33
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Global Expression Profiling of Transcription Factor Genes Provides New Insights into Pathogenicity and Stress Responses in the Rice Blast Fungus

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Because most efforts to understand the molecular mechanisms underpinning fungal pathogenicity have focused on studying the function and role of individual genes, relatively little is known about how transcriptional machineries globally regulate and coordinate the expression of a large group of genes involved in pathogenesis. Using quantitative real-time PCR, we analyzed the expression patterns of 206 transcription factor (TF) genes in the rice blast fungus Magnaporthe oryzae under 32 conditions, including multiple infection-related developmental stages and various abiotic stresses. The resulting data, which are publicly available via an online platform, provided new insights into how these TFs are regulated and potentially work together to control cellular responses to a diverse array of stimuli. High degrees of differential TF expression were observed under the conditions tested. More than 50% of the 206 TF genes were up-regulated during conidiation and/or in conidia. Mutations in ten conidiation-specific TF genes caused defects in conidiation. Expression patterns in planta were similar to those under oxidative stress conditions. Mutants of in planta inducible genes not only exhibited sensitive to oxidative stress but also failed to infect rice. These experimental validations clearly demonstrated the value of TF expression patterns in predicting the function of individual TF genes. The regulatory network of TF genes revealed by this study provides a solid foundation for elucidating how M. oryzae regulates its pathogenesis, development, and stress responses.

          Author Summary

          Rice blast disease, caused by Magnaporthe oryzae, destroys rice crop enough to feed 60 million people every year and has served as a model pathosystem for understanding host-parasite interactions. However, little is known about how M. oryzae globally regulates and coordinates its gene expression at the whole-genome scale. We analyzed the expression patterns of 206 M. oryzae genes encoding transcription factors (TFs) under 32 conditions, including infection-related developmental stages and various abiotic stresses, using quantitative real-time PCR. We focused on identifying the TF genes that are induced during the two most important infection-related morphogenetic changes; conidiation and infectious growth in rice. We identified 57 conidiation-specific TF genes and functionally characterized ten of them. Our data also showed that infectious growth in planta and oxidative stress responses in vitro involve largely overlapping groups of TFs. Comprehensive TF expression data and functional validation provided new insights into the regulatory mechanism underpinning pathogenicity and stress responses in M. oryzae. These data will also serve as a guide in studying the role of individual TF genes and the coordination of their expression in controlling development, pathogenicity, and abiotic stress responses in M. oryzae.

          Related collections

          Most cited references 45

          • Record: found
          • Abstract: found
          • Article: not found

          Automatic clustering of orthologs and in-paralogs from pairwise species comparisons.

          Orthologs are genes in different species that originate from a single gene in the last common ancestor of these species. Such genes have often retained identical biological roles in the present-day organisms. It is hence important to identify orthologs for transferring functional information between genes in different organisms with a high degree of reliability. For example, orthologs of human proteins are often functionally characterized in model organisms. Unfortunately, orthology analysis between human and e.g. invertebrates is often complex because of large numbers of paralogs within protein families. Paralogs that predate the species split, which we call out-paralogs, can easily be confused with true orthologs. Paralogs that arose after the species split, which we call in-paralogs, however, are bona fide orthologs by definition. Orthologs and in-paralogs are typically detected with phylogenetic methods, but these are slow and difficult to automate. Automatic clustering methods based on two-way best genome-wide matches on the other hand, have so far not separated in-paralogs from out-paralogs effectively. We present a fully automatic method for finding orthologs and in-paralogs from two species. Ortholog clusters are seeded with a two-way best pairwise match, after which an algorithm for adding in-paralogs is applied. The method bypasses multiple alignments and phylogenetic trees, which can be slow and error-prone steps in classical ortholog detection. Still, it robustly detects complex orthologous relationships and assigns confidence values for both orthologs and in-paralogs. The program, called INPARANOID, was tested on all completely sequenced eukaryotic genomes. To assess the quality of INPARANOID results, ortholog clusters were generated from a dataset of worm and mammalian transmembrane proteins, and were compared to clusters derived by manual tree-based ortholog detection methods. This study led to the identification with a high degree of confidence of over a dozen novel worm-mammalian ortholog assignments that were previously undetected because of shortcomings of phylogenetic methods.A WWW server that allows searching for orthologs between human and several fully sequenced genomes is installed at http://www.cgb.ki.se/inparanoid/. This is the first comprehensive resource with orthologs of all fully sequenced eukaryotic genomes. Programs and tables of orthology assignments are available from the same location. Copyright 2001 Academic Press.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Arabidopsis transcription factors: genome-wide comparative analysis among eukaryotes.

            The completion of the Arabidopsis thaliana genome sequence allows a comparative analysis of transcriptional regulators across the three eukaryotic kingdoms. Arabidopsis dedicates over 5% of its genome to code for more than 1500 transcription factors, about 45% of which are from families specific to plants. Arabidopsis transcription factors that belong to families common to all eukaryotes do not share significant similarity with those of the other kingdoms beyond the conserved DNA binding domains, many of which have been arranged in combinations specific to each lineage. The genome-wide comparison reveals the evolutionary generation of diversity in the regulation of transcription.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Real-time RT-PCR profiling of over 1400 Arabidopsis transcription factors: unprecedented sensitivity reveals novel root- and shoot-specific genes.

              Summary To overcome the detection limits inherent to DNA array-based methods of transcriptome analysis, we developed a real-time reverse transcription (RT)-PCR-based resource for quantitative measurement of transcripts for 1465 Arabidopsis transcription factors (TFs). Using closely spaced gene-specific primer pairs and SYBR Green to monitor amplification of double-stranded DNA (dsDNA), transcript levels of 83% of all target genes could be measured in roots or shoots of young Arabidopsis wild-type plants. Only 4% of reactions produced non-specific PCR products. The amplification efficiency of each PCR was determined from the log slope of SYBR Green fluorescence versus cycle number in the exponential phase, and was used to correct the readout for each primer pair and run. Measurements of transcript abundance were quantitative over six orders of magnitude, with a detection limit equivalent to one transcript molecule in 1000 cells. Transcript levels for different TF genes ranged between 0.001 and 100 copies per cell. Only 13% of TF transcripts were undetectable in these organs. For comparison, 22K Arabidopsis Affymetrix chips detected less than 55% of TF transcripts in the same samples, the range of transcript levels was compressed by a factor more than 100, and the data were less accurate especially in the lower part of the response range. Real-time RT-PCR revealed 35 root-specific and 52 shoot-specific TF genes, most of which have not been identified as organ-specific previously. Finally, many of the TF transcripts detected by RT-PCR are not represented in Arabidopsis EST (expressed sequence tag) or Massively Parallel Signature Sequencing (MPSS) databases. These genes can now be annotated as expressed.
                Bookmark

                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Pathog
                PLoS Pathog
                plos
                plospath
                PLoS Pathogens
                Public Library of Science (San Francisco, USA )
                1553-7366
                1553-7374
                June 2013
                June 2013
                6 June 2013
                : 9
                : 6
                Affiliations
                [1 ]Department of Agricultural Biotechnology, Fungal Bioinformatics Laboratory, Center for Fungal Genetic Resources, and Center for Fungal Pathogenesis, Seoul National University, Seoul, Korea
                [2 ]Center for Food and Bioconvergence, Seoul National University, Seoul, Korea
                [3 ]National Institute of Biological Resources, Ministry of Environment, Incheon, Korea
                [4 ]Department of Plant Biology, University of Georgia, Athens, Georgia, United States of America
                [5 ]Department of Plant Pathology and Environmental Microbiology, Pennsylvania State University, University Park, Pennsylvania, United States of America
                University of Toronto, Canada
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: S.Y. Park, S.E. Lim, J. Park, Y.H. Lee. Performed the experiments: S.Y. Park, S.R. Kim, S. Kim, H.S. Rho, S. Kong. Analyzed the data: S.Y. Park, J. Choi, G.W. Lee, J. Park, Y. Kim, J. Jeon, M.H. Chi, C.H. Khang, S. Kang, Y.H. Lee. Contributed reagents/materials/analysis tools: S.Y. Park, S.E. Lim, S.R. Kim, S. Kim, H.S. Rho, S. Kong, J. Choi, G.W. Lee, J. Park. Wrote the paper: S.Y. Park, S.E. Lim, J. Park, C.H. Khang, S. Kang, Y.H. Lee.

                Article
                PPATHOGENS-D-12-01636
                10.1371/journal.ppat.1003350
                3675110
                23762023

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Page count
                Pages: 13
                Funding
                This work was supported by National Research Foundation of Korea grants funded by the Korean government (Grant number: 2012-0001149 and 2012-0000141; http://www.nrf.re.kr), The Technology Development Program for Agriculture and Forestry (TDPAF) of the MIFAFF of the Korean government (Grant number: 309015-04-SB020; http://www.mifaff.go.kr), and The Next-Generation BioGreen 21 Program of Rural Development Administration in Korea (Grant number: PJ00821201; www.rda.go.kr). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Agriculture
                Biology

                Infectious disease & Microbiology

                Comments

                Comment on this article