Abstract

BackgroundRice blast, caused by the fungal pathogen Magnaporthe grisea, is a devastating disease causing tremendous yield loss in rice production. The public availability of the complete genome sequence of M. grisea provides ample opportunities to understand the molecular mechanism of its pathogenesis on rice plants at the transcriptome level. To identify all the expressed genes encoded in the fungal genome, we have analyzed the mycelium and appressorium transcriptomes using massively parallel signature sequencing (MPSS), robust-long serial analysis of gene expression (RL-SAGE) and oligoarray methods.ResultsThe MPSS analyses identified 12,531 and 12,927 distinct significant tags from mycelia and appressoria, respectively, while the RL-SAGE analysis identified 16,580 distinct significant tags from the mycelial library. When matching these 12,531 mycelial and 12,927 appressorial significant tags to the annotated CDS, 500 bp upstream and 500 bp downstream of CDS, 6,735 unique genes in mycelia and 7,686 unique genes in appressoria were identified. A total of 7,135 mycelium-specific and 7,531 appressorium-specific significant MPSS tags were identified, which correspond to 2,088 and 1,784 annotated genes, respectively, when matching to the same set of reference sequences. Nearly 85% of the significant MPSS tags from mycelia and appressoria and 65% of the significant tags from the RL-SAGE mycelium library matched to the M. grisea genome. MPSS and RL-SAGE methods supported the expression of more than 9,000 genes, representing over 80% of the predicted genes in M. grisea. About 40% of the MPSS tags and 55% of the RL-SAGE tags represent novel transcripts since they had no matches in the existing M. grisea EST collections. Over 19% of the annotated genes were found to produce both sense and antisense tags in the protein-coding region. The oligoarray analysis identified the expression of 3,793 mycelium-specific and 4,652 appressorium-specific genes. A total of 2,430 mycelial genes and 1,886 appressorial genes were identified by both MPSS and oligoarray.ConclusionThe comprehensive and deep transcriptome analysis by MPSS and RL-SAGE methods identified many novel sense and antisense transcripts in the M. grisea genome at two important growth stages. The differentially expressed transcripts that were identified, especially those specifically expressed in appressoria, represent a genomic resource useful for gaining a better understanding of the molecular basis of M. grisea pathogenicity. Further analysis of the novel antisense transcripts will provide new insights into the regulation and function of these genes in fungal growth, development and pathogenesis in the host plants.

Highlights

  • Rice blast, caused by the fungal pathogen Magnaporthe grisea, is a devastating disease causing tremendous yield loss in rice production

  • Since the fabrication of most types of microarrays relies on sequences that are derived from ESTs, full-length cDNAs or computer program-predicted coding regions, many rare transcripts may be absent from these arrays

  • Using the same RNA samples of appressoria and mycelia, oligoarray hybridization was performed to check if these three approaches can detect similar sets of expressed genes in M. grisea

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Summary

Introduction

Rice blast, caused by the fungal pathogen Magnaporthe grisea, is a devastating disease causing tremendous yield loss in rice production. The experimental approaches such as ESTs (expressed sequenced tags) [2], full-length cDNA sequencing [3], SAGE (serial analysis of gene expression) [4,5], MPSS (massively parallel signature sequencing) [6,7], RATE (robust analysis of transcript ends [8] and microarrays [9,10] are essential tools to validate the annotated putative transcriptional units (TUs) in the sequenced genomes. SAGE and MPSS are two powerful methods for genome-wide expression profiling and novel gene identification [4,5,7] Both methods determine the sequence of short tags derived from a defined position at the 3' regions of expressed mRNAs in a cell. The newly developed 5'-RATE method simplifies tag cloning and sequencing procedures and should have a broad application for transcriptome analysis of complex genomes [8]

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