Proteinaceous effector discovery and characterization in filamentous plant pathogens

Abstract

The complicated interplay of plant-pathogen interactions occurs on multiple levels as pathogens evolve to constantly evade the immune responses of their hosts. Many economically important crops fall victim to filamentous pathogens that produce small proteins called effectors to manipulate the host and aid infection/colonization. Understanding the effector repertoires of pathogens is facilitating an increased understanding of the molecular mechanisms underlying virulence as well as guiding the development of disease control strategies. The purpose of this review is to give a chronological perspective on the evolution of the methodologies used in effector discovery from physical isolation and in silico predictions, to functional characterization of the effectors of filamentous plant pathogens and identification of their host targets.

Keywords: bioinformatic effector predictions; effector host-target interactions; effectors; fungal phytopathogens; in planta methodologies; oomycete phytopathogens.

FIGURE 1

A timeline showing the progression of filamentous plant pathogen effector prediction and identification from the pregenomic era to the present day. The first effectors identified using these methods are included as well as the elicitins used for homology‐based searches. Increasingly, pangenome data are used to predict core and novel candidates but as yet none have been characterized using this technique. For a recent review of pangenomics see Golicz et al. (2019). Details on individual effectors named are given in Table 1.

FIGURE 2

The host‐induced gene silencing (HIGS) construct encodes an inverted sequence that forms a hairpin double‐stranded (ds) RNA following transcription and is introduced into the host plant either by transient or stable transformation. The dsRNA is processed to form small interfering RNA (siRNA), either before or after delivery to the pathogen cell using the plants innate RNAi machinery. Once inside the fungal cells the siRNA silences the target effector genes by interfering with the target mRNA transcripts (Koch et al., 2018). The movement of small RNA between host and pathogen is detailed by Wang and Dean (2020).

FIGURE 3

The BSMV‐VOX technology adapted from Lee et al. (2012). (a) Virus‐mediated overexpression (VOX) system. The heterologous protein coding sequence is inserted in the γ genome of barley stripe mosaic virus (BSMV), upstream of the in‐frame stop codon in the γb open reading frame (ORF). A gene for the autoproteolytic peptide 2A is also inserted between the 3′ terminus of the γb ORF and the gene of interest for processing the fusion protein during translation, thus releasing the heterologous protein of interest. (b) The BSMV genome is composed of three RNAs that are capped at the 5′ end and form a tRNA‐like hairpin secondary structure at the 3′ terminus. RNAα encodes the αa replicase protein containing methyltransferase and helicase domains. RNAβ encodes coat and movement proteins whilst RNAγ encodes the polymerase (POL) component of replicase, and the cysteine‐rich γb protein involved in viral pathogenicity.

FIGURE 4

Protein–protein interaction techniques. (a) Co‐immunoprecipitation, effectors are tagged with a peptide sequence such as green fluorescent protein (GFP) and expressed in planta. Antibodies are used to pull down the protein complexes that can then be analysed using liquid chromatography and mass spectrometry (LC‐MS/MS) (Petre et al., 2017). (b) Biotinylation, effectors are fused to mutant biotin ligase enzymes and expressed in vivo. The fusion protein catalyses the biotinylation of interacting and proximal proteins in the presence of biotin. The biotinylated proteins are captured using streptavidin beads (Roux et al., 2012). (c) Bimolecular fluorescence complementation, the effector and putative interactors are tagged with nonfluorescent fragments of yellow fluorescent protein (YFP). Direct interaction of the tagged effectors results in YFP reassembly visualized in vivo or quantified using flow cytometry (Kerppola, 2008; Graciet and Wellmer, 2010; Miller et al., 2015).