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Applied and Environmental Microbiology, April 2008, p. 2200-2209, Vol. 74, No. 7
0099-2240/08/$08.00+0     doi:10.1128/AEM.01962-07
Copyright © 2008, American Society for Microbiology. All Rights Reserved.

In Situ-Synthesized Virulence and Marker Gene Biochip for Detection of Bacterial Pathogens in Water ^,

Sarah M. Miller,^1, Dieter M. Tourlousse,^1, Robert D. Stedtfeld,¹ Samuel W. Baushke,¹ Amanda B. Herzog,¹ Lukas M. Wick,³ Jean Marie Rouillard,⁴ Erdogan Gulari,⁴ James M. Tiedje,² and Syed A. Hashsham^1,2*

Department of Civil and Environmental Engineering,¹ Center for Microbial Ecology,² National Center for Food Safety and Toxicology, Michigan State University, East Lansing, Michigan 48824,³ Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan 48109⁴

Received 27 August 2007/ Accepted 25 January 2008

Top ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
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.

Top ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Pathogen detection and identification using virulence and marker genes (VMGs) as genetic targets are receiving considerable interest due to the rapidly increasing availability of gene and whole-genome sequence information for most major pathogens. VMGs are expected to be more specific than the 16S rRNA gene due to the limited ability of the 16S rRNA gene to differentiate among closely related microorganisms. Information about the presence or absence of selected VMGs also yields information about the pathogenic potential of detected microorganisms. Screening for multiple VMGs for each pathogen is necessary to reduce the potential of erroneous calls due to differences in the occurrence of various VMGs among pathogenic strains belonging to the same species (59). Use of a single VMG or a limited number of VMGs may lead to an increased rate of false-negative calls if the selected VMGs are unevenly distributed among different strains.

One of the first microarrays for the detection of microbial pathogens using multiple VMGs was described by Wilson et al. (66), who used the Affymetrix platform. This microarray was validated for 11 bacterial, five viral, and two eukaryotic pathogens and employed genomic DNA (gDNA) of pathogens spiked in total DNA extracted from filtered air. This study also highlighted the advantage of using highly redundant probe sets to infer presence/absence calls based on the positive fraction (PF). The PF was defined as the number of positive probes for a given target divided by the total number of probes used to detect that target. Setting a sufficiently high threshold for the PF to define a target as present substantially reduces the rate of false-positive calls due to cross-hybridization. When redundant probe sets are used, the reliability of the presence/absence calls is expected to be somewhere between the reliability achieved by PCR-based detection of the VMGs and the reliability achieved by sequencing of the corresponding VMG amplicons. Utilization of redundant probe sets may also result in more efficient validation of newly designed microarrays. This is because the success levels for probe design are generally high and presence/absence calls based on redundant probe sets are not affected by failure of a small number probes within a set.

Among the challenges in the development of pathogen detection microarrays is the limited ability to detect low-abundance target sequences within complex mixtures. Microarrays without target gene amplification only allow detection of microorganisms at a relative abundance of approximately 1 to 5% (4, 11, 26, 50, 67). Obviously, pathogens may be present at levels well below this limit in various matrices. Hence, microarray-based pathogen detection generally relies upon target gene amplification strategies, typically using PCR (6, 7, 35). For conserved genes (e.g., the 16S rRNA gene), amplification using universal or group-specific primer sets in multitemplate PCR has been extensively used to enrich target genes prior to hybridization (12, 13, 16, 17, 24, 36-38, 43, 44, 51, 52, 55, 63, 65). Methods for simultaneous amplification of VMGs, however, are less well developed. Frequently, multiple PCRs or multiplex PCR has been used to amplify multiple VMGs to enhance probe signals and to improve detection limits for genotyping of isolates and pathogen detection in various matrices (1, 2, 8, 10, 20, 25, 31, 32, 45, 54, 61, 62, 66). However, development of robust and sensitive multiplex PCR assays for multiple VMGs still requires careful primer design and significant optimization of the reaction parameters (41). If a large number of VMGs could be simultaneously amplified in a robust manner in complex matrices, VMG-based microarrays could provide the required specificity, sensitivity, and target throughput, all in the same assay, for diagnostic purposes.

The sensitivity of microarrays is also influenced by the strength of probe signals obtained after hybridization. Probes with high target binding affinities may be preferred for detecting low-abundance targets. However, such probes are also more prone to cross-hybridization (23) and may decrease specificity. This trade-off between specificity and sensitivity needs to be assessed experimentally to define optimal probe selection criteria.

The main objective of this study was to design and validate an in situ-synthesized VMG biochip to detect 12 bacterial pathogens. Many of the pathogens included (Aeromonas hydrophila, Helicobacter pylori, Legionella pneumophila, Pseudomonas aeruginosa, Vibrio cholerae, Vibrio parahaemolyticus, and Yersinia enterocolitica) were waterborne. Other pathogens (Clostridium perfringens, Salmonella, Staphylococcus aureus, Campylobacter jejuni, and Listeria monocytogenes) were relevant to clinical diagnostics and food safety. gDNA extracted from tap water, river water, and tertiary effluent from a municipal wastewater treatment plant served as the matrix used to assess sensitivity and specificity after samples were spiked with pathogen gDNA. Target gene amplification using a split multiplex PCR assay allowed detection of pathogens at a relative abundance between 0.1 and 0.01%, depending on the pathogen and the VMG. Up to six VMG amplicons per pathogen and up to 35 probes per VMG amplicon were used to eliminate false-positive calls. The effect of characteristics of probes on their hybridization behavior was also evaluated in order to derive probe design rules yielding the best trade-off between sensitivity and specificity. The described VMG biochip may have applications in diagnostic areas where parallel screening of multiple pathogens with high levels of specificity is critical.

Top ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Probe design and biochip synthesis.
Two sets of probes were included on the VMG biochip (see the supplemental material for probe sequences). The first set of probes (n = 791) was designed to detect 35 VMGs covering 12 pathogens. A total of 47 PCR primer sets were designed for these genes; each amplicon was targeted by 7 to 35 probes, and the average was 17 probes per amplicon (Table 1). The second set of probes (n = 2,034) targeted 67 VMGs for the 12 pathogens mentioned above, as well as VMGs for five additional pathogens. For these genes, no primers were designed, and the probes were analyzed as nontargeted probes to assess hybridization specificity. All VMGs selected in this study were used previously to detect the selected pathogens in PCR-based assays. All probes were designed based on 671 sequences retrieved from the GenBank database and satisfied the following criteria: (i) the probes were complementary to all retrieved sequences of a given gene with species-level specificity, and (ii) the probes contained at least two mismatches to all other nontarget sequences within the database. The Gibbs free energy of probe-target duplex formation assuming complementary targets (G°_duplex) ranged from –14.1 to –23.9 kcal/mol; the mean was –18.4 kcal/mol, and the variability (standard deviation) was 2.1 kcal/mol.

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TABLE 1. Overview of the validation resultsa

The designed probes were synthesized in situ on microfluidic biochips using a proprietary light-directed synthesis technology developed by the University of Michigan (18, 19) and commercialized by Xeotron (Houston, TX; now part of Invitrogen, Carlsbad, CA). The microfluidic chip contained 8,000 microreactors with a diameter of 50 µm, which were interconnected with flow channels (Fig. 1a). The in situ synthesis technology employed conventional phosphoramidite chemistry in conjunction with photogenerated acid-triggered deprotection of the 5'-hydroxyl groups of nucleotide phosphoramidite monomers. Spatially resolved light patterns for light-directed acid deprotection were generated using a digital micromirror device. The probes were attached to the substrate via a spacer consisting of Ts and C₁₈ with an effective length of 12 nucleotides. The biochip also contained 22 randomly spaced control spots containing solely linker chemistry to assess background signal intensity (56, 64).

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FIG. 1. Evaluation of 791 targeted and 2,034 nontargeted probes with a composite target mixture containing all 47 VMG amplicons (24 amplicons labeled with Cy3 and 23 amplicons labeled with Cy5). (a) Part of the Xeotron chip after hybridization and washing up to a temperature of 30°C. (b) Distribution of the SNRs for 791 targeted probes (filled bars) and 2,034 nontargeted probes (open bars). (c) Distribution of the PFs for 47 targeted probe sets (filled bars) and 67 nontargeted probe sets (open bars). (d) Success of probe selection for targeted and nontargeted probe sets (• and , respectively). The indicated slopes of the dashed lines are numerically identical to the PFs.

Nucleic acid extraction.
Twelve reference bacteria were used to validate the VMG biochip. Cultures of A. hydrophila ATCC 7966, C. perfringens ATTC 12916, Salmonella sp. strain ATCC 13311, L. monocytogenes ATCC 15313, P. aeruginosa ATCC 10145, V. parahaemolyticus ATCC 43996, and Y. enterocolitica ATCC 55075 were obtained from the American Type Culture Collection (ATCC) (Manassas, VA). Cells were grown overnight according to the instructions provided by ATCC for the organisms. gDNA was extracted from 1 to 2 ml of the cultures using a DNeasy tissue kit (Qiagen, Valencia, CA) according to the manufacturer's instructions. For V. cholerae ATCC 39315, C. jejuni ATCC 700819, H. pylori ATCC 700392, S. aureus ATCC 700699, and L. pneumophila ATCC 33152, purified gDNA was obtained from the ATCC.

Tap water (40 liters), river water (10 liters; Red Cedar River, East Lansing, MI), and tertiary effluent (20 liters; wastewater treatment plant in East Lansing, MI) were sampled and filtered through 0.45-µm nitrocellulose filters (Millipore, Billerica, MA). gDNA was extracted from the filters using a MegaPrep UltraClean soil DNA kit (Mo Bio Laboratories, Carlsbad, CA) according to the manufacturer's instructions. The amount and quality of the extracted DNA were determined with a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE).

Primer design and PCR conditions.
A total of 47 gene-specific primer sets (see Table S1 in the supplemental material) were designed for 35 VMGs flanking gene regions with high probe density. Primers were selected so that they had similar annealing temperatures (one set had an annealing temperature of 53°C, and another set had an annealing temperature of 58°C) and covered most alleles of a given VMG available in the GenBank database. The uniqueness of the primers was confirmed by a BLAST search against the GenBank database. To ensure primer specificity, mismatches with related nontarget sequences were located near the 3' ends of the primers. For multiplex PCR, primers were segregated into five primer combinations, and each combination contained 9 or 10 primer pairs (see Table S2 in the supplemental material). Mixtures of primer sets were selected so that each pathogen (except P. aeruginosa) was targeted in at least two different multiplex PCRs. Primers were synthesized by Integrated DNA Technologies (Coralville, IA).

PCR mixtures (25 µl) consisted of 1x PCR buffer, 2 mM MgCl₂, 1.5 U (for monoplex PCR) or 3 U (for multiplex PCR) of AmpliTaq Gold (Roche Molecular Systems, Pleasanton, CA), each deoxynucleoside triphosphate at a concentration of 200 µM (Invitrogen), each primer at a concentration of 500 nM, 200 ng of bovine serum albumin (New England BioLabs, Beverly, MA), and 1 µl of DNA. After the initial enzyme activation at 94°C for 10 min, 35 cycles of the following temperature regimen was used for amplification: denaturation at 94°C for 60 s, annealing at 53 or 58°C for 60 s, and elongation at 72°C for 60 s. This was followed by a final elongation step at 72°C for 7 min. PCR mixtures were purified using a QIAquick PCR purification kit (Qiagen), and the amplification products were quantified using the NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies).

Fluorescent labeling of PCR products.
PCR products pooled from different PCRs were labeled using an aminoallyl dUTP (aa-dUTP) incorporation and cyanine dye coupling protocol as described previously (56, 64), with minor modifications. Briefly, PCR products were amplified, and aa-dUTP was incorporated into the amplification products with a Bioprime DNA labeling kit (Invitrogen, San Diego, CA) using a ratio of aa-dUTP to dTTP of 5:1 and an incubation time of 120 min. Purified products were then coupled with cyanine dye by incubation for 80 min in a 1:1 mixture of 0.1 M sodium carbonate buffer (pH 9.3) and N-hydroxysuccinimide ester cyanine dye (Amersham Biosciences).

Biochip hybridization and melting curve development.
Hybridization and melting profile generation were performed using previously established protocols (56, 64). The method used for melting profile analysis was based on methods used in previous studies utilizing this approach for microarrays using 16S rRNA targets (15, 29, 34, 60). Briefly, after priming of the biochips, target DNA (200 pmol of Cy dye) was hybridized overnight at 20°C using an M-2 hybridization station (Invitrogen [formerly Xeotron Corporation, Houston, TX]) with hybridization buffer containing 35% deionized formamide (Ambion), 6x SSPE (pH 6.6; Invitrogen), and 0.4% Triton X-100 (Sigma) (1x SSPE is 0.18 M NaCl, 10 mM NaH₂PO₄, and 1 mM EDTA [pH 7.7]). After the chips were washed, the initial point of the melting curve profile was obtained by washing each chip with high-stringency wash buffer (20 mM NaCl, 10 mM Na₂PO₄, 5 mM Na₂EDTA; pH adjusted to 6.6 with HCl) for 1.4 min at 25°C and imaging the chip. High-stringency wash buffer was degassed under a vacuum to prevent formation of air bubbles during the wash steps. Subsequent development of the melting profile was performed by using manual cycles of washing and scanning of the chip that were repeated at 1°C intervals until 60°C was reached. At the end of this procedure, the chip was additionally stripped at 60, 45 and 30°C (2.5 min each) with nuclease-free water (Sigma). The tubing of the hybridization station was washed for 20 min before and after each experiment to prevent carryover between experiments.

Experimental design.
Specificity and sensitivity were evaluated with samples consisting of pathogen gDNA spiked into DNA obtained from different water sources (tap water, river water, and tertiary effluent from a wastewater treatment plant), as has been done in numerous other validation studies (43, 66). A first set of samples was prepared by spiking 10 pg of gDNA of each pathogen (equivalent to approximately 1,400 to 5,500 genome copies depending on the pathogen) into 10 ng of DNA from the different water samples. This yielded a relative abundance of 0.1% for each of the pathogens, calculated as a mass-based percentage of pathogen gDNA in the total community gDNA. Both spiked and unspiked samples were subjected to the split multiplex PCR amplification step. The amplicons obtained from spiked water samples were labeled with Cy3, and the amplicons obtained from unspiked water samples were labeled with Cy5. Equal amounts of the two labeled products (100 pmol of each dye or 2.5 µg of DNA) were mixed and hybridized in duplicate. For the unspiked water samples, 2,034 nontargeted probes were evaluated to examine probe specificity. For the spiked samples, 673 probes were evaluated to examine probe sensitivity. A second set of samples contained pathogen gDNA spiked at a relative abundance of 0.01% into gDNA from river water and at a relative abundance of 0.001% in gDNA from tertiary effluent.

Data acquisition and processing.
Microarrays were scanned with a GenePix 4000B 16-bit laser scanner (Axon instruments, Union City, CA). Fluorescence signal intensities were extracted from the scanned images using GenePix5.0 (Axon Instruments, Union City, CA), which yielded values between 0 and 65,535 arbitrary units. The median of all pixel intensities within a spot was used as the raw spot intensity. Subsequent data analysis was done with Microsoft Excel (Microsoft, Redmond, WA), and plotting was done with SigmaPlot 9.0 (Systat Software, Point Richmond, CA).

Raw spot intensities were divided by the mean signal intensity of 22 empty control spots to obtain signal-to-noise ratios (SNRs) (56). The SNR was computed for each wash step between 30 and 45°C, and the values were subsequently averaged. Averaging of SNRs is equivalent to calculating an SNR based on the area under the melting curve within this temperature interval. The SNR was subsequently divided by the median SNR of the 2,034 nontargeted probes. A probe signal was considered positive if the SNR was greater than 3. The PF was computed by dividing the number of positive signal probes within a set by the number of probes within the set (66).

Gibbs free energy estimation.
The G°_duplex was calculated with the DINAMelt web server (14, 40, 53). This parameter reflects the energy released during probe-target duplex formation and is sequence dependent (53). It provides a thermodynamic measure of the affinity between a probe and a target, with more negative G°_duplex values being indicative of higher binding affinities. For all calculations, perfectly matching duplexes were assumed, and effects of target dangling ends were neglected. The temperature used for the calculations was 43°C instead of the actual hybridization temperature (20°C) to account for the presence of 35% formamide in the hybridization solution (3, 60), and the Na⁺ concentration used was 1 M. A linear relationship between the G+C content of a probe and its G°_duplex was observed. This relationship was used to convert an estimated G°_duplex into a corresponding G+C content.

Top ABSTRACT INTRODUCTION MATERIALS AND METHODS RESULTS AND DISCUSSION REFERENCES

 
Probe behavior with amplicon mixtures generated by monoplex PCR.
The performance of the VMG biochip was evaluated by hybridization of a target mixture containing all 47 VMG amplicons obtained in individual PCR amplifications. Of the 47 amplicons, 24 were labeled with Cy3 and 23 were labeled with Cy5. For the Cy3-labeled amplicons, 337 of 393 (85.5%) targeted probes displayed positive signals in all three replicates. Furthermore, only 10 of the 393 (2.5%) probes displayed positive signals in at least one of the replicates with the Cy5-labeled amplicons (i.e., nontargeted amplicons), with a median SNR of 3.4. For the Cy5-labeled amplicons, 383 of 398 (96.2%) targeted probes yielded positive signals in all three replicates. Furthermore, only 18 of 398 (4.5%) probes displayed positive signals in at least one of the replicates with the Cy3-labeled amplicons (i.e., nontargeted amplicons), with a median SNR of 3.5. Of the 2,034 nontargeted probes, 27 (1.3%) and 34 (1.7%) displayed positive signals with the Cy3- and Cy5-labeled amplicons, respectively, in at least one of the replicates.

Overall, of the 791 targeted probes, 720 (91.0%) produced positive signals with SNRs up to 1,000. Of the 2,034 nontargeted probes, 61 (3.0%) yielded positive signals with either the Cy3- or Cy5-labeled amplicons. The signal intensities for targeted and nontargeted probes were well separated, except for a small fraction of the probes (Fig. 1b), indicating the good discriminatory power of the probes. Altogether, 95% of the 5,650 individual probe-target interactions (twice the total number of targeted and nontargeted probes) were the expected interactions in terms of positive/negative probe signals.

The PF was calculated for each probe set (and corresponding amplicon) by dividing the number of positive probes by the number of probes within the set (66). A PF based on the number of initially designed probes is equivalent to the success level of probe selection for individual probe sets. Of the 47 targeted probe sets, 22 displayed a PF of 1, implying that 100% of the designed probes yielded positive signals. Of the remaining 25 sets, 18 displayed a PF between 0.8 to 1 and 7 displayed a PF between 0.6 to 0.8 (Fig. 1c). Of the 67 nontargeted probe sets, 34 displayed a PF of 0 and 31 displayed a PF between 0 and 0.1. The two remaining sets had PFs of 0.125 and 0.3. The success level of probe selection could also be illustrated by plotting the number of designed probes versus the number of positive probes (Fig. 1d). As Fig. 1d shows, the PFs for both targeted and nontargeted probes were not related to the number of designed probes.

Substantial variation in signal intensity was observed among probes for a given VMG amplicon (Fig. 2b). Extensive unevenness in signal intensity among probes for a given target is well documented (5, 44, 46, 56). This variability may be attributed to various factors, including probe and target secondary structure (9, 27, 47), target length (33), G°_duplex (23), and even the position of fluorescent labels (68). Also, sequence dissimilarities between the probes and hybridized targets may have contributed to this variability. When probe sets were sorted according to their median SNRs, a trend toward increasing PF with a higher median SNR was apparent (Fig. 2a). Interestingly, the G+C content of the VMG amplicons displayed an analogous tendency (Fig. 2c). This may also partially explain the overall lower success rate for the probe sets hybridized with the Cy3-labeled amplicons (85.5%) than for the probe sets targeted by the Cy5-labeled amplicons (96.2%). The G+C content for Cy3-labeled amplicons (36.5% ± 5.4%) was, on average, lower than the G+C content for the Cy5-labeled amplicons (48.6% ± 7.9%). These trends imply that probe design for genes or genomes (and hence detection of the corresponding microorganisms) with a considerably lower G+C content may be more challenging when continuous regions with a higher G+C content are not present in the selected gene targets.

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FIG. 2. PFs (a), SNRs (b), and G+C contents (c) for all 47 targeted VMG amplicons. The probe sets are sorted from bottom to top according to increasing median SNR. The boundaries of the boxes in panel b indicate the 25th and the 75th percentiles, and the whiskers indicate the 10th and the 90th percentiles. The median is given as a solid line, and outlying data points are shown as open symbols.

Success rate of probe design as a function of G°_duplex.
In order to optimize the probe selection criteria, whether probes with positive or negative signals could be identified in silico based on evaluation of their theoretical (thermodynamic) properties was evaluated. Based on previous studies, it was anticipated that probe-target binding affinity (quantified using G°_duplex) would be the most valuable parameter for this screening (23, 39, 42, 49). Only a weak linear relationship (r² = 0.34) was observed between G°_duplex and the natural logarithm of signal intensity (see Fig. S2 in the supplemental material). This variability in signal intensity for probes with comparable G°_duplex values precluded evaluation of probe design criteria in terms of signal intensity based on G°_duplex. To determine the relationship between G°_duplex and probe behavior, hybridization patterns were interpreted in terms of positive/negative probe signals, irrespective of their strength. For this analysis, all 791 targeted probes were sorted according to their G°_duplex values and binned, and the percentage of positive probes in each bin was calculated. The latter value is equivalent to the success level for probes with a G°_duplex within the bin range. A drastic decrease in the probe design success rate was observed for probes with a G°_duplex value less negative than –17 kcal/mol (Fig. 3) and a G+C content less than 34.4% (as estimated using the linear relationship between G+C content and G°_duplex). This trend was independent of the window size used for binning of the probes (see Fig. S3 in the supplemental material). A similar analysis with increasing SNR thresholds revealed analogous trends, but the results were shifted toward more negative G°_duplex values (data not shown). The factors contributing to the unevenness in probe signals for a given target may also explain the lack of hybridization signals for probes with a highly negative G°_duplex. In accordance with our observations, Reyes-Lopez et al. (49) previously reported that the proportion of predicted signals observed experimentally increased for 9-mer probes with increasingly negative G°_duplex values.

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FIG. 3. Success of probe design as a function of G°duplex. Each symbol indicates the percentage of positive probes for bins of 40 probes, and the error bars indicate the standard deviations for three replicates. The percentage of positive probes is plotted at the median G°duplex of each bin. The G+C content was derived from the linear relationship between G°duplex and G+C content.

Based on our analysis of the hybridization behavior of all targeted probes, a G°_duplex threshold of –17 kcal/mol could be used as a valuable design rule for future oligonucleotide probe design. Although this criterion was demonstrated only for 18-mers in this study, a similar approach to assess the effect of G°_duplex on the success level of probe design could be adopted for longer probes. The proposed G°_duplex design threshold is in accordance with the findings of Loy et al. (38), who proposed a value of –16 kcal/mol for 18-mers targeting the 16S rRNA gene. This value was suggested based on the observation that the majority of probes displaying positive signals (including cross-hybridization signals) were attributed to probe-target duplexes with a G°_duplex more negative than –16 kcal/mol. It should be noted that small deviations from these G°_duplex design thresholds may be observed for hybridizations performed under different experimental conditions and/or for theoretical G°_duplex estimates obtained using other nearest-neighbor model parameters (38). Finally, the G°_duplex criterion was derived from hybridization patterns with high-abundance and low-complexity target mixtures. For target mixtures having very different compositions in terms of target sequence abundance and diversity, these rules should be applied with caution.

Multiplex amplification of VMG targets.
A split multiplex PCR assay was developed to amplify all VMG amplicons (see Table S2 in the supplemental material). A gel image of the amplification products obtained with a mixture of gDNA from all 12 pathogens is shown in Fig. S1 in the supplemental material. Of the 720 probes displaying positive signals after hybridization of monoplex PCR amplicons, 673 (93.5%) yielded positive signals after hybridization of multiplex PCR amplicons (Table 1). For 28 of the 47 probe sets, all probes yielded positive signals. In general, the probes that displayed negative signals with the multiplex PCR amplicons also yielded low signals with the monoplex PCR amplicons (data not shown). For the ctxB gene of V. cholerae, only 2 of 20 probes yielded positive hybridization signals after hybridization of amplicons generated by multiplex PCR. This was attributed to poor amplification in the multiplex PCR rather than to low probe hybridization efficiency. With monoplex PCR, an amplification product of the expected length was observed, verifying that the designed primers successfully amplified the ctxB gene of V. cholerae ATCC 39315. In addition, all probes targeting the ctxB gene yielded positive signals after hybridization of the ctxB gene amplicon generated by monoplex PCR. The 27 targeted probes (3.9%, excluding probes targeting the ctxB gene) displaying negative signals were distributed among multiple VMG amplicons, and hence redesign or further optimization of the multiplex PCR assays was expected to be ineffective. In addition, the low levels of the signals of these probes with the monoplex PCR amplicons suggested that their sensitivity may be limited; therefore, these probes were masked during further analysis.

Performance of the VMG biochip with spiked water samples.
The performance of the VMG biochip probe sets was further tested with samples containing pathogen gDNA spiked into gDNA extracted from three different water samples: tap water, tertiary effluent from a wastewater treatment plant, and river water. For pathogens spiked at a relative abundance level of 0.1%, the median PFs for 46 targeted VMG amplicons were 0.79, 0.87, and 0.75 for tap water, tertiary effluent, and river water, respectively (Fig. 4). The median PFs for the 67 other probe sets were 0.05 for tap water, 0.09 for tertiary effluent, and 0.17 for river water (Fig. 4). By applying a PF threshold of 0.5, the numbers of spiked VMG amplicons assigned as present were 39 for tap water (85%), 40 for tertiary effluent (87%), and 39 for river water (85%). For the nontargeted probe sets, the PF was always less than 0.5, and consequently none of the corresponding genes were identified as present in the water samples. Probe sets targeting S. aureus VMG amplicons indicated the presence of this pathogen in all three spiked water samples, although the VMG amplicons assigned as present were in disagreement among samples. The VMG amplicons of C. perfringens could not be detected in any of the spiked water samples.

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FIG. 4. Performance of the VMG biochip with gDNA extracted from various water samples. DNA (10 pg) of each pathogen was spiked into 10 ng of background gDNA, yielding a relative abundance for each pathogen of 0.1%. For spiked samples, 673 probes (46 VMG amplicons) were analyzed. For unspiked samples, 2,034 probes (67 VMG amplicons) were analyzed. The box plots indicate the distribution of the PF among the VMG amplicons. The dashed line at a PF of 0.5 indicates the selected threshold for presence/absence calls. The boundaries of the boxes indicate the 25th and the 75th percentiles, and the whiskers indicate the 10th and the 90th percentiles. The median is given as a solid line, the mean is shown as a dashed line, and outlying data points are shown as open symbols.

When pathogen gDNA was spiked at a relative abundance of 0.01% into gDNA extracted from river water, the presence/absence calls for the VMG amplicons varied for a given pathogen (Table 1). A similar observation was made previously by Wilson et al. (66) and was explained by the variable sensitivity among probes (17). These researchers observed that for pathogens spiked at a fractional abundance of 0.025% (500 fg pathogen gDNA in 2 ng gDNA from air) only five of eight probe sets for Francisella tularensis and three of eight probe sets for Y. pestis were assigned as positive. Conceptually, presence/absence calls for a given pathogen could be based upon indices reflecting the PFs of all probe sets. Requiring that all probe sets for a given pathogen need to be scored as positive to assign the pathogen as present would drastically reduce false-positive calls. However, such an approach might lead to increased rates of false-negative calls due the unequal distribution of multiple VMGs in clinical and environmental isolates of most pathogenic species. For pathogen gDNA spiked into tertiary effluent gDNA at a relative abundance level of 0.001% none of the targeted VMG amplicons were assigned as present (data not shown). Hence, the detection limit of the VMG biochip was between a relative abundance of 0.1% and a relative abundance of 0.01%, depending on the pathogen and VMG. This detection limit is within the range of sensitivity previously reported for microbial diagnostic arrays targeting either the 16S rRNA gene (43, 44, 52) or multiple VMGs (66). However, advances that enhance the sensitivity are required to achieve reliable detection at abundance levels approaching the minimum infectious dose in environmental samples. These advances include improved experimental techniques for target gene enrichment and/or microarray signal enhancement and optimized data analysis algorithms.

The PF threshold applied in this study was less stringent than the cutoff values (at least 0.8) used by previous researchers (13, 66). Both target gene sequence diversity and abundance affect the selection of an optimal threshold for the PF. Uncharacterized sequence diversity within the target gene may lead to reduced PFs due to the increased potential for mismatching probe-target duplexes. Low target gene abundance may also yield decreased PFs due to variability in probe sensitivity. In both cases, a lower threshold PF needs to be used to reduce false-negative calls. Although the concept of redundant probe sets for enhanced reliability in presence/absence calls has been exploited widely (1, 12, 31, 32, 37, 38, 54, 61, 66), the effect of target gene abundance on PFs is less well documented. As demonstrated in this study and by Wilson et al. (66), lower PFs are expected with decreasing target gene amounts, and the extent of the decrease may vary among probe sets.

The use of mismatch probes is a common strategy to identify cross-hybridization signals on microarrays and to enhance the reliability of presence/absence calls (65, 66). In this study, excellent specificity was observed without inclusion of mismatch probes, even for environmental samples. A combination of various factors, including the use of redundant probe sets (with an average of 15 probes per VMG amplicon after experimental screening), the use of short probes with increased discriminatory power compared to long probes, reduction of sample complexity by target gene enrichment using multiplex PCR, and use of the melting curve approach, contributed to the elimination of false-positive calls.

Probe selectivity as a function of G°_duplex.
For selective detection, probes should yield high hybridization signals with intended target sequences and low signals with nontarget sequences. However, due to the biochemical nature of the probe-target hybridization process, these two criteria cannot be optimized independently. This translates into a trade-off between probe specificity and sensitivity that must be considered during probe selection. Based on our analysis shown in Fig. 3, G°_duplex was selected as the probe design parameter quantifying the trade-off between probe specificity and sensitivity. Probe sensitivity was derived from the hybridization patterns of the targeted probes, while probe specificity was quantified based on the hybridization patterns of the nontargeted probes. The hybridization results with the unspiked tap water sample were omitted from this analysis due the minimal amount of nontargeted probes with positive signals (Fig. 4). The hybridization results for all three water samples spiked with pathogen gDNA at a relative abundance of 0.1% were included in the analysis.

Both targeted and nontargeted probes were sorted according to their G°_duplex values and binned, and the percentage of positive probes was computed for each bin. After the percentage of positive probes was plotted as a function of G°_duplex, two distinct regions were apparent (Fig. 5b), and this effect was independent of the window size used for probe binning (see Fig. S4 in the supplemental material). In region 1 (G°_duplex more negative than –19.3 kcal/mol), 93.8% ± 2.8% of the targeted probes displayed positive signals, independent of the G°_duplex. The percentage of nontargeted probes with positive signals was influenced more by G°_duplex and increased from 14.7% for probes with a G°_duplex of –19.9 kcal/mol to 42.7% for probes with a G°_duplex of –23.2 kcal/mol (average increase, 6.8% per G°_duplex). In region 2 (G°_duplex less negative than –19.3 kcal/mol), a rapid decrease in the percentage of positive targeted probes was observed, and only 31.3% of the probes yielded positive signals for probes with a G°_duplex of –15.6 kcal/mol (average decrease, –16.1% per G°_duplex). The percentage of nontargeted probes yielding a positive signal was significantly lower in this region (average, 9.2% ± 3.9%).

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FIG. 5. Derivation of optimal probe design criteria. (a) Selectivity, expressed as the difference in the percentage of positive probes for targeted and nontargeted probes, as a function of G°duplex. (b) Percentage of positive probes as a function of G°duplex for targeted and nontargeted probes (• and , respectively). Each symbol indicates the percentage of positive probes for bins of 40 targeted and 80 nontargeted probes, and the error bars indicate the standard deviations for samples and replicates (n = 6 for targeted probes and n = 4 for nontargeted probes). The G+C content was derived from the linear relationship between G°duplex and G+C content.

As evident from Fig. 5b, an increase in the percentage of positive targeted probes was always associated with an increase in the percentage of positive nontargeted probes. Probe selectivity, reflecting the ability of a probe to detect intended target sequences and exclude nontarget sequences, quantifies this trade-off and is the parameter that needs to be maximized during probe selection. Probe selectivity, calculated by determining the difference in the percentages of positive probes for targeted and nontargeted probes as a function of G°_duplex, was estimated based on the linear regression lines in Fig. 5b. The highest selectivity (80%) was observed for probes with a G°_duplex of –19.3 kcal/mol and a G+C content of 47.2% (Fig. 5a). Thus, probes with a G°_duplex of –19.3 kcal/mol provide the best trade-off between sensitivity and specificity. Probes with a G°_duplex deviating from this optimum displayed lower selectivity. The selectivity was more than 70% for probes with a G°_duplex between –18.6 and –21.1 kcal/mol, which corresponds to a G+C content between 42.3 and 56.1% (Fig. 5a). Interestingly, the decrease in selectivity in region 2 was approximately twofold greater than that in region 1 (–6.6% per G°_duplex for region 1 and –14.0% per G°_duplex for region 2). This difference was attributed to the larger decrease in the percentage of positive targeted probes in region 2 than increase in the percentage of positive nontargeted probes in region 1 (Fig. 5b). It should be noted that for DNA samples containing more complex nontargeted sequences, the increase in the percentage of positive nontargeted probes (i.e., cross-hybridization) for increasingly negative G°_duplex values may be more pronounced, while for more abundant target sequences the decrease in the percentage of positive targeted probes for decreasingly negative G°_duplex values may be suppressed.

A number of studies have demonstrated the effectiveness of G°_duplex as a theoretical probe selection parameter for both long and short probes and RNA or DNA targets (22, 30, 38, 39, 42, 49, 50, 57, 58). In contrast, Pozhitkov et al. (46) recently suggested that all theoretical (thermodynamics-based) screening of oligonucleotide probes should be omitted due to poor correlations between G°_duplex (and G° for intra- and intermolecular self-structures) and experimentally observed signal strengths for rRNA targets. Similarly, only a weak linear correlation was observed between probe signal intensity and G°_duplex (see Fig. S2 in the supplemental material). This indicates that hybridization signals cannot be accurately predicted with current thermodynamics models derived from hybridizations in solution. However, thermodynamic indices may still be indicative of probe behavior by reflecting the probability that a probe will yield a signal intensity higher than a given threshold. Based on the effect of G°_duplex on probe selectivity (Fig. 5a), an optimal G°_duplex of –19.3 kcal/mol is suggested for 18-mer oligonucleotide probes under the described hybridization conditions. These probes should yield the highest confidence in presence/absence calls for a given number of probes per target gene. For probes with either more or less negative G°_duplex values with lower selectivity, more probes need to be designed for a given target to achieve high confidence in presence/absence calls. In general, probes with a more negative G°_duplex displayed better sensitivity but poorer specificity. Finally, it is interesting that the effect of G°_duplex on the trade-off between probe sensitivity and specificity is analogous to the effect of probe length. In general, long probes (50- to 70-mers) are more sensitive than short probes (15- to 30-mers) but display lower specificity (28, 48).

Cost and flexibility in microarray probe synthesis are often recognized as two of the major limiting factors in the use of microarray technology for diagnostic purposes (6, 21). In this study, a maskless light-directed in situ microarray synthesis technology developed at the University of Michigan was employed (19). This technology employs a digital micromirror device to generate preprogrammed light patterns on the chip surface, triggering deprotection of the 5'-hydroxyl group in conventional phosphoramidite monomers. Synthesis of oligonucleotides using this chemistry provides high fidelity and a stepwise yield and also allows synthesis of probes that are up 100 nucleotides long. In addition, synthesis of new chips is low cost, rapid, and flexible and involves simply uploading a list of probe sequences in the optical unit. Other advantages of in situ probe synthesis include high spot uniformity and probe molecule density. In addition, continuous recycling of the hybridization solution in the microfluidic chips used in this study provides increased signal uniformity within a spot and increased reproducibility of the hybridization signals. The higher cost per chip compared to conventional glass slides is the major limitation of this platform (21). The ability to rapidly synthesize biochips with updated and reiterated probe sets is considered critical for diagnostic purposes as new gene sequence information is appearing almost daily and this information should be incorporated in the probe selection exercise and data analysis as soon as it becomes available.

Conclusions.
In conclusion, we developed and validated a coupled format for multiplex PCR and an in situ-synthesized DNA biochip for detection of 12 bacterial pathogens in various water samples. By using redundant probe sets targeting various VMGs and inferring presence/absence calls based on the PF, false-positive calls were eliminated. Pathogens could be detected at a relative abundance of 0.1 to 0.01%, depending on the pathogen. Analysis of the hybridization patterns also showed that probes with a G°_duplex provided the best trade-off between sensitivity and specificity. In future studies, the VMG biochip will be applied to additional environmental samples, and the presence/absence calls will be verified independently using real-time PCR.

 
This work was funded in part by grants from the National Institutes of Health (grant 1 R01 RR018625-03), the Michigan Economic Development Corporation (grant GR-476 PO 085P3000517), and the MSU Foundation.

 
* Corresponding author. Mailing address: Department of Civil and Environmental Engineering, A126 Research Complex Engineering, Michigan State University, East Lansing, MI 48824. Phone: (517) 355-8241. Fax: (517) 355-0250. E-mail: hashsham{at}egr.msu.edu

Published ahead of print on 1 February 2008.

S.M.M. and D.M.T. contributed equally to this study.

Supplemental material for this article may be found at http://aem.asm.org/.

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Applied and Environmental Microbiology, April 2008, p. 2200-2209, Vol. 74, No. 7
0099-2240/08/$08.00+0     doi:10.1128/AEM.01962-07
Copyright © 2008, American Society for Microbiology. All Rights Reserved.

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