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The Long and Viscous Road: Uncovering Nuclear Diffusion Barriers in Closed Mitosis

Overview of attention for article published in PLoS Computational Biology, July 2014
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Title
The Long and Viscous Road: Uncovering Nuclear Diffusion Barriers in Closed Mitosis
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003725
Pubmed ID
Authors

Eder Zavala, Tatiana T. Marquez-Lago

Abstract

Diffusion barriers are effective means for constraining protein lateral exchange in cellular membranes. In Saccharomyces cerevisiae, they have been shown to sustain parental identity through asymmetric segregation of ageing factors during closed mitosis. Even though barriers have been extensively studied in the plasma membrane, their identity and organization within the nucleus remains poorly understood. Based on different lines of experimental evidence, we present a model of the composition and structural organization of a nuclear diffusion barrier during anaphase. By means of spatial stochastic simulations, we propose how specialised lipid domains, protein rings, and morphological changes of the nucleus may coordinate to restrict protein exchange between mother and daughter nuclear lobes. We explore distinct, plausible configurations of these diffusion barriers and offer testable predictions regarding their protein exclusion properties and the diffusion regimes they generate. Our model predicts that, while a specialised lipid domain and an immobile protein ring at the bud neck can compartmentalize the nucleus during early anaphase; a specialised lipid domain spanning the elongated bridge between lobes would be entirely sufficient during late anaphase. Our work shows how complex nuclear diffusion barriers in closed mitosis may arise from simple nanoscale biophysical interactions.

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Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 33%
Researcher 6 22%
Other 3 11%
Student > Master 3 11%
Professor 1 4%
Other 2 7%
Unknown 3 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 33%
Agricultural and Biological Sciences 7 26%
Physics and Astronomy 3 11%
Engineering 2 7%
Energy 1 4%
Other 3 11%
Unknown 2 7%