Comparative Assessment of Different Nucleic Acid Templates for Use as Standards in the qPCR‐Based Quantification of Virus Genomes

ABSTRACT

Quantitative PCR (qPCR) employing either non‐specific dyes or gene‐specific dye‐labelled probes is a commonly used technique for the quantification of viral nucleic acids. While commercial kits often come equipped with their own proprietary standards, many research endeavours necessitate the preparation of bespoke standards that suit the specific needs of the research. This study aimed to assess the appropriateness of various nucleic acid standards, namely circular plasmids, linear plasmids, PCR products, and transcribed RNA, in virus nucleic acid quantification. The evaluation of these standards was conducted through the implementation of qPCR and qRT‐PCR methods utilising two different targeted gene‐specific primer and probe sets. The findings of this investigation revealed notable differences between the standards. Notably, the study emphasises the necessity to choose standards carefully because their behaviour in PCR is not equal.

1. Introduction

Viruses are obligate parasites that are completely reliant on living cells to complete their replication cycle. Viruses can have either a DNA or RNA genome, and the genome can vary across species. It can be segmented or non‐segmented, double stranded or single stranded and the size can diverge dramatically—as small as approximately 1.7 kb and as big as nearly 400 kb (International Committee on Taxonomy of Viruses: ICTV 1966).

Traditionally, virus quantification was and still is carried out using techniques such as the plaque assay or TCID₅₀. Importantly, these techniques quantify replication‐competent virus particles. In contrast to that, PCR is now increasingly used to quantify virus genomes as a proxy for the amount of replication‐competent virus in a sample, and it can be used in relative or absolute terms to assess virus population increases or decreases (Maclachlan et al. 2016). The detection and amplification of genome fragments using quantitative PCR (qPCR) is facilitated through either non‐specific DNA‐binding dyes (such as Eva green or Sybr green) or Taqman‐based fluorophore‐labelled probes. Taqman‐based virus quantification utilises DNA polymerases with exonuclease activity at the 5’ side of the DNA, a feature absent in most synthetic DNA polymerases used in conventional PCR, enabling the release of the fluorophore dye at the 5' end of the probe, which is the essential principle of real‐time diagnostics (Luthra et al. 1998). Although some studies suggest the efficacy of SYBR green PCR to be equal to or more effective than Taqman‐based PCR, the latter is often still favoured due to the perception that it will increase specificity and sensitivity (Zhou et al. 2017; Tajadini et al. 2014).

Two main quantification methods for viruses are typically utilised. The first, relative quantification, primarily assesses virus gene expression levels, such as in drug studies, determining fold changes between two samples (Larionov et al. 2005; Ma et al. 2021). The second, absolute quantification, measures absolute copy numbers by comparing to a DNA or RNA standard with a known copy number (Li et al. 2016). Challenges can arise, however, when the nucleic acid type of the virus differs from the type of nucleic acid standard used (Li et al. 2016; Risalde et al. 2020). A small number of studies have examined the efficiency and stability of these different standards (Dhanasekaran et al. 2010; Bustin 2002; Devonshire et al. 2011). These studies have pointed to the importance of standardisation of materials used and also the lack of stability of some standards compared to others.

This study aimed to enhance our understanding of the difference between PCR standards to enhance decision making for investigators carrying out virus quantification by PCR.

2. Materials and Methods

Primers and Probe: Two primer sets were selected from established primer pairs used in routine diagnostic studies at the Department of Virology laboratories, Faculty of Veterinary Medicine, Ondokuz Mayıs University (Table 1). The first set targeted viral haemorrhagic septicaemia virus (VHSV), which is an ssRNA virus (Liu et al. 2008), and the second set targeted Infectious pancreatic necrosis virus (IPNV), which has a dsRNA genome (Maclachlan et al. 2016).

TABLE 1.

The nucleotide sequence, product sizes, GC ratios, and Tm grades of the primer sets used in this study.

Primer target	Primer/Probe sequence	Amplicon length	Tm (GC%)
VHSV (15)	CATCCATCTCCCGCTATCAGT		64 (52%)
	AGACAGTTTCGCCTCTAAGAT	145 bp	60 (43%)
	FAM−5'AGCGTCTCCGCAGTCGCGAGTGG 3'‐TAMRA		77 (70%)
IPNV	GTGGTCACAGTGGTCAGCTC		68 (60%)
	CCGTCTGCTGGTTGATCTTG	145 bp	66 (55%)
	FAM−5' TGACCCAGTCCATCCCGACCG 3'‐TAMRA		74 (67%)

Design of standards: Circular plasmid standards, linearised plasmid standards, PCR product standards, and transcribed RNA standards were prepared for both primer sets. Initially, end‐point PCR was used to generate PCR product standards for both primer sets. The pGEM‐T Easy Vector System (Promega, Cat No: A1380) was then used for the generation of plasmid standards. Each PCR product was cloned into the pGEM‐T Easy vector according to the manufacturer's instructions and subsequently linearised using the SacI‐HF restriction enzyme (NEB, Cat no: R3156S). Purification of all DNA standards was performed using the GeneJET Gel Extraction Kit (Thermo Fisher Scientific, Cat no: K0691) from a 1% agarose gel containing 0.5 µg/mL ethidium bromide.

The calculation of the number of DNA standard copies followed this formula:

$$\mathrm{Viral}\mathrm{dsDNA}\mathrm{copy}/1\mu \mathrm{L}=\frac{\mathrm{weight}\left(\frac{\mathrm{ng}}{\mu \mathrm{L}}\right)\times 6022\times {10}^{23}}{\left[\mathrm{Plasmid}+\mathrm{insert}(\mathrm{bp})]\times 1\times {10}^9\times 660\right]}$$

RNA standards were transcribed using the AmpliScribe T7 Transcription kit (Epicentre Biotechnologies, Cat No: AS 3107) following the manufacturer's instructions from the linearised gene inserted into the pGEM‐T easy plasmid. DNase treatment was applied to degrade all DNA that was present, and a non‐reverse transcription PCR was used to confirm that the DNA had been degraded.

The calculation of the number of RNA standard copies was performed using the formula:

$$\mathrm{Viral}\mathrm{ssRNA}\mathrm{copy}/1\mu \mathrm{L}=\frac{\mathrm{weight}\left(\frac{\mathrm{ng}}{\mu \mathrm{L}}\right)\times 6022\times {10}^{23}}{\left[\mathrm{Plasmid}+\mathrm{insert}(\mathrm{bp})]\times 1\times {10}^9\times 340\right]}$$

Quantitative PCR: The qPCR assays, using iTaq Universal Probes One‐Step Kit (Biorad, Cat no: 1725141), were performed in a BioRad CFX Connect device. Reaction conditions were set as per the manufacturer's instructions. Each reaction consisted of 10 µL 2× iTaq buffer, 320 nM forward primer, 320 nM reverse primer, 160 nM probe, 1 µL reverse transcriptase, 5 µL distilled water, and 1 µL of standards. The temperature conditions for all amplifications were as follows: 10 min RT at 50°C, 3 min predenaturation at 95°C, 5 s at 95°C, 10 s at 60°C x 40 repeated cycles, and cooling at 12°C.

Each standard was diluted tenfold, five times and five independent runs for each primer set (VHSV and IPNV) were carried out.

Minimum Detection limits: Minimum detection limits for each standard were determined through probit regression analysis, with calculations performed using Microsoft Office.

Statistics: Multiple unpaired t‐tests were performed using Graphpad Prism. Significance was determined by the p value was less than 0.05.

3. Results

3.1. Average Ct Value Differed Between Standards

Each PCR was set up with a fixed amount of each standard template, either 10⁴, 10³, 10², 10¹ or 1 molecule(s) per reaction. Following each PCR, the Ct values were recorded, and the mean of the Ct values was calculated. The results are reported in Table 2, illustrated in Figures 1 and 2 and individual Ct values for every run available in Tables S1 and S2. Unpaired t‐tests were carried out comparing each standard at each concentration. All analysis is reported in Tables S3 and S4.

TABLE 2.

Average Ct values for all runs (5 per target) along with standard deviations in brackets.

Number per reaction	Circular plasmid	Linear plasmid	PCR product	RNA
VHSV average Ct values (standard deviation)
104	26.07 (1.12)	25.80 (0.81)	24.77 (0.80)	27.68 (0.80)
103	28.40 (1.04)	28.56 (0.74)	26.87 (0.75)	29.76 (1.13)
102	29.38 (0.94)	29.32 (1.03)	28.40 (0.96)	30.77 (1.19)
101	32.16 (0.67)	30.29 (0.64)	28.81 (1.11)	31.20 (0.65)
1	32.68 (0.31)	31.00 (0.03)	30.07 (0.80)	—
IPNV average Ct values (standard deviation)
104	26.28 (0.67)	25.44 (0.57)	25.04 (0.36)	28.11 (0.54)
103	28.53 (0.63)	28.80 (0.95)	26.84 (0.30)	30.52 (0.74)
102	29.45 (0.79)	29.27 (0.69)	27.80 (0.59)	31.26 (0.85)
101	31.36 (0.86)	30.07 (0.25)	28.20 (0.61)	31.71 (0.71)
1	32.57 (0.69)	30.82 (0.71)	29.94 (0.24)	—

Note: Values on the left are the number of each molecule added to the PCR reaction.

FIGURE 1.

Average Ct values of each template standard following PCR using primers and probes targeting VHSV. Values for each starting amount of template are individually illustrated for comparative purposes.

FIGURE 2.

Average Ct values of each template standard following PCR using primers and probes that target IPNV. Values for each starting amount of template are individually illustrated for comparative purposes.

3.2. Detection Limits for Each Standard

Circular Plasmid Standards: The qRT‐PCR successfully detected 1 circular plasmid molecule/reaction in 4 out of 10 cases and 10¹ plasmid molecules/reaction in 6 out of 10 cases. Additionally, 10^{2,3 and 4} plasmid molecules/reaction were detected in all 10 cases (Figure 3). The 95% probit value for analytical sensitivity was computed as 88.75 plasmid molecules/reaction.

FIGURE 3.

PCR percentage success of detecting each standard at each different amount of starting material.

Linear Plasmid Standards: In the case of linear plasmid standards, the qRT‐PCR detected 1 plasmid molecule/reaction in 4 out of 10 cases and 10¹ plasmid molecules/reaction in 6 out of 10 cases. Furthermore, 10² plasmid molecules/reaction were detected in all 10 cases (Figure 3). The 95% probit value for analytical sensitivity was calculated as 88.75 DNA molecules/reaction.

PCR Product Standards: The qRT‐PCR was successful in detecting 1 DNA molecule/reaction in 8 out of 10 cases, while 10¹ DNA molecules/reaction were detected in all 10 cases (Figure 3). The 95% probit value for analytical sensitivity was determined as 7.75 DNA molecules/reaction.

Transcribed RNA Standards: The qRT‐PCR did not detect any nucleic acid when only 1 molecule/reaction was present, but detected 8 out of 10 when 10 molecules were present. While 100% of reactions were positive when higher amounts of cDNA were present (Figure 3). The 95% probit value for analytical sensitivity was established as 77.50 DNA molecules/reaction.

4. Discussion

The qPCR technique uses two distinct methods, relative and absolute quantification, to assess gene expression and determine viral loads, respectively. Relative quantification involves two primary methods: the comparative Ct method and the standard curve method. In contrast, absolute quantification, predominantly used for ascertaining viral loads, relies on diluted standards of known concentration (Conte et al. 2018). Although modern digital PCR allows absolute quantification without standards, the standard curve method remains the conventional approach, incorporating standards within the target gene region (Pavsic et al. 2016). This method extrapolates viral load in unknown samples by referencing curves derived from standards diluted at specific logarithmic ratios of known concentration.

Four standards were compared with the same primers in this study. If everything was equal, Ct values should have been consistent across standards, but this was not observed. Apart from at the highest concentration, the PCR product consistently amplified earlier than the other standards. The PCR product standard also had the lowest limit of detection and the highest PCR success rate at very low concentrations. PCR products may provide a simpler template for PCR primers and probes to access compared to plasmid DNA, which may explain these differences. There is also a lack of other sequences present in the PCR mixtures that could act as competition for the primers and probes. In a sense, this is the cleanest template possible, and although it is good for PCR, it perhaps creates an unrealistic target compared to a whole virus genome.

In contrast to the PCR product, the RNA template that was reverse transcribed before PCR, amplified significantly later than any of the DNA templates. The RNA template also had the largest amount of Ct value deviation between runs compared to the other templates. Factors such as the inherent instability of RNA and the reverse transcription process may influence this difference. The reverse transcription introduces an extra variable compared to the DNA templates and it may not be 100% efficient, potentially leading to amplification inconsistency in the PCR.

Both plasmid standards had similar limits of detection, and both amplified in a similar fashion at higher concentrations, but at lower concentrations, the average Ct values for the linear plasmids were consistently lower. This indicates that at lower concentrations, amplification is more efficient from a linearised template. When a plasmid is circularised, it can take on a supercoiled state that may limit access of primers to the template early in a PCR, while a linearised plasmid does not do this, perhaps explaining the difference observed. Interestingly, the circularised plasmid showed slightly higher Ct value deviation than the linearised plasmid, which may also be associated with the different forms that the circularised plasmid can take on. Two previous studies examining which of these templates was better for PCR produced contrasting results. Oldham and Duncan 2012 suggested that there was minimal difference between a linearised and circularised plasmid, while Hou et al. 2010 suggested that there were marked differences, with overestimation of copy number a distinct possibility with a circularised plasmid template. Our data support both studies to an extent, with consistency between templates being present at higher concentrations but inconsistency at lower concentrations.

The stability and uniformity of standards used in absolute quantification are crucial. For example, a previous study evaluating different DNA standards reported that plasmid standards exhibit greater stability over time than PCR products, and long‐term storage of PCR products can lead to greater variation in results (Li et al. 2016). In the study presented here, all standards were prepared and tested without long‐term storage, so stability is less of a concern, although even short‐term storage of RNA can lead to degradation, suggesting that RNA should be handled carefully or stored as cDNA long‐term.

While some studies use RNA standards for calculating the viral load of RNA viruses (Kwiatek et al. 2010) others opt for DNA standards (Bustin 2002; Devonshire et al. 2011). Nonetheless, most studies do not specify the type of standard used (Hasanoglu et al. 2018; Saravanan et al. 2021; Zhao et al. 2018). It is clear from this study that variation does exist between standards, and that careful consideration should be taken when designing PCR standards. It is recommended that if RNA viruses are being quantified then an RNA standard should be used because of the greater variation seen with RNA, while for a DNA virus, a DNA standard is recommended. A PCR product or a linearised plasmid is most suitable if seeking to quantify a non‐circularised virus template, but if the PCR is designed to target a region of a circularised plasmid virus genome, then it would be optimal to use a circularised plasmid as standard because a linearised plasmid or a PCR product is not directly comparable.

Moreover, the concentration of the standard clearly has an impact too, with each of the standards reducing in reliability at lower concentrations, although the PCR product was almost perfect, even at the lowest concentration. Both the linearised and circularised template standards showed poor reliability at lower concentrations, suggesting that concentration should be considered carefully when using such standards and amplifying from similar target templates.

In conclusion variation exists between templates and careful consideration should be taken when choosing what standard to use. This is particularly relevant to commercial kits where the standard is not necessarily appropriate for the target virus that is being quantified.

Author Contributions

C.T. conceived and planned the experiments. C.T. and H.N.K. carried out the molecular experiments. Z.Y., S.G., G.B., and H.A. planned statistical analyses and contributed to the interpretation of the results. C.T., G.B., and H.A. took the lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis and manuscript.

Funding

The authors have nothing to report.

Ethics Statement

This article describes experiments that are in vitro and do not involve humans or animals, so no ethical approvals are required.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Supporting information

Supporting Table 1: Ct values for all VHSV runs along with averages and standard deviations across runs.

Supporting Table 2: Ct values for all IPNV runs along with averages and standard deviations across runs.

Supporting Table 3: Multiple unpaired t‐tests were carried out comparing the Ct values for each standard to the other standards for each primer and probe set. The table above shows the p‐values for the VHSV pairs.

Supporting Table 4: Multiple unpaired t‐tests were carried out comparing the Ct values for each standard to the other standards for each primer and probe set. The table above shows the p‐values for the IPNV pairs.

Tamer, C. , Kurucay H. N., Barry G., Gumusova S., Yazici Z., and Albayrak H.. 2026. “Comparative Assessment of Different Nucleic Acid Templates for Use as Standards in the qPCR‐Based Quantification of Virus Genomes.” Veterinary Medicine and Science 12, no. 2: e70816. 10.1002/vms3.70816

Data Availability Statement

The data supporting this study's findings are available from the corresponding author upon reasonable request.