Genome-Enabled Analysis of Population Dynamics and Virulence-Associated Loci in the Oat Crown Rust Fungus Puccinia coronata f. sp. avenae.

Puccinia coronata f. sp. avenae (Pca) is an important fungal pathogen causing crown rust that impacts oat production worldwide. Genetic resistance for crop protection against Pca is often overcome by the rapid virulence evolution of the pathogen. This study investigated the factors shaping adaptive evolution of Pca using pathogen populations from distinct geographic regions within the United States and South Africa. Phenotypic and genome-wide sequencing data of these diverse Pca collections, including 217 isolates, uncovered phylogenetic relationships and established distinct genetic composition between populations from northern and southern regions from the United States and South Africa. The population dynamics of Pca involve a bidirectional movement of inoculum between northern and southern regions of the United States and contributions from clonality and sexuality. The population from South Africa is solely clonal. A genome-wide association study (GWAS) employing a haplotype-resolved Pca reference genome was used to define 11 virulence-associated loci corresponding to 25 oat differential lines. These regions were screened to determine candidate Avr effector genes. Overall, the GWAS results allowed us to identify the underlying genetic factors controlling pathogen recognition in an oat differential set used in the United States to assign pathogen races (pathotypes). Key GWAS findings support complex genetic interactions in several oat lines, suggesting allelism among resistance genes or redundancy of genes included in the differential set, multiple resistance genes recognizing genetically linked Avr effector genes, or potentially epistatic relationships. A careful evaluation of the composition of the oat differential set accompanied by the development or implementation of molecular markers is recommended. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

AvrSr27 is a zinc-bound effector with a modular structure important for immune recognition.

Effector proteins are central to the success of plant pathogens, while immunity in host plants is driven by receptor-mediated recognition of these effectors. Understanding the molecular details of effector-receptor interactions is key for the engineering of novel immune receptors. Here, we experimentally determined the crystal structure of the Puccinia graminis f. sp. tritici (Pgt) effector AvrSr27, which was not accurately predicted using AlphaFold2. We characterised the role of the conserved cysteine residues in AvrSr27 using in vitro biochemical assays and examined Sr27-mediated recognition using transient expression in Nicotiana spp. and wheat protoplasts. The AvrSr27 structure contains a novel ß-strand rich modular fold consisting of two structurally similar domains that bind to Zn2+ ions. The N-terminal domain of AvrSr27 is sufficient for interaction with Sr27 and triggering cell death. We identified two Pgt proteins structurally related to AvrSr27 but with low sequence identity that can also associate with Sr27, albeit more weakly. Though only the full-length proteins, trigger Sr27-dependent cell death in transient expression systems. Collectively, our findings have important implications for utilising protein prediction platforms for effector proteins, and those embarking on bespoke engineering of immunity receptors as solutions to plant disease.

Genotypic and Resistance Profile Analysis of Two Oat Crown Rust Differential Sets Urge Coordination and Standardization.

Puccinia coronata f. sp. avenae is the causal agent of the disease known as crown rust, which represents a bottleneck in oat production worldwide. Characterization of pathogen populations often involves race (pathotype) assignments using differential sets, which are not uniform across countries. This study compared the virulence profiles of 25 P. coronata f. sp. avenae isolates from Australia using two host differential sets, one from Australia and one from the United States. These differential sets were also genotyped using diversity arrays technology sequencing technology. Phenotypic and genotypic discrepancies were detected on 8 out of 29 common lines between the two sets, indicating that pathogen race assignments based on those lines are not comparable. To further investigate molecular markers that could assist in the stacking of rust resistance genes important for Australia, four published Pc91-linked markers were validated across the differential sets and then screened across a collection of 150 oat cultivars. Drover, Aladdin, and Volta were identified as putative carriers of the Pc91 locus. This is the first report to confirm that the cultivar Volta carries Pc91 and demonstrates the value of implementing molecular markers to characterize materials in breeding pools of oat. Overall, our findings highlight the necessity of examining seed stocks using pedigree and molecular markers to ensure seed uniformity and bring robustness to surveillance methodologies. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Virulence Patterns of Oat Crown Rust in Australia - Season 2022.

Puccinia coronata f. sp. avenae (Pca) is an important foliar pathogen of oat which causes crown rust disease. The virulence profile of 48 Pca isolates derived from different locations in Australia was characterized using a collection of oat lines often utilized in rust surveys in the United States and Australia. This analysis indicates that Pca populations in Eastern Australia are broadly virulent, which contrasts with the population in Western Australia (WA). Several oat lines/Pc genes are effective against all rust samples collected from WA, suggesting they may provide useful resistance in this region if deployed in combination. We identified 19 lines from the United States oat differential set that display disease resistance to Pca in WA, with some in agreement with previous rust survey reports. We adopted the 10-letter nomenclature system to define oat crown rust races in Australia and compare the frequency of those virulence traits to published data from the United States. Based on this nomenclature, 42 unique races were detected among the 48 isolates, reflecting the high diversity of virulence phenotypes for Pca in Australia. Nevertheless, the Pca population in the United States is substantially more broadly virulent than that of Australia. Close examination of resistance profiles for the oat differential set lines after infection with Pca supports hypotheses of allelism or redundancy among Pc genes or the presence of several resistance genes in some oat differential lines. These findings illustrate the need to deconvolute the oat differential set using molecular tools.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

EffectorP 3.0: Prediction of Apoplastic and Cytoplasmic Effectors in Fungi and Oomycetes.

Many fungi and oomycete species are devasting plant pathogens. These eukaryotic filamentous pathogens secrete effector proteins to facilitate plant infection. Fungi and oomycete pathogens have diverse infection strategies and their effectors generally do not share sequence homology. However, they occupy similar host environments, either the plant apoplast or plant cytoplasm, and, therefore, may share some unifying properties based on the requirements of these host compartments. Here, we exploit these biological signals and present the first classifier (EffectorP 3.0) that uses two machine-learning models: one trained on apoplastic effectors and one trained on cytoplasmic effectors. EffectorP 3.0 accurately predicts known apoplastic and cytoplasmic effectors in fungal and oomycete secretomes with low estimated false-positive rates of 3 and 8%, respectively. Cytoplasmic effectors have a higher proportion of positively charged amino acids, whereas apoplastic effectors are enriched for cysteine residues. The combination of fungal and oomycete effectors in training leads to a higher number of predicted cytoplasmic effectors in biotrophic fungi. EffectorP 3.0 expands predicted effector repertoires beyond small, cysteine-rich secreted proteins in fungi and RxLR-motif containing secreted proteins in oomycetes. We show that signal peptide prediction is essential for accurate effector prediction, because EffectorP 3.0 recognizes a cytoplasmic signal also in intracellular, nonsecreted proteins.[Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Long reads and Hi-C sequencing illuminate the two-compartment genome of the model arbuscular mycorrhizal symbiont Rhizophagus irregularis.

Chromosome folding links genome structure with gene function by generating distinct nuclear compartments and topologically associating domains. In mammals, these undergo preferential interactions and regulate gene expression. However, their role in fungal genome biology is unclear. Here, we combine Nanopore (ONT) sequencing with chromatin conformation capture sequencing (Hi-C) to reveal chromosome and epigenetic diversity in a group of obligate plant symbionts: the arbuscular mycorrhizal fungi (AMF). We find that five phylogenetically distinct strains of the model AMF Rhizophagus irregularis carry 33 chromosomes with substantial within-species variability in size, as well as in gene and repeat content. Strain-specific Hi-C contact maps reveal a 'checkerboard' pattern that underline two dominant euchromatin (A) and heterochromatin (B) compartments. Each compartment differs in the level of gene transcription, regulation of candidate effectors and methylation frequencies. The A-compartment is more gene-dense and contains most core genes, while the B-compartment is more repeat-rich and has higher rates of chromosomal rearrangement. While the B-compartment is transcriptionally repressed, it has significantly more secreted proteins and in planta upregulated candidate effectors, suggesting a possible host-induced change in chromosome conformation. Overall, this study provides a fine-scale view into the genome biology and evolution of model plant symbionts, and opens avenues to study the epigenetic mechanisms that modify chromosome folding during host-microbe interactions.

The stem rust fungus Puccinia graminis f. sp. tritici induces centromeric small RNAs during late infection that are associated with genome-wide DNA methylation.

BACKGROUND: Silencing of transposable elements (TEs) is essential for maintaining genome stability. Plants use small RNAs (sRNAs) to direct DNA methylation to TEs (RNA-directed DNA methylation; RdDM). Similar mechanisms of epigenetic silencing in the fungal kingdom have remained elusive. RESULTS: We use sRNA sequencing and methylation data to gain insight into epigenetics in the dikaryotic fungus Puccinia graminis f. sp. tritici (Pgt), which causes the devastating stem rust disease on wheat. We use Hi-C data to define the Pgt centromeres and show that they are repeat-rich regions (~250 kb) that are highly diverse in sequence between haplotypes and, like in plants, are enriched for young TEs. DNA cytosine methylation is particularly active at centromeres but also associated with genome-wide control of young TE insertions. Strikingly, over 90% of Pgt sRNAs and several RNAi genes are differentially expressed during infection. Pgt induces waves of functionally diversified sRNAs during infection. The early wave sRNAs are predominantly 21 nts with a 5' uracil derived from genes. In contrast, the late wave sRNAs are mainly 22-nt sRNAs with a 5' adenine and are strongly induced from centromeric regions. TEs that overlap with late wave sRNAs are more likely to be methylated, both inside and outside the centromeres, and methylated TEs exhibit a silencing effect on nearby genes. CONCLUSIONS: We conclude that rust fungi use an epigenetic silencing pathway that might have similarity with RdDM in plants. The Pgt RNAi machinery and sRNAs are under tight temporal control throughout infection and might ensure genome stability during sporulation.

The crystal structure of SnTox3 from the necrotrophic fungus Parastagonospora nodorum reveals a unique effector fold and provides insight into Snn3 recognition and pro-domain protease processing of fungal effectors.

Plant pathogens cause disease through secreted effector proteins, which act to promote infection. Typically, the sequences of effectors provide little functional information and further targeted experimentation is required. Here, we utilized a structure/function approach to study SnTox3, an effector from the necrotrophic fungal pathogen Parastagonospora nodorum, which causes cell death in wheat-lines carrying the sensitivity gene Snn3. We developed a workflow for the production of SnTox3 in a heterologous host that enabled crystal structure determination and functional studies. We show this approach can be successfully applied to study effectors from other pathogenic fungi. The ß-barrel fold of SnTox3 is a novel fold among fungal effectors. Structure-guided mutagenesis enabled the identification of residues required for Snn3 recognition. SnTox3 is a pre-pro-protein, and the pro-domain of SnTox3 can be cleaved in vitro by the protease Kex2. Complementing this, an in silico study uncovered the prevalence of a conserved motif (LxxR) in an expanded set of putative pro-domain-containing fungal effectors, some of which can be cleaved by Kex2 in vitro. Our in vitro and in silico study suggests that Kex2-processed pro-domain (designated here as K2PP) effectors are common in fungi and this may have broad implications for the approaches used to study their functions.

Machine learning in plant-pathogen interactions: empowering biological predictions from field scale to genome scale.

Machine learning (ML) encompasses statistical methods that learn to identify patterns in complex datasets. Here, I review application areas in plant-pathogen interactions that have recently benefited from ML, such as disease monitoring, the discovery of gene regulatory networks, genomic selection for disease resistance and prediction of pathogen effectors. However, achieving robust performance from ML is not trivial and requires knowledge of both the methodology and the biology. I discuss common pitfalls and challenges in using ML approaches. Finally, I highlight future opportunities for ML as a tool for dissecting plant-pathogen interactions using high-throughput data, for example, through integration of diverse data sources and the analysis with higher resolution, such as from individual cells or on elaborate spatial and temporal scales.

Identification and profiling of narrow-leafed lupin (Lupinus angustifolius) microRNAs during seed development.

BACKGROUND: Whilst information regarding small RNAs within agricultural crops is increasing, the miRNA composition of the nutritionally valuable pulse narrow-leafed lupin (Lupinus angustifolius) remains unknown. RESULTS: By conducting a genome- and transcriptome-wide survey we identified 7 Dicer-like and 16 Argonaute narrow-leafed lupin genes, which were highly homologous to their legume counterparts. We identified 43 conserved miRNAs belonging to 16 families, and 13 novel narrow-leafed lupin-specific miRNAs using high-throughput sequencing of small RNAs from foliar and root and five seed development stages. We observed up-regulation of members of the miRNA families miR167, miR399, miR156, miR319 and miR164 in narrow-leafed lupin seeds, and confirmed expression of miR156, miR166, miR164, miR1507 and miR396 using quantitative RT-PCR during five narrow-leafed lupin seed development stages. We identified potential targets for the conserved and novel miRNAs and were able to validate targets of miR399 and miR159 using 5' RLM-RACE. The conserved miRNAs are predicted to predominately target transcription factors and 93% of the conserved miRNAs originate from intergenic regions. In contrast, only 43% of the novel miRNAs originate from intergenic regions and their predicted targets were more functionally diverse. CONCLUSION: This study provides important insights into the miRNA gene regulatory networks during narrow-leafed lupin seed development.

ApoplastP: prediction of effectors and plant proteins in the apoplast using machine learning.

The plant apoplast is integral to intercellular signalling, transport and plant-pathogen interactions. Plant pathogens deliver effectors both into the apoplast and inside host cells, but no computational method currently exists to discriminate between these localizations. We present ApoplastP, the first method for predicting whether an effector or plant protein localizes to the apoplast. ApoplastP uncovers features of apoplastic localization common to both effectors and plant proteins, namely depletion in glutamic acid, acidic amino acids and charged amino acids and enrichment in small amino acids. ApoplastP predicts apoplastic localization in effectors with a sensitivity of 75% and a false positive rate of 5%, improving the accuracy of cysteine-rich classifiers by > 13%. ApoplastP does not depend on the presence of a signal peptide and correctly predicts the localization of unconventionally secreted proteins. The secretomes of fungal saprophytes as well as necrotrophic, hemibiotrophic and extracellular fungal pathogens are enriched for predicted apoplastic proteins. Rust pathogens have low proportions of predicted apoplastic proteins, but these are highly enriched for predicted effectors. ApoplastP pioneers apoplastic localization prediction using machine learning. It will facilitate functional studies and will be valuable for predicting if an effector localizes to the apoplast or if it enters plant cells.

Genome sequencing and comparative genomics of the broad host-range pathogen Rhizoctonia solani AG8.

Rhizoctonia solani is a soil-borne basidiomycete fungus with a necrotrophic lifestyle which is classified into fourteen reproductively incompatible anastomosis groups (AGs). One of these, AG8, is a devastating pathogen causing bare patch of cereals, brassicas and legumes. R. solani is a multinucleate heterokaryon containing significant heterozygosity within a single cell. This complexity posed significant challenges for the assembly of its genome. We present a high quality genome assembly of R. solani AG8 and a manually curated set of 13,964 genes supported by RNA-seq. The AG8 genome assembly used novel methods to produce a haploid representation of its heterokaryotic state. The whole-genomes of AG8, the rice pathogen AG1-IA and the potato pathogen AG3 were observed to be syntenic and co-linear. Genes and functions putatively relevant to pathogenicity were highlighted by comparing AG8 to known pathogenicity genes, orthology databases spanning 197 phytopathogenic taxa and AG1-IA. We also observed SNP-level "hypermutation" of CpG dinucleotides to TpG between AG8 nuclei, with similarities to repeat-induced point mutation (RIP). Interestingly, gene-coding regions were widely affected along with repetitive DNA, which has not been previously observed for RIP in mononuclear fungi of the Pezizomycotina. The rate of heterozygous SNP mutations within this single isolate of AG8 was observed to be higher than SNP mutation rates observed across populations of most fungal species compared. Comparative analyses were combined to predict biological processes relevant to AG8 and 308 proteins with effector-like characteristics, forming a valuable resource for further study of this pathosystem. Predicted effector-like proteins had elevated levels of non-synonymous point mutations relative to synonymous mutations (dN/dS), suggesting that they may be under diversifying selection pressures. In addition, the distant relationship to sequenced necrotrophs of the Ascomycota suggests the R. solani genome sequence may prove to be a useful resource in future comparative analysis of plant pathogens.

Comparative genomics and prediction of conditionally dispensable sequences in legume-infecting Fusarium oxysporum formae speciales facilitates identification of candidate effectors.

BACKGROUND: Soil-borne fungi of the Fusarium oxysporum species complex cause devastating wilt disease on many crops including legumes that supply human dietary protein needs across many parts of the globe. We present and compare draft genome assemblies for three legume-infecting formae speciales (ff. spp.): F. oxysporum f. sp. ciceris (Foc-38-1) and f. sp. pisi (Fop-37622), significant pathogens of chickpea and pea respectively, the world's second and third most important grain legumes, and lastly f. sp. medicaginis (Fom-5190a) for which we developed a model legume pathosystem utilising Medicago truncatula. RESULTS: Focusing on the identification of pathogenicity gene content, we leveraged the reference genomes of Fusarium pathogens F. oxysporum f. sp. lycopersici (tomato-infecting) and F. solani (pea-infecting) and their well-characterised core and dispensable chromosomes to predict genomic organisation in the newly sequenced legume-infecting isolates. Dispensable chromosomes are not essential for growth and in Fusarium species are known to be enriched in host-specificity and pathogenicity-associated genes. Comparative genomics of the publicly available Fusarium species revealed differential patterns of sequence conservation across F. oxysporum formae speciales, with legume-pathogenic formae speciales not exhibiting greater sequence conservation between them relative to non-legume-infecting formae speciales, possibly indicating the lack of a common ancestral source for legume pathogenicity. Combining predicted dispensable gene content with in planta expression in the model legume-infecting isolate, we identified small conserved regions and candidate effectors, four of which shared greatest similarity to proteins from another legume-infecting ff. spp. CONCLUSIONS: We demonstrate that distinction of core and potential dispensable genomic regions of novel F. oxysporum genomes is an effective tool to facilitate effector discovery and the identification of gene content possibly linked to host specificity. While the legume-infecting isolates didn't share large genomic regions of pathogenicity-related content, smaller regions and candidate effector proteins were highly conserved, suggesting that they may play specific roles in inducing disease on legume hosts.

EffectorP: predicting fungal effector proteins from secretomes using machine learning.

Eukaryotic filamentous plant pathogens secrete effector proteins that modulate the host cell to facilitate infection. Computational effector candidate identification and subsequent functional characterization delivers valuable insights into plant-pathogen interactions. However, effector prediction in fungi has been challenging due to a lack of unifying sequence features such as conserved N-terminal sequence motifs. Fungal effectors are commonly predicted from secretomes based on criteria such as small size and cysteine-rich, which suffers from poor accuracy. We present EffectorP which pioneers the application of machine learning to fungal effector prediction. EffectorP improves fungal effector prediction from secretomes based on a robust signal of sequence-derived properties, achieving sensitivity and specificity of over 80%. Features that discriminate fungal effectors from secreted noneffectors are predominantly sequence length, molecular weight and protein net charge, as well as cysteine, serine and tryptophan content. We demonstrate that EffectorP is powerful when combined with in planta expression data for predicting high-priority effector candidates. EffectorP is the first prediction program for fungal effectors based on machine learning. Our findings will facilitate functional fungal effector studies and improve our understanding of effectors in plant-pathogen interactions. EffectorP is available at http://effectorp.csiro.au.

Pooled effector library screening in protoplasts rapidly identifies novel Avr genes.

Crop breeding for durable disease resistance is challenging due to the rapid evolution of pathogen virulence. While progress in resistance (R) gene cloning and stacking has accelerated in recent years1-3, the identification of corresponding avirulence (Avr) genes in many pathogens is hampered by the lack of high-throughput screening options. To address this technology gap, we developed a platform for pooled library screening in plant protoplasts to allow rapid identification of interacting R-Avr pairs. We validated this platform by isolating known and novel Avr genes from wheat stem rust (Puccinia graminis f. sp. tritici) after screening a designed library of putative effectors against individual R genes. Rapid Avr gene identification provides molecular tools to understand and track pathogen virulence evolution via genotype surveillance, which in turn will lead to optimized R gene stacking and deployment strategies. This platform should be broadly applicable to many crop pathogens and could potentially be adapted for screening genes involved in other protoplast-selectable traits.

A comparative hidden Markov model analysis pipeline identifies proteins characteristic of cereal-infecting fungi.

BACKGROUND: Fungal pathogens cause devastating losses in economically important cereal crops by utilising pathogen proteins to infect host plants. Secreted pathogen proteins are referred to as effectors and have thus far been identified by selecting small, cysteine-rich peptides from the secretome despite increasing evidence that not all effectors share these attributes. RESULTS: We take advantage of the availability of sequenced fungal genomes and present an unbiased method for finding putative pathogen proteins and secreted effectors in a query genome via comparative hidden Markov model analyses followed by unsupervised protein clustering. Our method returns experimentally validated fungal effectors in Stagonospora nodorum and Fusarium oxysporum as well as the N-terminal Y/F/WxC-motif from the barley powdery mildew pathogen. Application to the cereal pathogen Fusarium graminearum reveals a secreted phosphorylcholine phosphatase that is characteristic of hemibiotrophic and necrotrophic cereal pathogens and shares an ancient selection process with bacterial plant pathogens. Three F. graminearum protein clusters are found with an enriched secretion signal. One of these putative effector clusters contains proteins that share a [SG]-P-C-[KR]-P sequence motif in the N-terminal and show features not commonly associated with fungal effectors. This motif is conserved in secreted pathogenic Fusarium proteins and a prime candidate for functional testing. CONCLUSIONS: Our pipeline has successfully uncovered conservation patterns, putative effectors and motifs of fungal pathogens that would have been overlooked by existing approaches that identify effectors as small, secreted, cysteine-rich peptides. It can be applied to any pathogenic proteome data, such as microbial pathogen data of plants and other organisms.

Heuristic RNA pseudoknot prediction including intramolecular kissing hairpins.

Pseudoknots are an essential feature of RNA tertiary structures. Simple H-type pseudoknots have been studied extensively in terms of biological functions, computational prediction, and energy models. Intramolecular kissing hairpins are a more complex and biologically important type of pseudoknot in which two hairpin loops form base pairs. They are hard to predict using free energy minimization due to high computational requirements. Heuristic methods that allow arbitrary pseudoknots strongly depend on the quality of energy parameters, which are not yet available for complex pseudoknots. We present an extension of the heuristic pseudoknot prediction algorithm DotKnot, which covers H-type pseudoknots and intramolecular kissing hairpins. Our framework allows for easy integration of advanced H-type pseudoknot energy models. For a test set of RNA sequences containing kissing hairpins and other types of pseudoknot structures, DotKnot outperforms competing methods from the literature. DotKnot is available as a web server under http://dotknot.csse.uwa.edu.au.

Predicting pseudoknotted structures across two RNA sequences.

MOTIVATION: Laboratory RNA structure determination is demanding and costly and thus, computational structure prediction is an important task. Single sequence methods for RNA secondary structure prediction are limited by the accuracy of the underlying folding model, if a structure is supported by a family of evolutionarily related sequences, one can be more confident that the prediction is accurate. RNA pseudoknots are functional elements, which have highly conserved structures. However, few comparative structure prediction methods can handle pseudoknots due to the computational complexity. RESULTS: A comparative pseudoknot prediction method called DotKnot-PW is introduced based on structural comparison of secondary structure elements and H-type pseudoknot candidates. DotKnot-PW outperforms other methods from the literature on a hand-curated test set of RNA structures with experimental support. AVAILABILITY: DotKnot-PW and the RNA structure test set are available at the web site http://dotknot.csse.uwa.edu.au/pw. CONTACT: janaspe@csse.uwa.edu.au SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A highly contiguous genome assembly reveals sources of genomic novelty in the symbiotic fungus Rhizophagus irregularis.

The root systems of most plant species are aided by the soil-foraging capacities of symbiotic arbuscular mycorrhizal (AM) fungi of the Glomeromycotina subphylum. Despite recent advances in our knowledge of the ecology and molecular biology of this mutualistic symbiosis, our understanding of the AM fungi genome biology is just emerging. Presented here is a close to T2T genome assembly of the model AM fungus Rhizophagus irregularis DAOM197198, achieved through Nanopore long-read DNA sequencing and Hi-C data. This haploid genome assembly of R. irregularis, alongside short- and long-read RNA-Sequencing data, was used to produce a comprehensive annotation catalog of gene models, repetitive elements, small RNA loci, and DNA cytosine methylome. A phylostratigraphic gene age inference framework revealed that the birth of genes associated with nutrient transporter activity and transmembrane ion transport systems predates the emergence of Glomeromycotina. While nutrient cycling in AM fungi relies on genes that existed in ancestor lineages, a burst of Glomeromycotina-restricted genetic innovation is also detected. Analysis of the chromosomal distribution of genetic and epigenetic features highlights evolutionarily young genomic regions that produce abundant small RNAs, suggesting active RNA-based monitoring of genetic sequences surrounding recently evolved genes. This chromosome-scale view of the genome of an AM fungus genome reveals previously unexplored sources of genomic novelty in an organism evolving under an obligate symbiotic life cycle.

Nuclear exchange generates population diversity in the wheat leaf rust pathogen Puccinia triticina.

In clonally reproducing dikaryotic rust fungi, non-sexual processes such as somatic nuclear exchange are postulated to play a role in diversity but have been difficult to detect due to the lack of genome resolution between the two haploid nuclei. We examined three nuclear-phased genome assemblies of Puccinia triticina, which causes wheat leaf rust disease. We found that the most recently emerged Australian lineage was derived by nuclear exchange between two pre-existing lineages, which originated in Europe and North America. Haplotype-specific phylogenetic analysis reveals that repeated somatic exchange events have shuffled haploid nuclei between long-term clonal lineages, leading to a global P. triticina population representing different combinations of a limited number of haploid genomes. Thus, nuclear exchange seems to be the predominant mechanism generating diversity and the emergence of new strains in this otherwise clonal pathogen. Such genomics-accelerated surveillance of pathogen evolution paves the way for more accurate global disease monitoring.