說明
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.
資料紀錄
此資源sampling event的資料已發佈為達爾文核心集檔案（DwC-A），其以一或多組資料表構成分享生物多樣性資料的標準格式。 核心資料表包含 506 筆紀錄。
亦存在 1 筆延伸集的資料表。延伸集中的紀錄補充核心集中紀錄的額外資訊。 每個延伸集資料表中資料筆數顯示如下。
此 IPT 存放資料以提供資料儲存庫服務。資料與資源的詮釋資料可由「下載」單元下載。「版本」表格列出此資源的其它公開版本，以便利追蹤其隨時間的變更。
版本
以下的表格只顯示可公開存取資源的已發布版本。
如何引用
研究者應依照以下指示引用此資源。:
Gomez Aparicio L, García Garrido S (2023). Fungal members of the rhizosphere microbiota of Quercus ilex subsp. ballota. Version 2.1. Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC). Samplingevent dataset. https://doi.org/10.15470/jow7hd
權利
研究者應尊重以下權利聲明。:
此資料的發布者及權利單位為 Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC)。 This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.
GBIF 註冊
此資源已向GBIF註冊，並指定以下之GBIF UUID: 0360980c-a656-482a-a689-3c1f1ee3f7d9。 Instituto de Recursos Naturales y Agrobiología de Sevilla (CSIC) 發佈此資源，並經由GBIF Spain同意向GBIF註冊成為資料發佈者。
關鍵字
samplingEvent; Biodiversity; Edafic Biodiversity
聯絡資訊
- 連絡人
- 出處
- 連絡人
https://orcid.org/0000-0001-5122-3579
- 連絡人
- 連絡人
地理涵蓋範圍
Huelva (Spain), Sevilla (Spain) and Córdoba (Spain)
| 界定座標範圍 | 緯度南界 經度西界 [-90, -180], 緯度北界 經度東界 [90, 180] |
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分類群涵蓋範圍
無相關描述
| Phylum | Ascomycota, Basidiomycota, Glomeromycota, Mortierellomycota, Chytridiomycota |
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計畫資料
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.
| 計畫名稱 | Sustainability for Mediterranean Hotspots in Andalusia integrating LifeWatch ERIC (SUMHAL). Working package 7: Improving sustainability of Mediterranean forests and silvopastoral agrosystems under climate change |
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| 辨識碼 | LIFEWATCH-2019-09-CSIC-4, POPE 2014-2020 |
| 經費來源 | This study was funded by MICINN through European Regional Development Fund [SUMHAL, LIFEWATCH-2019-09-CSIC-13, POPE 2014-2020] and by the Spanish Ministry of Economy, Industry and Competitiveness [AGL2015-66048-C2-1-R; RTI2018-098015-B-I00]. To be referred from 2023 onwards as SUMHAL, LIFEWATCH-2019-09-CSIC-4, POPE 2014-2020. |
| 研究區域描述 | 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. |
| 研究設計描述 | 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. |
參與計畫的人員:
取樣方法
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.
| 研究範圍 | 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. |
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方法步驟描述:
- 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).
引用文獻
- Abarenkov, K., Zirk, A., Piirmann, T., Pöhönen, R., Ivanov, F., Nilsson, R.H., Kõljalg, U., 2021. UNITE QIIME release for Fungi. Version 10.05.2021. [WWW Document]. UNITE Community. URL https://doi.plutof.ut.ee/doi/10.15156/BIO/1264708 (accessed 1.17.23)
- 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
- Bolyen, E., Rideout, J.R., Dillon, M.R., Bokulich, N.A., Abnet, C.C., Al-Ghalith, G.A., Alexander, H., Alm, E.J., Arumugam, M., Asnicar, F., Bai, Y., Bisanz, J.E., Bittinger, K., Brejnrod, A., Brislawn, C.J., Brown, C.T., Callahan, B.J., Caraballo-Rodríguez, A.M., Chase, J., Cope, E.K., Da Silva, R., Diener, C., Dorrestein, P.C., Douglas, G.M., Durall, D.M., Duvallet, C., Edwardson, C.F., Ernst, M., Estaki, M., Fouquier, J., Gauglitz, J.M., Gibbons, S.M., Gibson, D.L., Gonzalez, A., Gorlick, K., Guo, J., Hillmann, B., Holmes, S., Holste, H., Huttenhower, C., Huttley, G.A., Janssen, S., Jarmusch, A.K., Jiang, L., Kaehler, B.D., Kang, K.B., Keefe, C.R., Keim, P., Kelley, S.T., Knights, D., Koester, I., Kosciolek, T., Kreps, J., Langille, M.G.I., Lee, J., Ley, R., Liu, Y.-X., Loftfield, E., Lozupone, C., Maher, M., Marotz, C., Martin, B.D., McDonald, D., McIver, L.J., Melnik, A.V., Metcalf, J.L., Morgan, S.C., Morton, J.T., Naimey, A.T., Navas-Molina, J.A., Nothias, L.F., Orchanian, S.B., Pearson, T., Peoples, S.L., Petras, D., Preuss, M.L., Pruesse, E., Rasmussen, L.B., Rivers, A., Robeson, M.S., Rosenthal, P., Segata, N., Shaffer, M., Shiffer, A., Sinha, R., Song, S.J., Spear, J.R., Swafford, A.D., Thompson, L.R., Torres, P.J., Trinh, P., Tripathi, A., Turnbaugh, P.J., Ul-Hasan, S., van der Hooft, J.J.J., Vargas, F., Vázquez-Baeza, Y., Vogtmann, E., von Hippel, M., Walters, W., Wan, Y., Wang, M., Warren, J., Weber, K.C., Williamson, C.H.D., Willis, A.D., Xu, Z.Z., Zaneveld, J.R., Zhang, Y., Zhu, Q., Knight, R., Caporaso, J.G., 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol 37, 852–857. https://doi.org/10.1038/s41587-019-0209-9
- Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583. https://doi.org/10.1038/nmeth.3869
- Martin, M., 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12. https://doi.org/10.14806/ej.17.1.200
- Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., Glöckner, F.O., 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Research 41, D590–D596. https://doi.org/10.1093/nar/gks1219
- Rambaut, A., 2018. FigTree.
- Riddell, C.E., Frederickson-Matika, D., Armstrong, A.C., Elliot, M., Forster, J., Hedley, P.E., Morris, J., Thorpe, P., Cooke, D.E.L., Pritchard, L., Sharp, P.M., Green, S., 2019. Metabarcoding reveals a high diversity of woody host-associated Phytophthora spp. In soils at public gardens and amenity woodlands in Britain. PeerJ 7, e6931. https://doi.org/10.7717/PEERJ.6931/SUPP-3
- Stamatakis, A., 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313. https://doi.org/10.1093/bioinformatics/btu033
額外的詮釋資料
| 替代的識別碼 | 10.15470/jow7hd |
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| 0360980c-a656-482a-a689-3c1f1ee3f7d9 | |
| https://ipt.gbif.es/resource?r=fungal-rhizo-quercus |