Pub Date : 2018-01-01Epub Date: 2018-05-10DOI: 10.1007/s40858-018-0221-5
Sètondji Alban Paterne Etchiha Afoha, Antoine Affokpon, Lieven Waeyenberge, Nancy de Sutter, Clément Agbangla, Alexandre Dansi, Daniel L Coyne, Nicole Viaene
In Benin, yam production continues to face numerous production constraints, including yield and quality reduction by Scutellonema bradys. Implementation of efficient management techniques against this pest requires an improved understanding, including at the molecular level, of the pest. The current study aimed at identifying the Scutellonema spp. associated with yam in Benin and investigating the phylogenetic relationships between populations. Nematodes of the genus Scutellonema were obtained from tubers exhibiting external dry rot symptoms. DNA was extracted from nematodes belonging to 138 populations collected from 49 fields from 29 villages. For 51 of these populations, both the ITS1 and COI regions could be amplified via PCR, sequenced, compared with available sequences in the NCBI database and were identified as S. bradys. Maximum likelihood was used to construct 60% consensus phylogenetic trees based on 51 sequences. This phylogenetic analysis did not reveal any genetic separation between populations by cultivar, village, cropping system nor by agroecological zone. Neither could any subgroups within S. bradys be separated, indicating that no subspecies were present. An earlier published species-specific primer set was verified with the DNA of the 51 sequences and was considered a reliable and rapid method for S. bradys identification.
在贝宁，山药生产仍然面临许多生产制约因素，包括布氏黄粉虫（Scutellonema bradys）造成的产量和质量下降。要针对这种害虫实施有效的管理技术，就必须加深对这种害虫的了解，包括分子水平的了解。目前的研究旨在确定与贝宁山药相关的 Scutellonema 属，并调查种群之间的系统发育关系。研究人员从表现出外部干腐症状的块茎中获取了 Scutellonema 属线虫。从 29 个村庄 49 块田地中采集的 138 个种群的线虫中提取了 DNA。其中 51 个种群的 ITS1 和 COI 区域均可通过 PCR 扩增、测序，并与 NCBI 数据库中的可用序列进行比较，确定为 S. bradys。在 51 个序列的基础上，使用最大似然法构建了 60% 的共识系统发生树。该系统发育分析没有发现任何按栽培品种、村庄、耕作制度或农业生态区域划分的种群遗传分离现象。也无法在 S. bradys 中分离出任何亚群，这表明不存在亚种。早先发表的一套物种特异性引物与 51 个序列的 DNA 进行了验证，被认为是一种可靠而快速的 S. bradys 鉴定方法。
{"title":"Molecular diversity of <i>Scutellonema bradys</i> populations from Benin, based on ITS1 rDNA and COI mtDNA.","authors":"Sètondji Alban Paterne Etchiha Afoha, Antoine Affokpon, Lieven Waeyenberge, Nancy de Sutter, Clément Agbangla, Alexandre Dansi, Daniel L Coyne, Nicole Viaene","doi":"10.1007/s40858-018-0221-5","DOIUrl":"10.1007/s40858-018-0221-5","url":null,"abstract":"<p><p>In Benin, yam production continues to face numerous production constraints, including yield and quality reduction by <i>Scutellonema bradys</i>. Implementation of efficient management techniques against this pest requires an improved understanding, including at the molecular level, of the pest. The current study aimed at identifying the <i>Scutellonema</i> spp. associated with yam in Benin and investigating the phylogenetic relationships between populations. Nematodes of the genus <i>Scutellonema</i> were obtained from tubers exhibiting external dry rot symptoms. DNA was extracted from nematodes belonging to 138 populations collected from 49 fields from 29 villages. For 51 of these populations, both the ITS1 and COI regions could be amplified <i>via</i> PCR, sequenced, compared with available sequences in the NCBI database and were identified as <i>S. bradys</i>. Maximum likelihood was used to construct 60% consensus phylogenetic trees based on 51 sequences. This phylogenetic analysis did not reveal any genetic separation between populations by cultivar, village, cropping system nor by agroecological zone. Neither could any subgroups within <i>S. bradys</i> be separated, indicating that no subspecies were present. An earlier published species-specific primer set was verified with the DNA of the 51 sequences and was considered a reliable and rapid method for <i>S. bradys</i> identification.</p>","PeriodicalId":48767,"journal":{"name":"Tropical Plant Pathology","volume":"43 4","pages":"323-332"},"PeriodicalIF":2.5,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7370939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38203062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-01-01Epub Date: 2017-02-14DOI: 10.1007/s40858-017-0138-4
Jonathan S West, Gail G M Canning, Sarah A Perryman, Kevin King
Many pathogens are dispersed by airborne spores, which can vary in space and time. We can use air sampling integrated with suitable diagnostic methods to give a rapid warning of inoculum presence to improve the timing of control options, such as fungicides. Air sampling can also be used to monitor changes in genetic traits of pathogen populations such as the race structure or frequency of fungicide resistance. Although some image-analysis methods are possible to identify spores, in many cases, species-specific identification can only be achieved by DNA-based methods such as qPCR and LAMP and in some cases by antibody-based methods (lateral flow devices) and biomarker-based methods ('electronic noses' and electro-chemical biosensors). Many of these methods also offer the prospect of rapid on-site detection to direct disease control decisions. Thresholds of spore concentrations that correspond to a disease risk depend on the sampler (spore-trap) location (whether just above the crop canopy, on a UAV or drone, or on a tall building) and also need to be considered with weather-based infection models. Where disease control by spore detection is not possible, some diseases can be detected at early stages using optical sensing methods, especially chlorophyll fluorescence. In the case of Fusarium infections on wheat, it is possible to map locations of severe infections, using optical sensing methods, to segregate harvesting of severely affected areas of fields to avoid toxins entering the food chain. This is most useful where variable crop growth or microclimates within fields generate spatially variable infection, i.e. parts of fields that develop disease, while other areas have escaped infection and do not develop any disease.
{"title":"Novel Technologies for the detection of Fusarium head blight disease and airborne inoculum.","authors":"Jonathan S West, Gail G M Canning, Sarah A Perryman, Kevin King","doi":"10.1007/s40858-017-0138-4","DOIUrl":"https://doi.org/10.1007/s40858-017-0138-4","url":null,"abstract":"<p><p>Many pathogens are dispersed by airborne spores, which can vary in space and time. We can use air sampling integrated with suitable diagnostic methods to give a rapid warning of inoculum presence to improve the timing of control options, such as fungicides. Air sampling can also be used to monitor changes in genetic traits of pathogen populations such as the race structure or frequency of fungicide resistance. Although some image-analysis methods are possible to identify spores, in many cases, species-specific identification can only be achieved by DNA-based methods such as qPCR and LAMP and in some cases by antibody-based methods (lateral flow devices) and biomarker-based methods ('electronic noses' and electro-chemical biosensors). Many of these methods also offer the prospect of rapid on-site detection to direct disease control decisions. Thresholds of spore concentrations that correspond to a disease risk depend on the sampler (spore-trap) location (whether just above the crop canopy, on a UAV or drone, or on a tall building) and also need to be considered with weather-based infection models. Where disease control by spore detection is not possible, some diseases can be detected at early stages using optical sensing methods, especially chlorophyll fluorescence. In the case of <i>Fusarium</i> infections on wheat, it is possible to map locations of severe infections, using optical sensing methods, to segregate harvesting of severely affected areas of fields to avoid toxins entering the food chain. This is most useful where variable crop growth or microclimates within fields generate spatially variable infection, i.e. parts of fields that develop disease, while other areas have escaped infection and do not develop any disease.</p>","PeriodicalId":48767,"journal":{"name":"Tropical Plant Pathology","volume":"42 3","pages":"203-209"},"PeriodicalIF":2.5,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s40858-017-0138-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38203086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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