Characterization of Phytophthora infestans resistance to mefenoxam using FTIR spectroscopy

Accepted Manuscript Characterization of Phytophthora Infestans Resistance to Mefenoxam using FTIR Spectroscopy A. Pomerantz, Y. Cohen, E. Shufan, Y. Ben-Naim, S. Mordechai, A. Salman, M. Huleihel PII: DOI: Reference:

S1011-1344(14)00302-9 http://dx.doi.org/10.1016/j.jphotobiol.2014.10.005 JPB 9854

To appear in:

Journal of Photochemistry and Photobiology B: Biology

Received Date: Revised Date: Accepted Date:

3 July 2014 6 October 2014 11 October 2014

Please cite this article as: A. Pomerantz, Y. Cohen, E. Shufan, Y. Ben-Naim, S. Mordechai, A. Salman, M. Huleihel, Characterization of Phytophthora Infestans Resistance to Mefenoxam using FTIR Spectroscopy, Journal of Photochemistry and Photobiology B: Biology (2014), doi: http://dx.doi.org/10.1016/j.jphotobiol.2014.10.005

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CHARACTERIZATION OF PHYTOPHTHORA INFESTANS RESISTANCE TO MEFENOXAM USING FTIR SPECTROSCOPY A. Pomerantz1, Y. Cohen2, E. Shufan3, Y. Ben-Naim2, S. Mordechai4, A. Salman#*3, and M. Huleihel#*1 1

Department of Microbiology, Immunology and Genetics, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.

2

3

Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, 52900, Israel. Department

of

Physics,

SCE

-

Shamoon

College

of

Engineering,

Beer-Sheva 84100, Israel. 4

Department of Physics, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.

*Corresponding authors: Dr. Ahmad Salman Tel: +972-8-6475794 Fax: +972-8-8519161 E-mail: [email protected] # contributed equally

Prof. Mahmoud Huleihel Tel: +972-8-6479867 Fax: +972-8-6479867 E-mail: [email protected]

Table of contents entry: Major steps in this study: FTIR Ab sorption [A.U ]

0.06

spectroscopy may provide a

0.04

0.02

0.00

-0.02 4000

3500

3000

2500

2000

1500

specific, rapid, and inexpensive

1000

-1

Wavenumber[cm ]

Discrimination Results

Resistant

Non-Resistant

Multivariate analysis: PCA LDA

method

for

differentiation

successful between

resistant and non-resistant isolates P. infestans to mefenoxam fungicide.

Abstract Phytophthora infestans (P. infestans) is the causal agent of late blight in potato and tomato. This pathogen devastated the potato crops in Ireland more than a century years ago and is still causing great losses worldwide. Although fungicides controlling P. infestans have been used successfully for almost 100 years, some isolates have developed resistance to most common fungicides. Identification and characterization of these resistant isolates is required for better control of the disease. Current methods that are based on microbiological and molecular techniques are both expensive and time consuming. Fourier Transform Infra-Red spectroscopy (FTIR) is an inexpensive and reagent-free technique that provides accurate results in only a few minutes. In this study the infrared absorption spectra of the sporangia of P. infestans were measured to evaluate the potential of FTIR spectroscopy in tandem with multivariate analysis in order to classify those sporangia into those that were resistant and those that were nonresistant to the phenylamide fungicide mefenoxam. Based

on individual

measurements, our results show that FTIR spectroscopy enables classification of P. infestans isolates into mefenoxam resistant and mefenoxam non-resistant types with specificity of 81.9% and sensitivity of 75.5%. Using average spectra per leaf, it was possible to improve the classification results to 88% sensitivity and 95% specificity.

Key words: PCA, LDA, sporangia, fungicide resistance, rapid detection, infrared spectroscopy, multivariate analysis

1.

Introduction

Phytophthora is a large genus of oomycetes with over sixty disease-causing species [1, 2]

. Oomycetes, frequently referred to as water molds, are a group of fungus-like

eukaryotic microorganisms found throughout the world; they are both saprophytic and pathogenic. Phytophthora infestans (Mont.) de Bary is the cause of the late blight disease in Solanaceae. P. infestans are a major concern in Solanaceae diseases and cause severe losses to crops, mainly to potatoes and tomatoes. Annual losses in Europe due to crop destruction and control measures exceeds 15% of a close to €6 billion market [3, 4]. P. infestans is regarded today as a threat to global food security. P. infestans mainly has two mating types — A1 and A2— which can reproduce individually to produce short term spores. In the presence of the opposite mating type the oomycete will produce oospores, which are spores with a thick wall that allows survival in soil for several years. These oospores are usually genetically different from both parents and may have different characteristics [4]. Mefenoxam, a phenylamide fungicide, is the most commonly used fungicide for late blight. It blocks RNA transcription by inhibiting RNA polymerase 1 activity. Since their discovery in 1977, phenylamide fungicides have significantly contributed to an effective control of P. infestans and play an important role in modern disease control programs [5]. Shortly after the introduction of mefenoxam to crops, resistant isolates of P. infestans were detected in fields [6]. It is believed that mefenoxam resistance originated from naturally occurring resistant isolates that existed at a very low proportion in the population, even before any exposure to the fungicide [5]. Resistance to antibiotics usually develops as results of molecular changes in the host genome. Such changes might lead to expression of specific enzymes that can attack and disassemble

the antibiotic that is being used, or they might cause stability and resistance of the targeted enzyme to the used antibiotic. Although the molecular mechanism that controls resistance to mefenoxam is not known, specific mutations can cause some changes in the targeted enzyme (RNA polymerase 1 in this case), and can change its identity without affecting its activity [5]. Infrared spectroscopy is highly sensitive to molecular changes in cells. There are three

categories of sensitivity levels: mefenoxam resistant, non-resistant (sensitive), and intermediate

[5]

. Mefenoxam resistance is an important aspect of P. infestans and is

being examined for diagnosis and classification of isolates. This parameter is of utmost importance to the farmers who grow the crops. When late blight is detected in a field the farmer has to determine the fungicide to be used. Mefenoxam and its derivatives are the cheapest and most common fungicides available today, and thus are the first to be considered. Naturally, mefenoxam has little effect on resistant isolates, in which case other fungicides must be applied. The current method used for detecting resistant isolates relies on leaf lesions or potato tuber slices that are placed on moist filter paper in petri dishes and infected with oomycete inoculums. Control plates saturated with water are compared with parallel dishes saturated with the tested fungicide (such as mefenoxam). After seven days the two populations are compared in order to determine the resistance of the isolates according to their growing ability. These examinations, although simple to conduct, are extremely time-consuming when trying to determine the best treatment to apply to growing crops, because an aggressive isolate of P. infestans might devour an entire field in one week. Often farmers do not wait for laboratory results, but apply fungicides immediately after the disease is detected in the field, regardless the available information. Because non-resistant isolates tend to be the more aggressive ones, such decisions are somewhat rational [7].

FTIR spectroscopy is a powerful tool for chemical analysis and is used to provide detailed information about chemical composition at the molecular level. FTIR has high sensitivity, good resolution, and good signal-to-noise ratio (SNR) and is simple to use, quick, and economical [8, 9]. Since the 1990s FTIR spectroscopy has been used for the examination of biological samples, taking advantage of its ability to measure very small samples [10-15]. Since the early 2010s phytopathogenic fungi FTIR investigation began to rise in popularity, leading to successful classification and differentiation among phytopathogenic fungi genera species and isolates [15]. These successes were enhanced by combining FTIR with advanced mathematical methods such as Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA)[13, 14]. In the present work, we present a novel approach for quick identification and detection of P. infestans resistant to mefenoxam. Using FTIR micro-spectroscopy together with PCA and LDA methods we examined five isolates of P. infestans and successfully determined, using their characteristic spectra and specific biomarkers, whether they are mefenoxam resistant or non-resistant isolates. 2.

Materials and Methods 2.1.

Pathogen

The details regarding preparation of the samples and the infection process are described below. 2.1.1. Preparation of infectious pathogen. Five P. infestans isolates were supplied while infecting a tomato leaf (Lycopersicon esculentum L) of the susceptible inbred ZH line. Isolate data is presented in supplementary Table 1. The fresh sporangia were removed from the infected tomato

leaf by spraying the leaf with sterilized double-distilled water, then collected and kept on ice until use.

2.1.2. Pathogen infection. Fresh detached tomato leaves (cv. HA-870) were rinsed in alcohol for one minute, followed by two rinses of sterilized double-dehydrated water, and then placed on moist filter paper in plastic petri dishes. The leaves were then inoculated by droplet application of the sporangia water (10-µl each, with 10 droplets per leaf). Leaves were kept in the dark for 12 hours at 15°C and were then exposed to 12 hours of a light/day cycle at 17°C to 22°C, until symptoms appeared (usually within 3-6 days).

2.2

Sample preparation for FTIR measurements

Sporangia were collected 3-6 days after inoculation, depending on the isolate, when coverage of the leaf appeared. Once again, sporangia were removed from the leaf by spraying sterilized double-dehydrated water. The sporangia containing water were centrifuged for four minutes at 2600 rpm and rinsed twice. Excessive water was then removed and a droplet of 5 µL was placed on a 1" square ZnSe slide. The samples were examined using an optical microscope, evaluating sporangia density and purity (from bacterial contamination and leaf debris). The samples were then dried at room temperature for several minutes, until they were completely dehydrated.

2.3

FTIR measurements

FTIR measurements were performed using the FTIR microscope IR scope II with a liquid nitrogen cooled mercury-cadmium-telluride (MCT) detector, coupled to the FTIR spectrometer (Bruker Equinox model 55, OPUS software). To achieve a high

signal to noise ratio (SNR), 128 co-added scans were collected in each measurement in the 600 to 4000 cm-1 wavenumber region. All five P. infestans isolates were grown on petri dishes at different occasions, and samples were prepared from each dish. The number of replicate dishes and measurements from each leaf are detailed in Supplementary Table 1. Each sample was measured over 10 times and spectra were acquired. The measurement site was circular with a diameter of 100 microns, and a spectral resolution of 4 cm-1 was used. The measured regions were chosen to contain a maximum number of sporangia at a minimum contamination. In total, we measured 504 spectra from five P. infestans isolates. 2.4

Spectral Analysis

The acquired spectra were manipulated as follows: Atmospheric compensation was applied to eliminate influences of CO2 and air humidity. The 800-1775 cm-1 region of interest of was cut from the spectra. The remaining regions, which did not contain relevant information, were discarded. The cut spectra were then baseline corrected using the concave rubber band method, with 64 consecutive points and five iterations (in the manual mode). The corrected spectra were then normalized using vector normalization followed by offset correction. All spectral manipulations were performed using the standard tools of OPUS software version 7.

2.5

Statistical Analysis

As described in section 2.3, FTIR spectra were obtained from each sample. Each measurement belongs to one of two classes (either resistant or non-resistant). The possibility of determining the class type by pattern recognition methods—PCA followed by LDA—was examined. The spectrum of a sample, which is called, in the context of pattern recognition, “a feature vector”, is a fingerprint of the sample. The

number of points in each spectrum is of the order of hundreds. Separating between the classes working with hundreds of points for each measurement can be significantly improved if the number of dimensions is reduced by using the PCA method [15-17]. In this work the use of PCA enables the reduction of the number of dimensions from a few hundred to several dozen, keeping enough information for the category separation. Data reduction is obtained by choosing a partial set of PCs. In this work we choose the first k PCs, with 1 ≤ k ≤ 15 . After applying PCA for dimensionality reduction the LDA method [18, 19] was chosen for category decision. LDA is a classic statistical method used for variable classification [18, 19]. The input for the LDA is the data obtained after the PCA dimensionality reduction. For validation the leave-one-(LOO) out procedure was applied [16, 17]. This variant uses one point as the test set and the rest N − 1 points for training. The procedure is repeated N times, and for each repetition a different point is left out. The PCA and LDA calculations were done using codes that were developed in-house, based on MATLAB and Mathematica softwares. The LDA calculation was applied using the LOO algorithm that is used for cross-validation. This algorithm has been extensively explored in machine learning and used for estimating the error in small populations [20].

3.

Results

In this study we examined the potential of FTIR spectroscopy as a diagnostic tool to rapidly detect and differentiate between resistant and non-resistant isolates of P. infestans to mefenoxam. Figure 1 shows the averaged infrared absorption spectra and the standard deviation bars of each isolate belonging to the two groups of P. infestans isolates (Supplementary Table 1) investigated in the range of (800 -1775 cm-1). As can be seen from Figure 1, all main features appear in the five isolates, although the

differences between the two groups and within the groups are minute. The spectra of the two groups are very similar, sometimes overlapping, with slight variations in the bands intensities. No band shifts were observed between the two groups. The resistant group of isolates has higher intensities in the 1740 cm-1 lipids and the 1050 cm-1 carbohydrates bands, and lower intensity in the 1642 cm-1 amide I bands. Starting from the higher wavenumbers, the band at 1740 cm-1 is attributed mainly to the phospholipids C=O ester vibration. Amide I is a dominant band in this region, arising from the C=O, C=N and N-H stretching vibrations at 1642 cm-1. The Amid II and Amid III bands are visible at 1541 cm-1 and 1310 cm-1 respectively [21-23]. The lower part of the spectra reflects mainly carbohydrates. Cellulose is mainly attributed to the C-O and C-C stretching vibrations at 1110 and 1050 cm-1 respectively. Glycogen also contributes to these bands and to the C-C stretching vibration [24] located at 1151 cm-1. No band shifts were observed in the spectra between the different isolates. The aim of this study was to differentiate between oomycete isolates that are resistant to mefenoxam and those that are non-resistant. In this regard we first tried to derive spectral biomarkers from the measured mid-infrared absorption spectra. Area analysis of integrated band intensities enabled us to calculate the differences at specific bands and to show the differences quantitatively. Each spectrum was broken down to its major bands. The integrated area underneath each band was calculated. These areas may be considered as biomarkers that represent the concentration of biomolecules that can be attributed to bands in the calculated range. Observing the calculated intensity relations, we narrowed the study to four bands that offer the best differentiation. These bands are: carbohydrates bands in the range of 925-1180 cm-1,

phosphate bands in the range of 1180-1280 cm-1, amide I in the range of 1580-1710 cm-1, and the lipids bands in the range of 1710-1770 cm-1. The amide I and phosphate bands give higher absorbance for the non-resistant isolate group, while the bands for the carbohydrates and lipids give higher intensities in the resistant group. We next prepared two-dimensional graphs displaying, and therefore comparing, the intensity of one of these four bands against different band intensities. Four such graphs are shown in Figure 2: (a) Carbohydrates versus amide I; (b) Carbohydrates versus lipids; (c) Amide I versus lipids; and (d) Phosphates versus lipids. Each figure displays a separating straight line (a decision line) that divides the data points into two clusters—resistant (red points) and non-resistant (blue points). The decision line was established by applying LDA to the subspace spanned by the two chosen biomarkers. As can be seen from the figure, some points from each group overlap with others. This data set included 221 resistant and 163 non-resistant spectra. Using the 2D graphs of biological biomarkers data, the sensitivity and specificity of the classification procedure for each plot were calculated. The resistant and nonresistant isolate detection success rates were respectively considered as the test positive and test negative outcomes. For example, in Figure 2a, the 163 resistant dots under the line are true-positive and the 126 non-resistant dots above the line are truenegative. The 58 resistant dots above the line are false negative and the 37 nonresistant dots under the line are false positive. The sensitivity is calculated as the sum of true-positive divided by all the positive (resistant) dots. The specificity is the sum of true-negative divided by all the negative (non-resistant) dots. The resulting sensitivity and specificity of the resistant group of P. infestans are summarized in Table 1.

Multivariate analysis was carried out for the classification goal. First, we applied PCA on the entire spectral region (800-1775 cm-1), in order to lower the dimensions of the data. Second, in order to achieve better results despite the high levels of similarity between the spectra, we applied LDA – a statistical multivariate supervised method. The LOO algorithm was used for verification. LDA determines the best differentiation of the groups for each number of principal components (PCs), assigning he number of errors in identification of the spectra to the correct group. Identification errors (in percentage) derived using LDA, versus PC numbers, are displayed in Figure 3 for three different ranges. Due to the high similarity between the isolates of the two groups, 14 PCs were needed for the classification to achieve an overall good classification while simultaneously keep the highest loading (PC) meaningful and noiseless. A confusion matrix was generated (Table 2), based on the LDA calculations using 14 PCs. It was possible to achieve a success rate of 79.2%, calculated as the ratio between the total number of correctly identified spectra (sum of numbers on the diagonal of the matrix) and the total number of spectra.

The specificity and sensitivity of the classification of resistant and non-resistant isolates of P. infestans were 81.9 % and 75.5% respectively. These percentages were derived using LDA calculations with 14 PCs in the lipids, Amide I, carbohydrate, and phosphate ranges (925-1280, 1580-1770, cm-1). Average spectra were also calculated and examined for an alternative analysis. The average spectrum was calculated for each leaf plate prepared for the study. We obtained 20 average spectra from 3 resistant isolates and 17 average spectra from 2 non-resistant isolates; yielding an overall of 37 average spectra.

Figure 4 shows a typical average spectrum of 10 measurements from the same leaf that was infected with the non-resistant isolate 501 (Supplementary Table 1). The highlighted area is determined by the standard deviation in the 800-1775 cm-1 region. New biomarkers and multivariate analyses were performed on the leaves averages similarly to the method discussed earlier using the individual spectra. Using the same analysis procedure and based on the leaf averaged spectra, 86% sensitivity and 94% specificity were achieved based on analysis of spectral biomarkers. The classification of the studied isolates of P. infestans into resistant and non-resistant categories, using LDA with the LOO algorithm, and based on the average spectra of the leaves, is shown in Table 3. As can be seen from Table 3, the best classification results were derived using LDA calculations with 14 PCs in the 800-1775 cm-1 range, which yields 88% sensitivity and 95% specificity. Area and multivariate analysis studies of the different P. infestans isolates (data not shown) were performed on the 2800-2990 cm-1 high wavenumber region. However, this region gave poor results relative to the lower regions and therefore was not considered for further analysis.

4.

Discussion

Recently there have been attempts to determine mefenoxam resistance of P. infestans isolates by genetic means, in order to pinpoint the specific genes responsible for resistance[25]. These attempts have been insufficient and very preliminary. Similar studies in other species of phytophthora have led to identification of more specific genes[26]. However, classification is not yet possible. Determination of P. infestans mefenoxam resistance is performed today the same way it had been performed 25

years ago[27]. It takes about a week to detect resistant isolates to mefenoxam. In our study it was shown that by using FTIR microspectroscopy, it is possible to classify resistant and non-resistant isolates of P. infestans to mefenoxam in less than twenty minutes. From the standpoint of the farmer, the availability of a testing procedure that uses such a short time scale for differentiation, is crucial. FTIR spectroscopy of phytopathogenic fungi has become a popular research topic in the last few years[14, 28]. It has been also used for examination of oomycetes, although the studies did not focus on the oomycetes themselves, but on determining the reactions of resistant plants to the oomycete and FTIR markers[29, 30]. Similar studies have recently been conducted for the investigation of P. infestans influence on potatoes[31]. However, to our knowledge, FTIR spectroscopy had not yet been used for the examination of P. infestans and classifying them into groups that are either resistant and non-resistant to mefenoxam. Based on the spectral biomarker analysis carried out on the individual measurements, a good differentiation between resistant and non-resistant isolates was achieved, with up to 74% sensitivity and 77% specificity (Figure 2 and Table 1). Using PCA followed by LDA analysis with the LOO algorithm, 81.9% sensitivity and 75% specificity were achieved based on all the metabolites. The samples were examined by inverted microscopy for evaluating sporangia density and to ensure maximum purity. It should be noted, however, that some of the obtained samples might contain a mixture of mature and immature sporangia and a certain very low level of contamination (with bacteria and leaf debris), which may affect the spectrum and lead to a misidentification of the tested sample. As can be seen from Figure 4, there is some heterogeneity between the spectra measured from the same leaf. Part of this heterogeneity may be attributed to these factors and the sample

preparation process. Using the infrared microscope the spectrum was acquired from a circular spot 100 microns in diameter. Thus an individual spot, prepared from one leaf, may contain more contamination or immature sporangia, resulting in a misidentification. To minimize this effect, an average spectrum of each leaf plate prepared for the study was calculated. Each average spectrum is based on about 10 measurements. Using the leaves averages spectra the results of classification were improved remarkably (88% sensitivity and 95% specificity). An informed selection of the number of PCs can improve the classification success because the feature vectors are described by a low number of dimensions. This may possibly reduce the data noise. But the main aim of the PCA is not to improve the classification performance but rather to preserve the classification performance using features’ vectors with lower dimensionality. This dimensionality reduction improves the processing time and simplifies the classifier[32]. Choosing the number of PCs depends on the number of classes, the level of classification (genus, species or isolates), and the sample type [14, 15]. Our aim is to choose the minimum number of PCs that give a good classification. LDA calculations were repeated many times as shown in Figure 3 and Table 3. Initially, each spectrum was represented as one number (the coefficient of the PC1) and the error rate was calculated. If the results were satisfying the classification procedure was finished and the appropriate PC number is 1. Otherwise, we repeated the LDA calculation again, with each spectrum being represented by two numbers (the coefficients of PC1 and PC2 respectively). Again, the success rate was calculated. This calculation was repeated until a good classification (reflected in the success/error rate that is determined by the user, according to his needs) was achieved. Occasionally the dependence of

identification errors in PC number calculations reaches a plateau. When this occurs, the PC number is determined by taking the lowest value in that plateau. In this study the samples are isolates of the same species and are thus very similar (Figure 1). We therefore used the first 14 PCs for the classification procedure. Further studies should aim to enlarge the database to other isolates of P. infestans.

5.

Conclusions

FTIR may be used as a quick alternative to currently standard microbiological methods for examination of oomycetes. The FTIR methodoloby provides good differentiation results between mefenoxam-resistant isolates of P. infestans and those that are non-resistant.

6.

Figure Captions

Figure 1: Average absorption spectra of P. infestans isolates. (A) Mefenoxam resistant (red) and Mefenoxam non-resistant (blue), the major bands and their assignments are listed in the figure. (B) Mefenoxam resistant and (C) Mefenoxam non-resistant. Figure 2: Comparison between integrated bands of resistant and non-resistant isolates of P. infestans, using the entire data set of 221 resistant spectra and 163 non-resistant spectra. The figure contains four 2D graphs: (a) Integrated carbohydrates vs. integrated amide I (b) Carbohydrates vs. Lipids (c) amide I band vs. Lipids band (d) Phosphate band vs. Lipids band. In each of the four figures, a decision line was drawn between the two isolate groups namely the resistant and the non-resistant isolates. The decision line was found by implying LDA to the subspace spanned by the two chosen biomarkers.

Figure 3: Identification error in percentage versus PC number for three different ranges- the entire range 800-1775 cm-1 (red) lipids; amide I range 1580-1770 cm-1 (green); and lipids, amide I, carbohydrate and phosphate ranges (1580-1770, 9251280 cm-1) (blue). Figure 4: Typical average spectrum of 10 measurements acquired from the sample prepared from one infected leaf in the region 800-1775 cm-1 (solid line). The highlighted area is determined by the standard deviation in the 800-1775 cm-1 region.

7.

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Table 1: Sensitivity and specificity of resistant isolate group of P. infestans for the entire data set of 221 resistant and 163 non-resistant spectra. The sensitivity is calculated as true-positive divided by sum of true-positive + false negative (221). The specificity is true-negative divided by the sum of true- negative + false positive (163). Resistant Biomarker Pairs

Non-Resistant Sensitivity

Specificity

37

0.74

0.77

123

40

0.71

0.75

60

120

43

0.73

0.74

68

122

41

0.69

0.75

True

False

True

False

Positive

Negative

Negative

Positive

Amid I - Lipids

163

58

126

Carbohydrates - Amid I

156

65

Carbohydrates - Lipids

161

Phosphate I - Lipids

153

Table 2: Successful identification of P. infestans isolates. Identifications were obtained using LDA calculations and the LOO algorithm in the low wavenumber region of 800-1775 cm-1.

Non-resistant Resistant

Non-resistant 123 40

Resistant 40 181

Table 3: Identification errors versus PC number of P. infestans isolates as resistance and non-resistance to Mefenoxam.

The identification errors were

calculated using LDA with the LOO algorithm in different wavenumber ranges.

PC number

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

800-1775 cm-1 range 40.5 37.8 35.1 29.7 32.4 27.0 29.7 29.7 35.1 35.1 16.2 13.5 18.9 10.8 16.2

925-1280, 1580-1770, cm-1 37.8 29.7 29.7 29.7 29.7 29.7 32.4 29.7 21.6 21.6 24.3 21.6 27.0 24.3 10.8

Highlights

1. Infrared spectroscopy may classify resistant P. infestan isolates to mefenoxam fungicide. 2. A classification success rate of ~90% was achieved using PCA and LDA. 3. The averaged spectra gave better classification rates than the individual spectra.

Graphical abstract

Major steps in this study: FTIR spectroscopy may provide a specific, rapid, and inexpensive method for successful differentiation between resistant and non-resistant isolates P. infestans to mefenoxam fungicide.

Ab sorption [A.U ]

0.06

0.04

0.02

0.00

-0.02 4000

3500

3000

2500

2000

1500

1000

-1

Wavenumber[cm ]

Discrimination Results

Resistant

Non-Resistant

Multivariate analysis: PCA LDA