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Mechanistic Basis of Branch-Site Selection in Filamentous Bacteria

Overview of attention for article published in PLoS Computational Biology, March 2012
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Title
Mechanistic Basis of Branch-Site Selection in Filamentous Bacteria
Published in
PLoS Computational Biology, March 2012
DOI 10.1371/journal.pcbi.1002423
Pubmed ID
Authors

David M. Richards, Antje M. Hempel, Klas Flärdh, Mark J. Buttner, Martin Howard

Abstract

Many filamentous organisms, such as fungi, grow by tip-extension and by forming new branches behind the tips. A similar growth mode occurs in filamentous bacteria, including the genus Streptomyces, although here our mechanistic understanding has been very limited. The Streptomyces protein DivIVA is a critical determinant of hyphal growth and localizes in foci at hyphal tips and sites of future branch development. However, how such foci form was previously unknown. Here, we show experimentally that DivIVA focus-formation involves a novel mechanism in which new DivIVA foci break off from existing tip-foci, bypassing the need for initial nucleation or de novo branch-site selection. We develop a mathematical model for DivIVA-dependent growth and branching, involving DivIVA focus-formation by tip-focus splitting, focus growth, and the initiation of new branches at a critical focus size. We quantitatively fit our model to the experimentally-measured tip-to-branch and branch-to-branch length distributions. The model predicts a particular bimodal tip-to-branch distribution results from tip-focus splitting, a prediction we confirm experimentally. Our work provides mechanistic understanding of a novel mode of hyphal growth regulation that may be widely employed.

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The data shown below were compiled from readership statistics for 66 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
United States 1 2%
Netherlands 1 2%
China 1 2%
Unknown 62 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 26%
Student > Ph. D. Student 13 20%
Student > Doctoral Student 5 8%
Professor > Associate Professor 5 8%
Student > Bachelor 4 6%
Other 11 17%
Unknown 11 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 53%
Biochemistry, Genetics and Molecular Biology 11 17%
Immunology and Microbiology 4 6%
Mathematics 1 2%
Environmental Science 1 2%
Other 3 5%
Unknown 11 17%