Biology subjects

Liljeroth, E.

Publications and source records attributed to Liljeroth, E..

3 recordsLinked to original sources

bioRxiv2022

Double trouble or a blessing in disguise? Co-infection of potato with the causal agents of late and early blight

The simultaneous occurrence of multiple diseases is an understudied area in plant pathology; however, studies of animal and human diseases have shown that the presence of multiple pathogens can impact virulence, and the course of disease development. Furthermore, they also present an important driver of epidemiological dynamics. Global potato production is plagued by multiple pathogens, amongst which are Phytophthora infestans and Alternaria solani, the causal agents of potato late and early blight respectively. Both these pathogens have different lifestyles and are successful pathogens of potato, but despite observations of both pathogens infecting potato simultaneously in field conditions, the tripartite interactions between potato and these two pathogens are so far, poorly understood. Here we studied the interaction of A. solani and P. infestans first in vitro and subsequently in planta both in laboratory and field settings. We found that A. solani can inhibit P. infestans both in terms of growth in vitro and infection of potato, both in laboratory experiments and in an agriculturally relevant field setting.

microbiology↗

bioRxiv2021

Whole genome sequencing elucidates the species-wide diversity and evolution of fungicide resistance in the early blight pathogen Alternaria solani

Early blight of potato is caused by the fungal pathogen Alternaria solani and is an increasing problem worldwide. The primary strategy to control the disease is applying fungicides such as succinate dehydrogenase inhibitors (SDHI). SDHI-resistant strains, showing reduced sensitivity to treatments, appeared in Germany in 2013, five years after the introduction of SDHIs. Two primary mutations in the Sdh complex (SdhB-H278Y and SdhC-H134R) have been frequently found throughout Europe. How these resistances arose and spread, and whether they are linked to other genomic features, remains unknown. We performed whole-genome sequencing for A. solani isolates from potato fields across Europe (Germany, Sweden, Belgium, and Serbia) to better understand the pathogens genetic diversity in general and understand the development and spread of the genetic mutations that lead to SDHI resistance. We used ancestry analysis and phylogenetics to determine the genetic background of 48 isolates. The isolates can be grouped into 7 genotypes. These genotypes do not show a geographical pattern but appear spread throughout Europe. The Sdh mutations appear in different genetic backgrounds, suggesting they arose independently, and the observed admixtures might indicate a higher adaptive potential in the fungus than previously thought. Our research gives insights into the genetic diversity of A. solani on a genome level. The mixed occurrence of different genotypes and apparent admixture in the populations indicate higher genomic complexity than anticipated. The conclusion that SDHI tolerance arose multiple times independently has important implications for future fungicide resistance management strategies. These should not solely focus on preventing the spread of isolates between locations but also on limiting population size and the selective pressure posed by fungicides in a given field to avoid the rise of new mutations in other genetic backgrounds.

evolutionary biology↗

bioRxiv2020

Automatic late blight lesion recognition and severity quantification based on field imagery of diverse potato genotypes by deep learning

The plant pathogen Phytophthora infestans causes the severe disease late blight in potato, which results in a huge loss for potato production. Automatic and accurate disease lesion segmentation enables fast evaluation of disease severity and assessment of disease progress for precision crop breeding. Deep learning has gained tremendous success in computer vision tasks for image classification, object detection and semantic segmentation. To test whether we could extract late blight lesions from unstructured field environments based on high-resolution visual field images and deep learning algorithms, we collected ~500 field RGB images in a set of diverse potato genotypes with different disease severity (0-70%), resulting in 2100 cropped images. 1600 of these cropped images were used as the dataset for training deep neural networks. Finally, the developed model was tested on the 250 cropped images. The results show that the intersection over union (IoU) values of background (leaf and soil) and disease lesion classes in the test dataset are 0.996 and 0.386, respectively. Furthermore, we established a linear relationship (R2 = 0.655) between manual visual scores of late blight and the number of lesions at the canopy level. We also learned that imbalance weights of lesion and background classes improved segmentation performance, and that fused masks based on the majority voting of the multiple masks enhanced the correlation with the visual scores. This study demonstrates the feasibility of using deep learning algorithms for disease lesion segmentation and severity evaluation based on proximal imagery for crop resistance breeding in field environments.

plant biology↗