We thank the entire Sigma Life Science Mission Target ID Library development team, especially Kevin Gutshall for finding the technology and negotiating license terms, and Heather Holemon for her support and participation in fruitful troubleshooting discussions. We also thank Dr. Joop G&auml;ken of King&#x2019;s College London for freely sharing unpublished results and suggestions, and Qazi Hamid of RxBiosciences for preparing a great batch of cDNA and his persistence in getting it cloned. We would like to acknowledge Nan Lin and Scott Bahr from SAFC for performing cDNA arrays and data analysis.
MISSION is a registered trademark of Sigma-Aldrich Biotechnology L.P.

<ol>
	<li>Huang, Q., Gumireddy, K., Schrier, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+microRNAs+miR-373+and+miR-520c+promote+tumour+invasion+and+metastasis.">The microRNAs miR-373 and miR-520c promote tumour invasion and metastasis.</a> Nat. Cell Biol. 10 (2), 202-210 (2008).</li><li>Lichner, Z., Pall, E., Kerekes, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+miR-290-295+cluster+promotes+pluripotency+maintenance+by+regulating+cell+cycle+phase+distribution+in+mouse+embryonic+stem+cells.">The miR-290-295 cluster promotes pluripotency maintenance by regulating cell cycle phase distribution in mouse embryonic stem cells.</a> Differentiation. 81 (1), 11-24 (2011).</li><li>Subramanyam, D., Lamouille, S., Judson, R. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple+targets+of+miR-302+and+miR-372+promote+reprogramming+of+human+fibroblasts+to+induced+pluripotent+stem+cells.">Multiple targets of miR-302 and miR-372 promote reprogramming of human fibroblasts to induced pluripotent stem cells.</a> Nat. Biotechnol. 29 (5), 443-448 (2001).</li><li>Lim, L. P., Lau, N. C., Garrett-Engele, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Microarray+analysis+shows+that+some+microRNAs+downregulate+large+numbers+of+target+mRNAs.">Microarray analysis shows that some microRNAs downregulate large numbers of target mRNAs.</a> Nature. 433 (7027), 769-773 (2005).</li><li>Huntzinger, E., Izaurralde, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Gene+silencing+by+microRNAs:+contributions+of+translational+repression+and+mRNA+decay.">Gene silencing by microRNAs: contributions of translational repression and mRNA decay.</a> Nat. Rev. Genet. 12 (2), 99-110 (2011).</li><li>Thomas, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Desperately+seeking+microRNA+targets.">Desperately seeking microRNA targets.</a> Nat. Struc. Mol. Biol. 17 (10), 1169-1174 (2010).</li><li>Baek, D. <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), 71-71 (2008).</li><li>Guo, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mammalian+microRNAs+predominantly+act+to+decrease+target+mRNA+levels.">Mammalian microRNAs predominantly act to decrease target mRNA levels.</a> Nature. 466 (3708), 835-840 (2010).</li><li>Ma, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Tumour+invasion+and+metastasis+initiated+my+microRNA-10b+in+breast+cancer.">Tumour invasion and metastasis initiated my microRNA-10b in breast cancer.</a> Nature. 449 (7163), 682-688 (2007).</li><li>Visone, 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+miR-221+and+miR-222,+both+overexpressed+in+human+thyroid+papillary+carcinomas,+regulate+p27Kip1+protein+levels+and+cell+cycle.">MicroRNAs miR-221 and miR-222, both overexpressed in human thyroid papillary carcinomas, regulate p27Kip1 protein levels and cell cycle.</a> Endocrine-Related Cancer. 14 (3), 791-798 (2007).</li><li>Lu, Z., 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-21+promotes+cell+transformation+by+targeting+the+programmed+cell+death+4+gene.">MicroRNA-21 promotes cell transformation by targeting the programmed cell death 4 gene.</a> Oncogene. 27 (31), 4373-4379 (2008).</li><li>Guo, 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=The+noncoding+RNA,+miR-126,+suppresses+the+growth+of+neoplastic+cells+by+targeting+phosphatidylinositol+3-kinase+signaling+and+is+frequently+lost+in+colon+cancers.">The noncoding RNA, miR-126, suppresses the growth of neoplastic cells by targeting phosphatidylinositol 3-kinase signaling and is frequently lost in colon cancers.</a> Genes Chrom. Cancer. 47 (11), 939-946 (2008).</li><li>Yamakuchi, 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=miR-34a+repression+of+SIRT1+regulates+apoptosis.">miR-34a repression of SIRT1 regulates apoptosis.</a> Proc. Natl. Acad. Sci. U.S.A. 105 (36), 13421-13426 (2008).</li><li>Wickramasinghe, N. S., 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=Estradiol+downregulates+miR-21+expression+and+increases+miR-21+target+gene+expression+in+MCF-7+breast+cancer+cells.">Estradiol downregulates miR-21 expression and increases miR-21 target gene expression in MCF-7 breast cancer cells.</a> Nucleic Acid Res. 37 (8), 2584-2595 .</li><li>Ciaudo, 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=Highly+dynamic+and+sex-specific+expression+of+microRNAs+during+early+ES+cell+differentiation.">Highly dynamic and sex-specific expression of microRNAs during early ES cell differentiation.</a> PLoS Genetics. 5, e1000620 (2009).</li><li>Vallejo, D. 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=Targeting+Notch+signaling+by+the+conserved+miR-8/200+microRNA+family+in+development+and+cancer+cells.">Targeting Notch signaling by the conserved miR-8/200 microRNA family in development and cancer cells.</a> EMBO J. 30 (4), 756-769 (2011).</li><li>Jafarifar, F., 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=Repression+of+VEGFA+by+CA-rich+element-binding+microRNAs+is+modulated+by+hnRNAP.">Repression of VEGFA by CA-rich element-binding microRNAs is modulated by hnRNAP.</a> L. EMBO J. 30 (7), 1324-1334 (2011).</li><li>Clancy, J. L., 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=mRNA+isoform+diversity+can+obscure+detection+of+miRNA-mediated+control+of+translation.">mRNA isoform diversity can obscure detection of miRNA-mediated control of translation.</a> RNA. 17 (6), 1025-1033 (2011).</li></ol>

Authors declare financial relationships with commercial entities that have an interest in the submitted work.

microRNAs are 20-24 nucleotide RNAs that regulate gene expression post-transcriptionally by inhibiting mRNA translation, and frequently, destabilizing the targeted mRNA (reviewed in [6]). A single miRNA may regulate several hundred mRNAs to control a cell&#x2019;s response to developmental and environmental signals. Identifying and validating target mRNAs is essential in determining a miRNA&#x2019;s role and function in these pathways. However, target identification is not straightforward because, in animals, miRNAs and their target sites are not fully complementary. The &#x201C;seed&#x201D; region, bases 2 through 7 from the 5&#x2032;-end of the miRNA, is usually complementary to its targets. However, there are many exceptions to the seed rule, and downstream base-pairing can compensate for an imperfect seed match. A number of computer algorithms have been developed to predict miRNA targets based on seed matching and downstream compensation, target structure and position, sequence conservation, and various other parameters that have been observed for experimentally validated targets (reviewed in [7]). While in silico predictions are convenient and do identify many valid miRNA targets, most predicted genes fail experimental validation tests, and many actual targets are not predicted. Furthermore, since computer algorithms are based on previously determined target features, they do not allow discovery of targets that deviate from what is already known.

A number of experimental systems have been used successfully to identify or discover functional miRNA targets in living cells. These global screening methods include microarrays and RNA sequencing, RNA co-immunoprecipitation (RIP), and Stable Isotope Labeling with Amino acids in Cell culture (SILAC), a proteomic method (reviewed in [7]). Each method has advantages and disadvantages. Since miRNA targeting often destabilizes an mRNA and leads to its degradation, loss or gain in mRNA level after introducing a miRNA mimic or inhibitor, respectively, can identify miRNA targets. This loss or gain of mRNA is readily detected by microarray or deep sequencing. While mRNA detection methods are simpler and more sensitive than protein detection methods, mRNA detection will miss any miRNA targets that are not degraded. Recent reports from the Bartell lab comparing miRNA target results from RNA detection with those from SILAC [8] or ribosome profiling [9] indicate that mammalian miRNAs predominantly act by reducing target mRNA levels. However, many other labs working with individual miRNA targets have detected change in protein but no change in mRNA level. Reports of what appear to be translational regulation with no detectable mRNA loss include: miR-10b on HOXD10 [10], miR221/222 on p27kip1 [11], miR-21 on Pdcd4 [12], miR-126 on p85&#x03B2; [13], miR-34 on SIRT1 [14], miR-21 on PTEN [15], miR-302d on Arid4b [16], miR-200c on JAG1 [17], and miR-299, 297, 567, and 609 on VEGFA [18]. Furthermore, Clancy et al [19] recently reported that translational regulation by let-7 was only detected when individual mRNA isoforms that contain the let-7 target site were detected selectively. The latter suggests that, at least in some cases, translational regulation may be overlooked in a composite profile &#x2013; that is, when all mRNA isoforms are detected as one mRNA &#x2013; as obtained with many microarray and sequence analyses. Co-immunoprecipitation of miRNA-mRNA complexes via argonaute (usually ago2) or another associated protein (RNA immunoprecipitation, or RIP) will isolate miRNA targets regardless of regulation mechanism. In addition, RIP detects endogenous, and presumably biologically relevant, interactions. However, it is not known whether all miRNA-mRNA interactions are functional, and RIP would miss any mRNA targets that associate only transiently or are rapidly degraded. Furthermore, specific miRNA-mRNA partners must be inferred bioinformatically because all miRNA-mRNA pairs co-precipitated together. Finally, SILAC directly identifies the final product of miRNA regulation, the protein itself, but is insensitive and therefore misses rare proteins and small fold changes in protein levels.

In light of the limitations with current miRNA target identification methods, we felt an additional global assay to identify functional miRNA targets by an alternative mechanism was needed. To fulfill this need, we licensed a technology invented by Joop Gaken and Azim Mohamedali of King&#x2019;s College London. Their invention is a dual selection fusion protein, specifically, a thymidine kinase-zeocin fusion, regulated by a cDNA library of potential miRNA target sequences in its 3&#x2032;UTR. Cells stably transfected with and expressing TKzeo-cDNA constructs can be selected with zeocin, as illustrated in Fig 2. After expressing a miRNA of interest, that miRNA&#x2019;s targets can be selected with ganciclovir, as illustrated in Fig 3. Ganciclovir kills cells expressing thymidine kinase, that is, any cells expressing TKzeo-cDNA lacking a target for the miRNA of interest. Targeted cDNA can be isolated by PCR amplification of DNA from cells that survive ganciclovir using primers that flank the cDNA, and PCR products can be sequenced to identify targets. 

We have developed the King&#x2019;s College technology into a new tool for global identification and discovery of functional human miRNA targets &#x2013; the MISSION Target ID Library. With the Target ID Library, users can isolate miRNA targets by a series of mammalian cell culture transfection and drug selection steps. The Library is comprehensive, and contains 66-79% of human genes. Initial results indicate that both previously discovered as well as new targets can be isolated from the MISSION Target ID Library.

Although the protocol is of some length, use of the Target ID Library requires standard molecular lab techniques &#x2013; mammalian cell culture, transfection, drug selection, PCR, and sequencing &#x2013; and therefore, should be well within the means of most biologists. We suggest that sufficient attention be paid to proper experimental design, optimizing culture conditions, and most importantly, pre-testing each new cell type to determine optimum drug levels for selection steps (kill-curves) to ensure success with the Target ID Library.

As with all miRNA target identification methods, it is highly recommended that targets identified by screening the Target ID Library be validated with a second method, such as microarray, qRT-PCR, reporter assay (i.e., luciferase), or Western analysis.

<table border="1" >
 <tbody>
 <tr>
 <td>Name of the reagent</td>
 <td>Company</td>
 <td>Catalog Number</td>
 <td>Comments</td>
 </tr>
 <tr>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/P0982?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">JumpStart
 REDTaq 
 ReadyMix</a></td>
 <td><a href="http://www.sigmaaldrich.com/" target="_blank">Sigma-Aldrich</a></td>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/P0982?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">P0982</a></td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/G2536?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">Ganciclovir</a></td>
 <td><a href="http://www.sigmaaldrich.com/" target="_blank">Sigma-Aldrich</a></td>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/G2536?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">G2536</a></td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td>G418</td>
 <td><a href="http://www.sigmaaldrich.com/" target="_blank">Sigma-Aldrich</a></td>
 <td>G8168</td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/P9620?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">Puromycin</a></td>
 <td><a href="http://www.sigmaaldrich.com/" target="_blank">Sigma-Aldrich</a></td>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/P9620?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">P9620</a></td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td>Topo TA</td>
 <td>Invitrogen</td>
 <td>KNM4500-01
 &nbsp;
 </td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/g1n10?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">GenElute Mammalian Genomic DNA Miniprep Kit</a></td>
 <td><a href="http://www.sigmaaldrich.com/" target="_blank">Sigma-Aldrich</a></td>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/g1n10?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">G1N10</a></td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td>Cell Specific Nucleofection kit</td>
 <td>Lonza</td>
 <td>&nbsp;
 </td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td>Nucleofector</td>
 <td>Lonza</td>
 <td>AAD-1001</td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td>Zeocin</td>
 <td>Invitrogen</td>
 <td><a href="http://products.invitrogen.com/ivgn/en/US/adirect/invitrogen?cmd=catProductDetail&amp;productID=R25001">R250-01</a></td>
 <td>&nbsp;
 </td>
 </tr>
 <tr>
 <td height="1"><a href="http://www.sigmaaldrich.com/catalog/product/sigma/mreh01?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">Target ID Library</a></td>
 <td><a href="http://www.sigmaaldrich.com/" target="_blank">Sigma-Aldrich</a></td>
 <td><a href="http://www.sigmaaldrich.com/catalog/product/sigma/mreh01?lang=en&region=US&cm_mmc=affiliate-_-JOVE-_-video-_-target_id" target="_blank">MREH01</a></td>
 <td>&nbsp;</td>
 </tr>
 </tbody>
</table>

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%).

Watch this Scientific Journal Video about Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library at JoVE.com

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

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, ...

genome-wide-screen-for-mirna-targets-using-mission-target-id

Universidad San Sebastian

Research

JoVE Journal

Biology

17.5K Views.  Sigma-Aldrich. 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.

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

Genome-wide Analysis of Aminoacylation (Charging) Levels of tRNA Using Microarrays

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both prokaryotic and eukaryotic cells lead to translational regulation which is a major cellular mechanism of stress response. Familiar examples are the stringent response in E. coli and the Gcn2 stress response pathway in yeast ([2-6]). Recent work in E. coli and S. cerevisiae have shown that tRNA charging patterns are highly dynamic and depends on the type of stress experienced by cells [1, 6, 7]. The highly dynamic, variable nature of tRNA charging makes it essential to determine changes in tRNA charging levels at the genomic scale, in order to fully elucidate cellular response to environmental variations. In this review we present a method for simultaneously measuring the relative charging levels of all tRNAs in S. cerevisiae . While the protocol presented here is for yeast, this protocol has been successfully applied for determining relative charging levels in a wide variety of organisms including E. coli and human cell cultures[7, 8].

Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

We describe a method for microarray analysis to determine relative aminoacylation levels of all tRNAs from S. cerivisiae.

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both ...

MISSION esiRNA for RNAi Screening in Mammalian Cells

RNA interference (RNAi) is a basic cellular mechanism for the control of gene expression. RNAi is induced by short double-stranded RNAs also known as small interfering RNAs (siRNAs). The short double-stranded RNAs originate from longer double stranded precursors by the activity of Dicer, a protein of the RNase III family of endonucleases. The resulting fragments are components of the RNA-induced silencing complex (RISC), directing it to the cognate target mRNA. RISC cleaves the target mRNA thereby reducing the expression of the encoded protein1,2,3. RNAi has become a powerful and widely used experimental method for loss of gene function studies in mammalian cells utilizing small interfering RNAs.
Currently two main methods are available for the production of small interfering RNAs. One method involves chemical synthesis, whereas an alternative method employs endonucleolytic cleavage of target specific long double-stranded RNAs by RNase III in vitro. Thereby, a diverse pool of siRNA-like oligonucleotides is produced which is also known as endoribonuclease-prepared siRNA or esiRNA. A comparison of efficacy of chemically derived siRNAs and esiRNAs shows that both triggers are potent in target-gene silencing. Differences can, however, be seen when comparing specificity. Many single chemically synthesized siRNAs produce prominent off-target effects, whereas the complex mixture inherent in esiRNAs leads to a more specific knockdown10.
In this study, we present the design of genome-scale MISSION esiRNA libraries and its utilization for RNAi screening exemplified by a DNA-content screen for the identification of genes involved in cell cycle progression. We show how to optimize the transfection protocol and the assay for screening in high throughput. We also demonstrate how large data-sets can be evaluated statistically and present methods to validate primary hits. Finally, we give potential starting points for further functional characterizations of validated hits.

Here we use a human esiRNA library in a high-throughput screen for genes involved in cell division. We demonstrate how to set up and conduct an esiRNA screens, as well as how to analyze and validate the results.

RNA interference (RNAi) is a basic cellular mechanism for the control of gene expression. RNAi is induced by short double-stranded RNAs also known as ...

Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

MISSION LentiPlex Pooled shRNA Library Screening in Mammalian Cells

RNA interference (RNAi) is an intrinsic cellular mechanism for the regulation of gene expression. Harnessing the innate power of this system enables us to knockdown gene expression levels in loss of gene function studies.
There are two main methods for performing RNAi. The first is the use of small interfering RNAs (siRNAs) that are chemically synthesized, and the second utilizes short-hairpin RNAs (shRNAs) encoded within plasmids 1. The latter can be transfected into cells directly or packaged into replication incompetent lentiviral particles. The main advantages of using lentiviral shRNAs is the ease of introduction into a wide variety of cell types, their ability to stably integrate into the genome for long term gene knockdown and selection, and their efficacy in conducting high-throughput loss of function screens. To facilitate this we have created the LentiPlex pooled shRNA library.
The MISSION LentiPlex Human shRNA Pooled Library is a genome-wide lentiviral pool produced using a proprietary process. The library consists of over 75,000 shRNA constructs from the TRC collection targeting 15,000+ human genes 2. Each library is tested for shRNA representation before product release to ensure robust library coverage. The library is provided in a ready-to-use lentiviral format at titers of at least 5 x 108 TU/ml via p24 assay and is pre-divided into ten subpools of approximately 8,000 shRNA constructs each. Amplification and sequencing primers are also provided for downstream target identification.
Previous studies established a synergistic antitumor activity of TRAIL when combined with Paclitaxel in A549 cells, a human lung carcinoma cell line 3, 4. In this study we demonstrate the application of a pooled LentiPlex shRNA library to rapidly conduct a positive selection screen for genes involved in the cytotoxicity of A549 cells when exposed to TRAIL and Paclitaxel. One barrier often encountered with high-throughput screens is the cost and difficulty in deconvolution; we also detail a cost-effective polyclonal approach utilizing traditional sequencing.

Here we use a human LentiPlex pooled library and traditional sequencing methods to identify gene targets promoting cell survival. We demonstrate how to set up and deconvolute a LentiPlex screen and validate the results.

RNA interference (RNAi) is an intrinsic cellular mechanism for the regulation of gene expression. Harnessing the innate power of this system enables us to ...

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

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 ...

Using RNA-mediated Interference Feeding Strategy to Screen for Genes Involved in Body Size Regulation in the Nematode C. elegans

Double-strand RNA-mediated interference (RNAi) is an effective strategy to knock down target gene expression1-3. It has been applied to many model systems including plants, invertebrates and vertebrates. There are various methods to achieve RNAi in vivo4,5. For example, the target gene may be transformed into an RNAi vector, and then either permanently or transiently transformed into cell lines or primary cells to achieve gene knockdown effects; alternatively synthesized double-strand oligonucleotides from specific target genes (RNAi oligos) may be transiently transformed into cell lines or primary cells to silence target genes; or synthesized double-strand RNA molecules may be microinjected into an organism. Since the nematode C. elegans uses bacteria as a food source, feeding the animals with bacteria expressing double-strand RNA against target genes provides a viable strategy6. Here we present an RNAi feeding method to score body size phenotype. Body size in C. elegans is regulated primarily by the TGF- &beta; - like ligand DBL-1, so this assay is appropriate for identification of TGF-&beta; signaling components7. We used different strains including two RNAi hypersensitive strains to repeat the RNAi feeding experiments. Our results showed that rrf-3 strain gave us the best expected RNAi phenotype. The method is easy to perform, reproducible, and easily quantified. Furthermore, our protocol minimizes the use of specialized equipment, so it is suitable for smaller laboratories or those at predominantly undergraduate institutions.

Using RNA-mediated Interference Feeding Strategy to Screen for Genes Involved in Body Size Regulation in the Nematode C. elegans

We demonstrate how to use the RNAi feeding technique to knock down target genes and score body size phenotype in C. elegans. This method could be used for a large scale screen to identify potential genetic components of interest, such as those involved in body size regulation by DBL-1/TGF-&beta; signaling.

Double-strand RNA-mediated interference (RNAi) is an effective strategy to knock down target gene expression1-3. It has been applied to many model systems ...

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.

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.

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

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC (Crosslinking of Small Molecules to Isolate Chromatin)

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to dose-limiting toxicity and many adverse side effects. Targeting the genome with sequence-specific small molecules may enable molecules with increased therapeutic index and fewer off-target effects. N-methylpyrrole/N-methylimidazole polyamides are molecules that can be rationally designed to target specific DNA sequences with exquisite precision. And unlike most natural transcription factors, polyamides can bind to methylated and chromatinized DNA without a loss in affinity. The sequence specificity of polyamides has been extensively studied in vitro with cognate site identification (CSI) and with traditional biochemical and biophysical approaches, but the study of polyamide binding to genomic targets in cells remains elusive. Here we report a method, the crosslinking of small molecules to isolate chromatin (COSMIC), that identifies polyamide binding sites across the genome. COSMIC is similar to chromatin immunoprecipitation (ChIP), but differs in two important ways: (1) a photocrosslinker is employed to enable selective, temporally-controlled capture of polyamide binding events, and (2) the biotin affinity handle is used to purify polyamide&#8211;DNA conjugates under semi-denaturing conditions to decrease DNA that is non-covalently bound. COSMIC is a general strategy that can be used to reveal the genome-wide binding events of polyamides and other genome-targeting chemotherapeutic agents.

Genome-wide Mapping of Drug-DNA Interactions in Cells with COSMIC Crosslinking of Small Molecules to Isolate Chromatin

Identifying the direct targets of genome-targeting molecules remains a major challenge. To understand how DNA-binding molecules engage the genome, we developed a method that relies on crosslinking of small molecules to isolate chromatin (COSMIC).

The genome is the target of some of the most effective chemotherapeutics, but most of these drugs lack DNA sequence specificity, which leads to ...

Genome-wide Purification of Extrachromosomal Circular DNA from Eukaryotic Cells

Extrachromosomal circular DNAs (eccDNAs) are common genetic elements in Saccharomyces cerevisiae and are reported in other eukaryotes as well. EccDNAs contribute to genetic variation among somatic cells in multicellular organisms and to evolution of unicellular eukaryotes. Sensitive methods for detecting eccDNA are needed to clarify how these elements affect genome stability and how environmental and biological factors&#160;induce their formation in eukaryotic cells. This video presents a sensitive eccDNA-purification method called Circle-Seq. The method encompasses column purification of circular DNA, removal of remaining linear chromosomal DNA, rolling-circle amplification of eccDNA, deep sequencing, and mapping. Extensive exonuclease treatment&#160;was&#160;required for sufficient linear chromosomal DNA degradation. The rolling-circle amplification step by &#966;29 polymerase enriched for circular DNA over linear DNA. Validation of the Circle-Seq method on three S. cerevisiae CEN.PK populations of 1010 cells detected hundreds of eccDNA profiles in sizes larger than 1 kilobase. Repeated findings of ASP3-1, COS111, CUP1, RSC30,&#160;HXT6, HXT7 genes on circular DNA in both S288c and CEN.PK suggests that DNA circularization is conserved between strains at these loci. In sum, the Circle-Seq method has broad applicability for genome-scale screening for eccDNA in eukaryotes as well as for detecting specific eccDNA types.

This paper presents a sensitive method called Circle-Seq for purifying extrachromosomal circular DNA (eccDNA). The method encompasses column purification, removal of remaining linear chromosomal DNA, rolling-circle amplification and high-throughput sequencing. Circle-Seq is applicable to genome-scale screening of eukaryotic eccDNA and studying genome instability and copy-number variation.

Extrachromosomal circular DNAs (eccDNAs) are common genetic elements in Saccharomyces cerevisiae and are reported in other eukaryotes as well. EccDNAs ...