRNAs in Breast Cancer Cells

Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen receptors (ERs), ER&#945;, and ER&#946;. ER&#945; is frequently upregulated in breast cancer and drives the proliferation of breast cancer cells. The ERs function as transcription factors and regulate gene expression. Whereas ER&#945;&#39;s regulation of protein-coding genes is well established, its regulation of noncoding microRNA (miRNA) is less explored. miRNAs play a major role in the post-transcriptional regulation of genes, inhibiting their translation or degrading their mRNA. miRNAs can function as oncogenes or tumor suppressors&#160;and are also promising biomarkers. Among the miRNA assays available, microarray and quantitative real-time polymerase chain reaction (qPCR) have been extensively used to detect and quantify miRNA levels. To identify miRNAs regulated by estrogen signaling in breast cancer, their expression in ER&#945;-positive breast cancer cell lines were compared before and after estrogen-activation using both the &#181;Paraflo-microfluidic microarrays and Dual Labeled Probes-low density arrays. Results were validated using specific qPCR assays, applying both Cyanine dye-based and Dual Labeled Probes-based chemistry. Furthermore, a time-point assay was used to identify regulations over time. Advantages of the miRNA assay approach used in this study is that it enables a fast screening of mature miRNA regulations in numerous samples, even with limited sample amounts. The layout, including the specific conditions for cell culture and estrogen treatment, biological and technical replicates, and large-scale screening followed by in-depth confirmations using separate techniques, ensures a robust detection of miRNA regulations,&#160;and eliminates false positives and other artifacts. However, mutated or unknown miRNAs, or regulations at the primary and precursor transcript level, will not be detected. The method presented here represents a thorough investigation of estrogen-mediated miRNA regulation.

Molecular signaling through both estrogen and microRNAs are critical in breast cancer development and growth. Estrogen activates the estrogen receptors, which are transcription factors. Many transcription factors can regulate the expression of microRNAs, and estrogen-regulated microRNAs can be profiled using different large-scale techniques.

Estrogen plays vital roles in mammary gland development and breast cancer progression. It mediates its function by binding to and activating the estrogen ...

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, ...

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 ...

Highly Efficient Ligation of Small RNA Molecules for MicroRNA Quantitation by High-Throughput Sequencing

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide resolution. This technique captures miRNAs by attaching 3&#8217; and 5&#8217; oligonucleotide adapters to miRNA molecules and allows de novo miRNA discovery. Coupling with powerful next-generation sequencing platforms, miR-Seq has been instrumental in the study of miRNA biology. However, significant biases introduced by oligonucleotide ligation steps have prevented miR-Seq from being employed as an accurate quantitation tool. Previous studies demonstrate that biases in current miR-Seq methods often lead to inaccurate miRNA quantification with errors up to 1,000-fold for some miRNAs1,2. To resolve these biases imparted by RNA ligation, we have developed a small RNA ligation method that results in ligation efficiencies of over 95% for both 3&#8217; and 5&#8242; ligation steps. Benchmarking this improved library construction method using equimolar or differentially mixed synthetic miRNAs, consistently yields reads numbers with less than two-fold deviation from the expected value. Furthermore, this high-efficiency miR-Seq method permits accurate genome-wide miRNA profiling from in vivo total RNA samples2.

MicroRNAs (miRNAs) are a widely conserved class of regulatory molecules. Here we describe a miRNA cloning method that relies upon two potent ligation steps followed by high-throughput sequencing. Our method permits accurate genome-wide quantitation of miRNAs.

MiRNA cloning and high-throughput sequencing, termed miR-Seq, stands alone as a transcriptome-wide approach to quantify miRNAs with single nucleotide ...