| Literature DB >> 30146948 |
Edel Pérez-López1, Matthew Waldner2, Musharaf Hossain1, Anthony J Kusalik2, Yangdou Wei1, Peta C Bonham-Smith1, Christopher D Todd1.
Abstract
Clubroot is an economically important disease affecting Brassica plants worldwide. Plasmodiophora brassicae is the protist pathogen associated with the disease, and its soil-borne obligate parasitic nature has impeded studies related to its biology and the mechanisms involved in its infection of the plant host. The identification of effector proteins is key to understanding how the pathogen manipulates the plant's immune response and the genes involved in resistance. After more than 140 years studying clubroot and P. brassicae, very little is known about the effectors playing key roles in the infection process and subsequent disease progression. Here we analyze the information available for identified effectors and suggest several features of effector genes that can be used in the search for others. Based on the information presented in this review, we propose a comprehensive bioinformatics pipeline for effector identification and provide a list of the bioinformatics tools available for such.Entities:
Keywords: Brassica; Clubroot; P. brassicae; bioinformatics; effectors; pipeline
Mesh:
Substances:
Year: 2018 PMID: 30146948 PMCID: PMC6177251 DOI: 10.1080/21505594.2018.1504560
Source DB: PubMed Journal: Virulence ISSN: 2150-5594 Impact factor: 5.882
Figure 1.Brassica plant root affected by Plasmodiophora brassicae. A. Canola root with typical galls after 1 month of inoculation with P. brassicae resting spores. B. Life cycle of P. brassicae showing the steps involved in infection through to the production of secondary plasmodia in the host plant cortical cells. Scheme based on that of Kageyama and Asano [7], representing spindle-shaped resting spores, biflagellate primary zoospores, zoospores, and primary and secondary plasmodia (oval black figure in root hairs and cortical cells, respectively). Further steps in P. brassicae´s life cycle, such as the formation of resting spores in cortical cells and its ejection to the soil, are not shown in this scheme.
Steps to identify putative effectors within the secretome of P. brassicae in the European strain Pbe3 and the Canadian strain Pb3.
| Europe strain (Pbe3) | Canadian strain (Pb3) | |
|---|---|---|
| Signal Peptide/No trans-membrane domaina | 533* | 590* |
| D score > 0.7 | NA | 431 |
| Size (Small secreted proteins) b | 416 | 221 |
| Over-expression c | 300 | NA |
| Plant-free library d | 92 | NA |
NA, Not applied
a In the Canadian strain, other subcellular localization signals were also used to remove putative proteins from further analysis.
b For the European strain the cutoff was < 450 aa, while for the Canadian protein proteins < 300 aa were selected.
c At least 10 expected fragments per kilobase of transcript per million fragments (FPKM)
d FPKM log2 fold change > 5 in plant-free library
* Putative secreted proteins.
Figure 2.Coherent pipeline to identify putative effector proteins of Plasmodiophora brassicae. The pipeline assumes: (1) Researchers are starting with RNA-Seq reads from a host plant infected with P. brassicae; (2) The draft genomes available for P. brassicae, other plasmodiophorids, oomycetes, and other plant pathogens are used; (3) Motifs mentioned in the review or structural similarities with previously described effector proteins were identified.
Bioinformatics tools used in a coherent pipeline to identify putative effectors of P. brassicae.
| Bioinformatics tool | Description | Website | Reference |
|---|---|---|---|
| Trimmomatic | A fast, multithreaded command line tool that can be used to trim and crop Illumina data as well as to remove adapters. | [ | |
| STAR | An algorithm for aligning high-throughput long and short RNA-seq data to a reference genome. | [ | |
| SignalP | Software that predicts signal peptide cleavage sites in proteins from eukaryotes and prokaryotes. | [ | |
| WoLF PSORT | An algorithm that predicts the subcellular localization sites of proteins based on their amino acid sequences. It makes predictions based on both known sorting signal motifs and some correlative sequence features such as amino acid content. | [ | |
| TargetP | Tool that predicts subcellular location of eukaryotic proteins. | [ |