Abstract
We have established the Ustilago bromivora–Brachypodium spp. interaction as a new model pathosystem for biotrophic fungal plant infections of the head smut type (Rabe et al., 2016). In this protocol, the methodology used for comparing gene expression between saprophytic and in planta growth of the fungus is described. The experimental and analytical pipeline, how next generation RNA sequencing (Illumina RNA-Seq) analysis can be used to obtain lists of genes significantly up or down regulated in planta in comparison to axenic culture is given. Furthermore, different methods to identify functional categories that are over- or under-represented among specific classes of genes are presented.
Keywords: Plant infection, Biotrophic plant pathogens, Fungal pathogens, Smuts, Ustilago bromivora, RNA-seq, Differential expression, over/under representation analysis
Background
RNA deep sequencing (RNA-Seq) is a powerful and versatile tool to gain insights into the responses of cells and organisms to environmental changes and their adaptations to new developmental stages. A striking change of life situation comes with the switch from yeast-like growth to filamentous, pathogenicity associated growth modes in non-obligate pathogens. We studied this switch in the biotrophic fungal plant pathogen Ustilago bromivora (Rabe et al., 2016). RNA-Seq from infected tissue is a special situation, since reads from both–the host and the pathogen–will be identified. Here necessary considerations are described. These include the sequencing depth required to sufficiently cover the pathogen in the host tissue, and the methods used to align and quantify the resulting mixed pool of reads. Over/underrepresentation analysis (ORA) is a method to link expression changes to potential biological responses by looking if certain classes of transcripts respond in a concerted way. Three methodologies are described that can be used to statistically test for over- or underrepresentation of classes of transcripts: The first method tests ORA individually for defined classes of interest, such as predicted secreted proteins, using Fisher exact test (example for R implementation given). The other two approaches are ‘explorative’ analyses that test over/underrepresentation across all functional classes defined in a given functional annotation framework (FunCat or Mapman annotation).
Materials and Reagents
Equipment
Software
Procedure
Data analysis
Figure 2 shows a flow diagram of the different steps involved in RNA-Seq data analysis, indicating the software tools used. Figure 2. Workflow of RNA-Seq data analysis. Dark blue background: Input from lllumina RNA sequencing; Light blue: Sequencing quality control and read quantification; Green: Identification of differentially expressed genes; Orange: Three methods for over/under-representation analysis.
Notes
Recipes
Acknowledgments
This protocol is adapted from Rabe et al. (2016). We would like to thank all the people involved in the works for this protocol as well as the original publication that it is based upon. The research leading to these results received funding from the European Research Council under the European Union's Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement no. [EUP0012 ‘Effectomics’], the Austrian Science Fund (FWF): [P27429-B22, P27818-B22, I3033-B22], and the Austrian Academy of Science (OEAW).
References
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