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

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 <it>Brucella </it>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.</p> <p>Conclusions</p> <p>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.</p

Hybridization thermodynamics of NimbleGen Microarrays

Background While microarrays are the predominant method for gene expression profiling, probe signal variation is still an area of active research. Probe signal is sequence dependent and affected by probe-target binding strength and the competing formation of probe-probe dimers and secondary structures in probes and targets. Results We demonstrate the benefits of an improved model for microarray hybridization and assess the relative contributions of the probe-target binding strength and the different competing structures. Remarkably, specific and unspecific hybridization were apparently driven by different energetic contributions: For unspecific hybridization, the melting temperature Tm was the best predictor of signal variation. For specific hybridization, however, the effective interaction energy that fully considered competing structures was twice as powerful a predictor of probe signal variation. We show that this was largely due to the effects of secondary structures in the probe and target molecules. The predictive power of the strength of these intramolecular structures was already comparable to that of the melting temperature or the free energy of the probe-target duplex. Conclusions This analysis illustrates the importance of considering both the effects of probe-target binding strength and the different competing structures. For specific hybridization, the secondary structures of probe and target molecules turn out to be at least as important as the probe-target binding strength for an understanding of the observed microarray signal intensities. Besides their relevance for the design of new arrays, our results demonstrate the value of improving thermodynamic models for the read-out and interpretation of microarray signals

Design and Evaluation of Oligonucleotide Microarrays for the Detection of Bovine Pathogens

Two microarray designs were developed and produced to screen for multiple bovine pathogens commonly found in the cattle industry today. The first microarray was designed, built, and processed in-house using conventional material and equipment and targeted Pasteurella multocida, Manheimia haemolytica, Histophilus somni, and Arcanobacterium pyogenes. For each pathogen, 12 perfect-match oligonucleotide probes, which were also designed in-house, targeted different sections of the respective 16S ribosomal genes, and were coupled with 12 corresponding mismatched probes for background. These arrays were able to produce distinct hybridization patterns for each pathogen that were easily visible without the need for computer analysis. However, the need for PCR amplification of the 16S gene prior to hybridization motivated us to explore more efficient array options. The second designed microarray, a custom Affymetrix GeneChip, targeted Escherichia coli, Salmonella typhimurium, and Salmonella dublin in addition to the previously mentioned pathogens and was more successful in overall performance than the in-house arrays. In addition to the 16S gene, oligonucleotide probes targeted other genes (from 2 to \u3e4500, depending on whether the genome was sequenced) that were unique to each pathogen. This array also differed from the in-house arrays in that mismatched probes were not designed. The different probe sets performed at different detection limits as P. multocida, A. pyogenes, S. typhimurium, and S. dublin were detected with as little as 250ng of hybridized genomic DNA (gDNA), while M. haemolytica, H. somni, and E. coli required as much as 1μg gDNA. These pathogens were also spiked into bovine tissue to simulate multiorgan infections in which they were individually detected with the microarray design

Optimization and clinical validation of a pathogen detection microarray

New design and optimization of pathogen detection microarrays is shown to allow robust and accurate detection of a range of pathogens. The customized microarray platform includes a method for reducing PCR bias during DNA amplification

Development of a DNA-based microarray for the detection of zoonotic pathogens in rodent species

The demand for diagnostic tools that allow simultaneous screening of samples for multiple pathogens is increasing because they overcome the limitations of other methods, which can only screen for a single or a few pathogens at a time. Microarrays offer the advantages of being capable to test a large number of samples simultaneously, screening for multiple pathogen types per sample and having comparable sensitivity to existing methods such as PCR. Array design is often considered the most important process in any microarray experiment and can be the deciding factor in the success of a study. There are currently no microarrays for simultaneous detection of rodent-borne pathogens. The aim of this report is to explicate the design, development and evaluation of a microarray platform for use as a screening tool that combines ease of use and rapid identification of a number of rodent-borne pathogens of zoonotic importance. Nucleic acid was amplified by multiplex biotinylation PCR prior to hybridisation onto microarrays. The array sensitivity was comparable to standard PCR, though less sensitive than real-time PCR. The array presented here is a prototype microarray identification system for zoonotic pathogens that can infect rodent species

Development of a Flow-Trough Microarray based Reverse Transcriptase Multiplex Ligation-Dependent Probe Amplification Assay for the Detection of European Bunyaviruses

It is suspected that apart from tick-borne encephalitis virus several additional European Arboviruses such as the sandfly borne Toscana virus, sandfly fever Sicilian virus and sandfly fever Naples virus, mosquito-borne Tahyna virus, Inkoo virus, Batai virus and tick-borne Uukuniemi virus cause aseptic meningo-encephalitis or febrile disease in Europe. Currently, the microarray technology is developing rapidly and there are many efforts to apply it to infectious diseases diagnostics. In order to arrive at an assay system useful for high throughput analysis of samples from aseptic meningo-encephalitis cases the authors developed a combined multiplex ligation-dependent probe amplification and flow-through microarray assay for the detection of European Bunyaviruses. These results show that this combined assay indeed is highly sensitive, and specific for the accurate detection of multiple viruses

VARIATIONS IN MICROARRAY BASED GENE EXPRESSION PROFILING: IDENTIFYING SOURCES AND IMPROVING RESULTS

Two major issues hinder the application of microarray based gene expression profiling in clinical laboratories as a diagnostic or prognostic tool. The first issue is the sheer volume and high-dimensionality of gene expression data from microarray experiments, which require advanced algorithms to extract meaningful gene expression patterns that correlate with biological impact. The second issue is the substantial amount of variation in microarray gene expression data, which impairs the performance of analysis method and makes sharing or integrating microarray data very difficult. Variations can be introduced by all possible sources including the DNA microarray technology itself and the experimental procedures. Many of these variations have not been characterized, measured, or linked to the sources. In the first part of this dissertation, a decision tree learning method was demonstrated to perform as well as more popularly accepted classification methods in partitioning cancer samples with microarray data. More importantly, results demonstrate that variation introduced into microarray data by tissue sampling and tissue handling compromised the performance of classification methods. In the second part of this dissertation, variations introduced by the T7 based in vitro transcription labeling methods were investigated in detail. Results demonstrated that individual amplification methods significantly biased gene expression data even though the methods compared in this study were all derivatives of the T7 RNA polymerase based in vitro transcription labeling approach. Variations observed can be partially explained by the number of biotinylated nucleotides used for labeling and the incubation time of the in vitro transcription experiments. These variations can generate discordant gene expression results even using the same RNA samples and cannot be corrected by post experiment analysis including advanced normalization techniques. Studies in this dissertation stress the concept that experimental and analytical methods must work together. This dissertation also emphasizes the importance of standardizing the DNA microarray technology and experimental procedures in order to optimize gene expression analysis and create quality standards compatible with the clinical application of this technology. These findings should be taken into account especially when comparing data from different platforms, and in standardizing protocols for clinical applications in pathology

Universal ligation-detection-reaction microarray applied for compost microbes

<p>Abstract</p> <p>Background</p> <p>Composting is one of the methods utilised in recycling organic communal waste. The composting process is dependent on aerobic microbial activity and proceeds through a succession of different phases each dominated by certain microorganisms. In this study, a ligation-detection-reaction (LDR) based microarray method was adapted for species-level detection of compost microbes characteristic of each stage of the composting process. LDR utilises the specificity of the ligase enzyme to covalently join two adjacently hybridised probes. A zip-oligo is attached to the 3'-end of one probe and fluorescent label to the 5'-end of the other probe. Upon ligation, the probes are combined in the same molecule and can be detected in a specific location on a universal microarray with complementary zip-oligos enabling equivalent hybridisation conditions for all probes. The method was applied to samples from Nordic composting facilities after testing and optimisation with fungal pure cultures and environmental clones.</p> <p>Results</p> <p>Probes targeted for fungi were able to detect 0.1 fmol of target ribosomal PCR product in an artificial reaction mixture containing 100 ng competing fungal ribosomal internal transcribed spacer (ITS) area or herring sperm DNA. The detection level was therefore approximately 0.04% of total DNA. Clone libraries were constructed from eight compost samples. The LDR microarray results were in concordance with the clone library sequencing results. In addition a control probe was used to monitor the per-spot hybridisation efficiency on the array.</p> <p>Conclusion</p> <p>This study demonstrates that the LDR microarray method is capable of sensitive and accurate species-level detection from a complex microbial community. The method can detect key species from compost samples, making it a basis for a tool for compost process monitoring in industrial facilities.</p