skip to main content

Title: Competitiveness of Quantitative Polymerase Chain Reaction (qPCR) and Droplet Digital Polymerase Chain Reaction (ddPCR) Technologies, with a Particular Focus on Detection of Antibiotic Resistance Genes (ARGs)
With fast-growing polymerase chain reaction (PCR) technologies and various application methods, the technique has benefited science and medical fields. While having strengths and limitations on each technology, there are not many studies comparing the efficiency and specificity of PCR technologies. The objective of this review is to summarize a large amount of scattered information on PCR technologies focused on the two majorly used technologies: qPCR (quantitative polymerase chain reaction) and ddPCR (droplet-digital polymerase chain reaction). Here we analyze and compare the two methods for (1) efficiency, (2) range of detection and limitations under different disciplines and gene targets, (3) optimization, and (4) status on antibiotic resistance genes (ARGs) analysis. It has been identified that the range of detection and quantification limit varies depending on the PCR method and the type of sample. Careful optimization of target gene analysis is essential for building robust analysis for both qPCR and ddPCR. In our era where mutation of genes may lead to a pandemic of viral infectious disease or antibiotic resistance-induced health threats, this study hopes to set guidelines for meticulous detection, quantification, and analysis to help future prevention and protection of global health, the economy, and ecosystems.
Authors:
; ; ;
Award ID(s):
1818125
Publication Date:
NSF-PAR ID:
10344217
Journal Name:
Applied Microbiology
Volume:
1
Issue:
3
Page Range or eLocation-ID:
426 to 444
ISSN:
2673-8007
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Background Wastewater-based epidemiology (WBE) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be an important source of information for coronavirus disease 2019 (COVID-19) management during and after the pandemic. Currently, governments and transportation industries around the world are developing strategies to minimize SARS-CoV-2 transmission associated with resuming activity. This study investigated the possible use of SARS-CoV-2 RNA wastewater surveillance from airline and cruise ship sanitation systems and its potential use as a COVID-19 public health management tool. Methods Aircraft and cruise ship wastewater samples (n = 21) were tested for SARS-CoV-2 using two virus concentration methods, adsorption–extraction by electronegative membrane (n = 13) and ultrafiltration by Amicon (n = 8), and five assays using reverse-transcription quantitative polymerase chain reaction (RT-qPCR) and RT-droplet digital PCR (RT-ddPCR). Representative qPCR amplicons from positive samples were sequenced to confirm assay specificity. Results SARS-CoV-2 RNA was detected in samples from both aircraft and cruise ship wastewater; however concentrations were near the assay limit of detection. The analysis of multiple replicate samples and use of multiple RT-qPCR and/or RT-ddPCR assays increased detection sensitivity and minimized false-negative results. Representative qPCR amplicons were confirmed for the correct PCR product by sequencing. However, differences in sensitivity were observed among molecular assays and concentrationmore »methods. Conclusions The study indicates that surveillance of wastewater from large transport vessels with their own sanitation systems has potential as a complementary data source to prioritize clinical testing and contact tracing among disembarking passengers. Importantly, sampling methods and molecular assays must be further optimized to maximize detection sensitivity. The potential for false negatives by both wastewater testing and clinical swab testing suggests that the two strategies could be employed together to maximize the probability of detecting SARS-CoV-2 infections amongst passengers.« less
  2. Antimicrobial resistance is a well-documented public health concern. The role that drinking water distribution pipes have as sources of antibiotic resistance genes (ARGs) is not well known. Metals are a known stressor for antibiotic resistance development, implying that aging metal-pipe infrastructure could be a source of ARGs. The objective of this study was to determine if ARGs, metal resistance genes (MRGs), and intI 1 were pervasive across various pipe biofilm sample types (biomass surfaces, pipe surfaces, corrosion tubercles, and under corrosion tubercles) and if the resistance genes associated with particular microbial taxa. Eight sample types in triplicate ( n = 24) were taken from inside a >100 year-old, six ft. section of a full-scale chloraminated cast iron drinking water main. Droplet digital PCR (ddPCR) was employed as a novel approach to quantify ARGs in pipes from full-scale drinking water distribution systems (DWDS) because it yielded higher detection frequencies than quantitative PCR (qPCR). Illumina sequencing was employed to characterize the microbial community based on 16S rRNA genes. ARGs and MRGs were detected in all 24 pipe samples. Every sample contained targeted genes. Interestingly, the mean absolute abundances of ARGs and MRGs only varied by approximately one log value across sample types,more »but the mean relative abundances (copy numbers normalized to 16S rRNA genes) varied by over two log values. The ARG and MRGs concentrations were not significantly different between sample types, despite significant changes in dominant microbial taxa. The most abundant genera observed in the biofilm communities were Mycobacterium (0.2–70%), and β-lactam resistance genes bla TEM , bla SHV , and the integrase gene of class 1 integrons ( intI 1) were positively correlated with Mycobacterium . The detection of ARGs, MRGs, and class 1 integrons across all sample types within the pipe indicates that pipes themselves can serve as sources for ARGs in DWDS. Consequently, future work should investigate the role of pipe materials as well as corrosion inhibitors to determine how engineering decisions can mitigate ARGs in drinking water that stem from pipe materials.« less
  3. Nucleic acids are ubiquitous in biological samples and can be sensitively detected using nucleic acid amplification assays. To achieve highly accurate and reliable results, nucleic acid isolation and purification is often required and can limit the accessibility of these assays. Encapsulation of these workflows onto a single device may be achieved through fabrication methodologies featuring commercial three-dimensional (3D) printers. This study aims to characterize fused deposition modeling (FDM) filaments based on their compatibility with nucleic acid storage using quantitative polymerase chain reaction (qPCR). To study the adsorption of nucleic acids, storage vessels were fabricated using six common thermoplastics including: polylactic acid (PLA), nylon, acrylonitrile butadiene styrene (ABS), co-polyester (CPE), polycarbonate (PC), and polypropylene (PP). DNA adsorption of a short 98 base pair and a longer 830 base pair fragment to the walls of the vessel was shown to vary significantly among the polymer materials as well as the color varieties of the same polymer. PLA storage vessels were found to adsorb the least amount of the 98 base pair DNA after 12 hours of storage in 2.5 M NaCl TE buffer whereas the ABS and PC vessels adsorbed up to 97.2 ± 0.2% and 97.5 ± 0.2%. DNA adsorption couldmore »be reduced by decreasing the layer height of the 3D printed object, thereby increasing the functionality of the ABS storage vessel. Nylon was found to desorb qPCR inhibiting components into the stored solution which led to erroneous DNA quantification data from qPCR analysis.« less
  4. Marshall, Christopher W. (Ed.)
    ABSTRACT Identification of genes encoding β-lactamases (BLs) from short-read sequences remains challenging due to the high frequency of shared amino acid functional domains and motifs in proteins encoded by BL genes and related non-BL gene sequences. Divergent BL homologs can be frequently missed during similarity searches, which has important practical consequences for monitoring antibiotic resistance. To address this limitation, we built ROCker models that targeted broad classes (e.g., class A, B, C, and D) and individual families (e.g., TEM) of BLs and challenged them with mock 150-bp- and 250-bp-read data sets of known composition. ROCker identifies most-discriminant bit score thresholds in sliding windows along the sequence of the target protein sequence and hence can account for nondiscriminative domains shared by unrelated proteins. BL ROCker models showed a 0% false-positive rate (FPR), a 0% to 4% false-negative rate (FNR), and an up-to-50-fold-higher F1 score [2 × precision × recall/(precision + recall)] compared to alternative methods, such as similarity searches using BLASTx with various e-value thresholds and BL hidden Markov models, or tools like DeepARG, ShortBRED, and AMRFinder. The ROCker models and the underlying protein sequence reference data sets and phylogenetic trees for read placement are freely available through http://enve-omics.ce.gatech.edu/data/rocker-bla . Applicationmore »of these BL ROCker models to metagenomics, metatranscriptomics, and high-throughput PCR gene amplicon data should facilitate the reliable detection and quantification of BL variants encoded by environmental or clinical isolates and microbiomes and more accurate assessment of the associated public health risk, compared to the current practice. IMPORTANCE Resistance genes encoding β-lactamases (BLs) confer resistance to the widely prescribed antibiotic class β-lactams. Therefore, it is important to assess the prevalence of BL genes in clinical or environmental samples for monitoring the spreading of these genes into pathogens and estimating public health risk. However, detecting BLs in short-read sequence data is technically challenging. Our ROCker model-based bioinformatics approach showcases the reliable detection and typing of BLs in complex data sets and thus contributes toward solving an important problem in antibiotic resistance surveillance. The ROCker models developed substantially expand the toolbox for monitoring antibiotic resistance in clinical or environmental settings.« less
  5. To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020–2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident–rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copiesmore »(copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.« less