Aggregates of misfolded proteins are a hallmark of many age-related diseases.
Recently, they have been linked to aging of Escherichia coli (E. coli) where
protein aggregates accumulate at the old pole region of the aging bacterium.
Because of the potential of E. coli as a model organism, elucidating aging and
protein aggregation in this bacterium may pave the way to significant advances
in our global understanding of aging. A first obstacle along this path is to
decipher the mechanisms by which protein aggregates are targeted to specific
intercellular locations. Here, using an integrated approach based on
individual-based modeling, time-lapse fluorescence microscopy and automated
image analysis, we show that the movement of aging-related protein aggregates
in E. coli is purely diffusive (Brownian). Using single-particle tracking of
protein aggregates in live E. coli cells, we estimated the average size and
diffusion constant of the aggregates. Our results evidence that the aggregates
passively diffuse within the cell, with diffusion constants that depend on
their size in agreement with the Stokes-Einstein law. However, the aggregate
displacements along the cell long axis are confined to a region that roughly
corresponds to the nucleoid-free space in the cell pole, thus confirming the
importance of increased macromolecular crowding in the nucleoids. We thus used
3d individual-based modeling to show that these three ingredients (diffusion,
aggregation and diffusion hindrance in the nucleoids) are sufficient and
necessary to reproduce the available experimental data on aggregate
localization in the cells. Taken together, our results strongly support the
hypothesis that the localization of aging-related protein aggregates in the
poles of E. coli results from the coupling of passive diffusion- aggregation
with spatially non-homogeneous macromolecular crowding. They further support
the importance of "soft" intracellular structuring (based on macromolecular
crowding) in diffusion-based protein localization in E. coli.Comment: PLoS Computational Biology (2013
