Oral fluids as a diagnostic specimen

The terms ‘saliva’ and ‘oral fluid’ are often used interchangeably to refer to fluid samples collected from the oral cavity (Kintz et al., Reference Kintz, Cirimele and Ludes2000; Wong, Reference Wong2006). More accurately, saliva is the fluid produced by salivary glands whereas oral fluid is a composite of saliva, serum transudate, mucosal cells and cellular debris, microorganisms, digestive enzymes, and food residues (Schramm et al., Reference Schramm, Smith and Craig1993; Crouch, Reference Crouch2005; Cone and Huestis, Reference Cone and Huestis2007). This review will use the term ‘oral fluid’ as defined by Atkinson et al. (Reference Atkinson, Dawes, Ericson, Fox, Gandara, Malamud, Mandel, Navazesh and Tabak1993): ‘The fluid obtained by insertion of absorptive collectors into the mouth’.

Although various sampling strategies are used for human beings, oral fluid samples in veterinary medicine are usually collected by introducing an absorbent material into the oral cavity (Palmer et al., Reference Palmer, Whipple and Waters2001; Shin et al., Reference Shin, Cho, Cho, Kang, Kim, Kim, Park and Park2004; Cavalcante et al., Reference Cavalcante, Muniz, Jia, Augusto, Troccoli, Medeiros, Dias, Switzer, Soares and Santos2018). Depending on the size of animals, oral fluid samples could be collected by allowing large animals and primates to chew on absorbent material, e.g. cotton rope, or swabbing oral and buccal cavities in small animals (Larghi et al., Reference Larghi, Nebel, Lazaro and Savy1975; Thomas et al., Reference Thomas, Champoux, Suomi and Gunnar1995; Lutz et al., Reference Lutz, Tiefenbacher, Jorgensen, Meyer and Novak2000; Shin et al., Reference Shin, Cho, Cho, Kang, Kim, Kim, Park and Park2004; Smith et al., Reference Smith, Gray, Moxley, Younts-Dahl, Blackford, Hinkley, Hungerford, Milton and Klopfenstein2004; Gomes-Keller et al., Reference Gomes-Keller, Gonczi, Tandon, Riondato, Hofmann-Lehmann, Meli and Lutz2006; Prickett et al., Reference Prickett, Kim, Simer, Yoon and Zimmerman2008; Dietze et al., Reference Dietze, Moritz, Alexandrov, Krstevski, Schlottau, Milovanovic, Hoffmann and Hoffmann2018; Cheng et al., Reference Cheng, Buckley, Van Geelen, Lager, Henao-Diaz, Poonsuk, Pineyro, Baum, Ji, Wang, Main, Zimmerman and Gimenez-Lirola2020).

The presence of viable viral pathogens, pathogen-specific antibody, and nucleic acids in oral fluids has been well-described (Sirisinha and Charupatana, Reference Sirisinha and Charupatana1970; Garrett, Reference Garrett1975; Archibald et al., Reference Archibald, Zon, Groopman, Mclane and Essex1986). In people, the presence of infectious viruses in oral fluid was first demonstrated by bioassay, e.g. clinical signs in cats and monkeys inoculated with oral fluids from humans with mumps (Wollstein, Reference Wollstein1918; Johnson and Goodpasture, Reference Johnson and Goodpasture1934; Henle et al., Reference Henle, Henle, Wendell and Rosenberg1948). Later, it was used to confirm rabies infection in an infant by intracerebral inoculation of Swiss mouse pups with oral fluids from the child (Duffy et al., Reference Duffy, Woolley and Nolting1947). The fact that several viruses including cytomegalovirus, human immunodeficiency virus (HIV) (Groopman et al., Reference Groopman, Salahuddin, Sarngadharan, Markham, Gonda, Sliski and Gallo1984), herpesviruses (Kaufman et al., Reference Kaufman, Brown and Ellison1967; Douglas and Couch, Reference Douglas and Couch1970), Zika virus (Bonaldo et al., Reference Bonaldo, Ribeiro, Lima, Dos Santos, Menezes, Da Cruz, De Mello, Furtado, De Moura, Damasceno, Da Silva, De Castro, Gerber, De Almeida, Lourenco-De-Oliveira, Vasconcelos and Brasil2016), and influenza virus (Vinagre et al., Reference Vinagre, Martinez, Avendano, Landaeta and Pinto2003), added additional evidence to the role of oral fluids as a source of pathogens. In animals, Coxsackie b-1 virus from rabbits (Madonia et al., Reference Madonia, Bahn and Calandra1966), rabies virus from dogs (Larghi et al., Reference Larghi, Nebel, Lazaro and Savy1975), foot-and-mouth disease virus (FMDV) from cattle (Sellers et al., Reference Sellers, Burrows, Mann and Dawe1968), and influenza A virus and porcine reproductive and respiratory syndrome virus (PRRSV) from pigs (Wills et al., Reference Wills, Zimmerman, Yoon, Swenson, Mcginley, Hill, Platt, Christopher-Hennings and Nelson1997; Detmer et al., Reference Detmer, Patnayak, Jiang, Gramer and Goyal2011) can be isolated from oral fluid specimens.

Statement of the problem

In both basic research and diagnostic medicine, the repeatability of quantitative polymerase chain reaction (qPCR) testing is affected by the variation introduced at any point between sample collection and the final test report (Heid et al., Reference Heid, Stevens, Livak and Williams1996; Klein, Reference Klein2002; Hoorfar et al., Reference Hoorfar, Malorny, Abdulmawjood, Cook, Wagner and Fach2004). Ideally, proper controls can be used to verify the integrity of the process accounting for variation. Internal controls that were extracted or amplified concurrently with test samples verify that the procedure was performed correctly and functioned within expected parameters. In addition, external positive amplification controls (template control) containing fixed quantities of purified PCR target nucleic acids may be used to identify run-to-run variation, e.g. concentration of reagents, qPCR profiles, instrument settings. In contrast, external negative amplification controls (non-template controls) are used to detect reagent contamination.

Internal controls are nucleic acids that are either inherent to the specimen matrix (endogenous reference genes) or added (‘spiked’) into test samples (exogenous reference genes) prior to nucleic acid extraction. Importantly, qPCR results can be ‘normalized’ in the context of internal control results to compensate for variation arising from the initial sample nucleic acid quantity and/or concentration, differences among reverse transcription and amplification efficiencies, assay protocols, and/or instrument settings (Vandesompele et al., Reference Vandesompele, De Preter, Pattyn, Poppe, Van Roy, De Paepe and Speleman2002; Bustin and Nolan, Reference Bustin and Nolan2004; Huggett et al., Reference Huggett, Dheda, Bustin and Zumla2005; Bustin et al., Reference Bustin, Benes, Garson, Hellemans, Huggett, Kubista, Mueller, Nolan, Pfaffl, Shipley, Vandesompele and Wittwer2009; Biassoni and Raso, Reference Biassoni and Raso2014). A number of qPCR normalization-compatible internal reference genes have been described for diagnostic matrices in human medicine, e.g. reticulocytes, keratinocytes, oral fluids, bronchoalveolar lavage fluids, tissue samples (Glare et al., Reference Glare, Divjak, Bailey and Walters2002; Silver et al., Reference Silver, Best, Jiang and Thein2006; Bar et al., Reference Bar, Bar and Lehmann2009; Chervoneva et al., Reference Chervoneva, Li, Schulz, Croker, Wilson, Waldman and Hyslop2010; Koppelkamm et al., Reference Koppelkamm, Vennemann, Fracasso, Lutz-Bonengel, Schmidt and Heinrich2010; Martin, Reference Martin2016). In contrast, the use of internal reference genes is less frequently reported in veterinary research, perhaps because of the diversity of specimens and animal species (McIntosh et al., Reference Mcintosh, Tumber, Harding, Krakowka, Ellis and Hill2009; Pol et al., Reference Pol, Deblanc, Oger, Le Dimna, Simon and Le Potier2013; Yan et al., Reference Yan, Toohey-Kurth, Crossley, Bai, Glaser, Tallmadge and Goodman2020). Therefore, the objective of this review is to compare the use of internal reference genes reported in recent human and veterinary qPCR research involving the oral fluid matrix.

Inherent variations in real-time PCR

Although real-time PCR has been used to precisely quantify molecular substances, the data should be interpreted with caution because of the introduction of variations throughout the process. PCR results are typically reported as quantitation cycles (C _q), i.e. the number of cycles required for the cumulative fluorescent intensity to meet a pre-determined threshold (Schmittgen and Livak, Reference Schmittgen and Livak2008; Rao et al., Reference Rao, Huang, Zhou and Lin2013). In general terms, samples with a higher initial concentration of target DNA/RNA will require fewer PCR amplification cycles to reach the threshold than those with a lower initial concentration (Schmittgen and Livak, Reference Schmittgen and Livak2008). However, in the laboratory, the C _q of any given sample may be affected by extraneous factors, e.g. technicians' proficiency, test protocols, reagents, PCR conditions, and instruments (Johnson et al., Reference Johnson, Nolan, Bustin and Wilks2013; Kralik and Ricchi, Reference Kralik and Ricchi2017). For example, a recent study concluded the process of collecting nasopharyngeal swabs was a significant source of variability and could produce false-negative results in a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) real-time PCR assay (Basso et al., Reference Basso, Aita, Navaglia, Franchin, Fioretto, Moz, Bozzato, Zambon, Martin, Dal Pra, Crisanti and Plebani2020). To address the problem of variability introduced by extraneous factors, results can be expressed as the DNA/RNA copy number in the sample (absolute quantification) or expressed as the difference in target DNA/RNA (relative quantification) relative to known negative samples (Klein, Reference Klein2002; Schmittgen and Livak, Reference Schmittgen and Livak2008; Kralik and Ricchi, Reference Kralik and Ricchi2017).

Real-time PCR quantification

Absolute quantification converts a C _q result to DNA/RNA copy number using either digital qPCR or absolute standard curves. Digital qPCR is done by partitioning a sample into subsamples and then performing qPCR separately on each subsample. Thereafter, the distribution and proportion of subsamples containing molecules of interest are used to estimate the number of DNA/RNA copies based on the Poisson distribution (Dube et al., Reference Dube, Qin and Ramakrishnan2008; Huggett et al., Reference Huggett, Foy, Benes, Emslie, Garson, Haynes, Hellemans, Kubista, Mueller and Nolan2013). Alternatively, absolute quantification based on standard curves uses the relationship between the sample C _q and known concentrations of DNA/RNA to interpolate the concentration of target in the sample. Absolute standard curves are typically established by generating C _q results of serially diluted standards with known copy numbers of target DNA/RNA.

However, identifying the change of targets in unknown samples relative to negative calibrators may be sufficient for disease surveillance and diagnostic medicine (Livak and Schmittgen, Reference Livak and Schmittgen2001). Relative quantification of qPCR data may be achieved through two approaches: the relative standard curve and the comparative C _q (Liu and Saint, Reference Liu and Saint2002). Relative standard curves use methods similar to absolute standard curves except the standards do not have known DNA/RNA copy numbers. Instead, relative standard curves describe the relationship between C _q values and the mass of total DNA/RNA for each dilution. The sample C _q result can then be interpreted in the context of the relative standard curve. Because both absolute and relative quantification require that standard curves for targets and references be generated in each PCR run to account for run-to-run variation, a comparative C _q method (ΔΔC _q, pronounced ‘double delta C _q’), has been used in gene expression studies (Livak and Schmittgen, Reference Livak and Schmittgen2001; Pfaffl, Reference Pfaffl2001). This method quantifies the expression of a target gene in a treated sample relative to an untreated calibrator in terms of the fold change in gene expression (Rao et al., Reference Rao, Huang, Zhou and Lin2013). Conveniently, the treated sample and untreated calibrator can be collected at different time points, may be derived from different tissues, or obtained from individuals in different treatment groups (Rao et al., Reference Rao, Huang, Zhou and Lin2013). Unlike standard curve methods, the comparative C _q method eliminates the need to generate standard curves in each PCR run and, therefore, may be used in high-throughput molecular laboratories performing routine disease diagnostic and surveillance testing.

Real-time PCR data normalization

Data normalization is a statistical procedure designed to control variations introduced in the sampling/testing process and to ensure that results are comparable within and between laboratories (Bylesjö et al., Reference Bylesjö, Cloarec, Rantalainen, Brown, Tauler and Walczak2009; Biassoni and Raso, Reference Biassoni and Raso2014; Filzmoser and Walczak, Reference Filzmoser and Walczak2014). For example, Dahdouh et al. (Reference Dahdouh, Lazaro-Perona, Romero-Gomez, Mingorance and Garcia-Rodriguez2020) contended that direct estimation of SARS-CoV-2 viral load based on raw C _qs could neglect variation introduced during the sample collection process, e.g. patient tolerance to nasal swabbing, and concluded that the normalization of raw C _qs against marker nucleic acid genes inherent to sampled cell masses or mucosal surfaces should be implemented to ensure the comparability of coronavirus disease 2019 (COVID-19) qPCRs (Dahdouh et al., Reference Dahdouh, Lazaro-Perona, Romero-Gomez, Mingorance and Garcia-Rodriguez2020; Walsh et al., Reference Walsh, Jordan, Clyne, Rohde, Drummond, Byrne, Ahern, Carty, O'brien, O'murchu, O'neill, Smith, Ryan and Harrington2020).

Three methods commonly used for PCR normalization include consistently testing the same amount (mass) of sample, measuring total RNA/DNA, or using endogenous/exogenous reference genes (Huggett et al., Reference Huggett, Dheda, Bustin and Zumla2005):

1. (1) Testing the same amount of sample is standard practice in molecular and diagnostic laboratories that use standardized protocols, albeit the concentration of detectable target in clinical samples is still affected by sample collection, storage, and handling and, therefore, may not fully represent the initial concentration.

2. (2) Normalizing qPCR results against the total RNA/DNA content in sample extracts, i.e. prior to PCR, is a more precise approach for controlling sample-to-sample variation (Wang et al., Reference Wang, Zhao, Li, Liu, Ernst, Liu, Liu, Xi and Lei2015). Quantification of total RNA/DNA can be achieved by spectrophotometrically measuring the optical absorbance (OD₂₆₀) or the fluorescence of dyes that are randomly bound to nucleic acids of the extracted sample (Jones et al., Reference Jones, Yue, Cheung and Singer1998; Green and Sambrook, Reference Green, Sambrook, Green and Sambrook2018). However, using total DNA/RNA for data normalization assumes that the efficiency of reverse-transcription and PCR amplification is identical for each sample, i.e. does not take sample-to-sample variation into account (Bustin, Reference Bustin2002).

3. (3) The most common approach for qPCR data normalization is to express the C _q of target DNA/RNA in the context of the C _q of one or more reference genes (Wittwer et al., Reference Wittwer, Herrmann, Moss and Rasmussen1997). To serve this purpose, reference genes must have genetic sequences that differ from the target and be present at predictable concentrations in the sample (Vandesompele et al., Reference Vandesompele, De Preter, Pattyn, Poppe, Van Roy, De Paepe and Speleman2002; Huggett et al., Reference Huggett, Dheda, Bustin and Zumla2005; Bylesjö et al., Reference Bylesjö, Cloarec, Rantalainen, Brown, Tauler and Walczak2009; Guenin et al., Reference Guenin, Mauriat, Pelloux, Van Wuytswinkel, Bellini and Gutierrez2009). Pfaffl (Reference Pfaffl2001) proposed an approach that integrated data normalization and qPCR relative quantification using test sample and negative calibrator results (Equation 1). This method calculates the target-to-reference ratio (R) of the C _q difference between a sample and a calibrator (ΔC _q) while taking PCR amplification efficiencies for target (E _target) and reference (E _ref) sequences into account (Pfaffl, Reference Pfaffl2001):

   (1)$$R = \displaystyle{{{( {E_{{\rm target}}} ) }^{\Delta C_{\rm q}_{_{{\rm target\;}( {{\rm calibrator}-{\rm sample}} ) } } }} \over {{( {E_{{\rm ref}}} ) }^{\Delta C_{\rm q}_{_{{\rm ref\;}( {{\rm calibrator}-{\rm sample}} ) } } }}}$$

In gene expression studies, samples collected from individuals with no treatment, or prior to treatment, may be used as negative calibrators and/or as a baseline relative to the expression/detection level of target genes in samples from treated individuals. Therefore, the relative quantity of a target gene in a treated sample is expressed as the fold change relative to an untreated calibrator, using a reference gene as a normalizer (Rao et al., Reference Rao, Huang, Zhou and Lin2013).

Both exogenous and endogenous reference genes have been used for data normalization at the individual sample level (Ke et al., Reference Ke, Chen and Yung2000). Exogenous reference genes are artificially synthesized nucleic acids with genetic sequences distinct from the target's (Huggett et al., Reference Huggett, Dheda, Bustin and Zumla2005). These heterologous genes may be spiked into test samples prior to the DNA/RNA extraction procedure at a fixed copy number or concentration (Yan et al., Reference Yan, Toohey-Kurth, Crossley, Bai, Glaser, Tallmadge and Goodman2020) to monitor the efficiency of DNA/RNA extraction and the integrity of reverse transcription and PCR amplification in test samples (Guenin et al., Reference Guenin, Mauriat, Pelloux, Van Wuytswinkel, Bellini and Gutierrez2009; Johnston et al., Reference Johnston, Gallaher and Czaja2012). In contrast, endogenous reference genes are host-specific nucleic acids inherent to the specimen (Yan et al., Reference Yan, Toohey-Kurth, Crossley, Bai, Glaser, Tallmadge and Goodman2020). Since endogenous reference genes are processed concurrently with target DNA/RNA, the detection of these genes reflects both the sample-to-sample variation in the quantity and quality of initial amplifiable DNA/RNA and the variation introduced by the extraction and amplification procedures (Radonic et al., Reference Radonic, Thulke, Mackay, Landt, Siegert and Nitsche2004).

Internal reference genes in oral fluids

Endogenous reference genes have been widely used in gene expression analyses for the purpose of representing sample nucleic acid concentration and as the gold standard for qPCR data normalization (Vandesompele et al., Reference Vandesompele, De Preter, Pattyn, Poppe, Van Roy, De Paepe and Speleman2002; Bustin et al., Reference Bustin, Benes, Nolan and Pfaffl2005; Huggett et al., Reference Huggett, Dheda, Bustin and Zumla2005). However, the expression of common reference genes depends on a variety of factors, e.g. cell/specimen types, sample quality and handling, age of subjects, animal species, and disease/treatment status (Zhong and Simons, Reference Zhong and Simons1999; Hamalainen et al., Reference Hamalainen, Tubman, Vikman, Kyrola, Ylikoski, Warrington and Lahesmaa2001; Selvey et al., Reference Selvey, Thompson, Matthaei, Lea, Irving and Griffiths2001; Deindl et al., Reference Deindl, Boengler, Van Royen and Schaper2002; Glare et al., Reference Glare, Divjak, Bailey and Walters2002). Thus, endogenous reference genes must be validated for their consistency of expression and/or detection in test specimens and under the conditions in which target genes will be evaluated (Mestdagh et al., Reference Mestdagh, Van Vlierberghe, De Weer, Muth, Westermann, Speleman and Vandesompele2009). Typically, this involves comparing the variation in endogenous gene C _qs in samples from subjects with potentially impactful biological characteristics, e.g. age, gender, and disease status (Huggett et al., Reference Huggett, Dheda, Bustin and Zumla2005; Robinson et al., Reference Robinson, Sutherland and Sutherland2007; Becker et al., Reference Becker, Hammerle-Fickinger, Riedmaier and Pfaffl2010).

In this review, qPCR-based gene expression and disease diagnostic studies were evaluated for the use of endogenous and/or exogenous reference standards in oral fluid specimens from non-human vertebrate and human subjects. Initially, the MEDLINE^® database was searched (title and abstract fields) on 24 October 2020 for refereed scientific publications containing the following searching terms: (‘saliva*’ or ‘oral fluid*’ or ‘oral swab*’) and (‘qpcr*’ or ‘quantitative pcr*’ or ‘real time pcr*’ or ‘real-time pcr*’ or ‘realtime pcr’ or ‘RT-qPCR’ or ‘qRT-PCR’ or ‘real time RT-PCR’ or ‘real-time RT-PCR’ or ‘realtime RT-PCR’) not (review[Publication Type]). Articles were excluded if not written in English, if not applicable to non-human vertebrate animals, if the oral fluid specimen was not collected by insertion of an absorptive collector into the mouth (Atkinson et al., Reference Atkinson, Dawes, Ericson, Fox, Gandara, Malamud, Mandel, Navazesh and Tabak1993), or if only components of oral fluids, e.g. microorganisms, biofilms, salivary extracellular vesicles, were evaluated. The remaining publications were evaluated for the use of internal endogenous and/or exogenous reference genes. A total of 1566 articles were retrieved from MEDLINE^®. For the period 2003–2020, 136 met the language, research subject, and full-text criteria (Table 1). Among these, exogenous reference genes were used in 25.7% (35/136), endogenous reference genes in 27.2% (37/136), and 52.9% (72/136) did not include sufficient information on the use of internal reference genes.

Table 1. Frequency of reference genes reported in published qPCR studies using the oral fluid matrix

ACTB, β-actin; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; rRNAs, 5S, 18S, 28S ribosomal RNAs.

^a Buffalo, cattle, deer, goat, and sheep.

^b Chipmunk, gerbil, guinea pig, mongoose, mouse, rabbit, rat, shrew, squirrel, and vole.

^c Bat, elephant, horse, opossum, poultry, skunk, turtle, and weasel.

^d Endogenous reference genes with frequency < 5 pooled.

A similar strategy was used to retrieve oral fluid-based qPCR studies on human subjects from the MEDLINE^® database for the articles published between 2016 and 2020. Among the 772 articles retrieved, 184 met the language, species, and content criteria (Table 1).

Exogenous reference genes were used in 14.0% of reviewed studies (25/179), endogenous reference genes in 31.8% (57/179), and 57.0% (102/179) of the studies did not include sufficient information on the use of internal reference genes. As shown in Table 1, β-actin (ACTB) mRNA, ribosomal RNAs (18S and 28S rRNA), and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) mRNA, respectively, were the most frequently used endogenous reference genes in published studies on non-human vertebrates. In human studies, ACTB mRNA, GAPDH mRNA, and U6 small nuclear RNA (snRNA) were the most commonly reported.