Due to copyright issues, an electronic copy of the thesis must be ordered from the faculty. For the faculty to have time to process the order, the order must be received by the faculty at the latest 2 days before the public defence. Orders received later than 2 days before the defence will not be processed. After the public defence, please address any inquiries regarding the thesis to the candidate.
Trial Lecture over a chosen topic – time and place
See Trial Lecture.
Trial Lecture over a given topic – time and place
See Trial Lecture.
Adjudication committee
- First opponent: Professor Li Gan, Helen and Robert Appel Alzheimer Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, US,
- Second opponent: Associate Professor Edward B. Lee, University of Pennsylvania, US,
- Third member and chair of the evaluation committee: Associate Professor Rune Enger, University of Oslo
Chair of the Defence
Professor Magnar Bjørås, University of Oslo
Principal Supervisor
Associate Professor Evandro Fei Fang, University of Oslo
Summary
Dementia is a common disease in older people, affecting 50 million individuals worldwide. The most common form, Alzheimer's disease (AD), has no cure due to limited understanding of its causes. In AD, damage to mitochondria (the cell's powerhouses) and disrupted RNA splicing contribute to disease progression. Nicotinamide adenine dinucleotide (NAD+) is important for brain health. Supplementing NAD+ has shown promise in animal models by preserving mitochondrial function and improving memory. However, its effects on mitochondrial quality control pathways and RNA splicing in AD are still unclear. Artificial intelligence (AI) is used in medicine for analyzing data, diagnosing diseases, and discovering drugs. I combined AI with traditional wet laboratory methods to unveil novel mechanisms on how NAD+ inhibits AD.
I found that increasing NAD+ levels improved mitochondrial health and reduced AD-related issues by helping a protein called ATF5 to move to the cell's nucleus. I also explored NAD+'s influence on RNA splicing. I show NAD+ can normalize abnormal RNA splicing. This normalization may alleviate AD-related problems. Additionally, we developed an AI approach to identify compounds that promote mitophagy (a process that removes damaged mitochondria). Our algorithm identified two promising compounds that induce mitophagy, reduce AD-related problems, and preserve memory in AD models.
NAD+-related clinical trials targeting Alzheimer's disease (AD) are currently underway. These ongoing trials emphasize the significance of conducting more comprehensive mechanistic studies to deepen our understanding of this therapeutic approach. Artificial intelligence (AI) has emerged as a valuable tool in expediting the drug development process, and its integration with wet lab experiments has yielded promising results in the field of medicine. The combination of traditional laboratory research and AI-driven analysis, as I developed in this thesis work, offers a compelling example of how multidisciplinary approaches can contribute to advancements in medical research.
In conclusion, my thesis work provides insights into news causes of AD including compromised NAD+-mitophagy pathway and aberrant RNA splicing capacity. My work provides pre-clinical evidence of restoration of mitochondrial homeostasis and RNA splicing as possible therapeutic strategies against AD.
Additional information
Contact the research support staff.