Sebastian Schornack

Root colonisation by endomycorrhizal fungi has long been a widely used metric in mycorrhizal studies. However, due to the significant time required to assess colonisation using traditional microscopy techniques, studies of colonisation at large scales are impractical. Machine-learning-based approaches may increase output and facilitate ecosystem assessments. We trained a machine-learning-based tool (MycorrhizaFinder) on 42 field roots (132 unique 1 cm sections, with > 100,000 tiles) and validated on a further 37 sections from grasslands and heathlands hosting common Northern European plants with arbuscular (AM) and ericoid mycorrhizal (ErM) fungal structures, and dark septate endophytes (DSE), also common in field-sourced roots. We incorporated a user-customized confidence threshold to encourage engagement with morphological ambiguities, in e.g. ErM and DSE. A Macro F ₁ statistic was used to assess the tool’s development performance. We provide a sample workflow from root processing and microscope slide scanning to semi-automated model training and performance evaluation. Our model performance combining all classes, the Macro F ₁ , is conservatively 66% for arbuscular and 57% for ericoid mycorrhizal colonisation assessment. MycorrhizaFinder is user-friendly, requires no programming skills and offers flexibility for advanced agronomists or ecologists to train the tool using their own labelled mycorrhizal root datasets, including images acquired from different instruments or staining protocols. This adaptability allows users to customize the model for specific ecosystems or experimental designs. Leveraged with molecular identification and/or functional traits of fungi (e.g. arbuscules), MycorrhizaFinder supports scalable and repeatable nursery experiments and monitoring across ecosystems to assess mycorrhizal status and track land-use changes over time.

Rhizobia are soil bacteria that establish nitrogen-fixing symbioses with legumes. While many rhizobia use a Type III Secretion System to deliver “Nodulation Outer Protein” (Nop) effectors, some uniquely use these proteins to initiate nodule organogenesis, bypassing classical signalling. The molecular functions of these effectors remain largely unknown due to extreme sequence divergence. Using AlphaFold2-mediated structural proteomics, we identified a modular architecture in rhizobial effectors composed of 22 distinct structural units. We reveal that many Nop effectors are cryptic transcriptional or post-transcriptional regulators, harbouring unrecognised nucleic acid–binding modules and RNA-dependent RNA polymerase domains. Crucially, these modules are conserved in specific plant pathogens, such as gall-inducing Pantoea , where our predicted structural units align with experimentally validated DNA-binding domains. Furthermore, we discovered the BPN (B3 and PUA-like nucleic acid binding) domain as a structural mimic of plant B3-domain transcription factors, pointing to a direct mechanism for hijacking legume development. Our findings strongly suggest that rhizobia employ a modular domain-fusion strategy to act as direct genetic modulators, uncovering a conserved mechanism used by both symbionts and pathogens to hijack host developmental programmes.

Rhizobia are soil bacteria that establish nitrogen-fixing symbioses with legumes. While many rhizobia use a Type III Secretion System to deliver “Nodulation Outer Protein” (Nop) effectors, some uniquely use these proteins to initiate nodule organogenesis, bypassing classical signalling. The molecular functions of these effectors remain largely unknown due to extreme sequence divergence. Using AlphaFold2-mediated structural proteomics, we identified a modular architecture in rhizobial effectors composed of 22 distinct structural units. We reveal that many Nop effectors are cryptic transcriptional or post-transcriptional regulators, harbouring unrecognised nucleic acid–binding modules and RNA-dependent RNA polymerase domains. Crucially, these modules are conserved in specific plant pathogens, such as gall-inducing Pantoea , where our predicted structural units align with experimentally validated DNA-binding domains. Furthermore, we discovered the BPN (B3 and PUA-like nucleic acid binding) domain as a structural mimic of plant B3-domain transcription factors, pointing to a direct mechanism for hijacking legume development. Our findings strongly suggest that rhizobia employ a modular domain-fusion strategy to act as direct genetic modulators, uncovering a conserved mechanism used by both symbionts and pathogens to hijack host developmental programmes.

We perform a large-scale computational characterization of the map of protein primary to secondary structure using an AVR3a class protein effector domain from the plant pathogen \textit{P. palmivora} as a case study. We formulate a modified site-scanning approach for exploring the neutral component of secondary structure phenotypes based on predictions from the machine-learning algorithm Porter 5 and apply it to the AVR3a phenotype. We predict a set of sensitive sites within the effector domain that are generally located at or near the boundaries of structured regions, with restrictions on the possible amino acid residues at these sites dictated by the secondary structure type that they participate in within the WT. We characterize a set of mutated phenotypes derived through the exploration of the neutral component of the WT effector domain, selecting them so that they span a range including both very rarely and very commonly seen secondary structures, and that they include both secondary structures nearly identical to the WT and ones far removed from it. We find that all these diverse phenotypes have an estimated robustness of the same order as that of the WT, and that the robustness scales logarithmically phenotype frequency, as seen in other genotype-to-phenotype maps. Furthermore, we observe that the dependence of the estimated phenotype frequency on the Kolmogorov complexity indicates simplicity bias in the protein secondary structure map.

Class I homeodomain-leucine zipper (C1HDZ) transcription factors regulate diverse developmental and stress-related processes in land plants. While extensively studied in angiosperms, their functions in other lineages are poorly understood. In the liverwort Marchantia polymorpha, the sole C1HDZ orthologue (MpC1HDZ) controls herbivore defense by regulating oil body cell formation. This suggests that the function of C1HDZ genes may have diverged during the evolution of land plants. Here, we further investigated the function of MpC1HDZ in M. polymorpha and its integration into Arabidopsis (Arabidopsis thaliana) regulatory networks. Mpc1hdz mutants were more susceptible to herbivory by Spodoptera frugiperda caterpillars but displayed greater resistance to the oomycete Phytophthora palmivora, indicating that MpC1HDZ-mediated defenses extend to other herbivores but not oomycete pathogens. Loss-of-function Mpc1hdz mutants exhibited developmental alterations, including shortened gemma cups, diminished air pore density, and absence of surface papillae, in addition to the loss of oil bodies, highlighting a broader role for MpC1HDZ in epidermal cell differentiation. Heterologous expression of MpC1HDZ in the Arabidopsis athb13-2 mutant resulted in a smaller rosette diameter, greater leaf number and serration, and shortened inflorescence height; it also restored drought tolerance and improved the forage quality of the mutant tissues. These findings demonstrate that MpC1HDZ can be functionally integrated into endogenous HD-ZIP I regulatory networks, modulating C1HDZ-associated responses. Together, these results support an ancestral developmental role for C1HDZ genes, with abiotic stress functions emerging later in angiosperm evolution, enabled by conserved protein features that facilitate regulatory network integration.

Durable resistance to soil-borne pathogens remains elusive in cereals, partly because susceptibility (S) genes that facilitate root infection have not been identified in monocots. In the model legume Medicago truncatula , the SCAR/WAVE complex member MtAPI functions as a root S-gene for microbial invasion. Whether SCAR gene associated susceptibility function is conserved in monocots, and whether SCAR gene inactivation can enhance root resistance in cereals, remains unknown. Here, we identify and characterize three SCAR genes in barley: HvSCAR-A , HvSCAR-B , and HvSCAR-C . Cross-species complementation assays indicate that HvSCAR-B and HvSCAR-C are functionally similar to MtAPI . While hscar-b and hvscar-c single mutants exhibited no major growth defects, hvscar-a mutants showed strongly reduced seed production, and a hvscar-b/c double mutant displayed shorter root hairs. Notably, the hvscar-b/c double mutant exhibited increased resistance to the hemibiotrophic pathogen Phytophthora palmivora but greater colonization by the symbiotic arbuscular mycorrhizal fungus Funneliformis mosseae , underscoring a complex role in plant root - microbe interactions. Our findings reveal a conserved susceptibility function of SCAR genes in monocots and identify api monocot homologs as promising targets for engineering disease resistance in cereals. This study offers new insights into SCAR protein functional diversification and its potential for improving root health in crop plants.

: Background and aims: Root colonisation by endomycorrhizal fungi has long been one of the most widely used metrics in mycorrhizal studies. However, due to the significant time required to assess colonisation using traditional microscope techniques, studies of colonisation at large scales are impractical. AI-powered approaches may increase output and facilitate ecosystem assessments. Methods: We trained an AI-powered tool (MycorrhizaFinder) on field roots from diverse grasslands and heathlands hosting common Northern European plants with a range of arbuscular (AM) and ericoid mycorrhizal (ErM) fungal structures, and dark septate endophytes (DSE), also common in field-sourced roots. We incorporated a user-customized confidence threshold to encourage the user to engage with inevitable morphological ambiguities, in e.g. ErM and DSE. A Macro F ₁ statistic was used to assess the tool’s development. Results: We provide a sample workflow from root processing and microscope slide scanning to semi-automated model training and performance evaluation. Without human supervision, our automated baseline Macro F ₁ is 66% for arbuscular and at 57% for ericoid mycorrhizal colonisation assessment. Conclusion: MycorrhizaFinder is user friendly, requires no programming skills and offers flexibility for advanced agronomists or ecologists who wish to train the tool using their own labelled mycorrhizal root datasets, including images acquired from different instruments or staining protocols. This adaptability allows users to customize the model for specific ecosystems or experimental designs. Leveraged with molecular identification and/or functional assessment of fungi, MycorrhizaFinder could support scalable and repeatable monitoring across ecosystems to assess mycorrhizal status and track land-use changes over time.

Specialized host-microbe interfaces are central to cellular interactions in plants. Intracellular structures such as haustoria formed by filamentous pathogens mediate nutrient exchange and effector delivery to host cells. Despite their biological importance, the lack of quantitative frameworks has largely confined the study of these interfaces to qualitative observations, limiting our ability to compare infection strategies, cellular responses, and spatial organization across cells and tissues. Here, we present HFinder , a deep learning-based framework for automated detection, segmentation, and quantitative analysis of plant-microbe interfaces in confocal images. Using an object-centric deep learning approach, HFinder enables robust identification of haustoria, microbial hyphae, and host organelles across diverse imaging conditions and pathosystems. We demonstrate that this framework supports quantitative analyses of subcellular processes at host-microbe interfaces, including effector secretion, perturbation of host cellular processes, and immune receptor accumulation at haustoria. HFinder provides a practical and scalable solution for the systematic digitalization of plant infection imaging data and establishes a general framework for quantitative studies of cellular dynamics at host-microbe contact zones.