Introduction

The very first detection of the association of phloem colonizing prokaryotes with diseases of plants was made by Doi et al. (1967) while working on aster yellows disease. Initially referred to as mycoplasma-like organisms (MLOs) due to their morphological resemblance to animal mycoplasmas, the phytoplasmas turned out to be economically important plant pathogens with a wide host range and displaying a multiplicity of symptoms across different plants. Some of the phytoplasma diseases are lethal, killing the host plants upon infection while others debilitate their hosts ultimately reducing the yield. Phytoplasmas are associated with diseases in over 700 species of plants globally (Maejima et al. 2014). Phytoplasma-associated diseases include aster yellows, peach yellows, X-disease, rice yellow dwarf, potato witches’ broom, maize bushy stunt, sweet potato witches’ broom, cassava witches’ broom, grapevine yellows, apple proliferation, pear decline, peanut witches’ broom, sesame phyllody and soybean phyllody (Lee et al. 2000). Most of these diseases have high economic impact in the agricultural scenario, even restricting movement of plant materials across the world because of quarantine concerns. Throughout the world, economically important palm species are subjected to several phytoplasma diseases like the coconut yellow decline (Nejat et al. 2009), coconut lethal yellowing (Myrie et al. 2007; Harrison et al. 1994a, b; Ntushelo et al. 2013), Weligama coconut leaf wilt (Perera et al. 2012), oil palm stunting disease (Mehdi et al. 2012), oil palm lethal wilt (Alvarez et al. 2014), Al-wajam disease of date palm (Alhudaib et al. 2007), yellow decline disease of foxtail palm (Naderali et al. 2017), Hainan arecanut yellow leaf disease (Daquan et al. 2002), coconut root wilt disease (Manimekalai et al. 2010a), Indian arecanut yellow leaf disease (Manimekalai et al. 2010b) and so on.

Overview

Phytoplasmas are wall-less, pleomorphic, non-helical prokaryotes included within the class Mollicutes. Attempts to culture phytoplasma in vitro have been made in the past without much success. However, recently some progress has been made towards this end (Contaldo et al. 2012, 2016; Contaldo and Bertaccini, 2019). Contaldo et al. (2018) could isolate diverse phytoplasmas from grapevine using a single media (Contaldo et al. 2016); indicating that same medium supports the growth of different phytoplasmas. Contaldo et al. (2019) isolated phytoplasma from coconut palms affected by the lethal yellowing disease in Africa and also assessed the antibiotic susceptibility of the isolated 16SrXII-A and 16SrXXII-B phytoplasmas. The phytoplasma exhibited tobramycin susceptibility and cephalexin hydrate and rifampicin resistance.

Phytoplasmas are the smallest among bacterial species with cell size ranging from 200 to 1000 nm. They are obligate plant parasites colonizing the phloem sieve elements (Christensen et al. 2004). Sap sucking insect vectors from the order Hemiptera and the families Cicadellidae, Fulgoridae and Psyllidae mediate phytoplasma transmission across plant hosts (Weintraub and Beanland 2006).

The phytoplasma genome is miniature ranging in size from 530 to 1350 kb (Marcone et al. 1999) and is characterized by low GC content. The genome sequencing and annotation of Ca. P. asteris, OYstrain, revealed that the entire pentose phosphate pathway and the ATP synthase subunits were absent in the genome (Oshima et al. 2004). The genome also lacked many essential genes related to oxidative phosphorylation, tricarboxylic acid cycle, fatty acid and amino acid metabolism thus suggesting the occurrence of reductive evolution. Hence these intracellular pathogens depend entirely on their hosts for many products of the pathways which they do not have. Another distinguishing feature of phytoplasma genome is the presence of several multicopy repeat sequences called Potential Mobile Units which are important for phytoplasma’s interaction with the environment (Bai et al. 2006). Phytoplasma cell membrane proteins are in direct contact with plant cell cytoplasm and could play an important role in pathogenesis. Many of the membrane proteins are strongly antigenic (Seddas et al. 1996). These antigenic membrane proteins are the candidates for plant pathogen interaction. These proteins seem to vary among phytoplasma species and this variability accounts for host specificity of phytoplasma (Barbara et al. 2002). The Sec translocation system (secA, secY, dnaK, yidC, ftsY, ffh, grpE, groES and groEL) involved in secretion of phytoplasmal proteins directly into the host cell are key players in phytoplasma pathogenicity (Kakizawa et al. 2001).

Phytoplasma classification

Advances in molecular biology and accessibility to phytoplasma 16S rRNA gene sequence information contributed to better understanding of epidemiology and developing of phytoplasma classification systems. In the class Mollicutes, phytoplasmas form a distinct, monophyletic clade and are accommodated in the genus ‘Candidatus Phytoplasma’. The phylogenetic analysis of the phytoplasma 16S rRNA gene forms the basis of phytoplasma classification. In the genus ‘Ca. Phytoplasma’, new species are designated when organisms share less than 97.5% similarity in the sequence of their 16S rRNA gene (IRPCM 2004). The phytoplasma 16Sr group/sub group classification is done based on the RFLP profile of phytoplasma 16S rRNA gene (Lee et al. 1998). Based on the similarity coefficients derived from the RFLP analyses of phytoplasma 16S rRNA gene, Lee et. al. (1998) differentiated 34 representative phytoplasma strains into 14 major groups (termed as 16Sr groups) and 32 sub-groups. Further, the advent of in silico RFLP method which involves computer-simulated RFLP analysis using high-quality sequence data has enabled virtual phytoplasma classification (Wei et al. 2007). Apart from 16S rRNA gene, the use of other markers like ribosomal protein genes, 16S/23S rRNA intergenic spacer sequences, 23S rRNA gene, tuf gene, secA gene, secY gene, rpoB gene, GroEL gene and amp gene as additional keys for detection and differentiation of phytoplasmas has been reported (Martini et al. 2019). Manimekalai et al. (2013) used the phytoplasma secA gene for phylogenetic classification of the Indian arecanut yellow leaf disease phytoplasma (Fig. 1).

Fig. 1
figure 1

Phylogenetic dendrogram constructed by neighbor-joining method with MEGA software showing the phylogenetic relationships of partial secA gene of arecanut YLD phytoplasma and 41 other known phytoplasma sequences and Bacillus subtilis as the out group. Bootstrap values are expressed as percentage of 1000 replications

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Phytoplasma diagnostics

Phytoplasma diseases detection was based on symptom expression and transmission studies by grafting, insect vectors or dodder. Microscopy and serology based techniques enabled more reliable and specific pathogen detection. Light microscopy techniques with Diens staining, which gives blue colouration and the fluorescence based DAPI staining of phytoplasma nucleic acid have been used for phytoplasma detection (Deeley et al. 1979; Seemuller and Kirkpatrick 1996). Scanning Electron Microscopy (SEM) was applied for observing phytoplasma in plant tissues (Haggis and Sinha 1978). Marcone and Ragozzino (1996) compared the ultrastructural features of several genetically different phytoplasmas using SEM. Phytoplasmas appeared as polymorphic bodies attached to the inner surface of the sieve tubes. They could not observe significant morphological differences between phytoplasmas belonging to different phylogenetic clusters. Transmission Electron Microscopy (TEM) is a reliable technique for phytoplasma detection and the very first report on the association of phytoplasma with aster yellows disease was made by Doi et al. (1967) by employing TEM for localization of phytoplasmas in the sieve tubes of infected plants.

Serological methods for phytoplasma detection emerged as early as in 1980s (Jiang et al. 1989; Chen and Jiang, 1988). Enzyme Linked Immuno Sorbant Assay (ELISA) protocols have been optimised for several phytoplasma species (Cheng et al. 1995; Brzin et al. 2003; Sasikala et al. 2010; Kanatiwela-de Silva et al. 2019). Polyclonal antisera developed against partially purified phytoplasma sample from host plants are being routinely used for serological detection (Sasikala et al. 2001; Kanatiwela-de Silva et al. 2019). Since extraction of sufficient phytoplasma cells to be used as immunogen for antisera development is difficult, especially in woody hosts where pathogen titre is low, cross reaction to host material is a common problem with ELISA based detection (Kenyon et al. 1998). For more specific detection, phytoplasma membrane proteins like the immuno-dominant membrane protein and SecA protein have been used as immunogens for developing antisera (Wambua et al. 2017; Manimekalai et al. 2015). Shahryari et al. (2013) developed polyclonal antiserum against the Ca. P. aurantifolia immunodominant membrane protein gene and used it for DAS-ELISA and dot immunosorbent assay based phytoplasma detection. Koinuma et al. (2020) studied the spatio-temporal distribution of onion yellows phytoplasma in its insect vector using immune-histochemical staining, wherein the antibody raised against Amp (antigenic membrane protein) of phytoplasma was used for detection.

Phytoplasma detection has witnessed a new dawn with upsurge in the field of molecular biology. Along with accurate detection, the molecular techniques also enabled classification of phytoplasma. Krikpatrik et al. (1987) reported for the first time the cloning of DNA of phytoplasma associated with the Western X disease from the insect vector. This gave way to probe based detection and classification using phytoplasma DNA specific probes (Bonnet et al. 1990; Davis et al. 1988). Lefol et al. (1993) reported the attachment phytoplasma to insect vectors and shown the propagation of Flavescence dorée phytoplasma in the leafhopper vector Euscelidius variegatus Kbm (Lefol et al. 1994). Since then, different molecular detection platforms ranging from nested PCR to quantitative PCR and the isothermal amplification have been optimised for the detection of phytoplasma associated with diseases of various crops and insect vectors worldwide.

Nested PCR

Deng and Hiruki (1991) for the first time reported the amplification of phytoplasma 16S rRNA gene, thus leading to an era of PCR based phytoplasma detection. They designed five pairs of primers specifically amplifying 16S rRNA gene of Mollicutes; the plant and E. coli DNA remaining unamplified with these primers. Since then, PCR amplification of the 16S rDNA using phytoplasma specific universal and group-specific primers has been a part of phytoplasma molecular detection (Schaff et al. 1992; Harrison et al. 1994a, b; Jarausch et al. 1994; Harrison, 1996). The primer pair P1 (Deng and Hiruki 1991) and P7 (Schneider et al. 1995) is the routinely used universal primer pair for phytoplasma detection. The P1/P7 primer pair amplifies the entire 16S rRNA gene along with the 16S/23S rRNA spacer region. For PCR based phytoplasma detection, Gundersen and Lee (1996) developed a universal primer pair R16mF2/R1 and a modified primer pair R16F2n/R2 (Lee et al. 1993) by comparative analysis of 16S rRNA gene sequences from 19 phytoplasma and 48 related Mollicutes. Group specific primers for phytoplasma detection were developed targeting the sequence diversity of the phytoplasma 16S–23S intergenic spacer regions (Smart et al. 1996).

The classical molecular detection of phytoplasma was based on the amplification and characterization of highly conserved 16S rRNA gene. However, the concentration of these phloem-limited pathogens is often low in the host plants and is also subjected to seasonal variation (Nakamura et al. 1998). The titre is even low in woody hosts where the distribution of phytoplasma is mostly uneven within the host. This low titre of phytoplasma in host plants and the presence of inhibitors in DNA preparations necessities nested PCR reaction to increase the detection sensitivity with the additional advantage of increasing the detection specificity. There are many universal and group specific primers for nested PCR-based amplification of phytoplasma 16S rDNA. Lee et al. (1994) developed group specific oligonucleotide primers targeting phytoplasma 16S rDNA for sensitive nested PCR detection. Universal primers targeting the ribosomal RNA gene nested with group specific primers are used for the detection of phytoplasma belonging to well defined taxonomic groups. In case of less characterized phytoplasmas, nested PCR with a different universal primer pair is the method of choice (Marzachi 2004). However, in many cases, where the universal primers do not give high percentage of positive amplification from symptomatic samples, there is the need to design specific primers for that phytoplasma. Nested PCR based molecular detection techniques for coconut root wilt disease and arecanut yellow disease have been developed by designing specific primers (Manimekalai et al. 2010a, b). The semi-nested primers 1F7-7R3/1F7-7R2 amplifying 490 bp region of phytoplasma 16S rDNA efficiently detected phytoplasma from yellow leaf diseased arecanut samples (Fig. 2). The nested PCR approach has been used for detection of phytoplasma associated with diseases of diverse crops world-wide (Table 1). However, the nested PCR based detection system has associated disadvantages. The method is time-consuming since two rounds of PCR followed by agarose gel electrophoresis are needed for visualization of positive amplicons. Being an open tube system with several sample handling steps nested PCR is more prone to generate carry over contamination (Cordova et al. 2014; Galetto et al. 2005). This often ends up in false positives, thus reducing the efficiency of molecular diagnostics (Youssef et al. 2017).

Fig. 2
figure 2

Amplification of phytoplasma 16S rRNA gene using semi nested primers 1F7/7R3 – 1F7-7R2 (product size 490 bp) from yellow leaf disease symptomatic arecanut palms. Lanes 1–13 DNA samples from diseased palms; Lane 14 Positive control, Lane 15 Healthy arecanut sample, Lane 16 NTC (No template control), M Molecular weight marker

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Table 1 Phytoplasma detection based on nested PCR approach
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Quantitative PCR

The second generation PCR known as real time PCR, with the potential to increase the accuracy and reliability of pathogen detection, emerged as the new gold standard for pathogen detection (Alemu 2014). As the name suggests, the real time PCR technique enables us to monitor the PCR reaction progress in real time. The technique allows simultaneous quantification of the target DNA in a sample and is often called quantitative PCR. For pathogens which cannot be extracted from host tissue, or are present at low inocula in diseased samples, the quantitative PCR has emerged as a tool for accurate detection and quantification (Mirmajlessi et al. 2015; Abou-Jawdah et al. 2019). In real time PCR, the end point detection associated with conventional PCR is replaced with real time monitoring with the aid of fluorescent molecules incorporated in the PCR reaction mixture. The real time PCR is advantageous over conventional PCR in the field of diagnostics since it is a closed tube contained system, eliminating the need to further confirm the identity of PCR product and hence is less prone to cross contamination. Both the amplicon sequence independent and the sequence specific real time PCR detection chemistries have been utilised for phytoplasma detection from various hosts (Table 2).

Table 2 Real time PCR based diagnostics for phytoplasma
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The double stranded DNA intercalating dye, SYBR Green, has been widely used for real time PCR-based detection of phytoplasma. The real time PCR output is represented as the number of PCR cycles required to achieve a given level of fluorescence (Ponchel et al. 2003). The PCR cycle number at which distinct fluorescence is detected above background noise is called cycle threshold (Ct). When we plot the number of PCR cycles against the increasing fluorescence, we get the amplification plot in a real time PCR (Fig. 3a). The binding of SYBR Green dye to double stranded DNA molecules does not depend on the DNA sequence being amplified. Hence, any non-specific amplicon and primer dimmer need to be discriminated from the specific amplification. For this, a melt curve/dissociation curve analysis is programmed after the PCR amplification, wherein the temperature is raised from 30 to 40 °C to 95 °C whilst continuously reading the fluorescence. Since the melting temperature depends on the length and GC content of the PCR products, the PCR products of different lengths and sequences can be observed as distinct fluorescent peaks (Alemu 2014). A single melting peak represents a single specific product. Galetto et al. (2005) detected the flavescence doree, bois noir and apple proliferation phytoplasmas using SYBR Green real time PCR along with melt curve analysis. Nair et al. (2014) observed the unique melting peak at 82.3 ± 0.5 °C after amplifying a fragment of arecanut yellow leaf disease phytoplasma 16S rRNA gene using the SYBR Green real time PCR route (Fig. 3b).

Fig. 3
figure 3

a Amplification plot for the yellow leaf diseased arecanut samples and positive control (grassy shoot diseased sugarcane sample). The Ct value is calculated by plotting the normalized fluorescence against the number of cycles. b Dissociation curve analysis. Melting peak is obtained at 82.3 ± 0.5 °C for diseased arecanut and the positive control. NTC showed no melting peak (Nair et al., 2014)

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Fluorescent probes are DNA fragments complementary to the gene of interest and labelled with fluorescent dyes (Alemu 2014). The real time PCR with dual labelled probes, commonly called TaqMan probes (trade name of Roche Molecular Systems, Inc) utilizes the 3′ to 5′ exonuclease activity of Thermus aquaticus DNA polymerase for generating the signal for PCR product detection during the amplification step (Holland et al. 1991). Fluorophores like FAM, TET, HEX and ROX and quenchers like TAMRA, methyl red and DABCYL are commonly used to monitor the real time PCR assays (Wittwer et al. 2001). The probe based assay ensures specificity due to the need for precise binding of the probe to DNA template, thus eliminating the chances of false positives through mismatch primer annealing. The probe degradation occurs only in a target specific manner (Holland et al. 1991). The probe based real time PCR assays have well been utilized in the field of phytoplasma detection and quantification from diseased plant samples. Taqman real time PCR using FAM as the fluorescence emitter and TAMRA as the quencher has been developed for detecting the phytoplasma associated with the coconut root wilt disease and the arecanut yellow leaf disease in India (Nair et al. 2015, 2016b). Figure 4 shows the amplification plot in the probe based real time PCR detection of coconut root wilt disease phytoplasma. Nikolic et al. (2010) developed Taqman minor groove binder probes to specifically detect Ca. P. mali, Ca. P. pyri and Ca. P. prunorum. Nejat et al. (2010) observed high diagnostic sensitivity for Taqman real time PCR when compared to the conventional nested PCR for analysing the field collected coconut samples infected with coconut yellow decline phytoplasma by revealing some symptomatic samples which tested negative in the nested PCR assay. Marzachi and Bosco (2005) reported the absolute quantification of chrysanthemum yellows phytoplasma in terms of genomic units of phytoplasma DNA in each nanogram of host DNA using a dilution series of plasmid DNA cloned with 16S rRNA gene of the phytoplasma. In yet another study the real time PCR route has been utilized for monitoring the in planta distribution of pathogen in host plant (Tatineni et al. 2008). The Taqman real time PCR was employed for the detection and quantification of coconut lethal yellowing phytoplasma from different plant tissues. The highest concentration of phytoplasma was observed in trunk followed by root apex, mature inflorescence, spear leaf, flag leaf and mature leaf. Moreover, the assay was found to be superior to nested PCR in terms of sensitivity as a number of nested PCR negative samples also tested positive in the probe based real time PCR (Cordova et al. 2014). In terms of the time required for detection, the real time PCR was found to be more than two times faster than nested PCR in the detection of aster yellows phytoplasma (Demeuse et al. 2016). Hence, the probe based real time PCR has been widely used in phytoplasma detection and quantification from different hosts (Bahder et al. 2020; Kogej et al. 2020).

Fig. 4
figure 4

Amplification plot for root wilt diseased coconut samples. The Ct value has been calculated by plotting the normalized fluorescence against the number of cycles. NTC, NPC (No probe control) and Healthy have no Ct (Nair et al., 2016b)

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ddPCR

Digital PCR is the third generation nucleic acid amplification technology for sensitive and absolute nucleic acid quantification which works by partitioning a sample of DNA into a high number of single, parallel PCR reactions (Morcia et al. 2020). Reactions are carried out in separated and numerous small volume compartments ideally holding one or none of the target molecule; thus after absorbance measurements, a compartment containing no target molecule is counted as zero, whereas a compartment with one target is counted as one. Hindson et al. (2011) described the high throughput low cost droplet digital PCR (ddPCR) using water-in-oil droplets which support PCR amplification of single template molecules using chemistries similar to that of TaqMan real time PCR. Since then, the method finds use in absolute quantification of target DNA sequences. The use droplet digital PCR has also been applied for phytoplasma detection and quantification. Bahder et al. (2018) developed highly sensitive TaqMan based digital PCR assay for the detection of 16SrIV group phytoplasma infecting palms in Florida. The assay targeted the 16S rRNA gene of phytoplasma and could detect phytoplasmas at much lower concentrations than was possible through real time PCR and nested PCR. Perez-Lopez et al. (2017) obtained accurate quantification of strawberry green petal phytoplasma using ddPCR and they observed that the method could consistently detect samples with as few as 10 copies of phytoplasma per reaction. In another study, Mehle et al. (2014) used ddPCR targeting the secY gene of phytoplasma for absolute quantification of flavescence doree phytoplasma in grapevine.

Isothermal diagnostics approach—LAMP

The nucleic acid based diagnostics like PCR and real time PCR have revolutionized the field of plant pathogen detection. However, the need for expensive instrumentation for thermal cycling and in case of real time PCR, additional concurrent monitoring of fluorescence often increases the cost of detection and restricts the large scale use in routine diagnostics (Tomlinson and Boonham 2008). Moreover, inhibitors in crude DNA preparation hinder the Taq DNA polymerase activity and hence the DNA samples need to be purified so as to support PCR amplification (Dickinson 2015). Thus there is always a need for simple, low cost yet sensitive phytoplasma detection protocol amenable for on-field diagnostics. Numerous isothermal amplification methods like nucleic acid sequence-based amplification, helicase-dependent amplification, strand displacement amplification, rolling circle amplification and others have been reported so far for overcoming the need for thermal cycling (Van-Ness et al. 2003). The LAMP (Loop Mediated Isothermal Amplification), an isothermal nucleic acid amplification platform devised by Notomi et al. (2000), has emerged as a popular tool for phytoplasma molecular detection. It is an auto cycling strand displacement DNA synthesis catalysed by a strand displacing DNA polymerase using four specially designed primers (two inner primers and two outer primers) that bind to six distinct sequences on the target DNA. The essence of the reaction is the inner primer pair having two discrete regions corresponding to the sense and antisense sequences of the target DNA, one for the initial priming and other for self-priming during subsequent amplification. In the LAMP reaction, due to the interaction of the forward and reverse internal primers binding at each end of the target region, an intermediate structure with a stem-loop at both the ends forms and this acts as the template for further rounds of amplification. In the initial steps of LAMP, all four primers are needed, but in the later cycles only the inner primers are needed for strand displacement DNA synthesis (Notomi et al. 2000). Here amplification takes place at a constant temperature of 60–65 °C in the presence of a DNA polymerase with strand displacing property like the Bst polymerase. The method can produce 109 copies of a target in less than an hour with high specificity and speed. The inclusion of an additional pair of primer, the loop primers, further increases the rate of amplification by allowing every loop region to act as primer binding site (Nagamine et al. 2002). The LAMP products consist of a mixture of stem loop DNAs of different stem lengths and alternately inverted repeats of the target sequence forming cauliflower like structures with multiple loops (Notomi et al. 2000).

End point detection of positive LAMP reaction can be made by agarose gel electrophoresis where a typical ladder-like pattern is observed since the LAMP products contain varying numbers of inverted repeats of the target (Fig. 5a). The amount of magnesium pyrophosphate produced as the by-product of LAMP reaction is very high resulting in turbidity in case of positive LAMP reaction (Mori et al. 2001). Observing for the turbidity at the end of LAMP assay is also a method for identification of positives. Tomita et al. (2008) developed visual detection system for LAMP by including manganous ion and calcein to the reaction and observing variation in the fluorescence. A colorimetric detection for LAMP positives has been developed by adding a metal indicator hydroxy naphthol blue (HNB) to the reaction mixture prior to amplification and by monitoring the colour change with respect to change in the Mg2+ ion concentration (Goto et al. 2009). Positive samples show a colour change from violet to sky blue at the end of the reaction (Fig. 5b). As like real time PCR, real time monitoring of LAMP reaction is also possible by incorporating fluorescent molecules in the LAMP reaction mixture. The Genie II instrument (OptiGene, Horsham, UK) has been developed for performing real time LAMP assays. The real time LAMP produces amplification plots that are similar to that of real time PCR (Fig. 6a). Here also, the fidelity of the amplicons can be determined by an annealing curve analysis with concurrent fluorescence monitoring programmed at the end of the isothermal amplification process (Fig. 6b). The annealing temperature is characteristic to each amplicon (Tomlinson 2013).

Fig. 5
figure 5

a Detection of LAMP amplicons by agarose gel electrophoresis. Lanes 1–6 – YLD symptomatic arecanut samples; NTC no template control; S positive control; M1 1 Kb DNA ladder (Fermentas). b Colorimetric analysis of LAMP products using HNB dye. RWD1, RWD2 root wilt diseased coconut samples, NTC no template control, SCGS positive control (grassy shoot diseased sugarcane sample). The positive samples show blue color and the NTC remain violet. (Nair et al., 2016a)

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Fig. 6
figure 6

Amplification plot (a) and annealing peak (b) generated in the real time LAMP assay. Wells 1, 2 NTC (no amplification); 3 Healthy coconut sample (no amplification); 4, 5 root wilt disease symptomatic coconut samples; 6, 7 yellow leaf disease symptomatic arecanut samples, 8 Positive control, Sample 4 tested negative, samples 5–8 positive (Nair et al., 2016a)

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The LAMP technique has been successfully employed for the detection of phytoplasma associated with several devastating diseases world over (Table 3). The LAMP is advantageous over the conventional molecular detection protocols in terms of sensitivity, specificity and ease of detection. Obura et al. (2011) observed that the LAMP assay is 20 times more sensitive than conventional nested PCR assay while working on the detection of Napier grass stunt phytoplasma. Regions other than the phytoplasma 16SrRNA gene have also been targeted in LAMP based detection of phytoplasma. Sugawara et al. (2012) targeted the groEL gene of Ca. P. asteris and observed that the LAMP assay had ten-fold higher efficiency in phytoplasma detection compared to PCR. Shengjie et al. (2017) developed LAMP assay for detecting the 16SrI group phytoplasmas by targeting the phytoplasma tuf gene. Kogovsek et al. (2015) developed real time LAMP assay for field level detection of flavescence doree phytoplasma affecting grapevine. The whole assay from sample preparation to detection could be completed in an hour. Yankey et al. (2011) observed that in real time LAMP based detection of coconut lethal yellowing phytoplasma; amplicons could be observed as early as 12 min from the start of the reaction. The LAMP assay with the benefits of high amplification rate, short run times and simplified amplicon detection also has a potential disadvantage; the risk of carry-over contamination with amplicons from previous reaction is higher than that for methods where fewer products are generated (Tomlinson and Boonham 2008). Careful sample handling and closed tube detection methods will effectively address this concern. As real time detection requires no post-amplification manipulation, there is no risk of carryover contamination. The portable real time LAMP systems make field level diagnostics simple and convenient (Tomlinson 2013). The Bst-type DNA polymerases used in LAMP are much less susceptible to inhibitors in DNA preparations than Taq polymerase used in PCR and hence crude DNA preparations can be used for LAMP reaction (Dickinson 2015). The LAMP technique when combined with simple sample preparation protocols can be used for in-field detection of phytoplasma (Hodgetts 2019).

Table 3 Application of LAMP technique for molecular detection of phytoplasma
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Apart from LAMP, there are other isothermal amplification platforms like the Recombinase Polymerase Amplification (RPA) and helicase-dependent amplification (HDA) suitable for pathogen detection. Valasevich and Schneider (2017) developed the isothermal RPA assays for the specific detection of Ca. P. mali, the causal agent of apple proliferation disease. They targeted the phytoplasma imp gene and detection was made both by a fluorophore labelled probe and by direct visualization employing a lateral flow device. Villamor et al. (2019) developed RPA assays targeting the phytoplasma immuno-dominant protein coding regions for rapid detection of ‘Ca. P. pruni’ associated with the Western X disease of sweet cherry.

Second and third generation sequencing techniques—new trends in phytoplasma detection

Plant pathogen detection witnessed a quantum leap with the advent of next generation sequencing technologies. The high throughput second generation sequencing and the single molecule long read third generation sequencing have enabled accurate pathogen identification and simultaneous detection of multiple pathogens. Single DNA sequencing technologies will enable the development of portable on-site plant pathogen detection devices (Nezhad, 2014). Zikeli et al. (2018) applied next generation sequencing based on Illumina MiSeq platform for simultaneous detection of viruses, viroids and phytoplasmas in grapevine and fruit tree samples. The method allowed the detection of low titre infections in tissues as well as parallel detection of multiple pathogens form a single sample. Chalupowicz et al. (2019) reported the use of Oxford Nanopore single-molecule sequencing platform for the detection and identification of different plant pathogens including phytoplasma. They used the method to identify Ca. P. aurantifolia’ from infected Catharanthus roseus plants.

Nanotechnology in phytoplasma disease management

Futuristic approaches in phytoplasma disease detection and management will be based on the application of nanotechnology. The extension of molecular diagnostics to a nano scale is a promising technology for identifying pathogens; nano-phytopathology being a cutting-edge science using nanotechnology for diagnosing and controlling plant diseases and their pathogens at an early stage (Khiyami et al. 2014). Nanotechnology provides tools for cheap, rapid and accurate detection and monitoring of plant pathogens in terms of portable diagnostic equipment, nano particle-based bio barcoded DNA sensor, quantum dots (QDs), nanostructured platforms and nano diagnostic kits (Kashyap et al. 2017). Biotechnological advances and the precise diagnosis tools with the use of nanomaterials have a promising future in plant disease diagnosis at an early stage (Duhan et al. 2017). Biosensors that make use of nano and bio-molecular assemblies are now replacing conventional diagnostic methods. A biosensor is a self-contained analytical device with a biologically active material in contact with a transducer to sense chemical or biochemical phenomena occurring at sensor surface and to ultimately convert the biological recognition response into an electrical signal represented as output display (Kumar and Arora 2020).

An early investigation on the use of a nano-biotransducer for the detection of flavescence doree phytoplasma was made by Firrao et al. (2005). The nano-biotransducer performing as a molecular beacon bearing a fluorescein at its 5′ end and a 2 nm gold particle at its 3′ end, acting as the quencher, emitted a strong fluorescence signal when hybridised to target DNA. The probe was used to confirm the identity of PCR amplification obtained from DNA extracted from grapevine plants affected by flavescence doree. A quantum dot (QD)-based nano-biosensor for sensitive detection of Ca. P. aurentifolia associated with lime witches’ broom has been developed by conjugating the antibody against immunodominant membrane protein to tioglicolic acid-modified cadmium-telluride quantum dots. This immunosensor showed high sensitivity and 100 percent specificity in detection (Rad et al. 2012). A DNA biosensor has been developed for the detection of sugarcane white leaf (SCWL) phytoplasma (Wongkaew 2012). The label free DNA biosensor using the whole chromosomal single stranded DNA of SCWL phytoplasma immobilized on chitosan-modified glassy carbon electrode coupled with methylene blue intercalator allowed specific indication of the SCWL-DNA hybridization. Quantitative determination of SCWL phytoplasma DNA in the target sample with a detection limit of 0.1 nM was reported. Apart from pathogen detection, nanotechnology also finds application in phytoplasma disease control. Neda (2015) reported the antimicrobial activity of nano silver against coconut yellow decline phytoplasma where significant reduction of phytoplasma concentration was observed within five months of nano silver injection in to the palms. In another report, Czarnobai De Jorge and Gross (2018) described the management of phytoplasma vectoring psyllids in fruit trees of the family Rosaceae by using nanofibers for emitting insect repellent compounds. The use of nanomaterials in plant science is still in its infancy. More studies are needed in this direction to optimize the synthesis and bio-functionalization of nano materials for plant applications (Sanzari et al. 2019).

Role of host pathogen interaction studies in phytoplasma disease management

We know that phytoplasma disease management depends mainly on disease prevention. Deciphering the mechanism behind phytoplasma-plant interaction at a molecular level can provide new methods for disease management by developing resistant genotypes, improving diagnostic methods and controlling disease spread. The phytoplasma-host-vector interaction is a complex process. Phytoplasma secrete effectors which regulate plant growth and disrupt plant pathways to aid their colonization and transmission (Sugio et al. 2011; Sugio and Hogenhout 2012). Phytoplasma effectors modulate their plant and insect hosts, thus providing a fitness advantage to these pathogens. Some of the phytoplasma effectors include SAP11, SAP54, SAP09, SAP11, TENGU, etc.; and phytoplasmas have a functional Sec translocation pathway for the secretion of these effectors (Rashid et al. 2018). The abundant phytoplasma cell-surface protein namely antigenic membrane protein (Amp) is known to take part in phytoplasma–vector interactions (Tomkins et al. 2018). Rashidi et al. (2015) demonstrated the role of phytoplasma antigenic membrane protein in transmission of Ca. P. asteris by insect vectors. These proteins were associated with two phases of the vector transmission process namely, movement through the midgut epithelium and colonization of the salivary glands.

Elucidation of the differentially expressed genes in host under healthy and diseased conditions will enhance our understanding of disease development process and plant’s defence responses under phytoplasma infection. There are diverse techniques available for studying differential gene expression in plants like Suppression Subtractive Hybridization, Expressed Sequence Tags analysis, Differential Display, Serial Analysis of Gene Expression, cDNA-Amplified Fragment Length Polymorphism, microarrays and RNA Seq (Casassola et al. 2013). Differential gene expression studies are often based on transcriptome analysis. Comparative transcriptomics for unravelling host-phytoplasma interaction is now well exploited by phytoplasma researchers globally and hence our understanding towards that extent has substantially increased. Nejat et al. (2015) studied the phytoplasma mediated transcriptional alterations in coconut palm infected with yellow decline phytoplasma using RNA Seq technique and observed the reprogramming of several biological and cellular processes in response to phytoplasma infection. The disruption of host pathways in relation to phytoplasma infection may either be due to the host’s attempt to combat the pathogen or due to the effectors secreted by the phytoplasma ultimately modifying host pathways to suit its needs. Mardi et al. (2015) performed in depth transcriptome sequencing of Mexican lime trees infected with Ca. P. aurentifolia to study the host pathways that were deregulated during phytoplasma infection. Transcriptome analysis was also used to identify the mechanism behind the morphological alterations in paulownia plants in response to paulownia witches’ broom phytoplasma infection (Fan et al. 2015). In jujube plants with witches broom disease, co-regulation of multiple pathways including phytohormone biosynthesis, signal transduction, photosynthesis and secondary metabolite synthesis was observed (Wang et al. 2018). Abba et al. (2014) employed the RNA Seq approach for global transcriptome profiling in grapevine during phytoplasma infection and the results gave an idea about transcriptional organization and gene structure of flavescence doree phytoplasma. Liu et al. (2014) employed transcriptome analysis to understand leaf-flower transition in Catheranthus under peanut witches’ broom phytoplasma infection. They could identify differentially expressed genes involved in developmental reprogramming and plant’s defence response. In Cranberry false blossom disease, phytoplasma infection was found to increase the expression of genes associated with nutrient metabolism and suppress the genes associated with defensive pathways, hence following “host manipulation hypothesis,” whereby phytoplasma enhances host quality for insect vectors, thus promoting phytoplasma transmission (Pradit et al. 2019). Studies on host pathogen interaction will also enable the development of disease diagnostics based on altered host gene expression using quantitative real time PCR. Moreover, transcript profiling under healthy and diseased condition will throw lights in to host genes deregulated under infection and hence will provide specific leads for breeding resistant lines, which is important in the management of phytoplasma diseases.

Impact of changing climatic scenario on the occurrence and severity of phytoplasma diseases

As is known, plant disease is the result of interaction between a susceptible host plant, virulent pathogen, and the environment (Elad and Pertot 2014). Hence, the implications of changing environment on plant disease severity are considerable. On one side, plant resistance pathways and defence hormone networks are all influenced by environmental factors while on the other side, pathogen virulence mechanisms, as well as pathogen reproduction and survival are affected by temperature and humidity (Velásquez 2018). The impact of climate change is even higher in case of vector borne plant pathogens like viruses and phytoplasma wherein the changing climate influences the feeding behaviour and host range of the insect vector (Fig. 7). More and more studies are being conducted to understand the influence of climate change on plant diseases and voluminous data are being generated (Zhou et al. 2019; Gullino et al. 2018; Chitarra et al. 2015; Maggi et al. 2014).

Fig. 7
figure 7

Influence of environment on the occurrence of vector borne phytoplasma diseases

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In case of economically important phytoplasma diseases also, efforts have been made to unearth the influence of climatic variations on disease development and spread. The geographical distribution and impact of phytoplasma diseases primarily depends on the host range as well as the feeding preference of insect vectors (Kumari et al. 2019). Phytoplasma-related diseases are expected to increase in the coming years because global warming is advantageous to the cold-sensitive phytoplasma vectors (Hogenhout et al. 2008). The climate changes resulting from global warming may facilitate the spread of phytoplasma diseases to new areas and to additional crops, particularly as the vectors become more widespread and survive warmer winters (Krishnareddy 2013). Cagirgan et al. (2013) reported a positive influence of climate change in the West Mediterranean region of Turkey, wherein unusually cool and rainy growing season in the year 2010 led to the disappearance of vector borne sesame phyllody disease in the region where the disease had been devastating for the past five years. The cool season probably unmatched the vector’s cycle to develop phyllody. Galetto et al. (2011) studied the multiplication patterns of two phytoplasmas, chrysanthemum yellows and flavescence doree in their insect vectors and plant hosts under different climatic conditions, by providing controlled temperature and carbon dioxide. They observed that phytoplasma multiplication was faster under cooler conditions in insect vectors and under warmer conditions in plant hosts, suggesting that the influence of temperature and carbon dioxide on phytoplasma multiplication is host-dependent. More studies to understand the genomics of climatic adaptation in host plant, vector and phytoplasma need to be conducted in future to get better picture of the influence of changing environment on phytoplasma diseases.

Conclusion

Worldwide, devastating diseases in many crop plants are the result of phytoplasma infection. Prevention rather than treatment is the approach advocated in case of phytoplasma diseases. Early and efficient diagnostics, vector control and use of phytoplasma resistant varieties are the common strategies in disease management. Molecular diagnostic techniques have facilitated early, quick and accurate disease detection, thus enabling roughing-off of infected plants at an early stage thereby preventing disease spread followed by replanting with indexed disease free planting materials. Understanding the molecular response of host plant to phytoplasma infection will help in breeding resistant varieties. Planting of resistant varieties in disease endemic areas plays a salient role in disease prevention. As the change in earth’s climate in the years to come will further influence the severity and spread of phytoplasma diseases, more studies are to be undertaken to unravel the molecular mechanism of climate adaptability in plant host, insect vector and phytoplasma.