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The transcriptome of non-coding RNA (ncRNA) species is increasingly focused in Alzheimer’s disease (AD) research. NcRNAs comprise, among others, transfer RNAs, long non-coding RNAs and microRNAs (miRs), each with their own specific biological function. We used smallRNASeq to assess miR expression in the hippocampus of young (3 month old) and aged (8 month old) Tg4-42 mice, a model system for sporadic AD, as well as age-matched wildtype controls. Tg4-42 mice express N-truncated Aβ4–42, develop age-related neuron loss, reduced neurogenesis and behavioral deficits. Our results do not only confirm known miR-AD associations in Tg4-42 mice, but more importantly pinpoint 22 additional miRs associated to the disease. Twenty-five miRs were differentially expressed in both aged Tg4-42 and aged wildtype mice while eight miRs were differentially expressed only in aged wildtype mice, and 33 only in aged Tg4-42 mice. No significant alteration in the miRNome was detected in young mice, which indicates that the changes observed in aged mice are down-stream effects of Aβ-induced pathology in the Tg4-42 mouse model for AD. Targets of those miRs were predicted using miRWalk. For miRs that were differentially expressed only in the Tg4-42 model, 128 targets could be identified, whereas 18 genes were targeted by miRs only differentially expressed in wildtype mice and 85 genes were targeted by miRs differentially expressed in both mouse models. Genes targeted by differentially expressed miRs in the Tg4-42 model were enriched for negative regulation of long-term synaptic potentiation, learning or memory, regulation of trans-synaptic signaling and modulation of chemical synaptic transmission obtained. This untargeted miR sequencing approach supports previous reports on the Tg4-42 mice as a valuable model for AD. Furthermore, it revealed miRs involved in AD, which can serve as biomarkers or therapeutic targets.
Humanity is plagued by many diseases. Beside environmental influences, many --- if not all --- diseases are also subject to genetic predisposition and then display molecular alterations such as proteomic or metabolic aberrations. The elucidation of the molecular principles underlying human diseases is one of the prime goals of biomedical research. To this end, there has been an advent of large-scale omics profiling studies. While the field of molecular biology has experienced tremendous development, data analysis remains a bottleneck. In the context of this thesis, we developed a number of analysis strategies for different types of omics data resulting from different experimental settings. These include approaches for associations studies for plasma miRNAs and time-resolved plasma omics data. Furthermore, we devised analyses of different RNA-Seq transcriptome profiling studies coping with problems such as lack of replicates or multifactorial experimental design. We also designed machine learning frameworks for the identification of discriminatory biomolecular signatures analysing case-control or time-to-event data. All of the strategies mentioned above were developed and applied in the contexts of multi-disciplinary endeavours. They aided in the identification of plasma miRNAs associated with age, sex, and BMI as well as plasma miRNAs bearing potential as diagnostic biomarkers for non-alcoholic fatty liver disease (NAFLD). This thesis significantly contributed to a study demonstrating the utility of plasma miRNAs as prognostic biomarkers for major cardiovascular events such as ST-elevation myocardial infarction. Our approaches for analysing RNA-Seq data aided in the characterisation of murine models for Alzheimers disease and the transcriptional response of human gingiva fibroblasts to ionizing radiation exposure. Furthermore, the developed approaches were applied for studying a human model for thyrotoxicosis and for the successful identification of a multi-omics plasma biomarker signature of thyroid status. We are only beginning to understand the molecular principles underlying human diseases. The approaches and results presented in this thesis will contribute to improved understanding of biomolecular processes involved in common diseases such as Alzheimers disease, NAFLD, and cardiovascular diseases.
Helicobacter (H.) pylori is the most important cause for peptic ulcer disease and a risk factor for gastric carcinoma. How colonization with H. pylori affects the intestinal microbiota composition in humans is unknown. We investigated the association of H. pylori infection with intestinal microbiota composition in the population-based cohort Study-of-Health-in-pomerania (SHip)-tRenD. Anti-H. pylori serology and H. pylori stool antigen tests were used to determine the H. pylori infection status. the fecal microbiota composition of 212 H. pylori positive subjects and 212 matched negative control individuals was assessed using 16S rRNA gene sequencing. H. pylori infection was found to be significantly associated with fecal microbiota alterations and a general increase in fecal microbial diversity. in infected individuals, the H. pylori stool antigen load determined a larger portion of the microbial variation than age or sex. the highest H. pylori stool antigen loads were associated with a putatively harmful microbiota composition. this study demonstrates profound alterations in human fecal microbiota of H. pylori infected individuals. While the increased microbiota diversity associated with H. pylori infection as well as changes in abundance of specific genera could be considered to be beneficial, others may be associated with adverse health effects, reflecting the complex relationship between H. pylori and its human host.