Resistance to early blight in potato and genetic structure of the pathogen population in Southeast Sweden

Potato early blight caused by the necrotrophic fungus Alternaria solani is a common foliar disease in many potato-growing regions. Application of fungicides is commonly used to effectively control the disease, although they are undesirable due to environmental consequences. Use of resistant cultivars would be the most optimal solution, but there are no cultivars with high level of resistance available on the market. In the present thesis, assessments of early blight resistance both in leaves and tubers of potato cultivars/clones were performed by applying different screening methods (field and greenhouse). Plant defence signalling in response to A. solani infection with main emphasis on salicylic (SA) and jasmonic acid (JA) hormones, was also studied. Furthermore, the genetic variability in A. solani populations from different potato growing regions of southeast Sweden was investigated. The fungal isolates were analysed for the F129L substitutions, which are associated with loss of sensitivity to QoI fungicides. In addition, field experiments were conducted to determine the occurrence of the F129L substitution and genetic shifts in the population during one growing season in response to two different fungicide strategies. Cultivars/clones revealed significant differences in resistance to A. solani both in leaves and tubers irrespective of screening method. Results from field and intact plant inoculation experiments were significantly correlated but there were no correlations observed between these two methods and detached leaf assays. Some cultivars/clones showed relatively higher level of resistance to the pathogen. Results from the data suggested that SA appears to be responsible for regulating symptom development while JA dependent COI1 defense signaling is important to inhibit fungal growth during early stages of infection. Microarray analysis showed rapid defense responses to A. solani infection mediated by partially overlapping SA and COI1 dependent jasmonic acid (JA) signaling. It was also observed that JA/ethylene signaling responses dominate at later time points. The genetic variability was relatively high among isolates of A. solani and significant genetic differentiation was found among populations from different locations in southeast Sweden. Two mitochondrial genotypes (GI and GII) were found among the isolates but the F129L substitution was only detected in GII isolates. Results from the field experiment showed that application of azoxystrobin (QoI fungicide) alone did not control the disease; better disease control was achieved with boscalid combined with pyraclostrobin. Similar results were obtained for yield. Moreover, results of sensitivity tests showed that isolates with the F129L substitution were less sensitive to azoxystrobin. AFLP analysis indicated within season changes in the A.solani population, especially at the end of the season

Characterizing Winter Wheat Germplasm for Fusarium Head Blight Resistance Under Accelerated Growth Conditions

Fusarium head blight (FHB) is one of the economically important diseases of wheat as it causes severe yield loss and reduces grain quality. In winter wheat, due to its vernalization requirement, it takes an exceptionally long time for plants to reach the heading stage, thereby prolonging the time it takes for characterizing germplasm for FHB resistance. Therefore, in this work, we developed a protocol to evaluate winter wheat germplasm for FHB resistance under accelerated growth conditions. The protocol reduces the time required for plants to begin heading while avoiding any visible symptoms of stress on plants. The protocol was tested on 432 genotypes obtained from a breeding program and a genebank. The mean area under disease progress curve for FHB was 225.13 in the breeding set and 195.53 in the genebank set, indicating that the germplasm from the genebank set had higher resistance to FHB. In total, 10 quantitative trait loci (QTL) for FHB severity were identified by association mapping. Of these, nine QTL were identified in the combined set comprising both genebank and breeding sets, while two QTL each were identified in the breeding set and genebank set, respectively, when analyzed separately. Some QTLs overlapped between the three datasets. The results reveal that the protocol for FHB evaluation integrating accelerated growth conditions is an efficient approach for FHB resistance breeding in winter wheat and can be even applied to spring wheat after minor modifications

Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning

Background High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial. Results The results show that 40-42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow rust scores using Random Forest (RF) modelling. The SVIs were selected through RF-based recursive feature elimination (RFE), and the predicted scores in the resulting models had a prediction accuracy of r(s) = 0.50-0.61 when measuring the correlation between predicted and observed scores. Some of the most important spectral features for prediction were the Plant Senescence Reflectance Index (PSRI), Photochemical Reflectance Index (PRI), Red-Green Pigment Index (RGI), and Greenness Index (GI). Conclusions The proposed HTPP method of combining SVI data from spectral sensors in RF models, has the potential to be deployed in wheat breeding trials to score yellow rust

GWAS-Assisted Genomic Prediction to Predict Resistance to Septoria Tritici Blotch in Nordic Winter Wheat at Seedling Stage

Septoria tritici blotch (STB) disease caused by Zymoseptoria tritici is one of the most damaging diseases of wheat causing significant yield losses worldwide. Identification and employment of resistant germplasm is the most cost-effective method to control STB. In this study, we characterized seedling stage resistance to STB in 175 winter wheat landraces and old cultivars of Nordic origin. The study revealed significant (p < 0.05) phenotypic differences in STB severity in the germplasm. Genome-wide association analysis (GWAS) using five different algorithms identified ten significant markers on five chromosomes. Six markers were localized within a region of 2 cM that contained seven candidate genes on chromosome 1B. Genomic prediction (GP) analysis resulted in a model with an accuracy of 0.47. To further improve the prediction efficiency, significant markers identified by GWAS were included as fixed effects in the GP model. Depending on the number of fixed effect markers, the prediction accuracy improved from 0.47 (without fixed effects) to 0.62 (all non-redundant GWAS markers as fixed effects), respectively. The resistant genotypes and single-nucleotide polymorphism (SNP) markers identified in the present study will serve as a valuable resource for future breeding for STB resistance in wheat. The results also highlight the benefits of integrating GWAS with GP to further improve the accuracy of GP

Transformation and gene-disruption in the apple-pathogen, Neonectria ditissima

Background Apple production in Sweden and elsewhere is being threatened by the fungus, Neonectria ditissima, which causes a disease known as European canker. The disease can cause extensive damage and the removal of diseased wood and heavily infected trees can be laborious and expensive. Currently, there is no way to eradicate the fungus from infected trees and our knowledge of the infection process is limited. Thus, to target and modify genes efficiently, the genetic transformation technique developed for N. ditissima back in 2003 was modified. Results The original protocol from 2003 was upgraded to use enzymes currently available in the market for making protoplasts. The protoplasts were viable, able to uptake foreign DNA, and able to regenerate back into a mycelial colony, either as targeted gene-disruption mutants or as ectopic mutants expressing the green fluorescent protein (GFP). Conclusions A new genetic transformation protocol has been established and the inclusion of hydroxyurea in the buffer during the protoplast-generation step greatly increased the creation of knockout mutants via homologous recombination. Pathogenicity assays using the GFP-mutants showed that the mutants were able to infect the host and cause disease

Parametric mapping of QTL for resistance to European canker in apple in 'Aroma' x 'Discovery'

Resistance to European canker (Neonectria ditissima) in apple is currently one of the most important breeding targets for commercial production in Sweden. Previous research has identified significant genetic variation in susceptibility to the disease, with the local Swedish cultivar 'Aroma' considered as one of the most resistant cultivars. Identification of genetic regions underlying the resistance of this cultivar would be a valuable tool for future breeding. Thus, we performed Bayesian quantitative trait loci (QTL) mapping for resistance to European canker in a full-sib family of 'Aroma' x 'Discovery'. Mapping was performed with the area under the disease progression curves (AUDPCs) from all seven (AUDPC_All7) and the first four assessments (AUDPC_First4), and three parameters of a sigmoid growth model for lesion length. As a scale for the effect of the different parameters, historic phenotypic data from screenings of a genetically diverse germplasm was compiled and re-analyzed. The parametrization of the data on lesion growth increased the number of QTL that could be identified with high statistical power, and provided some insight into their roles during different stages of disease development in the current experimental setup. Five QTL regions with strong or decisive evidence were identified on linkage groups 1, 8, 15, and 16. The QTL regions could be assigned to either of the parameters lesion length at the first assessment ('LL_A1'), the maximal lesion growth rate (lesion length doubling time, 't_gen'), and the lesion length at girdling ('LL_G'). Three of these QTL were traced along the pedigrees of some known relatives of the FS family, and discussed in relation to future crosses for breeding and genetic research

Reduced efficacy of biocontrol agents and plant resistance inducers against potato early blight from greenhouse to field

Early blight in potato, caused by Alternaria solani, is mainly controlled by frequent applications of synthetic fungicides. Reducing the use of synthetic fungicides in agriculture is desired to reach an overall sustainable development since the active components can be harmful for humans and for the ecosystem. In integrated pest management, IPM, the idea is to combine various measures, including optimized crop management, crop rotation, use of resistant cultivars, biological control agents (BCAs), plant resistance inducers, and fertilizers, to decrease the dependence on traditional chemical fungicides. In this paper, we present the results from greenhouse and field trials where we evaluated the effect of strategies aimed at reducing our reliance on synthetic fungicides including treatments with biological control agents (BCAs) (Pythium oligandrum, Polygandron (R), and Bacillus subtilis, Serenade (R)) and plant resistance inducers (silicon products HortiStar (R) and Actisil (R)) for early blight in potato. The agents were applied separately or in combination with each other or with synthetic fungicides. In the greenhouse, trials application of these agents resulted in 50-95% reduction of infection by A. solani, but their combination did not generally improve the outcome. However, the effects were much smaller in the hand-sprayed field trials, 20-25% disease reduction and almost disappeared in full-scale field trials where application was done with tractor sprayers. In this article, we discuss possible reasons behind the drop in efficacy from greenhouse trials to full-size field evaluation

Affordable Imaging Lab for Noninvasive Analysis of Biomass and Early Vigour in Cereal Crops

Plant phenotyping by imaging allows automated analysis of plants for various morphological and physiological traits. In this work, we developed a low-cost RGB imaging phenotyping lab (LCP lab) for low-throughput imaging and analysis using affordable imaging equipment and freely available software. LCP lab comprising RGB imaging and analysis pipeline is set up and demonstrated with early vigour analysis in wheat. Using this lab, a few hundred pots can be photographed in a day and the pots are tracked with QR codes. The software pipeline for both imaging and analysis is built from freely available software. The LCP lab was evaluated for early vigour analysis of five wheat cultivars. A high coefficient of determination (R2 0.94) was obtained between the dry weight and the projected leaf area of 20-day-old wheat plants and R2 of 0.9 for the relative growth rate between 10 and 20 days of plant growth. Detailed description for setting up such a lab is provided together with custom scripts built for imaging and analysis. The LCP lab is an affordable alternative for analysis of cereal crops when access to a high-throughput phenotyping facility is unavailable or when the experiments require growing plants in highly controlled climate chambers. The protocols described in this work are useful for building affordable imaging system for small-scale research projects and for education

Genetic diversity and occurrence of the F129L substitutions among isolates of Alternaria solani in south-eastern Sweden

Background: Early blight, caused by the fungus Alternaria solani, occurs on potato mainly in the south-eastern part of Sweden, but also in other parts of the country. The aim of this study was to investigate the genetic diversity of A. solani populations from different potato growing regions in south-eastern Sweden using AFLP marker analysis. In addition, the cultured isolates were examined for substitutions in the gene encoding cytochrome b, associated with loss of sensitivity against QoI fungicides.Results: Nei's gene diversity index for the Swedish populations of A. solani revealed a gene diversity of up to 0.20. Also genetic differentiation was observed among populations of A. solani from different locations in south-eastern Sweden. The mitochondrial genotype of the isolates of A. solani was determined and both known genotypes, GI (genotype 1) and GII (genotype 2), were found among the isolates. The occurrence of the F129L substitution associated with a loss of sensitivity to strobilurins was confirmed among the GII isolates. In vitro conidial germination tests verified that isolates containing the F129L substitution had reduced sensitivity to azoxystrobin and, at a lower extent, to pyraclostrobin.Conclusions: Genetic diversity was relatively high among isolates of A. solani in south-eastern part of Sweden. F129L substitutions, leading to reduced sensitivity to strobilurins, have been established in field populations, which may have implications for the future efficacy of QoI fungicides

Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat

Phenotyping with proximal sensors allow high-precision measurements of plant traits both in the controlled conditions and in the field. In this work, using machine learning, an integrated analysis was done from the data obtained from spectroradiometer, infrared thermometer, and chlorophyll fluorescence measurements to identify most predictive proxy measurements for studying Septoria tritici blotch (STB) disease of wheat. The random forest (RF) models for chlorosis and necrosis identified photosystem II quantum yield (QY) and vegetative indices (VIs) associated with the biochemical composition of leaves as the top predictive variables for identifying disease symptoms. The RF model for chlorosis was validated with a validation set (R2: 0.80) and in an independent test set (R2: 0.55). Based on the results, it can be concluded that the proxy measurements for photosystem II, chlorophyll content, carotenoid, and anthocyanin levels and leaf surface temperature can be successfully used to detect STB. Further validation of these results in the field will enable application of these predictive variables for detection of STB in the field