تعیین تنوع قارچی در یک منطقه نیمه خشک بیابانی با استفاده از توالی یابی نسل دوم

Introduction

The dimension of the soil fungal diversity is poorly understood even though fungi are an essential group in ecosystem processes, and they have high levels of diversity (1-3). Emerging methods of direct extracting nucleic acids from soil and amplification of fungal ITS (Internal Transcribed Spacer) variable regions combined with new techniques in sequencing, permit us to extend our knowledge on fungal diversity in soil (4). In recent years, conducted research on fungal diversity by using new sequencing techniques like next-generation sequencing (NGS) has made a possible accurate estimation of soil fungal diversity and identification of novel taxa. Our information on the soil fungal diversity in terrestrial ecosystems is restricted (5-9). Most of the studies have been done on forest, tundra, compost, and grassland (10-12).  It makes the situation even worse in arid and semiarid ecosystems, especially in Iran.

 According to our survey, there is no study on fungal diversity in Iran, especially using next-generation sequencing techniques.

In this study, the diversity and composition of the fungal community were determined in the collected soil samples from a semiarid area. Plant and animal diversity in this area has been reported to some extent (13, 14), but according to the literature, there has been no published report on fungal diversity indices and their community structure. The present study aimed to monitor the possible variation in soil chemical properties and the fungal community, which has been affected by the site, located in cold or warm areas, and seasons (spring and autumn).

Methods and Materials

The study was carried out in Kerman province, Iran. The two selected plots were at the cold and warm sites of the Khabr National Park and Ruchun Wildlife Refuge (Table 1). Sampling was carried out in June (S, spring) and November (A, autumn). Thus, there were four treatments: (1) cold-spring (CS), (2) cold-autumn (CA), (3) warm-spring (WS), and (4) warm-autumn (WA). Soil samples were collected from 0-10 cm soil depth. Plant debris was removed from soil samples by sieving through a 2-mm sieve. Soil samples were divided into two parts: 1- was dried at room temperature and used for soil chemical analysis, and 2- was kept at -80 °C for the community analysis of the fungi by NGS.

Table 1- The General Properties of the Sampled Areas

Soil Chemical Analysis: The soil pH and electrical conductivity were measured (15). The gravimetric water content (GWC) was determined after drying soil at 105 °C for 24 h (16). The soil texture was determined by the hydrometer method (17). Soil organic carbon (SOC) was measured by the wet oxidation method (18). SOC was multiplied by 1.72 to calculate soil organic matter (SOM). The sodium bicarbonate was used to extract available phosphorous (AP) from soil samples (19). Total nitrogen in soil samples was measured using a micro Kjeldahl (20). Soil samples were extracted by ammonium acetate and then their available K was measured by atomic absorption spectroscopy (21).

.DNA Extraction, Illumina Sequencing, and Data Processing: PowerSoil®DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsdad, CA) was used to extract DNA. DNA was amplified by the ITS1f_ITS2 primer set (22) to make a library. ITS1f_ITS2 primer amplifies the ITS 1 region in fungi. Sequencing was done by Illumina MiSeq platforms at 2 × 150 of the paired-end reads. The obtained sequences were analyzed using QIIME, v 1.9.0 (23). After demultiplexing, individual sequences (R1 and R2) were downloaded from Illumina Base spaces. Barcodes were removed from raw Illumina fastq files of each pair R1 and R2, and combined by PANDAseq (24). The ''pick-open-reference.py'' for the operational taxonomic unit (OUT) was used. UNITE database v 5.0 (25) was used to assign the qualified sequences to all OTUs with 97% similarity. The Chao1 and J indices were determined using appropriate commands of QIIME.

Statistical Analysis: The data normality was done by the Shapiro-Wilk test via Jonson transformation in Minitab software whenever necessary. The two-way variance was used to analyze the data. The site and season effects were determined by the Mann-Whitney U test. The mean comparison was done by the Least Significant Difference (LSD) test (5%).

Results and Discussion.

Soil Chemical Properties: The sampling site soil was categorized in Haplic Calcisoils. Soil particles on average were clay (10%), silt (30%), and sand (60%) with a texture of sandy loam. The results indicated a significant effect (p<0.01) of the season on pH value (Table 2), which a higher pH value was seen in autumn soil samples (Table 2). Soil pH as a chemical indicator has a critical role in soil processes such as the cycling of elements, the solubility of nutrients, microbial activity, and enzymatic activity (26-30). The low pH value in spring soil samples can be explained by releasing carbon dioxide through plant roots as well as microbial activities in this natural ecosystem (31).

The quantity and quality of SOC and SOM have been considered soil quality indicators (32, 33). The SOM was affected by the site treatment (Table 2), and more SOM was at the cold site (Table 2). The less SOM in the warm site can be explained by the faster transformation of organic C to inorganic C and less vegetation coverage in this site. The negative relation between temperature and soil organic matter has been previously reported by other researchers (34).

The effect of site and season was significant (p<0.01) for total nitrogen (TN), available P (AP), and exchangeable potassium (AK) values. In general, the presence of higher vegetation coverage reduces the loss of micro and macro nutrients explaining the greater values of NPK (Table 2) at the cold site.

Table 2- Mean Comparison (n 3) of Soil Chemical Properties between Site (Cold with Warm) and Season (Spring with Autumn) Treatments

Different letters show significant differences at p< 0.05 level. TN: total nitrogen, EK: exchangeable potassium, AP: Available phosphorous, GWC: Gravimetric water content, SOC: soil organic carbon, and SOM: soil organic matter.

Table 3- Mean Comparison (n 3) of Soil Diversity Index between Site (Cold with Warm) and Season (Spring with Autumn) Treatments

Different letters show significant differences at p< 0.05 level. Chao1 and Shannon, H indicate the richness and evenness of soil samples, respectively

Whole Fungal Community Profile: A total of 390022 sequences were obtained via Illumina Miseq sequencing. Analysis was carried out on the qualified sequences (333201). The number of the detected sequences for each sample was 7002 up to 66494. There were 3057 OTUs at 97% similarity. As shown in Table 3, the effect of site, season, and their interactions was significant (p<0.01) on richness (chao1) and evenness (Shannon, H index). Statistical analysis showed higher richness in samples taken from cold sites (378±14) and spring season (435±23) than the warm site (330±28) and autumn season (380±27), while there was more evenness at the warm site (0.85±0.041) and autumn season samples (0.73±0.022) as compared to the cold site (0.73±0.028) and spring season samples (0.68±0.031).

Zhang, et al. (35) reported a positive correlation between soil microbial richness and moisture, supporting more richness of soil samples in the cold site and spring season samples. In addition, several studies have suggested that vegetation coverage and soil organic carbon can increase soil moisture (36- 39). Aridity has a negative impact on soil organic carbon content and the positive impact of SOC on the abundance and diversity of both bacteria and fungi has been reported in many studies (40). The reduction of microbial abundance changes negatively the function of the soil ecosystem (41). There are several studies, reporting seasonal variation in fungal richness and evenness (42, 43), which is inconsistent with our results. The more observed diversity in spring season sample soils can be related to the less pH of the soil because fungi prefer acidic pH for growth. We did not observe any difference in SOM values between the spring and autumn season samples. These observed results can be attributed to animal excreta.

The interaction effect of site and season indicated that the samples taken at the cold sites in spring (CS) and the warm sites in autumn (WA) had the highest and the lowest richness according to Chao1 values, respectively (Figure 1a).

There was higher richness and evenness at the cold site, indicating more diversity and even distribution of fungi (Figures 1a and 1b). Samples taken in the autumn season at the warm site showed fewer numbers of species, and a high evenness index clarified the uniform distribution of fungal species. A higher number of species and a low evenness index for spring season samples at the warm site indicated to more uneven distribution of species. These results were surprising for both the number of members and how evenly distributed in the community (43, 44). Thus, a community might have more species and its abundance is low.

Fig. 1- Estimated values of fungal community richness (a) and evenness index (b) under interaction effect of site and season. Significant differences are indicated by different letters. Chao1 provides an estimation of the observed species

Fungal Community Composition: The five major fungal phyla were observed in all samples with different abundance. Almost 45% of sequences were unidentified. Results have shown that sequences and OTUs belonging to Ascomycota were dominant in both cold and warm sites and spring and autumn season samples. Chytridiomycota and Zygomycota were more prevalent in cold sites, whereas more Glomeromycota was detected in warm sites (Fig 2).

Fig. 2- Relative Abundance of Fungal Phyla Determined by Next-generation Sequencing

The fungal community composition changed with the season as Ascomycota and Glomeromycota were more frequent in spring compared to autumn, whereas the Basidiomycota and Chytridiomycota were more prevalent in autumn samples (Fig. 3).

Fig. 3- Relative Abundance of Fungal Phyla Determined by Next-generation Sequencing in Different Seasons

The seasonal changes even were observed in other taxonomic levels too. The Sordariales and Pleosporales both belonging to Ascomycota were the dominant order in spring samples. However, Hypocreales and Capnodiales, which both belong to Ascomycota, had more abundance in autumn samples. There were several unique phylotypes in both sites (cold or warm) and seasons (spring or autumn), which can be used as indicators. The diversity in the Ascomycota phylum was more and it was distinguished into 6 classes, 12 orders, 18 families, and 27 genera. A total of 6 identified classes were observed across all treatments. Dothideomycete and Incertaesedis were common to all treatments. Chytridiomycetes were exclusive to the cold site. Agaricomycetes and Leotiomycetes were observed only in spring samples at the cold site. Eurotiomycetes was one unique class in autumn samples at the warm site. We detected 12 orders in total, two of which, Glomerales and Pleosporales, were common to all treatments. The highest number of unique orders observed in spring samples at the cold sites were Corticiales, Diversisporales, Helotiales, Sordariales, and Hypocreales. Eurotiales and Pezizales were observed only in autumn season samples at the warm sites.

There were 11 identified families, with one common family, Pleosporaceae, which was observed across all treatments. Chaetomiaceae, Corticiaceae, Diversisporaceae, Helotiaceae, Nectriaceae, and Olpidium were observed only in samples of the cold sites in spring. There was only one unique family in the warm site which was taken in autumn and named Pezizaceae.

There were no common genera (11 genera) among treatments. The detected genera belonged to different groups of fungi with different functions in soil. Mortierella and Phoma were observed in spring samples, taken from both cold and warm sites. There were seven unique genera in spring samples, which were taken from cold sites and identified as Chaetomium, Chalastospora, Diversispora, Gibberella, Olpidium brassicae, Rhizoctonia, and Rhizoscyphus. Only one unique genus was observed in autumn samples of the warm sites called Terfezia. The observed differences in fungal composition between the site and season have been reported by other researchers (45). The season has a special impact on fungal composition, as more fungi were observed during the wet season (46). The diversity of fungal communities depends on environmental factors such as temperature, precipitation, snow coverage, and nutrient availability (47, 48). The number of observed classes, orders, families, and genera related to each phylum has been presented in Table 4.

Table 4- Total Phylotypes at Five Taxonomic Levels in Soil Samples

Conclusion

The richness, evenness, and community composition of fungi were determined in a semiarid area. The impact of site and season treatments on fungal communities was studied by next-generation sequencing. The soil fungal communities are sensitive to soil chemical properties, especially elements concentration, soil organic matter, and moisture. We observed the most richness in the cold sites with higher element concentration, more organic matter, and higher moisture if compared to the warm sites. In addition, we observed seasonal changes in the soil fungal diversity due to alteration of soil chemicals such as the amount and kind of soil organic matter, moisture, and others.