Format:
1 Online-Ressource (PDF-Datei: 85 Seiten, 19682 Kilobyte)
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Illustrationen (farbig), Diagramme (farbig)
Content:
Aging, Artificial neural networks, Bioinformatics, Machine Learning, Photoaging, Skin
Content:
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.
Note:
Literaturverzeichnis: Seite 26-36. - Literaturangaben
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Dissertation Universitätsmedizin der Universität Greifswald 2023
Additional Edition:
Erscheint auch als Druck-Ausgabe Holzscheck, Nicholas Jonas, 1991 - Analyse intrinsischer und extrinsischer Aspekte von Hautalterung in hochdimensionalen biologischen Daten mittels bioinformatischer und maschineller Lernmethoden Greifswald, 2023
Language:
English
Keywords:
Bioinformatik
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Maschinelles Lernen
;
Haut
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Alterung
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Hochschulschrift
URN:
urn:nbn:de:gbv:9-opus-88551
