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Non-coding RNA (ncRNA) classes take over important housekeeping and regulatory functions and are quite heterogeneous in terms of length, sequence conservation and secondary structure. High-throughput sequencing reveals that the expressed novel ncRNAs and their classification are important to understand cell regulation and identify potential diagnostic and therapeutic biomarkers. To improve the classification of ncRNAs, we investigated different approaches of utilizing primary sequences and secondary structures as well as the late integration of both using machine learning models, including different neural network architectures. As input, we used the newest version of RNAcentral, focusing on six ncRNA classes, including lncRNA, rRNA, tRNA, miRNA, snRNA and snoRNA. The late integration of graph-encoded structural features and primary sequences in our MncR classifier achieved an overall accuracy of >97%, which could not be increased by more fine-grained subclassification. In comparison to the actual best-performing tool ncRDense, we had a minimal increase of 0.5% in all four overlapping ncRNA classes on a similar test set of sequences. In summary, MncR is not only more accurate than current ncRNA prediction tools but also allows the prediction of long ncRNA classes (lncRNAs, certain rRNAs) up to 12.000 nts and is trained on a more diverse ncRNA dataset retrieved from RNAcentral.
Plus‐strand RNA [(+)RNA] viruses are the largest group of viruses, medically highly relevant human pathogens, and are a socio‐economic burden. The current global pandemic of the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) shows how a virus has been rapidly spreading around the globe and that– without an antiviral treatment– virus trans mission is solely dependent on human behavior. However, other (+)RNA viruses such as rhino‐, noro‐, dengue‐ (DENV), Zika, and hepatitis C virus (HCV) are constantly spreading and expanding geographically. As in the case of hepatitis C, since its first identification in the 1970s, it took more than 30 years to understand the HCV structure, genome organiza t ion, life cycle, and virus‐host interplay leading to the cure of a chronic and life‐threatening disease. However, no vaccination or antiviral treatment exists for most (+)RNA viruses. Con sequently, a precise and comprehensive analysis of the viruses, their life cycles, and parasitic interactions with their hosts remains an important field of research. In the presented thesis, we use mathematical modeling to study the life cycles of (+)RNA viruses. We analyze replication strategies of closely related (+)RNA viruses, namely HCV, DENV, and coxsackievirus B3 (CVB3), to compare their life cycles in the presence and ab sence of the host’s immune response and antiviral drug treatment and consider different viral spreading mechanisms. Host dependency factors shape the viral life cycle, contribut ing to permissiveness and replication efficiency. Our mathematical models predicted that host dependency factors, such as ribosomes, and thus the virus’ ability to hijack the host cell’s translation machinery play an essential role in the viral genome replication efficiency. Furthermore, our mathematical model suggested that the availability of ribosomes in the vi ral life cycle is a crucial factor in disease outcome: the development of an acute or chronic disease. Even though the host developed strategies to attack the virus, e.g., by degrading the viral genome, blocking the viral protein production, and preventing viral spread, viruses found strategies to countermeasure those so‐called host restriction factors derived from the immune system. Our mathematical models predicted that DENV might be highly effective in blocking the cell’s attempts to recognize the invader. Moreover, we found ongoing HCV RNAreplication even with highly effective antiviral drugs that block processes in the viral life cycle. Furthermore, we found alternative pathways of infection spread, e.g., by HCV RNA carrying exosomes, which may be a possible explanation for reported plasma HCV RNA at the end of treatment, found in a subset of patients. Hence, the mathematical models presented in this thesis provide valuable tools to study the viral replication mechanism in detail. Even though being a simplification of reality, our model predictions confirm and explain known and suggest novel biological mechanisms. In the pre sented thesis, I will summarize and discuss key findings and contextualize model predictions in the broader scientific literature to improve our understanding of the viral dynamics and the virus‐host interplay.
Age is the single biggest risk factor for most major human diseases. As such, understanding the intricate molecular changes that drive biological aging holds great promise in attempting to slow
the onset of systemic diseases and thereby increase the effective health-span in modern societies.
This thesis explores several computational approaches to capture and analyze the molecular biological alterations triggered by intrinsic and extrinsic aging using skin as a model tissue to deliver genes and pathways as potential targets for intervention strategies.
Publication 1 demonstrates the utility of multi-omics data integration strategies for aging research, leading to the identification of four latent aging phases in skin tissue through an integrated cluster analysis of gene expression and DNA methylation data. The four phases improved the detection of molecular aging signals and were shown to be associated with sunbathing habits of the test subjects. Deeper analysis revealed extensive non-linear alterations in various biological pathways particularly at the transition into the fourth aging phase, coinciding with menopause, with potentially wide-reaching functional implications. Publication 2 describes the development of a novel type of age clock, that provides a new level of interpretability by embedding biological pathway information in the architecture of an artificial neural network. The clock not only generates meaningful biological age estimates from gene expression data, but further allows simultaneous monitoring of the aging states of various biological processes through the activations of intermediate neurons. Analyses of the inner workings of the clock revealed a wide-spread impact of aging on the global pathway landscape. Simulation experiments using the transcriptomic clock recapitulated known functional aging gene associations and allowed deciphering of the pathways by which accelerated aging conditions such as chronic sun exposure and Hutchinson-Gilford progeria syndrome exert their effects. Publication 3 further explores the molecular alterations caused by the pro-aging effector UV irradiation in the skin. The multi-omics data analysis of repetitively irradiated skin revealed signs of the immediate acquisition of aging- and cancer-related epigenetic signatures and concurrent wide-spread transcriptional changes across various biological processes. Investigations into the varying resilience to irradiation between subjects revealed prognostic biomarker signatures capable of predicting individual UV tolerances, with accuracies far surpassing the traditional Fitzpatrick classification scheme. Further analysis of the transcripts and pathways associated with UV tolerance identified a form of melanin-independent DNA damage protection in individuals with higher innate UV resilience.
Together, the approaches and findings described in this thesis explore several new angles to advance our understanding of aging processes and external drivers of aging such as UV irradiation in the human skin and deliver new insight on target genes and pathways involved.