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Localization of Protein Aggregation in Escherichia coli Is Governed by Diffusion and Nucleoid Macromolecular Crowding Effect

Overview of attention for article published in PLoS Computational Biology, April 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

Mentioned by

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4 X users
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1 Facebook page
wikipedia
4 Wikipedia pages

Citations

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115 Dimensions

Readers on

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224 Mendeley
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2 CiteULike
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Title
Localization of Protein Aggregation in Escherichia coli Is Governed by Diffusion and Nucleoid Macromolecular Crowding Effect
Published in
PLoS Computational Biology, April 2013
DOI 10.1371/journal.pcbi.1003038
Pubmed ID
Authors

Anne-Sophie Coquel, Jean-Pascal Jacob, Mael Primet, Alice Demarez, Mariella Dimiccoli, Thomas Julou, Lionel Moisan, Ariel B. Lindner, Hugues Berry

Abstract

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 provide 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 224 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 6 3%
France 2 <1%
Norway 1 <1%
Portugal 1 <1%
United Kingdom 1 <1%
Australia 1 <1%
Japan 1 <1%
Belgium 1 <1%
Unknown 210 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 56 25%
Researcher 54 24%
Student > Master 25 11%
Student > Bachelor 21 9%
Student > Doctoral Student 9 4%
Other 26 12%
Unknown 33 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 72 32%
Biochemistry, Genetics and Molecular Biology 41 18%
Physics and Astronomy 22 10%
Engineering 14 6%
Chemistry 9 4%
Other 28 13%
Unknown 38 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 October 2023.
All research outputs
#6,524,426
of 25,461,852 outputs
Outputs from PLoS Computational Biology
#4,470
of 8,981 outputs
Outputs of similar age
#51,480
of 206,161 outputs
Outputs of similar age from PLoS Computational Biology
#44
of 135 outputs
Altmetric has tracked 25,461,852 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 8,981 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 206,161 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 135 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.