development of quantitative pcr detection methods …

SUMMARY

In recent years quantitative PCR (qPCR) detectionmethods have been widely utilised to detect phytopath-ogenic fungi and oomycetes and have greatly con-tributed to the advancement of knowledge in plantpathology. However, major drawbacks and common er-rors, most typical of earlier reports, still affect manymethods currently available in the literature. Errors canbe made throughout the entire process for the develop-ment of qPCR methods, at the level of selection of ap-propriate DNA extraction and purification protocols,identification of suitable target regions, choice of thechemistry, design and validation of specific primers andprobes, analysis of sensitivity, choice of an absoluteand/or relative quantification approach and analysis ofthe risk of detecting target DNA from dead sources. Inthe present review the above mentioned steps areanalysed, highlighting their critical aspects and provid-ing a practical guide for the users.

Key words: qPCR, fungi, oomycetes, detection, quan-titative analyses.

INTRODUCTION

The development of real-time quantitative amplifica-tion technologies (qPCR) in the early 1990s has revolu-tionized basic and applied studies in all biological fields,including the detection of plant pathogens. There areseveral significant advantages of PCR-based detectionover the traditional methods of diagnosis, i.e microrgan-isms do not need to be cultured, even single target mol-ecules can be potentially detected in complex environ-ments and analyses are rapid and versatile. Further-more, qPCR enables the detection of amplicons through

Corresponding author: L. SchenaFax: +39.096.5312827E-mail: [email protected]

a specific fluorescent signal, thus eliminating the post-amplification processing steps needed in conventionalPCR (cPCR). This significantly reduces the time andcost of analyses and eliminates the use of harmful sub-stances like ethidium bromide, which is still utilized tostain DNA in electrophoretic gels. Health risks for op-erators and environmental contamination are reduced,and the credit of PCR testing as an automated diagnos-tic system suitable for large-scale applications increases.Moreover, qPCR is a versatile technique for the accu-rate, sensitive, reliable and high throughput quantifica-tion of target DNA in various environmental samples(Fig. 1).

Several reviews have been published in recent yearson the application of qPCR methods to detect plantpathogens (Schena et al., 2004; Okubara et al., 2005;Mumford et al., 2006; Cooke et al., 2007; Vincelli andTisserat, 2008; O’Brien et al., 2009; Bilodeau, 2011).However, methodological approaches have been rarelyanalysed in a comprehensive and critically way, so thatmajor drawbacks and common errors, more typical ofearlier reports, still affect protocols currently availablein literature. The aim of the present review is to providea comprehensive guide for the development of accurateqPCR methods through the detailed analysis of all as-pects that need to be considered to avoid errors (Fig. 2).The review focuses on filamentous fungi andoomycetes, which comprise major plant pathogens. Al-though these organisms represent some of the most dis-tantly related eukaryotes, they share several commonproblems related to their detection and quantification.Phylogenetic evidence has proven that multiple hori-zontal gene transfers have occurred from the ancestralfilamentous ascomycetes to the distantly relatedoomycetes, determining some similarity in both appear-ance and lifestyles (Richards et al., 2006).

EXTRACTION OF TARGET DNA

The extraction of target DNA remains one of themost challenging steps in the development of appropri-

Journal of Plant Pathology (2013), 95 (1), 7-24 Edizioni ETS Pisa, 2013 7

OFFERED REVIEW

DEVELOPMENT OF QUANTITATIVE PCR DETECTION METHODS FOR PHYTOPATHOGENIC FUNGI AND OOMYCETES

L. Schena1, M.G. Li Destri Nicosia1, S.M. Sanzani2, R. Faedda3, A. Ippolito2 and S.O. Cacciola3

1 Dipartimento di Agraria, Università Mediterranea di Reggio Calabria, Località Feo di Vito, 89124 Reggio Calabria, Italy2 Dipartimento di Scienze del Suolo, della Pianta e degli Alimenti, Università degli Studi Aldo Moro,

Via Amendola 165/A, 70126 Bari, Italy3 Dipartimento di Gestione dei Sistemi Agroalimentari e Ambientali, Università degli Studi,

Via S. Sofia 100, 95123 Catania, Italy

001_JPP_Review Schena_7colore 19-03-2013 11:55 Pagina 7

8 Quantitative PCR detection of fungi and oomycetes Journal of Plant Pathology (2013), 95 (1), 7-24

ate qPCR methods. An ideal DNA extraction protocolshould enable the obtention of good quality DNA witha low concentration of substances inhibiting PCR reac-tions and, at the same time, should be cost-effective,rapid and as simple as possible to allow its large-scaleapplication by a broad range of operators. Furthermore,DNA extraction protocols should provide comparableresults with samples differing widely in physical andchemical composition, organic content, microbial popu-lations and so on.

The section below provides general informationabout the most frequently utilized procedures to extractDNA for qPCR analyses of fungi and oomycetes.

Soil samples. Although a large number of differentprotocols have been developed to extract DNA fromthe soil, they can be grouped into two main categories:direct and indirect methods (Robe et al., 2003). Thefirst approach consists in the direct extraction of DNAthrough in situ cell lysis followed by purification. Con-versely, indirect methods are based on the separation oftarget organisms from the soil particles followed by celllysis and DNA purification. Direct extraction methodsprovide higher yields but DNA of a smaller size.

Direct methods are by far the most utilized in qPCRprotocols to detect fungi and oomycetes from soil, sincethey provide the highest DNA yields within acceptableprocessing times and the shearing of target DNA is aminor problem considering the very limited size of am-plified fragments obtained with most qPCR methods.However, an indirect method based on flotation has re-

cently been developed to extract prokaryotic and eu-karyotic DNAs from soil which gives yields comparableto those of direct methods (Parachin et al., 2010).

Direct extraction methods can all be considered asslight modifications of the original procedure proposedby Ogram et al. (1987), since they share two main steps:(i) the disruption of microbial cell walls to release nucle-ic acids and (ii) the separation of nucleic acids from soilparticles. The physical disruption of cells is commonlyobtained by grinding soil samples in liquid nitrogen orby vigorously shaking them with a vortex or a beadbeater in the presence of stainless steel balls, glass-beadsor zirconia/silica beads. The extraction of nucleic acidscan be particularly difficult when resting spores need tobe detected. For example, in a recent study, several at-tempts were made to detect P. infestans in soils, butnone of the methods released DNA from oospores di-rectly from soil or from oospores artificially added tosoils or buffers (Lees et al., 2012). In some cases, alyophilization and/or sonication step was added to im-prove extractions (Cullen et al., 2001; Williams et al.,2009). A pressure-cycle technology proved useful in im-proving DNA extraction of from Rhizoctonia and Pythi-um species in the presence of low pathogen populationdensities (Okubara et al., 2007).

Most extraction buffers contain sodium dodecyl sul-phate (SDS) or cetyltrimethylammonium bromide(CTAB), but their choice and efficacy vary dependingon the substrate of extraction (Demeke and Jenkins,2010). The addition of substances such as the abovementioned CTAB (Zhou et al., 1996), polyvinyl-

Fig. 1. Relative frequency of published qPCR methods grouped according to the matrices in which fungal biocontrol agents orphytopathogenic fungi and oomycetes have been detected. Frequencies (%) were calculated on a database of 201 representativearticles (Supplementary data, Annex 1) published in ISI journals since 1999.


Journal of Plant Pathology (2013), 95 (1), 7-24 Schena et al. 9

polypyrrolidone (PVPP) (Cullen et al., 2001), sodiumascorbate (Holben et al., 1988) or skim milk (Volos-siouk et al., 1995) to the soil slurry before cell lysis, canfavour the removal of humic compounds. Moreover,proteinase K is often used to digest contaminating pro-teins, especially in fungal DNA extractions (Zhang etal., 2005; Kernaghan et al., 2007). Once nucleic acidsare released and separated from soil particles, they areextracted with one or more organic solvents (mainlyphenol and chloroform), then concentrated byalcohol/salt precipitation according to standard proce-dures (Sambrook and Russell, 2011).

Since soil is a very complex matrix, many extractionprotocols include a further purification step. Cesiumchloride (CsCl) density gradient centrifugation is a veryeffective but inappropriate method for use with multi-ple samples because of its complexity and the long pro-cessing time (Ogram et al., 1987). More widely utilizedare DNA-binding columns based on the use of a varietyof gel filtration resins (Miller, 2001). These devices aremore effective for removing humic contaminants whenPVPP is combined with gel filtration resins (Ruano-Rosa et al., 2007). Columns can be manually packed inthe laboratory (Schena and Ippolito, 2003).

In recent years, commercial kits have increasinglybeen used for DNA extraction and purification fromsoils and sediments. Examples are: (i) the UltraClean™kit (Mo Bio Laboratories, USA) where cells are lysed bymechanical (bead beating) and chemical methods andthe extracted DNA is purified using a silica-based spincolumn (van Gent-Pelzer et al., 2010); (ii) the FastDNASpin kit for soil (MP Biomedicals, USA), designed witha dedicated FastPrep instrument (MP Biomedicals) forthe mechanical lysis of cells by homogenization with amixture of ceramic and silica particles, with a purifica-tion of the extracted DNA by a specific spin column(www.mpbio.com) (Luo et al., 2009); (iii) the rapid ex-traction kit for soil genomic DNA (BioTeke, China)used for recovering Thielaviopsis basicola DNA (Huangand Kang, 2010); (iv) the Wizard™ Magnetic DNA pu-rification System (Promega, USA), created for food sub-strates but utilized also for extracting Rhizoctonia solaninucleic acids from soil (Budge et al., 2009).

Specific reports have focused on the comparison ofDNA extraction efficiency of laboratory-devised proto-cols and commercial kits in terms of DNA yield and pu-rity, but results are often conflicting due to the extremeheterogeneity of soil samples (Mumy and Findlay, 2004;Rajendhran and Gunasekaran, 2008). It was suggestedthat, depending on the microorganism and the soil type,different combinations and modifications of lysis proto-cols may be needed (Jiang et al., 2011). Furthermore,most commercial kits were found inadequate for cal-careous soils, too time-consuming, and/or not robustenough for use on a wide range of soil types (Ophel-Keller et al., 2008). In general, since the protocol uti-

lized to extract DNA can markedly influence the esti-mation of soil microbial diversity, the use of a plethoraof different methods, each with its own bias, makes ac-curate data comparison difficult.

Apart from the extraction protocol, an important as-pect to be considered in DNA extraction from soil isthe representativeness of samples (Okubara et al.,2005). Propagules of soil-borne pathogens tend to benon-randomly distributed at small spatial scales, poten-tially leading to high levels of variation between samples(Rodríguez-Molina et al., 2000). High capacity directsoil extraction methods could increase the representa-tiveness of soil samples, since current techniques oftenwork on relatively small samples of less than one gramin total (Ophel-Keller et al., 2008; van Gent-Pelzer etal., 2010; Woodhall et al., 2012). Alternatively, sieving-centrifugation procedures can be utilized before extrac-tion to concentrate the pathogen from larger samples soas to increase sampling representativeness (Pavón et al.,2008). In addition to sample size, an appropriate sam-pling strategy and a congruous number of samples aremajor factors for a reliable soil-borne pathogen detec-tions (Okubara et al., 2005; Bilodeau, 2011).

Plant tissues. A vast range of methods is available forDNA isolation from plant material including very ardu-ous matrices such as bark, dry seeds, and pollen, butnone of these methods can be considered as universallyapplicable to all plant species (Varma et al., 2007). Theplant cell wall can make DNA extraction relatively diffi-cult although it greatly depends on plant material andorgan age. In particular, the extraction of DNA frommature organs as well as from many infected tissues isfrequently complicated by the presence of high concen-trations of polyphenols, tannins, and polysaccharides.

Most extraction methods are based on the physicaldisruption of tissues (by grinding in liquid nitrogenand/or bead beating) in association with chemical dis-ruption of cell components by means of cellulases,pectinases and carbohydrases. These enzymes releaselarge amounts of polysaccharides from the maceratedtissue that often co-precipitate with DNA. The use ofNaCl in combination with the cationic detergent CTABproved to be beneficial in DNA isolation from polysac-charide-rich plants since it forms insoluble complexeswith proteins and most acidic polysaccharides (Syamku-mar et al., 2003). Furthermore, polysaccharide-bindingresins can be utilized for host tissues containing an ex-cessive polysaccharide concentration, as in the case oftubers and seeds. The contamination with polyphenolscan be prevented by using antioxidants such as β-mer-captoethanol, bovine serum albumin (BSA), sodiumazide, ascorbic acid, dithiothreitol (DTT), sodium sul-fite, sodium iso-ascorbate, and so on (Chen and Ronald,1999; Michiels et al., 2002). Furthermore,polyvinylpyrrolidone (PVP) and PVPP are commonly



utilized to separate the polyphenolic compounds fromthe extracted DNA since they act as adsorbents of thesechemical compounds (Porebski et al., 1997). Other im-portant contaminating compounds during plant DNAextractions are RNAs and proteins that can be purgedusing DNase-free pancreatic RNase and chemicals likeSDS, DTT and β-mercaptoethanol, which destroy thestructural organization of proteins (Barnwell et al.,1998). Protein-hydrolyzing enzymes like proteinase Kare also suitable for the purpose as they break a varietyof peptide bonds and rapidly inactivate DNases andRNases in cell lysates, facilitating the isolation of highmolecular weight DNA (Aljanabi and Martinez, 1997).

A number of commercial kits, mainly based onDNA-binding silica columns, are also available to ex-tract DNA from plant tissues. In particular, widely usedkits are the DNeasy Plant kit (Qiagen Sciences, USA),the Nucleon Phytopure (Amersham Biosciences EuropeGmbH, Germany) and the Nucleospin Plant II® mini-prep and the Nucleo-Mag 96 Plant kit (Macherey-NagelGmbH, Germany). Some kits can also be processed in a96-well format, allowing large-scale analysis with in-creased throughput. Major advantages of commercialkits are their simplicity and rapidity together with theabsence of harmful chemical compounds, even thoughthey frequently are no cost effective and can be ineffi-cient when handling plants with high polysaccharide orhigh polyphenolic content (Michiels et al., 2002; Smithet al., 2005). Slight modifications of the protocols rec-ommended by the manufacturer are sometimes neces-sary to increase the performances. As an example, theaddition of PVP and a supplemental DNA purificationsteps removed putative inhibitors from soybean tissuesprocessed with the FastDN kit (Q-BIOgene/MP Bio-medicals, France) and improved the detection of thevascular fungal pathogen Phialophora gregata f. sp. sojae(Malvick and Grunden, 2005).

Air samples. When fungal propagules have to be de-tected in air samples, the low concentration of targetmolecules is a major difficulty to be faced due to smalldifferences between aerosol samples and concentrationstypically hovering near the method detection limit(Hospodsky et al., 2010). A wide variety of devicesbased on the use of trapping surfaces (wax/grease coat-ed tape, filter, agar or glass slides, etc.) are available forcollecting bio-aerosol samples (Jackson and Bayliss,2011). In particular, gelatin filters are appropriate forcollecting air-borne fungal spores for molecular analysessince they are soluble in the DNA extraction buffer,thus avoiding additional steps which are instead neces-sary when other type of filters (e.g. polycarbonate orglass filters) are utilized. Once the spores have beentrapped and collected in a microcentrifuge tube, con-ventional DNA extraction procedures, including com-mercial kits, can be utilized. However, many aspects

need to be optimized according to the characteristics ofthe sample (fungal species, type of spores, environment,etc.) (Yamamoto et al., 2010). Furthermore, consideringthe low concentration of target DNA the addition of ex-ogenous DNA (e.g. salmon sperm DNA) as carriercould be beneficial (Carisse et al., 2009).

As for other kinds of matrices, non-target particlesand non-biological particles (mostly air pollutants) caninfluence accuracy, precision, and detection limits ofqPCR approaches. It has been demonstrated that ex-traction of DNA from cells and PCR inhibition aresome of the most important variables that influenceqPCR analysis of microorganisms collected from the air(Hospodsky et al., 2010). As a consequence, the use ofinternal controls is extremely important for quantitativeanalyses of airborne pathogens.

Water samples. Two main strategies, centrifugationand filtration, can be utilized to collect microorganismpropagules from water samples for subsequent DNAextraction. In a comparative assay for detection of Phy-tophthora species an equal performance was obtainedwith both methods, but filtration was more rapid andsimple and enabled the on-field sampling in remotelocations (Scibetta et al., 2012). The size of the filterdepends on that of the microorganism to be detected(Scibetta et al., 2012). Once propagules have been col-lected, common soil DNA extraction protocols can beutilized. Some filters can directly be subjected to me-chanical and chemical treatment in order to disrupt thecells and to release nucleic acids. A number of filtration-based approaches can be utilized for both aerosol andwater samples (Haugland et al., 2002; Paez-Rubio et al.,2005; Hospodsky et al., 2010).

IDENTIFICATION OF TARGET GENES

A crucial step in qPCR assay development is theidentification of appropriate target DNA regions. Agood target gene should be sufficiently variable to en-able the differentiation of closely related species but, atthe same time, should not contain intraspecific varia-tion that would jeopardize the detection of all strains.A single optimal target gene for all phytopathogenicfungi and oomycetes does not exist and, therefore,compromises are required. For some assays, a quiteconserved target region is critical to enable the designof generic primers that amplify target DNA from agroup of species (e.g. all species of a genus), whereasfor other assays the requirement may be to differentiatelineages of a single species. Furthermore, the presenceof non-orthologous or duplicated divergent alleles cancomplicate the use of specific genes as appropriate tar-gets (Geiser et al., 2004). A good target gene shouldreadily be amplified and sequenced and, ideally, multi-



copied to enable the development of sensitive detectionmethods. However, the use of multicopy genes is notalways preferable since the variable number of copiescan potentially complicate the development of quanti-tative assays because diverse strains of the pathogencan be characterized by a different number of repeatunits present in the nuclear genome. For instance, Ver-ticillium dahliae ribosomal DNA (rDNA) was estimat-ed to range from ca. 24 to 73 copies per haploidgenome by comparing quantification cycles (Cq) ofmultiple and single-copy genes (Bilodeau et al., 2012).Since single-copy genes are not affected by the numberof tandem repeats, there is the potential to more accu-rately correlate Cq values with the quantity ofpathogen. Furthermore, it has been reported that single

pathogen propagules can also be detected with single-copy gene, providing that DNA extraction protocolsand amplification conditions are optimized (Schena etal., 2006).

Internal transcribed spacer (ITS) region. The inter-nal transcribed spacer (ITS) regions includes the ITS1and ITS2 regions, separated by the 5.8S gene and locat-ed between the 18S (SSU) and 28S (LSU) genes in therDNA. These regions are the most commonly se-quenced for fungi and oomycetes and have been widelyutilized for phylogenetic studies and diagnostic assaydevelopment (Supplementary data, Annex 1; Fig. 3).The ITS regions provide attractive targets because theyare highly stable, usually conserved within a species but

Fig. 2. Schematic flow process chart showing actions required to develop reliable qPCR methods for thedetection and quantification of phytopathogenic fungi and oomycetes.


variable enough to differentiate related taxa, easily am-plified and sequenced using universal primers targetingconserved flanking regions, and occur in multiplecopies (White et al., 1990). Different universal primersare available to amplify ITS regions and the selectionshould be made according to the taxonomic group(Cooke et al., 2000; Bellemain et al., 2010). The hugenumber of fungal and oomycetes ITS sequences cur-rently deposited in the international nucleotide se-quence databases provides a wide range of referencematerial for the identification of taxa and for the devel-opment of specific detection methods. Issues with unre-liable annotations of sequences in public DNA reposito-ries remain an obstacle to all sequence-based speciesidentification (Nilsson et al., 2006). However, the ITSregion is also the preferred DNA barcoding marker forboth the identification of single taxa and mixed envi-ronmental templates (‘environmental DNA barcoding’)and has recently been proposed as the official primarybarcoding marker for fungi and oomycetes (Bellemainet al., 2010). The standardization process needed for theuse of ITS sequences as fungal and oomycete barcodingmarkers will progressively increase the reliability of se-quences and favour their use as target gene for specificdetection methods (www.fungalbarcoding.org).

A limit to the use of the ITS regions as target genefor the development of species-specific detection meth-ods can be an insufficient genetic variability. In suchcases the design of primers and probes to identify anddetect closely related taxa can be very difficult or im-possible. As an example, the recent discovery and ITSsequencing of many new Phytophthora species haveraised concern about the specificity of some ITS-basedmolecular detection methods (Cooke et al., 2007).

Intergenic spacer (IGS) region. The intergenic spac-er (IGS) region or non-transcribed spacer (NTS) is lo-cated between the LSU and the small subunit SSUgenes of adjacent rDNA units (tandem repeats). De-pending on the taxon, it can be composed by a single ortwo distinct regions (IGS1 and IGS2) separate by the5S gene. This region can be a valid alternative to theITS region when closely related taxa or even differentformae speciales need to be differentiated or detectedsince it evolves faster than the ITS region and, as such,more sequence polymorphisms are present (Diguta etal., 2010; Bilodeau et al., 2012). Like the ITS, the IGSregion is multicopy but its length (approximately 2-4kbp in fungi and 3-5 kbp in oomycetes) provides con-siderable scope for primer and probe development.

The wide utilization of the IGS region as target fordeveloping specific molecular markers is primarily limit-ed by difficulties in amplifying long fragments and thelack of effective universal primers. However, the in-creasing number of complete IGS sequences depositedin GenBank should facilitate the use of this gene as a

routine target. Furthermore, specific attempts havebeen made to develop universal primers to amplifyshort fragments of the IGS region from Phytophthoraspecies (Schena and Cooke, 2006).

Ribosomal DNA. Ribosomal DNA genes (18S, 5.8Sand 28S) have also been reported as targets for the de-tection of phytopathogenic fungi and oomycetes. Thesegenes are commonly conserved within fungi andoomycetes and, as such, can be useful for detecting abroad range of species (Yamamoto et al., 2010). Howev-er, they also contain variable regions that proved valu-able to design species-specific primers and probes (Gaoet al., 2004; Ioos et al., 2012). Since the rDNA genes aremulti-copy, a qPCR approach developed using these tar-gets has the potential to be very sensitive.

Alternative nuclear genomic regions. Like with oth-er eukaryotes, most genes of fungi and oomycetes areorganized into exons and introns. Because exons areusually more conserved than introns, primers designedon exons can generally be applied across a wider taxo-nomic range of organisms (universal primers) and canbe utilized to sequence introns by designing primers onadjacent exon regions. Once the entire gene or a part ofit has been sequenced, the presence of different levelsof polymorphism is strategic to develop primer andprobes with the desired level of specificity. For in-stance, the b-tubulin gene is one of the most frequentlyutilized targets for fungi and oomycetes (Fig. 3). Theuse of this target is favored by the availability of univer-sal primers designed in conserved coding regions (ex-ons) (Glass and Donaldson, 1995). Amplified fragmentsalso contain variable regions (mainly introns) thatproved to effectively differentiate closely related taxa(Aroca et al., 2008). Since the b-tubulin gene can readi-ly be amplified and sequenced from a broad range ofspecies with universal primers it has also frequentlybeen utilized for phylogenetic studies (Schmitt et al.,2009). As a consequence, a conspicuous number of fun-gal and oomycete sequences are currently deposited inthe international databases, greatly favouring the designand the preliminary in silico evaluation of primer andprobe specificity.

Another promising target gene for phylogenetic stud-ies and for the development of specific molecular detec-tion methods is the ras-related protein (Ypt1) gene (Iooset al., 2006). The non-coding regions of the Ypt1 geneshowed sufficient variation to differentiate Phytophthoraspecies that are almost identical in ITS sequences andwere utilized to develop a molecular tool box for theidentification and detection of 15 different forest Phy-tophthora species and a multiplex qPCR detectionmethod to simultaneously detect and quantify P. ramo-rum, P. kernoviae, P. citricola and P. quercina (Schena etal., 2006, 2008). The same gene has recently been uti-



lized to detect P. cryptogea in Gerbera jamesonii and Cal-endula officinalis (Minerdi et al., 2008; Li et al., 2008).

Apart from b-tubulin and Ypt1 genes, many othernuclear regions have been reported as targets in qPCRmethods for fungi and oomycetes (Supplementary data,Annex 1; Fig. 3). Interesting is also the use of targetgenes involved in the synthesis of mycotoxins in orderto detect and distinguish toxigenic and non-toxigenicstrains. In general, recent molecular analyses have sub-stantially increased our understanding of the phyloge-netic relationships within oomycetes and fungal speciesand provided an enormous source of data for develop-ing molecular detection methods (Schoch et al., 2009).Furthermore, the exponential increase of sequences inpublic databases together with the availability of datafrom genome sequencing projects is giving new insightsin this field (http://www.ncbi.nlm.nih.gov/genome/).

Mitochondrial DNA. The sequencing of the entiremitochondrial genomes from an increasing number offungi and oomycetes (http://www.ncbi.nlm.nih.gov/genomes/GenomesHome.cgi?taxid=2759&hopt=html)has prompted the use of this cytoplasmic genome as auseful target for phylogenetic analysis and species iden-tification. In many organisms, mitochondrial DNA has ahigher rate of evolution than nuclear DNA and mayserve as an alternative to differentiate closely relatedspecies. For instance, a partial sequence of the mito-chondrial small subunit (mtSSU) rRNA gene was uti-

lized to differentiate between Fusarium solani f. sp.glycines and other F. solani isolates (Li et al., 2000) andto develop a specific qPCR assay to quantify F. solani f.sp. glycines in inoculated soybean roots (Li et al., 2008).

The mitochondrial DNA is present in multiple copiesper cell, thus molecular methods based on this targetare potentially very sensitive (Tooley et al., 2006). Ageneral disadvantage of mitochondrial DNA is the veryhigh AT/GC ratio. In some intergenic regions theAT/GC ratio can easily reach 80-90%, making the de-sign of effective primers very challenging. Furthermore,mitochondrial DNA is generally more difficult to ampli-fy and requires a higher concentration of MgCl2 com-pared to genomic DNA. Another potential complica-tion of using only mitochondrial-based marker systemsfor pathogen identification is the presence of species hy-brids (Olson and Stenlid, 2002). The mitochondrialgenome is uni-parentally inherited along with the mater-nal line. Thus, species hybrids may inherit the mito-chondrial genome of either parent and confound the re-sults of diagnostic assays. A potentially damaging hybridspecies may be mis-identified as one of the parentalspecies or, if the assay is not designed to detect bothparental mtDNA haplotypes, may remain undetected.

Random DNA fragments. Different PCR-based tech-niques such as random amplified polymorphic DNA(RAPD), arbitrarily primed PCR (AP-PCR) and ampli-fied fragment length polymorphism (AFLP) can be uti-


Fig. 3. Relative frequency of target genes utilized to develop qPCR methods for the detection of fungal biocontrol agents and phy-topathogenic fungi and oomycetes. Frequencies (%) were calculated using a database of 201 representative articles (Supplemen-tary data, Annex 1) published in ISI journals since 1999. For more details about target genes refer to Annex 1.



lized to randomly amplify portions of the genome. Frag-ments unique in length can be cloned and sequenced togenerate a sequence-characterized amplified region(SCAR) on which specific primers and probes can bedesigned. This approach has been utilized for a numberof different fungi and oomycetes (Fig. 3). In particular,SCAR primers proved very useful when other targetgenes do not enable the differentiation of closely relatedtaxa or when sub-specific groups need to be identifiedand detected, as in the case of specific strains of a bio-control agent (Schena et al., 2002a; Feng et al., 2011).However, the identification of potentially specific targetregions is quite laborious and time-consuming. Further-more, in most cases, no background information regard-ing the origin, localization, function and/or stability ofthe selected targets is available. There is, therefore, aneed for time-consuming screening to confirm thespecificity, reliability and stability of the selected primersets against a large and representative selection of iso-lates from both target and related species. Even aftersuch screening, monitoring of the pathogen populationmay be needed to check for any false negatives resultingfrom deletions or mutations in the target region.

CHOICE OF THE CHEMISTRY

Chemistries commonly utilized to develop qPCRmethods for the detection of agriculturally relevant fungiand oomycetes can be grouped into sequence non-specif-ic and specific methods (Schena et al., 2004). The firstgroup is based on dyes that emit fluorescent light wheninterposed into double-stranded DNA (dsDNA). SYBRGreen I is by far the most common dye but several validalternatives are also available (Gudnason et al., 2007).Since dyes do not discriminate between the different ds-DNA molecules, the formation of non-specific ampli-cons, as well as primer dimers, could lead to false positiveresults or to inaccurate quantification results. As a conse-quence, amplification reactions need to be accurately op-timized and the possible presence of non-specific prod-ucts needs to be examined at the end of the reaction byprogressively heating the reaction mix whilst continuous-ly monitoring the fluorescence (melting curve analysis).Products with different melting temperatures (Tm) willbe observed as distinct fluorescent peaks. Providing thatthe reactions are perfectly optimized, intercalating dyescan be a simple, flexible and reliable low-cost method asconfirmed by its wide utilization for the quantification offungi and oomycetes (Fig. 4).

Amplicon sequence-specific methods primarily in-clude hydrolysis probes (formerly known as TaqMan),Molecular beacons and Scorpion-PCR (Fig. 4). Thesemethods are based on the use of oligonucleotide probeslabeled with a donor fluorophore (reporter) and an ac-ceptor dye (quencher). The advantage of fluorogenic

probes over DNA binding dyes is that specific hy-bridization between probe and target DNA sequence isrequired to generate a fluorescent signal. As a conse-quence, these methods guarantee higher specificity lev-els, which is extremely important when they are utilizedto detect and/or quantify pathogens within natural sam-ples and, in particular, in certification schemes relatedto production of healthy propagative materials or to ex-clusion of the presence of quarantine pathogens. Thehigher specificity of amplicon sequence-specific meth-ods enables the discrimination of single base pair mis-matches. Specific methods are thus ideal when closelyrelated species need to be differentiated by using fewpolymorphic bases, as well as for genotyping single nu-cleotide polymorphisms (SNPs) and for mutation analy-ses (Thelwell et al., 2000). Scorpion PCR chemistriesrepresent an advancement as compared to Molecularbeacons and hydrolysis probes since they are based on auni-molecular mechanism in which the hybridization re-action occurs within the same strand and leads tostronger signals, shorter reaction times and better dis-crimination (Thelwell et al., 2000; Tomlinson et al.,2007). However, hydrolysis probes are the most utilizedin plant pathology because they associate high levels ofreliability and performance to reduced costs of theprobes, which can be easily designed (Fig. 4).

DESIGN AND VALIDATION OF SPECIFIC PRIMERS AND PROBES

A number of reports indicate the possible utilizationof primers previously designed for cPCR in qPCR as-says (Vandemark and Barker, 2003; Montes-Borrego etal., 2011). Nevertheless, at least for amplicon-specificmethods, this approach should be avoided since it doesnot enable the accurate design of primers and probes.

Fig. 4. Relative frequency of chemistries utilized in qPCRmethods for the detection of fungal biocontrol agents and phy-topathogenic fungi and oomycetes. Frequencies (%) were cal-culated on a database of 201 representative articles (Supple-mentary data, Annex 1), published in ISI journals since 1999.


In particular, amplicons commonly utilized for cPCRare almost always too long to enable the design of effi-cient qPCR assays.

Specific primers and probes can manually be de-signed and some suppliers provide very accurate guide-lines (see for instance “The Real-Time qPCR Hand-book” by Eurogentec, http://www.eurogentec.com/up-loads/GRT-QQPCR-BOOKLET-0604-V2.pdf). How-ever, appropriate software can greatly facilitate this task.Primer Express (PE Applied Biosystems, USA) enablesthe design of conventional and MGB hydrolysis probes.In the latter, the DNA probe is conjugated with a minorgroove binder (MGB) group and forms extremely stableduplexes with single-stranded DNA targets, allowingshorter probes to be used (Kutyavin et al., 2000). A sim-ilar result can be obtained with the locked nucleic acid(LNA) technology which relies on the use of a specialnucleotide-enhancing base stacking and backbone pre-organization (Obika et al., 1997; Koshkin et al., 1998).The exclusive rights to the LNA technology were se-cured in 1997 by Exiqon A/S (Denmark), which pro-vides a freely available design programme (http://www.exiqon.com/mRNA-probes). The software, Bea-con designer (PREMIER Biosoft International, USA)enables the design of primers and probes for the mostimportant chemistries including SYBR Green, hydroly-sis probes, Molecular beacons and Scorpion PCR andcan also be utilized to specifically develop multiplexqPCR assays. Another specific software called PrimerS-elect can be purchased from DNASTAR (USA). Othertools are freely available on the web. For instance,Primer3 (Whitehead Institute, USA) is a largely utilizedfree software for designing conventional primers thatcan also be used to design probes. A recently releasednew tool allows designing specific primers within a fam-ily of related sequences in such a way that each primerset amplifies only its target sequence and none of theothers (Fredslund and Lange, 2007).

Once primers and probes have been designed, theirspecificity needs to be determined. Preliminary evalua-tions can be performed by in silico analyses using theBasic Local Alignment Search Tool (BLAST) to verifythe presence of similar sequences among those deposit-ed in GenBank (http://www.ncbi.nlm.nih.gov). Ap-proximately 2-3 unique bases are enough to generatehighly specific primer and probes using average strin-gent amplification conditions. To increase specificity,polymorphic bases should be preferentially localizedclose to the 3’ end of the sequence (Fredslund andLange, 2007). In silico analyses are very useful for se-quenced genes for a preliminary checking of the speci-ficity of primers and probes, although the results needto be critically analysed because of the frequent unrelia-bility of sequences in public DNA repositories (Nilssonet al., 2006). Much more reliable sequences are availableand can be analysed within barcoding projects

(http://www.fungalbarcoding.org/), but currently theycover a quite limited number of taxa. In any case, in sili-co analyses can never be considered sufficient to evalu-ate primer and probe specificity, which ultimately needto be tested using actual samples, and a range of non-target species selected within related taxa. In particular,specificity tests need to be particularly accurate in thecase of complex environmental samples such as soil,since they are complex ecosystems with a diverse micro-bial community harbouring hundreds of different fun-gal and oomycete species besides many other groups oforganisms. The assays must have absolute specificity forthe target pathogen of interest and should not detect se-quences from other closely related species or formaespeciales. Specificity tests conducted by cPCR do notguarantee specificity of the primers in qPCR reactionssince amplification conditions can be significantly dif-ferent.

MULTIPLEX ANALYSES

A significant advantage of amplicon-specific com-pared to the non-specific methods is that fluorogenicprobes can be labelled with different distinguishable re-porter dyes for detecting two or more distinct se-quences in a single PCR reaction tube (multiplex PCR).Unlike cPCR, amplicons of the same length can be dif-ferentiated in qPCR reactions avoiding interferencesfrom different size products that are amplified with dif-ferent efficiency. A number of diverse dyes are currentlyavailable and most PCR equipments enable the simulta-neous detection of up to 4-5 different dyes. The discov-ery and application of non-fluorescent quencher (darkquencher) has made available wavelength emissions thatwere previously occupied by the emission of thequenchers themselves, thus permitting the inclusion of agreater number of spectrally discernible oligoprobes perreaction. However, a great limitation to the use of multi-plex PCR in both cPCR and qPCR is the competitionbetween different primers and probes which can resultin lower sensitivity and specificity (Ippolito et al., 2004;Tooley et al., 2006; Scauflaire et al., 2012). As a conse-quence, most multiplex qPCR protocols used in plantpathology provide the simultaneous detection of justtwo target DNAs which can be from different microor-ganisms (Ippolito et al., 2004; Tooley et al., 2006; Fenget al., 2011) or from a microorganism and an internalcontrol (Haudenshield and Hartman, 2011; Ioos et al.,2012). However, the detection of three to four differenttargets without reduction of sensitivity against singleamplifications has also been reported (Schena et al.,2006; Rodríguez et al., 2012). To obtain this result, sev-eral factors were optimized, including primer and probedesign, amplification conditions and amplicon length,which was reduced as much as possible. A method for



the simultaneous detection of more than one speciescan offer important advantages especially for testingprograms dealing with many samples, as in the case ofnational surveys when high throughput and reliabilityare critical (Cooke et al., 2007).

SENSITIVITY OF QUANTITATIVE PCR REACTIONS

Quantitative PCR is known as the most sensitive de-tection method currently available, although this as-sumption has recently been questioned by Bastien et al.(2008) who stressed the concept that qPCR is not neces-sarily more sensitive than cPCR since many factors caninfluence the detection limit in both approaches. How-ever, at least in plant pathology, the assumption thatqPCR is more sensitive than cPCR is widely accepted.Using an identical couple of primers a hundred-foldlower detection limit was reported for qPCR comparedto cPCR for detecting Phytophthora cryptogea (Minerdiet al., 2008). The higher sensitivity of qPCR enabled thedetection of the pathogen in symptomless plants and inthe re-circulating nutrient solution of soilless cultivationsystems. Furthermore qPCR enabled P. cryptogea detec-tion four days before cPCR and six days before the ap-pearance of disease symptoms in potted marigold andgerbera plants (Li et al., 2008). Phaeoacremoniumspecies were detected on grapevine wood with highersensitivity and reproducibility using qPCR comparedwith conventional nested-PCR (Aroca et al., 2008). Acouple of Puccinia horiana-specific primers enabled thedetection of the pathogen from 1 ng of DNA isolatedfrom infected chrysanthemum leaf tissue in convention-al PCR assays and from 1 pg in qPCR assays (Pedley,2009).

The higher sensitivity of qPCR compared to cPCR isdetermined by two main features (i) faint PCR bandsare hardly visualized in common agarose gels afterethidium bromide or SYBR staining whereas their fluo-rescence is readily detected by qPCR equipment; (ii)qPCR technologies and in particular, amplicon se-quence-specific methods, commonly amplify very shortDNA fragments (70-100 bp) which favour a higher levelof PCR efficiency and sensitivity compared to cPCR.The production of very short amplicons is best avoidedin cPCR due to their difficult visualization in agarosegels and the possible confusion with primer dimers.

A further increase in sensitivity can be achieved usinga nested PCR approach in which two sequential amplifi-cations with conventional (first amplification) and la-belled primers (second amplification) are combined(Schena et al., 2002b; Ippolito et al., 2004). The use of anested approach could be useful when very low levels ofpathogen infestations need to be detected in complexenvironmental samples such as soils, since PCR reac-tions are frequently partially or totally inhibited by com-

pounds co-extracted with target DNA. However, nestedPCR greatly increases the risks of false positives due tocross contaminations of reaction mixtures. Althoughfalse positives can result from sample-to-sample con-tamination, a more serious source of false positives isthe carry-over of DNA from a previous amplification ofthe same target. In qPCR, the reduced level of samplemanipulation and the monitoring of fluorescence with-out opening reaction tubes, greatly decrease the poten-tial of cross contamination compared to cPCR. Yet,when a double amplification is required (nested PCR),cross contamination can occur between the two amplifi-cations. Furthermore, quantitative analyses are muchmore challenging in nested PCR even if a direct correla-tion between the quantity of target DNA and thresholdcycles was reported within a restricted range of concen-trations (Schena et al., 2002b; Ippolito et al., 2004).

Based on the above considerations, attempts to in-crease sensitivity and reduce the risks of false negativesshould primarily focus on the optimization of qPCR andDNA extraction protocols rather than on the use ofnested approaches. For instance, a double amplificationwith conventional and real-time PCR primers (nestedScorpion PCR) was required to achieve the high level ofsensitivity necessary to detect Rosellinia necatrix in nat-urally infested soils (Schena and Ippolito, 2003). Morerecently, it was found that this pathogen can also be de-tected by a single qPCR reaction by improving DNAextraction and purification protocols (Ruano-Rosa etal., 2007).

Different detection limits have been reported in litera-ture (mainly between 10 pg and 10 fg of target DNA),depending on the target genes and the efficiency of thereactions. Lower detection limits can be achieved usingprimers developed on multicopy genes such rDNA genesand spacers or the mitochondrial DNA, since these se-quences are present in multiple copies in each cell. Thesedetection limits make it possible the detection of singlepropagules of plant pathogens in complex environmentalsamples (Demontis et al., 2008; van Gent-Pelzer et al.,2010; Sanzani et al., 2012). The genome size of differentoomycete species including Achlya spp., Albugo spp.,Bremia spp., Paraperonospora spp., Peronospora spp., andPlasmopara spp. is comprised between 40 and 200 fg(Voglmayr and Greilhuber, 1998). Similarly, the size ofthe haploid genome of important oomycete pathogenssuch as P. infestans (243 fg), P. sojae (96 fg), P. ramorum(66 fg) and Hyaloperonospora arabidopsidis (78 fg) canreadily be deduced from their complete genomic se-quences (http:// oomycetes.genomeprojectsolutions-data-bases.com/). Smaller genome sizes have been determinedfor a very large number of true fungi comprising Ascomy-cota (911 records), Basidiomycota (358 records), Chytrid-iomycota (3 records), Glomeromycota (11 records) andZygomycota (13 records) [Gregory et al. (2007);www.zbi.ee/fungal-genomesize/]. The size of 90% of the



available fungal genomes is comprised within 10-61 fg,with an average of 37 fg and a median of 28 fg. Thelargest genome reported to date (the mycorrizal Scutel-lospora castanea) is 810 fg in size whereas the smallest (7fg) belongs to the vertebrate pathogen Pneumocystiscarinii f. sp. muris.

If on the one hand, data on genome size confirm thepossible detection of single propagules or cells, on theother, they suggest that the very low limits of detection(1-10 ag of total DNA) sometimes reported in the litera-ture are questionable. Such detection limits can only bepartially justified by the multicopy nature of the targetgenes. According to Bustin et al. (2009) the lowest theo-retically possible detection limit is three DNA copiesper PCR so that, if a Poisson distribution is assumed,there is a 95% chance of including at least one copy inthe PCR, and of detecting a single DNA copy. Experi-mental results lower than the theoretically possible de-tection limit should never be reported (Bustin et al.,2009). In agreement with the above speculation, a nest-ed PCR assay based on a first amplification round withconventional genus-specific primers and a second am-plification with species-specific qPCR primers was assensitive (100 fg of target DNA) as a single round ofqPCR with the latter primers (Schena et al., 2006). Innested PCR, a very early increase in fluorescence (aver-age Ct 19.5) was observed with the amplification mix-ture containing the lowest detectable concentration(100 fg) of target DNA, but no amplification occurredat any lower DNA concentration, suggesting that nomore DNA was available for amplification. This resultwas consistent with the fact that a single copy targetgene (Ypt1 gene) was amplified by a single PCR round,thus the nested assay only improved the signal strengthwithout increasing sensitivity.

QUANTIFICATION AND CONTROL METHODS

The most innovative and powerful characteristic ofqPCR is its appropriateness for quantitative analyses. Todate, real-time PCR is the only technique that allows anaccurate, reliable, and high throughput quantification oftarget nucleic acids (Schmittgen, 2001). CompetitivePCR approaches have been proposed in the recent pastfor quantifying target DNA with cPCR but these meth-ods have never been widely utilized because they are la-borious and little accurate. Since amplification efficien-cy decreases during late PCR cycles, the final ampliconconcentration is not directly correlated with the concen-tration of target DNA (Ginzinger, 2002). In qPCR theamplicons can be measured at an early stage of the reac-tion when the efficiency is still constant. The number ofPCR cycles necessary to generate a fluorescent signalsignificantly above noise level is denoted quantificationcycle (Cq) or threshold cycle (Ct) and can be utilized to

quantify precisely the initial amount of target molecules(Bustin, 2002).

The quantification of target DNA requires the pre-liminary construction of a specific calibration curve. Tocover a wide range of concentrations a known quantityof target DNA is serially diluted (five- or ten-fold dilu-tion series are commonly utilized) and amplified byqPCR. Calibration curves and corresponding linearequations are automatically generated by software asso-ciated with qPCR equipments, by interpolating experi-mentally determined Cq values and the logarithm of theknown DNA concentrations. Calibration curves are alsouseful to determine the dynamic range over which a re-action is linear (linear dynamic range) and the efficiencyof the reaction which is determined from the slope ofthe log-linear portion of the calibration curve: PCR effi-ciency = 10-1/slope-1 (Bustin et al., 2009). In an goodqPCR method, PCR efficiency should be as close aspossible to 100%. Commonly, triplicate measurementsof each sample concentration are made to determinewhether differences in Cq values are statistically signifi-cant and are expressed as correlation coefficients (r2 val-ues). Standard curves are generally constructed usingserial dilutions of the total DNA of the microorganismof interest (Taylor et al., 2010) but the use of syntheticDNA oligonucleotides spanning the complete PCR am-plicon or plasmid DNA constructs has also been report-ed (Savazzini et al., 2008; Minerdi et al., 2008). Laterapproaches enable the evaluation of the actual numberof target gene copies and potentially give a more gen-uine measure of the amount of the microorganism pres-ent in the sample (Savazzini et al., 2008).

Specific calibration curves can also be created usingenvironmental samples artificially inoculated withknown quantities of pathogen propagules or biomass(Demontis et al., 2008; Luo et al., 2009; Sanzani et al.,2012). Such approaches are particularly fascinatingsince they yield immediately intelligible results that canbe compared with data from conventional detectionmethods. However, results must be used with precau-tion since artificially inoculated samples are differentfrom the naturally infected/infested ones that may con-tain pathogens under a number of different forms(mycelium, spores, conidia, conidiophores, sclerotia,etc.). For example, a very high correlation between thecolony forming units (CFU) of P. nicotianae and P. cit-rophthora and the corresponding DNA quantity evalu-ated by qPCR was found using artificially inoculatedsoils (Ippolito et al., 2004). The correlation was signifi-cantly lower with naturally infected soils since, as specu-lated by the above authors, the two methods detecteddifferent Phytophthora propagules (mycelium,zoospores, oospores, etc.) with differing efficiency. Itshould also be taken into account that different propag-ules are all determined as single CFU on a medium, buttheir DNA content can be significantly different.



Once a calibration curve has been constructed theabsolute quantification of target DNA is determined byinterpolating Cq values from unknown samples with thecurve. According to the calibration curve utilized, thequantitative data will be expressed in units of mass(commonly pg or fg of DNA), as number of copies oftarget DNA, as biomass of the microorganism or asnumber of propagules.

The efficiency of PCR reactions can be influencedby non-target DNA from host plants and other organ-isms and, in particular, by compounds of differentchemical nature co-extracted with DNA. Inhibiting fac-tors can act in several ways by interfering with the celllysis necessary for DNA extraction, by capturing or de-grading nucleic acids and by inhibiting polymerase ac-tivity. A total inhibition of PCR reaction will lead tofalse negatives while a partial inhibition will delay theexponential phase of PCR and determine higher Cq val-ues with a reduced sensitivity of the assays. Whateverthe level of inhibition, quantitative results will be al-tered since small differences in amplification efficiencycan result in dramatic changes in the calculated DNAconcentrations. The risk of PCR inhibition needs to bealways considered no matter what the origin of DNAand it becomes particularly relevant when nucleic acidsare extracted from complex environmental samplessuch as soil. It should also be taken into considerationthat different PCR reactions may not be equally suscep-tible to inhibition by substances co-purified with nucle-ic acid extracts and that different DNA polymerasesshow different levels of susceptibility to inhibitors (Al-Soud and Radstrom, 1998).

Since none DNA extraction protocol can guaranteethe absence of PCR inhibitors, the use of internal con-trols is mandatory to exclude their negative impact onthe accuracy of detection and quantification of the tar-get organism. When pathogens are detected in theirhost plants, the most common approach to normalizedata is the use of control primer and probes targetingendogenous plant genes that are co-extracted from thesample along with any potential target nucleic acid. Asimple approach is to multiply uncorrected pathogenDNA concentrations by a correction factor, i.e. averageof host DNA concentration/host DNA concentration ofthe specific sample under investigation (Sanzani et al.,2012). Commonly results are expressed as quantity oftarget DNA per quantity of host DNA (Gayoso et al.,2007). This kind of normalization is largely utilizedsince it takes into account variations in sample size(weight of tissue used for DNA extraction), extractionefficiency from one sample to another, and efficiency ofthe PCR reaction that can be influenced by a number offactors including the presence of inhibitors (van Gent-Pelzer et al., 2010). The use of a plant gene as a targetcontrol is also useful for checking the potential risk offalse negatives due to DNA degradation (van Gen-

Peltzer et al., 2010). For an accurate quantification,PCR efficiencies of both target and internal controlgenes should be close to 100% and not differ by morethan 10%.

Recently this procedure has been questioned becauseit could lead to an over-estimation of pathogen biomassdue to the degradation of host DNA during plant cellcollapse, such as that occurring during infection by anecrotrophic pathogen (Eshraghi et al., 2011). To avoidthis, some scientists have chosen to normalize their re-sults to sample surface area or weight but the results canbe influenced by the variability of DNA extractionyields (De Coninck et al., 2012). A good alternative canbe the addition of known quantities of exogenous mi-croorganisms or DNAs to the samples just before totalDNA extractions. For example, an internal control con-stituted by a green fluorescent protein (gfp) construct inEscherichia coli was detected by multiplex qPCR togeth-er with the target pathogen Synchytrium endobioticum(van Gent-Pelzer et al., 2010). In another study aimed atdetecting B. cinerea in grapevines the yeast Yarrowialipolytica was added to the samples before DNA extrac-tion, then detected together with the target DNA(Diguta et al., 2010). Recently, 1 ng of a specific plasmidDNA (ScFvB1) was added to all extracts just before thefirst centrifugation step of the DNA extraction protocol(Eshraghi et al., 2011).

A more sophisticated approach consists in the devel-opment of synthetic control templates that contain thesame primer binding sites as the target pathogen but havea distinct internal sequence between the primers, thusthey can be detected by a different fluorescent-labeledprobe (Bilodeau et al., 2012). By spiking test reactionswith this construct, the performance of the target primercan also be monitored alongside that of the other reac-tion components. Important limits of this approach arethe possible negative effects on the assay sensitivity dueto competition between target and control DNA and theconsiderable effort required to construct and synthesizeinternal controls (Mumford et al., 2006).

Although the absolute quantification of target DNA isthe most commonly utilized approach for fungi andoomycetes a relative quantification can equally be usefulto evaluate relative changes of pathogen DNA. The mostcommon approach for the relative quantification of nu-cleic acids is known as ∆∆Cq method and is widely uti-lized to quantify messenger RNA and determine the rela-tive expression of specific genes (Livak and Schmittgen,2001). This method has been slightly modified andadapted to study the accumulation of genomic DNA ofF. solani f. sp. glycines in inoculated soybean roots (Gaoet al., 2004; Li et al., 2008). In the latter study, to correctdifferences in the amount of soybean root DNA in eachsample, Cq values from a host specific probe (control)were subtracted from the corresponding pathogen spe-cific probe as follows: ∆Cq = Cq pathogen - Cq host.



The fold change in the amount of fungal DNA fromtime 1 to time 2 were then calculated by the ∆∆Ctmethod using the formula 2-∆∆Ct(T1-T2), in which T1 =∆Cq (14 days) and T2 = ∆Cq (21 days). A simpler alter-native is the ∆Cq method in which the relative amountof a pathogen is calculated by subtracting the Cq valueof an endogenous host control from the Cq value ofthe pathogen. A low ∆Cq value corresponds to highpathogen proliferation (De Coninck et al., 2012). Anoth-er approach consists in the calculation of the relative ra-tio of fungal and plant nucleic acids within each specificsample using the following formula: (ng of fungalgene/gene copy number in fungal genome)/(ng of plantgene/gene copy number in plant genome) (Berruyer etal., 2006; Baumgartner et al., 2010).

Finally a simple method for the relative quantificationof pathogen DNA is to express its quantity as a percent-age of the highest concentration. In a recent study, P. in-festans DNA was monitored over a 6-day period in artifi-cially inoculated potato leaf and tuber discs. The meanvalues at 6-day post-inoculation (dpi) were set at 100%,and all data from previous surveys were normalized tothe 6 dpi values (Llorente et al., 2010).

Along with specific methods to normalize quantita-tive results, a number of useful strategies can be utilizedto monitor possible inhibition phenomena. The simplestapproach consists in the routine use of dilution series todemonstrate that observed Cq values are consistent withthe anticipated result. When differences in Cq valuesbetween the undiluted and 10-fold-diluted DNA sam-ples are below the expected difference (approximately3.3 cycles) it can be assumed that inhibitory PCR sub-stances are present in the undiluted samples and/or thatthere are excess levels of template. The spiking of nucle-ic acids extracted from natural samples with exogenousDNA (positive controls) can be utilized to evaluate totaland partial inhibitions. A retarded increase in fluores-cence (higher Cq value) against the same concentrationof control DNA diluted in pure water will indicate apartial inhibition of PCR reactions (Taylor et al., 2010).Another possible strategy is the use of universal primersand markers designed on highly conserved genes withindifferent kingdoms (Schena et al., 2006). Such methodscould be valuable for complex environmental samplessuch as the soil where a single specific host does not ex-ist. In general, a good practice is to construct calibrationcurves by serially diluting target DNA with extractsfrom non-infected/contaminated samples rather thanpure water.

RISK OF DETECTING TARGET DNA FROM DEAD CELLS

A major limitation of molecular detection methods,especially when applied to soil-borne pathogens, is the

lack of discrimination between living and dead material.Because both traditional and qPCR assays detect nucle-ic acids rather than living cells, there is a risk that nucle-ic acids released from dead or unviable cells may lead topositive PCR signals. Nucleases are widely diffused inthe environment and can degrade DNA after the deathof microorganisms but the degradation rate is greatly in-fluenced by the environmental conditions and survivalstructures (sclerotia, resting spores, etc.) produced bythe pathogens. Indeed, although several reports indicatethat nucleic acids are quickly digested by DNases in thesoil (England et al., 1998; Schena and Ippolito, 2003),other studies have shown that DNA can persist in soilfor long periods of time by forming complexes with soilcomponents (England et al., 1997). Quantitative studiesof DNA degradation kinetics determined by qPCR haveshown that the rate of degradation of DNA after celldeath is variable, according to the DNA binding poten-tial of the substrate (Wolffs et al., 2005).

A possible approach to avoid false positives due tothe detection of DNA in dead cells is the combinationof qPCR with culturing (nutritive media) or baiting(host tissues) methods. The biological detection of thetarget microorganism also reduces the risks of false neg-atives as it increases the sensitivity of molecular detec-tion and reduces the problems of DNA extraction fromcomplex environmental samples since nucleic acids donot need to be concentrated before PCR. However,such methods have never been widely utilized since theydo not enable quantitative analyses and are much moretime-consuming, costly and laborious compared toqPCR.

Another possible strategy to exclude the detection ofdead cells is the use of mRNAs as an indicator of livingcells because these molecules are rapidly degraded indead cells. However, RNA analysis is more complex andcostly since RNA must be first reverse transcribed to beanalyzed and detection and quantification of mRNA arehighly dependent both on expression levels of the targetgene and on the extraction protocols (Schmittgen,2001). Furthermore, the easy degradation of RNA dur-ing sample processing could lead to false negatives. In arecent study, a mRNA-based qPCR method to detect P.ramorum proved less sensitive compared with a DNAqPCR, but more sensitive than isolation on a selectivemedium (Chimento et al., 2012).

CONCLUDING REMARKS

Quantitative PCR is an ideal method to evaluate in-oculum threshold levels and to study the epidemiology,biology and ecology of phytopathogenic fungi andoomycetes, thus opening to new research opportunitiesto investigate host-pathogen interactions and to addresstasks related to quarantine, eradication and biosecurity.



This technique possesses several advantages comparedwith traditional detection methods based on morpho-logical and cultural criteria which can only be utilizedby skilled operators, are time-consuming as they requiredays or weeks to completion, and provide not alwaysconclusive results since they do not allow discriminationof closely related organisms. Furthermore, traditionalmethods do not provide accurate quantitative data andmay not be sensitive enough to detect pathogens in pre-symptomatic infections (Cooke et al., 2007). Due to thegreat potentiality of qPCR, a large number of specificmethods to detect and quantify phytopathogenicoomycetes and fungi have been developed in the last 15years and have significantly contributed to the advance-ment of knowledge in the plant pathology sector.

In recent years, several new isothermal amplificationmethods have been proposed as alternatives to qPCR andcould help to further reduce the cost of molecular testing(Boonham et al., 2008). It is likely that the use of these al-ternative approaches will progressively increase, but cur-rently they are still utilised by few operators. By contrast,the expanding use of qPCR to detect and quantify phy-topathogenic fungi and oomycetes (Supplementary data,Annex 1) suggests that this technique will remain thepreferential approach for the next future. We are now atthe beginning of a new phase characterized by the routineutilization of qPCR in plant pathology laboratories thanksto the wide diffusion, at least in developed countries, ofthe required equipment and skills. The adoption of acommon format (96-well and latterly 384-well plates) andthe progressive reduction of the cost of equipment andreagents have made the technique well suited to high-throughput detection of target pathogens. Furthermore,the development and application of portable qPCR appa-ratuses for on-site detection of target organisms shouldfurther favour this process (Tomlinson et al., 2007).

However, considerable room for improvement ofqPCR approaches is still available. In general, protocolshave been progressively improved over the years butcommon drawbacks, mainly related to the inadequateor insufficient evaluation of specificity and sensitivity aswell as the lack of appropriate internal controls still af-fect some currently published methods. Although someaspects, such as the extraction of suitable target DNAand the risk of detecting nucleic acids from dead mate-rials remain challenging, the accurate knowledge andcritical analyses of all phases needed to develop accu-rate qPCR methods can greatly contribute to increasereliability and soundness of results in present and futureapplications.

ACKNOWLEDGEMENTS

This work funded by MIUR-PRIN 2008 “Molecularmethods for evaluating the effect of organic amend-

ments on the populations of root pathogens and micro-bial antagonists in the citrus rhizosphere” and by MI-UR-FIRB 2010 “Metagenomic strategies to assess genet-ic diversity in soil-borne Phytophthora species”.

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