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Efficacy of PCR-bassed techniqes and remote sensing technology for detecting potato brown rot infection caused by R. solanacearum

Bacterial wilt of potatoes (Solanum tuberosum) caused by Ralstonia solanacearum phylotype II sequevar I (race 3 biovar 2), is one of the most serious diseases of potatoes. This study aimed to detect such deteriorations through the remotely sensed data. In this regard, naturally infested potato fields at Abo Alkhawi, Al Tayareya and Om Saber villages at Behera governorate, Egypt, were used for selective isolation of R. solanacearum. On Semi Selective South Africa (SMSA) medium, 30 isolates were randomly screened and tested by immunofluorescence antibody staining (IFAS) and confirmed biologically by tomato plantlets (cv. Pinto) reaction. All isolates showed positive results with Real-time PCR. Based on the phylotype specific multiplex (Pmx) – PCR data, all isolates in concern were R. solanacearum and affiliated to phylotype II as 372bp amplicon. Spectral indices were applied on the Landsat 8 and RapidEye satellite images acquired in January and April 2019 including Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Salinity Index (NDSI) and Modified Chlorophyll Absorption Ratio Index (MCARI). Results from the remote sensing analyses showed medium to relatively high salinity and moisture content of soils concerened. Moreover, plants chlorophyll levels are in medium levels which is not typical to condition associated with usual circumstances. Further investigation, in a greater academic depth, are needed for pathological field surveys.


Introduction
El Beheira governorate is one of the largest governorates of Egypt in the cultivation and production of potatoes, where, its total cultivated area of potatoes reaches 69768 fedden out of a total of 251508. While its production of potato crop reaches 793404 ton out of a total of 2915342 (Economic Affairs Sector, 2018). For this reason, precautionary determinations should be taken while planting such strategic crop by using the modern lab determinations and the possible remote sensing technologies to detect potato infection with brown rot disease caused by R. solanacearum.
R. solanacearum pathogen is soil and tuber-borne bacterium (Kabeil et al., 2008), which causes bacterial wilt disease of potatoes or potato brown rot. Accordingly, it is a serious disease of potatoes and many other cultivated plants (Hayward & Hartman, 1994). This pathogen causes wilt syndrome for more than 450 plant species belonging to 54 different botanicals families (Caitilyn et al., 2005). The pathogen invades the plant through the points of root emergence and rapid multiplication leading to brownish discoloration and collapse of the vascular tissue (Hayward, 1991). Symptoms associated with infected plants are wilting, stunting and yellowing of the foliage (Kelman, 1953). The rate of disease development is depending on host susceptibility and the aggressiveness of the pathogenic strain.
In potato tubers the internal symptoms of the disease display a greyish-brown discoloration of vascular elements and milky-white sticky bacterial ooze is seeping out while in severe infection it may be visible at tuber eyes or at the stolon end scar. In some cases, these tuber symptoms are visibly absent and be as a latent infections.
Disease transmission may be through contaminated farm tools and equipments, latently infected planting material, plant debris, contaminated irrigation water, some weeds, insects, deep wounding during cultivation and nematodes. Moreover, this bacterium can move from the roots of infected plants to adjacent roots of healthy plants, as root to root infection (Ji et al., 2005).
R. solanacearum can survive long-term in soil under natural conditions because of a lack of sensitive detection methods for studying low pathogen populations amongst high numbers of saprophytic bacteria in the soil environment. Standard methods to monitor the present of pathogen in potato tubers include using of immunofluorescence antibody staining (IFAS), isolation on selective medium such as (SMSA) medium (OEPP/ EPPO, 1990). While the most commonly used techniques to confirm positive results in IFAS test are the bioassay on tomato plants (as indicator plant). Also, using polymerase chain reaction (PCR) from pure single colony that has been isolated from selective medium (OEPP/EPPO, 1990).
Many studies and trials have been carried out worldwide to control this serious disease through farming practices but without much success. Although crop rotation with non-host plants was recommended but had proven as inefficient method. While, using soil fumigants is environmentally unacceptable, expensive and largely ineffective against the disease (Saddler, 2005). The same conclusion was reached with antibiotics and chemical control (Murakoshi andTakaha shi 1984, andHabashy et al., 1993) while, breeding of new resistant varieties is consider a new stratiegy for controlling the disease (Lo´pez and Biosca 2004). As the outcome of failure and difficulties of control, the early detection of the disease is highly desired as preliminary aspects of management. Chiwaki et al. (2005) mentioned that using thermal infrared imagery induced a decrease in photosynthesis, transpiration and stomatal conductance which are closely related to the increase in plant leaf temperature. So, they depend on this method to detect the infection 4 days earlier to the appearance of visual diagenostic symptoms where, the invasion of vascular bundles and bacterial multiplication were lowering transpiration and raising leaf temperature. Also, remote sensing technique and image analysis allows the early detection of any morphological and anatomical changes even if these changes are small, without any physical contact between the measuring device and the studied object.
Moreover, the object can be analyzed many times noninvasively and without damage (Nilsson, 1995and Cha´vez et al. 2009, 2010. As the remarkable attractive symptom of infection being the decrease in the chlorophyll content resulted from wilting and yellowing change on foliage and the fact that remote sensing technique may accurately detect those changes. The current study aimed to monitor and detect such symptoms changes through the remotely sensed data obtained from satellite images. For this purpose, satellite images obtained from Landsat-8 -Operational Land Imager (OLI) and RapidEye were used to detect the presence of wilting conditions on potato plant at wider scale and confirmatory detection.

Material and Methods:
The data and methodology of the current study are simply displayed in Figure (1), where they included two main parts, A) Fieldwork and lab analyses, B) Remote Sensing Analyses. Soil samples were collected from three selected areas of investigation and examined for the presence of R. solanacearum bacterium that cause potato brown rot infection. Sixteen samples from each field were taken at approximately 20 cm depth, using a sampling auger, mixed well and sieved through 2.0 mm mesh size in the laboratory for homogenization. For the microbiological analyses the tested sample was splitted into (4) fractions.

Isolation of Ralstonia solanacearum:
Isolation from soil was made on SMSA as selective medium for the pathogen (Elphinstone et al., 1996). Decimal dilutions of the original suspension were made in 90 mL sterile phosphate buffer (0.05 M), then they were shaked for 2 hours at 15°C and plated onto SMSA medium.
The plates were incubated at 28°C for few days (almost 5 days).
The typical isolates which appeared as fluidal, slightly irregular, white colonies with pink centers were selected (Buddenhagen & Kelman, 1964). Thirty isolates were randomly selected and cultured on nutrient agar medium for 48 hours, and then they were verified serologically using IFAS test (Janse, 1988). The selected isolates were kept as suspensions in sterile tap water for long-term storage as bench incubation then they were revived by culturing on tetrazolium chloride (TTC) medium (Kelman, 1954), when being desired.

Biovar determination of the pathogen:
Biovar characterization of the bacterium was done according to its ability to oxidize sugar and sugar alcohols as described by the standard procedure of Hayward, (1964).

Pathogenic potential of isolates:
Pathogenicity of randomly selected isolates recovered from different area of investigation were biologically confirmed by inoculating tomato (Solanum lycopersicum cv. Pinto) plantlets (3 leaves/ seedling), grown in pots under greenhouse conditions, using the stem puncture technique (Janse, 1988). Injection was made at the leaf axis by a needle laden with the bacterial growth of the pathogen. Few drops of sterile water were injected instead of bacteria as a control treatment. The inoculated plants were covered with polyethylene bags for one day, kept at 30°C, then, the bags were removed, and pots were irrigated and examined for wilting symptoms.

Real-time PCR assay:
Real-time PCR (Taq-Man) for DNA amplification was preformed as descriped by Weller et al. (2000) using Applied Biosystem7500 real-time machine. All selected isolates from different soils were tested by this technique. The reaction mixture consistes of: 12.5µl of master mix, 1.0 µl of primer forward, 1.0 µl of primer reverse, 1.0 µl of probe and 7.0 µl of water and 2.5µl of nucleic acid extract. The sequence of used primers and probe is shown in Table (1) and were provided by OPRON, USA. Positive control of extracted DNA was used while, the water was used as a negative control.

Phylotype analysis of R. solanacearum by Multiplex-PCR
Phylotype analysis of selected R. solanacearum isolates was made by using the Opina primers 759/760 as internal markers which is specific for the R. solanacearum species and a set of 4 phylotypes-specific forward primers with a unique and conserved reverse primer targeted in the 16S-23S Intergenic Spacer region (Opina et al., 1997). Table (2) shows the sequences of these primers. One colony is suspended in 100.0µl of sterile water and is heated for 5 minutes at 100°C and the debris are spined down. The reaction mixture consists of: 12.5µl of ready master mix, 1.0 µl from each primer, 3.5µl of water and 2.0µl of nucleic acid extract (Sagar et al., 2014).
The cycling program was used in a thermal cycler (Biometra T personal) as following: 96°C for 5 min and followed by 30 cycles of 94°C for 15s, 59°C for 30s and 72°C for 30s, then followed by a final extension period of 10 min at 72°C. A 13.0μl aliquot of each amplified PCR product was subjected to electrophoresis on 2% (w/v) agarose gels, contains ethidium bromide (0.5% μgL-1) and then the bands were visualized on a UV-transilluminator. The Pmx-PCR test amplifies the 280-bp universal R. solanacearum specific reference band plus following phylotype-specific PCR products: a 144-bp amplicon from phylotype I strain; a 372-bp amplicon from phylotype II strains; a 91-bp amplicon from phylotypeIII strains and a 213bp amplicon from phylotype IV strains (Sagar et al.,2014).   The RapidEye is a multispectral and high-resolution satellite that consists of a constellation of five earth imaging satellites through identical sensors that are in the same orbital plane and are calibrated equally to one another. This means an image from one RapidEye satellite will be identical in characteristics to an image from any of the other four satellites (Planet Labs 2016).
Data from RapidEye are produced as individual 25km by 25km tiles and offered in different processing levels. The sensor is having a five-channel multispectral system operating in the visible and near-infrared (Warner et al., 2009) with a spatial resolution of 5 meters as shown in

Landsat 8-OLI sensor
The Landsat 8 satellite is carrying two different sensors the Thermal Infrared Sensor (TIRS) and Operational Land Imager (OLI) (Roy et al., 2014). The Landsat 8 two sensors providing a seasonal coverage of the world at different spatial resolutions 30m (visible, NIR, SWIR), 100m (thermal), and 15m (panchromatic) (Ennaji et al., 2018) as shown in Table (5). Only one scene from Landsat 8 was used in the date before planting, also bands from 1 to 7 were stacked together to make the image ready to apply the atmospheric corrections .The effects of the atmosphere (scattering and absorption) and the pixel values were finally converted to surface radiance, then the corrected image was clipped to match the boundary of the study area.

Construction of soil and vegetation indices:
Four different indices were applied to the downloaded and preprocessed satellite images in before and after planting dates including the Index (NDVI), (NDSI), (NDMI), and (MCARI).

Before planting indices construction
Three indices were applied to the images of RapidEye and Landsat 8 starting from the NDVI, NDSI, and NDMI. Both NDVI and NDSI were applied to the images of RapidEye, while the NDMI was applied to Landsat 8 image.
The NDVI index is used to detect the density of plants on a particular part of land (Weier & Herring, 2018). Where, it utilizes the absorptive and reflective characteristics of the vegetation in the red and near-infrared portions of the electromagnetic spectrum. Therefore, the NDVI was used in the current study and specifically on the January image ( Figure, 3) to make sure that the field is bare and not vegetated which will give a great opportunity to apply and measure the soil characteristics of the land.
The NDVI calculated using the following equation: Where: NIR = Near Infrared band (RapidEye, band no. 5) RED =RED band (RapidEye, band no. 3) The NDSI which has been proposed by (Aldakheel et al., 2005) is the widely used index to extract and measure the soil salinity due to its significant correlation with Electric Conductivity (EC) (Ennaji et al., 2018). The index can be calculated by using the following equation: Where: NIR = Near Infra-Red band (RapidEye band no. 5)

RED =RED band (RapidEye band no. 3)
The resulted NDSI map is dimensionless and ranges between −1 (low salinity content) to +1 (high salinity content) as shown in Figure (4).
The NDMI is a measure of the amount of water in the soil which in turn reflects and estimate the moisture content of the soils (Serrano et al., 2019). The following equation is used to calculate the NDMI: Where:

NIR = Near Infra-Red band (Landsat 8 band no. 5)
SWIR =Short Wave Infra-Red band (Landsat 8 band no. 6) As seen in the above NDMI equation the presence of the SWIR spectral band is required to run the analysis on the study area. For this reason, the Landsat 8 image was used to calculate and measure the moisture content of the soils of the study area (Figure ,5).

3.After planting indices construction
Two determinations were made (NDVI and MCARI) using the images of RapidEye. The NDVI was applied again over that date for double-checking the planting status of the field (Figure, 6).
MCARI is strongly affected and sensitive to chlorophyll concentrations in plant leaves (Xue & Su, 2017). It can be calculated using the following equation: Where: . Figure

Isolation of Ralstonia solanacearum from different potato fields:
Naturally infested soil samples from different potato fields in villages in concern were used for isolation of R. solanacearum bacterium. Morphological characteristics of isolates were described on SMSA medium. On that medium, all isolates showed fluidal, milky white, irregular with blood red center was identified as typical colonies of R. solanacearum. The numbers of bacteria were 20 x 10 4 , 31 x 10 4 and 48 x 10 4 from Al Tayreya, Om Saber and Abo Al Khawi villages respectively. Thirty isolates were randomly selected (12 isolates from Abo Al Khawi area, 9 isolates from Om Saber, as well as 9 isolates from Al Tayreya area).

Immunofluorescent antibody staining test (IFAS)
All randomly selected colonies (thirty isolates) developed on SMSA medium were tested by IFAS test. The cells showed short rod shaped morphology, stained evenly as bright green fluorescent ( Figure.,8).

Biovar determination of the pathogen
The biovars determination was based on the ability of isolates to produce acids from hexose and alcohol sugars. Data in Table (6) show that the thirty isolates were able to produce acids from lactose, maltose, and cellibiose. However, they were unable to produce acids from sorbitol, mannitol, and dulcitol denoting that, these thirty tested isolates were assigned to biovar 2. So, the dominant race in Egypt is race 3, biovar 2.

Pathogenic potentials of selected bacterial isolates
Pathogenicity showed that all tested isolates representing different locations were able to wilt tomato seedlings 3 days after stem inoculation under greenhouse conditions.

Real-time PCR (Taq-Man) assay
Real-time PCR is a sensitive test for detection of low concentrations of R. solanacearum and is being considered a confirmatory test in the detection work. The RS primers and probe were employed to detect all biovars and races of R. solanacearum. Positive results were shown with all the tested isolates indicating that the thirty isolates were R. solanacearum ( Figure., 9).

Phylotype assignment
Based on the results of the Pmx-PCR, all selected isolates belonged to the phylotype II sequevar I, as 372-bp amplicon was observed for the tested isolates ( Figure,10: a,b and c) .

B(Al Tayreya area)
A(Abo Al Khawi area) C (Om Saber area) Figure, 10(a,b and c): PCR products of (30) isolate of R. solanacearum using different primers, 280-bp amplicon from R.solanacearum species and 372-bp amplicon from phylotype II strains.

Before planting indices construction
The results obtained from the application of the remote sensing technology over the infested locations before planting potato are shown in Table (

After planting indices construction
After applying both NDVI and MCARI analysis over the RapidEye images covering the study locations the results are shown in Table (  Based on the analyses made on the soils (both in the field and isolated samples) and foliage of plants using both lab tests and remote sensing analyses, the effects of bacterial infections with R. solanacearum can be monitored continuously over vast areas of fields planted with potato crops to detect any reduction in the chlorophyll levels in the leaves which may be caused by such bacterium type or any other disease that may cause an uncharacteristic decrease in chlorophyll contents.

Discussion
Bacterial wilt of potato caused by R. solanacearum (Syn. P. solanacearum) is the most serious bacterial disease predominating in warm and temperate potato districts at certain conditions (Yabuuchi et al., 1995). This disease has causes a lot of quarantine problems for Egyption potato exportation since the first report made by Sabet (1961) and has been studied intensively by Farag(1970) and(1976). The bacterium is classified as tuber-borne disease, in principal, with extended longivity in infested fields that complicate agriculture practices for control, along with possible occurence of susceptible and multiple weed flora (unpublished data). The bacterium has more than 4 races at the time being with predominance of "Race 3" at certain old potato cultivation district in Egypt. No evidence is being made of the occurence of the most serious and hazardous race 1 bacteria in Egypt. The disease produced by race 3 has been surveyed in Egypt by Michail et al. (1974) by the classical methods of diagnosis and diseased areas were coded and prohibited from potato exportation.
Because of complexity of field work during the survey and extended planting potatoes, more accurate and rapid screening methods were followed. Among these modern and advanced methods of detection is to follow the analyses of the occurence of the disease through remote sensing (Nilsson, 1995 andChavez et al., 2012).
In the present work, isolation of the pathogen was made from naturally infested soil(s) from previously enlisted potato cultivation districts in three villages (Aboelkhawy, Tayareya, Omsaber) at Behera governorate (North of Nile delta). Several investigators reported occurrence of potato brown rot bacteria in other location of Egypt (Hamad et al., 2008(Hamad et al., & 2016Hassan et al., 2017 andHanafy et al., 2018). Methods currently used for the detection of R. solanacearum through plating on modified semi-selective medium (Elphinstone et al., 1996) and immunofluorescent antibody staining (Janse, 1988). Complimentary procedures including a tomato bioassay (Janse, 1988), real-time PCR assays (Weller et al., 2000) and Phylotype assignment (Sagar et al., 2014). Many researchers clarified that these methods differ in their degree of sensitivity (Mikhail, et al., 2016). Plating technique on modified (SMSA) medium is a microbiological selective method although some samples may show saprophytic bacteria (Pradhanang et al., 2000). It may requires skilled and well trained personnel who can distinguish target colonies from other saprophytes that can grow on the medium. Immunofluorescent antibody staining (IFAS) is a serological technique and considerd one of the first standard screening tests for detecting latent infection of R. solanacearum in potato tubers. This method with the polyclonal antisera was described as a rapid and inexpensive method but lack in sensitivity beside giving a false positive results due to cross-reactions with other bacteria (Balabel, 2014). Using tomato seedling as biological test was reliable to detect as few as 10 4 cells / mL (Janse, 1988 andElphinstone et al., 1996). Tomato seedlings are showing wilt symptoms within a week from inoculation depending on the starting inoculum (inoculum potential) and optimization of conditions (Elphinstone et al., 1996), however, if temperature falls below 21ºC the infection takes place but no symptoms could be recognized i.e, the wilt may fail to develop (Singh et al., 2014). The results of biovar determination indicating that all the tested isolates belong to biovar 2. On the other hand, phylotype specific multiplex (Pmx)-PCR revealed that all thirty isolates of R. solanacearum belonged to phylotype II as a 372-bp amplicon was observed for all the tested isolates after electrophoresis (Agarose gel 2% w/v). These results indicating that, the race 3, biovar 2 (phylotype II, sequevar I) is dominant race in Egypt, the same results were observed by other searchers (Hamad et al., 2016;Hassan, et al., 2017).
Remote sensing technology was applied in this research by using satellite images from Landsat 8 and RapidEye before planting (bare soil conditions) and after planting. Soil salinity, soil moisture, and plant chlorophyll content were checked over the acquired satellite images through the application of Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Salinity Index (NDSI) and Modified Chlorophyll Absorption Ratio Index (MCARI) indices. It is known that at early stages of bacterial infection, the first recognized symptoms developed on the plant leaves as wilting of young leaves followed by general wilting and yellowing of the leaves and finally plant death (Champoiseau & Momol, 2009) Results of remote sensing analysis revealed chlorophyll levels were in medium levels which was not typical to conditions associated with usual conditions. Moreover, analyses showed medium or relatively high salinity along with high moisture content. The latter two factors might be involved in disease progress, as shown by chlorophyll level change. It is known that, the capability of remotely sensing light reflectance to detect the damage caused by bacteria is depending on changes in reflectance introduced approximately by the blockage of vascular tissue of the infected plants (Grimault et al. 1994;Herna´ndez et al. 2005). Any stress on the plant leads to different physiological and morphological changes in affected plants and remote sensing technique can detect early morphological and anatomical changes even if these changes are small (Cha´vez et al. 2009(Cha´vez et al. , 2010. Moreover, Cha´vez et al., (2012) noted that different plant reactions, caused by different factors (e.g. bacteria, virus, water deficits, meterological aspects) may be well detected by test sensing devices and analytical ways. Also, Mahlien, 2016 used different imaging sensors to detect different plant diseases.
Therefore, the results of processing of satellite images denoting unusual growth conditions that may be attributed to disease progress. Such an approach can be applied by decision-makers as precautionary aspects to rapid and presumptive surveys of disease incidence of vast areas cultivated with potato crops and /or any other crop. Also, it could help farmers and producers to be more focalized and timely response to intra-field and intra-crop variations such as the outbreak of disease. For clarification , remote sensing has been used to detect many diseases that affect plants, for example: phytophthora foot rot in citrus trees (Fletcher et al.,2001) ;late blight in tomatoes (Zhang et al.,2005); yellow rust in wheat (Huang et al.,2007); citrus greening disease ( Kumar et al.,2012); downy mildew in cucumber (Tian and Zhang,2012) ; powdery mildew in winter wheat ( Yuan et al.,2014) ; grapevine leafroll disease -GLD (MacDonald,2016); yellow leaf curl disease in tomato leaves (Lu et al.,2018) and fusarium wilt in Banana ( Ye et al.,2020).
It is very important to note that successful use of remote sensing depends on the accurate study of healthy and diseased plants. Otherwise we must compare and analyze remote sensing data from healthy plants with data from plants under stress. In this point, Lowe et al., 2017 used the imaging techniques to detect biotic and abiotic stress in plants. However, further work is advised to explore the validity of remote sensing of plant diseases at larger scale to help in disease control.