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Mechanistic Model of Rothia mucilaginosa Adaptation toward Persistence in the CF Lung, Based on a Genome Reconstructed from Metagenomic Data

Overview of attention for article published in PLOS ONE, May 2013
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Title
Mechanistic Model of Rothia mucilaginosa Adaptation toward Persistence in the CF Lung, Based on a Genome Reconstructed from Metagenomic Data
Published in
PLOS ONE, May 2013
DOI 10.1371/journal.pone.0064285
Pubmed ID
Authors

Yan Wei Lim, Robert Schmieder, Matthew Haynes, Mike Furlan, T. David Matthews, Katrine Whiteson, Stephen J. Poole, Christopher S. Hayes, David A. Low, Heather Maughan, Robert Edwards, Douglas Conrad, Forest Rohwer

Abstract

The impaired mucociliary clearance in individuals with Cystic Fibrosis (CF) enables opportunistic pathogens to colonize CF lungs. Here we show that Rothia mucilaginosa is a common CF opportunist that was present in 83% of our patient cohort, almost as prevalent as Pseudomonas aeruginosa (89%). Sequencing of lung microbial metagenomes identified unique R. mucilaginosa strains in each patient, presumably due to evolution within the lung. The de novo assembly of a near-complete R. mucilaginosa (CF1E) genome illuminated a number of potential physiological adaptations to the CF lung, including antibiotic resistance, utilization of extracellular lactate, and modification of the type I restriction-modification system. Metabolic characteristics predicted from the metagenomes suggested R. mucilaginosa have adapted to live within the microaerophilic surface of the mucus layer in CF lungs. The results also highlight the remarkable evolutionary and ecological similarities of many CF pathogens; further examination of these similarities has the potential to guide patient care and treatment.

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Geographical breakdown

Country Count As %
United States 1 1%
Denmark 1 1%
Unknown 83 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 22%
Researcher 16 19%
Student > Doctoral Student 8 9%
Student > Master 7 8%
Student > Bachelor 7 8%
Other 15 18%
Unknown 13 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 27 32%
Medicine and Dentistry 12 14%
Pharmacology, Toxicology and Pharmaceutical Science 7 8%
Biochemistry, Genetics and Molecular Biology 7 8%
Immunology and Microbiology 6 7%
Other 7 8%
Unknown 19 22%