Fungal members of the rhizosphere microbiota of Quercus ilex subsp. ballota

Lorena Gomez Aparicio; Sara García Garrido

doi:10.15470/jow7hd

Fungal members of the rhizosphere microbiota of Quercus ilex subsp. ballota

Citation

Gomez Aparicio L, García Garrido S (2024). Fungal members of the rhizosphere microbiota of Quercus ilex subsp. ballota. Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). Sampling event dataset https://doi.org/10.15470/jow7hd accessed via GBIF.org on 2024-12-12.

Description

The general objective of thE WP7 is to advance our understanding of the impacts of global change drivers - mainly climate change and exotic pathogens - on the above- and below-ground biodiversity of Mediterranean forests and silvopastoral agrosystems, and to use the resulting information to propose management tools aim to improve the resistance and resilience of these forests in scenarios of increasing abiotic and biotic stress. We will focus our research on forests and dehesas of evergreen Quercus species (Quercus suber and Quercus ilex) in Andalusia, due to their strategic ecological and economic importance and their current vulnerability status as a result of increasing aridity and the invasion of the aggressive exotic pathogen Phytophthora cinammomi.

Sampling Description

Study Extent

Soil at 5 to 20 cm depth attached to the secondary roots of every tree (Quercus ilex subsp. ballota) was collected, transported on ice and frozen at -80 ºC until processed. The DNA from each sample was extracted using DNeasy Power Soil Pro kit (QIAGEN) according to the manufacturer’s instructions. V3-V4 16S rRNA and ITS2 regions from Bacteria and Fungi kingdoms, respectively, were amplified. Likewise, in order to study the presence of Phytophthora species, amplicon libraries using the Phytophthora-specific primers that amplify ITS1 region were created using a nested PCR approach. The libraries were sequenced with Illumina MiSeq platform using 2 x 275 bp paired-end reads.

Sampling

The Illumina paired-end raw sequences were processed using the freely available bioinformatics software QIIME 2 version 2022.2.0 (Bolyen et al., 2019). The sequences from each target (bacteria, fungi or Phytophthora spp.) and each sequencing run were processed equally but separately throughout the analysis. The sequences were trimmed by implementing cutadapt (Martin, 2011) in QIIME 2 with q2-cutadapt plugin and trim-paired command. Chimeric sequences were identified and deleted after quality filtering and de-noising using the Divisive Amplicon Denoising Algorithm 2 (DADA2) pipeline implemented in QIIME 2 with q2-dada2 plugin and denoise-paired command (Callahan et al., 2016). The resulting amplicon sequence variants (ASVs) identified were curated using mumu by removing taxonomically redundant and erroneous ASVs. This involved constructing a database of the ASV sequences using makeblastdb application from the BLAST v.2.9.0 software suite, from which match lists were created using the blastn algorithm (query coverage: 80; percent identity cutoff: 84). These match lists and the ASV feature tables were input into the mumu algorithm to produce curated ASV tables. Singletons were excluded from the analysis. Taxonomic classification of the ASVs for bacteria and fungi was performed with pre-trained Naive Bayes classifiers and the q2-feature-classifier plugin (Bokulich et al., 2018). For the bacterial dataset, the SILVA v.138 database (Quast et al., 2013) was applied using a pre-trained classifier specifically curated for the sequenced 16SV3V4 region. In the case of the fungal dataset, the UNITE dynamic database v.8.3 (Abarenkov et al., 2021), which has been manually curated by experts in these particular fungal lineages, was used. Sequences assigned to mitochondria, chloroplasts and archaea were removed using the q2-taxa plugin in QIIME 2 and a taxonomy-based filtering step using the qiime taxa filter-seqs and qiime taxa filter-table commands.

Method steps

The taxonomic assignment to the ASVs identified in the analysis of Phytohthora species was performed by generating a reference database from a combination of sequences from five different sources: the UNITE dynamic database v.8.3 (consisting of 58,440 eukaryotic sequences), reference sequences from phytophthoradb (http://www.phytophthoradb.org/; 340 sequences), reference sequences from Phytopthora-id (http://Phytophthora-id.org; 270 sequences), 174 sequences of Phytophthora spp. from the database generated in Riddell et al. (2019), and 39,701 sequences from Genbank matching the search "oomycota 'internal transcribed spacer'". In the latter case, the taxonomy for the Genbank accessions was obtained using the taxonomizr package (v 0.10.2) in R v 4.2.2. Finally, the reference sequence database, namely the combined fasta file (98,925 sequences), and the associated taxonomy description file were imported into QIIME 2 and used together with the qiime feature-classifier plugin and the classify-consensus-blast command. As a first step, the sequences of the potential Phytophthora ASVs were aligned against the custom reference database using strict homology parameters (query coverage: 90; percent identity cutoff: 99) to ensure that successful matches belong to a Phytophthora species. The unaligned ASVs were submitted to a second step with relaxed parameters (query coverage: 75; percent identity cutoff: 65). The third step consisted of comparing the unassigned ASVs from the second step to the entire NCBI non-redundant protein database (release-255) using default parameters. In the fourth step, the low confidence ASVs assigned in the second and third steps were concatenated, aligned with MAFFT, and used to construct a maximum likelihood (ML) phylogenetic tree using RAxML (v 8.2.12; Stamatakis, 2014). The tree was inferred employed a general time reversible substitution model with a computational work–around (GTRCAT) without rate heterogeneity with a correction for ascertainment bias. Statistical support was calculated by applying bootstrap runs in an automated approach (autoMRE), where RaxML executes a maximum of 1000 BS replicate searches, although convergence may occur earlier. The best-scoring ML tree of the search analysis was then visualized using the software FIGTREE version 1.4.4 (Rambaut, 2018).

Taxonomic Coverages

Ascomycota
rank: phylum
Basidiomycota
rank: phylum
Glomeromycota
rank: phylum
Mortierellomycota
rank: phylum
Chytridiomycota
rank: phylum

Geographic Coverages

Huelva (Spain), Sevilla (Spain) and Córdoba (Spain)

Bibliographic Citations

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Bokulich, N.A., Kaehler, B.D., Rideout, J.R., Dillon, M., Bolyen, E., Knight, R., Huttley, G.A., Gregory Caporaso, J., 2018. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome 6, 90. https://doi.org/10.1186/s40168-018-0470-z -
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Contacts

Lorena Gomez Aparicio
originator
position: principal investigator
IRNAS-CSIC
Sevilla
ES

Sara García Garrido
originator
position: management technician
Instituto de Recursos Naturales y Agrobiología de Sevilla (IRNAS)
Av. Reina Mercedes, 10. Sevilla
Sevilla
41012
Sevilla
ES
Telephone: 954624711
email: sara.garcia@csic.es
userId: https://www.linkedin.com/profile/view?id=https://www.linkedin.com/in/saragargar/

Lorena Gomez Aparicio
administrative point of contact
position: Permanent Researcher
IRNAS-CSIC
Seville
Seville
ES
homepage: https://www.irnas.csic.es/en/lorena-gomez-aparicio/
userId: https://orcid.org/0000-0001-5122-3579

Cristina Zamora Ballesteros
administrative point of contact
position: postdoctoral researcher
Forest Genetics, Faculty of Environment and Natural Resources, Albert-Ludwigs-Universität Freiburg
Freiburg
DE
email: cristinazamoraballesteros@gmail.com
userId: https://orcid.org/0000-0002-9728-5553

Marta Gil Martinez
administrative point of contact
position: Postdoctoral Researcher
University of Copenhagen
Copenhagen
DK
email: marta.gil@irnas.csic.es
userId: https://orcid.org/0000-0001-7629-5130