Nous tenons à remercier toute la vie Sigma sciences cible Mission ID développement équipe de la Bibliothèque, en particulier Kevin Gutshall pour trouver la technologie et de la négociation des conditions de licence, et Heather Holemon pour son soutien et la participation à des discussions fructueuses de dépannage. Nous remercions également le Dr Joop Gäken du Kings College de Londres pour partager librement les résultats non publiés et des suggestions, et Qazi Hamid RxBiosciences pour la préparation d&#39;un lot de grande ADNc et sa persistance à l&#39;obtenir cloné. Nous tenons à remercier Nan Lin et Scott Bahr de SAFC pour effectuer des puces à ADNc et l&#39;analyse des données. MISSION est une marque déposée de Sigma-Aldrich Biotechnology LP

<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>

Les auteurs déclarent des relations financières avec des entités commerciales qui ont un intérêt dans le travail présenté.

microARN sont des ARN 20-24 nucléotides qui régulent l&#39;expression génique post-transcriptionnelle par inhibition de la traduction des ARNm, et souvent, la déstabilisation de l&#39;ARNm ciblé (revue dans [6]). Un seul miARN peuvent réguler plusieurs centaines ARNm pour contrôler la réponse d&#39;une cellule à signaux développement et d&#39;environnement. Identifier et valider les ARNm cibles est essentielle pour déterminer le rôle d&#39;un miARN et de la fonction dans ces voies. Cependant, l&#39;identification des cibles n&#39;est pas simple parce que, chez les animaux, les miRNAs et de leurs sites cibles ne sont pas tout à fait complémentaire. La «graine» région, fonde 2 à 7 de l&#39;extrémité 5 &#39;du miRNA, est généralement complémentaire à ses objectifs. Cependant, il ya de nombreuses exceptions à la règle de semences, et en aval d&#39;appariement de bases peut compenser pour un match de semences imparfaite. Un certain nombre d&#39;algorithmes informatiques ont été développés pour prédire les cibles des miRNA sur la base de correspondance de semences et de compensation en aval, la structure cible d&#39;unla position d, conservation de la séquence, et divers autres paramètres qui ont été observées pour des cibles validées expérimentalement (revue dans [7]). Bien que les prédictions in silico sont pratiques et permettent d&#39;identifier de nombreuses cibles des miRNA valides, la plupart des gènes prédit échouent aux tests de validation expérimentale, et de nombreux objectifs réels ne sont pas prévues. En outre, parce que les algorithmes informatiques sont basés sur les caractéristiques cibles préalablement déterminées, elles ne permettent pas de découverte de cibles qui s&#39;écartent de ce qui est déjà connu. Un certain nombre de systèmes expérimentaux ont été utilisés avec succès pour identifier ou de découvrir des cibles des miRNA fonctionnels dans les cellules vivantes. Ces méthodes de dépistage mondiaux comprennent les microréseaux et séquençage de l&#39;ARN, l&#39;ARN co-immunoprécipitation (RIP), et l&#39;étiquetage des isotopes stables avec les acides aminés en culture cellulaire (SILAC), une méthode protéomique (revue dans [7]). Chaque méthode a ses avantages et ses inconvénients. Depuis miARN ciblant déstabilise souvent un ARNm et conduit à sa dégradation, la perte ogain de r dans le niveau d&#39;ARNm après l&#39;introduction d&#39;un miRNA imiter ou d&#39;un inhibiteur, respectivement, peuvent identifier les cibles des miRNA. Cette perte ou le gain de l&#39;ARNm est facilement détectée par puces à ADN ou par séquençage en profondeur. Bien que les méthodes de détection d&#39;ARNm sont plus simples et plus sensible que les méthodes de détection de protéines, de détection d&#39;ARNm va manquer les cibles des miRNA qui ne sont pas dégradées. Des rapports récents du laboratoire Bartell comparant les résultats cibles des miRNA de détection de l&#39;ARN avec ceux de SILAC [8] ou le profilage des ribosomes [9] indiquent que les miRNAs mammifères principalement agissent en réduisant les niveaux d&#39;ARNm cibles. Cependant, de nombreux autres laboratoires qui travaillent avec des objectifs individuels miARN ont détecté le changement dans la protéine, mais aucun changement dans le niveau d&#39;ARNm. Rapports de ce qui semble être régulation de la traduction sans perte ARNm détectable comprennent: miR-10b sur HOXD10 [10], miR221/222 sur p27kip1 [11], miR-21 sur Pdcd4 [12], miR-126 sur p85β [13] , miR-34 sur SIRT1 [14], miR-21 sur PTEN [15], miR-302d sur Arid4b [16], miR-200c sur JAG1 [17], et miR-299, 297, 567, une 609 sur VEGFA [18]. En outre, Clancy et al [19] a récemment rapporté que la réglementation en translation par let-7 a été détectée lorsque isoformes d&#39;ARNm individuels qui contiennent le site let-7 ont été détectés cible de manière sélective. Celui-ci suggère que, au moins dans certains cas, régulation de la traduction peut être négligé dans un profil composite - qui est, lorsque toutes les isoformes d&#39;ARNm sont détectés comme un ARNm - obtenue avec un microréseau nombreux et analyses de séquences. Co-immunoprécipitation des miARN-ARNm complexes via Argonaute (généralement Ago2) ou d&#39;une autre protéine associée (ARN immunoprécipitation, ou RIP) permet d&#39;isoler les cibles des miRNA indépendamment du mécanisme de règlement. En outre, RIP détecte endogène, et probablement biologiquement pertinente, les interactions. Cependant, on ne sait pas si toutes les interactions miARN-ARNm sont fonctionnels, et RIP serait manquer aucun ARNm cibles qui associent uniquement de façon transitoire ou sont rapidement dégradées. En outre, spécifiques miARN-ARNm des partenaires doit être déduite bioinformatiment parce que toutes les paires de miARN-ARNm co-précipités ensemble. Enfin, SILAC identifie directement le produit final de la réglementation miRNA, la protéine elle-même, mais elle est insensible et manque donc de protéines rares et de petits changements de pliage dans les taux de protéines. À la lumière des limites des méthodes actuelles d&#39;identification des miRNA cibles, nous nous sommes sentis un test global supplémentaire pour identifier les cibles des miRNA fonctionnelles par un autre mécanisme a été nécessaire. Pour répondre à ce besoin, nous sous licence une technologie inventée par Joop Gaken et Azim Mohamedali du Kings College de Londres. Leur invention est une protéine de fusion double sélection, en particulier, une thymidine kinase-zéocine fusion, régie par une banque d&#39;ADNc de séquences cibles potentielles de miARN dans son 3&#39;UTR. Les cellules transfectées de façon stable avec et exprimant TKzeo-ADNc constructions peuvent être sélectionnés avec la zéocine, comme illustré dans la figure 2. Après avoir exprimé un miRNA d&#39;intérêt, les objectifs que miARN peuvent être sélectionnés wie ganciclovir, comme l&#39;illustre la figure 3. Ganciclovir tue les cellules exprimant la thymidine kinase, qui est, toutes les cellules exprimant TKzeo-ADNc défaut une cible pour la miARN d&#39;intérêt. Ciblée d&#39;ADNc peut être isolé par amplification par PCR de l&#39;ADN à partir de cellules qui survivent au ganciclovir en utilisant des amorces qui flanquent les produits d&#39;ADNc et la PCR peut être séquencés pour identifier les cibles. Nous avons développé une technologie Collège du Roi dans un nouvel outil pour l&#39;identification globale et la découverte de cibles fonctionnelles miRNA humains - la Bibliothèque ID MISSION cible. Avec la Bibliothèque ID cible, les utilisateurs peuvent isoler des cibles des miRNA par une série de transfection de culture cellulaire de mammifère et des mesures de sélection des médicaments. La bibliothèque est complète, et contient 66-79% des gènes humains. Les premiers résultats indiquent que les deux précédemment découvert ainsi que de nouvelles cibles peuvent être isolés de la Bibliothèque ID MISSION cible. Bien que le protocole est d&#39;une certaine longueur,l&#39;utilisation de la Bibliothèque ID cible nécessite des techniques standard de laboratoire moléculaires - culture cellulaire de mammifère, la transfection, la sélection des médicaments, la PCR et le séquençage - et, par conséquent, devraient être bien dans les moyens de la plupart des biologistes. Nous suggérons qu&#39;une attention suffisante soit accordée à la conception expérimentale proprement dite, l&#39;optimisation des conditions de culture, et surtout, de pré-tester chaque nouveau type de cellule pour déterminer les niveaux de médicaments optimales pour les étapes de sélection (kill-courbes) pour assurer le succès avec la Bibliothèque ID cible. Comme avec les méthodes d&#39;identification des miRNA tous les cibles, il est fortement recommandé que les objectifs identifiés par criblage de la bibliothèque ID cible être validée par une deuxième méthode, tels que puces, qRT-PCR, test rapporteur (c.-à-luciférase), ou l&#39;analyse de l&#39;Ouest.

<table border="1" ><tbody><tr><td> Nom du réactif </td><td> Entreprise </td><td> Numéro de catalogue </td><td> Commentaires </td></tr><tr><td> JumpStart REDTaq Readymix </td><td> Sigma-Aldrich </td><td> P0982 </td><td></td></tr><tr><td> Le ganciclovir </td><td> Sigma-Aldrich </td><td> G2536 </td><td></td></tr><tr><td> G418 </td><td> Sigma-Aldrich </td><td> G8168 </td><td></td></tr><tr><td> Puromycine </td><td> Sigma-Aldrich </td><td> P9620 </td><td></td></tr><tr><td> Topo TA </td><td> Invitrogen </td><td> KNM4500-01 </td><td></td></tr><tr><td> GenElute ADN génomique des mammifères Miniprep Kit </td><td> Sigma-Aldrich </td><td> G1N10 </td><td></td></tr><tr><td> K nucléofection Cell Specificil </td><td> Lonza </td><td></td><td></td></tr><tr><td> Nucleofector </td><td> Lonza </td><td> AAD-1001 </td><td></td></tr><tr><td> Zéocine </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></td></tr><tr><td height="1"> ID cible Bibliothèque </td><td> Sigma-Aldrich </td><td> MREH01 </td><td></td></tr></tbody></table>

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

La Bibliothèque ID cible est conçu pour aider à la découverte des cibles et l&#39;identification des microARN (miARN). La Bibliothèque ID cible est une base de plasmide, l&#39;échelle du génome ADNc cloné dans les 3&#39;UTR aval de la protéine de fusion à double sélection, la thymidine kinase-zéocine (TKzeo). Le premier tour de sélection est pour les transformants stables, suivie avec l&#39;introduction d&#39;un miRNA d&#39;intérêt, et enfin, la sélection pour les ADNc contenant la cible du miRNA de. ADNc sélectionnés sont identifiés par séquençage (voir la figure 1-3 pour Workflow Library ID cible et les détails). Pour assurer une large couverture du transcriptome humain, ID cible Bibliothèque ADNc ont été générés via oligo-dT amorçage utilisant un pool d&#39;ARN total préparé à partir de plusieurs tissus humains et des lignées cellulaires. Résultant gamme d&#39;ADNc de 0,5 à 4 ko, avec une taille moyenne de 1,2 kb, et ont été clonés dans le p3TKzeo double plasmide de sélection (voir la figure 4 pour une carte de plasmide). Les gènes cibles représentées dans le lithèque peut être trouvée sur la page web de Sigma-Aldrich. Les résultats de séquençage Illumina (tableau 3), montrent que la bibliothèque comprend 16 922 des 21,518 gènes uniques dans UCSC RefGene (79%), ou 14.000 gènes avec 10 ou plus de lectures (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

La Bibliothèque ID cible est une base de plasmide, l&#39;échelle du génome de collecte de l&#39;ADNc cloné utilisé pour identifier les cibles des miRNA. Ici, nous la preuve de son utilisation et l&#39;application.

L&#39;échelle du génome d&#39;écran pour cibles des miRNA l&#39;aide de l&#39;ID de bibliothèque MISSION cible

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

Research

JoVE Journal

Biology

17.5K vues.  Sigma-Aldrich. La Bibliothèque ID cible est une base de plasmide, l'échelle du génome de collecte de l'ADNc cloné utilisé pour identifier les cibles des miRNA. Ici, nous la preuve de son utilisation et l'application.

Vidéo: 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.

L&#39;échelle du génome Analyse des aminoacylation (recharge) Niveaux d&#39;ARNt utilisant des 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 ...

Recherche

Biologie

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.

Analyse du génome entier en utilisant ChIP d&#39;identifier les gènes cibles spécifiques Isoform

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.

LentiPlex MISSION dépistage sur mélange d&#39;Bibliothèque shRNA dans les cellules de mammifères

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

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene 1. While the creation of libraries of RNAi clones covering most of the C. elegans genome 2,3 opened the way for true functional genomic studies (see for example 4-7), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens 8. The approach relies on microscopic imaging and image analysis. 
	Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture.
	We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing9. When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

We describe a protocol using C. elegans and RNAi feeding libraries that allows automated measurement of multiple parameters such as fluorescence, size and opacity of individual worms in a population. We give one example of a screen to identify genes involved in anti-fungal innate immunity in C. elegans.

Quantitatifs et automatisé à haut débit du génome à l&#39;échelle écrans ARNi dans C. elegans</em

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. ...

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.

A Novel bayésienne changement de point Algorithme pour l&#39;échelle du génome Analyse des données ChIPseq divers types de

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.

La délétion de gènes dans le génome entier<em&gt; Streptococcus sanguinis</em&gt; Par PCR à haut débit

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

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.

Détection de cibles miARN dans à haut débit utilisant le test de 3&#39;LIFE

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).

La cartographie du génome à l&#39;échelle du médicament-ADN interactions dans les cellules avec COSMIC (réticulation de petites molécules pour isoler la chromatine)

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.

Purification de l&#39;ensemble du génome de l&#39;ADN extrachromosomique circulaire à partir de cellules eucaryotes

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

Comprehensive Workflow for the Genome-wide Identification and Expression Meta-analysis of the ATL E3 Ubiquitin Ligase Gene Family in Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the prediction of family functions based on several features, such as the presence of sequence motifs or of particular sites for post-translational modification and the expression profile of family members in different conditions. This work describes a detailed protocol for gene family characterization. Here, the procedure is applied to the characterization of the Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligase family in grapevine. The methods include the genome-wide identification of family members, the characterization of gene localization, structure, and duplication, the analysis of conserved protein motifs, the prediction of protein localization and phosphorylation sites as well as gene expression profiling across the family in different datasets. Such procedure, which could be extended to further analyses depending on experimental purposes, could be applied to any gene family in any plant species for which genomic data are available, and it provides valuable information to identify interesting candidates for functional studies, giving insights into the molecular mechanisms of plant adaptation to their environment.

This article describes the procedure for the identification and characterization of a gene family in grapevine applied to the family of Arabidopsis T&#243;xicos in Levadura (ATL) E3 ubiquitin ligases.

Flux de travail complet pour le génome-large d’Identification et de la Meta-analyse de l’Expression de la famille des gènes ATL E3 ubiquitine Ligase à Grapevine

Classification and nomenclature of genes in a family can significantly contribute to the description of the diversity of encoded proteins and to the ...