Open AccessArticle

Evaluation of the Effects of Epicoccum nigrum on the Olive Fungal Pathogens Verticillium dahliae and Colletotrichum acutatum by ¹H NMR-Based Metabolic Profiling

Federica Angilè

^1,†

Mario Riolo

^2,†

Santa Olga Cacciola

Francesco Paolo Fanizzi

^1,*

and

Elena Santilli

^3,*

Department of Biological and Environmental Sciences and Technologies, University of Salento, Prov.le Lecce-Monteroni, 73100 Lecce, Italy

Department of Agriculture, Food and Environment, University of Catania, 95123 Catania, Italy

Council for Agricultural Research and Economics, Research Centre for Olive, Fruit and Citrus Crops (CREA-OFA), Rende, 87036 Cosenza, Italy

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

J. Fungi 2025, 11(2), 129; https://doi.org/10.3390/jof11020129

Submission received: 13 January 2025 / Revised: 3 February 2025 / Accepted: 6 February 2025 / Published: 8 February 2025

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Figure 1
Phylogenetic trees of Verticillium dahliae (A) and Epicoccum nigrum (B) based on the internal transcribed spacer (ITS) and, for E. nigrum, also on the β-tubulin (Tub2) gene. In panel (A), the phylogenetic tree for V. dahliae was constructed using only ITS sequences, with Gibellulopsis nigrescens as the outgroup and a log likelihood of −1029.50. In panel (B), the multilocus phylogenetic tree of E. nigrum was developed using both ITS and Tub2 sequences, with Epicoccum dendrobii as the outgroup and a log likelihood of −1560.69. Maximum likelihood estimation was used to infer the trees, and the Tamura–Nei model was applied. "> Figure 2
(A) Petri dish cultures: C9D2C + RD4C, dual-culture of C. acutatum and E nigrum; RD4C, mono-culture of E nigrum; ER 1357 + RD4C, dual-culture of V. dahliae and E nigrum; ER 1357, mono-culture of V. dahliae. (B) Percentage of mycelial growth inhibition of Colletotrichum acutatum (C9D2C) and Verticillium dahliae (ER 1357) observed in the dual-culture test against Epicoccum nigrum (RD4C). Inhibition values were calculated after 10 days for C. acutatum and 15 days for V. dahliae following incubation. "> Figure 3
Typical 1H-NMR ZGCPPR spectra of aqueous extracts of three pathogens: E. nigrum (bottom), V. dahliae (middle) and C. acutatum (top); a representative expansion of aromatic region is also shown. "> Figure 4
Typical 1H NMR spectra of lipid extracts of three pathogens: E. nigrum (bottom), V. dahliae (middle) and C. acutatum (top). "> Figure 5
(a) OPLS-DA t[1]/t[2] score plot performed on aqueous extracts of E. nigrum cultivated in mono- and dual-assay; (b) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the 1H NMR spectra. "> Figure 6
(a) OPLS-DA t[1]/to [1] score plot performed on V. dahliae aqueous extracts cultivated in mono- and dual-assay; mono, mono-culture assay (V. dahliae); dual, dual-culture assay (V. dahliae + E. nigrum); (b) loading plot for the OPLS-DA model; (c) OPLS-DA t[1]/to [1] score plot performed on C. acutatum aqueous extracts cultivated in mono- and dual-assay; mono, mono-culture assay (C. acutatum); dual, dual-culture assay (C. acutatum + E. nigrum); (d) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the 1H NMR spectra. "> Figure 7
Variation in discriminating metabolite content between mono- and dual-culture assay for aqueous extract; (a) E. nigrum; (b) V. dahliae; (c) C. acutatum. Signif. codes ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05. "> Figure 8
(a) OPLS-DA t[1]/t[2] score plot performed on lipid extracts of E. nigrum cultivated in mono- and dual-assay; (b) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicated the chemical shift value (ppm) in the 1H NMR spectra. "> Figure 9
(a) OPLS-DA t[1]/to [1] score plot performed on V. dahliae lipid extracts cultivated in mono- and dual-assay; mono, mono-culture assay (V. dahliae); dual, dual-culture assay (V. dahliae + E. nigrum); (b) loading plot for the OPLS-DA model; (c) OPLS-DA t[1]/to [1] score plot performed on C. acutatum lipid extracts cultivated in mono- and dual-assay; mono, mono-culture assay (C. acutatum); dual, dual-culture assay (C. acutatum + E. nigrum); (d) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the 1H NMR spectra. "> Figure 10
Variation in discriminating fatty acid contents between mono- and dual-culture assay for aqueous extracts; (a) E. nigrum; (b) V. dahliae; (c) C. acutatum. Signif. codes ‘***’ 0.001 ‘**’ 0.01. ">

Versions Notes

Abstract

Olive trees are a cornerstone of Mediterranean agriculture but face significant threats from diseases such as Verticillium wilt and olive anthracnose. These diseases, caused by Verticillium dahliae and Colletotrichum spp., respectively, result in significant economic losses and degrade olive oil quality. While traditional chemical treatments present environmental risk, sustainable alternatives such as biological control agents (BCAs) are gaining attention. Epicoccum nigrum, an antagonistic fungus, has shown potential as a BCA due to its production of antimicrobial secondary metabolites. This study aimed to observe whether E. nigrum has an antagonistic ability against V. dahliae and C. acutatum, and to elucidate the metabolic interactions between these fungi using NMR-based metabolomics. E. nigrum showed inhibitory effects on the growth of C. acutatum and V. dahlia of 44.97% and 38.73% respectively. Metabolomic profiling revealed distinct biochemical responses in E. nigrum, V. dahliae, and C. acutatum under mono- and dual-culture. Multivariate statistical analysis highlighted the metabolic shifts in mycelia and identified the primary metabolites, such as glutamine, 4-aminobutyrate, and phenylalanine that are involved in adaption for survival in stress conditions such as the presence of a competitor. The results could be important for a better understanding of the primary fungal metabolism, which is still poorly characterized. Further investigation is needed, but these results suggest that E. nigrum could serve as a BCA, offering a more sustainable approach to managing olive diseases.

Keywords:

NMR-based metabolomics; BCA; fungal interaction; Epicoccum nigrum; Verticillium dahliae; Colletotrichum acutatum; olive trees

1. Introduction

The olive (Olea europaea subsp. L. europaea var. europaea) has a rich history in the Near East, spreading westward into the Mediterranean basin, where it now thrives as a widely cultivated fruit tree [1]. Its ability to adapt to the dry and rural conditions of the region makes it not only an agricultural commodity but also a cultural and culinary icon that supports economies throughout the Mediterranean [2]. Globally, more than 11.6 million hectares of olive cultivation were reported according to data from the International Olive Oil Council [3].

Despite its agricultural importance, the olive tree is highly vulnerable to pests and diseases, two of the most severe being Verticillium wilt and olive anthracnose (OA).

The soil-borne, vascular-colonizing fungus Verticillium dahliae Kleb., particularly the highly virulent defoliating (D) pathotype, is the causal agent of Verticillium wilt of olive (VWO). V. dahliae can survive in the soil for many years, it has a wide host range, from annual plants to woody crops [4], and available fungicides are not effective [5]. The disease is considered one of the most threatening biotic constraints in many olive-growing countries [2]. This disease severely impacts olive trees by infecting their vascular system, leading to wilting, tree mortality, and reduced fruit yield. The disease not only causes substantial economic and cultural losses but also affects the quality of virgin olive oil [2,6].

Over the last two decades, Verticillium wilt has killed thousands of olive trees world-wide, with mortality exacerbated by factors such as the presence of susceptible cultivars, extensive irrigation, and the use of soils previously cultivated with susceptible annual hosts like cotton, tomato, or potato [2,7]. Valverde et al. (2023) report that disease incidence on olive trees varies significantly depending on irrigation practices, with rates of 9%, 17%, and 4% observed in non-irrigated orchards, compared to 21%, 40%, and 13% in irrigated orchards in Morocco, southern Spain, and Syria, respectively [7].

In addition to Verticillium wilt, olive anthracnose (OA) poses a major phytopathological concern, especially in the Mediterranean region. OA, predominantly caused by Colletotrichum acutatum and Colletotrichum nymphaeae, has a significant impact on olive cultivation globally [8,9]. Around 18 species of Colletotrichum, predominantly within the C. acutatum, C. gloeosporioides, and C. boninense species complexes, have been identified as causing olive anthracnose worldwide [8,9]. Alongside Mediterranean olive fruit fly and olive leaf spot, OA is a major concern for olive crops in the Mediterranean region [10,11]. OA leads to substantial deterioration in oil quality, influenced by the proportion of infected fruits, the specific Colletotrichum acutatum strains, and the olive cultivar [12,13,14]. In southern Italy, where conditions—such as warm, rainy, and humid climates during fruit ripening—favor disease development, OA outbreaks are endemic and severe [9,15,16]. Globally, Colletotrichum acutatum and Colletotrichum nymphaeae are the predominant species associated with OA outbreaks, particularly in the Mediterranean region. They are known for their aggressiveness and role in causing significant disease epidemics [8,9,11,17,18,19,20].

Olive anthracnose is a major disease of olive in Portugal, competing with Bactrocera oleae (Gmelin). With an average severity of 14%, OA causes 30–50% crop losses in Portugal but only 2.6% in Spain [13]. Anthracnose has been reported on all continents, with economically significant impacts in certain areas [9]. Expanding olive cultivation into new regions complicates predictions about OA incidence due to novel host–pathogen interactions [9,21].

Conventional management strategies for these diseases have largely relied on chemical treatments and the development of resistant cultivars. Presently, the management of olive diseases caused by fungi sensu latu, including fruit rots, primarily depends on pre-harvest treatments with copper and synthetic fungicides. However, European Directive 2009/128/EC, which aims to substantially reduce pesticide use in agriculture, has fostered the search for alternative, environmentally friendly, and toxicologically safer strategies. These alternatives include the use of generally recognized as safe (GRAS) natural substances and microorganisms or their metabolites that can stimulate natural plant defense mechanisms [22]. Consequently, there has been a growing interest in sustainable and environmentally friendly agricultural practices, particularly the use of biological control agents (BCAs) [23,24,25].

In recent years, there has been a growing interest in exploring natural and sustainable alternatives for managing plant diseases. One promising approach is the use of antagonistic fungi, or their secondary metabolites, as biocontrol agents [26,27,28,29,30]. Among the promising biological control agents (BCAs), Epicoccum spp., particularly Epicoccum nigrum, have gained recognition for their antagonistic properties against several fungal pathogens. These fungi produce a wide range of secondary metabolites with antimicrobial properties, such as β-carotene, γ-carotene, rhodoxanthin, torularhodin, epicocconone, and antibiotics like epicorazine A and B, and flavipin [31]. E. nigrum has shown efficacy in inhibiting the growth of several fungal pathogens, including Fusarium spp., Botrytis cinerea, Rhizoctonia solani, Pythium spp., and Colletotrichum falcatum [31,32,33,34,35]. Its potential extends beyond agriculture into pharmaceuticals and other industrial applications, such as biotransformation of pharmaceuticals, biosynthesis of nanoparticles, degradation of biogenic amines in wine, and lipid synthesis for biodiesel production [33,36,37,38].

To address these challenges, biological control agents are being extensively researched for their potential to manage plant diseases sustainably. Recent studies have highlighted the effectiveness of various BCAs, such as Trichoderma spp., Bacillus spp., and non-pathogenic Fusarium oxysporum, in controlling Verticillium wilt. These BCAs exert their effects through mechanisms such as antibiosis, competition, and induction of plant defense responses [2,22,23]. Similarly, other biocontrol agents are being explored for their ability to manage olive anthracnose by inhibiting the growth of Colletotrichum species [39].

Metabolomics, an advanced omics technology following genomics, proteomics, and transcriptomics, offers a powerful tool to study the biochemical interactions between beneficial and pathogenic fungi. In recent years, metabolomics has largely been used in several research fields regarding fungi, such as the identification and classification of pathogens, metabolic pathways of fungi, plant–fungal interaction and the study of fungal natural products (secondary metabolites) [40,41,42]. Furthermore, metabolomics allows the analysis and identification of hundreds of metabolites in a single experiment, exploiting advances in analytical techniques, enhanced instrumentation and bioinformatics tools [43]. Avant-garde instrumental approaches for metabolomics studies include Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS) and Nuclear Magnetic Resonance spectroscopy (NMR) [44]. Recently, NMR has been widely employed in metabolomics studies, being a non-destructive analytical technique [41,44,45]. Moreover, NMR allows identification of molecular components of biological complex matrices, assigning up to 20 and 100 metabolites in vivo and in vitro, respectively. The low sensitivity of NMR, when compared with MS, is overcome by new pulse sequences for observing hetero- or homo-nuclear correlation [41,44,45]. Furthermore, NMR coupled with Multivariate Statistical Analysis (MVA) provides quantitative insights into the dynamic metabolic responses of living systems to environmental stimuli or genetic modifications [40,45]. In the context of fungal interactions, metabolomics enables the comprehensive profiling of metabolites involved in pathogenicity and biocontrol mechanisms [46,47]. However, the molecular mechanisms and metabolic interactions underlying its biocontrol efficacy remain underexplored. The aims of the study were the following: (i) to assess the in vitro antagonistic ability of Epicoccum nigrum against Colletotrichum acutatum and Verticillium dahlia; (ii) to analyze and compare the metabolic profiles of Epicoccum nigrum, Verticillium dahliae and Colletotrichum acutatum in mono- and dual-culture conditions using NMR spectroscopy; (iii) to interpret the metabolic data by MVA to understand the biochemical responses and interactions among the fungal isolates in different culture conditions.

2. Materials and Methods

2.1. Fungal Isolates

This study includes isolates of three different fungal genera, Colletotrichum acutatum (C9D2C), Verticillium dahliae (ER 1357) and Epicoccum nigrum (RD4C). Colletotrichum acutatum isolate was identified in previous studies [9]. Epicoccum nigrum (RD4C) and Verticillium dahlia (ER 1357) were identified molecularly in this study. Fungal isolates were sourced from the Council for Agricultural Research and Economics, Research Centre for Olive, Fruit and Citrus Crops (CREA-OFA, Rende (CS), Italy), the Research Centre for Plant Protection and Certification (CREA-DC, Roma, Italy) and the Molecular Plant Pathology Laboratory of the Department of Agriculture, Food and Environment, University of Catania (Italy). All fungal isolates were obtained from olive trees. Specifically, Epicoccum nigrum was isolated from olive drupes, Colletotrichum acutatum from symptomatic drupes [9] and Verticillium dahliae from soil collected from a symptomatic olive tree.

All isolates were cultured on Potato Dextrose Agar (PDA) at 24 ± 1 °C in the dark for 15 days until their use for morphological and molecular characterization in in vitro tests.

2.2. Morphological Identification of Epicoccum nigrum and Verticillium dahliae

The isolates Verticillium ER 1357 and Epicoccum RD4C were characterized morphologically based on colony and microscopic features. Both isolates were grown on PDA in Petri plates at 25 °C in the dark for 14 days.

For Verticillium ER 1357, morphological identification focused on the analysis of conidia and microsclerotia. Microsclerotia were examined under a light microscope, while conidia were obtained by preparing spore suspensions in sterile distilled water (SDW).

For Epicoccum RD4C, observations included colony characteristics such as size, color, and hyphal morphology. The dimensions and morphological features of mature conidia were measured at ×400 magnification to evaluate variability in size and shape, following the methodology described by Yu et al. (2016) [48] for Verticillium dahliae.

2.3. Molecular Identification of Epicoccum nigrum and Verticillium dahliae

RD4C and ER 1357 isolates, which had not been previously identified, were grown on PDA for 15 days at 24 ± 1 °C. Then, the mycelium of the isolates was harvested with a sterile scalpel, and approximately 1 g (fresh weight) of lyophilized mycelium was ground to fine powder in liquid nitrogen. The genomic DNA was extracted following the manufacturer’s protocol and using a PowerPlant Pro DNA isolation Kit (MO BIO Laboratories, Inc., Hilden, Germany). The DNA was preserved at −20 °C. The ITS1–5.8S–ITS2 region was amplified with primers ITS1 and ITS4 [49] and sequenced to confirm the isolate species. PCR amplifications were performed on a GeneAmp PCR System 9700 (Applied Biosystems, Foster City, CA, USA). All PCRs were carried out using Taq DNA polymerase recombinant (Invitrogen, Carlsbad, CA, USA) in a total volume of 25 μL containing PCR buffer (1×), dNTP mix (0.2 mM), MgCl2 (1.5 mM), forward and reverse primers (0.5 μM each), Taq DNA polymerase (1 U) and 1 μL of genomic DNA. Reaction conditions were 94 °C for 3 min; followed by 35 cycles of 94 °C for 30 s, 58 °C for 30 s, and 72 °C for 30 s; followed by an additional 10 min extension at 72 °C. Amplified products were analyzed by agarose gel electrophoresis and single bands of the expected size were purified with the QIAquick PCR Purification Kit (Qiagen, Duesseldorf, Germany) and sequenced with both forward and reverse primers by Macrogen Europe (Amsterdam, The Netherlands). The ChromasPro v. 1.5 software was used to evaluate the reliability of sequences and to create consensus sequences.

For RD4C, the β-tubulin (Tub2) gene was also analyzed. Amplifications of Tub2 were performed using primers Btub2Fd/Btub4Rd [50]. PCR conditions for Tub2 included an initial denaturation step at 94 °C for 2 min, followed by 35 cycles of 94 °C for 30 s, 54 °C for 30 s, and 72 °C for 30 s, with a final extension at 72 °C for 2 min. The amplified Tub2 fragments were processed as described for ITS, including purification and sequencing.

The ITS and Tub2 sequences obtained in the present study were deposited in GenBank. The reference sequences of Epicoccum spp. and Verticillium spp. used to construct the multilocus phylogenetic tree are shown in Table 1. The phylogenetic tree for Epicoccum was constructed using a multilocus approach, while for Verticillium only the ITS region was used. MUSCLE was used for sequence alignment, and phylogenetic trees were generated with MEGA11.

2.4. In Vitro Antagonistic Ability

The selected strain of E. nigrum (RD4C) was screened for its ability to inhibit the mycelial growth (%) of C. acutatum (C9D2C) and V. dahliae (ER 1357) strains by in vitro dual- culture assays [22]. The percentage inhibition of growth was calculated using the following formula:

I n h i b i t i o n o f g r o w t h (%) = (\frac{X - Y}{X}) \times 100

where X is the radial growth of pathogen alone without antagonist (control) and Y is the radial growth of pathogen along with antagonist.

The dual-culture test was carried out in 9 cm Petri dishes with 15 mL of PDA by placing 5 mm diameter agar plugs of each pathogen and antagonist taken from the margin of 1-week-old colonies grown on PDA. The dual-cultures were set up by placing pathogen and antagonist 4 cm apart on PDA plates. Single cultures of each pathogen were used as control. Plates were incubated at 24 ± 1 °C in the dark, and radial mycelial growth was measured when C. acutatum (C9D2C) and V. dahliae (ER 1357) mycelium of control cultures covered the whole Petri dishes (namely, 10 days for C. acutatum and 15 days for V. dahliae after incubation). Overall, the experimental set-up consisted of the following four treatments (including controls) made of 10 replicates each: (i) C. acutatum isolate C9D2C, (ii) V. dahliae isolate ER 1357, (iii) C. acutatum isolate C9D2C + E. nigrum isolate RD4C, and (iv) V. dahliae isolate ER 1357 + E. nigrum isolate RD4C.

2.5. Sample Preparation for NMR Analysis

Mycelium extracts, aqueous and lipid, were obtained according to a modified Bligh and Dyer two-step method, a standard procedure for metabolomics profiling of cellular substrates [54]. In particular, for each culture test (mono and dual-culture assay) two biological replicates were carried out. For each sample, ~100 mg of lyophilized material, in three technical replicates, were added to methanol (400 μL), deionized water (285 μL) and chloroform (400 μL). The solution was mixed and placed on ice for 10 min and then was centrifuged for 20 min at 10,000 rpm. The two phases obtained, aqueous and lipid, were separated and dried by a SpeedVac vacuum concentrator. Successively, aqueous extracts were dissolved in 500 μL K₂HPO₄ buffer 0.1 M (pH 7.4) in D₂O containing 0.01% of salt of 3-trimethylsilylpropionic acid-d4 (TSP-d4) as internal reference (δ = 0.00). Lipid extracts were dissolved in 600 μL CDCl₃/CD₃OD (2:1) containing 0.03% v/v trimethylsilane as internal standard (TMS, δ = 0.00). Aqueous and lipid extracts were then transferred into 5 mm NMR tubes and analyzed.

2.6. NMR Measurement

The ¹H NMR spectra, ZGCPPR and ZG for aqueous and lipid fraction, respectively, were recorded using a Bruker Avance III NMR spectrometer (Bruker, Ettlingen, Germany) operating at 600.13 MHz for ¹H observation, equipped with a TCI cryoprobe, incorporating a z-axis gradient coil and automatic tuning-matching. Experiment was acquired in automation mode after loading individual samples on a Bruker Automatic Sample Charger, interfaced with the software IconNMR Version 5 (Bruker), at 300 K. In detail, for mycelium aqueous extracts, 1D sequence with pre-saturation and composite pulse for selection (zgcppr Bruker standard pulse sequence) was acquired, with 128 transients, 16 dummy scans, 5 s relaxation delay, size of Free Induction Decay (FID) of 64 K data point, a spectral width of 20.0276 ppm and acquisition time of 2.73 s. For each lipid extract, a standard 1D ¹H one-dimensional spectrum was acquired by performing the following experimental condition: 64 scans, 16 dummy scans, 64 K time domain, spectral width of 20.0276 ppm, 2 s delay, p1 8 μs and 2.73 s acquisition time. After acquisition, the resulting FIDs were multiplied by an exponential weighting function corresponding to a line broadening of 0.3 Hz before Fourier transformation, automated phasing and baseline correction.

For both extracts, the metabolites were assigned on the basis of 2D NMR spectral analysis (2D ¹HJres, ¹H COSY, ¹H-¹³C HSQC, and HMBC) and by comparison with published data [54,55,56,57]. The metabolite assignments were verified using the Human Metabolome Data Base (HMDB).

2.7. Data Processing and Multivariate Statistical Analysis

In order to reduce the data dimensionality, the bucketing process and subsequent normalization on the total area (sum of each resonance intensity) within each bucket were calculated. In particular, all the spectra were divided into buckets with uniform spectral width (0.04 ppm for both extracts) by using AMIX 3.9.15 (Analysis of Mixture, Bruker Biospin GmbH, Rheinstetten, Germany) software. For aqueous extracts, the region 10.00–0.5 ppm was analyzed, with the exclusions of 4.98–4.74 ppm and 3.70–3.30 ppm containing the residual peaks from the suppressed water and methanol resonances, respectively. Analogously, for lipid spectra, the range 5.80–0.5 ppm was considered, excluding the methanol resonance region 4.46–4.26 ppm. For both extracts, Multivariate Statistical Analyses were performed, using as input variables the Pareto scaled NMR bucketed data [54]. A principal component analysis (PCA) was preliminarily performed on the whole dataset, and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to more deeply examine differences among samples and to identify the metabolites that contribute to their discrimination. Multivariate data analysis was performed using SIMCA-14 software (Sartorius Stedim Biothech, Umeå, Sweden). Commonly, PCA is applied to obtain and demonstrate the systematic variation in matrix X consisting of rows (the considered observation) and columns (the variables) of the buckets obtained from NMR spectra. The OPLS-DA analysis allows filtering out of variations that are not directly connected to the response, and produces models of clearer interpretation, focusing the predictive information in one component [54,58]. The PCA and OPLS-DA quality model is defined by R² and Q² parameters. In detail, R² is a cross-validation parameter defined as the portion of data variance explained by the models, and indicates the goodness of fit. On the other hand, the Q² parameter is the portion of variance in the data predictable by the model [54,59]. The minimal number of components required can be easily defined since R² (cum) and Q² (cum) parameters display completely divergent behavior as the model’s complexity increases [59,60]. The results were reported by the optimal bi-dimensional scores plots and relative loading plots; the latter were used to identify the metabolites responsible for separation among sample groups [54,58].

3. Results

3.1. Morphological and Molecular Identification

The morphological characteristics of Verticillium dahliae ER 1357 were consistent with typical features of the species. On PDA, the colony initially appeared creamy white, then darkened and became compact and appressed to the agar. The colony produced abundant hyaline conidia, which were cylindrical to oval, measuring approximately 4.4 ± 1.23 μm in length, with a maximum length of 6.0 μm. Conidiophores were hyaline, verticillately branched, and contained two to four phialides per node. Microsclerotia were dark brown to black, spherical, and compact, measuring 20–100 μm in diameter, with aggregates of microsclerotia reaching up to 200 μm in diameter. These features align with the description of V. dahliae provided in the study by Inderbitzin et al. (2011) [52].

In the case of Epicoccum nigrum RD4C, the colony on PDA was initially white with cottony mycelium, which later turned reddish-orange after 14 days of incubation. The reverse of the colony also produced an orange pigment. The mycelium spread across the entire surface of the plate, covering an area of approximately 633 mm². The colony produced numerous conidia, which were globular to subglobose or pyriform in shape, with some conidia being multicellular and resembling a football shape. The conidia measured 14.5 to 26.4 μm in length and 15.9 to 28.4 μm in width. Immature conidia often had a brown stalk cell. These morphological features are consistent with E. nigrum [61]. The morphological characteristics of both fungi are summarized in Table 2.

Molecular analyses were conducted on the RD4C and ER 1357 isolates. Phylogenetic analysis based on the ITS sequence grouped Verticillium dahliae ER 1357 with isolates SDV 1025 and CBS 318.66 (Figure 1A). Similarly, the multilocus phylogenetic analysis of Epicoccum nigrum RD4C, based on both ITS and Tub2 sequences, clustered it with the reference isolates CBS 140523 and CBS 125.82 (Figure 1B). The GenBank accession numbers for the ITS sequences of RD4C and ER 1357 are PP413698 and PP809450, respectively.

Additionally, the Tub2 gene sequence of Epicoccum nigrum RD4C was deposited in GenBank under Accession No. PP420949.

3.2. In Vitro Antagonistic Ability

In dual-culture assays, E. nigrum (RD4C) inhibited the growth of C. acutatum by 44.97 ± 1.37% and V. dahliae by 38.73 ± 4.77% (Figure 2A), showing significant antagonistic potential (Figure 2B).

3.3. NMR Profiles Obtained from E. nigrum, V. dahliae, C. acutatum

NMR-based metabolomics was applied to explore metabolic profiles of mycelia. Both aqueous and lipid extracts were considered for E. nigrum, V. dahliae and C. acutatum (mono- and dual-culture assay) obtaining a specific profile. Typical ¹H-NMR ZGCPPR (600 MHz) spectra of aqueous extracts obtained from mycelia samples are reported in Figure 3. The aqueous fraction was dominated, in the high and middle region (0.00–5.60 ppm), by signals ascribable to amino acids, organic acids (succinate, lactate) and sugars (mainly α and β glucose, mannitol and trehalose). The low-field aromatic region (6–10 ppm) showed signals assigned to phenolic compounds such as aromatic amino acids (phenylalanine and tyrosine), and alkaloids (trigonelline) (Table 3). Furthermore, NMR spectra obtained from E. nigrum extracts showed a typical signal of propanol (δ = 0.83). Moreover, the ¹H NMR spectra of aqueous extracts of V. dahliae were characterized by signals ascribed to nicotinate at 8.94, 8.61, 8.25 and 7.53 ppm, that were absent in E. nigrum and C. acutatum extracts. A malonate signal at 3.12 ppm was found only in extracts of C. acutatum.

Representative 1D ¹H NMR spectra of lipid extracts are shown in Figure 4, showing the characteristic signals of fatty acids (saturated, SFAs, mono--, MUFAs, di-, DUFAs, and polyunsaturated, PUFAs, fatty acids), triglycerides (TAG) and phospholipids, in particular phosphatidylcholine (PC) (Table 4).

3.4. Multivariate Statistical Analysis

3.4.1. Aqueous Extracts

An exploratory principal component analysis (PCA) of the ¹H NMR spectra from all aqueous extracts was performed, in order to reveal the main trends of samples. The preliminary unsupervised PCA model was obtained, together with the corresponding t[1]/t[2] score plot, with five principal components being enough to describe more than 80% of the total variance of the entire dataset (t[1] and t[2] explained 38% and 18%, respectively), and R²X = 0.843 and Q² = 0.691. The PCA score plot displayed a certain degree of separation among samples (Figure S1). In particular, visual inspection of the PCA showed that V. dahliae (obtained from mono- V. dahliae, and dual- V. dahliae + E. nigrum) and C. acutatum (obtained from mono- C. acutatum and dual- C. acutatum + E. nigrum) grouped according to mycelium, while E. nigrum resulted in two clearly separated sub-clusters on the basis of culture assay performed (mono- E. nigrum, or dual-culture assay E. nigrum + V. dahliae and E. nigrum + C. acutatum) (Figure S1).

In order to better evaluate the differences between the various growth conditions (mono- and dual-culture assays), a supervised analysis was performed for each pathogen. From the preliminary PCA (Figure S1) examination, at first instance, the metabolic profile variations in E. nigrum aqueous extracts between mono- and dual-culture assay were assessed. The OPLS-DA analysis for E. nigrum gave a good model, with a total variance of R²X = 0.918 R²Y = 0.959 and predictability of Q² = 0.813 (Figure 5a). In the OPLS-DA score plot, the aqueous extracts of E. nigrum in mono-culture assay were well separated along the predictive component t[1] compared to mycelium grown in dual-culture. The molecular components responsible for separation between the different growth conditions were observed by examining the OPLS-DA loading plot for the model (Figure 5b). In particular, higher levels of glutamate, glutamine, succinate and lower levels of sugars (glucose, trehalose, mannitol) were found in E. nigrum cultivated in mono-culture assay. Furthermore, E. nigrum cultivated in dual-culture assay with V. dahliae was characterized from higher levels of ethanolamine, while higher relative content of 4-aminobutyrate and tyrosine were observed in E. nigrum cultivated with C. acutatum.

The same approach was also used for V. dahliae and C. acutatum, obtaining two new supervised models (Figure 6). For both mycelia, the OPLS-DA analysis demonstrated a good degree of sample separation based on the different culture conditions.

In particular, the OPLS-DA models obtained with 1 + 2 + 0 components gave, respectively, the following parameters: R²X = 0.554, R²Y = 0.965, Q² = 0.258 for V. dahliae (Figure 6a), and R²X = 0.867, R²Y = 0.987 and Q² = 0.947 for C. acutatum (Figure 7c). Examining the loading plots for the models, it is possible to identify the metabolites responsible for separation. Concerning V. dahliae, higher levels of organic acids (lactate, acetate, succinate, 4 aminobutyrate), amino acids (alanine, threonine, valine, leucine, isoleucine, phenylalanine) and nicotinate characterized the mycelium cultivated in mono-assay. Instead, sugars, such as glucose, mannitol and UDP-glucose were found in V. dahlia aqueous extracts obtained from co-culture with E. nigrum (Figure 6b).

An opposite trend for sugar content was observed for C. acutatum compared to V. dahliae; in fact, higher levels of glucose, mannitol and trehalose were found in C. acutatum cultivated in mono-culture assay (Figure 6d). Moreover, a higher relative content of trigonelline was characteristic of C. acutatum obtained from mono-culture. On the other hand, amino acids (in detail: valine, leucine, isoleucine, threonine, tyrosine, phenylalanine), 4-aminobutyrate, malonate and ethanolamine were found at higher levels in C. acutatum obtained from co-culture with E. nigrum (Figure 6d).

Furthermore, the variation in principal discriminating metabolite content, such as sugars and 4-aminobutyrate, for E. nigrum, V. dahliae and C. acutatum between mono- and dual-culture assay was calculated by the integration of the selected and distinctive NMR signals. In particular, for each mycelium, the differences between the two growth conditions (mono and dual) were observed and reported as Log₂ Fold Change (FC) ratio in Figure 7. In detail, metabolites at negative values were higher in mono- culture assay, while metabolites at positive values were characteristic of dual-culture assay. Especially, statistically significant differences were observed between E. nigrum aqueous extracts obtained from dual-culture assay with V. dahliae as well as C. acutatum compared to mono-culture assay (Figure 7a).

3.4.2. Lipid Extracts

The same approach applied to aqueous extracts was also performed for lipid extracts, carrying out a preliminary unsupervised analysis of all samples (Figure S2). In the PCA analysis, the total variance of 80% was explained by seven principal components, R²X = 0.972 and Q² = 0.920 (with t[1], t[2] of 61% and 17%, respectively). The PCA score plot showed a separation between mono- and dual-culture assay only for E. nigrum (mono-, E. nigrum, and dual-culture assay E. nigrum + V. dahliae and E. nigrum + C. acutatum), while V. dahliae (obtained from mono-, V. dahliae, and dual-, V. dahliae + E. nigrum) and C. acutatum (obtained from mono-, C. acutatum and dual-, C. acutatum + E. nigrum) samples showed mixed and scattered results.

In order to better understand the differences between E. nigrum in mono- and dual-culture assay, analogously to aqueous extracts, the supervised analysis was performed considering E. nigrum in different growth conditions, in particular mono-culture assay versus dual-culture assay (E. nigrum + V. dahliae and E. nigrum + C. acutatum).

The supervised OPLS-DA analysis performed on E. nigrum gave a good mode (2 + 1 + 0, R²X = 0.921 R²Y = 0.782 and Q² = 0.655) (Figure 8). In detail, E. nigrum samples separated according to culture assay (lipid extracts from mono- and dual-culture assay were found at positive and negative values to the orthogonal component, respectively). Furthermore, OPLS-DA demonstrated a clear separation of E. nigrum samples based on co-culture assay along the predictive [1] component (E. nigrum + V. dahliae at positive values and E. nigrum + C. acutatum at negative values). From loading plot analysis, it was possible to define the lipids responsible for separation. In particular, E. nigrum cultivated in mono-culture was characterized by higher level of SFAs, while higher relative content of phosphatidylcholine (PC), unsaturated fatty acids, principally poly-unsaturated (PUFA) and di-unsaturated (DUFA) were found in E. nigrum grown with C. acutatum. Instead, E. nigrum obtained from co-culture with V. dahliae was characterized by greater relative content of short FAs.

In addition, the differences for V. dahliae and C. acutatum lipid extracts, between mono- and dual-culture assays respectively, were evaluated by supervised OPLS-DA analysis (Figure 9). Both pathogens showed separation along the predictive component in their respective score plots (Figure 9a,c). The analysis of loading plot (Figure 9b,d) showed for V. dahliae as well as for C. acutatum higher relative content in short FAs in pathogen lipid extracts obtained from mono-culture assay. By contrast, both pathogens V. dahliae and C. acutatum obtained from co-culture with E. nigrum were characterized by higher relative content of UFAs, such as PUFAs (Figure 9b,d). Furthermore, a greater level of sterol was found in C. acutatum grown in dual-culture assay (Figure 9c).

In addition, the variations in discriminating fatty acids between different culture assays (mono- and dual-) for each pathogen E. nigrum, V. dahliae and C. acutatum were verified by the integration of unbiased signals (NMR-specific resonances) and reported as Log₂FC (Figure 10). In bar graphs, fatty acids found at negative values were more prevalent in mono-assay, whereas lipids at positive values characterized the mycelium extract obtained from dual-culture assay. Concerning E. nigrum extracts, statistically significant levels of SFA, DUFA and PC were observed principally between mono-culture assay and dual-culture with C. acutatum (Figure 10a). No significant differences were observed between mono- and dual-culture assays of lipid C. acutatum extracts (Figure 10c).

4. Discussion

The olive tree is highly vulnerable to pests and diseases. Those related to fungal infections are highly relevant, although they have not been fully investigated in terms of their specific primary metabolism. The present study investigates the antagonistic potential of Epicoccum nigrum (RD4C isolate) against two significant plant pathogens, Colletotrichum acutatum (C9D2C isolate) and Verticillium dahliae (ER 1357 isolate). Through a combination of NMR-based metabolomic analysis and MVA, we explored the interactions between these fungi and elucidated the biochemical basis of E. nigrum’s antagonistic effects. This study demonstrates that E. nigrum effectively inhibits the growth of both C. acutatum and V. dahliae.

The dual-culture assays confirmed the potential of E. nigrum as a BCA, with a clear inhibition of both pathogens. Specifically, E. nigrum inhibited the growth of C. acutatum by 44.97% and V. dahliae by 38.73%. These results are consistent with previous studies highlighting the biocontrol potential of E. nigrum against various plant pathogens [31,62,63,64]. The observed inhibition percentages indicate a significant antagonistic effect, suggesting that E. nigrum could be an effective biocontrol agent for managing these pathogens in agricultural settings.

The NMR-based metabolomics analysis provided detailed insight into the metabolic changes associated with the fungal interactions. Multivariate statistical analysis, mainly OPLS-DA, demonstrated clear metabolic distinctions between mono- and dual-culture assay, for both extracts, aqueous and lipid. In particular, alterations in fungal primary metabolism with changes in sugar, amino acid, organic acid and fatty acid relative contents were observed. It is well known that in the presence of stress, biotic or abiotic, an organism triggers a series of reactions in its metabolism as a defense mechanism to counteract the deleterious effects being caused. Despite the fact that the primary metabolism of fungi plays a crucial role in their response to stresses, pathogenesis and survival, it has been poorly studied so far [56]. On the contrary, considering the potential uses of secondary metabolites in several sectors, such as the pharmaceutical and agri-food industries, the secondary metabolism of pathogenic fungi has been amply studied [56]. Nevertheless, more research is still needed in order to better understand the overall metabolism of these microorganism, since several genes are not expressed under the usual laboratory conditions [56].

In this study, the ¹H NMR characterization of aqueous and lipid extracts of E. nigrum, V. dahliae and C. acutatum provided advanced information on the metabolite composition of these pathogens. In particular, aqueous extracts revealed a dominance of amino acids, organic acids and sugars, with specific metabolites like propanol in E. nigrum and nicotinate in V. dahliae being distinctive markers. From statistical analysis, E. nigrum showed higher levels of glutamate, glutamine, and succinate in mono-cultures, whereas dual-cultures showed increased ethanolamine and 4-aminobutyrate, indicating a shift in metabolic pathways when in the presence of the pathogens. Similarly, V. dahliae and C. acutatum exhibited significant metabolic changes in dual-cultures, with higher levels of sugars in V. dahliae and increased amino acids and organic acids in C. acutatum. These metabolic shifts likely reflect the competitive interactions and stress responses induced by the presence of E. nigrum. Among these discriminant metabolites, some are involved in energy and amino acid metabolism and oxidative stress. As is known, in all organisms UDP-glucose is important for several processes in cell physiology [65]. In particular, in fungi, UDP-glucose is implicated in the synthesis of trehalose (osmoprotector) and of β-glucans (essential components of cell walls) [65,66]. Trehalose, a non-reducing disaccharide, is important not only as a reserve carbohydrate, but it also plays a crucial rule in different processes, such as glycolysis control, pathogenicity, sporulation and protection against osmotic stress. Changes in trehalose contents suggest a mechanism of adaptation of plant pathogens to unfavorable or stressful growth conditions [55,56,67]. Additionally, mannitol also showed a shift in relative content between mono- and dual-culture assays. In fungi, mannitol is implicated in several physiological functions, such as carbon storage and pathogenesis [57,68].

In metabolic profiles obtained from aqueous extracts of E. nigrum, V. dahliae and C. acutatum in mono- and dual-assay, variations in amino acid content were also observed. As already reported in the literature, aromatic amino acids (tyrosine and phenylalanine), branched-chain amino acids (leucine, valine, isoleucine) are strictly necessary as precursors in protein synthesis and energy production [65]. Glutamate appears to play a protective role against osmotic stress and acids [56]. Moreover, threonine is important as a nitrogen source for microorganisms, whereas glutamine content reflects the nitrogen state for fungi [56]. In addition, aromatic amino acids represent the essential precursors of numerous secondary metabolites (such as phenylpropanoid, alkaloids) that are produced through several biosynthetic pathways [10]. It is well-known that secondary metabolites are involved in the defense mechanisms of organisms. Furthermore, most fungi convert phenylalanine to phenyl-acetate [69,70]. This latter compound is a bioactive metabolite that is studied not only for its phytotoxicity but also for its antimicrobial activity [70]. In fact, some phenylalanine derivatives have been shown to be effective against both Gram-positive and Gram-negative bacteria, likely due to their interaction with bacterial membranes [71]. However, to our knowledge, there is no evidence in the literature that Epicoccum spp. produce phenylalanine-derived antimicrobial compounds.

Furthermore, succinate, 4-aminobutyrate (or GABA) and malonate proved to be discriminating metabolites. In particular, succinate is the main intermediate of the TCA cycle, while the rule of 4-aminobutyrate and its metabolism in phytopathogenic fungi is still unclear, although it is known that 4-aminobutyrate represents a nitrogen and carbon source [72]. Anyway, recent studies have demonstrated that 4-aminobutyrate plays a crucial role in response to environmental stress conditions, with a positive effect on asexual development [72]. In fact, in most fungi, the 4-aminobutyrate is synthesized by decarboxylation of glutamate and it is involved in sporulation [73]. Specifically, in Alternaria alternata, GABA metabolism was linked to oxidative stress responses and fungal development, suggesting its potential involvement in pathogenicity and environmental adaptation [74]. Nevertheless, there is no current evidence that Epicoccum spp. actively synthesize or utilize GABA as part of their metabolic strategy. Finally, malonate is related to nitrogen metabolism and could play a protective role, also participating in the fatty acid biosynthetic pathways [57].

In this study, besides the aqueous extracts, the lipid profile of E. nigrum, V. dahliae and C. acutatum were further analyzed. The visual inspection of ¹H NMR spectra revealed the presence of different classes of lipids such as Saturated and Unsaturated Fatty Acids (SFA and UFA, respectively), triglycerides and phospholipids (principally PC). As already observed for aqueous extracts, the OPLS-DA analysis of lipid extracts also exhibited a clear separation between mono- and dual-culture assay for all pathogenic mycelia, buttressing the observed shift in metabolic pathways in the presence of the pathogens. In fungi, lipids represent the major constituent of the membrane system but they occur also as minor components in the cell wall. Lipids can store energy in lipid bodies and, sometimes, lipids act as intra or extracellular signals [75,76]. The variation in lipid composition reflects the greater complexity and cell-size of fungi [76]. The content and the different classes of lipids, in fungi, derive not only from microorganisms but also from developmental stages, nutrition and growth conditions [76]. Furthermore, recent studies suggest a role for lipids in cell signaling, since during host–pathogen interaction there is an exchange of lipid signals between the organisms [75]. Fatty Acids (FA) represent the crucial lipid, being an important energy source and they are incorporated in phospholipids, an essential building block of membranes. Moreover, FAs can be an energy reservoir in the form of TAGs or esters accumulated in lipid drops or globules in cells [75]. As already reported in the literature, in vitro, UFAs (such as linoleic and linolenic acids) are able to reduce the fungal growth in different mycelia [75,77]. TAGs are usually considered as storage lipids, but during mycelium development, they are used as carbon skeletons and energy sources [76]. Besides TAGs, other important lipids are sterols and phospholipids. Sterols influence permeability, thus regulating molecular movement and viscosity of the lipid layer. Furthermore, they also act as precursors of steroid hormones essential in fungal reproduction [76]. In eukaryotes, including most species of fungi, phosphatidylcholine (PC) together with phosphatidylethanolamine are the main phospholipids. Phospholipids are the major constituents of membranes, contributing to their integrity and function, but they also play a crucial rule in fungal vegetative growth [78].

In conclusion, the distinct metabolic profiles observed for both extracts, across mono- and dual-cultures, suggest that the presence of E. nigrum may exert its antagonistic effects. This is done through multiple mechanisms, including competition for nutrients, production of inhibitory compounds, and alteration of the pathogens’ metabolic pathways. The changes in relative content of certain amino acids, sugars, organic acids and lipids in the presence of E. nigrum indicate a possible disruption of the mycelial metabolic balance, which could hinder its growth and virulence.

5. Conclusions

This study provides a comprehensive analysis of the antagonistic interactions in vitro between E. nigrum and two major plant pathogens, V. dahlia and C. acutatum, supported by robust molecular and metabolomic data. The findings highlight the potential of E. nigrum as a biocontrol agent and pave the way for further research into its application in agricultural practices. Future studies should focus on field trials to evaluate the practical effectiveness of E. nigrum in diverse crop systems and explore the molecular mechanisms underlying its biocontrol activity in greater detail.

By enhancing our understanding of fungal antagonism and metabolic interactions, we can develop more sustainable and effective strategies for managing plant diseases and improving crop health.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jof11020129/s1, Figure S1: PCA score plot t[1]/t[2] (five PCs R²X = 0.843 and Q² = 0.691) performed on aqueous extracts of E. nigrum, V. dhaliae and C. acutatum. Abbreviation: mono, mono culture assay; dual, dual culture assay.; Figure S2: PCA score plot t[1]/t[2] (seven PCs R²X = 0.972 and Q² = 0.920) performed on lipid extracts of E. nigrum, V. dhaliae and C. acutatum. Abbreviation: mono, mono culture assay; dual, dual culture assay.

Author Contributions

Conceptualization, F.P.F., S.O.C. and E.S.; methodology, F.A., M.R., E.S. and F.P.F.; validation, S.O.C., E.S. and F.P.F.; formal analysis, F.A. and M.R.; investigation, F.A. and M.R.; resources, E.S.; data curation, F.A., M.R. and S.O.C.; writing—original draft preparation, F.A. and M.R.; writing—review and editing, S.O.C., F.P.F. and E.S.; supervision, F.P.F. and E.S.; funding acquisition, E.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the 2018–2022 project “Salvaguardia e valorizzazione del patrimonio olivicolo italiano con azioni di ricerca nel settore della difesa fitosanitaria”—SALVAOLIVI—Italian Ministry of Agriculture, Food and Forestry Policies, DM. 0033437 of 21 December 2017. CUP: C11I17000100001.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author due to privacy.

Acknowledgments

We are grateful to Luca Riccioni for providing Verticillium dahliae isolates.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Phylogenetic trees of Verticillium dahliae (A) and Epicoccum nigrum (B) based on the internal transcribed spacer (ITS) and, for E. nigrum, also on the β-tubulin (Tub2) gene. In panel (A), the phylogenetic tree for V. dahliae was constructed using only ITS sequences, with Gibellulopsis nigrescens as the outgroup and a log likelihood of −1029.50. In panel (B), the multilocus phylogenetic tree of E. nigrum was developed using both ITS and Tub2 sequences, with Epicoccum dendrobii as the outgroup and a log likelihood of −1560.69. Maximum likelihood estimation was used to infer the trees, and the Tamura–Nei model was applied.

Figure 2. (A) Petri dish cultures: C9D2C + RD4C, dual-culture of C. acutatum and E nigrum; RD4C, mono-culture of E nigrum; ER 1357 + RD4C, dual-culture of V. dahliae and E nigrum; ER 1357, mono-culture of V. dahliae. (B) Percentage of mycelial growth inhibition of Colletotrichum acutatum (C9D2C) and Verticillium dahliae (ER 1357) observed in the dual-culture test against Epicoccum nigrum (RD4C). Inhibition values were calculated after 10 days for C. acutatum and 15 days for V. dahliae following incubation.

Figure 3. Typical ¹H-NMR ZGCPPR spectra of aqueous extracts of three pathogens: E. nigrum (bottom), V. dahliae (middle) and C. acutatum (top); a representative expansion of aromatic region is also shown.

Figure 4. Typical ¹H NMR spectra of lipid extracts of three pathogens: E. nigrum (bottom), V. dahliae (middle) and C. acutatum (top).

Figure 5. (a) OPLS-DA t[1]/t[2] score plot performed on aqueous extracts of E. nigrum cultivated in mono- and dual-assay; (b) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the ¹H NMR spectra.

Figure 6. (a) OPLS-DA t[1]/to [1] score plot performed on V. dahliae aqueous extracts cultivated in mono- and dual-assay; mono, mono-culture assay (V. dahliae); dual, dual-culture assay (V. dahliae + E. nigrum); (b) loading plot for the OPLS-DA model; (c) OPLS-DA t[1]/to [1] score plot performed on C. acutatum aqueous extracts cultivated in mono- and dual-assay; mono, mono-culture assay (C. acutatum); dual, dual-culture assay (C. acutatum + E. nigrum); (d) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the ¹H NMR spectra.

Figure 7. Variation in discriminating metabolite content between mono- and dual-culture assay for aqueous extract; (a) E. nigrum; (b) V. dahliae; (c) C. acutatum. Signif. codes ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05.

Figure 8. (a) OPLS-DA t[1]/t[2] score plot performed on lipid extracts of E. nigrum cultivated in mono- and dual-assay; (b) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicated the chemical shift value (ppm) in the ¹H NMR spectra.

Figure 9. (a) OPLS-DA t[1]/to [1] score plot performed on V. dahliae lipid extracts cultivated in mono- and dual-assay; mono, mono-culture assay (V. dahliae); dual, dual-culture assay (V. dahliae + E. nigrum); (b) loading plot for the OPLS-DA model; (c) OPLS-DA t[1]/to [1] score plot performed on C. acutatum lipid extracts cultivated in mono- and dual-assay; mono, mono-culture assay (C. acutatum); dual, dual-culture assay (C. acutatum + E. nigrum); (d) loading plot for the model, colored according to the correlation-scaled vector (p(corr)). The variables indicate the chemical shift value (ppm) in the ¹H NMR spectra.

Figure 10. Variation in discriminating fatty acid contents between mono- and dual-culture assay for aqueous extracts; (a) E. nigrum; (b) V. dahliae; (c) C. acutatum. Signif. codes ‘***’ 0.001 ‘**’ 0.01.

Table 1. The reference isolates of Epicoccum spp. and Verticillium spp. used to construct the multilocus phylogenetic tree.

Species	Strain Identification	GenBank Accession Number		References
Species	Strain Identification	ITS	Tub2	References
Epicoccum nigrum	CBS 140523	MN972952	MN983966	[51]
E. nigrum	CBS 125.82	FJ426995	FJ427106	[51]
E. poae	LC:8160	KY742113	KY742355	[51]
E. poae	LC:8162	KY742115	KY742357	[51]
E. layuense	E22	MH643920	MH643930	[51]
E. layuense	E23	MH643921	MH643931	[51]
E. mackenziei	MFLUCC16-0335	KX698039	MW186824	[51]
E. dendrobii	SMEL1	MT020087	MT024597	[51]
Gibellulopsis nigrescens	PD595	JN187976	-	[48]
Verticillium albo-atrum	PD670	JN187990	-	[48]
V. albo-atrum	PD748	JN188017	-	[48]
V. dahliae	PD332	HQ206718	-	[52]
V. dahliae	SDV1025	KC834733	-	[48]
V. dahliae	CBS 381.66	KT362918	-	[53]
V. longisporum	PD687	HQ206893	-	[48]
V. longisporum	PD730	HQ206920	-	[48]
V. nubilum	IMI278734	AY935948	-	[48]

Table 2. Morphological characteristics of Verticillium dahliae and Epicoccum nigrum observed on PDA.

Fungal Species	Colony	Conidia
Verticillium dahliae (ER 1357)	Initially creamy white, it becomes dark, compact and appressed with age	Hyaline, cylindrical to oval, average length 4.4 ± 1.23 µm (max 6.0 µm)
Epicoccum nigrum (RD4C)	Initially white with cottony mycelium, it turns reddish-orange after 14 days; the reverse of the colony produces orange pigments	Globose to subglobose or pyriform, some multicellular; size: 14.5–26.4 µm in length and 15.9–28.4 µm in width

Table 3. Chemical shift and assignments of main peaks in the ¹H NMR spectra of D₂O extracts of E. nigrum, V. dahliae and C. acutatum. (d: doublet; m: multiplet; s: singlet; t: triplet; q: quartet).

Compound	Assignment	¹H (ppm, Multiplicity)
Leucine	CH₃, CH	0.96 (d), 1.70 (m)
Isoleucine	CH₃, CH	0.90 (t), 1.01 (d)
Valine	CH₃,CH	0.98 (d), 1.05 (d)
Lactate	CH₃,CH	1.33 (d), 4.16 (q)
Threonine	γ CH₃, αCH	1.33 (d), 4.25
Alanine	CH₃,CH	1.48 (d), 3.79 (m)
Acetate	CH₃	1.92 (s)
4 amino-butyrate		1.9, 2.29 (t), 3.01
Glutamate	γ CH_2, βCH₂	2.07 (m), 2.36 (m)
Glutamine	γ CH_2, βCH₂	2.14 (m), 2.46 (m)
Succinate	CH₂/CH₂	2.41 (s)
Malonate	CH₂	3.12 (s)
Ethanolamine		3.14
Choline	N(CH₃)₃	3.20 (s)
Phosphocholine	N(CH₃)₃	3.22 (s)
Mannitol	CH-1	3.68, 3.76, 3.80, 3.86
Trehalose	CH	5.20 (d)
β-Glucose	CH	4.66 (d)
α-glucose	CH-2/CH-6; CH-3/CH5	5.24 (d)
Tyrosine	CH-2/CH-6; CH-4; CH-3/CH5	6.9 (m), 7.20 (m)
Phenylalanine		7.3 (m), 7.38 (m), 7.42 (m)
Adenosine	CH-7; CH-4	8.2, 8.3
Tryptophan		7.53 (d), 7.72 (d)
UDP glucose		7.9, 5.95
Fumarate	CH	6.54 (s)
Formate	CH	8.46 (s)
Nicotinate		8.9, 8.6, 8.25, 7.5
Trigonelline		9.10, 8.84, 8.08

Table 4. Chemical shift and assignments of main peaks in the ¹H NMR spectra of CDCl₃/CD₃OD extracts of E. nigrum, V. dahliae, C. acutatum.

Compound	Assignment	¹H ppm
Sterol		0.63
Short chain length FA	CH₃	0.82–0.86
FA except ω-3	CH₃	0.88
All FA	-CH₂- =CH-CH COOCH₂CH₂ COOCH₂	1.26 1.31 1.55–1.66 2.32–2.37
UFA	CH₂CH=CH -CH=CH-	1.99–2.10 5.30–5.42
DUFA ω-6	CH=CH-CH₂-CH=CH	2.78
PUFA	CH=CH-CH₂-CH=CH	2.81
Phosphatidylcholine (PC)	-N(CH₃)₃	3.22
TAG	-CH₂-	4.17 4.31 5.26
DAG (sn1,2) DAG (sn1,2; sn2,3)	-CH₂- HO–CH₂–CH–	4.18 4.35 3.68 5.10

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Angilè, F.; Riolo, M.; Cacciola, S.O.; Fanizzi, F.P.; Santilli, E. Evaluation of the Effects of Epicoccum nigrum on the Olive Fungal Pathogens Verticillium dahliae and Colletotrichum acutatum by ¹H NMR-Based Metabolic Profiling. J. Fungi 2025, 11, 129. https://doi.org/10.3390/jof11020129

AMA Style

Angilè F, Riolo M, Cacciola SO, Fanizzi FP, Santilli E. Evaluation of the Effects of Epicoccum nigrum on the Olive Fungal Pathogens Verticillium dahliae and Colletotrichum acutatum by ¹H NMR-Based Metabolic Profiling. Journal of Fungi. 2025; 11(2):129. https://doi.org/10.3390/jof11020129

Chicago/Turabian Style

Angilè, Federica, Mario Riolo, Santa Olga Cacciola, Francesco Paolo Fanizzi, and Elena Santilli. 2025. "Evaluation of the Effects of Epicoccum nigrum on the Olive Fungal Pathogens Verticillium dahliae and Colletotrichum acutatum by ¹H NMR-Based Metabolic Profiling" Journal of Fungi 11, no. 2: 129. https://doi.org/10.3390/jof11020129

APA Style

Angilè, F., Riolo, M., Cacciola, S. O., Fanizzi, F. P., & Santilli, E. (2025). Evaluation of the Effects of Epicoccum nigrum on the Olive Fungal Pathogens Verticillium dahliae and Colletotrichum acutatum by ¹H NMR-Based Metabolic Profiling. Journal of Fungi, 11(2), 129. https://doi.org/10.3390/jof11020129

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Evaluation of the Effects of Epicoccum nigrum on the Olive Fungal Pathogens Verticillium dahliae and Colletotrichum acutatum by ¹H NMR-Based Metabolic Profiling

Abstract

1. Introduction