4.7 Article

Characterization of Grapevine Wood Microbiome Through a Metatranscriptomic Approach

Journal

MICROBIAL ECOLOGY
Volume 83, Issue 3, Pages 658-668

Publisher

SPRINGER
DOI: 10.1007/s00248-021-01801-z

Keywords

High-throughput sequencing; RNAseq; Dysbiosis; Co-occurrence network; Grapevine trunk diseases

Funding

  1. Instituto Nacional de Tecnologia Agropecuaria [PRET-1251101]

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Grapevine trunk diseases, particularly hoja de malvon (HDM), pose a significant threat to wine and table grape production in Argentina and Uruguay. The study revealed differences in the microorganisms present in grapevine wood between symptomatic and asymptomatic plants affected by HDM, with a higher presence of certain fungi in plants with more severe symptoms.
Grapevine trunk diseases threaten wine and table grape production worldwide, primarily by reducing yields and, in its advanced stages, causing plant death. Among those diseases, the complex etiology disease known as hoja de malvon (HDM) significantly concerns Argentinian and Uruguayan viticulture. At least four different fungi are associated with this disease, but their role and interactions with other wood microorganisms are understudied. In this sense, analyzing grapevine wood microbiome composition could help understand microbial interactions occurring in HDM onset. Hence, a metatranscriptomic study was performed for the microbiome characterization of mature field-grown Vitis vinifera cv. Malbec, leaf-symptomatic or leaf-asymptomatic. The microbiome was mainly represented by Dothideomycetes and Actinobacteria. In the plant with more marked symptoms, higher levels of the Basidiomycota Arambarria destruens and Phellinus laevigatus were detected. Despite this particular difference, discriminating symptomatic from asymptomatic plants based on the presence or abundance of HDM pathogens was not possible. Alpha diversity and rank-abundance curve analyses indicated that plants with foliar symptoms have lower microbial evenness than asymptomatic plants. The co-occurrence network modeled microbial interkingdom interactions. Molecular data generated in this study will help develop future targeted molecular quantification for specific taxa.

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