Saproxylic fungi of fine woody debris studied by metabarcoding-based MycoPins method in Oulanka, Finland, 2022-2023

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Shumskaya M, Lim J, Saarinen P K, Apgar S, Hoyte B, Nunez M, Gayathri M S, Vengine L, Salib C, Seidle M, Inoa A, Nguyen T, Twdroos J, Luna A, Herrera-Juarez J (2025). Saproxylic fungi of fine woody debris studied by metabarcoding-based MycoPins method in Oulanka, Finland, 2022-2023. Version 1.14. Kean University. Occurrence dataset. https://ipt.gbif.us/resource?r=mycopinsfinland23-24&v=1.14

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此資料的發布者及權利單位為 Kean University。 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: 63283fef-d82f-40ba-9346-c4810e9690dc。  Kean University 發佈此資源，並經由GBIF-US同意向GBIF註冊成為資料發佈者。

關鍵字

dead wood; molecular ecology; metabarcoding; Samplingevent; Specimen

聯絡資訊

地理涵蓋範圍

Oulanka Research Station https://eu-interact.org/field-sites/oulanka-research-station/ 25 km south of the Arctic Circle Sub-Arctic (Boreal zone) No permafrost

分類群涵蓋範圍

Saproxylic fungi from Ascomycetes and Basidiomycetes were identified from DNA extracted from saw dust of wooden pins (pine, spruce, birch) using MycoPins method (Shumskaya, 2023).

時間涵蓋範圍

計畫資料

The MycoPins (previously: ezDowels) project aimed to develop a rapid affordable protocol to monitor early colonization events in communities of boreal wood-inhabiting (saproxylic) fungi. Building upon research experiences of applicants, earlier INTERACT data, and the latest metabarcoding methods, setting and testing of sampling techniques was performed, as well as sample processing, data processing and analysis of the development of early dead wood fungal communities. The fieldwork carried out at INTERACT sites was followed by the laboratory analysis at Kean university. The project aimed to add a new monitoring method for INTERACT portfolio, and pave a way for a broader scale Remote Access project, and establish a well-documented internationally standardized data publishing routine for research stations to increase visibility and current Virtual Access through global data discoverability portals.

參與計畫的人員:

取樣方法

A sextet of sterilized wooden pins of three types of wood, each in a duplicate (softwood - pine and spruce, and hardwood - birch), were placed on the top soil along three different sampling sites: transect A - conifer forest with reindeer access, transect B - conifer forest without reindeer access, transect C - broadleaf forest with access to tourists. The pins were collected approximately every 2 weeks during summer and fall seasons between July 1, 2022 and October 6, 2023, with the exceptions for the time periods when the transects were not accessible (e.g. winter).

方法步驟描述:

1. MycoPin placement Three 10 m wires (transects) were prepared with pin sets (MycoPins) attached to each one of them at every meter. Each MycoPin set consisted of 6 pins (a sextet): a pair of pine pins (softwood), a pair of birch pins (hardwood), and a pair of spruce pins (softwood). Each sextet was labeled with a sample number (six numbers and a letter: A and B for pine, C and D for birch, E and F for spruce). Each transect was attached to a tree and then placed on the top soil with the pin sets buried under the top leaf and soil matter. One transect was placed at three different sampling sites: (A) An area of a boreal forest unprotected from reindeers. (B) An area of a boreal forest located next to A, but protected from grazing by reindeers. (C) An area of a mixed broadleaf forest, accessed by random visitors.
2. Extraction and storage. One MycoPin sextet from each transect was located using the wire transect as a guide and collected every two weeks with exceptions for weather conditions. The collected sextets were dried in separate waxed paper bags for 2-3 hours at 45°C and stored dry at room temperature.
3. DNA isolation. The core of each pin from each sexted was drilled using a 2 mm fire-sterilized drill bit. The resultant sawdust was collected in a sterile centrifuge tube. The sawdust was then used to isolate genomic DNA using PowerSoil DNA Isolation kit from Qiagen (USA) according to the manufacturer instructions. Homogenization was performed using BeadBug homogenizers (BenchMark Scientific). DNA concentration was measured using NanoDrop (ThermoFisher). Genomic DNA was stored at −80°C.
4. PCR. Tagged primers for the ITS2 fungal region were used to perform PCR according to Clemmensen (2016). Forward and reverse primers were ITS7 andITS4, . Using this F/R primer sequence, a set of 40 tagged primer pairs was generated to individualize each PCR procedure. 10-nucleotide long unique tags were added to primers during oligonucleotide synthesis. Each DNA extracted from each MycoPin was subjected to PCR with a uniquely tagged primer pair. The amplification was verified via agarose gel electrophoresis. The amplified DNA was purified and stored at −20°C. E.Z.N.A® Cycle Pure Kit (Omega Bio-tek) was used for the amplicons purification. Positive control was used to verify the PCR and subsequent NGS in a form of mock fungal community made of 12 plasmids (Palmer, 2018), negative control (water) was used to exclude false-positive results. Tagging of PCR fragments allowed for mixing them into a single multiplex for a subsequent Next Generation Sequencing; the resultant sequence file can be sorted into clusters by tags, allowing to segregate individual amplicons.
5. Next-Generation Sequencing. The amplified tagged DNA samples were combined at equal amounts of 100 ng to create a multiplex for next-generation sequencing. The multiplex was sequenced using AmpliconEZ service at Genewiz (Azenta Life Sciences, New Jersey, USA).
6. Bioinformatics.

   Two paired FASTQ files for each multiplex were analyzed using SCATA (https://scata.mykopat.slu.se/), a bioinformatic tool designed for analyzing sequenced tagged amplicons. The FASTQ files were uploaded and verified as SCATA datasets. Low quality sequences were excluded, similar sequences were clustered, and abundance data for each cluster was calculated.

   The sequence quality was based on several criteria using SCATA default parameters, they include (1) a 90% primer match on tag identification, (2) a minimum sequence length of 200, (3) a minimum base quality of 10, and (4) a minimum mean base quality of 20. The FASTQ files were overlapped and merged. Kmer size for overlap search was set to 7. The minimum number of adjacent kmers to form high-scoring segment pairs during overlap search was set to 5. The minimum number of shared kmers to merge a read pair was set to 10.

   SCATA uses the USEARCH algorithm for clustering. Using the SCATA clustering criteria defaults, the clustering distance was set to 0.015, the minimum proportion of the longest sequence in a sequence pair to consider for clustering was set to 0.85, the penalty for mismatch was set to 1, and no penalty was set on an introduction of an open gap. However, a penalty of 1 is incurred for each succeeding gap. No weights were used for end gaps. Homopolymers longer than 3 before clustering were collapsed. No downsampling and no removal of low frequency genotypes were performed during clustering. Up to 3 representative sequences were reported for each cluster. Double clusters and clusters present in positive and negative controls were excluded.

   Each set of representative sequences is matched to a species found in the UNITE v. 9.0 (2023-07-18) fungi database. For each of the clusters without a match, a BLASTn search against the NCBI database was performed. The search result with the lowest e-value and the highest percent identity was considered the best match species for the cluster. The BLASTn match results with a score less than 200 were excluded. If there are multiple best matches, the first match in the best match list is selected.

   The abundance data (DNA sequence reads) of the same species were amalgamated.

   Each species identification was aligned with the taxonomy of GBIF Backbone using statistical software R and rgbif package v. 3.7.9. Non-fungal species were rejected. Fungal species not identified on the genus-level, at the minimum, were also discarded. Fungal traits were assigned according to FungalTraits database (from an Excel sheet, supplementary data of Põlme, 2020). Historical weather data for each transect were gathered from Weatherstack (www.weatherstack.com).

   Note:The abundance data is used as the Organism Quantity in the GBIF occurrence dataset with “DNA sequence reads” as the Organism Quantity Type. The value is used to pertain to the abundance of a species relative to other species present for a particular event (date-transect-substrate).

收藏資料

引用文獻

1. Shumskaya M, Lorusso N, Patel U, Leigh M, Somervuo P, Schigel D (2023) MycoPins: a metabarcoding-based method to monitor fungal colonization of fine woody debris. Mycokeys: 77-95. 10.3897/mycokeys.96.101033
2. Clemmensen KE, Ihrmark K, Durling MB, Lindahl BD (2016) Sample Preparation for Fungal Community Analysis by High-Throughput Sequencing of Barcode Amplicons. Methods in Molecular Biology (Clifton, NJ). Humana Press: New York, NY, USA, 61-88. 10.1007/978-1-4939-3369-3_4
3. Palmer JM, Jusino MA, Banik MT, Lindner DL (2018) Non-biological synthetic spike-in controls and the AMPtk software pipeline improve mycobiome data. Peerj 6: e4925. 10.7717/peerj.4925
4. Abarenkov, K.; Zirk, A.; Piirmann, T.; Pöhönen, R.; Ivanov, F.; Nilsson, H.; Kõljalg, U.(2023): UNITE general FASTA release for Fungi 2. Version 18.07.2023. UNITE Community. 10.15156/BIO/2938068
5. Põlme, S., Abarenkov, K., Henrik Nilsson, R., Lindahl, B. D., Clemmensen, K. E., Kauserud, H., Nguyen, N., Kjøller, R., Bates, S. T., Baldrian, P., Frøslev, T. G., Adojaan, K., Vizzini, A., Suija, A., Pfister, D., Baral, H. O., Järv, H., Madrid, H., ... Pradeep, C. K. (2020). FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Diversity, 105(1), 1-16. 10.1007/s13225-020-00466-2

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