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Article

Comparative Genomics Reveals Host-Specific Adaptation of Pyricularia oryzae Strains Isolated from Rice and Barnyard Grass

by
Wenda Sun
1,†,
Xiaohan Zhang
1,†,
Zhuan Zhang
1,
Xiaofang Xie
1,
Song Tang
1,
Tian Song
1,
Baoxu Lu
1,
Jiafeng Wang
2,
Zhibin Liang
1,
Xiaofan Zhou
1,* and
Yizhen Deng
1,*
1
Guangdong Provincial Key Laboratory of Microbial Signals and Disease Control, Integrative Microbiology Research Centre, South China Agricultural University, Guangzhou 510642, China
2
Nation Engineering Research Center of Plant Space Breeding, South China Agricultural University, Guangzhou 510642, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
J. Fungi 2026, 12(2), 109; https://doi.org/10.3390/jof12020109
Submission received: 11 January 2026 / Revised: 31 January 2026 / Accepted: 2 February 2026 / Published: 5 February 2026
(This article belongs to the Section Fungal Genomics, Genetics and Molecular Biology)
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Abstract

Barnyard grass, a widespread and persistent weed in rice paddies, belongs to the same family as rice and may act as a bridge host for the rice blast fungus. This study utilized comparative genomics to analyze six Pyricularia oryzae strains isolated from barnyard grass (Baicao series) and rice (GDYJ7 and ZJX18), integrating pathogenicity assays, whole-genome sequencing, and functional annotation. Pathogenicity tests demonstrated host specificity, as Baicao series strains caused typical lesion symptoms on barnyard grass but not on rice leaves, while GDYJ7 and ZJX18 caused lesions mainly on rice. Genomic analyses indicated that Baicao series strains possessed larger genomes (41.04 Mb to 41.16 Mb) with a higher content of repetitive sequences (6.68% to 7.09%) compared to rice strains GDYJ7 and ZJX18 (38.69 Mb and 39.05 Mb; 3.66% and 3.71% repeats). Phylogenetic analysis confirmed that Baicao series strains represent a grass-infecting pathotype of P. oryzae species, as they were grouped with the established grass-isolated P. oryzae strains, while GDYJ7 and ZJX18 were grouped with rice-isolated P. oryzae strains. However, Baicao series, GDYJ7 and ZJX18 are all relatively distant from P. grisea species. PCR amplification revealed that Baicao series strains harbored significantly fewer avirulence genes (Avr-Pib, Avr-Pizt, PWL3) than GDYJ7 and ZJX18 (Avr-Pib, Avr-Pizt, Avr-Pi9, Avr-Pik, PWL2), with Baicao9 retaining only Avr-Pib. In summary, our results suggested that the genomic sequences of the barnyard grass-isolated strains serve as a valuable resource for the study of P. oryzae strains with differential host preference and provide novel insights into the evolution of pathogen genomes during host adaptation.

1. Introduction

Rice (Oryza sativa L.) is one of the most important food crops for people worldwide [1]. Rice blast caused by Pyricularia oryzae (syn. Magnaporthe oryzae) is one of the most devastating diseases affecting rice crops worldwide, leading to 10% to 30% losses of global rice yield every year [2,3,4]. In the past three decades, rice blast has been developed as a model system to study fungal–plant interactions [5,6]. P. oryzae was the first plant pathogenic fungus to have its genome sequenced and made available to the public [7]. Pyricularia species can infect not only cereal crops such as rice, wheat [8], and barley [9] but also a wide range of grasses including crabgrass (Digitaria spp.), foxtail (Setaria spp.), and ryegrass (Lolium spp.) [10,11,12]. Studies have also shown that certain Pyricularia species are capable of infecting non-gramineous hosts such as banana (Musa spp.) and ginger (Zingiber spp.) [13,14]. Host shift and host expansion by fungal plant pathogens have increased the frequency of emerging pathogens and the incidence of disease across crops, posing a serious threat to global food security [15,16,17]. A notable case of host shift and co-evolution of this fungus in recent decades is the outbreak of wheat blast caused by P. oryzae in South America in the early 1990s [18]. Additionally, the infection of wheat plants displaying white head symptoms in Kentucky, USA, has been suggested as another key example of host shift, where the pathogen likely jumped from ryegrass (Lolium spp.) to wheat as its new host [19]. Another study suggests that P. oryzae may shift from millet (Setaria spp.) to rice [20]. The broad host range of P. oryzae, coupled with its potential for host shift, significantly contributes to the emergence and outbreak of novel diseases and is thus worthy of in-depth investigation on the molecular mechanism of host adaptation and expansion of the P. oryzae population. Availability of genome sequences of P. oryzae population thus provides the basis for making such analyses.
The publication of the P. oryzae strain 70-15 genome has since spurred the sequencing and assembly of numerous field isolates [7], such as Guy11 [21], B157 [22], Y34 [23], and 98-06 [24]. Comparative genomic and functional studies of these strains have uncovered substantial genome plasticity and highlighted the contribution of lineage-specific genes to pathogenicity [25,26,27]. More recently, advances in sequencing technologies have enabled large-scale sequencing of field isolates from rice as well as isolates collected from various grass and cereal hosts [28,29]. Population-level analyses of these genomes indicate that rapidly evolving genes acts as a key driver of pathogen specialization [30,31,32]. Nevertheless, the genomic landscape of most species within the Pyricularia spp. remains largely unexplored. At present, genomic information on rice-infecting Pyricularia strains is abundantly available but less reported in the Poaceae grass-infecting Pyricularia strains. Therefore, comparative genomic analysis of P. oryzae, the fungal pathogen responsible for rice blast, could aid in monitoring its host shift and/or spread, thereby helping to predict and prevent potential epidemics.
Pathogenic strains of Pyricularia contain more genes encoding secretory proteins and avirulence (Avr) genes in their genomes compared to non-pathogenic strains, which may contribute to their successful host invasion [33]. The interaction between rice and the rice blast fungus follows the classic gene-to-gene interaction theory [34]. The host specificity of the plant pathogen is determined by the interaction between the plant host’s resistance (R) genes and the fungal pathogen’s Avr genes [35]. To date, more than 40 Avr genes have been identified in the rice blast fungus, 12 of which have been cloned [36]. Variation in Avr genes leads to differences in virulence among strains of the fungus [37]. Mechanisms such as gene deletion, frameshift mutations, and transposon insertion can alter the function of Avr genes, thereby affecting the pathogenicity of strains carrying such Avr genes [38,39]. For example, Pot3 transposon has been identified as a major reason to generate different variations of Avr-Pizt and Avr-Pib. The insertion of Pot3 transposon at the −462 bp site in the promoter region of the AvrPiz-t gene enabled the P. oryzae strains Guy11 to evade recognition by rice carrying the Piz-t resistance gene, thereby causing a shift from an “avirulent” to a “virulent” phenotype [40]. In all 248 P. oryzae isolates from Philippines, insertions of the Pot3 transposon were identified within the AvrPib gene, mainly occurring at three distinct sites: two in the promoter region (at positions −304 bp and −125 bp) and one within the coding region (at 169 bp). Such Pot3 insertion resulted in the failure of AvrPib gene expression, and consequently, to the evasion of recognition by the rice Pib resistant gene, leading to a shift from an “avirulent” to a “virulent” phenotype [41]. This type of genetic variation provided the P. oryzae population with a rapid adaptive advantage, thereby enabling it to overcome crop resistance. Therefore, monitoring the loss and variation of Avr genes in populations of the rice blast fungus is crucial for safeguarding rice production in the region.
Barnyard grass, a weed commonly found in and around rice fields, belongs to the same family as rice (Poaceae). It may serve as a bridge host for the rice blast fungus, facilitating the survival and dispersal of the pathogen. In this study, four P. oryzae strains were isolated from blast-like lesions on barnyard grass growing near paddy fields at the Baisha Experimental Base (111°50′ E, 21°52′ N) in Yangjiang, Guangdong Province, China. Together with two previously isolated rice-infecting P. oryzae strains, GDYJ7 and ZJX18, from Xian rice (Indica) in Guangdong and Zhejiang province, respectively, we assessed the pathogenicity of these six strains on detached leaves of barnyard grass and rice and concurrently examined the presence or absence of several known Avr genes. To further investigate their taxonomic classification and pathogenic mechanisms at the genomic level, we performed whole-genome sequencing on all six isolates. Overall, this study aims to reveal the molecular mechanism of host adaptation by different strains within the P. oryzae population.

2. Results

2.1. Isolation and Identification of Six Pyricularia Strains from Barnyard Grass and Rice with Leaf Blast Symptoms

We isolated a total of four Pyricularia single-spore strains from the same diseased barnyard grass leaves collected near rice fields in Yangjiang, designating them Baicao4, Baicao5, Baicao6, and Baicao9. In addition, we included the previously isolated P. oryzae strains GDYJ7 from diseased rice in Yangjiang and ZJX18 from diseased rice in Zhejiang Province (Figure 1A,B). On RPA medium, strains of the Baicao series formed white, fluffy colonies with dense mycelium. Strain GDYJ7 developed grayish-black, fluffy colonies with moderately dense mycelium, while strain ZJX18 produced grayish-white colonies that developed black speckles at later stages, accompanied by relatively sparse mycelium (Figure 1C). The conidia of all the above-mentioned strains were pyriform in shape and contained two septa (Figure 1C).

2.2. Pathogenicity Analysis of the Isolated Strains

We conducted pathogenicity assays on these six strains, among which the pathogenicity of GDYJ7 was validated [42,43] and therefore served as a positive control for rice blast. The results (Figure 2A,B) showed that all Baicao strains successfully infected barnyard grass (with Baicao9 showing weaker pathogenicity) and caused typical lesion symptom but showed almost no infection on rice. Strain GDYJ7 was able to infect the susceptible rice cultivars CO39 and NPB, exhibited limited infection on the moderately resistant cultivar ZH11, and failed to infect barnyard grass. Strain ZJX18 was the most virulent, capable of infecting all three rice cultivars (CO39, NPB, and ZH11) with prominent lesions, and also caused mild lesions on barnyard grass (Figure 2A,B). These findings confirm that isolates from different host origins exhibit clear host preference.

2.3. Genome Sequencing and Assembly

Next, we performed whole-genome sequencing for the six Pyricularia strains using Illumina technology. After quality control of the raw data, de novo assembly generated genome sequences of Baicao4 (size: 41.04 Mb, 1339 contigs), Baicao5 (41.18 Mb, 1278 contigs), Baicao6 (41.44 Mb, 1149 contigs), Baicao9 (41.46 Mb, 1117 contigs), GDYJ7 (38.69 Mb, 1358 contigs), and ZJX18 (39.05 Mb, 1463 contigs). The Contig N50 values ranged from 99,612 bp to 141,887 bp across the six strains, indicating reasonable levels of genome assembly continuity. The GC contents of the four Baicao series strains (50.15% to 50.31%) were slightly lower than those of the two rice-isolated strains GDYJ7 and ZJX18 (51.24% and 51.22%) (Table 1).
Assessment with BUSCO showed that all strains contained at least 4309 (95.9%) complete single-copy genes, indicating high levels of genome completeness (Table 2). Gene prediction analysis identified 11,114 to 11,440 protein-coding genes in the genomes of the six strains (Table 3). Repetitive sequences are widespread in eukaryotic genomes, and their proportion is closely associated with genome size [44]. In this study, repetitive sequence annotation revealed higher levels of repeat content in the four Baicao strains (6.68% to 7.09%) relative to GDYJ7 and ZJX18 (3.66% and 3.71%) (Table 3). Transposable elements represent a major component of repetitive sequences and can drive gene evolution through chromosomal rearrangements and other forms of genomic variation [45]. A total of 650 to 760 DNA transposons were predicted in all six strains genomes, accounting for less than 1% of the total length of each genome. Notably, long interspersed nuclear elements (LINEs) were present in all six strains, whereas short interspersed nuclear elements (SINEs) were only detected in Baicao4, Baicao5, GDYJ7, and ZJX18. In addition, long terminal repeat (LTR) retrotransposons of the Gypsy/DIRS1 and Ty1/Copia types were identified in all six strains, while the BEL/Pao type of LTR retrotransposons was found in strain Baicao9 only (Table 3).
Non-coding RNAs (ncRNAs) are key regulators involved in cellular processes including growth, differentiation, survival, and apoptosis [46]. Our prediction across the six genomes identified all major ncRNA types, including transfer RNAs (tRNAs; 215 to 320), ribosomal RNAs (rRNAs; 40 to 54), small nuclear RNAs (snRNAs, 22 in each strain), and small RNAs (sRNAs, four in each strain) (Table 3).

2.4. Phylogenetic Analyses

Comparative genomic analysis based on average nucleotide identity (ANI) revealed that the four Baicao series strains shared 99.05% to 99.07% identity with P. oryzae strain D15/s47. Strain GDYJ7 showed 99.74% and 99.69% identity to P. oryzae strain 70-15 and P131, respectively, and strain ZJX18 with 99.75% and 99.82% identity with 70-15 and P131 (Table S1). We previously reported a barnyard grass isolated P. grisea strain SCAU-2 [29]. Here, we found that Baicao series strains shared only 86.74 to 86.75% and 86.90 to 86.94% identity with two P. grisea strain SCAU-2 and NI907, respectively (Table S1).
We further constructed a maximum-likelihood (ML) phylogenetic tree using single-copy orthologous gene sequences. The Baicao series strains clustered within the same evolutionary clade as P. oryzae E34, MZ5-1-6 (isolated from Eleucine), and P. grisea D15 (isolated from Digitaria). The strains GDYJ7 and ZJX18 grouped together with the P. oryzae strains 70-15, Y34, and ZJG1 (isolated from rice). SCAU-2 was grouped with P. grisea strains NI907 and D1, separated from the clade containing Baicao series strains (Figure 3). Interestingly, the Baicao series strains are closer to the P. oryzae strains preferring the host Eleusine but relatively distant from the P. grisea strains preferring Digitaria. Therefore, we conclude that Baicao series strains belong to the grass pathotype of P. oryzae species, rather than P. grisea species.

2.5. Prediction and Analyses of Pathogenicity-Related Genes

To identify potential virulence genes in the fungal pathogens, we performed gene annotation using the Pathogen–Host Interactions database (PHI) and the Database of Fungal Virulence Factors (DFVF). Based on the PHI database, 1416, 1430, 1417, 1412, 1422, and 1434 PHI-related genes were annotated in strains Baicao4, Baicao5, Baicao6, Baicao9, GDYJ7, and ZJX18, respectively. Among these, the numbers of genes associated with increased pathogenicity (enhanced virulence) were 19, 19, 19, 19, 18, and 17, respectively (Figure 4A). Details of PHI-related genes with sequence identity > 90% are provided in Table S2. According to the DFVF database, 1258 (11.3%), 1290 (11.6%), 1294 (11.6%), 1287 (11.2%), 1288 (11.6%), and 1293 (11.6%) protein-coding genes were successfully annotated in strains Baicao4, Baicao5, Baicao6, Baicao9, GDYJ7, and ZJX18, respectively (Figure 4B). Detailed information on DFVF-related genes with sequence identity > 90% is listed in Table S3.
Additionally, carbohydrate-active enzymes are closely linked to fungal growth, development, and pathogenicity [47,48]. Based on the Carbohydrate-Active Enzymes (CAZy) database, strains Baicao4, Baicao5, Baicao6, Baicao9, GDYJ7, and ZJX18 were annotated with 2392, 2399, 2399, 2405, 2411, and 2439 related genes, respectively. The CAZyme annotation profiles of the six strains were largely similar in composition and quantity. Among these, glycoside hydrolases (GHs) were the most abundant (1160 to 1183), which was followed by glycosyltransferases (GTs; 548 to 557). Polysaccharide lyases (PLs) represented the lowest proportion among all enzyme classes (Figure 4C). The functional composition of plant cell wall-degrading enzymes (PCWDEs) across the six strains exhibited overall high consistency (Table S4), with hemicellulases predominating, followed by cellulases and lignin-related enzymes. Only in strain GDYJ7 was there a slight reduction in the proportion of hemicellulases (Figure 4D).

2.6. Prediction and Analyses of Membrane Transport Proteins

Membrane transport proteins play a crucial role in pathogen–host interactions. In this study, we predicted 898, 900, 905, 898, 903, and 911 membrane transport proteins in strains Baicao4, Baicao5, Baicao6, Baicao9, GDYJ7, and ZJX18, respectively (Table 4). Among these membrane transport proteins, the composition was generally similar across the six strains. In the four Baicao series strains, secondary transporters represented the highest proportion, with counts of 232, 236, 236, and 228, respectively. In strains GDYJ7 and ZJX18, the numbers of secondary transporters were 228 and 233, respectively. Next, primary active transporters showed a proportion largely comparable to that of secondary transporters. The six strains contained 226, 226, 226, 226, 231, and 231 primary active transporters, respectively (Figure 5A).
The number of major facilitator superfamily (MFS) transporters outnumbered that of ATP-binding cassette (ABC) transporters in all six strains: the counts of MFS transporters in Baicao4, Baicao5, Baicao6, Baicao9, GDYJ7, and ZJX18 were 66, 67, 70, 60, 64, and 69, respectively, whereas the corresponding numbers of ABC transporters were 43, 43, 42, 42, 45, and 42 (Table 4).
Both the ABC and MFS superfamilies contain multidrug transporters, which help pathogens resist adverse environmental stresses [49]. Among the six strains in this study, multidrug transporters accounted for the largest proportion and showed relatively similar abundance. In terms of specific subtypes, the most abundant multidrug transporters in all six strains were proteins of the DHA1 family and multidrug resistance (MDR) proteins, followed by DHA2 family proteins and pleiotropic drug resistance (PDR) proteins (Figure 5B).

2.7. Prediction and Analyses of Secondary Metabolite Biosynthetic Gene Clusters

Plant pathogens produce a wide array of secondary metabolites, which can serve as virulence factors and facilitate host infection [50,51]. In this study, we identified 71, 70, 70, 71, 74, and 83 secondary metabolite biosynthetic gene clusters in the genomes of strains Baicao4, Baicao5, Baicao6, Baicao9, GDYJ7, and ZJX18, respectively. The composition of these clusters was largely similar across strains, with terpene and T1PKS clusters being the most abundant; each strain contained 18–21 clusters of these two types. NRPS and NRPS-like clusters varied somewhat among strains: GDYJ7 and ZJX18 possessed relatively fewer NRPS clusters, while NRPS-like clusters were slightly more numerous in GDYJ7. In contrast, indole clusters were low in number and relatively consistent in all strains (Figure 5C).

2.8. Presence and Variation of Avr Genes

We successfully obtained the sequences of six established Avr genes, Avr-Pib, Avr-Pizt, Avr-Pi9, Avr-Pik, PWL2, and PWL3, from the whole genomes of the six strains. We further verified these Avr genes by PCR amplification, using the primers listed in Table 5. Combined with genome sequencing and PCR amplification, we found that Baicao4, Baicao5, and Baicao6 shared a conserved set of Avr genes including Avr-Pib, Avr-Pizt, and PWL3 but lacked Avr-Pik and PWL2. However, Baicao9 strain contained only Avr-Pib, exhibiting marked uniqueness compared to the other Baicao strains. This suggests a potential divergence in pathogenic mechanisms or host adaptation compared to the other barnyard grass isolates. In stark contrast, the two rice-isolated strains GDYJ7 and ZJX18 possessed Avr-Pib, Avr-Pizt, Avr-Pik, Avr-Pi9, and PWL2. PWL3 was missing in GDYJ7 but present in ZJX18 (Figure S1; Table 6). Two Avr genes, Avr-Pia and Avr-Pita2, were detected in our previously isolated P. grisea strain SCAU-2 [29] but not found in genomes of the six strains in this study, and neither did they amplified by PCR (Figure S1).
We aligned the sequences of Avr-Pib, Avr-Pizt, and PWL3 shared by the isolates from barnyard grass or rice obtained in this study, the P. oryzae strains from different hosts (Oryza, Triticum, Eleusine), and P. grisea strains infecting Digitaria (if available). Simultaneous alterations at two sites, A137T and A158C, were found in the coding sequences of Avr-Pib in the four Baicao series strains and their closest strain MZ5-1-6 (based on phylogenetic analysis showed in Figure 3), resulting in amino acid changes as E46Vand Y53S, respectively, compared to other strains (Figure S2). Two P. grisea strains displayed multiple point mutations that are distinct from P. oryzae strains, but at A103G, G178A, and A213T they are same as one of the wheat-infecting strain 131021 (Figure S2). Avr-Pizt is absent in P. griase strains and Baicao9. Altered sites in the Avr-Pizt coding sequences could clearly distinguish the P. oryzae strains infecting rice, wheat, and grass (Figure S3). The coding sequences of the PWL3 gene are present in three Baicao strains (except for Baicao9) and several other P. oryzae strains. Interestingly, we noticed a clear pattern of nucleotide point mutations, and corresponding changes in amino acid sequences, that could divide all the tested strains into three groups: Baicao series strains and MZ5-1-6 in a group (group A) that is closer with four rice-infecting strains (group B), while the two wheat-infecting strains are grouped with the other grass-infecting strains (group C) (Figure S4). Based on these observations, we hypothesized that the host range of P. oryzae populations could be inferred based on a combination of Avr gene variations.
In summary, differences in the Avr gene profiles between the barnyard grass-isolated Baicao strains and the rice-isolated strains (GDYJ7, ZJX18), as well as established P. grisea strain (SCAU-2), may reflect differential adaptation strategy towards different hosts.

3. Discussion

This study conducted an integrated comparative genomic analysis of six P. oryzae strains obtained from barnyard grass and rice. It revealed dynamic changes and specific adaptive strategies at the genomic level during the pathogen’s adaptation to different Poaceae hosts. Our findings not only confirmed the central role of host specificity in the evolution of the blast fungus but also provided novel genomic perspectives for understanding host jump and potential epidemic risks associated with this important pathogen.
First, we confirmed a clear correlation between the host origin and pathogenic phenotype. The Baicao series strains from barnyard grass successfully infected their original host but were nearly non-pathogenic to rice. In contrast, the rice-isolated strains GDYJ7 and ZJX18 exhibited strong pathogenicity towards rice but showed no or mild infection capability on barnyard grass (Figure 2). Phylogenetic analysis grouped the Baicao series strains with P. oryzae strains isolated from Eleusine, while GDYJ7 and ZJX18 clustered with typical rice-infecting P. oryzae strains (Figure 3), all separated from the P. grisea strains NI907 and SCAU-2. Eleusine genus is represented by species Eleusine indica and Eleusine coracana. E. indica is a widely distributed weed in many regions [56], while E. coracana is utilized as a dual-purpose grain and forage plant in Africa [57]. Our Baicao series strains cluster phylogenetically with multiple E. coracana-isolated strains, suggesting that, during evolution, some P. oryzae strains may have utilized grasses as hosts before they gained ability to infect grain crops. This supported their classification into different pathotypes from an evolutionary relationship perspective. Barnyard grass, a pervasive and difficult-to-eradicate weed in paddy ecosystems, has long been considered a potential alternative host and “refuge” for P. oryzae [10,58]. Previous studies have indicated the existence of gene flow between pathogens on grasses and cereals [59], and host jump is a key factor in the outbreak of emerging diseases such as wheat blast [19,60]. In this study, interestingly, the rice-infecting P. oryzae strain ZJX18 caused a mild lesion symptom on grass (Figure 3). This could be either a residual infectivity to an old host after it jumped from grass to rice, or alternatively, it may broaden its host range from rice to the grass growing at the rice paddy.
Second, analysis of genomic components revealed potential genomic plasticity associated with host adaptation. Transposable elements (TEs), as major drivers of repetitive sequences and genomic plasticity, likely played a key role through differences in their abundance and type [61,62]. For instance, BEL/Pao-type LTR retrotransposons were detected exclusively in the strains Baicao9. Existing research suggests that host specialization is linked to TE-associated dynamic gene gain and loss [26]. In this study, the genome size of barnyard grass-isolated strains was slightly larger than that of rice-isolated strains (Table 1), and their repetitive sequence proportion was significantly higher than that of rice-isolated strains (Table 3). Active TEs might have rapidly promoted genetic variation within pathogen populations by causing chromosomal rearrangements, gene inactivation, or novel gene generation, thereby facilitating adaptation to different host selection pressures [63,64]. For example, Pot3 has been identified as a major reason to generate different variations of Avr-Pizt and Avr-Pib [40,41]. Studies on other plant pathogens, such as Fusarium species causing Fusarium head blight (FHB) or rust fungi causing wheat stem rust, have also shown that rapid changes in transposon-rich genomic regions constitute a common strategy for pathogen genome evolution and rapid adaptation to different environments [65,66,67].
Furthermore, annotation of pathogenesis-related genes indicated that despite host specialization, strains from different origins maintained a high degree of conservation in core pathogenic mechanisms. Annotations based on the PHI and DFVF databases showed that all strains possessed a similar number of pathogenesis-related genes and virulence factors. The composition and quantity of CAZymes were also highly similar among strains, with GHs being the most abundant family (Figure 4). This implied that fundamental infection strategies, such as plant cell wall degradation, represented a common toolkit for Pyricularia pathogens infecting different Poaceae hosts. However, finer regulation might have occurred at the level of effector secretion and secondary metabolite synthesis [68,69]. Membrane transport proteins, particularly multidrug transporters from the ABC and MFS families, are crucial in pathogen–host interactions. They might have been involved in effector secretion or helped the pathogen counteract antimicrobial substances produced by the host [28,70,71]. For instance, Botrytis cinerea AI18 neutralizes phytoalexins from Solanaceae and Fabaceae plants via a dual mechanism involving the efflux transporter BcatrB and complementary metabolizing enzymes [72]. Despite no significant difference in the number of transporters between the two types of Pyricularia strains, differences in their expression levels might determine their differential roles in effector secretion or host infection. The accumulation of diterpenoid phytoalexins (DPs) in rice, notably momilactones and phytocassanes, is induced upon infection by the blast fungus [73]. Therefore, P. oryzae may enhance its survival during infection by upregulating the expression of multidrug transporters, such as those from the ABC and MFS families, to efflux toxic compounds like DPs out of the cell. Fungi are capable of producing a vast array of secondary metabolites, which serve various functions, such as acting as signaling molecules for inter-microbial communication or as virulence factors in interactions with plant and animal hosts [74]. In this context, differences in secondary metabolite biosynthesis gene clusters, such as the relatively lower number of NRPS clusters in GDYJ7 and ZJX18, might have indicated that different strains synthesized distinct specific toxins or signaling molecules to adapt to their respective host environments (Figure 5C). The Baicao series strains derived from barnyard grass may harbor more diverse NRPS clusters, enabling the synthesis of compounds specifically adapted to the paddy field companion weeds host. This genetic capacity could enhance their colonization or virulence on this specific host.
The rice-isolated strains GDYJ7 and ZJX18 harbored a more diverse repertoire of Avr genes, including Avr-Pi9, Avr-Pizt, Avr-Pik, and PWL2. In contrast, less Avr genes were detected in the Baicao series strains, among which Baicao9 retains only Avr-Pib (Table 5). Products of the Avr genes were initially acting as a virulence factor towards the host lacking the corresponding R genes. Therefore, variations of the Avr gene coding sequences may be attributed to host adaptation and provide clues for origin of wheat- or rice-infecting strains from the P. oryzae population. Avr-Pizt is a small secreted peptide consisting of 108 amino acids [40], targeting the rice E3 ubiquitin ligases APIP6 and APIP10 to modulate plant defense responses and playing a crucial role in virulence [75,76,77]. Avr-Pik is a 113-amino acid protein with a 21-amino acid signal peptide and exhibits high variability, with multiple allelic forms present [78]. In susceptible rice (without corresponding R gene), Avr-PikD (the ancestral allele of Avr-Pik) inhibits the activity of the rice LSD1 transcriptional activator AKIP30, thereby facilitating P. oryzae infection via effector-triggered susceptibility (ETS) [79]. Both Avr-Pib and Avr-Pi9 exhibit multiple types of variations, such as TEs insertions, point mutations, or deletions, which constitute the primary mechanisms leading to the loss of their avirulence function [36]. Avr-Pib weakens rice blast resistance by inhibiting the interaction between OsMAPKKK72 and OsMKK9, thereby reducing MAPK activation [80]. Avr-Pi9 may function in compromising the basal resistance in rice by targeting the rice RING-type E3 ubiquitin ligase OsRGLG5 [81]. PWL2 and PWL3 are members of a dynamic, rapidly evolving gene family, functioning as host-specificity determinants [82,83]. The effector protein PWL2 acts as a virulence factor that suppresses plant immunity by interfering with the localization of the host protein HIPP43 at plasmodesmata [84]. In this study, we observed clear patterns in Avr-Pizt and PWL3 coding sequence variation reflecting host preference to rice, wheat, or grasses (Figures S3 and S4). Furthermore, for PWL3 coding sequences, Baicao series strains and MZ5-1-6 are closer with rice-infecting strains, while other grass-infecting strains were closer to wheat-infecting strains (Figure S4). The PWL3 gene (absent in Baicao9) in the Baicao series strains and MZ5-1-6 was identical to the reference sequence U36995.1 that was reported from grass-isolated P. grisea strain, indicating that it may play a role in pathogenicity towards grass. Further mutations in the PWL3 gene differentiate rice-infecting strains (including ZJX18) from Baicao series strains and MZ5-1-6 (Figure S4). Another rice-infecting strain, GDYJ7, lacks the PWL3 gene. We infer that the point mutations of the PWL3 gene, leading to alteration of amino acid sequence and premature termination in rice-infecting strains (Figure S4; Table 6), may be responsible for their loss of pathogenicity towards grass. To support this hypothesis, the ZJX18 strain showed residual infectivity to grass while GDYJ7 was completely non-pathogenic to grass; absence of PWL3 in Baicao9 may be responsible for its weak pathogenicity to grass, compared to other Baicao strains (Figure 2). The presence of Avr-Pi9 and Avr-Pik in GDYJ7 and ZJX18, but absence in Baicao series strains, may be a reason for higher virulence of the rice-isolated strains than Baicao series strains towards the rice host (without the corresponding R genes). In addition, presence of PWL2 in GDYJ7 and ZJX18 may be a reason for their incapability to infect grass hosts, as reported [70]. The presence of PWL3 in Baicao series strains supports the fact that PWL3 does not function in preventing the host expanding to grass as PWL2 does, as it was reported in [70]. On the other hand, the presence of Avr genes could be recognized by the host harboring the corresponding R genes, thus granting such host with race resistance to the particular strains. In such circumstance, TE insertion to disrupt the Avr gene function is a strategy used by the fungal strains to escape such gene-for-gene resistance and shift from avirulent to virulent [85,86].
Overall, we proposed that variations in Avr genes profiles may correlate with the host range of P. oryzae strains, so that based on analysis of DNA or amino acid sequences of some chosen Avr genes, a host range of a particular P. oryzae may be predicted. This hypothesis awaits further validation by experiments. Simultaneously, enhancing the monitoring of Avr gene variations in field populations of P. oryzae will not only deepen our understanding of the molecular mechanisms underlying its host selection and adaptive evolution but also provide a critical decision-making basis for the rational deployment and rotation of resistant rice varieties, thereby safeguarding regional rice production security. With the publication of the genomes of Baicao series [accession numbers: PRJNA1395695], other researchers could also perform further analyses with this information to investigate the molecular mechanisms underlying host adaptation of the P. oryzae population.

4. Conclusions

In summary, this study, by integrating phenotypic and genomic data, delineated the differentiation landscape of host adaptation in P. oryzae strains infecting barnyard grass and rice. This differentiation resulted from the combined effects of the Avr gene repertoire, genomic plasticity (particularly TEs activity), and differences in specific pathogenesis-related gene modules. As a common weed in rice agroecosystems, barnyard grass harbors pathogen populations that constitute a unique genetic pool, for which this study provides genomic resources. Our findings emphasized the importance of focusing on grass hosts and their associated pathogen populations in the integrated management of rice blast.

5. Materials and Methods

5.1. Isolation of Pyricularia Strains

In 2024, leaf samples of barnyard grass exhibiting rice blast symptoms were collected from the Yangjiang Institute of Agricultural Sciences (Baisha) Experimental Base (111°50′ E, 21°52′ N). Single-spore isolation was performed to obtain the Pyricularia “Baicao” series of monosporic isolates [87]. Leaf samples containing necrotic lesions were collected using scissors. The leaves were immersed in a 1.2% sodium hypochlorite solution for 3 min, followed by thorough rinsing with sterile water several times until the chlorine odor dissipated. Using sterile forceps, the leaves were transferred to a Petri dish. Unaffected portions of the leaves were excised and discarded, while the remaining segments containing necrotic lesions were placed in a moist chamber. They were then incubated at 25 °C under a 16 h light/8 h dark photoperiod for 3–5 days. During incubation, the leaf segments were observed daily for the emergence of Pyricularia mycelia and spores. Under a dissecting microscope, a sterilized fine needle was used to pick a single Pyricularia conidium. The picked single spore was transferred onto a RPA medium supplemented with 50 µg/mL streptomycin. The plate was sealed with Parafilm and incubated in darkness at 28 °C for 3–5 days, until a single small colony appeared. Subsequently, mycelia from the edge of the colony were picked with a sterile toothpick and transferred onto a fresh RPA medium containing 50 µg/mL streptomycin for a round of purification. To ensure the purity of the single-spore isolates, P. oryzae Strains GDYJ7 and ZJX18, which were previously single-spore isolates from diseased rice plants in Guangdong and Zhejiang provinces, respectively, and preserved in the laboratory, were used as references. Six isolates were cultured on rice polish agar medium (RPA; 20 g/L rice polish, 2 g/L yeast extract, and 12 g/L agar) overlaid with sterile filter papers and cultured in darkness at 28 °C for 14 days until the mycelia fully covered the filter papers. The colonized filter papers were then aseptically collected with sterile forceps, air-dried at 28 °C, and stored at −20 °C [36].

5.2. Pathogenicity Assay

Rice blast fungal isolates were cultured on RPA media at 28 °C with 12 h light followed by 12 h dark treatment for producing mycelium. Seven days later, the mycelia were scraped with a cell spreader and the plates were exposed to continuous light at 28 °C for 3–5 days to promote sporulation. Then, the conidia were washed with 5 mL double-distilled water (ddH2O) per Petri dish to collect them, and the suspension was adjusted to the concentration of 1 × 105 conidia/mL [88].
The barnyard grass and rice seeds were immersed in water for 2 days at 35 °C in darkness for germination and then grown in the greenhouse with 28–30 °C day/night temperature and 14 h/10 h light/dark period. At three-leaf to five-leaf seedling stages, the second youngest leaves were detached and placed on the ddH2O with 6-benzylaminopurinehydrochloride (6-BA) in a square Petri dish after punch wounded. Detached leaves of barnyard grass (Echinochloa phyllopogon) or rice cultivars (ZH11, NPB, and CO39) were inoculated with 10 μL of spore suspension. The inoculated leaves were first incubated in darkness at 28 °C and 90% relative humidity for 24 h and then transferred under an alternate dark–light cycle (12 h:12 h) at 28 °C for an additional 4–5 days. Disease symptoms were then recorded and photographed. The experiment included three independent biological replicates [89,90].

5.3. DNA Extraction, Amplification and Sequencing

To prepare the genomic DNA for sequencing, the six isolate strains (Baicao series, GDYJ7, and ZJX18) were cultured in the liquid simple complete medium (CM; 6 g/L casaminoacid, 6 g/L yeast extract, and 10 g/L sucrose) in a 120 rpm shaker at 28 °C for 3–4 days. The mycelia were then collected for the preparation of genomic DNA using the CTAB method [91,92]. The Avr sequences of all six Pyricularia strains were amplified by PCR using Green Taq Mix (Vazyme, Nanjing, China) and the primers listed in Table 5, with the following amplification conditions: 1 cycle at 95 °C for 3 min, 30 cycles at 95 °C for 15 s, 62 °C for 15 s, and 72 °C for 60 s/kb, followed by 1 cycle at 72 °C for 5 min. PCR amplicons were resolved in 1.5% (w/v) agarose gel through electrophoresis.
The whole genome was sequenced via massively parallel sequencing (MPS) on the Illumina NovaSeq 6000 platform, by Novogene (Beijing) Co., Ltd. (Beijing, China).

5.4. Assembly, Prediction and Annotation

In this study, de novo whole-genome assembly was performed using SPAdes v4.1.0 on quality-controlled sequencing reads, with assembly parameters set to-k31, 51, 71, 91 [93]. To ensure consistency of the assembly results, experimental replicates were conducted for validation. The completeness of the assembled genomes was assessed with BUSCO v5.8.3 [94]. The average nucleotide identity (ANI) between the sequenced strains and published genomes of closely related species in Table 7 was calculated using FastANI v1.34, allowing phylogenetic relationships to be evaluated at the whole-genome level [95].
Based on the assembled genomes, homology-based gene prediction was performed using protein sequences from closely related species with the Braker v3.0.8 pipeline. Braker is an automated genome annotation pipeline for eukaryotes that integrates tools such as GeneMark and AUGUSTUS [96]. tRNA and rRNA were predicted using tRNAscan-SE V2.0.12 and barrnap v0.9, respectively. For the annotation of non-coding RNAs including snRNA, miRNA, and sRNA, the assembled genomes were compared against the Rfam database (http://rfam.xfam.org/, accessed on 21 October 2025) using Infernal v1.1.5 with its cmsearch program under default parameters [97].
For repetitive sequence prediction, a species-specific repeat library was first constructed using RepeatModeler v2.0.6 to identify repeat types within the library. This library was then merged with the Repbase database (https://www.girinst.org/server/RepBase/index.php, accessed on 7 April 2025) to build the final repeat database. The genome sequences were aligned against this database using RepeatMasker v4.1.8 to predict interspersed repeats [98], while tandem repeats were predicted with TRF v4.09.1 [99].
Protein sequence functional annotation was performed using DIAMOND v2.0.14.152, with alignments conducted against the following databases: KOG (https://www.ncbi.nlm.nih.gov/research/COG/, accessed on 22 May 2025), GO (https://www.geneontology.org/, accessed on 25 May 2025), KEGG (https://www.kegg.jp/, accessed on 7 September 2025), TCDB (http://www.tcdb.org, accessed on 22 May 2025), Pfam (http://pfam.xfam.org/, accessed on 23 May 2025), and CAZy (http://www.cazy.org, accessed on 9 April 2025). The alignment parameter was set to an E-value cutoff of 1 × 10−10.
Pathogen virulence factors were predicted by aligning protein sequences against the PHI database (http://www.phi-base.org/, accessed on 19 June 2025) and the Database of Fungal Virulence Factors (DFVF) (https://doi.org/10.6084/m9.figshare.31239886, accessed on 1 Februay 2026), with the parameters set to an E-value cutoff of 1 × 10−10 and a sequence identity threshold of >60%.
Secondary metabolite biosynthetic genes and gene clusters were identified using antiSMASH v8.0.0 (https://fungismash.secondarymetabolites.org/, accessed on 10 December 2025) [100], which performs predictions based on profile hidden Markov models (HMMs) specific to biosynthetic types. The presence of core biosynthetic genes in the genomes was further validated by BLAST v2.17.0 analysis, with parameters set to an E-value cutoff of 1 × 10−5 and a sequence coverage threshold of 75%.

5.5. Phylogenetic Analysis and Comparative Genomic Analysis

In this study, we employed OrthoFinder v3.0.1 to perform gene family clustering analysis on the genomes of the six isolated strains [101], along with those of 15 reference strains downloaded from the NCBI genome database (https://www.ncbi.nlm.nih.gov/genome, accessed on 4 December 2025). Orthologous single-copy protein sequences were extracted from the clustering results and aligned using MAFFT v7.525 [102]. A phylogenetic tree was subsequently constructed under the maximum-likelihood (ML) criterion with IQ-TREE v2.4.0, with model selection set to AUTO and branch support assessed via 1000 bootstrap replicates [103].

5.6. Statistical Analysis

Section 2.2 (Pathogenicity Analysis of the Isolated Strains): Lesion area was assessed as described, statistical analyses were performed with GraphPad_Prism 8. DPS 9.01 software, and one-way analysis of variance (ANOVA) tests or Student’s t-test was carried out. p values < 0.05 were considered as statistically significance.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jof12020109/s1, Figure S1: Agarose gel electrophoresis image of amplified Avr genes from isolated strains; Figure S2: Sequence alignment of Avr-Pib; Figure S3: Sequence alignment of Avr-Pizt; Figure S4: Sequence alignment of PWL3; Table S1: Average nucleotide identity (ANI) analysis; Table S2: The coding genes of the six tested strains with sequence identity higher than 90% to those in the PHI database; Table S3: Comparison of the predicted virulence genes in six tested strains (identity > 90%); Table S4: Distribution of plant cell wall degradation enzymes in six tested strains.

Author Contributions

Y.D., Z.L. and X.Z. (Xiaofan Zhou) conceptualized the study. Y.D. acquired funding. W.S., Z.Z. and X.X. isolated and characterized the Pyricularia single-spore strains. W.S., Z.Z., X.X., T.S., S.T. and B.L. helped in conducting experiments and analyzing data. J.W. supplied the Avr gene primers. X.Z. (Xiaohan Zhang) and X.Z. (Xiaofan Zhou) conducted the whole-genome sequencing and evolutionary analysis. W.S., X.Z. (Xiaohan Zhang) and Y.D. drafted the original manuscript. W.S. and X.Z. (Xiaohan Zhang) revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the National Key R&D Program of China (2023YFD1400200). The funders had no role in study design, data collection, and interpretation, or the decision to submit the work for publication.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The all strains whole-genome sequences data have been deposited in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under accession number PRJNA1395695. Supporting data for this study are available in the Supplementary Information.

Acknowledgments

We thank Jiafeng Wang (SCAU) for providing the Avr gene primers.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

ANI: average nucleotide identity; NCBI: National Center for Biotechnology Information; RPA: rice polish agar medium; LINEs: long interspersed nuclear elements; SINEs: short interspersed nuclear elements; LTR: long terminal repeat; ncRNAs: non-coding RNAs; tRNAs: transfer RNAs; rRNAs: ribosomal RNAs; snRNAs: small nuclear RNAs; sRNAs: small RNAs; PHI: pathogen–host interactions; DFVF: database of fungal virulence factors; CAZy: carbohydrate-active enzymes; PCWDEs: plant cell wall-degrading enzymes; GHs: glycoside hydrolases; GTs: glycosyltransferases; PLs: polysaccharide lyases; ABC: ATP-binding cassette; MFS: major facilitator superfamily; MDR: multidrug resistance; PDR: pleiotropic drug resistance; ML: maximum-likelihood; TEs: transposable elements; ddH2O: double-distilled water; 6-BA: 6-benzylaminopurinehydrochloride; MPS: massively parallel sequencing; HMMs: hidden Markov models; FHB: Fusarium head blight; DPs: diterpenoid phytoalexins.

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Figure 1. Isolation and morphological characteristics of six Pyricularia strains. (A) Lesion symptoms on barnyard grass or rice leaves in the field. (B) Single-spore isolation workflow. (C) Colony and conidia morphology. Scale bars = 10 μm. All strains were grown for 7 days on RPA medium before being photographed.
Figure 1. Isolation and morphological characteristics of six Pyricularia strains. (A) Lesion symptoms on barnyard grass or rice leaves in the field. (B) Single-spore isolation workflow. (C) Colony and conidia morphology. Scale bars = 10 μm. All strains were grown for 7 days on RPA medium before being photographed.
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Figure 2. Pathogenicity assay of the strains. (A) Spore suspensions (1 × 105/mL) were inoculated on the detached leaves of barnyard grass and different rice varieties (ZH11, NPB, and CO39). P. oryzae strain GDYJ7 served as a positive control and H2O as a blank control. Scale bars = 1 cm. (B) Quantitative analysis of lesion area based on the results of (A). The mean ± S.D. was derived from three independent biological repeats. Statistical analysis was performed using a two-tailed unpaired Student’s t-test versus GDYJ7. ns, **, ***: no significance, p < 0.01, p < 0.001, respectively.
Figure 2. Pathogenicity assay of the strains. (A) Spore suspensions (1 × 105/mL) were inoculated on the detached leaves of barnyard grass and different rice varieties (ZH11, NPB, and CO39). P. oryzae strain GDYJ7 served as a positive control and H2O as a blank control. Scale bars = 1 cm. (B) Quantitative analysis of lesion area based on the results of (A). The mean ± S.D. was derived from three independent biological repeats. Statistical analysis was performed using a two-tailed unpaired Student’s t-test versus GDYJ7. ns, **, ***: no significance, p < 0.01, p < 0.001, respectively.
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Figure 3. Phylogenetic analysis. Constructing a phylogenetic tree based on single-copy orthologous gene sequences using the maximum-likelihood (ML) method, and the ANI analysis was integrated for validation. The red typeface represents the strains isolated in this study. A heatmap of the ANI values between P. oryzae 70-15/P. pennisetigena Br36/P. grisea NI907 and the 17 representative Pyricularia strains is shown on the right side of the tree.
Figure 3. Phylogenetic analysis. Constructing a phylogenetic tree based on single-copy orthologous gene sequences using the maximum-likelihood (ML) method, and the ANI analysis was integrated for validation. The red typeface represents the strains isolated in this study. A heatmap of the ANI values between P. oryzae 70-15/P. pennisetigena Br36/P. grisea NI907 and the 17 representative Pyricularia strains is shown on the right side of the tree.
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Figure 4. Comparative analysis of gene annotations and plant cell wall-degrading enzymes for six strains based on the PHI, DFVF, CAZy, and PCWDEs databases. (A) Annotation statistics of the six pathogenic strains in the PHI database. (B) Proportion of genes annotated in the DFVF database for the six strains. (C) CAZy functional annotation results for the six strains. (D) Comparative analysis of fungal PCWDEs among the six strains.
Figure 4. Comparative analysis of gene annotations and plant cell wall-degrading enzymes for six strains based on the PHI, DFVF, CAZy, and PCWDEs databases. (A) Annotation statistics of the six pathogenic strains in the PHI database. (B) Proportion of genes annotated in the DFVF database for the six strains. (C) CAZy functional annotation results for the six strains. (D) Comparative analysis of fungal PCWDEs among the six strains.
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Figure 5. Prediction of membrane transport proteins and secondary metabolite synthases. (A) Annotation statistics of the six pathogenic strains in the TCDB database. (B) Comparative analysis of multidrug transporters from the ABC and MFS families in the six pathogenic strains. (C) Statistical results of secondary metabolite biosynthetic gene clusters in the six pathogenic strains.
Figure 5. Prediction of membrane transport proteins and secondary metabolite synthases. (A) Annotation statistics of the six pathogenic strains in the TCDB database. (B) Comparative analysis of multidrug transporters from the ABC and MFS families in the six pathogenic strains. (C) Statistical results of secondary metabolite biosynthetic gene clusters in the six pathogenic strains.
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Table 1. Genome assembly statistics.
Table 1. Genome assembly statistics.
FeaturesBaicao4Baicao5Baicao6Baicao9GDYJ7ZJX18
All Length (Mb)41.0441.1841.4441.4638.6939.05
Contig num133912781149111713581463
Contig max len (bp)732,390477,166581,875627,432731,285688,677
Contig average len (bp)30,650.1032,21936,061.9037,118.6028,487.5026,690.40
Contig N50 (bp)99,61292,052119,374102,581147,078141,887
GC Ratio50.31%50.26%50.16%50.15%51.24%51.22%
Table 2. Genome assembly evaluation.
Table 2. Genome assembly evaluation.
FeaturesBaicao4Baicao5Baicao6Baicao9GDYJ7ZJX18
Complete and single-copy4315 (96.1%)4316 (98.0%)4319 (98.1%)4319 (97.8%)4321 (96.2%)4309 (95.9%)
Complete and duplicated4 (0.1%)4 (0.1%)4 (0.1%)4 (0.1%)4 (0.1%)5 (0.1%)
Fragmented90 (2.0%)92 (2.0%)87 (1.9%)89 (0.7%)87 (1.9%)81 (2.0%)
Missing
(not recovered in assembly)		83 (1.8%)80 (1.8%)82 (1.8%)83 (1.2%)80 (1.8%)87 (1.9%)
Total BUSCO4492 (100%)4492 (100%)4492 (100%)4492 (100%)4492 (100%)4492 (100%)
Table 3. Genome component analysis.
Table 3. Genome component analysis.
Prediction FeaturesBaicao4Baicao5Baicao6Baicao9GDYJ7ZJX18
Gene Number11,11411,11611,15111,17111,44011,118
tRNA215216216216312320
rRNA535240474854
sRNA444444
snRNA222222222222
DNA transposons691697730729650760
LINE121142146112380404
SINE88--99
LTR195422501732189013701347
BEL/Pao---8--
Gypsy/DIRS112101573707125511921181
Ty1/Copia457455972444178166
Effect Protein Number317631703189319130973172
Cytoplasmic effector276327512770277627152783
Apoplastic effector413419419415382763
“-” stands for non-existence.
Table 4. The number of transporters.
Table 4. The number of transporters.
TransportersBaicao4Baicao5Baicao6Baicao9GDYJ7ZJX18
ABC434342424542
MFS666770606469
other789790793788794800
total898900905890903911
Table 5. Primers used in this study.
Table 5. Primers used in this study.
Gene NamePrimer Sequence (5′-3′)Source
Avr-PiaF: CATCGCTTTGCCCTCATTThis study
R: ACTTGATTCCTCCCGTAAACAG
Avr-PibF: AAGTCCTTCCCATTACCCTA[52]
R: GCAATAACCATCCAGCCATA
Avr-PiztF: GATCAAATGAACACCAGGAA[53]
R: CGATGAAGAATGGAAGAATG
Avr-Pi9F: CCTTCTAGTCATTCCTTTGGThis study
R: AGGCGAATGTGCTTACTACT
Avr-PikF: AATTTATTCAACTGCCACTCTG[52]
R: AACCTCGTCAAACCTCCCTA
Avr-Pita2F: TTTCGGCCCAACTCCGGTCC[54]
R: TAAAGGGTCCACTGACCCCG
PWL2F: ATGAAATGCAACAACATC[55]
R: CCTCACACTTAAGTTAACAC
PWL3F: GCGTGCTCATTTGTAAACCThis study
R: TTCCTTCATTTCTCTCCCTG
Table 6. Avirulence genes distribution.
Table 6. Avirulence genes distribution.
StrainsAvr-PibAvr-PiztAvr-Pi9Avr-PikPWL2PWL3
Baicao4++---+
Baicao5++---+
Baicao6++---+
Baicao9+-----
GDYJ7+++++-
ZJX18++++++/aa sequence altered; remature termination
“-” stands for non-existence. “+” stands for existence.
Table 7. Strains GenBank whole genome assembly accession number.
Table 7. Strains GenBank whole genome assembly accession number.
StrainsGenBank Accession Number
P. oryzae 131002 GCA_049355375.1
P. oryzae 131021 GCA_049355335.1
P. oryzae 70-15 GCF_000002495.2
P. oryzae Y34 GCA_000292585.1
P. oryzae ZJG1 GCA_025135345.1
P. pennisetigena Br36 GCF_004337985.1
P. grisea D1/s49GCA_024704135.1
P. grisea Nl907GCF_004355905.1
P. oryzae D10/s71GCA_024704055.1
P. oryzae K17 GCA_024704195.1
P. oryzae D15/s47GCA_024704025.1
P. oryzae K65/159wGCA_024704275.1
P. oryzae E34GCA_021845515.1
P. oryzae MZ5-1-6GCA_004346965.1
P. oryzae D15/s6GCA_024704165.1
P. oryzae D10/s9GCA_024704035.1
P. grisea SCAU-2GCA_040113025.1
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Sun, W.; Zhang, X.; Zhang, Z.; Xie, X.; Tang, S.; Song, T.; Lu, B.; Wang, J.; Liang, Z.; Zhou, X.; et al. Comparative Genomics Reveals Host-Specific Adaptation of Pyricularia oryzae Strains Isolated from Rice and Barnyard Grass. J. Fungi 2026, 12, 109. https://doi.org/10.3390/jof12020109

AMA Style

Sun W, Zhang X, Zhang Z, Xie X, Tang S, Song T, Lu B, Wang J, Liang Z, Zhou X, et al. Comparative Genomics Reveals Host-Specific Adaptation of Pyricularia oryzae Strains Isolated from Rice and Barnyard Grass. Journal of Fungi. 2026; 12(2):109. https://doi.org/10.3390/jof12020109

Chicago/Turabian Style

Sun, Wenda, Xiaohan Zhang, Zhuan Zhang, Xiaofang Xie, Song Tang, Tian Song, Baoxu Lu, Jiafeng Wang, Zhibin Liang, Xiaofan Zhou, and et al. 2026. "Comparative Genomics Reveals Host-Specific Adaptation of Pyricularia oryzae Strains Isolated from Rice and Barnyard Grass" Journal of Fungi 12, no. 2: 109. https://doi.org/10.3390/jof12020109

APA Style

Sun, W., Zhang, X., Zhang, Z., Xie, X., Tang, S., Song, T., Lu, B., Wang, J., Liang, Z., Zhou, X., & Deng, Y. (2026). Comparative Genomics Reveals Host-Specific Adaptation of Pyricularia oryzae Strains Isolated from Rice and Barnyard Grass. Journal of Fungi, 12(2), 109. https://doi.org/10.3390/jof12020109

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