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Medicago truncatula and Glomus intraradices gene expression in cortical cells harboring arbuscules in the arbuscular mycorrhizal symbiosis

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      Abstract

      Background

      Most vascular flowering plants have the capacity to form symbiotic associations with arbuscular mycorrhizal (AM) fungi. The symbiosis develops in the roots where AM fungi colonize the root cortex and form arbuscules within the cortical cells. Arbuscules are enveloped in a novel plant membrane and their establishment requires the coordinated cellular activities of both symbiotic partners. The arbuscule-cortical cell interface is the primary functional interface of the symbiosis and is of central importance in nutrient exchange. To determine the molecular events the underlie arbuscule development and function, it is first necessary to identify genes that may play a role in this process. Toward this goal we used the Affymetrix GeneChip ® Medicago Genome Array to document the M. truncatula transcript profiles associated with AM symbiosis, and then developed laser microdissection (LM) of M. truncatula root cortical cells to enable analyses of gene expression in individual cell types by RT-PCR.

      Results

      This approach led to the identification of novel M. truncatula and G. intraradices genes expressed in colonized cortical cells and in arbuscules. Within the arbuscule, expression of genes associated with the urea cycle, amino acid biosynthesis and cellular autophagy was detected. Analysis of gene expression in the colonized cortical cell revealed up-regulation of a lysine motif (LysM)-receptor like kinase, members of the GRAS transcription factor family and a symbiosis-specific ammonium transporter that is a likely candidate for mediating ammonium transport in the AM symbiosis.

      Conclusion

      Transcript profiling using the Affymetrix GeneChip ® Medicago Genome Array provided new insights into gene expression in M. truncatula roots during AM symbiosis and revealed the existence of several G. intraradices genes on the M. truncatula GeneChip ®. A laser microdissection protocol that incorporates low-melting temperature Steedman's wax, was developed to enable laser microdissection of M. truncatula root cortical cells. LM coupled with RT-PCR provided spatial gene expression information for both symbionts and expanded current information available for gene expression in cortical cells containing arbuscules.

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      Most cited references 108

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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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        Statistical significance for genomewide studies.

        With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
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          Primer3 on the WWW for general users and for biologist programmers.

           H Skaletsky,  S Rozen (1999)
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            Author and article information

            Affiliations
            [1 ]Boyce Thompson Institute for Plant Research, Tower Road, Ithaca, NY 14853, USA
            [2 ]Plant Biology Division, Samuel Roberts Noble Foundation, 2510 Sam Noble Parkway, Ardmore, OK 73401, USA
            [3 ]CEA/Cadarache IBEB, Service de Biologie Végétale et de Microbiologie Environnementales, UMR 6191 CNRS-CEA-Aix Marseille Univ., F-13108 St. Paul Lez Durance, France
            Contributors
            Journal
            BMC Plant Biol
            BMC Plant Biology
            BioMed Central
            1471-2229
            2009
            22 January 2009
            : 9
            : 10
            2649119
            1471-2229-9-10
            19161626
            10.1186/1471-2229-9-10
            Copyright © 2009 Gomez et al; licensee BioMed Central Ltd.

            This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

            Categories
            Research Article

            Plant science & Botany

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