Toward Decentralized Agrigenomic Surveillance? A Polymerase Chain Reaction–Restriction Fragment Length Polymorphism Approach for Adaptable and Rapid Detection of User-Defined Fungal Pathogens in Potato Crops

Abstract

Agrigenomics is one of the emerging focus areas for omics sciences. Yet, agrigenomics differs from medical omics applications such as pharmacogenomics and precision medicine, by virtue of vastly distributed geography of applications at the intersection of agriculture, nutrition, and genomics research streams. Crucially, agrigenomics can address diagnostics and safety surveillance needs in remote and rural farming communities or decentralized food, crop, and environmental monitoring programs for prompt, selective, and differential identification of pathogens. A case in point is the potato crop that serves as a fundamental nutritional source worldwide. Decentralized potato crop and plant protection facilities are pivotal to minimize unnecessary, preemptive use of broad-spectrum fungicides, thus helping to curtail the costs, environmental burden, and the development of resistance in opportunistic human pathogenic fungi. We report here a polymerase chain reaction–restriction fragment length polymorphism approach that is sensitive and adaptable in detection and broad identification of fungal pathogens in potato crops, with a view to future decentralized agrigenomic surveillance programs. Notably, the fingerprinting patterns obtained by the method fully differentiated 12 fungal species examined in silico, with 10 of them also tested in vitro. The method can be scaled up through improvements in electrophoresis and enzyme panel for adaption to other crops and/or pathogens. We suggest that decentralized and integrated agrosurveillance programs and translational agrigenomic programs can inform future innovations in multidomain biosecurity, particularly across omics applications from agriculture and nutrition to clinical medicine and environmental biosafety.

Introduction

Omics technologies have tended to find vast applications in the medical knowledge domain, but the potential applications of omics science extend to a much wider context, so as to include, among others, agriculture and environmental health, biosafety, and biosecurity.

A notable example is agrigenomics, at the intersection of agriculture, plant biology, nutrition, and genomic research streams. One of its key differences with medical omics fields, such as pharmacogenomics and precision medicine, is the highly distributed geography of applications (Dwivedi et al., 2016; Taheri et al., 2018). Crucially, agrigenomics can address plant diagnostics, nutritional and environmental security, and, most importantly, safety surveillance needs in remote and rural farming communities. Very efficient application of the field may be envisaged at decentralized food, crop, and environmental monitoring setups for prompt, selective, and differential identification of a wide range of pathogens.

Indeed, both naturally occurring and perpetrated plant diseases might escalate to catastrophic events, affecting the health and economy of whole communities or destabilizing nation states and societies by causing food shortage or disrupting agriculture-based economies (Khalil and Shinwari, 2014). The prompt and accurate identification of putative plant pathogens are pivotal for the containment and suppression of disease outbreaks, especially, but not exclusively, if combined with the use of translational agrigenomics (Katara et al., 2012). The latter provides specific targeting, thus eliminating intense, proactive, or reactive interventions such as spraying with wide-spectrum fungicides.

Decentralized surveillance units usually operate on austere infrastructure, equipment, and manning conditions, especially in countries with unreliable transportation as a result of discontinuous terrain, challenging weather, and/or vast distances between reference laboratories and crops.

We report here a polymerase chain reaction–restriction fragment length polymorphism (PCR-RFLP) approach that is sensitive and adaptable in detection and broad identification of fungal pathogens in potato crops, in a context of decentralized agrigenomic surveillance. Notably, the fingerprinting patterns obtained by the method fully differentiated 12 fungal species examined in silico, with 10 of them also tested in vitro. The method can be scaled up through improvements in electrophoresis and enzyme panel for adaption to other crops and/or pathogens.

Materials and Methods

Context for the research methodology

This approach to agricultural surveillance is to support locally deployed phytopathologists who, up to now, examine diseased plants by the form and location of symptoms or disease signs, and perhaps cultivation of a number of samples.

Both host organ selectivity and disease signs and symptoms should be regarded as specific events of a defined, but up to now elusive host-symbiont interaction, with solid causality, affected by circumstance, but not randomness. Unfortunately, this functional network approach is only recently attracting interest in an integrated, multifaceted, and multiomic manner, even in human subjects (Culibrk et al., 2016).

Pasting the above to diseased/infected plants and amassing enough data, comprehension, and understanding to establish causality for signs and selectivity in sites of infection will take a while, maybe not very short. Until then, experience, statistics and empiricism will reign supreme and will be prone to misidentification due to similarity or outright relaxed specificity of signs.

In such conditions, state-of-the-art concepts (Lees et al., 2012), permitting very high sensitivity (Atallah et al., 2007) and specificity (Tooley et al., 1997; Trout et al., 1997; Tsui et al., 2011), even in the most demanding task of detecting and identifying a pathogen in soil samples (Cullen et al., 2005; Errampalli et al., 2001) are inapplicable. In addition, ready-to use, commercially available standardized sample preparation kits and dedicated hardware (Brierley et al., 2009; Okubara et al., 2007) are likely to prove cumbersome and unresponsive.

To overcome these difficulties, well-proven, adaptable, and low-cost solutions with minimal investment in equipment and expendables are needed. Their use might prove instrumental for curtailing time and expenditure in sample processing and avoiding sample surges. The latter may affect and even saturate the state-of-the-art (Atallah et al., 2007), highly capable, but always few reference laboratories (Ramage et al., 2016), especially when a crisis develops.

In such a context, an early-warning function is envisaged for rapid identification of potato fungal pathogens. This approach could contribute to prompt decision-making for effective control of potato fungal pathogens in decentralized/remote areas, without previous dedicated phytobiomic and agrigenomic analyses and data.

Later on, such an approach may also be linked to a wider alert network based on real-time crop disease monitoring, assisting in disease control and outbreak suppression. In such a context, it can be integrated into novel, multipotent biosurveillance structures engaged in biosecurity tasks and encompassing all three biorisk areas: public health, animal health, and agriculture/environment. Such multipotent or omnipotent facilities, even austere ones, follow the prerogatives of initiatives like “Global Health Security Agenda” and “One Health” (Hekim and Ozdemir, 2017; Nuzzo, 2017; Schoch-Spana et al., 2017; Thelaus et al., 2017; Wolicki et al., 2016).

To allow fast and reliable molecular diagnosis of potato fungal pathogens in the widely dispersed four main potato growing areas in Greece, a weighted, branched approach was initiated. Pathogen detection and identification were restricted to 12 in-country detected pathogens from a universal panel of the American Phytopathological Society (APS) (Weingartner et al., 2001), totaling 26 fungal genera (causing 28 diseases).

The depth of identification for the isolates used for spiking or as pure culture samples was initially focused at the species level, except if more than one species of a genus were potato related, whence genus-level identification could be deemed enough. An exception was allowed for the Sclerotinia sp. isolate, which could not be identified to species level, but has been included nevertheless, to blind test the identification potential of the in silico method and to confirm compatibility of the genus with the molecular steps of the method.

A two-option DNA extraction step was selected, as it offers a choice for either the robustness of a phenol-based extraction method (Velegraki et al., 1999a) or the high sensitivity of the CTAB (cetyl-trimethyl ammonium bromide) extraction protocols (Trout et al., 1997; Velegraki et al., 1999b). The PCR step is similarly based on three alternative universal primer pairs, targeting two different loci. All three primer pairs are suitable for selective fungal signal detection, based on differences in amplicon lengths between fungal and background genomes (Solanum tuberosum).

The actual identification is performed by the patterns produced by restriction enzyme analysis (as previously illustrated in Kambouris et al., 1999; Velegraki et al., 1999a) in combination with undigested amplicon total length (as in Velegraki et al., 1999b), organotropism, and symptomatology of potato plants (Arora and Khurana, 2004).

The research thus undertaken aimed at introducing a rapid and more accurate method for the detection and initial identification of fungal pathogens. This method is to be applicable in austere, dispersed facilities with existing and relatively low-cost and easily acquired and operated equipment and infrastructure. In this way, it is bound to increase the biosafety level of, initially, potato crops, while lowering the production costs through sparing use of fungicides.

Plants and fungal strains

Strains of potato pathogens were kindly donated from the BPIC (Benaki Phytopathological Institute Collection), and the UOA/HPFC (University of Athens/Hellenic Collection of Pathogenic Fungi) to supplement available strains in the IPPP (Plant Protection Institute of Patras), as illustrated in Table 1. Both healthy and late blight-infected potato plant parts from the experimental field of IPPP were used, along with commercially available tubers. The latter and the healthy plant parts were cleaned thoroughly with 70% ethanol before being spiked with Fusarium solani culture spores. The blighted stems were treated for 5 sec in 0.7% w/v NaClO. The blighted leaves, although, could not tolerate this treatment and were thus processed directly ex vivo. Both spiked and diseased plant tissue were put in 1.5 mL plastic microcentrifuge tubes for extraction.

Table 1.

In Vitro Compatibility Among Sample Organs-DNA Extraction Method-Primer Pairs

Fungus	Origin	Extraction	Spiked tuber	Spiked stem	Spiked leaf	Diseased stem	Diseased leaf	Pure culture
Fusarium solani	UOA/HCPF^a	Phenol	3,5^b	3	3
F. solani	“	CTAB	3 [10⁻²]^c	+	+
Phytophthora infestans	IPPP^d	Phenol				3	−^e
P. infestans	“	CTAB				+^f	3
Alternaria solani	UOA/HCPF	CTAB						3
A. solani	“	Phenol						3,5
Verticillium dahliae	IPPP	Phenol						3,5
Sclerotinia sp.	IPPP	Phenol						3,5
Phoma sp.	UOA/HCPF	Phenol						3,5
Sclerotium sp.	IPPP	Phenol						3,5
Rhizoctonia solani	BPIC^g	Phenol						3,5
Pythium ultimum	BPIC	Phenol						3,5

Strains obtained from the University of Athens/Hellenic Collection of Pathogenic Fungi.

Amplification product with primer pair ITS-1/4: “1”; with primer pair LR3F63: “3”; with Amplification product with primer pair ITS-5/4: “5”.

Dilution factor of the extracted DNA solution.

Strains from the collection of the Plant Protection Institute of Patras.

No amplicon with any of the three primer pairs.

Amplification product with all three primer pairs.

Strains obtained from the collection of Benaki Phytopathological Institute.

ITS, internal transcribed spacer.

DNA extraction

The molecular methods used were adapted from earlier, medical protocols developed by the authors due to own expertize and know-how, along with proven compatibility with the molecular assay. Moreover, they satisfied specifications focused on robustness, simplicity, and affordability, as they are supposed to be massively implemented in decentralized, austere facilities. Moreover, the method being compatible with medical applications allows seamless incorporation or absorption of agrosurveillance units to public and veterinary health structures in the future.

The whole process from sampling to PCR can be implemented within Eppendorf or similar sealable plastic microcentrifuge tubes of 1.5–2 mL. This choice simplifies and accelerates the procedure, while curtailing costs and logistical footprint; most importantly, it does restrict crossover contamination and the logistical footprint compared to mortar-based techniques. The phenol extraction protocol is a modification of one previously described (Velegraki et al., 1999a), with most differences focused at the pre-extraction steps. The CTAB protocol, selected to enable extraction of miniscule amounts of target DNA in heavy clutter background, was also based on previous work (Velegraki et al., 1999b) with similar modifications.

Phenol protocol

Cells were disrupted mechanically with an orbital homogenizer in lysis buffer (200 mM Tris-HCl, pH 8, 250 mM NaCl, 25 mM EDTA, and 0.5% sodium dodecyl sulfate-SDS), followed phenol–chloroform extraction step with a mixture of 25 phenol (Sigma, MO):24 chloroform (BDH, Poole, United Kingdom):1 isoamyl alcohol (Ferak, Berlin, Germany), vigorous vortexing for 5 min, and 1-h centrifugation at 4°C at 13,000 g. The upper aqueous layer was then removed and after a pure chloroform extraction step with the same vortexing and centrifugation parameters, the supernatant was precipitated with an equal volume of cold isopropanol (Merk, Darmstadt, Germany) at −20°C for 15 min and centrifuged at 15,000 g for 15 min.

The pellet was washed with 70% aqueous solution of ethanol, briefly dried, suspended in sterile double-distilled water, photometrically assessed at 260/280 nm, and the DNA concentration adjusted to 10–30 ng/μL; PCR amplification was performed on 5 μL portions of DNA extracts in 50 μL total reaction volume.

CTAB protocol

Samples were inserted in microcentrifuge tubes containing 500 μL of extraction buffer (2% CTAB, Sigma; 700 mM NaCl; 50 mM Tris-HCl, pH 8; 10 mM EDTA; and 2% 2-mercaptoethanol). They were again mechanically disrupted with an orbital homogenizer for 1–2 min and incubated for 30 min at 65°C under agitation. Subsequently, 500 μL of chloroform:isoamyl alcohol mix (24:1, v/v) was added, vigorously shaken until homogeneity, and spun at 8,000 g for 10 min at room temperature. The supernatant, aqueous phase was transferred into a clean tube, mixed with an equal volume of cold isopropanol, and placed at −20°C for 30 min. The samples were then spun for 10 min at 8,000 g, the supernatant was poured off, and the pellet was washed in 500 μL of wash buffer (76% ethanol and 10 mM ammonium acetate) and allowed to stand at room temperature for 15 min.

The samples were centrifuged again at 8,000 g for 5 min at room temperature, the wash buffer poured off, and the pellet washed with 70% aqueous solution of ethanol, briefly dried, suspended in sterile double-distilled water, photometrically assessed at 260/280 nm, and the DNA concentration adjusted to 10–30 ng/μL; PCR amplification was performed on 5 μL portions of DNA extracts in 50 μL total reaction volume.

Database sequence research-in silico PCR-RFLP

National center for biotechnology information (NCBI)/Nucleotide entries of the fungi of interest (as per Table 1) were retrieved and scanned for internal transcribed spacer (ITS) primers' annealing loci as well as restriction sites for HaeIII and MspI using the Vector NTI program (Invitrogen). The MspI restriction endonuclease was selected for the first run of the in silico examination and the HaeIII and AluI for the second. The running order was based on previous experience (Christakis et al., 2005; Velegraki et al., 1999a). Amplicon and digests' sizes were thus predicted and differentiation criteria were determined. Delineated data are presented in Tables 1 and 2.

Table 2.

In Silico Differentiation of Fungi Based on Internal Transcribed Spacer Region Molecular Signals

Organism	GenBank Acc #	Taxon	ITS1/4	ITS5/4	ITS1/4Ø MspI^a	ITS1/4 Ø HaeIII
Helminthosporium solani	KC106739.1	A^b	569	∼591	X^c	540, 40
Colletotrichum coccodes	JQ005775.1	A	576	598	430, 140	112, 10, 170, 280
Phoma exigua	HQ713781.1	A	543	565	140, 390	X
Alternaria solani	KC584217.1	A	577	∼599	60, 140, 370	365, 140, 65
Fusarium solani	EU326207.1	A	565	587	210, 340	90, 15, 10, 120, 85, 240
Verticillium dahliae	AF104926.1	A	540	562	200, 240, 100	150, 30, 25, 340
Sclerotinia sclerotiorum	JQ739461.1	A	542	564	307, 235	430, 110
Sclerotium rolfsii	KC292637.1	B^d	684	706	130, 540	20, 110, 180,370
Rhizoctonia solani	KM013470.1	B	682	714	X	60, 120, 540
Synchytrium endobioticum	KF160870.1	C^e	633	655	270, 370	600, 30
Pythium ultimum	JN695787.1	O^f	∼920	∼942	X	450, 280, 180
Phytophthora infestans	AJ854292.1	O	884	906	370, 225, 280	X
Solanum tuberosum	AY875827.1	Plant	∼675	∼700	67, >130, 420, ?^g	70,>120, 240, >220

Sizes and RFLP patterns of ITS-5/4 and ITS-1/4 PCR products of 12 potato fungal pathogens and host plant as mined from NCBI/Nucleotide database.

Ø symbol meaning “digested by”.

Ascomycetes.

Undigested amplicon.

Basidiomycetes.

Chytridiomycetes.

Oomycetes.

Numerical value unavailable.

Deposited sequences were selected to contain, if possible, sizeable lengths (>50 bp) of both the 18S and 28S rDNA coding sequences so as to contain the ITS-5 and ITS-4 annealing targets respectively. If no suitable entries were available, the anticipated amplicon and fragments' sizes were deduced based on the fact that in most entries, the sites of pair of amplifying primers were located at standard distances from the external edges of ITS1 and ITS2 areas.

In vitro PCR amplification

To further reduce the logistical footprint, in all PCR, the same conditions were applied (Velegraki et al., 1999a). The primers ITS-5, ITS-4, LR3, and F63 have been used before (Fell et al., 2000; Trout et al., 1997; White et al., 1990).

ITS-4: 5′-TCCTCCGCTTATTGATATGC-3′

ITS-5: 5′-GGAAGTAAAAGTCGTAACAAGG-3′

LR3: 5′-GGTCCGTGTTTCAAGACGG- 3′

F63: 5′-GCATATCAATAAGCGGAGGAAAAG-3′

The former pair is universally used for fungal identification (although not consistently for all taxa, Balajee et al., 2009; Bellemain et al., 2010) and as it targets a multicopy (up to 250 copies per genome, Bellemain et al., 2010; Vilgalys and Gonzalez, 1990) locus, it offers factual sensitivity about two orders of magnitude higher than the single-copy loci.

The reaction mixture (50 μL) contained 1.5 mM MgCl₂, 100 μM each of the dNTPs, 50 pmol of each primer per reaction, and 2 U of Taq DNA polymerase per reaction (Promega). Amplification was performed with a Robocycler (Stratagene) for 30 cycles with the following conditions: 1 min at 95°C, 1 min at 55°C, and 1 min 30 sec at 72°C, and for the final cycle, 1 min at 95°C, 1 min at 55°C, and 5 min at 72°C. The PCR products were stored at 4°C until run in 2.5% agarose gels in 0.5 × TBE at 100 mA or less, with standard loading buffer and ethidium bromide stain.

In vitro RFLPs

MspI digestions were carried out as required using 12.5 μL of crude amplicon, 1.5 μL of enzyme-specific 10 × buffer, and 1.5 μL of the enzyme, in conditions specified by the manufacturer (NEB, MA) for 2–3 h. Electrophoresis in 2.5–3% agarose gels (precise concentration depending on product nature, that is, amplicon or digest) stained with ethidium bromide was run as required at <100 mA with digested and undigested amplicons run side by side and next to 100-bp ladder size marker.

Molecular controls and standards

PCR amplicons and the respective RFLP patters were highly specific; thus no negative controls were used. In batch-negative amplifications, PCR was repeated with the addition of external positive controls and in case of repetitive batch negativity, a second extraction was performed. For pure cultures, two independent extractions were used to define negativity in amplification or digestion. For spiked/diseased plant tissue, three amplifications/digestions were used to define negativity, which was further tested by up to three independent extractions; each repetition extraction was used only once for amplification/digestion. Compatibility/verification was scored whenever a result was turning positive, as the object of the research was a proof-of-principal methodology and not its development to maturity and field application.

Results

Our working hypothesis proved mostly applicable, as even with a single primer pair and a single restriction endonuclease, 12 species could be discriminated in silico. The 10 of them, which were tested in vitro as well, were shown to produce molecular patterns as expected, although in some cases, interpretation was challenging, especially when working with spiked or diseased plant tissue. The kind of tissue will affect the extraction method of choice.

The ITS-1/4 primer pair was tested initially, only to be discarded early on, in favor of the more reliable ITS-5/4; the ITS-5 primer is located just 3 bp before the 5′ end of the ITS-1. The ITS-5/4 performed much better in producing signal with the same DNA extracts from both plant tissue and pure fungal cultures. More specifically, the ITS-1/4 pair failed to produce amplicons from phenol extracts of pure culture of Verticillium dahliae and of F. solani-spiked tuber and leaf; in contrary, ITS-5/4 produced acceptable amplicons in these instances (Supplementary Fig. S1).

In many sequences submitted to NCBI database, the genomic data for the ITS-5/4 require a degree of deduction, as the submissions are of insufficient lengths. However, the in vitro restriction fragment patterns obtained were perfect matches to those predicted by our in silico analysis.

As seen in Table 2, the amplicons of the selected species of ascomycetes and oomycetes were readily discernible from the background signal (amplicon of S. tuberosum DNA). Yet, oomycetes species produced regularly a two-band pattern, in which the specific band was nearly at 1000 bp of size as predicted, whereas the secondary, unpredicted band, was ∼600 bp. This byproduct was consistent, even in pure culture extractions, a fact that eliminates any notion of co-amplification of other fungi, possibly present in the diseased field samples. (Supplementary Fig. S2).

Things were different with the two Basidiomycota anamorphs, Rhizoctonia solani and Sclerotium rolfsii. Their ITS-5/4 amplicons are similar in size to a potato plant-associated respective amplicon (714 and 706 bp compared to ∼700 bp, respectively), and a restriction digestion reaction with MspI is required to differentiate the specific fungal signals from each other and from the plant background signal. The amplicon of R. solani remains indigestible, whereas the one of S. rolfsii is clearly digested, producing a major fragment at ∼550 bp; similarly, the major fragment of the amplicon of the potato plant runs at ∼420 bp, as seen in Table 2. Such a restriction reaction is also needed to identify the ascomycetes involved in potato plant diseases. The studied ascomycetes produce amplicons clearly different in size from that of the potato plant, allowing easy detection (Table 2 & lane 5 of Supplementary Fig. S3).

However, resolution among different ascomycetes, which is imperative for identification, may be occasionally complicated in terms of specific bands. In some cases, or rather in some queries between ascomycetes with similar restriction patterns, full pattern recognition of the restriction fragments of a sample may be advisable instead of seeking fragments of specific seize (Lane 2 of SM Figure 3 is a good example of a characteristic pattern).

For example, as evident from Table 2, Synchytrium endobioticum and Alternaria solani have identical major fragments at 307 bp; thus, the minor fragments become important, with the former presenting a single minor band at 270 bp, while the latter produces minor bands under 200 bp, which are rarely visible in agarose gels. Similarly, Sclerotinia sclerotiorum and F. solani have similar, two-band patterns, the former at 235 and 307 bp and the latter at 210 and 340 bp. The major and minor bands are indistinguishable in agarose gels, but the whole pattern of the latter is wider, with more distance between the bands than the former, and the combined difference in both band sizes makes the pattern discernible from each other, whereas each band individually might not be so.

The use of the LR3-F63 primer pair was found very promising, even though in silico prediction approach was not successful. Its use shows two clear advantages, compared to the ITS-5/4 primer pair: (a) it bears a better yield, which improves the anticipated specific sensitivity (expressed in [DNA]) by a factor of two compared with ITS-5/4 and (b) it is compatible with more combinations of extraction methods with plant organs' samples (Table 1 and Supplementary Fig. S4). On the other hand, the in vitro reactions, especially restriction digestions, were performed in blind, trial-and-error formats due to the lack of in silico data, as the NCBI database contains very few and poor quality respective entries. The discrimination from the potato plant background noise is achievable, but prospect for identification of genera and species is limited.

Thus, in the case of ascomycetes, many bands less than 600 bp were obtained, all of which were digestible with MspI, without, however, any species-characteristic restriction patterns. All ascomycetes had similar, single-site restriction patterns, which set them apart from the potato plant background noise and from other fungi. The LR3-F63 product with S. tuberosum is indigestible with MspI (Supplementary Fig. S5), contrary to all tested ascomycetes (Supplementary Figs. S3, S5–S7), but similar to oomycetes and basidiomycetes (Supplmentary Fig. S8), which produce no restriction patterns either. Still, amplicon size alone allows differentiation among oomycetes, basidiomycetes, and potato plant.

The size of LR3-F63 amplicons for R. solani and S. rolfsii is near 600 bp; for S. tuberosum clearly over 600 bp; for Phytophthora infestans <800 bp; and for Pythium ultimum ∼800 bp (Supplementary Fig. S8). Both oomycetes show a two-band product, with the large band permitting clear discrimination from both the plant's and true fungi's signal (Table 3 and Supplementary Figs. S3, S5–S7).

Table 3.

In Vitro Detection and Differentiation by Primer Pair LR3F63

Organism	Origin	LR3F63 amplicon	Ø MspI^a
Phytophthora infestans	IPPP^b	∼600, ∼800	−^c
Pythium ultimum	BPIC^d	∼450, >800	−
Alternaria solani	UOA/HCPF^e	∼600	+^f
Verticillium dahlia	IPPP	<600	+
Fusarium solani	UOA/HCPF	<600	+
Rhizoctonia solani	BPIC	>600	−
Phoma sp	UOA/HCPF	∼600	+
Sclerotium sp	IPPP	>600, >200	−
Sclerotinia sp	IPPP	500<, >600	+
Solanum tuberosum		∼600	−

Amplicon number and size and digestion status of amplicon incubation with MspI of nine potato fungal pathogens.

Digested by.

Strains from the collection of the Plant Protection Institute of Patras.

Undigested amplicon.

Strains obtained from the collection of Benaki Phytopathological Institute.

Strains obtained from the University of Athens/Hellenic Collection of Pathogenic Fungi.

Digested amplicon.

Discussion

The usual procedure for the detection and identification of fungal pathogens causing disease in plants is by symptom examination. However, extrapolation of the causal agent(s) based on symptoms alone is unduly laborious and of insufficient specificity. With the possible exception of very few, highly specific symptoms, each associated with a single pathogen, most symptoms and a considerable number of signs may attest to more than one pathogen. Indeed, in several cases, particularly at the early stages of disease development, symptoms overlap, thus confusing agronomists and resulting in wrong field control recommendations.

The situation becomes more challenging in isolated, remote areas, due to the broken terrain (islands, plateaus, gorges, etc), physical distance from research and advisory centers, and other similar reasons. In such regions, scientific support is usually lacking and most cases are dealt with by continuous overlapping spraying with wide-spectrum fungicides, a practice both expensive and ill-advised. It creates resistant fungal strains, which increase the problem, affecting not only crops but also human subjects (Balis et al., 2006; Georgiadou et al., 2014; Meneau and Sanglard, 2005; Natarajan et al., 2013; Pastor and Guarro, 2008; Scheel et al., 2013; Verweij et al., 2009).

However, small local support stations, public or private, which could afford low cost, but effective techniques for pathogen identification, would be able to contribute significantly toward effective crop pathogen control. In this study, we propose an approach for the identification of fungal pathogens of potato, a plant that is economically important and usually cropped in isolated areas.

Given that the method is developed as a diagnostic tool and not for area surveillance, infected plant parts are expected to be highly enriched in the causative pathogen compared with other commensals, microflora, and random or opportunistic phytobiome genomes. In this context, relaxed sensitivity is acceptable, if not preferable, as it also resolves specificity issues inherent in field conditions, especially if diseased parts cannot be treated for initial, external decontamination. The expected background was limited to possible amplification products of the host plant proper plus any noise by byproducts of amplification; the latter have always been plighting in-house developed, affordable, and flexible protocols.

Given that the panels of relevant pathogens depend on geography and field conditions, any diseased plant processing resolves one single disease triangle. Thus, the experimental issues to be tested were for a proof-of-concept approach, which may be fine-tuned by the actual users according to the expected potato pathogens at a given location.

The key issues were the confirmation of the in silico predicted amplicons' and restriction fragments' sizes by actual experiments; the resolution of the pathogens' signal in the presence of the host's DNA and cell debris environment; and, finally, the compatibility of the extraction protocols with the molecular method. Once the main uncertainty lies with the host tissue (more or less or no chlorophyll, more or less starch) and with the pre-extraction processing, successful implementation with one disease substantiates proof of concept. Detailed fine-tuning is left to different end users so as to form their own detection and discrimination panels as needed.

A method for rapid identification of fungal pathogens in crops might allow more specific, focused, and on-demand administration of fungicides, thus lowering expenditure and environmental burden, enhancing sustainability, and optimizing the use of antifungal resources for agricultural, medical, and veterinary applications (Erukhimovitch et al., 2010; Katan, 1998). The concept described herein allows for efficient discrimination among fungal diseases with similar symptoms. Such examples are, among others, the discrimination between Fusarium and Verticillium causing potato wilt disease, between silver scurf and black dot in potato tubers, between potato stem rot and potato white mold in stem, between potato late blight and potato early blight in foliage, and among late blight, Fusarium dry rot and Phoma gangrene of tubers (Table 2).

The discrimination between similar symptoms, caused by fungal and non-fungal pathogens (as with tobacco rattle virus-infected tubers), is even safer and simpler.

More detailed identification, to subspecies-level taxa, such as anastomosis groups in R. solani, formae specialis in Fusaria, and P. infestans types A1/A2, has not been within the scope of this study. Most probably, this level of discrimination is not achievable through the ITS region; in real life, detailed identification can be leisurely performed in specialized laboratories, where samples, or extracted DNA, will have been sent, while the initial identification will be proceeding.

Modern detection approaches, of course, fully exploit 20th century technology (Tooley et al., 1997), such as PCR in different platforms and formats, some of which were quite high tech at the time of conception: multiplex protocols (of rather low order, up to now) are developed (Atallah et al., 2007), as are real-time (Lees et al., 2012) and combined multiplex-nested tandem protocols or array reading of PCR products (Tsui et al., 2011).

In each of the above cases, the object seems to be a specific PCR producing specific amplicons, which unequivocally identify a given fungus amidst others (Tooley et al., 1997; Trout et al., 1997; Tsui et al., 2011). The latter might be pathogens or probable non-pathogenic, environmental contaminants. Instead of seeking only one pathogen, some of the proposed approaches allow the detection of more specific, differential signals simultaneously, in a parallel, exclusive format (Erukhimovitch et al., 2010; Qu et al., 2011; Tsui et al., 2011). Still, to cover any number of possibilities, multiple reactions must be performed either sequentially, or simultaneously, if there are more than one PCR cyclers/blocks available.

Such limitations make the process cumbersome; they could, however, be overcome, if discrimination was not performed at the amplification step, but at a subsequent, product (signal) processing step, which can be easier to develop, less demanding to perform, and more cost-effective to routinely utilize than a specific PCR.

The quest for extreme specificity and excellent sensitivity is mainly driven by the expressed intent to use PCR-based methods for routine testing of soil and for performing screening, that is, examining proactively seed and propagative material, and even plant samples before the development of symptoms (Schaad and Frederick, 2002). Once such intent is dropped, more basic and austere, plainer and cheaper approaches may be used. This is the case with diagnosis, where tissue presenting signs and symptoms will be preferentially sampled, meaning a substantial presence of pathogen's DNA. This fact negates any need for top sensitivity and specificity performance; the pathogen is both present and prominent.

The ITS primers have not demonstrated top performance regarding sensitivity (Velegraki et al., 1999a), which is rather an asset to avoid co-amplification of fungal microflora in the field, especially in the soil.

Any envisaged pathogen causing an actual infection should be prominent in cell numbers and DNA presence at the site of the infection. So, it is fair to suppose that a fungal signal detected from infected sites and verified by comparison with in silico data would rather be a specific signal from the involved pathogen and not some mysterious contaminant. If no such signal is produced, the pathogen (or rather the causal agent) should be of non-fungal/oomycetal nature. The reduced sensitivity of the ITS family of primers is used as an enhancer of relevance in a pathogen-rich environment, such as infected plant parts.

The decentralized laboratories are to be manned with phytopathologists; thus, the diagnostic question is de facto limited by the infected plant organ and the signs, a fact that reduces the complexity of the decision rationale. If unexpected findings occur, then both kinds of samples, DNA extracts, and PCR amplicons can always be dispatched to reference laboratories for specialist evaluation.

Regarding the ability of the method to resolve mixed infections, it is theoretically possible. The same principle that filters the background signal of the potato plant out of the specific fungal signal, that is differential digestion and restriction fragment pattern recognition, allows resolution of mixed fungal infections.

There are limitations and conditions characteristic of PCR-RFLP. For instance, the restriction patterns, as seen in Table 2, must be specific and discrete. These qualities are greatly affected by different conditions, gel types, and gel concentrations, all of which can be used or even combined to enhance discrimination and accuracy.

For standard applications, the resolution of a typical gel might prove an issue: there are cases, especially among Ascomycetes, where both amplicons and main or major restriction fragments are similar. Such conditions can be resolved by a combination of fungal selectivity to plant tissues and organs, and meticulous band analysis. The latter takes into consideration all the visible fragments of the restriction digestion and compares the complete pattern to the marker pattern and, preferably, to the pattern of previously treated samples of the probable species, now employed as controls.

Amassing experience and samples make such an approach progressively easier; still, acquisition of pure fungal strains and treatment with the desired protocols, from extraction to electrophoresis of digests, is a much more prudent way to initiate similar capabilities and initiatives. Moreover, the ratio of the fungal loads, the target DNA yields, and the priming efficiency determine whether all implicated fungal pathogens are amplified to visible and digestible levels; viruses and bacteria produce no signal with the primer pairs employed herein (Velegraki et al., 1999a).

No duplex PCR formats were attempted in this study, to reduce primer pair crosstalk, which would increase noise and byproducts.

The cost and turn-around time (TAT) of the method cannot be assessed properly, as too many parameters are to affect the outcome. The panel of the prospective pathogens, the level of discrimination required, contractual issues in acquisition deals, and manning strategies, coupled to the standard operating procedures of each user may result in considerable differences in expenditure and reaction time. Still, a rudimentary assessment, based on similar protocols that have been used for clinical samples, points to a man-day of work for initial results if samples need no pre-treatment and a maximum of 3 working days from sample reception to final result if retreatment and restriction digestion are both needed. Moreover, a 1- or 2-day timeframe will be needed for implementation on pure cultures, which means that the time of the incubation of the culture must be factored in.

In all above cases, the extraction, PCR, and electrophoresis costs are approximately one Euro (roughly 1.2 Dollars) per sample. This sum does not include RFLP, the cost of which is provisional and depends on the selected enzyme, and has not factored in the cost of facilities, equipment, and labor; still, it provides a rough estimate, taking into consideration that it has been achievable with 90's technology of cyclers. More modern solutions might drop TAT times considerably, and affect the cost.

Other options have been and are still considered. Although enzyme-linked immunosorbent assay may be an ill-advised approach when identification is needed, as there are no specific antibodies for all the fungi of interest, non-DNA-based methods, such as fourier-transform infra-red spectroscopy (FTIR) (Erukhimovitch et al., 2010) and fluorescent in situ hybridization (FISH) (Tsui et al., 2011) allow some kind of optimism in terms of cost and time, but are plighted by some prerequisites: the former depends on current spectral libraries of the highest fidelity for discriminating just three pathogens, whereas the procedure is less familiar to both scientists and technicians.

The latter allows selectable discrimination scope, but this is not adjustable if more than one taxon is considered. Furthermore, this approach cannot achieve segregation without another round of FISH, thus increasing cost and time, while leaf sampling is rather challenging for the method per se.

The last issue to be discussed is the extraction method. At a time of standardized, commercial kit extraction mainstream and dedicated hardware (Brierley et al., 2009; Okubara et al., 2007), this study insists on a 1990 s-type in-house approach (Velegraki et al., 1999a, 1999b). The latter is focused on low cost, adaptability, customization, and robustness; it is based on inexpensive, widely available chemicals (Tsui et al., 2011) and can be performed wholly within sealable standard microtubes.

Similar DNA extraction methods might be used for other molecular tests of phytopathological interest, in similar or dissimilar pathogens and in a variety of crops, thus simplifying logistics of austerely equipped and manned field units and outposts.

To prove this adaptability, a similar protocol was developed in IPPP in silico to detect and differentiate the entomopathogenic endophytic fungi Beauveria bassiana, Paecilomyces fumosoroseus, and Metarhizium robertsii (Vega, 2008) with HaeIII digestion of ITS-5/4 amplicons (Manousopoulos and Mantzoykas, unpublished data).

Conclusions

As the new generation of OMICS comes of age, new disciplines spawning from genomics need not be dedicated, extra expensive, extra complicated, extra novel, or extravagant to acquire a functional niche and applications: they simply need to be responsive and adaptive, that is, relevant.

This study is an example of effect-based research rather than technology-driven research. It aims at a method with widespread routine application to at least tens of outposts throughout rural Greece by moderately trained technicians and technologists and not at dedicated, smart research formats. Thus, it embodies the opposite paradigm to all aforementioned approaches.

Trained mycologists in the field of plant protection and phytopathology are few, and the numbers of the ones able to accurately identify fungi by culture and microscopy dwindle by the day due to age. Specialists are not easy, nor cheap, to train and hire, whereas molecular protocols are applicable throughout the spectrum of phytopathology with limited methodological differentiations.

The key factor(s) for the use of a molecular application in routine phytopathological practice in austere, but extended networks of local services and infrastructures is NOT its exotic technology. They are its applicability, relevance, ease, and low cost for upfront investments, training, adaptations/upgrades, and routine use (expendables and instrumentation maintenance), while retaining a favorable cost-effectiveness for its life cycle.

The concept presented herein does not claim sensitivity at femtogram level (Atallah et al., 2007) or even at picogram level, neither does it quantify fungal signal to estimate fungal load (Atallah and Stevenson, 2006) nor does it resolve the fungal load of soil samples (Errampalli et al., 2001; Lees et al., 2002). However, in its present setup, it detects fungal presence in affected tissue and differentiates a dozen of potato fungal pathogens. All these are achievable without resorting even to simple means to increase the method's discriminatory power; such means are the testing of more endonucleases and/or the use of polyacrylamide gels. At the same time, the whole extraction process is performed within microcentrifuge tubes, for ease of application and contamination control.

The adaptability of the method along with its origin from medical mycology makes it an excellent candidate for multipotent biosurveillance stations, where smart, multitasking assays will assist in implementing integrated biopreparedness networks able to detect residual, common, and (re)emerging pathogens threatening plant, animal, and human assets (Hekim and Ozdemir, 2017; Thelaus et al., 2017; Nuzzo, 2017).

Footnotes

Acknowledgments

This research was funded under the Project “Research & Technology Development Innovation Projects” AgroETAK, MIS 453350, within the framework of the Operational Program “Human Resources Development”. It is co-funded by the European Social Fund through the National Strategic Reference Framework (Research Funding Program 2007–2013) coordinated by the Hellenic Agricultural Organization–DEMETER (Plant Protection Institute of Patras/Scientific supervisor: Dr Y. Manousopoulos). The authors acknowledge and are grateful for the invaluable help of Ms Milioni N, Ms Goudoudaki S, Ms Karmakolia Ai, Ms Paulopoulou J, and Mr Lagogiannis G.

Author Disclosure Statement

The authors declare that no conflicting financial interests exist.

Abbreviations Used

References

Arora

, and Khurana

SMP.

(2004). Major fungal and bacterial diseases of potato and their management. In: Fruit and Vegetable Diseases. Disease Management of Fruits and Vegetables, vol 1. Mukerji

, ed. Dordrecht, Springer.

Atallah

, and Stevenson

. (2006). A methodology to detect and quantify five pathogens causing potato tuber decay using real-time quantitative PCR. Phytopathol, 96, 1037–1045.

Atallah

, Bae

, Jansky

, Rouse

, and Stevenson

. (2007). Multiplex real-time quantitative PCR to detect and quantify Verticillium dahliae colonization in potato lines that differ in response to verticillium wilt. Phytopathol, 97, 864–872.

Balajee

, Borman

, Brandt

, et al. (2009). Sequence-based identification of Aspergillus, Fusarium and Mucorales in the clinical mycology laboratory: Where are we and where should we go from here?. J Clin Microbiol, 47, 877–884.

Balis

, Velegraki

, Fragou

, Pefanis

, Kalabokas

, and Mountokalakis

. (2006). Lung mass caused by Phoma exigua. Scand J Infect Dis, 38, 552–555.

Bellemain

, Carlsen

, Brochmann

, Coissac

, Taberlet

, and Kauserud

. (2010). ITS as an environmental DNA barcode for fungi: An in silico approach reveals potential PCR biases. BMC Microbiol, 10, 189–197.

Brierley

, Stewart

, and Lees

. (2009). Quantifying potato pathogen DNA in soil. Appl Soil Ecol, 41, 234–238.

Christakis

, Perlorentzou

, Chalkiopoulou

, Megalakaki

, and Velegraki

. (2005). Disseminated infection of Fusarium solani group in a patient with acute myelogenous leukaemia: Case report and literature review. Acta Microbiol Hellen, 50, 170–182(in Greek).

Culibrk

, Croft

, and Tebbutt

. (2016). Systems biology approaches for host-fungal interactions: An expanding multi-omics frontier. OMICS, 20, 127–139.

10.

Cullen

, Toth

, Pitkin

, et al. (2005). Use of quantitative molecular diagnostic assays to investigate fusarium dry rot in potato stocks and soil. J Phytopathol, 95, 1462–1471.

11.

Dwivedi

, Tiwari

, Prasanna

, Awasthi

, Singh

, and Pandey

. (2016) Citrus functional genomics and molecular modeling in relation to Citrus sinensis (Sweet Orange) Infection with Xylella fastidiosa (Citrus Variegated Chlorosis). OMICS, 20, 485–490.

12.

Errampalli

, Saunders

, and Cullen

. (2001). A PCR-based method for detection of potato pathogen, Helminthosporium solani, in silver scurf infected tuber tissue and soils. J Microbiol Methods, 44, 59–68.

13.

Erukhimovitch

, Tsror

, Hazanovsky

, and Huleihel

. (2010). Direct identification of potato's fungal phyto-pathogens by Fourier-transform infrared (FTIR) microscopy. Spectroscopy, 24, 609–619.

14.

Fell

, Boekhout

, Fonseca

, Scorzetti

, and Statzell-Tallman

. (2000). Biodiversity and systematics of basidiomycetous yeasts as determined by large-subunit rDNA D1/D2 domain sequence analysis. Int J Syst Evol Microbiol, 50, 1351–1371.

15.

Georgiadou

, Velegraki

, Arabatzis

, et al. (2014). Cluster of Fusarium verticillioides bloodstream infections among immunocompetent patients in an internal medicine department after reconstruction works in Larissa, Central Greece. J Hosp Infect, 86, 267–271.

16.

Hekim

, and Ozdemir

. (2017). A General Theory for “Post” systems biology: Iatromics and the environtome. OMICS, 21, 359–360.

17.

Kambouris

, Reichard

, Legakis

, and Velegraki

. (1999). Sequences from the aspergillopepsin PEP gene of Aspergillus fumigatus: Evidence on their use in selective PCR identification of Aspergillus species in infected clinical samples. FEMS Immunol Med Microbiol, 25, 255–264.

18.

Katan

(1998). Principles in plant pathology. In: Plant Diseases in Israel. Rotem

, Palti

, Ben-Yephet

, eds. Bet-Dagan, Israel: Volcani Center, 25–43.

19.

Katara

, Grover

, and Sharma

. (2012). In Silico prediction of drug targets in phytopathogenic Pseudomonas syringae pv. phaseolicola: Charting a course for agrigenomics translation research. OMICS, 16, 700–707.

20.

Khalil

, and Shinwari

. (2014). Threats of agricultural bioterrorism to an agro dependent economy; what should be done?. J Bioterror Biodef, 5, 127–134.

21.

Lees

, Cullen

, Sullivan

, and Nicolson

(2002). Development of conventional and quantitative real-time PCR assays for the detection and identification of Rhizoctonia solani AG-3 in potato and soil. Plant Pathol, 51, 293–302.

22.

Lees

, Sullivan

, Lynott

, and Cullen

. (2012). Development of a quantitative real-time PCR assay for Phytophthora infestans and its applicability to leaf, tuber and soil samples. Plant Pathol, 61, 867–876.

23.

Meneau

, and Sanglard

. (2005). Azole and fungicide resistance in clinical and environmental Aspergillus fumigatus isolates. Med Mycol, 43, Suppl 1:S307–S311.

24.

Natarajan

, Rekha

, Sharda

, and Mahalingam

. (2013). Colletotrichum keratitis: A rare but definite clinical entity. J Clin Diagn Res, 7, 1430–1433.

25.

Nuzzo

JB.

(2017). Improving biosurveillance systems to enable situational awareness during public health emergencies. Health Secur, 15, 17–19.

26.

Okubara

, Li

, Schroeder

, Schumacher

, and Lawrence

. (2007). Improved extraction of Rhizoctonia and Pythium DNA from wheat roots and soil samples using pressure cycling technology. Can J Plant Pathol, 29, 304–310.

27.

Pastor

, and Guarro

. (2008). Alternaria infections: Laboratory diagnosis and relevant clinical features. Clin Microbiol Infect, 14, 734–746.

28.

, Wanner

, and Christ

. (2011). Multiplex real-time PCR (TaqMan) assay for the simultaneous detection and discrimination of potato powdery and common scab diseases and pathogens. J Appl Microbiol, 110, 769–777.

29.

Ramage

, Prentice

, DePalma

, et al. (2016). Comprehensive laboratory evaluation of a highly specific lateral flow assay for the presumptive identification of Bacillus anthracis spores in suspicious white powders and environmental samples. Health Secur, 14, 351–365.

30.

Schaad

, and Frederick

. (2002). Real-time PCR and its application for rapid plant disease diagnostics. Can J Plant Pathol, 24, 250–258.

31.

Scheel

, Hurst

, Barreiros

, Akiti

, Nucci

, and Balajee

. (2013). Molecular analyses of Fusarium isolates recovered from a cluster of invasive mold infections in a Brazilian hospital. BMC Infect Dis, 13, 49–60.

32.

Schoch-Spana

, Cicero

, Adalja

, et al. (2017). Global catastrophic biological risks: Toward a working definition. Health Secur, 15, 323–328.

33.

Taheri

, Lee Abdullah

, Yusop

, et al. (2018) Mining and development of novel SSR markers using next generation sequencing (NGS) data in plants. Molecules, 23, pii: E399.

34.

Thelaus

, Lindberg

, Lambertz

, et al. (2017). Network experiences from a cross-sector biosafety level-3 laboratory collaboration: A swedish forum for biopreparedness diagnostics. Health Secur, 15, 385–391.

35.

Tooley

, Bunyard

, Carras

, and Hatziloukas

. (1997). Development of PCR primers from internal transcribed spacer region 2 for detection of Phytophthora species infecting potatoes. Appl Environ Microbiol, 63, 1467–1475.

36.

Trout

, Ristaino

, Madritch

, and Wangsomboondee

. (1997). Rapid detection of Phytophthora infestans in late blight-infected potato and tomato using PCR. Plant Dis, 81, 1042–1048.

37.

Tsui

CKM

, Woodhall

, Chen

, et al. (2011). Molecular techniques for pathogen identification and fungus detection in the environment. IMA Fungus, 2, 177–189.

38.

Vega

FE.

(2008). Insect pathology and fungal endophytes. J Invertebr Pathol, 98, 277–279.

39.

Velegraki

, Kambouris

, Kostourou

, Chalevelakis

, and Legakis

. (1999). Rapid extraction of fungal DNA from clinical samples for PCR amplification. Med Mycol, 37, 69–73.

40.

Velegraki

, Kambouris

, Skiniotis

, Savala

, Mitroussia- Ziouva

, and Legakis

. (1999). Identification of medically significant fungal genera by polymerase chain reaction followed by restriction enzyme analysis. FEMS Immunol Med Microbiol, 23, 303–312.

41.

Verweij

, Snelders

, Kema

GHJ

, Mellado

, and Melchers

WJG

. (2009). Azole resistance in Aspergillus fumigatus: A side-effect of environmental fungicide use?. Lancet Infect Dis, 9, 789–795.

42.

Vilgalys

, and Gonzalez

. (1990). Organisation of ribosomal DNA in the basidiomycete Thanatephorus praticola. Curr Genet, 18, 277–280.

43.

Weingartner

, Hooker

, and Stevenson

. (2001). Diseases of potato. https://www.apsnet.org/publications/commonnames/Pages/Potato.aspx. Accessed February 20, 2018.

44.

White

, Bruns

, Lee

, and Taylor

(1990). In: PCR Protocols. A Guide to Methods and Applications. Innis

, Gelfand

, Sninsky

, White

, eds. San Diego, USA: Academic Press, Inc., 315–322.

45.

Wolicki

, Nuzzo

, Blazes

, Pitts

, Iskander

, and Tappero

. (2016). Public health surveillance: At the core of the global health security agenda. Health Secur, 14, 85–88.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB

0.07 MB