We will apply the data and insights generated in the 3 following approaches to further study both aging and age-dependent diseases (with a focus on oncology and auto-immune diseases)

Approach 1: The information theory of aging is a theoretical framework that seeks to explain the aging process in terms of information theory. It is based on the idea that aging is a result of the accumulation of stochastic errors and loss of information. As we age, our epigenome, which is the collection of all epigenetic marks in our cells, undergoes significant changes. One of the main epigenetic changes that occur during aging is DNA methylation. There is a global decrease in DNA methylation, but local increase particularly in regions of the genome that are important for regulating gene expression. By measuring the compressibility of a system, we can gain insight into its stochastic nature.  In a highly ordered system, methylation events might be highly locally correlated in a specific and predictable manner, making it more compressible. In a system with disorder or randomness, the methylation events are not arranged in a specific pattern, making it less compressible. We want to explore if compressibility of epigenome-wide information confirms the information theory of aging on the epigenetic level.

Approach 2: Epigenetic deconvolution is a technique that is used to separate and analyse the distinct cell populations present in a mixed sample of cells, such as peripheral blood mononuclear cells (PBMCs) or circulating free DNA (cfDNA). This technique utilises epigenetic markers, such as DNA methylation and histone modifications, to identify and isolate specific cell types within a mixture. In the case of PBMCs, this can include T cells, B cells, and monocytes, while cfDNA can come from multiple sources such as cancer, fetal, or other tissue. Additionally, this technique can also be used for diagnostic and therapeutic applications, such as identifying the origin of cancer cells in cfDNA samples. Next to enhanced methode development we plan to generate better reference datasets within our core-facility NXTGNT using reference material from the different ongoing collaborations

Approach 3: Epigenetic Foundational Models are currently heavily research within a genetic-AI-context. Which “foundational” properties allow a generative model to produce synthetic DNA that is indistinguishable from experimentally measured genomes ? In addition to rediscover fundamental properties like transcription and translation we might gain deeper insight in genome regulation. Given that epigenetics evolved we hope to expand these foundational models with reference epigenomes to further enhance their capabilities to generate synthetic epigenomes that will increase our knowledge on the convoluted interactions between genetics and epigenetics.