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Quantitative-structure-activity relationship study to predict the antifungal activity of essential oils against Fusarium verticillioides
Food Control 108 (2020) 106836
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Quantitative-structure-activity relationship study to predict the antifungal activity of essential oils against Fusarium verticillioides
T
Romina P. Pizzolittoa,b,1, Andrés G. Jacquata,b,1, Virginia L. Usseglioa,b, Fernanda Achimóna,b, Alejandro E. Cuellob,c, Julio A. Zygadloa,b, José S. Dambolenaa,b,∗ a
Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Universidad Nacional de Córdoba, Av. Vélez Sarsfield 1611, X5016GCA, Edificio de Investigaciones Biológicas y Tecnológicas, Ciudad Universitaria, Córdoba, Argentina b Instituto de Ciencia y Tecnología de los Alimentos (ICTA-FCEFyN), Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Av. Vélez Sarsfield 1611, X5016GCA, Edificio de Investigaciones Biológicas y Tecnológicas, Ciudad Universitaria, Córdoba, Argentina c Universidad Nacional de Río Negro (UNRN), Belgrano 526, Viedma, 8500, Río Negro, Argentina
ARTICLE INFO
ABSTRACT
Keywords: Essential oils Antifungal activity F. verticillioides QSAR Predictive model
Fusarium verticillioides is the principal fungal pathogen affecting corn maize both in the field and/or during grain storage. The efficiency of essential oils (EOs) as antifungals has been well established against many phytopathogenic fungi, so the aim of this QSAR study was to design a mathematical model using arbitrary descriptors based on EO components to predict antifungal activity. The EOs that reported the highest activity were Origanum spp. The results obtained in the QSAR analysis revealed a statistically significant model (P < 0.0001) which predicted the antifungal activity in a linear equation based on three EO component descriptors: the content of oxygenated sesquiterpenes, content of phenolic compounds and/or the content of peroxide compounds by their composition. These descriptors permitted antifungal activity to be predicted of new EOs without any additional information required, and thus this could be an aid in the search for EOs with the capacity to affect F. verticillioides growth. To our knowledge, the present study constitutes the first QSAR study on the antifungal activity of the EOs. In addition, the strategy used to design the descriptors can be used in the study of other types of complex mixtures.
1. Introduction Maize is one of the main cereals grown worldwide and forms the basis of the human and animal diet. In 2017, the maize worldwide production was 1134 million tons, with Argentina being the fifth largest producer in the world (FAO, 2017). However, in Argentina, significant losses of the annual corn production (17–40%) are caused by the action of fungi and their mycotoxins at both the pre- and post-harvest stages (Montemarani, Sartori, Nesci, Etcheverry, & Barros, 2018). The fungal Fusarium verticillioides is considered to be the main cause of maize ear rot (Chulze, Ramirez, Torres, & Leslie, 2000) and one of the main producers of mycotoxins (referred to as fumonisins), with toxicological implications in humans and farm animals and resulting economic losses (Pitt, 2000). The combined management of agricultural practices that integrate the use of resistant genotypes, the control of insects by transgenic events and the use of synthetic fungicides has been the most
popular strategy for the control of ear rot (Ferrigo, Raiola, & Causin, 2016). However, it is imperative to develop new environmentallyfriendly protection techniques, in view of the unsatisfactory results obtained from synthetic fungicides with respect to the reduction in disease levels and mycotoxin concentrations, and also because of the potential negative impacts on the environment (Boyer, Zhang, & Lempérière, 2012) and food security (Hu et al., 2014), as well as the development of fungal resistance. In recent years, there has been an increasing interest towards the use of natural bioactive compounds for fungal control. Related to this, essential oils (EOs) have been shown to provide a safer alternative as they are biodegradable and less toxic for the environment (Djordjevic et al., 2013). In addition, they are effective as antimicrobials, with their antifungal activity having been demonstrated against a large number of phytopathogenic fungi under in vitro conditions (Babagoli & Behdad, 2012; Bahraminejad, Seifolahpour, & Amiri, 2016; Bajpai & Kang,
Corresponding author. Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Instituto de Ciencia y Tecnología de los Alimentos (ICTA), Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina Avenida Vélez Sarsfield 1611, X5016GCA, Córdoba, Argentina. E-mail address: [email protected] (J.S. Dambolena). 1 Both authors contributed equally to this work. ∗
https://doi.org/10.1016/j.foodcont.2019.106836 Received 3 June 2019; Received in revised form 13 August 2019; Accepted 20 August 2019 Available online 21 August 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.
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2010; Dambolena et al., 2010; Peschiutta et al., 2016; Sridhar, Rajagopal, Rajavel, Masilamani, & Narasimhan, 2003; Viuda-Martos, Ruiz-Navajas, Fernández-Lopéz, & Pérez-Álvarez, 2006). Essential oils contain a natural mixture of monoterpenes, diterpenes and hydrocarbons, with a variety of functional groups being responsible for their biological activity (Tabassum & Vidyasagar, 2013). Moreover, many investigations have reported antifungal and antifumonsin activity of the EOs on F. verticillioides (López-Meneses et al., 2017; Vilaplana, Pérezrevelo, & Valencia-chamorro, 2018). Structure/activity and structure/property quantitative relationships studies (QSAR/QSAP) employ variables such as structural characteristics and physical-chemical properties to generate descriptors that can be correlated quantitatively with the biological activity of the compounds of interest, and thereby obtain a mathematical model able to predict this biological activity in new structurally related compounds (Gallucci et al., 2014). QSAR studies predict the biological activity of structurally related compounds by considering the small changes occurring between them, called homologous series (Dambolena, Zygadlo, & Rubinstein, 2011) and these studies are widely used for the study and rational design of molecules with specific activities. Previous reports have used QSAR analysis to predict the biological activity of the individual EO components on bacteria such as Escherichia coli, Staphylococcus aureus (Owen, Laird, & Wilson, 2018),Mycobacterium tuberculosis and M. bovis (Andrade-Ochoa et al., 2015) and also on fungi (Dambolena, López, Meriles, Rubinstein, & Zygadlo, 2012; Pizzolitto et al., 2015) and insects (Herrera et al., 2015). However, QSAR studies to predict the biological effects of complex mixtureshave been poorly explored. Recently, a QSAR study on the activity of the EOs against insects of the genus Carpophilus and Oryzaephilus was published (Comelli et al., 2018). However, to our knowledge, there are no references available related to QSAR studies being used to predict the antifungal activity of complex mixtures of components, such as EOs. Thus, the aim of this investigation was to determine the effects of fourteen EOs on F. verticillioides growth, and to design a mathematical model using arbitrary descriptors based on EO components to predict the antifungal activity of EOs. To our knowledge, the present study constitutes the first QSAR study on the antifungal activity of EOs.
was collected with a Pasteur glass pipette and dried over anhydrous sodium sulfate. The EOs were stored in glass vials with Teflon caps at −18 °C until analysis, and C. x limon and C. sinensis EOs were obtained by cold-pressing method on the fruit peel, using a screwless cold press. With respect to the hydrodistillation method, fewer hydrocarbon compounds are extracted in the EO by the pressing method (Radan, Parčina, & Burčul, 2018). 2.3. Analysis of EO compositions The EO compositions were determined by GC-MS analysis performed using a PerkinElmer Clarus 600 equipped with a DB-5 capillary column (60m × 0.25 mm i. d, 0.25um coating thickness: Agilent Technologies-J&W, Palo Alto, CA, USA), and a polar Supelcowax 10 (Bellefonte; PA, USA) coated with a phase polyethylene glycol (30m × 0.25 mm i. d, 0.25um coating thickness) (Dambolena et al., 2016). The GC analytical conditions were: injector and detector temperature of 250 °C, oven temperature programmed from 60 to 240 °C at 4 °C/min. Helium (He) was the carrier at a constant flow of 0.9 ml/min and the ionization was 70 eV. The compounds were identified by comparison with the retention index (RI) with reference to a homologous series of n-alkanes (C9 – C25), and by comparison with the mass spectra of NIST libraries, and confirmed by co-injection with the standards (Sigma, USA). 2.4. Fungal strain and conidia suspension
2. Materials and methods
Fusarium verticillioides (Sacc) Nirenberg (= F. moniliforme Sheldon teleomorph Giberella fujikuroi [Sawada] Ito in Ito & Kimura (Leslie & Summerel, 2006) strain M3125 (provided by Dr. Robert Proctor, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, Peoria, IL, United States) was employed in the antifungal activity of the EOs. A conidia suspension was prepared according to Dambolena et al. (2011). Briefly, F. verticillioides was cultured on Czapek-Dox Agar (CDA) for 7 days at 28 °C in order to obtain profuse sporulation. Then, sterile water was added to the culture plate and the conidial suspension was homogenized. The concentration was adjusted to 1 × 106 CFU/ml with a Neubauer chamber.
2.1. Plant material
2.5. Antifungal activity of essential oils
The plant species used in this study were Origanum vulgare L. spp. virens, Origanum x applii (Domin Boros) and Origanum vulgare L. spp. vulgare, which were obtained from the Experimental Station Santa Lucía (INTA, San Juan, Argentina), La Consulta (INTA, Mendoza, Argentina) and the Faculty of Agronomy, University of La Pampa, respectively. Tagetes riojana M. Ferraro Biurrum 8753 and Aloysia polystachya (Griseb.) Moldenke Biurrum 8755 (A. polystachya 1) were collected from the “Los Llanos” region (La Rioja, Argentina). Chenopodium ambrosioides L. was purchased from “Establecimiento Don Luis: hierbas medicinales y aromáticas” (La Paz, Córdoba, Argentina). Aloysia polystachya Griseb. (Verbenaceae) (A. polystachya 2), Laurus nobilis L. (Lauraceae), Minthostachys verticillata Griseb., Eucalyptus globulus Labill., Schinus molle L. and Mentha × piperita L. were collected from the “El Espinal” region (Córdoba, Argentina). Citrus x limon L. and Citrus sinensis L. were obtained from the Experimental Station INTA, Entre Ríos, Argentina.
The antifungal activity of the EOs was tested using radial growth of the fungal colony by the Minimum Inhibitory Concentration (MIC) technique, following a methodology proposed by Meriles, Gil, Haro, March, and Guzma (2006). Briefly, EOs were added and mixed with CDA (45 °C) to obtain concentrations of 31.25, 62.50, 125.00, 250.00, 500.00 y 1000.00 μL/L, and then poured into the Petri dishes (9.0 cm in diameter). As the control, an EO-free medium CDA was used. Ten μl of conidia suspensions (1 × 106 CFU/ml), prepared as described above, were added aseptically to the center of the Petri dishes and incubated in the dark at 25 °C, until the fungus completely covered the plate. The colony diameter was measured daily and the colony area (radial growth) was then calculated, with the inhibitory concentrations IC25 and IC50 being calculated by Probit analysis of the antifungal assays. 2.6. Descriptor construction The components of the EO were clustered into different categories according to their chemical structures, in order to use them as descriptors (independent variable) in the QSAR study, with the clustering being performed arbitrarily. The descriptors created for each EO are indicated in Table 1. Each of these descriptors was designed as the sum of the relative percentage of each compound that qualifies as this
2.2. Essential oil extraction The EOs of the plant material were obtained by hydrodistillation in a Clevenger-like apparatus for 2 h, and the obtained emulsion was cooled to 4 °C and centrifuged at 3000 rpm. The upper volatile fraction
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3. Results
Table 1 Descriptors created for each essential oil. Descriptors
Code
Total oxygenated components content Total hydrocarbon components content Total monoterpene content Total sesquiterpene content Total alcohol compounds content Total phenol compounds content Total aldehyde compounds content Total ketone compounds content Total etherified compounds content Total peroxide compounds content Monoterpene hydrocarbons Oxygenated monoterpenes Sesquiterpene hydrocarbons Oxygenated Sesquiterpenes Total monocyclic compounds content Total bicyclic compounds content Total aliphatic compounds content Monocyclic ether-aldheyde compounds content Monocyclic alcohol compounds content Monocyclic phenol compounds content Monocyclic aldehyde compounds content Monocyclic ketone compounds content Monocyclic hydrocarbon compounds content Monocyclic etherified compounds content Bicyclic ether-aldheyde compounds content Bicyclic peroxide compounds content Bicyclic alcohol compounds content Bicyclic phenol compounds content Bicyclic aldehyde compounds content Bicyclic ketone compounds content Bicyclic hydrocarbon compounds content Bicyclic etherified compounds content Aliphatic alcohol compounds content Aliphatic phenol compounds content Aliphatic aldehyde compounds content Aliphatic ketone compounds content Aliphatic hydrocarbon compounds content Aliphatic etherified compounds content
b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag ah ai aj ak al am
3.1. Essential oils composition The main components (≥5%) of the EOs are shown in Table 2 (a full table including all the components is shown in Supplemental Material Table S2). The main EO components from O. x applii, O. vulgare spp. vulgare and O. vulgare spp. virens were thymol (37.77%; 29.95% and 34.04%, respectively) and cis-sabinene hydrate (31.67%; 24.65% and 19.09%, respectively), with ascaridole being the principal component of C. ambrosioides EO (92.79%). α-pinene was the major component of S. molle EO (13.8%), followed by limonene (12.81%). The main components of M. verticillata EO were pulegone and menthone (43.66% and 40.06%, respectively), whereas the three major components characterizing the T. riojana EO were dihydro tagetone (37.69%), spathulenol (36.22%) and ocimenone (20.89%). L. nobilis EO was characterized by abundant amounts of 1.8-cineole (22.58%) and eugenol methyl ether (21.46%), while the EO from A. polystachya 2 mainly constituted of carvone (62.95%) followed by α-thujone (27.62%). Menthol was the principal constituent of M. × piperita EO (64.95%), while 1.8-cineole was the major component of E. globulus EO (98.9%), with A. polystachya 1 EO being mainly composed of α-thujone (98.71%). Finally, limonene was the major component of C. x limon and C. sinensis EO (62.5% and 87.6%, respectively). 3.2. Antifungal assay The antifungal activity of the evaluated EOs is shown in Table 3. In general, the EOs presented a dose-dependence effect. The EOs that showed the highest activity were O. x appli, O. vulgare spp. virens and O. vulgare spp. vulgare, with IC50 values of 66.79uL/L 101.71uL/L and 108.27uL/L, respectively, followed by the EOs of C. ambrosioides, T. riojana, A. polystachya 2, S. molle, M. verticillata, C. x sinensis and L. nobilis, with IC50 values of 243.12uL/L, 761.75uL/L, 1082.43uL/L, 1226.76uL/L, 1552.43uL/L, 1604.82uL/L and 1846.87uL/L, respectively. The EOs of M. piperita, A. polystachya 1, E. globosus and C. x limon showed no antifungal activity, with IC50 > 2000uL/L. IC25 and IC50 were used in a cluster analysis to evaluate possible similarities in the antifungal activities of the compounds used. According to the bioactivity, this analysis identified the following three antifungal groups (Fig. 1): Group I (most bioactive): O. x appli, O. vulgare spp. virens, O. vulgare spp. vulgare, C. ambrosioides and T. riojana. Group II (intermediate bioactivity): A. polystachya 2, S. molle, M. verticillata, C. x sinensis, L. nobilis. Group III (least bioactive): A. polystachya 1, M. piperita, C. x limon and E. globosus.
descriptor according to its chemical characteristics. A full list including the relative percentages of the descriptors for each EO is shown in Table S1 of the supplemental material. 2.7. Statistical analysis Multivariate analyses were used to study the relationships between the essential oil components and the antifungal activity of the essential oils. Hierarchical cluster analyses were carried out based on the antifungal doses (IC25 and IC50) to evaluate possible similarities in the antifungal activities of the compounds. The “average linkage” clustering algorithm and the “standard Euclidean distance” method were used in this study. A principal component analysis (PCA) was performed on the essential oil component descriptors to determine which of these contributed to the conformation of the groups. Multiple linear regression analyses (MLR) were calculated in order to examine the quantitative relationships between linear combinations of the dependent variable (Log 1/IC25) and the predictor variables (essential oil components). In the MLR equations, N is the number of data points, r is the correlation coefficient between observed values of the dependent variable and the values calculated from the equation, and R2 is the square of the correlation coefficient (representing the goodness of fit). The QSAR model was validated with the root mean square prediction error (RMSPE) obtained by the cross validation leaveone-out procedure. Results with p values < 0.05 were considered to be significant. All statistical analyses were calculated by using the InfoStat Professional 2010p software (Di Rienzo et al., 2010).
3.3. Structure/activity relationships (SAR) The EO components were classified arbitrarily into different categories (Table S1) according to their chemical structures, in order to use them as descriptors (independent variables) in the QSAR study. To determine qualitatively the composition variables responsible for the antifungal activities observed in the EOs, a principal component analysis was carried out (PCA). Fig. 2 shows the composition variable correlation associated with EO activity on F. verticillioides. The PCA plot revealed that group I, the most active EOs, was separated from the other EO groups and accounted for 52.9% of the total variance. Group I was characterized by the variables: g, k, o, r, u, ab, ad and ak. Then, a second PCA (Fig. 3) was carried out in order to determine if the variables that were able to discriminate group 1 in the first PCA could separate O. x applii, O. vulgare spp. virens, O. vulgare spp. vulgare, C. ambrosioides and T. riojana from the rest of the EOs. The first two components (PC1 + PC2) of the PCA accounted for 67.8% of the total variance, with the most antifungal EO being well separated from 3
4
α pinene camphene β pinene β myrcene myrcene α phellandrene limonene 1.8-cineole α terpinene dihydro tagetone γ-terpinene linalool α thujone cis -sabinene hydrate mentone isomentone Neomenthol menthol terpinen-4-ol ocimene E pulegone carvone thymol ascaridol piperitenone eugenol methyl eter β cariofilene germacrene D biciclogermacrene spathulenol
936 954 983 987 991 1003 1033 1034 1034 1051 1064 1097 1108 1125 1158 1167 1169 1179 1198 1231 1242 1247 1287 1331 1344 1398 1430 1485 1494 1578
tr – tr tr – – tr tr tr – tr – – 31.679 tr – – – 5.887 – – – 37.774 – – tr tr – – tr
O. x appli
tr tr tr tr – tr tr – tr – 12.045 – – 24.654 tr – – – 7.764 – – – 29.950 – – tr tr – – tr
O. vulgare spp. vulgare tr tr tr tr – tr tr – tr – 6.811 – – 19.094 – – – – 6.660 – – – 34.045 – – tr – – – tr
O. vulgare spp. virens tr tr tr tr – – tr tr tr – tr – tr – – – – – – – – – – 92.790 – tr tr tr – tr
C. ambrosioides 13.800 12.620 5.800 5.830 – 6.760 12.810 tr – – – – – – – – – – – – – – – – – – 11.880 8.950 5.160 tr
S. molle tr – tr tr – – tr tr – – – – – – 40.061 tr – – – – 43.666 – – – tr – tr – – –
M. verticillata – – – – – – – – – 37.697 – – – – – – – – – 20.898 – – – – – – – – – 36.227
T. riojana tr – tr – – – tr 22.581 – – – 8.116 – – – – – – 6.327 – tr tr – – 16.636 21.461 tr tr – tr
L. nobilis – – – – – – tr – – – – tr 27.623 – tr – – – – tr tr 62.958 tr – tr – tr – – tr
A. polystachya 1 tr – 12.600 – tr – 62.500 – – – 8.500 – – – – – – – – – – – – – – – – – – –
C. x limon tr – tr – – – tr – tr – – – tr – 11.308 5.944 5.274 64.958 – – tr – – – – tr – – – –
M. piperita tr – tr tr – – – – – – – – 98.717 – – – – – – – – tr – – – – – – – –
A. polystachya 2
tr – – – – – – – 98.900 – tr – – – – – – – – – – – – – – – – – – –
E. globosus
tr – – – 6.500 – 87.600 – – – – – – – – – – – – – – – – – – – – – – –
C. x sinensis
a Compounds are listed in order of elution in the DB-5 column. tr, compounds lower than 5%. Percentages were calculated from the peak area without correction. Kovats Index: retention index relative to homologous alkanes. “-“: Compound not present.
Compoundsa
Kovats Index
Table 2 Composition of essential oils analyzed by GC-MS.
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activity of the EO rises with a corresponding increase in the content of oxygenated sesquiterpenes (o), the content of phenolic compounds (g) and/or the content of peroxide compounds (k) present in their composition.
Table 3 Antifungal activity of EOs on F. verticillioides: Inhibitory concentrations (IC25 and IC50) and Minimum Inhibitory Concentration (MIC). Essential Oils
IC25 (uL/L)
IC50 (uL/L)
MIC (uL/L)
O. vulgare spp. virens M. piperita A. polystachya 1 T. riojana A. polystachya 2 O. x applii O. vulgare spp. vulgare M. verticillata E. globosus C. x limon C. x sinensis L. nobilis S. molle C. ambrosioides
62.38 > 1000 > 1000 332.68 973.71 41.75 60.5 1000.09 > 1000 > 1000 1373.64 1167.09 906.07 205.14
101.71 > 1000 > 1000 761.75 1082.43 66.79 108.27 1552.43 > 1000 > 1000 1604.82 1846.87 1226.76 243.12
250 > 1000 > 1000 > 1000 > 1000 250 250 > 1000 > 1000 > 1000 > 1000 > 1000 > 1000 500
4. Discussion Due to their safety characteristics, essential oils can be used as a natural alternative to synthetic compounds for fungal control. Numerous studies have reported inhibitory activity of EOs against fungal growth in in vitro conditions, and in the present investigation, the effects of fourteen EOs were evaluated against Fusarium verticillioides. The results revealed that O. x appli, O. vulgare spp virens, O. vulgare spp vulgare, C. ambrosoides and T. riojana were the most active of the evaluated EOs, with the antifungal activity of some of the evaluated EOs having been previously reported on different fungal species. Our results are in agreement with Lavin, Saravia, and Guiamet (2016), López, Theumer, Zygadlo, and Rubinstein (2004) and Velluti, Sanchis, Ramos, and Turon (2004), who reported an inhibitory effect of oregano EO on the Fusarium species. Similarly, the antifungal activity of C. ambrosioides has also been previously reported, with Kumar, Kumar, Dubey, and Tripathi (2007) demonstrating a high antifungal activity of C. ambrosioides essential oil on A. flavus. On the other hand, the antifungal property observed here of T. riojana EO has not been previously reported. Although a variety of EOs have been shown to possess the ability to reduce fungal development, in our study we found that the A. polytschya, M. piperita, C. x lemon and E. globosus EOs did not affect Fusarium verticillioides growth. In agreement with our results, López et al. (2004) reported less antifungal activity of A. polystachya EO compared to oregano EO on F. verticillioides, while Benomari et al. (2017) demonstrated the inhibitory activity of M. piperita EO against Botrytis cinerea, Monilinia fructigena and Monilinia laxa growth. Many studies have attributed the antifungal effect of the EOs to their main chemical components, and have suggested that there is a relationship between antifungal activity and the chemical structures of these main components following the rule: phenols > alcohols > aldehydes > ketones > esters > hydrocarbons.(Kurita & Koike, 1983; López et al., 2004). This is in agreement with our results, because most of the active EOs evaluated (Origanum spp) revealed a high content of phenolic compounds, while the less bioactive EOs showed a high hydrocarbon monoterpene content. Related to this, the antifungal activity of phenolic compounds has been associated with an effect on the cell membrane permeability, resulting in the loss of cellular components and the inhibition of the cell metabolisms (Cosmo Andrade et al., 2019; Ilić et al., 2019). Ascaridole, a bicyclic monoterpene with a peroxide functional group, was found to be the main component in C. ambrosoides. Although compounds with a peroxide functional group have not been included in the rule cited above, the highly reactive peroxide functional group has been shown to destroy cell membranes and inhibit germination of the conidia (Li et al., 2019). On the other hand, EOs with high ketone compounds content revealed different antifungal activities on F. verticillioides, with the T. riojana EO showing a high antifungal activity (Group I), while the A. polystachya 1 and M. piperita EOs did not have any significant effects on fungal growth. These last results suggest that other structural or chemical characteristics of the ketone monoterpenes, in addition to the functional group, should be considered when evaluating antifungal activity of the essential oils. Many studies on the antifungal activity of the EO components have been performed (López-Meneses et al., 2017; Sampietro et al., 2016; Vilaplana et al., 2018), with a new research trend emerging oriented at developing tools to predict their activity, and which can then be used as a guide in the synthesis of new compounds with antifungal capacity. In this way, several QSAR studies have allowed the antifungal activity of individual EO components to be predicted. For example, Voda, Boh, and Vrtacnik (2004) reported a QSAR study using PLS regression
Fig. 1. Hierarchical clustering of 14 essential oils based on their antifungal activity (IC25 and IC50) on F. verticillioides. The “average linkage” clustering algorithm and the “standard Euclidean distance” method were used.
the rest and mainly influenced by the variables o, g and k. 3.4. Quantitative structure/activity relationships (QSAR) In order to predict the EO activities, a multiple linear regression (MLR) was carried out to determine the mathematical function that best estimated the EO action. Thus, for the EOs that did not present antifungal activity, the IC25 was adjusted to 2000 ppm (the highest concentration evaluated) and the dependent variable (IC25) was converted to log (1/IC25). The model obtained was statistically significant (P = 0.0001), and predicted antifungal activity in a linear equation (1) based on three descriptors.
log
1 = 0.04(g ) + 0.02(o) + 0.01(k ) + 1.82 IC25
(1)
N = 14; R2 = 0.96; RMSPE = 6.3%; P < 0.0001 This model demonstrated a good correlation between antifungal activity and the selected essential oil components (Fig. 4), which represented 96.0% of the total variance (R2 = 0.96), with an error prediction of 6.3%. In addition, the model indicated that the antifungal 5
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Fig. 2. Principal components analysis (PCA) of the composition variable correlations associated with EO activity on F. verticillioides.
analysis to predict the antifungal activity of oxygenated aromatic EO compounds on two wood-decaying fungi. Furthermore, QSAR studies have been carried out to predict the antifungal activity of phenolic EO compounds on F. verticillioides (Dambolena et al., 2012), Aspergillus parasiticus (Pizzolitto et al., 2015), and different Candida species (Gallucci et al., 2014). In these QSAR analyses, different molecular properties of the compounds have been proposed as descriptors to
explain antifungal activity. However, due to the difficulty of defining descriptors in complex matrices, the QSAR study of essential oils has been poorly explored. In our investigation, we arbitrary designed several descriptors based on the EO compositions to be used in the SAR and QSAR analyses, and to predict the antifungal activity of essential oils against F. verticillioides. The SAR study (Figs. 1 and 2) allowed us to define a qualitative
Fig. 3. Principal components analysis (PCA) of the composition variables to discriminate O. x applii, O. vulgare spp. virens, O. vulgare spp. vulgare, C. ambrosioides and T. riojanafrom the rest of the EOs. The separation was mainly influenced by o (content of oxygenated sesquiterpenes), g (total content of phenols) and k (total content of peroxides). 6
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Fig. 4. Predicted versus observed antifungal activity (IC25) of EOs on F. verticillioides from a structural–activity relationship (or QSAR) model.
relationship between some of the EO component descriptors and the antifungal activities of the EOs. This information was then utilized to reduce the number of descriptors that were used in the QSAR analysis, with the results obtained in the QSAR analysis revealing a statistically significant model (P < 0.0001), which predicted antifungal activity in a linear equation based on three EO component descriptors. This model indicated that the antifungal activity of the EO rises with a corresponding increase of the content of oxygenated sesquiterpenes (o), content of phenolic compounds (g) and/or the content of peroxide compounds (k) in their compositions. The statistical parameters used for the evaluation of the regression equations showed the validity of the obtained model (RMSPE: 6.3%) and the goodness of fit between the model-predicted and experimental values (r2: 0.96) (Fig. 4). The term “content of phenolic compounds” in the QSAR model seems to explain the high antifungal activity of the evaluated oregano EOs, which is in agreement with the high antifungal activity previously reported in EOs rich in phenolic compounds (López et al., 2004). Moreover, the term “content of peroxide compounds” in the QSAR model appears to explain the antifungal activity of the C. ambrosoides EO. It is well known that EOs rich in phenols have a strong antifungal activity, whereas peroxide compounds are not usually reported in EOs. However, in agreement with the results obtained by QSAR, the high antifungal of C. ambrosoides found has been attributed to the content of peroxide compounds (Jardim, Jham, Dhingra, & Moriera Freire, 2008). The term “content of oxygenated sesquiterpenes” seems to explain the antifungal activity of the T. riojana EO. Although the antifungal property of T. riojana EO has not been previously reported, several oxygenated sesquiterpenes have been shown to have high antifungal activity (Ioannou, Poiata, Hancianu, & Tzakou, 2007) in agreement with our results. Of the fourteen EOs evaluated in the present work, only T. riojana EO revealed a high oxygenated sesquiterpene content. However, Voda et al. (2004) and Dambolena et al. (2012) reported differences in the antifungal activity of compounds with the same functional group, suggesting that chemical properties such as lipophilicity, electrostatic effects and steric characteristics may explain the antifungal variability found among phenolic related compounds. Hence, other EOs with a high oxygenated sesquiterpene content should be evaluated in order to determine which of their chemical or structural characteristics, in addition to the functional group, should be considered in the QSAR model to predict the antifungal activity of these EOs. As mentioned above, although numerous studies have been carried
out to investigate the structure-activity relationship of pure compounds, there are few bibliographical references concerning the generation of new descriptors and QSAR analysis to predict the bioactivity of complex mixtures such as EOs. In particular, to the best of our knowledge, there is no information available about QSAR studies to estimate the antifungal activity of EOs. Recently, Comelli et al. (2018) reported an innovative QSAR study to predict the insecticide activity of six EOs based on descriptors such as molecular size, branchedness, charge distribution, and electronegativity, with the descriptors designed by these authors considering the chemical properties and the relative proportion of each compound in the EOs. In the present work, we generated arbitrary descriptors which clustered the components of the EOs into different categories according to their chemical structures, with the relative proportion of the compounds being considered for each EO. Then, the QSAR model obtained by this strategy allowed us predict the bioactivity of the EOs when the chemical composition was known. This strategy could now be used to predict the bioactivity of other types of complex mixtures. 5. Conclusions In summary, in the present study we have reported a new strategy to design descriptors which are able to be used in QSAR analysis of complex mixtures. Based on the antifungal activity of fourteen essential oils, we performed a QSAR analysis and generated mathematical models based on the following three descriptors obtained from the EO composition: Content of phenolic compounds; Content of peroxide compounds; Content of oxygenated sesquiterpenes. These descriptors enabled us to predict the antifungal activity of new essential oils without any additional information being required, and thus this could be an aid in the search for EOs with the capacity to affect F. verticillioides growth. To our knowledge, the present study constitutes the first QSAR study on the antifungal activity of the EOs. In addition, the strategy used for designing the descriptors could now be used in the study of other types of complex mixtures. Conflicts of interest The authors declare no conflict of interests.
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Acknowledgments
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A review of control methods and resistance mechanisms in stored-product insects. Bulletin of Entomological Research, 102, 213–229. http://doi.org/10.1017/S0007485311000654. Chulze, S. N., Ramirez, M. L., Torres, A., & Leslie, J. F. (2000). Genetic variation in Fusarium section Liseola from no-till maize in Argentina. Applied and Environmental Microbiology, 66(12), 5312–5315. http://doi.org/10.1128/AEM.66.12.5312-5315. 2000. Comelli, N. C., Romero, O. E., Diez, P. A., Marinho, C. F., Schliserman, P., Carrizo, A., et al. (2018). QSAR study of biologically active essential oils against beetles infesting the walnut in catamarca, Argentina. Journal of Agricultural and Food Chemistry, 66, 12855–12865. research-article http://doi.org/10.1021/acs.jafc.8b04161. Cosmo Andrade, J., Pereira da Silva, A. R., Freitas, M. A., de Azevedo Ramos, B., Sampaio Freitas, T., de Assis, G., et al. (2019). Control of bacterial and fungal biofilms by natural products of Ziziphus joazeiro Mart. (Rhamnaceae). 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(2016). Fusarium toxins in cereals: Occurrence, legislation, factors promoting the appearance and their management. Molecules, 21, 1–35. http://doi.org/10.3390/molecules21050627. Gallucci, M. N., Carezzano, M. E., Oliva, M. M., Demo, M. S., Pizzolitto, R. P., Zunino, M. P., et al. (2014). In vitro activity of natural phenolic compounds against fluconazoleresistant Candida species: A quantitative structure-activity relationship analysis. Journal of Applied Microbiology, 116(4)http://doi.org/10.1111/jam.12432. Herrera, J. M., Zunino, M. P., Dambolena, J. S., Pizzolitto, R. P., Gañan, N. A., Lucini, E. I., et al. (2015). Terpene ketones as natural insecticides against Sitophilus zeamais.
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Quantitative-structure-activity relationship study to predict the antifungal activity of essential oils against Fusarium verticillioides
T
Romina P. Pizzolittoa,b,1, Andrés G. Jacquata,b,1, Virginia L. Usseglioa,b, Fernanda Achimóna,b, Alejandro E. Cuellob,c, Julio A. Zygadloa,b, José S. Dambolenaa,b,∗ a
Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Universidad Nacional de Córdoba, Av. Vélez Sarsfield 1611, X5016GCA, Edificio de Investigaciones Biológicas y Tecnológicas, Ciudad Universitaria, Córdoba, Argentina b Instituto de Ciencia y Tecnología de los Alimentos (ICTA-FCEFyN), Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Av. Vélez Sarsfield 1611, X5016GCA, Edificio de Investigaciones Biológicas y Tecnológicas, Ciudad Universitaria, Córdoba, Argentina c Universidad Nacional de Río Negro (UNRN), Belgrano 526, Viedma, 8500, Río Negro, Argentina
ARTICLE INFO
ABSTRACT
Keywords: Essential oils Antifungal activity F. verticillioides QSAR Predictive model
Fusarium verticillioides is the principal fungal pathogen affecting corn maize both in the field and/or during grain storage. The efficiency of essential oils (EOs) as antifungals has been well established against many phytopathogenic fungi, so the aim of this QSAR study was to design a mathematical model using arbitrary descriptors based on EO components to predict antifungal activity. The EOs that reported the highest activity were Origanum spp. The results obtained in the QSAR analysis revealed a statistically significant model (P < 0.0001) which predicted the antifungal activity in a linear equation based on three EO component descriptors: the content of oxygenated sesquiterpenes, content of phenolic compounds and/or the content of peroxide compounds by their composition. These descriptors permitted antifungal activity to be predicted of new EOs without any additional information required, and thus this could be an aid in the search for EOs with the capacity to affect F. verticillioides growth. To our knowledge, the present study constitutes the first QSAR study on the antifungal activity of the EOs. In addition, the strategy used to design the descriptors can be used in the study of other types of complex mixtures.
1. Introduction Maize is one of the main cereals grown worldwide and forms the basis of the human and animal diet. In 2017, the maize worldwide production was 1134 million tons, with Argentina being the fifth largest producer in the world (FAO, 2017). However, in Argentina, significant losses of the annual corn production (17–40%) are caused by the action of fungi and their mycotoxins at both the pre- and post-harvest stages (Montemarani, Sartori, Nesci, Etcheverry, & Barros, 2018). The fungal Fusarium verticillioides is considered to be the main cause of maize ear rot (Chulze, Ramirez, Torres, & Leslie, 2000) and one of the main producers of mycotoxins (referred to as fumonisins), with toxicological implications in humans and farm animals and resulting economic losses (Pitt, 2000). The combined management of agricultural practices that integrate the use of resistant genotypes, the control of insects by transgenic events and the use of synthetic fungicides has been the most
popular strategy for the control of ear rot (Ferrigo, Raiola, & Causin, 2016). However, it is imperative to develop new environmentallyfriendly protection techniques, in view of the unsatisfactory results obtained from synthetic fungicides with respect to the reduction in disease levels and mycotoxin concentrations, and also because of the potential negative impacts on the environment (Boyer, Zhang, & Lempérière, 2012) and food security (Hu et al., 2014), as well as the development of fungal resistance. In recent years, there has been an increasing interest towards the use of natural bioactive compounds for fungal control. Related to this, essential oils (EOs) have been shown to provide a safer alternative as they are biodegradable and less toxic for the environment (Djordjevic et al., 2013). In addition, they are effective as antimicrobials, with their antifungal activity having been demonstrated against a large number of phytopathogenic fungi under in vitro conditions (Babagoli & Behdad, 2012; Bahraminejad, Seifolahpour, & Amiri, 2016; Bajpai & Kang,
Corresponding author. Instituto Multidisciplinario de Biología Vegetal (IMBIV-CONICET), Instituto de Ciencia y Tecnología de los Alimentos (ICTA), Facultad de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de Córdoba, Córdoba, Argentina Avenida Vélez Sarsfield 1611, X5016GCA, Córdoba, Argentina. E-mail address: [email protected] (J.S. Dambolena). 1 Both authors contributed equally to this work. ∗
https://doi.org/10.1016/j.foodcont.2019.106836 Received 3 June 2019; Received in revised form 13 August 2019; Accepted 20 August 2019 Available online 21 August 2019 0956-7135/ © 2019 Elsevier Ltd. All rights reserved.
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2010; Dambolena et al., 2010; Peschiutta et al., 2016; Sridhar, Rajagopal, Rajavel, Masilamani, & Narasimhan, 2003; Viuda-Martos, Ruiz-Navajas, Fernández-Lopéz, & Pérez-Álvarez, 2006). Essential oils contain a natural mixture of monoterpenes, diterpenes and hydrocarbons, with a variety of functional groups being responsible for their biological activity (Tabassum & Vidyasagar, 2013). Moreover, many investigations have reported antifungal and antifumonsin activity of the EOs on F. verticillioides (López-Meneses et al., 2017; Vilaplana, Pérezrevelo, & Valencia-chamorro, 2018). Structure/activity and structure/property quantitative relationships studies (QSAR/QSAP) employ variables such as structural characteristics and physical-chemical properties to generate descriptors that can be correlated quantitatively with the biological activity of the compounds of interest, and thereby obtain a mathematical model able to predict this biological activity in new structurally related compounds (Gallucci et al., 2014). QSAR studies predict the biological activity of structurally related compounds by considering the small changes occurring between them, called homologous series (Dambolena, Zygadlo, & Rubinstein, 2011) and these studies are widely used for the study and rational design of molecules with specific activities. Previous reports have used QSAR analysis to predict the biological activity of the individual EO components on bacteria such as Escherichia coli, Staphylococcus aureus (Owen, Laird, & Wilson, 2018),Mycobacterium tuberculosis and M. bovis (Andrade-Ochoa et al., 2015) and also on fungi (Dambolena, López, Meriles, Rubinstein, & Zygadlo, 2012; Pizzolitto et al., 2015) and insects (Herrera et al., 2015). However, QSAR studies to predict the biological effects of complex mixtureshave been poorly explored. Recently, a QSAR study on the activity of the EOs against insects of the genus Carpophilus and Oryzaephilus was published (Comelli et al., 2018). However, to our knowledge, there are no references available related to QSAR studies being used to predict the antifungal activity of complex mixtures of components, such as EOs. Thus, the aim of this investigation was to determine the effects of fourteen EOs on F. verticillioides growth, and to design a mathematical model using arbitrary descriptors based on EO components to predict the antifungal activity of EOs. To our knowledge, the present study constitutes the first QSAR study on the antifungal activity of EOs.
was collected with a Pasteur glass pipette and dried over anhydrous sodium sulfate. The EOs were stored in glass vials with Teflon caps at −18 °C until analysis, and C. x limon and C. sinensis EOs were obtained by cold-pressing method on the fruit peel, using a screwless cold press. With respect to the hydrodistillation method, fewer hydrocarbon compounds are extracted in the EO by the pressing method (Radan, Parčina, & Burčul, 2018). 2.3. Analysis of EO compositions The EO compositions were determined by GC-MS analysis performed using a PerkinElmer Clarus 600 equipped with a DB-5 capillary column (60m × 0.25 mm i. d, 0.25um coating thickness: Agilent Technologies-J&W, Palo Alto, CA, USA), and a polar Supelcowax 10 (Bellefonte; PA, USA) coated with a phase polyethylene glycol (30m × 0.25 mm i. d, 0.25um coating thickness) (Dambolena et al., 2016). The GC analytical conditions were: injector and detector temperature of 250 °C, oven temperature programmed from 60 to 240 °C at 4 °C/min. Helium (He) was the carrier at a constant flow of 0.9 ml/min and the ionization was 70 eV. The compounds were identified by comparison with the retention index (RI) with reference to a homologous series of n-alkanes (C9 – C25), and by comparison with the mass spectra of NIST libraries, and confirmed by co-injection with the standards (Sigma, USA). 2.4. Fungal strain and conidia suspension
2. Materials and methods
Fusarium verticillioides (Sacc) Nirenberg (= F. moniliforme Sheldon teleomorph Giberella fujikuroi [Sawada] Ito in Ito & Kimura (Leslie & Summerel, 2006) strain M3125 (provided by Dr. Robert Proctor, United States Department of Agriculture, Agricultural Research Service, National Center for Agricultural Utilization Research, Peoria, IL, United States) was employed in the antifungal activity of the EOs. A conidia suspension was prepared according to Dambolena et al. (2011). Briefly, F. verticillioides was cultured on Czapek-Dox Agar (CDA) for 7 days at 28 °C in order to obtain profuse sporulation. Then, sterile water was added to the culture plate and the conidial suspension was homogenized. The concentration was adjusted to 1 × 106 CFU/ml with a Neubauer chamber.
2.1. Plant material
2.5. Antifungal activity of essential oils
The plant species used in this study were Origanum vulgare L. spp. virens, Origanum x applii (Domin Boros) and Origanum vulgare L. spp. vulgare, which were obtained from the Experimental Station Santa Lucía (INTA, San Juan, Argentina), La Consulta (INTA, Mendoza, Argentina) and the Faculty of Agronomy, University of La Pampa, respectively. Tagetes riojana M. Ferraro Biurrum 8753 and Aloysia polystachya (Griseb.) Moldenke Biurrum 8755 (A. polystachya 1) were collected from the “Los Llanos” region (La Rioja, Argentina). Chenopodium ambrosioides L. was purchased from “Establecimiento Don Luis: hierbas medicinales y aromáticas” (La Paz, Córdoba, Argentina). Aloysia polystachya Griseb. (Verbenaceae) (A. polystachya 2), Laurus nobilis L. (Lauraceae), Minthostachys verticillata Griseb., Eucalyptus globulus Labill., Schinus molle L. and Mentha × piperita L. were collected from the “El Espinal” region (Córdoba, Argentina). Citrus x limon L. and Citrus sinensis L. were obtained from the Experimental Station INTA, Entre Ríos, Argentina.
The antifungal activity of the EOs was tested using radial growth of the fungal colony by the Minimum Inhibitory Concentration (MIC) technique, following a methodology proposed by Meriles, Gil, Haro, March, and Guzma (2006). Briefly, EOs were added and mixed with CDA (45 °C) to obtain concentrations of 31.25, 62.50, 125.00, 250.00, 500.00 y 1000.00 μL/L, and then poured into the Petri dishes (9.0 cm in diameter). As the control, an EO-free medium CDA was used. Ten μl of conidia suspensions (1 × 106 CFU/ml), prepared as described above, were added aseptically to the center of the Petri dishes and incubated in the dark at 25 °C, until the fungus completely covered the plate. The colony diameter was measured daily and the colony area (radial growth) was then calculated, with the inhibitory concentrations IC25 and IC50 being calculated by Probit analysis of the antifungal assays. 2.6. Descriptor construction The components of the EO were clustered into different categories according to their chemical structures, in order to use them as descriptors (independent variable) in the QSAR study, with the clustering being performed arbitrarily. The descriptors created for each EO are indicated in Table 1. Each of these descriptors was designed as the sum of the relative percentage of each compound that qualifies as this
2.2. Essential oil extraction The EOs of the plant material were obtained by hydrodistillation in a Clevenger-like apparatus for 2 h, and the obtained emulsion was cooled to 4 °C and centrifuged at 3000 rpm. The upper volatile fraction
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3. Results
Table 1 Descriptors created for each essential oil. Descriptors
Code
Total oxygenated components content Total hydrocarbon components content Total monoterpene content Total sesquiterpene content Total alcohol compounds content Total phenol compounds content Total aldehyde compounds content Total ketone compounds content Total etherified compounds content Total peroxide compounds content Monoterpene hydrocarbons Oxygenated monoterpenes Sesquiterpene hydrocarbons Oxygenated Sesquiterpenes Total monocyclic compounds content Total bicyclic compounds content Total aliphatic compounds content Monocyclic ether-aldheyde compounds content Monocyclic alcohol compounds content Monocyclic phenol compounds content Monocyclic aldehyde compounds content Monocyclic ketone compounds content Monocyclic hydrocarbon compounds content Monocyclic etherified compounds content Bicyclic ether-aldheyde compounds content Bicyclic peroxide compounds content Bicyclic alcohol compounds content Bicyclic phenol compounds content Bicyclic aldehyde compounds content Bicyclic ketone compounds content Bicyclic hydrocarbon compounds content Bicyclic etherified compounds content Aliphatic alcohol compounds content Aliphatic phenol compounds content Aliphatic aldehyde compounds content Aliphatic ketone compounds content Aliphatic hydrocarbon compounds content Aliphatic etherified compounds content
b c d e f g h i j k l m n o p q r s t u v w x y z aa ab ac ad ae af ag ah ai aj ak al am
3.1. Essential oils composition The main components (≥5%) of the EOs are shown in Table 2 (a full table including all the components is shown in Supplemental Material Table S2). The main EO components from O. x applii, O. vulgare spp. vulgare and O. vulgare spp. virens were thymol (37.77%; 29.95% and 34.04%, respectively) and cis-sabinene hydrate (31.67%; 24.65% and 19.09%, respectively), with ascaridole being the principal component of C. ambrosioides EO (92.79%). α-pinene was the major component of S. molle EO (13.8%), followed by limonene (12.81%). The main components of M. verticillata EO were pulegone and menthone (43.66% and 40.06%, respectively), whereas the three major components characterizing the T. riojana EO were dihydro tagetone (37.69%), spathulenol (36.22%) and ocimenone (20.89%). L. nobilis EO was characterized by abundant amounts of 1.8-cineole (22.58%) and eugenol methyl ether (21.46%), while the EO from A. polystachya 2 mainly constituted of carvone (62.95%) followed by α-thujone (27.62%). Menthol was the principal constituent of M. × piperita EO (64.95%), while 1.8-cineole was the major component of E. globulus EO (98.9%), with A. polystachya 1 EO being mainly composed of α-thujone (98.71%). Finally, limonene was the major component of C. x limon and C. sinensis EO (62.5% and 87.6%, respectively). 3.2. Antifungal assay The antifungal activity of the evaluated EOs is shown in Table 3. In general, the EOs presented a dose-dependence effect. The EOs that showed the highest activity were O. x appli, O. vulgare spp. virens and O. vulgare spp. vulgare, with IC50 values of 66.79uL/L 101.71uL/L and 108.27uL/L, respectively, followed by the EOs of C. ambrosioides, T. riojana, A. polystachya 2, S. molle, M. verticillata, C. x sinensis and L. nobilis, with IC50 values of 243.12uL/L, 761.75uL/L, 1082.43uL/L, 1226.76uL/L, 1552.43uL/L, 1604.82uL/L and 1846.87uL/L, respectively. The EOs of M. piperita, A. polystachya 1, E. globosus and C. x limon showed no antifungal activity, with IC50 > 2000uL/L. IC25 and IC50 were used in a cluster analysis to evaluate possible similarities in the antifungal activities of the compounds used. According to the bioactivity, this analysis identified the following three antifungal groups (Fig. 1): Group I (most bioactive): O. x appli, O. vulgare spp. virens, O. vulgare spp. vulgare, C. ambrosioides and T. riojana. Group II (intermediate bioactivity): A. polystachya 2, S. molle, M. verticillata, C. x sinensis, L. nobilis. Group III (least bioactive): A. polystachya 1, M. piperita, C. x limon and E. globosus.
descriptor according to its chemical characteristics. A full list including the relative percentages of the descriptors for each EO is shown in Table S1 of the supplemental material. 2.7. Statistical analysis Multivariate analyses were used to study the relationships between the essential oil components and the antifungal activity of the essential oils. Hierarchical cluster analyses were carried out based on the antifungal doses (IC25 and IC50) to evaluate possible similarities in the antifungal activities of the compounds. The “average linkage” clustering algorithm and the “standard Euclidean distance” method were used in this study. A principal component analysis (PCA) was performed on the essential oil component descriptors to determine which of these contributed to the conformation of the groups. Multiple linear regression analyses (MLR) were calculated in order to examine the quantitative relationships between linear combinations of the dependent variable (Log 1/IC25) and the predictor variables (essential oil components). In the MLR equations, N is the number of data points, r is the correlation coefficient between observed values of the dependent variable and the values calculated from the equation, and R2 is the square of the correlation coefficient (representing the goodness of fit). The QSAR model was validated with the root mean square prediction error (RMSPE) obtained by the cross validation leaveone-out procedure. Results with p values < 0.05 were considered to be significant. All statistical analyses were calculated by using the InfoStat Professional 2010p software (Di Rienzo et al., 2010).
3.3. Structure/activity relationships (SAR) The EO components were classified arbitrarily into different categories (Table S1) according to their chemical structures, in order to use them as descriptors (independent variables) in the QSAR study. To determine qualitatively the composition variables responsible for the antifungal activities observed in the EOs, a principal component analysis was carried out (PCA). Fig. 2 shows the composition variable correlation associated with EO activity on F. verticillioides. The PCA plot revealed that group I, the most active EOs, was separated from the other EO groups and accounted for 52.9% of the total variance. Group I was characterized by the variables: g, k, o, r, u, ab, ad and ak. Then, a second PCA (Fig. 3) was carried out in order to determine if the variables that were able to discriminate group 1 in the first PCA could separate O. x applii, O. vulgare spp. virens, O. vulgare spp. vulgare, C. ambrosioides and T. riojana from the rest of the EOs. The first two components (PC1 + PC2) of the PCA accounted for 67.8% of the total variance, with the most antifungal EO being well separated from 3
4
α pinene camphene β pinene β myrcene myrcene α phellandrene limonene 1.8-cineole α terpinene dihydro tagetone γ-terpinene linalool α thujone cis -sabinene hydrate mentone isomentone Neomenthol menthol terpinen-4-ol ocimene E pulegone carvone thymol ascaridol piperitenone eugenol methyl eter β cariofilene germacrene D biciclogermacrene spathulenol
936 954 983 987 991 1003 1033 1034 1034 1051 1064 1097 1108 1125 1158 1167 1169 1179 1198 1231 1242 1247 1287 1331 1344 1398 1430 1485 1494 1578
tr – tr tr – – tr tr tr – tr – – 31.679 tr – – – 5.887 – – – 37.774 – – tr tr – – tr
O. x appli
tr tr tr tr – tr tr – tr – 12.045 – – 24.654 tr – – – 7.764 – – – 29.950 – – tr tr – – tr
O. vulgare spp. vulgare tr tr tr tr – tr tr – tr – 6.811 – – 19.094 – – – – 6.660 – – – 34.045 – – tr – – – tr
O. vulgare spp. virens tr tr tr tr – – tr tr tr – tr – tr – – – – – – – – – – 92.790 – tr tr tr – tr
C. ambrosioides 13.800 12.620 5.800 5.830 – 6.760 12.810 tr – – – – – – – – – – – – – – – – – – 11.880 8.950 5.160 tr
S. molle tr – tr tr – – tr tr – – – – – – 40.061 tr – – – – 43.666 – – – tr – tr – – –
M. verticillata – – – – – – – – – 37.697 – – – – – – – – – 20.898 – – – – – – – – – 36.227
T. riojana tr – tr – – – tr 22.581 – – – 8.116 – – – – – – 6.327 – tr tr – – 16.636 21.461 tr tr – tr
L. nobilis – – – – – – tr – – – – tr 27.623 – tr – – – – tr tr 62.958 tr – tr – tr – – tr
A. polystachya 1 tr – 12.600 – tr – 62.500 – – – 8.500 – – – – – – – – – – – – – – – – – – –
C. x limon tr – tr – – – tr – tr – – – tr – 11.308 5.944 5.274 64.958 – – tr – – – – tr – – – –
M. piperita tr – tr tr – – – – – – – – 98.717 – – – – – – – – tr – – – – – – – –
A. polystachya 2
tr – – – – – – – 98.900 – tr – – – – – – – – – – – – – – – – – – –
E. globosus
tr – – – 6.500 – 87.600 – – – – – – – – – – – – – – – – – – – – – – –
C. x sinensis
a Compounds are listed in order of elution in the DB-5 column. tr, compounds lower than 5%. Percentages were calculated from the peak area without correction. Kovats Index: retention index relative to homologous alkanes. “-“: Compound not present.
Compoundsa
Kovats Index
Table 2 Composition of essential oils analyzed by GC-MS.
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activity of the EO rises with a corresponding increase in the content of oxygenated sesquiterpenes (o), the content of phenolic compounds (g) and/or the content of peroxide compounds (k) present in their composition.
Table 3 Antifungal activity of EOs on F. verticillioides: Inhibitory concentrations (IC25 and IC50) and Minimum Inhibitory Concentration (MIC). Essential Oils
IC25 (uL/L)
IC50 (uL/L)
MIC (uL/L)
O. vulgare spp. virens M. piperita A. polystachya 1 T. riojana A. polystachya 2 O. x applii O. vulgare spp. vulgare M. verticillata E. globosus C. x limon C. x sinensis L. nobilis S. molle C. ambrosioides
62.38 > 1000 > 1000 332.68 973.71 41.75 60.5 1000.09 > 1000 > 1000 1373.64 1167.09 906.07 205.14
101.71 > 1000 > 1000 761.75 1082.43 66.79 108.27 1552.43 > 1000 > 1000 1604.82 1846.87 1226.76 243.12
250 > 1000 > 1000 > 1000 > 1000 250 250 > 1000 > 1000 > 1000 > 1000 > 1000 > 1000 500
4. Discussion Due to their safety characteristics, essential oils can be used as a natural alternative to synthetic compounds for fungal control. Numerous studies have reported inhibitory activity of EOs against fungal growth in in vitro conditions, and in the present investigation, the effects of fourteen EOs were evaluated against Fusarium verticillioides. The results revealed that O. x appli, O. vulgare spp virens, O. vulgare spp vulgare, C. ambrosoides and T. riojana were the most active of the evaluated EOs, with the antifungal activity of some of the evaluated EOs having been previously reported on different fungal species. Our results are in agreement with Lavin, Saravia, and Guiamet (2016), López, Theumer, Zygadlo, and Rubinstein (2004) and Velluti, Sanchis, Ramos, and Turon (2004), who reported an inhibitory effect of oregano EO on the Fusarium species. Similarly, the antifungal activity of C. ambrosioides has also been previously reported, with Kumar, Kumar, Dubey, and Tripathi (2007) demonstrating a high antifungal activity of C. ambrosioides essential oil on A. flavus. On the other hand, the antifungal property observed here of T. riojana EO has not been previously reported. Although a variety of EOs have been shown to possess the ability to reduce fungal development, in our study we found that the A. polytschya, M. piperita, C. x lemon and E. globosus EOs did not affect Fusarium verticillioides growth. In agreement with our results, López et al. (2004) reported less antifungal activity of A. polystachya EO compared to oregano EO on F. verticillioides, while Benomari et al. (2017) demonstrated the inhibitory activity of M. piperita EO against Botrytis cinerea, Monilinia fructigena and Monilinia laxa growth. Many studies have attributed the antifungal effect of the EOs to their main chemical components, and have suggested that there is a relationship between antifungal activity and the chemical structures of these main components following the rule: phenols > alcohols > aldehydes > ketones > esters > hydrocarbons.(Kurita & Koike, 1983; López et al., 2004). This is in agreement with our results, because most of the active EOs evaluated (Origanum spp) revealed a high content of phenolic compounds, while the less bioactive EOs showed a high hydrocarbon monoterpene content. Related to this, the antifungal activity of phenolic compounds has been associated with an effect on the cell membrane permeability, resulting in the loss of cellular components and the inhibition of the cell metabolisms (Cosmo Andrade et al., 2019; Ilić et al., 2019). Ascaridole, a bicyclic monoterpene with a peroxide functional group, was found to be the main component in C. ambrosoides. Although compounds with a peroxide functional group have not been included in the rule cited above, the highly reactive peroxide functional group has been shown to destroy cell membranes and inhibit germination of the conidia (Li et al., 2019). On the other hand, EOs with high ketone compounds content revealed different antifungal activities on F. verticillioides, with the T. riojana EO showing a high antifungal activity (Group I), while the A. polystachya 1 and M. piperita EOs did not have any significant effects on fungal growth. These last results suggest that other structural or chemical characteristics of the ketone monoterpenes, in addition to the functional group, should be considered when evaluating antifungal activity of the essential oils. Many studies on the antifungal activity of the EO components have been performed (López-Meneses et al., 2017; Sampietro et al., 2016; Vilaplana et al., 2018), with a new research trend emerging oriented at developing tools to predict their activity, and which can then be used as a guide in the synthesis of new compounds with antifungal capacity. In this way, several QSAR studies have allowed the antifungal activity of individual EO components to be predicted. For example, Voda, Boh, and Vrtacnik (2004) reported a QSAR study using PLS regression
Fig. 1. Hierarchical clustering of 14 essential oils based on their antifungal activity (IC25 and IC50) on F. verticillioides. The “average linkage” clustering algorithm and the “standard Euclidean distance” method were used.
the rest and mainly influenced by the variables o, g and k. 3.4. Quantitative structure/activity relationships (QSAR) In order to predict the EO activities, a multiple linear regression (MLR) was carried out to determine the mathematical function that best estimated the EO action. Thus, for the EOs that did not present antifungal activity, the IC25 was adjusted to 2000 ppm (the highest concentration evaluated) and the dependent variable (IC25) was converted to log (1/IC25). The model obtained was statistically significant (P = 0.0001), and predicted antifungal activity in a linear equation (1) based on three descriptors.
log
1 = 0.04(g ) + 0.02(o) + 0.01(k ) + 1.82 IC25
(1)
N = 14; R2 = 0.96; RMSPE = 6.3%; P < 0.0001 This model demonstrated a good correlation between antifungal activity and the selected essential oil components (Fig. 4), which represented 96.0% of the total variance (R2 = 0.96), with an error prediction of 6.3%. In addition, the model indicated that the antifungal 5
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Fig. 2. Principal components analysis (PCA) of the composition variable correlations associated with EO activity on F. verticillioides.
analysis to predict the antifungal activity of oxygenated aromatic EO compounds on two wood-decaying fungi. Furthermore, QSAR studies have been carried out to predict the antifungal activity of phenolic EO compounds on F. verticillioides (Dambolena et al., 2012), Aspergillus parasiticus (Pizzolitto et al., 2015), and different Candida species (Gallucci et al., 2014). In these QSAR analyses, different molecular properties of the compounds have been proposed as descriptors to
explain antifungal activity. However, due to the difficulty of defining descriptors in complex matrices, the QSAR study of essential oils has been poorly explored. In our investigation, we arbitrary designed several descriptors based on the EO compositions to be used in the SAR and QSAR analyses, and to predict the antifungal activity of essential oils against F. verticillioides. The SAR study (Figs. 1 and 2) allowed us to define a qualitative
Fig. 3. Principal components analysis (PCA) of the composition variables to discriminate O. x applii, O. vulgare spp. virens, O. vulgare spp. vulgare, C. ambrosioides and T. riojanafrom the rest of the EOs. The separation was mainly influenced by o (content of oxygenated sesquiterpenes), g (total content of phenols) and k (total content of peroxides). 6
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Fig. 4. Predicted versus observed antifungal activity (IC25) of EOs on F. verticillioides from a structural–activity relationship (or QSAR) model.
relationship between some of the EO component descriptors and the antifungal activities of the EOs. This information was then utilized to reduce the number of descriptors that were used in the QSAR analysis, with the results obtained in the QSAR analysis revealing a statistically significant model (P < 0.0001), which predicted antifungal activity in a linear equation based on three EO component descriptors. This model indicated that the antifungal activity of the EO rises with a corresponding increase of the content of oxygenated sesquiterpenes (o), content of phenolic compounds (g) and/or the content of peroxide compounds (k) in their compositions. The statistical parameters used for the evaluation of the regression equations showed the validity of the obtained model (RMSPE: 6.3%) and the goodness of fit between the model-predicted and experimental values (r2: 0.96) (Fig. 4). The term “content of phenolic compounds” in the QSAR model seems to explain the high antifungal activity of the evaluated oregano EOs, which is in agreement with the high antifungal activity previously reported in EOs rich in phenolic compounds (López et al., 2004). Moreover, the term “content of peroxide compounds” in the QSAR model appears to explain the antifungal activity of the C. ambrosoides EO. It is well known that EOs rich in phenols have a strong antifungal activity, whereas peroxide compounds are not usually reported in EOs. However, in agreement with the results obtained by QSAR, the high antifungal of C. ambrosoides found has been attributed to the content of peroxide compounds (Jardim, Jham, Dhingra, & Moriera Freire, 2008). The term “content of oxygenated sesquiterpenes” seems to explain the antifungal activity of the T. riojana EO. Although the antifungal property of T. riojana EO has not been previously reported, several oxygenated sesquiterpenes have been shown to have high antifungal activity (Ioannou, Poiata, Hancianu, & Tzakou, 2007) in agreement with our results. Of the fourteen EOs evaluated in the present work, only T. riojana EO revealed a high oxygenated sesquiterpene content. However, Voda et al. (2004) and Dambolena et al. (2012) reported differences in the antifungal activity of compounds with the same functional group, suggesting that chemical properties such as lipophilicity, electrostatic effects and steric characteristics may explain the antifungal variability found among phenolic related compounds. Hence, other EOs with a high oxygenated sesquiterpene content should be evaluated in order to determine which of their chemical or structural characteristics, in addition to the functional group, should be considered in the QSAR model to predict the antifungal activity of these EOs. As mentioned above, although numerous studies have been carried
out to investigate the structure-activity relationship of pure compounds, there are few bibliographical references concerning the generation of new descriptors and QSAR analysis to predict the bioactivity of complex mixtures such as EOs. In particular, to the best of our knowledge, there is no information available about QSAR studies to estimate the antifungal activity of EOs. Recently, Comelli et al. (2018) reported an innovative QSAR study to predict the insecticide activity of six EOs based on descriptors such as molecular size, branchedness, charge distribution, and electronegativity, with the descriptors designed by these authors considering the chemical properties and the relative proportion of each compound in the EOs. In the present work, we generated arbitrary descriptors which clustered the components of the EOs into different categories according to their chemical structures, with the relative proportion of the compounds being considered for each EO. Then, the QSAR model obtained by this strategy allowed us predict the bioactivity of the EOs when the chemical composition was known. This strategy could now be used to predict the bioactivity of other types of complex mixtures. 5. Conclusions In summary, in the present study we have reported a new strategy to design descriptors which are able to be used in QSAR analysis of complex mixtures. Based on the antifungal activity of fourteen essential oils, we performed a QSAR analysis and generated mathematical models based on the following three descriptors obtained from the EO composition: Content of phenolic compounds; Content of peroxide compounds; Content of oxygenated sesquiterpenes. These descriptors enabled us to predict the antifungal activity of new essential oils without any additional information being required, and thus this could be an aid in the search for EOs with the capacity to affect F. verticillioides growth. To our knowledge, the present study constitutes the first QSAR study on the antifungal activity of the EOs. In addition, the strategy used for designing the descriptors could now be used in the study of other types of complex mixtures. Conflicts of interest The authors declare no conflict of interests.
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Acknowledgments
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