Study site and sampling strategy
The sampling site is located in one of the Chinese truffle hotspots in Huidong county (26°22'48"N, 102°24'36"E, 2745 m a.s.l.) (Fu et al. 2016), Sichuan province, southwest China. The site is a pure P. armandii Franch forest with the following ecological traits: annual variation of air temperature ranged between 11 ~ 24°C; annual precipitation was 1099 mm (Fu et al. 2016), sandy loam (Haplic Luvisol, FAO Soil Classification System) soil (Fu et al. 2016). Sampling of soil and truffles was carried out at the truffle producing period on December 2018. In order to capture the variation present in the forest, we adopted a “3 × 6” sampling strategy, that is, 3 plots (100 m away from each other; size 200 × 200 m) were chosen as field ecological replicates, within each plot we randomly digged six truffles. Soil samples in each plot were respectively collected from soil around and below the ascomata of T. indicum (STi), T. pseudohimalayense (STp), and bulk or control soils (SC, ten meters away where no ascomata were detected). Soils were immediately stored in a cooler and transported to the laboratory where they were sieved (2 mm) to remove stone, root, and microfauna under aseptic conditions. Half of each composite soil sample (six samples from each plot) was stored at − 20°C for microbial analysis and the rest soils were air-dried for chemical analyses.
Three composited fungal tissue samples (each having 18 cutting slices obtained with a sterilized scalpel from six fruiting bodies of T. indicum or T. pseudohimalayense) were also respectively collected from the gleba or peridium of T. indicum (GTi or PTi) and T. pseudohimalayense (GTp or PTp). After clean with sterilized milli-Q water, the peridium and gleba tissues of six selected ascomata from each plot were sampled using a sterilized scalpel, composited and then stored in sterilized self-sealing bags (60 mm × 85 mm) at − 20°C for subsequent DNA extraction.
Soil property analysis
Soil pH was determined in a soil and distilled water (1:2.5, W/V) mixture using a Delta 320 pH meter (Mettler-Toledo Instruments, Shanghai, China). Soil moisture was gravimetrically measured by oven drying at 105°C for 24 h. Soil organic matter was determined with the potassium dichromate external heating method (Guo 2009). Soil total carbon (TC) and total nitrogen (TN) were measured with an elemental analyzer (Vario MAX C/N, Hanau, Germany) (Parkinson and Allen 1975). Determination of alkaline hydrolyzable N, calcium (Ca2+), and magnesium (Mg2+) was based on the Chinese national standard method (Nu 1999).
DNA extraction and PCR amplification
DNA from soil and truffle samples were extracted using the MoBioPower Soil DNA kit (12888) and the DNeasy Plant Mini Kit (Qiagen SA, Germany), respectively. The ascomata of Ti and Tp were identified by both morphological and molecular techniques in the Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, China. Polymerase chain reactions (PCR) were carried out following the previously described method (Xiong et al. 2016). Internal transcribed spacer 1 (ITS 1) was amplified for fungal community analyses, using universal primers ITS5-1737F and ITS2-2043R (Schultz 2005; Jeandroz et al. 2008). For PCR, all the samples were uniformly diluted to 20 ng/µL and PCR reactions were performed in triplicate in a 25 µL mixture (5 µL of 5× reaction buffer, 5 µL of 5×GC buffer, 2 µL of dNTP(2.5 mM), 1 µL of each primer, 2 µL of template DNA, 8.75 µL of DNase free water and 0.25 µL Q5 DNA polymerase). PCR thermal cycling conditions were 94°C for 5 min (initial denaturation), 30 cycles of 30 s at 94°C, 52°C 30 s, 72° C 30 s, with a final extension for 10 min at 72°C. Amplicons were extracted from 1 % agarose gels and purified with the EZNA Gel Extraction Kit (Omega, Bio-Tech, New York, USA) according to the manufacturer’s guidelines and quantified with PicoGreen using a FLUOstar Optima microplate reader (BMG Labtech, Jena, Germany).
Illumina MiSeq sequencing and bioinformatics
Purified amplicons were pair-end sequenced 2 × 300 on the Illumina MiSeq platform (MAGIGE, Guangdong, China) using the MiSeq Reagent Kit v2 (600-cycles-PE, MS-102-3003). Sequences were processed and quality-filtered using the QIIME (V1.9.1) pipeline. Paired-end reads were truncated to 100 bp to remove low-quality sequence tails (average quality values < 20 over a 10-bp sliding window. The ≥ 10 bp that passed through quality screening overlapping sequences were assembled using the FLASH software (v1.2.11) (Magoč and Salzberg 2011). The remaining high-quality sequences were clustered into operational taxonomic units (OTUs) at a 97% similarity cutoff. For species identification, we compared our sequence with the one deposited the UNITE database (for ITS, http://unite.ut.ee/index.php) using a confidence threshold ≥ 0.5. The OTUs assigned to the same phylum, class, genus, and species level were grouped together based on their taxonomic affiliations.
Data processing and statistical analysis
Shannon index and the observed species were used to evaluate fungal diversity and richness in soils and ascomata, respectively. One-way analysis of variance (ANOVA) followed by Tukey HSD (at P < 0.05) was used to compare significant differences in soil properties of the control, T. indicum and T. pseudohimalyense surrounding soils. Independent samples T-tests were applied to compare significant differences in diversity indices between T. indicum and T. pseudohimalyense. Beta-diversity from the overall microbial communities between paired samples were determined using the UniFrac metric (Lozupone et al. 2011) in the MOTHUR program (http://www.mothur. org). Principal Coordinate Analysis (PCoA) was performed by the vegan package of R software based on the weighted Unifrac distance matrix, and the obtained coordinate points were plotted using the ggplot 2 package in R software. Analysis of similarity (Anosim), non-parametric multivariate analysis of variance (Adonis) using distance matrices, and a multi-response permutation procedure (Mrpp) were used to examine fungal community differences (CLARKE 1993; Sickle 1997; Zapala and Schork 2006). Redundancy analysis (RDA) was used to analyze the relationship between fungal communities and soil properties. RDA is advantageous of assessing the explanatory power of each defined variable by parsing out other terms as constraints to calculate its proportion of total variance (O’Connor 1988).