Introduction

Rice sheath blight (ShB), caused by Rhizoctonia solani Kühn (R.solani), is one of the most devasting diseases in rice worldwide. Under intensive and high-input production system and favorable conditions, ShB can cause up to 50% of grain yield losses (Cu et al. 1996; Lee and Rush 1983). These intensified systems rely heavily on the use of high-yielding, semi-dwarf rice varieties, high crop densities, and high rate of nitrogen fertilizers. All these factors make ShB a major constraint for rice production in Asia, America, and other rice-growing areas (Gnanamanickam 2009; Savary et al. 1995). Spraying fungicides has been the major approach to control ShB because of very few rice varieties with complete or high resistance to ShB, which severely hinders the progress on developing ShB resistant varieties. A few germplasms, like Tetep, Jasmine 85, Teqing and YSBR1, were reported to carry good resistance to ShB (Channamallikarjuna et al. 2010; Pan et al. 1999; Zuo et al. 2009). Among them, YSBR1, derived from an inter-subspecies rice hybrid, showed high-level, stably reliable ShB resistance at stages from seedling to adult plants and in different environments, including field, growth chamber and greenhouse (Zuo et al. 2009). Most recently, YSBR1 was found to significantly suppress R. solani hypha growth through the observation of R. solani hyphal behavior on the surface of leaf sheaths (Cao et al. 2022).

The current breeding practice has been using varieties with certain levels of resistance to ShB, namely quantitative resistance controlled by quantitative trait loci (QTLs) (Li et al. 1995a; Zou et al. 2000). To date, more than 50 ShB resistance, QTLs have been preliminarily mapped on all rice 12 chromosomes (Molla et al. 2020), and some of them, have been validated, such as qSB-9TQ、qSB-11LE、qSB11-1、qSB-11HJX、qSB9-2、qSB-7TQ (Li et al. 1995a; Eizenga et al. 2015; Molla et al. 2020; Zhu et al. 2014). However, due to the fact that many factors, such as the micro-climates, canopy density, plant height and heading date, may disturb the phenotyping of ShB disease, only a very few ShB resistance QTLs have been fine mapped so far. To decrease these affections and enhance the accuracy of phenotypic evaluation, further improving the phenotypic evaluation method and developing specific mapping population have been recommended, which leaded to the successfully fine mapping of qSB-9TQ and qSB-11LE. The qSB-9TQ was mapped into a 146-kb region, and 12 candidates were identified (Zuo et al. 2014). Eleven genes in a 78.87-kb region were identified as the most likely candidates for qSB-11LE (Zuo et al. 2013). To date, no ShB QTLs were successfully characterized, which has, to a great extent, hindered the utilization of ShB QTLs in the breeding against the disease.

It is known that R. solani secretes host-specific toxins, cell wall degrading enzymes, and effectors as weapons to counter rice defense, while in turn rice uses diverse strategies to fight against it. Host defense strategies mainly include phytohormone signaling, pathogenesis-related proteins (PR), antimicrobial compounds and secondary metabolites (Li et al. 2021; Molla et al. 2020). Besides these universal defense-response genes/compounds in host–pathogen interactions, some specific components have been identified either. For example, Li et al. (2019b) recently identified a F-box protein ZmFBL41 conferring ShB resistance in maize, and its homologous gene OsFBX61 in rice also negatively regulated ShB resistance by targeting and degrading ZmCAD, a lignin biosynthesis enzyme. Loose Plant Architecture 1 (LPA1) interacts with a kinesin-like protein (KLP) to promote rice resistance to ShB via activation of PIN-FORMED 1a (PIN1a)-dependent auxin redistribution and subsequent activation of auxin signaling (Chu et al. 2021). Tissue-specific activation of DOF11 promotes both rice ShB resistance and grain yield through the activation of SWEET14 (Kim et al. 2021). Reactive oxygen species (ROS) appears to have dual roles as it is benefit for R. solani infection but also involved in plant basal defense responses (Foley et al. 2016; Shetty et al. 2008). OsRSR1 and OsRLCK5 are involved in ShB resistance by regulating host ROS contents induced by R. solani through the glutathione (GSH)-ascorbic acid (AsA) antioxidant system (Wang et al. 2021). Overexpression of OsNYC, a chlorophyll degradation gene, significantly enhances ShB susceptibility by inhibiting ROS scavenging processes (Cao et al. 2022). The qLN11 ShB resistance QTL was considered to involve in the activation of genes associated with the ROS-redox pathway and alleviates ROS accumulation in rice, thus delaying R. solani colonization (Oreiro et al. 2022). However, the molecular mechanisms underlying the rice-R. solani interaction remain unclear and the breeding potential of these genes remain inadequate.

In this study, we fine-mapped an ShB resistance QTL qSB12YSB, which carries a good breeding potential, and preliminarily analyzed its possible resistance mechanism. We repeatedly evaluated the phenotypes of 34 chromosomal segment substitution lines (CSSLs) derived from YSBR1 and Lemont (LE) cross using three inoculation methods in two years, and finally fine-mapped qSB12YSB to a 289-kb region on chromosome 12. Through the integration analysis of transcriptomic and resequencing data, we identified 18 candidate genes for qSB12YSB and found that qSB12YSB may activate secondary metabolites biosynthesis and ROS scavenging system to enhance rice ShB resistance.

Materials and methods

Plant materials

Rice variety YSBR1, developed by pedigree breeding from the progeny of a Japonica/Indica hybrid, carries a reliable high resistance to rice ShB evaluated by several methods in different locations and years (Zuo et al. 2009). LE, a japonica rice cultivar from Louisiana, USA, demonstrates high susceptibility to ShB (Bollich et al. 1985; Li et al. 1995b). The CSSL P5585 line carrying increased ShB resistance was identified in a set of backcross lines between LE (the recipient parent) and YSBR1 (the donor parent). P5585 was backcrossed with LE (recurrent parent) to generate a segregating BC4F2 population for primary QTL mapping. One BC4F2 line sharing, the same genetic background with LE but contains a YSBR1 chromosomal segment over the region between markers RM28553 and RM28819 was selected to generate a set of BC5F3 lines for fine mapping of qSB12YSB. Simultaneously, a near-isogenic line, NIL-qSB12YSB (CSSL-25), which harbored only a short chromosomal segment containing qSB12YSB from YSBR1, was selected for RNA-seq analysis. NJ9108, NJ5055, and NJ44, commercial rice cultivars widely planted in Jiangsu province for their premium eating quality, all are susceptible or highly susceptible to ShB (Li et al. 2019a; Wang et al. 2007, 2012, 2013). All plants were grown in the experimental fields of Yangzhou University in Yangzhou (Jiangsu, China) under normal cultivation conditions.

Fungal inoculation and phenotyping of rice ShB tolerance

YN-7, a R. solani AGI-1A isolate with strong pathogenicity, was used in inoculation assays. The YN-7 strain was inoculated on potato dextrose agar (PDA) medium and cultured at 28 °C for 3 days. Mycelial discs (ca. 0.7 cm in diameter) were transferred to potato dextrose broth (PDB) medium containing autoclaved veneers (1.0 cm long × 0.3 cm wide × 0.8 mm thick) and grown at 28 °C until mycelia twined around the veneers. The veneers with mycelia were used as the inoculum.

All plants were grown in the field in Yangzhou (Jiangsu, China) and evaluated for ShB resistance by three different methods, adult plant inoculation in the field, adult plant inoculation in greenhouse and detached tiller inoculation in growth chamber.

Adult plant inoculation in the field: Rice plants at late tillering stage were inoculated by inserting the wood inoculum colonized with R. solani into the inside of the third leaf sheath from the top. Each plant was inoculated on three largest tillers. Disease severity was rated at around 30 days after heading using a ‘0 ~ 9’ ShB rating system (Zuo et al. 2013). The disease score of each rice line/variety was calculated by averaging the disease score of 24 plants from three replications.

Adult plant inoculation in greenhouse: Rice plants at late tillering stage were selected and transplanted to a long plastic basin in a greenhouse with controlled humidity (45%-60%) and temperature (30℃/13 h, 26℃/11 h). Each basin contains 5 rice plants as one replication. Five tillers in each plant were inoculated as described above. The lesion length of each plant was measured at 14 days after inoculation (DAI). The average lesion length of 15 plants from three replications was used to represent the lesion length of the corresponding line or variety.

Detached tiller inoculation in a growth chamber was done as described by He et al. (2020). A single tiller was detached from the plant and fixed on flower mud. Leaves of the tiller were trimmed. The wood inoculum colonized with R. solani was inserted into the inside of 2nd leaf sheath from the top. All inoculated sheathes were placed on an inoculation shelf with controlled temperature and humidity, then the lesion length was measured at 7 DAI. Each rice variety or line was inoculated at least 20 sheathes separated in two to three inoculation shelves. The experiment was repeated two times. Two-tailed student’s t-tests were used for comparison between two groups. Data are presented as mean (average) ± sd. P < 0.05 was considered to be statistically significant.

Primer design and QTL mapping

For fine-mapping of qSB12YSB, we developed 142 polymorphic markers, including SSR (simple sequence repeat) markers and Indel (insertion and deletion) markers. SSR markers and Indel markers were designed based on the sequence differences obtained from resequencing results of YSBR1 and LE. Sequences of Indel primers are listed in Supplemental table 2.

The phenotypic values of ShB resistance of the 150 BC4F2 CSSLs from field test were used to detect QTLs by biparental populations module (BIP) packaged within IciMapping 4.2.

DNA extraction and resequencing

Genomic DNA from rice plants was extracted using young leaves with the CTAB (hexadecyltrimethylammonium bromide) method described by Porebski et al. (1997). The extracted DNA yield and purity were measured using a Nanodrop (Thermo Fisher Scientific) and visualized in a 1.2% agarose gel after electrophoresis. After the quality of genomic DNA samples was confirmed, DNA resequencing was conducted by Huazhi Biotech Co. Ltd. Raw reads were filtered to obtain clean reads to ensure the quality of subsequent information analyses. The sequencing reads were compared with the reference genome and relocated to the reference genome for subsequent analysis.

RT-qPCR and RNA-seq assays

Sheathes were collected at indicated time points in 1.5 ml RNase free centrifuge tubes and frozen in liquid nitrogen. Total RNA was extracted by the Trizol method (Invitrogen) and genomic DNA digested by DNase I (TaKaRa). Then, RT-qPCR assays were performed with SYBR Green regent (Takara) on the CFX96™ Real-Time PCR Detection System (Bio-Rad, Hercules, CA) to detect specific genes. The level of actin mRNA was used as a standard control to normalize relative transcript levels. Primers used for RT-qPCR are listed in Supplemental Table 1. For candidate genes analysis, the relative expression levels of each gene in LE and NIL-qSB12YSB in 0 and 20 HAI were analyzed.

Table 1 The candidate genes in qSB12YSB region
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For RNA-seq analysis, sheathes of 8-week-old rice plants 0 and 20 HAI were collected. RNA integrity was evaluated using the Agilent 2100 Bioanalyzer (Agilent Technologies). Samples with an RNA integrity number of ≥ 7 were processed for the subsequent analysis. RNA sequencing was conducted by OE Biotech. Transcriptomic profiling was performed according to the standard protocol of the Affymetrix Rice Genome Array (CapitalBio). Differentially expressed genes were selected using | Log2 Fold change |≥ 1 and q-value < 0.05 as the cutoffs. Hierarchical cluster analysis of differential expression genes was performed to examine transcript expression patterns. Heat map and KEGG pathway enrichment analysis of differential expression genes were performed using the R package.

Enzymatic activity assay

In order to determine the activity of superoxide dismutase (SOD), glutathione S-transferase (GST), ascorbate peroxidase (APX), catalase (CAT), the infected and un-infected plant sheathes were sampled and measured using SOD, GST, APX and CAT Activity Assay Kit (Beijing Solarbio Science & Technology Co.,Ltd).

Evaluation of agronomic traits in the field

Rice lines were planted in 3 rows per line in the field with three replications. Each row contained 12 plants. Under slight-disease conditions, the rice lines were sprayed chemical fungicides thifluzamide during the tillering, jointing, booting and full heading stage. Under severe-disease conditions, 5 tillers per plant were inoculated with R. solani and didn’t spray any fungicides.

The middle 10 plants in the central row were selected for measuring agronomic traits at the maturing stage. Various agronomic traits such as plant height, tiller number, tiller angle, heading date, panicle length, flag leaf length, flag leaf width, 1000 grain weight, grain length and width, panicle number per plant, grain number per main panicle, number of filled grains were measured as described (Chen et al. 2014). Seed-setting rate was calculated as: (Total number of filled grains per main panicle/total number of grains per main panicle) × 100%. The average value of the 10 plants represents the value of this rice line.

To measure grain yields of LE and NIL-qSB12YSB in slight-disease and severe-disease conditions, each line was planted with three replications in the field; each replication contains 10 rows. Grain yields were determined when the seeds were harvested and normalized to yield per 10 000 m2 (hm2).

Results

Preliminary mapping of qSB12 YSB

We previously generated several advanced backcrossing (BC4) lines using YSBR1 as the donor and LE as the recipient parent. In a ShB nursery field, we found that line P5585 showed the highest resistance to ShB. To further confirm its resistance, we then artificially inoculated this line with R. solani in the field and found that its average disease scores were 5.08, significantly lower than that of LE (score = 6.81) (Fig. 1a). Heading date and morphological traits, including plant height and tiller angle, have been found to be tightly associated with ShB resistance (Srinivasachary and Savary 2011). Therefore, we measured these traits plus tiller number, flag leaf length and width for both LE and line P5585. The results showed that P5585 and LE significantly differed only in plant height, and P5585 (105 cm) was significantly higher than LE of 96 cm (Fig. 1b). To eliminate the interference of plant height on the resistant phenotype of P5585, we further evaluated its ShB resistance by detached tiller inoculation assay, in which the sheath on detached tiller was inoculated in a growth chamber, and adult plant inoculation method in green house under controlled temperature and humidity. We found that P5585 showed an average lesion length of 9.13 cm, significantly shorter than LE of 13.75 cm, in the detached tiller inoculation assay (Fig. 1c). Because this assay excludes the effect from plant height, it indicates that the taller plant height of P5585 is not responsible for its stronger ShB resistance. Inoculation of the adult plants displayed a similar trend as the detached tiller results (Fig. 1d). Taken together, we conclude that the ShB resistance of P5585 is reliable and mainly not from the indirect effect of plant height.

Fig. 1
figure 1

Sheath blight resistance and agronomic traits of P5585. a Disease phenotype of LE, P5585 and YSBR1 after ShB inoculation under field conditions. Disease score was determined at 40 days after inoculation (DAI). Values are means ± SD (n = 24 plants). Different letters represent significant differences, P < 0.05, one-way ANOVA. Scale bar, 10 cm. b Agronomic traits of LE and P5585 under field conditions. Plant height, heading date, tiller angle, tiller number, flag leaf length and flag leaf width were measured at maturing stage. Values are means ± SD (n = 20 plants). Heading date were scored in three replications. Values are means ± SD (n = 3). **, P < 0.01; ns, not significant using Student’s t-tests. c Disease phenotype of LE and P5585 after ShB inoculation on sheath in a detached tiller. Single tillers from 8-week-old plants were detached, leaves trimmed and sheathes inoculated with R. solani. Lesion lengths were measured at 7 DAI. Values are means ± SD (n = 30). **, P < 0.01 using Student’s t-tests. Scale bar, 10 cm. d Disease phenotype of LE and P5585 inoculated in green house. Lesion length was measured at 14 DAI. Values are means ± SD (n = 12 plants). **, P < 0.01 using Student’s t-tests. Scale bar, 5 cm

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We subsequently analyzed the genetic background of P5585 using 142 polymorphic molecular markers that differentiate between YSBR1 and LE (Supplemental Fig. 1). The results showed that P5585 and LE differed on 15 SSR loci and 7 of them concentrated on the end part of chromosome 12. The remaining 8 SSR markers located on 5 chromosomes, with 2 on each of chromosome 1, 3 and 6, and 1 on each of chromosome 2 and 10. To preliminarily determine the location of the resistance gene and simultaneously eliminate background affection, we backcrossed P5585 with LE to construct a BC5F2 population consisting of 150 plants. We firstly inoculated these plants artificially in the green house and then conducted a QTL linkage analysis using the phenotype data and genotype data on 142 polymorphic markers of each plant. A significant logarithm of odds (LOD) peak than the threshold value of 2.5 was detected on the end of chromosome 12 (Fig. 2a), indicating an ShB resistance QTL located in this region. No QTL was inferred on other polymorphic markers or regions. Based on the linkage map, we named this ShB resistance QTL as qSB12YSB hereafter and found it most likely located in the interval of markers RM28553 and RM28819 covering 3.35 Mb on the rice physical map.

Fig. 2
figure 2

Fine mapping of qSB12YSB. a LOD graph for the detection of sheath blight resistance QTLs on chromosome 12 by inclusive composite interval mapping in BC5F2 (P5585 x LE). b A diagram for the genotypes and resistance phenotypes of CSSLs and controls. Dark blue and light blue bars represent YSBR1 and LE chromosomal segments, respectively. Horizontal red line represents the fine mapping region of qSB12YSB. Vertical lines represent the marker positions. 2021 and 2022 represent inoculations done in years 2021 and 2022. Field, GH, and DT represent whole plants inoculated in the field, whole plants inoculated in a greenhouse, and single tillers inoculated in a growth chamber, respectively. Pink and green blocks represent highly susceptible and partially resistant phenotypes, respectively. Yellow blocks represent taller plant heights than LE and light blue blocks indicate LE height

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Fine mapping of qSB12 YSB

In order to further narrow down the region of qSB12YSB, we selected one BC5F2 plant, which shares the same genetic background to LE but carries an YSBR1 chromosomal segment over the qSB12YSB region, to self-pollenate to develop lines carrying contiguous substitution segments in this region. Seven new polymorphic markers in the qSB12YSB region were developed and used to determine the recombinants by marker-assisted selection (MAS). In the BC5F3 generation, we obtained 34 chromosomal segment substitutional lines (CSSLs), in which the contiguous and overlapping YSBR1 segments completely cover the qSB12YSB region between markers RM28553 and RM28819 (Fig. 2b). At least one cross-over point was found between adjacent markers in these 34 CSSLs, except between markers RM6306 and RM28819. The average marker intervals of 10 markers were 372 kb, which led to a relatively high resolution for further mapping of qSB12YSB.

We repeatedly evaluated the ShB resistance of the 34 CSSLs using three different methods in 2021 and 2022. We found that most lines showed consistent phenotypes with different methods in different years. A few lines showed ambiguous phenotypes in one method, which did not significantly affect the determination of their phenotypes because their phenotypes were consistent in two other methods. We then determined the phenotype of each of the 34 CSSLs and classified them into two clusters according to their disease scores or ratings (Supplemental Fig. 2). We also evaluated the plant height of these CSSLs and found no association between plant height and ShB resistance (Fig. 2b), which further confirmed that qSB12YSB against ShB resistance was not caused by the indirect effect of plant height.

We found that all CSSLs containing the YSBR1 segment over the region between Ind25.916 and Ind26.205 showed significantly higher resistance than LE, indicating that this region co-segregated with qSB12YSB. Eight CSSLs had crossovers in the Ind25.916∼Ind26.205 region, and all these CSSLs contained an YSBR1 segment covering Ind26.205 but not Ind25.916. Moreover, while CSSLs-23 and 24 were susceptible, the other CSSLs displayed a relatively resistant phenotype. This indicates that Ind25.916 is the left border and Ind26.205 is the right border of qSB12YSB (Fig. 2b). Finally, we fine mapped the qSB12YSB to the region between Ind25.916 and Ind26.205 covering 289 kb in length on the rice physical map.

Determination of the candidate genes for qSB12 YSB

In the rice genome annotation database, 43 putative genes were annotated in the qSB12YSB region. We compared the sequences of the 43 genes between YSBR1 and LE after whole genome resequencing and found 423 SNPs/Indels between YSBR1 and LE in 39 genes. Among them, 287 SNPs/Indels exist in the upstream untranslated regions (UTRs) or promoters and 34 SNPs in coding sequences (CDSs) with 22 of them causing amino acid changes. These SNPs/Indels potentially contribute to the qSB12YSB phenotype.

We also performed RNA-seq analysis on LE and CSSL-25 (hereafter namely NIL-qSB12YSB) with or without R. solani infection and analyzed the expression patterns of these 43 genes in LE and NIL-qSB12YSB after inoculation. We compared the RNA-seq and RT-qPCR data for the candidate genes and a few randomly selected genes and found a consistent trend between the two data, indicating the reliability of RNA-seq data (Supplemental Fig. 3).

We corrected the expression patterns of 5 candidate genes according to their RT-qPCR results (Table 1). Finally, the transcriptomic and resequencing data were combined and visualized in Fig. 3. Among the 43 genes, 16 genes did not express in either inoculated or not (No expression genes, n) and were therefore ruled out. Total of 14 genes were induced by R. solani differentially between LE and NIL-qSB12YSB (Differential expression genes, DEGs), and 13 of them harbor SNPs/Indels in UTRs or promoters. Twelve genes expressed in the corresponding region of qSB-12YSB (Expression genes, EGs), which were not induced by R. solani infection, and 4 of them harbor a nonsynonymous mutation in CDS. One gene harbors a nonsynonymous mutation in CDS and was induced by R. solani but no differential expression between LE and NIL-qSB12YSB (Induced expression gene, IEG). Therefore, these 18 genes, 1 IEG, 13 DEGs and 4 EGs, were candidate genes for qSB12YSB.

Fig. 3
figure 3

Putative genes in the qSB12YSB region. Dark blue, green, yellow and gray blocks represent the genes induced by R. solani differentially between LE and NIL-qSB12YSB (DEG), genes induced by R. solani but no differential expression between LE and NIL-qSB12YSB (IG), genes expressed but not induced by R. solani (EG), non-expression genes (n), respectively. Red line, light grey line, and blue dashes represent nonsynonymous SNPs, synonymous SNPs, and Indels, respectively. The heat maps besides a gene represent the expression levels of this gene in LE (upper) and NIL-qSB12YSB (lower) in 0 and 20 h after inoculation (HAI)

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The expression levels of these 18 candidate genes in LE and NIL-qSB12YSB at 0 and 20 h after inoculation are shown in Fig. 3. Most of these genes encode proteins with a known domain or predicted function as described below (Table 1): methyltransferase small domain containing protein (Os12g0612500), class III homeodomain leucine zipper gene (Os12g0612700), SET domain group protein (Os12g0613200), auxin response factor (Os12g0613700), nucleolar protein NOP5 (Os12g0614000), Hypothetical protein (Os12g0614050), OsWAK127b (Os12g0614900), OsWAK129c (Os12g0615200), OsWAK129b (Os12g0615300), methyltransferase domain-containing protein (Os12g0615400), PPR repeat-containing protein (Os12g0615600), G-patch domain-containing protein (Os12g0615800), kinesin motor domain-containing protein (Os12g0616000), ribosomal protein (Os12g0616200), similar to cation-proton exchanger (Os12g0616500), putative Deg protease homologue (Os12g0616600), protein of unknown function DUF761 (Os12g0616800), transketolase (Os12g0616900).

Resistant line NIL-qSB12 YSB shows stronger ability on scavenging ROS than susceptible Lemont after the infection of R. solani

Based on our RNA-seq data, we found 2594 and 5384 R. solani-induced/suppressed genes in LE and NIL-qSB12YSB, respectively (Supplement Fig. 4a). Cluster analysis grouped these genes into 11 significantly enriched gene clusters (Fig. 4a). KEGG enrichment analysis was performed for each cluster. Clusters 2 and 6 were either upregulated or downregulated in LE and NIL-qSB12YSB; the upregulated genes were enriched in the biosynthesis of secondary metabolites, metabolic pathways, MAPK signaling pathway, alpha-linolenic acid metabolism or plant hormones signal transduction whereas the downregulated genes were enriched in photosynthesis. The specifically upregulated genes in NIL-qSB12YSB (cluster 7) were enriched in the biosynthesis of secondary metabolites, the biosynthesis of amino acids, glutathione metabolism and phenylpropanoid biosynthesis. In addition, the activation of glycolysis pathway has been reported accompanied with the activation of phenylpropanoid pathway to produce resistance-related secondary metabolites (Mutuku and Nose 2012). We found that compared with susceptible lines, infected resistant line NIL-qSB12YSB has higher expression levels of genes in these pathways, suggesting that NIL-qSB12YSB may produce more resistance-related antitoxins to defend R. solani (Supplemental Fig. 4c).

Fig. 4
figure 4

Transcriptomic analysis of differentially expressed genes regulated by qSB12YSB. a Hierarchical clustering and heatmap of 2326 differential expressed genes in response to R.solani and their related KEGG pathways. The differential expressed genes are grouped into 11 co-expressed clusters. Colors represent log2- fold change comparing relative expression. b, c, d The relative expression levels of genes involved in cell redox homeostasis, carbon fixation and OPPP in LE and NIL-qSB12YSB after inoculation. Colors represent log2-fold change comparing relative expression. G6PD, glucose-6-phosphate dehydrogenase; PGL, 6-phosphogluconolactonase; 6PGDH, 6-phosphogluconate dehydrogenase. e DAB staining results of inoculated LE and NIL-qSB12YSB leaves at 20 HAI

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According to previous reports showing that R. solani induces plant cell death rapidly (Anderson et al. 2016). Enhancing the ability of plant cells to maintain redox homeostasis may significantly suppress R. solani growth (Oreiro et al. 2022). We therefore analyzed the genes in cell redox homeostasis pathways in LE and NIL-qSB12YSB after inoculation and identified 38 ROS scavenging-related proteins (Fig. 4b), including peroxiredoxins, glutathione reductase (GSR), glutathione peroxidase (GRX), ascorbate peroxidase (APX), glutaredoxin, disulfide isomerase (PDI), catalase (CAT), superoxide dismutase (SOD), glutathione S-transferase (GST). Most of them were upregulated in NIL-qSB12YSB upon R. solani infection but only few were upregulated in LE (Fig. 4b). Furthermore, we measured the activities of these enzymes, SOD, GST, APX and CAT, and as expected, we observed that three enzymes of them (SOD, GST, APX) showed apparently higher levels in NIL-qSB12YSB than those in LE after inoculation of R.solani (Supplemental Fig. 4b). We also observed that the downregulated genes in NIL-qSB12YSB were enriched in carbon fixation (Cluster 4). The deceleration of carbon fixation will free reducing power (in the form of NADPH) to cope with R. solani-induced oxidants, thereby favoring ROS scavenging (Michelet et al. 2005, 2006, 2013; Schürmann and Buchanan 2008). Ribulose-1,5 bisphosphate carboxylase/oxygenase (Rubsico), phosphoglycerate kinase (PGK), glyceraldehyde-3-phosphate dehydrogenase (GAPDH) are main genes involved in carbon fixation. Most of these genes were downregulated in NIL-qSB12YSB but not in LE, suggesting the downregulation of the Calvin Benson cycle in NIL-qSB12YSB (Fig. 4c). In higher plants, the oxidative pentose phosphate pathway (OPPP) is also a major source of reducing power (Scharte et al. 2009). We did observed that upregulation of the pentose phosphate pathway was more specifically in NIL-qSB12YSB (Fig. 4d). The DAB (3,3-Diaminobenzidine) staining result verified that NIL-qSB12YSB accumulated apparently less ROS than LE (Fig. 4e). Taken together, these data suggest that resistant NIL-qSB12YSB has stronger ability to counter ROS bursts induced by R. solani than susceptible LE.

qSB12 YSB displays stably resistant effects in different rice varieties

To determine the resistant effects of qSB12YSB in different rice varieties, we further introduced qSB12YSB by MAS into three commercial rice varieties, NJ9108, NJ5055 and NJ44. These three varieties have been widely planted in Jiangsu province, China, with over 306 million hectares in total. In the BC2F3 generation, we obtained at least two lines in each variety background. These lines were evaluated together with their corresponding controls for ShB resistance in both field and green house conditions. The results (Fig. 5a–c) showed that the qSB12YSB-containing lines in all three genetic backgrounds showed significantly lower disease scores than their corresponding controls in the field. In average, the qSB12YSB reduced disease scores by 1.17–2.02 in different genetic backgrounds. Similar results were found in green house inoculations. These data clearly demonstrate that qSB12YSB is able to improve ShB resistance in the three commercial rice varieties.

Fig. 5
figure 5

Breeding potential of qSB12YSB in improving sheath blight resistance. a, b, c Disease phenotypes of NJ9108 (a), NJ5055 (b), NJ44 (c) and their respective qSB12YSB introgression lines in field and greenhouse tests. Images showed the phenotype in 14DAI in green house. **P < 0.01 using Student’s t-test. Scale bar, 10 cm. d Disease severity and yields of LE and NIL-qSB12YSB after ShB inoculation. The LE and NIL-qSB12YSB lines were planted for yield tests under two different ShB disease conditions: non-disease to severe-disease conditions. Each test contained 3 replications per line for LE and NIL-qSB12YSB. A total of 300 plants for each line were harvested and the yield measured. The filled grains in the main panicle were collected and photographed. e ShB resistance and morphological traits of LE and NIL-qSB12YSB. Disease score, tiller number (TN), and grain number per main panicle (GNP) were measured from 10 plants per line. Values are means ± SD (n = 10 plants). 1000-grain weight (1000GW) was measured from three replications, Values are means ± SD (n = 3 plants). Yield per plant was measured from 300 plants

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To further evaluate the breeding potential of qSB12YSB, we measured the yields of LE and NIL-qSB12YSB under slight-disease and severe-disease conditions in field trials (Fig. 5d). The results showed that in slight-disease condition, LE and NIL-qSB12YSB had similar grain yields (Fig. 5e). However, NIL-qSB12YSB performed significantly better than LE in severe-disease condition: While LE suffered a 25.3% yield loss compared with slight-disease, NIL-qSB12YSB only sustained an 11.8% loss under severely diseased condition. Plant yield is comprised by tiller number (TN), grain number per panicle (GNP), ratio of filled grains (RFG) and 1000-grain weight (1000GW), we then further analyzed these traits in these trials. The results showed that no significant differences were found in these traits between LE and NIL-qSB12YSB, except for the ratio of filled grains (RFG) (Fig. 5e). While LE sustained a 25.6% loss in RFG, NIL-qSB12YSB sustained only a 10.1% loss in RFG under severe-disease condition, representing a 60% improvement in preventing RFG loss by qSB12YSB. Therefore, the rescued yield losses by qSB12YSB is mainly attributed to the reduction in RFG loss. Taken together, we conclude that qSB12YSB can retrieve about 13.5% (25.3%-11.8%) of the yield losses caused by R. solani by mainly reducing the loss in RFG.

Discussion

Comparison of the location of qSB12 YSB with previous studies and its breeding potential

Here, we identified an ShB resistance QTL from the resistant YSBR1 variety and fine-mapped it to a 289-kb region between 25.916 and 26.205 Mb on chromosome 12 in the physical map. To date, a total of 7 independent studies have identified the existence of ShB resistance QTLs on chromosome 12 (summarized in Supplemental Table 2 and Supplemental Fig. 5). However, among these ShB QTLs, almost no was repeatedly detected in different years or in different environments, except qSBD-12–2(E1) and qSBPL-12(E2). qSB12YSB is located on the end of the long arm of chromosome 12, in or around the two previously identified QTLs, qSB-12 and qRTL12 (Supplemental Fig. 5) (Pinson et al. 2005; Taguchi-Shiobara et al. 2013). So far, fine-mapping of qSB12 has not been reported, which hinders the utilization of this QTL in breeding practice.

Using a pair of NILs in the LE background, we found that compared with LE, the NIL-qSB12YSB decreased 13.5% yield losses in severe ShB disease condition, indicating a great potential of qSB12YSB in rice breeding against ShB. Furthermore, we introduced qSB12YSB into three commercial varieties (NJ9108, NJ5055 and NJ44) with different genetic backgrounds, and confirmed that qSB12YSB did significantly improve the ShB resistance of Japonica rice. Besides, we also found different degrees of increased plant height in these lines compared with their respective recurrent parents. However, we did not find an association between qSB12YSB-confered ShB resistance and plant height. There are significant differences between YSBR1 and the three commercial varieties in plant height, which might be due to residual YSBR1 chromosomal segments in the background of these BC2F3 lines that lead to the increased plant height. We will continue to backcross the BC2F3 lines to their recurrent parents to exclude the interference of other chromosomal segments in the future.

Candidate genes and resistance mechanisms of qSB12 YSB

Based on the transcriptomic and resequencing data, we comprehensively compared the expression patterns and sequence variations of the genes in the qSB12YSB fine mapping region and ultimately inferred 18 most likely candidates for qSB12YSB. To further determine the priority of these candidate genes for next step verification test, the functions of these genes were discussed, which allowed us to consider the following 10 genes that would be of higher priority for functional verification: Os12g0613700, namely, ARF25, encodes an auxin response factor. Suppressing ARFs by targeting with miR167 reduced rice resistance to Magnaporthe oryzae (Zhao et al. 2020). Os12g0614000 encodes mediator subunit 19a (MED19a). Mediator plays important roles in plant development and resistance (Kidd et al. 2011). AtMED19a, the homologue of OsMED19a, regulates resistance to biotrophic and hemi-biotrophic pathogens. Degradation of AtMED19a led to downregulation of the SA signaling pathway while upregulation of the JA/ET signaling pathway (Caillaud et al. 2013). Os12g0616000 encodes a kinesin motor domain containing protein. A kinesin-like protein (KLP) has been shown a role in promoting ShB resistance via activation of auxin redistribution and subsequent activation of auxin signaling (Chu et al. 2021). Os12g0614900, Os12g0615200, Os12g0615300 all encode wall-associated kinases (WAK) named OsWAK127b, OsWAK129c, OsWAK129b, respectively. WAKs play important roles in both plant development and pathogen response (Kohorn & Kohorn 2012). Os12g0616200 encodes a ribosomal protein. Ribosomal proteins are major components of ribosomes involved in the cellular process of protein biosynthesis and play a minor role in basal resistance against virulent pathogens. GhARPL18A-6 (ribosomal protein L18A) can induce lignin deposition, ROS bursts and phenylpropanoid biosynthesis defense response pathways to increase resistance to Verticillium dahlia (Zhang et al. 2019). Os12g0616500 encodes a cation/H+ exchanger protein. Cation/H+ exchangers play an important role in response to abiotic stresses in plants (Pandey 2015). Os12g0616600 encodes a putative Deg protease homologue. Deg protease plays an important role in the photosystem II repair cycle under light stress condition (Kapri-pardes et al. 2007). Os12g0616900 encodes a transketolase (TK). TK catalyzes reactions in the Calvin cycle and OPPP. Upregulation of TKs would produce more erythrose-4-phosphate (E4P), which is a precursor for the shikimate pathway, leading to enhanced phenylpropanoid biosynthesis (Henkes et al. 2001). Upregulation of OPPP would also provide more NADPH to inhibit oxidative bursts (Linnenbrügger et al. 2022). These most likely candidates for qSB12YSB have been cloned for functional verification through gene knock out and overexpression.

A number of studies have shown that pathogens often attack and disrupt plant photosynthetic systems, especially photosystem II complex, to promote pathogenesis (Serrano et al. 2016; Torres-Zabala et al. 2015). Recently, Ghosh et al. (2017) found that R. solani infection can destroy chloroplasts and inhibit photosynthesis in rice. The inhibition of photosynthesis will produce less NADPH and cause production of excess ROS in plants (Gan et al. 2019). In general, ROS has a dual function in regulating plant defenses. In plant-biotrophs/hemibiotrophs interactions, ROS functions as a signal molecule to initiate defense responses against fungal pathogens (Kou et al. 2019), and therefore, host-derived ROS can inhibit further expansion of pathogen to adjacent cells (Liu et al. 2014). To inhibit host ROS-dependent immunity, pathogens activate their antioxidation systems and block host ROS bursts to facilitate pathogenicity (Molina and Kahmann 2007; Liu and Zhang 2022). On the contrary, in plant-necrotrophs interactions, necrotrophic pathogens often stimulate excessive ROS accumulation in the host to induce cell death, which is conducive to the growth and colonization of necrotrophic fungi, while the host may activate antioxidation systems to inhibit ROS bursts induced by the pathogen (Heller and Tudzynski 2011). In rice-R. solani interaction, several lines of evidences have been shown to support this hypothesis. Our ShB resistant line exhibits a significantly lower ROS accumulation compared with the susceptible control at 48 h post inoculation with R. solani, further supporting the above hypothesis. Similarly, QTL qLN11 indirectly activates genes involved in the ROS-redox pathway, alleviates ROS accumulation in rice cells, and thus delays R. solani colonization (Oreiro et al. 2022). Disease resistance protein OsRSR1 and protein kinase OsRLCK5 are involved in sheath blight resistance by regulating ROS levels through the glutathione-ascorbic acid antioxidant system (Wang et al. 2021). Suppression of R. solani-induced rice chlorophyll degradation improves rice ShB resistance via reducing R. solani-induced ROS bursts (Cao et al. 2022).

Here, we present a model for qSB12YSB-mediated ShB resistance to summarize our results (Fig. 6). R. solani attacks host photosynthetic systems to promote pathogenesis during infection. Inhibition of host photosynthesis causes lower levels of NADPH that affects cell redox balance, leading to cell death due to excess ROS. NIL-qSB12YSB initiates three steps to counter excess ROS to protect plants from the oxidative damage. First, qSB12YSB slows down the Calvin-Benson cycle freeing reducing power to cope with increased oxidants. Second, qSB12YSB activates OPPP to produce more NADPH. Third, qSB12YSB activates expression of cell redox genes to maintain ROS homeostasis to prevent cell death. These three steps help NIL-qSB12YSB counter the ROS bursts induced by R. solani infection. In addition, NIL-qSB12YSB also activates the glycolysis pathway and the phenylpropanoid pathway to produce more resistance-related secondary metabolites to defend against R. solani.

Fig. 6
figure 6

Potential mechanisms of ShB resistance regulated by qSB12YSB. Model of qSB12YSB-mediated ShB resistance. R. solani attacks host photosynthetic systems to promote pathogenesis during infection, inducing plant cell death due to excessive ROS. To protect plants from the oxidative damage, qSB12YSB slows down the Calvin-Benson cycle and activates OPPP to improve NADPH level in plants. qSB12YSB also activates cell redox-related genes to maintain ROS homeostasis to reduce cell death. These three steps finally help NIL-qSB12YSB to inhibit ROS bursts induced by R. solani infection. qSB12YSB also activates the glycolysis and phenylpropanoid pathways to produce more resistance-related secondary metabolites to defend against R. solani. Red lines represent the down expressed pathways. Blue lines represent the up expressed pathways. Black lines represent the pathways none differentially expressed pathway

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