Application of broad-spectrum, sequence-based pathogen identification in an urban population.

A broad spectrum detection platform that provides sequence level resolution of target regions would have a significant impact in public health, case management, and means of expanding our understanding of the etiology of diseases. A previously developed respiratory pathogen microarray (RPM v.1) demonstrated the capability of this platform for this purpose. This newly developed RPM v.1 was used to analyze 424 well-characterized nasal wash specimens from patients presenting with febrile respiratory illness in the Washington, D. C. metropolitan region. For each specimen, the RPM v.1 results were compared against composite reference assay (viral and bacterial culture and, where appropriate, RT-PCR/PCR) results. Across this panel, the RPM assay showed >or=98% overall agreement for all the organisms detected compared with reference methods. Additionally, the RPM v.1 results provide sequence information which allowed phylogenetic classification of circulating influenza A viruses in approximately 250 clinical specimens, and allowed monitoring the genetic variation as well as antigenic variability prediction. Multiple pathogens (2-4) were detected in 58 specimens (13.7%) with notably increased abundances of respiratory colonizers (esp. S. pneumoniae) during viral infection. This first-ever comparison of a broad-spectrum viral and bacterial identification technology of this type against a large battery of conventional "gold standard" assays confirms the utility of the approach for both medical surveillance and investigations of complex etiologies of illness caused by respiratory co-infections.

Using a resequencing microarray as a multiple respiratory pathogen detection assay.

Simultaneous testing for detection of infectious pathogens that cause similar symptoms (e.g., acute respiratory infections) is invaluable for patient treatment, outbreak prevention, and efficient use of antibiotic and antiviral agents. In addition, such testing may provide information regarding possible coinfections or induced secondary infections, such as virally induced bacterial infections. Furthermore, in many cases, detection of a pathogen requires more than genus/species-level resolution, since harmful agents (e.g., avian influenza virus) are grouped with other, relatively benign common agents, and for every pathogen, finer resolution is useful to allow tracking of the location and nature of mutations leading to strain variations. In this study, a previously developed resequencing microarray that has been demonstrated to have these capabilities was further developed to provide individual detection sensitivity ranging from 10(1) to 10(3) genomic copies for more than 26 respiratory pathogens while still retaining the ability to detect and differentiate between close genetic neighbors. In addition, the study demonstrated that this system allows unambiguous and reproducible sequence-based strain identification of the mixed pathogens. Successful proof-of-concept experiments using clinical specimens show that this approach is potentially very useful for both diagnostics and epidemic surveillance.

Broad-spectrum respiratory tract pathogen identification using resequencing DNA microarrays.

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.