Back

3D Reconstruction of Dinoflagellate Chromosomes from Hi-C Data Refutes the Cholesteric Liquid Crystal Hypothesis

Philipp, L.; Marinov, G. K.; Todd, S.; Weber, S. C.

2025-01-26 biophysics
10.1101/2025.01.24.634729 bioRxiv
Show abstract

Dinoflagellates have permanently condensed chromosomes that are often described as liquid crystalline. Specifically, a Cholesteric Liquid Crystal (CLC) model was proposed in which DNA is organized into parallel fibers within stacked discs, such that the fiber orientation rotates by a constant angle between adjacent discs. Extrachromosomal loops extending from the discs were hypothesized to be more accessible and thus to contain transcriptionally active genes. Although the CLC model captures some features of dinoflagellate chromosome structure, its validity has not been rigorously tested against modern genomic data. Here, we use chromatin conformation capture (Hi-C) data to simulate 3D conformations of chromosome scaffolds for three dinoflagellate species: Fugacium kawagutii, Symbiodinium microadriaticum, and Breviolum minutum. Consensus and population-based modeling generate diverse polymer conformations with moderate orientational and nematic order. However, we find no evidence of cholesteric discs. Moreover, contact probability curves from empirical Hi-C data are inconsistent with the CLC model. Nevertheless, we show that introducing locus-specific boundaries into the CLC model can produce simulated Hi-C contact maps with topologically associating domains (TADs), which are observed in experimental Hi-C contact maps for these species. Finally, by mapping RNA-seq data onto our simulated conformations, we show that actively transcribed genes are present throughout entire chromosomes, and not exclusively on extrachromosomal loops or at the surface. Our results challenge the long-standing CLC model and suggest that dinoflagellate chromosomes are organized into condensed but non-crystalline structures that do not impede transcription.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.