2 MATERIAL AND METHODS
2.1 Sampling and DNA
extraction
Microhabitat samples were collected in October 2017 in cooperation with
the Canopy Crane Facility in the floodplain forest in Leipzig, Germany
(51.3657 N, 12.3094 E). We sampled three different specimens of three
autochthonous tree species: The small-leaved lime (Tilia
cordata ), the European ash (Fraxinus excelsior ) and the
pedunculate oak (Quercus robur ). The samples can be classified
into two strata: (i) canopy samples and (ii) ground samples. Canopy
samples were taken at 20 to 30 m height with replicates at all four
cardinal directions of each tree. At each of the four replicate sites on
every tree, following seven microbial microhabitat compartments related
to tree surface were sampled: Fresh leaves, dead wood, bark, arboreal
soil and three cryptogam epiphytes (lichen and two moss genera,Hypnum and Orthotrichum ). In addition, two ground samples
(soil and leaf litter with four replicates per tree) at 2 m distance
from each trunk were sampled. The soils were collected at the surface
layer (~10 cm depth after removal of leaf litter and
stones) throughout each station. All 324 samples were stored at -22°C
until further processing. For DNA extraction, all canopy and litter
samples were decorticated and/or chopped with a sterile razor blade and
cut into small, regular pieces. DNA extraction was done according to the
manufacturer’s instruction with the DNeasy PowerSoil kit (QIAGEN,
Hilden, Germany). DNA concentration and quality were checked using a
NanoDrop Spectrophotometer (NanoDrop Technologies, Wilmington, USA). For
following PCR amplification, all four replicates of each microhabitat
per tree were pooled.
2.2 PCR amplification, barcoding and
sequencing
PCRs with taxon specific primers were conducted in two steps. The
hypervariable V4 region of the 18S ribosomal RNA gene (SSU rDNA) was
used for cercozoan community profiling with specific primers
(Fiore-Donno, Richter-Heitmann, & Bonkowski, 2020). For the first PCR
the forward primers S616F_Cerco and S616F_Phyt were mixed in the
proportions of 50% and 50%, and used with the reverse primer
S963R_Phyt. For a following semi‐nested PCR a mixture of the reverse
primers S947_Phyt and S947_Vamp in an equal proportion has been used.
The thermal program consisted of an initial denaturation step at 95°C
for 2 min, 24 cycles at 95°C for 30 s, 52°C for 30 s, 72°C for 30 s; and
a final elongation step at 72°C for 5 min. For amplifying the ITS 1 of
the oomycete communities we used the specific primer pair ITS_177F and
58SR_Oom (Fiore-Donno & Bonkowski, 2020). Amplicons of the first PCR
were again used as template for a semi-nested PCR with the primer pair
I1786F_Stra and 58SR_Oom. The thermal program started with a
denaturation step at 95°C for 2 min, followed by 24 cycles at 95°C for
30 s, 58°C for 30 s, 72°C for 30 s; and a final extension step at 72°C
for 5 min.
We used 1 μl of DNA template for the first PCR amplification and 1 μl of
the obtained amplicons as a template for a second semi‐nested PCR which
was conducted with tagged primers. Tags were designed as described in
Fiore-Donno, Richter-Heitmann, & Bonkowski (2020). The used primers and
tag combinations are provided in Supplementary Tables 3 and 4.
We applied the following final concentrations: DreamTaq polymerase
(Thermo Fisher Scientific, Dreieich, Germany) 0.01 units, Thermo
Scientific DreamTaq Green Buffer, dNTPs 0.2 mM and primers 1 µM. To
reduce the artificial dominance of few amplicons by PCR competition, all
PCRs were carried out twice. PCR products were pooled, then purified and
normalized using SequalPrep Normalization Plate Kit (Invitrogen GmbH,
Karlsruhe, Germany). Sequencing was performed with a MiSeq v2 Reagent
kit of 500 cycles for the shorter ITS amplicons (c. 250 bp) of Oomycota
and a MiSeq v3 Reagent kit of 600 cycles for the amplified V4 Region
fragments (c. 350 bp) of Cercozoa. Sequencing was conducted by a MiSeq
Desktop Sequencer (Illumina Inc., San Diego, CA, USA) at the Cologne
Center for Genomics (Germany).
2.3 Sequence processing
All bioinformatic and statistical methods were applied to both Oomycota
and Cercozoa datasets independently if not stated otherwise. Raw reads
were merged using vsearch v2.10.3 (Rognes et al. , 2016) at
default settings. Merged contigs were demultiplexed with cutadapt v1.18
(Martin, 2011) allowing no mismatches in neither primer nor tag
sequence. Cutadapt was also used to trim primer and tag sequences after
demultiplexing. Sequences were then clustered into operational taxonomic
units (OTUs) using swarm v2.2.2 (Mahé et al. , 2015) with d
= 1 and fastidious option on. Chimeras were de novo detected
using vsearch. OTUs were removed from the final OTU table if they were
flagged as chimeric, showed a quality value of less than 0.0002, were
shorter than 150bp (Oomycota) or 300bp (Cercozoa), or were represented
by less than 0.005% of all reads (i.e. 141 reads for Oomycota or 269
reads for Cercozoa).
For taxonomic assignment, OTUs were first tentatively assigned by using
BLAST+ v2.9.0 (Camacho et al. , 2009) with default parameters
against the non-redundant NCBI Nucleotide database (as of June 2019).
OTUs were removed if the best hit in terms of bitscore was a
non-oomycete or non-cercozoan sequence, respectively. For a finer
taxonomic assignment, two databases were used: The PR2 database
(v4.12.0, Guillou et al. , 2012) served as a taxonomic reference
set for cercozoan V4 sequences, while for the Oomycota all available
oomycete sequences were downloaded from NCBI Nucleotide (as of July
2019). Both databases were used as a template for an in-silicoPCR with cutadapt, with the same primer sequences used in this study.
The resulting virtual amplicons served as a database with the same
length and genetic origin as our sequenced amplicons, which offers the
advantage of penalising terminal gaps during the taxonomic annotation -
which was performed with vsearch. The annotation was refined by
assigning the species name of the best vsearch hit to the corresponding
OTU if the pairwise identity was over 95%. OTUs with lower percentages
were assigned higher taxonomic levels.
To account for random effects due to low sequencing depth, the final OTU
table was loaded into Qiime2 v2018.11 (Bolyen et al. , 2019) to
explore the sequencing depth by sample metadata. The minimum sequencing
depth was determined depending on how many samples per metadata would be
excluded. It was set as high as possible while retaining at least five
samples per microhabitat and 15 samples per tree species.
2.4 Statistical analyses
All statistical analyses were conducted in R v3.5.3 (R Core Team, 2019).
Rarefaction curves were carried out with the iNEXT package (Chaoet al. , 2014; Hsieh, Ma, & Chao, 2019) to determine if a higher
sequencing depth would have revealed more OTUs. Alpha diversity indices
were calculated for each sample using the diversity function in
the vegan package (Oksanen et al. , 2019). Both former methods
were applied on the OTU table with absolute abundances. To explore
differences in the community composition across the samples, the
following beta diversity-based methods were conducted on relative
abundances. Non-metric multidimensional scaling was performed on the
Bray-Curtis dissimilarity matrix of the log transformed table (functionsvegdist and metaMDS in the vegan package, respectively).
The same method was used in a permutational multivariate analysis of
variance (permANOVA, function adonis ), to test if oomycete and
cercozoan OTU diversity differed across the strata, habitats and tree
species. To analyse the effects of environmental factors to the variance
of the community composition, a redundancy analysis was carried out on
the Hellinger-transformed table (function rda in the vegan
package). The function nestedtemp was used to test if the
community of a microhabitat might be a subset of a larger one. Species
accumulation curves were calculated using the specaccum function
and the number of shared OTUs between different combinations of
microhabitats was visualised using the UpSetR package (Lex et
al. , 2014; Gehlenborg, 2019). All figures were plotted with the ggplot2
package (Wickham, 2016).