Mol Genet Genomics (2007) 278:421–431

DOI 10.1007/s00438-007-0260-y

ORIGINAL PAPER

RL-SAGE and microarray analysis of the rice transcriptome after Rhizoctonia solani infection

R. C. Venu · Yulin Jia · Malali Gowda · Melissa H. Jia · Chatchawan Jantasuriyarat · Eric Stahlberg · Huameng Li · Andrew Rhineheart · Prashanth Boddhireddy · Pratibha Singh · Neil Rutger · David Kudrna · Rod Wing · James C. Nelson · Guo-Liang Wang

Received: 20 March 2007 / Accepted: 14 May 2007 / Published online: 20 June 2007© Springer-Verlag 2007

Abstract Sheath blight caused by the fungal pathogenRhizoctonia solani is an emerging problem in rice produc-tion worldwide. To elucidate the molecular basis of ricedefense to the pathogen, RNA isolated from R. solani-infected leaves of Jasmine 85 was used for both RL-SAGElibrary construction and microarray hybridization. RL-SAGEsequence analysis identiWed 20,233 and 24,049 distinct tagsfrom the control and inoculated libraries, respectively.Nearly half of the signiWcant tags (¸2 copies) from

both libraries matched TIGR annotated genes and KOMEfull-length cDNAs. Among them, 42% represented senseand 7% antisense transcripts, respectively. Interestingly,60% of the library-speciWc (¸10 copies) and diVerentiallyexpressed (>4.0-fold change) tags were novel transcriptsmatching genomic sequence but not annotated genes.About 70% of the genes identiWed in the SAGE librariesshowed similar expression patterns (up or down-regulated)in the microarray data obtained from three biological repli-cations. Some candidate RL-SAGE tags and microarraygenes were located in known sheath blight QTL regions.The expression of ten diVerentially expressed RL-SAGEtags was conWrmed with RT-PCR. The defense genes asso-ciated with resistance to R. solani identiWed in this studyare useful genomic materials for further elucidation of themolecular basis of the defense response to R. solani andWne mapping of target sheath blight QTLs.

Keywords Plant defense · Candidate genes · QTLs · Sheath blight · Necrotrophic pathogen

Introduction

Rice (Oryza sativa L.) is a staple food crop of more thanhalf the world population and also a model monocot cropplant for genetic and genomics studies. Sheath blight dis-ease, caused by the pathogenic fungus Rhizoctonia solaniKühn, is one of the most prevalent rice diseases, causingsevere damage to rice yield worldwide (Lee and Rush1983; Rush and Lindberg 1996). The fungus survives eitheras sclerotia or mycelia in plant debris in soil, Xoats to thesurface of Xoodwater, germinates, forms infection cushionsand/or lobate appressoria on the plant surface for infection.After the initial infection, the pathogen moves up the plant

Communicated by K. Shirasu.

R.C.Venu, Yulin Jia, and Malali Gowda Contributed equally to the project.

Electronic supplementary material The online version of this article (doi:10.1007/s00438-007-0260-y) contains supplementary material, which is available to authorized users.

R. C. Venu · M. Gowda · C. Jantasuriyarat · H. Li · A. Rhineheart · G.-L. Wang (&)Department of Plant Pathology, The Ohio State University, 201 Kottman Hall, 2021 CoVey Rd, Columbus, OH 43210, USAe-mail: wang.620@osu.edu

Y. Jia · M. H. Jia · P. Singh · N. RutgerUSDA-ARS Dale Bumpers National Rice Research Center, Stuttgart, AR 72160, USA

E. StahlbergOhio Supercomputer Center, The Ohio State University, Columbus, OH 43212, USA

P. Boddhireddy · J. C. NelsonDepartment of Plant Pathology, Kansas State University, Manhattan, KS 66506, USA

D. Kudrna · R. WingArizona Genomics Institute and Department of Plant Sciences, University of Arizona, Tucson, AZ 85721-0036, USA

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by surface hyphae and develops new infection structuresover the entire plant, causing signiWcant necrotic damage(Ou 1985).

Because of its semi-saprophytic nature, R. solani has awide host range and uncharacterized pathogenicity mecha-nisms. No highly resistant rice germplasm has been foundto date (Lee and Rush 1983; Rush and Lindberg 1996, Pin-son et al. 2005). It is generally believed that resistance to R.solani is a typical quantitative trait controlled by polygenes(Sha and Zhu 1989) and that only partially resistant or tol-erant varieties exist in rice germplasm (Lee et al. 1999,2000; Raina et al. 1999; Chen et al. 2000; Meena et al.2000). There are multiple minor genes or QTLs for resis-tance to sheath blight in some rice varieties (Sha and Zhu1989; Li et al. 1995; Zou et al. 2000). However, genes withmajor eVects were observed in cultivars Jasmine 85 andTeqing (Pan et al. 1999). Since none of the identiWed QTLgenes have been cloned, the underlying mechanism ofresistance to R. solani is largely unknown.

Gene expression proWling is a promising approach tostudy the regulatory mechanisms and signaling networksthat underlie plant defense responses and pathogenesis. Bothserial analysis of gene expression (SAGE) and oligoarrayshave been widely used for transcriptome proWling in manyorganisms. Microarrays are based on the sequences derivedfrom expressed sequence tags (ESTs), full-length cDNAs orcomputer program-predicted coding regions (Tan et al.2003). Microarrays allow for rapid measurement and visual-ization of diVerential expression between genes on a largescale. In contrast, SAGE is a tag-based digital analysis ofgene expression that is achieved by generation of short tags(14 or 21 bp) from expressed transcripts that are used todetermine the transcript abundance based on the frequencyof tags (Velculescu et al. 1995; Gowda et al. 2004).

To elucidate the transcriptome change in rice after theinfection of R. solani, both RL-SAGE (Gowda et al. 2004)and microarray methods were used in this study. The sameRNA samples were used in the RL-SAGE library construc-tion and microarray hybridizations so that the results fromthe two platforms can be compared. Both methods identi-Wed numerous transcripts potentially associated with sheathblight resistance. Our Wrst attempt at dissection of thedefense transcriptome to R. solani provides a starting pointto elucidate the molecular basis of resistance to this impor-tant pathogen.

Materials and methods

Detached-leaf inoculation assay and RNA isolation

The US rice cultivar Jasmine 85 co-developed by the Inter-national Rice Research Institute (IRRI), USDA-ARS and

Texas university experiment station (Marchetti et al. 1998)exhibited moderate resistance to R. solani. RR0102 is oneof the most virulent isolates collected from US rice Welds.Previously, a series of time-course experiments were con-ducted from 2 to 48 h after inoculation. A 16-h samplingtime was selected for a study of early host defense responseand pathogenesis because a number of defense genesshowed a peak expression level around this time (Jia et al.unpublished data).

Plants were grown to the V11 growth stage (Counceet al. 2000) and at least 6 cm of a detached leaf was usedfor inoculation. RR0102 mycelia were grown on potatodextrose agar, and uniform inoculum was transferredonto the abaxial surfaces of the detached penultimateleaves placed in a container with moistened Wlter paper.After inoculation, the container was sealed with ParaWlmto maintain the high humidity required for successfulinfection by R. solani. Cool white light and temperatureof 22–24°C were maintained. A pure agar plug wasplaced on the detached leaves as the control. The controland inoculated 6-cm detached leaves were used for totalRNA isolation using the Qiagen RNeasy Kit followingthe manufacturer’s instructions (Qiagen, Valencia, CA).To ensure successful infection, inoculated leaves werekept for up to 48 h after inoculation to observe diseasesymptoms.

Construction of the RL-SAGE libraries and sequence analysis

About 50 ng of mRNAs isolated from the control and inoc-ulated leaves were used for the construction of the two RL-SAGE libraries as described previously by Gowda et al.(2004). The two libraries were sequenced at the ArizonaGenomics Institute. Bioinformatics analysis was done usingSAGEspy and other programs developed at the Ohio Super-computer Center (http://www.bioinformatics.osc.edu/sag-espy). Ditags of 40–42 bp were identiWed from thesequence reads obtained from the RL-SAGE clones. Thenindividual tags were identiWed and the total tags were nor-malized to 100,000, for tag-frequency comparison acrosslibraries. Clustering analysis was done using MicrosoftAccess. All the ESTs (ftp://ftp.tigr.org/pub/data/tgi/Oryza_sativa/) and pseudomolecules (ftp://ftp.tigr.org/pub/data/Eukaryotic_Projects/o_sativa/annotation_ dbs/) were down-loaded from the TIGR version 4 databases.

Oligoarray hybridization

Total RNA was isolated using the Qiagen RNeasy kit. Twohundred and Wfty nano grams of total RNA was labeledwith Cy3 and Cy5 using Agilent’s low-input ampliWcationkit following the manufacturer’s instructions. Seven hundred

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Mol Genet Genomics (2007) 278:421–431 423

and Wfty nano grams of ampliWed cRNA was prepared forhybridization using Agilent’s in situ hybridization kit fol-lowing the manufacturer’s instructions. Hybridization withthe Agilent’s 22 K 60mer-oligo chip representing 22,000rice genes was performed at 60°C for 17 h in a SciGeneoven. Slides were washed using Agilent’s SSC washingprotocol (http://www.chem.agilent.com/scripts/pds.asp?lPage = 12133). Three biological replications were per-formed for the control and inoculated RNA samples. Onedye-swap hybridization was conducted for the Wrst two rep-lications and two dye-swap hybridizations were conductedfor the third replication. Slides were scanned in an Axonscanner using default settings. Oligoarray data analysis wasperformed using GeneSpring software (http://www.rosettabio.com/products/resolver/default.htm). An average of signalintensities of three biological replications was performed asper GeneSpring’s recommendations. All the microarrayexperiments were carried out in accordance with MIAMEstandards.

Comparative analysis of the RL-SAGE and oligo array data

Because RL-SAGE and microarray are very diVerent proWl-ing platforms, a direct comparison of the gene expressiondata is not possible. So, virtual 21 bp RL-SAGE tags(“CATG” followed by 17 bp) were isolated from thesequences of the spotted genes on the array. These virtualtags were then matched to the control and inoculated RL-SAGE tags. The RL-SAGE tags that matched only one vir-tual tag of a gene on the array and the genes containing onlyone RL-SAGE tag were used in the comparison. RL-SAGEtag frequency was directly compared with the oligoarrayerror-weighted average of signal intensity. Regression andPearson’s correlation analyses were calculated usingMicrosoft Excel.

RT-PCR analysis

The total RNA obtained from the control and inoculatedleaves was treated with RNAse-free DNAse (Ambion) for60 min at 37°C. First-strand cDNA synthesis employed 1�g of total RNA, using the Qiagen RT-PCR kit accordingto the manufacturer’s instructions. The control PCR of theActin 1 gene was performed with the sense primer 5�-CGTCTGCGATAATGGAACTGG-3� and antisenseprimer 5�-CTGCTGGAATGTGCTGAGAGAT-3�. Genesexpressed only in the control or only in the inoculatedlibrary were ampliWed using gene speciWc primers listed inSupplemental Table S1. PCR assays were performed for 25cycles, where each cycle includes denaturing at 95°C for30 s, annealing at 58°C for 45 s, and elongation at 72°C for60 s. The PCR amplicons were separated on a 1.0% agarosegel.

QTL and gene placement on a reference genetic map

Sheath-blight QTLs from Gramene and the literature wereplaced on the reference genetic map, Cornell IRMI SSR2003 in the Gramene database (http://www.gramene.org),as follows. QTL Xanking-marker sequences were obtainedby searches of Gramene or GenBank and BLASTed againstthe TIGR version 4 pseudomolecule assembly. From thephysical position (bp) of the match on the pseudomolecule(almost invariably the top-scoring hit) representing themarker’s linkage group on the original map, its geneticposition was calculated by linear interpolation betweenSSR markers previously mapped either genetically or bythe same interpolation process. The lengths of chromosomelabeled as containing the QTLs were estimated from the lit-erature sources or adopted from Gramene. Genes identiWedby RL-SAGE tag searches were genetically mapped in asimilar manner by interpolation based on physical position.

Results

Sequence analysis of the control and inoculated RL-SAGE libraries

The characteristics of the control and inoculated libraries ofJasmine 85 are summarized in Table 1. A total of 3,456 and3,264 clones randomly picked from the control and inocu-lated libraries, respectively, were sequenced. From the rawsequence reads, 90,230 and 78,218 individual tags wereisolated from the control and inoculated libraries, respec-tively. Distinct tags from the control (20,233) and inocu-lated (24,049) libraries were classiWed into signiWcant (¸2copies) and non-signiWcant (one copy or singleton) tags(Table 1). The highest copy number in the control andinfected libraries was 423 and 490, respectively (Supple-mental Table S2). The total number of signiWcant tags inboth libraries (11,448 in the control and 11,317 in the inoc-ulated) was approximately the same despite the diVerencein total number of distinct tags (Supplemental Table S2).However, about 10% more singleton tags were found in theinoculated library.

Matching analysis to rice genomic and EST sequences

SigniWcant tags were searched against several rice EST,cDNA and genomic databases (see “Materials and meth-ods”) to identify sense, antisense and novel transcript tags(Fig. 1, Supplemental Table S3). About 68% (control) and65% (inoculated) of tags matched Nipponbare (japonica)genomic sequence, while 71% and 69% matched the 93–11(indica) genomic sequences, suggesting a closer relation-ship between Jasmine 85 and 93–11. This is consistent with

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424 Mol Genet Genomics (2007) 278:421–431

Jasmine 85’s pedigree of indica origin (http://www.agco-mintl.com/jasmin.htm, Marchetti et al. 1998). Nearly 68and 65% of the signiWcant tags from the control and inocu-lated libraries gave perfect matches to TIGR-ESTs. Ofthose matched to TIGR ESTs, around 60% and 33% of thecontrol library tags and 58 and 32% of the inoculatedlibrary tags represented sense and antisense transcripts,respectively. Approximately 50% of the tags from the con-trol and inoculated libraries matched either KOME full-length cDNAs or TIGR annotated genes. Among thematched tags, about 42 and 50% of the tags matched thesense strands and about 6 and 8% matched the antisensestrands of KOME full-length cDNAs and annotated genes,

respectively (Supplemental Table S3). The search againstTIGR-annotated genes indicated that about 1% of the tagsfrom either library matched both sense and antisense tran-scripts of the same gene (data not shown). A total of 72 and50 distinct signiWcant tags from the control and inoculatedlibraries, respectively, matched annotated genes thatencode hypothetical proteins, providing experimental evi-dence for these predicted genes (Supplemental Table S4).In addition, alternative transcript tags including thosederived from alternative splicing and alternative termina-tion events were also found. The rice TIGR EST database(release 17.0) in which alternative splice forms were clus-tered was used to determine whether multiple RL-SAGEtags are matched to the same cluster. A total of 610 and 728unique clusters had two or more control and inoculatedlibrary RL-SAGE tags, respectively (Supplemental TableS4). These results supported the presence of alternativelyspliced or terminated transcripts in both libraries. Interest-ingly, about 10% of the tags matched the intergenic regionsin the rice genome (Supplemental Table S3). These sequenceswere missed in the genome annotation.

Transcriptome analysis of the control and inoculated libraries

SpeciWcally expressed, induced and suppressed genes

To identify speciWcally and highly induced and suppressedgenes in the two libraries, tags with only a single match inthe Nipponbare genome were selected. Among speciWcexpressed tags with 10 copies or more, 115 and 189 tagswere speciWc to the inoculated and control library, respec-tively, with 41 of the former and 71 of the latter matchingannotated genes. Among the tags common to both libraries,148 tags were highly induced (¸4.0-fold) and 129 highlysuppressed (·4.0-fold) in the inoculated library, with 61 ofthe former and 54 of the latter matching annotated genes.

Table 1 Characteristics of the two Jasmine 85 RL-SAGE libraries

a Tags were normalized to 100,000

Control library

Inoculated library

Sequenced clones 3,456 3,264

Total tags 90,230a 78,218a

1 copy tag 8,785 12,732

2–4 copy tags 6,695 6,493

5–9 copy tags 2,717 3,092

10–24 copy tags 1,546 1,281

25–49 copy tags 316 297

50–99 copy tags 114 98

¸100 copy tags 60 56

Distinct tags 20,233 24,049

SigniWcant tags 11,448 (56.6%) 11,317 (47%)

Non-signiWcant tags 8,785 (43.4%) 12,732 (53%)

Library speciWc signiWcant tags

5,562 (48.6%) 6,003 (35.5%)

Library speciWc total tags 13,074 (64.0%) 16,890 (70.2%)

SigniWcant common tags 3,961 (55.3%)

Total common tags 7,159

Fig. 1 The control and inocu-lated RL-SAGE library tags were matched to diVerent rice nucleotide databases

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Mol Genet Genomics (2007) 278:421–431 425

Tags matching annotated genes are listed in SupplementalTable S5 and those not matching, in Supplemental Table S6.

A total of 2,939 signiWcant tags from both the librariesmatched either 93–11 or Nipponbare genomic sequence butdid not match TIGR annotated genes. Of these, 1,828(16.0%) and 1,778 (15.7%) tags belonged to the control andinoculated libraries, respectively. These transcripts are des-ignated novel transcripts because they were missed in thegene annotation.

Antisense transcripts

About 8% of the tags from the control and inoculatedlibraries represented antisense transcripts of annotatedgenes. A total of 422 genes that produced antisense tran-scripts were speciWc to the inoculated library (Supplemen-tal Table S7). Some of them encode proteins containingNB-ARC, zinc Wnger or kinase domain and other defense-related motifs. Many annotated genes contain both senseand antisense transcripts in the control (558 genes) andinoculated (525 genes) libraries. Many pathogenicity-related genes generated both sense and antisense transcriptsand were present only in the inoculated library (Table 2).Some of these encode endo-chitinase, non-speciWc lipidtransfer proteins, phenylalanine ammonia-lyase, metallothi-onein-like protein, NAC domain and WRKY transcriptionfactors, ubiquitin-conjugating enzyme, serine/threonineprotein kinase, lipoxygenase 8, and Ras-related protein.Further, expression of these antisense transcripts was con-Wrmed by matching signiWcant tags from both the librarieswith rice antisense FL-cDNAs in the KOME database(Osato et al. 2003; http://cdna01.dna.aVrc.go.jp/cDNA/

Analysis/antisenseweb/riceantisense.fasta). About 63 and70 antisense genes were found to be expressed in the con-trol and inoculated libraries (Supplemental Table S7).

Transcription factors

Transcription factors (TFs) play an important role in activa-tion of defense related genes after pathogen infection (Oñate-Sanchez et al. 2006; Ryu et al. 2006). A total of 327 and 340unique TFs were expressed in the control and inoculatedlibraries, respectively, with 132 TFs speciWc to the controland 145 to the inoculated library. TFs pooled from bothlibraries represented 54 unique TF families. The frequenciesof selected library-speciWc TFs are shown in Table 3.

Functional classiWcation of expressed genes in control and inoculated libraries

Using Kyoto Encyclopedia of Genes and Genomes(KEGG) database (http://www.genome.jp/kegg/) all thegenes expressed in the control and inoculated library wereclassiWed into diVerent functional groups (Fig. 2). Only tagsrepresented by ¸4 copies and matching a single chromo-somal region were used in the analysis. In the inoculatedlibrary, more genes related to signal transduction, metabo-lism of cofactors, nucleotide metabolism, lipid metabolismand translation are expressed than that in the controllibrary. In contrast, genes related to amino acid metabolism,sorting and degradation, transcription, carbohydrate metab-olism, energy metabolism, cell growth and death, replica-tion and repair were relatively suppressed in the inoculatedlibrary.

Table 2 Pathogenesis related genes that contained both sense and antisense pairs and were speciWcally expressed in the inoculated library

Gene ID Gene description Copy number (sense)

Copy number (antisense)

Os10g39680 Acidic 27 kda endochitinase precursor, putative, expressed 212 5

Os11g02400 NonspeciWc lipid-transfer protein precursor, putative, expressed 171 3

Os02g41630 Phenylalanine ammonia-lyase, putative, expressed 119 6

Os02g41650 Phenylalanine ammonia-lyase, putative, expressed 43 5

Os08g44400 Glutathione S-transferase, N-terminal domain containing protein, expressed

31 3

Os02g08440 WRKY transcription factor, putative, expressed 14 3

Os07g09680 Ras-related protein ARA-3, putative, expressed 9 3

Os03g63400 NAC domain containing protein, expressed 10 4

Os12g01200 Leucine-rich repeat family protein, putative, expressed 8 4

Os02g42314 Ubiquitin-conjugating enzyme family protein, expressed 8 4

Os08g12750 Serine/threonine protein kinase, putative, expressed 8 3

Os08g39850 Lipoxygenase 8, chloroplast precursor, putative, expressed 8 3

Os12g38064 Metallothionein-like protein 1, putative, expressed 5 4

Os05g51420 Hypersensitive-induced response protein, putative, expressed 5 3

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426 Mol Genet Genomics (2007) 278:421–431

Microarray analysis of the rice transcriptome after R. solani infection

To compare the transcriptomes revealed by RL-SAGE andmicroarray, the same RNA samples isolated from the controland R. solani-inoculated leaves were used in hybridizationswith Agilent rice 22 K oligo arrays. A total of 588 and 526

genes were ¸1.5-fold up- and down-regulated in the inocu-lated library compared to the control sample (Fig. 3). Therewere more genes with high fold changes in the up-regulatedgroup (158 up-regulated vs. 2 down-regulated with ¸3.0-fold change). Fifteen genes showed more than 10 foldinduction after inoculation, with the highest fold change(26.7) seen for the gene encoding a putative NAD-depen-

Table 3 Transcription factors speciWcally expressed in control and inoculated libraries

Gene ID Control Inoculated Family Description

Os06g39906 13 0 HB Homeobox domain containing protein, expressed

Os06g49040 13 0 GARP-G2-like Myb-like DNA-binding domain, SHAQKYF class family protein, expressed

Os07g49030 12 0 PHD PHD-Wnger family protein, expressed

Os11g03390 11 0 FHA FHA domain containing protein, expressed

Os10g40390 11 0 GRAS GRAS family transcription factor containing protein, expressed

Os02g06584 10 0 C3H Zinc Wnger C-x8-C-x5-C-x3-H type family protein, expressed

Os11g08080 10 0 MYB-related SWIRM domain containing protein, expressed

Os08g05510 9 0 MYB-related Myb-like DNA-binding domain, SHAQKYF class family protein, expressed

Os02g49326 9 0 PcG SET domain-containing protein, putative, expressed

Os02g07840 9 0 bZIP bzip transcription factor family protein, expressed

Os07g42370 0 23 ZIM ZIM motif family protein, expressed

Os03g51220 0 22 MYB-related Myb-like DNA-binding domain containing protein, expressed

Os06g44010 0 15 WRKY WRKY2 protein, putative, expressed

Os07g38240 0 12 C2H2 Zinc Wnger protein, putative, expressed

Os05g46020 0 10 WRKY WRKY DNA binding domain containing protein, expressed

Os05g43950 0 10 VOZ Expressed protein

Os02g03580 0 9 bZIP Transcription factor HBP-1a, putative, expressed

Os09g35910 0 9 HB Homeobox domain containing protein, expressed

Os07g04560 0 8 NAC No apical meristem protein

Os03g62379 0 8 MYB-related Myb-like DNA-binding domain containing protein, expressed

Fig. 2 Functional classiWcation of rice genes expressed in re-sponses to the infections of R. solani using the KEGG (Kyoto Encyclopedia of Genes and Genomes) database

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dent formate dehydrogenase (Supplemental Table S8).Many defense related genes were highly induced. In con-trast, the highest fold reduction among the suppressedgenes was 3.6, which encodes an unknown protein.

Transcriptome comparison of the R. solani infected leaves revealed by microarray and RL-SAGE

An error-weighted average of signal intensities of threebiological replications of the control or inoculated RNAlabeled with Cy3 and Cy5 were calculated and compared withthe copy number of RL-SAGE tags in the two RL-SAGE

libraries. The two data sets were moderately correlated(Fig. 4). About 70% of the genes identiWed in RL-SAGElibraries showed similar expression pattern (up- or down-regulation) in the microarray data (Supplemental Table S9).To compare the expression results obtained from the twoplatforms, a Pearson’s correlation analysis was performed.When all the data of the control sample from the RL-SAGEand microarray platforms were used in the comparativeanalysis, a moderate correlation (0.56) was observed(Table 4). But removal of three outliers in the microarraydata increased the correlation to 0.75 for high copy numbertags. Similarly, removal of the Wve outliers in the micro-array data of the inoculated sample increased the correla-tion coeYcient from 0.47 to 0.60 (Table 4). For the controlsample, the correlation was not improved when the tagswith high copy number (¸15 copies) were used in the data-set without the three outliers. However, the correlationincreased from 0.47 to 0.60 in the inoculated sample whenthe high-copy tags were used in the dataset without the Wveoutliers.

RL-SAGE data validation using RT-PCR

To validate the RL-SAGE data, ten tags were selectedbased on these two conditions: (1) high to moderate expres-sion and speciWc to one library, and (2) matching only oneKOME full length cDNA to avoid non-speciWc ampliWca-tion. The tag copy number, gene annotation and primer

Fig. 3 Up- and down-regulated genes in the inoculated library withdiVerent fold changes

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Fig. 4 Comparison of RL-SAGE tag frequency with average signal intensity of microarray for the control (a) and inoculated (b) libraries. Both RL-SAGE tag frequency and microarray average signal intensity are on logarithmic scale

1215.2+x5000.0=yR2 5343.0=

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800006000040000200000

Oligoarray average signal intensity

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RL-

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A B

Table 4 Comparison of RL-SAGE and oligoarray methods used in transcriptome proWling of control and Rhizoctonia solani inoculated rice

Oligoarray average signal intensities of three biological replications of microarray were compared with RL-SAGE tag frequencya,c Correlation before removing genesb Correlation after removing 3 genes out of 803 genes (Standard residuals range >3)d Correlation after removing 5 genes out of 864 genes (Standard residuals range >3)

Technology comparison CorrelationcoeYcient using all tags

Correlation coeYcient using only signiWcant tags

Correlation coeYcient using tags ¸5 copies

Correlation coeYcient using tags ¸10 copies

Correlation coeYcient using tags ¸15 copies

Control: RL-SAGE vs Oligoarraya 0.56 0.56 0.57 0.61 0.70

Control: RL-SAGE vs Oligoarrayb 0.59 0.58 0.60 0.66 0.75

Inoculated: RL-SAGE vs Oligoarrayc 0.47 0.48 0.53 0.53 0.45

Inoculated: RL-SAGE vs Oligoarrayd 0.52 0.54 0.60 0.64 0.60

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sequences are listed in Supplemental Table S1. ExpectedPCR product sizes were obtained from all the ampliWca-tions. As shown in Fig. 5, stronger or weaker expressionwas detected for the induced (glutathione peroxidase 1, glu-tathione S-transferase, scarecrow-like 3 protein, glutamate1-semialdehyde aminotransferase) or suppressed (G-boxbinding protein, aldolase C-1, methyltransferase, ribosomalprotein S10) transcripts in the inoculated library, respec-tively, corroborating the tag frequency in the library. Out of

10 genes tested, eight had similar expression patternsbetween RL-SAGE and microarray data (SupplementalTable S10).

Mapping candidate RL-SAGE tags on the rice linkage map

To investigate whether some of the candidate RL-SAGEtags and candidate genes from the microarray analysis arelocated in the proximity of sheath blight QTLs that havebeen mapped on the rice linkage map, these sequences wereplaced on the rice genetic map constructed on the RiceCAPWWW site (http://www.uark.edu/ua/ricecap/rcapdata/Home.php). For RL-SAGE tags, two groups of tags werechosen for the mapping analysis. The Wrst group comprised304 library-speciWc high-copy (>10) tags, of which 192lacked TIGR annotations. The second group comprised 277tags common to both libraries at a 4-fold increase ordecrease between libraries, of which 162 lacked TIGRannotations. All these candidate tags are listed in Supple-mental Tables S5 and S6. For microarray data, candidategenes with 2.0-fold increase (344 genes) or decrease (43genes) were chosen for mapping (Supplemental Table S8).In general, the candidate tags tended to be clustered on thelinkage map (Fig. 6). Many tags were located in the known

Fig. 5 Validation of up- (a) and down- (b) regulated transcripts in theinoculated library by RT-PCR

A

277260KA

InoculatedControl1Kb InoculatedControl1Kb

996301KA

268860KA

877070KA

628460KA

nitcaeciR

B

602801KA

644990KA

077370KA

899301KA

091201KA

nitcaeciR

Fig. 6 Mapping candidate RL-SAGE tags and microarray candidategenes to known sheath blight QTL regions on the rice linkage map.qSB-2, 3, 7 and 9-2 were identiWed from Jasmine 85 by Zou et al.(2000). “SB QTL in Lemont and Teqing” were obtained from Pinson

et al. (2005). “QTLs not in Gramene” were described in Han et al.(2002), Kunihiro et al. (2002) and Sato et al. (2004). “SB_QTLs” wereobtained from Gramene (http://www.gramene.org)

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QTL intervals. Interestingly, ten RL-SAGE tags and micro-array candidate genes were mapped between 50 and 60 cMon chromosome 2 where qSB-2 is mapped. The sequencesand function of these two genes are shown in SupplementalTable 11. qSB-2 together with three other QTLs (qSB-3,qSB-7 and qSB9-2) were identiWed in Jasmine 85 by Zouet al. (2000, Fig. 6). The sequence and location of eachmapped candidate tag can be found on the RiceCAP WWWsite (http://www.uark.edu/ua/ricecap/database.htm).

Discussion

In addition to the genetic approach, a direct assessment ofthe biochemical and physiological changes during diseasedevelopment can be used to identify genes involved in thedisease response pathway. Enhanced and suppressedexpression of genes during infection could be interpreted asinvolvement of these genes in host defense. This so-calledcandidate gene approach has been used in a number of stud-ies to associate defense-related genes with disease resis-tance to M. grisea (Wang et al. 2001 and Liu et al. 2004). Inboth studies, several candidate defense genes were mappedonto the regions with known major and QTLs to M. grisea.Although a large number of rice collections have beenscreened for resistance to R. solani, only a dozen moder-ately resistant cultivars have been identiWed and are beingused in rice breeding programs. Genetic analysis of theresistance to sheath blight was initiated a decade ago. Atotal of 37 QTLs have been mapped on the rice linkage map(http://www.gramene.org/qtl/). Because of the complexityof the resistance and lack of reliable inoculation method,the resistant mechanism to sheath blight is still largelyunknown. Jasmine 85 confers moderate resistance to USand Chinese R. solani isolates and contains several QTLs tothe pathogen (Pan et al. 1999; Zou et al. 2000). It is one ofthe top cultivars with a relatively high level of resistance toR. solani in both greenhouse and Weld screens to the R.solani population in Columbia (F. Correa, personal com-munication). In this study, the defense response in Jasmine85 after sheath blight infection was proWled using both RL-SAGE and microarray methods. A large number of genesthat were highly induced or suppressed in the inoculatedJasmine 85 leaves were identiWed from both approaches.Many novel genes that are not present in the existing ESTcollections were identiWed from the inoculated RL-SAGElibrary. Some of these genes (581 RL-SAGE and 344microarray) that are speciWcally or diVerentially expressedin the RL-SAGE libraries were mapped on the rice linkagemap. Interestingly, many candidate genes are mapped intoknown QTL intervals and could be useful for Wne mappingof these QTLs. If one of the candidate genes is the causalagent for the underlying variation of the QTL, cloning of

the gene will be straightforward. In addition, silencing orover-expression of selected candidate genes might generatehighly resistant transgenic lines. Our study provides initialgenomic materials for further elucidation of the function ofnovel genes that are involved in the defense response toR. solani.

In both RL-SAGE and microarray analyses, manydefense- and metabolism-related genes were identiWed inthe infected rice leaves of Jasmine 85 whose expression iseither highly induced or suppressed. Many of them are alsoinvolved in the interaction with other pathogens such as therice blast fungus M. grisea (Kim et al. 2001; Lu et al. 2004,Jantasuriyarat et al. 2005). For example, many genes identi-Wed in this study are also present in the large EST collec-tions made from M. grisea infected leaves (Jantasuriyaratet al. 2005, http://www.mgosdb.org/). These genes may beinvolved in pathogenesis, common points of signal recogni-tion and overlapping networks of defense pathways. Toverify these Wndings, four RL-SAGE libraries from controland M. grisea-infected leaf tissues were recently con-structed and analyzed (Gowda et al. unpublished). Thecomparison of the defense transcriptomes between R.solani and M. grisea-infected leaves indicates that a largenumber of the genes are induced or suppressed in bothcases, suggesting a possible conserved defense pathway toboth pathogens. Further detailed comparative analysis ofthe two defense transcriptomes may provide new insightsinto the molecular mechanism to these two important fun-gal pathogens.

It is intriguing that more than half of the high-copylibrary-speciWc or diVerentially expressed tags matched thegenome sequence of Nipponbare and 93–11 but did notmatch annotated rice genes. These novel transcripts couldbe located in an exonic region of an expressed gene that hasbeen missed in the annotation. Alternatively, they could bederived from the coding region of a gene that has not beenidentiWed in the rice genome. One possible reason for theidentiWcation of this large set of novel genes in this study isthat a very limited expression analysis has been done in therice/sheath blight pathosystem. Although a large number ofEST and full length cDNA sequences have been reported(Yamamoto and Sasaki 1997; Ewing et al. 1999, Kikuchiet al. 2003), the genes associated with the defense to R.solani may not be present in the cDNA libraries challengedby other rice pathogens (Jantasuriyarat et al. 2005; Choet al. 2004). It is worthwhile investigating whether thesenovel genes are also involved in the defense to rice blastand bacterial blight or unique to sheath blight. Detailedfunctional analysis of these genes may provide new infor-mation in the defense to the sheath blight pathogen.

The correlation that we observed between the results ofdiVerent methods for deep transcriptome proWling ofeukaryotic genomes has been examined previously. Coughlan

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et al. (2004) compared the proWling results from bothmicroarray and massively parallel signature sequencing(MPSS, Meyers et al. 2004) using the same RNA samplesof Arabidopsis. A moderate and low correlation among thetwo data types was observed when the abundance of theMPSS tags and microarray signal intensity were compared.Correlation was improved when MPSS tags with <10 TPM(transcripts per million) were removed in the comparison.In this study, we obtained similar results when the abun-dance of the RL-SAGE tags and microarray signal intensitywere compared. The moderate correlation between RL-SAGE and microarray is not surprising, since the correla-tion in gene expression levels across diVerent microarrayplatforms was only 0.53 (Tan et al. 2003). Since bothmicroarray and RL-SAGE methods could identify diVerentbut overlapping sets of candidate genes, they could be usedsimultaneously in transcriptome studies. It is noteworthythat novel genes can be easily identiWed in RL-SAGElibraries since it is an open platform.

It is noteworthy that many antisense transcripts wereidentiWed in the R.sonali-infected RL-SAGE library in thisstudy, which is consistent with the Wnding from the M. gri-sea RL-SAGE libraries (Gowda et al. 2007). The expres-sion of four selected antisene transcripts after M. griseainfection was conWrmed using strand-speciWc RT-PCR.Interestingly, one of the antisense transcripts was the classi-cal defense gene PR1. The function of the induced anti-sense transcripts in M. grisea or R. sonali-infected riceplants is not known yet. One possibility is that these anti-sense transcripts may pair with the corresponding sensetranscripts to form double-stranded RNA (dsRNA) mole-cules, which may trigger the RNA interference pathway inthe infected cells (Jen et al. 2005). Molecular analysis ofthese antisense transcripts should shed light on the role ofantisense genes in plant and fungus interactions.

Acknowledgments We thank CuWanda Flowers for excellent tech-nical support. Financial support for this project came from the Rice-CAP project of the USDA-NRI, National Program NP301 on“Genomic characterization of rice germplasm”, the University ofArkansas Rice Research and Promotion Board on “Development ofmolecular strategies to control rice sheath blight disease” and the NSF-Plant Genome Research Program (#0115642 and #0321437).

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