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Massively parallel pathogen identification using high-density microarrays

delete2007-11-22
delete37
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OA
AI
N
Nicolás Berthet
P
Philip Dickinson
I
Ingrid Filliol
A
Anita Reinhardt
C
Christophe Batéjat
T
Tatiana Vallaeys
K
Katherine A. Kong
C
Christopher Davies
W
Walter Lee
S
Shenglan Zhang
Y
Yaron Turpaz
B
Béate Heym
G
Gilberte Coralie
L
Laurent Dacheux
A
Ana Maria Burguière
H
Hervé Bourhy
I
Iain G. Old
J
Jean‐Claude Manuguerra
S
Stewart T. Cole
G
Giulia C. Kennedy *
DOI:10.1111/j.1751-7915.2007.00012.xdelete
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摘要

摘要

En 中文
Identification of microbial pathogens in clinical specimens is still performed by phenotypic methods that are often slow and cumbersome, despite the availability of more comprehensive genotyping technologies. We present an approach based on whole-genome amplification and resequencing microarrays for unbiased pathogen detection. This 10 h process identifies a broad spectrum of bacterial and viral species and predicts antibiotic resistance and pathogenicity and virulence profiles. We successfully identify a variety of bacteria and viruses, both in isolation and in complex mixtures, and the high specificity of the microarray distinguishes between different pathogens that cause diseases with overlapping symptoms. The resequencing approach also allows identification of organisms whose sequences are not tiled on the array, greatly expanding the repertoire of identifiable organisms and their variants. We identify organisms by hybridization of their DNA in as little as 1-4 h. Using this method, we identified Monkeypox virus and drug-resistant Staphylococcus aureus in a skin lesion taken from a child suspected of an orthopoxvirus infection, despite poor transport conditions of the sample, and a vast excess of human DNA. Our results suggest this technology could be applied in a clinical setting to test for numerous pathogens in a rapid, sensitive and unbiased manner.
Keyword:
HIGH-THROUGHPUT
SPECIES IDENTIFICATION
DNA

期刊

Microbial Biotechnology 封面图
Microbial Biotechnology
IF:
5.2
论文数:
2.6K
被引数:
8.8K

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A
affymetrix
学者数:
182
论文数: 75
被引数: 0
U
Universite Paris Cite
学者数:
8.9W
论文数: 6.3W
被引数: 604
I
institut pasteur paris
学者数:
5.3K
论文数: 3.6K
被引数: 13
P
pasteur network
学者数:
2.0W
论文数: 1.3W
被引数: 32
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