Elucidating the potential of microRNAs : towards a functional landscape of microRNAs in the model organisms Tribolium castaneum and Galleria mellonella
Insects have been utilized by humans for thousands of years. At first in a very primitive way as direct food provider for example for honey or as material source like lac dye or silk. Nowadays, with emerging technical possibilities, resources and needs, the usage of insects, as the world s largest animal class, is getting much more skillful.Technologies like Next Generation Sequencing emerged in the past decades, enabling a broad scientific community to study organisms, like insects, at a molecular genetic level for a comparable cheap price. This technological jump led to findings that promoted insects as valuable model organisms and resource of molecules for various applications. For example, the study of effects of pathogens to an insect organism and a potential transfer of knowledge to humans.One of the emerging fields in the molecular genetic research is the investigation of gene expression regulation by microRNAs. Since their misregulation can lead to serious malformations of body parts, cancer and even death, the large research community is diversified and the numbers of microRNA publications is increasing to nearly 18,000 per year. Despite their popular impact, the in silico analysis of microRNAs needs the knowledge of a broad portfolio of task-specific tools and settings in order to compute results. Furthermore, due to their small size of only around 21 nucleotides, the assignment of a corresponding mRNA is a non-trivial task, leading to a broad variety of approaches in the laboratory and in silico. On the one hand, the target prediction algorithms mostly try to generalize microRNA behavior and filter for microRNA-mRNA bindings with these features. This leads to a large number of false-positives. On the other hand, many of the laboratory approaches are not high- throughput, making a validation of microRNA targets time consuming. With Next Generation Sequencing, certain methods (like CLIP-sequencing) are able to raise signals of microRNA binding sites on mRNAs in a high-throughput manner. However, these methods have the drawback that they are very difficult to perform. Datasets are therefore very rare in public databases. Within this thesis I present the assessment of a best practice workflow for microRNA analysis, providing a guideline for other researchers. My benchmarking of microRNA isoform tools not only identified the most suitable tool for high-throughput pipelines, but also highlighted the broader impact on microRNA isoforms in Tribolium castaneum early development. Outgoing from the assessment, I created a scripted workflow which was then translated into a novel pipeline, called microPIECE, covering the widely used microRNA analysis tasks, including microRNA isoform detection, combined with a novel microRNA target prediction approach that relies on transferred and evolutionary conserved CLIP-sequencing data from closely related species. This approach makes the previously mentioned rare data available to other species.Finally, I applied my pipeline exemplarily to Galleria mellonella in order to identify the impact of microRNAs to the immune response against pathogenic Escherichia coli strains, indicating a benefit for human pathogen investigations and shedding light on a potential insect utilization for humans.The results of my research are publicly available in three different scientific journals (BMC Bioinformatics, JOSS- Journal of Open Source Software and Nature Scientific Reports). The source code of the microPIECE pipeline, as well as a Docker environment is available via GitHub.com.
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