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Development and evaluation of a multiplex droplet digital polymerase chain reaction method for simultaneous detection of five biothreat pathogens.

ABSTRACT: Biothreat agents pose a huge threat to human and public health, necessitating the development of rapid and highly sensitive detection approaches. This study establishes a multiplex droplet digital polymerase chain reaction (ddPCR) method for simultaneously detecting five high-risk bacterial biothreats: Yersinia pestis, Bacillus anthracis, Brucella spp., Burkholderia pseudomallei, and Francisella tularensis. Unlike conventional multiplex real-time PCR (qPCR) methods, the multiplex ddPCR assay was developed using two types of probe fluorophores, allowing the assay to perform with a common two-color ddPCR system. After optimization, the assay performance was evaluated, showing a lower limit of detection (LOD) (0.1-1.0 pg/μL) and good selectivity for the five bacteria targets. The multiplex assay's ability to simultaneously detect two or more kinds of targets in a sample was also demonstrated. The assay showed strong sample tolerance when testing simulated soil samples; the LOD for bacteria in soil was 2 × 10²-2 × 10³ colony-forming unit (CFU)/100 mg soil (around 5-50 CFU/reaction), which was 10-fold lower than that of the single-target qPCR method. When testing simulated soil samples at bacterial concentrations of 2 × 10³-2 × 10⁴ CFU/100 mg soil, the assay presented a higher sensitivity (100%, 35/35) than that of the qPCR method (65.71%, 23/35) and a good specificity (100%, 15/15). These results suggest that the developed 5-plex ddPCR method is more sensitive than conventional qPCR methods and is potentially suitable for rapidly detecting or screening the five selected bacterial biothreats in suspicious samples.

SUBMITTER: Du Y

PROVIDER: S-EPMC9366144 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Development and evaluation of a multiplex droplet digital polymerase chain reaction method for simultaneous detection of five biothreat pathogens.

Du Yipu Y Yan Ziheng Z Song Kai K Jin Junyan J Xiao Liting L Sun Zhulin Z Tan Yafang Y Zhang Pingping P Du Zongmin Z Yang Ruifu R Zhao Yong Y Song Yajun Y

Frontiers in microbiology 20220728

Biothreat agents pose a huge threat to human and public health, necessitating the development of rapid and highly sensitive detection approaches. This study establishes a multiplex droplet digital polymerase chain reaction (ddPCR) method for simultaneously detecting five high-risk bacterial biothreats: Yersinia pestis, Bacillus anthracis, Brucella spp., Burkholderia pseudomallei, and Francisella tularensis. Unlike conventional multiplex real-time PCR (qPCR) met ...[more]

PMID: 35966705

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Project description:BackgroundBlood cultures are the cornerstone of diagnosis for detecting the presence of bacteria or fungi in the blood, with an average detection time of 48 hours and failure to detect a pathogen occurring in approximately 50% of patients with sepsis. Rapid diagnosis would facilitate earlier treatment and/or an earlier switch to narrow-spectrum antibiotics.ObjectiveThe aim of this study is to develop and implement a multiplex droplet digital polymerase chain reaction (ddPCR) assay as a routine diagnostic tool in the detection and identification of pathogens from whole blood and/or blood culture after 3 hours of incubation.MethodsThe study consists of three phases: (1) design of primer-probe pairs for accurate and reliable quantification of the most common sepsis-causing microorganisms using a multiplex reaction, (2) determination of the analytical sensitivity and specificity of the multiplex ddPCR assay, and (3) a clinical study in patients with sepsis using the assay. The QX200 Droplet Digital PCR System will be used for the detection of the following species-specific genes in blood from patients with sepsis: coa (staphylocoagulase) in Staphylococcus aureus, cpsA (capsular polysaccharide) in Streptococcus pneumoniae, uidA (beta-D-glucuronidase) in Escherichia coli, oprL (peptidoglycan-associated lipoprotein) in Pseudomonas aeruginosa, and the highly conserved regions of the 16S rRNA gene for Gram-positive and Gram-negative bacteria. All data will be analyzed using QuantaSoft Analysis Pro Software.ResultsIn phase 1, to determine the optimal annealing temperature for the designed primer-probe pairs, results from a gradient temperature experiment will be collected and the limit of detection (LOD) of the assay will be determined. In phase 2, results for the analytical sensitivity and specificity of the assay will be obtained after an optimization of the extraction and purification method in spiked blood. In phase 3, clinical sensitivity and specificity as compared to the standard blood culture technique will be determined using 301 clinical samples.ConclusionsSuccessful design of primer-probe pairs in the first phase and subsequent optimization and determination of the LOD will allow progression to phase 3 to compare the novel method with existing blood culture methods.International registered report identifier (irrid)PRR1-10.2196/33746.

Project description:Bovine enterovirus (BEV), bovine coronavirus (BCoV), and bovine rotavirus (BRV) are still the major worldwide concerns in the health care of cattle, causing serious economic losses in the livestock industry. It is urgent to establish specific and sensitive methods to detect viruses for the early control of diseases. Droplet digital PCR (ddPCR) has been proposed to effectively detect viral particles, and it does not involve Ct values or standard curves. In this study, we designed specific primers and probes, based on conserved regions of viral genomes, to optimize protocols for a dual ddPCR assay for detecting BCoV and BRV and a multiplex ddPCR assay for BEV, BCoV, and BRV. Sensitivity assays revealed that the lower limit of detection for qPCR was 1,000 copies/μL and for ddPCR for BEV, BCoV, and BRV, 2.7 copies/μL, 1 copy/μL and 2.4 copies/μL, respectively. Studying 82 samples collected from diarrheal calves on a farm, our dual ddPCR method detected BCoV, BRV, and co-infection at rates of 18.29%, 14.63%, and 6.1%, respectively. In contrast, conventional qPCR methods detected BCoV, BRV, and co-infection at rates of 10.98%, 12.2%, and 3.66%, respectively. On the other hand, studying 68 samples from another farm, qPCR detected BCoV, BRV, BEV, and co-infection of BCoV and BEV at rates of 14.49%, 1.45%, 5.80%, and 1.45%, respectively. Our multiplex ddPCR method detected BCoV, BRV, BEV, co-infection of BCoV and BEV, and co-infection of BRV and BEV. at rates of 14.49%, 2.9%, 8.7%, 2.9%, and 1.45%, respectively. Studying 93 samples from another farm, qPCR detected BCoV, BRV, BEV, and co-infection of BCoV and BEV was detected at rates of 5.38%, 1.08%, 18.28%, and 1.08%, respectively. Co-infection of BCoV, BRV, BEV, BCoV, and BEV, and co-infection of BRV and BEV, were detected by multiplex ddPCR methods at rates of 5.38%, 2.15%, 20.45%, 1.08%, and 1.08%, respectively. These results indicated that our optimized dual and multiplex ddPCR methods were more effective than conventional qPCR assays to detect these viral infections.

Project description:Digital PCR in droplets (ddPCR) is an emerging method for more and more applications in DNA (and RNA) analysis. Special requirements when establishing ddPCR for analysis of genetically modified organisms (GMO) in a laboratory include the choice between validated official qPCR methods and the optimization of these assays for a ddPCR format. Differentiation between droplets with positive reaction and negative droplets, that is setting of an appropriate threshold, can be crucial for a correct measurement. This holds true in particular when independent transgene and plant-specific reference gene copy numbers have to be combined to determine the content of GM material in a sample. Droplets which show fluorescent units ranging between those of explicit positive and negative droplets are called 'rain'. Signals of such droplets can hinder analysis and the correct setting of a threshold. In this manuscript, a computer-based algorithm has been carefully designed to evaluate assay performance and facilitate objective criteria for assay optimization. Optimized assays in return minimize the impact of rain on ddPCR analysis. We developed an Excel based 'experience matrix' that reflects the assay parameters of GMO ddPCR tests performed in our laboratory. Parameters considered include singleplex/duplex ddPCR, assay volume, thermal cycler, probe manufacturer, oligonucleotide concentration, annealing/elongation temperature, and a droplet separation evaluation. We additionally propose an objective droplet separation value which is based on both absolute fluorescence signal distance of positive and negative droplet populations and the variation within these droplet populations. The proposed performance classification in the experience matrix can be used for a rating of different assays for the same GMO target, thus enabling employment of the best suited assay parameters. Main optimization parameters include annealing/extension temperature and oligonucleotide concentrations. The droplet separation value allows for easy and reproducible assay performance evaluation. The combination of separation value with the experience matrix simplifies the choice of adequate assay parameters for a given GMO event.

Dataset Information

Development and evaluation of a multiplex droplet digital polymerase chain reaction method for simultaneous detection of five biothreat pathogens.

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Development and evaluation of a multiplex droplet digital polymerase chain reaction method for simultaneous detection of five biothreat pathogens.

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