Artsprosjekt Endofyttisk sopp i trær 23-19

Data Records

The data in this occurrence resource has been published as a Darwin Core Archive (DwC-A), which is a standardized format for sharing biodiversity data as a set of one or more data tables. The core data table contains 303 records.

This IPT archives the data and thus serves as the data repository. The data and resource metadata are available for download in the downloads section. The versions table lists other versions of the resource that have been made publicly available and allows tracking changes made to the resource over time.

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How to cite

Researchers should cite this work as follows:

Norden B, Andreasen M (2022): Artsprosjekt Endofyttisk sopp i trær 23-19. v1.0. Norwegian Institute for Nature Research. Dataset/Occurrence. https://ipt.nina.no/resource?r=artsprosjekt_23_19_endophytes&v=1.0

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The publisher and rights holder of this work is Norwegian Institute for Nature Research. This work is licensed under a Creative Commons Attribution (CC-BY 4.0) License.

GBIF Registration

This resource has been registered with GBIF, and assigned the following GBIF UUID: bdfff433-2114-4b18-86cb-d2d281dbe097.  Norwegian Institute for Nature Research publishes this resource, and is itself registered in GBIF as a data publisher endorsed by GBIF Norway.

Keywords

Occurrence; Endopytic fungi; Ascomycota; Eurotiomycetes; Dothideomycetes

Contacts

Björn Norden

• Originator ●
• Point Of Contact

Seniorforsker

Norsk institutt for naturforskning

NO

bjorn.norden@nina.no

http://orcid.org/0000-0002-2739-9774

Mathias Andreasen

• Originator

Norsk institutt for naturforskning

NO

mathias.andreasen@nina.no

http://orcid.org/0000-0002-2390-8412

Roald Vang

• Metadata Provider

rv@nina.no

Geographic Coverage

Southern Norway

Taxonomic Coverage

Fungal endophyte diversity associated with living sapwood and twigs of beech, linden, maple, and oak trees in Norway. The dataset includes identifiyed samples of Ascomata from trunks and twigs, along with pure cultures, from core samples drilled from tree trunks, identified by Sanger sequencing together with morfological identification.

Temporal Coverage

Project Data

Trees harbor an enormous diversity of fungi spending at least part of their life-cycles as symptomless and secretive inhabitants. These, the so-called endophytic fungi, often form very species rich communities, but most species remain unidentified or undescribed. The primary objective of the project was to map fungal endophyte diversity associated with living sapwood and twigs of beech, linden, maple, and oak trees in Norway. A total of 193 trees were drilled and sapwood samples were taken for metabarcoding. Since identifying environmental sequences of Ascomycota involves many challenges and pitfalls (e.g., most species globally remain to be named and described, many described species do not have any available reference sequences, a relatively high percentage of reference sequences are known to be mis-labelled as well as numerous taxonomic issues), great care is needed when interpreting lists of species names generated by environmental sequencing and run against databases. By using 99.5% similarity with a known reliable sequence in UNITE and/or Genbank, by only considering sequences of over >250 base pairs in length, and checking original publications, we minimized the risk of misidentification. Sequencing of wood samples resulted in 105000 sequences and 1837 fungal species hypotheses. By applying our strict criteria for sequence data, we identified 60 Ascomycota species new to Norway. While relaxing the criteria for “safe identification” somewhat, more species could have been recorded, but we suggest that our criteria are reasonable. For the rest of the species hypotheses, the majority may represent so called “dark taxa”. These are equally scientifically important but how to handle them remains a great challenge. Knowledge of morphology and taxonomy are indispensable for identification, and it was our hope that complementing environmental sequencing with sampling of ascomata on trunks and twigs, and pure culturing from the drill samples, would increase the chances of reliable identification of fungal species. However, as the latter methods often failed to detect as many and the same species as the sequencing, the value of these complimentary methods was limited. Nevertheless, in a minority of cases we were able to confirm the identity by studies of anamorph and/or teleomorph morphology. Sampling of ascomata from trunks and twigs of the drilled trees yielded two additional species new to Norway including one undescribed species but showed little overlap with sequence data. Sampling from additional trees yielded two undescribed genera and several undescribed species (descriptions in progress). To generate data valuable for management strategies and the conservation of biodiversity, we compared species composition and species richness of our entire sequence dataset among the four studied tree species, trees of different sizes and among trees in three different regions of S Norway. Controlling for sample size, oak had significantly higher species density than Norway maple, beech, and linden. The effect of tree size on species density was small and not statistically significant and there was no effect of region. Regarding species composition, oak was significantly separate from the other three tree species, which all had largely over-lapping species communities. Tree size had a significant overall effect on species composition and there were significant interactions among tree species and the size of the trees. There was no significant difference in composition among the three regions. Our results emphasize the unique value of oak and large temperate deciduous trees for biodiversity and show that metabarcoding techniques can be very relevant for conservation-oriented studies of for instance the endophytic fungal communities in old trees. As such they can also generate a lot of new specific information about species new to Norway, distribution and ecology. However, only for a minority of species the identity can be decided with certainty, and complimentary morphology-based methods proved to be of limited value in this respect. For the future, Ascomycota biodiversity mapping projects based on ascomata collection, and were relevant, single spore isolation, pure culturing, morphological studies of the fungal holomorph, and multi-marker sequencing are needed. In addition, metabarcoding datasets can be useful for biodiversity mapping purposes if studied by taxonomists.

The personnel involved in the project:

Björn Norden

• Principal Investigator

http://orcid.org/0000-0002-2739-9774

Mathias Andreasen

http://orcid.org/0000-0002-2390-8412

Sampling Methods

10 cm trunk core samples was drilled with a sterilized wood drill from 193 trees. One sample from each tree was freeze dried for DNA metabarcoding and the other incubated at different medium in Petri duchess for isolation of mycelium for later Sanger sequencing and identification. Results from each of several methods used for the detection of Ascomycota species 1. Sampling of ascomata on trunks and twigs The bark of the living tree trunks drilled for DNA sequencing supported relatively few ascomata of the target groups. On oak, the most common species was Hysterium pulicare and Navicella pileata, but we also found for example Rebentischia unicaudata and Zignoella fallax. However, the most interesting find was a possibly undescribed species of Nigrograna, which is now under cultivation. On beech, Ascodichaena rugosa clearly dominated and we found little else of interest. On one maple, Decaisnella mesascium was found. Linden trees almost entirely lacked ascomata and we did not manage to find for example Peridiothelia fuliguncta that we looked specifically for. After one year of “incubation” in an outdoor environment the marked twigs were checked under the lens and about 40 different species of non-lichenized Ascomycota were noted along with lichenized ascomycetes such as Arthonia radiata, Arthonia sp, Arthopyrenia cinereopruinosa and Graphis scripta. Most of the species were asexual and represented genera with species that cannot be determined without sequencing like Cytospora spp (Valsa; 13 twigs), Diplodia (1), Fusarium (including Gibberella state; 3 twigs), Phoma (3). The most common species was Ascodichaena rugosa (14 finds on beech and oak). Other Ascomycota species included Cosmospora sp (2), Hymenoscyphus herbarum (1), and Nectria nigricans (1). Several coelomycetous species were impossible to identify even to genus based on morphology. The most interesting Ascomycota species was Lophiotrema cf tilia, which is now under cultivation and will be sequenced. Concerning basidiomycetes, only one twig with Cylindrobasidion evolvens and two with Peniophora incarnata were found. Twenty-five twigs had no fungi, perhaps indicating that the twigs may have been stored in a too dry environment.   2. Sequencing of wood samples The metabarcoding pipeline produced 105000 sequences which were automatically blasted against UNITEs database and resulted in 1842 species hypothesises. After DNA extraction and amplification, a total of 2181 OTUs were detected in 366 tree samples. Excluding the data collected from non-fungal species, a total of 1837 species hypotheses remained. Following quality filtering, results from 21 trees had no high-quality sequences and were excluded, which resulted in 187 samples left for analyses. Species new to Norway identified from DNA meeting rigorous criteria are listed in section 1.   3. Identification of anamorphs (asexual stages) in pure culture and Sanger sequencing of cultures Fungi were isolated and pure cultured from part of the same material that was used for metabarcoding. The most interesting cultures were thereafter Sanger sequenced and we matched the results from best hits (usually > 99%) in the UNITE database with morphological identification in about 40 cases.

Method step description:

1. A. Induction of ascomata formation in pure culture. Ascomata were formed in culture for just a few species, for example Chaetomium globosum and various species of Nectriaceae. As these are formed by single spore mycelia, they are often sterile (except for homothallic species) and for many species ascospore formation would probably need another mycelium of a complementary mating type. As we expected, ascomata formation made little contribution to our mapping results. Field sampling, DNA work and bioinformatics. We sampled wood from the trunks of Acer platanoides, Fagus silvatica, Quercus robur and Tilia cordata trees with diameter at dbh of 30-50 cm. We used an increment borer at breast height, taking one 20 cm-long and five mm in diameter wood core sample from the sapwood (incl. the bark) per tree. In addition, a living twig of similar dimensions was collected from each tree. To be able to compare methods we sampled all visible ascomata and characteristic asexual stages on 80 trees (20 of each tree species). We surveyed the bark of the entire trunk up to two m above ground, and carefully scrutinized the surface with a 10 × illuminated handlens. Most ascomata are very small, about 50% of ascomycetes < 1 mm (Purhonen 2018), and some down to 0.05 mm. Sporocarps will be identified in the laboratory. Björn Nordén will be responsible for identification and will supervise Mathias Andreasen. In addition, parts of collected fruit bodies will be used for DNA isolation and Sanger sequencing of ITS rRNA. Wood core samples were extracted by drilling 10 cm deep into the core wood and extracting a 5 mm diam × 10 cm wood core sample. During sampling, the increment borer was sterilised between samples by dipping in ethanol and burning with a gas burner in order to avoid cross-contaminations. Two wood samples were taken, one from the S and another from the N side of the tree, both drilled at a hight of 1.5 m and stored in sterile falcon centrifuge tubes at 4 ˚C. One wood core sample was used for A. Fungal cultivation and Sanger sequencing and incubated for fungal growth. The other wood core sample was used for B. Sawdust grinding and metabarcoding. Below we give a description of the DNA work and bioinformatics pipeline used and thereafter we describe the process of additional Blasting and the logic used for assigning species names to individual sequences. A. Fungal cultivation and Sanger sequencing Wood core samples were transferred onto a sterile tissue and 1 cm of the core sample containing the bark were removed with a sterile scalpel and discarded. Each wood core sample was then parted into two, disinfected in 33% H2O2 for 60 seconds, purified in distilled water for another 60 seconds and subsequently transferred to each its different medium on Petri dishes holding Hagem agar (0.5% malt extract, 2% agar in water, 0.05% NH4NO3, 0.05% KH2PO4, 0.05% MgSO4 0.05%, Glycose 0.5%) or malt agar (MEA: 3% malt extract, 1.5% agar in water). Cultures emerging from the wood core samples were immediately isolated to pure Petri diches and grown for later transfer of sterile mycelium for Sanger sequencing and microscopic examination. The method of Sanger sequencing follows the Andreasen et al. (2021) with modifications. DNA extraction and sequencing DNA was extracted from cultured mycelia using the Phire Plant Direct PCR Kit (Thermo Scientific, Waltham, USA) following the manufacturer’s manuals for both DNA isolation and Polymerase Chain Reaction (PCR). Efforts were made to amplify the ribosomal DNA regions of internal transcribed spacer (ITS) containing ITS1, 5.8S and ITS2. The following PCR protocols were used to amplify the molecular regions: 2 min at 95 °C, 40 cycles of 15 s at 95 °C, denaturation for 15 s at 95 °C, annealing at 20 s at 53 °C and followed by an elongation for 1 min and 10 s at 70 °C, with a terminal extension of 3 min at 70 °C. PCR products were checked with electrophoresis on 1.5% agarose gels. Five μL PCR product was purified with 0.2 μL ExoSAP-IT (GE Healthcare, Waukesha, WI) and 1.8 μL water. Samples were then run on a thermocycler at 37 °C for 15 min, followed by 80 °C for 15 min. Cleaned PCR product was diluted with 45 μL water per sample. Five μL PCR product and 5 μL sequencing primer was added to clean tubes and labelled before sequencing. Sanger sequencing was performed by Eurofins, Luxemburg. The bioinformatic for the sequences generated by Sanger sequencing were first assembled, edited, and visually controlled in the software Geneious Prime v. 2020.0.5 (Kearse et al. 2012). Filtering the sequences resulted in removing sequences shorter than 200 bp, primer dimer, homopolymers, and sequences difficult to read caused by low quality. Automated identification of fungal species relied on the GenBank (NCBI) database and the BLASTN algorithm. During identification, the criteria used were sequence coverage >80%, genus-level 94-97%, and species-level ≥ 98%. If the criteria were not achieved, the samples were unidentified. Taxa identified as Lecanoromycetes were excluded because most of the species belonging to the taxa represent lichen mycobionts. Moreover, it could result from contamination from the bark during sampling. B. Sawdust grinding and metabarcoding DNA sampling The freeze-dried wood core samples were firstly sterilized for 60 seconds in 33% H2O2, purified in distilled water for another 60 seconds and subsequently grinded down to sawdust samples. Individual sawdust samples used for DNA extraction were weighed at 2-g increments and sealed in filter paper packages that were freeze-dried at 105 ˚C for 72 h in a VirTis SP Scientific Freeze Dryer (SP Industries Inc., Suffolk, UK). Remaining sawdust was stored at -20 ˚C in sterile zip lock bags. 2 mL centrifugation screw cap tubes containing 0.4 g freeze-dried sawdust from the individual trees and a 3% CTAB solution were homogenised through metal bolts with a Precellys 24 homogeniser (Bertin Corp, Rockville, MD, USA). The resulting solution, after homogenisation, was then incubated at a temperature of 65 ˚C for 1.5 h and later cleaned with chloroform. The upper phase was cleansed using the Technum NucleoSpin soil kit (Macherey-Nagel, Düren, Germany), and the upper phase was cleaned following the recommendations from the company. An ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) and the DNA concentration were changed to 10 ng/mL resulting in an evaluation of the DNA content. The DNA samples were amplified using a primer pair gITS7, and ITS4 to individual amplify each sample. The two primers contained sample identification barcodes and resulted in 208 barcode samples. 15 µl reactions containing an amount of 1.2 µl DNA, 1% Taq polymerase (5 u/ µl), (DreamTaq Green, Thermo Scientific, Waltham, USA), 11% of 10 x buffer, 11% dNTPs (10mM), 1% MgCl₂ (25 nM), 2% each primer (200 mM) and 72% Milli-Q water were used to run the PCR. The reactions were put through Applied biosystems 2700 thermal cycler (Applied Biosystems, Foster City, CA, USA) and performed 35 cycles. The process steps for the amplification consisted of initial denaturation at a temperature of 95 ˚C for 2 min followed by 30 s denaturation at 95 ˚C, 30 s annealing at 56 ˚C, continued with further annealing for 1 min at 72 ˚C with a terminal extension of 7 min at 72 °C. The amplified samples from the PCR were placed on a 1.5% agarose gel (Agarose D1, Conda, Madrid, Spain) with an electrophoresis scan performed for 30 min with 300 V using QuantityOne software (Piovanelli, 2006). The resulting DNA concentration from the electrophoresis was analysed/evaluated with a NanoDrop 3300 flurospectrometer (Thermo Fisher Scientific, Waltham, MA, USA). After evaluation, the 208 equimolar PCR samples were produced and cleansed using EZN.A cycle pure kit (Omega Bio-tek, Norcross, GA, USA). The quality was controlled with NanoDrop 3300 flurospectrometer and an Invitrogen QUBIT fluorometer (Fisher Scientific, Loughborough, UK). The sequencing was completed at SciLifeLab (Uppsala Genome Centre, NGI Uppsala, Sweden) using the Pacific Biosciences (PacBio RSII) platform. The bioinformatic sequences generated were controlled and clustered in the SCATA NGS sequencing pipeline. Filtering the sequences resulted in removing sequences shorter than 200 bp, primer dimer, homopolymers, and sequences difficult to read caused by low quality. The cause of the removal was collapsing to 3 bp during sequencing. Also, sequences with missing barcodes or primers were removed. The barcodes and primers were removed but stored as meta-data to keep the information. The sequences clustered together in different taxa with single-linkage clustering as a method that is based on 98% similarity. The most commonly occurring taxa were chosen to represent the taxon. The automated identification of fungal species relied on the GenBank (NCBI) database and the BLASTN algorithm. During identification, the criteria used were sequence coverage >80%, genus-level 94-97%, and species-level ≥ 98%. If the criteria were not achieved, the samples were unidentified. Taxa identified as Lecanoromycetes were excluded because most of the species belonging to the taxa represent lichen mycobionts. Moreover, it could result from contamination from the bark during sampling. For both method A. (Sanger sequencing) and B. (metabarcoding) the result from the samplings and automated analysis were cautiously interpreted due to the risk of contamination with different fungi and we used our ecological and taxonomic knowledge to exclude such cases. For reporting any species as new to Norway based on DNA data alone, we used > 99.5% similarity with a known reliable sequence in UNITE and/or Genbank as a threshold. Only sequences of over >250 bp in length were considered. We then manually blasted each sequence against both databases and noted congruence/incongruence for the best hits, synonyms et c. Thereafter, we checked the sources given and in case of doubt read any published papers. Unpublished records that could not be traced were not considered further. This resulted in a rather short but well-pruned and credible list compared to the OTUs or species hypotheses produced by the automated process.

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