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Development of multiplex PCR based diagnostics assay for simultaneous detection of major citrus pathogens

Indian Phytopathology(2024)

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摘要
Citrus, most economically significant fruit crop globally and represents India’s third largest fruit industry after banana and mango. Susceptibility of citrus spp. to more than 150 diseases can result into diminishing productivity and yield. Potential citrus threats including citrus tristeza virus (CTV), Indian citrus ringspot virus (ICRSV), citrus yellow mosaic virus (CYMV), and Candidatus Liberibacter asiaticus (CLas). Currently, there is no control strategy available for these systemic pathogens viz., resistance-host, antimicrobial, or antiviral therapeutics. Present efforts were initiated to develop a simple, reliable, and sensitive nucleic acid-based multiplex PCR assay designed for the detection of citrus pathogens. A field survey was conducted to identify samples exhibiting a mixed infection of major citrus pathogens. The gene specific primer sets were designed and optimized to achieve the targeted amplification individually and simultaneously. Assay can detect all four major pathogens viz., CLas: 236 bp, CTV: 627 bp, ICRSV: 309 bp, and CYMV: 811 bp by amplifying specific targeted genomic regions, 16 S rRNA, RNA binding protein (p23), and Coat protein (CP) genes respectively. The multiplex PCR (mPCR) assay was highly sensitive, truly specific, and could detect nucleic acid template ranging from 50 pg/µl to 0.40 ng/µl. Further, validation was done using different field samples with a excellent diagnostic concurrence of Kappa value = 0.884. mPCR technique exhibits the capability to efficiently identify all four prominent citrus pathogens simultaneously. Thereby holding the potential to diagnose samples on large scale in a time-efficient manner, while maintaining cost-effectiveness for citrus budwood programs, especially in the context of quarantine applications.
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关键词
Multiplex PCR (mPCR) assay,Disease surveillance,Diagnostic efficiency,Primer design,Nucleic acid-based assay,Disease identification
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