Messelink, J. J. B. | bioRxiv Science

bioRxiv2025

Learning the dynamic organization of a replicating bacterial chromosome from time-course Hi-C data

Bacterial chromosomes are in continual motion as they undergo concurrent transcription, replication, and segregation. Time-course Hi-C experiments hold promise for studying chromosome organization across the cell cycle, but interpreting Hi-C data from dynamic systems remains challenging. Here, we develop a fully data-driven 4D Maximum Entropy approach to extract a model for the dynamic organization of a replicating bacterial chromosome directly from time-course Hi-C and microscopy data. After validating our 4D data-driven model for Caulobacter crescentus against independent microscopy data, we infer quantitative information about changes in chromosome organization across the bacterial replication cycle. Our model reveals a sustained global linear organization of the C. crescentus chromosome during replication, as well as dynamic patterns of local chromosome extension induced by the replication forks. We use these data-driven inferences to constrain a mechanistic model for a replicating bacterial chromosome. Our model demonstrates that origin-pulling by a ParABS -like system, together with loop extrusion by condensin, can explain our inferred large-scale chromosome segregation patterns. The inferred replication-induced local changes in chromosome compaction, however, require additional mechanisms, which we attribute to replication-induced NAP unbinding and positive supercoiling. Overall, our work introduces a rigorous data-driven framework for quantitatively interpreting time-course Hi-C data, and offers new mechanistic insights into bacterial chromosome organization across the cell cycle.

biophysics↗

bioRxiv2022

Multi-contact statistics distinguish models of chromosome organization

Chromosome organization in both eukaryotes and prokaryotes is highly regulated. Organizing mechanisms, such as loop-extrusion, have been extensively studied using Hi-C methods, which measure pairwise contacts between chromosomal regions. New multi-contact methods additionally measure which chromosomal contacts occur simultaneously. Here, we develop three predictors of baseline multi-contact frequencies given pairwise contact data, corresponding to distinct physical limits, and argue that a comparison between data and prediction can lead to biological insight. We test these predictors for two simulated polymer models with cross-linking or loop-extrusion, and find that simulated three-point contacts are only predicted by the physically appropriate approximation. Finally, we apply our approach to previously published experimental multi-contact data from human chromosomes. Strikingly, we discover that observed three-point contact frequencies are well predicted by a formula based on loop-extrusion, suggesting that multi-contact data can give insight into chromosome organization mechanisms.

biophysics↗