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.

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.

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.

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.

Hierarchical clustering of MS/MS spectra from the firefly metabolome identifies new lucibufagin compounds.

Metabolite identification is the greatest challenge when analysing metabolomics data, as only a small proportion of metabolite reference standards exist. Clustering MS/MS spectra is a common method to identify similar compounds, however interrogation of underlying signature fragmentation patterns within clusters can be problematic. Previously published high-resolution LC-MS/MS data from the bioluminescent beetle (Photinus pyralis) provided an opportunity to mine new specialized metabolites in the lucibufagin class, compounds important for defense against predation. We aimed to 1) provide a workflow for hierarchically clustering MS/MS spectra for metabolomics data enabling users to cluster, visualise and easily interrogate the identification of underlying cluster ion profiles, and 2) use the workflow to identify key fragmentation patterns for lucibufagins in the hemolymph of P. pyralis. Features were aligned to their respective MS/MS spectra, then product ions were dynamically binned and resulting spectra were hierarchically clustered and grouped based on a cutoff distance threshold. Using the simplified visualization and the interrogation of cluster ion tables the number of lucibufagins was expanded from 17 to a total of 29.

A specific fungal transcription factor controls effector gene expression and orchestrates the establishment of the necrotrophic pathogen lifestyle on wheat.

The fungus Parastagonospora nodorum infects wheat through the use of necrotrophic effector (NE) proteins that cause host-specific tissue necrosis. The Zn2Cys6 transcription factor PnPf2 positively regulates NE gene expression and is required for virulence on wheat. Little is known about other downstream targets of PnPf2. We compared the transcriptomes of the P. nodorum wildtype and a strain deleted in PnPf2 (pf2-69) during in vitro growth and host infection to further elucidate targets of PnPf2 signalling. Gene ontology enrichment analysis of the differentially expressed (DE) genes revealed that genes associated with plant cell wall degradation and proteolysis were enriched in down-regulated DE gene sets in pf2-69 compared to SN15. In contrast, genes associated with redox control, nutrient and ion transport were up-regulated in the mutant. Further analysis of the DE gene set revealed that PnPf2 positively regulates twelve genes that encode effector-like proteins. Two of these genes encode proteins with homology to previously characterised effectors in other fungal phytopathogens. In addition to modulating effector gene expression, PnPf2 may play a broader role in the establishment of a necrotrophic lifestyle by orchestrating the expression of genes associated with plant cell wall degradation and nutrient assimilation.

Geographic distance to pediatric surgical care within the continental United States.

PURPOSE: Geographic proximity to pediatric surgical care has not been evaluated using the Decennial Census nor have racial, ethnic, gender, or urbanization variations been reported. This study's aim is to describe proximity of children living in the continental U.S. to a pediatric surgeon with respect to these variations. METHODS: The 2010 American Pediatric Surgical Association member file and the 2010 Decennial Census were used to calculate straight-line distances between pediatric surgeons' zip code centroids and census block centroids. RESULTS: In 2010, 716 practicing pediatric surgeons were identified, 6,182,882 populated Census blocks were identified, and 73,690,271 children were enumerated. Of white non-Hispanic children, 30.1% lived greater than 40 miles from care. Of Native American children, 40.5% lived more than 60 miles from care. Among children 0-5 years of age, the median (IQR) miles to closest pediatric surgeon was 14.2 (6.2, 39.6), and 3,010,698 of these children lived more than 60 miles from care. CONCLUSION: More than 10 million children lived greater than 60 miles from a pediatric surgeon in 2010. Racial, ethnic, age, and urbanization variations in proximity to pediatric surgeons were present. This method is feasible to describe distance-to-care with the upcoming 2020 Decennial Census and may benefit future allocation of pediatric surgeons. LEVEL OF EVIDENCE: IV.

"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.

Low Amplitude Boom-and-Bust Cycles Define the Septoria Nodorum Blotch Interaction.

INTRODUCTION: Septoria nodorum blotch (SNB) is a complex fungal disease of wheat caused by the Dothideomycete fungal pathogen Parastagonospora nodorum. The fungus infects through the use of necrotrophic effectors (NEs) that cause necrosis on hosts carrying matching dominant susceptibility genes. The Western Australia (WA) wheatbelt is a SNB "hot spot" and experiences significant under favorable conditions. Consequently, SNB has been a major target for breeders in WA for many years. MATERIALS AND METHODS: In this study, we assembled a panel of 155 WA P. nodorum isolates collected over a 44-year period and compared them to 23 isolates from France and the USA using 28 SSR loci. RESULTS: The WA P. nodorum population was clustered into five groups with contrasting properties. 80% of the studied isolates were assigned to two core groups found throughout the collection location and time. The other three non-core groups that encompassed transient and emergent populations were found in restricted locations and time. Changes in group genotypes occurred during periods that coincided with the mass adoption of a single or a small group of widely planted wheat cultivars. When introduced, these cultivars had high scores for SNB resistance. However, the field resistance of these new cultivars often declined over subsequent seasons prompting their replacement with new, more resistant varieties. Pathogenicity assays showed that newly emerged isolates non-core are more pathogenic than old isolates. It is likely that the non-core groups were repeatedly selected for increased virulence on the contemporary popular cultivars. DISCUSSION: The low level of genetic diversity within the non-core groups, difference in virulence, low abundance, and restriction to limited locations suggest that these populations more vulnerable to a population crash when the cultivar was replaced by one that was genetically different and more resistant. We characterize the observed pattern as a low-amplitude boom-and-bust cycle in contrast with the classical high amplitude boom-and-bust cycles seen for biotrophic pathogens where the contrast between resistance and susceptibility is typically much greater. Implications of the results are discussed relating to breeding strategies for more sustainable SNB resistance and more generally for pathogens with NEs.

Accessories Make the Outfit: Accessory Chromosomes and Other Dispensable DNA Regions in Plant-Pathogenic Fungi.

Fungal pathogen genomes can often be divided into core and accessory regions. Accessory regions ARs) may be comprised of either ARs (within core chromosomes (CCs) or wholly dispensable (accessory) chromosomes (ACs). Fungal ACs and ARs typically accumulate mutations and structural rearrangements more rapidly over time than CCs and many harbor genes relevant to host-pathogen interactions. These regions are of particular interest in plant pathology and include host-specific virulence factors and secondary metabolite synthesis gene clusters. This review outlines known ACs and ARs in fungal genomes, methods used for their detection, their common properties that differentiate them from the core genome, and what is currently known of their various roles in pathogenicity. Reports on the evolutionary processes generating and shaping AC and AR compartments are discussed, including repeat induced point mutation and breakage fusion bridge cycles. Previously ACs have been studied extensively within key genera, including Fusarium, Zymoseptoria, and Alternaria, but are growing in frequency of observation and perceived importance across a wider range of fungal species. Recent advances in sequencing technologies permit affordable genome assembly and resequencing of populations that will facilitate further discovery and routine screening of ACs.

Bioinformatic prediction of plant-pathogenicity effector proteins of fungi.

Effector proteins are important virulence factors of fungal plant pathogens and their prediction largely relies on bioinformatic methods. In this review we outline the current methods for the prediction of fungal plant pathogenicity effector proteins. Some fungal effectors have been characterised and are represented by conserved motifs or in sequence repositories, however most fungal effectors do not generally exhibit high conservation of amino acid sequence. Therefore various predictive methods have been developed around: general properties, structure, position in the genomic landscape, and detection of mutations including repeat-induced point mutations and positive selection. A combinatorial approach incorporating several of these methods is often employed and candidates can be prioritised by either ranked scores or hierarchical clustering.

Comparative analysis of the predicted secretomes of Rosaceae scab pathogens Venturia inaequalis and V. pirina reveals expanded effector families and putative determinants of host range.

BACKGROUND: Fungal plant pathogens belonging to the genus Venturia cause damaging scab diseases of members of the Rosaceae. In terms of economic impact, the most important of these are V. inaequalis, which infects apple, and V. pirina, which is a pathogen of European pear. Given that Venturia fungi colonise the sub-cuticular space without penetrating plant cells, it is assumed that effectors that contribute to virulence and determination of host range will be secreted into this plant-pathogen interface. Thus the predicted secretomes of a range of isolates of Venturia with distinct host-ranges were interrogated to reveal putative proteins involved in virulence and pathogenicity. RESULTS: Genomes of Venturia pirina (one European pear scab isolate) and Venturia inaequalis (three apple scab, and one loquat scab, isolates) were sequenced and the predicted secretomes of each isolate identified. RNA-Seq was conducted on the apple-specific V. inaequalis isolate Vi1 (in vitro and infected apple leaves) to highlight virulence and pathogenicity components of the secretome. Genes encoding over 600 small secreted proteins (candidate effectors) were identified, most of which are novel to Venturia, with expansion of putative effector families a feature of the genus. Numerous genes with similarity to Leptosphaeria maculans AvrLm6 and the Verticillium spp. Ave1 were identified. Candidates for avirulence effectors with cognate resistance genes involved in race-cultivar specificity were identified, as were putative proteins involved in host-species determination. Candidate effectors were found, on average, to be in regions of relatively low gene-density and in closer proximity to repeats (e.g. transposable elements), compared with core eukaryotic genes. CONCLUSIONS: Comparative secretomics has revealed candidate effectors from Venturia fungal plant pathogens that attack pome fruit. Effectors that are putative determinants of host range were identified; both those that may be involved in race-cultivar and host-species specificity. Since many of the effector candidates are in close proximity to repetitive sequences this may point to a possible mechanism for the effector gene family expansion observed and a route to diversification via transposition and repeat-induced point mutation.