Pan-genome survey of Septoria pistaciarum, causal agent of Septoria leaf spot of pistachios, across three Aegean sub-regions of Greece.

Septoria pistaciarum, a causal agent of Septoria leaf spot disease of pistachio, is a fungal pathogen that causes substantial losses in the cultivation, worldwide. This study describes the first pan-genome-based survey of this phytopathogen-comprising a total of 27 isolates, with 9 isolates each from 3 regional units of Greece (Pieria, Larissa and Fthiotida). The reference isolate (SPF8) assembled into a total of 43.1 Mb, with 38.6% contained within AT-rich regions of approximately 37.5% G:C. The genomes of the 27 isolates exhibited on average 42% gene-coding and 20% repetitive regions. The genomes of isolates from the southern Fthiotida region appeared to more diverged from each other than the other regions based on SNP-derived trees, and also contained isolates similar to both the Pieria and Larissa regions. In contrast, isolates of the Pieria and Larissa were less diverse and distinct from one another. Asexual reproduction appeared to be typical, with no MAT1-2 locus detected in any isolate. Genome-based prediction of infection mode indicated hemibiotrophic and saprotrophic adaptations, consistent with its long latent phase. Gene prediction and orthology clustering generated a pan-genome-wide gene set of 21,174 loci. A total of 59 ortholog groups were predicted to contain candidate effector proteins, with 36 (61%) of these either having homologs to known effectors from other species or could be assigned predicted functions from matches to conserved domains. Overall, effector prediction suggests that S. pistaciarum employs a combination of defensive effectors with roles in suppression of host defenses, and offensive effectors with a range of cytotoxic activities. Some effector-like ortholog groups presented as divergent versions of the same protein, suggesting region-specific adaptations may have occurred. These findings provide insights and future research directions in uncovering the pathogenesis and population dynamics of S. pistaciarum toward the efficient management of Septoria leaf spot of pistachio.

The Molecular Docking of MAX Fungal Effectors with Plant HMA Domain-Binding Proteins.

Fungal effector proteins are important in mediating disease infections in agriculturally important crops. These secreted small proteins are known to interact with their respective host receptor binding partners in the host, either inside the cells or in the apoplastic space, depending on the localisation of the effector proteins. Consequently, it is important to understand the interactions between fungal effector proteins and their target host receptor binding partners, particularly since this can be used for the selection of potential plant resistance or susceptibility-related proteins that can be applied to the breeding of new cultivars with disease resistance. In this study, molecular docking simulations were used to characterise protein-protein interactions between effector and plant receptors. Benchmarking was undertaken using available experimental structures of effector-host receptor complexes to optimise simulation parameters, which were then used to predict the structures and mediating interactions of effector proteins with host receptor binding partners that have not yet been characterised experimentally. Rigid docking was applied for both the so-called bound and unbound docking of MAX effectors with plant HMA domain protein partners. All bound complexes used for benchmarking were correctly predicted, with 84% being ranked as the top docking pose using the ZDOCK scoring function. In the case of unbound complexes, a minimum of 95% of known residues were predicted to be part of the interacting interface on the host receptor binding partner, and at least 87% of known residues were predicted to be part of the interacting interface on the effector protein. Hydrophobic interactions were found to dominate the formation of effector-plant protein complexes. An optimised set of docking parameters based on the use of ZDOCK and ZRANK scoring functions were established to enable the prediction of near-native docking poses involving different binding interfaces on plant HMA domain proteins. Whilst this study was limited by the availability of the experimentally determined complexed structures of effectors and host receptor binding partners, we demonstrated the potential of molecular docking simulations to predict the likely interactions between effectors and their respective host receptor binding partners. This computational approach may accelerate the process of the discovery of putative interacting plant partners of effector proteins and contribute to effector-assisted marker discovery, thereby supporting the breeding of disease-resistant crops.

The pursuit of genetic gain in agricultural crops through the application of machine-learning to genomic prediction.

Current practice in agriculture applies genomic prediction to assist crop breeding in the analysis of genetic marker data. Genomic selection methods typically use linear mixed models, but using machine-learning may provide further potential for improved selection accuracy, or may provide additional information. Here we describe SelectML, an automated pipeline for testing and comparing the performance of a range of linear mixed model and machine-learning-based genomic selection methods. We demonstrate the use of SelectML on an in silico-generated marker dataset which simulated a randomly-sampled (mixed) and an unevenly-sampled (unbalanced) population, comparing the relative performance of various methods included in SelectML on the two datasets. Although machine-learning based methods performed similarly overall to linear mixed models, they performed worse on the mixed dataset and marginally better on the unbalanced dataset, being more affected than linear mixed models by the imposed sampling bias. SelectML can assist in the training, comparison, and selection of genomic selection models, and is available from https://github.com/darcyabjones/selectml.

Ab Initio Modelling of the Structure of ToxA-like and MAX Fungal Effector Proteins.

Pathogenic fungal diseases in crops are mediated by the release of effector proteins that facilitate infection. Characterising the structure of these fungal effectors is vital to understanding their virulence mechanisms and interactions with their hosts, which is crucial in the breeding of plant cultivars for disease resistance. Several effectors have been identified and validated experimentally; however, their lack of sequence conservation often impedes the identification and prediction of their structure using sequence similarity approaches. Structural similarity has, nonetheless, been observed within fungal effector protein families, creating interest in validating the use of computational methods to predict their tertiary structure from their sequence. We used Rosetta ab initio modelling to predict the structures of members of the ToxA-like and MAX effector families for which experimental structures are known to validate this method. An optimised approach was then used to predict the structures of phenotypically validated effectors lacking known structures. Rosetta was found to successfully predict the structure of fungal effectors in the ToxA-like and MAX families, as well as phenotypically validated but structurally unconfirmed effector sequences. Interestingly, potential new effector structural families were identified on the basis of comparisons with structural homologues and the identification of associated protein domains.

Template-Based Modelling of the Structure of Fungal Effector Proteins.

The discovery of new fungal effector proteins is necessary to enable the screening of cultivars for disease resistance. Sequence-based bioinformatics methods have been used for this purpose, but only a limited number of functional effector proteins have been successfully predicted and subsequently validated experimentally. A significant obstacle is that many fungal effector proteins discovered so far lack sequence similarity or conserved sequence motifs. The availability of experimentally determined three-dimensional (3D) structures of a number of effector proteins has recently highlighted structural similarities amongst groups of sequence-dissimilar fungal effectors, enabling the search for similar structural folds amongst effector sequence candidates. We have applied template-based modelling to predict the 3D structures of candidate effector sequences obtained from bioinformatics predictions and the PHI-BASE database. Structural matches were found not only with ToxA- and MAX-like effector candidates but also with non-fungal effector-like proteins-including plant defensins and animal venoms-suggesting the broad conservation of ancestral structural folds amongst cytotoxic peptides from a diverse range of distant species. Accurate modelling of fungal effectors were achieved using RaptorX. The utility of predicted structures of effector proteins lies in the prediction of their interactions with plant receptors through molecular docking, which will improve the understanding of effector-plant interactions.

Genome Analysis of the Broad Host Range Necrotroph Nalanthamala psidii Highlights Genes Associated With Virulence.

Guava wilt disease is caused by the fungus Nalanthamala psidii. The wilt disease results in large-scale destruction of orchards in South Africa, Taiwan, and several Southeast Asian countries. De novo assembly, annotation, and in-depth analysis of the N. psidii genome were carried out to facilitate the identification of characteristics associated with pathogenicity and pathogen evolution. The predicted secretome revealed a range of CAZymes, proteases, lipases and peroxidases associated with plant cell wall degradation, nutrient acquisition, and disease development. Further analysis of the N. psidii carbohydrate-active enzyme profile exposed the broad-spectrum necrotrophic lifestyle of the pathogen, which was corroborated by the identification of putative effectors and secondary metabolites with the potential to induce tissue necrosis and cell surface-dependent immune responses. Putative regulatory proteins including transcription factors and kinases were identified in addition to transporters potentially involved in the secretion of secondary metabolites. Transporters identified included important ABC and MFS transporters involved in the efflux of fungicides. Analysis of the repetitive landscape and the detection of mechanisms linked to reproduction such as het and mating genes rendered insights into the biological complexity and evolutionary potential of N. psidii as guava pathogen. Hence, the assembly and annotation of the N. psidii genome provided a valuable platform to explore the pathogenic potential and necrotrophic lifestyle of the guava wilt pathogen.

An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens.

Fungal plant-pathogens promote infection of their hosts through the release of 'effectors'-a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction of effectors remains a major challenge in plant pathology, but if achieved will facilitate rapid improvements to host disease resistance. This study presents a novel tool and pipeline for the ranking of predicted effector candidates-Predector-which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and applies a pairwise learning to rank approach. Predector outperformed a typical combination of secretion and effector prediction methods in terms of ranking performance when applied to a curated set of confirmed effectors derived from multiple species. We present Predector ( https://github.com/ccdmb/predector ) as a useful tool for the ranking of predicted effector candidates, which also aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner.

Remote homology clustering identifies lowly conserved families of effector proteins in plant-pathogenic fungi.

Plant diseases caused by fungal pathogens are typically initiated by molecular interactions between 'effector' molecules released by a pathogen and receptor molecules on or within the plant host cell. In many cases these effector-receptor interactions directly determine host resistance or susceptibility. The search for fungal effector proteins is a developing area in fungal-plant pathology, with more than 165 distinct confirmed fungal effector proteins in the public domain. For a small number of these, novel effectors can be rapidly discovered across multiple fungal species through the identification of known effector homologues. However, many have no detectable homology by standard sequence-based search methods. This study employs a novel comparison method (RemEff) that is capable of identifying protein families with greater sensitivity than traditional homology-inference methods, leveraging a growing pool of confirmed fungal effector data to enable the prediction of novel fungal effector candidates by protein family association. Resources relating to the RemEff method and data used in this study are available from https://figshare.com/projects/Effector_protein_remote_homology/87965.

Gene Validation and Remodelling Using Proteogenomics of Phytophthora cinnamomi, the Causal Agent of Dieback.

Phytophthora cinnamomi is a pathogenic oomycete that causes plant dieback disease across a range of natural ecosystems and in many agriculturally important crops on a global scale. An annotated draft genome sequence is publicly available (JGI Mycocosm) and suggests 26,131 gene models. In this study, soluble mycelial, extracellular (secretome), and zoospore proteins of P. cinnamomi were exploited to refine the genome by correcting gene annotations and discovering novel genes. By implementing the diverse set of sub-proteomes into a generated proteogenomics pipeline, we were able to improve the P. cinnamomi genome annotation. Liquid chromatography mass spectrometry was used to obtain high confidence peptides with spectral matching to both the annotated genome and a generated 6-frame translation. Two thousand seven hundred sixty-four annotations from the draft genome were confirmed by spectral matching. Using a proteogenomic pipeline, mass spectra were used to edit the P. cinnamomi genome and allowed identification of 23 new gene models and 60 edited gene features using high confidence peptides obtained by mass spectrometry, suggesting a rate of incorrect annotations of 3% of the detectable proteome. The novel features were further validated by total peptide support, alongside functional analysis including the use of Gene Ontology and functional domain identification. We demonstrated the use of spectral data in combination with our proteogenomics pipeline can be used to improve the genome annotation of important plant diseases and identify missed genes. This study presents the first use of spectral data to edit and manually annotate an oomycete pathogen.

Chromosome-level genome assembly and manually-curated proteome of model necrotroph Parastagonospora nodorum Sn15 reveals a genome-wide trove of candidate effector homologs, and redundancy of virulence-related functions within an accessory chromosome.

BACKGROUND: The fungus Parastagonospora nodorum causes septoria nodorum blotch (SNB) of wheat (Triticum aestivum) and is a model species for necrotrophic plant pathogens. The genome assembly of reference isolate Sn15 was first reported in 2007. P. nodorum infection is promoted by its production of proteinaceous necrotrophic effectors, three of which are characterised - ToxA, Tox1 and Tox3. RESULTS: A chromosome-scale genome assembly of P. nodorum Australian reference isolate Sn15, which combined long read sequencing, optical mapping and manual curation, produced 23 chromosomes with 21 chromosomes possessing both telomeres. New transcriptome data were combined with fungal-specific gene prediction techniques and manual curation to produce a high-quality predicted gene annotation dataset, which comprises 13,869 high confidence genes, and an additional 2534 lower confidence genes retained to assist pathogenicity effector discovery. Comparison to a panel of 31 internationally-sourced isolates identified multiple hotspots within the Sn15 genome for mutation or presence-absence variation, which was used to enhance subsequent effector prediction. Effector prediction resulted in 257 candidates, of which 98 higher-ranked candidates were selected for in-depth analysis and revealed a wealth of functions related to pathogenicity. Additionally, 11 out of the 98 candidates also exhibited orthology conservation patterns that suggested lateral gene transfer with other cereal-pathogenic fungal species. Analysis of the pan-genome indicated the smallest chromosome of 0.4 Mbp length to be an accessory chromosome (AC23). AC23 was notably absent from an avirulent isolate and is predominated by mutation hotspots with an increase in non-synonymous mutations relative to other chromosomes. Surprisingly, AC23 was deficient in effector candidates, but contained several predicted genes with redundant pathogenicity-related functions. CONCLUSIONS: We present an updated series of genomic resources for P. nodorum Sn15 - an important reference isolate and model necrotroph - with a comprehensive survey of its predicted pathogenicity content.

Crop-Zone Weed Mycobiomes of the South-Western Australian Grain Belt.

In the absence of a primary crop host, secondary plant hosts may act as a reservoir for fungal plant pathogens of agricultural crops. Secondary hosts may potentially harbor heteroecious biotrophs (e.g., the stripe rust fungus Puccinia striiformis) or other pathogens with broad host ranges. Agricultural grain production tends toward monoculture or a limited number of crop hosts over large regions, and local weeds are a major source of potential secondary hosts. In this study, the fungal phyllospheres of 12 weed species common in the agricultural regions of Western Australia (WA) were compared through high-throughput DNA sequencing. Amplicons of D2 and ITS were sequenced on an Illumina MiSeq system using previously published primers and BLAST outputs analyzed using MEGAN. A heatmap of cumulative presence-absence for fungal taxa was generated, and variance patterns were investigated using principal components analysis (PCA) and canonical correspondence analysis (CCA). We observed the presence of several major international crop pathogens, including basidiomycete rusts of the Puccinia spp., and ascomycete phytopathogens of the Leptosphaeria and Pyrenophora genera. Unrelated to crop production, several endemic pathogen species including those infecting Eucalyptus trees were also observed, which was consistent with local native flora. We also observed that differences in latitude or climate zones appeared to influence the geographic distributions of plant pathogenic species more than the presence of compatible host species, with the exception of Brassicaceae host family. There was an increased proportion of necrotrophic Ascomycete species in warmer and drier regions of central WA, compared to an increased proportion of biotrophic Basidiomycete species in cooler and wetter regions in southern WA.

Reference Genome Assembly for Australian Ascochyta rabiei Isolate ArME14.

Ascochyta rabiei is the causal organism of ascochyta blight of chickpea and is present in chickpea crops worldwide. Here we report the release of a high-quality PacBio genome assembly for the Australian A. rabiei isolate ArME14. We compare the ArME14 genome assembly with an Illumina assembly for Indian A. rabiei isolate, ArD2. The ArME14 assembly has gapless sequences for nine chromosomes with telomere sequences at both ends and 13 large contig sequences that extend to one telomere. The total length of the ArME14 assembly was 40,927,385 bp, which was 6.26 Mb longer than the ArD2 assembly. Division of the genome by OcculterCut into GC-balanced and AT-dominant segments reveals 21% of the genome contains gene-sparse, AT-rich isochores. Transposable elements and repetitive DNA sequences in the ArME14 assembly made up 15% of the genome. A total of 11,257 protein-coding genes were predicted compared with 10,596 for ArD2. Many of the predicted genes missing from the ArD2 assembly were in genomic regions adjacent to AT-rich sequence. We compared the complement of predicted transcription factors and secreted proteins for the two A. rabiei genome assemblies and found that the isolates contain almost the same set of proteins. The small number of differences could represent real differences in the gene complement between isolates or possibly result from the different sequencing methods used. Prediction pipelines were applied for carbohydrate-active enzymes, secondary metabolite clusters and putative protein effectors. We predict that ArME14 contains between 450 and 650 CAZymes, 39 putative protein effectors and 26 secondary metabolite clusters.

Prediction of pathogenicity genes involved in adaptation to a lupin host in the fungal pathogens Botrytis cinerea and Sclerotinia sclerotiorum via comparative genomics.

BACKGROUND: Narrow-leafed lupin is an emerging crop of significance in agriculture, livestock feed and human health food. However, its susceptibility to various diseases is a major obstacle towards increased adoption. Sclerotinia sclerotiorum and Botrytis cinerea - both necrotrophs with broad host-ranges - are reported among the top 10 lupin pathogens. Whole-genome sequencing and comparative genomics are useful tools to discover genes responsible for interactions between pathogens and their hosts. RESULTS: Genomes were assembled for one isolate of B. cinerea and two isolates of S. sclerotiorum, which were isolated from either narrow-leafed or pearl lupin species. Comparative genomics analysis between lupin-derived isolates and others isolated from alternate hosts was used to predict between 94 to 98 effector gene candidates from among their respective non-conserved gene contents. CONCLUSIONS: Detection of minor differences between relatively recently-diverged isolates, originating from distinct regions and with hosts, may highlight novel or recent gene mutations and losses resulting from host adaptation in broad host-range fungal pathogens.

A whole genome scan of SNP data suggests a lack of abundant hard selective sweeps in the genome of the broad host range plant pathogenic fungus Sclerotinia sclerotiorum.

The pathogenic fungus Sclerotinia sclerotiorum infects over 600 species of plant. It is present in numerous environments throughout the world and causes significant damage to many agricultural crops. Fragmentation and lack of gene flow between populations may lead to population sub-structure. Within discrete recombining populations, positive selection may lead to a 'selective sweep'. This is characterised by an increase in frequency of a favourable allele leading to reduction in genotypic diversity in a localised genomic region due to the phenomenon of genetic hitchhiking. We aimed to assess whether isolates of S. sclerotiorum from around the world formed genotypic clusters associated with geographical origin and to determine whether signatures of population-specific positive selection could be detected. To do this, we sequenced the genomes of 25 isolates of S. sclerotiorum collected from four different continents-Australia, Africa (north and south), Europe and North America (Canada and the northen United States) and conducted SNP based analyses of population structure and selective sweeps. Among the 25 isolates, there was evidence for two major population clusters. One of these consisted of 11 isolates from Canada, the USA and France (population 1), and the other consisted of nine isolates from Australia and one from Morocco (population 2). The rest of the isolates were genotypic outliers. We found that there was evidence of outcrossing in these two populations based on linkage disequilibrium decay. However, only a single candidate selective sweep was observed, and it was present in population 2. This sweep was close to a Major Facilitator Superfamily transporter gene, and we speculate that this gene may have a role in nutrient uptake from the host. The low abundance of selective sweeps in the S. sclerotiorum genome contrasts the numerous examples in the genomes of other fungal pathogens. This may be a result of its slow rate of evolution and low effective recombination rate due to self-fertilisation and vegetative reproduction.

Adapting legume crops to climate change using genomic approaches.

Our agricultural system and hence food security is threatened by combination of events, such as increasing population, the impacts of climate change, and the need to a more sustainable development. Evolutionary adaptation may help some species to overcome environmental changes through new selection pressures driven by climate change. However, success of evolutionary adaptation is dependent on various factors, one of which is the extent of genetic variation available within species. Genomic approaches provide an exceptional opportunity to identify genetic variation that can be employed in crop improvement programs. In this review, we illustrate some of the routinely used genomics-based methods as well as recent breakthroughs, which facilitate assessment of genetic variation and discovery of adaptive genes in legumes. Although additional information is needed, the current utility of selection tools indicate a robust ability to utilize existing variation among legumes to address the challenges of climate uncertainty.

"CATAStrophy," a Genome-Informed Trophic Classification of Filamentous Plant Pathogens - How Many Different Types of Filamentous Plant Pathogens Are There?

The traditional classification of fungal and oomycete phytopathogens into three classes - biotrophs, hemibiotrophs, or necrotrophs - is unsustainable. This study highlights multiple phytopathogen species for which these labels have been inappropriately applied. We propose a novel and reproducible classification based solely on genome-derived analysis of carbohydrate-active enzyme (CAZyme) gene content called CAZyme-Assisted Training And Sorting of -trophy (CATAStrophy). CATAStrophy defines four major divisions for species associated with living plants. These are monomertrophs (Mo) (corresponding to biotrophs), polymertrophs (P) (corresponding to necrotrophs), mesotrophs (Me) (corresponding to hemibiotrophs), and vasculartrophs (including species commonly described as wilts, rots, or anthracnoses). The Mo class encompasses symbiont, haustorial, and non-haustorial species. Me are divided into the subclasses intracellular and extracellular Me, and the P into broad and narrow host sub-classes. This gives a total of seven discrete plant-pathogenic classes. The classification provides insight into the properties of these species and offers a facile route to develop control measures for newly recognized diseases. Software for CATAStrophy is available online at https://github.com/ccdmb/catastrophy. We present the CATAStrophy method for the prediction of trophic phenotypes based on CAZyme gene content, as a complementary method to the traditional tripartite "biotroph-hemibiotroph-necrotroph" classifications that may encourage renewed investigation and revision within the fungal biology community.

The western Mediterranean region provided the founder population of domesticated narrow-leafed lupin.

KEY MESSAGE: This study revealed that the western Mediterranean provided the founder population for domesticated narrow-leafed lupin and that genetic diversity decreased significantly during narrow-leafed lupin domestication. The evolutionary history of plants during domestication profoundly shaped the genome structure and genetic diversity of today's crops. Advances in next-generation sequencing technologies allow unprecedented opportunities to understand genome evolution in minor crops, which constitute the majority of plant domestications. A diverse set of 231 wild and domesticated narrow-leafed lupin (Lupinus angustifolius L.) accessions were subjected to genotyping-by-sequencing using diversity arrays technology. Phylogenetic, genome-wide divergence and linkage disequilibrium analyses were applied to identify the founder population of domesticated narrow-leafed lupin and the genome-wide effect of domestication on its genome. We found wild western Mediterranean population as the founder of domesticated narrow-leafed lupin. Domestication was associated with an almost threefold reduction in genome diversity in domesticated accessions compared to their wild relatives. Selective sweep analysis identified no significant footprints of selection around domestication loci. A genome-wide association study identified single nucleotide polymorphism markers associated with pod dehiscence. This new understanding of the genomic consequences of narrow-leafed lupin domestication along with molecular marker tools developed here will assist plant breeders more effectively access wild genetic diversity for crop improvement.

Correction to: Comparative genomics of the wheat fungal pathogen Pyrenophora tritici-repentis reveals chromosomal variations and genome plasticity.

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Pan-Parastagonospora Comparative Genome Analysis-Effector Prediction and Genome Evolution.

We report a fungal pan-genome study involving Parastagonospora spp., including 21 isolates of the wheat (Triticum aestivum) pathogen Parastagonospora nodorum, 10 of the grass-infecting Parastagonospora avenae, and 2 of a closely related undefined sister species. We observed substantial variation in the distribution of polymorphisms across the pan-genome, including repeat-induced point mutations, diversifying selection and gene gains and losses. We also discovered chromosome-scale inter and intraspecific presence/absence variation of some sequences, suggesting the occurrence of one or more accessory chromosomes or regions that may play a role in host-pathogen interactions. The presence of known pathogenicity effector loci SnToxA, SnTox1, and SnTox3 varied substantially among isolates. Three P. nodorum isolates lacked functional versions for all three loci, whereas three P. avenae isolates carried one or both of the SnTox1 and SnTox3 genes, indicating previously unrecognized potential for discovering additional effectors in the P. nodorum-wheat pathosystem. We utilized the pan-genomic comparative analysis to improve the prediction of pathogenicity effector candidates, recovering the three confirmed effectors among our top-ranked candidates. We propose applying this pan-genomic approach to identify the effector repertoire involved in other host-microbe interactions involving necrotrophic pathogens in the Pezizomycotina.