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This study investigated the generalizability of Arabidopsis thaliana immune responses across diverse pathogens, including Botrytis cinerea, Sclerotinia sclerotiorum, and Pseudomonas syringae, using a data-driven, machine learning approach. Machine learning models were trained to predict disease development from early transcriptional responses. Feature selection techniques based on network science and topology were used to train models employing only a fraction of the transcriptome. Machine learning models trained on one pathosystem where then validated by predicting disease development in new pathosystems. The identified feature selection gene sets were enriched for pathways related to biotic, abiotic, and stress responses, though the specific genes involved differed between feature sets. This suggests common immune responses to diverse pathogens that operate via different gene sets.The study demonstrates that machine learning can uncover both established and novel components of the plant's immune response, offering insights into disease resistance mechanisms. These predictive models highlight the potential to advance our understanding of multigenic outcomes in plant immunity and can be further refined for applications in disease prediction. 

Fungal pathogens display remarkable variation in genome content and organization that directly impacts their survival and host interactions. Although numerous models have been proposed to explain the origins of this variation, they generally fail to explain or predict the mechanisms that generate the genome variation observed in natural populations.Starshipsare a recently discovered group of giant fungal transposons that carry dozens of genes as cargo and horizontally transfer both within and between species. Here, we identify the features of a newly defined 'Starshipcompartment' in the major fungal plant pathogenPyricularia oryzae. We test the hypothesis that theStarshipcompartment makes distinct contributions to fungal genome evolution by explicitly comparing its transferability, mutability, and epigenetic modifications with those of the canonical core and accessory compartments. To enable this, we developed an updated and user-friendly version of the annotation tool stargraph for the comprehensive annotation ofStarshipsandStarship-like regions. Using this approach, we identified two distinct families ofStarshipsand relatedStarship-like regions inP. oryzaethat differ in their activity, impacts on genome organization, modes of sequence evolution, and epigenetic modifications. Elements from the more active family exhibit higher rates of structural variation than all other genomic compartments in the predominantly clonal isolates infecting rice. Both families ofStarshipsencode specific suites of known effector sequences that contribute to plant disease andStarshipactivity accounts for avirulence gene turnover, which suggests that evolutionary change within theStarshipcompartment may subsequently impact the evolution of plant-fungal interactions.Starshipsfrom the more active family have repeatedly transferred across thePyriculariagenus and tend to be depleted in heterochromatic histone modifications and repeat-induced point mutations. However, contrasting histone modification profiles in this family suggests a genomic conflict between silencing or maintainingStarshipactivity. Our findings demonstrate that variation in the mode of sequence diversification and epigenetic modification within theStarshipcompartment underpins the impacts of these giant transposons on fungal genome evolution. We argue for the explicit consideration of not only theStarshipcompartment but of element-specific dynamics when investigating the evolution of host-fungal interactions. 

Abstract The genomes of the fungus Magnaporthe oryzae that causes blast diseases on diverse grass species, including major crops, have indispensable core-chromosomes and may contain supernumerary chromosomes, also known as mini-chromosomes. These mini-chromosomes are speculated to provide effector gene mobility, and may transfer between strains. To understand the biology of mini-chromosomes, it is valuable to be able to detect whether a M. oryzae strain possesses a mini-chromosome. Here, we applied recurrent neural network models for classifying DNA sequences as arising from core- or mini-chromosomes. The models were trained with sequences from available core- and mini-chromosome assemblies, and then used to predict the presence of mini-chromosomes in a global collection of M. oryzae isolates using short-read DNA sequences. The model predicted that mini-chromosomes were prevalent in M. oryzae isolates. Interestingly, at least one mini-chromosome was present in all recent wheat isolates, but no mini-chromosomes were found in early isolates collected before 1991, indicating a preferential selection for strains carrying mini-chromosomes in recent years. The model was also used to identify assembled contigs derived from mini-chromosomes. In summary, our study has developed a reliable method for categorizing DNA sequences and showcases an application of recurrent neural networks in predictive genomics. 

A genome of Pyricularia oryzae (synonym Magnaporthe oryzae), the fungus that causes blast disease on diverse grass species, has seven core chromosomes and may contain supernumerary mini-chromosomes. The P. oryzae Triticum (PoT) pathotype is the phylogenetic lineage responsible for devastating epidemics of wheat blast disease. Genomic analysis of wheat blast field isolates from the initial outbreak in 1985 in Brazil through recent field isolates in South America revealed dynamic presence and structure of mini-chromosomes. Two earliest field isolates representing founder lineages for the Triticum pathotype contain similar mini-chromosomes. Another PoT founder isolate from 1986 and 37 out of 39 Triticum field isolates collected between 1986 and 1992 lack mini-chromosomes. Mini-chromosomes present in the founder strains each contain two copies of the PWT7 wheat blast avirulence gene, and PWT7 was lost from subsequent early strains through mini-chromosome loss. Almost all PoT field isolates from 2005 to 2020 have regained mini-chromosomes in which PWT7 sequences have been replaced by other sequences. Telomere-to-telomere assemblies of 11 mini-chromosomes identified two major mini-chromosome types in the South American PoT population, and demonstrated significant within-mini-chromosome sequence alterations as well as recombination with other mini-chromosomes or core chromosome ends. Additionally, our data indicate horizontal mini-chromosome transfer between Pyricularia species, resulting in nearly identical genomic fragments shared between P. oryzae and Pyricularia pennisetigena isolates in the PWT4 avirulence gene region. Our genomic analysis depicts the dynamic mini-chromosome compartment in the diverse South American Triticum field population through time, indicating important roles for mini-chromosomes in pathogen adaptation and pathogenicity. 

Through the association of protein complexes to DNA, the eukaryotic nuclear genome is broadly organized into open euchromatin that is accessible for enzymes acting on DNA and condensed heterochromatin that is inaccessible. Chemical and physical alterations to chromatin may impact its organization and functionality and are therefore important regulators of nuclear processes. Studies in various fungal plant pathogens have uncovered an association between chromatin organization and expression of in planta - induced genes that are important for pathogenicity. This review discusses chromatin-based regulation mechanisms as determined in the fungal plant pathogen Verticillium dahliae and relates the importance of epigenetic transcriptional regulation and other nuclear processes more broadly in fungal plant pathogens. 

Summary The fungal pathogen,Magnaporthe oryzae Triticumpathotype, causing wheat blast disease was first identified in South America and recently spread across continents to South Asia and Africa. Here, we studied the genetic relationship among isolates found on the three continents.Magnaporthe oryzaestrains closely related to a South American field isolate B71 were found to have caused the wheat blast outbreaks in South Asia and Africa. Genomic variation among isolates from the three continents was examined using an improved B71 reference genome and whole‐genome sequences.We found strong evidence to support that the outbreaks in Bangladesh and Zambia were caused by the introductions of genetically separated isolates, although they were all close to B71 and, therefore, collectively referred to as the B71 branch. In addition, B71 branch strains carried at least one supernumerary mini‐chromosome. Genome assembly of a Zambian strain revealed that its mini‐chromosome was similar to the B71 mini‐chromosome but with a high level of structural variation.Our findings show that while core genomes of the multiple introductions are highly similar, the mini‐chromosomes have undergone marked diversification. The maintenance of the mini‐chromosome and rapid genomic changes suggest the mini‐chromosomes may serve important virulence or niche adaptation roles under diverse environmental conditions. 

Abstract DNA double-strand breaks require repair or risk corrupting the language of life. To ensure genome integrity and viability, multiple DNA double-strand break repair pathways function in eukaryotes. Two such repair pathways, canonical non-homologous end joining and homologous recombination, have been extensively studied, while other pathways such as microhomology-mediated end joint and single-strand annealing, once thought to serve as back-ups, now appear to play a fundamental role in DNA repair. Here, we review the molecular details and hierarchy of these four DNA repair pathways, and where possible, a comparison for what is known between animal and fungal models. We address the factors contributing to break repair pathway choice, and aim to explore our understanding and knowledge gaps regarding mechanisms and regulation in filamentous pathogens. We additionally discuss how DNA double-strand break repair pathways influence genome engineering results, including unexpected mutation outcomes. Finally, we review the concept of biased genome evolution in filamentous pathogens, and provide a model, termed Biased Variation, that links DNA double-strand break repair pathways with properties of genome evolution. Despite our extensive knowledge for this universal process, there remain many unanswered questions, for which the answers may improve genome engineering and our understanding of genome evolution. 

Abstract Cellular biological networks represent the molecular interactions that shape function of living cells. Uncovering the organization of a biological network requires efficient and accurate algorithms to determine the components, termed communities, underlying specific processes. Detecting functional communities is challenging because reconstructed biological networks are always incomplete due to technical bias and biological complexity, and the evaluation of putative communities is further complicated by a lack of known ground truth. To address these challenges, we developed a geometric-based detection framework based on Ollivier-Ricci curvature to exploit information about network topology to perform community detection from partially observed biological networks. We further improved this approach by integrating knowledge of gene function, termed side information, into the Ollivier-Ricci curvature algorithm to aid in community detection. This approach identified essential conserved and varied biological communities from partially observed Arabidopsis protein interaction datasets better than the previously used methods. We show that Ollivier-Ricci curvature with side information identified an expanded auxin community to include an important protein stability complex, the Cop9 signalosome, consistent with previous reported links to auxin response and root development. The results show that community detection based on Ollivier-Ricci curvature with side information can uncover novel components and novel communities in biological networks, providing novel insight into the organization and function of complex networks.