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>

<ol>
	<li>Bartel, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNAs:+genomics,+biogenesis,+mechanism,+and+function.">MicroRNAs: genomics, biogenesis, mechanism, and function.</a> Cell. 116 (2), 281-297 (2004).</li><li>Pasquinelli, A. E., 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=Conservation+of+the+sequence+and+temporal+expression+of+let-7+heterochronic+regulatory+RNA.">Conservation of the sequence and temporal expression of let-7 heterochronic regulatory RNA.</a> Nature. 408 (6808), 86-89 (2000).</li><li>Reinhart, B. J., Weinstein, E. G., Rhoades, M. W., Bartel, B., Bartel, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNAs+in+plants.">MicroRNAs in plants.</a> Genes Dev. 16 (13), 1616-1626 (2002).</li><li>Ivey, K. N., 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+regulation+of+cell+lineages+in+mouse+and+human+embryonic+stem+cells.">MicroRNA regulation of cell lineages in mouse and human embryonic stem cells.</a> Cell Stem Cell. 2 (3), 219-229 (2008).</li><li>Lee, R. C., Feinbaum, R. L., Ambros, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+C.+elegans+heterochronic+gene+lin-4+encodes+small+RNAs+with+antisense+complementarity+to+lin-14.">The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14.</a> Cell. 75 (5), 843-854 (1993).</li><li>Wienholds, E., 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+expression+in+zebrafish+embryonic+development.">MicroRNA expression in zebrafish embryonic development.</a> Science. 309 (5732), 310-311 (2005).</li><li>Darnell, D. K., 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+expression+during+chick+embryo+development.">MicroRNA expression during chick embryo development.</a> Dev Dyn. 235 (11), 3156-3165 (2006).</li><li>Jin, P., Alisch, R. S., Warren, S. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=RNA+and+microRNAs+in+fragile+X+mental+retardation.">RNA and microRNAs in fragile X mental retardation.</a> Nat Cell Biol. 6 (11), 1048-1053 (2004).</li><li>Kim, J., 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+MicroRNA+feedback+circuit+in+midbrain+dopamine+neurons.">A MicroRNA feedback circuit in midbrain dopamine neurons.</a> Science. 317 (5842), 1220-1224 (2007).</li><li>Poy, M. N., 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+pancreatic+islet-specific+microRNA+regulates+insulin+secretion.">A pancreatic islet-specific microRNA regulates insulin secretion.</a> Nature. 432 (7014), 226-230 (2004).</li><li>Calin, G. A., Croce, C. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNA+signatures+in+human+cancers.">MicroRNA signatures in human cancers.</a> Nature Reviews Cancer. 6 (11), 857-866 (2006).</li><li>Friedman, R. C., Farh, K. K., Burge, C. B., Bartel, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Most+mammalian+mRNAs+are+conserved+targets+of+microRNAs.">Most mammalian mRNAs are conserved targets of microRNAs.</a> Genome Res. 19 (1), 92-105 (2009).</li><li>Paraskevopoulou, M. 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=DIANA-microT+web+server+v5.0:+service+integration+into+miRNA+functional+analysis+workflows.">DIANA-microT web server v5.0: service integration into miRNA functional analysis workflows.</a> Nucleic Acids Res. 41 (Web Server issue), W169-W173 (2013).</li><li>Krek, 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=Combinatorial+microRNA+target+predictions.">Combinatorial microRNA target predictions.</a> Nat Genet. 37 (5), 495-500 (2005).</li><li>Bartel, D. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroRNAs:+target+recognition+and+regulatory+functions.">MicroRNAs: target recognition and regulatory functions.</a> Cell. 136 (2), 215-233 (2009).</li><li>Vella, M. C., Choi, E. Y., Lin, S. Y., Reinert, K., Slack, F. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+C.+elegans+microRNA+let-7+binds+to+imperfect+let-7+complementary+sites+from+the+lin-41+3'UTR.">The C. elegans microRNA let-7 binds to imperfect let-7 complementary sites from the lin-41 3'UTR.</a> Genes Dev. 18 (2), 132-137 (2004).</li><li>Lal, 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=miR-24+Inhibits+cell+proliferation+by+targeting+E2F2,+MYC,+and+other+cell-cycle+genes+via+binding+to+'seedless'+3'UTR+microRNA+recognition+elements.">miR-24 Inhibits cell proliferation by targeting E2F2, MYC, and other cell-cycle genes via binding to 'seedless' 3'UTR microRNA recognition elements.</a> Molecular Cell. 35 (5), 610-625 (2009).</li><li>Cevec, M., Thibaudeau, C., Plavec, J. N. M. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NMR+structure+of+the+let-7+miRNA+interacting+with+the+site+LCS1+of+lin-41+mRNA+from+Caenorhabditis+elegans.">NMR structure of the let-7 miRNA interacting with the site LCS1 of lin-41 mRNA from Caenorhabditis elegans.</a> Nucleic Acids Res. 38 (21), 7814-7821 (1093).</li><li>Shin, C., 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=Expanding+the+microRNA+targeting+code:+functional+sites+with+centered+pairing.">Expanding the microRNA targeting code: functional sites with centered pairing.</a> Molecular Cell. 38 (6), 789-802 (2010).</li><li>Azzouzi, I., 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-96+directly+inhibits+gamma-globin+expression+in+human+erythropoiesis.">MicroRNA-96 directly inhibits gamma-globin expression in human erythropoiesis.</a> PLoS One. 6 (7), e22838 (2011).</li><li>Chen, J., 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=miR-193b+Regulates+Mcl-1+in+Melanoma.">miR-193b Regulates Mcl-1 in Melanoma.</a> Am J Pathol. 179 (5), 2162-2168 (2011).</li><li>Chi, S. W., Hannon, G. J., Darnell, R. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=An+alternative+mode+of+microRNA+target+recognition.">An alternative mode of microRNA target recognition.</a> Nat Struct Mol Biol. 19 (3), 321-327 (1038).</li><li>Jiao, L. R., 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=MicroRNAs+targeting+oncogenes+are+down-regulated+in+pancreatic+malignant+transformation+from+benign+tumors.">MicroRNAs targeting oncogenes are down-regulated in pancreatic malignant transformation from benign tumors.</a> PLoS One. 7 (2), e32068 (2012).</li><li>Wolter, J. M., Kotagama, K., Pierre-Bez, A. C., Firago, M., Mangone, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=3'LIFE:+a+functional+assay+to+detect+miRNA+targets+in+high-throughput.">3'LIFE: a functional assay to detect miRNA targets in high-throughput.</a> Nucleic Acids Res. , (2014).</li><li>Mangone, M., 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+landscape+of+C.+elegans+3'UTRs.">The landscape of C. elegans 3'UTRs.</a> Science. 329 (5990), 432-435 (2010).</li><li>Ekdahl, Y., Farahani, H. S., Behm, M., Lagergren, J., Ohman, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A-to-I+editing+of+microRNAs+in+the+mammalian+brain+increases+during+development.">A-to-I editing of microRNAs in the mammalian brain increases during development.</a> Genome Res. 22 (8), 1477-1487 (2012).</li><li>Fu, Y., Dominissini, D., Rechavi, G., He, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+expression+regulation+mediated+through+reversible+m(6)A+RNA+methylation.">Gene expression regulation mediated through reversible m(6)A RNA methylation.</a> Nat Rev Genet. 15 (5), 293-306 (2014).</li><li>Zhou, P., 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=Large-scale+screens+of+miRNA-mRNA+interactions+unveiled+that+the+3'UTR+of+a+gene+is+targeted+by+multiple+miRNAs.">Large-scale screens of miRNA-mRNA interactions unveiled that the 3'UTR of a gene is targeted by multiple miRNAs.</a> Plos One. 8 (7), e68204 (2013).</li><li>Dyer, B. W., Ferrer, F. A., Klinedinst, D. K., Rodriguez, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+noncommercial+dual+luciferase+enzyme+assay+system+for+reporter+gene+analysis.">A noncommercial dual luciferase enzyme assay system for reporter gene analysis.</a> Anal Biochem. 282 (1), 158-161 (2000).</li><li>Seiler, C. 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 individu...

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 border="0" cellpadding="0" cellspacing="0" width="406">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Reagents</td>
			<td style="width:71px;"></td>
			<td style="width:119px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">glycylglycine</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">G1127-25G</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Kx PO4</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">P2222</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">EGTA</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">E3889</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">ATP</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">FLAAS</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">DTT</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">D0632</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">MgSO4</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">M7506</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">CoA</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">C4282</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">luciferin</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">L9504</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">NaCl</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">S7653</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Na2EDTA</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">E0399</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">K H2 P O4</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">1551139</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">BSA</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">A2153</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">NaN3</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">S2002</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;">Coelenterazine</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">C3230</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">PBS/HEPES</td>
			<td style="width:71px;">Corning</td>
			<td style="width:119px;">21-040-CV</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">DMEM</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">D5546</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">FBS</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">F2442</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Pennicilin/Streptomicin</td>
			<td style="width:71px;">Sigma</td>
			<td style="width:119px;">P4333</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Trypsin</td>
			<td style="width:71px;"></td>
			<td style="width:119px;">T2600000</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;"></td>
			<td style="width:71px;"></td>
			<td style="width:119px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Consumables</td>
			<td style="width:71px;"></td>
			<td style="width:119px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">MaxiPrep Kit</td>
			<td style="width:71px;">Promega</td>
			<td style="width:119px;">A2392</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">96-well miniprep plate</td>
			<td style="width:71px;">Pall</td>
			<td>8032</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">96-well shuttle plates</td>
			<td style="width:71px;">Lonza</td>
			<td style="width:119px;">V4SP-2096</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">5x Lysis Buffer</td>
			<td style="width:71px;">Promega</td>
			<td>E1941&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;"></td>
			<td style="width:71px;"></td>
			<td style="width:119px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Instruments</td>
			<td style="width:71px;"></td>
			<td style="width:119px;"></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">96-well GloMax Plate Reader</td>
			<td style="width:71px;">Promega</td>
			<td style="width:119px;">E9032</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Biomech FX Liquid Handler Robot</td>
			<td style="width:71px;">Beckmann</td>
			<td style="width:119px;">A31842</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">4D-Nucleofector Core Unit</td>
			<td style="width:71px;">Lonza</td>
			<td style="width:119px;">AAF-1001</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">96-well Shuttle System</td>
			<td style="width:71px;">Lonza</td>
			<td style="width:119px;">AAM-1001</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Cell Counter Countess</td>
			<td style="width:71px;">Invitrogen</td>
			<td style="width:119px;">C10227</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 negativ...

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&#039;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.

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 ...

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

Research

JoVE Journal

Biology

10.0K visualizações.  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.

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

High-throughput Saccharification Assay for Lignocellulosic Materials

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing a biorefinery. These raw materials would constitute a new industrial platform, which is both sustainable and carbon neutral, to replace 
the current dependency on fossil fuel. The recalcitrance to deconstruction observed in lignocellulosic materials is produced by several intrinsic properties 
of plant cell walls. Crystalline cellulose is embedded in matrix polysaccharides such as xylans and arabinoxylans, and the whole structure is encased by the 
phenolic polymer lignin, that is also difficult to digest 1. In order to improve the digestibility of plant materials we need to discover the main bottlenecks 
for the saccharification of cell walls and also screen mutant and breeding populations to evaluate the variability in saccharification 2. These tasks require 
a high throughput approach and here we present an analytical platform that can perform saccharification analysis in a 96-well plate format. This platform has 
been developed to allow the screening of lignocellulose digestibility of large populations from varied plant species. We have scaled down the reaction volumes 
for gentle pretreatment, partial enzymatic hydrolysis and sugar determination, to allow large numbers to be assessed rapidly in an automated system.
This automated platform works with milligram amounts of biomass, performing ball milling under controlled conditions to reduce the plant 
materials to a standardised particle size in a reproducible manner. Once the samples are ground, the automated formatting robot dispenses specified and recorded 
amounts of material into the corresponding wells of 96 deep well plate (Figure 1). Normally, we dispense the same material into 4 wells to have 4 replicates for 
analysis. Once the plates are filled with the plant material in the desired layout, they are manually moved to a liquid handling station (Figure 2). In this 
station the samples are subjected to a mild pretreatment with either dilute acid or alkaline and incubated at temperatures of up to 90&deg;C. The pretreatment solution 
is subsequently removed and the samples are rinsed with buffer to return them to a suitable pH for hydrolysis. The samples are then incubated with an enzyme
mixture for a variable length of time at 50&deg;C. An aliquot is taken from the hydrolyzate and the reducing sugars are automatically determined by the MBTH 
colorimetric method.

A simple, rapid method for determining the saccharification potential of large numbers of plant biomass samples is described. The automated platform for this analysis involves the preparation of the plant biomass for analysis in 96 well plates and the subsequent performance of pretreatment, hydrolysis and quantification of the sugars released.

Polysaccharides that make up plant lignocellulosic biomass can be broken down to produce a range of sugars that subsequently can be 
used in establishing ...

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, ...

High-throughput Crystallization of Membrane Proteins Using the Lipidic Bicelle Method

Membrane proteins (MPs) play a critical role in many physiological processes such as pumping specific molecules across the otherwise impermeable membrane bilayer that surrounds all cells and organelles. Alterations in the function of MPs result in many human diseases and disorders; thus, an intricate understanding of their structures remains a critical objective for biological research. However, structure determination of MPs remains a significant challenge often stemming from their hydrophobicity. 
MPs have substantial hydrophobic regions embedded within the bilayer. Detergents are frequently used to solubilize these proteins from the bilayer generating a protein-detergent micelle that can then be manipulated in a similar manner as soluble proteins. Traditionally, crystallization trials proceed using a protein-detergent mixture, but they often resist crystallization or produce crystals of poor quality. These problems arise due to the detergent&prime;s inability to adequately mimic the bilayer resulting in poor stability and heterogeneity. In addition, the detergent shields the hydrophobic surface of the MP reducing the surface area available for crystal contacts. To circumvent these drawbacks MPs can be crystallized in lipidic media, which more closely simulates their endogenous environment, and has recently become a de novo technique for MP crystallization.
Lipidic cubic phase (LCP) is a three-dimensional lipid bilayer penetrated by an interconnected system of aqueous channels1. Although monoolein is the lipid of choice, related lipids such as monopalmitolein and monovaccenin have also been used to make LCP2. MPs are incorporated into the LCP where they diffuse in three dimensions and feed crystal nuclei. A great advantage of the LCP is that the protein remains in a more native environment, but the method has a number of technical disadvantages including high viscosity (requiring specialized apparatuses) and difficulties in crystal visualization and manipulation3,4. Because of these technical difficulties, we utilized another lipidic medium for crystallization-bicelles5,6 (Figure 1). Bicelles are lipid/amphiphile mixtures formed by blending a phosphatidylcholine lipid (DMPC) with an amphiphile (CHAPSO) or a short-chain lipid (DHPC). Within each bicelle disc, the lipid molecules generate a bilayer while the amphiphile molecules line the apolar edges providing beneficial properties of both bilayers and detergents. Importantly, below their transition temperature, protein-bicelle mixtures have a reduced viscosity and are manipulated in a similar manner as detergent-solubilized MPs, making bicelles compatible with crystallization robots. 
Bicelles have been successfully used to crystallize several membrane proteins5,7-11 (Table 1). This growing collection of proteins demonstrates the versatility of bicelles for crystallizing both alpha helical and beta sheet MPs from prokaryotic and eukaryotic sources. Because of these successes and the simplicity of high-throughput implementation, bicelles should be part of every membrane protein crystallographer&prime;s arsenal. In this video, we describe the bicelle methodology and provide a step-by-step protocol for setting up high-throughput crystallization trials of purified MPs using standard robotics.

Bicelles are lipid/amphiphile mixtures that maintain membrane proteins (MPs) within a lipid bilayer but have unique phase behavior that facilitates high-throughput screening by crystallization robots. This technique has successfully produced a number of high-resolution structures from both prokaryotic and eukaryotic sources. This video describes protocols for generating the lipidic bicelle mixture, incorporating MPs into the bicelle mixture, setting up crystallizations trials (manually as well as robotically) and harvesting crystals from the medium.

Membrane proteins (MPs) play a critical role in many physiological processes such as pumping specific molecules across the otherwise impermeable membrane ...

High-throughput Physical Mapping of Chromosomes using Automated in situ Hybridization

Projects to obtain whole-genome sequences for 10,000 vertebrate species1 and for 5,000 insect and related arthropod species2 are expected to take place over the next 5 years. For example, the sequencing of the genomes for 15 malaria mosquitospecies is currently being done using an Illumina platform3,4. This Anopheles species cluster includes both vectors and non-vectors of malaria. When the genome assemblies become available, researchers will have the unique opportunity to perform comparative analysis for inferring evolutionary changes relevant to vector ability. However, it has proven difficult to use next-generation sequencing reads to generate high-quality de novo genome assemblies5. Moreover, the existing genome assemblies for Anopheles gambiae, although obtained using the Sanger method, are gapped or fragmented4,6.
Success of comparative genomic analyses will be limited if researchers deal with numerous sequencing contigs, rather than with chromosome-based genome assemblies. Fragmented, unmapped sequences create problems for genomic analyses because: (i) unidentified gaps cause incorrect or incomplete annotation of genomic sequences; (ii) unmapped sequences lead to confusion between paralogous genes and genes from different haplotypes; and (iii) the lack of chromosome assignment and orientation of the sequencing contigs does not allow for reconstructing rearrangement phylogeny and studying chromosome evolution. Developing high-resolution physical maps for species with newly sequenced genomes is a timely and cost-effective investment that will facilitate genome annotation, evolutionary analysis, and re-sequencing of individual genomes from natural populations7,8.
Here, we present innovative approaches to chromosome preparation, fluorescent in situ hybridization (FISH), and imaging that facilitate rapid development of physical maps. Using An. gambiae as an example, we demonstrate that the development of physical chromosome maps can potentially improve genome assemblies and, thus, the quality of genomic analyses. First, we use a high-pressure method to prepare polytene chromosome spreads. This method, originally developed for Drosophila9, allows the user to visualize more details on chromosomes than the regular squashing technique10. Second, a fully automated, front-end system for FISH is used for high-throughput physical genome mapping. The automated slide staining system runs multiple assays simultaneously and dramatically reduces hands-on time11. Third, an automatic fluorescent imaging system, which includes a motorized slide stage, automatically scans and photographs labeled chromosomes after FISH12. This system is especially useful for identifying and visualizing multiple chromosomal plates on the same slide. In addition, the scanning process captures a more uniform FISH result. Overall, the automated high-throughput physical mapping protocol is more efficient than a standard manual protocol.

High-throughput Physical Mapping of Chromosomes using Automated in situ Hybridization

Genome assemblies based on massively parallel DNA sequencing technologies are usually highly fragmented. The development of physical chromosome maps can potentially improve genome assemblies. Here, we demonstrate innovative approaches to chromosome preparation, fluorescent in situ hybridization, and imaging that significantly increase throughput of the physical map development.

Projects to obtain whole-genome sequences for 10,000 vertebrate species1 and for 5,000 insect and related arthropod species2 are expected to take place ...

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

RNA Secondary Structure Prediction Using High-throughput SHAPE

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed "high-throughput selective 2' hydroxyl acylation analyzed by primer extension", or SHAPE, allows prediction of RNA secondary structure with single nucleotide resolution. This approach utilizes chemical probing agents that preferentially acylate single stranded or flexible regions of RNA in aqueous solution. Sites of chemical modification are detected by reverse transcription of the modified RNA, and the products of this reaction are fractionated by automated capillary electrophoresis (CE). Since reverse transcriptase pauses at those RNA nucleotides modified by the SHAPE reagents, the resulting cDNA library indirectly maps those ribonucleotides that are single stranded in the context of the folded RNA. Using ShapeFinder software, the electropherograms produced by automated CE are processed and converted into nucleotide reactivity tables that are themselves converted into pseudo-energy constraints used in the RNAStructure (v5.3) prediction algorithm. The two-dimensional RNA structures obtained by combining SHAPE probing with in silico RNA secondary structure prediction have been found to be far more accurate than structures obtained using either method alone.

High-throughput selective 2' hydroxyl acylation analyzed by primer extension (SHAPE) utilizes a novel chemical probing technology, reverse transcription, capillary electrophoresis and secondary structure prediction software to determine the structures of RNAs from several hundred to several thousand nucleotides at single nucleotide resolution.

Understanding the function of RNA involved in biological processes requires a thorough knowledge of RNA structure. Toward this end, the methodology dubbed ...

High-throughput Functional Screening using a Homemade Dual-glow Luciferase Assay

We present a rapid and inexpensive high-throughput screening protocol to identify transcriptional regulators of alpha-synuclein, a gene associated with Parkinson&#39;s disease. 293T cells are transiently transfected with plasmids from an arrayed ORF expression library, together with luciferase reporter plasmids, in a one-gene-per-well microplate format. Firefly luciferase activity is assayed after 48 hr to determine the effects of each library gene upon alpha-synuclein transcription, normalized to expression from an internal control construct (a hCMV promoter directing Renilla luciferase). This protocol is facilitated by a bench-top robot enclosed in a biosafety cabinet, which performs aseptic liquid handling in 96-well format. Our automated transfection protocol is readily adaptable to high-throughput lentiviral library production or other functional screening protocols requiring triple-transfections of large numbers of unique library plasmids in conjunction with a common set of helper plasmids. We also present an inexpensive and validated alternative to commercially-available, dual luciferase reagents which employs PTC124, EDTA, and pyrophosphate to suppress firefly luciferase activity prior to measurement of Renilla luciferase. Using these methods, we screened 7,670 human genes and identified 68 regulators of alpha-synuclein. This protocol is easily modifiable to target other genes of interest.

We present a rapid and inexpensive screening method for identifying transcriptional regulators using high-throughput robotic transfections and a homemade dual-glow luciferase assay. This protocol rapidly generates direct side-by-side functional data for thousands of genes and is easily modifiable to target any gene of interest.

We present a rapid and inexpensive high-throughput screening protocol to identify transcriptional regulators of alpha-synuclein, a gene associated with ...

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays focus on transcriptional regulation, being essentially aimed at the identification of promoter-targeting molecules. As a result, post-transcriptional mechanisms are largely uncovered by gene expression targeted drug development. Here we describe a cell-based assay aimed at investigating the role of the 3&#39; untranslated region (3&#8217; UTR) in the modulation of the fate of its mRNA, and at identifying compounds able to modify it. The assay is based on the use of a luciferase reporter construct containing the 3&#8217; UTR of a gene of interest stably integrated into a disease-relevant cell line. The protocol is divided into two parts, with the initial focus on the primary screening aimed at the identification of molecules affecting luciferase activity after 24 hr of treatment. The second part of the protocol describes the counter-screening necessary to discriminate compounds modulating luciferase activity specifically through the 3&#8217; UTR. In addition to the detailed protocol and representative results, we provide important considerations about the assay development and the validation of the hit(s) on the endogenous target. The described cell-based reporter gene assay will allow scientists to identify molecules modulating protein levels via post-transcriptional mechanisms dependent on a 3&#8217; UTR.

Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3&#8217; UTR.

Both transcriptional and post-transcriptional regulation have a profound impact on genes expression. However, commonly adopted cell-based screening assays ...

Identification of Kinase-substrate Pairs Using High Throughput Screening

We have developed a screening platform to identify dedicated human protein kinases for phosphorylated substrates which can be used to elucidate novel signal transduction pathways. Our approach features the use of a library of purified GST-tagged human protein kinases and a recombinant protein substrate of interest. We have used this technology to identify MAP/microtubule affinity-regulating kinase 2 (MARK2) as the kinase for a glucose-regulated site on CREB-Regulated Transcriptional Coactivator 2 (CRTC2), a protein required for beta cell proliferation, as well as the Axl family of tyrosine kinases as regulators of cell metastasis by phosphorylation of the adaptor protein ELMO. We describe this technology and discuss how it can help to establish a comprehensive map of how cells respond to environmental stimuli.

Protein phosphorylation is a central feature of how cells interpret and respond to information in their extracellular milieu. Here, we present a high throughput screening protocol using kinases purified from mammalian cells to rapidly identify kinases that phosphorylate a substrate(s) of interest.


We have developed a screening platform to identify dedicated human protein kinases for phosphorylated substrates which can be used to elucidate novel ...

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of energy homeostasis and obesity. The small size of zebrafish allows for assays on embryos to be conducted in a 96- or 384-well plate format, Morpholino and CRISPR based technologies promote ease of genetic manipulation, and drug treatment by bath application is viable. Moreover, zebrafish are ideal for forward genetic screens allowing for novel gene discovery. Given the relative novelty of zebrafish as a model for obesity, it is necessary to develop tools that fully exploit these benefits. Herein, we describe a method to measure energy expenditure in thousands of embryonic zebrafish simultaneously. We have developed a whole animal microplate platform in which we use 96-well plates to isolate individual fish and we assess cumulative NADH2 production using the commercially available cell culture viability reagent alamarBlue. In poikilotherms the relationship between NADH2 production and energy expenditure is tightly linked. This energy expenditure assay creates the potential to rapidly screen pharmacological or genetic manipulations that directly alter energy expenditure or alter the response to an applied drug (e.g. insulin sensitizers).

High Throughput Danio Rerio Energy Expenditure Assay

Zebrafish are an important model organism for the study of energy homeostasis. By utilizing a NADH2 sensitive redox indicator, alamar Blue, we have developed an assay that measures the metabolic rate of zebrafish larvae in a 96 well plate format and can be applied to drug or gene discovery.

Zebrafish are an important model organism with inherent advantages that have the potential to make zebrafish a widely applied model for the study of ...