Nicht-codierende RNAs sind ein bedeutender Bestandteil genregulatorischer Prozesse. Ihre Fehlregulierung wird mit zellulärer Dysfunktion und der Entstehung von Krankheiten in Zusammenhang gebracht. Ziel dieser Arbeit war die Entwicklung verschiedener Testsysteme zur Untersuchung nicht codierender RNAs mit dem Schwerpunkt microRNA (miRNA), precursor miRNA (pre-miRNA) und circular RNA (circRNA). Für eine Zyklisierung und Funktionalisierung von circRNA mittels Cu-katalysierter Click-Chemie zur Identifizierung zellulärer Interaktionspartner und zugehöriger Wirkmechanismen wurden die Termini linearer RNA-Template modifiziert. Mit Hilfe enzymatischer Techniken wie Transkription und Ligation konnte in vitro die Inkorporation Azid- und Alkin-funktionalisierter Nukleotid-Bausteine am 5‘- und 3‘-Terminus gezeigt werden. Zur Untersuchung der miRNA-Reifung in cellulo wurde die pre-miRNA-134 unter Verwendung chemo-enzymatischer Methoden mit einem Fluorophor/Quencher-Paar an den Termini ausgestattet. Durch intrazelluläre Reifung der gelabelten pre-miRNA mit einhergehender Fluoreszenzfreisetzung sollte die Visualisierung und damit die Lokalisierung des miRNA-Reifungsortes innerhalb von Neuronen realisiert werden. Zudem gelang die Entwicklung eines auf branched rolling-circle amplification (BRCA) basierenden Argonaute2(Ago2)-vermittelten Spaltungsassays. Ein Enzymkomplex aus rekombinantem, humanem Ago2 und der miRNA miR 122, genannt minimal RISC, wurde dabei zur Substrat-Spaltung eingesetzt. Zur Etablierung des BRCA-basierenden Ago2-vermittelten Spaltungsassays als Screening-Tool für die Identifizierung potentieller Inhibitoren der mRNA-Spaltung wurden exemplarisch sechs Testsubstanzen aus der Gruppe der Aminoglykoside untersucht. Der BRCA-basierende Ago2-vermittelte Spaltungsassay stellte eine einfache und zuverlässige Detektionsmethode dar, der die Untersuchung einer größeren Probenzahl mit geringem Aufwand und ohne Verwendung von fluorogen gelabeltem Substrat ermöglichte. / Non-coding RNAs are an important factor in gene regulation in which their deregulation is associated with cellular dysfunction and disease. Here, the development of different test systems for the investigation of non-coding RNAs, namely microRNA (miRNA), precursor miRNA (pre-miRNA), and circular RNA (circRNA), was on focus. In order to circularize and functionalize circRNA with the purpose of identifying cellular interaction partners and possible mechanisms of action, 5‘- and 3‘-terminal modifications were added to a linear RNA template. This was accomplished by using azide- and alkyne-functionalized nucleotides which were incorporated by enzymatic approaches like transcription and ligation to be followed by Cu-catalyzed click chemistry for circularization. For investigating miRNA maturation in neuronal cells, pre-miR-134 was modified by chemo-enzymatic approach with fluorophore and quencher at its 5‘ and 3‘ ends, respectively. Intracellular maturation of labeled pre-miRNA would produce a fluorescent signal upon cleavage, thus enabling visualization and localization of miRNA maturation in neuronal cells. Furthermore, the development of Ago2-mediated mRNA cleavage assay based on branched rolling-circle amplification (BRCA) was accomplished. A complex of recombinant human Ago2 and miRNA miR-122, called minimal RISC, was used for substrate cleavage. To establish this assay as adequate screening method for identifying potential inhibitors of mRNA cleavage, a group of six aminoglycosides was tested. The BRCA-based Ago2-mediated cleavage assay showed to be a simple and reliable detection method and screening tool for small molecule binders with little effort and without fluorescent labeling of substrate.

RNA Sequencing has recently emerged as a breakthrough technology for microRNA (miRNA) discovery. This technology has allowed the discovery of thousands of miRNAs in a large number of species. However, despite the benefits of this technology, it also carries its own limitations, including the need for sequencing read libraries and of the genome. Differently, ab initio computational methods need only the genome as input to search for genonic locus likely to give rise to novel miRNAs. In the core of most of these methods, there are predictive models induced by using data mining techniques able to distinguish between real (positive) and pseudo (negative) miRNA precursors (pre-miRNA). Nevertheless, the applicability of current literature ab initio methods have been compromised by high false detection rates and/or by other computational difficulties. In this work, we investigated how the main aspects involved in the induction of predictive models for pre-miRNA affect the predictive performance. Particularly, we evaluate the discriminant power of feature sets proposed in the literature, whose computational costs and composition vary widely. The computational experiments were carried out using sequence data from 45 species, which covered species from eight phyla. The predictive performance of the classification models induced using large training set sizes (≥ 1; 608) composed of instances extracted from real and pseudo human pre-miRNA sequences did not differ significantly among the feature sets that lead to the maximal accuracies. Moreover, the differences in the predictive performances obtained by these models, due to the learning algorithms, were neglectable. Inspired by these results, we obtained a feature set which can be computed 34 times faster than the less costly among those feature sets, producing the maximal accuracies, albeit the proposed feature set has achieved accuracy within 0.1% of the maximal accuracies. When classification models using the elements previously discussed were induced using small training sets (120) from 45 species, we showed that the feature sets that produced the highest accuracies in the classification of human sequences were also more likely to produce higher accuracies for other species. Nevertheless, we showed that the learning complexity of pre-miRNAs vary strongly among species, even among those from the same phylum. These results showed that the existence of specie specific features indicated in previous studies may be correlated with the learning complexity. As a consequence, the predictive accuracies of models induced with different species and same features and instances spaces vary largely. In our results, we show that the use of training examples from species phylogenetically more complex may increase the predictive performances for less complex species. Finally, by using ensembles of computationally less costly feature sets, we showed alternative ways to increase the predictive performance for many species while keeping the computational costs of the analysis lower than those using the feature sets from the literature. Since in miRNA discovery the number of putative miRNA loci is in the order of millions, the analysis of putative miRNAs using a computationally expensive feature set and or inaccurate models would be wasteful or even unfeasible for large genomes. In this work, we explore most of the learning aspects implemented in current ab initio pre-miRNA prediction tools, which may lead to the development of new efficient ab initio pre-miRNA discovery tools / O sequenciamento de pequenos RNAs surgiu recentemente como uma tecnologia inovadora na descoberta de microRNAs (miRNA). Essa tecnologia tem facilitado a descoberta de milhares de miRNAs em um grande número de espécies. No entanto, apesar dos benefícios dessa tecnologia, ela apresenta desafios, como a necessidade de construir uma biblioteca de pequenos RNAs, além do genoma. Diferentemente, métodos computacionais ab initio buscam diretamente no genoma regiões prováveis de conter miRNAs. A maioria desses métodos usam modelos preditivos capazes de distinguir entre os verdadeiros (positivos) e pseudo precursores de miRNA - pre-miRNA - (negativos), os quais são induzidos utilizando técnicas de mineração de dados. No entanto, a aplicabilidade de métodos ab initio da literatura atual é limitada pelas altas taxas de falsos positivos e/ou por outras dificuldades computacionais, como o elevado tempo necessário para calcular um conjunto de atributos. Neste trabalho, investigamos como os principais aspectos envolvidos na indução de modelos preditivos de pre-miRNA afetam o desempenho preditivo. Particularmente, avaliamos a capacidade discriminatória de conjuntos de atributos propostos na literatura, cujos custos computacionais e a composição variam amplamente. Os experimentos computacionais foram realizados utilizando dados de sequências positivas e negativas de 45 espécies, cobrindo espécies de oito filos. Os resultados mostraram que o desempenho preditivo de classificadores induzidos utilizando conjuntos de treinamento com 1608 ou mais vetores de atributos calculados de sequências humanas não diferiram significativamente, entre os conjuntos de atributos que produziram as maiores acurácias. Além disso, as diferenças entre os desempenhos preditivos de classificadores induzidos por diferentes algoritmos de aprendizado, utilizando um mesmo conjunto de atributos, foram pequenas ou não significantes. Esses resultados inspiraram a obtenção de um conjunto de atributos menor e que pode ser calculado até 34 vezes mais rapidamente do que o conjunto de atributos menos custoso produzindo máxima acurácia, embora a acurácia produzida pelo conjunto proposto não difere em mais de 0.1% das acurácias máximas. Quando esses experimentos foram executados utilizando vetores de atributos calculados de sequências de outras 44 espécies, os resultados mostraram que os conjuntos de atributos que produziram modelos com as maiores acurácias utilizando vetores calculados de sequências humanas também produziram as maiores acurácias quando pequenos conjuntos de treinamento (120) calculados de exemplos de outras espécies foram utilizadas. No entanto, a análise destes modelos mostrou que a complexidade de aprendizado varia amplamente entre as espécies, mesmo entre aquelas pertencentes a um mesmo filo. Esses resultados mostram que a existência de características espécificas em pre-miRNAs de certas espécies sugerida em estudos anteriores pode estar correlacionada com a complexidade de aprendizado. Consequentemente, a acurácia de modelos induzidos utilizando um mesmo conjunto de atributos e um mesmo algoritmo de aprendizado varia amplamente entre as espécies. i Os resultados também mostraram que o uso de exemplos de espécies filogeneticamente mais complexas pode aumentar o desempenho preditivo de espécies menos complexas. Por último, experimentos computacionais utilizando técnicas de ensemble mostraram estratégias alternativas para o desenvolvimento de novos modelos para predição de pre-miRNA com maior probabilidade de obter maior desempenho preditivo do que estratégias atuais, embora o custo computacional dos atributos seja inferior. Uma vez que a descoberta de miRNAs envolve a análise de milhares de regiões genômicas, a aplicação prática de modelos preditivos de baixa acurácia e/ou que dependem de atributos computacionalmente custosos pode ser inviável em análises de grandes genomas. Neste trabalho, apresentamos e discutimos os resultados de experimentos computacionais investigando o potencial de diversas estratégias utilizadas na indução de modelos preditivos para predição ab initio de pre-miRNAs, que podem levar ao desenvolvimento de ferramentas ab initio de maior aplicabilidade prática

RNA Sequencing has recently emerged as a breakthrough technology for microRNA (miRNA) discovery. This technology has allowed the discovery of thousands of miRNAs in a large number of species. However, despite the benefits of this technology, it also carries its own limitations, including the need for sequencing read libraries and of the genome. Differently, ab initio computational methods need only the genome as input to search for genonic locus likely to give rise to novel miRNAs. In the core of most of these methods, there are predictive models induced by using data mining techniques able to distinguish between real (positive) and pseudo (negative) miRNA precursors (pre-miRNA). Nevertheless, the applicability of current literature ab initio methods have been compromised by high false detection rates and/or by other computational difficulties. In this work, we investigated how the main aspects involved in the induction of predictive models for pre-miRNA affect the predictive performance. Particularly, we evaluate the discriminant power of feature sets proposed in the literature, whose computational costs and composition vary widely. The computational experiments were carried out using sequence data from 45 species, which covered species from eight phyla. The predictive performance of the classification models induced using large training set sizes (≥ 1; 608) composed of instances extracted from real and pseudo human pre-miRNA sequences did not differ significantly among the feature sets that lead to the maximal accuracies. Moreover, the differences in the predictive performances obtained by these models, due to the learning algorithms, were neglectable. Inspired by these results, we obtained a feature set which can be computed 34 times faster than the less costly among those feature sets, producing the maximal accuracies, albeit the proposed feature set has achieved accuracy within 0.1% of the maximal accuracies. When classification models using the elements previously discussed were induced using small training sets (120) from 45 species, we showed that the feature sets that produced the highest accuracies in the classification of human sequences were also more likely to produce higher accuracies for other species. Nevertheless, we showed that the learning complexity of pre-miRNAs vary strongly among species, even among those from the same phylum. These results showed that the existence of specie specific features indicated in previous studies may be correlated with the learning complexity. As a consequence, the predictive accuracies of models induced with different species and same features and instances spaces vary largely. In our results, we show that the use of training examples from species phylogenetically more complex may increase the predictive performances for less complex species. Finally, by using ensembles of computationally less costly feature sets, we showed alternative ways to increase the predictive performance for many species while keeping the computational costs of the analysis lower than those using the feature sets from the literature. Since in miRNA discovery the number of putative miRNA loci is in the order of millions, the analysis of putative miRNAs using a computationally expensive feature set and or inaccurate models would be wasteful or even unfeasible for large genomes. In this work, we explore most of the learning aspects implemented in current ab initio pre-miRNA prediction tools, which may lead to the development of new efficient ab initio pre-miRNA discovery tools / O sequenciamento de pequenos RNAs surgiu recentemente como uma tecnologia inovadora na descoberta de microRNAs (miRNA). Essa tecnologia tem facilitado a descoberta de milhares de miRNAs em um grande número de espécies. No entanto, apesar dos benefícios dessa tecnologia, ela apresenta desafios, como a necessidade de construir uma biblioteca de pequenos RNAs, além do genoma. Diferentemente, métodos computacionais ab initio buscam diretamente no genoma regiões prováveis de conter miRNAs. A maioria desses métodos usam modelos preditivos capazes de distinguir entre os verdadeiros (positivos) e pseudo precursores de miRNA - pre-miRNA - (negativos), os quais são induzidos utilizando técnicas de mineração de dados. No entanto, a aplicabilidade de métodos ab initio da literatura atual é limitada pelas altas taxas de falsos positivos e/ou por outras dificuldades computacionais, como o elevado tempo necessário para calcular um conjunto de atributos. Neste trabalho, investigamos como os principais aspectos envolvidos na indução de modelos preditivos de pre-miRNA afetam o desempenho preditivo. Particularmente, avaliamos a capacidade discriminatória de conjuntos de atributos propostos na literatura, cujos custos computacionais e a composição variam amplamente. Os experimentos computacionais foram realizados utilizando dados de sequências positivas e negativas de 45 espécies, cobrindo espécies de oito filos. Os resultados mostraram que o desempenho preditivo de classificadores induzidos utilizando conjuntos de treinamento com 1608 ou mais vetores de atributos calculados de sequências humanas não diferiram significativamente, entre os conjuntos de atributos que produziram as maiores acurácias. Além disso, as diferenças entre os desempenhos preditivos de classificadores induzidos por diferentes algoritmos de aprendizado, utilizando um mesmo conjunto de atributos, foram pequenas ou não significantes. Esses resultados inspiraram a obtenção de um conjunto de atributos menor e que pode ser calculado até 34 vezes mais rapidamente do que o conjunto de atributos menos custoso produzindo máxima acurácia, embora a acurácia produzida pelo conjunto proposto não difere em mais de 0.1% das acurácias máximas. Quando esses experimentos foram executados utilizando vetores de atributos calculados de sequências de outras 44 espécies, os resultados mostraram que os conjuntos de atributos que produziram modelos com as maiores acurácias utilizando vetores calculados de sequências humanas também produziram as maiores acurácias quando pequenos conjuntos de treinamento (120) calculados de exemplos de outras espécies foram utilizadas. No entanto, a análise destes modelos mostrou que a complexidade de aprendizado varia amplamente entre as espécies, mesmo entre aquelas pertencentes a um mesmo filo. Esses resultados mostram que a existência de características espécificas em pre-miRNAs de certas espécies sugerida em estudos anteriores pode estar correlacionada com a complexidade de aprendizado. Consequentemente, a acurácia de modelos induzidos utilizando um mesmo conjunto de atributos e um mesmo algoritmo de aprendizado varia amplamente entre as espécies. i Os resultados também mostraram que o uso de exemplos de espécies filogeneticamente mais complexas pode aumentar o desempenho preditivo de espécies menos complexas. Por último, experimentos computacionais utilizando técnicas de ensemble mostraram estratégias alternativas para o desenvolvimento de novos modelos para predição de pre-miRNA com maior probabilidade de obter maior desempenho preditivo do que estratégias atuais, embora o custo computacional dos atributos seja inferior. Uma vez que a descoberta de miRNAs envolve a análise de milhares de regiões genômicas, a aplicação prática de modelos preditivos de baixa acurácia e/ou que dependem de atributos computacionalmente custosos pode ser inviável em análises de grandes genomas. Neste trabalho, apresentamos e discutimos os resultados de experimentos computacionais investigando o potencial de diversas estratégias utilizadas na indução de modelos preditivos para predição ab initio de pre-miRNAs, que podem levar ao desenvolvimento de ferramentas ab initio de maior aplicabilidade prática

La famille des microARN (miARN) let-7 comprend treize membres qui jouent des rôles critiques dans de nombreux processus biologiques, notamment la différenciation et le développement cellulaires. Plus spécifiquement, ils fonctionnent comme des suppresseurs de tumeurs en ciblant plusieurs oncogènes. La dérégulation des niveaux de miARN let-7 a été associée à diverses maladies humaines, y compris des cancers et des troubles neurodégénératifs. Il est bien établi que Drosha et Dicer, appartenant à la famille des RNases III, sont deux enzymes clés de la voie de maturation des miARN, et qu'un traitement défectueux par ces endoribonucléases pourrait affecter l'expression des gènes. Au cours des dix dernières années, plusieurs recherches ont permis d'identifier les caractéristiques structurales de l'ARN et les protéines qui régulent la voie de maturation des miARN. Cependant, les détails moléculaires menant à la régulation des niveaux d’expression des miARN nécessitent des investigations supplémentaires. L'objectif principal de cette thèse est d'étudier l'activité de clivage in vitro des endoribonucléases Drosha et Dicer envers leurs substrats let-7, en se concentrant sur la façon dont diverses caractéristiques de séquence et de structure affectent leurs activités. Tout d'abord, un criblage structural de type SHAPE suivi d'investigations thermodynamiques et cinétiques détaillées pour les treize pré-miARN de la famille let-7 ont été réalisés avec une enzyme Dicer purifiée in vitro. Cette étude a révélé que malgré les différences structurales des membres de la famille let-7, Dicer ne discrimine pas entre ses substrats, y compris les pré-miARN avec une extension de 1-nt et 2-nt à leur extrémité 3'. L'ensemble de ces travaux met en évidence la remarquable promiscuité de Dicer vis-à-vis divers pré-miARN de la famille let-7. Deuxièmement, le mécanisme enzymatique du clivage du pré-let-7a-1 a été examiné. Les résultats de la cinétique de l'état stable, de l'état pré-stable et de l'impulsion-chase sont conformes à l'opinion dominante, soutenue par de récentes structures de cryo-EM, selon laquelle le ou les changements de conformation d'un complexe enzyme-substrat dans une conformation catalytiquement productive sont importants pour l'activité de clivage. Troisièmement, nous avons étudié la séquence et les déterminants structuraux du clivage du pri-let-7 par le complexe microprocesseur (MP) composé de Drosha et de son partenaire obligatoire DGCR8. Sur la base d'études de clivage de plusieurs substrats pri-let-7 avec un complexe MP reconstitué in vitro, il a été constaté que le clivage du pri-let-7g donne des produits multiples. En utilisant des variantes de pri-let-7g, il a été révélé qu'un élément structural conservé de pri-let-7g favorise un clivage improductif, peut-être en raison du clivage de son substrat par la MP dans l'orientation inverse. Cette étude fournit un cadre pour des investigations futures dans l'étude du clivage de pri-let-7g par Drosha et éventuellement l'identification de nouveaux mécanismes de régulation. Dans l'ensemble, nos résultats donnent un aperçu de la façon dont les caractéristiques structurales des pri-miARN et des pré-miARN de la famille let-7 modulent le traitement par Drosha et Dicer et ouvrent la voie à de futures études visant à examiner le rôle des facteurs protéiques dans la régulation de la maturation des miARN let-7. / The let-7 family of microRNAs (miRNAs) comprises of thirteen members that play critical roles in many biological processes, including cell differentiation and development. More specifically, they function as tumor suppressors by targeting several oncogenes. Deregulation in let-7 miRNA levels has been associated with various human diseases, including cancers and neurodegenerative disorders. It is well established that Drosha and Dicer are the two key enzymes of the miRNA maturation pathway, and that faulty processing by these endoribonucleases could affect gene silencing. Thus, it is crucial to better understand how Drosha and Dicer respectively process the primary miRNAs (pri-miRNAs) and precursor miRNAs (pre-miRNAs) to yield mature miRNAs, and how these enzymes are regulated. In the last decade of miRNA research, several investigations have identified RNA structural features and RNA-binding proteins that regulate the miRNA maturation pathway, adding another layer of regulation in this pathway. However, the molecular detail of this regulation requires further investigations. The main goal of this thesis is to investigate the in vitro processing activity of Drosha and Dicer toward their let-7 substrates, focusing on how diverse sequence and structural features affect their activities. First, SHAPE structural probing followed by detailed thermodynamic and kinetic investigations for all thirteen pre-miRNAs of the let-7 family were performed with in vitro purified Dicer. Surprisingly, this study revealed that despite structural differences in the pre-let-7 members, Dicer does not discriminate between these substrates, including pre-miRNAs with a 1 nt and a 2-nt overhang at their 3'-end. Additional binding and cleavage investigations of pre let-7 substrates carrying 3'-end modifications (mono- and oligo-uridylation, mono- and oligo-adenylation) were performed to clarify how these modifications affect Dicer binding and cleavage activities. Together, this work highlights the remarkable substrate promiscuity of Dicer toward diverse pre-miRNAs of the let-7 family. Second, the enzymatic mechanism of pre-let-7 cleavage by Dicer was examined using pre-let-7a-1 as a model substrate. The results from the steady-state, pre-steady state and pulse-chase kinetics are consistent with the prevailing view, supported by recent cryo-EM structures, that the conformational change(s) of an enzyme-substrate complex into a catalytically productive conformation are important for cleavage activity. Third, the sequence and structural determinants of pri-let-7 processing by the Microprocessor (MP) complex composed of Drosha and its obligatory partner DGCR8 were investigated. Based on cleavage studies of several pri-let-7 substrates with an in vitro reconstituted MP complex, it was found that cleavage of pri-let-7g yields multiple products. Using pri-let-7g variants, it was revealed that a conserved structural element of pri-let-7g promotes unproductive cleavage, possibly as a result of the MP cleaving its substrate in the reverse orientation. This study provides the framework for future investigations in studying pri-let-7g processing by Drosha and possibly identifying novel mechanisms of regulation. Overall, our findings provide insights on how the structural features of pri-miRNAs and pre-miRNAs of the let-7 family modulate processing by Drosha and Dicer and pave the way for future studies aimed at examining the role of protein factors in regulating the maturation of let-7 miRNAs.

Das Ziel dieser Arbeit war die Synthese niedermolekularer Verbindungen, die an die prä-miRNA binden und dadurch die Reifung zur miRNA inhibieren. Daher sollten die RNA-Binder 2-Desoxystreptamin sowie Neamin mit Alkinen funktionalisiert und durch Kupfer-katalysierte Azid-Alkin 1,3-dipolare Cycloaddition (CuAAC) mit verschiedenen bivalenten Aziden verknüpft werden. Im Rahmen dieses Projekts wurde der synthetische Zugang zu den benötigten Alkin- sowie Azid-funktionalisierten Grundbausteinen optimiert. Ferner wurde ein effektives und zuverlässiges Protokoll für die CuAAC erarbeitet, welches es ermöglichte, 88 Testsubstanzen in guter Ausbeute und hoher Reinheit zu isolieren. Anschließend wurde die Substanzbibliothek in einem BRCA-Reifungsassay unter kompetitiven Bedingungen auf die Inhibition der miRNA-Reifung getestet. Dabei wurden mehrere potente Inhibitoren der miRNA-Reifung mit IC50-Werten von bis zu 0.5 µM identifiziert. Der zweite Schwerpunkt dieser Arbeit lag auf der Etablierung einer chemo-enzymatische RNA-Funktionalisierungsstrategie um prä-miRNA-Sonden herzustellen: In-vitro-Transkriptionen mit der T7-RNA-Polymerase sowie Ligationen mit der T4 RNA Ligase 1 waren das Fundament der enzymatischen RNA-Synthese, während die Funktionalisierung der RNA durch CuAAC erzielt wurde. Dafür wurden die neuen Verbindungen O-(5‘-Guanosin)-O-propargylmonophosphat sowie 3‘,5‘-O,O-Bisphosphat-5-ethinyluridin synthetisiert, durch in-vitro-Tran¬skription an das 5‘-Ende von prä-miRNAs eingeführt und anschließend durch CuAAC mit einem Fluoreszenzlöscher modifiziert. Das Uridinbisphosphat wurde durch CuAAC mit einem Fluorophor markiert und anschließend effizient mit der T4 RNA Ligase 1 an das 3‘-Ende verschiedener prä-miRNAs ligiert. Darüber hinaus war es auch möglich, das Alkin-modifizierte Uridinbisphosphat an das 3‘-Ende von prä-miRNAs zu ligieren, dieses durch eine zweite Ligation an eine definierte interne Position zu verschieben und abschließend durch CuAAC zu funktionalisieren. / The objective of this work was the synthesis of small molecules, which bind to pre-miRNAs to prevent their maturation to fully active miRNAs. To create a substance library of bivalent inhibitors, the RNA binding motifs 2-deoxystreptamine as well as neamine were alkyne modified and linked with several bisazides via a copper catalyzed alkyne-azide cycloaddition (CuAAC). Hence, optimized syntheses of the basic building blocks along with an effective and reliable CuAAC-protocol were established. 88 test substances were isolated in good yield and high purity. Finally they were analyzed with regard to their potential to selectively inhibit the miRNA maturation. For this purpose, the assay was performed under competitive conditions with a set of two pre-miRNA-pairs. The initial screening revealed several inhibitors with IC50-values in the lower µM range. The second focus of this work was on the development of a synthetic access to alkyne modified ribonucleotides to establish a chemo-enzymatic functionalization strategy for RNAs using in-vitro-transcription, ligation and CuAAC. In this context, the syntheses of 3‘,5‘-O,O-bisphosphate-5-ethinyl uridine and O-(5‘-guanosine)-O-propargyl monophos-phate are described for the first time. The guanosine monophosphate was used as transcription starter to address the 5’-end of RNA and was consecutively labeled with an azido-tagged quencher. The uridine bisphosphate was conjugated with a fluorophor and introduced to the 3’-end of RNAs by T4 RNA ligase 1. Moreover, the uridine bisphosphate can be ligated to the 3’-end without a fluorophor attached, to serve as a connecting point for a further ligation with an oligonucleotide of any length. Thereby, the former terminal alkynylated uridine was shifted to a defined internal position by successive enzymatic reactions and was successfully derivatized with a fluorophor by CuAAC.

No description available.

Neurotrophins and their receptors are key molecules in the development of thenervous system. Neurotrophin-3 binds preferentially to its high-affinity receptorNTRK3, which exists in two major isoforms in humans, the full-length kinaseactiveform (150 kDa) and a truncated non-catalytic form (50 kDa). The twovariants show different 3'UTR regions, indicating that they might be differentiallyregulated at the post-transcriptional level. In this work we explore howmicroRNAs take part in the regulation of full-length and truncated NTRK3,demonstrating that the two isoforms are targeted by different sets of microRNAs.We analyze the physiological consequences of the overexpression of some of theregulating microRNAs in human neuroblastoma cells. Finally, we providepreliminary evidence for a possible involvement of miR-124 - a microRNA with noputative target site in either NTRK3 isoform - in the control of the alternativespicing of NTRK3 through the downregulation of the splicing repressor PTBP1. / Las neurotrofinas y sus receptores constituyen una familia de factores crucialespara el desarrollo del sistema nervioso. La neurotrofina 3 ejerce su funciónprincipalmente a través de una unión de gran afinidad al receptor NTRK3, del cualse conocen dos isoformas principales, una larga de 150KDa con actividad de tipotirosina kinasa y una truncada de 50KDa sin dicha actividad. Estas dos isoformasno comparten la misma región 3'UTR, lo que sugiere la existencia de unaregulación postranscripcional diferente. En el presente trabajo se ha exploradocomo los microRNAs intervienen en la regulación de NTRK3, demostrando que lasdos isoformas son reguladas por diferentes miRNAs. Se han analizado lasconsecuencias fisiológicas de la sobrexpresión de dichos microRNAs utilizandocélulas de neuroblastoma. Finalmente, se ha estudiado la posible implicación delmicroRNA miR-124 en el control del splicing alternativo de NTRK3 a través de laregulación de represor de splicing PTBP1.