Predication of the Effector Proteins Secreted by Fusarium sacchari Using Genomic Analysis and Heterogenous Expression

One of the causative agents of pokkah boeng disease (PBD), which affects sugarcane crops globally, is the fungus Fusarium sacchari. These fungal infections reduce sugar quality and yield, resulting in severe economic losses. Effector proteins play important roles in the interactions between pathogenic fungi and plants. Here, we used bioinformatic prediction approaches to identify 316 candidate secreted effector proteins (CSEPs) in the complete genome of F. sacchari. In total, 95 CSEPs contained known conserved structures, representing 40 superfamilies and 18 domains, while an additional 91 CSEPs contained seven known motifs. Of the 130 CSEPs containing no known domains or motifs, 14 contained one of four novel motifs. A heterogeneous expression system in Nicotiana benthamiana was used to investigate the functions of 163 CSEPs. Seven CSEPs suppressed BAX-triggered programmed cell death in N. benthamiana, while four caused cell death in N. benthamiana. The expression profiles of these eleven CSEPs during F. sacchari infection suggested that they may be involved in sugarcane-F. sacchari interaction. Our results establish a basis for further studies of the role of effector molecules in pathogen–sugarcane interactions, and provide a framework for future predictions of pathogen effector molecules.

Potentiating Lung Mucosal Immunity Through Intranasal Vaccination

Yearly administration of influenza vaccines is our best available tool for controlling influenza virus spread. However, both practical and immunological factors sometimes result in sub-optimal vaccine efficacy. The call for improved, or even universal, influenza vaccines within the field has led to development of pre-clinical and clinical vaccine candidates that aim to address limitations of current influenza vaccine approaches. Here, we consider the route of immunization as a critical factor in eliciting tissue resident memory (Trm) populations that are not a target of current licensed intramuscular vaccines. Intranasal vaccination has the potential to boost tissue resident B and T cell populations that reside within specific niches of the upper and lower respiratory tract. Within these niches, Trm cells are poised to respond rapidly to pathogen re-encounter by nature of their anatomic localization and their ability to rapidly deliver anti-pathogen effector functions. Unique features of mucosal immunity in the upper and lower respiratory tracts suggest that antigen localized to these regions is required for the elicitation of protective B and T cell immunity at these sites and will need to be considered as an important attribute of a rationally designed intranasal vaccine. Finally, we discuss outstanding questions and areas of future inquiry in the field of lung mucosal immunity.

A single amino acid polymorphism in a conserved effector of the multihost blast fungus pathogen expands host-target binding spectrum

Accelerated gene evolution is a hallmark of pathogen adaptation and specialization following host-jumps. However, the molecular processes associated with adaptive evolution between host-specific lineages of a multihost plant pathogen remain poorly understood. In the blast fungus Magnaporthe oryzae (Syn. Pyricularia oryzae), host specialization on different grass hosts is generally associated with dynamic patterns of gain and loss of virulence effector genes that tend to define the distinct genetic lineages of this pathogen. Here, we unravelled the biochemical and structural basis of adaptive evolution of APikL2, an exceptionally conserved paralog of the well-studied rice-lineage specific effector AVR-Pik. Whereas AVR-Pik and other members of the six-gene AVR-Pik family show specific patterns of presence/absence polymorphisms between grass-specific lineages of M. oryzae, APikL2 stands out by being ubiquitously present in all blast fungus lineages from 13 different host species. Using biochemical, biophysical and structural biology methods, we show that a single aspartate to asparagine polymorphism expands the binding spectrum of APikL2 to host proteins of the heavy-metal associated (HMA) domain family. This mutation maps to one of the APikL2-HMA binding interfaces and contributes to an altered hydrogen-bonding network. By combining phylogenetic ancestral reconstruction with an analysis of the structural consequences of allelic diversification, we revealed a common mechanism of effector specialization in the AVR-Pik/APikL2 family that involves two major HMA-binding interfaces. Together, our findings provide a detailed molecular evolution and structural biology framework for diversification and adaptation of a fungal pathogen effector family following host-jumps.

Cell scientist to watch – Yasin Dagdas

ABSTRACT Yasin Dagdas studied biotechnology at the Middle East Technical University in Ankara, Turkey. In 2009, he moved to the UK to join the lab of Nicholas Talbot for his PhD at University of Exeter. There, he studied the role of cellular morphogenesis in the pathogenicity of the rice blast fungus Magnaporthe oryzae. Yasin then did a postdoc with Sophien Kamoun from 2013–2016 at The Sainsbury Laboratory in Norwich, where he discovered how a plant pathogen effector has evolved to antagonize a host autophagy cargo receptor. In 2017, he established his own group at the Gregor Mendel Institute in Vienna. Research in his lab focusses on autophagy-mediated cellular quality control mechanisms in plants.

Novel effector recognition capacity engineered into a paired NLR complex

The Arabidopsis RRS1-R Resistance gene confers recognition of the bacterial acetyltransferase PopP2 and another bacterial effector, AvrRps4. The RRS1-S allele recognizes AvrRps4 but not PopP2. RRS1-R/RRS1-S heterozygotes cannot recognize PopP2. RRS1-R and RRS1-S also suppress the constitutive RPS4-dependent autoactivity of RRS1-Rslh1. Phytoplasmas cause important plant diseases, and their effectors can cause degradation of specific host proteins. We tested whether attaching a pathogen effector-dependent degron to RRS1-R, enabling its degradation by phytoplasma effector SAP05, could derepress RRS1-Rslh1 autoactivity, resulting in SAP05-dependent resistance. In transient assays in tobacco, RRS1-R-derived constructs can confer a hypersensitive response (HR) to SAP05. However, phytoplasma infection assays in transgenic Arabidopsis resulted in delayed disease symptoms but not full resistance. We provide a proof-of-concept strategy utilizing the recessiveness of a plant immune receptor gene to engineer recognition of a pathogen effector that promotes degradation of a specific host protein.

An NLR Integrated Domain toolkit to identify plant pathogen effector targets

SUMMARYPlant resistance genes (or NLR “Nod-like Receptors”) are known to contain atypical domains procuring them with a decoy capacity. Some of these integrated domains (or ID) allow the plant to lure the virulence determinants (“effectors”) of pathogens and triggering a specific NLR immune reaction.In this work, our goal was to generate a library of known IDs that could be screened with plant pathogen effectors in order to identify putative new effector virulence targets and NLR-effector pairs.We curated the IDs contained in NLRs from seven model and crop plant species. We cloned 52 IDs representing 31 distinct Pfam domains. This library was screened for interaction by yeast-two-hybrid with a set of 31 conserved Ralstonia solanacearum type III effectors. This screening and the further in planta interaction assay allowed us to identify three interactions, involving different IDs (kinase, DUF3542, WRKY) and two type III effectors (RipAE and PopP2).PopP2 was found to physically interact with ID#85, an atypical WRKY domain integrated in the GmNLR-ID85 NLR protein from Soybean. Using a imaging method in living plant cells, we showed that PopP2 associates with ID#85 in the nucleus. But unlike the known WRKY-containing Arabidopsis RRS1-R NLR receptor, this newly identified soybean WRKY domain could not be acetylated by PopP2 and its atypical sequence (WRKYGKR) also probably renders it inefficient in plant immunity triggering.This ID toolkit is available for screening with other plant pathogen effectors and should prove useful to discover new effectors targets and potentially engineer new plant resistance genes.

Intrinsically disordered pathogen effector alters the STAT1 dimer to prevent recruitment of co-transcriptional activators CBP/p300

Signal transducer and activator of transcription (STATs) proteins signal from cell-surface receptors to drive transcription of immune response genes. The parasite Toxoplasma gondii blocks STAT1-mediated gene expression by secreting the intrinsically disordered protein TgIST that traffics to the host nucleus, binds phosphorylated STAT1 dimers, and occupies nascent transcription sites that unexpectantly remain silenced. Here we define a core repeat region within internal repeats of TgIST that is necessary and sufficient to block STAT1-mediated gene expression. Cellular, biochemical, mutational, and structural studies demonstrate that the repeat region of TgIST adopts a helical conformation upon binding to STAT1 dimers. The binding interface is defined by a groove formed from two loops in the STAT1 SH2 domains that reorient during dimerization. TgIST binding to this newly exposed site at the STAT1 dimer interface altered its conformation and prevented recruitment of co-transcriptional activators, thus defining the mechanism of blocked transcription.

Accurate plant pathogen effector protein classification ab initio with deepredeff: an ensemble of convolutional neural networks

Background Plant pathogens cause billions of dollars of crop loss every year and are a major threat to global food security. Effector proteins are the tools such pathogens use to infect the cell, predicting effectors de novo from sequence is difficult because of the heterogeneity of the sequences. We hypothesised that deep learning classifiers based on Convolutional Neural Networks would be able to identify effectors and deliver new insights. Results We created a training set of manually curated effector sequences from PHI-Base and used these to train a range of model architectures for classifying bacteria, fungal and oomycete sequences. The best performing classifiers had accuracies from 93 to 84%. The models were tested against popular effector detection software on our own test data and data provided with those models. We observed better performance from our models. Specifically our models showed greater accuracy and lower tendencies to call false positives on a secreted protein negative test set and a greater generalisability. We used GRAD-CAM activation map analysis to identify the sequences that activated our CNN-LSTM models and found short but distinct N-terminal regions in each taxon that was indicative of effector sequences. No motifs could be observed in these regions but an analysis of amino acid types indicated differing patterns of enrichment and depletion that varied between taxa. Conclusions Small training sets can be used effectively to train highly accurate and sensitive deep learning models without need for the operator to know anything other than sequence and without arbitrary decisions made about what sequence features or physico-chemical properties are important. Biological insight on subsequences important for classification can be achieved by examining the activations in the model

Conserved Opposite Functions in Plant Resistance to Biotrophic and Necrotrophic Pathogens of the Immune Regulator SRFR1

Plant immunity is mediated in large part by specific interactions between a host resistance protein and a pathogen effector protein, named effector-triggered immunity (ETI). ETI needs to be tightly controlled both positively and negatively to enable normal plant growth because constitutively activated defense responses are detrimental to the host. In previous work, we reported that mutations in SUPPRESSOR OF rps4-RLD1 (SRFR1), identified in a suppressor screen, reactivated EDS1-dependent ETI to Pseudomonas syringae pv. tomato (Pto) DC3000. Besides, mutations in SRFR1 boosted defense responses to the generalist chewing insect Spodoptera exigua and the sugar beet cyst nematode Heterodera schachtii. Here, we show that mutations in SRFR1 enhance susceptibility to the fungal necrotrophs Fusarium oxysporum f. sp. lycopersici (FOL) and Botrytis cinerea in Arabidopsis. To translate knowledge obtained in AtSRFR1 research to crops, we generated SlSRFR1 alleles in tomato using a CRISPR/Cas9 system. Interestingly, slsrfr1 mutants increased expression of SA-pathway defense genes and enhanced resistance to Pto DC3000. In contrast, slsrfr1 mutants elevated susceptibility to FOL. Together, these data suggest that SRFR1 is functionally conserved in both Arabidopsis and tomato and functions antagonistically as a negative regulator to (hemi-) biotrophic pathogens and a positive regulator to necrotrophic pathogens.