In this episode of the Epigenetics Podcast, we talked with Alena van Bömmel from the Biomedical Center (BMC) in Munich about her work on the development of interpretable epigenetic clocks and statistical models of epigenetic dynamics during aging, and the unique epigenetic signatures associated with various cancers, such as brain tumors or leukemias to detect powerful diagnostic markers or predictors of therapeutic response.

The Interview starts with Dr. van Bömmel sharing her work on co-occurring transcription factors within cell-type specific enhancers, describing the pioneering use of DNA sequencing and its substantial implications in understanding chromatin accessibility. We explore the findings that revealed varying transcription factor interactions across cell types, emphasizing the complexity inherent in gene regulation. Although her research largely remained in silico, its findings paved the way for potential validation through advanced sequencing techniques.

The discussion broadens to encompass Dr. van Bömmel's work on pediatric acute lymphoblastic leukemia, where she elaborates on the epigenetic dynamics observed in patient samples. We discuss her collaboration on a large project that aimed to elucidate the methylation profiles of leukemia patients and how specific epigenetic modifications might indicate cancer subtypes.

As the conversation shifts towards aging, Dr. van Bömmel explains her research on DNA methylation trajectories in mouse models. This work unearthed unexpected patterns of abrupt changes in methylation that correspond to distinct life stages, reflecting the potential applicability of these findings in understanding human aging processes.

Delving further into her innovative research, she introduces 'Methylizer,' a groundbreaking DNA methylation-based classifier designed for brain tumor diagnostics. We examine the rapid diagnostic capabilities this tool offers in surgical contexts, illustrating a paradigm shift in how epigenetic data can inform real-time clinical decisions.

Now at the LMU in Munich, Dr. van Bömmel shares her experiences establishing her lab and her intent to foster a computational-focused research environment that collaborates closely with wet lab scientists. We discuss her aspirations to integrate various layers of epigenetic data through advanced statistical methods and to investigate the aging dynamics of brain cells, specifically in the context of neurodegenerative diseases like Alzheimer’s.

References

Van Bömmel, A., Love, M. I., Chung, H.-R., & Vingron, M. (2018). coTRaCTE predicts co-occurring transcription factors within cell-type specific enhancers. PLOS Computational Biology, 14(8), e1006372. https://doi.org/10.1371/journal.pcbi.1006372
Olecka, M., van Bömmel, A., Best, L., Haase, M., Foerste, S., Riege, K., Dost, T., Flor, S., Witte, O. W., Franzenburg, S., Groth, M., von Eyss, B., Kaleta, C., Frahm, C., & Hoffmann, S. (2024). Nonlinear DNA methylation trajectories in aging male mice. Nature communications, 15(1), 3074. https://doi.org/10.1038/s41467-024-47316-2
Brändl, B., Steiger, M., Kubelt, C., Rohrandt, C., Zhu, Z., Evers, M., Wang, G., Schuldt, B., Afflerbach, A. K., Wong, D., Lum, A., Halldorsson, S., Djirackor, L., Leske, H., Magadeeva, S., Smičius, R., Quedenau, C., Schmidt, N. O., Schüller, U., Vik-Mo, E. O., … Müller, F. J. (2025). Rapid brain tumor classification from sparse epigenomic data. Nature medicine, 31(3), 840–848. https://doi.org/10.1038/s41591-024-03435-3

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