Development and experimental validation of a predictive threshold cycle equation for quantification of virulence and marker genes by high-throughput nanoliter-volume PCR on the OpenArray platform.

Development of quantitative PCR (QPCR) assays typically requires extensive screening within and across a given species to ensure specific detection and lucid identification among various pathogenic and nonpathogenic strains and to generate standard curves. To minimize screening requirements, multiple virulence and marker genes (VMGs) were targeted simultaneously to enhance reliability, and a predictive threshold cycle (C(T)) equation was developed to calculate the number of starting copies based on an experimental C(T). The empirical equation was developed with Sybr green detection in nanoliter-volume QPCR chambers (OpenArray) and tested with 220 previously unvalidated primer pairs targeting 200 VMGs from 30 pathogens. A high correlation (R(2) = 0.816) was observed between the predicted and experimental C(T)s based on the organism's genome size, guanine and cytosine (GC) content, amplicon length, and stability of the primer's 3' end. The performance of the predictive C(T) equation was tested using 36 validation samples consisting of pathogenic organisms spiked into genomic DNA extracted from three environmental waters. In addition, the primer success rate was dependent on the GC content of the target organisms and primer sequences. Targeting multiple assays per organism and using the predictive C(T) equation are expected to reduce the extent of the validation necessary when developing QPCR arrays for a large number of pathogens or other targets.

In situ-synthesized virulence and marker gene biochip for detection of bacterial pathogens in water.

Pathogen detection tools with high reliability are needed for various applications, including food and water safety and clinical diagnostics. In this study, we designed and validated an in situ-synthesized biochip for detection of 12 microbial pathogens, including a suite of pathogens relevant to water safety. To enhance the reliability of presence/absence calls, probes were designed for multiple virulence and marker genes (VMGs) of each pathogen, and each VMG was targeted by an average of 17 probes. Hybridization of the biochip with amplicon mixtures demonstrated that 95% of the initially designed probes behaved as predicted in terms of positive/negative signals. The probes were further validated using DNA obtained from three different types of water samples and spiked with pathogen genomic DNA at decreasing relative abundance. Excellent specificity for making presence/absence calls was observed by using a cutoff of 0.5 for the positive fraction (i.e., the fraction of probes yielding a positive signal for a given VMG). A split multiplex PCR design for simultaneous amplification of the VMGs resulted in a detection limit of between 0.1 and 0.01% relative abundance, depending on the type of pathogen and the VMG. Thermodynamic analysis of the hybridization patterns obtained with DNA from the different water samples demonstrated that probes with a hybridization Gibbs free energy of approximately -19.3 kcal/mol provided the best trade-off between sensitivity and specificity. The developed biochip may be used to detect the described bacterial pathogens in water samples when parallel and specific detection is required.

Virulence factor activity relationships: challenges and development approaches.

Virulence factor activity relationships (VFAR) is a predictive approach proposed by the National Research Council's Committee on Drinking Water Contaminants (Washington, D.C.) to classify and rank waterborne pathogens. It is based on the presumption that health threats of waterborne pathogens can be predicted from descriptors at different levels of cellular organization. This paper summarizes challenges that need to be addressed while developing VFAR, with a focus on genomics, such as genomic variability among related pathogens and the need to incorporate genetic descriptors for persistence and host susceptibility. Three key components of VFAR development and validation are also presented, including (1) compilation of a comprehensive VFAR database, (2) development of predictive mathematical models relating descriptors to health effects and other microbial responses, and (3) high-throughput molecular monitoring of drinking water supplies and sources. Bayesian approach and on-chip polymerase chain reaction are discussed as examples of mathematical models and molecular monitoring.

Diagnostic microarray for 14 water and foodborne pathogens using a flatbed scanner.

Parallel detection approaches are of interest to many researchers interested in identifying multiple water and foodborne pathogens simultaneously. Availability and cost-effectiveness are two key factors determining the usefulness of such approaches for laboratories with limited resources. In this study, we developed and validated a high-density microarray for simultaneous screening of 14 bacterial pathogens using an approach that employs gold labeling with silver enhancement (GLS) protocol. In total, 8887 probes (50-mer) were designed using an in-house database of virulence and marker genes (VMGs), and synthesized in quadruplicate on glass slides using an in-situ synthesis technology. Target VMG amplicons were obtained using multiplex polymerase chain reaction (PCR), labeled with biotin, and hybridized to the microarray. The signals generated after gold deposition and silver enhancement, were quantified using a flatbed scanner having 2-µm resolution. Data analysis indicated that reliable presence/absence calls could be made, if: i) over four probes were used per gene, ii) the signal-to-noise ratio (SNR) cutoff was greater than or equal to two, and iii) the positive fraction (PF), i.e., number of probes with SNR≥2 for a given VMG was greater than 0.75. Hybridization of the array with blind samples resulted in 100% correct calls, and no false positive. Because amplicons were obtained by multiplex PCR, sensitivity of this method is similar to PCR. This assay is an inexpensive and reliable technique for high throughput screening of multiple pathogens.

Identification of non-specific hybridization using an empirical equation fitted to non-equilibrium dissociation curves.

Non-equilibrium dissociation curves (NEDCs) have the potential to identify non-specific hybridizations on high throughput, diagnostic microarrays. We report a simple method for the identification of non-specific signals by using a new parameter that does not rely on comparison of perfect match and mismatch dissociations. The parameter is the ratio of specific dissociation temperature (T(d-w)) to theoretical melting temperature (T(m)) and can be obtained by automated fitting of a four-parameter, sigmoid, empirical equation to the thousands of curves generated in a typical experiment. The curves fit perfect match NEDCs from an initial experiment with an R(2) of 0.998±0.006 and root mean square of 108±91 fluorescent units. Receiver operating characteristic curve analysis showed low temperature hybridization signals (20-48°C) to be as effective as area under the curve as primary data filters. Evaluation of three datasets that target 16S rRNA and functional genes with varying degrees of target sequence similarity showed that filtering out hybridizations with T(d-w)/T(m)<0.78 greatly reduced false positive results. In conclusion, T(d-w)/T(m) successfully screened many non-specific hybridizations that could not be identified using single temperature signal intensities alone, while the empirical modeling allowed a simplified approach to the high throughput analysis of thousands of NEDCs.

Influence of dangling ends and surface-proximal tails of targets on probe-target duplex formation in 16S rRNA gene-based diagnostic arrays.

Dangling ends and surface-proximal tails of gene targets influence probe-target duplex formation and affect the signal intensity of probes on diagnostic microarrays. This phenomenon was evaluated using an oligonucleotide microarray containing 18-mer probes corresponding to the 16S rRNA genes of 10 waterborne pathogens and a number of synthetic and PCR-amplified gene targets. Signal intensities for Klenow/random primer-labeled 16S rRNA gene targets were dissimilar from those for 45-mer synthetic targets for nearly 73% of the probes tested. Klenow/random primer-labeled targets resulted in an interaction with a complex mixture of 16S rRNA genes (used as the background) 3.7 times higher than the interaction of 45-mer targets with the same mixture. A 7-base-long dangling end sequence with perfect homology to another single-stranded background DNA sequence was sufficient to produce a cross-hybridization signal that was as strong as the signal obtained by the probe-target duplex itself. Gibbs free energy between the target and a well-defined background was found to be a better indicator of hybridization signal intensity than the sequence or length of the dangling end alone. The dangling end (Gibbs free energy of -7.6 kcal/mol) was found to be significantly more prone to target-background interaction than the surface-proximal tail (Gibbs free energy of -64.5 kcal/mol). This study underlines the need for careful target preparation and evaluation of signal intensities for diagnostic arrays using 16S rRNA and other gene targets due to the potential for target interaction with a complex background.