Main objectives

pond.Photo: S.Anslan

The project is organized into five work packages (WP’s):

WP1. Extending the DNA reference sequence database UNITE to arctic fungi

Our aim is to include about 1500 new fungal accessions from arctic environments to UNITE. Fruit-body collections from arctic environments are already available at the Mycological Herbaria in Oslo and Copenhagen. However, it is difficult to obtain high quality DNA from decades old specimens. Hence, during 2013, a field cruise covering large parts of the Svalbard archipelago will be conducted. Freshly collected specimens are vouchered and subjected to DNA extraction and amplification of the nuclear rDNA ITS region, and partial LSU gene that will be sequenced using Sanger method that provides the best sequence quality. Lab technicians in Oslo and at the University Center at Svalbard will conduct the sequencing and the obtained reference data will be incorporated into the UNITE database.


WP2. Validating high-throughput sequencing approaches

Various high-throughput sequencing approaches are compared for analysis of fungal communities. First, we will perform sequence analysis of fungal mock communities, where the taxonomic composition (and biomass of each species) is known. Different fungal mock communities will be established from fungal cultures, including primarily genome sequenced fungal strains, where information about the number of rDNA repeats is available. From the mock communities, both ITS and partial LSU will be targeted. Quantitative PCR will be run in parallel to check the relative abundance of taxa, which will be compared to their abundance in the obtained sequence data. We will also add different levels of ‘control DNA templates’ to the mock communities (‘spiking’) to evaluate qualitative and quantitative biases. Second, environmental samples (from plant roots) collected during the field studies will be analyzed using the same sequencing techniques. We will here implement the spiking approach to improve quantitative comparisons across samples.

            The Norwegian Sequencing Centre (NSC), University of Oslo, will be used for the sequence analyses. NSC (http://www.sequencing.uio.no/services/) is a well-equipped sequencing facility currently operating the following sequencing systems: 1) Illumina MiSeq, 2) Roche GS FLX (454), 3) IonTorrent (PGM and Proton), and 4) PacBio. The mock community samples, and environmental samples will be analysed using all these four approaches and compared. New emerging approaches (e.g. nanopore sequencing) will also be evaluated when these become commercially available.


WP3. Data mining from reference data and high-throughput sequencing
Different bioinformatics data-mining approaches will be evaluated for the combined analysis of fungal reference sequence data and environmental high throughput sequence data from multiple datasets. Our primary aim is to develop a bioinformatics tool for rapid parsing of high-throughput sequence data and associated metadata that are typically deposited in different formats in separate databases. To improve taxonomic placement of the relatively short environmental sequences, we will incorporate alignment and phylogenetics tools with clustering algorithms for automated assignment of sequences into operation taxonomic units that carry an identification tag from the reference phylogenies. The algorithm will use additional data from other genes (such as LSU and protein-encoding genes) and genomes for more robust backbone phylogeny. The workflow resulting from WP3 will be implemented in WP4 and WP5, and thus form a basis for identification in ecological studies.


WP4. Phylogenetic diversity of key fungal groups
Recent studies have revealed a vast diversity of several fungal groups in the arctic, many of which are associated with ectomycorrhizal plants. The basidiomycete groups Inocybe (Agaricales), Sebacinales and Thelephorales, and the ascomycete order Helotiales stand out as especially species rich but poorly studied groups in the arctic (Geml et al. 2011; Blaalid et al. 2012; Timling et al. 2012). In WP4 we will perform combined analyses of reference sequence data from these three groups (generated in WP1, and other data) and environmental sequence data (generated in WP2 and WP5) using bioinformatics approaches established in WP3. The analyses will reveal the phylogenetic diversity of these ecologically important groups and shed light into the proportion of belowground diversity that cannot be recovered from fruit-bodies.

 

WP5. Uniqueness of arctic fungi

In this WP, we seek to establish biogeographic patterns and affinities of arctic fungi. It has been recently suggested that arctic ectomycorrhizal fungi display negligible dispersal barriers since the majority of species exhibit circumarctic distribution (Geml et al. 2011). We hypothesize that certain fungi are adapted exclusively to arctic conditions, but most of the mycota constitutes a depauperate (nested) version of the community encountered in boreal forests, mires and meadows. First, we will establish parallel latitudinal transects through Scandinavia and the Baltic states to document changes in fungal richness and community composition in relation to climate and changing vegetation. Second, we will sample roots from relict populations of arctic plants in temperate habitats to be able to understand the relative role of host plant species in the distribution of arctic ectomycorrhizal fungi. Third, we combine previously published data from typical arctic plants from arctic and temperate and Alpine habitats. We will use both Sanger sequencing and high-throughput sequencing for identification and traditional community-level statistics plus comparative phylogenetics tools to model the relative effects of climate, soil nutrients, plant species and confounding spatial distance (i.e. autocorrelation) on dominant fungal species, major lineages and the entire community. This latitudinal gradient will also be applied to predict long-term and large-scale effects of climate warming on arctic fungi.