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      Epigenetic Control of Effector Gene Expression in the Plant Pathogenic Fungus Leptosphaeria maculans

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          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

          Plant pathogens secrete an arsenal of small secreted proteins (SSPs) acting as effectors that modulate host immunity to facilitate infection. SSP-encoding genes are often located in particular genomic environments and show waves of concerted expression at diverse stages of plant infection. To date, little is known about the regulation of their expression. The genome of the Ascomycete Leptosphaeria maculans comprises alternating gene-rich GC-isochores and gene-poor AT-isochores. The AT-isochores harbor mosaics of transposable elements, encompassing one-third of the genome, and are enriched in putative effector genes that present similar expression patterns, namely no expression or low-level expression during axenic cultures compared to strong induction of expression during primary infection of oilseed rape ( Brassica napus). Here, we investigated the involvement of one specific histone modification, histone H3 lysine 9 methylation (H3K9me3), in epigenetic regulation of concerted effector gene expression in L. maculans. For this purpose, we silenced the expression of two key players in heterochromatin assembly and maintenance, HP1 and DIM-5 by RNAi. By using HP1-GFP as a heterochromatin marker, we observed that almost no chromatin condensation is visible in strains in which LmDIM5 was silenced by RNAi. By whole genome oligoarrays we observed overexpression of 369 or 390 genes, respectively, in the silenced- LmHP1 and - LmDIM5 transformants during growth in axenic culture, clearly favouring expression of SSP-encoding genes within AT-isochores. The ectopic integration of four effector genes in GC-isochores led to their overexpression during growth in axenic culture. These data strongly suggest that epigenetic control, mediated by HP1 and DIM-5, represses the expression of at least part of the effector genes located in AT-isochores during growth in axenic culture. Our hypothesis is that changes of lifestyle and a switch toward pathogenesis lift chromatin-mediated repression, allowing a rapid response to new environmental conditions.

          Author Summary

          Effectors are key players in pathogenicity of microbes toward plants. Effector genes usually show concerted expression during plant infection but how this concerted expression is generated remains a largely unexplored research topic. Epigenetic mechanisms are involved in genome maintenance and integrity but are increasingly considered as important for regulation of gene expression in numerous and diverse organisms. Here we show that the genomic environment has impact on expression of Leptosphaeria maculans effector genes, and that an epigenetic mechanism that relies on two proteins involved in heterochromatin formation and maintenance, HP1 and DIM-5, modulates this expression, leading to repression during growth in axenic culture. Chromatin decondensation by removal of histone H3 lysine 9 methylation and/or HP1 is presumably a prerequisite for effector gene expression during primary infection of oilseed rape. Thus we show chromatin-based transcriptional regulation that can act on effector gene expression in fungi. Our study highlights the importance of heterochromatic landscapes, not only for genome maintenance but also in rapid and efficient adaptation of organisms to changing environmental situations.

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          Most cited references 94

          • Record: found
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          Basic local alignment search tool.

          A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
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            Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

            In this paper we report exploratory analyses of high-density oligonucleotide array data from the Affymetrix GeneChip system with the objective of improving upon currently used measures of gene expression. Our analyses make use of three data sets: a small experimental study consisting of five MGU74A mouse GeneChip arrays, part of the data from an extensive spike-in study conducted by Gene Logic and Wyeth's Genetics Institute involving 95 HG-U95A human GeneChip arrays; and part of a dilution study conducted by Gene Logic involving 75 HG-U95A GeneChip arrays. We display some familiar features of the perfect match and mismatch probe (PM and MM) values of these data, and examine the variance-mean relationship with probe-level data from probes believed to be defective, and so delivering noise only. We explain why we need to normalize the arrays to one another using probe level intensities. We then examine the behavior of the PM and MM using spike-in data and assess three commonly used summary measures: Affymetrix's (i) average difference (AvDiff) and (ii) MAS 5.0 signal, and (iii) the Li and Wong multiplicative model-based expression index (MBEI). The exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values. We evaluate the four expression summary measures using the dilution study data, assessing their behavior in terms of bias, variance and (for MBEI and RMA) model fit. Finally, we evaluate the algorithms in terms of their ability to detect known levels of differential expression using the spike-in data. We conclude that there is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities.
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              A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

              When running experiments that involve multiple high density oligonucleotide arrays, it is important to remove sources of variation between arrays of non-biological origin. Normalization is a process for reducing this variation. It is common to see non-linear relations between arrays and the standard normalization provided by Affymetrix does not perform well in these situations. We present three methods of performing normalization at the probe intensity level. These methods are called complete data methods because they make use of data from all arrays in an experiment to form the normalizing relation. These algorithms are compared to two methods that make use of a baseline array: a one number scaling based algorithm and a method that uses a non-linear normalizing relation by comparing the variability and bias of an expression measure. Two publicly available datasets are used to carry out the comparisons. The simplest and quickest complete data method is found to perform favorably. Software implementing all three of the complete data normalization methods is available as part of the R package Affy, which is a part of the Bioconductor project http://www.bioconductor.org. Additional figures may be found at http://www.stat.berkeley.edu/~bolstad/normalize/index.html
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                1553-7390
                1553-7404
                March 2014
                6 March 2014
                : 10
                : 3
                Affiliations
                [1 ]INRA, UR 1290 BIOGER-CPP, Thiverval-Grignon, France
                [2 ]Department of Biochemistry and Biophysics, Center for Genome Research and Biocomputing, Oregon State University, Corvallis, Oregon, United States of America
                University of Exeter, United Kingdom
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: JLS MF TR IF. Performed the experiments: JLS MEG NG BO JL MHB MF LRC IF. Analyzed the data: JLS JL JG MF TR IF. Wrote the paper: JLS MF TR IF.

                Article
                PGENETICS-D-13-03255
                10.1371/journal.pgen.1004227
                3945186
                24603691

                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: 19
                Funding
                JLS was funded by a Young Scientist Funding by INRA. Part of this work was funded by Agence Nationale de la Recherche (FungIsochores project; ANR- 09-GENM-028). 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
                Genetics
                Epigenetics
                Chromatin
                Genomics
                Genome Analysis Tools
                Transcriptomes
                Functional Genomics
                Genome Expression Analysis
                Microbiology
                Mycology
                Fungi
                Host-Pathogen Interaction
                Microbial Control
                Microbial Pathogens
                Pathogenesis
                Plant Science
                Plant Pathology
                Plant Pathogens

                Genetics

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