Detection of Cercospora beticola and Phoma betae on Table Beet Seed using Quantitative PCR

Nighttime Applications of Germicidal UV Light to Suppress Cercospora Leaf Spot in Table Beet

Cercospora leaf spot (CLS), caused by the hemibiotrophic fungus Cercospora beticola, is a destructive disease affecting table beet. Multiple applications of fungicides are needed to reduce epidemic progress to maintain foliar health and enable mechanized harvest. The sustainability of CLS control is threatened by the rapid development of fungicide resistance, the need to grow commercially acceptable yet CLS-susceptible cultivars, and the inability to manipulate agronomic conditions to mitigate disease risk. Nighttime applications of germicidal UV light (UV-C) have recently been used to suppress several plant diseases, notably those caused by ectoparasitic biotrophs such as powdery mildews. We evaluated the efficacy of nighttime applications of UV-C for suppression of CLS in table beet. In vitro lethality of UV-C to germinating conidia increased with increasing dose, with complete suppression at 1,000 J/m2. Greenhouse-grown table beet tolerated relatively high doses of UV-C without lethal effects despite some bronzing on the leaf blade. A UV-C dose >1,500 J/m2 resulted in phytotoxicity severities greater than 50%. UV-C exposure to ≤750 J/m2 resulted in negligible phytotoxicity. Older (6-week-old) greenhouse-grown plants were more susceptible to UV-C damage than younger (2- and 4-week-old) plants. Suppression of CLS by UV-C was greater when applied within 6 days of C. beticola inoculation than if delayed until 13 days after infection in greenhouse-grown plants. In field trials, there were significant linear relationships between UV-C dose and CLS control and phytotoxicity severity, and a significant negative linear relationship between phytotoxicity and CLS severity at the final assessment. Significant differences between UV-C doses on the severity of CLS and phytotoxicity indicated an efficacious dose near 800 J/m2. Collectively, these findings illustrate significant and substantial suppression by nighttime applications of UV-C for CLS control on table beet, with potential for incorporation in both conventional and organic table beet broadacre production systems.

An in-field heat treatment to reduce Cercospora beticola survival in plant residue and improve Cercospora leaf spot management in sugarbeet

Introduction

Sugarbeets account for 55 to 60% of U.S. sugar production. Cercospora leaf spot (CLS), primarily caused by the fungal pathogen Cercospora beticola, is a major foliar disease of sugarbeet. Since leaf tissue is a primary site of pathogen survival between growing seasons, this study evaluated management strategies to reduce this source of inoculum.

Methods

Fall- and spring-applied treatments were evaluated over three years at two study sites. Treatments included standard plowing or tilling immediately post-harvest, as well as the following alternatives to tillage: a propane-fueled heat treatment either in the fall immediately pre-harvest or in the spring prior to planting, and a desiccant (saflufenacil) application seven days pre-harvest. After fall treatments, leaf samples were evaluated to determine C. beticola viability. The following season, inoculum pressure was measured by monitoring CLS severity in a susceptible beet variety planted into the same plots and by counting lesions on highly susceptible sentinel beets placed into the field at weekly intervals (fall treatments only).

Results

No significant reductions in C. beticola survival or CLS were observed following fall-applied desiccant. The fall heat treatment, however, significantly reduced lesion sporulation (2019-20 and 2020-21, P < 0.0001; 2021-22, P < 0.05) and C. beticola isolation (2019-20, P < 0.05) in at-harvest samples. Fall heat treatments also significantly reduced detectable sporulation for up to 70- (2021-22, P < 0.01) or 90-days post-harvest (2020-21, P < 0.05). Reduced numbers of CLS lesions were observed on sentinel beets in heat-treated plots from May 26-June 2 (P < 0.05) and June 2-9 (P < 0.01) in 2019, as well as June 15-22 (P < 0.01) in 2020. Both fall- and spring-applied heat treatments also reduced the area under the disease progress curve for CLS assessed the season after treatments were applied (Michigan 2020 and 2021, P < 0.05; Minnesota 2019, P < 0.05; 2021, P < 0.0001).

Discussion

Overall, heat treatments resulted in CLS reductions at levels comparable to standard tillage, with more consistent reductions across year and location. Based on these results, heat treatment of fresh or overwintered leaf tissue could be used as an integrated tillage-alternative practice to aid in CLS management.

Seed Infection Rate, but Not Pathogen Titer, Positively Correlates with Disease Index of Cephalosporium Stripe in Winter Wheat

Cephalosporium gramineum survives primarily in colonized plant residue but is also transmitted by seed at a low frequency. The purpose of this study was to correlate disease intensity in the field with percentage of infected seed and amount of pathogen DNA using a high-throughput PCR method. Field-grown seed of three wheat cultivars was collected over 4 years from plots with a known disease index. The culture-based seed infection rate was determined by isolation of C. gramineum from 2,016 seeds per seed lot. DNA of 380 seeds from each seed lot was extracted individually, and a PCR assay with a fluorescent-labeled forward primer for detecting C. gramineum was performed on each seed. C. gramineum was isolated from 0.12% of the seed on average (range 0 to 0.74%), whereas it was detected in 3.7% on average (range 1.3 to 7.6%) using PCR detection. The single-seed PCR assay was more sensitive than either the culture-based method or conventional PCR. DNA of 674 seeds that tested positive by this PCR was quantified using a real-time PCR with newly designed primers for the amount of pathogen per seed. Seed contained 0.017 to 77.1 pg/seed of C. gramineum DNA (mean 3.0 pg/seed). Disease index was positively correlated with seed infection rate but not with pathogen titer in seed. This fluorescent-labeled PCR, along with quantitative PCR, improved our understanding of seed transmission of C. gramineum in wheat.

Control of Phoma Leaf Spot and Root Decay of Table Beet in New York

Disease caused by Neocamarosporium betae (syn. Phoma betae, Pleospora betae) results in reductions in plant populations, foliar disease (Phoma leaf spot [PLS]), and root disease and decay in table beet. Disease caused by N. betae has reemerged as prevalent in organic table beet production in New York. The disease can also cause substantial issues in conventional table beet production. To evaluate in-field control options for conventional and organic table beet production, small-plot, replicated trials were conducted in each of two years (2019 and 2021). The fungicides, propiconazole and difenoconazole, and premixtures, pydiflumetofen + fludioxonil or pydiflumetofen + difenoconazole, provided excellent PLS and root decay control. Azoxystrobin provided excellent (69.9%) control of PLS in 2019 and lesser (40%) control in 2021. Field trial results complemented in vitro sensitivity testing of 30 New York N. betae isolates that were all highly sensitive to azoxystrobin (mean effective concentration to reduce mycelial growth by 50%, EC₅₀ = 0.0205 µg/ml) and propiconazole (mean EC₅₀ = 0.0638 µg/ml). Copper octanoate and microbial biopesticides containing either Bacillus amyloliquefaciens D747 or B. mycoides strain J provided moderate (68.5 to 74.6%) PLS control as reflected in epidemic progress. The Gompertz model provided the best fit to PLS epidemics reflecting a polycyclic epidemic. Reductions in PLS severity were associated with significant decreases in Phoma root decay and increases in canopy health and the time-to-death of leaves compared with nontreated control plots. Prolonging leaf survival is critical for mechanical harvest of roots. These findings underpin the design of programs for foliar disease control in conventional and organic table beet production. Assessment of PLS severity in the field will better inform postharvest management decisions.

Seedborne Cercospora beticola Can Initiate Cercospora Leaf Spot from Sugar Beet (Beta vulgaris) Fruit Tissue

Cercospora leaf spot (CLS) is a globally important disease of sugar beet (Beta vulgaris) caused by the fungus Cercospora beticola. Long-distance movement of C. beticola has been indirectly evidenced in recent population genetic studies, suggesting potential dispersal via seed. Commercial sugar beet "seed" consists of the reproductive fruit (true seed surrounded by maternal pericarp tissue) coated in artificial pellet material. In this study, we confirmed the presence of viable C. beticola in sugar beet fruit for 10 of 37 tested seed lots. All isolates harbored the G143A mutation associated with quinone outside inhibitor resistance, and 32 of 38 isolates had reduced demethylation inhibitor sensitivity (EC₅₀ > 1 µg/ml). Planting of commercial sugar beet seed demonstrated the ability of seedborne inoculum to initiate CLS in sugar beet. C. beticola DNA was detected in DNA isolated from xylem sap, suggesting the vascular system is used to systemically colonize the host. We established nuclear ribosomal internal transcribed spacer region amplicon sequencing using the MinION platform to detect fungi in sugar beet fruit. Fungal sequences from 19 different genera were identified from 11 different sugar beet seed lots, but Fusarium, Alternaria, and Cercospora were consistently the three most dominant taxa, comprising an average of 93% relative read abundance over 11 seed lots. We also present evidence that C. beticola resides in the pericarp of sugar beet fruit rather than the true seed. The presence of seedborne inoculum should be considered when implementing integrated disease management strategies for CLS of sugar beet in the future.

Detection of Ramularia collo-cygni from barley in Australia using triplex quantitative and droplet digital PCR

BACKGROUND

Ramularia leaf spot (RLS), caused by Ramularia collo-cygni, is an emerging threat to barley (Hordeum vulgare L.) production. RLS has been reported in Australia, however only minimal information is available regarding its detection and distribution. Due to initial asymptomatic growth in planta, slow growth in vitro and symptomatic similarities to net blotch and physiological leaf spots, detection of this pathogen can be challenging. Quantitative polymerase chain reaction (PCR)-based methods for R. collo-cygni-specific identification and detection have been described, however these assays have been demonstrated to lack specificity. False-positive detections may have serious implications, thus we aimed to design a robust R. collo-cygni-specific PCR method.

RESULTS

Using the phylogenetically informative RNA polymerase II second largest subunit (rpb2) and translation elongation factor 1-alpha (tef1-α) genes, along with the tef1-α gene of H. vulgare, a triplex assay was developed for both quantitative and droplet digital PCR. The triplex assay detected R. collo-cygni DNA in barley leaves from New South Wales, South Australia, Tasmania, Victoria and Western Australia. No R. collo-cygni DNA was detected in barley seed grown in Western Australia.

CONCLUSION

The presence of R. collo-cygni DNA has been confirmed in Australian barley crops, suggesting a distribution ranging across the southern barley growing regions of Australia. The R. collo-cygni-specific assay will be a valuable tool to assist with monitoring the distribution and impact of R. collo-cygni in Australia and other regions. © 2021 Society of Chemical Industry.

Weather-Based Predictive Modeling of Cercospora beticola Infection Events in Sugar Beet in Belgium

Cercospora leaf spot (CLS; caused by Cercospora beticola Sacc.) is the most widespread and damaging foliar disease of sugar beet. Early assessments of CLS risk are thus pivotal to the success of disease management and farm profitability. In this study, we propose a weather-based modelling approach for predicting infection by C. beticola in sugar beet fields in Belgium. Based on reported weather conditions favoring CLS epidemics and the climate patterns across Belgian sugar beet-growing regions during the critical infection period (June to August), optimum weather conditions conducive to CLS were first identified. Subsequently, 14 models differing according to the combined thresholds of air temperature (T), relative humidity (RH), and rainfall (R) being met simultaneously over uninterrupted hours were evaluated using data collected during the 2018 to 2020 cropping seasons at 13 different sites. Individual model performance was based on the probability of detection (POD), the critical success index (CSI), and the false alarm ratio (FAR). Three models (i.e., M1, M2 and M3) were outstanding in the testing phase of all models. They exhibited similar performance in predicting CLS infection events at the study sites in the independent validation phase; in most cases, the POD, CSI, and FAR values were ≥84%, ≥78%, and ≤15%, respectively. Thus, a combination of uninterrupted rainy conditions during the four hours preceding a likely start of an infection event, RH > 90% during the first four hours and RH > 60% during the following 9 h, daytime T > 16 °C and nighttime T > 10 °C, were the most conducive to CLS development. Integrating such weather-based models within a decision support tool determining fungicide spray application can be a sound basis to protect sugar beet plants against C. beticola, while ensuring fungicides are applied only when needed throughout the season.

Development of a Sequential Sampling Plan using Spatial Attributes of Cercospora Leaf Spot Epidemics of Table Beet in New York

Sampling strategies that effectively assess disease intensity in the field are important to underpin management decisions. To develop a sequential sampling plan for the incidence of Cercospora leaf spot (CLS), caused by Cercospora beticola, 31 table beet fields were assessed in the state of New York. Assessments of CLS incidence were performed in six leaves arbitrarily selected in 51 sampling locations along each of three to six linear transects per field. Spatial pattern analyses were performed, and results were used to develop sequential sampling estimation and classification models. CLS incidence (p) ranged from 0.13 to 0.92 with a median of 0.31, and beta-binomial distribution, which is reflective of aggregation, best described the spatial patterns observed. Aggregation was commonly detected (>95%) by methods using the point-process approach, runs analyses, and autocorrelation up to the fourth spatial lag. For Spatial Analysis by Distance Indices, or SADIE, 45% of the datasets were classified as a random pattern. In the sequential sampling estimation and classification models, disease units are sampled until a prespecified target is achieved. For estimation, the goal was sampling CLS incidence with a preselected coefficient of variation (C). Achieving the C = 0.1 was challenging with <51 sampling units, and only observed on datasets with incidence >0.3. Reducing the level of precision, i.e., increasing C to 0.2, allowed the preselected C to be achieved with a lower number of sampling units and with an estimated incidence ([Formula: see text]) close to the true value of p. For classification, the goal was to classify the datasets above or below prespecified thresholds (p_t) used for CLS management. The average sample number, or ASN, was determined by Monte Carlo simulations, and was between 20 and 45 at disease incidence values close to p_t, and approximately 11 when far from p_t. Correct decisions occurred in >76% of the validation datasets. Results indicated these sequential sampling plans can be used to effectively assess CLS incidence in table beet fields.

Cercospora beticola: The intoxicating lifestyle of the leaf spot pathogen of sugar beet

Cercospora leaf spot, caused by the fungal pathogen Cercospora beticola, is the most destructive foliar disease of sugar beet worldwide. This review discusses C. beticola genetics, genomics, and biology and summarizes our current understanding of the molecular interactions that occur between C. beticola and its sugar beet host. We highlight the known virulence arsenal of C. beticola as well as its ability to overcome currently used disease management strategies. Finally, we discuss future prospects for the study and management of C. beticola infections in the context of newly employed molecular tools to uncover additional information regarding the biology of this pathogen.

TAXONOMY

Cercospora beticola Sacc.; Kingdom Fungi, Phylum Ascomycota, Class Dothideomycetes, Order Capnodiales, Family Mycosphaerellaceae, Genus Cercospora.

HOST RANGE

Well-known pathogen of sugar beet (Beta vulgaris subsp. vulgaris) and most species of the Beta genus. Reported as pathogenic on other members of the Chenopodiaceae (e.g., lamb's quarters, spinach) as well as members of the Acanthaceae (e.g., bear's breeches), Apiaceae (e.g., Apium), Asteraceae (e.g., chrysanthemum, lettuce, safflower), Brassicaceae (e.g., wild mustard), Malvaceae (e.g., Malva), Plumbaginaceae (e.g., Limonium), and Polygonaceae (e.g., broad-leaved dock) families.

DISEASE SYMPTOMS

Leaves infected with C. beticola exhibit circular lesions that are coloured tan to grey in the centre and are often delimited by tan-brown to reddish-purple rings. As disease progresses, spots can coalesce to form larger necrotic areas, causing severely infected leaves to wither and die. At the centre of these spots are black spore-bearing structures (pseudostromata). Older leaves often show symptoms first and younger leaves become infected as the disease progresses.

MANAGEMENT

Application of a mixture of fungicides with different modes of action is currently performed although elevated resistance has been documented in most employed fungicide classes. Breeding for high-yielding cultivars with improved host resistance is an ongoing effort and prudent cultural practices, such as crop rotation, weed host management, and cultivation to reduce infested residue levels, are widely used to manage disease. USEFUL WEBSITE: https://www.ncbi.nlm.nih.gov/genome/11237?genome_assembly_id=352037.