Speaker
Description
Recent technologies, such as Hi-C [1], have revealed that the mammalian genome has a complex, far from
random three-dimensional (3D) organization, intimately linked to vital biological processes. To rationalize
the complexity of experimental data, polymer models from Statistical Physics and a variety of
computational methods have been developed [2,3]. However, they typically cannot explain data at the scale
of the full genome.
In this talk, I will present the first genome-wide extension of PRISMR [4], our approach that combines
Machine Learning and Polymer Physics to infer the different types of DNA binding sites determining
genome 3D structure. The genome-wide study allowed us to develop a code linking chromosome 3D
structure to chromatin states through our inferred binding domains. Interestingly, they have an overlapping,
combinatorial organization along chromosomes necessary to accurately explain contact specificity. The
binding domains and the associated architectural code were tested by making predictions on the changes of
the 3D structure caused by a set of genomic mutations at the Sox9 locus linked to human diseases and our
predictions were confirmed by independent data from cells carrying such mutations. Finally, in a reverse
approach based on the discovered code, we predicted de novo the 3D structure of an independent set of
chromosomes from only their 1D chromatin marks, thus validating the inferred epigenetic-architecture
code [4].
Overall, our results shed light on how 3D information is encrypted in 1D chromatin via the specific
combinatorial arrangement of binding sites.
References
[1] R. Kempfer, A. Pombo, Nature reviews Genetics 21 (2020)
[2] Barbieri et al. Proc. Natl. Acad. Sci. USA 109 (2012)
[3] Bianco et al. Nat. Genet. 50 (2018)
[4] Esposito et al. Cell Reports 38 (2022)
