Impact of high atmospheric carbon dioxide on the biotic stress response of the model cereal species Brachypodium distachyon

Losses due to disease and climate change are among the most important issues currently facing crop production. It is therefore important to establish the impact of climate change, and particularly of high carbon dioxide (hCO2), on plant immunity in cereals, which provide 60% of human calories. The aim of this study was to determine if hCO2 impacts Brachypodium distachyon immunity, a model plant for temperate cereals. Plants were grown in air (430 ppm CO2) and at two high CO2 conditions, one that is relevant to projections within the coming century (1000 ppm) and a concentration sufficient to saturate photosynthesis (3000 ppm). The following measurements were performed: phenotyping and growth, salicylic acid contents, pathogen resistance tests, and RNAseq analysis of the transcriptome. Improved shoot development was observed at both 1000 and 3000 ppm. A transcriptomic analysis pointed to an increase in primary metabolism capacity under hCO2. Alongside this effect, up-regulation of genes associated with secondary metabolism was also observed. This effect was especially evident for the terpenoid and phenylpropanoid pathways, and was accompanied by enhanced expression of immunity-related genes and accumulation of salicylic acid. Pathogen tests using the fungus Magnaporthe oryzae revealed that hCO2 had a complex effect, with enhanced susceptibility to infection but no increase in fungal development. The study reveals that immunity in B. distachyon is modulated by growth at hCO2 and allows identification of pathways that might play a role in this effect

Retrograde signalling from the mitochondria to the nucleus translates the positive effect of ethylene on dormancy breaking of Arabidopsis thaliana seeds

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Analyzing Multifactorial RNA-Seq Experiments with DicoExpress

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RFLOMICS: R package and Shiny interface for Integrative analysis of omics data

International audienceThe acquisition of multi-omics data in the context of complex experimental design is a widely used practice to identify entities and decipher the biological processes they are involved. The investigation of each omics layer is a good first step to explore and extract relevant biological variability. The statistical integration could then be restrained to pertinent omics levels and features. Such analysis of heterogeneous data remains a technical challenge with the needs of expertise methods and parameters to take into account data specificity. Furthermore, applying different statistical methods from several tools is also a technical challenge in term of data management. In this context, we developed RFLOMICS, an R package with a shiny interface, to ensure the reproducibility of analysis, with a guided and comprehensive analysis and visualization of data in a framework which can manage several omics-data and analysis results. RFLOMICS currently supports up to three types of omics (RNAseq, proteomics, and metabolomics), and can deal with multi-factorial experiments (up to 3 biological factors). It includes methods chosen based on expert feedback. This application is divided into three key steps. The first step allows the user to import the experimental design file and abundance matrix for each dataset (read counts for RNA-Seq, signal intensity for metabolomics and proteomics), and set up the statistical model and contrasts associated to the biological issues. The second step is to perform a full analysis for each dataset, which includes : i- quality control to check for batch effects or identify outlier samples that can be removed, ii- filtering and normalization of RNA-Seq data, or transformation of prot/meta data, iii- differential expression analysis using edgeR for RNA- Seq and limma for prot/meta data, iv- co-expression analysis using coseq, and finally, v- functional enrichment analysis. The third step is to integrate selected omics layers using the unsupervised methods proposed by MOFA. All the results as well as the raw data, and all information necessary for reproducibility of analysis are managed and stored thanks to the MultiAssayExperiment object. An HTML report can be generated, summarizing all analysis steps, using rmarkdown R package. RFLOMICS provides the same framework that allows the user to perform the analysis of multi-omics project from A to Z, taking into account the complexity of the design. It guarantees the relevance of the used methods, and ensures the reproducibility of the analysis. The interface offers an interesting flexibility between the visualization of the results and the data manipulation (filtering, parameter setting). Future development will include the implementation of supervised integration methods, and a docker image to facilitate deployment

DiCoExpress: a tool to process multifactorial RNAseq experiments from quality controls to co-expression analysis through differential analysis based on contrasts inside GLM models

International audienceBackground: RNAseq is nowadays the method of choice for transcriptome analysis. In the last decades, a high number of statistical methods, and associated bioinformatics tools, for RNAseq analysis were developed. More recently, statistical studies realised neutral comparison studies using benchmark datasets, shedding light on the most appropriate approaches for RNAseq data analysis. Results: DiCoExpress is a script-based tool implemented in R that includes methods chosen based on their performance in neutral comparisons studies. DiCoExpress uses pre-existing R packages including FactoMineR, edgeR and coseq, to perform quality control, differential, and co-expression analysis of RNAseq data. Users can perform the full analysis, providing a mapped read expression data file and a file containing the information on the experimental design. Following the quality control step, the user can move on to the differential expression analysis performed using generalized linear models thanks to the automated contrast writing function. A co-expression analysis is implemented using the coseq package. Lists of differentially expressed genes and identified co-expression clusters are automatically analyzed for enrichment of annotations provided by the user. We used DiCoExpress to analyze a publicly available RNAseq dataset on the transcriptional response of Brassica napus L. to silicon treatment in plant roots and mature leaves. This dataset, including two biological factors and three replicates for each condition, allowed us to demonstrate in a tutorial all the features of DiCoExpress. Conclusions: DiCoExpress is an R script-based tool allowing users to perform a full RNAseq analysis from quality controls to co-expression analysis through differential analysis based on contrasts inside generalized linear models. DiCoExpress focuses on the statistical modelling of gene expression according to the experimental design and facilitates the data analysis leading the biological interpretation of the results

Analyzing Multifactorial RNA-Seq Experiments with DicoExpress

International audienceThe proper use of statistical modeling in NGS data analysis requires an advanced level of expertise. There has recently been a growing consensus on using generalized linear models for differential analysis of RNA-Seq data and the advantage of mixture models to perform co-expression analysis. To offer a managed setting to use these modeling approaches, we developed DiCoExpress that provides a standardized R pipeline to perform an RNA-Seq analysis. Without any particular knowledge in statistics or R programming, beginners can perform a complete RNA-Seq analysis from quality controls to co-expression through differential analysis based on contrasts inside a generalized linear model. An enrichment analysis is proposed both on the lists of differentially expressed genes, and the co-expressed gene clusters. This video tutorial is conceived as a step-by-step protocol to help users take full advantage of DiCoExpress and its potential in empowering the biological interpretation of an RNA-Seq experiment

Genome-Wide Transcriptomic Analysis of the Effects of Infection with the Hemibiotrophic Fungus Colletotrichum lindemuthianum on Common Bean

International audienceBean anthracnose caused by the hemibiotrophic fungus Colletotrichum lindemuthianum is one of the most important diseases of common bean (Phaseolus vulgaris) in the world. In the present study, the whole transcriptome of common bean infected with C. lindemuthianum during compatible and incompatible interactions was characterized at 48 and 72 hpi, corresponding to the biotrophy phase of the infection cycle. Our results highlight the prominent role of pathogenesis-related (PR) genes from the PR10/Bet vI family as well as a complex interplay of different plant hormone pathways including Ethylene, Salicylic acid (SA) and Jasmonic acid pathways. Gene Ontology enrichment analysis reveals that infected common bean seedlings responded by down-regulation of photosynthesis, ubiquitination-mediated proteolysis and cell wall modifications. In infected common bean, SA biosynthesis seems to be based on the PAL pathway instead of the ICS pathway, contrarily to what is described in Arabidopsis. Interestingly, ~30 NLR were up-regulated in both contexts. Overall, our results suggest that the difference between the compatible and incompatible reaction is more a question of timing and strength, than a massive difference in differentially expressed genes between these two contexts. Finally, we used RT-qPCR to validate the expression patterns of several genes, and the results showed an excellent agreement with deep sequencing

Brassinosteroid signaling-dependent root responses to prolonged elevated ambient temperature

Due to their sessile nature, plants have to cope with and adjust to their fluctuating environment. Temperature elevation stimulates the growth of Arabidopsis aerial parts. This process is mediated by increased biosynthesis of the growth-promoting hormone auxin. How plant roots respond to elevated ambient temperature is however still elusive. Here we present strong evidence that temperature elevation impinges on brassinosteroid hormone signaling to alter root growth. We show that elevated temperature leads to increased root elongation, independently of auxin or factors known to drive temperature-mediated shoot growth. We further demonstrate that brassinosteroid signaling regulates root responses to elevated ambient temperature. Increased growth temperature specifically impacts on the level of the brassinosteroid receptor BRI1 to downregulate brassinosteroid signaling and mediate root elongation. Our results establish that BRI1 integrates temperature and brassinosteroid signaling to regulate root growth upon long-term changes in environmental conditions associated with global warming.Moderate heat stimulates the growth of Arabidopsis shoots in an auxin-dependent manner. Here, Martins et al. show that elevated ambient temperature modifies root growth by reducing the BRI1 brassinosteroid-receptor protein level and downregulating brassinosteroid signaling