Multiplex species-specific real-time PCR assays for the detection of European Armillaria spp. and Desarmillaria spp. in different environmental substrates

Abstract

Armillaria root rot, caused by basidiomycete fungi of the genera Armillaria and Desarmillaria , affects numerous agronomic crops and forest trees worldwide. In Europe, seven species have been described, namely Armillaria mellea , A. ostoyae , A. gallica , A. borealis , A. cepistipes , Desarmillaria ectypa , and D. tabescens . In trees, these species can invade roots and the trunk base (bark and wood). Degraded samples may hamper reliable diagnostics of Armillaria spp. and Desarmillaria spp. As the disease progresses, successful isolation of the fungus may be compromised by the extensive overgrowth of fungal saprophytes and bacteria. Real-time polymerase chain reaction (qPCR) has become the gold standard for plant pathogen diagnostics owing to its speed, specificity, and ability to detect target DNA across a wide range of matrices, including highly degraded samples. This study aimed to develop seven species-specific assays targeting the European Armillaria and Desarmillaria species, along with a broad-range assay targeting both genera. The internal transcribed spacer (ITS), the intergenic spacer (IGS) and four single-copy genes retrieved from publicly available genomic resources were used to design specific qPCR oligonucleotides. To improve the practical implementation of the assays, the individual species-specific tests were multiplexed in two groups according to the frequency of occurrence of each species in Europe, without compromising the assay specificity or sensitivity. The new diagnostics methods were assessed on environmental samples collected from French forests with suspicion of Armillaria / Desarmillaria infections, and successfully demonstrated their efficacy in detecting the different species causing root rot disease. As the pathogenic behavior (primary or opportunistic) depends on Armillaria species, these tools have great interest to improve forest health surveys and diagnosis.