Search for: All records

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. 

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. 

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. 

Single-phase body-centered cubic (bcc) refractory medium- or high-entropy alloys can retain compressive strength at elevated temperatures but suffer from extremely low tensile ductility and fracture toughness. We examined the strength and fracture toughness of a bcc refractory alloy, NbTaTiHf, from 77 to 1473 kelvin. This alloy’s behavior differed from that of comparable systems by having fracture toughness over 253 MPa·m^1/2, which we attribute to a dynamic competition between screw and edge dislocations in controlling the plasticity at a crack tip. Whereas the glide and intersection of screw and mixed dislocations promotes strain hardening controlling uniform deformation, the coordinated slip of <111> edge dislocations with {110} and {112} glide planes prolongs nonuniform strain through formation of kink bands. These bands suppress strain hardening by reorienting microscale bands of the crystal along directions of higher resolved shear stress and continually nucleate to accommodate localized strain and distribute damage away from a crack tip. 

Environmental observation networks, such as AmeriFlux, are foundational for monitoring ecosystem response to climate change, management practices, and natural disturbances; however, their effectiveness depends on their representativeness for the regions or continents. We proposed an empirical, time series approach to quantify the similarity of ecosystem fluxes across AmeriFlux sites. We extracted the diel and seasonal characteristics (i.e., amplitudes, phases) from carbon dioxide, water vapor, energy, and momentum fluxes, which reflect the effects of climate, plant phenology, and ecophysiology on the observations, and explored the potential aggregations of AmeriFlux sites through hierarchical clustering. While net radiation and temperature showed latitudinal clustering as expected, flux variables revealed a more uneven clustering with many small (number of sites < 5), unique groups and a few large (> 100) to intermediate (15–70) groups, highlighting the significant ecological regulations of ecosystem fluxes. Many identified unique groups were from under-sampled ecoregions and biome types of the International Geosphere-Biosphere Programme (IGBP), with distinct flux dynamics compared to the rest of the network. At the finer spatial scale, local topography, disturbance, management, edaphic, and hydrological regimes further enlarge the difference in flux dynamics within the groups. Nonetheless, our clustering approach is a data-driven method to interpret the AmeriFlux network, informing future cross-site syntheses, upscaling, and model-data benchmarking research. Finally, we highlighted the unique and underrepresented sites in the AmeriFlux network, which were found mainly in Hawaii and Latin America, mountains, and at under- sampled IGBP types (e.g., urban, open water), motivating the incorporation of new/unregistered sites from these groups. 

Abstract CRISPR-Cas mediated genome engineering has revolutionized functional genomics. However, understanding of DNA repair following Cas-mediated DNA cleavage remains incomplete. Using Cas12a ribonucleoprotein genome editing in the fungal pathogen, Magnaporthe oryzae , we detail non-canonical DNA repair outcomes from hundreds of transformants. Sanger and nanopore sequencing analysis reveals significant variation in DNA repair profiles, ranging from small INDELs to kilobase size deletions and insertions. Furthermore, we find the frequency of DNA repair outcomes varies between loci. The results are not specific to the Cas-nuclease or selection procedure. Through Ku80 deletion analysis, a key protein required for canonical non-homologous end joining, we demonstrate activity of an alternative end joining mechanism that creates larger DNA deletions, and uses longer microhomology compared to C-NHEJ. Together, our results suggest preferential DNA repair pathway activity in the genome that can create different mutation profiles following repair, which could create biased genome variation and impact genome engineering and genome evolution. 

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 observedArabidopsisprotein 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.