Anastasia L. Lagopodi

Insights on the Evolution of Mycoparasitism from the Genome of Clonostachys rosea

Clonostachys rosea is a mycoparasitic fungus that can control several important plant diseases. Here, we report on the genome sequencing of C. rosea and a comparative genome analysis, in order to resolve the phylogenetic placement of C. rosea and to study the evolution of mycoparasitism as a fungal lifestyle. The genome of C. rosea is estimated to 58.3 Mb, and contains 14,268 predicted genes. A phylogenomic analysis shows that C. rosea clusters as sister taxon to plant pathogenic Fusarium species, with mycoparasitic/saprotrophic Trichoderma species in an ancestral position. A comparative analysis of gene family evolution reveals several distinct differences between the included mycoparasites. Clonostachys rosea contains significantly more ATP-binding cassette (ABC) transporters, polyketide synthases, cytochrome P450 monooxygenases, pectin lyases, glucose-methanol-choline oxidoreductases, and lytic polysaccharide monooxygenases compared with other fungi in the Hypocreales. Interestingly, the increase of ABC transporter gene number in C. rosea is associated with phylogenetic subgroups B (multidrug resistance proteins) and G (pleiotropic drug resistance transporters), whereas an increase in subgroup C (multidrug resistance-associated proteins) is evident in Trichoderma virens. In contrast with mycoparasitic Trichoderma species, C. rosea contains very few chitinases. Expression of six group B and group G ABC transporter genes was induced in C. rosea during exposure to the Fusarium mycotoxin zearalenone, the fungicide Boscalid or metabolites from the biocontrol bacterium Pseudomonas chlororaphis. The data suggest that tolerance toward secondary metabolites is a prominent feature in the biology of C. rosea.

Inhibitory and stimulatory effects of essential oils and individual monoterpenoids on growth and sporulation of four soil-borne fungal isolates of Aspergillus terreus, Fusarium oxysporum, Penicillium expansum, and Verticillium dahliae

The effect of essential oils and individual monoterpenoids on soil-borne fungi, in pure and mixed cultures, in growth media and in the soil environment, was investigated. Essential oils were extracted from lavender (Lavandula stoechas), oregano (Origanum vulgare subsp. hirtum), sage (Salvia fruticosa) and spearmint (Mentha spicata). The monoterpenoids tested were fenchone, carvacrol, 1,8-cineole, carvone, α-pinene and terpinen-4-ol. Their effect was examined on growth and sporulation of Aspergillus terreus, Fusarium oxysporum, Penicillium expansum and Verticillium dahliae isolated from an organic cultivation of tomato. All tested essential oils and individual monoterpenoids inhibited mycelial growth in all fungi and conidial production in most fungi. The strongest inhibitory activity on mycelial growth was exhibited by oregano and spearmint oils and by carvacrol and carvone, respectively their main constituents. The inhibitory activity was clearly fungistatic in A. terreus and F. oxysporum but fungicidal in V. dahliae. On sporulation, clearly stimulatory effects were observed alongside inhibitory ones. Conidial production was always promoted by α-pinene in P. expansum and by sage oil in F. oxysporum. At certain dosages it was promoted by cineole and carvone in F. oxysporum, and by lavender oil in A. terreus and V. dahliae. Experiments with carvone and carvacrol against mixed fungal cultures in a soil environment showed that V. dahliae was the most sensitive and A. terreus the most tolerant of the four fungi. Our results demonstrate strong but divergent effects and selectivity of action of the lower terpenoids on fungal strains that can become serious pests of tomato. Of special importance is the complete inhibition of growth and conidial production of V. dahliae, a pathogen otherwise very resistant to chemical control.

Evaluation of UAV imagery for mapping Silybum marianum weed patches

ABSTRACT Silybum marianum (L.) Gaertn weed has the tendency to grow in patches. In order to perform site-specific weed management, determining the spatial distribution of weeds is important for its eradication. Remote sensing has been used to perform species discrimination and it offers promising techniques for operational weed mapping. In the present study, the feasibility of high-resolution imaging for S. marianum detection and mapping is reported. A multispectral camera (green–red–near-infrared) mounted on a fixed wing unmanned aerial vehicle (UAV) was used for the acquisition of high-resolution images with pixel size of 0.1 m. The maximum likelihood (ML) classifier was used to classify the S. marianum among other weed species present in a field, with Avena sterilisL. being predominant. As input to the classifier, the three spectral bands and the texture were used. The scale of the mapping was varied by degrading the image resolution to evaluate classification performance, with 1 m resolution providing the highest classification accuracy. The classification rates obtained using ML reached an overall accuracy of 87.04% with a K-hat statistic of 74%. The results prove the feasibility of operational S. marianum mapping using UAV and multispectral imaging.

Investigating the compatibility of the biocontrol agent Clonostachys rosea IK726 with prodigiosin-producing Serratia rubidaea S55 and phenazine-producing Pseudomonas chlororaphis ToZa7

This study was carried out to assess the compatibility of the biocontrol fungus Clonostachys rosea IK726 with the phenazine-producing Pseudomonas chlororaphis ToZa7 or with the prodigiosin-producing Serratia rubidaea S55 against Fusarium oxysporum f. sp. radicis-lycopersici. The pathogen was inhibited by both strains in vitro, whereas C. rosea displayed high tolerance to S. rubidaea but not to P. chlororaphis. We hypothesized that this could be attributed to the ATP-binding cassette (ABC) proteins. The results of the reverse transcription quantitative PCR showed an induction of seven genes (abcB1, abcB20, abcB26, abcC12, abcC12, abcG8 and abcG25) from subfamilies B, C and G. In planta experiments showed a significant reduction in foot and root rot on tomato plants inoculated with C. rosea and P. chlororaphis. This study demonstrates the potential for combining different biocontrol agents and suggests an involvement of ABC transporters in secondary metabolite tolerance in C. rosea.

Novelty Detection Classifiers in Weed Mapping: Silybum marianum Detection on UAV Multispectral Images

In the present study, the detection and mapping of Silybum marianum (L.) Gaertn. weed using novelty detection classifiers is reported. A multispectral camera (green-red-NIR) on board a fixed wing unmanned aerial vehicle (UAV) was employed for obtaining high-resolution images. Four novelty detection classifiers were used to identify S. marianum between other vegetation in a field. The classifiers were One Class Support Vector Machine (OC-SVM), One Class Self-Organizing Maps (OC-SOM), Autoencoders and One Class Principal Component Analysis (OC-PCA). As input features to the novelty detection classifiers, the three spectral bands and texture were used. The S. marianum identification accuracy using OC-SVM reached an overall accuracy of 96%. The results show the feasibility of effective S. marianum mapping by means of novelty detection classifiers acting on multispectral UAV imagery.

Application of Multilayer Perceptron with Automatic Relevance Determination on Weed Mapping Using UAV Multispectral Imagery

Remote sensing techniques are routinely used in plant species discrimination and of weed mapping. In the presented work, successful Silybum marianum detection and mapping using multilayer neural networks is demonstrated. A multispectral camera (green-red-near infrared) attached on a fixed wing unmanned aerial vehicle (UAV) was utilized for the acquisition of high-resolution images (0.1 m resolution). The Multilayer Perceptron with Automatic Relevance Determination (MLP-ARD) was used to identify the S. marianum among other vegetation, mostly Avena sterilis L. The three spectral bands of Red, Green, Near Infrared (NIR) and the texture layer resulting from local variance were used as input. The S. marianum identification rates using MLP-ARD reached an accuracy of 99.54%. Τhe study had an one year duration, meaning that the results are specific, although the accuracy shows the interesting potential of S. marianum mapping with MLP-ARD on multispectral UAV imagery.

Identification of purple spot disease on asparagus crops across spatial and spectral scales

Abstract Purple spot caused by the fungus Stemphylium vesicarium is a disease that causes heavy damage in asparagus crops. It infects the leaves and stem and results in degradation of the quality in the harvested asparagus shoots, lowering their commercial value. Precautionary spraying with fungicides is practiced to prevent its appearance, posing a threat to consumers and the environment. The aim of this work was to evaluate the ability of visible and near-infrared field spectroscopy (UNISPEC-DC) to identify the appearance of the purple spot symptoms at the level of asparagus plants and the ability of very high resolution (Pleiades-1A) and wide area (Landsat 8) satellite images to map fungal distribution within asparagus fields and across the river plain, respectively. Field measurements were used as reference to select the optimum infection identification models. Results indicate that field spectroscopy and wide area remote sensing can be used to create sufficiently accurate quantification models of disease severity in asparagus plants (R2 = 0.83 and RMSE = 9.68%) or across asparagus fields (R2 = 0.85 and RMSE = 8.26%). The accuracy drops when using satellite data to quantify infection within field (R2 = 0.34 and RMSE = 13.13%). The contributing wavelengths, bands and indices can be utilized to provide timely information to farmers and agronomists in order to support precision plant protection applications.

Spectral Identification of Disease in Weeds Using Multilayer Perceptron with Automatic Relevance Determination

Microbotryum silybum, a smut fungus, is studied as an agent for the biological control of Silybum marianum (milk thistle) weed. Confirmation of the systemic infection is essential in order to assess the effectiveness of the biological control application and assist decision-making. Nonetheless, in situ diagnosis is challenging. The presently demonstrated research illustrates the identification process of systemically infected S. marianum plants by means of field spectroscopy and the multilayer perceptron/automatic relevance determination (MLP-ARD) network. Leaf spectral signatures were obtained from both healthy and infected S. marianum plants using a portable visible and near-infrared spectrometer (310–1100 nm). The MLP-ARD algorithm was applied for the recognition of the infected S. marianum plants. Pre-processed spectral signatures served as input features. The spectra pre-processing consisted of normalization, and second derivative and principal component extraction. MLP-ARD reached a high overall accuracy (90.32%) in the identification process. The research results establish the capacity of MLP-ARD to precisely identify systemically infected S. marianum weeds during their vegetative growth stage.