Update and Validation of the 16S rDNA qPCR Assay for the Detection of Three ‘Candidatus Liberibacter Species’ Following Current MIQE Guidelines and Workflow
- Fatima Osman1
- Tyler Dang2
- Sohrab Bodaghi2
- Rukhama Haq3
- Irene Lavagi-Craddock2
- Amanda Rawstern4
- Esteban Rodriguez4
- MaryLou Polek4
- Nelson A. Wulff5
- Ronel Roberts6
- Gerhard Pietersen7
- Anna Englezou8
- Nerida Donovan8
- Svetlana Y. Folimonova9
- Georgios Vidalakis2 †
- 1Department of Plant Pathology, University of California, Davis, CA, U.S.A.
- 2Department of Microbiology and Plant Pathology, University of California, Riverside, CA, U.S.A.
- 3Department of Biotechnology, Lahore College for Women University, Lahore, Pakistan
- 4US Department of Agriculture, Agricultural Research Service, National Clonal Germplasm Repository for Citrus & Dates, Riverside, CA, U.S.A.
- 5Fundecitrus, Fundo de Defesa da Citricultura, Araraquara, Brazil
- 6Agricultural Research Council, Tropical and Subtropical Crops, Mbombela, South Africa
- 7Department of Genetics, Stellenbosch University, Stellenbosch, South Africa
- 8Elizabeth Macarthur Agricultural Institute, New South Wales Department of Primary Industries, Menangle, NSW, Australia
- 9Department of Plant Pathology, University of Florida, Gainesville, FL, U.S.A.
An updated real-time multiplex quantitative polymerase chain reaction (qPCR) assay was designed and validated for the simultaneous detection of three ‘Candidatus Liberibacter species’ (CLsp), ‘Ca. Liberibacter asiaticus’ (CLas), ‘africanus’ (CLaf), and ‘americanus’ (CLam), associated with the huanglongbing disease of citrus. The multiplex assay was designed based on the qPCR assay published in 2006 by Li et al., considering all available CLsp 16S rRNA gene sequences in GenBank and the MIQE guidelines and workflow for qPCR optimization, which became available after 2006. When using the updated multiplex CLsp qPCR assay compared with singleplex qPCR, no significant increase in quantitative cycle (Cq) values was detected. The specificity and sensitivity of the updated qPCR assay was optimal, and measuring the intra- and interassay variations confirmed the reproducibility and repeatability of the assay. The assay was also successfully used with a large number of diverse samples at independent laboratories in four countries, thus demonstrating its transferability, applicability, practicability, and robustness as different qPCR reaction conditions or instruments had a minor effect on Cq values. This updated multiplex CLsp qPCR assay can be used in a variety of citrus surveys, germplasm, or nursery stock programs that require different pathogen detection tools for their successful operation.
Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.
Citrus huanglongbing (HLB) is a devastating disease of citrus, posing a major risk for the citrus industry all over the world (Bové 2006). HLB is associated with three nonculturable, gram-negative, phloem-limited α-proteobacteria, ‘Candidatus Liberibacter asiaticus’ (CLas), ‘Ca. Liberibacter africanus’ (CLaf), and ‘Ca. Liberibacter americanus’ (CLam) (Wang et al. 2017; Wulff et al. 2014), with CLas vectored by the Asian citrus psyllid (ACP, Diaphorina citri Kuwayama) being the most pervasive (Bové 2006; Gottwald 2010). HLB symptoms include yellow shoots or chlorotic mottling on leaves, branch dieback, fruit discoloration and deformation, yield reduction, and eventual death of the tree (Bové 2006; da Graça et al. 2016). There is currently no cure for HLB, so detection of the disease-associated bacteria in the insect vector and citrus host is necessary for vector treatment and tree removal to reduce inoculum levels and spread (Albrecht et al. 2020; Graham et al. 2020).
Various tools and assays have been developed for HLB diagnostics (Valdés et al. 2016). These include visual identification of disease symptoms in the field or biological indexing on sweet orange seedlings (Citrus sinensis (L.) Osbeck) (Belasque et al. 2010; Cevallos-Cevallos et al. 2009; Folimonova and Achor 2010; Gottwald et al. 2007). Other existing detection methods include HLB-specific fluorescent markers (Schwarz 1968), serological methods with monoclonal and polyclonal antibodies (Ding et al. 2015; Gao et al. 1993; Garnier et al. 1987; Pagliaccia et al. 2017), loop-mediated isothermal amplification (Rigano et al. 2014), and conventional polymerase chain reaction (PCR) (Fujikawa and Iwanami 2012; Hocquellet et al. 1999; Hung et al. 1999; Jagoueix et al. 1996; Rigano et al. 2014; Tian et al. 1996). Currently, quantitative PCR (qPCR) and droplet digital qPCR have become the preferred detection method of ‘Ca. Liberibacter spp.’ (CLsp) (Ananthakrishnan et al. 2013; Coy et al. 2014; Kunta et al. 2014; Li et al. 2006, 2009; Lin et al. 2010; Maheshwari et al. 2021; Morgan et al. 2012; Orce et al. 2015; Park et al. 2018; Tatineni et al. 2008; Teixeira et al. 2008; Zafarullah et al. 2021; Zheng et al. 2016) because of its high-throughput capabilities and increased sensitivity, reported to be 10 to 1,000 times higher than nested and conventional PCR, respectively (Morgan et al. 2012). The increased sensitivity of qPCR and digital qPCR (Selvaraj et al. 2018; Zhong et al. 2018) increases the chances of detecting the low-tittered CLsp bacteria in plant and insect samples. The most widely used qPCR assay for the detection of CLsp was developed by Li et al. (2006); it has been extensively adopted as the CLsp detection tool in HLB management and regulatory programs (Albrecht et al. 2020; Floyd and Krass 2006; Gottwald and McCollum 2017), also referred to in the National Diagnostic Protocol for ‘Candidatus Liberibacter asiaticus’, the putative causal agent of HLB-NDP25 V1. The Li et al. (2006) assay was designed to target the bacterial 16S rRNA gene sequence, a region evolutionarily conserved and shown to support qPCR assays that are 10 times more sensitive than qPCR assays targeting 16S rDNA (Kim and Wang 2009; Yarza et al. 2010). More recently, a CLas-specific qPCR assay was developed targeting the nrdB gene, which encodes β-subunit of ribonucleotide reductase (RNR) that exists as five multicopy genes of CLas (Maheshwari et al. 2021; Zheng et al. 2016). The RNR assay was demonstrated to be more sensitive than the Li et al. (2006) 16S rDNA assay and has also been adopted as a CLsp detection tool by HLB management programs (Albrecht et al. 2020; Zheng et al. 2016). A phage-based qPCR primer set (LJ900f/LJ900r) was developed and tested for the detection of CLas but recent studies showed that CLas prophages and their sequences were highly variable, and in some cases, the prophage sequences were absent, which could impede detection reliability or accuracy (Katoh et al. 2014; Morgan et al. 2012; Zheng et al. 2016). Other less adopted qPCR assays have been developed for CLas detection, including the rplJ/rplL ribosomal protein gene for CQULA (Teixeira et al. 2008; Wang et al. 2006) and hyvI/hyvII multiple tandem repeats for LJ900fpr (Morgan et al. 2012).
In this study, we demonstrate that all pathogen detection assays require updates as more current pathogen or technology information becomes available and that the process of assay design, validation, and implementation is a multistage collaborative effort that needs to adhere to specific guidelines. More specifically, the qPCR assay developed by Li et al. (2006), targeting 16S rDNA, was updated and validated considering current 16S rRNA CLsp gene sequences and the MIQE guidelines for quantitative and qualitative qPCR, as well as the qPCR assay design workflow that became available after 2006 (Broeders et al. 2014; Bustin and Huggett 2017; Bustin et al. 2009). The updated multiplex qPCR assay was also tested with a large number of diverse biological samples at five independent laboratories and compared with the RNR assay (Zheng et al. 2016). This updated multiplex CLsp qPCR assay is an additional tool that can be used to combat HLB and has the potential to benefit the various citrus survey, quarantine, germplasm, and nursery stock programs requiring an array of different pathogen detection tools to succeed.
MATERIALS AND METHODS
Singleplex and multiplex qPCR assay design
All GenBank sequences available for the 16S rDNA of the three CL species were utilized in this study. More specifically, three alignments were generated separately for each of the CLas-784, CLam-57, and CLaf-35 sequences to design their respective singleplex qPCR assay. A fourth multiple alignment of all the 16S rDNA of CLas, CLam, and CLaf sequences was generated to design the multiplex qPCR assay (Fig. 1). Nucleotide sequences alignments were produced using Sequence Analysis and Molecular Biology Data Management Software Vector NTI Advance 11 (Thermo Fisher Scientific, Carlsbad, CA, U.S.A.). The aligned sequences were used to identify conserved sequences in the 16S rRNA region and to design primers and qPCR probes used for singleplex and multiplex qPCR experiments (Table 1). The fluorophore used for all CLsp singleplex and multiplex qPCR probes was FAM, and the 3′ quencher was the Black Hole Quencher. The qPCR assay was designed using the Primer Express Software (Thermo Fisher Scientific), considering the MIQE guidelines for quantitative and qualitative qPCR and the qPCR in silico and empirical design and optimization parameters and workflow (Broeders et al. 2014; Bustin and Huggett 2017; Bustin et al. 2009; dMIQE Group and Huggett 2020; Udvardi et al. 2008). The homology of the primers and qPCR probes was confirmed by BLAST (NCBI) searching against the nonredundant database.
Singleplex qPCR for individual CLsp is as follows: HLBas2 (HLBas2 and HLBas.3 forward primers, HLBp and HLB3r; Zafarullah et al. ), HLBaf (HLBaf2, HLBp and HLB3r), and HLBam2 (HLBam.2, HLBp and HLB3r). The updated multiplex qPCR assay for the detection CLsp (CLas, CLam, and CLaf) is as follows: HLBas2, HLBas.3, HLBaf, and HLBam.2 forward primers; HLBp probe; and HLB3r reverse primer (Table 1).
Singleplex and multiplex qPCR assays
All primers and qPCR probes were synthesized by Thermo Fisher Scientific and prepared to be 100 pmol/µl concentration. The same qPCR primer/probe mixes for both singleplex and multiplex were prepared by mixing 20 µl each of the 100 pmol/µl forward primers, 20 µl each of 100 pmol/µl reverse primers, and 4 µl each of the 100 pmol/µl probes per bacterium in a final volume of 240 µl of water. Both singleplex qPCR for individual CLsp and the updated multiplex CLsp qPCR assay for the detection CLsp (CLas, CLam, and CLaf) were carried out in 12 µl reactions with 0.3 µl of water, 0.58 µl of qPCR primer/probe mix (100 pmol/µl), 6 µl of TaqMan Universal Master Mix II (2×) (Thermo Fisher Scientific), and 5 µl of the 1:5 diluted DNA using the manufacturer's amplification conditions (2 min at 50°C, 10 min at 95°C, then 40 cycles of 15 s at 95°C and 60 s at 60°C). The samples were placed in a 384-well plate and amplified in a 7900HT FAST Real-time PCR system (Applied Biosystems, Foster City, CA, U.S.A.). The appropriate no-template, negative, and positive controls were included for all qPCR runs. Fluorescent signals were collected during the annealing temperature, and the quantitative cycle (Cq) was calculated and exported with a threshold of 0.06 and a baseline of 3-12 for the targets of interest. The Cq was calculated by the qPCR machine using an algorithm with a set range of cycles at which the first detectable significant increase in fluorescence occurs.
CLas, CLam, CLaf DNA, and non-CLsp tested samples
CLsp samples (true positives) used in the validation and evaluation of the updated 16S RNA CLsp multiplex qPCR detection assays originated from various geographical regions, sample types, citrus species, and nucleic acid extraction methods to account for CLsp diversity and different laboratory practices (Table 2). CLas samples from 2-year-old citrus trees grown from seeds in an insect-proof greenhouse that were graft-inoculated with HLB-infected tissue from field citrus trees were provided by the University of Florida, Gainesville, or the United States Department of Agriculture (USDA), Agricultural Research Service (ARS), Fort Pierce, FL. Collection of CLas samples from symptomatic field trees (i.e., chlorotic canopies, shoots dieback, and blotchy mottled leaves) was conducted by the authors in commercial citrus orchards in Florida. Additional Florida CLas samples, from psyllids and symptomatic field trees, were acquired from a research farm of the University of Florida in Polk County. Other CLas samples were provided by the USDA-ARS Exotic Pathogens of Citrus (EPCC), Beltsville, Maryland, and the California Department of Food and Agriculture (CDFA). CLas samples provided by the New South Wales (NSW) Department of Primary Industries’ Elizabeth Macarthur Agricultural Institute (EMAI), NSW, Australia, were collected in different countries from field trees exhibiting asymmetric blotchy mottle on foliage, tree decline, and ACP feeding loci. CLas samples were collected from orchard trees at the Citrus Research Institute, Sargodha, Pakistan, exhibiting overall chlorosis, corky and yellowish leaf veins, and nutrient deficiency symptoms. CLaf samples provided by Stellenbosch University, Stellenbosch, South Africa, originated from field citrus samples collected in Pretoria at the experimental farm of the University of Pretoria (Roberts et al. 2015) and exhibited asymmetrical leaf blotchiness. CLam samples provided by Fundecitrus, Araraquara, SP, Brazil, were derived from symptomatic trees having general chlorotic canopies, leaf blotchy mottle and misshapen fruit. All CLsp samples were provided either as DNA or freeze-dried tissue (USDA Permit P526P-16-00352). Additional CLas samples were intercepted as citrus variety introductions from China, Pakistan, and Puerto Rico by the Citrus Clonal Protection Program (CCPP) at the University of California (UC) Riverside (USDA permit PCIP-14-00356, PCIP-16-00029, PCIP-17-00613, and P526P-18-01680).
Noninoculated controls (true negatives) were maintained under screenhouse at the CCPP Lindcove Foundation Facility, UC Agriculture and Natural Resources (UC ANR), Lindcove Research and Extension Center (LREC) (Table 3). Nontarget citrus pathogens (i.e., true negatives: viruses, viroids, and other prokaryotes) were maintained in the greenhouses and screenhouses of the Citrus Clonal Protection Program (CCPP) at the Rubidoux Quarantine Facility, UC, Riverside (CDFA permit 3096), or were provided by the USDA ARS EPCC as nucleic acids (Table 4).
Sample processing and DNA isolation
Citrus samples were collected from phloem-rich tissue such as the bark of stems of the last matured flush (approximately 12 to 18 months old) or petioles of fully expanded leaves. To account for the possible uneven distribution of the pathogen within a plant, samples were collected from around the tree canopy and combined in a single sample. Tissues in the California laboratories were processed as described in Dang et al. (2022). DNA was extracted with the MagMAX Express-96 (Thermo Fisher Scientific) instrument, using the MagMax-96 DNA multi-sample kit (Thermo Fisher Scientific) (Ginnan et al. 2020), whereas RNA for pathogens used for validation was extracted using the MagMax-96 viral RNA isolation kit as previously described in Dang et al. (2022). The final extracts were eluted in 100 μl of H2O, and the quality, purity, and integrity of the nucleic acid was evaluated. Samples from Brazil and South Africa had DNA extracted by CTAB and eluted in 100 μl of H2O (Teixeira et al. 2008). Samples in the Australian laboratory were processed for DNA extraction using different methods: The Bioline Isolate II Plant DNA kit (Meridian Biosciences, Memphis, TN, U.S.A.) was used for samples from the Lao People's Democratic Republic and Timor Leste; the Qiagen Purification kit (QIAGEN, Hilden, Germany) was used for samples from Thailand; and CTAB and a RedExtract-N-Amp kit (Sigma-Aldrich, New Castle, NSW, Australia) were used for samples from Bhutan.
Validation of the updated qPCR assays
The updated CLsp qPCR assays for the detection of CLas, CLam, and CLaf, were validated using applicable quantitative and qualitative real time PCR parameters as previously described (Broeders et al. 2014; Bustin et al. 2009; dMIQE Group and Huggett 2020). The multiplex CLsp qPCR assay was compared with the singleplex qPCR assays for each CLsp, and the data were analyzed by comparing sample using the singleplex and multiplex qPCR assays (Table 2). The TaqMan Universal PCR Master Mix (Thermo Fisher Scientific) kit was used to test the DNA samples listed in Table 2 following the manufacturer's recommendation. The same primer probe concentrations were used for both singleplex and multiplex qPCR reactions, using 5 µl of 1:5 diluted DNA.
The specificity of the updated assays was evaluated in silico by performing BLAST against nucleotide sequences in GenBank and NCBI and experimentally by testing against a variety of samples with known CLsp infection status and noninfected controls (Tables 2 and 3). Cross-reactivity was assessed using other nontarget citrus pathogens that include viruses, viroids, and other prokaryotic organisms (Table 4).
The sensitivity (absolute limit of detection, LOD) and quantification of the amount of CLsp in the samples (bacterial load) was calculated by generating an absolute standard curve for each of the three singleplex and the multiplex assays to determine the starting number of copies. The bacterial load, calculated as copies per microliter of infected sample in this study, roughly equals 0.55 mg of fresh or 0.27 mg of freeze-dried budwood.
More specifically, amplicons for CLas, CLam, and CLaf were obtained for each primer set (Table 1) and individually cloned into plasmids (Eurofins MWG Operon, Huntsville, AL, U.S.A.). The plasmid DNA was linearized using the HindIII enzyme to increase the efficiency of dilutions. Serial 10-fold dilutions of plasmids carrying a known copy number of CLas, CLam, and CLaf inserts were produced to construct absolute standard curves for each of the qPCR assays (Fig. 2). Reactions were performed in triplicate to establish the linear response between the Cq values and the log of known copy numbers. The copy numbers for each sample were calculated as described by Leutenegger (2001). The slope of the standard curve and the coefficient of determination (R2) were calculated using linear regression (Rasmussen 2001). The amplification efficiency (E) of the singleplex and multiplex assays was calculated with the formula E = 10(−1/slope) − 1 (Pfaffl 2004; Svec et al. 2015).
The inter- and intra-assay variations of the singleplex and multiplex qPCR assays were assessed within one plate and in different plates, respectively. DNA from samples representing the three CLsp, with a range of different Cq values, were selected and tested by singleplex and multiplex qPCR in triplicate reactions. The intra- and interassay variations were calculated by determining the percentage of the coefficient of variation (CV%), which was calculated for each sample as follows: mean of the standard deviations of the triplicates divided by the grand mean of the triplicates × 100 (Table 5).
The applicability, practicability, and transferability of the assay were demonstrated by testing a large number of diverse biological and DNA preparation samples at the UC Davis Real-time PCR Research & Diagnostic Core Facility (qPCR instrument: 7900HT FAST and master mix: TaqMan Universal PCR Master Mix, Thermo Fisher Scientific) and by deploying the assay to four independent laboratories: University of California (UC) Riverside-CCPP and the USDA-ARS National Clonal Germplasm Repository for Citrus and Dates (NCGRCD), both in Riverside, CA, U.S.A.; EMAI, NSW Department of Primary Industries, Menangle, NSW, Australia; and the Department of Genetics, Stellenbosch University, South Africa (Table 6; Supplementary Table S1).
The robustness of the assay was evaluated with a series of deviation experiments in annealing temperatures (±2°C) and reaction volumes (±2 μl). The robustness experiments were performed at the UCR-CCPP, USDA-ARS NCGRCD, and EMAI laboratories using locally available CLas and CLaf DNA samples, master mixes (TaqMan Universal PCR Master Mix, Thermo Fisher Scientific; iTaq Universal Probe Supermix and SsoAdvanced Universal Probes Supermix, Bio-Rad, Hercules, CA, U.S.A.) and qPCR instruments (CFX96 Real-Time PCR Detection System, Bio-Rad; QuantStudio 5, QuantStudio 12K Flex and ViiA 7, Thermo Fisher Scientific; Rotor-Gene Q, QIAGEN) (Table 7).
Statistical analysis was performed using PASW Statistics 18 software (PSS, Chicago, IL, U.S.A.). P values < 0.05 were considered significant. The reproducibility of the singleplex and multiplex qPCR assay was tested by analysis of variance (ANOVA) comparing the Cq values of the samples (Table 2).
Comparison of the updated multiplex CLsp qPCR assay with previously published assays
The updated multiplex CLsp qPCR assay was compared with the original qPCR assay by Li et al. (2006). Fifty-seven known positive samples (true positives) for one of the three CLsp, 13 samples of noninfected controls (true negatives), and 28 samples of 10 nontargeted citrus pathogens were tested with the updated CLsp qPCR assay and Li et al.’s (2006) qPCR assay (Table 2).
Based on the principle that a well-performing diagnostic test correctly identifies the diseased individuals in a population, a series of statistical measurements, as reviewed by Bewick et al. (2004), were used to compare the performance of the two qPCR CLsp detection assays. An assay performs well when sensitivity (Sn) = true positives/(true positives + false negatives) and specificity (Sp) = true negatives/(true negatives + false positives) approach 100%. High positive likelihood ratio (LR+) = sensitivity/(1 − specificity) and low (close to zero) negative likelihood ratio (LR–) = (1 − sensitivity)/specificity also indicate a well-performing diagnostic test. Finally, Youden's index, (J) = sensitivity + specificity – 1, can attain the maximum value of 1 when the diagnostic test is perfect and the minimum value of 0 when the test has no diagnostic value (Bewick et al. 2004).
Although the experiments of this study were completed and the majority of the available biological samples were exhausted by testing with the updated and Li et al.’s (2006) qPCR assays, the RNR assay was published and later on adapted for use in citrus HLB management programs (Albrecht et al. 2020; Selvaraj et al. 2018; Zheng et al. 2016). Therefore, a small number of new CLas samples was acquired by the CDFA and tested with the RNR, Li et al. (2006), and updated qPCR assays as a minimum reference point for this study and the updated qPCR for the detection of CLsp (Table 8).
Updated singleplex and multiplex qPCR assay design
In total, 876 sequences of CLas, CLam, and CLaf 16S rRNA genes available at GenBank (NCBI) were aligned and used to update the qPCR primers and probe from Li et al. (2006). The alignment included newer GenBank accessions such as the CLas sequences of the California positive HLB trees (Kumagai et al. 2013) not available at the time of Li et al.’s (2006) study (Fig. 1).
The updated multiplex CLsp qPCR assay included two forward primers for CLas (HLBas.2 and HLBas.3; Zafarullah et al. 2021) and one forward primer for each of the CLam (HLBam.2) and CLaf (HLBaf.2), as well as a shortened probe (HLBp2) (Table 1). The sequences of the updated primers and probe covered all sequence variations of the three CLsp (Fig. 1) and had optimum annealing temperatures and amplicon length (i.e., 113 bp) per MIQE guidelines, published after Li et al.’s (2006) study (Table 1) (Broeders et al. 2014; Bustin et al. 2009). Annealing temperatures for the primers and probes were adjusted from Li et al. (2006) and ranged from 58.4 to 60°C for the primers and 69°C for the probe (Table 1), following the guidelines for optimal real-time qPCR reactions and for prevention of dimer formation (Bustin and Huggett 2017; dMIQE Group and Huggett 2020; Udvardi et al. 2008).
Li et al.’s (2006) forward primer was updated to include a G nucleotide in position 14 (HLBas.3) corresponding to a mutation present only in some CLas sequences (Fig. 1). The single reverse primer for the three CLsp (HLBr) from Li et al. (2006) was not updated as it covers all the targeted genomic areas of CLsp and had the annealing temperature of 58.4°C (Table 1).
Updated qPCR assay validation
The specificity of the updated qPCR assay was determined in silico using all available GenBank sequences. The CLsp primers and probe did not cross-react with any 16S rRNA gene sequence from any other organism (data not shown). Additionally, the specificity of the assay (singleplex and multiplex) was evaluated qualitatively with the correct classification (false negative and positive rate 0%) of 95 known CLsp infected and noninfected samples (Table 2). Samples with Cq values below the established Y intercept of singleplex and multiplex qPCR assays for the current study were considered positive (Fig. 2). Every detected Cq above the established Y intercept for each qPCR assay will require additional testing either by diluting the samples (i.e., reducing qPCR inhibitors effect) or resampling and re-extraction (i.e., uneven pathogen distribution in tree) to confirm the positive result. Samples with a Cq of 40 were considered negative.
More specifically, the CLsp qPCR assay was able to reliably detect the three targeted CLsp species in 54 known positive samples (true positives) regardless of geographic origin, citrus host, or pathogen detection status from Li et al.’s (2006) assay (Table 2). In addition, the assay did not cross-react with 13 samples of noninfected controls (true negatives) from different citrus varieties (Table 3) or 28 samples of 10 nontarget citrus pathogens (Table 4), and its performance measurements (Sn, Sp, LR+, LR-, and J) were optimum (Table 9). The updated singleplex and multiplex qPCR assays were successfully used with over 120 additional samples of various hosts (citrus and psyllids) and different DNA mixtures of CLsp in four independent laboratories in California, Brazil, and South Africa. In all cases, the primers and probe detected the targeted bacterium, and no cross-reactivity among the three assays designed for CLas, CLam, and CLaf was detected (Supplementary Table S1).
The sensitivity of the CLsp multiplex qPCR showed a linear dynamic range of 107, whereas the singleplex assays ranged from 105 to 107 copies per microliter down to <10 copies per microliter (LOD) when samples were tested with 10-fold serial dilutions (Fig. 2).
The amplification efficiency of the multiplex qPCR assay was 95.7% (Fig. 2). The amplification efficiencies of the CLas singleplex assays using the common reverse primer (HLBr.2) and probe (HLBp) with the two different forward primers (HLBas.2 and HLBas.3) were 94.5% and 90.3%, respectively (Fig. 2). The amplification efficiency of the singleplex qPCR for CLam and CLaf was 93.8% and 92.7%, respectively. The R2 for all assays (singleplex and multiplex) was at the 0.99 level (Fig. 2). All amplification efficiency and R2 values were in the acceptable range set by the MIQE guidelines of quantitative and qualitative real-time qPCR experiments (Broeders et al. 2014; Bustin et al. 2009).
There were no significant differences in the calculation of the bacterial load for the different CLsp using the singleplex or the multiplex qPCR assay (P > 0.05). The mean bacterial load calculated for the samples tested using the multiplex assay was 2.61 × 107 copies per microliter of infected sample. The mean bacterial load calculated for the CLas-positive samples tested using the two singleplex assays was 2.65 × 107 (HLBas.2) and 2.48 × 107 (HLBas.3) per microliter of infected sample copies of CLas. The mean bacterial load calculated by the CLam and CLaf singleplex assays was 2.76 × 107 and 2.97 × 107 per microliter of infected sample copies, respectively.
The CV values for the singleplex and multiplex qPCR assays was in the range of 0.22 to 1.19% (intra-assay variation) and 0.49 to 0.97% (interassay variation), indicating low variation between different repetitions and different runs of the assays (Table 5).
The transferability, robustness, applicability, and practicability of the multiplex assay was demonstrated by testing a number of diverse biological samples at independent laboratories with consistent reproducible results for the identification of CLsp-positive samples. More specifically, the transferability of the assay was tested in two California laboratories using four different qPCR instruments and two different qPCR reagent kits and available CLas DNA samples with consistent results (Table 6). The robustness of the assay was tested in three independent laboratories in California and Australia using available CLas and CLaf DNA samples and laboratory equipment. The assay was proven to be robust because different annealing temperatures, reaction volumes, qPCR instruments, and master mixes had a minor effect on the Cq values and did not affect the classification of samples as positive (Table 7). The applicability and practicability of the assay was demonstrated by testing a large number of CLsp-positive and -negative biological samples from different geographic origins and citrus and psyllid hosts, including different DNA mixtures of CLsp, in four laboratories in California, Brazil, and South Africa, with consistent results (Supplementary Table S1).
Comparison of the updated multiplex CLsp qPCR assay with previously published assays
The updated CLas detection assay was compared with Li et al.’s (2006) and the RNR assay (Selvaraj et al. 2018; Zheng et al. 2016). All assays were able to detect CLas in the tested samples (Table 8). The minimum and maximum Cq values of the updated assay were 23.72 and 34.51, respectively, very close to the Li et al. (2006) and RNR assay values (Table 8). Overall, the updated qPCR assay produced lower Cq values than Li et al.’s (2006) and the RNR assays and calculated higher copy numbers of the CLas target, including for the kumquat sample #7, which had the highest Cq values for all assays (i.e., over 10 copies versus fewer than 2 copies) (Table 8).
No effective cure for HLB is currently available. As a result, management of HLB depends on the successful exclusion of CLsp from citrus-producing regions using pathogen-tested nursery stock, quarantines, removal of CLsp-infected trees, and control of the psyllid vector. Therefore, it is critical to detect HLB-associated CLsp bacteria in citrus trees and psyllid vectors for the deployment of appropriate intervention strategies (Albrecht et al. 2020; Bassanezi et al. 2020; Belasque et al. 2010; Graham et al. 2020). As more CLsp genome information or pathogen detection technologies become available, it is crucial to update and streamline the CLsp detection assays so that inoculum pressure can be reduced and the spread of HLB to disease-free areas is restricted (Gottwald and McCollum 2017; Graham et al. 2020; Kogenaru et al. 2014).
From the plethora of existing CLsp detection assays, the most widely used real-time qPCR assay targeting the 16S rRNA gene region is the assay developed by Li et al. (2006). This qPCR assay was the first of its kind at the early stages of the HLB epidemic in the United States (Floyd and Krass 2006; Halbert 2005; Li et al. 2006). It has served the U.S. industry and regulatory agencies for many years in testing large numbers of citrus trees and psyllids in all citrus-producing states (Albrecht et al. 2020; Graham et al. 2020). Therefore, it was selected to be the official regulatory HLB diagnostic tool in state and federal HLB management and regulatory programs in the United States not only because of all the desirable qPCR properties and its multiplex capacity, including amplification of the cytochrome oxidase (COX) citrus reference gene, but also because it was designed to detect and differentiate the three CLsp associated with HLB using different forward primers but the same reverse primer and probe (Albrecht et al. 2020; Floyd and Krass 2006; Gottwald and McCollum 2017; Li et al. 2006).
However, as the authors highlight in their 2006 paper, samples of CLaf were not available at the time of the study; therefore, data for the detection of all three CLsp were not presented (Li et al. 2006). In a follow-up publication (Li et al. 2007), the authors indicated that because the design of all the available qPCR primers and probes was based only on a few genetic loci, they recommended that as genome sequencing projects progress, new sequences should be used for the design and validation of more species-specific primer or probe sets (Li et al. 2007). Our results support this need, as highlighted by the 2006 and 2007 Li et al. studies, because the Li et al. (2006) assay calculated less than one copy number (i.e., 0 to 0.34; CLsp bacteria absent) for different samples of CLas, CLaf, and CLam (Table 2).
Recent developments in bacterial whole genome sequencing, and the generation of sequence information in the 16S rRNA gene for all CLsp, allowed us to update Li et al.’s (2006) assay considering the qPCR MIQE and design guidelines, which became available after 2006 (Broeders et al. 2014; Bustin and Huggett 2017; Bustin et al. 2009; dMIQE Group and Huggett 2020; Udvardi et al. 2008). All GenBank sequences of CLsp were separately aligned, and qPCR assays were designed in areas showing 100% consensus at the 16S rRNA gene region. This region has been extensively investigated as a tool for the detection of prokaryotes due to its evolutionarily conserved nature in combination with the presence of a range of variable sites (Yarza et al. 2010). The 16S rRNA gene region has also been reported to be the best characterized region among the three CLsp (Coletta-Filho et al. 2014; Li et al. 2006; Teixeira et al. 2008). One significant advantage of using the 16S rRNA gene sequences is that reference sequences from numerous organisms are available in GenBank to reliably examine the specificity of newly designed primers in silico (Coletta-Filho et al. 2005; Subandiyah et al. 2000; Teixeira et al. 2005). Each rrn operon (containing all three ribosomal genes = 16S/23S/S) has been independently sequenced to assess intra-operon variability, and it was determined that all three rrn operons are 100% identical (Wulff et al. 2014), confirming that validity of using such sequences to design primer/probe combinations (Wulff et al. 2014; GenBank accession number CP006604). In addition, the sequences of the 16S rRNA gene are highly conserved among the CLsp, but variation is sufficient to design specific primers capable of detecting each of the three species separately (i.e., CLas, CLam, and CLaf). Our updates in qPCR assay design targeting the 16S rRNA gene sequence included altering the sequence of Li et al.’s (2006) forward primers to improve their GC content requirements (i.e., ∼50%), annealing temperature (i.e., ∼60°C), and annealing temperature difference from the probe (i.e., ∼10°C), per recent qPCR design guidelines (Broeders et al. 2014; Bustin and Huggett 2017; Bustin et al. 2009; dMIQE Group and Huggett 2020; Udvardi et al. 2008).
When using the updated multiplex CLsp qPCR assay compared with singleplex qPCR, no significant increase in Cq values was detected, with their ratios maintained as one or very close to one. This indicated that specificity was not lost when combining all the updated primers and qPCR probe in a single multiplex qPCR reaction. The specificity and sensitivity of the updated qPCR assay was optimum, and measuring the intra- and interassay variations confirmed the reproducibility and repeatability of the assay. The assay was also successfully used with a large number of diverse samples, diverse citrus hosts, at independent laboratories in four countries, demonstrating its transferability, applicability, and practicability. The updated qPCR assay was also robust as different qPCR reaction conditions or instruments had a minor effect on Cq values and did not affect the classification of samples as positive. This is of particular importance as there are regulatory Cq value thresholds that could affect the deployment of valuable resources and change the course of HLB eradication responses or management programs (Graham et al. 2020).
In the validation experiments of this study, samples with low copy numbers (i.e., <10) and Cq values below the established Y intercept of the singleplex and multiplex qPCR assays (Fig. 2) were considered positive (Table 2). However, in regulatory, survey, germplasm, or other programs, samples with low copy numbers and Cq values approaching the regulatory thresholds should undergo additional testing following dilution of the original sample (i.e., reducing qPCR inhibitors) or by resampling at different time intervals (i.e., titer of CLsp increasing over time in the host) and re-extracting an increased sample size per tree (i.e., addressing the uneven pathogen distribution) before the determination that such samples originated from CLsp-infected trees.
In this publication, we presented a case study and a collaborative effort for the updated design, validation, and implementation of a multiplex CLsp detection assay based on new genome sequences and qPCR guidelines and workflows (Broeders et al. 2014; Bustin and Huggett 2017; Bustin et al. 2009; dMIQE Group and Huggett 2020; Udvardi et al. 2008). The comparison of the updated assay with the most commonly used CLsp qPCR assays (i.e., Li et al. 2006 and Zheng et al. 2016), and their extensive use in HLB management programs, clearly demonstrates that qPCR assays are a reliable tool for pathogen detection regardless of their specific design or targets (Albrecht et al. 2020; Graham et al. 2020; Kogenaru et al. 2014; Selvaraj et al. 2018; Zheng et al. 2016). It is therefore important for such programs to address CLsp testing protocol bottlenecks, such as number of leaf samples tested per tree, number of trees tested per target area, and the strict manual processing protocols of samples, to maximize the impact that qPCR assays can have in HLB exclusion, quarantine, or management programs (Gottwald and McCollum 2017; Graham et al. 2020).
The authors acknowledge the Cahuilla people as the Traditional Custodians of the Land on which the experimental work was completed. We are grateful to all past and current CCPP personnel for their dedicated work and especially for creating and maintaining the CCPP disease bank and foundation materials. Collaborators kindly supplied CLsp samples: Cristina Paul-USDA-ARS, Beltsville, MD; Greg McCollum, USDA-ARS, Fort Pierce, FL; Lucita Kumagai-CDFA; Shagufta Naz, Lahore College for Women University, Pakistan; and Grant Chambers- EMAI, NSW, Australia.
The author(s) declare no conflict of interest.
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Author contributions: F.O. and G.V. conceived the study and designed the experiments. F.O., T.D., S.B., R.H., I.L.-C., A.R., E.R., M.P., N.W., R.R., G.P., A.E., N.D., and S.F. performed the experiments. F.O. and T.D. prepared figures and tables and wrote the manuscript with G.V. All authors contributed to the article and approved the submitted version.
The authors dedicate this paper to the memory of Dr. Laurene Levy, USDA, for her service in agriculture, especially in the field of regulatory diagnostics and for inspiring this work to continue protecting citrus from deadly diseases.
The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA, CDFA, U.S., or state government determination or policy. Mention of trade names or commercial products in this publication is solely for the purpose of providing specific information and does not imply recommendation or endorsement by the UC, CDFA, or USDA. UC is an equal opportunity provider and employer.
Funding: This research was funded by the California Citrus Nursery Board project “The Future of the Cooperative Registration Program of Nursery Owned Citrus Source Trees” awarded to G. Vidalakis. Additional support was provided in part by the Citrus Research Board (project 6100), the USDA National Institute of Food and Agriculture (Hatch project 1020106), and the National Clean Plant Network–USDA Animal and Plant Health Inspection Service (14-8130-0419-CA, 15-8130-0419-CA, 16-8130-0419-CA, AP17PPQS&T00C118, AP18PPQS&T00C107, AP19PPQS&T00C148) awarded to G. Vidalakis.
The author(s) declare no conflict of interest.