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.