PCR-Mediated Detection and Quantification of the Goss’s Wilt Pathogen <i>Clavibacter michiganensis</i> subsp. <i>nebraskensis</i> Via a Novel Gene Target

McNally, R. R.; Ishimaru, Carol A.; Malvick, Dean

doi:10.1094/phyto-05-16-0190-r

Cited by 13 publications

(8 citation statements)

References 28 publications

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“…Since the first report in 1969 from Nebraska, the disease has been spreading and adversely impacting corn production in the Midwestern United States including western Indiana, Illinois, Iowa, Missouri, eastern Nebraska, and eastern Kansas, as well as Canada. Goss's disease shows two major types/phases of symptoms -a leaf blight and a systemic vascular wilt [6,7,8]. The leaf blight phase involves leaves and above-ground parts of the plant which exhibit small, dark and discontinuous watersoaked spots; orange shiny bacterial exudates are also observed in advanced stages.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Dobhal and collaborators [10] developed a loop-mediated isothermal amplification (LAMP) method to discriminate all known subspecies of C. michiganensis; however, no effective qPCR/PCR protocol is available. Although, serological and molecular diagnostic methods for economically important subspecies of C. michiganensis have been developed [2,7,11,12,13,14], these diagnostic tests are time consuming and may exhibit cross reactivity with Gram-positive bacteria and/or Gram-negative bacteria demanding further molecular test for cross confirmation [11,15].…”

Section: Introductionmentioning

confidence: 99%

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VIDHOP, viral host prediction with Deep Learning

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Viehweger

Barth

et al. 2019

Preprint

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Zoonosis, the natural transmission of infections from animal to human, is a far-reaching global problem. The recent outbreaks of Zika virus and Ebola virus are examples of viral zoonosis, which occur more frequently due to globalization. In case of a virus outbreak, it is helpful to know which host organism was the original carrier of the virus. Once the reservoir or intermediate host is known, it can be isolated to prevent further spreading of the viral infection. Recent approaches aim to predict a viral host based on the viral genome, often in combination with the potential host genome and using arbitrary selected features. This methods have a clear limitation in either the amount of different hosts they can predict or the accuracy of the prediction. Here, we present a fast and accurate deep learning approach for viral host prediction, which is based on the viral genome sequence only. To assure a high prediction accuracy we developed an effective selection approach for the training data, to avoid biases due to a highly unbalanced number of known sequences per virus-host combinations. We tested our deep neural network on three different virus species (influenza A virus, rabies lyssavirus, rotavirus A) and reached for each virus species a AUC between 0.94 and 0.98, outperforming previous approaches and allowing highly accurate predictions while only using fractions of the viral genome sequences. We show that deep neural networks are suitable to predict the host of a virus, even with a limited amount of sequences and highly unbalanced available data. The deep neural networks trained for this approach build the core of the virus host predicting tool VIDHOP (VIrus Deep learning HOst Prediction). doi: bioRxiv preprint shown to perform well with character sequences 28 , such as DNA or RNA sequences, potentially allowing for a furthermore increase in the prediction quality. Author Contributions

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VIDHOP, viral host prediction with Deep Learning

Mock

Viehweger

Barth

et al. 2019

Preprint

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“…Since the first report in 1969 from Nebraska, the disease has been spreading and adversely impacting corn production in the Midwestern United States including western Indiana, Illinois, Iowa, Missouri, eastern Nebraska, and eastern Kansas, as well as Canada. Goss’s disease shows two major symptom types—a leaf blight and a systemic vascular wilt [6,7,8]. The leaf blight phase involves leaves and above-ground parts of the plant which exhibit small, dark and discontinuous water-soaked spots; orange shiny bacterial exudates are also observed in advanced stages.…”

Section: Introductionmentioning

confidence: 99%

“…Although serological and molecular diagnostic methods for economically important subspecies of C . michiganensis have been developed [2,7,11,12,13,14], these diagnostic tests are time consuming and may exhibit cross reactivity with Gram-positive bacteria and/or Gram-negative bacteria demanding further molecular test for cross confirmation [11,15].…”

Section: Introductionmentioning

confidence: 99%

Synergetic effect of non-complementary 5’ AT-rich sequences on the development of a multiplex TaqMan real-time PCR for specific and robust detection of Clavibacter michiganensis and C. michiganensis subsp. nebraskensis

2019

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Clavibacter is an agriculturally important genus comprising a single species, Clavibacter michiganensis , and multiple subspecies, including, C . michiganensis subsp. nebraskensis which causes Goss's wilt/blight of corn, accounts for high yield losses and is listed among the five most significant diseases of corn in the United States of America. Our research objective was to develop a robust and rapid multiplex TaqMan real-time PCR (qPCR) to detect C . michiganensis in general and C . michiganensis subsp. nebraskensis with enhanced reliability and accuracy by adding non-complementary AT sequences to the 5’ end of the forward and reverse primers. Comparative genomic analyses were performed to identify unique and conserved gene regions for primer and probe design. The unique genomic regions, ABC transporter ATP-binding protein CDS/ABC-transporter permease and MFS transporter were determined for specific detection of C . michiganensis and C . m . subsp. nebraskensis , respectively. The AT-rich sequences at the 5’ position of the primers enhanced the reaction efficiency and sensitivity of rapid qPCR cycling; the reliability, accuracy and high efficiency of the developed assay was confirmed after testing with 59 strains from inclusivity and exclusivity panels–no false positives or false negatives were detected. The assays were also validated through naturally and artificially infected corn plant samples; all samples were detected for C . michiganensis and C . m . subsp. nebraskensis with 100% accuracy. The assay with 5’ AT-rich sequences detected up to 10- and 100-fg of C . michiganensis and C . michiganensis subsp. nebraskensis genome targets, respectively. No adverse effect was observed when sensitivity assays were spiked with host genomic DNA. Addition of 5’ AT-rich sequences enhanced the qPCR reaction efficiency from 0.82 (M = -3.83) and 0.91 (M = -3.54) to 1.04 (with optimum slope value; M = -3.23) for both C . michiganensis and C . michiganensis subsp. nebraskensis , respectively; an increase of 10-fold sensitivity was also obtained with C . michiganensis primer set. The methodology proposed here can be used to optimize reaction efficiency and to harmonize diagnostic protocols which have prodigious applications in routine diagnostics, biosecurity and microbial forensics.

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“…PCR is rapid, highly sensitive and can be specific for identification of bacterial 97 pathogens (Schaad & Frederick, 2002;Vincelli & Tisserat, 2008;McNally et al 2016).…”

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confidence: 99%

Comparative Genomics to Develop a Specific Multiplex PCR Assay for Detection of Clavibacter michiganensis

et al. 2020

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Clavibacter michiganensis is a Gram-positive bacterial pathogen that proliferates in the xylem vessels of tomato, causing bacterial wilt and canker symptoms. Accurate detection is a crucial step in confirming outbreaks of bacterial canker and developing management strategies. A major problem with existing detection methods are false-positive and -negative results. Here, we report the use of comparative genomics of 37 diverse Clavibacter strains, including 21 strains sequenced in this study, to identify specific sequences that are C. michiganensis detection targets. Genome-wide phylogenic analyses revealed additional diversity within the genus Clavibacter. Pathogenic C. michiganensis strains varied in plasmid composition, highlighting the need for detection methods based on chromosomal targets. We utilized sequences of C. michiganensis-specific loci to develop a multiplex PCR-based diagnostic platform using two C. michiganensis chromosomal genes (rhuM and tomA) and an internal control amplifying both bacterial and plant DNA (16s ribosomal RNA). The multiplex PCR assay specifically detected C. michiganensis strains from a panel of 110 additional bacteria, including other Clavibacter spp. and bacterial pathogens of tomato. The assay was adapted to detect the presence of C. michiganensis in seed and tomato plant materials with high sensitivity and specificity. In conclusion, the described method represents a robust, specific tool for detection of C. michiganensis in tomato seed and infected plants.

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PCR-Mediated Detection and Quantification of the Goss’s Wilt Pathogen Clavibacter michiganensis subsp. nebraskensis Via a Novel Gene Target

Cited by 13 publications

References 28 publications

VIDHOP, viral host prediction with Deep Learning

VIDHOP, viral host prediction with Deep Learning

Synergetic effect of non-complementary 5’ AT-rich sequences on the development of a multiplex TaqMan real-time PCR for specific and robust detection of Clavibacter michiganensis and C. michiganensis subsp. nebraskensis

Comparative Genomics to Develop a Specific Multiplex PCR Assay for Detection of Clavibacter michiganensis

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