Evidence map›Paper›PMID 21672191›Full record

ArticleBMC microbiology2011

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

Shamira J Shallom, Jenni N Weeks, Cristi L Galindo, Lauren McIver, Zhaohui Sun, John McCormick, L Garry Adams, Harold R Garner

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Article in BMC microbiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Shamira J ShallomVirginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA.
Jenni N Weeks
Cristi L Galindo
Lauren McIver
Zhaohui Sun
John McCormick
L Garry Adams
Harold R Garner

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe 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.

resultsA 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.

conclusionsThis 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.

Indexed as

AnimalsBrucellaCommunicable DiseasesForensic MedicineHumansMicroarray AnalysisMicrobiological Techniques

Identifiers

PMID21672191
PMCPMC3130645

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.