https://orcid.org/0009-0002-8208-0413
Figures
Abstract
With the rapid development of society and economy, most lakes in southwest China are in a state of eutrophication. As decomposers and producers in lake ecosystems, the overlying water bacterioplankton communities play an important role in maintaining material circulation and energy flow. However, the response mechanism of their composition to eutrophication remains unclear. This research sampled the overlying water of Sancha Lake, a typical sub-deep-water lake in Southwest China, at nine different sampling sites in April (spring) and November (autumn) 2017. High-throughput sequencing of 16S rRNA gene and molecular ecological network approach are used to analyze the structure and network topology of bacterioplankton community and the response mechanism of key species to eutrophication in the overlying water of Sancha Lake. The results show that the bacterial species diversity is abundant, belonging to 55 phyla, 64 classes and 732 genera. Pseudomonadota, Actinomycetota and Bacillota are the dominant phyla, while Pseudomonas and Bacillus are the dominant genera. The relative abundance of bacterial groups in the overlying water in spring and autumn has significant seasonal differences (P < 0.05 or P < 0.01). There are seasonal differences between spring and autumn in bacterioplankton community structure in the overlying water (P < 0.01). The bacterioplankton community structure is mainly influenced by phosphorus, permanganate index (CODMn), dissolved oxygen (DO) and pH. In spring and autumn, the overlying water bacterioplankton communities are mainly affected by deterministic processes and random processes, respectively. In spring and autumn, there are more positive interactions than negative interactions among bacterioplankton in the overlying water. The closeness of the overlying water bacterioplankton network is higher in spring than in autumn, and the interaction degree of the components of the overlying water bacterioplankton network is higher in autumn than in spring. The key species in the spring bacterioplankton molecular ecological network are Methylobacter, Candidatus Magnetoovum, Pseudomonas and Thiobacillus. In autumn, Methylacidimicrobium, Thiothrix and Clostridium are found. The relative abundance of key species is positively correlated with soluble reactive phosphate (SRP) content in spring, but negatively correlated with SRP content in autumn. The results of this research indicate that the bacterioplankton community in the overlying water of Sancha Lake is abundant in diversity, and its composition and structure changes are dynamic responses to the changes of eutrophication environmental factors, and the key species of bacterioplankton may play an important role in the phosphorus cycle of eutrophication.
Citation: Li Y, Gong S, Liu H, Li Y, Luo W, Gong Z (2025) Bacterioplankton community structure and molecular ecological network characteristics in the overlying water of Sancha Lake. PLoS One 20(7): e0327903. https://doi.org/10.1371/journal.pone.0327903
Editor: Xingbin Sun, Northeast Forestry University, CHINA
Received: February 22, 2025; Accepted: June 23, 2025; Published: July 15, 2025
Copyright: © 2025 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The datasets generated during the current study are available in the NCBI Sequence Read Archive (SRA) database repository, [Accession Number PRJNA623159].
Funding: This research was funded by the Key Laboratory Open Project of Southwest Jiaotong University (ZD2022210017). This research was funded by the Student Research Training Program (2023145). The grant recipient of both fundings is Y.L.
Competing interests: The authors have declared that no competing interests exist.
1 Introduction
Water eutrophication refers to the process and phenomenon of excessive content of nitrogen, phosphorus and other nutrients in water, which causes the aquatic ecosystem to change from a poor state to a higher state of eutrophication [1]. In eutrophic water, bacteria are the most important contributors to the transformation of complex organic matter and mineral elements, and the research of their diversity and structural composition is particularly important for the understanding of eutrophic aquatic ecosystems [2]. In lake ecosystems, bacterioplankton in the water plays an important role in regulating the nutrient balance between aquatic organisms and primary producers through metabolic pathways such as degradation of organic matter. Bacterioplankton in the overlying water (5–10 cm above the sediment) is also an important component of lake ecosystems, and it plays an important role in material cycling (e.g., carbon and phosphorus cycling) and energy flow at the sediment-overlying water interface, mainly participating in the decomposition of organic matter and mineralization of organic matter into inorganic compounds that can be used by plants for primary production [3]. The diversity and structural composition of bacterioplankton in water have significant response relationships to environmental factors [4,5]. Sensitive to changes in the external environment, the diversity and colony composition of bacterioplankton in water can truly reflect the status of the quality of the water environment through a sensitive response to changes in the external environment [6]. The bacterial community in the overlying water body exhibits greater sensitivity to fluctuations in the external environment. This heightened sensitivity is attributed to the direct exposure of these bacteria to the water column, which renders them more responsive to variations in physicochemical parameters, as well as anthropogenic pollutants [7]. Therefore, variations in the abundance and community structure of bacteria in the overlying water are frequently utilized as indicators to reflect the nutrient cycling and ecological conditions of lakes and reservoirs, and are considered crucial parameters for assessing ecological restoration. [8–10]. At present, studies on the diversity and structural composition of bacterioplankton in lake water mainly focus on the surface water of external polluted shallow and deep-water lakes [11–13], northern lakes [14], and southern lakes [15]. For example, Zhang Yajie et al. [14] collected water samples from Beihai Lake in four seasons respectively, and analyzed the water quality and the structure and composition of microbial community, and concluded from the study that the community structure of bacteria in Beihai Lake was closely related to environmental factors. Zou Shenjuan et al. [15] analyzed and compared the differences in the bacterial community structure and its diversity between the dry and abundant water periods in the water of Lake Daye, using 16S rRNA gene high-throughput sequencing technology, and found that bacterioplankton in Lake Daye were abundant in diversity and had a complex community structure. However, there are few reports on the diversity and composition of bacterioplankton in the internally polluted sub-deep-water lakes in southwest China (especially the overlying water) [16].
In complex bacterial communities, there are a series of interactions among species, such as competition, cooperation, and symbiosis, etc. Molecular ecological networks based on depicting and characterizing the interrelationships of species within microbial communities provide a reliable method to understand the potential interactions among complex microorganisms [17]. Network analysis enables researchers to effectively examine the interspecific interactions of microorganisms in water systems [18]. This method has been widely used to explore species interactions of microorganisms in various ecosystems, such as lakes [19] and soils [20]. It has been shown that ecological network analysis can also identify “key species” in a community [21]. Key taxa are highly connected species in microbial communities, and the increase or decrease of these species causes changes in the interactions of the entire ecological network [22,23], and they play an important role in maintaining the stability of bacterial community composition and function [24].
Sancha Lake is located in the eastern New District of Sichuan Province (E104°11 ‘16 “~E104°17’ 16”, N30°13 ‘08” ~ N30°19’ 56 “), in the eastern suburbs of Chengdu, upstream of Jiangxi River, a tributary of the Tuojiang River in the Yangtze River system. The lake area of Sancha Lake is 27 km2, the drainage area above the dam site is 161.25 km2, the average water depth is 8.3 m, and the deepest water depth is 32.5 m. The main source of water for Sancha Lake is Min River, accounting for about 80% of the total water in the reservoir, and about 20% comes from rainfall and the two streams of the Tiaodeng River and the Longyun River. The agricultural irrigation period is from March to July every year, and the water diversion period is from July to November. The area where Sancha Lake is located is a subtropical humid monsoon climate, with an average annual temperature of 15.2~16.9°C, no ice in the four seasons, and an average annual precipitation of 786.5 mm. Sancha Lake is a sub-deep-water lake with internal pollution. It is an important source of drinking water in Tianfu New District. It also has the functions of irrigation, maintaining biodiversity, regulating surface runoff and climate. With the development of “Two lakes and one Mountain International Tourism Cultural functional Area” in Tianfu New District, the population has begun to increase substantially, urbanization has accelerated, environmental protection awareness is weak and water pollution has intensified, and the contradiction between supply and demand, development and protection of water resources in Sancha Lake has become increasingly serious. In recent years, the pollution of Sancha Lake has also aroused the great attention of the relevant departments, and the sewage in the four towns and cities in the basin has been centralized regulation. A network of sewage intercepts around the lake has been built to collect sewage from hotels, resort resorts and farmhouses near the lake and enter the Sancha Town sewage treatment plant for treatment and then enter the downstream Jiangxi River. Ban cage farming altogether. Strictly control the number of boats used for lake tourism. The Sancha Lake water pollution treatment project has effectively controlled exogenous pollutants, and the water quality is generally good, but the phosphorus index value of the water is still poor, and there is eutrophication in the water, and the extent of eutrophication is progressively intensifying [25]. In recent years, the water storage capacity, water depth, functional attributes of the water body, and the surrounding environmental conditions and so on of Sancha Lake have all remained relatively stable, exhibiting no significant fluctuations. Previously, our group conducted a research on the structure of the microbial community in the sediment of Sancha Lake and its relationship with environmental factors, which concluded that the microbial community in the sediment of eutrophic Sancha Lake was diversified, and its composition and distribution differed significantly between the spring and the non-spring, and that the environmental factors, such as pH and total phosphorus (TP), had a significant effect on the composition and structure of the community [26]. The seasonal variations in the concentrations of dissolved oxygen (DO), chemical permanganate index (CODMn), biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), and TP in the overlying water of Sancha Lake follow the order: spring > winter > summer > autumn. This indicates that these parameters are significantly higher in spring compared to other seasons [26]. During spring, Sancha Lake enters the agricultural irrigation period, during which the reservoir’s storage capacity decreases, resulting in an elevated relative concentration of nutrients such as phosphorus. Additionally, the increase in water temperature during spring exacerbates the eutrophication of the water body compared to other seasons [26]. Furthermore, the substantial temporal gap between spring and autumn facilitates the investigation of the community structure of airborne bacteria and the characteristics of their molecular ecological networks caused by differences in environmental factors. Therefore, in this research, nine sampling sites are selected in Sancha Lake, and samples are collected in April (spring) and November (autumn) 2017, to determine the physicochemical factors of the overlying water. Illumina Miseq high-throughput sequencing and molecular ecological network analysis method are used to study the diversity, structural composition and molecular ecological network characteristics of the bacterioplankton in the overlying water of Sancha Lake. The key species that have important influence on the eutrophication of Sancha Lake are revealed. We hypothesize that the bacterioplankton community in the overlying water of Sancha Lake is abundant in diversity, its community structure is related to environmental factors, and key species contribute to the SRP of the water.
2 Materials and methods
2.1 Time and location of water sample collection
Based on the characteristics of eutrophication distribution in Sancha Lake, a total of 9 sampling sites were established across the lake (Fig 1). The geographic coordinates of each sampling site were determined using GPS, with their specific locations and descriptions provided in Table 1. Surface overlying water samples from sediments were collected during April (spring) and November (autumn) of 2017.
2.2 Water sample collection and physicochemical factor determination
At each sampling site, 2 L of overlying water were collected using a gas-tight water sampler equipped with a 5-cm base and an upper floating plate. The sampler was vertically lowered into the water: during descent, the floating plate ascended to open the sampler, allowing water to flow freely through it. When the base gently contacted the sediment surface, the sampler was retrieved. During retrieval, the floating plate descended to seal the sampler, ensuring collection of overlying water. 1 L water sample was immediately filtered with a microporous filter membrane with a pore size of 0.45μm to remove impurities, and then filtered with a microporous filter membrane vacuum pump of 0.22μm. The filter membrane was removed and stored in a −80°C refrigerator for DNA extraction. The remaining 1L water sample was used for physicochemical factor determination.
Temperature (T), pH and conductivity (EC) were determined with a portable multi-parameter thermometer HI991301. DO was measured with HQ3OD portable dissolved oxygen meter. The determination methods of CODMn, BOD5, total nitrogen (TN), NH3-N and TP were determined in accordance with the Water and wastewater monitoring and analysis methods editorial board (4th edition) [27]. Dissolved total phosphorus (DTP) and SRP were determined according to the experimental protocol of Li et al. [28]. Alkaline phosphatase (ALP) was measured by p-NPP method [29], and the activity of ALP was expressed as the number of nmol of p-nitrophenol (PNP) produced per liter of water sample per minute.
2.3 DNA extraction and Illumina Miseq sequencing
The above-mentioned microporous filter membrane with a pore size of 0.22μm was taken, the filter membrane and the filter substance were cut into 1 ~ 2 mm fragments under sterile conditions, and put into the Eppendorf tube. Total DNA extraction from the overlying water was performed using Power Water® DNA Isolation Kit (MO BIO laboratories, Carlsbad, CA, USA). The integrity of DNA extraction was detected by 0.8% agarose gel electrophoresis and DNA concentration quantification was performed with a NanoDrop2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, 02454). The DNA extract was stored at −80°C for subsequent experiments. Each sample was repeated three times.
Since the optimal insertion fragment for Illumina MiSeq sequencing ranges from 200 to 520 bp, the V3 to V4 hypervariable region of bacterial 16S rRNA gene with a length of about 468 bp was selected for amplification in this research. The PCR was amplified by ABI Applied Biosystems®2720 (Applied Biosystems, foster city, CA, USA). The amplification primer [30] was:
- 338F(5’-ACTCCTACGGGAGGCAGCAG-3’)
- 806R(5’-GG ACTACHVGGGTWTCTAAT-3’)
PCR amplification system (25 μL): 0.25 μL Q5® High-Fidelity DNA Polymerase (5U·μL), 5μl reaction buffer (5×), 5 μL High GC buffer (5×), 2 μL dNTPs (10 mM), 2 μL template DNA (about 10 ng·μL-1), 1 μL Forwardprimer (10μM), 1 μL Reverseprimer (10μM), 8.75 μL ddH2O.
The PCR amplification procedure was: Pre-denatured at 98°C for 30 seconds, 27 cycles (denatured at 98°C for 15 seconds, annealed at 50°C for 30 seconds, extended at 72°C for 30 seconds), and finally extended at 72°C for 5 min and saved at 4°C. PCR amplification products were detected by 2% agarose Gel electrophoresis, and target fragments were recovered by AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA). Each sample was repeated three times.
The high-throughput sequencing work was completed by Shanghai Personal Biotechnology Co., Ltd. All purified PCR products were quantitatively detected by QuantiFluor™-ST (Promega, Madison, Wisconsin, USA). Illumina’s TruSeq Nano DNA LT Library Prep Kit was used to construct the library. Qualified libraries were sequenced 2 × 300 bp double-ended using the MiSeq Reagent Kit V3 (600cycles) on the Illumina MiSeq platform (Illumina, San Diego, USA). The raw data was uploaded to the NCBI database.
The original sequencing was controlled by Trimmomatic software. Raw sequencing reads with exact matches to the barcodes were assigned to respective samples and identified as valid sequences. The low-quality sequences were filtered through following criteria [31,32]: sequences that had a length of <150 bp, sequences that had average Phred scores of <20, sequences that contained ambiguous bases, and sequences that contained mononucleotide repeats of >8 bp. Paired-end reads were assembled using FLASH (version 2.7, http://ccb.jhu.edu/software/FLASH/, Center for Bioinformatics and Computational Biology, Iowa City, IA, USA) [33]. After chimera detection, the remaining high-quality sequences were clustered into operational taxonomic units (OTUs) at 97% sequence identity by UCLUST [34]. A representative sequence was selected from each OTU using default parameters. OTU taxonomic classification was conducted by BLAST searching the representative sequences set against the Greengenes Database (http://greengenes.secondgenomes) [35] using the best hit [36]. According to 97% (16S rRNA genes) [37] the similarity of the use of UPARSE software (version 7.1 http://drive5.com/uparse/, Edgar, R.C., Tiburon, CA, USA) for OTU sequence clustering, the single sequence and chimera are eliminated in the process of clustering. The sequences with the highest abundance in each OTU were annotated by using RDP classifier (http://rdp.cme.msu.edu/). Sequence comparison of 16S rRNA gene Silva database (Release115, http://www.arb-silva.de) [38]. Set the comparison threshold to 70%.
2.4 Statistical analysis
Based on the results of OTU clustering analysis, each sample was analyzed for richness indices (Ace and Chaol) and diversity indices (Shannon and Simpson) separately using QIIME software (version 2.0, http://qiime.org/, Rob Knight Lab, Boulder, CO, USA) [39]. Using Mothur software (Version 1.35.1, Patrick Schloss, University of Michigan, MI, USA), Call Metastats(http://metastats.cbcb.umd.edu/) statistical algorithm [40], for each taxon in the sequence difference between the sample group comparing two tests, according to the test results, using R software to the most significant difference between the sample group of taxa as abundance distribution. Welch’s t test was used to analyze and compare whether the distribution of each taxon in the two sample groups was significantly different [41]. Wilcoxon rank-sum test was used to test the hypothesis of species between the microbial communities of the two groups, and bacterial groups with significant differences in abundance between different groups were identified [42]. In order to investigate the similarity and difference of community structure among different samples, principal coordinates analysis (PCoA) was used [43]. Adonis test was conducted with Vegan software of R software [44] to determine the size of differences within and between sample groups, so as to evaluate the size and statistical significance of differences between original sample groups.
Variance Inflation Factor (VIF) was used to screen environmental factors [45]. The redundancy analysis (RDA) was conducted on the spatial and temporal distribution characteristics of bacterial community and environmental factors in water by using R language vegan package [46]. The principles for quantifying community assembly processes were based on phylogeny (β-nearest taxon index, βNTI) and taxonomic β diversity measures (based on Bray-Curtis’ Raup Crick, RCbray values) by exploring the building mechanisms of microbial communities through Stegen’s null model. |βNTI| > 2 indicated that deterministic process dominated the construction of microbial communities, and can be further divided into homogeneous selection (βNTI<−2) and heterogeneous selection (βNTI > 2). |βNTI| < 2 indicated that random process dominated the construction of microbial community, which was further divided into homogenizing dispersal (RCbray<−0.95) and dispersal limitation (RCbray>0.95) and undominated processes (|RCbray| < 0.95) [47]. The Gephi software was used to visualize the molecular ecological network at the genus level. In order to reduce the complexity of the network, the bacteria genera that accounted for the top 50 relative abundance and appeared in ≥50% of the samples were retained. Spearman’s correlation coefficient r ≥ 0.6, and the significance was P < 0.05 [48,49]. The Majorbio cloud platform (https://cloud.majorbio.com/page/tools/) online tools were used to construct the molecular ecological network structure of bacterioplankton genera community in spring and autumn and to calculate the intra-module connectivity values Zi and participation coefficients Pi. In the molecular ecological network, different nodes represent different microbial species, and the key species of the community can be identified according to the topological characteristics of the nodes. The attribute types of nodes are generally divided into four categories: peripheral nodes (Zi < 2.5, Pi < 0.62), connectors (Zi < 2.5, Pi ≥ 0.62), module hubs (Zi ≥ 2.5, Pi < 0.62), and network hubs (Zi ≥ 2.5, Pi ≥ 0.62). According to previous researches, nodes with Zi ≥ 2.5 or Pi ≥ 0.62 are defined as key species [50]. Analysis of variance (ANOVA) was used to compare the significance of gene copy number and physicochemical factors among samples. SPSS statistical software (version 20.0, IBM, Armonk, NY, USA) was used for statistical analysis, and regression analysis was used to test the correlation between microbial key species and environmental factors. The significant level was set as P = 0.05, and the extremely significant level was set as P = 0.01.
3 Results and analysis
3.1 Analysis of physicochemical properties of the overlying water
The measured values of physicochemical factors of the overlying water at nine sampling sites in spring and autumn 2017 are shown in Table 2. It can be seen that the measured values of physicochemical factors in the overlying water of Sancha Lake show seasonal changes differences in 2017. In the physical index determination, EC and T values do not change significantly in spring and autumn, DO value is significantly higher in spring than in autumn (P < 0.01), pH value is lower in spring than in autumn (P < 0.01). The contents of CODMn, BOD5, TP, DTP and SRP are significantly higher in spring than in autumn (P < 0.05), while the contents of NH3-N and TN are slightly higher in spring than in autumn. ALP enzyme activity in autumn is significantly higher than that in spring (P < 0.01). The average and quarterly average of CODMn, BOD5 and NH3-N at each sampling point meet the environmental quality standard for surface water (GB 3838−2002), class III. TP is more seriously polluted, seriously exceeding the surface water class III standard.
3.2 Bacterioplankton diversity and abundance
Illumina Miseq high-throughput sequencing was performed on the V3 ~ V4 highly variable region of 16S rRNA gene coated with water DNA. After quality control, splicing and removal of chimera, a total of 860,510 valid analysis sequences are obtained from 18 samples. Among them, the sequence number of each sample ranges from 36,292–65,196, and the average sequence number reaches 47,806. The coverage of all samples is greater than 0.9, indicating that the sequencing depth is sufficient to cover most of the microbial species’ information, and the sample size is sufficient to reflect the diversity differences among different communities.
After removing the few OTUs belonging to archaea obtained by annotation, a total of 9,557 OTUs are obtained from 860,510 valid analysis sequences, among which 3,733 OTUs are shared in spring and autumn, 4,348 OTUs are unique in spring, and 1476 OTUs are unique in autumn. OTUs and sequence number show a higher change in spring than in autumn. The number of OTUs in each sample ranges from 1,149–3,691. The distribution of OTUs and the number of valid analysis sequences in the sample is shown in Table 3.
Using the above OTUs statistics of overlying water bacteria, the bacterial diversity index (Shannon and Simpson) and abundance index (Ace and Chaol) are calculated. In the overlying water of Sancha Lake is abundant in bacterial diversity, with Shannon index ranging from 6.42 to 10.71, Ace index ranging from 1,451.67 to 4,445.10, Chaol index ranging from 1,358.39 to 4,227.71, and Simpson index ranging from 0.949 to 0.998 (Table 3). The Shannon index, Chaol index and Ace index in spring are significantly higher than those in autumn (P < 0.05), but the diversity index of spatial loci is not significantly different (P > 0.05).
3.3 Taxonomic composition and spatiotemporal variation of bacterioplankton community
A total of 55 phyla, 64 classes, 130 orders, 262 families and 732 genera are detected in the overlying water of Sancha Lake in spring and autumn. The statistical results of the number of bacterial groups at each taxonomic level at each sampling point in the two seasons are shown in Table 3.
3.3.1 Phyla level.
At the phyla level, the distribution and relative abundance of the top 20 bacterial groups in the overlying water of each sampling point in spring and autumn of Sancha Lake are shown in Fig 2. A total of 55 bacterial phyla have been detected in the overlying water of Sancha Lake. Among them, Pseudomonadota (average abundance 30.1%, relative abundance 24.20%−51.60%), Actinomycetota (12.6%, 6.9%−27.0%) and Bacillota (11.7%, 0.6% ~ 26.4%) have a significant advantage, and the overall abundance accounted for 54.4% of the whole sequence. The other main phyla (average abundance > 1%) are Chloroflexota (5.1%, 0.2–18.3%), Bacteroidota (3.5%, 1.3–6.4%), Nitrospirota (1.9%, 0.1–8.6%), Planctomycetota (1.8%, 0.4–5.6%), Verrucomicrobiota (1.6%, 0.1–2.6%), Cyanobacteriota (1.5%, 0.1–2.6%), Candidatus Aminicenantes (1.0%, 0–2.8%).
SPW and AUW correspond to spring and autumn overlying water, respectively. L is the sample, and the number is the sampling point number. Others are 20 after relative abundance and are not identified. The same below.
In terms of the spatial distribution of bacterial community, except for Candidatus Aminicenantes, bacterial groups with relatively high abundance (average abundance > 1%) are distributed at each sampling point, but there is spatial heterogeneity in the distribution of different bacterial community. Pseudomonadota is more distributed in the center and dam of the lake, while it is less distributed in the inlet water area and at the end of the lake, but the difference is not obvious. The relative abundance of Actinomycetota is high in the tail of the lake and in the influent area, but decreases significantly in the center of the lake. Bacillota distributes more in the center of the lake and less in the tail and bay of the lake.
In the temporal distribution of bacterial community, the bacterial groups with higher relative abundance (average abundance > 1%) exist in both spring and autumn, but their relative abundance is different. According to the results of Metastats test and Wilcoxon test, the relative abundance of Pseudomonadota, Verrucomicrobiota and Cyanobacteriota in spring and autumn has no significant difference (P > 0.05). The distribution of Actinomycetota and Bacillota in spring is significantly lower than that in autumn (P < 0.01). The relative abundance of Chloroflexota, Bacteroidota, Nitrospirota, Planctomycetota and Candidatus Aminicenantes in spring is significantly higher than that in autumn (P < 0.05).
3.3.2 Genera level.
At the genera level, the distribution and relative abundance of the top 20 bacterial groups in the overlying water of each sampling point in spring and autumn are shown in Fig 3. At the genera level, the genera with high relative abundance (average abundance > 1%) are Pseudomonas (average abundance 7.2%), Bacillus (5.7%), Sulfuricurvum (3.2%), Thermomarinilinea (2.6%), Longilinea (1.12%), Candidatus Magnetobacterium (1.08%) and Dechloromonas (1.02%). Among them, Pseudomonas and Bacillus have a clear advantage, with an overall abundance of 12.9% of all sequences.
In terms of spatial distribution, the relative abundance of bacteria with higher relative abundance (average abundance > 1%) shows different distribution characteristics at each sampling point. The bacterial groups Pseudomonas, Dechloromonas and Sulfuricurvum with high relative abundance (average abundance > 1%) are widely distributed in each sampling site, and the relative abundance differences are not obvious. Bacillus is mainly distributed in lake tail, bay and inflow area, but is less distributed in lake center and deep-water area of dam. Thermomarinilinea is mainly distributed at sampling points outside the incoming water area. Longilinea and Candidatus Magnetobacterium are mainly distributed in the lake tail and bay areas.
3.4 Comparative analysis of composition and structure of bacterioplankton community
As can be seen from Fig 4, PCo1 (64.49%) of the X-axis represents the first principal axis that can best distinguish all samples and explain 64.49% of all differences in the samples, PCo2 (14.32%) of the Y-axis represents the second principal axis that can best distinguish all samples, they could explain 14.32% of all differences in the sample. The plane formed by these two axes shows more than 78% of the difference between the samples. In spring, bacterial community at nine sampling sites clusters along the first principal axis in the first and fourth quadrants. The bacterial community of the nine sampling sites in autumn is scattered in the 2nd and 3rd quadrants, which is far away from the bacterial community gathering areas of the nine sampling sites in spring.
There are significant seasonal differences in the bacterial community structure between different samples in the overlying water of Sancha Lake in spring and autumn (P < 0.01). Seasonal changes in spring and autumn significantly affect bacterial community structure. In Fig 4, the sampling sites represented by different shape dots and triangles can be well distinguished, that is, the differences between samples are mainly due to the different sources of samples, namely spring and autumn.
Spatially, sampling sites in the same season can be distinguished on the second axis, indicating that environmental types, namely spatial heterogeneity, of different sampling sites have an impact on bacterial community, but the impact is smaller than that of sampling sites in different seasons. In spring, the distribution and aggregation of bacterial community at different sampling sites indicate that the influence of environmental type (spatial heterogeneity) on bacterial community structure is limited. In autumn, the sampling sites show discrete ordering positions with different environmental types, indicating that the change of environmental types of sampling sites in autumn has a greater impact on bacterial community structure.
Adonis test shows that the difference between groups is greater than the difference within groups (R2 = 0.294, P = 0.001), which also indicates that it is meaningful for spring and autumn samples to be grouped according to spring and autumn samples.
Wilcoxon test is used to calculate the different bacteria genera in spring and autumn, as shown in Fig 5. In terms of time distribution, most of the bacterial groups are distributed in spring and autumn, but the relative abundance is different, and the seasonal variation has a great effect on the community structure. According to the results of Metastats test and Wilcoxon test, the relative abundance of Pseudomonas and Bacillus in the bacterial group with higher relative abundance (>1%) in autumn is significantly higher than that in spring (P < 0.01). The distributions of Thermomarinilinea, Longilinea, Candidatus Magnetobacterium and Dechloromonas in spring are significantly higher than those in autumn (P < 0.01). There are differences in the distribution of Sulfuricurvum between the two seasons, but the difference is not significant (P > 0.05).
The horizontal coordinate indicates the species name under different classification levels, the vertical coordinate indicates the percentage value of the abundance of a certain species in the sample, and different colors indicate different groups. The far right is the P value, *P < 0.05; **P < 0.01.
3.5 Correlation between bacterioplankton community and environmental physicochemical factors
The environmental factors selected by VIF method are analyzed by RDA at the genera level. The results show that, at the genera level, the dynamic changes of the bacterioplankton community structure in the overlying water of Sancha Lake are affected by physicochemical factors in the water (Fig 6). In spring and autumn, DO, pH, TP and CODMn are the main influencing factors, which could significantly explain 30.22% of overlying water bacterial community distribution at genera level (P < 0.05), and the two axes provide 20.78% and 9.44% explanatory power, respectively. Although the effects of environmental factors on bacterial community structure are different in different seasons, phosphorus, CODMn, DO and pH, which are closely related to seasonal changes, are the main environmental factors affecting the bacterioplankton community structure in the overlying water of Sancha Lake.
3.6 Construction mechanism of bacterioplankton community
Stegen null model analysis is used to explore the construction mechanism of bacterioplankton community in spring and autumn overlying water of Sancha Lake (Fig 7). The results show that deterministic processes play a greater role in controlling community aggregation in the construction of overlying bacterioplankton community in spring, especially heterogeneous selection, which accounted for 75.6%. Random processes (dispersal limitation (11.1%), homogenizing dispersal (4.4%) and undominated processes (44.5%)) dominate the construction of overlying bacterioplankton community in autumn, accounting for 60%.
3.7 Molecular ecological network analysis of bacterioplankton community
The molecular ecological network is used to study the characteristics of the bacterioplankton community in the overlying water of Sancha Lake, and the molecular ecological network of the bacterioplankton community in the overlying water in spring and autumn is constructed (Fig 8). The spring bacterioplankton molecular ecological network consists of 50 nodes and 288 connections (200 positively correlated and 88 negatively correlated). The autumn bacterioplankton molecular ecological network consists of 50 nodes and 299 edges (162 positively correlated and 137 negatively correlated). In spring and autumn, the number of positive correlated edges is greater than the number of negative correlated edges, indicating that the synergistic effect between the major bacterial community is large, and the antagonistic effect is small. In spring, the synergistic effect is the largest, and the proportion of positive correlation reaches 69.44%. In addition, the average network node degree and the average network path length are lower in spring than in autumn.
Nodes represent bacterial species, connections between nodes represent interactions between species, red indicates positive correlation, and blue indicates negative correlation.
The topological functions of the molecular ecological network nodes of the overlying water bacterioplankton community in spring and autumn are shown in Fig 9. Topological analysis of the molecular ecological network nodes of overlying water bacterioplankton in spring and autumn shows that the number of key species in spring is higher than that in autumn. A total of six connectors are identified from the spring bacterioplankton, and the key species for identification of the genus are Methylobacter, Candidatus Magnetoovum, Pseudomonas and Thiobacillus. A total of three connectors are identified in the autumn bacterioplankton, and the key species for identification of the genus are Methylacidimicrobium, Thiothrix and Clostridium.
In order to further reveal the important role of key species in the eutrophication process of Sancha Lake, regression analysis of SRP and overlying water is carried out. The key species and SRP regression analysis of bacterioplankton in spring overlying water of Sancha Lake show that the relative abundance of Methylobacter(y = 0.564x + 0.088, R = 0.172, N = 9) and Pseudomonas(y = 0.112x + 0.106, R = 0.045, N = 9) is positively correlated with SRP content, the relative abundance of Candidatus Magnetoovum(y = 8.305x-0.231, R = 0.8, N = 9), Thiobacillus(y = 3.308x-0.025, R = 0.683, N = 9) is significantly positively correlated with SRP content (P < 0.05). In order to further analyze its mechanism of action, regression analysis is conducted between it and the pH of the overlying water. The results show that the relative abundance of Methylobacter(y = −0.098x + 0.742, R = −0.494, N = 9), Candidatus Magnetoovum(y = −0.170x + 1.203, R = −0.271, N = 9), Pseudomonas(y = −0.093x + 0.710, R = −0.619, N = 9) and Thiobacillus(y = −0.111x + 0.829, R = −0.382, N = 9) is negatively correlated with pH. The key species and SRP regression analysis of bacterioplankton in autumn overlying water of Sancha Lake show that the relative abundance of Methylacidimicrobium(y = −1.683x + 0.138, R = −0.372, N = 9), Thiothrix(y = −2.676x + 0.154, R = −0.301, N = 9) and Clostridium(y = −1.780x + 0.140, R = −0.273, N = 9) is negatively correlated with SRP content. In order to further analyze the mechanism of its action, regression analysis is conducted between it and the ALP of the overlying water. The results show that the relative abundance of Thiothrix (y = 0.015x-0.104, R = 0.216, N = 9) and Clostridium (y = 0.006x + 0.032, R = 0.108, N = 9) is positively correlated with the content of ALP. The relative abundance of Methylacidimicrobium (y = −0.003x + 0.154, R = −0.084, N = 9) is negatively correlated with ALP content.
4 Discussion
4.1 Composition and diversity of bacterioplankton in water
In this research, Illumina Miseq high-throughput sequencing is applied, and the results show that there are significant seasonal differences in the structural composition and diversity of the bacterioplankton community in the overlying water of Sancha Lake (P < 0.05), The diversity of bacterioplankton community in spring is significantly higher than that in autumn (P < 0.05). Xue Lu et al. [51] found that Alpha diversity of bacterial community in surface water of Baoan Lake was significantly higher in summer and autumn than in spring and winter. Cao Xinyi et al. [52] found that the Alpha diversity index of the plankton bacterial community in the surface water of Mochou Lake and Zixia Lake was the highest in summer and autumn, and the lowest in spring and winter. The microbial diversity in the overlying water of Sancha Lake in spring is higher than that observed in autumn. This phenomenon may be attributed to the increase in dissolved oxygen levels in spring, as well as the floating of sediments and the increase of nitrogen and phosphorus nutrients, which is conducive to the reproduction and growth of bacterioplankton, resulting in large bacterial community differences between spring and autumn, and different habitats show different bacterial diversity.
Pseudomonadota, Actinomycetota and Bacillota are the dominant phyla in the overlying water of Sancha Lake. The composition of the bacterioplankton community in Sancha Lake is similar to that in Danjiangkou Reservoir [53] and Beihai Lake [14] and other freshwater lake reservoirs reported in previous researches. Most of them belong to the phyla Pseudomonadota, Actinomycetota, Cyanobacteriota, Bacillota and Verrucomicrobiota.
In the phylum Pseudomonadota, Methylobacter, Pseudomonas, Thiobacillus and Thiothrix belong to it. Pseudomonadota can make extensive use of organic matter, participate in the metabolism of various substances, and play a very important role in the cycle of elements. Pseudomonadota usually exists as the first dominant phylum in water environments, such as wetlands [54]. Pseudomonadota is also the first dominant phylum of bacterioplankton in lake water [55].
In Bacillota, Clostridium belongs to it. Bacillota is an important chemoheterotrophic bacterium with strong tolerance, and its existence is usually observed in stressed environments such as eutrophic lake sediments [56,57].
In the phyla Nitrospirota and Verrucomicrobiota, the genera Candidatus Magnetoovum and Methylacidimicrobium belong to the phyla Nitrospirota and Verrucomicrobiota, respectively. Relevant studies have reported that Nitrospirota and Verrucomicrobiota are the main bacterial groups in the freshwater ecosystem [58,59], and a relatively low abundance of these two bacterial groups is detected in Sancha Lake. The difference in relative abundance of the same bacterial community may be due to their different environments and the different physiological and ecological characteristics of the bacterial community itself.
4.2 The main controlling factors of bacterioplankton community structure distribution in water
The results of the PCoA and Adonis test revealed highly significant seasonal differences in bacterial community structure between spring and autumn across different samples of the overlying water in Sancha Lake (P < 0.01). This could potentially be attributed to the increased dissolved oxygen levels in spring, as well as variations in environmental factors such as pH, T, phosphorus nutrients, and CODMn. The seasonal dynamic changes of bacterioplankton groups in aquatic ecosystems are a common feature. In the research on the factors affecting the bacterioplankton in the lake and reservoir ecosystem, Liu Lanying et al. [60] studied the composition of the bacterioplankton community in Zuohai Lake in Fuzhou and found that seasonal changes were an important factor determining the bacterioplankton community structure. In Beihai Lake, bacterioplankton were inactive in spring and winter when T, TP and TN were low, but the composition of bacterioplankton community changes significantly when T, TP and TN were increased in summer and autumn [14]. In Lake Erie, the microbial community composition of samples in different seasons is significantly different due to productivity [61]. There are notable variations in the bacterial community structure at the sampling points within the same season for the sample group, suggesting that alterations in the environmental type of the sampling points significantly influence the bacterial community structure. As depicted in Fig 4, the disparities in bacterial communities at the tail of the reservoir (L2), the center of the reservoir (L4), and the dam (L6) are more pronounced in spring than in autumn. This may be attributed to the greater variation in SRP values at these three sampling points in spring compared to autumn. The dissimilarities between the spring and autumn sample groups of Sancha Lake were significantly higher than those within the groups in either season (P < 0.01), indicating that spatial heterogeneity had a limited impact on bacterial community composition. This could be due to the more substantial differences in environmental factors such as DO, pH, T, phosphorus nutrients, and CODMn between the spring and autumn sample groups compared to those within each group. Additionally, the limited number of samples (only 9 in this study) might have constrained the statistical power for detailed spatial subgroup analysis. Dang et al. [62] investigated the comprehensive spatiotemporal dynamics of airborne bacteria in the water of Danjiangkou Reservoir across all four seasons. They similarly concluded that seasonal changes in Danjiangkou Reservoir significantly affected the biodiversity and structure of airborne bacterial communities, while spatial heterogeneity had a weaker influence.
There is a significant correlation between bacterioplankton community structure and environmental factors in Sancha Lake. In RDA at genera level, bacterial community structure is significantly correlated with DO, pH, CODMn, and TP (P < 0.05). Aerobic bacteria need oxygen to grow and develop, while anaerobic bacteria need to survive in an anoxic environment. Therefore, DO is also a major factor in the formation of bacterioplankton community structure [63]. pH value not only directly affects the growth state of bacteria, but also affects the structure and diversity of bacterial community by changing the physical and chemical properties of water [64]. CODMn is a conventional method for measuring organic matter and oxidizable inorganic matter pollution in water samples. The community structure of bacterioplankton varies with the level of chemical oxygen demand [65]. Water nutrients are a key determinant of bacterioplankton community structure [66]. Phosphorus, CODMn, DO and pH are the main environmental factors affecting the community structure of bacterioplankton. This is similar to the results reported in the research of factors affecting the composition of bacterioplankton in 25 small watersheds [67]. T, pH and NH4+-N significantly affected the composition of bacterioplankton community in Danjiangkou reservoir area [53]. It can be seen that the combination of environmental factors can explain the dynamic changes in the composition of the bacterioplankton community structure in the lake reservoir.
Seasonal thermal stratification has long been considered a critical factor closely associated with the eutrophication of reservoirs [68]. This phenomenon typically initiates in spring and reaches maturity during summer [69,70]. The formation of a thermocline limits the supply of DO throughout the water column, leading to seasonal hypoxia in bottom waters [71]. Such conditions can exacerbate eutrophication and compromise the health and functionality of aquatic ecosystems. In sub-deepwater lakes and reservoirs, a thermocline is likely to develop when water depth exceeds 7 meters [72,73]. Consequently, except for sampling points L2 and L8, thermoclines may form in the water bodies at other sampling. However, due to the lack of temperature measurements at varying depths within the same sampling point, it remains uncertain whether the water body at this sampling point is situated within the thermocline. Future studies could focus on elucidating the effects of seasonal thermal stratification in the overlying water body of Sancha Lake on environmental factors and microbial community structure, as well as its implications for eutrophication. These findings would provide a more robust scientific foundation for the restoration of eutrophic water bodies and water quality management in Sancha Lake.
4.3 Construction process of bacterioplankton community in water
In this research, it is found that the construction process of overlying water bacterioplankton community in spring is dominated by deterministic processes, while that in autumn is dominated by random processes. The relative importance of deterministic and random processes to microbial community construction is closely related to the research scale [74]. In terms of time scale, the microbial community construction process in different time scales is often dominated by different ecological processes, which may be due to the change of direction and intensity of ecological processes in a long-time span, or because time itself can be used as a driving factor for community construction, such as the intensity of random or deterministic events increases over time, exposing the microbial community to more random or deterministic processes [75]. Due to the high dissolved oxygen content in Sancha Lake during spring, the strong water circulation capacity of the lake, and variations in environmental factors, the planktonic bacterial community in the overlying water of Sancha Lake is primarily governed by deterministic processes dominated by heterogeneous selection. Meanwhile, Fig 6 illustrates the correlation between physicochemical factors and airborne bacteria in the spring overlying water of Sancha Lake. These findings suggest that DO, pH, phosphorus, and CODMn exhibit certain environmental selectivity for airborne bacteria in the spring overlying water of Sancha Lake. Similar results have been reported in studies on planktonic bacteria in lakes conducted by other researchers. For instance, Bai et al. [76] demonstrated that the community structure of planktonic bacteria in the grassy, algal, and central areas of Taihu Lake was significantly correlated with chlorophyll a, transparency, and total suspended solids, respectively. In different lakes, due to variations in geographical location and human activity influences, the environmental selection factors governing the distribution patterns of planktonic bacterial communities differ. Nonetheless, these research findings collectively confirm that deterministic processes (environmental selection) play a significant role in shaping the composition of planktonic bacterial communities in lakes. In autumn, the dissolved oxygen content in Sancha Lake is relatively low, resulting in poor water circulation and a limited spatial scale. These factors contribute to mild or gradual changes in water quality, as well as significant homogenization, leading to the floating bacterial community in the overlying water being primarily driven by stochastic processes during this season. Tripathi et al. [77] and Stegen et al. [78] argue that deterministic processes occur under conditions of drastic or sudden environmental changes, whereas random combinations dominate under mild or gradual changes. Liu et al. [79] investigated the construction process of the bacterial community in the reservoir area of Jinze Reservoir and similarly found that the relative importance of random and deterministic processes in bacterial community succession exhibited distinct seasonal characteristics.
4.4 Network characteristics and key species functions of bacterioplankton community in water
In the molecular ecological network of overlying water bacterioplankton in spring and autumn, most nodes belong to the Pseudomonadota phylum, accounting for more than 40% of all nodes. There are more positive connections than negative connections in the overlying water in spring and autumn, indicating that the bacterioplankton may be more inclined to co-exist in a synergistic and interactive way. This is consistent with the molecular ecological network characteristics of bacterial communities in other habitats. For example, Zhao et al. [80] established the molecular ecological network of bacterial communities in lakes in six cities in China, and found that most species in the community are positively correlated. This positive correlation can be considered as the existence of symbiosis and commensal relationships among bacterial populations, so these results may indicate that bacterial communities are self-organizing and self-sustaining [21]. The average network node degree and average network path length in spring are lower than those in autumn. The average degree of network nodes reveals the connectivity of each component in the bacterial network. The higher the average degree, the higher the degree of network interaction; the average path length of the network reveals the tightness of each component of the network; the smaller the average path length, the higher the tightness of the network [81]. The results show that the bacterioplankton network in the overlying water of Sancha Lake is more compact in spring than in autumn, and the interaction degree of the components of the bacterioplankton network in autumn is higher than that in spring.
The key species of spring bacterioplankton in this research are Methylobacter, Candidatus Magnetoovum, Pseudomonas and Thiobacillus. The key species of autumn bacterioplankton are Methylacidimicrobium, Thiothrix and Clostridium. There are differences in the quantity and types of key species of airborne bacteria in spring and autumn, which indicates that when external environmental factors change, that is, environmental factors such as phosphorus significantly affect the structural composition of the microbial community by regulating the abundance of key bacterial genera. This change further leads to alterations in the topological structure of the microbial co-occurrence network, causing it to exhibit obvious seasonal adaptation characteristics. Furthermore, the seasonal differences in the community construction mechanism indicate that when the construction mechanism changes, it will directly affect the composition of species in different seasons. This change in species composition will further alter the interactions among species, have an impact on the abundance and function of key species, and cause changes in the network topology. The changes in the network topology will ultimately affect the stability and function of the ecosystem. Compared with other species, key species play an important role in maintaining network structure, function and lake material circulation. For example, Pseudomonas, widely distributed in freshwater systems, is an extremely important phosphorus solubilizing bacteria [82]. Thiobacillus plays an important role in nitrogen and phosphorus cycling in ecological environment, and Thiobacillus is also a bacterium with phosphorus solubilization ability [83,84]. Thiothrix is one of the major genera with denitrification and phosphorus removal [85,86].
The key species and SRP regression analysis of bacterioplankton in spring overlying water of Sancha Lake show that the relative abundance of Methylobacter and Pseudomonas is positively correlated with SRP content, the relative abundance of Candidatus Magnetoovum, Thiobacillus is significantly positively correlated with SRP content (P < 0.05). This may be because in spring, due to water mobility is enhanced, dissolved oxygen is increased, sediments float up, and inorganic phosphorous solubilizing bacteria grow vigorously in an environment with high oxygen content [87], secreting organic acids to dissolve insoluble inorganic phosphorus, thus increasing SRP content in water. The key species and SRP regression analysis of bacterioplankton in autumn overlying water of Sancha Lake show that the relative abundance of Methylacidimicrobium, Thiothrix and Clostridium is negatively correlated with SRP content. This may be due to the fact that in autumn, the water quality is alkaline, phytoplankton flourish in Sancha Lake, and the water environment is short of active phosphorus. In the case of phosphorus shortage, key species promote the decomposition of organophosphorus by secreting ALP, so that it can decompose and produce SRP. Phosphorus is usually the first limiting nutrient for the primary productivity of lakes, and the phosphorus directly available to bacterioplankton is SRP. Therefore, SRP is an important factor in determining the nutrient status and productivity of lakes [88]. Phosphorus in water is decomposed or mineralized by bacteria through acid production or alkaline phosphatase secretion, and then SRP is released to participate in the chemical cycle of phosphorus and cause eutrophication in lakes [89]. Therefore, we speculate that these key species may contribute to the eutrophication of Sancha Lake. However, the high-throughput sequencing technology of 16S rRNA genes only focuses on the characteristic level of microbial communities and does not indicate the functional level of microbial communities. In the subsequent experiments, relevant studies such as macrogenes need to be conducted to further analyze and determine the roles of key species in the phosphorus cycle process of eutrophication.
5 Conclusion
The overlying water of Sancha Lake has abundant diversity of bacterioplankton community. The bacterioplankton in the spring and autumn overlying water of Sancha Lake belong to 55 phyla, 64 classes, 130 orders, 262 families and 732 genera of the bacterial domain, among which Pseudomonadota, Actinomycetota and Bacillota are the dominant phyla, and Pseudomonas and Bacillus are the dominant genera. In spring and autumn, Sancha Lake has common bacterioplankton taxa, but some bacterial communities have significant seasonal differences and insignificant spatial differences. Seasonal variation in spring and autumn significantly affects bacterial community structure. Phosphorus, CODMn, DO and pH are the main environmental factors affecting the bacterioplankton community structure in Sancha Lake. The construction process of the overlying water bacterioplankton community in spring is dominated by deterministic process dominated by heterogeneous selection, while the construction process of the overlying water bacterioplankton community in autumn is dominated by random process dominated by undominated processes. Both spring and autumn bacterioplankton are more mutually reinforcing than mutually inhibitory, the closeness of the overlying water bacterioplankton network is higher in spring than in autumn, and the interaction degree of the components of the overlying water bacterioplankton network is higher in autumn than in spring. The number of key species of bacterioplankton is higher in spring than in autumn, and these key species may play an important role in the phosphorus cycle of eutrophication process.
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