Back

DNA bridging explains sub-diffusive movement of chromosomal loci in bacteria

Subramanian, S.; Murray, S. M.

2022-11-18 biophysics
10.1101/2022.11.18.517049 bioRxiv
Show abstract

Chromosomal loci in bacterial cells show a robust sub-diffusive scaling of the mean square displacement, MSD({tau}) [~]{tau} , with < 0.5. On the other hand, recent experiments have also shown that DNA-bridging Nucleoid Associated Proteins (NAPs) play an important role in chromosome organisation and compaction. Here, using polymer simulations we investigate the role of DNA bridging in determining the dynamics of chromosomal loci. We find that bridging compacts the polymer and reproduces the sub-diffusive elastic dynamics of monomers at timescales shorter than the bridge lifetime. Consistent with this prediction, we measure a higher exponent in a NAP mutant ({Delta}H-NS) compared to wild-type E. coli. Furthermore, bridging can reproduce the rare but ubiquitous rapid movements of chromosomal loci that have been observed in experiments. In our model the scaling exponent defines a relationship between the abundance of bridges and their lifetime. Using this and the observed mobility of chromosomal loci, we predict a lower bound on the average bridge lifetime of around 5 seconds. Significance StatementThe bacterial chromosome exhibits dynamics that cannot be explained by simple polymer models. In particular, the mean square displacement of individual chromosomal loci exhibits a power law scaling with an exponent less than that predicted by polymer theory. Here, we use polymer simulations and experiments to show that DNA bridging by Nucleoid Associated Proteins can explain these anomalous dynamics. Consistent with this, we show that in the absence of the bridging protein H-NS, the scaling exponent increases. Chromosomal loci also display rare rapid movements not explainable by polymer theory, even accounting for the viscoelasticity of the cytoplasm. Our simulations show that bridging can additionally explain this behaviour. Finally, we predict a lower bound on the average bridge lifetime within cells.

Matching journals

The top 1 journal accounts 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.