Differential expression profiling of the early response to Ustilaginoidea virens between false smut resistant and susceptible rice varieties
- Yanqing Han†1, 2,
- Kang Zhang†1, 2,
- Jun Yang1, 2,
- Nan Zhang1, 2,
- Anfei Fang1, 2,
- Yong Zhang1, 2,
- Yongfeng Liu3,
- Zhiyi Chen3,
- Tom Hsiang4 and
- Wenxian Sun1, 2Email author
© Han et al. 2015
Received: 5 September 2014
Accepted: 3 November 2015
Published: 16 November 2015
Rice false smut caused by Ustilaginoidea virens has recently become one of the most devastating rice diseases worldwide. Breeding and deployment of resistant varieties is considered as the most effective strategy to control this disease. However, little is known about the genes and molecular mechanisms underlying rice resistance against U. virens.
To explore genetic basis of rice resistance to U. virens, differential expression profiles in resistant ‘IR28’ and susceptible ‘LYP9’ cultivars during early stages of U. virens infection were compared using RNA-Seq data. The analyses revealed that 748 genes were up-regulated only in the resistant variety and 438 genes showed opposite expression patterns between the two genotypes. The genes encoding receptor-like kinases and cytoplasmic kinases were highly enriched in this pool of oppositely expressed genes. Many pathogenesis-related (PR) and diterpene phytoalexin biosynthetic genes were specifically induced in the resistant variety. Interestingly, the RY repeat motif was significantly more abundant in the 5’-regulatory regions of these differentially regulated PR genes. Several WRKY transcription factors were also differentially regulated in the two genotypes, which is consistent with our finding that the cis-regulatory W-boxes were abundant in the promoter regions of up-regulated genes in IR28. Furthermore, U. virens genes that are relevant to fungal reproduction and pathogenicity were found to be suppressed in the resistant cultivar.
Our results indicate that rice resistance to false smut may be attributable to plant perception of pathogen-associated molecular patterns, activation of resistance signaling pathways, induced production of PR proteins and diterpene phytoalexins, and suppression of pathogenicity genes in U. virens as well.
Rice false smut (RFS) caused by the Clavicipitaceous fungus Ustilaginoidea virens, also known as Villosiclava virens, has recently become one of the most devastating grain diseases in the majority of rice-planting regions worldwide . RFS was first reported in Tirunelveli district of Tamil Nadu State of India and previously categorized as a minor disease due to its sporadic occurrence . However, the disease has expanded rapidly in China due to large-scale planting of high-yield rice cultivars and hybrids, heavy application of nitrogenous fertilizer and global warming in the past two decades, and has been found in about one third of rice cultivation areas in severe years [1, 3]. RFS outbreaks have also been reported in some American, Italian and Southern Asian rice-growing regions . The disease incidence rate was estimated to be 15.85 % in 2011 across northern India, and the smut balls formed on up to 100 grains per panicle in some fields with high disease severity .
Aside from huge yield losses (up to 40 % in severe years) caused by RFS, U. virens produces abundant amounts of mycotoxins that often contaminate rice products and are poisonous to both human and animals [6–8]. Due to the economic importance of the disease, many studies have been performed on the occurrence, pathogen detection, mycotoxin identification, infection lifecycle and chemical control of the disease [4, 9–12]. However, research on screening of rice germplasm for RFS resistance, molecular mechanisms underlying RFS resistance and the pathogenicity of U. virens is scarce . Breeding for rice cultivars with durable resistance to RFS is considered to be one of the most economical, environmentally safe and effective strategies for disease management. A rapid and effective inoculation method has been developed to evaluate rice resistance to U. virens and screen resistant germplasm for breeding [14, 15]. Although no rice variety has yet been identified to have complete or high level of resistance, cultivars do exhibit significant differences in quantitative resistance to U. virens [16, 17]. Much effort has been taken to identify quantitative trait loci (QTL) associated with rice resistance to U. virens [17–19]. It was reported that the rice cultivar IR28 has a relatively high resistance to RFS, which was controlled by two major and multiple minor resistance genes . Eight QTLs controlling RFS resistance were also found in the resistant rice variety Lemont . However, no QTL for RFS resistance in rice has yet been isolated and resistance mechanisms are largely unknown .
In plants, multiple strategies have evolved to recognize pathogens and thus trigger immune systems to defend against pathogen invasion. Recognition of conserved pathogen-associated molecular patterns (PAMPs) by pattern recognition receptors (PRRs) activates PAMP-triggered immunity (PTI) and prevents further colonization on the hosts by microbial pathogens . Perception of pathogen effectors by intercellular R proteins in plants activates effector-triggered immunity (ETI), which includes rapid and acute cell death responses in plants and restricts multiplication of pathogens . Furthermore, systemic acquired resistance (SAR) induced by the signal molecule salicylic acid (SA) may confer long-lasting protection against a wide range of pathogens .
Pathogenesis-related (PR) genes are often induced in plant defense signaling through the action of plant hormones including salicylic acid, jasmonic acid or ethylene . In Arabidopsis, expression of PR1, PR2 and PR5 is induced by SA and used as a signature for SAR . These induced PR proteins possess antimicrobial activities through their hydrolytic, proteinase-inhibitory and membrane-permeabilizing abilities, or serve as defense signals [22, 23]. As an example, PR-2 proteins function as β-1,3-glucanases that catalyze the hydrolytic cleavage of 1,3-β-D-glucosidic linkages in β-1,3-glucans present in the fungal cell walls. The disrupted cell walls cause cell lysis and death in fungi . The PR-3 proteins possess endo-chitinase activities and retard fungal growth by the enzymatic hydrolysis of chitin, the predominant constituent of fungal cell walls. The released chitin fragments often act as endogenous triggers to stimulate plant defenses . Peroxidases (PR-9) are heme-containing glycoproteins that participate in a number of physiological processes, such as biosynthesis of ethylene, suberization and lignification of plant cells in response to pathogen infection, wounding and abiotic stresses [27, 28].
Comprehensive transcriptome analyses during the interaction of plants and pathogens are commonly used to provide new insights into molecular mechanisms of plant resistance. Transcriptome comparisons between durable resistant and susceptible rice varieties in response to attack by the blast fungus Magnaporthe oryzae revealed that chitin-oligosaccharide sensing factors, wall-associated kinases, MAPK cascades and WRKY transcription factors were involved in rice blast resistance . In addition, gene expression profiling of rice in response to the infection of rice stripe virus (RSV) and small brown plant-hopper (SBPH) revealed by transcriptome analyses indicated that the jasmonate signaling pathway was important in rice resistance to SBPHs . Transcriptome analyses were also performed for other host-pathogen interaction through RNA-Seq, including wheat and Fusarium graminearum , maize and Sporisorium reilianum f. sp. zeae , cotton and the wilt fungus Verticillium dahliae , soybean and Xanthomonas axonopodis pv. glycines , banana and F. oxysporum f. sp. cubense . Many genes were thereby revealed to be involved in resistance-associated signal transduction and defense mechanism in plants. For example, PR genes were found to be significantly up-regulated in rice after blast fungus inoculation  and in the maize resistant variety Mo17 in response to S. reilianum f. sp. zeae .
Recently, RNA-Seq has been used to reveal stage-specific biological processes related to the compatible rice-U. virens interaction and expression profiling in rice varieties at the late stage of U. virens infection [37, 38]. It was reported that the primary site of U. virens colonization was at the base of the filaments with the inner spikelets becoming infected by hyphae at 24 h post inoculation (hpi) . Here, we analyzed and compared gene expression profiles of the RFS resistant variety IR28 and susceptible LYP9 after U. virens inoculation at early stages (24 hpi and 48 hpi) using transcriptome data. The results indicate that several major gene families might be involved in rice resistance to U. virens infection, including receptor-like kinases, PR genes, diterpene phytoalexin biosynthesis genes and WRKY transcription factors. These results provide important information to further understand molecular mechanisms of rice reaction and resistance to false smut.
Disease symptoms of false smut in rice cultivars IR28 and LYP9
Virulence assays of three U. virens isolates (37–1, 39–3 and P1) to the varieties IR28 and LYP9, showing that IR28 is significantly more resistant to U. virens infection than LYP9
Infected panicle rate
False smut balls per panicle
0 % (n = 20)
0 % (n = 20)
20 % (n = 20)
50 % (n = 20)
0.45 ± 0.23
1.05 ± 0.30
90 % (n = 20)
95 % (n = 20)
1.05 ± 0.20
4.80 ± 0.65
100 % (n = 20)
100 % (n = 15)
5.75 ± 0.74
26.2 ± 2.40
RNA-Seq data and aligning to the reference genomes
Changes in gene expression level of rice cultivars IR28 and LYP9 at 24 h and 48 h after P1 inoculation were analyzed using RNA-Seq data. A total of 64.4 million clean reads, each of which was 49 bp in length, were generated from eight cDNA libraries (the susceptible cultivar LYP9 and resistant cultivar IR28 at 24 and 48 hpi and four mock-inoculated controls). About 82 % of the clean reads were successfully aligned to the Oryza sativa L. spp. indica reference genome (Additional file 2: Table S1). Saturation analysis showed that newly emerging tags were gradually reduced as the total number of sequence tags increased, and the detectable tags approached saturation when the number of sequencing tags reached ~3 million (Additional file 3: Figure S2). These results indicate that the gene transcript data were reliable, and suitable for further transcriptome analysis.
Expression profiling analyses in resistant and susceptible cultivars in response to U. virens inoculation
Comparison between transcriptomes of IR28 and LYP9 in response to U. virens infection by cluster analysis
Gene ontology enrichment analysis
To investigate functions or biological processes that the differentially regulated genes might be involved in, gene ontology (GO) enrichment analysis was performed to classify up-regulated DEGs (Additional file 5: Table S3). Within three major GO categories (cellular components, molecular functions and biological processes), 14 common GO terms, 2 IR28-specific and 31 LYP9-specific GO terms were enriched at 24 hpi, while 12 common GO, 1 IR28-specific and 30 LYP9-specific GO terms were enriched at 48 hpi. The gene names in the GO terms enriched specifically by IR28 were searched for items that might be related to RFS resistance. Among them, the GO term “transferase activity” was the only one that was significantly enriched (P ≤ 0.05) in IR28 at both inoculation time points. It is most likely that some genes with transferase activity are involved in RFS resistance (Additional file 6: Figure S3).
Some protein kinases including receptor-like kinases are likely involved in RFS resistance
Expression profiles of pathogenesis-related genes
Five β-1,3-glucanase genes belonging to the PR2 family exhibited significantly different expression patterns between IR28 and LYP9 after P1 inoculation (Additional file 9: Table S6). In IR28, these genes were transcriptionally induced at 24 hpi and up-regulated even more dramatically at 48 hpi. In contrast, these genes were generally suppressed or not significantly regulated at both time points in LYP9. Extensive transcriptome analyses in both cultivars also showed that three class I (PR3), two class II (PR4) and 13 class III chitinase genes (PR8) were up-regulated at 24 hpi and 48 hpi in IR28, while these genes were generally down-regulated at the two time points in LYP9 (Fig. 4 and Additional file 9: Table S6). It is interesting to note that genes BGIOSGA035717 to 21, BGIOSGA033526, BGIOSGA033527, BGIOSGA033529 and BGIOSGA033530 were tandemly arranged in a chitinase gene cluster on chromosome 11. In addition, 16 peroxidase genes (PR9), 3 thaumatin-like genes (PR5) and 5 proteinase inhibitor genes (PR6) were identified as being induced in IR28 while most were inhibited in LYP9 after P1 inoculation. Phenylalanine ammonia-lyases (PALs), sometimes classified as PR proteins, are involved in the synthesis of both phytoalexins and lignin, to inhibit pathogens from penetrating cell walls . Three PAL genes (BGIOSGA014703, BGIOSGA018017 and BGIOSGA005998) involved in the phenylalanine metabolism and phenylpropanoid biosynthesis pathways were also up-regulated only in IR28 (Fig. 4 and Additional file 9: Table S6). Taken together, our finding that many defense-related genes including PR and PAL genes showed opposite expression patterns between IR28 and LYP9 after U. virens inoculation indicates that these genes play essential roles in RFS resistance in IR28.
Diterpene phytoalexin biosynthesis genes
Differential expression of WRKY transcription factors
The cis-acting regulatory element analysis
RY repeat motifs enriched in the 5’-regulatory regions of 47 PR genes, particularly in 9 chitinase genes, which were up-regulated in IR28 and suppressed in LYP9
PR genes (47)
PR genes (660)
Protein kinases (52)
RY repeat motif
RY repeat motif
RY repeat motif
RY repeat motif
Validation of DEGs by quantitative RT-PCR analyses
Comparison of U. virens transcriptome in the resistant and susceptible cultivars during infection
To compare expression profiles of U. virens during infection of the resistant and susceptible cultivars, clean RNA-Seq reads were mapped to the reference genome of U. virens  (Additional file 15: Table S10). Expression profiles of U. virens from the infected resistant cultivar IR28 were analyzed and compared with those from LYP9 described previously . In IR28, 614 and 542 fungal genes were up-regulated significantly at 24 and 48 hpi compared with that from axenic cultures, respectively. Meanwhile, 425 and 247 genes were identified to be suppressed at 24 and 48 hpi, respectively. Interestingly, predicted host-pathogen interaction database (PHI-base) genes  that are probably involved in host-pathogen interactions were found to be significantly enriched in fungal DEGs from both rice genotypes, indicating their potential roles in pathogenicity of U. virens.
As shown in Venn diagrams (Additional file 16: Figure S6), gene expression profiles of U. virens in the resistant cultivar IR28 were much different from those in the susceptible LYP9, although 426 (266 up-regulated and 160 down-regulated) and 433 (285 up-regulated and 148 down-regulated) genes have similar expression patterns during infection of the resistant and susceptible cultivars at 24 and 48 hpi, respectively. GO enrichment analyses revealed that U. virens DEGs in two cultivars, especially for down-regulated genes, were enriched in different GO terms (Additional file 17: Table S11). Interestingly, GO terms in biological processes that are related to fungal multiplication and pathogenicity, such as reproductive process, sexual and asexual reproduction, sporulation and cell adhesion, were significantly enriched in down-regulated genes in the resistant IR28, but not in the susceptible LYP9. These results suggest that biological processes required for successful infection of U. virens are greatly suppressed in the resistant cultivar.
RFS is a newly emerging fungal disease that causes severe yield loss and toxin contamination in rice grains . Screening of rice genetic germplasm for RFS resistance revealed that certain cultivars exhibit relatively stable RFS resistance although no resistance gene has been reported so far. However, little is known about molecular mechanisms underlying durable resistance to RFS in rice. RNA-Seq is a recently developed approach that can be used in transcriptome analyses to reveal genome-wide expression profiling and regulation in plant hosts in response to pathogen infection. The technique has several advantages over other methods. First, RNA-Seq, unlike hybridization-based approaches, can detect gene transcripts despite not having the genome sequence of the target species. Second, RNA-Seq has low background noice . Third, the technology has a higher sensitivity than DNA microarray and can be used to detect a larger dynamic range of expression levels of gene transcripts [48, 49].
In this study, RNA-Seq was used to identify genes differentially expressed between the cultivar IR28 with durable RFS resistance and susceptible cultivar LYP9 in response to U. virens at early infection stages. Comparative transcriptome analyses suggest that some important protein families including receptor-like kinases, WRKY transcription factors, PR proteins, and phytoalexin biosynthetic enzymes play important roles in RFS resistance. A clear correlation between RNA-Seq and qRT-PCR data confirmed expression patterns of the tested genes in response to U. virens infection (Fig. 7 and Additional file 16: Figure S6). Several transcriptome studies on the interaction of rice and U. virens have been reported recently [37, 38]. Different from other transcriptome analyses, we analyzed and compared transcriptome profiles of the resistant and susceptible rice cultivars at the very early stage of infection (24 hpi and 48 hpi). Although gene expression profiles were partially different among those studies, a large proportion of DEGs revealed here were also reported in other transcriptome analyses. For instance, WRKY transcription factors, such as WRKY53 and WRKY69, were induced in different transcriptome studies. Additionally, some genes that had unique responses to U. virens infection revealed by Chao et al. , such as LOC_Os07g07870.1 and LOC_Os08g23790.1, had similar expression patterns in this study. Difference in expression patterns of partial DEGs might be due to different infection stages and different rice genotypes. It has been found that many rice genes had opposite regulation patterns between the early and late stages of U. virens infection .
Pathogenesis-related proteins may be crucial for RFS resistance
Cluster analyses showed that the majority of DEGs (inoculated vs. non-inoculated) in both genotypes were differentially regulated between the two cultivars in response to U. virens inoculation (Fig. 2). Among the group II genes, 47 PR genes were identified including members in the PR2-6, PR8 and PR9 families (Fig. 4). Some PR proteins, such as β-1,3-glucanases, chitinases and proteinases have direct antifungal activities and hydrolyze molecules on the cell walls of fungal pathogens, including glucans, chitins and proteins directly [50, 51]. Other PR proteins including thaumatin-like proteins and proteinase inhibitors have enzyme inhibitory activities and exert an effect against fungi by inactivating proteinases secreted by pathogens . In addition, the peroxidase activity of PR-9 also contributes to fungal disease resistance by cross-linking and strengthening plant cell walls .
Consistent with our findings, PR genes in rice have been shown to be induced by diverse biotic stresses including infection by the rice blast fungus M. oryzae , the bacterial blight pathogen Xanthomonas oryzae pv. oryzae , the sheath blight fungus Rhizoctonia solani [55, 56], and the rice dwarf virus (RDV) . These expression data suggest that PR genes have important roles in plant defenses against pathogen infection, which has been experimentally verified. Previous studies demonstrated that over-expression of the PR genes encoding β-1,3-glucanases, chitinases and thaumatin-like proteins enhanced resistance to Fusarium head blight in wheat [58–61].
Preliminary mapping using 157 recombinant inbred lines derived from an inter-subspecies cross of Daguandao/IR28 identified a QTL conferring RFS resistance in the chromosome 11 in IR28 . The QTL is physically close to the chitinase gene cluster region, out of which, nine chitinase genes were identified to be highly induced after U. virens inoculation (Fig. 4 and Additional file 9: Table S6). Another study showed that a QTL conferring resistance to R. solani was also mapped near to the chitinase gene cluster region , suggesting that the chitinase gene cluster might be involved in broad-spectrum and durable disease resistance. Notably, clean RNA-Seq reads of the susceptible cultivar LYP9 were mapped to these chitinase genes and it was found that no gene in the chitinase cluster was absent from the genome of LYP9. Collectively, these differentially regulated PR genes in the resistant and susceptible genotypes might play essential roles in rice resistance against U. virens.
Diterpene phytoalexins are important for RFS resistance
Diterpene phytoalexins, secondary metabolites with a low molecular mass, have anti-microbial activity and play important roles in plant defense responses [64, 65]. In this study, seven diterpene phytoalexin biosynthesis genes were identified to be significantly up-regulated in the resistant variety and weakly or not induced in the susceptible variety after inoculation (Fig. 5 and Additional file 10: Table S7). Among them, OsCPS4, CYP99A2, CYP99A3 and OsMAS are responsible for different steps in the biosynthesis of momilactone A and B (Fig. 5). Knock-down of OsCPS4 caused lower accumulation levels of momilactones and oryzalexin S and the cps4 rice mutant is more susceptible to M. oryzae infection than the wild-type . Simultaneous knock-down of CYP99A2 and CYP99A3 specifically suppressed elicitor-inducible production of momilactones . Additionally, OsCPS2 and CYP76M7 are physically located on the same gene cluster involved in biosynthesis of the antifungal phytocassanes . OsCPS2 expression in the resistant rice cultivar IL7 was up-regulated at 2 d after M. oryzae inoculation, resulting in enhanced phytoalexin production . OsKSL11 is another gene where expression was elevated in IR28 after U. virens infection. OsKSL11 has been found to react with syn-CDP and produce syn-stemod-13(17)-ene . These results suggest that production of phytoalexins, in particular momilactones, is highly induced by U. virens infection in rice and can play a key role in RFS resistance.
Conserved cis-elements are involved in the regulation of defense responses against U. virens infection
A recent study reported that the U. virens regulated genes shared highly conserved cis-elements in the promoters including W-boxes, the DNA binding sites of Myb and Dof proteins, which is highly consistent with our cis-element enrichment analyses (Additional file 13: Table S9) . WRKY transcription factors are vital components in plant defense against pathogens . WRKY proteins can regulate phytoalexin production and PR gene expression through binding to the cis-regulatory element W-box. This study revealed that 13 WRKY transcription factors were differentially regulated in both the resistant and susceptible cultivars after U. virens infection. In particular, OsWRKY53, OsWRKY69 and OsWRKY71 were found to be highly up-regulated in IR28 and suppressed in LYP9 (Additional file 11: Table S8). It was demonstrated that transgenic rice plants over-expressing OsWRKY53 and OsWRKY71 exhibited enhanced resistance to blast disease and X. oryzae pv. oryzae infection [71–73]. Both Dof and Myb proteins are also important transcription factors that are involved in the regulation of plant defenses and biotic stress resistance [74, 75]. Taken together, these findings imply that some WRKY, Dof and Myb transcription factors, such as OsWRKY53, OsWRKY69 and OsWRKY71, play important roles in rice transcriptome regulation during U. virens infection.
Furthermore, the cis-regulatory RY repeat motif was found to be significantly more abundant in the promoter regions of these differentially regulated PR genes than other PR genes, even though the motif is generally enriched in the PR gene promoters. These results suggest that the seed-specific cis-element may be also involved in the expression regulation of defense-related genes in response to U. virens infection.
Defense-oriented reprogramming of protein kinase genes in rice during early infection of U. virens
Many protein kinase genes were reported to be transcriptionally regulated in host plants upon pathogen infection . In agreement with this, we found here that 52 protein kinase genes were highly induced in IR28 after U. virens infection. Among these, three categories of receptor-like kinases including lectin-, LRR- and LysM-containing transmembrane kinases were identified which are often involved in the recognition of pathogens by sensing pathogen-associated molecular patterns . Many LysM receptor-like kinases can mediate plant defense responses against fungal pathogens likely through chitin perception [78, 79]. BGIOSGA016815, a lectin receptor kinase, was also identified to be induced in response to bacterial, parasite, fungal and viral infection in rice . Other up-regulated kinase genes encode cytoplasmic kinases that function in the phospho-relay and are essential components in defense signaling. For instance, OsMAPKK4 is phosphorylated by upstream MAPKKK7 (BGIOSGA000957) that was induced by U. virens infection in IR28, which prompts signal transduction in response to various biotic and abiotic stresses including pathogen, insect, drought, salinity, flood and cold . Therefore, we speculate that these differential regulated protein kinases may play crucial roles in RFS resistance signaling.
In the present study, comparison of expression profiles between the resistant cultivar IR28 and the susceptible LYP9 during early stages of U. virens infection uncovered a clear difference in the regulation of defense responses against U. virens between the two genotypes. A genome-wide view of expression profiles of the resistant rice cultivar in response to U. virens infection promotes understanding of molecular mechanisms underlying RFS resistance. A specific set of protein kinases, PR proteins, WRKY transcription factors, and secondary metabolites including phytoalexins were found to be crucial for RFS resistance. Transgenic rice plants over-expressing some of the identified genes are being developed to confirm their biological functions in RFS resistance. The information revealed by transcriptome analyses will also facilitate the isolation of QTLs associated with resistance to U. virens in rice.
Rice materials and fungal inoculation
Oryza sativa L. spp. indica cultivars IR28 (resistant to RFS) and LYP9 (highly susceptible but high-yielding) were grown at the experiment station of Jiangsu Academy of Agricultural Sciences in Nanjing, Jiangsu, China. U. virens 37–1 and 39–3 were monospore isolates from samples collected at paddy fields in Jiangsu Province, China, and the P1 isolate originating from Kansas, USA was courtesy of Professor Jinrong Xu, Purdue University. Rice panicles were inoculated with a mixture of conidial and hyphal fragments as described with minor modifications . Briefly, the U. virens isolates were cultured in potato sucrose broth (PSB, fresh potato extract and 2 % sucrose) on an incubator shaker at 120 rpm and 28 °C for a week. The panicles of rice plants at the booting stage were inoculated with conidial suspensions (2 × 105 conidia ml−1) at 5 to 7 days before earing. Rice panicles injected with PSB were used as mock controls. The pathogen- or mock-inoculated panicles were harvested at 24 and 48 hpi, immediately frozen in liquid nitrogen, and then kept at −70 °C for RNA isolation. Some inoculated rice plants were grown further for disease symptom observations three weeks after inoculation.
Preparation of cDNA libraries for RNA-Seq
Total RNA was isolated using RNApure® total RNA rapid extraction kit according to the manufacturer’s instruction (Aidlab Biotechnologies, Beijing). The yield and purity of RNA were evaluated by measurement of absorbance at 260 and 280 nm. RNA integrity was confirmed using Agilent 2100 Bioanalyzer (Agilent Technologies) with a minimum RNA integrated number (RIN) value of 7.0. Total RNA isolated from the samples of three biological replicates at each time point (24 and 48 hpi) was combined for RNA-Seq. Poly(A) + mRNA was enriched from total RNA using oligo(dT) magnetic beads and used for library construction. RNA-Seq libraries were constructed following the standard pipeline at Beijing Genomics Institute (BGI) in Shenzhen, China. Reads of 49 bp length were generated with the Illumina HiSeq™ 2000 sequencing platform at BGI.
Mapping reads to the reference genome and annotated genes
Raw reads were downloaded from BGI in FASTQ format. The reference genome of Oryza sativa L. ssp. indica 93–11 and associated gene information were downloaded from Gramene (http://www.gramene.org/) and the Rice Genome Annotation Project (http://rice.plantbiology.msu.edu). The genome of U. virens isolate UV-8b was used as the reference for analyzing U. virens transcriptome . Prior to mapping reads to the reference databases, all reads were filtered to remove adaptor sequences, and eliminate reads in which the percentage of unknown bases (N) was greater than 10 %, or the percentage of the low quality bases (bases with Phred quality score ≤ 5) in a read exceeded 50 %. The resultant clean reads were mapped to rice and U. virens genomes using SOAP2 . No more than two mismatches were allowed in the alignment for each read.
Analysis and screening of differentially expressed genes
RPKM (Reads per kb per Million reads) was used to represent the gene expression level of rice and U. virens transcripts . Differentially expressed genes (DEGs) in rice cultivars were identified through comparing gene expression levels between U. virens- and mock-inoculated panicles with the criteria of the absolute log2 ratio value ≥ 1 and false discovery rate (FDR) ≤ 0.001 . DEGs of U. virens were identified by comparing the gene expression level during infection with that in axenic cultures using the same criteria. The DEGs of rice and U. virens were then subjected to GO enrichment analyses using the WEGO (Web Gene Ontology Annotation Plotting) program, respectively . P-values were calculated by comparing the observed frequency of an annotation term with the frequency expected in respective genome using Pearson’s chi-squared test. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed to identify significantly enriched metabolic pathways or signal transduction pathways in rice DEGs comparing with the whole genome background. Pathways with Q-values ≤ 0.05 are considered significantly enriched in DEGs as assessed with the PAICE program . Hierarchical clustering of all DEGs was performed using cluster 3.0 .
Conserved cis-elements searches
The 1.5 kb sequences upstream of the start codon of selected genes in rice were scanned for putative conserved cis-elements identical with or similar to the motifs in PLACE database . The enriched motifs in the up-regulated genes were determined by comparing frequency in the up-regulated genes with that in down-regulated genes (chi-square test, P < 0.01). Alternatively, Relative Appearance Ratio (RAR) of motifs was calculated using the formula (motif counts in a selected promoter set/number of promoters in the set)/(motif counts in total promoters/number of total promoters) . P values comparing motif frequency in selected gene sets with that in total genes were calculated using Fisher’s exact test. The conserved motifs were identified with the criteria of RAR ≥ 3 and P value < 0.01.
Validation of RNA-Seq data by quantitative real-time RT-PCR
Some differentially regulated genes identified through RNA-Seq were validated by qRT-PCR. The primer sets used for qRT-PCR were designed based on exon sequences of the selected genes using the online program, oligo analyzer (http://www.idtdna.com) and the specificity of PCR primers was evaluated by blasting primer sequences against the NCBI database (Additional file 18: Table S12). Total RNA (2 μg) was used for cDNA synthesis with MLV reverse transcriptase (Invitrogen). PCR was performed in 20 μl of reaction mix containing 0.4 μl cDNA, 10 μl SYBR Premix Ex Taq™ (Takara, Dalian), 0.4 μl ROX reference dye, and 0.4 μl of each primer (10 μM) using an ABI Prism 7000 System (Applied Biosystems, Foster City, CA). Three replicates for each biological replicate were performed with similar results. Relative gene expression was calculated using the 2-▵▵Ct method .
We thank Jinrong Xu at Purdue University for providing the U. virens isolate P1. The work is supported by the transgenic crop project 2012ZX08009003-003, National Natural Science Foundation of China grant 31471728, the National High Technology Research and Development program of China 2012AA100703, and the 111 project B13006 to W. S.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Ladhalakshmi D, Laha GS, Singh R, Karthikeyan A, Mangrauthia SK, Sundaram RM, et al. Isolation and characterization of Ustilaginoidea virens and survey of false smut disease of rice in India. Phytoparasitica. 2012;40(2):171–6.View ArticleGoogle Scholar
- Cooke MC. Some extra-European fungi. Grevillea. 1878;7:13–5.Google Scholar
- Guo X, Li Y, Fan J, Li L, Huang F, Wang W. Progress in the study of false smut disease in rice. J Agr Sci Tech. 2012;A2:1211–7.Google Scholar
- Rush MC, Shahjahan A, Jones JP, Groth DE. Outbreak of false smut of rice in Louisiana. Plant Dis. 2000;84(1):100.View ArticleGoogle Scholar
- Sanghera GS, Ahanger MA, Kashyap SC, Bhat ZA, Rather AG, Parray GA. False smut of rice (Ustilaginoidea virens) under temperate agro-climatic conditions of Kashmir, India. Elixir Bio Tech. 2012;49:9827–31.Google Scholar
- Koiso Y, Li Y, Iwasaki S, Hanaoka K, Kobayashi T, Sonoda R, et al. Ustiloxins, antimitotic cyclic peptides from false smut balls on rice panicles caused by Ustilaginoidea virens. J Antibiot. 1994;47(7):765–73.View ArticlePubMedGoogle Scholar
- Zhou YL, Pan YJ, Xie XW, Zhu LH, Xu JL, Wang S, et al. Genetic diversity of rice false smut fungus, Ustilaginoidea virens and its pronounced differentiation of populations in North China. J Phytopathol. 2008;156(9):559–64.View ArticleGoogle Scholar
- Ludueña RF, Roach MC, Prasad V, Banerjee M, Koiso Y, Li Y, et al. Interaction of ustiloxin A with bovine brain tubulin. Biochem Pharmacol. 1994;47(9):1593–9.View ArticlePubMedGoogle Scholar
- Miyazaki S, Matsumoto Y, Uchihara T, Morimoto K. High-performance liquid chromatographic determination of ustiloxin A in forage rice silage. J Vet Med Sci. 2009;71(2):239–41.View ArticlePubMedGoogle Scholar
- Kim KW, Park EW. Ultrastructure of spined conidia and hyphae of the rice false smut fungus Ustilaginoidea virens. Micron. 2007;38(6):626–31.View ArticlePubMedGoogle Scholar
- Zhou YL, Izumitsu K, Sonoda R, Nakazaki T, Tanaka E, Tsuda M, et al. PCR-based specific detection of Ustilaginoidea virens and Ephelis japonica. J Phytopathol. 2003;151(9):513–8.View ArticleGoogle Scholar
- Ashizawa T, Takahashi M, Moriwaki J, Hirayae K. Quantification of the rice false smut pathogen Ustilaginoidea virens from soil in Japan using real-time PCR. Eur J Plant Pathol. 2010;128(2):221–32.View ArticleGoogle Scholar
- Zhang Y, Zhang K, Fang A, Han Y, Yang J, Xue M, et al. Specific adaptation of Ustilaginoidea virens in occupying host florets revealed by comparative and functional genomics. Nat Commun. 2014;5:3849.PubMedGoogle Scholar
- Wang LP, Dong MZ, Hao ZN, Tao RX. Preliminary study on resistance of rice varieties to rice false smut by using method of natural infection in Zhejiang province. Acta Agric Jiangxi. 2010;22(7):73–4.Google Scholar
- Cartwright RD, Lee FN, Beaty T, Sutton EA, Parsons CE. Reaction of rice cultivars/lines to false smut, stem rot, and black sheath rot disease. Univ Arkansas Agric Exp Stn Res Ser. 2000;476:158–68.Google Scholar
- Biswas A. False smut disease of rice: a review. Environ Ecol. 2001;19(1):67–83.Google Scholar
- Li Y, Zhu Z, Zhang Y, Zhao L, Wang C. Genetic analysis of rice false smut resistance using mixed major genes and polygenes inheritance model. Acta Agron Sin. 2008;34(10):1728–33.View ArticleGoogle Scholar
- Xu J, Xue Q, Luo L, Li Z. Preliminary report on quantitative trait loci mapping of false smut resistance using near-isogenic introgression lines in rice. Acta Agric Zhejiang. 2001;14(1):14–9.Google Scholar
- Zhou YL, Xie XW, Zhang F, Wang S, Liu XZ, Zhu LH, et al. Detection of quantitative resistance loci associated with resistance to rice false smut (Ustilaginoidea virens) using introgression lines. Plant Pathol. 2014;63(2):365-72.Google Scholar
- Jones JD, Dangl JL. The plant immune system. Nature. 2006;444(7117):323–9.View ArticlePubMedGoogle Scholar
- Muthamilarasan M, Prasad M. Plant innate immunity: An updated insight into defense mechanism. J Biosci. 2013;38(2):1–17.Google Scholar
- Fu ZQ, Dong X. Systemic acquired resistance: turning local infection into global defense. Annu Rev Plant Biol. 2013;64:839–63.View ArticlePubMedGoogle Scholar
- van Loon LC, Rep M, Pieterse C. Significance of inducible defense-related proteins in infected plants. Annu Rev Phytopathol. 2006;44:135–62.View ArticlePubMedGoogle Scholar
- Uknes S, Mauch-Mani B, Moyer M, Potter S, Williams S, Dincher S, et al. Acquired resistance in Arabidopsis. Plant Cell. 1992;4(6):645–56.PubMed CentralView ArticlePubMedGoogle Scholar
- Leubner-Metzger G, Meins F. Functions and regulation of plant β-1, 3-glucanases (PR-2). In: Datta SK, Muthukrishnan S, editors. Pathogenesis-related proteins in plants. Boca Raton, Fla: CRC Press; 1999. p. 49–76.Google Scholar
- Kasprzewska A. Plant chitinases-regulation and function. Cell Mol Biol Letters. 2003;8(3):809–24.Google Scholar
- Almagro L, Ros LG, Belchi-Navarro S, Bru R, Barceló AR, Pedreño MA. Class III peroxidases in plant defence reactions. J Exp Bot. 2009;60(2):377–90.View ArticlePubMedGoogle Scholar
- Hiraga S, Sasaki K, Ito H, Ohashi Y, Matsui H. A large family of class III plant peroxidases. Plant Cell Physiol. 2001;42(5):462–8.View ArticlePubMedGoogle Scholar
- Bagnaresi P, Biselli C, Orrù L, Urso S, Crispino L, Abbruscato P, et al. Comparative transcriptome profiling of the early response to Magnaporthe oryzae in durable resistant vs susceptible rice (Oryza sativa L.) genotypes. PLoS ONE. 2012;7(12):e51609.PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng W, Ma L, Zhao J, Li Z, Sun F, Lu X. Comparative transcriptome analysis of two rice varieties in response to rice stripe virus and small brown planthoppers during early interaction. PLoS ONE. 2013;8(12), e82126.PubMed CentralView ArticlePubMedGoogle Scholar
- Xiao J, Jin X, Jia X, Wang H, Cao A, Zhao W, et al. Transcriptome-based discovery of pathways and genes related to resistance against Fusarium head blight in wheat landrace Wangshuibai. BMC Genomics. 2013;14:197.PubMed CentralView ArticlePubMedGoogle Scholar
- Zhang S, Xiao Y, Zhao J, Wang F, Zheng Y. Digital gene expression analysis of early root infection resistance to Sporisorium reilianum f. sp. zeae in maize. Mol Genet Genomics. 2013;288(1–2):21–37.View ArticlePubMedGoogle Scholar
- Xu L, Zhu L, Tu L, Liu L, Yuan D, Jin L, et al. Lignin metabolism has a central role in the resistance of cotton to the wilt fungus Verticillium dahliae as revealed by RNA-Seq-dependent transcriptional analysis and histochemistry. J Exp Bot. 2011;62(15):5607–21.PubMed CentralView ArticlePubMedGoogle Scholar
- Kim KH, Kang YJ, Kim DH, Yoon MY, Moon J, Kim MY, et al. RNA-Seq analysis of a soybean near-isogenic line carrying bacterial leaf pustule-resistant and-susceptible alleles. DNA Res. 2011;18(6):483–97.PubMed CentralView ArticlePubMedGoogle Scholar
- Li C, Deng G, Yang J, Viljoen A, Jin Y, Kuang R, et al. Transcriptome profiling of resistant and susceptible Cavendish banana roots following inoculation with Fusarium oxysporum f. sp. cubense tropical race 4. BMC Genomics. 2012;13:374.PubMed CentralView ArticlePubMedGoogle Scholar
- Kawahara Y, Oono Y, Kanamori H, Matsumoto T, Itoh T, Minami E. Simultaneous RNA-Seq analysis of a mixed transcriptome of rice and blast fungus interaction. PLoS ONE. 2012;7(11), e49423.PubMed CentralView ArticlePubMedGoogle Scholar
- Chao J, Jin J, Wang D, Han R, Zhu R, Zhu Y, et al. Cytological and transcriptional dynamics analysis of host plant revealed stage-specific biological processes related to compatible rice-Ustilaginoidea virens interaction. PLoS ONE. 2014;9(3), e91391.PubMed CentralView ArticlePubMedGoogle Scholar
- Fan J, Guo XY, Li L, Huang F, Sun WX, Huang YY, et al. Infection of Ustilaginoidea virens intercepts rice seed formation but activates grain-filling-related genes. J Integr Plant Biol. 2015;57(6):577-590.Google Scholar
- Hu M, Luo L, Wang S, Liu Y, Li J. Infection processes of Ustilaginoidea virens during artificial inoculation of rice panicles. Eur J Plant Pathol. 2014;139(1):67–77.View ArticleGoogle Scholar
- Dixon RA. Natural products and plant disease resistance. Nature. 2001;411(6839):843–7.View ArticlePubMedGoogle Scholar
- Hasegawa M, Mitsuhara I, Seo S, Imai T, Koga J, Okada K, et al. Phytoalexin accumulation in the interaction between rice and the blast fungus. Mol Plant-Microbe Interact. 2010;23(8):1000–11.View ArticlePubMedGoogle Scholar
- Grayer RJ, Kokubun T. Plant - fungal interactions: the search for phytoalexins and other antifungal compounds from higher plants. Phytochemistry. 2001;56(3):253–63.View ArticlePubMedGoogle Scholar
- Okada K. The biosynthesis of isoprenoids and the mechanisms regulating it in plants. Biosc Biotech Bioch. 2011;75(7):1219–25.View ArticleGoogle Scholar
- Pandey SP, Somssich IE. The role of WRKY transcription factors in plant immunity. Plant Physiol. 2009;150(4):1648–55.PubMed CentralView ArticlePubMedGoogle Scholar
- Rushton PJ, Somssich IE, Ringler P, Shen QJ. WRKY transcription factors. Trends Plant Sci. 2010;15(5):247–58.View ArticlePubMedGoogle Scholar
- Winnenburg R, Baldwin TK, Urban M, Rawlings C, Kohler J, Hammond-Kosack KE. PHI-base: a new database for pathogen host interactions. Nucleic Acids Res. 2006;34:D459–64.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009;10(1):57–63.PubMed CentralView ArticlePubMedGoogle Scholar
- Nagalakshmi U, Wang Z, Waern K, Shou C, Raha D, Gerstein M, et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science. 2008;320(5881):1344–9.PubMed CentralView ArticlePubMedGoogle Scholar
- Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods. 2008;5(7):621–628.50.View ArticlePubMedGoogle Scholar
- van Loon LC, Van Strien EA. The families of pathogenesis-related proteins, their activities, and comparative analysis of PR-1 type proteins. Physiol Mol Plant Pathol. 1999;55(2):85–97.View ArticleGoogle Scholar
- Edreva A. Pathogenesis-related proteins: research progress in the last 15 years. Gen Appl Plant Physiol. 2005;31(1–2):105–24.Google Scholar
- Hejgaard J, Jacobsen S, Svendsen IB. Two antifungal thaumatin-like proteins from barley grain. FEBS Lett. 1991;291(1):127–31.View ArticlePubMedGoogle Scholar
- Passardi F, Penel C, Dunand C. Performing the paradoxical: how plant peroxidases modify the cell wall. Trends Plant Sci. 2004;9(11):534–40.View ArticlePubMedGoogle Scholar
- Yin Z, Chen J, Zeng L, Goh M, Leung H, Khush GS, et al. Characterizing rice lesion mimic mutants and identifying a mutant with broad-spectrum resistance to rice blast and bacterial blight. Mol Plant-Microbe Interact. 2000;13(8):869–76.View ArticlePubMedGoogle Scholar
- Lee J, Bricker TM, Lefevre M, Pinson SR, Oard JH. Proteomic and genetic approaches to identifying defence-related proteins in rice challenged with the fungal pathogen Rhizoctonia solani. Mol Plant Pathol. 2006;7(5):405–16.View ArticlePubMedGoogle Scholar
- Zhao C, Wang A, Shi Y, Wang L, Liu W, Wang Z, et al. Identification of defense-related genes in rice responding to challenge by Rhizoctonia solani. Theor Appl Genet. 2008;116(4):501–16.View ArticlePubMedGoogle Scholar
- Shimizu T, Satoh K, Kikuchi S, Omura T. The repression of cell wall-and plastid-related genes and the induction of defense-related genes in rice plants infected with rice dwarf virus. Mol Plant-Microbe Interact. 2007;20(3):247–54.View ArticlePubMedGoogle Scholar
- Li WL, Faris JD, Muthukrishnan S, Liu DJ, Chen PD, Gill BS. Isolation and characterization of novel cDNA clones of acidic chitinases and β-1, 3-glucanases from wheat spikes infected by Fusarium graminearum. Theor Appl Genet. 2001;102(2–3):353–62.View ArticleGoogle Scholar
- Anand A, Zhou T, Trick HN, Gill BS, Bockus WW, Muthukrishnan S. Greenhouse and field testing of transgenic wheat plants stably expressing genes for thaumatin‐like protein, chitinase and glucanase against Fusarium graminearum. J Exp Bot. 2003;54(384):1101–11.View ArticlePubMedGoogle Scholar
- Chen WP, Chen PD, Liu DJ, Kynast R, Friebe B, Velazhahan R, et al. Development of wheat scab symptoms is delayed in transgenic wheat plants that constitutively express a rice thaumatin-like protein gene. Theor Appl Genet. 1999;99(5):755–60.View ArticleGoogle Scholar
- Pritsch C, Muehlbauer GJ, Bushnell WR, Somers DA, Vance CP. Fungal development and induction of defense response genes during early infection of wheat spikes by Fusarium graminearum. Mol Plant-Microbe Interact. 2000;13(2):159–69.View ArticlePubMedGoogle Scholar
- Li YS, Huang SD, Yang J, Wang CL. Analysis of quantitative trait loci for resistance to rice false smut. Acta Agron Sin. 2011;37:778–83.View ArticleGoogle Scholar
- Channamallikarjuna V, Sonah H, Prasad M, Rao G, Chand S, Upreti HC, et al. Identification of major quantitative trait loci qSBR11-1 for sheath blight resistance in rice. Mol Breeding. 2010;25(1):155–66.View ArticleGoogle Scholar
- Hammerschmidt R. Phytoalexins: what have we learned after 60 years? Annu Rev Phytopathol. 1999;37:285–306.View ArticlePubMedGoogle Scholar
- Pedras MSC, Yaya EE, Glawischnig E. The phytoalexins from cultivated and wild crucifers: chemistry and biology. Nat Prod Rep. 2011;28(8):1381–405.View ArticlePubMedGoogle Scholar
- Toyomasu T, Usui M, Sugawara C, Otomo K, Hirose Y, Miyao A, et al. Reverse-genetic approach to verify physiological roles of rice phytoalexins: characterization of a knockdown mutant of OsCPS4 phytoalexin biosynthetic gene in rice. Physiol Plant. 2014;150(1):55–62.View ArticlePubMedGoogle Scholar
- Shimura K, Okada A, Okada K, Jikumaru Y, Ko K, Toyomasu T, et al. Identification of a biosynthetic gene cluster in rice for momilactones. J Biol Chem. 2007;282(47):34013–8.View ArticlePubMedGoogle Scholar
- Swaminathan S, Morrone D, Wang Q, Fulton DB, Peters RJ. CYP76M7 is an ent-cassadiene C11 α-hydroxylase defining a second multifunctional diterpenoid biosynthetic gene cluster in rice. Plant Cell. 2009;21(10):3315–25.PubMed CentralView ArticlePubMedGoogle Scholar
- Morrone D, Jin Y, Xu M, Choi S, Coates RM, Peters RJ. An unexpected diterpene cyclase from rice: functional identification of a stemodene synthase. Arch Biochem Biophys. 2006;448(1-2):133–40.View ArticlePubMedGoogle Scholar
- Ross CA, Liu Y, Shen QJ. The WRKY gene family in rice (Oryza sativa). J Integr Plant Biol. 2007;49(6):827–42.View ArticleGoogle Scholar
- Liu X, Bai X, Wang X, Chu C. OsWRKY71, a rice transcription factor, is involved in rice defense response. J Plant Physiol. 2007;164(8):969–79.View ArticlePubMedGoogle Scholar
- Qiu D, Xiao J, Ding X, Xiong M, Cai M, Cao Y, et al. OsWRKY13 mediates rice disease resistance by regulating defense-related genes in salicylate-and jasmonate-dependent signaling. Mol Plant-Microbe Interact. 2007;20(5):492–9.View ArticlePubMedGoogle Scholar
- Chujo T, Takai R, Akimoto-Tomiyama C, Ando S, Minami E, Nagamura Y, et al. Involvement of the elicitor-induced gene OsWRKY53 in the expression of defense-related genes in rice. BBA-Gene Struct Expr. 2007;1769(7):497–505.View ArticleGoogle Scholar
- Yanagisawa S. Dof domain proteins: plant-specific transcription factors associated with diverse phenomena unique to plants. Plant Cell Physiol. 2004;45(4):386–91.View ArticlePubMedGoogle Scholar
- Ambawat S, Sharma P, Yadav NR, Yadav RC. MYB transcription factor genes as regulators for plant responses: an overview. Physiol Mol Biol Plants. 2013;19(3):307–21.PubMed CentralView ArticlePubMedGoogle Scholar
- Yazawa T, Kawahigashi H, Matsumoto T, Mizuno H. Simultaneous transcriptome analysis of Sorghum and Bipolaris sorghicola by using RNA-Seq in combination with de novo transcriptome assembly. PLoS ONE. 2013;8(4), e62460.PubMed CentralView ArticlePubMedGoogle Scholar
- Wu Y, Zhou JM. Receptor-like kinases in plant innate immunity. J Integr Plant Biol. 2013;55(12):1271–86.View ArticlePubMedGoogle Scholar
- Wan J, Zhang X, Neece D, Ramonell KM, Clough S, Kim S, et al. A LysM receptor-like kinase plays a critical role in chitin signaling and fungal resistance in Arabidopsis. Plant Cell. 2008;20(2):471–81.PubMed CentralView ArticlePubMedGoogle Scholar
- Stacey G, Stacey M, Wan J, Zhang X. LysM receptor-like kinases to improve plant defense response against fungal pathogens. In: U.S. Patent 8,097,771, issued Jan 17, 2012.Google Scholar
- Narsai R, Wang C, Chen J, Wu J, Shou H, Whelan J. Antagonistic, overlapping and distinct responses to biotic stress in rice (Oryza sativa) and interactions with abiotic stress. BMC Genomics. 2013;14:93. http://www.freepatentsonline.com/8097771.pdf PubMed CentralView ArticlePubMedGoogle Scholar
- Dóczi R, Brader G, Pettkó-Szandtner A, Rajh I, Djamei A, Pitzschke A, et al. The Arabidopsis mitogen-activated protein kinase kinase MKK3 is upstream of group C mitogen-activated protein kinases and participates in pathogen signaling. Plant Cell. 2007;19(10):3266–79.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang S, Li M, Dong H, Liu XZ, Bai YJ, Yang H. Sporulation, inoculation methods and pathogenicity of Ustilaginoidea albicans, the cause of white rice false smut in China. J Phytopathol. 2008;156(11–12):755–7.View ArticleGoogle Scholar
- Li R, Yu C, Li Y, Lam T, Yiu S, Kristiansen K, et al. SOAP2: an improved ultrafast tool for short read alignment. Bioinformatics. 2009;25(15):1966–7.View ArticlePubMedGoogle Scholar
- Benjamini Y, Yekutieli D. The control of the false discovery rate in multiple testing under dependency. Ann Stat. 2001;29(4):1165–88.View ArticleGoogle Scholar
- Ye J, Fang L, Zheng H, Zhang Y, Chen J, Zhang Z, et al. WEGO: a web tool for plotting GO annotations. Nucleic Acids Res. 2006;34 Suppl 2:W293–7.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36 Suppl 1:D480–4.PubMed CentralPubMedGoogle Scholar
- de Hoon M, Imoto S, Nolan J, Miyano S. Open Source Clustering Software. Bioinformatics. 2004;20(9):1453-54.Google Scholar
- Higo K, Ugawa Y, Iwamoto M, Korenaga T. Plant cis-acting regulatory DNA elements (PLACE) database: 1999. Nucleic Acids Res. 1999;27(1):297–300.PubMed CentralView ArticlePubMedGoogle Scholar
- Yamamoto YY, Yoshioka Y, Hyakumachi M, Maruyama K, Yamaguchi-Shinozaki K, Tokizawa M, et al. Prediction of transcriptional regulatory elements for plant hormone responses based on microarray data. BMC Plant Biol. 2011;11:39.PubMed CentralView ArticlePubMedGoogle Scholar
- Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2 − ΔΔCT Method. Methods. 2001;25(4):402–8.View ArticlePubMedGoogle Scholar