Methods

Broad-spectrum respiratory tract pathogen identification using resequencing DNA microarrays

    • 1 Center for Bio/Molecular Science & Engineering, Code 6900, Naval Research Laboratory, Washington, District of Columbia 20375, USA;
    • 2 NOVA Research Incorporated, Alexandria, Virginia 22308, USA;
    • 3 Epidemic Outbreak Surveillance Advanced Diagnostics Laboratory, Lackland Air Force Base, San Antonio, Texas 78236, USA;
    • 4 Texas A&M University System, San Antonio, Texas 78223, USA;
    • 5 Department of Infectious Disease, Wilford Hall USAF Medical Center, Lackland Air Force Base, San Antonio, Texas 78236, USA;
    • 6 Department of Defense Center for Deployment Health Research, Naval Health Research Center, San Diego, California 92186, USA;
    • 7 School of Computational Sciences, George Mason University, Manassas, Virginia 20110, USA;
    • 8 Air Force Institute for Operational Health, Brooks Air Force Base, San Antonio, Texas 78235, USA;
    • 9 HQ USAF/SGR, Falls Church, Virginia 22041, USA
    • 10 These authors contributed equally to this study.
    • 11 Corresponding author. E-mail [email protected]; fax (202) 767-9598.
Published February 15, 2006. Vol 16 Issue 4, pp. 527-535. https://doi.org/10.1101/gr.4337206
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Abstract

The exponential growth of pathogen nucleic acid sequences available in public domain databases has invited their direct use in pathogen detection, identification, and surveillance strategies. DNA microarray technology has offered the potential for the direct DNA sequence analysis of a broad spectrum of pathogens of interest. However, to achieve the practical attainment of this potential, numerous technical issues, especially nucleic acid amplification, probe specificity, and interpretation strategies of sequence detection, need to be addressed. In this report, we demonstrate an approach that combines the use of a custom-designed Affymetrix resequencing Respiratory Pathogen Microarray (RPM v.1) with methods for microbial nucleic acid enrichment, random nucleic acid amplification, and automated sequence similarity searching for broad-spectrum respiratory pathogen surveillance. Successful proof-of-concept experiments, utilizing clinical samples obtained from patients presenting adenovirus or influenza virus-induced febrile respiratory illness (FRI), demonstrate the ability of this approach for correct species- and strain-level identification with unambiguous statistical interpretation at clinically relevant sensitivity levels. Our results underscore the feasibility of using this approach to expedite the early surveillance of diseases, and provide new information on the incidence of multiple pathogens.

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