Polydomus karssenii gen. nov. sp. nov. is a dark septate endophyte with a bifunctional lifestyle parasitising eggs of plant parasitic cyst nematodes (Heterodera spp.).

In this study fungal strains were investigated, which had been isolated from eggs of the cereal cyst nematode Heterodera filipjevi, and roots of Microthlaspi perfoliatum (Brassicaceae). The morphology, the interaction with nematodes and plants and the phylogenetic relationships of these strains originating from a broad geographic range covering Western Europe to Asia Minor were studied. Phylogenetic analyses using five genomic loci including ITSrDNA, LSUrDNA, SSUrDNA, rpb2 and tef1-α were carried out. The strains were found to represent a distinct phylogenetic lineage most closely related to Equiseticola and Ophiosphaerella, and Polydomus karssenii (Phaeosphaeriaceae, Pleosporales) is introduced here as a new species representing a monotypic genus. The pathogenicity tests against nematode eggs fulfilled Koch's postulates using in vitro nematode bioassays and showed that the fungus could parasitise its original nematode host H. filipjevi as well as the sugar beet cyst nematode H. schachtii, and colonise cysts and eggs of its hosts by forming highly melanised moniliform hyphae. Light microscopic observations on fungus-root interactions in an axenic system revealed the capacity of the same fungal strain to colonise the roots of wheat and produce melanised hyphae and microsclerotia-like structure typical for dark septate endophytes. Confocal laser scanning microscopy further demonstrated that the fungus colonised the root cells by predominant intercellular growth of hyphae, and frequent formation of appressorium-like as well as penetration peg-like structures through internal cell walls surrounded by callosic papilla-like structures. Different strains of the new fungus produced a nearly identical set of secondary metabolites with various biological activities including nematicidal effects irrespective of their origin from plants or nematodes.

High-throughput phenotyping of nematode cysts.

The beet cyst nematode Heterodera schachtii is a plant pest responsible for crop loss on a global scale. Here, we introduce a high-throughput system based on computer vision that allows quantifying beet cyst nematode infestation and measuring phenotypic traits of cysts. After recording microscopic images of soil sample extracts in a standardized setting, an instance segmentation algorithm serves to detect nematode cysts in these images. In an evaluation using both ground truth samples with known cyst numbers and manually annotated images, the computer vision approach produced accurate nematode cyst counts, as well as accurate cyst segmentations. Based on such segmentations, cyst features could be computed that served to reveal phenotypical differences between nematode populations in different soils and in populations observed before and after the sugar beet planting period. The computer vision approach enables not only fast and precise cyst counting, but also phenotyping of cyst features under different conditions, providing the basis for high-throughput applications in agriculture and plant breeding research. Source code and annotated image data sets are freely available for scientific use.

Learning to Segment Fine Structures Under Image-Level Supervision With an Application to Nematode Segmentation.

Image segmentation models trained only with image-level labels have become increasingly popular as they require significantly less annotation effort than models trained with scribble, bounding box or pixel-wise annotations. While methods utilizing image-level labels achieve promising performance for the segmentation of larger-scale objects, they perform less well for the fine structures frequently encountered in biological images. In order to address this performance gap, we propose a deep network architecture based on two key principles, Global Weighted Pooling (GWP) and segmentation refinement by low-level image cues, that, together, make segmentation of fine structures possible. We apply our segmentation method to image datasets containing such fine structures, nematodes (worms + eggs) and nematode cysts immersed in organic debris objects, which is an application scenario encountered in automated soil sample screening. Supervised only with image-level labels, our approach achieves Dice coefficients of 79.72% and 58.51 % for nematode and nematode cyst segmentation, respectively.

Tare Soil Disinfestation from Cyst Nematodes Using Inundation.

The dissemination of soil tares in the potato and sugar beet processing industry is one of the main paths for the spread of potato cyst nematodes (PCN), a severe quarantine pest. Efficient measures for the disinfestation of tare soil from PCN, but also from beet cyst nematodes (BCN), are needed. In our study, Globodera pallida (a PCN) and Heterodera schachtii (a BCN) cysts were sealed in gauze bags and imbedded in sedimentation basins. The cysts were either placed (a) in a presedimentation basin (Brukner basin) for three days, (b) in the presedimentation basin for three days and subsequently in sedimentation basins for nine weeks or (c) in sedimentation basins for nine weeks (without presedimentation). We tested the viability of the eggs and juveniles by hatching assays and using the reproduction rates in bioassays. We demonstrated that PCN and BCN imbedded in a sedimentation basin were only still showing some hatching activity after 2.5 weeks, while no hatching was observed when an additional Brukner basin treatment was conducted before sedimentation.

Instance Segmentation of Nematode Cysts in Microscopic Images of Soil Samples.

Nematodes are plant parasites that cause damage to crops. In order to quantify nematode infestation based on soil samples, we propose an instance segmentation method that will serve as the basis of automatic quantitative analysis. We consider light microscopic images of cluttered object collections as they occur in realistic soil samples. We introduce an algorithm, LMBI (Local Maximum of Boundary Intensity) to propose instance segmentation candidates. In a second step, a SVM classifier separates the nematode cysts among the candidates from soil particles. On a data set of soil sample images, the LMBI detector achieves near-optimal recall with a limited number of candidate segmentations, and the combined detector/classifier achieves recall and precision of 0.7. The pipeline only requires simple dot annotations and ≈moderately sized training data, which enables quick annotating and training in laboratory applications.

Effector gene vap1 based DGGE fingerprinting to assess variation within and among Heterodera schachtii populations.

Populations of beet cyst nematodes Heterodera schachtii vary in aggressiveness and virulence toward sugar beet varieties, but also in traits like host range, or decline rate in the field. Diversity of their essential pathogenicity gene vap1 is shaped by diversifying selection and gene flow. The authors developed a technique to study inter-population variation and intra-population diversity and dynamics of H. schachtii based on the gene vap1. Degenerate primers were designed to amplify, clone, and sequence this gene from diverse species and populations of cyst nematodes. This resulted in a high diversity of sequences for H. schachtii, and allowed to design non-degenerated primers to amplify a fragment suitable for sequence dependent separation of gene variants in denaturing gradient gel electrophoresis (DGGE). The developed primers span a highly variable intron and part of a slightly variable exon. A marker comprised of the 14 mostly detected gene variants was established for gel-to-gel comparisons. For individual juveniles up to six gene variants were resolved and substantial variation within and among cysts was observed. A fast and easy DNA extraction procedure for 20 pooled cysts was established, which provided DGGE patterns with high similarity among replicate samples from field populations. Permutation tests on pairwise similarities within and among populations showed significant differences among vap1 patterns of field populations of H. schachtii. Similarly, gene diversity as expressed by the Shannon index was statistically different among field populations. In conclusion, the DGGE technique is a fast and - compared to sequencing approaches - inexpensive tool to compare populations of H. schachtii and link observed biological characteristics to genetic pattern.Populations of beet cyst nematodes Heterodera schachtii vary in aggressiveness and virulence toward sugar beet varieties, but also in traits like host range, or decline rate in the field. Diversity of their essential pathogenicity gene vap1 is shaped by diversifying selection and gene flow. The authors developed a technique to study inter-population variation and intra-population diversity and dynamics of H. schachtii based on the gene vap1. Degenerate primers were designed to amplify, clone, and sequence this gene from diverse species and populations of cyst nematodes. This resulted in a high diversity of sequences for H. schachtii, and allowed to design non-degenerated primers to amplify a fragment suitable for sequence dependent separation of gene variants in denaturing gradient gel electrophoresis (DGGE). The developed primers span a highly variable intron and part of a slightly variable exon. A marker comprised of the 14 mostly detected gene variants was established for gel-to-gel comparisons. For individual juveniles up to six gene variants were resolved and substantial variation within and among cysts was observed. A fast and easy DNA extraction procedure for 20 pooled cysts was established, which provided DGGE patterns with high similarity among replicate samples from field populations. Permutation tests on pairwise similarities within and among populations showed significant differences among vap1 patterns of field populations of H. schachtii. Similarly, gene diversity as expressed by the Shannon index was statistically different among field populations. In conclusion, the DGGE technique is a fast and ­ compared to sequencing approaches ­ inexpensive tool to compare populations of H. schachtii and link observed biological characteristics to genetic pattern.