Structure and Co-occurrence Network Characteristics of Rhizosphere Soil Fungal Communities ofAlsophila spinulosa in Subtropical Chishui River Valley, China
Abstract:
Aims The co-occurrence of soil microorganisms and plants is of great significance in revealing the material cycle. The study of the community structure and co-occurrence network relationship of rhizosphere soil fungi of the relict plant Alsophila spinulosacan reveal the mechanism of constructing soil fungal communities.
Methods The community structure and co-occurrence network characteristics of soil fungi in the rhizosphere of A. spinulosawere analysed using Illumina Miseq sequencing technology and co-occurrence networks.
Results The rhizosphere soil fungal communities of A. spinulosa are significantly different from those in the nonrhizosphere soil. The rhizosphere soil fungal phylogeny of A. spinulosa was concentrated in Ascomycota, Mortierellomycota, and Rozellomycota. Aggregation of Cutaneotrichosporon , the main differential species, significantly affected the construction of the rhizosphere fungal community of A. spinulosa . The indicator fungal groups of the rhizosphere soil fungal community of A. spinulosa were significantly influenced by habitat. Saprotrophs are the main fungi responsible for material exchange in A. spinulosa . Increase in the relative abundance of animal pathogens was the main factor affecting the percentage of pathotroph. The rhizosphere soil fungal co-occurrence networks of A. spinulosa had high synergism and network connectivity, and more intense interspecies competition at the order level.
Conclusions Overall, the rhizosphere soil fungal community ofA. spinulosa altered significantly, with a stable co-occurrence network. Continuous in-depth study on the growth of the key soil fungi can help understand the growth mechanism of A. spinulosa .
Keywords: Subtropical river valley; Alsophila spinulosa ; Soil fungi; Community structure; Co-occurrence network; Functional prediction
Introduction
Microorganisms play a crucial role in maintaining ecosystem function (Coban et al. 2022). Fungi are the dominant players of the soil microbial community, with a broad scope, high spatial heterogeneity, and crucial ecological functions (Bahram et al. 2015). The soil fungal community structure and co-occurrence network relationships can reveal the construction pattern of soil microbial communities, reflect the functional status of soil ecosystems, and elucidate the material cycling characteristics of plant–soil negative feedback systems (Bever et al. 2012). The soil fungal community structure reportedly responds differently to plants, with higher soil fungal co-occurrence network stability in plant root systems (Wang C et al. 2018; Yuqi Wang et al. 2023; Lei Zhang et al. 2021). Therefore, investigating the structure and co-occurrence network of soil fungal communities is important for revealing the “microbe–plant” relationship.
As an ancient relict tree fern from the Cretaceous age, A. spinulosa is a crucial material for studying plant origin and transformation and geographic zones (Qinqin He et al. 2022); the stem is rich in flavonoids, alkaloids, and other active substances, which are of high medicinal value in epidemics treatment and bacterial inhibition (Shuhua Li et al. 2013; Xin Zhang et al. 2018). Harsh breeding conditions, serious habitat destruction, interference by natural enemies, and intense interspecific competition have adversely declined the A. spinulosa population and drastically reduced their distribution range. Although recent studies have focused on A. spinulosa and developed conservation programs worldwide,A. spinulosa is still present on the endangered conservation list. To improve its endangered status, many researchers have performed relevant studies on the population structure and dynamics (Xie et al. 2022), spatial distribution (Yuan 2021), breeding (Lang et al. 2021), natural enemies (Du 2022), community structure (Hui Li et al. 2021; Qin Liu et al. 2019), interspecific relationships (Jiang et al. 2021), and diversity (Zhao 2018) of A. spinulosa . Certain relationships between A. spinulosa and other plants and animals are known biologically, but studies on A. spinulosa –microbial relationships remain relatively scarce. Endophytic fungal diversity reportedly varied in different A. spinulosa tissue masses such as pinnae, leaf rachis, petioles, bark, roots, and petiolar apoplasts.Aspergillus sydowii and Dactylonectria pauciseptata are two endophytic fungi that are widely present in A. spinulosa(YongLan Liu et al. 2021; Wei Zang et al. 2020). Xylaria ,Colletotrichum , and Pestalotiopsis constitute the dominant genera of endophytic fungi in the root, stem, and leaf tissues ofA. spinulosa (Wenna Zhou 2015). Ectomycorrhizal fungi affect the ecological functions of A. spinulosa such as material cycling and nutrient supply (Tedersoo et al. 2020). Acaulospora andFunneliformis constitute the dominant mycorrhizal fungal genera of A. spinulosa . They help improve the resistance of A. spinulosa and are of high value for its successful breeding and survival (Lara-Pérez et al. 2014). Studies on A. spinulosa –fungus relationship have mainly focused on endophytic fungi, and the effect of fungi on the environment remains unclear, making it difficult to fully understand the relationship between A. spinulosa -microorganisms.
Ecological specialization of fungi is reportedly higher than that of bacteria in the soil (Xi 2022). Soil serves as an important site for species exchange, and the soil fungal community structure in the root system of A. spinulosa could significantly impact nutrient turnover functions (Lu Han 2022; Wei Li et al. 2022). In the soil, high relative abundances of Basidiomycota are observed in mixed evergreen deciduous broadleaved forests, montane dwarf forests, and deciduous broadleaved forests. Ascomycota was not significantly different and widespread, and Peridiomycota appeared to display a U-shaped variation pattern (Man et al. 2021). It remains unclear whether Ascomycota is similarly widespread in the soil fungal community of theA. spinulosa root system. It is important to identify the fungal taxa that differentiate soil fungal communities in theA. spinulosa root system based on fungal ecological specialization of fungi. Determination of the ecological functions of soil fungal taxa in the A. spinulosa root system would help influence A. spinulosa growth. Defining the stability state of the soil fungal community in the inter-root zone of A. spinulosaalong with the key fungal taxa that critically influence community stability is also crucial. Therefore, an in-depth study on the soil fungal community structure and co-occurrence network in theA. spinulosa root system is of great significance to improveA. spinulosa –microbial relationships and provide new references for developing A. spinulosa conservation strategies.
This study aimed to focus on the rhizosphere soil fungal community ofA. spinulosa in the subtropical Chishui River Valley, China. Using microbial Illumina Miseq sequencing technology, we revealed the structure of the rhizosphere soil fungal community of A. spinulosa and its co-occurrence network to provide rich data and scientific reference for studying this community.
Materials and Methods
Study area
This study was conducted in Chishui City, Guizhou Province (28°16′19′′N, 105°36′35′′E to 28°46′0′′N, 106°15′0′′E, southwestern China), which has a central subtropical humid monsoon climate and is located in the transition zone from the Yunnan–Guizhou Plateau to the Sichuan Basin. This terrain is mainly of the plateau–canyon type and mountain–plain canyon type, with heavy mountains and deep canyons in the southeast, rolling hills in the northwest, and open and gentle river valleys. The terrain is high in the southeast and low in the northwest regions, with the altitude decreasing from the southeast to the northwest. The average annual temperature at the time of this study was 18.1°C, with an annual difference in temperature of 20.1°C–20.5°C. The extreme minimum temperature was −4°C and the extreme maximum temperature was 39°C. The average annual rainfall in this region is 1292.3 mm, mainly concentrated from April to October, accounting for approximately 80% of the annual rainfall. The wind direction is prevalently north, southeast in summer, and north in winter. The soils are mostly neutral and slightly acidic sandy purple soils, and there are many river tributaries. The vegetation type can be classified into four groups: subtropical evergreen broadleaved forest, mixed coniferous forest, coniferous forest, and bamboo forest.
Sample area square setting
The sampling sites were selected in the Chishui area with A. spinulosa growth, and the rhizosphere soil was collected within a 4 mm depth from the base of the trunk of each A. spinulosa fern with good growth conditions and a similar growth profile. Nonrhizosphere soil samples were also collected from a locality away from the plant root zone. A total of 23 sampling sites were selected, and 46 soil samples were collected. The collected samples were sieved to remove stones, plant remains, roots, and other impurities and then immediately packed into sterile centrifuge tubes, labeled, and stored in liquid nitrogen. The samples were sent to the laboratory for Illumina Miseq sequencing of soil microorganisms.
DNA extraction and high-throughput sequencing
DNA kit (OMEGA, USA) was used to extract total DNA from the soil fungal flora of each sample, and the extraction quality (1% agarose gel electrophoresis) and DNA quantity (UV spectrophotometry, NanoDrop 2000 Spectrophotometer) were measured. Polymerase chain reaction (PCR) amplification was performed with ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′)_ITS2R (5′- GCTGCGTTCTTCATCGATGC-3′) under the following conditions: predenaturation at 95°C for 3 min; followed by 27 cycles of denaturation at 95°C for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 30 s; stable extension at 72°C for 10 min; and final storage at 4°C. The PCR system comprised 4 μL of 5× TransStart FastPfu buffer, 2 μL of dNTPs (2.5 mmol/L), 0.8 μL of upstream primer (5 μmol/L), 0.8 μL of downstream primer (5 μmol/L), 0.4 μL of TransStart FastPfu DNA polymerase, and 10 ng of template DNA, which made up the volume to 20 μL, with three replicates for each sample. The PCR products from the same sample were mixed and detected via 2% agarose gel electrophoresis and then recovered by cutting the gel using AxyPrepDNA Gel Recovery Kit (AXYGEN). The recovered products were eluted using Tris_HCl for 2% agarose electrophoresis detection. Based on the initial quantification via electrophoresis, the PCR products were detected and quantified using the QuantiFluor™ -ST Blue Fluorescence Quantification System (Promega) and then mixed in appropriate proportions according to the sequencing volume required for each sample.
Based on the Illumina library construction and sequencing results, the obtained PE reads were first spliced according to the overlapping relationship, and the sequence was quality controlled and filtered, after which the samples were differentiated and subjected to OTU clustering analysis. OTU clustering of the nonrepeated sequences (excluding single sequences) was performed at a 97% similarity level, and species annotation analysis was performed using the RDP classifier Bayesian algorithm with the Unite Fungal Database. The community species composition of each sample was determined at each taxonomic level: domain, kingdom, phylum, class, order, family, genus, and species. This technology and sequencing equipment were provided by Shanghai Majorbio Bio-Pharm Technology Co.
Data processing and analysis
Analytical methods
The fungal community in the rhizosphere soil was subjected to corresponding NMDS analyses. LEfSe analysis was performed to compare the species with significant differences among the groups. The construction of evolutionary relationships of soil fungal communities based on microbial phylogenetic evolutionary trees was performed. The functional status of soil fungi was analyzed based on FUNGuild functional prediction. The microbial co-occurrence network visualization was applied to characterize the topology of the soil fungal community network of A. spinulosa and screen out the key species. According to the network visualization status, the r and p values of the symbiotic network at different taxonomic levels were as follows: phylum (r > 0.5, p < 0.5), order (r > 0.6, p < 0.5), family (r > 0.5, p < 0.5), genus (r > 0.68, p < 0.05), and species (r > 0.68, p < 0.05). Based on the indicator species analysis method of screening the indicator groups of soil fungi at each taxonomic level, the indicator value IndVa was calculated as follows:
\begin{equation} IndVal=\frac{N_{\text{ij}}}{N_{i}}\times\frac{M_{\text{ij}}}{M_{j}}\times 100\nonumber \\ \end{equation}
where \(N_{\text{ij}}\) is the average abundance value of taxon i in the subgroup j, \(N_{i}\) is the sum of the average abundance values of taxon i in all subgroups; \(M_{\text{ij}}\)is the number of samples of taxon i occurring in the subgroup j, and \(M_{j}\ \)is the total number of samples of taxon i in the subgroup j.
Taxa with IndVal of ≥25% were used as indicator taxa, taxa with IndVal of ≥70% were used as character indicator taxa, and taxa with IndVal of 50%–70% were used as habitat indicator taxa. The indicator taxa were analyzed at five taxonomic levels: phylum, order, family, genus, and species.
Data processing
Data analyses were performed using Microsoft Excel 2019, R software, and Shanghai Meguiar’s BioCloud platform (https://www.isanger.com). Species annotation and assessment were performed using Uparse (version 7.0.1090;http://drive5.com/uparse/) software. Dilution curves were constructed using Mothur (version 1.30.1) software to calculate the alpha diversity index for random samples. The beta diversity distance matrix was calculated using Qiime software, NMDS analysis, and graphing in R. Linear discriminant analysis of the samples was performed according to different grouping conditions using LEfSe software (http://huttenhower.sph.harvard.edu/galaxy/root?tool_id=lefse_upload) based on the taxonomic composition. Phylogenetic evolutionary trees were constructed using FastTree software (version 2.1.3;http://www.microbesonline.org/fasttree/) by selecting the sequences corresponding to the taxonomic information at the species level according to the maximum likelihood method. Taxonomic analysis of fungal communities was performed using FUNGuild via the Megisign cloud platform. Microbial network visualization was performed based on “igraph,” “vegan,” “tidyverse,” “psych’,” “ggsci,” “magrittr,” “ggplot2,” ”RColorBrewer,” and other packages.
Results
Soil fungal community structure
Community composition
The species composition reflects the community structure status. In this study, 259,1494 optimized sequences were obtained for the fungus, with an average sequence length of 227 bp. Clustering at 97% similarity yielded 9986 OTUs belonging to 16 phyla, 65 orders (class), 157 orders, 389 families, 948 genera, and 1624 species.
Dilution curves indicate the rationality of sequencing data. In this study (Fig. 1a), the dilution curves of nonrhizosphere and rhizosphere soil samples of A. spinulosa exhibited a gradual rise after the sequencing volume reached a certain depth and then gradually flattened out. The sequencing depth was sufficient to cover most microbial taxa, and increasing the sequencing volume would only lead to the addition of a small number of new species, indicating the rationality of the sequencing volume in this study. According to NMDS analysis, a stress value of 0.083 (Fig. 1b) indicates a significant difference in the fungal community structure between nonrhizosphere and rhizosphere soils of A. spinulosa , suggesting that the grouping of nonrhizosphere and rhizosphere soils was well represented.
Note: R indicates soil fungi of A. spinulosa ; NO indicates soil fungi of A. spinulosa . (a) Dilution curve. (b) NMDS analysis. Points of different colors or traits in the graph represent different sample groups. The closer the points of the two samples, the more similar their species composition. The horizontal and vertical coordinates indicate relative distances and have no practical significance. stress: test for the merit of NMDS analysis. A stress value of <0.2 can be represented by a two-dimensional point plot of NMDS analysis; this graph has some interpretative significance. The stress value of <0.1 indicates a good ranking. When the stress value is <0.05, it is considered well represented.