A survey on tomato leaf grey spot in the two main production areas of Argentina led to the isolation of Stemphylium lycopersici representatives which were genetically diverse and differed in their virulence

Franco, Mario Emilio Ernesto; Troncozo, María Inés; López, Silvina Marianela Yanil; Lucentini, César Gustavo; Medina, Rocío; Saparrat, Mario Carlos Nazareno; Ronco, L.; Balatti, Pedro Alberto

doi:10.1007/s10658-017-1248-z

Cited by 19 publications

(21 citation statements)

References 39 publications

(24 reference statements)

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“…Several studies mainly focused on morphological, physiological, pathogenic and genetic variation in different populations of S . lycopersici have been performed [ 7 – 10 ], but much research remains to be done concerning the molecular mechanisms underlying all these aspects. Regarding this, the whole-genome sequencing project of S .…”

Section: Introductionmentioning

confidence: 99%

The mitochondrial genome of the plant-pathogenic fungus Stemphylium lycopersici uncovers a dynamic structure due to repetitive and mobile elements

et al. 2017

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Stemphylium lycopersici (Pleosporales) is a plant-pathogenic fungus that has been associated with a broad range of plant-hosts worldwide. It is one of the causative agents of gray leaf spot disease in tomato and pepper. The aim of this work was to characterize the mitochondrial genome of S. lycopersici CIDEFI-216, to use it to trace taxonomic relationships with other fungal taxa and to get insights into the evolutionary history of this phytopathogen. The complete mitochondrial genome was assembled into a circular double-stranded DNA molecule of 75,911 bp that harbors a set of 37 protein-coding genes, 2 rRNA genes (rns and rnl) and 28 tRNA genes, which are transcribed from both sense and antisense strands. Remarkably, its gene repertoire lacks both atp8 and atp9, contains a free-standing gene for the ribosomal protein S3 (rps3) and includes 13 genes with homing endonuclease domains that are mostly located within its 15 group I introns. Strikingly, subunits 1 and 2 of cytochrome oxidase are encoded by a single continuous open reading frame (ORF). A comparative mitogenomic analysis revealed the large extent of structural rearrangements among representatives of Pleosporales, showing the plasticity of their mitochondrial genomes. Finally, an exhaustive phylogenetic analysis of the subphylum Pezizomycotina based on mitochondrial data reconstructed their relationships in concordance with several studies based on nuclear data. This is the first report of a mitochondrial genome belonging to a representative of the family Pleosporaceae.

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Section: Introductionmentioning

confidence: 99%

The mitochondrial genome of the plant-pathogenic fungus Stemphylium lycopersici uncovers a dynamic structure due to repetitive and mobile elements

et al. 2017

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“…In this case, we set the empirical threshold to 90% according to the cases of the relative humidity model application in disease warning systems; an optimal RHM threshold (RH ≥ 90%) was used in an existing cucumber downy mildew warning system [11], a strawberry disease-warning systems in four US states [30], and a potato late blight model [31]. 2 The DPM is based on the difference value (DPD) between the air temperature (T) and the dew point temperature (dewT) [4]. Dew occurs only when the difference is below a certain threshold (2 • C) [32].…”

Section: Model Descriptionmentioning

confidence: 99%

“…It is caused by over irrigation, rainfall or condensation, and acts as a catalyst for disease onset as it favors fungal infections, therefore causing a high possibility of heavy yield losses. For example, LWD facilitates gray leaf spot dispersal, which is an important fungal disease in maize and tomato [2,3], as it influences the processes of germination, infection, sporulation and, to a lesser extent, the lag time for symptom development (the latent period) [4]. However, leaf wetness duration is not only a meteorological variable, like temperature or precipitation, and it has not been routinely measured by most national meteorological agencies as part of general observation programs [5].…”

Section: Introductionmentioning

confidence: 99%

Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning

Wang

Sánchez-Molina

et al. 2019

Water

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Leaf wetness duration (LWD) is a key driving variable for peat and disease control in greenhouse management, and depends upon irrigation, rainfall, and dewfall. However, LWD measurement is often replaced by its estimation from other meteorological variables, with associated uncertainty due to the modelling approach used and its calibration. This study uses the decision learning tree method (DLT) for calibrating four LWD models-RH threshold model (RHM), the dew parameterization model (DPM), the classification and regression tree model (CART) and the neural network model (NNM)-whose performances in reproducing measured data are assessed using a large dataset. The relative importance of input variables in contributing to LWD estimation is also computed for the models tested. The LWD models were evaluated at two different greenhouse locations: in a Chinese (CN) greenhouse over three planting seasons (April 2014-October 2015 and in a Spanish (ES) greenhouse over four planting seasons (April 2016-February 2018). Based on multi-evaluation indicators, the models were given a ranking for their assessment capabilities during calibration (in the Spanish greenhouse from . The models were then evaluated on an independent set of data, and the obtained areas under the receiver operating characteristic curve (AUC) of the LWD models were over 0.73. Therein, the best LWD model in this case was the NNM, with positive predict values (PPVs) of 0.82 (SP) and 0.90 (CN), and mean absolute errors (MAEs) of 1.85 h (SP) and 1.30 h (CN). Consequently, the DLT can decrease LWD estimation error by calibrating the model threshold and choosing black box model input variables. simulation were developed, and are routinely used to provide inputs to early disease warning systems of disease outbreaks [6,7]. The LWD models can be divided into two broad categories: empirical and physical models. The latter are data demanding because they require inputs that are not always available, such as cloud cover and net radiation [8,9]. Conversely, estimation of LWD is based on its relationships with meteorological variables available in standard agro-meteorological stations. Some examples of empirical LWD models are the simple relative humidity threshold model (RHM), which simulates the leaf wetness occurrence when humidity is above a threshold [10,11]; the dew point depression method (DPM), based on the principle of dew formation [12,13]; and the classification and regression tree (CART) model, which considers the non-linear relationship between leaf wetness, wind speed, rainfall, dew temperature and relative humidity in decision nodes to determine leaf wetness [14]. Kim et al. demonstrated the spatial portability of the CART model through its application in different environments [15]. Gillespie et al. achieved good results when estimating LWD by a dew point depression model [16]. Further attempts have been made for estimation of LWD using neural network models (NNMs), which can be considered black box models. These are self-adaption models trained with refe...

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“… Stemphylium lycopersici is one of the causative agents of gray leaf spot disease in tomato ( 5 ). In a previous study, we reported the draft genome sequence of S. lycopersici strain CIDEFI-216, a virulent and sporulating isolate ( 6 , 7 ). The purpose of this work was to obtain the draft genome sequences of two strains that proved to be less virulent than isolate CIDEFI-216 ( 6 ).…”

Section: Announcementmentioning

confidence: 99%

“…The purpose of this work was to obtain the draft genome sequences of two strains that proved to be less virulent than isolate CIDEFI-216 ( 6 ). While strain CIDEFI-213 sporulates under the conditions assayed on potato dextrose agar (PDA) ( 7 ) and V8 medium, strain CIDEFI-212 does not.…”

Section: Announcementmentioning

confidence: 99%

Draft Genome Sequences of Sporulating (CIDEFI-213) and Nonsporulating (CIDEFI-212) Strains of Stemphylium lycopersici

Medina

Franco

Lucentini

et al. 2018

Microbiol Resour Announc

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A survey on tomato leaf grey spot in the two main production areas of Argentina led to the isolation of Stemphylium lycopersici representatives which were genetically diverse and differed in their virulence

Cited by 19 publications

References 39 publications

The mitochondrial genome of the plant-pathogenic fungus Stemphylium lycopersici uncovers a dynamic structure due to repetitive and mobile elements

The mitochondrial genome of the plant-pathogenic fungus Stemphylium lycopersici uncovers a dynamic structure due to repetitive and mobile elements

Improving the Performance of Vegetable Leaf Wetness Duration Models in Greenhouses Using Decision Tree Learning

Draft Genome Sequences of Sporulating (CIDEFI-213) and Nonsporulating (CIDEFI-212) Strains of Stemphylium lycopersici

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