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Identification of genes differentially expressed in a resistant reaction to Mycosphaerella pinodes in pea using microarray technology.

ABSTRACT: BACKGROUND: Ascochyta blight, caused by Mycosphaerella pinodes is one of the most important pea pathogens. However, little is known about the genes and mechanisms of resistance acting against M. pinodes in pea. Resistance identified so far to this pathogen is incomplete, polygenic and scarce in pea, being most common in Pisum relatives. The identification of the genes underlying resistance would increase our knowledge about M. pinodes-pea interaction and would facilitate the introgression of resistance into pea varieties. In the present study differentially expressed genes in the resistant P. sativum ssp. syriacum accession P665 comparing to the susceptible pea cv. Messire after inoculation with M. pinodes have been identified using a M. truncatula microarray. RESULTS: Of the 16,470 sequences analysed, 346 were differentially regulated. Differentially regulated genes belonged to almost all functional categories and included genes involved in defense such as genes involved in cell wall reinforcement, phenylpropanoid and phytoalexins metabolism, pathogenesis- related (PR) proteins and detoxification processes. Genes associated with jasmonic acid (JA) and ethylene signal transduction pathways were induced suggesting that the response to M. pinodes in pea is regulated via JA and ET pathways. Expression levels of ten differentially regulated genes were validated in inoculated and control plants using qRT-PCR showing that the P665 accession shows constitutively an increased expression of the defense related genes as peroxidases, disease resistance response protein 39 (DRR230-b), glutathione S-transferase (GST) and 6a-hydroxymaackiain methyltransferase. CONCLUSIONS: Through this study a global view of genes expressed during resistance to M. pinodes has been obtained, giving relevant information about the mechanisms and pathways conferring resistance to this important disease. In addition, the M. truncatula microarray represents an efficient tool to identify candidate genes controlling resistance to M. pinodes in pea.

SUBMITTER: Fondevilla S

PROVIDER: S-EPMC3027157 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Identification of genes differentially expressed in a resistant reaction to Mycosphaerella pinodes in pea using microarray technology.

Fondevilla Sara S Küster Helge H Krajinski Franziska F Cubero José I JI Rubiales Diego D

BMC genomics 20110113

<h4>Background</h4>Ascochyta blight, caused by Mycosphaerella pinodes is one of the most important pea pathogens. However, little is known about the genes and mechanisms of resistance acting against M. pinodes in pea. Resistance identified so far to this pathogen is incomplete, polygenic and scarce in pea, being most common in Pisum relatives. The identification of the genes underlying resistance would increase our knowledge about M. pinodes-pea interaction and would facilitate the introgression ...[more]

PMID: 21226971

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Project description:Bananas and plantains are considered an important crop around the world. Banana production is affected by several constraints, of which Black Sigatoka Disease, caused by the fungus Mycosphaerella fijiensis, is considered one of the most important diseases in banana plantations. The banana accession 'Calcutta-4' has a natural resistance to Black Sigatoka; however, the fruit is not valuable for commercialization. Gene identification and expression studies in 'Calcutta-4' might reveal possible gene candidates for resistant to the disease and elucidate mechanisms for resistance. A subtracted cDNA library was generated from leaves after 6, 9 and 12 days inoculated with M. fijiensis conidia on greenhouse banana plants of the accession 'Calcutta-4'. Bioinformatic analysis revealed 99 good quality sequences. Blast2go analysis revealed that 31% of the sequences could not be categorized and, according to the Biological Process Category, 32 and 28 ESTs are related to general metabolic and cellular processes, respectively; while 10 ESTs response to stimulus. Seven sequences were redundant and one was similar to genes that may be involved in pathogen resistance including the putative disease resistance protein RGA1. Genes encoding zinc finger domains were identified and may play an important role in pathogen resistance by inducing the expression of downstream genes. Expression analysis of four selected genes was performed using RT-qPCR during the early stage of the disease development at 6, 9, 12 and 15 days post inoculation showing a peak of up regulation at 9 or 12 days post inoculation. Three of the four genes showed an up-regulation of expression in 'Calcutta-4' when compared to 'Williams' after inoculation with M. fijiensis, suggesting a fine regulation of specific gene candidates that may lead to a resistance response. The genes identified in early responses in a plant-pathogen interaction may be relevant for the resistance response of 'Calcutta-4' to Black Sigatoka. Genes with different functions may play a role in plant response to the disease. The present study suggests a fine up regulation of these genes that might be needed to perform an incompatible interaction. Further gene functional studies need to be performed to validate their use as candidate resistance genes in susceptible banana cultivars.

Project description:BackgroundMicroarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).ResultsWe hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.ConclusionsFactors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Project description:BACKGROUND: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. RESULTS: The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. CONCLUSION: This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

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Identification of genes differentially expressed in a resistant reaction to Mycosphaerella pinodes in pea using microarray technology.

Publications

Identification of genes differentially expressed in a resistant reaction to Mycosphaerella pinodes in pea using microarray technology.

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