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Article

High-Throughput Sequencing Reveals Fungal Microbiome of Apricots Grown Under Organic and Integrated Pest Management Systems

by
Milan Navrátil
1,
Dana Šafářová
1,
Radek Čmejla
2,
Martin Duchoslav
3 and
Jiří Sedlák
2,*
1
Department of Cell Biology and Genetics, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
2
Research and Breeding Institute of Pomology Holovousy Ltd., Holovousy 129, 508 01 Holovousy, Czech Republic
3
Department of Botany, Faculty of Science, Palacký University Olomouc, Šlechtitelů 27, 783 71 Olomouc, Czech Republic
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(17), 1825; https://doi.org/10.3390/agriculture15171825
Submission received: 10 July 2025 / Revised: 22 August 2025 / Accepted: 25 August 2025 / Published: 27 August 2025
(This article belongs to the Special Issue Strategies to Improve the Security and Nutritional Quality of Crop Species—2nd Edition)
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Abstract

Apricots are affected by many abiotic and biotic factors that could negatively impact their vitality and yield, leading to branch and tree dieback. Knowledge of the microbiome composition is key to choosing the optimal measurement strategy. The effect of the two different growing systems, i.e., organic (ORG) and integrated pest management (IPM), on the apricot fungal microbiome was studied. The inner bark was used to isolate DNA, and the present fungi were analyzed using a metagenomics high-throughput sequencing (HTS) profiling approach of the data obtained based on the Illumina sequencing of the ITS1-ITS2 amplicons of the 18S rRNA gene. Of the 20 analyzed samples, Ascomycota was the dominant phylum, and Dothiomycetes was the most abundant. Basidiomycota was the less frequent, with Tremellomycetes being the predominant within this phylum. PCA analysis showed the complete separation of the samples obtained from the orchards grown under the ORG and IPM systems. Cladosporia, Alternaria, Aureobasidium, and Visniacozyma were detected in all samples, but they dominated the IPM samples. Filobasiadiales were recognized as an indicator species for ORG management, while Caliciales, Lecanorales, Lichinales, Mycosphaerellales, Myriangiales, Phacidiales, Teloschistales, and Thelebolales were identified as indicator species for IPM management. Based on the order and genus levels, a significantly higher fungal microbiome richness was detected in the ORG samples. This could be connected to the environmentally beneficial growing system applied in the orchard, but it is impossible to assess the risk of trunk disease development or premature apricot tree decline.

1. Introduction

Apricots are among the most popular and important stone fruits in Europe. Over the last decade, EU apricot production has ranged from 400,000 to 680,000 tons, mainly depending on spring frosts [1]. The top EU producers are Italy (projected to produce 214,282 tons in 2024), Spain (134,352 tons), Greece (87,500 tons), and France (87,852 tons) [2]. In the Czech Republic, apricot orchards cover 597 ha, yielding 6536 and 5413 tons in 2022 and 2023, respectively [3].
Healthy fruit trees are prerequisite for sustainable and profitable crop production. The health and yield of apricot trees (Prunus armeniaca L., family Rosaceae) can be affected by many biotic and abiotic stresses, with various fungal pathogens playing an important role. Knowledge of these pathogens and their detection are necessary for successfully managing orchards and avoiding losses in the quality and quantity of harvested fruit. Historically and epidemiologically, studies have primarily focused on various fungi of the genera Phytophthora, Verticillium, Cytospora, Monilia, Eutypa, Apiognomonia, Venturia, and Wilsonomyces, which cause most significant damage and/or disease. From a grower’s perspective, the most important fungal diseases are cytospora canker, caused by Cytospora cincta and C. leucostoma, and eutypa dieback, caused by Eutypa lata. All fungal infections exhibit similar symptoms, including canker, gummosis, wood and phloem necrosis, and branch dieback. These infections have been noted not only in apricots but also in other prunus species such as cherries, peaches, and plums. They have been reported worldwide, including in many European countries [4,5,6]. Next in economic importance is Monilia spp., which causes a wide range of symptoms such as brown rot blossom, twig blight, and fruit rot. Less commonly, they cause branch decline. Infection results in reduced tree vitality, lower yield, and lower production quality [7]. Phytophthora cactorum, the causal agent of apricot crown and root rot, is a soil-borne pathogen with a very wide host range, widespread in temperate regions [8]. Verticilium albo-atrum and V. dahlia are the causal agents of verticilium wilt, which manifests as yellowing and wilting of leaves on individual branches or whole trees, and bark necrosis [9].
Apiognomonia erythrostoma and Venturia carpophila cause brown and/or black spots on leaves and fruit, as well as premature leaves and fruit drop, and fruit gummosis. Similarly to the above-mentioned pathogens, apricot shot hole disease, caused by Wilsonomyces carpophilus, could reduce the tree vitality, longevity, and productivity of apricot orchards [10,11,12].
Fruit growers in Europe have reported frequent damage to apricot orchards, resulting in tree decline (apoplexy) until now [13,14,15]. The initial symptoms resemble those caused by the ‘Candidatus Phytoplasma prunorum’ (leaf yellowing, rolling, and branch decline) and Pseudomonas syringae pv. syringae (blossom blast, dried leaves attached to trees, branch dieback, bark necrosis, and trunk cankers) and the fungi Cytospora spp. (canker, gummosis, and decline) [16]. Apricot decline appears to be the result of abiotic and biotic factors that colonize and ultimately destroy tree tissues. These fungal infections often create initial infection courts and physiological stress that facilitate bacterial colonization, particularly by Pseudomonas syringae, which emerges as a pivotal bacterial pathogen in this complex disease system [17,18,19,20].
Apricots are susceptible to many fungal pathogens, which are directly associated with premature apricot tree decline. Few studies have examined the fungal microbial diversity of apricot trees. Using biological isolation methods followed by morphological and/or molecular identification and phytopatogenicity tests, the fungi were found to belong to the following orders: Botryosphaeriales (genera Aplosporella, Botryosphaeria, Diplodia, Dothiorella, Lasiodiplodia, and Neofusicoccum), Calosphaeriales (Calosphaeria), Coniochaetales (Coniochaeta), Diaporthales (Cytospora, Diaporthe, and Leucostoma), Dothideales (Aureobasidium), Helotiales (Monilia), Leotiales (Collophora), Phaeomoniellales (Phaeomoniella), Pleosporales (Coniothyrium), Togniniales (Phaeoacremonium and Togninia), and Xylariales (Eutypa and Eutypella) [21,22,23,24,25,26,27,28,29,30,31].
New techniques based on high-throughput sequencing (HTS) allow us to identify complete fungal microbiomes. This information obtained can then be used to analyze community relationships in and to elucidate new pathogens, as well as synergism or antagonism among community members. Nevertheless, subsequent biological isolation of the detected fungal pathogens and their pathogenicity testing to confirm pathogenicity and/or relationships remain essential. Currently, HTS techniques are being applied to study viral communities and identify new viruses, such as the apricot vein clearing-associated virus, mume virus A, Prunus virus T, and plum bark necrosis stem pitting-associated virus, as well as to analyze the bacterial microbiome [32,33,34,35].
In the Czech Republic, the apricots are grown using two managements systems: integrated pest management (IPM) and organic management (ORG). These systems are strictly regulated by EC Directives and national measures. IPM is the standard growing system used for intensive production orchards and must comply with the EC Directive 2009/128/EC and national measures, such as Decree No. 205/2012 Coll. and SISPO guidelines [36,37,38]. IPM prioritizes biological, cultural, and mechanical methods of orchard management and allows for chemical protection under defined conditions and thresholds. In contrast, the ORG is must adhere to EU Regulation 2018/848, as well as Czech Act No. 242/2000 Coll. and Decree No. 16/2006 Coll. [39,40]. This system strictly limits inputs to approved natural substances, such as copper, mineral oils, and microbial preparations [38,40]. The main benefit of this system is reducing the negative chemical and harmful ecological impact of the fruit production on the environment, which leads to sustainable food production [41].
This study aimed to compare the taxonomic composition and biodiversity characteristics of the fungal microbiome of apricots, focusing on to the trees showing symptoms of the dieback or apoplexy in important production areas of the Czech Republic maintained under two contrasting management systems: ORG (sustainable) and IPM.

2. Materials and Methods

2.1. Sample Collection

The study was focused on apricot-growing regions in the Czech Republic: south Moravia and Eastern Bohemia. The orchards in these regions had been managed under IPM or ORG systems for a long time. Orchards trees around 15 years old were visually inspected and samlples were collected from 2018 to 2020. Branches with phloem and/or xylem necrosis were collected. The sampled trees were randomly selected, as the trees in the orchards mainly exhibited similar habitus. At least one 30–50 cm long branch with fully developed wood and phloem parts was cut off and individually packed in a plastic bag. A total of 20 samples were collected from both types of orchards. Two to six samples per orchard were selected from each orchard for detailed metagenomic analyses (see Table 1).

2.2. Extraction of Nucleic Acids

Approximately 1 cm3 of stem segment (xylem and phloem) was cut out from around the border of damaged/healthy tissue, with 5–10 samples per branch. The samples were lyophilized as a mixed sample for 48 h using a Power Dry PL3000 (Heto-Thermo Fisher Scientific, Waltham, MA, USA), then stored at −70 °C until DNA isolation. Prior to nucleic acid isolation, samples were homogenized for 60 s at 1500 rpm using a Mixer Mill MM 200 (Retsch GmbH, Haan, Germany). Total DNA was extracted from 100 mg of homogenized tissue using a Plant/Fungi DNA Isolation Kit (Norgen Biotek Corp., Thorold, ON, Canada), following the manufacturer’s instructions.
Total RNA was extracted from 50 mg of tissue and homogenized as described above, using a Ribospin Plant isolation kit (GeneAll Biotechnology, Co., Ltd., Seoul, Republic of Korea) according to the manufacturer’s instructions. The quality of the isolated DNA or RNA was assessed using a Nanodrop 1000 (Thermo Fisher Scientific, Waltham, MA, USA).

2.3. Library Preparation and Next-Generation Sequencing

Metagenomic HTS profiling was performed commercially by SEQMe, s.r.o. (Dobříš, Czech Republic). In brief, isolated DNA was amplified using the universal primers ITS1F (5′-GGTCATTTAGAGGAAGTAA-3′) and ITS4R (5′-TCCTCCGCTTATTGATATGC-3′), which target the eukaryotic ITS1+ITS2 region of the 18S rRNA gene. The purified amplicons were then sequenced in a multiplex run of 250 bp paired reads using a NovaSeq6000 (Illumina, San Diego, CA, USA). The raw data were automatically processed using the Basespace cloud interface (Illumina, San Diego, CA, USA) with the default settings. Base calling, adapter clipping, and quality filtering were performed using bcl2fastq v2.20 Conversion Software (Illumina, San Diego, CA, USA).
The amplicon sequences were deposited in the SRA archive at NCBI under the project number PRJNA1287318.

2.4. Bioinformatic Analysis

The raw data were paired, and the paired reads were trimmed using BBDuk with a quality score of 20. The final datasets were mapped against the Prunus chloroplast sequence (Acc. No. NC_043901) to remove the plant-derived reads (Q30, low sensitivity, and default parameters). Clean reads were assembled using the Geneious assembler (Custom Sensitivity parameters: minimum overlap of 100, minimum overlap identity of 98%, word length of 24, maximum mismatch per read of 2, index word length 24, allow gaps—maximum per read of 1, maximum gap size of 1). Obtained consensus sequences and unique reads were identified using MegaBLAST against the microbiome NCBI database. Contigs were de-duplicated, and the obtained operational taxonomic units (OTUs) were clustered into a dataset. The dataset was subsequently analyzed using the Geneious Sequence Classifier in default High Sensitivity mode (to cover higher variability) with the following parameters: 99% identity for species, 95% for genus, and 90% for the family (the default setting) to annotate the individual sequences. All bioinformatics analyses of HTS data were performed using Geneious Prime v.2021.0 (Biomatters Ltd., Auckland, New Zealand).

2.5. Data Analyses

Two taxonomic resolutions (order level and genus level) were prepared and analyzed separately. The input data (paired reads) represented a proportional representation of each taxon within the sample. Because of percentages summing to 100, multivariate data represent compositional data. To eliminate spurious correlations within the dataset, a centered log-ratio transformation was applied to the compositional data before multivariate analysis. This transformation is equivalent to a simple log transform followed by subtraction of the mean. First, a principal component analysis (PCA) was performed on the variance–covariance matrix of the transformed data [42]. The number of significant components was estimated by comparing the estimated and random models using the Broken Stick method [43]. Samples were assigned to two groups according to the management practice applied at the locality (IPM = integrated pest management, ORG = organic pest management), and their centroids were passively projected into the resulting ordination plot. Hierarchical clustering was applied to the transformed data (i.e., the order-level dataset) using the two-way unweighted pair-group average (UPGMA) algorithm with the Euclidean distance metric [42]. The support of each node in the sample dendrogram was estimated by bootstrapping with 999 bootstrap replicates. The numbers at the nodes represent the percentage of replicates in which each node is still supported, if the percentage exceeds 90.
To identify the taxa indicative of the given sample groups, an Indicator Species Analysis (IndVal) was carried out on the original absolute frequency matrix for each taxon. This resulted in an indicator value ranging from 0 to 100% for taxon i in group j. The statistical significance of the indicator values (praw) was estimated using 999 random sample permutations of groups. The p values were additionally Bonferroni-corrected (pBonf), representing a highly conservative approach.
For each sample, alpha diversity indices were calculated, using abundance data and the number of reads representing individuals: the number of taxa (S), the Simpson index (1-D), which measures the evenness of taxa within samples, and the Shannon index (H), which takes into account the number of reads as well as the number of taxa. We compared the indices between the ORG and IPM groups using a randomization test with 10,000 permutations and t-statistics for unequal variances in the groups. All calculations were performed using PAST 4.10 [44].

2.6. RT-QPCR Detection of Microbial Pathogens—‘Candidatus Phytoplasma Prunorum’, Pseudomonas Syringae, Cytospora sp.

Additional screening was performed to detect the presence of the following pathogens, which were considered important agents that cause the apricot dieback: ‘Candidatus Phytoplasma prunorum’ and Pseudomonas syringae bacteria, as well as Cytospora sp. in the sampled trees.
To increase the sensitivity of these detections, the pathogens were tested using RNA, since ribosomal RNA is highly expressed. Up to 1 μg of total RNA isolated was used for cDNA preparation with M-MLV Reverse Transcriptase (Invitrogen, Waltham, MA, USA) and Primer Random (dN)6 (Roche AG, Basel, Switzerland), following the manufacturer’s recommendations. Briefly, up to 1 µg of total RNA was mixed with 100 ng random primers, 0.5 mM dNTP, and DEPC-treated water to each a final total volume of 12 µL. The mixture was heated to 65 °C for 5 min and immediately chilled on ice. Meanwhile, a mixture of RT buffer (1×), RiboSafe RNase Inhibitor (40 U), DTT (10 mM) and DEPC-treated water was prepared to a final total volume of 7 µL. This mixture was added to the previous one, and the resulting mixture was incubated at 37 °C for 2 min. Finally, M-MLV RT (200 U) was added. Reversion transcription was performed by incubating at 25 °C for 10 min, followed by 50 min at 37 °C. The enzyme was inactivated by heating the reaction at 70 °C for 15 min. The Nad5 gene was used as an internal control to check the RNA quality and the effectiveness of RT-qPCR. RT-qPCR was performed for all pathogens using qPCR 2× Blue Master Mix (Top-Bio, Vestec, Czech Republic) and a Rotor-Gene Q PCR cycler (Qiagen, Hilden, Germany). Primers, probes, and their concentrations shown in Table 2 were adopted from a previous publication [45]. The cycling profile was as follows: one cycle at 94 °C for 5 min followed by 40 cycles at 94 °C for 20 s, 58 °C for 20 s, and 72 °C for 20 s. The threshold cycle (Ct) cutoff value for positive samples was ≤32.9.

3. Results

A total of 20 samples were analyzed: 7 obtained from apricot trees grown in the 2 ORG orchards and 13 from the 2 orchards under IPM (Table 2). After analyses of all the reads obtained, Illumina sequencing using the NovaSeq6000 yielded 2,849,805 high-quality fungal sequences, with an average of 142,340.25 sequences per sample (Table S1).

3.1. Composition of Fungal Microbiome

Three different phyla were identified: Ascomycota, Basidiomycota, and Mucoromycota. Figure 1 and Table S2 show the distributions of the most abundant fungal orders and genera.
Ascomycota was the dominant fungal phylum in all samples, accounting for 46.2–95.7% of the fungal reads, with an average of 74.2%. The most abundant Ascomycota class was Dothideomycetes (69.2%), followed by Sordariomycetes (13.4%), Lecanoromycetes (6.9%), Eurotiomycetes (5.4%), Leotiomycetes (4.8%), Candelariomycetes (0.2%), and Arthoniomycetes, Lichinomycetes, Pezizomycetes, Saccharomycetes, Taphrinomycetes (less than 0.1% each). The most frequently identified order was Pleosporales (905,353 reads or 37.8% of the Ascomycota phylum orders), followed by Cladosporiales (301,088; 12.6%), Mycosphaerellales (222,134; 9.3%), and Dothideales (220,358; 9.2%), all of which are in the class Dothideomycetes (Table S2).
In the Basidiomycota phylum, which on average accounted for 14.4% of the reads with a wide range from 0.7 to 46.5% per sample, Tremellomycetes was the dominant class, representing 91.1%. The relative abundance of Cystobasidiomycetes reads reached 7.8%, Microbotrymycetes 0.7%, Agaricomycetes and Agaricistilbymycetes reached 0.2% each, and Malasseziomycetes, Pucciniomycetes, Ustilaginomycetes, and Atractiellomycetes reached less than 0.1% each. The most abundant order was Tremellales (class Tremellomycetes; 267,245 reads; that is 86.3% of the Basidiomycota phylum orders). Less abundant orders were Cystobasidiales (class Cystobasidiomycetes; 19,302; 6.2%) and Filobasidiales (class Tremellomycetes; 13,579; 4.4%) (Table S2).
The only sporadic reads found were classified as members of the Mucoromycota, and unclassified reads represented 5.5% of all reads.

3.2. Comparison of Identified Fungal Orders and Genera Among Samples

A total of 69 orders and 245 genera were identified in the 20 analyzed samples. The most common fungal taxa across all samples were Pleosporales (27.98%), identified in twenty out of twenty samples (20/20), followed by Tremellales (12.39%, 20/20), Dothideales (9.36%, 20/20), Cladosporiales (12.57%, 19/20), and Mycosphaerellales (7.27%, 19/20). Other orders with an average frequency of more than 1% of fungal reads were Lecanorales (3.90%, 18/20), Diaporthales (3.12%, 17/20), Chaetosphaeriales (3.03%, 4/20), Eurotiales (2.79%, 10/20), Phacidiales (2.25%, 18/20), Hypocreales (1.95%, 16/20), Xylariales (1.35%, 18/20), and Chaetothyriales (1.02%, 15/20) (Table S2).
The most abundant genera in all samples were Cladosporium (12.57%, 20/20), Alternaria (11.66%, 20/20), Aureobasidium (9.06%, 20/20), and Vishniacozyma (7.34%, 20/20). These were followed by the genera Cytospora (2.66%, 14/20), Uzbekistanica (2.09%, 15/20), Pseudopithomyces (1.75%, 18/20), Geosmithia (1.59%, 6/20), Epicoccum (1.57%, 20/20), Talaromyces (1.36%, 5/20), and Nothophoma (1.33%, 18/20) (Table S2).

3.3. Comparison of Fungal Communities Between Organic and Integrated Pest Management Orchards

PCA analysis of the apricot microbiome data, conducted using order-level taxonomic resolution, revealed a complete separation of samples into two distinct groups corresponding to the management categories (ORG and IPM) along the first component (Figure 2A). The first component explained 29.3% of the total variation, and the broken stick model suggested that other components were non-significant. Six taxa were most positively correlated with the first component (r > 0.6), i.e., Cladosporiales, Dothideales, Filobasidiales, Sporidiobolales, Tremellales, and Malasseziales. On the other hand, nine taxa were strongly negatively correlated with the first component (r < −0.6), i.e., Caliciales, Eurotiales, Leotiales, Lichinales, Mycosphaerellales, Myriangiales, Phacidiales, Teloschistales, and Thelebolales (Figure 2B). The second component explained only 11.1% of the total variation, separating two samples (101N and 103N, from the same locality) from the rest (Figure 2A). These two samples were characterized by an increased proportion of several taxa, including Chaetosphaeriales, Botryosphaeriales, Eurotiales, and Sordariales (Figure 2B).
Two-way UPGMA clustering grouped the ORG and IPM samples into separate clusters with high bootstrap support. Within the IPM cluster, two subclusters are recognizable. Sample 110BN is distinct from the other IPM samples due to its singular presence of several taxa (e.g., Atractiellales, Pezizales, Rhytismatales) (Figure 3). Within the ORG cluster, each locality was clustered separately. Considering the clustering of taxa, four interpretable clusters were identified. Cluster I included the most frequent taxa in the dataset (Pleosporales, Dothideales, Cladosporiales, Tremellales, and unclassified), which clearly showed their dominance in the IPM samples, contrasting with the ORG samples. Cluster II comprised taxa with an intermediate overall frequency that showed a different preference for IPMs (e.g., the Filobasidiales and Cystobasidiales) or ORGs (e.g., the Phacidiales, Lecanorales, and Mycosphaerellales). Cluster III included taxa with low overall frequencies and usually random occurrences within the dataset. Cluster IV included taxa that were less frequent and typically occurred in ORG samples (e.g., Eurotiales, Thelebolales, and Myrianglales) (Figure 3).
Indicator species analysis (IndVal) identified 7 and 20 taxa as indicators of IPM and ORG management, respectively (Figure 3). After applying the Bonferroni correction, one taxon remained significant as an IPM indicator (Filobasidiales), and eight taxa remained significant as ORG indicators (Caliciales, Lecanorales, Lichinales, Mycosphaerellales, Myriangiales, Phacidiales, Teloschistales, and Thelebolales). The indicator values, raw p values, and Bonferroni-corrected p values for each taxon and each group are provided in Table S3.
Biodiversity measures of the apricot fungal microbiome were compared between the two management groups. Taxonomic richness, Shannon diversity, and the Simpson index were significantly higher in the ORG group compared to the IPM group (randomization test, all p < 0.05). The ORG samples were approximately ten orders richer per sample than the IPM samples (Figure 4A). The ORG samples were also more diverse (Figure 4B) and had more evenly distributed taxa (i.e., less dominant) than the IPM samples (Figure 4C).
We also compared the biodiversity measures of the apricot microbiome between two management groups using genus-level resolution, and we obtained results that were only partly identical to those using order-level resolution. Taxonomic richness, the Shannon index, and the Simpson index were significantly different between the ORG and IPM groups (randomization test, all p < 0.05). While the ORG samples were, on average, approximately twice as rich as the IPM samples, the IPM samples showed less dominance, i.e., they were more even. However, this was due to a disproportionately high percentage of one taxon (‘unclassified taxa’), which constituted an average of 69.3% of the total reads in the ORG samples and 19.1% in the IPM samples.
PCA of the apricot fungal microbiome data, with taxonomic resolution at the genus level, showed an almost identical pattern to that with order-level taxonomic resolution: a complete separation of the samples into two clusters corresponding to the management categories along the first component. Samples from ORG were on the right side of the ordination diagram, and samples from the IPM were on the left (Figure 5). Twelve taxa showed the strongest positive correlations with the first component (r > 0.6), (Table S3). These taxa are considered typical of ORG samples. Eight taxa showed the strongest negative correlations with the first component (r < −0.6), (Table S3). They are considered as typical of IPM samples. The pattern along the second axis is mainly driven by two outlier samples (AK230N, AK231N), which differ primarily in the frequencies of the ten taxa that strongly correlated with the second component (r > |0.6|) (Table S3).
Indicator species analysis (IndVal) identified 13 taxa and 69 as indicators of IPM and 69 as indicators of ORG management (Table S4). After Bonferroni correction, 1 taxon remained a significant indicator of IPM (Vishniacozyma), and 2 taxa remained indicators of ORG (Constantinomyces, Neophaeococcomyces).

3.4. PCR Detection of Pathogenic ‘Candidatus Phytoplasma Prunorum’, Pseudomonas Syringae and Cytospora sp.

In parallel with the HTS analysis of the fungal microbiome, PCR detection of important bacterial pathogens suspected to be involved in apricot dieback was performed. These pathogens include the ‘Candidatus Phytoplasma prunorum’ and the pathogenic bacterium Pseudomonas syringae. At the same time, the presence of Cytospora spp. was tested by PCR to compare the two methods. ‘Candidatus Phytoplasma prunorum’ and Pseudomonas syringae were only detected in the orchards in southern Moravia (Martinice, Lednice, and Velké Pavlovice), regardless of whether the apricot orchards were maintained in the IPM or ORG system. The situation was different for the pathogenic fungus Cytospora spp., which was prevalent in all the sites studied in both the Bohemian and Moravian regions (Table 3).

4. Discussion

These divergent regulatory systems are expected to exert distinct selection pressures on apricot-associated microbial communities in the phyllosphere, resulting in different risks of developing various disease symptoms’ development and premature tree decline.
Thus, we determined the composition of the fungal communities associated with apricot tree branches (inner bark) grown in an orchard using IPM and ORG management practices using an amplicon metagenomic approach based on the ITS2 region of fungal rDNA. As expected, the Ascomycota was the dominant phylum (74.2%), followed by Basidiomycota, with Mucoromycota comprising a small minority. Apricot trees were found to have a high microbial community diversity, with a total of 245 genera being identified. The four most frequently detected genera were Cladosporium, Alternaria, Aureobasidium and Vishniacozyma, representing 40.63% of the total relative abundance of all analyzed sequences. These results reflect observations on the structure of the fungal communities on the other fruit trees. A similar taxonomic structure of fungal phyla and genera was previously described on the bark and leaves of apple trees [46,47]. The highest abundance of three of these fungi, Cladosporidium, Alternaria, and Aureobasidium, was also found on harvested apple fruits [48]. These fungi have been reported to commonly occur on cherry fruits after flowering [49] and in the endophytic fungal microbiome of the annual shoots of the Manchurian apricot, Prunus mandshurica [50]. Interestingly, no difference in their relative abundance was found for apples. However, a slight upward trend was observed for apricots in the IPM system for all of these fungi.
The Aureobasidium species, typically A. pullulans, occur naturally on fruit surfaces and are generally considered to be good biocontrol agents against post-harvest fungal diseases in fruits such as apricots, peaches, cherries, apples, and grapes [51,52,53,54,55,56,57]. Interestingly, A. pululans was found in wood samples taken from the branches and trunks of stone fruit trees (almonds, apricots, cherries, and peaches), showing symptoms of decline and dieback in Iran [58]. However, the significance of this endophyte is not discussed further. In contrast, A. pululans is used in the agricultural practice to actively protect various crops, mainly grapevines and, more recently, apricots [53,56,59,60].
Alternaria sp. and Cladosporium sp., on the other hand, are relevant fruit pathogens. Both Alternaria alternata and Cladosporium herbarum are both involved in the rotting of harvested stone fruits. In addition, Alternaria spp. has also been associated with shot hole disease, which damages buds and induces lesions on the twigs of stone fruits, including apricots, during winter. Cladosporium carpophilum causes peach scab on apricots, resulting in necrotic lesions on twigs that can lead to dieback. Leaf spots develop on the infected leaves and the affected tissue may dry out and fall off. Leaves affected by scab usually drop prematurely as well [21,61,62,63].
Vishniacozyma sp. often co-occurs with other fungi on fruit such as apples, grapes, nectarines, and apricots. Members of this genus are not pathogenic and are considered as biocontrol organisms, along with Aureobasidium, Sporobolomyces, and Filobasidium [64,65,66].
Currently, there is limited knowledge about the composition of the fungal fruit tree microbiomes. Studies comparing the microbiomes of perennial plants such as fruit trees grown under organic or integrated conditions are scarce. The few existing studies mainly focus on grapevines and pome fruits, such as apples and pears. They mostly compare the rhizosphere, which consists of fungi that directly affect plant nutrition and growth, and on the phyllosphere, which consists of fruit microbes that affect the composition and flavor of fruits, and their post-harvest storage [67,68,69,70,71,72,73].
The taxonomic richness and the Simpson and Shannon diversity indices indicate that the diversity of fungi varies significantly between apricots under different management modes. This is to be expected as a consequence of the different measurement strategies applied. However, this has not been studied or reported often for the leaves and shoots. Contradictory results were found for the grapevines. No significant difference in the total number of fungal species was found in the bark, leaves, or grapes of the grapevines grown under organic or integrated pest management in Germany. However, the opposite situation was identified in Portugal or Italy [74,75]. The composition and abundance of fungal endosymbiots should be evaluated as a complex reaction to the local conditions, including the management system, cultivars, and local abiotic factors, at least for the grapevine. Similarly, the different protection measures applied in organic/ecological growth systems were repeatedly directly connected to higher taxon diversity in the fungal microbiome of flowers and fruits, in the case of apples, pears, and cranberries, as well as the positive effect of Alternaria presence [47,76,77,78].
During our study, we observed other taxa with fungal trunk diseases: Paraphoma (Pleosporales), Diplodia (Botryosphaeriales), Phaeoacremonium (Tognitiales), Coniochaeta (Coniochaetales), and Nectria (Hypocreales) occurred in ORG orchards, in addition to the demonstrated higher overall taxonomic richness. Other beneficial fungi belonging to the genera Bullera, Cryptococcus, Dioszegia, Erythrobasidium, Sporobolomyces, Arthrinium, Aureobasidium, Rhodotorula, and Saccharomyces were also identified in both ORG and IPM orchards. The presence of taxa that affects apricot trees and fruit production negatively or positively is a topic of repeated discussion, mainly in association with the apricot tree dieback, specifically apoplexy. In Europe, apricot dieback has been linked to several causal agents of fungal trunk diseases belonging to the genera Colletotrichum, Collophorina, Diplodia, Dothiorella, Phaeoacremonium, Schizophyllum, Botryosphaeria, Diplodia, Dothiorella, Calosphaeria, Coniochaeta, Diaporthe, Collophorina, and Phaeoacremonium [22,25,27,65,79,80,81,82]. The situation in the Czech Republic and the former Czechoslovakia is no different. There, the most abundant genera and/or causal agents of fungal trunk detected are Dactylonectria, Biscogniauxia, Thelonectria, Eutypa, Dothiorella, Diplodia, Diaporthe, Ilyonectria, Cadophora, Cryptovalsa, Botryosphaeria, Irpex lacteus, Fomes fomentarius, Calosphaeria pulchella, and Collophora [15,45,83,84]. In Slovakia (formerly part of Czechoslovakia), apoplexy of apricots results in annual losses of 5%, which is associated with Cytospora sp. and Monilia sp., together with the occurrence of pathogenic fungi, including Schizophyllum sp., Gnomonia sp., and Verticillium sp. [85].
The risk assessment for disease development in ORG and/or IPM orchards is complicated by the fact that various forms of bark necrosis, branch decline, and premature apricot tree decline are primarily associated with fungi, except for Cytospora spp. and Eutypa spp. Cytospora sp. was present at all of our studied localities, but was significantly more prevalent in IPM orchards. Bacterial infections by pseudomonads, especially by Pseudomonas syringae pv. syringae and ‘Candidatus Phytoplasma prunorum’, are a key factor emphasized by the various authors, too [13,86,87,88,89,90,91]. We detected both in the orchards and trees again. P. syringae pathovars demonstrate remarkable adaptability, and the ability to colonize host tissues even during dormancy periods. The production of ice-nucleation proteins by this bacterium raises the freezing point of water in plant tissues, exacerbating frost damage and creating additional entry points for primary and opportunistic fungal pathogens [92,93,94]. The resulting synergistic relationship between fungal and bacterial infections creates a destructive amplification loop. P. syringae effector proteins suppress host immune responses [95,96], enabling rapid bacterial proliferation and simultaneously weakening the tree defences against the already established fungal colonies. The interplay between diverse fungal pathogens and specialized bacterial infections frequently culminates in a cascade of symptoms progressing from localized infections to systemic failure. This represents one of the most challenging disease complexes in stone fruit production systems in agricultural businesses worldwide. These findings suggest that fungi in apricot orchards may act as primary pathogens, opportunists, or potential antagonists, thereby influencing the outcome of host–pathogen interactions. This ecological complexity further complicates the interpretation of plant immune responses and the overall risk assessment of trunk disease development in different management systems. It is important to emphasize that the current study does not allow for a definitive assessment of whether increased fungal diversity in organic (ORG) systems is associated with a higher or lower risk of trunk diseases.

5. Conclusions

This study presents new information on the fungal composition and insights into the differences in the structure of fungal microbiomes in apricots grown under integrated pest management and organic systems. Distinct fungal communities were detected for each management type. ORG orchards showed significantly higher taxonomic richness, diversity, and evenness. Indicator species analysis revealed that the order Filobasidiales was characteristic of IPM orchards, while several other orders, including Caliciales and Mycosphaerellales, were associated with ORG orchards. Fungal taxa associated with trunk diseases, such as Cytospora spp., were present in all orchards but were more prevalent in IPM sites. Their co-occurrence with pathogenic bacteria, such as Pseudomonas syringae and ‘Candidatus Phytoplasma prunorum’, suggests the existence of a synergistic disease complex that contributes to apricot decline. Stone fruit orchards comprising Prunus species are substantially affected by fungal pathogens, which act as essential components of complex microbial communities and frequently facilitate subsequent bacterial infections within the vascular tissues of host trees. Comprehensive understanding of these pathogens, including their ecological relationships, infection mechanisms, and interactions with bacterial counterparts, is crucial for developing sustainable protection strategies, particularly in colder, marginal apricot-growing regions where climate stress further increases susceptibility to disease.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agriculture15171825/s1, Table S1: List of HTS outputs assigned to fungal OTU; Table S2: List of the HTS reads classified at the order (A) and genus (B) level; Table S3: Pearson correlations of taxa with the first two PCA components, Table S4: List of indicators’ genera recognized in Indicator species analysis (IndVal).

Author Contributions

Conceptualization, M.N. and D.Š.; methodology, M.N., D.Š., R.Č. and M.D.; software, D.Š. and M.D.; validation, M.N., D.Š. and M.D.; formal analysis, M.N., D.Š. and M.D.; investigation, M.N., D.Š., R.Č. and J.S.; resources, M.N., D.Š. and J.S.; data curation, D.Š.; writing—original draft preparation, M.N., D.Š., M.D. and J.S.; writing—review and editing, M.N., D.Š., M.D., R.Č. and J.S.; visualization, M.N., M.D. and D.Š.; project administration, M.N.; funding acquisition, M.N. and J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Agriculture of the Czech Republic, National Agency for Agricultural Research, grant numbers QK1920124 and QL24010352, and by the Ministry of Agriculture of the Czech Republic, grant number RO1525.

Data Availability Statement

Amplicon sequences were deposited at the SRA archive (NCBI) under the BioProject number PRJNA1287318.

Conflicts of Interest

Authors Radek Čmejla and Jiří Sedlák are employed by the company Research and Breeding Institute of Pomology Holovousy Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Fungal community composition at order (A) and genus (B) levels. The relative abundance of each taxon in a group was calculated by dividing its total abundance in the group by the total abundance of all taxa in the group. The group Others covers taxa with an average abundance of less than 0.1%. Classification into Ascomycota (A) and Basidiomycota (B) is marked in the table legend.
Figure 1. Fungal community composition at order (A) and genus (B) levels. The relative abundance of each taxon in a group was calculated by dividing its total abundance in the group by the total abundance of all taxa in the group. The group Others covers taxa with an average abundance of less than 0.1%. Classification into Ascomycota (A) and Basidiomycota (B) is marked in the table legend.
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Figure 2. Principal component analysis of the apricot fungal microbiome data using order-level taxonomic resolution. (A) Ordination diagram of samples. Two types of management practices are visualized and colored in the diagram (red = IPM, green = ORG). The centroids of samples representing each management practice (IPM, ORG) and convex hulls for each management group are plotted in the diagram. (B) Ordination diagram of taxa. Taxa with small loadings on both axes are not visualized in the diagram.
Figure 2. Principal component analysis of the apricot fungal microbiome data using order-level taxonomic resolution. (A) Ordination diagram of samples. Two types of management practices are visualized and colored in the diagram (red = IPM, green = ORG). The centroids of samples representing each management practice (IPM, ORG) and convex hulls for each management group are plotted in the diagram. (B) Ordination diagram of taxa. Taxa with small loadings on both axes are not visualized in the diagram.
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Figure 3. Two-way UPGMA clustering (simultaneous clustering in R-mode and Q-mode) of apricot fungal microbiome data using order-level taxonomic resolution. The heat map shows the transformed abundance values in each sample (more heat indicates greater abundance). Sample color codes represent management categories (green = ORG and red = IPM). Taxa are colored by three colors according to the results of Indicator Species Analysis (IndVal) (see Table S3 for details), which identifies taxa indicative of given groups of samples (p < 0.05). Taxa colored in black represent non-indicators (p > 0.05). Taxa marked with an asterisk remained significant indicators after the application of Bonferroni correction of p. Four taxa clusters were interpreted (coded as numbers I–IV). Bootstrap support values of nodes greater than 90% are reported on the dendrogram of samples with an asterisk.
Figure 3. Two-way UPGMA clustering (simultaneous clustering in R-mode and Q-mode) of apricot fungal microbiome data using order-level taxonomic resolution. The heat map shows the transformed abundance values in each sample (more heat indicates greater abundance). Sample color codes represent management categories (green = ORG and red = IPM). Taxa are colored by three colors according to the results of Indicator Species Analysis (IndVal) (see Table S3 for details), which identifies taxa indicative of given groups of samples (p < 0.05). Taxa colored in black represent non-indicators (p > 0.05). Taxa marked with an asterisk remained significant indicators after the application of Bonferroni correction of p. Four taxa clusters were interpreted (coded as numbers I–IV). Bootstrap support values of nodes greater than 90% are reported on the dendrogram of samples with an asterisk.
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Figure 4. Boxplots of biodiversity measures of the apricot microbiome using order-level (top) and genus-level (bottom) taxonomic resolution data, which compare samples from two management groups (ORG, IPM). (A) Taxonomic richness (S), (B) the Shannon index (H), and (C) the Simpson index (1-D). Results of randomization tests, using t-statistics and unequal variances in the groups, comparing ORG and IMP for each biodiversity measure, are shown in the figure (*** p ≤ 0.001, ** 0.001 < p ≤ 0.01, * 0.01 < p ≤ 0.05). Sample color codes represent management categories (green = ORG and red = IPM).
Figure 4. Boxplots of biodiversity measures of the apricot microbiome using order-level (top) and genus-level (bottom) taxonomic resolution data, which compare samples from two management groups (ORG, IPM). (A) Taxonomic richness (S), (B) the Shannon index (H), and (C) the Simpson index (1-D). Results of randomization tests, using t-statistics and unequal variances in the groups, comparing ORG and IMP for each biodiversity measure, are shown in the figure (*** p ≤ 0.001, ** 0.001 < p ≤ 0.01, * 0.01 < p ≤ 0.05). Sample color codes represent management categories (green = ORG and red = IPM).
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Figure 5. Ordination diagram of samples (PCA) of the apricot microbiome data using genus-level taxonomic resolution. The diagram visualizes and colors two types of management practices (red = IPM, green = ORG). The centroids of the samples representing each management practice (IPM and ORG) and the convex hulls for each management group are plotted in the diagram.
Figure 5. Ordination diagram of samples (PCA) of the apricot microbiome data using genus-level taxonomic resolution. The diagram visualizes and colors two types of management practices (red = IPM, green = ORG). The centroids of the samples representing each management practice (IPM and ORG) and the convex hulls for each management group are plotted in the diagram.
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Table 1. List of sampling locations, samples, and symptoms observed.
Table 1. List of sampling locations, samples, and symptoms observed.
Sampling LocationGrowing SystemSample IDSampling DateSymptoms
Holovousy
(50.419383 N, 15.665607 E)		IPMAI362N28 June 2019Apoplexy
AI373NApoplexy
AG854N4 September 2018Branch dieback, leaf wilting
AH180N12 October 2018Leaf wilting
AH166NApoplexy
AG797N31 August 2018Stunting, premature leaf dropping
Martinice
(49.0059833 N, 16.8208667 E)		IPM109N3 May 2019Branch dieback
110BNBranch dieback
111NBranch dieback
114NBranch dieback
115NBranch dieback
117NBranch dieback
118NBranch dieback
Lednice
(48.7920883 N, 16.7951131 E)		ORGAK227N3 August 2020Stunting, bark cracking
AK228NBranch dieback
AK229NShot hole
AK230NLeaf yellowing
AK231NBranch dieback
Velké Pavlovice (48.9161167 N, 16.8181167 E)ORG101N3 May 2019Apoplexy
103NApoplexy
Table 2. Primers and probes used in the experiment.
Table 2. Primers and probes used in the experiment.
Identified MicroorganismPrimer or ProbeSequence (5’-3’)Final Concentration [µM]
‘Candidatus Phytoplasma prunorum’ForwardGCAGCTGCGGTAATACATGG0.50
ReverseGAATTCCACTTGCCTCTATCCAA0.50
ProbeAGTTCAACGCTTAACGTTGTGATGCTAT0.25
Pseudomonas syringaeForwardTCGAGCGGCAGCACGGGT0.50
ReverseAGGCCCGAAGGTCCCCTG0.50
ProbeTTGTACCTGGTGGCGAGCGG0.25
Cytospora sp.ForwardACCCAGAAACCCTTTGTGAACTTAT0.50
ReverseCCGGCGGGCCTGCTGTCC0.50
ProbeCGTTGCCTCGGCGCTGGCTGC0.25
Table 3. List of samples and pathogen detection.
Table 3. List of samples and pathogen detection.
Sample IDGrowing SystemPCR
ESFY		PCR
PS		PCR
Cyt		HTS
Cyt
AI362NIPM−−++
AI373N−−++
AG854N−−++
AH180N−−++
AH166N??++
AG797N?−++
109NIPM++−−
110BN++++
111N++++
114N++−−
115N++++
117N++++
118N+++−
AK227NORG+++−
AK228N++++
AK229N++++
AK230N−+−−
AK231N+++−
101NORG++−+
103N++++
ESFY—‘Candidatus Phytoplasma prunorum’, PS—Pseudomonas syringae, Cyt—Cytospora spp., + positive, − negative, ?—inconclusive results.
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Navrátil, M.; Šafářová, D.; Čmejla, R.; Duchoslav, M.; Sedlák, J. High-Throughput Sequencing Reveals Fungal Microbiome of Apricots Grown Under Organic and Integrated Pest Management Systems. Agriculture 2025, 15, 1825. https://doi.org/10.3390/agriculture15171825

AMA Style

Navrátil M, Šafářová D, Čmejla R, Duchoslav M, Sedlák J. High-Throughput Sequencing Reveals Fungal Microbiome of Apricots Grown Under Organic and Integrated Pest Management Systems. Agriculture. 2025; 15(17):1825. https://doi.org/10.3390/agriculture15171825

Chicago/Turabian Style

Navrátil, Milan, Dana Šafářová, Radek Čmejla, Martin Duchoslav, and Jiří Sedlák. 2025. "High-Throughput Sequencing Reveals Fungal Microbiome of Apricots Grown Under Organic and Integrated Pest Management Systems" Agriculture 15, no. 17: 1825. https://doi.org/10.3390/agriculture15171825

APA Style

Navrátil, M., Šafářová, D., Čmejla, R., Duchoslav, M., & Sedlák, J. (2025). High-Throughput Sequencing Reveals Fungal Microbiome of Apricots Grown Under Organic and Integrated Pest Management Systems. Agriculture, 15(17), 1825. https://doi.org/10.3390/agriculture15171825

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