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. 2024 Apr 12;29(8):1749.
doi: 10.3390/molecules29081749.

Detecting Pathogenic Phytophthora Species Using Volatile Organic Compounds

Affiliations

Detecting Pathogenic Phytophthora Species Using Volatile Organic Compounds

Patrick Sherwood et al. Molecules. .

Abstract

There are several highly damaging Phytophthora species pathogenic to forest trees, many of which have been spread beyond their native range by the international trade of live plants and infested materials. Such introductions can be reduced through the development of better tools capable of the early, rapid, and high-throughput detection of contaminated plants. This study utilized a volatilomics approach (solid-phase microextraction coupled to gas chromatography-mass spectrometry) to differentiate between several Phytophthora species in culture and discriminate between healthy and Phytophthora-inoculated European beech and pedunculate oak trees. We tentatively identified 14 compounds that could differentiate eight Phytophthora species from each other in vitro. All of the Phytophthora species examined, except Phytophthora cambivora, uniquely produced at least one compound not observed in the other species; however, most detected compounds were shared between multiple species. Phytophthora polonica had the most unique compounds and was the least similar of all the species examined. The inoculated seedlings had qualitatively different volatile profiles and could be distinguished from the healthy controls by the presence of isokaurene, anisole, and a mix of three unknown compounds. This study supports the notion that volatiles are suitable for screening plant material, detecting tree pathogens, and differentiating between healthy and diseased material.

Keywords: Fagus sylvatica; Quercus robur; gas chromatography–mass spectrometry; tree disease; volatilomics.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Principal component analysis (PCA) components plot of volatile compounds detected at 14 and 30 days post-inoculation (DPI) from eight Phytophthora species grown in vitro. N = 4 for each species by time point.
Figure 2
Figure 2
Scaled hierarchical cluster analysis of Phytophthora species based on the volatile compounds detected at 14 and 30 days post-inoculation. Species names are abbreviated to the specific epithet and are followed by the numeral for the sampling date post inoculation.
Figure 3
Figure 3
Average stem lesion lengths on Fagus sylvatica and Quercus robur saplings artificially inoculated with Phytophthora cinnamomi (P. cinn) or Phytophthora plurivora (P. plur) compared to mock-inoculated controls (MIC). Lesion lengths were recorded 50 days post inoculation. N = 3 for each bar; error bars are ± SD. Asterisks denote significant differences in lesion length compared to the MIC trees of the same species as determined by Dunnett’s test at the α = 0.05 level.

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