The phosphorylation landscape of infection-related development by the rice blast fungus.

Many of the world's most devastating crop diseases are caused by fungal pathogens that elaborate specialized infection structures to invade plant tissue. Here, we present a quantitative mass-spectrometry-based phosphoproteomic analysis of infection-related development by the rice blast fungus Magnaporthe oryzae, which threatens global food security. We mapped 8,005 phosphosites on 2,062 fungal proteins following germination on a hydrophobic surface, revealing major re-wiring of phosphorylation-based signaling cascades during appressorium development. Comparing phosphosite conservation across 41 fungal species reveals phosphorylation signatures specifically associated with biotrophic and hemibiotrophic fungal infection. We then used parallel reaction monitoring (PRM) to identify phosphoproteins regulated by the fungal Pmk1 MAPK that controls plant infection by M. oryzae. We define 32 substrates of Pmk1 and show that Pmk1-dependent phosphorylation of regulator Vts1 is required for rice blast disease. Defining the phosphorylation landscape of infection therefore identifies potential therapeutic interventions for the control of plant diseases.

The transcriptional landscape of plant infection by the rice blast fungus Magnaporthe oryzae reveals distinct families of temporally co-regulated and structurally conserved effectors.

The rice blast fungus Magnaporthe oryzae causes a devastating disease that threatens global rice (Oryza sativa) production. Despite intense study, the biology of plant tissue invasion during blast disease remains poorly understood. Here we report a high-resolution transcriptional profiling study of the entire plant-associated development of the blast fungus. Our analysis revealed major temporal changes in fungal gene expression during plant infection. Pathogen gene expression could be classified into 10 modules of temporally co-expressed genes, providing evidence for the induction of pronounced shifts in primary and secondary metabolism, cell signaling, and transcriptional regulation. A set of 863 genes encoding secreted proteins are differentially expressed at specific stages of infection, and 546 genes named MEP (Magnaportheeffector protein) genes were predicted to encode effectors. Computational prediction of structurally related MEPs, including the MAX effector family, revealed their temporal co-regulation in the same co-expression modules. We characterized 32 MEP genes and demonstrate that Mep effectors are predominantly targeted to the cytoplasm of rice cells via the biotrophic interfacial complex and use a common unconventional secretory pathway. Taken together, our study reveals major changes in gene expression associated with blast disease and identifies a diverse repertoire of effectors critical for successful infection.

An Improved Assembly of the Albugo candida Ac2V Genome Reveals the Expansion of the "CCG" Class of Effectors.

Albugo candida is an obligate oomycete pathogen that infects many plants in the Brassicaceae family. We resequenced the genome of isolate Ac2V using PacBio long reads and constructed an assembly augmented by Illumina reads. The Ac2VPB genome assembly is 10% larger and more contiguous compared with a previous version. Our annotation of the new assembly, aided by RNA-sequencing information, revealed a 175% expansion (40 to 110) in the CHxC effector class, which we redefined as "CCG" based on motif analysis. This class of effectors consist of arrays of phylogenetically related paralogs residing in gene sparse regions, and shows signatures of positive selection and presence/absence polymorphism. This work provides a resource that allows the dissection of the genomic components underlying A. candida adaptation and, particularly, the role of CCG effectors in virulence and avirulence on different hosts.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Plant pathogens convergently evolved to counteract redundant nodes of an NLR immune receptor network.

In plants, nucleotide-binding domain and leucine-rich repeat (NLR)-containing proteins can form receptor networks to confer hypersensitive cell death and innate immunity. One class of NLRs, known as NLR required for cell death (NRCs), are central nodes in a complex network that protects against multiple pathogens and comprises up to half of the NLRome of solanaceous plants. Given the prevalence of this NLR network, we hypothesised that pathogens convergently evolved to secrete effectors that target NRC activities. To test this, we screened a library of 165 bacterial, oomycete, nematode, and aphid effectors for their capacity to suppress the cell death response triggered by the NRC-dependent disease resistance proteins Prf and Rpi-blb2. Among 5 of the identified suppressors, 1 cyst nematode protein and 1 oomycete protein suppress the activity of autoimmune mutants of NRC2 and NRC3, but not NRC4, indicating that they specifically counteract a subset of NRC proteins independently of their sensor NLR partners. Whereas the cyst nematode effector SPRYSEC15 binds the nucleotide-binding domain of NRC2 and NRC3, the oomycete effector AVRcap1b suppresses the response of these NRCs via the membrane trafficking-associated protein NbTOL9a (Target of Myb 1-like protein 9a). We conclude that plant pathogens have evolved to counteract central nodes of the NRC immune receptor network through different mechanisms. Coevolution with pathogen effectors may have driven NRC diversification into functionally redundant nodes in a massively expanded NLR network.

Chromatin accessibility landscapes activated by cell-surface and intracellular immune receptors.

Activation of cell-surface and intracellular receptor-mediated immunity results in rapid transcriptional reprogramming that underpins disease resistance. However, the mechanisms by which co-activation of both immune systems lead to transcriptional changes are not clear. Here, we combine RNA-seq and ATAC-seq to define changes in gene expression and chromatin accessibility. Activation of cell-surface or intracellular receptor-mediated immunity, or both, increases chromatin accessibility at induced defence genes. Analysis of ATAC-seq and RNA-seq data combined with publicly available information on transcription factor DNA-binding motifs enabled comparison of individual gene regulatory networks activated by cell-surface or intracellular receptor-mediated immunity, or by both. These results and analyses reveal overlapping and conserved transcriptional regulatory mechanisms between the two immune systems.

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.

High-resolution expression profiling of selected gene sets during plant immune activation.

The plant immune system involves detection of pathogens via both cell-surface and intracellular receptors. Both receptor classes can induce transcriptional reprogramming that elevates disease resistance. To assess differential gene expression during plant immunity, we developed and deployed quantitative sequence capture (CAP-I). We designed and synthesized biotinylated single-strand RNA bait libraries targeted to a subset of defense genes, and generated sequence capture data from 99 RNA-seq libraries. We built a data processing pipeline to quantify the RNA-CAP-I-seq data, and visualize differential gene expression. Sequence capture in combination with quantitative RNA-seq enabled cost-effective assessment of the expression profile of a specified subset of genes. Quantitative sequence capture is not limited to RNA-seq or any specific organism and can potentially be incorporated into automated platforms for high-throughput sequencing.

Protein engineering expands the effector recognition profile of a rice NLR immune receptor.

Plant nucleotide binding, leucine-rich repeat (NLR) receptors detect pathogen effectors and initiate an immune response. Since their discovery, NLRs have been the focus of protein engineering to improve disease resistance. However, this approach has proven challenging, in part due to their narrow response specificity. Previously, we revealed the structural basis of pathogen recognition by the integrated heavy metal associated (HMA) domain of the rice NLR Pikp (Maqbool et al., 2015). Here, we used structure-guided engineering to expand the response profile of Pikp to variants of the rice blast pathogen effector AVR-Pik. A mutation located within an effector-binding interface of the integrated Pikp-HMA domain increased the binding affinity for AVR-Pik variants in vitro and in vivo. This translates to an expanded cell-death response to AVR-Pik variants previously unrecognized by Pikp in planta. The structures of the engineered Pikp-HMA in complex with AVR-Pik variants revealed the mechanism of expanded recognition. These results provide a proof-of-concept that protein engineering can improve the utility of plant NLR receptors where direct interaction between effectors and NLRs is established, particularly where this interaction occurs via integrated domains.

Using CRISPR/Cas9 genome editing in tomato to create a gibberellin-responsive dominant dwarf DELLA allele.

The tomato PROCERA gene encodes a DELLA protein, and loss-of-function mutations derepress growth. We used CRISPR/Cas9 and a single guide RNAs (sgRNA) to target mutations to the PROCERA DELLA domain, and recovered several loss-of-function mutations and a dominant dwarf mutation that carries a deletion of one amino acid in the DELLA domain. This is the first report of a dominant dwarf PROCERA allele. This allele retains partial responsiveness to exogenously applied gibberellin. Heterozygotes show an intermediate phenotype at the seedling stage, but adult heterozygotes are as dwarfed as homozygotes.

The use of quantitative imaging to investigate regulators of membrane trafficking in Arabidopsis stomatal closure.

Expansion of gene families facilitates robustness and evolvability of biological processes but impedes functional genetic dissection of signalling pathways. To address this, quantitative analysis of single cell responses can help characterize the redundancy within gene families. We developed high-throughput quantitative imaging of stomatal closure, a response of plant guard cells, and performed a reverse genetic screen in a group of Arabidopsis mutants to five stimuli. Focussing on the intersection between guard cell signalling and the endomembrane system, we identified eight clusters based on the mutant stomatal responses. Mutants generally affected in stomatal closure were mostly in genes encoding SNARE and SCAMP membrane regulators. By contrast, mutants in RAB5 GTPase genes played specific roles in stomatal closure to microbial but not drought stress. Together with timed quantitative imaging of endosomes revealing sequential patterns in FLS2 trafficking, our imaging pipeline can resolve non-redundant functions of the RAB5 GTPase gene family. Finally, we provide a valuable image-based tool to dissect guard cell responses and outline a genetic framework of stomatal closure.

A workflow for simplified analysis of ATAC-cap-seq data in R.

Background: Assay for Transposase-Accessible Chromatin (ATAC)-cap-seq is a high-throughput sequencing method that combines ATAC-seq with targeted nucleic acid enrichment of precipitated DNA fragments. There are increased analytical difficulties arising from working with a set of regions of interest that may be small in number and biologically dependent. Common statistical pipelines for RNA sequencing might be assumed to apply but can give misleading results on ATAC-cap-seq data. A tool is needed to allow a nonspecialist user to quickly and easily summarize data and apply sensible and effective normalization and analysis. Results: We developed atacR to allow a user to easily analyze their ATAC enrichment experiment. It provides comprehensive summary functions and diagnostic plots for studying enriched tag abundance. Application of between-sample normalization is made straightforward. Functions for normalizing based on user-defined control regions, whole library size, and regions selected from the least variable regions in a dataset are provided. Three methods for detecting differential abundance of tags from enriched methods are provided, including bootstrap t, Bayes factor, and a wrapped version of the standard exact test in the edgeR package. We compared the precision, recall, and F-score of each detection method on resampled datasets at varying replicate, significance threshold, and genes changed and found that the Bayes factor method had the greatest overall detection power, though edgeR was slightly stronger in simulations with lower numbers of genes changed. Conclusions: Our package allows a nonspecialist user to easily and effectively apply methods appropriate to the analysis of ATAC-cap-seq in a reproducible manner. The package is implemented in pure R and is fully interoperable with common workflows in Bioconductor.

The ash dieback invasion of Europe was founded by two genetically divergent individuals.

Accelerating international trade and climate change make pathogen spread an increasing concern. Hymenoscyphus fraxineus, the causal agent of ash dieback, is a fungal pathogen that has been moving across continents and hosts from Asian to European ash. Most European common ash trees (Fraxinus excelsior) are highly susceptible to H. fraxineus, although a minority (~5%) have partial resistance to dieback. Here, we assemble and annotate a H. fraxineus draft genome, which approaches chromosome scale. Pathogen genetic diversity across Europe and in Japan, reveals a strong bottleneck in Europe, though a signal of adaptive diversity remains in key host interaction genes. We find that the European population was founded by two divergent haploid individuals. Divergence between these haplotypes represents the ancestral polymorphism within a large source population. Subsequent introduction from this source would greatly increase adaptive potential of the pathogen. Thus, further introgression of H. fraxineus into Europe represents a potential threat and Europe-wide biological security measures are needed to manage this disease.

An automated quantitative image analysis tool for the identification of microtubule patterns in plants.

High throughput confocal imaging poses challenges in the computational image analysis of complex subcellular structures such as the microtubule cytoskeleton. Here, we developed CellArchitect, an automated image analysis tool that quantifies changes to subcellular patterns illustrated by microtubule markers in plants. We screened microtubule-targeted herbicides and demonstrate that high throughput confocal imaging with integrated image analysis by CellArchitect can distinguish effects induced by the known herbicides indaziflam and trifluralin. The same platform was used to examine 6 other compounds with herbicidal activity, and at least 3 different effects induced by these compounds were profiled. We further show that CellArchitect can detect subcellular patterns tagged by actin and endoplasmic reticulum markers. Thus, the platform developed here can be used to automate image analysis of complex subcellular patterns for purposes such as herbicide discovery and mode of action characterisation. The capacity to use this tool to quantitatively characterize cellular responses lends itself to application across many areas of biology.

The Arabidopsis Protein Phosphatase PP2C38 Negatively Regulates the Central Immune Kinase BIK1.

Plants recognize pathogen-associated molecular patterns (PAMPs) via cell surface-localized pattern recognition receptors (PRRs), leading to PRR-triggered immunity (PTI). The Arabidopsis cytoplasmic kinase BIK1 is a downstream substrate of several PRR complexes. How plant PTI is negatively regulated is not fully understood. Here, we identify the protein phosphatase PP2C38 as a negative regulator of BIK1 activity and BIK1-mediated immunity. PP2C38 dynamically associates with BIK1, as well as with the PRRs FLS2 and EFR, but not with the co-receptor BAK1. PP2C38 regulates PAMP-induced BIK1 phosphorylation and impairs the phosphorylation of the NADPH oxidase RBOHD by BIK1, leading to reduced oxidative burst and stomatal immunity. Upon PAMP perception, PP2C38 is phosphorylated on serine 77 and dissociates from the FLS2/EFR-BIK1 complexes, enabling full BIK1 activation. Together with our recent work on the control of BIK1 turnover, this study reveals another important regulatory mechanism of this central immune component.

A pigeonpea gene confers resistance to Asian soybean rust in soybean.

Asian soybean rust (ASR), caused by the fungus Phakopsora pachyrhizi, is one of the most economically important crop diseases, but is only treatable with fungicides, which are becoming less effective owing to the emergence of fungicide resistance. There are no commercial soybean cultivars with durable resistance to P. pachyrhizi, and although soybean resistance loci have been mapped, no resistance genes have been cloned. We report the cloning of a P. pachyrhizi resistance gene CcRpp1 (Cajanus cajan Resistance against Phakopsora pachyrhizi 1) from pigeonpea (Cajanus cajan) and show that CcRpp1 confers full resistance to P. pachyrhizi in soybean. Our findings show that legume species related to soybean such as pigeonpea, cowpea, common bean and others could provide a valuable and diverse pool of resistance traits for crop improvement.

blastjs: a BLAST+ wrapper for Node.js.

BACKGROUND: To cope with the ever-increasing amount of sequence data generated in the field of genomics, the demand for efficient and fast database searches that drive functional and structural annotation in both large- and small-scale genome projects is on the rise. The tools of the BLAST+ suite are the most widely employed bioinformatic method for these database searches. Recent trends in bioinformatics application development show an increasing number of JavaScript apps that are based on modern frameworks such as Node.js. Until now, there is no way of using database searches with the BLAST+ suite from a Node.js codebase. RESULTS: We developed blastjs, a Node.js library that wraps the search tools of the BLAST+ suite and thus allows to easily add significant functionality to any Node.js-based application. CONCLUSION: blastjs is a library that allows the incorporation of BLAST+ functionality into bioinformatics applications based on JavaScript and Node.js. The library was designed to be as user-friendly as possible and therefore requires only a minimal amount of code in the client application. The library is freely available under the MIT license at https://github.com/teammaclean/blastjs.

Lessons from Fraxinus, a crowd-sourced citizen science game in genomics.

In 2013, in response to an epidemic of ash dieback disease in England the previous year, we launched a Facebook-based game called Fraxinus to enable non-scientists to contribute to genomics studies of the pathogen that causes the disease and the ash trees that are devastated by it. Over a period of 51 weeks players were able to match computational alignments of genetic sequences in 78% of cases, and to improve them in 15% of cases. We also found that most players were only transiently interested in the game, and that the majority of the work done was performed by a small group of dedicated players. Based on our experiences we have built a linear model for the length of time that contributors are likely to donate to a crowd-sourced citizen science project. This model could serve a guide for the design and implementation of future crowd-sourced citizen science initiatives.

bio-samtools 2: a package for analysis and visualization of sequence and alignment data with SAMtools in Ruby.

MOTIVATION: bio-samtools is a Ruby language interface to SAMtools, the highly popular library that provides utilities for manipulating high-throughput sequence alignments in the Sequence Alignment/Map format. Advances in Ruby, now allow us to improve the analysis capabilities and increase bio-samtools utility, allowing users to accomplish a large amount of analysis using a very small amount of code. bio-samtools can also be easily developed to include additional SAMtools methods and hence stay current with the latest SAMtools releases. RESULTS: We have added new Ruby classes for the MPileup and Variant Call Format (VCF) data formats emitted by SAMtools and introduced more analysis methods for variant analysis, including alternative allele calculation and allele frequency calling for SNPs. Our new implementation of bio-samtools also ensures that all the functionality of the SAMtools library is now supported and that bio-samtools can be easily extended to include future changes in SAMtools. bio-samtools 2 also provides methods that allow the user to directly produce visualization of alignment data.

Physical and chemical properties of pomegranate fruit accessions from Croatia.

The objective was to evaluate physical and chemical properties of eight pomegranate accessions (seven cultivars and one wild genotype) collected from the Mediterranean region of Croatia. Accessions showed high variability in fruit weight and size, calyx and peel properties, number of arils per fruit, total aril weight, and aril and juice yield. Variables that define sweet taste, such as low total acidity (TA; 0.37-0.59%), high total soluble solids content (TSS; 12.5-15.0%) and their ratio (TSS/TA) were evaluated, and results generally aligned with sweetness classifications of the fruit. Pomegranate fruit had a high variability in total phenolic content (1985.6-2948.7 mg/L). HPLC-MALDI-TOF/MS analysis showed that accessions with dark red arils had the highest total anthocyanin content, with cyanidin 3-glucoside as the most abundant compound. Principal component analysis revealed great differences in fruit physical characteristics and chemical composition among pomegranate accessions.

Using 2k + 2 bubble searches to find single nucleotide polymorphisms in k-mer graphs.

MOTIVATION: Single nucleotide polymorphism (SNP) discovery is an important preliminary for understanding genetic variation. With current sequencing methods, we can sample genomes comprehensively. SNPs are found by aligning sequence reads against longer assembled references. De Bruijn graphs are efficient data structures that can deal with the vast amount of data from modern technologies. Recent work has shown that the topology of these graphs captures enough information to allow the detection and characterization of genetic variants, offering an alternative to alignment-based methods. Such methods rely on depth-first walks of the graph to identify closing bifurcations. These methods are conservative or generate many false-positive results, particularly when traversing highly inter-connected (complex) regions of the graph or in regions of very high coverage. RESULTS: We devised an algorithm that calls SNPs in converted De Bruijn graphs by enumerating 2k + 2 cycles. We evaluated the accuracy of predicted SNPs by comparison with SNP lists from alignment-based methods. We tested accuracy of the SNP calling using sequence data from 16 ecotypes of Arabidopsis thaliana and found that accuracy was high. We found that SNP calling was even across the genome and genomic feature types. Using sequence-based attributes of the graph to train a decision tree allowed us to increase accuracy of SNP calls further. Together these results indicate that our algorithm is capable of finding SNPs accurately in complex sub-graphs and potentially comprehensively from whole genome graphs. AVAILABILITY AND IMPLEMENTATION: The source code for a C++ implementation of our algorithm is available under the GNU Public Licence v3 at: https://github.com/danmaclean/2kplus2. The datasets used in this study are available at the European Nucleotide Archive, reference ERP00565, http://www.ebi.ac.uk/ena/data/view/ERP000565.