A Basic Guide to Real Time PCR in Microbial Diagnostics

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  REVIEW published: 02 February 2017doi: 10.3389/fmicb.2017.00108Frontiers in Microbiology | www.frontiersin.org  1  February 2017 | Volume 8 | Article 108  Edited by:  Jean-christophe Augustin,Ecole Nationale Vétérinaire d’Alfort,France  Reviewed by: Silvana Vero,University of the Republic, Uruguay Bastien Fremaux,French Pork and Pig Institute, France *Correspondence: Petr Kralik  kralik@vri.cz  Specialty section: This article was submitted toFood Microbiology, a section of the journal Frontiers in Microbiology   Received:  31 October 2016  Accepted:  16 January 2017   Published:  02 February 2017  Citation: Kralik P and Ricchi M (2017) A BasicGuide to Real Time PCR in Microbial Diagnostics: Definitions, Parameters, and Everything.Front. Microbiol. 8:108.doi: 10.3389/fmicb.2017.00108  A Basic Guide to Real Time PCR inMicrobial Diagnostics: Definitions,Parameters, and Everything Petr Kralik  1 *  and  Matteo Ricchi   2 1 Department of Food and Feed Safety, Veterinary Research Institute, Brno, Czechia,  2 Istituto Zooprofilattico Sperimentaledella Lombardia e dell’Emilia Romagna “Bruno Ubertini,” National Reference Centre for Paratuberculosis, Piacenza, Italy  Real time PCR (quantitative PCR, qPCR) is now a well-established method for thedetection, quantification, and typing of different microbial agents in the areas of clinicaland veterinary diagnostics and food safety. Although the concept of PCR is relativelysimple, there are specific issues in qPCR that developers and users of this technologymust bear in mind. These include the use of correct terminology and definitions,understanding of the principle of PCR, difficulties with interpretation and presentation of data, the limitations of qPCR in different areas of microbial diagnostics and parametersimportant for the description of qPCR performance. It is not our intention in this reviewto describe every single aspect of qPCR design, optimization, and validation; however,it is our hope that this basic guide will help to orient beginners and users of qPCR in theuse of this powerful technique. Keywords: quantitative PCR, limit of detection, limit of quantification, accuracy, precision, trueness INTRODUCTION The phrase “Polymerase chain reaction” (PCR) was first used more than 30 years ago in a paperdescribing a novel enzymatic amplification of DNA (Saiki et al., 1985). The first applications of  PCR were rather unpractical due to the usage of thermolabile Klenow fragment for amplification,which needed to be added to the reaction after each denaturation step. The crucial innovationwhich enabled routine usage of PCR was utilization of thermostable polymerase from  Thermusaquaticus  (Saiki et al., 1988). This improvement, together with the availability of PCR cyclers andchemical components, led to the worldwide recognition of PCR as the tool of choice for the specificenzymatic amplification of DNA  in vitro . It must be noted that the general concept of PCR, whichincludes primers, DNA polymerase, nucleotides, specific ions, and DNA template, and consistingof cycles that comprise steps of DNA denaturation, primer annealing, and extension, have not beenchanged since 1985. The invention of PCR has greatly boosted research in various areas of biology and this technology has significantly contributed to the current level of human knowledge in many spheres of research.The most substantial milestone in PCR utilization was the introduction of the concept of monitoring DNA amplification in real time through monitoring of fluorescence (Holland et al.,1991;Higuchietal.,1992).InrealtimePCR(alsodenotedasquantitativePCR—qPCR;usageofRT-PCR is inappropriate as this abbreviation is dedicated to reverse transcription PCR), fluorescenceis measured after each cycle and the intensity of the fluorescent signal reflects the momentary amount of DNA amplicons in the sample at that specific time. In initial cycles the fluorescenceis too low to be distinguishable from the background. However, the point at which the fluorescence  Kralik and Ricchi A Basic Guide to Real Time PCR intensity increases above the detectable level correspondsproportionally to the initial number of template DNA moleculesin the sample. This point is called the quantification cycle(C q ; different manufactures of qPCR instruments use their ownterminology, but since 2009, the term C q  is used exclusively)and allows determination of the absolute quantity of target DNAin the sample according to a calibration curve constructed of serially diluted standard samples (usually decimal dilutions) withknown concentrations or copy numbers (Yang and Rothman,2004; Kubista et al., 2006; Bustin et al., 2009).Moreover, qPCR can also provide semi-quantitative resultswithout standards but with controls used as a reference material.It this case, the observed results can be expressed as higher orlower multiples with reference to control. This application of qPCR has been extensively used for gene expressions studies(Bustin et al., 2009), but did not obtain the same success inmicrobiologyquantificationsinceitisunabletoproduceabsolutequantitative values.There are two strategies for the real time visualization of amplified DNA fragments—non-specific fluorescent DNA dyesand fluorescently labeled oligonucleotide probes. These twoapproaches were developed in parallel (Holland et al., 1991;Higuchi et al., 1992) and are used in pathogen detection;however, probe-based chemistry prevails. This is due to itshigher specificity mediated by the additional oligonucleotide—the probe—and the lower susceptibility to visualize non-specificPCR products, e.g., primer dimers (Bustin, 2000; Kubista et al., 2006).To fully understand the possibilities of qPCR in detectingand quantifying target DNA in samples it is essential to describethe mathematical principle of this method. The PCR is anexponential process where the number of DNA moleculestheoretically doubles after each cycle (if the efficiency of thereaction is 100%). More generally, the amplification reactionfollows this equation: N  n  =  N  0  ×  (1  +  E ) n (1)where N n  is the number of PCR amplicons after n cycles, N 0 is the initial number of template copies in the sample, E is thePCR efficiency that can assume values in the range from 0 to1 (0–100%) and n is number of cycles. In a scenario wherethere is initially one copy of the template in the reaction andPCR efficiency is 100%, it is possible to simplify the equation asfollows: N  n  =  2 n (2)If a calibration curve is run, usually 10-fold serial dilutions areused. The difference in C q  values between two 10-fold serialdilutions could be expressed as10 =  2 n (3)Then  n = 3.322. When E should be determined the (1) is startingpoint and the equation is E  =  10 − ( 1 n ) −  1 (4)If n is taken to be 3.322, then  E = 1, i.e., 100%.The PCR efficiency is therefore a significant factor for thequantification of the target DNA in unknown samples. Thereliability of the calibration curve in enabling quantification isthendeterminedbythespacingoftheserialdilutions.IftheLog 10 of the concentration or copy number of each standard is plottedagainst its C q  value ( Figure 1 ), the E can be derived from theregression equation describing the linear function:  y   =  kx   +  c  (5)Wherexandy,theconcentration/amountoftargetandC q  valuesrespectively, characterize the coordinates in the plot, k is theregression coefficient or slope and c is the intercept. Taking themodel regression equation from  Figure 1 , the slope is  − 3.322,which mean that  E = 100% according to (4). The intercept showsthe C q  value when one copy would be theoretically detected(Kubista et al., 2006; Johnson et al., 2013). The concentrationor amount of target nucleic acid in unknown samples is thencalculated according to the C q  value through Equation (5).From the definitions above it is evident that C q  values areinstrumental readings, and must be recalculated to values withspecific units, e.g., copies of organism, ng of DNA, variousconcentrations, etc., (Bustin et al., 2009; Johnson et al., 2013). However, referral to C q  values in scientific papers is widespreadand interpretations based on C q  values can lead to misleadingconclusions. Concentrations in qPCR are expressed in thelogarithmic scale ( Figure 1 ) and C q  differences between 10-foldserial dilutions are theoretically always 3.322 cycles. Therefore,although the numerical difference between C q  20 and 35 is rathernegligible, the difference in real numbers (copies, ng) is almostfive orders of magnitude (Log 10 ).This feature must be reflected in the subsequent calculations.For example, the coefficient of variation (CV, ratio betweenstandard deviation and mean) calculated from the C q  valuesand real numbers results in profoundly different results. Thesame applies for any statistical tests where C q  values are used,even for cases where the logarithm of C q  values is used for FIGURE 1 | Model calibration curve with the regression equation(characterized by the slope and intercept) and regression coefficient. Frontiers in Microbiology | www.frontiersin.org  2  February 2017 | Volume 8 | Article 108  Kralik and Ricchi A Basic Guide to Real Time PCR the normalization of data before the statistical evaluation. Thecorrect procedure should include initial recalculation to realnumbers followed by logarithmic transformation. PROS AND CONS OF USING qPCR INDETECTION AND QUANTIFICATION OFPATHOGENS Since PCR is capable of amplifying a specific fragment of DNA, it has been used in pathogen diagnostics. With theincreasing amount of sequencing data available, it is literally possible to design qPCR assays for every microorganism (groupsand subgroups of microorganisms, etc.) of interest. The mainadvantagesofqPCRarethatitprovidesfastandhigh-throughputdetectionandquantificationoftargetDNAsequencesindifferentmatrices. The lower time of amplification is facilitated by thesimultaneous amplification and visualization of newly formedDNA amplicons. Moreover, qPCR is safer in terms of avoidingcross contaminations because no further manipulation withsamples is required after the amplification. Other advantagesof qPCR include a wide dynamic range for quantification (7–8Log 10 ) and the multiplexing of amplification of several targetsinto a single reaction (Klein, 2002). The multiplexing option is essential for detection and quantification in diagnostic qPCR assaysthatrelyontheinclusionofinternalamplificationcontrols(Yang and Rothman, 2004; Kubista et al., 2006; Bustin et al.,2009).qPCR assays are used not only for the detection, but alsoto determine the presence of specific genes and alleles, e.g.,typing of strains and isolates, antimicrobial resistance profiling,toxin production, etc., However, the mere presence of genesresponsible for resistance to antimicrobial compounds or fungaltoxin production does not automatically mean their expressionor production. Therefore, although qPCR-based typing tests arefaster, their results should be correlated with phenotypic andbiochemical tests (Levin, 2012; Osei Sekyere et al., 2015). As for the microbial diagnostics, there are differentconsiderations in detecting and quantifying viral, bacterial,and parasitic agents. These considerations are based on thetarget (DNA or RNA), cultivability, interpretation of results, andclinical significance of qPCR results.qPCR plays an important role in the detection, quantification,and typing of viral pathogens. This is because detectionof important clinical and veterinary viruses using culturemethods is time-consuming or impossible, while ELISA testsare not universally available and suffer from comparatively low sensitivity and specificity. qPCR (with the inclusion of reversetranscription for the diagnostics of RNA viruses) provides theappropriate sensitivity and specificity  (Hoffmann et al., 2009). Moreover, determination of the viral load by (RT)-qPCR is usedas an indicator of the response to antiviral therapies (Watzingeret al., 2006). For these reasons (RT)-qPCR has become an indispensable tool in virus diagnostics (Yang and Rothman,2004).The situation is similar in the case of intestinal protozoandiagnostics (Rijsman et al., 2016). The gold standard technique for the detection of protozoan agents, the microscopicexamination of feces, is laborious, time-consuming, andrequires specifically trained personnel. Similarly, ELISA testingsuffers from low sensitivity and specificity (Rijsman et al.,2016). Therefore, qPCR is now emerging as a powerful tool inthe routine detection, quantification, and typing of intestinalparasitic protozoa.In contrast to viral and protozoan detection andquantification, many bacteria of clinical, veterinary, andfood safety significance, can be cultured. For this reason, cultureis considered as the gold standard in bacterial detection andquantification. However, in cases when critical and timely intervention for infectious disease is required, the traditional,slow, and multistep culture techniques cannot provide resultsin a reasonable time. This limitation is compounded by thenecessity of culturing fastidious pathogens and additional testing(species determination, identification of virulence factors, andantimicrobial resistance). qPCR is capable of providing therequired information in a short time; however, the phenotypicand biochemical features must be confirmed from bacterialisolates (Yang and Rothman, 2004).In food safety, all international standards for food quality rely on the determination of pathogenic microorganisms usingtraditional culture methods. qPCR techniques represent anexcellent alternative to existing standard culture methods asthey enable reliable detection and quantification (for severalpathogens) and harbor many other advantages as discussedabove. However, there are limitations with respect to thesensitivity of assays based on qPCR. As culture methods rely on the multiplication of bacteria during the pre-culture steps(pre-enrichment), samples for DNA isolation usually initially contain very low numbers of target bacteria (Rodriguez-Lazaroet al., 2013). This limitation leads to the most importantdisadvantage of qPCR, which is its inherent incapability of distinguishing between live and dead cells. The usage of qPCR itself is therefore limited to the typing of bacterial strains,identification of antimicrobial resistance, detection, and possibly quantification in non-processed and raw food. It is important tonote that processed food can still contain amplifiable DNA evenif all the potentially pathogenic bacteria in food are devitalizedand the foodstuff is microbiologically safe for consumption(Rodriguez-Lazaroetal.,2013).Toovercomethisproblem,apre- enrichment of sample in culture media could be placed priorto the qPCR. This step may include non-selective enrichmentin buffered peptone water or specific selective media for therespective bacterium. This procedure is primarily intendedto allow resuscitation/recovery and subsequent multiplicationof the bacteria for the downstream qPCR detection; thesecond advantage is dilution and elimination of possible PCR inhibitorspresentintothesample(presenceofsalts,conservationsubstances, etc.). The extraction of the DNA from the culturemedia is easier than that from the food samples, which are muchmore heterogeneous in terms of composition (Margot et al.,2015).Although qPCR itself cannot distinguish among viable anddead cells attempts have been made to adapt qPCR for viability detection. It was shown that RNA has low stability and should be Frontiers in Microbiology | www.frontiersin.org  3  February 2017 | Volume 8 | Article 108  Kralik and Ricchi A Basic Guide to Real Time PCR degraded in dead cells within minutes. However, the correlationofcellviabilitywiththepersistenceofnucleicacidspeciesmustbewell characterized for a particular situation before an appropriateamplification-based analytical method can be adopted as asurrogate for more traditional culture techniques (Birch et al.,2001). Moreover, difficulties connected with RNA isolation fromsamples like food, feces or environmental samples can providefalse-negative results especially when low numbers of target cellsare expected.Another option for determination of viability using qPCR isthe deployment of intercalating fluorescent dyes like propidiummonoazide (PMA) and ethidium monoazide (EMA; Nocker andCamper, 2009). In these methods, the criterion for viability determination is membrane integrity. Metabolically active cells(regardless of their cultivability) with full membrane integrity keep the dyes outside the cells and are therefore considered as viable. However, if plasma membrane integrity is compromised,the dyes penetrate the cells, or react with the DNA outsideof dead cells. The labeled DNA is then not available for theamplification by qPCR and the difference between treated anduntreated cells provides information about the proportion of  viable cells in the sample. The limitation of this method is thenecessitytohavethecellsinalight-transparentmatrix,e.g.,watersamples, cell cultures, etc., as the intercalation of the dye to DNArequires exposure to light. Therefore, samples of insufficient lighttransparency do not permit the application of these dyes. Thereis a preference for PMA over EMA, as it was shown that EMApenetrates the membranes of live bacterial cells (Nocker et al.,2006).Moreover, another topic we want to just to mention here isthe generation and use of standards required for the calibrationcurves. In general, two are the most diffused approaches forthe generation of calibration curves. One employs dilutions of target genomic nucleic acid and the other plasmid standards.Both strategies can lead to a final quantification of the target,but plasmids containing specific target sequences offer theadvantages of easy production, stability, and cheapness. Onthe other hand, in principle, PCR efficiency obtained by plasmid standards sometimes could differ compared to theefficiency obtained using genomic standard, which instead, fororganisms fastidious to growth, could be isolated only startingfrom a given matrix, and thus susceptible to degradation andlosses (Chaouachi et al., 2013). Finally, the production and  validation of international quantification standards for qPCR assaysistechnicallydemandingandthesestandardsarecurrently available only for a few targets (Pavšiˇc et al., 2015). qPCR PARAMETERS IN MICROBIALDETECTION AND QUANTIFICATION Analytical Specificity (Selectivity) This parameter in qPCR refers to the specificity of primersfor target of interest. Analytical specificity consists of twoconcepts: inclusivity describes the ability of the method todetect a wide range of targets with defined relatedness e.g.,taxonomic, immunological, genetic composition (Anonymous, 2009, 2015a). Another definition describes inclusivity as thestrains or isolates of the target analyte(s) that the method candetect (Anonymous, 2012). ISO 16140 and other standardsrecommendthatinclusivityshouldbedeterminedon20–50well-defined (certified) strains of the target organism (Anonymous,2009, 2011, 2012, 2015a; Broeders et al., 2014), or for  Salmonella ,it is recommended that 100 serovars should be included forinclusivity testing (Anonymous, 2012).On the other hand, exclusivity describes the ability of themethod to distinguish the target from similar but genetically distinct non-targets. In other words, exclusivity can also bedefined as the lack of interference from a relevant range of non-target strains, which are potentially cross-reactive (Anonymous,2009, 2011, 2012, 2015a). The desirable number of positivesamples in exclusivity testing is zero (Johnson et al., 2013).  Analytical Sensitivity (Limit of Detection,LOD) Many official documents have discussed theories and proceduresfor the correct definition of the LOD for different methods. Ageneral consensus was reached around the definition of the LODas the lowest amount of analyte, which can be detected withmore than a stated percentage of confidence, but, not necessarily quantified as an exact value (Anonymous, 2011, 2013, 2014). Inthis regard, the confidence level obtained or requested for thedefinition of LOD can reflect the number of replicates (bothtechnical and experimental) needed by the assay in order toreach the requested level of confidence (e.g., 95%). It is clear thatthe more replicates are tested, the narrower will be the intervalof confidence. Another definition describes the LOD as thelowest concentration level that can be determined as statistically differentfromablankataspecifiedlevelofconfidence.Thisvalueshould be determined from the analysis of sample blanks andsamples at levels near the expected LOD (Anonymous, 2015a). However, it should be noted that LOD definitions describedabove were reported for chemical methods, and are not perfectly suited for PCR methods (Burns and Valdivia, 2008). This is because, for limited concentrations of analyte (nucleic acids),the output of the reaction can be a success (amplification), ora failure (no amplification at all), without any blank, or criticallevel at which it is possible to set a cut-off value over which thesample can be considered as positive one. Moreover, it should berememberedherethat,bydefinition,ablanksampleshouldneverbe positive in PCR.Since the definitions reported above are not practicable forPCRs, other approaches have been proposed. A conservativeapproach is to consider the LOD value as the minimumconcentration of nucleic acid or number of cells, which alwaysgives a positive PCR result in all replicates tested, or in the majorpart (over 95%) of them (Nutz et al., 2011). In practice, multiple aliquots of a specific matrix are spiked with serial dilutions of the target organism and undergo the whole process of nucleicacid isolation and qPCR. The LOD is then defined as the spikeamount of target organism in dilution that could be detected in95%ofreplicates.Forexample,10replicatesofmilksampleswerespikedwithserialdilutionsof  Campylobacterjejuni inamountsof  Frontiers in Microbiology | www.frontiersin.org  4  February 2017 | Volume 8 | Article 108
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