Isolation and Molecular Identification of Monilinia fructigena in Almaty Region of Kazakhstan

Abstract

Monilinia fructigena, a causal agent of brown rot in apple and other fruit crops, poses a significant threat to fruit production and postharvest quality in temperate regions. This study reports on the molecular and morphological identification of M. fructigena isolates obtained from symptomatic apple fruits in the Almaty region of Kazakhstan. Nine isolates were characterized through a combination of morphological assessment, real-time PCR, target locus (ITS and TEF1-α gene) sequencing, and whole genome sequencing using nanopore sequencings. Morphological analysis revealed typical features of M. fructigena, including blastoconidia and microconidia. Pathogenicity tests on ‘Idared’ and ‘Golden Delicious’ apples confirmed the high aggressiveness of the isolates, with lesion development observed within 24–48 h post-inoculation. Molecular identification via real-time PCR and target sequencing confirmed all isolates as M. fructigena with high mapping quality and sequence identity. The whole genome sequencing of a representative isolate further validated the species identity based on comparative alignment with Monilinia reference genomes. Thus, the combination of the used traditional and molecular methods allowed us to unambiguously identify the isolated fungus as M. fructigena. This integrative approach enhances the understanding of Monilinia species in Central Asia and supports the implementation of modern molecular tools for phytopathogen surveillance and agricultural biosecurity.

1. Introduction

Fungi of the genus Monilinia (family Sclerotiniaceae, order Helotiales) are widespread phytopathogens affecting both stone and pome fruit crops. The species Monilinia fructicola, M. fructigena, and M. laxa cause a disease commonly known as brown rot of fruits in apple (Malus domestica), leading to significant yield losses at all stages—from storage to transportation and market distribution [1].

Monilinia fructigena poses a particularly serious epidemiological and economic threat and is primarily distributed in the temperate climates of Europe and Asia [2]. It can infect the fruits, blossoms, and shoots of apple (M. domestica), pear (Pyrus communis), quince (Cydonia oblonga), and plum (Prunus domestica) [3]. The postharvest period is when M. fructigena is the most dangerous: under storage and transport conditions, infection can develop rapidly, causing widespread fruit decay and considerable economic losses. The total worldwide losses caused by Monilinia species on fruit tree crops have been estimated at EUR 1.7 million annually [4]. According to the research findings, mature fruits at the late stages of development exhibit increased susceptibility to Monilinia fructigena infection. Although mechanical damage to the fruit skin is typically required for pathogen entry, there remains a significant risk of symptom development during the postharvest period, especially under favorable storage conditions. Improper harvesting practices that result in fruit injuries may promote the formation of infection foci and facilitate disease spread during storage and transportation [5]. The management of the infection by Monilinia species is difficult as the known chemical and physical treatments have shown efficiency in laboratory environments but lack sustainability in the field [6]. This fact underscores the critical importance of early detection to ensure phytosanitary safety during harvest and transportation. At present, no international phytosanitary protocols issued by EPPO (European and Mediterranean Plant Protection Organization) or EFSA (European Food Safety Authority) have been published regarding the detection of Monilinia fructigena, as well as measures for the isolation and destruction of infected fruits. This complicates diagnosis and control at the international level, especially in the context of cross-border fruit trade. Currently, the monitoring and identification of the pathogen are primarily based on national or provisional methodological guidelines, as well as general protocols for the Monilinia genus. The development and implementation of unified international standards remain a pressing task to prevent the further spread of the pathogen into new regions.

In Kazakhstan, the first confirmed cases of apple fruit damage by brown rot caused by M. fructigena were reported in the Almaty region [7]. However, large-scale data on the species composition of pathogens of the genus Monilinia in the country are still limited. The lack of accurate information about the prevailing taxa, their aggressiveness, and potential resistance makes it difficult to develop effective monitoring and bio-control measures. The classical diagnosis of Monilinia spp. is based on morphological features—the structure of conidia, the shape of the mycelium, and the type of sporulation. However, due to the high morphological similarity of species (especially M. fructigena and M. polystroma), the use of phenotypic characteristics alone often leads to erroneous identification [8]. In recent years, molecular methods—including PCR and sequencing of ribosomal DNA regions (primarily ITS1 and ITS2) and nuclear genes (e.g., TEF1-α and β-tubulin genes)—have become particularly important for species identification and the phylogenetic analysis of pathogens, including Monilinia species [9,10,11,12].

The advent of high-throughput sequencing (HTS) technologies, including the Oxford Nanopore Technologies platform, has expanded the possibilities for analyzing phytopathogen genomes and has accelerated taxonomic verification [13]. Despite the growing number of papers related to the genomics of the genus Monilinia, most of the research focuses on European and Chinese populations. Central Asia remains insufficiently covered in global phytoepidemiological maps, which is especially important in light of international trade which is associated with the risk of pathogen spread.

The present study is aimed at the molecular identification and phylogenetic analysis of M. fructigena isolates obtained from affected apple trees in the Almaty region, as well as the morphological and pathogenic characteristics of the strains. Despite the economic relevance of brown rot, the molecular epidemiology of Monilinia spp. in Central Asia remains underexplored, hindering phytosanitary decision-making and regional biosecurity planning. The use of a complex of microscopic, molecular, and sequencing methods makes it possible to obtain reliable taxonomic information and assess the epidemiological potential of the identified pathogens in a regional context. Besides traditional pathogenicity tests and microscopic examination, molecular identification has been conducted using three methods—RT-PCR, the target sequencing of target barcode loci, and whole genome sequencing—to ensure the precise determination of the pathogen. Moreover, whole genome sequencing data will help to lay a basis for future OMICS-based studies and the phytosanitary monitoring of Monilinia fructigena in Kazakhstan and Central Asia in general.

2. Materials and Methods

2.1. Collection of Monilinia Isolates

Fruits of the cultivated apple variety ‘Aport’ exhibiting characteristic symptoms of brown rot of fruit—namely, concentric rings of gray or white conidial pads on the surface—were collected in November of 2024 from commercial orchards in the Almaty region (Esik town, 43.364223° N, 77.478391° E).

To isolate the fungus from the plant material, conidia from the fruit surface (or from mummified fruits) and a portion of the mycelium from the fruit, avoiding any plant tissue, were placed on 2% potato dextrose agar (PDA) in Petri dishes. Subsequently, to obtain pure cultures, two sequential transfers of uniform mycelium were made into fresh nutrient broth. Cultures were incubated at 25 °C for seven days with the daily observation of the pathogen’s characteristic growth pattern. The fungal pathogen collection was stored at 4 °C.

2.2. Morphological Characterization and Assessment of Isolate Aggressiveness

The aggressiveness of the studied isolates was evaluated on locally grown apple fruits of the Idared and Golden Delicious cultivars, following the protocol described by Fischer et al. [10] with minor modifications. A total of eighteen apples were used for each cultivar, with two apples inoculated per isolate. Fruits were selected based on the absence of biotic or abiotic damage, uniform size within each cultivar, and consistent phenological stage. Apples were sequentially disinfected in a soap solution, 70% ethanol, and rinsed twice in sterile distilled water. After drying, a 1–1.5 cm longitudinal incision was made at the equatorial region of each fruit using a sterile scalpel. Mycelial plugs from 4-day-old cultures were inserted into the wounds, which were subsequently sealed with adhesive tape to prevent external contamination. Non-inoculated fruits served as negative controls.

The inoculated fruits were incubated in a desiccator at a relative humidity of 80–90% and a temperature of 23–25 °C under a 12 h photoperiod. Following the appearance of initial symptoms and every two days thereafter, disease progression was assessed. Two key parameters were recorded: the incubation period (time from inoculation to symptom onset) and the latent period (time from inoculation to the onset of sporulation) for each cultivar.

In addition to visual observation, infection progression was analyzed using light microscopy (EvosM5000, Therm Fisher Scientific, Waltham, MA, USA), phase-contrast microscopy (EvosM5000, Invitrogen, Carlsbad, CA, USA), and stereomicroscopy (Ivesta 3, Leica, Wetzlar, Germany). Microscopic preparations were made by suspending fungal mycelium in water, followed by heat fixation over an open flame. Staining was performed using a 0.025% solution of aniline blue in lactoglycerol (a mixture of glycerol, lactose, and water in a 1:1:1 ratio), supplemented with 5% benzyl alcohol. To monitor conidial germination, a fragment of the mycelium was transferred onto a slightly moistened nutrient medium. The time-lapse imaging of conidial germination on a thin layer of PDA medium was conducted in real time for 5 h using EvosM5000.

2.3. Real-Time PCR Identification

DNA extraction from 21-day-old pure cultures was performed using a commercial Plant/Fungi DNA Isolation Kit (Norgen Biotek Corp., Thorold, ON, Canada) according to the manufacturer’s instructions, using liquid nitrogen for mycelium homogenization. Real-time PCR was conducted using the Luna Universal Probe qPCR Master Mix (New England Biolabs, Ipswich, MA, USA) according to the manufacturer’s instructions. The primers used for amplification were designed by Wang et al., 2016 [14].

2.4. Target Sequencing of Ribosomal and Genomic DNA Regions

Partial representative regions of ITS and TEF1-α were amplified using the primers listed in

Table 1. Nucleotide sequences of primers used for target amplification for sequencing.

Primer Name	Nucleotide Sequence (5′–3′)	Amplification Program	Reference
ITS86F	GTGAATCATCGAATCTTTGAA	95 °C—2 min; 40 cycles: 95 °C—30 secs, 52 °C—30 secs, 72 °C—1 min; 72 °C—10 min	[15]
ITS4R	TCCTCCGCTTATTGATTGC
EF1-688F	CGGYCACTTGATCTACAAGTGC	94 °C—7 min; 25 cycles: 95 °C—1 min, 50 °C—1 min, 72 °C—1 min; 72 °C—10 min	[16]
EF1-1251R	CCTCGAACTCACCAGTACCG

. The PCR conditions were as follows: 1 U of Taq DNA Polymerase (New England Biolabs), 1× Taq buffer, 0.2 mM dNTPs, 0.2 µM of each primer, and 20 ng of DNA template in a final volume of 25 µL.

Sequencing was performed using the MinION Mk1B device from Oxford Nanopore Technologies (ONT) with the SQK-RBK114.96 library preparation kit. Library preparation was carried out according to the manufacturer’s protocol. Sequencing was run with the High-accuracy setting and default quality filtering parameters. Basecalling was performed using Dorado v7.4.12 with simultaneous barcode trimming. Subsequently, alignment was performed against reference sequences of the most common Monilinia species in the Asian region (

Table 2. Reference sequences used for alignment of reads obtained from target sequencing.

Species Name	Target Accession No.
	ITS	TEF1-α
Monilinia fructicola	KY038837.1	OP090631.1
Monilinia fructigena	KX982698.1	LT632536.1
Monilinia laxa	OM686844.1	OP090640.1
Monilinia polystroma	OQ170786.1	LT632541.1

) using minimap2.

For phylogenetic analysis, sequences were retrieved from the publicly available NCBI database. Sequence alignment was performed using the MAFFT algorithm [17] in UGENE v. 50.0. [18] Phylogenetic inference was based on the Maximum Composite Likelihood model, and the tree was constructed using the UPGMA method with bootstrap support calculated from 1000 replicates in MEGA 11 (v. 11.0.11) [19]. Further editing and visualization of the phylogenetic tree were carried out in FigTree v. 1.4.4.

2.5. Monilinia sp. Identification Using Whole Genome Sequencing

One isolate was selected for whole genome sequencing based on data on the preliminary target sequencing of all isolates. Sequencing was performed using the PromethION system with the FLO-PRO114M flow cell and SQK-NBD114-96 library preparation kit (Oxford Nanopore, Oxford, UK), following the manufacturer’s protocols. Sequencing was run with the High-accuracy setting and default quality filtering parameters. Basecalling was performed using Dorado v7.4.12 with simultaneous barcode trimming. The obtained reads were mapped against a set of the available reference genomes of M. fructigena (GCA_003260565), M. fructicola (GCA_008692225), M. polystroma (GCA_002909645), M. laxa (GCA_009299455), M. aucupariae (GCA_002162555), and M. vaccinii-corymbosi (GCA_017357885), retrieved from the NCBI database. Mapping was performed using BWA MEM (v. 0.7.17) with -ont2d settings [20]. Read mapping statistics were calculated using the Stats and Coverage tools of the Samtools (v. 1.19.2) package [21]. Consensus sequences corresponding to the scaffolds covered with reads were obtained using the Consensus command of Samtools.

Whole genome phylogenetic analysis was performed using all complete genome assemblies of Monilinia species available in the NCBI Genomes database along with several additional fungal genomes as the outgroup. The final dataset included, in addition to the consensus genome sequence, four M. fructigena assemblies (GCA_003260565—reference genome; GCA_003671625, GCA_02318975, GCA_002909635); five M. fructicola genomes (GCA_008692225—reference genome; GCA_029929755, GCA_016906325, GCA_002909715, GCA_002162545); three M. laxa genomes (GCA_009299455—reference genome; GCA_002938945, GCA_0029097225); two M. vaccinii-corymbosi genomes (GCA_017357885—reference; GCA_047671335); reference genomes of M. aucupariae (GCA_002162555) and M. polystroma (GCA_002909645); and reference genomes of the outgroup species—Botrytis cinerea (GCA_000143535), Scletotinia sclerotiorum (GCA_000146945), Erysiphe necator (GCA_024703715), Podosphaera leucoricha (GCA_013170925), Alternaria alternata (GCA_001642055), Fusarium oxysprum (GCA_013085055), Venturia inequalis (GCA_003689225), Moniliophthora roreri (GCA_001466705), and Puccinia graminis (GCA_000149925). The UFCG v.1.0.5 [22] pipeline was used to identify 758 protein coding genes of the BUSCO [23] dataset and create a neighbor-joining tree of concatenated gene sequences with the FastTree [24] tool. Additionally, a neighbor-joining tree with 1000 bootstrap replicates was created in MEGA11 (v. 11.0.11) to test the probabilities of all branches. A consensus network of all trees for individual genes consistent across all selected fungal genomes was constructed using the Phangorn R package (v.2.7.1) [25].

3. Results and Discussion

3.1. Morphological Characterization of Monilinia Isolates and Assessment of Their Pathogenicity

A total of nine isolates of Monilinia spp. were obtained and grown in the culture. To test the aggressiveness and pathogenic potential of Monilinia isolates, inoculation was performed on the fruits of two apple cultivars—Idared and Golden Delicious—with several replicates. The incubation period required for symptoms to develop and the latency period ranged from 24 to 48 h, which shows a high level of pathogenicity of isolates. The characteristic pre-sporulation phase was noted as early as 24 h after inoculation. The size of the lesions was determined by measuring two mutually perpendicular diameters, the values of which ranged from approximately 20 × 15 mm to 50 × 40 mm. The variation in lesion sizes over a wide range may reflect both the genetic characteristics of the isolates and the influence of varietal differences or incubation conditions [26]. Characteristic concentric rings of conidial pustules typical for M. fructigena were observed, colored white, light brown, or brown [26], depending on the age of the sporulating structures. Thus, the obtained isolates were preliminary identified as M. fructigena based on the colony morphology. More detailed analysis included microscopic examination and molecular analysis.

At the conidial stage of development, M. fructigena formed ellipsoid conidia aggregated into branched chains (Figure 1). The morphological analysis of the isolates revealed that the majority of the conidia were ovate or lemon-shaped, with sizes approximately in the range of 8–12 × 14–18 μm. The approximate length of germ tubes was 90–200 µm, with germination observed in 16% of 550 conidia and branching occurring in 35% of the germinated tubes (Figure 2C,D). Intense mycelial growth and active conidial formation were observed by the fourth day of cultivation, along with copious sporulation associated with the increased risk of pathogen spread in natural infection conditions. Using time-lapse microscopy on a thin layer of PDA medium, the dynamics of spore germination were recorded every 30 s over 5 h (Video S1 in Supplementary Materials). During the first 2–2.5 h, spores swelled, increasing in diameter from 12 to 14 μm, followed by active germ tube growth. Most spores developed a single tube, although in some cases double and triple tubes were observed. These findings align with previous reports demonstrating plasticity in the spore germination of Monilinia spp. [27].

Hyphal fragmentation, a process by which mycelial filaments split into brief segments that morphologically resemble arthroconidia (arthrospores), was also observed by a microscopic examination of M. fructigena cultures (Figure 2E,F). Both infected fruits and micropreparations from cultures cultivated on nutrient broth showed these structures. No correlation was found between hyphal fragmentation and isolate-specific traits or culture growth conditions. Such structures may visually resemble asexual spores; however, to date, no publications mentioning arthrospore formation in Monilinia species are available. Neither classical mycological descriptions nor modern molecular and morphological studies mention this feature. Considering this, the phenomenon of hyphal fragmentation observed here requires further investigation. It may represent a physiological adaptation of the fungus to stress conditions during storage, analogous to phenomena described for other phytopathogenic ascomycetes [28]. The size of the observed fragmented hyphal segments was 4–10 µm, which is approximately two–three times smaller than that of blastoconidia.

Additionally, it was established that Monilinia species are capable of producing microconidia—spherical spores distinct from typical macroconidia (Figure 2G,H). In the present work, such structures, both solitary and grouped on phialides, were observed in long-term cultures stored at 4–6 °C (30 days or longer). The size of the observed microconidia was approximately seven times smaller than that of macroconidia, measuring about 1.8–2.4 µm. In contrast, microconidia were not detected on infected fruits incubated for more than 21 days. Previously, the timing of microconidia appearance in PDA cultures was noted as 21 days [29]. The detection of microconidia under unfavorable storage conditions supports the hypothesis that these structures likely play a role in pathogen survival under stress rather than in the initial stages of plant infection.

3.2. Species Identification of Monilinia Isolates Using PCR and Target Sequencing Methods

Pure cultures of M. fructigena were obtained from nine isolates. On nutrient broth, colonies of this species exhibited a milky-white coloration with irregular margins. A well-defined rosette pattern was observed in the colony center (Figure 2A,B). The morphological features characteristic of M. fructigena, previously described in the literature, were observed in their structure, including ellipsoid and lemon-shaped conidia forming chains [27]. Note that the morphological differentiation of species of the genus Monilinia is difficult because they exhibit similar phenotypic features and growth parameters [30]. In this regard, molecular methods, particularly real-time PCR, play a key role in the accurate and reliable identification of pathogens. The obtained results of real-time PCR identification showed an ambiguous positive outcome for M. polystroma and M. fructigena (Figure 3), although the maximal ∆Rn values were observed as being two times higher for M. fructigena compared to M. polystroma (

Table 3. Levels of positive RT-PCR signals for detection of Monilinia species in nine isolates. Ct—cycle threshold; ∆R_nmax—maximum value of relative level of fluorescence.

Isolate		1_esik	2_esik	3_esik	4_esik	5_esik	6_esik	7_esik	8_esik	9_esik
M. fructigena	Ct	24	21	26	22	22	23	23	22	21
	∆Rnmax	65,823.0	64,061.8	57,020.2	57,278.0	57,742.2	54,084.1	59,017.6	58,518.2	65,440.0
M. polystroma	Ct	25	23	27	24	23	23	24	24	23
	∆Rnmax	35,181.4	34,681.2	31,014.6	31,946.8	30,499.6	30,235.1	31,137.8	26,368.2	31,721.0

). As the pure cultures of the putative M. fructigena isolates were tested, we exclude the possibility of the mixed presence of two species and suppose that the probe for M. polystroma could have ambiguous specificity, and this is not reliable for distinction between M. polystroma and M. fructigena. Thus, for more precise species identification, sequencing-based methods should be deployed.

In addition to morphological and PCR-based molecular analyses, the high-throughput sequencing (NGS) of two representative genetic regions, ITS and TEF1, was performed to verify taxonomic affiliation. The analysis explicitly confirmed that all samples belonged to M. fructigena (

Table 4. Quantitative metrics of target sequencing quality.

Sample	Total Reads	Mean MAPQ	Percentage of the Mapped Reads *	Total Bases Aligned	Mean Coverage Depth
ITS
isolate_1_esik	11,253	59.4010	95.24%	1,906,357	6748.09
isolate_2_esik	3048	59.3599	95.04%	503,094	1946.62
isolate_3_esik	9859	59.2935	94.72%	1,637,886	6437.12
isolate_4_esik	3483	59.4175	95.34%	589,480	2316.46
isolate_5_esik	6493	59.2566	94.52%	1,076,168	4195.96
isolate_6_esik	5966	59.4346	95.47%	1,032,229	3076.02
isolate_7_esik	9537	59.3728	95.22%	1,613,809	6243.78
isolate_8_esik	6633	59.5251	96.32%	1,140,997	2282.48
isolate_9_esik	3936	59.3501	3743	657,793	2545.00
TEF-1
isolate_1_esik	4068	59.9543	99.8%	1,816,901	3804.63
isolate_2_esik	25,907	59.9646	99.8%	11,736,748	24,581.78
isolate_3_esik	1121	60.0000	100%	508,897	1065.77
isolate_4_esik	7185	59.9634	99.8%	3,232,201	6769.51
isolate_5_esik	20,091	59.9716	99.85%	9,095,295	19,048.28
isolate_6_esik	18,992	59.9672	99.83%	8,657,001	18,131.47
isolate_7_esik	7259	59.9759	99.86%	3,273,728	6855.90
isolate_8_esik	13,774	59.9808	99.88%	6,240,709	13,068.47
isolate_9_esik	25,915	59.9491	99.74%	11,673,685	24,447.62

* Against the reference sequences of the ITS and TEF-1 of Monilinia fructigena (KY038837.1 and OP090631.1, correspondingly).

). For the ITS region, the median read length was 243 bp. The number of reads ranged from 3483 to 11,253 per isolate, of which between 2897 (95.0%) and 10,717 (95.2%) were uniquely classified as M. fructigena. Coverage levels varied between 99.6% and 100%, with an average MAPQ score not less than 59.25. The high degree of identity with the reference sequence (≥98.6%) indicates the reliability of the sequencing and the accuracy of the alignment.

For the TEF1 gene, the minimum read count was 1121, with 100% identity to the reference sequence. The median read length was 589 bp. The maximum number of reads reached 25,915, with an identity level of 99.74%. The average MAPQ scores ranged from 59.94 to 60, and coverage breadth for all samples at the TEF1 marker was 100%.

3.3. Phylogenetic Analysis of Monilinia Isolates Based on ITS and TEF-1 Regions

The multiple sequence alignment of the ITS–5.8S region, which included 419 entries from the publicly available NCBI database, revealed extremely low intraspecific variability among Monilinia species. Specifically, all studied M. fructigena isolates—both from our sample and from France, Spain, China, Serbia, Hungary, and the Netherlands, obtained from various hosts such as apple, plum, and quince—were found to be completely homologous in this fragment. Previously, ITS-based markers were used for intraspecific analyses of M. laxa, M. fructicola, and M. fructigena, but this region demonstrated limited resolving power [31]. This allows us to conclude that the ITS–5.8S region is insufficiently informative for assessing intraspecific diversification in M. fructigena.

To construct a phylogenetic tree with stronger statistical support, combined sequences of the TEF-1α and ITS regions were additionally aligned (Figure 4). The TEF-1α gene exhibited significantly higher variability and proved to be more informative for phylogenetic analysis, which is consistent with the findings from other studies on filamentous fungi [16]. In the resulting dendrogram, two Dutch M. fructigena isolates clustered together with nine of our strains into a single monophyletic clade, all members of which possessed completely identical sequences. However, identical genetic profiles do not allow for the unique determination of the geographic source of pathogen introduction. Such genetic uniformity within M. fructigena has been previously reported [9,32], confirming its status as a genetically conserved species across diverse geographic locations and hosts. The closest related species is M. polystroma, which is consistent with previously published data [9,33]. Meanwhile, M. ibarroaxa and M. fructicola formed separate clades, reflecting deeper evolutionary divergences within the genus [34]. Among the used molecular markers, the TEF-1α gene demonstrated the greatest phylogenetic informativeness in the analysis of the intraspecific structure because it contained the largest number of polymorphic regions in the studied sequences. In contrast, the ITS-5.8S area showed low resolution, which makes it insufficiently informative for the correct reconstruction of phylogenetic relationships within the species. Thus, the use of ITS–5.8S as a sole marker for analyzing intraspecific diversification appears to be of limited suitability.

3.4. Whole Genome Sequencing of Selected Monilinia Fructigena Isolate

To resolve the discrepancy between the results of real-time PCR and target nanopore sequencing, whole genome sequencing was performed on the PromethION platform to clarify the species identity of the selected isolate. As the targeted sequence displayed no variability between out isolates, isolate 9 was arbitrarily selected from the collected isolates as a representative sample (

Table 5. Summary of read alignment from whole genome sequencing of Monilinia isolate 9 against available reference genomes.

Species	Genome Accession (NCBI Reference Genomes)	Number of Scaffolds	Total Assembly Length, Mb	Mapped Read Percentage	Average Alignment Coverage Per Scaffold, Percent	Average Coverage Depth Per Scaffold	Average Mapping Quality Per Scaffold
M. frucigena	GCA_003260565	131	43.1	94.3095	95.8022	32.3539	42.8123
M. fructicola	GCA_008692225	20	44	65.8185	89.2557	27.0221	37.2900
M. laxa	GCA_009299455	49	42.8	84.2919	78.9134	41.7116	30.7935
M. polysroma	GCA_002909645	1188	44.5	91.9791	59.5105	13.1558	27.9712
M. aucupariae	GCA_002162555	505	54.4	53.2453	47.1104	80.2229	18.0861
M. vaccinii-corymbosi	GCA_017357885	9	30	39.9030	79.186	9.8027	38.0222

). Using the default read quality settings of Nanopore software, a total of 1,352,624 reads covering 670,351,071 bases were obtained. The median read length was 689 bp. Of six species of Monilinia with the available reference genomes, M. fructigena demonstrated the highest statistics in terms of genome coverage: about 94% of all reads were successfully mapped with an average scaffold coverage of 95.8%. The closest species were M. polystroma (92% and 59.5%, correspondingly), M. laxa (84.3% and 78.9%, correspondingly), and M. fructicola (65.8.3% and 89.3%, correspondingly). The highest average mapping quality per scaffold was observed in M. fructigena. Assuming that the completeness of read mapping and mapping quality depends on the selection of the correct reference genome, we consider these results evidence of the species identification of M. fructigena.

The results of the whole genome phylogenetic analysis were consistent with the known classification of Monilinia within the Fungi kingdom (Figure 5A). The results of bootstrap analysis showed 100% support for all tree nodes. Isolate 9 was unambiguously placed into a sub-cluster of M. fructigena, thus finally confirming its identification. The closest strain was Mfrg269 (Italy), which was also used as a reference for read mapping; the other available M. fructigena genomes originated from Spain (strain gena6) and the USA (strain Mfg5-SP-A). A consensus network summarizing the trees of the individual genes was built based on a total of 381 gene trees containing all 26 genomes and demonstrating similar structures, with isolate 9 having a connection to Mfrg269 (Figure 5B). Although the higher similarity between isolates can be considered an artifact of read mapping (aligned reads of isolate 9 reproduce possible structure features of the reference), the overall results leave no doubt in terms of the specificity of the obtained isolates. A comparison of the ITS and TEF-1 sequences between isolates (see above) allows us to extend this conclusion to other isolates found in this study.

The integrated use of morphological, molecular–genetic, and sequencing approaches enabled the reliable confirmation of the taxonomic affiliation of all studied isolates to M. fructigena and demonstrated the high quality of the obtained data. The use of the combination of different pathogen identification methods allows us to overcome their limitations and provide a comprehensive conclusion.

Previously, we discovered the presence of M. fructigena in Kazakhstan for the first time [7]. The present study aimed to further confirm the identity of the present pathogen and deepen the knowledge about its distribution. Here we showed that the combination of the traditional approaches (macro- and micromorphology, PCR) with modern sequencing technology, such as nanopore sequencing in our case, allows their limitations to be overcome and ensures high reliability. In addition to traditionally used barcoding markers such as ITS and TEF-1α, whole genome sequencing allows us to separate species within the genus based on the statistics of read mapping against the alternative genomes of Monilinia species and multi-gene phylogenetic analysis.

The comparison of the three molecular methods demonstrated the difference between their fidelity corresponding to their difficulty and running costs. RT-PCR analysis resulted in mixed signals between M. frucigena and its related species, M. polystroma. As the analysis material was obtained from the pure culture and the identity of M. fructigena was further confirmed by sequencing, we conclude that the resolving power of the tested system [14] is low between closely related species. However, it still provides a faster and more affordable method for detecting Monilinia and narrows the choice of the candidate species. The target sequencing of the combination of ITS and TEF-1α loci provided a clear distinction between M. fructigena and M. polystroma, whereas no intraspecific variation was detected. Finally, whole genome sequencing demonstrated that the existence of intraspecific variations was limited, however, by the low diversity of the available whole genome sequences of M. fructigena. Here we showed that the differences in the basic mapping metrics for scaffolds from several Monilinia species reflected the phylogenetic structure, and thus they are not only important as technical quality criteria but could also be used for the preliminary determination of the species. However, the efficient use of whole genome data has limitations. First, the number of the complete genomes of Monilinia sp. available to date is limited: for example, the NCBI Genome database contains only 16 genome assemblies of six partially assembled (scaffold-level) Monilinia species. Second, the available genomes lack annotation data, thus limiting possible functional inferences. However, the further collection of genomic data will help in studying Monilinia species on the scale of global population genomics and thus in better understanding their evolution. Third, the isolates obtained in the present study originated from one location and thus would likely represent one strain or several genetically close strains and thus could not reflect genetic variability in the pathogen in Kazakhstan. Therefore, further studies extending genomic analysis to a wider range of populations are required. Finally, the costs of whole genome sequencing remain high, and this limits its applicability to larger sample sizes. Thus, the target sequencing of the standard barcoding loci provides a reasonable trade-off allowing for precise species identification with much lower costs than those of whole genome sequencing. Traditional RT-PCR is a relatively cheap and reliable method; however, one should take into account the possibility of the false identification of related species such as, in our case, M. fructigena and M. polystroma.

Despite the unique status of Kazakhstan as being the link between Europe and Asia and a land of unique combinations of natural and agrarian ecosystems, studies on plant pathogens are still limited and rely mostly on outdated description-based methods. The deployment of modern molecular techniques to study the local phytopathogenetic landscape, including but not limited to Monilinia and other fungi, is necessary to develop and maintain agriculture and the environment in the modern world. The case of Monilinia species is of particular importance for Kazakhstan as fruit crops affected by these pathogens, especially apple trees, are crucial parts of agriculture in this country. The present work is one of many common efforts to establish such modern practices in Kazakhstan and thus intensify plant protection and environmental studies. However, the first results presented here have limitations due to the origin of the sample: as all isolates were obtained from geographically close locations, they lack intraspecific variability. Thus, obtaining a better understanding of the genetics of Monilinia distributed in the orchards of Kazakhstan requires further studies with larger geographic coverage and sample sizes. The higher diversity of isolates would also be useful for obtaining transcriptomic data to elucidate the mechanisms of virulence and host specificity in the local conditions. With the obtained results, the present study could be a good starting point for further wide-scale investigations of Monilinia spp. in Kazakhstan on the genomic level.

4. Conclusions

The results confirmed the species identity of the studied isolates as M. fructigena using a combination of morphological, molecular–genetic, and sequencing methods. The combination of RT-PCR and sequencing allowed us to overcome the limitations of differentiating Monilinia species based on phenotypic traits. The reliability of the results obtained was confirmed by the high identity of all isolates with reference M. fructigena sequences revealed by ITS and TET-1α region sequencing. The sequences of ITS and TET-1α loci and the whole genome of M. fructigena were obtained for the first time in Kazakhstan and will contribute to furthering the knowledge about brown rot fungi in Central Asia.