A total of 86 single-spore isolates of M. fructicola were included in the study. The isolates were collected from a wide range of hosts comprising pome and stone fruits from various locations in Korea (Table 1, Fig. 1): 58 isolates from peach (Prunus persica), 10 from apricot (Prunus armeniaca), 3 from Japanese plum (Prunus salicina), 3 from plum (Prunus mume), 9 from pluot (Prunus salicina × armeniaca), and 3 from cherry (Prunus pauciflora Bunge). All collected isolates were cultured from sporodochia originating from various parts of infected host tissues. Isolates were maintained on potato dextrose agar (PDA; potato dextrose 20 g, agar 15 g per liter) medium in Petri dishes in the dark condition at 25°C. For long-term storage, the isolated fungi were preserved at −80°C with a solution containing 20% glycerol and 17% skim milk.

For molecular identification of the isolated pathogens, DNA extraction was carried out. The isolated pathogens were inoculated onto one-fifth strength PDA (potato dextrose 4.8 g, agar 15 g per liter) and cultivated at 25°C for 10 days. The cultivated pathogens were subjected to DNA extraction using the cetyltrimethylammonium bromide (CTAB) method. Fungal spores and mycelium were transferred to a 1.5-ml E-tube containing 500 μl of CTAB (CTAB 2.0% (w/v), 100 mM Tris-HCl, pH 8.0, 20 mM EDTA pH 8.0, 1.4 M NaCl), and 8 μl of proteinase K solution (20 mg/ml) was added. The mixture was then crushed, and a 30 min reaction was carried out 65°C. After the reaction, it was cooled at room temperature for 10 min, followed by the addition of PCI (phenol:chloroform:isoamyl alcohol, 24:25:1) 500 μl, mixed by inverting 5 to 7 times. The mixture was centrifuged at 12,470 ×g for 10 min to remove proteins. The supernatant was transferred to a new E-tube, and 400 μl of isopropanol was added. After centrifugation at 12,470 ×g for 5 min at 4°C, the supernatant was removed. Finally, 70% ethanol (500 μl) was added, centrifuged for 5 min, and the supernatant was removed. The pellet was air-dried for 1-2 h. The DNA concentration was measured by adding 20 μl of TE buffer to the E-tube and using the NanoDrop 2000C spectrophotometer (Thermo Scientific, Waltham, MA, USA). The extracted DNA was used for the amplification of the ITS, TEF1-α, β-tubulin, and cytB regions. The primers used were ITS1 (5′-TCC GTA GGT GAA CCT GCG G-3′)/ITS4 (5′-TCC TCC GCT TAT TGA TAT GC-3′) for the ITS region, EF1-728F (5′-CAT CGA GAA GTT CGA GAA GG-3′)/EF1-986R (5′-TAC TTG AAG GAA CCC TTA CC-3′) for TEF1-α, TubF1 (5′-ATG CGT GAG ATT GTA CGT AT-3′)/TubR1 (5′-GTACCAATGCAAGAAAGCCT-3′) for β-tubulin, and colaexon3-fwd (5′-TTT ACC TTA CGG TCA AAT GAG CCT-3′)/colaexon4-rev (5′-AAC TCA ACA ATA TCA CCT CCA ATT CAT-3′) for cytB (Fischer et al., 2017; Hily et al., 2011; Poniatowska et al., 2021; White et al., 1990). The sequence analysis was conducted by Cosmogentech (Seoul, Korea), and the obtained sequences were identified using the NCBI nucleotide blast search program.

EcoRI (10 unit) and MspI (10 unit) were used to digest 200 ng of genomic DNA in a 20 μl reaction at 37°C for 5 h. The digested DNA was ligated to EcoRI adapter (0.1 μM: 5′-GACGATGAGTCCTGAT-3′, 5′-CGATCAGGACTCATC-3′) and MspI adapter (0.1 μM: 5′-GACGATGAGTCCTGAG-3′, 5′-CGCTCAGGACTCAT-3′) using 40 units of T4 DNA ligase, and 0.2 mM ATP in a 50 μl reaction at 37°C for 3 h. Following a 20-fold dilution of the ligated product, nonselective amplification was carried out using EcoRI+0 (5′-GACTGCGTACCAATTC-3′) and MspI+0 (5′-GATGAGTCCTGAGCGG-3′) primers. The thermal conditions included pre-denaturation at 94°C for 2 min, 20 amplification cycles (94°C for 30 s, 56°C for 1 min, and 72°C for 1 min), and final extension at 72°C for 90 s. The PCR products from the pre-amplification step were diluted 10-fold, and 1 μl of the dilution was used for selective amplification with the same core primers as the first amplification, along with two arbitrary (selective) nucleotides at the 3′ end. Selective reactions were performed in a 20 μl PCR reaction mix with the same components as the pre-amplification step, using 0.3 U of Taq polymerase and 50 ng of each selected primer. EcoRI primers were FAM labeled at their 5′ end for automated laser fluorescence analysis. The reaction involved an initial touch-down protocol: 12 cycles of primer-sensitive amplification (94°C for 30 s, 65°C decreased by 0.7°C during each cycle for 30 s, and 72°C for 60 s), 23 normal cycles (94°C for 30 s, 56°C for 30 s, and 72°C for 60 s), and a final elongation at 72°C for 10 min. The FAM-labeled amplicon was analyzed using a DNA Analyzer ABI3730XL (Perkin Elmer, Waltham, MA, USA) at the National Instrumentation Center for Environmental Management. From an initial screening of 40 different primer combinations on the fungal isolates, 20 primer combinations (comprising 4 EcoRI [E] and 10 MspI [M] primers) were selected based on amplification profile intensity, number of bands, and indicative polymorphism. M-TA, M-GT, M-TC, M-CT, M-TG, and M-AG were combined with E-GA; E-GA was combined with M-CG, M-CT, M-AT, M-TA, M-AG, M-TC, and M-GT; E-GT was combined with M-CG, M-GA, and M-GC; and E-TC was combined with M-TA, M-GA, M-AG, and M-CG (Gril et al., 2010).

The FAM-labeled DNA fragments generated from the DNA analyzer were obtained as fragment analysis (FSA) files. Fragment information within the FSA files was managed using the Fragman package (version 1.0.9) and analyzed with stats (version 4.3.2), ape (analysis of phylogenetic and evolution, version 5.7.1), nortest (version 1.0.4), vegan (version 2.6.4), and pairwiseAdonis (version 0.4.1) package in R (version 4.3.2). AFLP was evaluated based on sensitivity, geographical separation, and host plant for previously studied fungicides including bitertanol, pyraclostrobin, procymidone, and fluxapyroxad. The reported raw measurements involved the calculation of half-maximal effective concentration (EC₅₀) for fungicides using a three or four-parameter log-logistic model in the drc package (version 3.0.1) in R. The log-logistic model and EC₅₀ results were included in the supplementary data (data not shown). The results were visualized using ggplot2 (version 3.4.2) and ggpubr (version 0.6.0) packages in R.

To obtain information based on pathogen variation, this study was designed to investigate genetic diversity using 86 of the pathogens isolated from stone fruit cultivation in Korea (Fig. 1) and examine the relationship between AFLP and strain characteristics. As a result, a total of 1,482 well-defined AFLP bands were amplified. Primer combination E-GA+ M-CG, M-CT, M-AT, M-TA, M-AG, M-TC, and M-GT amplified the highest number of fragments (379). On the other hand, the primer combination E-GT+ M-CG, M-GA, and M-GC showed the fewest bands (28). On average, 304.92 AFLP markers were amplified per primer combination in the size range of 40-500 bp. The fragment patterns were utilized to construct a phylogenetic tree using Bray-Curtis distance and the Weighted Pair Group Method with Averaging (WPGMA) hierarchical clustering. All four primer combinations were grouped into three major clusters based on a phylogenetic distance of 0.3, and isolates from Jeolla province show a high frequency in all clusters (Figs. 2 and 3). In particular, distinct patterns were observed between strains isolated from Jeolla province and those from other regions, especially with the primer combinations E-GA+M-CG, M-CT, M-AT, M-TA, M-AG, M-TC, M-GT (Fig. 2), and E-TC+M-TA, M-GA, M-AG, G-CG (Fig. 3). Furthermore, when comparing AFLP banding patterns obtained from an AFLP study on M. laxa, a causative agent of brown rot disease, previously reported by Gril et al. (2008), completely different electrophoretic profiles were observed. This suggests that the AFLP technique is a suitable tool for studying closely related groups of brown rot pathogens.

In order to analyze the relationship more thoroughly between AFLP analysis and characteristics, we conducted a dimensional analysis using PCoA on primer combinations E-GA+M-CG, M-CT, M-AT, M-TA, M-AG, M-TC, M-GT, and E-TC+M-TA, M-GA, M-AG, G-CG pattern combinations (Figs. 4 and 5). Then PERANOVA observed whether the distribution of the pattern of the fragment varies by characteristic (Tables 2 and 3, Figs. 4 and 5). For the category of fungicides EC₅₀ were based on their quartiles (data not shown). Subsequently, PERMANOVA and pairwise PERMANOVA results (Tables 2 and 3), calculated based on point distribution, indicated significant differences in distribution (PERMANOVA, P < 0.10; pairwise PERMANOVA, P_adj < 0.10) at the large-scale locational unit (province). The results from Tables 2 and 3 diverge from the findings of the AFLP pattern analysis. While the AFLP pattern analysis indicates a difference between Jeolla province and other regions, the results from Tables 2 and 3 suggest a significant difference between Gangwon province and other regions. Moreover, significant distribution differences were identified among groups within the AFLP phylogeny (PERMANOVA, P < 0.001; pairwise PERMANOVA, P_adj < 0.001). However, no significant differences were observed between host plants and fungicide resistance groups (Figs. 4 and 5). In previous studies, isolates from diverse geographical locations (Spain, New Zealand, and Japan) from the same hosts were clustered, also suggesting a potential correlation with host specificity (Gril et al., 2010). Additionally, in another study, strains isolated from Virginia and Washington in the United States, though geographically separated, were considered to share a common origin based on being collected from the same host plant (Fulton et al., 1999). This suggests that, despite the diversity of host plants in Korea, significant differences occurred depending on the geographical separation.

This study investigated the genetic differentiation of M. fructicola, examining its influence based on cultivation regions, host plants, fungicide usage, and various environmental conditions. Different patterns were observed depending on the region, and significant differences were identified. However, the number of fungicide-resistant strains was limited. This highlights the need for further research, diverse approaches, and genetic diversity monitoring for sustainable agriculture.