One-Step Detection of Monilinia fructicola, M. fructigena, and M. laxa on Prunus and Malus by a Multiplex Real-Time PCR Assay

Abstract

Brown rot is an economically important fungal disease affecting stone and pome fruit orchards, as well as harvested fruit during storage and on the market. Monilinia fructicola, M. laxa, and M. fructigena are the main causal agents of this disease and each have a different regulatory status depending on regional regulations. In this study, a new multiplex tool based on real-time polymerase chain reaction was developed to detect the three pathogenic fungi in a single reaction on fruit, twigs, and flowers of Prunus and Malus spp. Species-specific primer-hydrolysis probe combinations were designed to amplify a region located in a previously described MO368 sequenced characterized amplified region marker, and used in a quadruplex format coupled with the 18S Uni universal primer-probe test in order to check the quality of the DNA template. The assay was designed and optimized with the objective to provide high performance values. Experimental data supported its sensitivity, specificity, reproducibility, and robustness. In addition, a set of quality controls was implemented to minimize the risk of false-positive and false-negative results, thus making this new test fit for use in serial analyses and reliable in the framework of official controls.

Peach Brown Rot: Still in Search of an Ideal Management Option

“…More recently, the RT-PCR method developed by Brouwershaven et al [88] was validated against all four brown rot-causing Monilinia species (M. laxa, M. fructicola, M. fructigena, and M. polystroma). In 2016, Guinet et al [93] used a multiplex real-time PCR (RT-PCR) to detect and discriminate the three common species of Monilinia (M. laxa, M. fructicola, and M. fructigena) on Prunus and Malus. Other authors, [94] applying high-resolution melting (HRM) techniques, distinguished six different species of Monilinia in peach (M. laxa, M. fructicola, M. fructigena, M. mumecola, M. lithartiana, and M. yunnanensis) by analyzing the melting curve of amplicons of two universal primer pairs.…”

“…The merits of DNA-based detection methods include reliability, timesaving, and higher sensitivity and specificity, when compared to the traditional and serological assays techniques employed during the processes of artificial cultivation [93,95]. For example, the molecular technique of multiplex RT-PCR assay (One step) developed in 2016 by Guinet et al [93] is efficient and prompt in characterizing the three major species of Monilinia responsible for brown rot (M. laxa, M. fructicola, and M. fructigena).…”

“…For example, the molecular technique of multiplex RT-PCR assay (One step) developed in 2016 by Guinet et al [93] is efficient and prompt in characterizing the three major species of Monilinia responsible for brown rot (M. laxa, M. fructicola, and M. fructigena). These authors also inferred that the exceptional reliability of their results is of paramount importance in the framework of phytosanitary regulations, considering that the performance data were generated, and the assay was fully validated in accordance to the EPPO guidelines [72].…”

Epidemiología y control de la podredumbre parda del melocotón en postcosecha

“…We detected bigger amounts of M. fructicola than M. laxa DNA and M. fructigena DNA in latently infected flowers using qPCR. We found that latent M. laxa infections had bigger DNA amounts Although conventional PCR and qPCR-based methods have already been developed for identifying and discriminating Monilinia species, these methods rely on sampling plant material with visible disease (Côté et al, 2004;Gell et al, 2007;Guinet et al, 2016;Hughes et al, 2000;Ioos and Frey, 2000;van Brouwershaven et al, 2010). We found that the qPCR-based method can detect the pathogen in artificial and natural latent brown rot infections.…”

“…Three different groups have developed qPCR methods for detection of Monilinia spp. from fungal cells collected from symptomatic plant material (Guinet et al, 2016;Luo et al, 2007;van Brouwershaven et al, 2010). But they have not been used to detect latent infection.…”