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Counting nematodes is a labor-intensive and time-consuming task, yet it is a pivotal step in various quantitative nematological studies; preparation of initial population densities and final population densities in pot, micro-plot and field trials for different objectives related to management including sampling and location of nematode infestation foci. Nematologists have long battled with the complexities of nematode counting, leading to several research initiatives aimed at automating this process. However, these research endeavors have primarily focused on identifying single-class objects within individual images. To enhance the practicality of this technology, there’s a pressing need for an algorithm that cannot only detect but also classify multiple classes of objects concurrently. This study endeavors to tackle this challenge by developing a user-friendly Graphical User Interface (GUI) that comprises multiple deep learning algorithms, allowing simultaneous recognition and categorization of nematode eggs and second stage juveniles of Meloidogyne spp. In total of 650 images for eggs and 1339 images for juveniles were generated using two distinct imaging systems, resulting in 8655 eggs and 4742 Meloidogyne juveniles annotated using bounding box and segmentation, respectively. The deep-learning models were developed by leveraging the Convolutional Neural Networks (CNNs) machine learning architecture known as YOLOv8x. Our results showed that the models correctly identified eggs as eggs and Meloidogyne juveniles as Meloidogyne juveniles in 94% and 93% of instances, respectively. The model demonstrated higher than 0.70 coefficient correlation between model predictions and observations on unseen images. Our study has showcased the potential utility of these models in practical applications for the future. The GUI is made freely available to the public through the author’s GitHub repository (https://github.com/bresilla/nematode_counting). While this study currently fo

To establish persistent infections in host plants, herbivorous invaders, such as root-knot nematodes, must rely on effectors for suppressing damage-induced jasmonate-dependent host defenses. However, at present, the effector mechanisms targeting the biosynthesis of biologically active jasmonates to avoid adverse host responses are unknown. Using yeast two-hybrid, in planta co-immunoprecipitation, and mutant analyses, we identified 12-oxophytodienoate reductase 2 (OPR2) as an important host target of the stylet-secreted effector MiMSP32 of the root-knot nematode Meloidogyne incognita. MiMSP32 has no informative sequence similarities with other functionally annotated genes but was selected for the discovery of novel effector mechanisms based on evidence of positive, diversifying selection. OPR2 catalyzes the conversion of a derivative of 12-oxophytodienoate to jasmonic acid (JA) and operates parallel to 12-oxophytodienoate reductase 3 (OPR3), which controls the main pathway in the biosynthesis of jasmonates. We show that MiMSP32 targets OPR2 to promote parasitism of M. incognita in host plants independent of OPR3-mediated JA biosynthesis. Artificially manipulating the conversion of the 12-oxophytodienoate by OPRs increases susceptibility to multiple unrelated plant invaders. Our study is the first to shed light on a novel effector mechanism targeting this process to regulate the susceptibility of host plants.

Nematode migration, feeding site formation, withdrawal of plant assimilates, and activation of plant defence responses have a significant impact on plant growth and development. Plants display intraspecific variation in tolerance limits for root-feeding nematodes. Although disease tolerance has been recognized as a distinct trait in biotic interactions of mainly crops, we lack mechanistic insights. Progress is hampered by difficulties in quantification and laborious screening methods. We turned to the model plant Arabidopsis thaliana, since it offers extensive resources to study the molecular and cellular mechanisms underlying nematode–plant interactions. Through imaging of tolerance-related parameters, the green canopy area was identified as an accessible and robust measure for assessing damage due to cyst nematode infection. Subsequently, a high-throughput phenotyping platform simultaneously measuring the green canopy area growth of 960 A. thaliana plants was developed. This platform can accurately measure cyst nematode and root-knot nematode tolerance limits in A. thaliana through classical modelling approaches. Furthermore, real-time monitoring provided data for a novel view of tolerance, identifying a compensatory growth response. These findings show that our phenotyping platform will enable a new mechanistic understanding of tolerance to below-ground biotic stress.

To establish persistent infections in host plants, herbivorous invaders, such as root-knot nematodes, must rely on effectors for suppressing damage-induced jasmonate-dependent host defenses. However, at present, the effector mechanisms targeting the biosynthesis of biologically active jasmonates to avoid adverse host responses are unknown.Using yeast-two-hybrid, in planta co-immunoprecipitation, and mutant analyses, we identified 12-oxophytodienoate reductase 2 (OPR2) as an important host target of the stylet-secreted effector MiMSP32 of the root-knot nematode Meloidogyne incognita. MiMSP32 has no informative sequence similarities with other functionally annotated genes but was selected for the discovery of novel effector mechanisms based on evidence of positive, diversifying selection.OPR2 catalyzes the conversion of a derivative of 12-oxophytodienoate to jasmonic acid (JA) and operates parallel to 12-oxophytodienoate reductase 3 (OPR3), which controls the main pathway in the biosynthesis of jasmonates. We show that MiMSP32 targets OPR2 to promote parasitism of M. incognita in host plants independent of OPR3-mediated JA biosynthesis.Artificially manipulating the conversion of the 12-oxophytodienoate by OPRs increases susceptibility to multiple unrelated plant invaders. Our study is the first to shed light on a novel effector mechanism targeting this process to regulate susceptibility of host plants

Cyst nematodes are considered a dominant threat to yield for a wide range of major food crops. Current control strategies are mainly dependent on crop rotation and the use of resistant cultivars. Various crops exhibit single dominant resistance (R) genes that are able to activate effective host-specific resistance to certain cyst nematode species and/or populations. An example is the potato R gene Gpa2, which confers resistance against the potato cyst nematode (PCN), Globodera pallida population D383. Activation of Gpa2 results in a delayed resistance response, which is characterized by a layer of necrotic cells formed around the developing nematode feeding structure. However, knowledge about the Gpa2-induced defense pathways is still lacking. Here, we uncover the transcriptional changes and gene expression network induced upon Gpa2 activation in potato roots infected with G. pallida. To this end, in vitro-grown Gpa2-resistant potato roots were infected with the avirulent population D383 and virulent population Rookmaker. Infected root segments were harvested at 3 and 6 dpi and sent for RNA sequencing. Comparative transcriptomics revealed a total of 1,743 differentially expressed genes (DEGs) upon nematode infection, of which 559 DEGs were specifically regulated in response to D383 infection. D383-specific DEGs associated with Gpa2-mediated defense mainly relates to calcium-binding activity, salicylic acid (SA) biosynthesis, and systemic acquired resistance (SAR). These data reveal that cyst nematode resistance in potato roots depends on conserved downstream signaling pathways involved in plant immunity, which are also known to contribute to R genes-mediated resistance against other pathogens with different lifestyles.