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Journal of Clinical Microbiology, July 2009, p. 2252-2255, Vol. 47, No. 7
0095-1137/09/$08.00+0     doi:10.1128/JCM.00033-09
Copyright © 2009, American Society for Microbiology. All Rights Reserved.

Rapid Identification of Biothreat and Other Clinically Relevant Bacterial Species by Use of Universal PCR Coupled with High-Resolution Melting Analysis

Samuel Yang,^1*, Padmini Ramachandran,^1, Richard Rothman,^1,2 Yu-Hsiang Hsieh,¹ Andrew Hardick,² Helen Won,² Aleksandar Kecojevic,¹ Joany Jackman,³ and Charlotte Gaydos^1,2

Johns Hopkins University, Department of Emergency Medicine, Baltimore, Maryland,¹ Johns Hopkins University, Division of Infectious Diseases, Baltimore, Maryland,² Applied Physics Laboratory, Johns Hopkins University, Laurel, Maryland³

Received 7 January 2009/ Returned for modification 13 February 2009/ Accepted 6 May 2009

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A rapid assay for eubacterial species identification is described using high-resolution melt analysis to characterize PCR products. Unique melt profiles generated from multiple hypervariable regions of the 16S rRNA gene for 100 clinically relevant bacterial pathogens, including category A and B biothreat agents and their surrogates, allowed highly specific species identification.

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Rapid and accurate diagnostic tools are critical for infectious disease surveillance and early diagnosis of disease (8, 12). A simple platform which could deliver broad-based screening and specific pathogen identification would be invaluable for the timely recognition of emerging and biothreat (BT) outbreaks, as well as commonly encountered clinical infections (2, 7, 9, 11, 12).

We previously reported a probe-based PCR assay, which utilizes conserved and variable 16S rRNA gene sequences for initial broad-based eubacterial detection and subsequent identification of specific bacterial agents (11). The assay demonstrated high analytical sensitivity but was limited by an inability to differentiate closely related pathogens due to decreased specificity of the TaqMan probe chemistry and high sequence homology within selected hypervariable regions of the 16S rRNA gene. Probe-based amplicon characterization accordingly limits testing to a finite number of anticipated pathogens. Alternative strategies for amplicon analysis, such as sequencing and mass spectrometry, allow broader-scale product characterization but are costly, time-consuming, and lacking in throughput (1, 6). High-resolution melt analysis (HRMA) offers a simple, low-cost, closed-tube approach to amplicon analysis with the capacity for single-nucleotide discrimination and easy integration with PCR analysis (10). We report a unique strategy for the rapid, highly specific identification of BT- related and non-BT-related bacterial pathogens which couples eubacterial PCR with HRMA.

Three hypervariable regions (V1, V3, and V6), each flanked by highly conserved sequences within the 16S rRNA gene, were selected for primer design (3). Sequence data for clinically or BT-relevant bacteria were obtained from GenBank and aligned using ClustalW (www.ebi.ac.uk/clustalw/) to determine sequence variability. Primer pairs used to target hypervariable regions were as follows: V1-F (5'-GYGGCGNACGGGTGAGTAA-3') and V1-R (5'-TTACCCCACCAACTAGC-3'), V3-F (5'-CCAGACTCCTACGGGAGGCAG-3') and V3-R (5'-CGTATTACCGCGGCTGCTG-3'), and V6-F (5'-TGGAGCATGTGGTTTAATTCGA-3') and V6-R (5'-AGCTGACGACANCCATGCA-3').

One hundred common, BT-related, and BT-surrogate organisms composed of 58 different bacterial species of American Type Culture Collection (ATCC) strains, clinical isolates, or inactivated or nonpathogenic strains were used for analysis (Table 1). Ten to 15 colonies of each bacterial organism were inoculated in to 200 µl of molecular-grade water (Roche Molecular Diagnostics, Indianapolis, IN), and DNA was extracted using a Roche MAGNA Pure instrument (Roche Molecular Corporation, Indianapolis, IN). Archived DNA extracted as previously described from 40 archived clinical synovial fluid (14) and cerebral spinal fluid samples collected from patients suspected of having septic arthritis or bacterial meningitis, respectively, were also used for blinded analyses.

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TABLE 1. Melting analysis of non-BT-related and BT-related organisms

Extracted DNA from each organism or clinical sample was subjected to three PCR analyses, targeting V1, V3, and V6 hypervariable regions, respectively. Every PCR analysis was performed in a 10-µl total volume comprised of 8 µl of PCR master mix and 2 µl of target input. The PCR master mix contained 4 µl of 2x Universal PCR mix (Idaho Technology, Salt Lake City, UT) and LC Green dye (Idaho Technology) for high-resolution melting. A total of 1.0 µl of 1.5-µM forward primer and reverse primer was added to the master mix. Each PCR analysis contained one primer pair. The PCR was performed using a GeneAmp Thermocycler (ABI, Foster City, CA). Cycling conditions were as follows: denaturation at 95°C for 30 s, followed by 45 cycle repeats at 95°C for 30 s and annealing/extension at 60°C/72°C for 60 s, and 1 cycle at 95°C for 30 s and 28°C for 30 s.

Each post-PCR sample amplicon was subjected to HRMA on the LightScanner instrument (Idaho Technology). Melting temperatures ranged from 60°C to 95°C. Data acquisition was performed for every 0.1°C increase in temperature. HRMA for each PCR sample was performed in triplicate and analyzed using the LightScanner software version 2.0 (Idaho Technology). The software function "negative filter" was first used to identify negative controls and any failed PCRs. Melt analysis of the positive samples was then subjected to fluorescence normalization and temperature shift to obtain the minimum inter- and intra-run variabilities (LightScanner version 2.0 operator's manual; Idaho Technology, Salt Lake City, UT). Specifically, normalization minimized the variations in fluorescence magnitude between samples due to differences in starting template or optics, and a temperature shift will overcome the effect of absolute temperature variation from position to position across the plate. Derivative plots were generated to assess the number of melting peaks. Analysis subsets (V1, V3, and V6) were defined by the primer sets used for amplification. Using the melting curves of Staphylococcus aureus as the reference curve, the difference plot for each positive sample was generated for subsequent grouping analysis. "Auto grouping" was performed on the difference plots to group all positive samples with a similar curve shape within the same analysis subset. A unique letter code was manually assigned for each group identified, starting with the letter "a" and progressing alphabetically. A combination of each letter from each of the variable regions was then accumulated to provide a signature code for each organism.

Each of the 100 bacterial organisms tested had a melting curve generated from HRMA for each of the analysis subsets (V1, V3, and V6) based on the primer set used. Each derivative plot revealed a single dominant peak, which was absent in the nontemplate control, indicating the presence of a single amplified sequence. The melting curves were demonstrated to be reproducible from run to run despite various target DNA concentrations over a 10,000-fold range (data not shown). Using the melting curve of Staphylococcus aureus as the reference, difference plots of the 100 tested organisms generated were compared within their analysis subset. The S. aureus melting curve was chosen as the reference curve, due mainly to the high sequence homology between various S. aureus strains (n = 8) compared within our target amplified regions. After grouping analysis, each difference plot was assigned a unique code letter and only plots with similar characteristics within the same analysis subset shared the same code letter (Fig. 1; Table 1). Although different species were observed to share similar plots within the same analysis subset, each species was associated with a unique three-letter signature code when all three analysis subsets were included. Even closely related species (e.g., Bacillus anthracis versus Bacillus cereus) with a single-nucleotide difference within some of our target regions could be differentiated (Fig. 1). Identical signature codes were observed among various strains of the same species (Table 1).

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FIG. 1. The difference plots of all the category A BT bacterial organisms and their surrogates. A grouping code letter (indicated on the top left corner of each graph) is assigned for each plot based on similarity in curve shape with other organisms under the same analysis subset (V1, V3, or V6).

We also performed HRMA on eubacterial PCR products from 40 blinded archived clinical samples, which included synovial fluids and cerebral spinal fluids previously collected from patients suspected of having septic arthritis or bacterial meningitis, respectively. HRMA correctly identified all 20 culture-negative samples as being negative. The signature codes generated from each of the 20 remaining positive samples were compared to our reference database of 58 different bacterial species for identification (Table 2). The species identified based on their signature codes correctly matched their respective culture organisms in all samples tested.

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TABLE 2. Melting analysis results of 20 blinded culture-positive clinical cerebrospinal and synovial fluids testeda

In this study, we demonstrate as proof of concept a simple, powerful approach to amplicon analysis for rapid bacterial species identification and differentiation of BT agents from their related surrogates. This approach relies on eubacterial real-time PCR analysis followed by HRMA. Unlike probe-based approaches to amplicon analysis, melt curve analysis can characterize PCR products without a priori knowledge of anticipated organisms. Further work will be required to develop a comprehensive database of signature codes from all common pathogens. Once established, nonmatching code generated from a positive amplification reaction may signify the presence of an uncommon, mutant, or emerging pathogen. This approach offers a simple work flow with a total turnaround time of 2 h (from sample collection to species identification) and obviates the need for laborious post-PCR procedures. Due to the ease of integrating the melt analysis, this approach has the potential to be used as a point-of-care test and may be feasible in resource-deficient clinical settings.

Despite the high discriminatory precision of HRMA, we found that amplicons of very different sequences may generate similar melt curves. These findings have been reported previously (4). To resolve "melting groups," Cheng et al. performed heteroduplex melt analyses between amplicons of unknown and reference bacterial species (4). A potential drawback with this approach is that closely related species with identical sequences within the amplified region may not be readily differentiated. We chose to analyze the melt profiles based on three instead of one of the 16S, hypervariable regions (3, 5). This yielded a unique set of melt plots for every non-BT or BT-relevant bacterial organism tested, with even closely related species able to be discerned (13). As expected, different strains of the same species with identical target sequences shared similar melt profiles. Future studies will determine whether the triple-PCR analyses are more cost-effective when performed in parallel or in a series for routine diagnostic testing and/or surveillance.

Potential limitations of using melt analysis for pathogen identification include nucleotide polymorphisms, which may exist between intragenomic copies of the 16S rRNA gene in some bacterial species, as well as polymicrobial infections. The number of peaks in the derivative plot may allow discrimination of single versus multiple infections. Future studies will focus on assay reproducibility and specificity using expanded panels of clinically relevant bacterial species, animal studies with BT agents, and human clinical validation studies of patients with suspected systemic bacterial infections.

 
The work described was supported by grant 2 U54 AI057168 from NIH/NIAID.

 
* Corresponding author. Mailing address: Johns Hopkins University Department of Emergency Medicine, 5801 Smith Avenue, Suite 3220, Davis Building, Baltimore, MD 21209. Phone: (410) 735-6441. Fax: (410) 735-6440. E-mail: syang10{at}jhmi.edu

Published ahead of print on 20 May 2009.

Samuel Yang and Padmini Ramachandran both contributed equally to the manuscript.

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Journal of Clinical Microbiology, July 2009, p. 2252-2255, Vol. 47, No. 7
0095-1137/09/$08.00+0     doi:10.1128/JCM.00033-09
Copyright © 2009, American Society for Microbiology. All Rights Reserved.

This Article Abstract Full Text (PDF) Alert me when this article is cited Alert me if a correction is posted Citation Map Services E-mail this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Reprints and Permissions Copyright Information Books from ASM Press MicrobeWorld Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Yang, S. Articles by Gaydos, C. Search for Related Content PubMed PubMed Citation Articles by Yang, S. Articles by Gaydos, C.