The impact of Verticillium longisporum on single plant and whole plot yield of oilseed rape (OSR) was studied in field experiments with natural infection or artificial soil infestation. Disease incidence (DI) and disease severity (DS) correlated with the amount of inoculum provided at four different levels by addition of infested rapeseed straw to the soil. DI and DS were higher in 2003, a year with higher average soil and air temperatures, than in 2004, at similar levels of added inoculum. Maximum DI and DS levels achieved with artificial soil infestation were 54.3% and 0.57 (rating scale from 0 to 2), respectively, which was insufficient to induce significant yield reduction in whole plots. In contrast, a significant decrease in yield was recorded on single naturally infected plants removed from the field at growth stage (GS) 83 to 85. Yield losses of single plants accounted for 20 to more than 80% at DS levels above 5 (scale 0 to 9). The systemic spread of V. longisporum was significantly delayed in plants in the field with substantial colonization of the shoots not occurring before maturity stages. However, fungal systemic spread into the shoots was faster in 2003, consistent with the visual disease assessment in the field. Conversely, the pathogen systemically spread after 28 days in plants (susceptible cultivar ÔFalconÕ) in the greenhouse. Root dip inoculation with conidia suspension induced earlier and more severe disease symptoms than microsclerotia added to the soil. In contrast to the field, intense stunting was recorded on inoculated plants in the greenhouse. The susceptible cultivar, ÔFalconÕ, showed higher DS, stronger reduction in root and shoot lengths and a faster fungal spread in the plant tissue than the moderate susceptible cultivar, ÔTalentÕ. Climatic conditions appear to be responsible for the strong delay in fungal invasion of plants in the field. This may prevent the plants from stunting and mitigate the overall yield effects of the disease. This study indicates a significant yield damage potential of V. longisporum in areas with DI above 60% and conditions accelerating the systemic pathogen spread in the plants.
Background In research questions such as in resistance breeding against the Beet necrotic yellow vein virus it is of interest to compare the virus concentrations of samples from different groups. The enzyme-linked immunosorbent assay (ELISA) counts as the standard tool to measure virus concentrations. Simple methods for data analysis such as analysis of variance (ANOVA), however, are impaired due to non-normality of the resulting optical density (OD) values as well as unequal variances in different groups. Methods To understand the relationship between the OD values from an ELISA test and the virus concentration per sample, we used a large serial dilution and modelled its non-linear form using a five parameter logistic regression model. Furthermore, we examined if the quality of the model can be increased if one or several of the model parameters are defined beforehand. Subsequently, we used the inverse of the best model to estimate the virus concentration for every measured OD value. Results We show that the transformed data are essentially normally distributed but provide unequal variances per group. Thus, we propose a generalised least squares model which allows for unequal variances of the groups to analyse the transformed data. Conclusions ANOVA requires normally distributed data as well as equal variances. Both requirements are not met with raw OD values from an ELISA test. A transformation with an inverse logistic function, however, gives the possibility to use linear models for data analysis of virus concentrations. We conclude that this method can be applied in every trial where virus concentrations of samples from different groups are to be compared via OD values from an ELISA test. To encourage researchers to use this method in their studies, we provide an R script for data transformation as well as the data from our trial.
The major resistance gene BvCR4 recently bred into sugar beets provides a high level of resistance to Cercospora leaf spot caused by the fungal pathogen Cercospora beticola. The occurrence of pathogen strains virulent to BvCR4 was studied using field trials in Switzerland and Germany. Virulence of a subset of these strains was tested in a field trial conducted under elevated artificial disease pressure. We created a new C. beticola reference genome and mapped whole genome sequences of 256 field-collected isolates and combined this with the virulence phenotypes to conduct three separate GWAS to identify candidate avirulence genes. GWAS analyses identified a locus associated with avirulence containing a single candidate avirulence effector gene named AvrCR4. All virulent isolates either lacked AvrCR4 or had non-synonymous mutations within the gene. AvrCR4 was present in all 74 unique isolates obtained from non-BvCR4 hybrids, whereas 33 of 89 unique isolates obtained from BvCR4 hybrids carried this deletion. We also mapped genomic data from 190 publicly available isolates from the U.S to our new reference genome. The AvrCR4 deletion was found in only one of 95 unique isolates from non-BvCR4 hybrids in the U.S. AvrCR4 presents a unique example of an avirulence effector in which virulent alleles have only recently emerged. Most likely these were selected out of the standing genetic variation after deployment of a new major resistance gene. Identification of AvrCR4 will enable real-time screening of C. beticola populations for the emergence and spread of virulent isolates as well as long-term studies of effector evolution.
The Beet necrotic yellow vein virus (BNYVV) causes rhizomania in sugar beet (Beta vulgaris L.), which is one of the most destructive diseases in sugar beet worldwide. In breeding projects towards resistance against BNYVV, the enzyme-linked immunosorbent assay (ELISA) is used to determine the virus concentration in plant roots and, thus, the resistance levels of genotypes. Here, we present a simulation study to generate 10,000 small samples from the estimated density functions of ELISA values from susceptible and resistant sugar beet genotypes. We apply receiver operating characteristic (ROC) analysis to these samples to optimise the cutoff values for sample sizes from two to eight and determine the false positive rates (FPR), true positive rates (TPR), and area under the curve (AUC). We present, furthermore, an alternative approach based upon Bayes factors to improve the decision procedure. The Bayesian approach has proven to be superior to the simple cutoff approach. The presented results could help evaluate or improve existing breeding programs and help design future selection procedures based upon ELISA. An R-script for the classification of sample data based upon Bayes factors is provided.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.