Combining Big Data to Understand Mechanisms of Alzheimer’s Disease

What biomedical issue did you address in your research and what did your studies find?

My thesis project aimed to analyze multiple types of big data to identify important mechanisms in Alzheimer's disease (AD). Previous studies have identified pathways that are important to AD, but the genomic mechanisms that uniquely underlie the key hallmarks of AD have yet to be fully characterized. My studies focused on understanding how genetic, epigenetic, and transcriptomic (gene expression) mechanisms impact the variability of proteins and pathological features of AD. My goal was to understand how changes in these mechanisms collectively and individually influence these core features. To investigate this, I performed large-scale genome-, epigenome-, and transcriptome-wide association studies as well as multi-omic integrative analyses.

A notable innovative approach I developed was in the epigenetic arm of my thesis project. I investigated DNA methylation, a process that can regulate gene expression, and its association with variability in levels of proteins and neuropathology. Many prior studies have explored DNA methylation on the level of individual methyl groups. However, DNA methylation is known to interact with neighboring sites to influence gene expression. I developed a new methodology that grouped individual methyl sites into regions based on a previously published chromatin map model. Through this approach, which leveraged fundamental biology, I was able to identify many more significant regions of DNA methylation associated with key AD proteins.

Overall, the results of my studies identified important genetic changes related to levels of brain amyloid as well as epigenetic and gene expression changes related to brain tau levels, both of which are known to accumulate in AD. These findings may help pave the way for the future identification of therapeutic targets addressing neurodegeneration.

What aspects of Mayo's culture helped you grow as a scientist and as a thinker?

Mayo's team approach to science helped me grow as a scientist. I learned that being a good scientist involves bringing together information from different sources and working in Mayo's collaborative culture makes that approach possible. This was especially evident in our lab's collaboration with the Mayo Clinic Brain Bank, headed by Dennis Dickson, M.D., at Mayo Clinic in Florida. For my research, I was able to study brain tissue samples donated by patients and get critical insight from world-expert neuropathologists.

Mayo also provided opportunities for me to advance my training. I took advantage of multiple funding opportunities provided by the graduate school and the Biochemistry and Molecular Biology track to attend courses and workshops, including a two-week course at Cold Spring Harbor Laboratory for statistical methods in data science. I also attended the American Association for the Advancement of Science's "Catalyzing Advocacy in Science and Engineering" workshop in Washington, D.C. These experiences expanded my scientific network, increased my analytic skills and taught me how federal policies shape science.