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Intracellular Bacteria Encode Inhibitory SNARE-Like Proteins

Overview of attention for article published in PLOS ONE, October 2009
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Title
Intracellular Bacteria Encode Inhibitory SNARE-Like Proteins
Published in
PLOS ONE, October 2009
DOI 10.1371/journal.pone.0007375
Pubmed ID
Authors

Fabienne Paumet, Jordan Wesolowski, Alejandro Garcia-Diaz, Cedric Delevoye, Nathalie Aulner, Howard A. Shuman, Agathe Subtil, James E. Rothman

Abstract

Pathogens use diverse molecular machines to penetrate host cells and manipulate intracellular vesicular trafficking. Viruses employ glycoproteins, functionally and structurally similar to the SNARE proteins, to induce eukaryotic membrane fusion. Intracellular pathogens, on the other hand, need to block fusion of their infectious phagosomes with various endocytic compartments to escape from the degradative pathway. The molecular details concerning the mechanisms underlying this process are lacking. Using both an in vitro liposome fusion assay and a cellular assay, we showed that SNARE-like bacterial proteins block membrane fusion in eukaryotic cells by directly inhibiting SNARE-mediated membrane fusion. More specifically, we showed that IncA and IcmG/DotF, two SNARE-like proteins respectively expressed by Chlamydia and Legionella, inhibit the endocytic SNARE machinery. Furthermore, we identified that the SNARE-like motif present in these bacterial proteins encodes the inhibitory function. This finding suggests that SNARE-like motifs are capable of specifically manipulating membrane fusion in a wide variety of biological environments. Ultimately, this motif may have been selected during evolution because it is an efficient structural motif for modifying eukaryotic membrane fusion and thus contribute to pathogen survival.

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Geographical breakdown

Country Count As %
United States 2 1%
Germany 1 <1%
United Kingdom 1 <1%
India 1 <1%
Japan 1 <1%
Spain 1 <1%
Unknown 127 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 40 30%
Researcher 26 19%
Professor > Associate Professor 9 7%
Student > Bachelor 9 7%
Professor 8 6%
Other 22 16%
Unknown 20 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 62 46%
Biochemistry, Genetics and Molecular Biology 23 17%
Immunology and Microbiology 12 9%
Computer Science 4 3%
Pharmacology, Toxicology and Pharmaceutical Science 2 1%
Other 9 7%
Unknown 22 16%