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Oomycete composition in Proteaceae orchards and natural stands on three continents
Migliorini, Duccio; Vivas, Maria; Wingfield, Michael J.; Shaw, Christopher; Burgess, Treena I.
The Proteaceae, a diverse family of woody flowering plants in the Southern Hemisphere, contains many species known to be
susceptible to Phytophthora cinnamomi, both in the natural environment and in cut-flower orchards. Very little is known about
the prevalence of P. cinnamomi and other oomycetes across these landscapes. To address this knowledge gap, we used a double
ITS1 and RPS10 gene metabarcoding approach and traditional isolation protocols to investigate oomycetes in orchards and natural
stands of Proteaceae across South Africa, South Africa (eastern and western), Australia, and Europe. The RPS10 primers
amplified more samples, including various Pythium species, while the ITS primers detected more Phytophthora phylotypes. Both
datasets showed that geographic regions influenced oomycete species richness and community composition, while they did not
show any variation between orchards and natural vegetation. RPS10 metabarcoding detected the largest number of species and
provided greater statistical confidence than ITS1 when considering oomycete species composition. Metabarcoding also showed
that orchards had a higher abundance of P. cinnamomi compared to native stands, although this was not found when isolating
through baiting of roots and rhizosphere soil. Direct isolation and metabarcoding are complementary, with metabarcoding serving
as an early detection tool. However, it cannot distinguish living viable propagules from residual DNA of dead propagules, limiting
its use for diagnostic purposes related to Phytophthora management and control. These results, along with those of other recent
studies, show that metabarcoding offers an effective tool to describe the dynamics of soil oomycetes in different ecosystems.
Description:
DATA AVAILABILITY : The ITS gene dataset generated and analyzed during
the current study is available in the GenBank repository, https:// www.
ncbi. nlm. nih. gov/ genba nk/. The metabarcoding dataset generated and
analyzed during the current study are available from the corresponding
author on reasonable request.