Cercospora nicotianae Isolates from Flue-Cured Tobacco in North Carolina Found with G143A and F129L Mutations in Cytochrome b Gene

2020 ◽  
Vol 21 (4) ◽  
pp. 288-290
Author(s):  
Andrew Ernst ◽  
Lindsey Thiessen

Frogeye leaf spot of tobacco caused by Cercospora nicotianae (Ellis & Everhart) is a widespread disease of cultivated tobacco. Recently, flue-cured tobacco producers in North Carolina reported losses due to frogeye leaf spot disease despite the use of strobilurin fungicides. Isolates (n = 4) were obtained in 2018 from affected tobacco leaves from Cumberland, Lenoir, and Nash counties. In 2019, isolates (n = 28) were collected from a field in Wilson county. After sequencing the cytb region of 32 isolates, 30 contained a single point mutation conferring a G143A or F129L amino acid change that resulted in quinone outside inhibitor (QoI) fungicide resistance. Although these resistance mutations have been found in air-cured tobacco in Kentucky, to the best of our knowledge, the present study is the first to report QoI resistance mutations in C. nicotianae populations in flue-cured tobacco and a first report in North Carolina.

Plant Disease ◽  
2015 ◽  
Vol 99 (4) ◽  
pp. 544-550 ◽  
Author(s):  
F. Zeng ◽  
E. Arnao ◽  
G. Zhang ◽  
G. Olaya ◽  
J. Wullschleger ◽  
...  

Frogeye leaf spot of soybean, caused by the fungus Cercospora sojina, reduces soybean yields in most of the top-producing countries around the world. Control strategies for frogeye leaf spot can rely heavily on quinone outside inhibitor (QoI) fungicides. In 2010, QoI fungicide-resistant C. sojina isolates were identified in Tennessee for the first time. As the target of QoI fungicides, the cytochrome b gene present in fungal mitochondria has played a key role in the development of resistance to this fungicide class. The cytochrome b genes from three QoI-sensitive and three QoI-resistant C. sojina isolates were cloned and sequenced. The complete coding sequence of the cytochrome b gene was identified and found to encode 396 amino acids. The QoI-resistant C. sojina isolates contained the G143A mutation in the cytochrome b gene, a guanidine to cytosine transversion at the second position in codon 143 that causes an amino acid substitution of alanine for glycine. C. sojina-specific polymerase chain reaction primer sets and TaqMan probes were developed to efficiently discriminate QoI-resistant and -sensitive isolates. The molecular basis of QoI fungicide resistance in field isolates of C. sojina was identified as the G143A mutation, and specific molecular approaches were developed to discriminate and to track QoI-resistant and -sensitive isolates of C. sojina.


2020 ◽  
Vol 21 (4) ◽  
pp. 230-231 ◽  
Author(s):  
Danilo L. Neves ◽  
Martin I. Chilvers ◽  
Tamra A. Jackson-Ziems ◽  
Dean K. Malvick ◽  
Carl A. Bradley

Frogeye leaf spot, caused by Cercospora sojina, is an important disease of soybean (Glycine max) in the United States. An important tactic to manage frogeye leaf spot is to apply foliar fungicides. Isolates of C. sojina were collected from soybean fields in one county in Michigan, three counties in Minnesota, and 10 counties in Nebraska in 2019, and they were tested for resistance to quinone outside inhibitor (QoI) fungicides using a discriminatory dose assay, a PCR assay, and DNA sequencing. Results of the testing indicated that QoI fungicide-resistant isolates were detected in isolates from all counties. Testing results also indicated that the G143A mutation was responsible for the QoI fungicide resistance. This is the first report of QoI fungicide-resistant C. sojina isolates in Michigan, Minnesota, and Nebraska and expands the geographical distribution of QoI fungicide-resistant C. sojina isolates to 18 states in total.


Plant Disease ◽  
2021 ◽  
Author(s):  
Bennett Harrelson ◽  
Bikash Ghimire ◽  
Robert Kemerait ◽  
Albert Culbreath ◽  
Zenglu Li ◽  
...  

Frogeye leaf spot (FLS), caused by the fungal pathogen Cercospora sojina K. Hara, is a foliar disease of soybean (Glycine max L. (Merr.)) responsible for yield reductions throughout the major soybean producing regions in the world. In the United States, management of FLS relies heavily on the use of resistant cultivars and in-season fungicide applications, specifically within the class of quinone outside inhibitors (QoIs), which has resulted in the development of fungicide resistance in many states. In 2018 and 2019, 80 isolates of C. sojina were collected from six counties in Georgia and screened for QoI fungicide resistance using molecular and in vitro assays, with resistant isolates being confirmed from three counties. Additionally, 50 isolates, including a “baseline isolate” with no prior fungicide exposure, were used to determine the percent reduction of mycelial growth to two fungicides, azoxystrobin and pyraclostrobin, at six concentrations: 0.0001, 0.001, 0.01, 0.1, 1, and 10 g ml-1. Mycelial growth observed for resistant isolates varied significantly from both the sensitive isolates and the baseline isolate for azoxystrobin concentrations of 10, 1, 0.1, and 0.01 g ml-1 and for pyraclostrobin concentrations of 10, 1, 0.1, 0.01 and 0.001 g ml-1. Moreover, 40 isolates were used to evaluate pathogen race on six soybean differential cultivars by assessing susceptible or resistant reactions. Isolate reactions suggested 12 races of C. sojina present in Georgia, four of which have not been previously described. Species richness indicators (rarefaction and abundance-based coverage estimator - ACE) indicated that within-county C. sojina race numbers were undersampled in the present study, suggesting the potential for the presence of either additional undescribed races or known but unaccounted for races in Georgia. However, no isolates were pathogenic on differential cultivar ‘Davis’, carrying the Rcs3 resistance allele, suggesting the gene is still an effective source of resistance in Georgia.


2015 ◽  
Vol 16 (2) ◽  
pp. 84-87 ◽  
Author(s):  
Yoshinao Aoki ◽  
Yumi Kawagoe ◽  
Nozomi Fujimori ◽  
Sayumi Tanaka ◽  
Shunji Suzuki

The use of the carboxylic acid amide (CAA) fungicide mandipropamid to manage grapevine downy mildew in vineyards in Japan has been increasing since 2010, because of widespread quinone outside inhibitor fungicide resistance in the Plasmopara viticola population. However, CAA fungicide resistance in P. viticola is becoming a serious problem worldwide. In 2013, we monitored for the presence of a single point mutation at codon 1105 of the cellulose synthase gene PvCesA3, which confers resistance to mandipropamid in P. viticola samples collected from four vineyards in Yamanashi prefecture in Japan. Five out of 157 samples were found to be heterozygotes, carrying both the mutated and nonmutated PvCesA3 alleles. Although CAA fungicide-resistant P. viticola isolates have not been reported yet in Japan, the emergence of heterozygous P. viticola populations indicates the potential risk of emergence of resistant homozygotes. Accepted for publication 14 March 2015. Published 1 May 2015


Author(s):  
Navjot Kaur ◽  
Hillary Mehl

Stagonospora nodorum blotch (SNB) caused by Parastagonospora nodorum is an important leaf spot disease in the mid-Atlantic U.S. Disease management approaches include use of resistant varieties, cultural control, and foliar fungicides. Frequent use of foliar fungicides can select for fungicide resistance within pathogen populations. Recently, the first report of quinone outside inhibitor (QoI) fungicide resistance in the U.S. was made based on a relatively small collection of P. nodorum isolates from Virginia. The objective of this study was to conduct a state-wide, two-year survey of P. nodorum populations in Virginia wheat and quantify frequencies of the target-site mutation that confers QoI resistance. A total of 318 isolates of P. nodorum were obtained from wheat collected at seven locations distributed throughout the wheat-growing regions of Virginia in 2018 and 2019. A previously designed pyrosequencing assay that detects the G143A substitution in the cytochrome b gene of P. nodorum was used to screen isolates for the presence or absence of the target site mutation. The G143A substitution was detected in all sampled fields. Among locations and years, frequencies of the mutation in P. nodorum populations ranged from 5-32% (mean = 19%). Thus, the QoI-resistance conferring G143A mutation was widespread in P. nodorum populations in Virginia and it occurred at a relatively high frequency. Results suggest that fungicides containing QoI active ingredients may not be effective for controlling SNB in Virginia and the surrounding region, and application of stand-alone QoI fungicides for disease control in wheat is not recommended.


2003 ◽  
Vol 47 (12) ◽  
pp. 3799-3805 ◽  
Author(s):  
Glenn P. Morlock ◽  
Beverly Metchock ◽  
David Sikes ◽  
Jack T. Crawford ◽  
Robert C. Cooksey

ABSTRACT Ethionamide (ETH) is a structural analog of the antituberculosis drug isoniazid (INH). Both of these drugs target InhA, an enzyme involved in mycolic acid biosynthesis. INH requires catalase-peroxidase (KatG) activation, and mutations in katG are a major INH resistance mechanism. Recently an enzyme (EthA) capable of activating ETH has been identified. We sequenced the entire ethA structural gene of 41 ETH-resistant Mycobacterium tuberculosis isolates. We also sequenced two regions of inhA and all or part of katG. The MICs of ETH and INH were determined in order to associate the mutations identified with a resistance phenotype. Fifteen isolates were found to possess ethA mutations, for all of which the ETH MICs were ≥50 μg/ml. The ethA mutations were all different, previously unreported, and distributed throughout the gene. In eight of the isolates, a missense mutation in the inhA structural gene occurred. The ETH MICs for seven of the InhA mutants were ≥100 μg/ml, and these isolates were also resistant to ≥8 μg of INH per ml. Only a single point mutation in the inhA promoter was identified in 14 isolates. A katG mutation occurred in 15 isolates, for which the INH MICs for all but 1 were ≥32 μg/ml. As expected, we found no association between katG mutation and the level of ETH resistance. Mutations within the ethA and inhA structural genes were associated with relatively high levels of ETH resistance. Approximately 76% of isolates resistant to ≥50 μg of ETH per ml had such mutations.


2019 ◽  
Vol 20 (2) ◽  
pp. 104-105 ◽  
Author(s):  
Febina M. Mathew ◽  
Emmanuel Byamukama ◽  
Danilo L. Neves ◽  
Carl A. Bradley

Resistance to quinone outside inhibitor (QoI) fungicides was detected in Cercospora sojina (causal agent of frogeye leaf spot) isolates collected from soybean (Glycine max) fields in four South Dakota counties during the 2018 growing season. A discriminatory dose assay was used to detect QoI-resistant isolates, and a follow-up polymerase chain reaction assay was used to determine the presence of the G143A mutation in QoI-resistant isolates. This is the first report of resistance to QoI fungicides in C. sojina isolates from South Dakota.


2018 ◽  
Vol 19 (4) ◽  
pp. 295-302 ◽  
Author(s):  
Guirong Zhang ◽  
Tom W. Allen ◽  
Jason P. Bond ◽  
Ahmad M. Fakhoury ◽  
Anne E. Dorrance ◽  
...  

Isolates of Cercospora sojina, causal agent of frogeye leaf spot of soybean (Glycine max), were collected across Alabama, Arkansas, Delaware, Illinois, Indiana, Iowa, Kentucky, Louisiana, Mississippi, Missouri, North Carolina, Ohio, Tennessee, and Virginia and were evaluated for quinone outside inhibitor (QoI) fungicide resistance. Collection of these isolates from these 14 states occurred between 2010 and 2017. QoI fungicide-resistant C. sojina isolates were detected in all 14 states surveyed and represent a total of 240 counties or parishes. In 2017, these 240 counties and parishes represented approximately 13% of the harvested soybean hectares in the United States. In light of this widespread occurrence of QoI fungicide-resistant C. sojina isolates, management of frogeye leaf spot should focus on integrated management practices such as planting resistant soybean cultivars, rotating with nonhost crops, and tilling to speed up decomposition of infested soybean residue. When foliar fungicide application is warranted, fungicide products that contain active ingredients from chemistry classes other than the QoI class should be applied for frogeye leaf spot management.


PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257008
Author(s):  
Shuang Liu ◽  
Haiye Yu ◽  
Yuanyuan Sui ◽  
Haigen Zhou ◽  
Junhe Zhang ◽  
...  

In this study, the feasibility of classifying soybean frogeye leaf spot (FLS) is investigated. Leaf images and hyperspectral reflectance data of healthy and FLS diseased soybean leaves were acquired. First, image processing was used to classify FLS to create a reference for subsequent analysis of hyperspectral data. Then, dimensionality reduction methods of hyperspectral data were used to obtain the relevant information pertaining to FLS. Three single methods, namely spectral index (SI), principal component analysis (PCA), and competitive adaptive reweighted sampling (CARS), along with a PCA and SI combined method, were included. PCA was used to select the effective principal components (PCs), and evaluate SIs. Characteristic wavelengths (CWs) were selected using CARS. Finally, the full wavelengths, CWs, effective PCs, SIs, and significant SIs were divided into 14 datasets (DS1–DS14) and used as inputs to build the classification models. Models’ performances were evaluated based on the classification accuracy for both the overall and individual classes. Our results suggest that the FLS comprised of five classes based on the proportion of total leaf surface covered with FLS. In the PCA and SI combination model, 5 PCs and 20 SIs with higher weight coefficient of each PC were extracted. For hyperspectral data, 20 CWs and 26 effective PCs were also selected. Out of the 14 datasets, the model input variables provided by five datasets (DS2, DS3, DS4, DS10, and DS11) were more superior than those of full wavelengths (DS1) both in support vector machine (SVM) and least squares support vector machine (LS-SVM) classifiers. The models developed using these five datasets achieved overall accuracies ranging from 91.8% to 94.5% in SVM, and 94.5% to 97.3% in LS-SVM. In addition, they improved the classification accuracies by 0.9% to 3.6% (SVM) and 0.9% to 3.7% (LS-SVM).


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