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Yli-Harja, O.

Publications and source records attributed to Yli-Harja, O..

2 recordsLinked to original sources

Integrative DNA methylation analysis of pediatric brain tumors reveals tumor type-specific developmental trajectories and epigenetic signatures of malignancy

Atypical teratoid/rhabdoid tumors (AT/RTs) are pediatric brain tumors known for their aggressiveness, exceptionally low mutation rate, and aberrant but still unresolved epigenetic regulation. To evaluate methylation associated regulation in AT/RTs, we compared them to medulloblastomas and choroid plexus tumors by integrating DNA methylation (507 samples), gene expression (120 samples), and public transcription factor (TF) binding data. We showed that elevated DNA methylation masks the binding sites of TFs driving neural development and is associated with reduced transcription for specific neural regulators in AT/RTs. Part of the hypermethylated sites behaved similarly in AT/RTs and pluripotent stem cells, revealing DNA methylation -driven halted cell differentiation. AT/RT-unique DNA hypermethylation was associated with polycomb repressive complex 2 members, like EZH2, and linked to suppressed genes with a role in neural development and tumorigenesis. The obtained results highlight and characterize these DNA methylation programs as drivers of AT/RT malignancy.

cancer biology↗

Data-driven multiscale modeling reveals the role of metabolic coupling for the spatio-temporal growth dynamics of yeast colonies

MotivationMulticellular entities, such as mammalian tissues or microbial biofilms, typically exhibit complex spatial arrangements that are adapted to their specific functions or environments. These structures result from intercellular signaling as well as from the interaction with the environment that allow cells of the same genotype to differentiate into well-organized communities of diversified cells. Despite its importance, our understanding on how cell-cell and metabolic coupling produce functionally optimized structures is still limited.\n\nResultsHere, we present a data-driven spatial framework to computationally investigate the development of one multicellular structure, yeast colonies. Using experimental growth data from homogeneous liquid media conditions, we develop and parameterize a dynamic cell state and growth model. We then use the resulting model in a coarse-grained spatial model, which we calibrate using experimental time-course data of colony growth. Throughout the model development process, we use state-of-the-art statistical techniques to handle the uncertainty of model structure and parameterization. Further, we validate the model predictions against independent experimental data and illustrate how metabolic coupling plays a central role in colony formation.\n\nAvailabilityExperimental data and a computational implementation to reproduce the results are available at http://research.cs.aalto.fi/csb/software/multiscale/code.zip.\n\nContactjukka.intosalmi@aalto.fi, alexander.skupin@uni.lu

bioinformatics↗