*Corresponding author Introduction
Disease is one of the major problems in potato cultivation in Indonesia, especially late blight caused by Phytophthora infestans, regarded as the most devastating disease in potatoes (Agrios, 2005). It has become a major disease and a limiting factor for potato production (Fry et al., 2015; Grünwald and Flier, 2005), especially in Indonesia where yield loss of ≤100% was estimated under adverse rainy season conditions. However, it is one of the most profitable crops grown in the highlands (Adiyoga, 2009).
P. infestans is a heterothallic species with two mating types, A1 and A2, for the sexual cycle beside the asexual form containing diploid and triploid cells which are a result of two reproduction cycles (Choi et al., 2020; Ericsson, 2023; Grünwald and Flier, 2005). Both mating types enable P. infestans to produce progeny with enhanced genetic variability, possess a rapid rate of pathogen evolution, and have plasticity in adaptability to different environmental changes including selection pressure from fungicide application, called fungicide resistance. The sexual and asexual recombination of the diploid and triploid cells results in nucleotide mutations and alters the genetic variability and adaptability to fungicides (Andrivon, 1995; Fry et al., 2015; Goodwin, 1997; Goodwin et al., 1994; Li et al., 2017).
Fungicides are one of the important tools used in disease management, including late blight. However, the emergence of resistance has become a limiting factor in food production and modern agriculture. This issue becomes more problematic while managing late blight (Brent and Hollomon, 2007a, 2007b; Ericsson, 2023). Oxathiapiprolin (OXTP) belonging to fungicide group 49 (9F) per the Fungicide Resistance Action Committee (FRAC) list was introduced to the Indonesian market, around 2018 by Dupont. The low sensitivity of P. infestans against OXTP has been reported in non-European countries, including Indonesia (East Java and Central Java, 2022). Sequencing of the oxysterol binding protein inhibitor (OSBPI) gene in a sample collected from Indonesia revealed point mutations at G770V (potato, tomato) and N837F/L (tomato) (FRAC, 2023; Mboup et al., 2021).
Polymerase chain reaction (PCR) followed by sequencing is a powerful molecular technique that can investigate P. infestans resistance, enabling nucleotide mutation analysis of the target sequence and establishing a correlation with the resistance levels. Moreover, for genotype determination studies related to resistance emergence, simple sequence repeats (SSRs) utilizing 12 plex microsatellites (100-300 bp target) are a newly developed marker employed to analyze genetic variability and plasticity against fungicide selection pressure caused by sexual and asexual recombination, genetic population structure due to migration, and ascertaining phylogenetic relationships within and between species and populations. These markers are highly polymorphic, multiallelic, and codominant (Fry, 2020; Li et al., 2013). Such analysis is vital for accurate recommendations in disease control management.
The objective of this report was to monitor the sensitivities of the P. infestans samples collected across Java to several fungicide actives registered and used in Indonesia. The focus was more on OXTP, which reported inducing resistance during a few seasons since its introduction. The conventional poisoned food assay method was applied to determine the sensitivity level and EC50 (effective concentration 50%). Additionally, a molecular approach involving PCR followed by sequencing for mutation detection in OSBPI and genotype determination using limited SSR markers and genetic population structure analysis was also utilized.
Materials and Methods
Sampling, isolation, and field resistance determination interviews
A total of 24 P. infestans samples consisting of 12 pure culture isolates and an additional 12 leaf samples collected from infected potato plants in the main cultivation areas of West and East Java (Table 1, Fig. 1) were used. Field sampling location information like the resistance level status, number of fungicide applications, and fungicide type were collected based on personal surveys and farmer interviews.
Fig. 1.
Sampling location of the isolates and DNA samples collected across Java Island during 2022-2024.
Table 1.
Sampling location and type of P. infestans samples across Java Island, Indonesia
Samples of infected leaves were used for pure culture and direct DNA isolation. For pure culture isolation, infected leaves, 0.5×0.5 cm were put onto 10% V-8 agar medium consisting of 1 g CaCO3, 0.05 g β-sitosterol, and 15 g of agar; added with 19 mg nystatin, 20 mg rifamycin, 200 mg ampicillin; and 100 mg of benomyl 50%; all mixed in 1 l distilled water (Gamboa et al., 2019). They were dark-incubated for 3-5 days and 18-20°C. Mycelium was propagated by transferring a 10-14 days-old pure culture agar plugs to pea broth medium (120 g of frozen peas dissolved in 1 l of distilled water and autoclaved for 15 min) and incubated for 10-14 days. The harvested mycelium was stored at −20°C. DNA was directly isolated from the pure culture and infected leaves employing the GP100 DNA Mini Kit (2017; Geneaid, New Taipei City, Taiwan) by following the manufacturer's instructions. The DNA template, 10-50 ng, was stored at −20°C.
Species and mating type identification
The 14-21-day-old culture isolates grown on 15% V-8 agar were identified morphologically under a microscope with sporangia and mycelium as specific attributes. Meanwhile, the marker O8-1F/2R (Judelson and Tooley, 2000) (Table 2) was employed for molecular species identification followed by nucleotide sequencing for validation. Phylogenetic tree analysis using the MEGA 11 software (https://www.megasoftware.net/) evaluated the phylogenetic relationship among the samples based on the comparative alignment of the P. infestans strain T30-4, P. infestans strain PI-BAC-11A5 INF1/INF4, P. palmivora, Fusarium solani, Verticillium dahlia, and Alternaria solani sequences accessed from the GenBank (https://www.ncbi.nlm.nih.gov/genbank/).
Table 2.
List of markers used
The mating type was identified by PCR-restriction fragment length polymorphism (RFLP) using the W16-1F/2R marker and the BsuRI enzyme following the protocol designed by Judelson et al. (1995); Judelson (1996) and verified by Brylińska et al. (2018) (Table 2). PCR reaction mixes for both markers (O8-1F/R and W16-1F/2R), 10 µl each, contained Ready Mix (5 µl, Bioline, London, UK); 1 µl each of the forward/reverse primer; DNA template (1 µl; ∼10 ng) and Milli-Q water (2 µl). RFLP was performed with the kit (Thermo Scientific, Waltham, MA, USA) by following the manufacturer's instructions. The 30 µl reaction mix consisted of Milli-Q Water (17 µl), 10X FastDigest Green Buffer (2 µl), PCR product (10 µl), and FastDigest Enzyme (1 µl), incubated at 37°C (5 min). The PCR products were separated on 1.5% agarose gel electrophoresis and a ultraviolet transilluminator was used to visualize the restriction digestion fragments. A1 was classified with 1-3 DNA fragments: 557-700 or 557-700; 457; and 100 bp, whereas A2 was categorized with two fragments: 457 and 100 bp.
The pairing test for mating type identification involved pairing and crossing two isolates in a Petri dish containing 10% V-8 agar media as described by Gamboa et al. (2019) and Mazáková et al. (2006). Briefly, two agar plugs of each isolate, 8 mm were placed in a Petri dish at a distance of 6 mm to each other and incubated for 10-14 days. The mating type was determined by observing hyphal contact and oospore formation with a microscope.
Fungicide sensitivity tests
Poisoned food assay was employed to evaluate the sensitivity levels of the 12 pure cultures against different concentrations of several registered fungicides applied in Indonesia, including metalaxyl for phenotypic analysis (Table 3). Single and double actives of the fungicides were dissolved in distilled water to prepare the concentration range required. It included spraying three concentrations of each fungicide i.e., 1X dose (printed on the label), 2X dose (which was still affordable by the farmer), and 5X dose (which was the maximum). The sensitivity test consisted of two steps using different fungicides, where all the representative resistance isolates were tested in the second step and three replications were conducted for each active ingredient. Poisoned food assay was prepared by growing and transferring a P. infestans agar plug, 0.5 cm onto a Petri dish containing 8 ml of 10% V-8 agar medium added with 1 ml of antibiotics (Gamboa et al., 2019; Peterson, 2015) and 1 ml of each concentration of the fungicide solution prepared.
Table 3.
A list of the active ingredients of the fungicides tested
Mycelium growth inhibition (%) and EC50 were calculated by applying the formula described by El-Aswad et al. (2023), i.e., GI (%) = (Gc − Gt) / Gc × 100, where GI was the mycelial growth inhibition, Gc was the radius of the mycelial growth on the negative control, and Gt was the radius of the mycelial growth on treatment medium. EC50 calculation was aimed to know effectiveness of active ingredient against fungal growth with 50% inhibition. Microsoft Excel 2019 was employed for EC50 Probit analysis. Using these values, the sensitivity level was determined based on a range devised in our laboratory i.e., resistance (R; <30%), moderate resistance (MR; 30-40%), reduced sensitivity (LS; 40-60%), sensitivity (S; >60%). This interval range was used to determine level of sensitivity based on EC50 value from poisoned food assay data.
Detection of mutations in OSBPI
The OSBPI marker, PLBf/r (Mboup et al., 2021) (Table 2), was used to amplify the OXTP target site on the OSBPI sequences of the 13 representative DNA samples (three DNA isolates and 10 leaf DNA samples) to detect any possible mutations an indication of resistance. The 10 µl PCR reaction consisted of Ready Mix (5 µl; Bioline), Primer forward/reverse (1 µl each), DNA template (1 µl), and Milli-Q water (2 µl).
DNA was sequenced at the Integrated Laboratory for Research and Testing (ILRT), Universitas Gadjah Mada with the Sanger sequencing method on a 3500 Genetic Analyzer 2500 (Applied Biosystem, Lexington, MA, USA). A consensus nucleotide sequence was created using the bioinformatics software BioEdit v.5.0.6 (https://loschmidt.chemi.muni.cz/bioinf/files/BioEdit_help.pdf). Nucleotide Basic Local Alignment Search Tool (BLAST) available on the NCBI website (https://blast.ncbi.nlm.nih.gov/Blast.cgi) with the P. infestans strain T30-4 (PITG_10462; 2,139-3,030 bp; 713-1,010 protein) as a reference identified any mutations and functional changes in the protein that play a major role in fungicide resistance.
Genotype determination and population genetic structure analysis
The genotyping of the 24 DNA templates used the four florescent SSR markers: PinfSSR11, PinfSSR8, PinfSSR4, and Pi4B F (Table 2) (Gamboa et al., 2019; Li et al., 2013). Each reaction, 10 µl, consisted of Ready Mix (5 μl; Bioline); primer forward/reverse (1 µl each); DNA template (1 µl), and Milli-Q water (2 µl). The PCR product was Sanger sequenced at the ILRT, Universitas Gadjah Mada on 3500 Genetic Analyzer 2500 (Applied Biosystems). GeneMapper v5.0 (https://www.thermofisher.com/order/catalog/product/4370784) was employed for multiloci genotype (MLG) identification. LIZ 600 (https://www.thermofisher.com/order/catalog/product/4408399) and Geneious Prime 2024.0 (https://www.ge-neious.com/updates/geneious-prime-2024-0) were used for allele size determination, sizing, and binning, while genotype classification was performed using the SSR matcher (https://strain-classifier.plant-aid.org/). The population genetic structure was analyzed utilizing Structure v.2.3.4 (https://web.stanford.edu/group/pritchardlab/structure_software/release_versions/v2.3.4/html/structure.html) and the web-based StructureSelector (https://lmme.ac.cn/StructureSelector/).
Results
Sampling, isolation, and field resistance-related interviews
Between 2022 and 2024, 24 DNA samples were collected, comprising 12 from pure cultures and 12 from infected potato leaves across major cultivation areas in Java (Table 1). Sampling sites were selected based on fungicide use and reported resistance, with data obtained from farmer interviews (S1). Fungicide regimes were classified into three groups: (i) group 1, 15-20 applications per season at 4-5-day intervals, mainly older fungicides (mancozeb, metalaxyl, cymoxanil) with no resistance reported; (ii) group 2, ∼18-20 applications at 2-3-day intervals using both older and newer fungicides (e.g., famoxadone, mandipropamid, oxathiapiprolin), with low to moderate resistance reported; and (iii) group 3, 20-25 + applications at 1-3-day intervals, characterized by intensive use of oxathiapiprolin and mixtures of multiple actives, where technically advanced farmers reported emerging control failures.
Species and mating type identification
Morphological identification revealed lemon-shaped sporangia in isolates #5, #6, #8, #30, #32, #35, #37, #38, #39, #40, #41, and #42, with hyphal aggregates additionally observed in #6, #8, #30, #32, #35, #37, #38, #39, #40, and #42 (S2.1; S2.2). Molecular verification using the O8-1F/2R marker on 12 leaf-derived and 12 pure culture DNA samples produced the expected 245 bp fragment (S3), confirming P. infestans. BLAST analysis of consensus sequences from representative samples (#5, #6, #25, #39, #42) showed 97-100% identity to the P. infestans T30-4 reference sequence (GenBank) (S4.1-4.2). Phylogenetic analysis further clustered all samples with P. infestans strains, distinct from P. palmivora, Fusarium solani, Verticillium dahliae, and Alternaria solani (Fig. 2).
Fig. 2.
Phylogenetic tree was constructed by MEGA 11 using Maximum Parsimony method based on Kimura-2 parameter with 1,000 boot-straps from nucleotide sequence resulted from marker O8-1F/2R amplification.
Mating type analysis identified isolates #32, #37, #41, and #42 as A1, producing one or two DNA fragments (∼457-700 bp). Most leaf-derived DNA samples were also A1 with two fragments (457-600 bp), except #83 (S5). Pairing tests with isolate #42 (A1 control) confirmed all isolates as A1 due to the absence of oospore formation, except isolates #5, #6, and #8, which were excluded due to poor growth (Table 4; S6.1-6.3).
Table 4.
The list of samples identified based on SSR fingerprints using four loci showed clonal lineage (multi loci genotype) from SSR matcher and mating analysis results employing molecular, pairing test and matcher
Phenotyping and fungicide sensitivity test (EC50).
Metalaxyl phenotyping of 12 isolates identified four resistants (#6, #8, #30, #32), two moderately resistant (#5, #35), one less sensitive (#39), and five sensitive (#37, #38, #40, #41, #42) strains (Tables 4, 5; S7.1-S7.4). Poisoned food assays against various fungicides revealed variable inhibition percentages across isolates and concentrations (Table 5; S7.1-7.4, S8.1-8.12), affecting EC50 estimates and resistance classification. Low inhibition (0-53%) was observed for isolates #5, #6, #8, #30, #32, and #39 against dimethomorph, oxathiapiprolin + famoxadone, and metalaxyl, indicating resistance to reduced sensitivity.
Table 5.
EC50 and sensitivity levels of Phytophthora infestans collected from across Java Island against several fungicide actives
Isolates #6, #32, #35, and #39 showed strong resistance to carboxylic acid amides (CAA) fungicides (mandipropamid, dimethomorph + ametoctradin, and oxathiapiprolin + famoxadone). However, #6, #32, and #35 were sensitive to oxathiapiprolin + chlorothalonil, while #39 remained resistant. Cross-resistance was evident among #6, #32, #35, and #39 against dimethomorph and mandipropamid. Resistant isolates displayed markedly elevated EC50 values, e.g., isolate #6 had an EC50 of 96,791 ppm against oxathiapiprolin + famoxadone compared with the recommended 1,200 ppm (Table 5; S7.1-7.4).
Conversely, isolates #38, #40, #41, and #42 exhibited high sensitivity (93-100% inhibition) to dimethomorph, oxathiapiprolin + famoxadone, and metalaxyl, particularly #38, which was sensitive to all actives. Their low EC50 values, below recommended rates, confirmed these results (e.g., isolate #42, EC50 = 1,739 ppm vs. 2,000 ppm for metalaxyl). Overall, sensitivity patterns varied: dimethomorph (nine resistant, three sensitive), oxathiapiprolin + famoxadone (eight resistant, four sensitive), metalaxyl (four resistant, two moderately resistant, one less sensitive, five sensitive), mandipropamid (four resistant, one sensitive), dimethomorph + ametoctradin (four resistant, one sensitive), and oxathiapiprolin + chlorothalonil (four sensitive, one less sensitive) (Table 5; S7.1-7.4).
OSBPI mutation analysis
Thirteen consensus sequences from representative samples were aligned against the reference strain T30-4 (OSBPI) using NCBI BLAST to detect mutations. Mutations (insertions, deletions, or substitutions) were identified in 12 of 13 samples (#6, #32, #39, #58, #78, #83, #93, #105, #110, #116, #122, #126), while no mutation was found in sample #61. The highest mutation frequency occurred in #6 (Wonosobo, ID genotype; 36 sites) and #93 (Batu, ID genotype; 75 sites), whereas the worst mutation frequency was found in #32 (Banjarnegara, EU_2_A1), #58 (Banjarnegara, ID genotype), and #83 (Banjarnegara, ID genotype), with only one site each (Table 6; S9.1-9.7).
Table 6.
List of mutation sites for each representative sequence isolate
| Code | No. mutation sites | No. insertion/deletion | Mutation site |
|---|---|---|---|
| 6 | 36 | 2 | K884Ea; N897Q; P898S; D899R; A900C; S902V; G903W; M904Y; Q864T; I865C; G866R; G867S; L868V; L869G; W870Y; G871C; D872G; R873E; D876R; I877L; M878T; G879L; N880W; M881A; V882T; F883W; K884Ca; D885S; K887T; N888R; L890T; Q891V; E893S; L894V; R895S; and insertion on C+896 and A+897 |
| 32 | 1 | 0 | K884Ea |
| 39 | 8 | 0 | L733S; L741M; A746K; R753G; Y756C; H767R; G770V; K884Ea |
| 58 | 1 | 0 | K884Ea |
| 61 | 0 | 0 | - |
| 78 | 14 | 0 | F783I; V820M; H823N; V841M; D885N; K887N; S917P; D916H; K912N; P738Q; H766K; H767K; S768Ra; G770Aa |
| 83 | 1 | 0 | K884Ea |
| 93 | 75 | 14 | F754N; K755A; Y756L; V757S; I758T; A761H; A763R; L765C; H766N; H767T; S768Da; L772M; F775Y; G780A; E781L; T782K; F783L; L787T; N788E; D789N; T791R; V793L; S794R; C795S; E796T; H797Q; T798P; S799T; H800T; H801R; P802Q; P803S; I804S; S805R; N806T; F807S; Q808S; F809L; T810P; G811V; E812K; W870G; K884Ra; D885K; K886Q; K887E; N888D; R889N; Q891R; C892S; E893A; L894V; L896V; N897A; D899Q; S902C; G903K; G905W; M907R; F908A; S909I; S910C; K912L; T913A; T915K; D916H; S917Q; L918C; R919W; D924N; S928P; R931C; and deletion on L-751; E-752; V-762; L-890; R-895; G-909; also insertion on R+765; S+765; S+784; E+789; R+790; R+915; T+917; R+918 |
| 105 | 14 | 2 | N788K; D789G; G790W; T791N; S794G; C795R; H797L; T798R; S799A; H801K; I804P; S805P; G818R; D885N; and deletion on D-792 and V-793 |
| 110 | 4 | 0 | P738Q; S768Na; K884Ea; L918M |
| 116 | 19 | 4 | S768Na; A817P; K884Ea; S902Y; P914C; T915R; D916Q; S917T; G920C; I922V; A927S; S928G; P929T; E932R; I933L; C934L; D935H; L923R; T950R; and deletion on L-918; L-923; D-924, T-925 |
| 122 | 3 | 0 | S768Ra; L918M; V921C |
| 126 | 6 | 0 | H767Q; S768Ra; K884Ea; W940G; V945F; N948K |
Substitution mutations at K884C/E/R and S768R/D/N were predominant in all samples except #105. The most extensive mutations were observed in #6 and #93, while minimal mutations occurred in #32, #58, and #83. Mutation G770V was detected in #39 (Magelang, Central Java), consistent with a site previously reported in Indonesian isolates (FRAC, 2023). No mutations were detected at N837L, as described by Mboup et al. (2021) and FRAC (2024). A novel mutation, G770A, was identified in #78 (Batu, East Java) (Fig. 3B).
Fig. 3.
Population structure analysis based on the Bayesian clustering model utilizing Structure.2.3.4, StructureSelector, and the Evanno curve were performed on 1,000,000 MCMCs; 250,000 burning periods; value K (1-6), and 100 iterations. Histogram of the population structure of all samples with single (A) and multiple lines (B). A dendrogram of the genetic diversity (C). The population codes were Bandung (1), Brebes (2), Banjarnegara (3), Wonosobo (4), Magelang (5), and Batu (6). The sample codes were arranged based on Table 3 and counted from 1 to 24.
Genotype determination
Microsatellite analysis of 24 samples using four SSR markers (PinfSSR8, PinfSS4, PinfSSR11, Pi4B) generated 30 alleles: seven (PinfSSR8), nine (PinfSS4), eight (PinfSSR11), and six (Pi4B) (Table 5). Based on SSR matcher analysis with these limited markers, 10 samples were assigned to five known genotypes: US_13 (#122, Brebes), EU_33_A2 (#8, #61, Banjarnegara), EU_2_A1 (#30, #32, Banjarnegara; #37, #39, #41, Magelang), EU_5_A1 (#5, Wonosobo), and an unidentified genotype (#35, Banjarnegara), which matched an existing database profile but lacked classification. The remaining 12 samples were predicted as unique Indonesian genotypes due to database mismatches. Mating type A2 was confirmed in #8 and #61 (EU_33_A2), with #61 showing no mutation sites. Overall, four genotypes were of European origin and one of U.S. origin (US_13), the latter displaying resistance to metalaxyl (Tables 4, 5).
Population genetic structure analysis
Population structure analysis of six sampling sites: Bandung (1), Brebes (2), Banjarnegara (3), Wonosobo (4), Magelang (5), and Batu (6) was performed using Structure v.2.3.4 with a Bayesian clustering model. The Evanno method supported K=5 (S.10), revealing six populations. Populations 3 (Banjarnegara) and 4 (Wonosobo) were genetically closest, with population 4 likely a subcluster of 3; both were also related to population 5 (Magelang), though less closely. Samples #14 and #15 from Wonosobo exhibited the same cluster pattern as population 3 (Fig. 3). Populations 1 (Bandung) and 6 (Batu) were genetically proximal despite geographic distance. In contrast, population 2 (Brebes, Central Java) was genetically distinct from other Central Java populations but showed affinity with populations 1 (Bandung, West Java) and 6 (Batu, East Java).
Discussion
All isolates tested using the poisoned food assay exhibited varying inhibition percentages against each active ingredient (Table 5; S7.1-7.4), demonstrating differential responses among P. infestans isolates. Most isolates were resistant to dimethomorph, oxathiapiprolin + famoxadone, metalaxyl, mandipropamid, and dimethomorph + ametoctradin, whereas only a few (#38, #40, #41, and #42) remained sensitive. Cross-resistance between dimethomorph and mandipropamid within the CAA group was observed in isolates #6, #32, #35, and #39, with resistance persisting even against dimethomorph + ametoctradin. This resistance is associated with a mutation at G1105S in the CAA target sequence (PiCesA3), which reduces sensitivity to mandipropamid and dimethomorph (FRAC, 2024; Kaur et al., 2024). Farmer practices of mixing three to four active ingredients, often including these compounds, further underscore their reduced efficacy.
OXTP resistance was widespread, except in isolates #6, #32, and #35, which were resistant to oxathiapiprolin + famoxadone but sensitive to oxathiapiprolin + chlorothalonil. This suggests that chlorothalonil, a multi-site MoA fungicide, can enhance efficacy and mitigate oxathiapiprolin resistance (Brent and Hollomon, 2007a; FRAC, 2023). In contrast, isolate #39 exhibited resistance to both mixtures. Mutation analysis revealed a common K884/E/C substitution in OSBPIs of #6 and #32, while #39 carried an additional G770V mutation (FRAC, 2023). This additional mutation likely confers a higher resistance level, as chlorothalonil theoretically provides limited efficacy and was unable to restore activity in #39. Further investigation is required to confirm this hypothesis. Overall, these findings demonstrate that single-site fungicides such as OXTP, metalaxyl, famoxadone, and dimethomorph pose a high risk of resistance development in P. infestans and require careful management in field applications.
The EC50 values of isolates varied according to inhibition percentages. Highly resistant isolates, such as #39 against oxathiapiprolin + famoxadone, exhibited EC50 values more than tenfold higher than the recommended rate (14,076 vs. 1,200 ppm), whereas sensitive isolates required only 50-75% of the recommended dose (Table 5; S7.1-7.4). Thus, achieving 50% control in resistant isolates would require a tenfold increase in application rates, rendering management economically unsustainable and environmentally harmful due to excessive fungicide use, resistance selection, and health risks (Brent and Hollomon, 2007a).
Resistance levels correlated positively with fungicide application frequency and type. Isolates from Magelang (#38, #40, #41, and #42) with low to medium resistance (score 2-3) exhibited high sensitivity (93-100% inhibition) under relatively low application pressure. By contrast, isolates #32 and #35 from Banjarnegara (score 4, high resistance) displayed minimal inhibition (∼0-34%) following frequent OXTP use. Notably, highly resistant isolates also emerged in low-resistance areas, such as #6 from Wonosobo and #39 from Magelang (Table 5; S1, S7.1-7.4), suggesting that broader sampling is needed. Selection pressure likely drives resistance through gene flow and recombination, increasing genetic variation and enabling P. infestans to generate novel genotypes with enhanced adaptability under fungicide pressure. This supports the conclusion that the ID genotype code may align with that reported by Dangi et al. (2021). Because resistant populations can spread via migration, further genotyping using 12-plex SSR markers is required, as this study was limited to four markers.
Migration of new genotypes and both mating types broadens genetic diversity, reshaping population structure (Fry et al., 1993; Goodwin, 1997; Goodwin et al., 1994). Consequently, more aggressive and resistant strains may emerge and rapidly displace older ones (Ericsson, 2023; Peterson, 2015). Indeed, P. infestans populations are often temporarily dominated by one or a few clonal lineages (Fry, 2020), and the introduction of exotic genotypes can alter local population composition. Analogous to resistance genotypes, these newcomers may exhibit novel phenotypic profiles, resist conventional management, and sustain epidemics. When such strains prevail, the local population structure shifts dramatically. Moreover, while sexual recombination generates genetic diversity, clonal propagation (asexual) drives multi-plication and dissemination (Li et al., 2012).
In this study, both A1 and A2 mating types were detected, with proportions varying across locations and A1 generally dominant. Balanced ratios promote sexual reproduction, which enhances genetic diversity, accelerates adaptation, and increases the likelihood of fungicide resistance through recombination (Gisi and Cohen, 1996; Goodwin, 1997; Goodwin et al., 1996). By contrast, populations dominated by a single mating type reproduce clonally, spreading resistance mutations via mitotic propagation but with relatively slow genetic change (Grünwald et al., 2006; Grünwald and Flier, 2005; Hansen et al., 2016; Mazáková et al., 2006, 2010). While spontaneous mutations can confer resistance under both reproductive modes, sexual populations evolve resistance more rapidly by combining multiple alleles and uncoupling them from fitness costs (McDonald and Linde, 2002; Milgroom and Fry, 1997). Regional evidence supports this view: higher A2 frequencies in northern and eastern Europe correlate with elevated resistance levels, and the emergence of self-fertile isolates further accelerates resistance by enabling recombination without opposite mating types (Ludwiczewska et al., 2025). Resistance to phenylamides also emerged in A1 populations prior to A2 establishment (Gisi and Cohen, 1996). These insights underscore the need to integrate mating type distribution into resistance management, particularly in regions where both types coexist, with fungicide rotation and mixtures serving as critical strategies (Fry, 2016; Grünwald et al., 2006).
Structure analysis (Fig. 3B; S.1) indicated that isolates #6, #93, #15, and #24 shared genetic patterns with populations from Magelang and Banjarnegara, suggesting these regions represent high-risk zones for resistance emergence despite weak direct correlations. Resistant isolates carrying the G770V mutation were detected mainly in Banjarnegara and Wonosobo, which clustered closely and appeared linked through shared ancestry or gene flow, while also genetically proximate to Magelang. The limited variation among these populations likely reflects common ancestry or uniform selection pressure from fungicide application, raising concern that resistant isolates (#6, #32, #39, #58, #83) may become dominant if preventive measures are not adopted (Fig. 3A, B). Mutation analysis confirmed that multiple substitutions in OSBPI underlie OXTP resistance, with nucleotide changes altering amino acid sequences and protein function as an adaptive response to fungicide pressure. A positive correlation between mutation frequency and fungicide application intensity was evident, particularly for OXTP: isolates #93 and #78 from Sumber Berantas (resistance score 4) carried extensive mutations (75 + 14 indels in #93; 14 in #78), consistent with reports of intensive fungicide use.
This study confirmed the G770V mutation as the principal determinant of OXTP resistance in Indonesian populations, consistent with FRAC reports, with three major genotypes involved (ID genotype; EU_2_A1; EU_33_A2). Additional mutations (G770A, K884C/E/R, S768R/D/N) were also identified, with K884C/E/R strongly implicated alongside G770A. Poisoned food assays coupled with PCR sequencing effectively characterized resistance and associated mutations, while microsatellite markers proved valuable for genotyping and population structure analysis, providing insights into origin, dominant genotypes, and migration risks. Regular population genetic monitoring is therefore critical for resistance management. In line with FRAC (2024) and van den Bosch et al. (2014), combining high-risk fungicides such as OXTP with partners from different MoAs reduces selection pressure, prolongs efficacy, and stabilizes disease control. Supporting this approach, assays demonstrated that oxathiapiprolin + chlorothalonil effectively inhibited mycelial growth in resistant isolates (#6, #32, #35, #38), except for #39.
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