TY - JOUR
AU - Zhao,, Lichao
AB - ABSTRACT This study reports the development and optimization of a real-time loop-mediated isothermal amplification (qLAMP) method for rapid detection of Acetobacter aceti strain in red wine samples. Our results showed that the primers and probes designed for 16S rRNA were effective for A. aceti detection. The quantification limit of real-time polymerase chain reaction (qPCR) and qLAMP in pure culture was 2.05 × 101 colony forming units (CFU) mL−1. qLAMP had a sensitivity of 6.88 × 101 CFU mL−1 in artificially contaminated Changyu dry red wine (CDRW) and Changyu red wine (CRW), and 6.88 × 102 CFU mL−1 in artificially contaminated Greatwall dry red wine (GDRW), which was 10 times higher than that of qPCR. In conclusion, this newly developed qLAMP is a reliable, rapid and accurate method for the detection and quantification of A. aceti species in red wine samples. Furthermore, our work provides a standard reference method for the quantitative detection of A. aceti and other acetic acid bacteria during the fermentation and storage of red wine samples. real-time loop-mediated isothermal amplification (qLAMP), real-time polymerase chain reaction (qPCR), Acetobacter aceti, quantitative detection, red wine INTRODUCTION Acetic acid bacteria (AAB) are a group of spoilage microorganisms belonging to the family Acetobacteraceae. Recently, AAB have been reclassified, including two traditional genera, Gluconobacter and Acetobacter, and several new genera, Acidomonas, Gluconacetobacter, Asaia, Kozakia, etc. (Guillamón et al. 2017). In red wines, AAB produce the characteristic volatile odor of acetic acid that negatively impacts the taste of the wine (Kántor et al. 2020b). Acetobacter species play a negative role in wine making, among which Acetobacter aceti is one of the main causes of spoilage. Low levels [∼102 colony forming units (CFU) ml−1] of A. aceti during the initial stage of fermentation can have unfavorable effects on the quality of wines. The small surviving populations exhibit rapid reproduction on short-term exposure of the red wine to air and quickly increase the level of acetic acid (Campaniello et al.2017). During the final stages of red wine fermentation, A. aceti species account for the greatest proportion of AAB (Guillamón et al. 2017). Furthermore, A. aceti becomes more prevalent as the wines become spoiled. To reduce or avoid the spoilage of red wine, methods to quantify low levels of A. aceti are necessary for monitoring and controlling the spoilage microorganisms. Current detection methods for AAB include a plate separation detection method, microscopy, and nucleic acid molecule based technology. The conventional method used for testing of spoilage organisms like AAB in red wine is the plate counting method, including non-selective pre-enrichment in the wine and enrichment on selective or differential agar (Sievers, Sellmer and Teuber 1992; Gammon et al. 2010). The full detection process is time-consuming, taking 2–4 days to complete. Culture-independent methods are needed due to the limited culture recovery of many microorganisms. DNA-based methods aimed at target gene markers are useful tools for the identification of AAB. The DNA targets have high thermal stability and are not affected by phenotype variability because the approaches are highly sensitive and specific. In addition, methods such as denaturing gradient gel electrophoresis (DGGE), random amplified polymorphic DNA-PCR (RAPD-PCR), enterobacterial repetitive intergenic consensus-PCR (ERIC-PCR), fluorescence in situ hybridization (FISH) and real-time polymerase chain reaction (qPCR) have the advantage of allowing for detection of individual species in the red wine matrix. DGGE, RAPD-PCR and ERIC-PCR are typically used for for routine identification of AAB (Guillamón et al. 2017). What is more, FISH coupled with fluorescence microscopy is a faster and culture-independent quantification method (Blasco et al. 2010). However, this method is time-consuming, and poor in sensitivity and specificity. Therefore, a simpler DNA-based detection assay with high sensitivity and specificity is urgently needed. In particular, qPCR technology has been used to identify and quantify AAB in wine and proved to be a quantitative tool with high specificity and sensitivity and remarkable accuracy and reproducibility (Soares-Santos et al. 2017; Kántor et al. 2020a). So far, there are still unexploited DNA-based technologies that have the potential to detect AAB in red wine. The real-time loop-mediated isothermal amplification (qLAMP) method, known as a rapid detection method with low cost and high sensitivity, is a new method developed in recent years (Oscorbin et al. 2016). qLAMP employs a DNA polymerase and a set of four specially designed primers that recognize six different sequences on target DNA, providing high sensitivity. Furthermore, the qLAMP reaction occurs with a constant temperature amplification process that requires only a simple thermostat. qLAMP technology eliminates the annealing and renaturation processes of qPCR, avoids the time consumption caused by temperature cycling, and can meet the needs of rapid detection of clinical samples (Duarte-Guevara, Duarte-Guevara and Ornob 2016). qLAMP is currently employed for quantitative assays of many pathogens such as Listeria monocytogenes and Ustilago maydis (Ye et al. 2015; Cao et al. 2017). However, qLAMP has not been applied for the detection of A.aceti−a representative specie of AAB in red wine, and standard test methods using this technique have not been established. Therefore, we applied qLAMP techniques to address the problems of wine microbial detection. In the work presented here, we have developed a high-sensitivity qLAMP method for the detection of a representative A. aceti strain of AAB, optimized the qLAMP system, evaluated the primer specificity, and determined the sensitivity in pure DNA and artificially contaminated red wines. Therefore, our work could provide an alternative reference method for the detection of A. aceti and other AAB and for quality control in the wine fermentation and storage process. MATERIALS AND METHODS Strains and culture conditions Acetobacter aceti strain GDMCC 1.367 and 19 other strains were used to develop and evaluate the qPCR and qLAMP assays (Table 1). All strains used to determine the specificity of the two methods were stored in the Food Safety and Systems Microbiology Laboratory of South China Agricultural University. Acetobacter aceti was cultivated on laboratory-prepared culture medium containing 10 g L−1 glucose, 10 g L−1 yeast extract, 20 g L−1 calcium carbonate, 30 g L−1 anhydrous ethanol and 18 g L−1 agar. The incubation time was 48 h and the incubation temperature was 30°C. The pH was 6.8 ± 0.2. On the same agar medium, we determined the CFU counts using plate dilution. For plate dilution, 1 mL of bacterial suspension was added into 9 mL of physiological saline (0.9% NaCl) to represent 1:10 dilution, and 1 mL of diluted wine was further added into 9 mL of physiological saline to represent 1:100 dilution, and so on (Kántor, Petrová and Kačániová 2014). Table 1. Specificity results of qPCR and qLAMP methods. . . Resultsb . Serial number . Straina . qPCR . qLAMP . 1 Acetobacter aceti GDMCC 1.367 + + 2 Gluconobacter cerinus GDMCC 1.1207 − − 3 Komagataeibacter xylinus GDMCC 1.423 − − 4 Komagataeibacter hansenii CGMCC 1.1811 − − 5 Lactobacillus plantarum GDMCC 1.1516 − − 6 Lactobacillus brevis GDMCC 1.288 − − 7 Saccharomyces cerevisiae Hansen GDMCC 2.36 − − 8 Pichia membranaefaciens Hansen GDMCC 2.25 − − 9 Saccharomyces willianus Saccardo GDMCC 2.60 − − 10 Hansenula GDMCC 2.96 − − 11 Saccharomyces cerevisiae Hansen GDMCC 2.90 − − 12 Enterobacter sakazakii GDMCC 1.296 − − 13 Gluconacetobacter sp. CICC 10 773 − − 14 Asaia sp. CICC 10 562 − − 15 Asaia sp. CICC 10 571 − − 16 Hansenula anomala CICC 1295 − − 17 Escherichia. coli O157: H7 ATCC43889 − − 18 Escherichia coli O157: H7 ATCC 35 150 − − 19 Staphylococcus aureus ATCC 25 923 − − 20 Listeria monocytogenes ATCC 19 115 − − . . Resultsb . Serial number . Straina . qPCR . qLAMP . 1 Acetobacter aceti GDMCC 1.367 + + 2 Gluconobacter cerinus GDMCC 1.1207 − − 3 Komagataeibacter xylinus GDMCC 1.423 − − 4 Komagataeibacter hansenii CGMCC 1.1811 − − 5 Lactobacillus plantarum GDMCC 1.1516 − − 6 Lactobacillus brevis GDMCC 1.288 − − 7 Saccharomyces cerevisiae Hansen GDMCC 2.36 − − 8 Pichia membranaefaciens Hansen GDMCC 2.25 − − 9 Saccharomyces willianus Saccardo GDMCC 2.60 − − 10 Hansenula GDMCC 2.96 − − 11 Saccharomyces cerevisiae Hansen GDMCC 2.90 − − 12 Enterobacter sakazakii GDMCC 1.296 − − 13 Gluconacetobacter sp. CICC 10 773 − − 14 Asaia sp. CICC 10 562 − − 15 Asaia sp. CICC 10 571 − − 16 Hansenula anomala CICC 1295 − − 17 Escherichia. coli O157: H7 ATCC43889 − − 18 Escherichia coli O157: H7 ATCC 35 150 − − 19 Staphylococcus aureus ATCC 25 923 − − 20 Listeria monocytogenes ATCC 19 115 − − aGDMCC, Guangdong Microbial Culture Collection Center, China; ATCC, American Type Culture Collection, USA; CICC, China Center of Industrial Culture Collection. b‘+/−’indicates positive and negative real-time DNA amplification signal; ‘+’ indicates that both of the replicates within the samples succeed in being amplified. ‘−’ indicates that replicates within the sample failed to be amplified. Open in new tab Table 1. Specificity results of qPCR and qLAMP methods. . . Resultsb . Serial number . Straina . qPCR . qLAMP . 1 Acetobacter aceti GDMCC 1.367 + + 2 Gluconobacter cerinus GDMCC 1.1207 − − 3 Komagataeibacter xylinus GDMCC 1.423 − − 4 Komagataeibacter hansenii CGMCC 1.1811 − − 5 Lactobacillus plantarum GDMCC 1.1516 − − 6 Lactobacillus brevis GDMCC 1.288 − − 7 Saccharomyces cerevisiae Hansen GDMCC 2.36 − − 8 Pichia membranaefaciens Hansen GDMCC 2.25 − − 9 Saccharomyces willianus Saccardo GDMCC 2.60 − − 10 Hansenula GDMCC 2.96 − − 11 Saccharomyces cerevisiae Hansen GDMCC 2.90 − − 12 Enterobacter sakazakii GDMCC 1.296 − − 13 Gluconacetobacter sp. CICC 10 773 − − 14 Asaia sp. CICC 10 562 − − 15 Asaia sp. CICC 10 571 − − 16 Hansenula anomala CICC 1295 − − 17 Escherichia. coli O157: H7 ATCC43889 − − 18 Escherichia coli O157: H7 ATCC 35 150 − − 19 Staphylococcus aureus ATCC 25 923 − − 20 Listeria monocytogenes ATCC 19 115 − − . . Resultsb . Serial number . Straina . qPCR . qLAMP . 1 Acetobacter aceti GDMCC 1.367 + + 2 Gluconobacter cerinus GDMCC 1.1207 − − 3 Komagataeibacter xylinus GDMCC 1.423 − − 4 Komagataeibacter hansenii CGMCC 1.1811 − − 5 Lactobacillus plantarum GDMCC 1.1516 − − 6 Lactobacillus brevis GDMCC 1.288 − − 7 Saccharomyces cerevisiae Hansen GDMCC 2.36 − − 8 Pichia membranaefaciens Hansen GDMCC 2.25 − − 9 Saccharomyces willianus Saccardo GDMCC 2.60 − − 10 Hansenula GDMCC 2.96 − − 11 Saccharomyces cerevisiae Hansen GDMCC 2.90 − − 12 Enterobacter sakazakii GDMCC 1.296 − − 13 Gluconacetobacter sp. CICC 10 773 − − 14 Asaia sp. CICC 10 562 − − 15 Asaia sp. CICC 10 571 − − 16 Hansenula anomala CICC 1295 − − 17 Escherichia. coli O157: H7 ATCC43889 − − 18 Escherichia coli O157: H7 ATCC 35 150 − − 19 Staphylococcus aureus ATCC 25 923 − − 20 Listeria monocytogenes ATCC 19 115 − − aGDMCC, Guangdong Microbial Culture Collection Center, China; ATCC, American Type Culture Collection, USA; CICC, China Center of Industrial Culture Collection. b‘+/−’indicates positive and negative real-time DNA amplification signal; ‘+’ indicates that both of the replicates within the samples succeed in being amplified. ‘−’ indicates that replicates within the sample failed to be amplified. Open in new tab Wine samples Greatwall dry red wine (GDRW, China Oil & Foodstuffs Corporation, Hebei province, China), Changyu red wine (CRW, Changyu Pioneer Wine Company Limited Yantai, Shandong province, China) and Changyu dry red wine (CDRW, Changyu Pioneer Wine Company Limited Yantai, Shandong province, China) were purchased from a local supermarket. The CDRW contained 4 g L−1 total sugar, 11% (v/v) ethanol and 1.5 mg L−1 total phenols. The CRW contained 75 g L−1 total sugar, 11% (v/v) ethanol and 1.1 mg L−1 total phenols. The GDRW contained 4 g L−1 total sugar, 13% (v/v) ethanol and 2.6 mg L−1 total phenols. The wine samples used for the artificial contamination were treated as follows: first, wine samples were filtered with sterile cotton (Jiaozuo league Hygiene group Co., Ltd, Henan, China) to remove impurities; and second, filtered solutions were sterilized using a 0.22 μm filter (Jinteng Co., Ltd, Tianjin, China). Then, wine samples and culture medium were incubated with known populations of A. aceti or other AAB species. After incubation, wine samples were washed twice with ethylenediaminetetraacetic acid-polyvinyl pyrrolidone (EDTA-PVP), 0.15 M NaCl, 0.1 M EDTA and 2% (v/w) PVP, Sigma-Aldrich) to eliminate possible PCR inhibitors (Jara et al. 2008). DNA extraction Extraction of genomic DNA was performed by using a bacterial genomic DNA extraction kit (Double Helix Gene Technology, Guangzhou, China) and Rapid Fungi Genomic DNA Isolation Kit (Sangon Biotech, Shanghai, China) according to the manufacturer's protocol. The extracted genomic DNA was stored at −20°C until use. Gene selection and primer design for qPCR and qLAMP According to the sequence published in the Genebank database, the primers designed with conserved sequence 16S rRNA (accession number: NR_026121) were selected (Table 2). For the 16S rRNA gene of A. aceti, the qLAMP primer was designed by Primer Explorer V4 software (Eiken Chemical Co. Ltd., Japan, http://primer.explorer.jp/elamp4.0.0/index.html) and the qPCR primers were designed by Primer Express software package v.3.0. Sequence-specific alignment of the primers was verified on the NCBI website. Table 2. The sequences of the primers and probes for qPCR and qLAMP methods. Detection method . Primer . Sequence (5′→3′) . qLAMP F3 AGGTGGGGATGACGTCAAG B3 CGGGAACGTATTCACCGC FIP CTAGCTTCCCACTGTCACCGC-TCCTCATGGCCCTTATGTCC BIP AACCGTCTCAGTTCGGATTGCA-TCCGCGATTACTAGCGATTC LF AGCACGTGTGTAGCCCA LB CTCTGCAACTCGAGTGCATG qPCR F CGGAATGACTGGGCGTAAAG R CAGTAATGAGCCAGGTTGCC Probe FAM-CGGGCTTAACCTGGGAGCTGCATT-BHQ1 Detection method . Primer . Sequence (5′→3′) . qLAMP F3 AGGTGGGGATGACGTCAAG B3 CGGGAACGTATTCACCGC FIP CTAGCTTCCCACTGTCACCGC-TCCTCATGGCCCTTATGTCC BIP AACCGTCTCAGTTCGGATTGCA-TCCGCGATTACTAGCGATTC LF AGCACGTGTGTAGCCCA LB CTCTGCAACTCGAGTGCATG qPCR F CGGAATGACTGGGCGTAAAG R CAGTAATGAGCCAGGTTGCC Probe FAM-CGGGCTTAACCTGGGAGCTGCATT-BHQ1 Open in new tab Table 2. The sequences of the primers and probes for qPCR and qLAMP methods. Detection method . Primer . Sequence (5′→3′) . qLAMP F3 AGGTGGGGATGACGTCAAG B3 CGGGAACGTATTCACCGC FIP CTAGCTTCCCACTGTCACCGC-TCCTCATGGCCCTTATGTCC BIP AACCGTCTCAGTTCGGATTGCA-TCCGCGATTACTAGCGATTC LF AGCACGTGTGTAGCCCA LB CTCTGCAACTCGAGTGCATG qPCR F CGGAATGACTGGGCGTAAAG R CAGTAATGAGCCAGGTTGCC Probe FAM-CGGGCTTAACCTGGGAGCTGCATT-BHQ1 Detection method . Primer . Sequence (5′→3′) . qLAMP F3 AGGTGGGGATGACGTCAAG B3 CGGGAACGTATTCACCGC FIP CTAGCTTCCCACTGTCACCGC-TCCTCATGGCCCTTATGTCC BIP AACCGTCTCAGTTCGGATTGCA-TCCGCGATTACTAGCGATTC LF AGCACGTGTGTAGCCCA LB CTCTGCAACTCGAGTGCATG qPCR F CGGAATGACTGGGCGTAAAG R CAGTAATGAGCCAGGTTGCC Probe FAM-CGGGCTTAACCTGGGAGCTGCATT-BHQ1 Open in new tab qPCR detection The samples for quantitative qPCR were prepared after DNA extraction. The assay's total reaction volume was 25 µL, including 10 µM of each primer and probe, 12.5 µL of AceQ® qPCR Probe Master Mix (Vazyme Biotech, NanJing, China) and 5 µL of DNA template. The reactions were carried out on a 7500 Fast Real-Time PCR System (Applied Biosystems, New York, USA) according to the thermal profile: hot start at 95°C for 10 min, followed by 45 cycles of dissociation at 95°C for 15 s and annealing–extension at 60°C for 1 min. All samples were tested in triplicate. qLAMP detection The samples for qLAMP detection were prepared after DNA extraction. The 25 μL reaction system included inner primers FIP and BIP (1.6 μmol L−1), external primers F3 and B3 (0.2 μmol L−1), loop primers LF and LB (0.8 μmol L−1), 20 mmol L−1 Tris-HCl (pH 8.8), 10 mmol L−1 KCl (≥99.0%, Sigma-Aldrich), 10 mmol L−1 MgSO4 (≥97%, Sigma-Aldrich, USA), 10 mmol L−1 (NH4)2SO4, 0.1% Tween-20, betaine (≥99%, Sigma-Aldrich, USA) or DMSO (Dimethyl Sulphoxide, ≥99.5%, Sigma-Aldrich, USA), 1.6 mmol L−1 dNTP (Takara, Dalian, China), 8 U Bst DNA large fragment polymerase (Biolabs, USA), 0.2 μmol L−1 SYTO-9 (Invitrogen, Carlsbad, CA, USA) and 5 μL of DNA template. Reaction amplification was accomplished at 63°C for 60 min, with a post-amplification melting analysis consisting of heating up to 95°C for 15 s, cooling down to 60°C for 1 min, and then heating up to 95°C for 30 s, cooling down to 60°C for 15 s. In this study the amplification of RealAmp was tested using the ZYD-S1TM detection system (Double Helix Gene Technology, Guangzhou, China). The results of qLAMP are expressed as Time-to-threshold (Tt). This is a measure of the reaction time at which the fluorescence of each sample exceeds the threshold. Optimization of the qLAMP assay To obtain the optimal performance of the newly designed qLAMP method, parameters were optimized including the Mg2+ concentration and the enhancers of the reaction, betaine and DMSO. Mg2+ concentration ranging from 4 to 12 mM was evaluated. With regard to the supplements, betaine was added to the reactions at 0.8 and 1.0 M final concentrations, while DMSO was tested at 5, 7.5 and 10%. All tests were performed in duplicate. Pure culture (∼106 CFU mL−1) was used to do this optimization. Evaluation of the limit of detection with pure DNA For pure DNA detection limit, the initial bacterial population was calculated using a conventional plate colony counting method after 48 h incubation, and the samples were subjected to 10-fold gradient dilution with physiological saline. Then the diluted solutions of DNA were extracted and subjected to qPCR and qLAMP amplification under optimized conditions. The reaction parameters (quantification limit and correlation coefficient) were calculated by plotting the Ct values or Tt values against the colony counts (log10 CFU/mL). The efficiency of reaction (E) of qPCR is given by the following formula: $$\begin{equation*} {\rm{E}} = {10^{ - 1/\kappa }} - 1 \end{equation*}$$ where κ is the slope value of each standard curve. However, the E value of qLAMP cannot be calculated by this formula for its special amplification product. All amplifications were done in triplicate. Evaluation of the limit of detection with artificially contaminated samples The wine samples used for artificial contamination were treated according to the previous description in section "Wine samples" to ensure that no bacteria were present before inoculation. Acetobacter aceti, which was used to prepare an artificially contaminated wine sample, was cultured after 48 h of incubation. A 10-fold dilution series of the culture was prepared with physiological saline, and the initial bacterial population was obtained by the plate count method using laboratory-prepared culture medium. A 100 μL aliquot of each dilution was added to 900 μL of wine sample. The quantification was calculated from the liquid mixture. Then the diluted solutions of DNA were extracted and subjected to qPCR and qLAMP amplification under optimized conditions. The reaction parameters (quantification limit and correlation coefficient) were calculated by plotting the Ct values or Tt values against the colony counts (log10 CFU/mL). All amplifications were done in triplicate. Data analysis All the Ct and Tt values are averages of three repetitions. Standard curves and other figures were completed by Origin 8.6. RESULTS Optimization of the qLAMP assay In the optimization tests, the Mg2+ concentration resulting in the highest sensitivity was 8 mmol L−1 (Fig. 1 A) and this was selected as the final concentration in subsequent tests. Optimization of the supplement in the qLAMP assay showed that better results were obtained with betaine than with DMSO. Furthermore, the best results were obtained with 0.8 M betaine (Fig. 1B). Figure 1. Open in new tabDownload slide Supplements optimization results for qLAMP. (A) Mg2+ concentration optimization results. (B) Betaine and DMSO optimization results. All these tests were performed in duplicate. ‘△Rn’ refers to fluorescence intensity. Figure 1. Open in new tabDownload slide Supplements optimization results for qLAMP. (A) Mg2+ concentration optimization results. (B) Betaine and DMSO optimization results. All these tests were performed in duplicate. ‘△Rn’ refers to fluorescence intensity. Figure 2. Open in new tabDownload slide Standard curves obtained by qPCR and qLAMP from serially diluted pure DNA. (A) Standard curve generated from Ct values against the enumeration of A. aceti by qPCR in pure DNA. (B) Standard curve generated from Tt values against the enumeration of A. aceti by qLAMP in pure DNA. Ct and Tt values are the average of three repetitions. Figure 2. Open in new tabDownload slide Standard curves obtained by qPCR and qLAMP from serially diluted pure DNA. (A) Standard curve generated from Ct values against the enumeration of A. aceti by qPCR in pure DNA. (B) Standard curve generated from Tt values against the enumeration of A. aceti by qLAMP in pure DNA. Ct and Tt values are the average of three repetitions. Specificity of qPCR and qLAMP We used the consensus sequence of the 16S rRNA gene for A. aceti to design primers and probes. The specificity of all newly designed primers was satisfactorily confirmed by BLAST. Considering the NCBI BLAST result, we believe that the primers and probes are valid for the detection and quantification of Acetobacter species (data not shown). We analyzed a total of 20 strains (Table 1). Both methods showed specificity for A. aceti and no false positives were observed with the other 19 species tested. Quantification limits of qLAMP and qPCR As shown in the Fig. 2, both qPCR and qLAMP standard curves presented a suitable linear correlation with the R2 values greater than 0.98. The qPCR standard curve was linear from 2.05 × 101 to 2.05 × 105 CFU mL−1 in pure DNA and the threshold cycles ranged from 17.03 Ct to 31.55 Ct. Similarly, the qLAMP standard curve was linear from 2.05 × 101 to 2.05 × 106 CFU mL−1 in pure DNA and the time-to-threshold varied from 9.94 Tt to 21.00 Tt. Lower concentrations of some replicates failed to be amplified. Figure 3. Open in new tabDownload slide Standard curves obtained by qPCR and qLAMP from 10-fold serial dilutions of CDRW, CRW and GDRW. Standard curves generated from Ct values against the enumeration of A. aceti by qPCR in (A) CDRW sample; (B) CRW sample; (C) GDRW sample. Standard curve generated from Tt values against the enumeration of A. aceti by qLAMP in (D) CDRW sample; (E) CRW sample; (F) GDRW sample. Figure 3. Open in new tabDownload slide Standard curves obtained by qPCR and qLAMP from 10-fold serial dilutions of CDRW, CRW and GDRW. Standard curves generated from Ct values against the enumeration of A. aceti by qPCR in (A) CDRW sample; (B) CRW sample; (C) GDRW sample. Standard curve generated from Tt values against the enumeration of A. aceti by qLAMP in (D) CDRW sample; (E) CRW sample; (F) GDRW sample. Acetobacter aceti detection in artificially contaminated wines We also analyzed the sensitivity of the two assays using artificially contaminated red wines that were inoculated with A. aceti. We studied the effects of three kinds of wines on DNA amplification by both methods. The qPCR and qLAMP standard curves presented a suitable linear correlation, with the R2 values greater than 0.98. These correlation coefficients demonstrated that qPCR and qLAMP assays were linear. The lowest detectable concentration by qPCR was 6.88 × 102, 6.88 × 102, and 6.88 × 103 CFU mL−1 in CDRW, CRW and GDRW, respectively. qLAMP detected the lowest limit, reaching 6.88 × 101, 6.88 × 101 and 6.88 × 102 CFU mL−1, respectively. The best results were obtained by qLAMP at 6.88 × 101 CFU mL−1, followed by qPCR at 6.88 × 102 CFU mL−1. Furthermore, taking CDRW as an example, Ct and Tt values ranged from 16.52 to 32.99 and from 9.85 to 26.07 for qPCR and qLAMP, respectively (Fig. 3). DISCUSSION Accurate identification and quantification of AAB is extremely important in wine production. AAB are common vinegar bacteria found in red wines that have been exposed to oxygen and higher temperature, and cause wine spoilage. There is currently a lack of effective, rapid and convenient detection methods for AAB detection. In addition, traditional detection methods have some common problems. First, the plate culture method is time consuming, and cultivation on plates directly from wine samples is difficult (Janssen et al. 2002). Second, AAB tend to remain at the surface of the liquid, forming aggregates that may produce biofilms due to their aerobic metabolism, leading to limitations in direct micro observation. Third, traditional quantification limits of AAB detection methods are high at 1 × 102–1 × 103 CFU mL−1) (González et al.2010; Torija et al. 2010). Fourth, qPCR based on DNA amplification can accurately recognize and quantify various bacteria with high sensitivity, however, an expensive thermocycling device is required. (Gavin et al. 2014). Thus, we set out to solve these problems by alternative DNA-based technology, i.e. qLAMP. The qLAMP method has been increasingly applied in point-of-care and limited-resource settings because nucleic acid amplification can be performed by the constant temperature detection system without expensive laboratory equipment (Cao et al. 2017). We are the first to apply qLAMP to A. aceti detection in red wine. Red wine samples contain complex matrices of ethanol, polysaccharides, anthocyanin and other pigments, and tannins (Gambuti et al. 2018). These compounds negatively interfere with qPCR and qLAMP methods by causing DNA precipitation and denaturation of DNA and polymerase (Soares-Santos, Pardo and Ferrer 2017). qLAMP-based methods need to be thoroughly optimized and evaluated for application in such complex wine matrix conditions since there are several factors that might influence their speed and analytical sensitivity. We focused on the concentration of Mg2+ and reaction supplementation with either betaine or DMSO. Mg2+ concentration affects amplification specificity and DNA polymerase activity, with extremely high concentrations leading to false positives due to nonspecific amplification (Saiki, Gelfand and Stoffel 1988). We found that the optimal concentration of Mg2+ was 8 mM. Concerning the optimization of supplements added to the reaction, our results agreed with those of Garrido-maestu et al. and Wang et al., who reported increased performance of qLAMP reactions when betaine was added rather than DMSO (Wang et al. 2015; Garrido-maestu et al. 2018). The efficiency of qLAMP amplification could be enhanced by further optimizing other components of the reaction system, such as the annealing events taking place that might compete with each other and effect the speed of isothermal reactions, and the influence of the template's innate secondary structures due to the absence of multiple denaturation steps. The quantification limit of qPCR and qLAMP assays were determined by testing serial 10-fold dilutions of A. aceti samples that had previously been quantified via plate count, and the results indicated that both assays could efficiently detect low numbers of A. aceti. The sensitivities of the qPCR and qLAMP assays for A. aceti in pure DNA cultures were both 2.05 × 101 CFU mL−1. Our results were better than those of Kántor etal. (2014), where the sensitivity was 102 CFU/mL for pure A. aceti culture. For the contaminated CDRW and CRW, qPCR quantification limits showed higher results of 6.88 × 102 CFU mL−1 than those of pure DNA, while qLAMP maintained a similar quantification limit of 6.88 × 101 CFU mL−1 when compared with pure DNA. The quantification limit was not significantly changed, with a quantification limit of 2.05 × 101 CFU mL−1 in pure culture and 6.88 × 101 CFU mL−1 in CDRW and CRW, respectively. It is worth mentioning that even though positive results were obtained with qLAMP, a delay in the Tt values was also observed. For the contaminated GDRW, the qLAMP method demonstrated a quantification limit of 6.88 × 102 CFU mL−1, which was 10-fold higher than the qPCR method. A similar result was reported by Soares-Santos et al. (2018), whose results showed that the DNA quantification range was 102–108 cells/mL for Oenococcus oeni in wine by cells-qLAMP. Soares-Santos et al. also reported similar quantification ranges obtained by cells-qPCR from Lactobacillus plantarum, Brettanomyces bruxellensis and Saccharomyces cerevisiae of 104–108, 104–107 and 103–107 cells/mL, respectively (Soares-Santos et al. 2017). The sensitivity difference between these two methods can be attributed to the high amplification efficiency of LAMP (Gonçalves et al. 2019). Comparing the GDRW to the other two wines, we found that the quantification limit of the GDRW was higher than that of the other two. Moreover, GDRW contains higher ethanol and total phenol than the others. Alcohol and phenol are inhibitors of PCR. Alcohol can inhibit nucleic acid amplification by precipitating DNA and causing loss of DNA in the cotton filtration step. In addition, polyphenolic substances, especially tannins, can also inhibit the PCR reaction (Soares-Santos et al. 2017). In terms of cost and convenience, qLAMP is superior to qPCR. The qLAMP reactions require only thermostatic equipment to achieve nucleic acid amplification, while qPCR needs expensive equipment to execute complex heating and cooling protocols. Furthermore, the qLAMP instrument is portable and suitable for field sample evaluation. The applications of qLAMP reported include detection of Ustilago maydis and Vibrio parahaemolyticus (Cao et al. 2017). The qLAMP developed in this study has the potential to become a foundation for quality management practices to prevent pollution by AAB of red wine. The qLAMP approach also proved to be faster than qPCR, with qLAMP analyses completed in about 60 min compared to 98 min for qPCR, which is another advantage of qLAMP over qPCR. The qLAMP reaction can be performed at a constant temperature because the amplification does not require prior double-stranded DNA thermal denaturation, eliminating the time-consuming annealing and renaturation processes in the PCR reaction, which makes qLAMP more suitable for field detection than qPCR (Wang et al. 2012). In conclusion, this study provides a highly sensitive tool and sheds new light on the current knowledge of quantification in red wine samples. The alternative qLAMP method developed in this study will guide accurate identification and quantification of A. aceti in wine, providing a reference for detection of some of the main AAB species as well as closely related groups of species. ACKNOWLEDGEMENTS This work was supported by the Science and Technology Planning Project of Guangdong Province (2017B020207004, 2020A1515011561); National Natural Science Foundation of China (31771940); The Science and Technology Program of Guangzhou (202002030264). 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TI - Development and application of a real-time loop-mediated isothermal amplification method for quantification of Acetobacter aceti in red wine
JF - FEMS Microbiology Letters
DO - 10.1093/femsle/fnaa152
DA - 2020-10-20
UR - https://www.deepdyve.com/lp/oxford-university-press/development-and-application-of-a-real-time-loop-mediated-isothermal-tPRSL9aH1F
VL - 367
IS - 19
DP - DeepDyve
ER -