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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

Resistance gene enrichment sequencing (RenSeq) enables reannotation of the NB-LRR gene family from sequenced plant genomes and rapid mapping of resistance loci in segregating populations.

RenSeq is a NB-LRR (nucleotide binding-site leucine-rich repeat) gene-targeted, Resistance gene enrichment and sequencing method that enables discovery and annotation of pathogen resistance gene family members in plant genome sequences. We successfully applied RenSeq to the sequenced potato Solanum tuberosum clone DM, and increased the number of identified NB-LRRs from 438 to 755. The majority of these identified R gene loci reside in poorly or previously unannotated regions of the genome. Sequence and positional details on the 12 chromosomes have been established for 704 NB-LRRs and can be accessed through a genome browser that we provide. We compared these NB-LRR genes and the corresponding oligonucleotide baits with the highest sequence similarity and demonstrated that ~80% sequence identity is sufficient for enrichment. Analysis of the sequenced tomato S. lycopersicum 'Heinz 1706' extended the NB-LRR complement to 394 loci. We further describe a methodology that applies RenSeq to rapidly identify molecular markers that co-segregate with a pathogen resistance trait of interest. In two independent segregating populations involving the wild Solanum species S. berthaultii (Rpi-ber2) and S. ruiz-ceballosii (Rpi-rzc1), we were able to apply RenSeq successfully to identify markers that co-segregate with resistance towards the late blight pathogen Phytophthora infestans. These SNP identification workflows were designed as easy-to-adapt Galaxy pipelines.

Reference-free SNP detection: dealing with the data deluge.

Reference-free SNP detection, that is identifying SNPs between samples directly from comparison of primary sequencing data with other primary sequencing data and not to a pre-assembled reference genome is an emergent and potentially disruptive technology that is beginning to open up new vistas in variant identification that reveals new applications in non-model organisms and metagenomics. The modern, efficient data structures these tools use enables researchers with a reference sequence to sample many more individuals with lower computing storage and processing overhead. In this article we will discuss the technologies and tools implementing reference-free SNP detection and the potential impact on studies of genetic variation in model and non-model organisms, metagenomics and personal genomics and medicine.

Assemblathon 1: a competitive assessment of de novo short read assembly methods.

Low-cost short read sequencing technology has revolutionized genomics, though it is only just becoming practical for the high-quality de novo assembly of a novel large genome. We describe the Assemblathon 1 competition, which aimed to comprehensively assess the state of the art in de novo assembly methods when applied to current sequencing technologies. In a collaborative effort, teams were asked to assemble a simulated Illumina HiSeq data set of an unknown, simulated diploid genome. A total of 41 assemblies from 17 different groups were received. Novel haplotype aware assessments of coverage, contiguity, structure, base calling, and copy number were made. We establish that within this benchmark: (1) It is possible to assemble the genome to a high level of coverage and accuracy, and that (2) large differences exist between the assemblies, suggesting room for further improvements in current methods. The simulated benchmark, including the correct answer, the assemblies, and the code that was used to evaluate the assemblies is now public and freely available from http://www.assemblathon.org/.

Gene gain and loss during evolution of obligate parasitism in the white rust pathogen of Arabidopsis thaliana.

Biotrophic eukaryotic plant pathogens require a living host for their growth and form an intimate haustorial interface with parasitized cells. Evolution to biotrophy occurred independently in fungal rusts and powdery mildews, and in oomycete white rusts and downy mildews. Biotroph evolution and molecular mechanisms of biotrophy are poorly understood. It has been proposed, but not shown, that obligate biotrophy results from (i) reduced selection for maintenance of biosynthetic pathways and (ii) gain of mechanisms to evade host recognition or suppress host defence. Here we use Illumina sequencing to define the genome, transcriptome, and gene models for the obligate biotroph oomycete and Arabidopsis parasite, Albugo laibachii. A. laibachii is a member of the Chromalveolata, which incorporates Heterokonts (containing the oomycetes), Apicomplexa (which includes human parasites like Plasmodium falciparum and Toxoplasma gondii), and four other taxa. From comparisons with other oomycete plant pathogens and other chromalveolates, we reveal independent loss of molybdenum-cofactor-requiring enzymes in downy mildews, white rusts, and the malaria parasite P. falciparum. Biotrophy also requires "effectors" to suppress host defence; we reveal RXLR and Crinkler effectors shared with other oomycetes, and also discover and verify a novel class of effectors, the "CHXCs", by showing effector delivery and effector functionality. Our findings suggest that evolution to progressively more intimate association between host and parasite results in reduced selection for retention of certain biosynthetic pathways, and particularly reduced selection for retention of molybdopterin-requiring biosynthetic pathways. These mechanisms are not only relevant to plant pathogenic oomycetes but also to human pathogens within the Chromalveolata.

Biogem: an effective tool-based approach for scaling up open source software development in bioinformatics.

SUMMARY: Biogem provides a software development environment for the Ruby programming language, which encourages community-based software development for bioinformatics while lowering the barrier to entry and encouraging best practices. Biogem, with its targeted modular and decentralized approach, software generator, tools and tight web integration, is an improved general model for scaling up collaborative open source software development in bioinformatics. AVAILABILITY: Biogem and modules are free and are OSS. Biogem runs on all systems that support recent versions of Ruby, including Linux, Mac OS X and Windows. Further information at http://www.biogems.info. A tutorial is available at http://www.biogems.info/howto.html CONTACT: bonnal@ingm.org.

The microRNA miR393 re-directs secondary metabolite biosynthesis away from camalexin and towards glucosinolates.

flg22 treatment increases levels of miR393, a microRNA that targets auxin receptors. Over-expression of miR393 renders plants more resistant to biotroph pathogens and more susceptible to necrotroph pathogens. In contrast, over-expression of AFB1, an auxin receptor whose mRNA is partially resistant to miR393 degradation, renders the plant more susceptible to biotroph pathogens. Here we investigate the mechanism by which auxin signalling and miR393 influence plant defence. We show that auxin signalling represses SA levels and signalling. We also show that miR393 represses auxin signalling, preventing it from antagonizing SA signalling. In addition, over-expression of miR393 increases glucosinolate levels and decreases the levels of camalexin. Further studies on pathogen interactions in auxin signalling mutants revealed that ARF1 and ARF9 negatively regulate glucosinolate accumulation, and that ARF9 positively regulates camalexin accumulation. We propose that the action of miR393 on auxin signalling triggers two complementary responses. First, it prevents suppression of SA levels by auxin. Second, it stabilizes ARF1 and ARF9 in inactive complexes. As a result, the plant is able to mount a full SA response and to re-direct metabolic flow toward the most effective anti-microbial compounds for biotroph resistance. We propose that miR393 levels can fine-tune plant defences and prioritize resources.

High-throughput confocal imaging of intact live tissue enables quantification of membrane trafficking in Arabidopsis.

Membrane compartmentalization and trafficking within and between cells is considered an essential cellular property of higher eukaryotes. We established a high-throughput imaging method suitable for the quantitative detection of membrane compartments at subcellular resolution in intact epidermal tissue. Whole Arabidopsis (Arabidopsis thaliana) cotyledon leaves were subjected to quantitative confocal laser microscopy using automated image acquisition, computational pattern recognition, and quantification of membrane compartments. This revealed that our method is sensitive and reliable to detect distinct endomembrane compartments. We applied quantitative confocal laser microscopy to a transgenic line expressing GFP-2xFYVE as a marker for endosomal compartments during biotic or abiotic stresses, and detected markedly quantitative adaptations in response to changing environments. Using a transgenic line expressing the plasma membrane-resident syntaxin GFP-PEN1, we quantified the pathogen-inducible extracellular accumulation of this fusion protein at fungal entry sites. Our protocol provides a platform to study the quantitative and dynamic changes of endomembrane trafficking, and potential adaptations of this machinery to physiological stress.