Random Amplified Polymorphic DNA-Based Analysis of Genetic Diversity and Characterization of Representative Bipolaris oryzae Isolates from 2023–2024

Abstract

Rice brown spot, caused by Bipolaris oryzae, typically occurs during the rice harvest season and can cause substantial yield losses. In 2023–2024, this disease emerged in rice cultivation areas of Korea. However, the population structure and genetic diversity of B. oryzae isolates remain unclear. Such information is necessary to effectively target and control rice brown spot. Thus, this study aimed to investigate the population structure of 50 B. oryzae isolates collected from the leaves, neck, and panicles of rice infected with brown spot through random amplified polymorphic DNA analysis. Among 140 primers tested, 30 were selected and applied, of which 5 exhibited significant polymorphisms among the isolates. The generated dendrogram revealed five clades with 92% similarity. Group A was the most predominant, comprising 84.0% of the total isolates (42/50), followed by Group C (8.0%, 4/50). Groups B, D, and E each contained one isolate (2%). These isolates were collected from the southern region of Korea and exhibited high genetic similarity (>95%). Two strains from Group A (F1305 and F1318) and one each from Groups B (F1248), C (F1253), D (F1317), and E (F1409) were selected and tested for their mycological characteristics and pathogenicity. Compared with the other strains, F1253, F1317, and F1409 exhibited higher conidial production and caused larger diseased leaf areas in the pathogenicity tests. These results suggest that the B. oryzae isolates that caused rice brown spot in 2023 are genetically homogeneous. This study may serve as a basis for developing targeted control strategies against brown spot.

Rice (Oryza sativa L.) is a major staple food crop in several regions worldwide (Bandumula, 2018; Liu et al., 2014). Considerable yield losses in rice production are caused by various diseases, including rice blast by Magnaporthe oryzae, rice sheath blight by Rhizoctonia solani, bacterial leaf blight by Xanthomonas oryzae pv. oryzae, bacterial grain rot by Burkholderia glumae, and rice brown spot (RBS) by Bipolaris oryzae (Liu et al., 2014).

Bipolaris oryzae (teleomorph: Cochliobolus miyabeanus) typically causes brown spot in leaves under poor nutrition or moisture deficiency during the rice harvest season (Agrios, 2005). RBS is a re-emerging disease that poses outbreak threats due to climate change (Savary et al., 2019). Moreover, it is sensitive to temperature and rainfall, with the risk of occurrence increasing depending on climatic conditions (Ray et al., 2015; Savary et al., 2019).

In Korea, the incidence of RBS was reported between 2001 and 2008. High incidence rates were recorded in Gyeongnam (51.5%), Chungbuk (61.3%), and Gyeongbuk (79.2%) in 2001 and in Chungnam (60.5%), Jeonbuk (57.5%), and Jeonnam (47.2%) in 2005. From 2001 to 2008, the incidence rate of this disease consistently ranged from 30% to 50% (Lee et al., 2010). However, no precise incidence rates of RBS have been reported since then.

Understanding the genetic diversity of B. oryzae populations is essential for developing effective disease control strategies, including regional disease forecasting and resistant variety breeding. Previous studies investigated the genetic structure of B. oryzae populations using various molecular markers, such as randomly amplified polymorphic DNA (RAPD) (Motlagh and Anvari, 2010), intersimple sequence repeats (ISSR) (Archana et al., 2014), and repetitive element-based PCR (Nazari et al., 2015). Significant polymorphisms were reported in Iranian isolates analyzed for genetic diversity using RAPD (Motlagh and Anvari, 2010). Archana et al. (2014) demonstrated that universal rice primer and ISSR markers are effective in differentiating B. oryzae isolates from India. A distinct genetic cluster of West African B. oryzae was identified using genome-wide sequencing (genotyping by sequencing, GBS), which provided insights into the evolutionary dynamics of this pathogen (Kaboré et al., 2022).

In South Korea, where rice is the staple crop, the population structure and genetic diversity of B. oryzae isolates remain unclear despite recent RBS outbreaks and urgent reports from the Rural Development Administration (Rural Development Administration, 2023). This gap exists for several reasons, including the fact that RBS typically occurs during the rice harvest season and is not considered a significant threat. However, interest in B. oryzae has increased in recent years because of a shift in the timing of RBS outbreaks in South Korea (National Institute of Crop Science, Rural Development Administration, 2024). Therefore, understanding the genetic diversity of B. oryzae isolates collected from Korean fields could provide valuable insights for effective pathogen management.

This study aimed to assess the genetic diversity of B. oryzae isolates collected from rice-growing regions in Korea between 2023 and 2024. Based on the observed polymorphic patterns, we aimed to identify representative isolates and characterize their morphological and pathogenic traits. This study provides essential baseline data for understanding the population structure of B. oryzae in Korea and serves as a foundation for developing targeted control strategies against RBS.

Materials and Methods

Isolates and culture conditions

All isolates collected in 2023 and used in the experiments were provided by the National Institute of Crop Science of the Rural Development Administration. The remaining isolates were collected in 2024 from Sunchon National University (Table 1). The isolates were cultured and sporulated on potato dextrose agar (Difco Laboratories, Detroit, MI, USA) at 25°C in the dark. The strains were transferred to 20 mL of potato dextrose broth (Difco Laboratories) in 125 mL flasks and cultured at 25°C with shaking at 120 rpm for 7 days to obtain mycelia for genomic DNA extraction.

Table 1.

Isolates collected and used in this study

Isolate no.	NICS no.	GenBank accession no.	RAPD group	Collection date	Tissue from rice	Collection site	Area, GPS
SYP-F1277	CM23-001	PV962737	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1279	CM23-003	PV962738	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1280	CM23-004	PV962739	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1281	CM23-005	PV962740	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1282	CM23-006	PV962741	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1283	CM23-007	PV962742	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1284	CM23-008	PV962743	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1285	CM23-009	PV962744	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1286	CM23-010	PV962745	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1287	CM23-011	PV962746	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1288	CM23-012	PV962747	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1289	CM23-013	PV962748	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1290	CM23-014	PV962749	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1291	CM23-015	PV962750	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1292	CM23-016	PV962751	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.902″, 126°46′41.72″
SYP-F1293	CM23-017	PV962752	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.931″, 126°46′41.415″
SYP-F1294	CM23-018	PV962753	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.931″, 126°46′41.415″
SYP-F1295	CM23-019	PV962754	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.931″, 126°46′41.415″
SYP-F1296	CM23-020	PV962755	A	14 Sep 2023	Panicle	Gimje-si	35°51′48.931″, 126°46′41.415″
SYP-F1297	CM23-021	PV962756	A	14 Sep 2023	Panicle	Gimje-si	35°51′22.306″, 126°46′13.875″
SYP-F1298	CM23-022	PV962757	A	14 Sep 2023	Panicle	Gimje-si	35°51′22.306″, 126°46′13.875″
SYP-F1299	CM23-023	PV962758	A	14 Sep 2023	Panicle	Gimje-si	35°51′22.306″, 126°46′13.875″
SYP-F1300	CM23-024	PV962759	A	14 Sep 2023	Panicle	Gimje-si	35°51′22.306″, 126°46′13.875″
SYP-F1301	CM23-025	PV962760	A	14 Sep 2023	Panicle	Gimje-si	35°51′22.306″, 126°46′13.875″
SYP-F1302	CM23-026	PV962761	A	14 Sep 2023	Panicle	Gimje-si	35°51′22.306″, 126°46′13.875″
SYP-F1303	CM23-027	PV962762	A	14 Sep 2023	Panicle	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1304	CM23-028	PV962763	A	14 Sep 2023	Panicle	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1305	CM23-029	PV962764	A	14 Sep 2023	Panicle	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1306	CM23-030	PV962765	A	14 Sep 2023	Panicle	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1307	CM23-031	PV962766	A	14 Sep 2023	Panicle	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1308	CM23-032	PV962767	A	14 Sep 2023	Panicle	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1309	CM23-033	PV962768	A	14 Sep 2023	Panicle	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1310	CM23-034	PV962769	A	14 Sep 2023	Neck	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1311	CM23-035	PV962770	A	14 Sep 2023	Neck	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1312	CM23-036	PV962771	A	14 Sep 2023	Neck	Gunsan-si	35°55′18.131″, 126°47′6.594″
SYP-F1313	CM23-037	PV962772	A	14 Sep 2023	Panicle	Gunsan-si	35°55′2.201″, 126°47′14.114″
SYP-F1314	CM23-038	PV962773	A	14 Sep 2023	Panicle	Gunsan-si	35°55′2.201″, 126°47′14.114″
SYP-F1315	CM23-039	PV962774	A	14 Sep 2023	Panicle	Gunsan-si	35°55′2.201″, 126°47′14.114″
SYP-F1316	CM23-040	PV962775	A	14 Sep 2023	Panicle	Gunsan-si	35°55′2.201″, 126°47′14.114″
SYP-F1317	CM23-041	PV962776	A	14 Sep 2023	Panicle	Gunsan-si	35°55′2.201″, 126°47′14.114″
SYP-F1318	CM23-042	PV962777	A	14 Sep 2023	Neck	Gunsan-si	35°55′2.201″, 126°47′14.114″
SYP-F1319	CM23-043	PV962778	A	14 Sep 2023	Neck	Gunsan-si	35°55′2.201″, 126°47′14.114″
SYP-F1250	23CM6	PV962781	A	6 Sep 2023	Leaf	Jeonbuk-do	35°56′1.55″, 126°41′17.955″
SYP-F1248	23CM2	PV962779	B	21 Aug 2023	Leaf	Jeollanam-do	34°41′21.25″, 126°53′53.595″
SYP-F1249	23CM4	PV962780	C	6 Sep 2023	Leaf	Gyeongnam-do	35°30′0.101″, 127°53′33.896″
SYP-F1251	23CM8	PV962782	C	11 Sep 2023	Leaf	Gyeongbuk-do	35°49′3.774″, 129°15′25.657″
SYP-F1253	23CM10	PV962783	C	22 Sep 2023	Leaf	Gyeongnam-do	34°58′49.422″, 128°20′1.633″
SYP-F1254	23CM12	PV962784	C	22 Sep 2023	Leaf	Gyeongnam-do	35°0′53.906″, 128°3′25.725″
SYP-F1371	SBO-1	PV962785	D	12 Jul 2024	Leaf	Jeonnam-do	34°59′58.441″, 127°29′26.275″
SYP-F1409	SBO-2	PV962786	E	12 Aug 2024	Leaf	Jeonnam-do	34°59′58.441″, 127°29′26.275″

RAPD, randomly amplified polymorphic DNA.

DNA extraction and molecular identification

Cultured mycelia were harvested and lyophilized to extract genomic DNA. The lyophilized mycelia were ground into a fine powder using a bead beater (BioSpec Mini Bead Beater 8, Bartlesville, OK, USA) and extracted using the NucleoSpin Plant II Mini Kit (Macherey-Nagel, Düren, Germany) in accordance with the manufacturer’s instructions. DNA concentration was measured using a NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies Inc., Wilmington, NC, USA).

For molecular identification, the internal transcribed spacer (ITS) regions were amplified using the primer pair ITS4 (5′-TCCTCCGCTTATTGATATGC-3′) and ITS5 (5′-GGAAGTAAAAGTCGTAACAAGG-3′) (White et al., 1990). The amplified PCR products were analyzed at Bioneer Inc. (Daejeon, Korea). The resulting sequences were assembled and edited using the SeqMan program from DNAStar (Madison, WI, USA). All sequences generated in this study were submitted to the National Center for Biotechnology Information (NCBI, http://www.ncbi.nlm.nih.gov) (Table 1).

RAPD-PCR

The extracted DNA was diluted to 12.5 ng/μL. A total of 140 10-mer primers from Operon Technology were used in this experiment (Supplementary Table 1). Amplification reactions were performed using 25 ng of genomic DNA (at 12.5 ng/μL), 10 pmol of 10-mer random primers, and 10 μL of 2× i-StarMAX II PCR master mix (iNtRON Biotechnology Inc., Seongnam, Korea). PCR experiments were conducted in a total volume of 20 μL per tube. Amplification was carried out in an ABI 2720 Thermal Cycler (Applied Biosystems, Thermo Fisher Scientific, Marsiling, Singapore) for 40 cycles consisting of 94°C for 1 min, 40°C for 1 min, and 72°C for 2 min, with the fastest possible temperature transitions between steps. PCR products were electrophoresed on a 1.2% agarose gel containing EcoDye DNA staining solution (catalog number LD001-1000, Solgent, Daejeon, Korea) at 100 V for 2 h and 30 min and then photographed under a UV transilluminator to obtain band profiles for each isolate.

Clustering analysis

The RAPD profile for each isolate was scored as 1 if the DNA band was visually present and 0 if it was absent. Only amplification products of 2 kb or less were considered, and only the major amplification product was scored. This approach assumes that products of the same size and electrophoretic mobility are identical across different isolates.

Each DNA band in the dataset was used to construct a similarity matrix between pairs of data generated across all isolates based on the Dice coefficient [F = 2Nxy/(Nx + Ny)], where Nxy represents the number of bands present or absent in both isolates x and y, and Nx and Ny denote the total number of bands observed for that pair of isolates, respectively.

Based on this similarity matrix, a cluster dendrogram was generated among the isolates by using the average linkage method (unweighted pair-group method with arithmetic mean) for hierarchical agglomerative clustering in the SAHN program of the NTSYS-pc package (Rohlf, 1992).

Pathogenicity test

For the pathogenicity test, a 5 mL conidial suspension (5 × 10⁴ spores/mL) containing Tween 20 (250 ppm) was sprayed onto susceptible 4–5-week-old rice seedlings (Oryza sativa cv. Shindongjin). All inoculated plants were placed in a plastic box and incubated at 30°C with 16 h light and 80–90% humidity. Lesions were observed at 1 dpi, and photographs were taken at 3 dpi. Disease severity was measured at 3 dpi, and diseased leaf area (DLA) was measured using ImageJ software (http://imagej.nih.gov/ij/) from equal areas of infected leaves of each isolate. All experiments were performed in triplicate.

Statistical analysis

Statistical analyses were performed using the SPSS software version 22.0 (IBM Corp., Armonk, NY, USA).

Results

Collection of B. oryzae isolates

In September 2023, brown spots appeared on the leaves, necks, and panicles of rice cultivated in Gimje-si and Gunsan-si in Jeollabuk-do Province, and intensive collection was conducted. In addition, pathogens were collected from brown spot lesions that occurred on the leaves of rice cultivated in Jangheung-gun in Jeollanam-do Province; Sancheong-gun, Sacheon-si, and Goseong-eup in Gyeongsangnam-do Province; and Gyeongju-si in Gyeongsanbuk-do Province (Fig. 1). In 2024, two isolates were obtained from the leaves of rice with brown spot in Suncheon-si in Jeollanam-do Province. A total of 50 isolates were obtained (Table 1, Fig. 1).

Fig. 1.

Map showing the collection sites and the number of isolates obtained from rice leaf, neck, and panicle tissues in the southern region of Korea between 2023 and 2024.

The ITS region sequences of all the isolates collected from 2023 to 2024 were identical. These sequences (PV962737 to PV962786) showed 100% identity (562 bp/562 bp) with B. oryzae CBS 199.54 (MH85729).

RAPD analysis

To select primers suitable for genetic diversity testing, we randomly selected leaf (F1254) and panicle (F1286) strains isolated from different tissues and regions for experimentation. Among the 140 primers tested, 107 produced amplification bands in both strains (Supplementary Fig. 1). Of these, 62 primers that generated identical bands in both strains were excluded from the candidate pool. From the remaining 45 candidate primers, 30 that exhibited the highest number of polymorphic bands were selected (Table 2).

Table 2.

List of 30 primers selected through PCR screening from a pool of 140 primers

Oligo name	Sequence (5′–3′)	Total band score	No. of polymorphic bands	Percentage of polymorphism (%)
OPA-13	CAGCACCCAC	7	1	14.3
OPB-01	GTTTCGCTCC	2	0	0.0
OPB-06	TGCTCTGCCC	11	9	81.8
OPB-09	TGGGGGACTC	7	4	57.1
OPB-14	TCCGCTCTGG	10	8	80.0
OPC-02	GTGAGGCGTC	11	4	36.4
OPC-05	GATGACCGCC	13	7	53.8
OPC-06	GAACGGACTC	15	14	93.3
OPC-12	TGTCATCCCC	8	4	50.0
OPC-13	AAGCCTCGTC	6	1	16.7
OPC-15	GACGGATCAG	12	6	50.0
OPC-17	TTCCCCCCAG	5	4	80.0
OPD-02	GGACCCAACC	10	4	40.0
OPD-07	TTGGCACGGG	7	4	57.1
OPD-08	GTGTGCCCCA	11	5	45.5
OPD-09	CTCTGGAGAC	6	2	33.3
OPD-13	GGGGTGACGA	10	2	20.0
OPD-17	TTTCCCACGG	5	4	80.0
OPD-19	CTGGGGACTT	6	4	66.7
OPE-04	GTGACATGCC	10	4	40.0
OPE-19	ACGGCGTATG	10	5	50.0
OPF-04	GGTGATCAGG	12	7	58.3
OPF-12	ACGGTACCAG	10	7	70.0
OPG-04	AGCGTGTCTG	7	2	28.6
OPH-12	ACGCGCATGT	11	6	54.5
OPH-13	GACGCCACAC	11	7	63.6
OPI-05	TGTTCCACGG	7	4	57.1
OPP-14	CCAGCCGAAC	5	3	60.0
OPV-10	GGACCTGCTG	12	5	41.7
OPW-07	CTGGACGTCA	8	4	50.0
	Total	265	141	53.2

PCR was performed on 50 strains using the 30 primers. Representative images are shown in Fig. 2. The number of amplified DNA fragments ranged from 2 (OPB-01) to 16 (OPD-08). However, the presence of amplified bands did not always correspond to polymorphic band information. The primer that provided the most polymorphic information was OPC-06, with 14 of 15 bands (93.3%) being polymorphic. The following primers generated more than 10 readable bands and produced polymorphic bands: OPB-06, 9/11 polymorphic bands (81.8%); OPB-14, 8/10 polymorphic bands (80.0%); OPF-12, 7/10 polymorphic bands (70.0%); and OPD-02, 4/10 polymorphic bands (40.0%) (Table 2).

Fig. 2.

Amplified DNA band patterns of genomic DNAs from Bipolaris oryzae isolates by PCR with five primers. PCR products were separated on a 1.5% agarose gel at 100 V for 2 h and 30 min. The five representative gel images shown correspond to the primers that generated the greatest number of polymorphic bands.

A total of 265 bands were generated from 30 primers, of which 141 (53.2%) were polymorphic across the 50 strains. The combined data revealed high similarity among all isolates, with similarity values exceeding 77.5% (Fig. 3). Six groups were arbitrarily defined based on similarities ranging from 87% to 92%, as these groups tended to correlate with the geographic regions from which the isolates were collected (Fig. 3). However, none of these groups were supported by bootstrapping (<10% support).

Fig. 3.

Dendrogram of 50 Bipolaris oryzae isolates collected from rice, based on randomly amplified polymorphic DNA (RAPD) polymorphism. Data from 30 different RAPD primers were combined and used to construct the dendrogram using the unweighted pair-group method with arithmetic mean. Numbers in parentheses indicate the number of isolates collected.

The 50 isolates were classified into several major groups based on their genetic similarity coefficients, which showed some correlation with their regional distribution. For example, isolates from Gimje-si and Gunsan-si in Jeollabuk-do Province were included in Group A. One isolate from Group B that exhibited 92% similarity to Group A was collected from Jangheung-gun in Jeollanam-do Province. Four isolates with 88% similarity to Groups A and B were obtained from Sancheong-gun, Sacheon-si, and Goseong in Gyeongsangnam-do Province and from Gyeongju-si in Gyeongsangbuk-do Province. Although these four isolates were collected from different regions, their RAPD-PCR band patterns were identical, suggesting that they are genetically identical.

Interestingly, the two strains collected from Suncheon-si in 2024 were each classified into Groups D and E. These isolates were collected from the same cultivar, Saecheongmu, in July and August. Therefore, whether RAPD-based genotypes of B. oryzae vary across host cultivars is difficult to conclude. Notably, the isolates in Group C were collected from different locations and at different times.

Characterization of six representative isolates from each group

Representative isolates were selected from each group to examine their mycological differences. In Group A, isolates collected from the panicles (F1305) and necks (F1318) were randomly selected. In Group C, an isolate collected from the leaves (F1253) was randomly selected. The sole isolates from Groups B (F1248), D (F1371), and E (F1409) were selected.

All isolates grew rapidly, forming fluffy colonies that reached 45–70 mm in diameter on a 90 mm Petri dish within 5 days. The anterior surface of the mycelia varied in texture, with most of the central area exhibiting fluffy gray growth, sometimes bordered by a whitish margin (F1253 and F1371). The reverse side of the mycelia ranged from dark gray to nearly black (Fig. 4A).

Fig. 4.

Mycological characteristics of six representative strains selected. (A) Colony morphology on potato dextrose agar after 5 days of incubation at 25°C: front (top) and reverse view (bottom). Micromorphological characteristics of conidial production (B) and conidiophore (C) observed after slide culture following 3 days of incubation at 25°C. (D) Conidia. (E) Results of the pathogenicity test of Bipolaris oryzae on 4–5-leaf stage Oryza sativa (cv. Shindongjin) plants. Leaves were inoculated by conidial suspension (1 × 10⁵ conidia/mL). The inoculated plants were incubated in a growth chamber at 30°C with 100% humidity, under a photoperiod of 16 h light and 8 h darkness. Disease rating was conducted 3 days later.

To assess conidial production, we examined conidia and conidia-bearing conidiophores after 3 days of growth using a slide culture method. Our observations confirmed that all six isolates produced conidia, with F1253, F1371, and F1409 generating the highest number of spores (Fig. 4B). Notably, each conidiophore produced 2–4 conidia (Fig. 4C). Conidiation was evaluated after 2 weeks of growth on potato dextrose agar. Among the isolates, F1371 produced the highest number of conidia, with a statistically significant difference, followed by F1409 and F1253. F1305, F1318, and F1248 exhibited low sporulation levels (Table 3, Fig. 4D). Spore length and width also varied among the isolates (Table 3).

Table 3.

Characterization of six representative Bipolaris oryzae isolates by RAPD analysis

Representative isolate	Conidiationa (104/mL)	Range of conidium sizeb		Average of conidium size (μm) (length × width)	Diseased leaf area (%)c, Avg ± SD
		Length (μm)	Width (μm)
F1305	44.7 ± 13.6 Dd	37.6–68.5	8.9–14.2	53.1 × 11.2	5.7 ± 1.5 E
F1318	48.0 ± 8.5 D	57.1–93.0	9.6–16.8	67.8 × 13.7	11.8 ± 1.4 D
F1248	52.3 ± 14.6 D	27.4–82.8	6.0–17.1	55.7 × 10.8	24.1 ± 2.7 C
F1253	253.7 ± 5.5 C	56.4–96.3	11.2–19.8	70.5 × 14.7	31.5 ± 7.4 B
F1371	633.3 ± 125.8 A	46.2–95.3	11.4–19.8	75.0 × 15.2	43.0 ± 6.4 A
F1409	424.0 ± 31.7 B	45.1–87.7	10.3–16.7	62.5 × 13.3	35.3 ± 7.9 A

RAPD, randomly amplified polymorphic DNA; Avg, average; SD, standard deviation.

^aConidiation was measured by counting the number of conidia collected in 5 mL of sterilized distilled water from 14-day-old potato dextrose agar plates.

^bSizes of conidia were determined from at least two experiments with 50 conidia each.

^cDiseased leaf area percentage measured using ImageJ software.

^dTukey’s test was used to determine significance at the 95% probability level. The same letters in each column indicate no significant differences.

In the pathogenicity test, DLA measurement revealed that F1371 and F1409 caused the largest lesion areas at statistically significant levels, followed by F1253, F1248, and F1318. Among the experimental isolates, F1305 showed the smallest lesion area, indicating the lowest pathogenicity (Table 3, Fig. 4E). Considering its robust conidial formation and relatively high pathogenicity in rice seedlings, isolate F1371 (Group D) was identified as the most suitable representative isolate for subsequent pathogenicity and functional studies. This clarification emphasizes that the selection refers to a specific isolate rather than the entire group and highlights its potential as a model strain for future investigations into the pathogenic mechanisms of B. oryzae in Korea.

Discussion

Recent disease outbreaks caused by B. oryzae have been closely associated with climate change (Ray et al., 2015; Savary et al., 2019). The genetic diversity of the isolates could contribute to the ability of the pathogen to adapt to environmental changes, indicating possible shifts in disease patterns or emergence of drug-resistant isolates in the future. In the present study, we conducted a RAPD analysis on 50 B. oryzae isolates collected from domestic rice fields between 2023 and 2024 to assess their genetic diversity and regional genetic structure. The RAPD analysis revealed variability among the 30 primers. Notably, primers OPC-06 and OPB-06 exhibited high polymorphism rates of 93.3% and 81.8%, respectively, indicating that these primers are well-suited for elucidating genetic diversity within B. oryzae populations in Korea. These primers can be used to develop scar markers for distinguishing between the different groups.

Cluster analysis of the isolates revealed that those collected from Gimje-si and Gunsan-si in Jeonbuk-do Province formed identical clusters, whereas isolates collected from a single location belonged to different clusters. These findings may reflect the spread of the pathogen within the same region or similarities arising from shared agricultural environments such as cultivars. Alternatively, the observed polymorphisms possibly resulted from introductions from outside the region or genetic recombination. To address this limitation, a survey of the host plant cultivars and the presence of both mating types in the pathogen should be conducted.

Notably, a relatively small number of isolates collected from four geographically distinct locations (Jangheung-gun, Sacheon-si, Gyeongju-si, and Goseong-gun) showed identical genetic clusters. Similar results were reported in RAPD-PCR analyses of B. oryzae isolates collected from Iran, where no correlation was found between genetic diversity and geographical distribution (Motlagh and Anvari, 2010). Our study revealed that except for Gimje-si and Gunsan-si, the sample sizes within each region were too small, underscoring the need for future regional pathogen monitoring to validate these findings.

Another point is that the two representative isolates in Group A, F1305 and F1318, were collected from the panicles and necks, respectively. These isolates belonged to a different group than the four isolates collected from the leaves. Mycological analysis revealed that they exhibited relatively low conidiation and pathogenicity in the leaves compared with the other four isolates (Table 3). The isolation of these two strains from the panicles and necks suggests their potential tissue specificity in B. oryzae. Although F1250, a leaf-derived isolate belonging to Group A, was listed in Table 1, it was not included in our pathogenicity assays. Therefore, whether leaf-origin isolates in Group A, such as F1250, display higher virulence on leaves remains unclear. This indicates that the reduced pathogenicity observed in F1305 and F1318 may be related more to tissue origin than to genetic grouping. Accordingly, we cannot generalize that Group A isolates are uniformly less virulent on rice leaves. Additional pathogenicity tests with leaf-derived isolates from Group A are required to verify this possibility.

RAPD analysis offers the advantages of being relatively simple and rapid for identifying genetic structures; however, it has limitations, including limited reproducibility and low resolution (Hong et al., 2021; Oliveira and Azevedo, 2022). Therefore, the findings of this study should be complemented with high-resolution genotyping methods, such as amplified fragment length polymorphism, ISSR (Hong et al., 2021), or GBS (Everhart et al., 2021). Furthermore, this research should be integrated with functional studies on virulence genes and genes related to toxin production, such as ophiobolin. In summary, we quantitatively analyzed the genetic diversity of B. oryzae strains collected in Korea, including regional similarities and heterogeneities, by using RAPD analysis. Our results provide fundamental data for understanding disease outbreak patterns, developing region-specific disease management strategies, and breeding resistant varieties.

Electronic Supplementary Material

Supplementary materials are available at The Plant Pathology Journal website (http://www.ppjonline.org/).