Development of multiplex RT-PCR assay for simultaneous detection of four viruses infecting apple (Malus domestica)

Nabi, Sajad Un; Madhu, Gandrajapalli Shivareddy; Rao, G. P.

doi:10.1111/lam.13643

Cited by 9 publications

(4 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the research question related to the detection of multiple classes of plant diseases (suffering from different diseases at a time) [50] could be useful for implementing a cost-effective protection system. For instance, black spots on apples are normally treated with a fungicide spray, whereas no such treatment is available for apple viruses [51]. Hence, this research will be helpful for growers to take appropriate treatment measures after detecting multiple plant diseases in an organ.…”

Section: Discussionmentioning

confidence: 99%

A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand

2022

View full text Add to dashboard Cite

Deep learning-based plant disease detection has gained significant attention from the scientific community. However, various aspects of real horticultural conditions have not yet been explored. For example, the disease should be considered not only on leaves, but also on other parts of plants, including stems, canes, and fruits. Furthermore, the detection of multiple diseases in a single plant organ at a time has not been performed. Similarly, plant disease has not been identified in various crops in the complex horticultural environment with the same optimized/modified model. To address these research gaps, this research presents a dataset named NZDLPlantDisease-v1, consisting of diseases in five of the most important horticultural crops in New Zealand: kiwifruit, apple, pear, avocado, and grapevine. An optimized version of the best obtained deep learning (DL) model named region-based fully convolutional network (RFCN) has been proposed to detect plant disease using the newly generated dataset. After final the most suitable DL model, data augmentation techniques were successively evaluated. Subsequently, the effects of image resizers with interpolators, weight initializers, batch normalization, and DL optimizers were studied. Finally, performance was enhanced by empirical observation of position-sensitive score maps and anchor box specifications. Furthermore, the robustness/practicality of the proposed approach was demonstrated using a stratified k-fold cross-validation technique and testing on an external dataset. The final mean average precision of the RFCN model was found to be 93.80%, which was 19.33% better than the default settings. Therefore, this research could be a benchmark step for any follow-up research on automatic control of disease in several plant species.

show abstract

Section: Discussionmentioning

confidence: 99%

A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand

2022

View full text Add to dashboard Cite

show abstract

“…Due to the non-availability of chemical viricides, it is difficult to control viral pathogens. Hence, the only effective way to prevent viral spread in perennial plants is through the use of virus-free planting material via indexing of scion wood and rootstocks using robust, timely and precise detection methods [ 19 , 20 ]. Diagnostic assays play an important role in the detection of plant viruses through their spatial and temporal distribution or selected expression in symptomatic, as well as asymptomatic, host plants [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Tissue and Time Optimization for Real-Time Detection of Apple Mosaic Virus and Apple Necrotic Mosaic Virus Associated with Mosaic Disease of Apple (Malus domestica)

Nabi

Mir

Yasmin

et al. 2023

Viruses

View full text Add to dashboard Cite

Besides apple mosaic virus (ApMV), apple necrotic mosaic virus (ApNMV) has also been found to be associated with apple mosaic disease. Both viruses are unevenly distributed throughout the plant and their titer decreases variably with high temperatures, hence requiring proper tissue and time for early and real-time detection within plants. The present study was carried out to understand the distribution and titer of ApMV and ApNMV in apple trees from different plant parts (spatial) during different seasons (temporal) for the optimization of tissue and time for their timely detection. The Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Reverse Transcription-quantitative Polymerase Chain Reaction (RT-qPCR) was carried out to detect and quantify both viruses in the various plant parts of apple trees during different seasons. Depending on the availability of tissue, both ApMV and ApNMV were detected in all the plant parts during the spring season using RT-PCR. During the summer, both viruses were detected only in seeds and fruits, whereas they were detected in leaves and pedicel during the autumn season. The RT-qPCR results showed that during the spring, the ApMV and ApNMV expression was higher in leaves, whereas in the summer and autumn, the titer was mostly detected in seeds and leaves, respectively. The leaves in the spring and autumn seasons and the seeds in the summer season can be used as detection tissues through RT-PCR for early and rapid detection of ApMV and ApNMV. This study was validated on 7 cultivars of apples infected with both viruses. This will help to accurately sample and index the planting material well ahead of time, which will aid in the production of virus-free, quality planting material.

show abstract

“…Developing sensitive detection methods is vital for the successful screening of virus-free propagation materials for apple trees. To date, several molecular detection methods, such as reverse transcription-polymerase chain amplification (RT-PCR) (Nabi et al, 2022), RT-real-time quantitative PCR (RT-qPCR) (Malandraki et al, 2017), loop-mediated isothermal amplification (Lu et al, 2018), recombinase polymerase amplification (Jeong et al, 2021;Kim et al, 2019), and CRISPR-based assays (Jiao et al, 2021), have been developed to detect apple tree viruses. Currently, conventional RT-PCR and RT-qPCR have been used by Korea's Ministry of Agriculture, Food and Rural Affairs (KMAFRA), to screen for apple viruses for in vitro micropropagation of apple plants.…”

mentioning

confidence: 99%

“…ASGV infects Rosaceae fruit trees, including apple, pear, apricot, and cherry, and is considered a high-risk viral pathogen during cultivation of fruit trees and production of ASGV-free plantlets by KMAFRA (Lee et al, 2020). Considering the increasing economic impact of ASGV in the global fruit industry, a range of RT-PCR and TaqMan probe-based RT-qPCR assays have been developed as reliable and sensitive detection methods for monitoring ASGV infections in fruit trees and for ASGV-free certification programs (Malandraki et al, 2017;Nabi et al, 2022).…”

mentioning

confidence: 99%

Detection and Quantification of Apple Stem Grooving Virus in Micropropagated Apple Plantlets Using Reverse-Transcription Droplet Digital PCR

et al. 2022

View full text Add to dashboard Cite

Apple stem grooving virus (ASGV) is a destructive viral pathogen of pome fruit trees that causes significant losses to fruit production worldwide. Obtaining ASGV-free propagation materials is essential to reduce economic losses, and accurate and sensitive detection methods to screen ASGV-free plantlets during in vitro propagation are urgently necessary. In this study, ASGV was sensitively and accurately quantified from in vitro propagated apple plantlets using a reverse transcription droplet digital polymerase chain reaction (RT-ddPCR) assay. The optimized RT-ddPCR assay was specific to other apple viruses, and was at least 10-times more sensitive than RT-real-time quantitative PCR assay. Furthermore, the optimized RT-ddPCR assay was validated for the detection and quantification of ASGV using micropropagated apple plantlet samples. This RT-ddPCR assay can be utilized for the accurate quantitative detection of ASGV infection in ASGV-free certification programs, and can thus contribute to the production of ASGV-free apple trees.

show abstract

Development of multiplex RT-PCR assay for simultaneous detection of four viruses infecting apple (Malus domestica)

Cited by 9 publications

References 20 publications

A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand

A Performance-Optimized Deep Learning-Based Plant Disease Detection Approach for Horticultural Crops of New Zealand

Tissue and Time Optimization for Real-Time Detection of Apple Mosaic Virus and Apple Necrotic Mosaic Virus Associated with Mosaic Disease of Apple (Malus domestica)

Detection and Quantification of Apple Stem Grooving Virus in Micropropagated Apple Plantlets Using Reverse-Transcription Droplet Digital PCR

Contact Info

Product

Resources

About