Title |
Mining Virulence Genes Using Metagenomics
|
---|---|
Published in |
PLOS ONE, October 2011
|
DOI | 10.1371/journal.pone.0024975 |
Pubmed ID | |
Authors |
Pedro Belda-Ferre, Raúl Cabrera-Rubio, Andrés Moya, Alex Mira |
Abstract |
When a bacterial genome is compared to the metagenome of an environment it inhabits, most genes recruit at high sequence identity. In free-living bacteria (for instance marine bacteria compared against the ocean metagenome) certain genomic regions are totally absent in recruitment plots, representing therefore genes unique to individual bacterial isolates. We show that these Metagenomic Islands (MIs) are also visible in bacteria living in human hosts when their genomes are compared to sequences from the human microbiome, despite the compartmentalized structure of human-related environments such as the gut. From an applied point of view, MIs of human pathogens (e.g. those identified in enterohaemorragic Escherichia coli against the gut metagenome or in pathogenic Neisseria meningitidis against the oral metagenome) include virulence genes that appear to be absent in related strains or species present in the microbiome of healthy individuals. We propose that this strategy (i.e. recruitment analysis of pathogenic bacteria against the metagenome of healthy subjects) can be used to detect pathogenicity regions in species where the genes involved in virulence are poorly characterized. Using this approach, we detect well-known pathogenicity islands and identify new potential virulence genes in several human pathogens. |
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Poland | 1 | 3% |
Norway | 1 | 3% |
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Canada | 1 | 3% |
Unknown | 17 | 55% |
Demographic breakdown
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Members of the public | 11 | 35% |
Practitioners (doctors, other healthcare professionals) | 2 | 6% |
Mendeley readers
Geographical breakdown
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United Kingdom | 4 | 3% |
Spain | 3 | 2% |
Brazil | 3 | 2% |
Netherlands | 2 | 1% |
Sweden | 2 | 1% |
Russia | 1 | <1% |
Unknown | 125 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
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Researcher | 34 | 23% |
Student > Bachelor | 16 | 11% |
Student > Master | 15 | 10% |
Student > Doctoral Student | 12 | 8% |
Other | 27 | 18% |
Unknown | 8 | 5% |
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Biochemistry, Genetics and Molecular Biology | 14 | 10% |
Medicine and Dentistry | 13 | 9% |
Computer Science | 5 | 3% |
Immunology and Microbiology | 5 | 3% |
Other | 14 | 10% |
Unknown | 8 | 5% |