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MicroRNAs, kleine, nichtkodierende RNA-Moleküle, sind in den letzten Jahren aufgrund ihrer regulatorischen Eigenschaften verstärkt in den Fokus der Forschung gerückt. Auch in der Steuerung des Stammzellverhaltens wird ein Einfluss der microRNAs vermutet. Die vorliegende Arbeit befasst sich mit der Untersuchung der Funktion ausgewählter microRNAs in der Osteogenese von murinen embryonalen Stammzellen. Dazu wurden die microRNAs in diesen Zellen überexprimiert. Die Kultivierung der Zellen erfolgte in Medien mit unterschiedlichen Glukosekonzentrationen. Mit Hilfe verschiedener Auswertungsmöglichkeiten (qPCR, Kalzium-Assay, Färbungen) wurden die Zellen auf einen positiven bzw. negativen Effekt der microRNA auf die Knochenzellbildung analysiert. Dabei wurde auch der mögliche Einfluss der Kultivierungsbedingungen auf die Wirkung der microRNA berücksichtigt.
RNA tertiary contact interactions between RNA tetraloops and their receptors stabilize the folding of ribosomal RNA and support the maturation of the ribosome. Here we use FRET assisted structure prediction to develop structural models of two ribosomal tertiary contacts, one consisting of a kissing loop and a GAAA tetraloop and one consisting of the tetraloop receptor (TLR) and a GAAA tetraloop. We build bound and unbound states of the ribosomal contacts de novo, label the RNA in silico and compute FRET histograms based on MD simulations and accessible contact volume (ACV) calculations. The predicted mean FRET efficiency from molecular dynamics (MD) simulations and ACV determination show agreement for the KL-TLGAAA construct. The KL construct revealed too high FRET efficiency and artificial dye behavior, which requires further investigation of the model. In the case of the TLR, the importance of the correct dye and construct parameters in the modeling was shown, which also leads to a renewed modeling. This hybrid approach of experiment and simulation will promote the elucidation of dynamic RNA tertiary contacts and accelerate the discovery of novel RNA interactions as potential future drug targets.
Long-range tertiary interactions between RNA tetraloops and their receptors stabilize the folding of ribosomal RNA and support the maturation of the ribosome. Here, we use FRET-assisted structure prediction to develop a structural model of the GAAA tetraloop receptor (TLR) interaction and its dynamics. We build the docked TLR de novo, label the RNA in silico and compute FRET histograms based on MD simulations. The predicted mean FRET efficiency is remarkably consistent with single-molecule experiments of the docked tetraloop. This hybrid approach of experiment and simulation will promote the elucidation of dynamic RNA tertiary contacts and accelerate the discovery of novel RNA and RNA-protein interactions as potential future drug targets.
Ziel der Bachelorarbeit ist es die Verifizierung der Chou-Fasman-Präferenzen an RNA-Molekülen zu realisieren und in Entscheidungsbäume einzubinden. Des Weiteren soll auf Basis bekannter und bestehender Vorhersagealgorithmen ein Meta-Vorhersage-Server erschaffen werden. Dieser soll alle bisher bekannten Vorhersagen bündeln und auf diesem Weg eine Qualitätssteigerung erzielen. Ziel ist es, die Sekundärstruktur einer Base X, aber auch die Base X selbst in Abhängigkeit von Vorgänger- und Nachfolger-Base vorherzusagen. Dabei werden sowohl die Nukleosid-Präferenzen als auch, die durch Experimente ermittelten chemischen Verschiebungen, berücksichtigt
Our current research aims to establish a complete ribonucleic acid (RNA) production line from plasmid design to purification of in vitro transcribed RNA and labeling of RNA. RNA is the central molecule within the central dogma of molecular biology and is involved in most essential processes within a cell[1]. In many cases, only compact three-dimensional structures of the respective RNA are able to fulfill their function. In this context, RNA tertiary contacts such as kissing loops and pseudoknots are essential to stabilize three-dimensional folding[2]. We will produce a tertiary contact consisting of a kissing loop and a GAAA tetraloop that occurs in eukaryotic ribosomal RNA[3,4]. The RNA sequence is integrated into a vector plasmid. Subsequently, the plasmid is amplified in E. coli. After following plasmid purification steps, the RNA sequence will be transcribed in vitro[5,6]. In order for the RNA be used for Förster resonance energy transfer (FRET) experiments at the single molecule level, fluorescent dyes must be coupled to the RNA molecule[7].
The occurence of prostate cancer (PCa) has been consistently rising since three decades and remains the third leading cause of cancer-related deaths after lung and bowel cancer in Germany. Despite of new methods of early detection, such as prostate-specific antigen (PSA) testing, it persists to be the most common cancer in german men with over 63,400 new diagnoses in Germany every year and exhibits high prevalence in other countries of Northern andWestern Europe as well [64]. Men over the age of 70 are most commonly affected by the lethal disease, whereas an indisposition before 50 is rare. The malignant prostate tumor can be healed through operation or irradiation while the cancer hasn’t reached the stage of metastasis in which other therapeutic methods have to be employed [14] [15]. In the metastatic phase, the patient usually exhibits symptoms when the tumors size affects the urethra or the cancer spreads to other tissue, often the bones [16].
The high prevalence of this disease marks the importance of further research into prognosis and diagnosis methods, whereby identification of further biomarkers in PCa poses a major topic of scientific analysis. For this task, the effectiveness of high-throughput RNA sequencing of the transcriptome (RNA molecules of an organism or specific cell type) is frequently exploited [66]. RNA sequencing or RNA-Seq in short, offers the possibility of transcriptome assessment, enabling the identification of transcriptional aberrations in diseases as well as uncharacterized RNA species such as non-coding RNAs (ncRNAs) which remain undetected by conventional methods [49]. To alleviate interpretation of the sequenced reads they are assembled to reconstruct the transcriptome as close to the original state as possible, thus enabling rapid detection of relevant biomolecules in the data [49]. Transcriptomic studies often require highly accurate and complete gene annotations on the reference genome of the examined organism. However, most gene annotations and reference genomes are far from complete, containing a multitude of unidentified protein-coding and non-coding genes and transcripts. Therefore, refinement of reference genomes and annotations by inclusion of novel sequences, discovered in high quality transcriptome assemblies, is necessary [24].