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Open Access Highly Accessed Research article

A species independent universal bio-detection microarray for pathogen forensics and phylogenetic classification of unknown microorganisms

Shamira J Shallom1, Jenni N Weeks2, Cristi L Galindo1, Lauren McIver1, Zhaohui Sun1, John McCormick1, L Garry Adams3 and Harold R Garner1*

Author Affiliations

1 Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA

2 St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN, USA

3 Department of Veterinary Pathobiology, College of Veterinary Medicine, Texas A & M University, College Station, TX, USA

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BMC Microbiology 2011, 11:132  doi:10.1186/1471-2180-11-132

Published: 14 June 2011

Abstract

Background

The ability to differentiate a bioterrorist attack or an accidental release of a research pathogen from a naturally occurring pandemic or disease event is crucial to the safety and security of this nation by enabling an appropriate and rapid response. It is critical in samples from an infected patient, the environment, or a laboratory to quickly and accurately identify the precise pathogen including natural or engineered variants and to classify new pathogens in relation to those that are known. Current approaches for pathogen detection rely on prior genomic sequence information. Given the enormous spectrum of genetic possibilities, a field deployable, robust technology, such as a universal (any species) microarray has near-term potential to address these needs.

Results

A new and comprehensive sequence-independent array (Universal Bio-Signature Detection Array) was designed with approximately 373,000 probes. The main feature of this array is that the probes are computationally derived and sequence independent. There is one probe for each possible 9-mer sequence, thus 49 (262,144) probes. Each genome hybridized on this array has a unique pattern of signal intensities corresponding to each of these probes. These signal intensities were used to generate an un-biased cluster analysis of signal intensity hybridization patterns that can easily distinguish species into accepted and known phylogenomic relationships. Within limits, the array is highly sensitive and is able to detect synthetically mixed pathogens. Examples of unique hybridization signal intensity patterns are presented for different Brucella species as well as relevant host species and other pathogens. These results demonstrate the utility of the UBDA array as a diagnostic tool in pathogen forensics.

Conclusions

This pathogen detection system is fast, accurate and can be applied to any species. Hybridization patterns are unique to a specific genome and these can be used to decipher the identity of a mixed pathogen sample and can separate hosts and pathogens into their respective phylogenomic relationships. This technology can also differentiate between different species and classify genomes into their known clades. The development of this technology will result in the creation of an integrated biomarker-specific bio-signature, multiple select agent specific detection system.