Microbial sequencing
DNA was extracted from 0.75 g of soil using the Power Soil DNA
extraction kit (Qiagen) according to the manufacturer’s instructions.
The primers ITS4ngs and ITS3mix targeting the ITS2 region of fungal
genes (Tedersoo et al. 2015) and the primers 515FB and 806RB
(Caporaso et al. 2012; Apprill et al. 2015; Paradaet al. 2016) targeting the V4 region of the 16S rRNA gene in
bacteria were used. Presence of PCR product was verified using agarose
gel electrophoresis. The PCR products were purified using Agencourt
AMPure XP magnetic beads (Beckman Coulter). Adapters and barcodes were
added to samples using the Nextera XT DNA library preparation kit set A,
B, and C (Illumina, San Diego, CA, USA). The final PCR product was
purified again with AMPure beads, checked using agarose gel
electrophoresis and quantified using a Nanodrop spectrophotometer before
equimolar pooling. We pooled all fungal samples (192) from each time
point in one Illumina Miseq PE250 run and divided the bacterial samples
over two separate runs (96 samples each). With two time points analysed,
this resulted in two MiSeq runs for fungi and four runs for bacteria.
Libraries were sequenced at McGill University and Genome Quebec
Innovation Center. Extraction negatives and a mock community were used
and further sequenced in each sequencing run. This mock community
consisted of 10 fungal species and associated bacteria, and was included
to control for the potential variation between sequencing runs and to
increase the accuracy of the bioinformatics analysis.
Bacterial sequences and fungal sequences were analysed using the PIPITS
pipeline and the Hydra pipeline, respectively (Gweon et al. 2015;
De Hollander 2017). In short, fungal sequences were paired using VSEARCH
and quality was filtered using standard parameters of the pipeline. The
ITS2 region was extracted using ITSx (Bengtsson-Palme et al.2013). Short reads were removed, and sequences were clustered based on a
97% similarity threshold using VSEARCH and fungal chimeric sequences
were removed by comparing with the UNITE uchime database. The
representative sequences were identified using the RDP classifier
against the UNITE database (Abarenkov et al. 2010) and clustered
further into phylotypes. For bacterial sequences, VSEARCH was used to
pair and cluster sequences. For classification, the SINA classification
was used with the SILVA database. Fungi were assigned to potential
functions using FunGuild (Nguyen et al. 2016) and curated using
in-house databases (Hannula et al. 2017; Tedersoo et al.2015; Mommer et al. 2018). For potential-plant pathogenic fungi,
the target plant species was checked based on available literature
(Watanabe 2018). The sequences created in this study are deposited to
ENA with accession number PRJEB31856.
The data from the conditioning phase (May 2017) and two months after
establishment of the responding phase (September 2017) were analyzed
together. We first filtered out microbial taxa that were present in less
than ten samples and had an abundance of less than 0.01%. For ITS data,
the sequences derived from other organisms than fungi were removed and
for 16S data, sequences originating from mitochondria and chloroplasts
were removed. For both bacteria and fungi, samples with less than 1000
reads remaining were removed from the dataset and read numbers were
normalized using total sum scaling (TSS).
Mock communities were used to inspect the filtering done for fungi and
to compare the runs with each other. After filtering, we detected the
same 13 fungal OTUs in all mock communities sequenced and the abundances
of these OTUs in the mock communities in different sequencing runs were
highly correlated (Pearson correlation, R2=0.97,
p<0.001).