Sensitive and specific detection of Xanthomonas axonopodis pv. citri by PCR using pathovar specific primers based on hrpW gene sequences

Park, Dong Suk; Hyun, Jae-Wook; Park, Young Jin; Kim, Jung Sun; Kang, Hee Wan; Hahn, Jang Ho; Go, Seung Joo

doi:10.1016/j.micres.2005.07.005

Cited by 39 publications

(10 citation statements)

References 10 publications

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“…XACF/XACR primer pairs [7] were used to test for Xcc, and primer pairs 27F/1492R [32] were used as a control to test for bacteria. All amplifications were carried out in a final volume of 25 µL containing 12.5 µL of DreamTaq Green PCR Master Mix (ThermoFisher, Waltham, MA), 1 µL of each primer (Table 1), 9.5 µL of nuclease free water, and 1 µL of the macerated tissue/water mix.…”

Section: Polymerase Chain Reaction (Pcr) Analysismentioning

confidence: 99%

UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning

2019

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A remote sensing technique was developed to detect citrus canker in laboratory conditions and was verified in the grove by utilizing an unmanned aerial vehicle (UAV). In the laboratory, a hyperspectral (400–1000 nm) imaging system was utilized for the detection of citrus canker in several disease development stages (i.e., asymptomatic, early, and late symptoms) on Sugar Belle leaves and immature (green) fruit by using two classification methods: (i) radial basis function (RBF) and (ii) K nearest neighbor (KNN). The same imaging system mounted on an UAV was used to detect citrus canker on tree canopies in the orchard. The overall classification accuracy of the RBF was higher (94%, 96%, and 100%) than the KNN method (94%, 95%, and 96%) for detecting canker in leaves. Among the 31 studied vegetation indices, the water index (WI) and the Modified Chlorophyll Absorption in Reflectance Index (ARI and TCARI 1) more accurately detected canker in laboratory and in orchard conditions, respectively. Immature fruit was not a reliable tissue for early detection of canker. However, the proposed technique successfully distinguished the late stage canker-infected fruit with 92% classification accuracy. The UAV-based technique achieved 100% classification accuracy for identifying healthy and canker-infected trees.

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Section: Polymerase Chain Reaction (Pcr) Analysismentioning

confidence: 99%

UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning

2019

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“…Molecular diagnosis is a cost-effective option for identifying pathogens with great genetic proximity. Molecular biology techniques have been extensively used for identifying various species of the genus Xanthomonas (Adikini et al, 2011; Coletta-Filho et al, 2006; Mavrodieva, Levy & Gabriel, 2004; Munhoz et al, 2011; Ngoc et al, 2009; Park et al, 2006; Rigano et al, 2010; Trindade et al, 2007; Waite et al, 2016; Zhao et al, 2016). The genetic diversity of the Xanthomonas pathotypes that infect citus makes their correct identification a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Detection and identification ofXanthomonaspathotypes associated with citrus diseases using comparative genomics and multiplex PCR

Fonseca

Felestrino

Caneschi

et al. 2019

PeerJ

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Background In Citrus cultures, three species of Xanthomonas are known to cause distinct diseases. X. citri subsp. citri patothype A, X. fuscans subsp. aurantifolii pathotypes B and C, and X. alfalfae subsp. citrumelonis, are the causative agents of cancrosis A, B, C, and citrus bacterial spots, respectively. Although these species exhibit different levels of virulence and aggressiveness, only limited alternatives are currently available for proper and early detection of these diseases in the fields. The present study aimed to develop a new molecular diagnostic method based on genomic sequences derived from the four species of Xanthomonas. Results Using comparative genomics approaches, primers were synthesized for the identification of the four causative agents of citrus diseases. These primers were validated for their specificity to their target DNA by both conventional and multiplex PCR. Upon evaluation, their sensitivity was found to be 0.02 ng/µl in vitro and 1.5 × 104 CFU ml−1 in infected leaves. Additionally, none of the primers were able to generate amplicons in 19 other genomes of Xanthomonas not associated with Citrus and one species of Xylella, the causal agent of citrus variegated chlorosis (CVC). This denotes strong specificity of the primers for the different species of Xanthomonas investigated in this study. Conclusions We demonstrated that these markers can be used as potential candidates for performing in vivo molecular diagnosis exclusively for citrus-associated Xanthomonas. The bioinformatics pipeline developed in this study to design specific genomic regions is capable of generating specific primers. It is freely available and can be utilized for any other model organism.

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“…Some studies have already demonstrated the potential of molecular diagnosis for Xanthomonas, more specifically for XccA strains, based on several techniques such as conventional PCR, multiplex PCR, real-time PCR (qPCR), nested-PCR, loop-mediated isothermal amplification (LAMP-PCR), droplet digital polymerase chain reaction (ddPCR), BOX-PCR, and FISH [22][23][24][25][26][27]29,30,[48][49][50][51][52][53]. The wide variety of techniques used in the molecular diagnostics developed for citrus canker solved the problem of cost and low sensitivity presented by serological methods.…”

Section: Discussionmentioning

confidence: 99%

“…Molecular diagnosis is a cost-effective option for identifying pathogens with great genetic proximity. Molecular biology techniques have been extensively used for identifying various species of the genus Xanthomonas [21][22][23][24][25][26][27][28][29][30]. The genetic diversity of the Xanthomonas pathotypes that infect citus makes their correct identification a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Detection and identification of Xanthomonas pathotypes associated with citrus diseases using comparative genomics and multiplex PCR (v0.1)"

Studholme¹

2019

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Background: In Citrus cultures, three species of Xanthomonas are known to cause distinct diseases. X. citri subsp. citri patothype A, X. fuscans subsp. aurantifolii pathotypes B and C, and X. alfalfae subsp. citrumelonis, are the causative agents of cancrosis A, B, C, and citrus bacterial spots, respectively. Although these species exhibit different levels of virulence and aggressiveness, only limited alternatives are currently available for proper and early detection of these diseases in the fields. The present study aimed to develop a new molecular diagnostic method based on genomic sequences derived from the four species of Xanthomonas. Results: Using comparative genomics approaches, primers were synthesized for the identification of the four causative agents of citrus diseases. These primers were validated for their specificity to their target DNA by both conventional and multiplex PCR. Upon evaluation, their sensitivity was found to be 0.02 ng/µl in vitro and 1.5×10 4 CFU ml-1 in infected leaves. Additionally, none of the primers were able to generate amplicons in 19 other genomes of Xanthomonas not associated with Citrus and one species of Xylella, the causal agent of citrus variegated chlorosis (CVC). This denotes strong specificity of the primers for the different species of Xanthomonas investigated in this study. Conclusions: We demonstrated that these markers can be used as potential candidates for performing in vivo molecular diagnosis exclusively for citrus-associated Xanthomonas. The bioinformatics pipeline developed in this study to design specific genomic regions is capable of generating specific primers. It is freely available and can be

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Sensitive and specific detection of Xanthomonas axonopodis pv. citri by PCR using pathovar specific primers based on hrpW gene sequences

Cited by 39 publications

References 10 publications

UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning

UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning

Detection and identification ofXanthomonaspathotypes associated with citrus diseases using comparative genomics and multiplex PCR

Peer Review #1 of "Detection and identification of Xanthomonas pathotypes associated with citrus diseases using comparative genomics and multiplex PCR (v0.1)"

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