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Extreme trophic tales: deciphering bacterial diversity and potential functions in oligotrophic and hypereutrophic lakes

BMC Microbiology volume 24, Article number: 348 (2024) Cite this article

Abstract

Background

Oligotrophy and hypereutrophy represent the two extremes of lake trophic states, and understanding the distribution of bacterial communities across these contrasting conditions is crucial for advancing aquatic microbial research. Despite the significance of these extreme trophic states, bacterial community characteristics and co-occurrence patterns in such environments have been scarcely interpreted. To bridge this knowledge gap, we collected 60 water samples from Lake Fuxian (oligotrophic) and Lake Xingyun (hypereutrophic) during different hydrological periods.

Results

Employing 16S rRNA gene sequencing, our findings revealed distinct community structures and metabolic potentials in bacterial communities of hypereutrophic and oligotrophic lake ecosystems. The hypereutrophic ecosystem exhibited higher bacterial α- and β-diversity compared to the oligotrophic ecosystem. Actinobacteria dominated the oligotrophic Lake Fuxian, while Cyanobacteria, Proteobacteria, and Bacteroidetes were more prevalent in the hypereutrophic Lake Xingyun. Functions associated with methanol oxidation, methylotrophy, fermentation, aromatic compound degradation, nitrogen/nitrate respiration, and nitrogen/nitrate denitrification were enriched in the oligotrophic lake, underscoring the vital role of bacteria in carbon and nitrogen cycling. In contrast, functions related to ureolysis, human pathogens, animal parasites or symbionts, and phototrophy were enriched in the hypereutrophic lake, highlighting human activity-related disturbances and potential pathogenic risks. Co-occurrence network analysis unveiled a more complex and stable bacterial network in the hypereutrophic lake compared to the oligotrophic lake.

Conclusion

Our study provides insights into the intricate relationships between trophic states and bacterial community structure, emphasizing significant differences in diversity, community composition, and network characteristics between extreme states of oligotrophy and hypereutrophy. Additionally, it explores the nuanced responses of bacterial communities to environmental conditions in these two contrasting trophic states.

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Introduction

Lakes serve as sentinels and integrators, reflecting the impacts of environmental changes on watersheds [1,2,3]. Recently, the escalation of eutrophication, driven by excessive nutrient input such as nitrogen and phosphorus, poses a grate threat to the health and stability of lake ecosystems [4]. Lakes are categorized into oligotrophic, mesotrophic, and eutrophic (mild eutrophic, moderate eutrophic, and hypereutrophic) based on biological productivity and nutritional indices [5]. The consequential effects of eutrophication on biotic communities, particularly microbial communities, in lakes are noteworthy [6].

Bacteria, integral to aquatic ecosystems, play pivotal roles in biogeochemical cycles of organic matter and nutrients [7, 8]. Minor environmental variations in aquatic settings can impact bacterial community activity [9], underscoring their critical role in signaling environmental changes and sustaining the health and stability of aquatic ecosystems. Bacterial communities influence ecosystem functions and stability through parameters like diversity, species composition, interspecific relationships, and nutrient cycling via decomposition of organic compounds [6, 10]. Understanding variations in bacterial community structure among lakes with different trophic states is essential in aquatic microbial ecology.

The positive correlation between bacterial abundance and lake eutrophication is well-established [11, 12]. The nutritional status of lakes alters the taxonomic structure of bacterial communities, impacting metabolic pathways of carbon, nitrogen, sulfur, and other elements [13, 14]. Eutrophic lakes exhibit higher richness and diversity [15] but can also experience reduced diversity due to excessive nutrients [16]. Mild eutrophic lakes, in a transition state, have the most microbial species but weak species interactions [6, 17]. However, scant attention has been given to bacterial communities in oligotrophic and hypereutrophic lakes. Comparing bacterial community structures in these extremes could offer valuable insights for the preservation and remediation of highly nutritious lakes.

Eutrophic lakes often witness the proliferation of Cyanobacteria, including toxic Microcystis and Planktothrix [18, 19]. Cyanobacteria, Proteobacteria and Bacteroidetes dominate in eutrophic lakes [20, 21], while oligotrophic lakes, characterized by clear water and low nutrient content, are dominated by Actinobacteria and Proteobacteria [7, 22]. Nevertheless, research on the seasonal and spatial changes in bacterial community structure, especially interactions among bacterial species, in extreme oligotrophic and eutrophic environments remains limited.

Species in ecosystems form intricate interactions through substance, energy, and information exchange, creating complex ecological networks [23]. Bacteria contribute to these networks, with changes in structure affecting ecosystem function and stability [24, 25]. While higher species diversity is often associated with more complex networks [26,27,28], studies show a negative or nonlinear correlation between network complexity and biodiversity [29, 30]. Ecosystem complexity contributes to stability, as observed in macroecology [23]. Exploring the impact of climate change on microbial networks reveals a strong correlation between network complexity and stability [31]. Yet, the complexity of bacterial species interactions and how it affects community stability between oligotrophic and hypereutrophic lakes remain unclear.

In this study, we selected two freshwater lakes characterized by distinct trophic states. Lake Fuxian, an oligotrophic deep lake, contrasts with Lake Xingyun, a hypereutrophic shallow lake. The close proximity of these lakes, coupled with their unique trophic states, renders them ideal for in-depth exploration of variations in bacterial diversity and the intricate dynamics of network complexity and stability driven by their trophic conditions. Our primary objectives include: (1) To analyze and compare the bacterial diversity and taxonomic composition between Lake Fuxian and Lake Xingyun, which have distinctly different nutrient status. We hypothesize that Lake Fuxian, being oligotrophic, will exhibit lower bacterial diversity compared to the hypereutrophic Lake Xingyun. Additionally, we expect distinct taxonomic profiles reflective of their respective nutrient conditions. (2) To assess and compare the complexity and stability of bacterial networks in both lakes. We hypothesize that the bacterial networks in Lake Xingyun will demonstrate greater complexity due to the high nutrient availability, whereas Lake Fuxian’s bacterial networks will show lower stability, characteristic of oligotrophic conditions and lower species diversity. This study provided insights into how varying nutrient conditions influence bacterial communities and their ecological interactions, offering perspectives on how freshwater ecosystems might respond to eutrophication, which are escalating global concerns.

Materials and methods

Study area

Both Lake Fuxian and Lake Xingyun are situated on the Yunnan Plateau in Southwest China (Fig. 1a). Lake Fuxian, as a representative plateau deep-water freshwater lake, boasts a surface area of 216 km2 and an average depth of 89.6 m. It constitutes 78% of the total water storage in Yunnan Province, presenting a clear water body deficient in nutrients (Fig. 1b), indicative of an oligotrophic status [32].

Lake Xingyun, on the other hand, is a shallow lake with a surface area of 34.7 km2 and an average depth of 5.3 m [33]. Its hydrological recharge depends on atmospheric precipitation and the seasonal inflow from 14 small rivers [34]. The average nutrient states of Lake Xingyun is characterized as hypereutrophic (Fig. 1c). Since 2002, the lake has witnessed annual cyanobacterial blooms. The two lakes are interconnected by the Gehe River, spanning approximately 2.0 km. In 2008, to safeguard the water quality of Lake Fuxian, the local government initiated the “Lake Fuxian-Lake Xingyun Outflow Diversion Project” aimed at blocking the Gehe River.

Fig. 1
figure 1

(a) Geographical positioning of Lake Fuxian and Lake Xingyun within Yunnan Province, along with the delineation of sampling sites. (b) Upper: July 2020 images depicting Lake Xingyun’s water body covered with dense cyanobacterial blooms; Lower: Contrastingly, Lake Fuxian’s water body appears clear. (c) The comprehensive trophic level index (TLI) for Lake Fuxian and Lake Xingyun. TLI values indicate trophic states: TLI < 30 signifies oligotrophic, 30 ≤ TLI ≤ 50 denotes mesotrophic, 50 < TLI ≤ 60 implies mild eutrophic, 60 < TLI ≤ 70 indicates moderate eutrophic, and TLI > 70 represents hypereutrophic

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Sample collection and environmental attribute analysis

Surface water samples of twenty liters each (top 50 cm) were collected from 10 designated sites in each lake (Fig. 1a) during hydrological normal (April 2020), wet (July 2020), and dry (January 2021) seasons using a 5 L Schindler sampler. Transparency (SD) was measured in situ by a Secchi disk. In situ measurements of pH, water temperature (WT), dissolved oxygen (DO), and conductivity (Cond) were performed using a multiparameter water quality sonde (YSI 6600 V2, Yellow Springs Instruments Inc., USA). All samples were processed in the laboratory within 4 h.

For DNA extraction, water samples (200 ml for Lake Xingyun and 1000 ml for Lake Fuxian) were filtered through a 0.2 μm pore-size Polycarbonate membrane (Millipore Boston, MA, USA) using a vacuum pump, and the filtered materials were stored at − 80℃. The remaining water samples underwent chemical and biological analyses. Spectrophotometric methods were used to measure total nitrogen (TN), total dissolved nitrogen (TDN), ammonia nitrogen (NH4-N), nitrate nitrogen (NO3-N), total phosphorus (TP), total dissolved phosphorus (TDP), phosphate phosphorus (PO4-P) and chlorophyll-a (Chl-a) content. Total suspended solids (SS) concentration was calculated by filtering 200 ~ 500 ml water samples through a pre-dried and weighed GF/C membrane, followed by drying in an oven at 105℃ for 4 h and weighing. Subsequently, the samples underwent further processing in a muffle furnace at 550℃ for 2 h. Afterward, they were cooled in a desiccator and weighed again to ascertain the concentration of inorganic suspended solids (ISS). The disparity between SS and ISS was defined as the loss on ignition (LOI) [35]. The concentration of dissolved organic carbon (DOC) was determined using a total organic carbon analyzer with the standard method (HJ/T 104–2003). Permanganate index (CODMn) was determined by the potassium permanganate method (GB/T 15456 − 2008).

For algal and bacterial abundance counting, 5 ml of raw water was taken into a centrifuge tube, stored at 4 °C for later algal biomass counting, and another 5 ml of raw water was mixed with 0.24 ml of formaldehyde, stored at 4℃ overnight, then transferred to -20℃ for total bacterial abundance (TB) counting. Subsequently, the samples were brought back to the laboratory and counted using a flow cytometer [36].

The trophic state assessment of the two lakes utilized the comprehensive trophic level index (TLI), a widely adopted measure for assessing the eutrophication degree of Chinese lakes. In this study, three water environmental parameters, Chl-a, TN, and TP, were employed to calculate TLI [37].

DNA extraction and sequencing

Total bacterial DNA were extracted from filter membranes using the FastDNA® Spin Kit for soil kit (MP Biomedicals, USA) following the manufacturer’s instructions. The V3–V4 variable regions of the bacterioplankton 16S rRNA gene were PCR amplified using universal primer sets 341 F (5’-ACTCCTACGGGAGGCAGCAG-3’) and 806R (5’-GGACTACHVGGGTWTCTAAT-3’) [38]. The PCR amplification system includes a 20 ng DNA template, 0.4 µM primer, and NEB Phusion High-Fidelity PCR mix. PCR amplification program: pre-denaturation at 98 °C for 3 min; denaturation at 98 °C for 45 s, annealing at 55 °C for 45 s, extension at 72 °C for 45 s, a total of 30 cycles; final extension at 72 °C for 7 min. The amplified product was purified by Ampure XP magnetic bead method and dissolved in Elution Buffer to complete the construction of the genomic library. Finally, the DNA library was subjected to paired-end sequencing (2 × 300 bp) using the Illumina Miseq PE300 platform (Illumina, USA) of BGI Co. Ltd (Shenzhen, China).

Bioinformatic analysis of sequencing data

The raw 16S rRNA gene sequencing data underwent bioinformatic analysis using the QIIME2 Core 2023.2 distribution [39]. The process included importing, merging paired reads, splicing, and quality control of the original sequences. Sequences with lengths exceeding 550 bp or falling below 200 bp, as well as primers and other low-quality nucleotide sequences, were removed. Amplicon sequence variants (ASVs) were generated, and taxonomic classification was accomplished by comparing representative sequences against the SILVA v138 database [40]. To minimize random sequencing errors, low-abundance ASVs (< 10 sequences) were excluded from the ASV table. The raw sequencing reads have been deposited in the Genome Sequence Archive (GSA) database (http://gsa.big.ac.cn) under accession number CRA008654.

Bacterial diversity, taxonomic and predictive functional differences, and statistical analyses

Bacterial diversity analysis was performed using the vegan package in R (version 4.1.2). Kruskal-Wallis tests were employed to assess differences in physicochemical parameters between Lake Fuxian and Lake Xingyun, as well as across various hydrological periods. Bacterial α-diversity indices, namely the Chao1 index and Shannon index, were calculated at a uniform sequence depth (i.e., 36,377). Kruskal-Wallis tests were then applied to discern differences in bacterial α-diversity indices between the two lakes and across different sampling seasons.

For bacterial β-diversity, non-metric multidimensional scaling (NMDS) using Bray–Curtis distance among bacterial communities was performed [41]. Analysis of Similarities (ANOSIM) was employed to compare differences in bacterial community compositions (BCCs) between Lake Fuxian and Lake Xingyun, as well as among different sampling seasons. Following detrended correspondence analysis (DCA), redundancy analysis (RDA) was selected to explore associations between environmental variables and bacterial community structures. Prior to analysis, ASV data underwent Hellinger transformation. The vegan package’s forward selection method screened environmental factors with significant impacts on community composition, retaining those with a variance inflation factor (VIF) of less than 10 to mitigate collinearity issues. Significance was confirmed through a Monte Carlo test with 999 permutations. The “rdacca.hp” package was employed to evaluate the independent effect of each significant variable on the variation of BCCs [42].

To identify differentially abundant bacterial taxa between the two lakes, we employed LEfSe (linear discriminant analysis effect size) on ASVs with > 0.05% relative abundance [43]. This analysis was conducted using the online Galaxy interface (http://huttenhower.sph.harvard.edu/galaxy) with a Kruskal-Wallis alpha value of 0.01 and a linear discriminant analysis (LDA) threshold score of 4.0.

Functional predictive analysis was conducted using the Functional Annotation of Prokaryotic Taxa (FAPROTAX) tool on an online platform (http://www.cloud.biomicroclass.com/) [44], and results were visualized using STAMP (version 2.1.3). A P-value < 0.05 was considered statistically significant.

Network construction and visualization

Molecular ecological networks were constructed using the Network Analysis Pipeline (iNAP: http://mem.rcees.ac.cn:8081/) [45], based on ASV data. Prior to network analysis, ASVs with a relative abundance < 0.08% were filtered out, and Spearman correlation coefficients were then calculated. The correlation matrix underwent scanning with the cutoff set from 0.01 to 1, in increments of 0.01, to determine the optimal threshold conforming to the Poisson distribution, guided by the random matrix theory (RMT) within the iNAP program. To ensure comparability between lakes, a uniform similarity threshold of 0.87 was selected for constructing co-occurrence networks.

Key network properties, including total nodes, total links, R square of power-law, average degree (avgK), average path distance (GD), and average clustering coefficient (avgCC), were calculated to describe network size. Module division was carried out using greedy modularity optimization [46]. Additionally, one hundred random networks were generated by rewiring all nodes and links corresponding to empirical networks. Parameters of both random and empirical networks were subjected to t-tests to assess the meaningfulness of network construction.

The stability of co-occurrence networks was evaluated by randomly removing 50% of the nodes from the static network, measuring the rate of reduction in network robustness [31]. Visualization and analysis of networks were performed using the interactive platform Gephi (version 0.9.2) [47], in conjunction with Cytoscape (version 3.7.1) [48] .

Results

Characteristics of environmental parameters

The average TLI for Lake Fuxian and Lake Xingyun was 24 and 76, respectively, indicating oligotrophic and hypereutrophic conditions (Fig. 1c). Figure 2 illustrates the average water quality parameters of both lakes. Kruskal-Wallis tests revealed significant differences in measured physicochemical (SD, pH, Cond, TN, TDN, NO3-N, NH4-N, TP, TDP, PO4-P, CODMn, DOC, Chl-a, SS, and ISS) and biological parameters (abundances of algae and total bacteria) between the two lakes (P < 0.001), except for WT and DO (P > 0.05). The content of organic matter (OM%) in SS in Lake Fuxian was significantly higher than in Lake Xingyun (P < 0.01).

Furthermore, environmental parameters exhibited heterogeneity during different hydrological periods (Supplementary Table S1). Water temperature (WT), pH, Cond, TDP, PO4-P, and CODMn during the wet season were significantly higher than during the normal and dry seasons (P < 0.05). In Lake Xingyun, TN concentrations were significantly higher in the wet season compared to the normal and dry seasons (P < 0.05), whereas TN did not show significant variation in Lake Fuxian. The highest abundance of total bacteria (TB) in Lake Xingyun occurred in the normal season, while in Lake Fuxian, it peeked during the wet season.

Fig. 2
figure 2

Comparison of the main environmental parameters between Lake Fuxian and Lake Xingyun. WT, water temperature; SD, Secchi disk transparency; DO, dissolved oxygen; Cond, conductivity; TN, total nitrogen; TDN, total dissolved nitrogen; NO3-N, nitrate nitrogen; NH4-N, ammonia nitrogen; TP, total phosphorus; TDP, total dissolved phosphorus; PO4-P, phosphate phosphorus; CODMn, permanganate index; DOC, dissolved organic carbon; Chl-a, chlorophyll-a; SS, suspended solids; ISS, inorganic suspended solids; OM, organic matter content; TB, total bacterial abundance. The non-parametric Kruskal-Wallis test was performed to examine differences among the lakes. At the top of each boxplot: NS indicates no significant differences (P > 0.05); **, P < 0.01; ***, P < 0.001. In the boxplot, bold short black line and yellow dot denote the median and the mean of each parameter, respectively

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Diversity of bacterial communities

Both Chao1 and Shannon indices in Lake Fuxian were significantly lower than those in Lake Xingyun (Fig. 3; P < 0.001). In Lake Fuxian, the Chao1 index was significantly higher in the dry and normal seasons compared to the wet season ( P < 0.001), while the Shannon index in the normal season was significantly higher than in the dry and wet seasons (P < 0.01). In Lake Xingyun, the dynamics of the Chao1 index exhibited a trend similar to that of the oligotrophic Lake Fuxian, with the highest value occurring in the dry season. However, the Shannon index showed no significant differences across different hydrological periods (P > 0.05).

NMDS illustrated distinct separation of BCCs based on nutrient status between lakes and among hydrological periods within each lake (Fig. 3c). ANOSIM indicated that BCCs between the oligotrophic Lake Fuxian and hypereutrophic Lake Xingyun were significantly different (ANOSIM R = 0.99, P < 0.001). The BCCs among different hydrological periods in both lakes exhibited significant separation (R = 0.99, P < 0.001). Additionally, in the NMDS plot, the points representing different sampling seasons in Lake Xingyun are more dispersed compared to Lake Fuxian, indicating higher β-diversity of bacterial communities in Lake Xingyun.

Fig. 3
figure 3

Comparisons of bacterial α-diversity and β-diversity between the oligotrophic Lake Fuxia and hypereutrophic Lake Xingyun, as well as among different hydrological periods. (a) Chao1 index, (b) Shannon index. ***, P < 0.001; **, P < 0.01; ns, no significant. (c) Nonmetric multidimensional scaling (NMDS) plot based on Bray–Curtis dissimilarity. Differences between bacterial community structures between the two lakes were tested using Analysis of Similarities (ANOSIM). The results are presented in the NMDS plot. Ellipses cover 95% of the data for each hydrological period

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Taxonomy of bacterial communities

At the phylum level, Lake Fuxian exhibited the presence of 22 bacterial phyla, while Lake Xingyun showed 23. Notably, Actinobacteria stood out as the most abundant phylum in Lake Fuxian, with an average relative abundance exceeding 79.0%. In contrast, Lake Xingyun displayed Actinobacteria (32.0%), Cyanobacteria (23.9%), Proteobacteria (21.0%), and Bacteroidetes (20.0%) as the most abundant phyla (Fig. 4a). A significant shift in bacterial taxa occurred during the wet season in Lake Fuxian, characterized by a substantial decrease in the relative abundance of Proteobacteria and a remarkable increase in Cyanobacteria.

At the class level, Lake Fuxian revealed the presence of 43 bacterial classes, whereas Lake Xingyun exhibited 47. Acidimicrobiia (54.0%) and Actinobacteria (25.2%) dominated Lake Fuxian, while Lake Xingyun was characterized by Actinobacteria (25.2%), Oxyphotobacteria (23.8%), Bacteroidia (17.2%), Gammaproteobacteria (14.9%), and Alphaproteobacteria (10.8%) (Fig. 4b).

Moving to the genus level, Lake Fuxian was dominated by the CL500-29 marine group (52.7%), hgcI clade (19.8%), and Cyanobium (4.9%). Conversely, Lake Xingyun featured Microcystis (19.9%), hgcI clade (15.7%), CL500-29 marine group (6.6%), Flavobacterium (4.3%), and unclassified Microscillaceae (4.3%) (Supplementary Fig. S1 & Table S2).

To discern differences in bacterial taxonomy between Lake Fuxian and Lake Xingyun, LEfSe was employed. At the phylum level, LEfSe revealed a significant enrichment of Actinobacteria in Lake Fuxian, whereas Bacteroidetes, Cyanobacteria, and Proteobacteria were notably enriched in Lake Xingyun (Fig. 4c). At the family level, substantial differences were observed, including Ilumatobacteria and Cyanobiaceae in Lake Fuxian, and Microscillaceae, Flavobacteriaceae, Microcystaceae, Acetobacteraceae, and Burkholderiaceae in Lake Xingyun (Fig. 4c).

Fig. 4
figure 4

Bacterial taxonomy of Lake Fuxian and Lake Xingyun, categorized at the (a) phylum and (b) class levels. Only the 10 most abundant taxa are included in the figure, while other rare taxa are grouped into “Others”. (c) The LEfSe cladogram shows significant differences in bacterial taxa between the two lakes. Colored dots on the cladogram denote taxa with noteworthy differences in abundance across lakes, while the cladogram circles delineate phylogenetic taxa from phylum to family

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Environmental drivers influencing BCCs

RDA unveiled that the seasonal variations in BCCs between Lakes Fuxian and Xingyun were primarily influenced by CODMn, DOC, PO4-P, NH4-N, WT, and DO (Fig. 5a). Together, these six variables accounted for 80.2% of the total variations, with CODMn and DOC emerging as the most crucial contributors (Fig. 5b). In Lake Fuxian, the first and second axes of the RDA explained 24.3% and 15.4% of the variance in bacterial community (Fig. 5c). Through forward selection of RDA and the Monte Carlo permutation test, it was determined that the variations in BCCs in Lake Fuxian were significantly driven by DOC, PO4-P, TDP, TN, and NO3-N. Similarly, in Lake Xingyun, the first and second axes of the RDA explained 27.7% and 21.3% of the variance in BCCs (Fig. 5d). The forward selection of RDA, along with the Monte Carlo permutation test, indicated that the variations in BCCs in Lake Xingyun could be significantly elucidated by Cond, NO3-N, pH, PO4-P, and WT.

Fig. 5
figure 5

Redundancy analyses (RDA) plots depict the prominent environmental varibles influencing variations in BCCs in both lakes (a) and within each specific lake, namely Lake Fuxian (c) and Lake Xingyun (d). The individual effects of each significant environmental variable are illustrated in figure (b). The significance levels: *P < 0.05, **P < 0.01, ***P < 0.001. The abbreviations used in the plots are as follows: CODMn, permanganate index; DOC, dissolved organic carbon; PO4-P, phosphate phosphorus; NH4-N, ammonia nitrogen; WT, water temperature; DO, dissolved oxygen; NO3-N, nitrate nitrogen; TN, total nitrogen; TDP, total dissolved phosphorus; Cond, conductivity

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Functional prediction analysis

The results of functional prediction revealed notable differences in the abundance of various potential functions between Lake Fuxian and Lake Xingyun (Fig. 6). In Lake Fuxian, functions such as intracellular parasites, methanol oxidation, methylotrophy, fermentation, photoheterotrophy, and aromatic compound degradation were found to be more abundant. Additionally, functions associated with the nitrogen cycle, including nitrogen/nitrate respiration and nitrogen/nitrate denitrification, were enriched in Lake Fuxian. Conversely, Lake Xingyun exhibited significant enrichments in functions related to ureolysis, human pathogens, animal parasites or symbionts, and phototrophy.

Fig. 6
figure 6

Mean proportion of bacterial functional groups with the significant difference (P < 0.05) between Lake Fuxian and Lake Xingyun

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Characteristics of co-occurrence networks

Co-occurrence networks were constructed based on datasets from all hydrological seasons, and the observed empirical network parameters significantly surpassed those of the corresponding random networks, indicating a nonrandom nature (Table 1). Moreover, the “small-world” coefficients (σ) indicated that both co-occurrence networks exhibited “small-world” properties, suggesting their ability to respond rapidly and effectively to disturbances [31]. Additionally, both lakes’ co-occurrence networks displayed predominantly positive relationships, with higher values in Lake Fuxian (71.6%) compared to Lake Xingyun (67.4%), indicative of ecological cooperation within the microbiome.

Table 1 Topological properties of bacterial ecological co-occurrence networks
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Contrasting the oligotrophic Lake Fuxian, co-occurrence networks from the hypereutrophic Lake Xingyun exhibited higher avgK (9.330 vs. 4.639) and avgCC (0.415 vs. 0.371), indicating increased complexity. The Lake Xingyun network demonstrated higher robustness and lower vulnerability, suggesting a more stable characteristic. Conversely, the co-occurrence network from Lake Fuxian displayed a shorter GD, implying greater sensitivity to environmental disturbances. Both lakes’ co-occurrence networks exhibited modular structures, clearly divided into four major modules (Fig. 7). In Lake Fuxian, modules I were predominantly present in the wet season, while modules IV were mainly observed in the dry season. In Lake Xingyun, modules I and III were primarily associated with the wet season.

Fig. 7
figure 7

Co-occurrence networks and their topological properties and stability of bacterial communities from Lake Fuxian (a) and Lake Xingyun (b). ASVs were selected based on their relative abundance (≥ 0.08%) among the total bacterial sequences. The size of each node is proportional to the number of connections to that node, and different colors indicate distinct modules within the networks. The distribution of bacterial taxa in each module from Lake Fuxian (c) and Lake Xingyun (d) is presented across different hydrological periods. To assess network stability, robustness (e) was quantified as the proportion of remaining taxa in a network after random removal of node (50%). Vulnerability (f) was determined by identifying the maximum node vulnerability in each network

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Discussion

Building upon our primary objectives outlined in the introduction, we explored the distinct differences in bacterial diversity, taxonomic composition, and predicted functions between the oligotrophic Lake Fuxian and the hypereutrophic Lake Xingyun. As hypothesized, our findings revealed significant disparities in the bacterial communities of these lakes, driven by their contrasting nutrient states. The results provide valuable insights into how environmental factors, specifically trophic conditions, shape bacterial community composition and ecological interactions within freshwater ecosystems. The following, we will discuss in detail from three aspects.

Distinct differences in bacterial diversity, taxonomic composition and predicted functions between oligotrophic Lake Fuxian and hypereutrophic Lake Xingyun

Our exploration of bacterial diversity, taxonomic composition, and functional predictions in oligotrophic Lake Fuxian and hypereutrophic Lake Xingyun reveals distinct disparities (Fig. 3a, b). The bacterial communities in the hypereutrophic lake exhibit considerably greater α-diversity than those in the oligotrophic counterpart. Our results suggest that in hyper-eutrophic lakes, the increased availability of growth-limiting resources may lead to the diversification of bacterial niches, which could enhance species diversity at the local scale [49, 50]. Additionally, due to its small surface area and shallow depth, Lake Xingyun is more susceptible to external river inputs. Therefore, the influx of external microorganisms may also be one of the reasons for the high bacterial alpha diversity in Lake Xingyun.

Despite both lakes sharing common bacterial phyla with other freshwater bodies [10, 51], their relative abundances diverge significantly, illustrating how eutrophication alters bacterial diversity and community structures [52]. In Lake Fuxian, Actinobacteria dominate the bacterial community, comprising 79.0% of its composition. This phylum, widespread in various aquatic environments, has traditionally been associated with different water bodies [7], including mesotrophic drinking water reservoirs [53, 54], oligotrophic and eutrophic lakes [55,56,57]. Mesocosm experiments revealed that the occurrence of Actinobacteria was correlated with less eutrophic conditions [58]. Our LEfSe analysis further emphasizes the significant enrichment of Actinobacteria in Lake Fuxian compared to Lake Xingyun, suggesting an adaptation to the lake’s upper water layers from oligotrophic environments [58]. Conversely, Lake Xingyun exhibits higher enrichment of Proteobacteria, Cyanobacteria, and Bacteroidetes, attributed to its heightened nutrient sensitivity and human-induced waste contributions. This hypereutrophic lake, characterized by significant human disturbance, heavily relies on precipitation and inflow from surrounding rivers, intensifying its exposure to agricultural runoff [34]. Proteobacteria and Bacteroidetes exhibit high sensitivity to nutrient enrichment and cyanobacterial blooms, evident in their prevalence in environments with such conditions [59, 60]. Additionally, Bacteroidetes have been linked to areas with elevated nitrogen levels and the presence of human metabolic wastes in lakes [61], highlighting their adaptability to nutrient-rich and anthropogenically influenced aquatic ecosystems.

Significant differences in functional composition between the two trophic lakes were observed. In Lake Fuxian, more functions related to element cycling were identified, while Lake Xingyun exhibited a higher abundance of functions related to human interference. In Lake Fuxian, functions associated with carbon decomposition (fermentation, and aromatic compound degradation) were significantly enriched, with the dominant bacterial genus CL500-29 marine group and hgcI clade showing high efficiency in utilizing carbon compounds [62,63,64]. Furthermore, functions related to methanol utilization (methanol oxidation, and methylotrophy) in Lake Fuxian were also significantly enriched. This is consistent with the fact that the relative abundance of methylotrophs (i.e., Candidatus Methylopumilus, also known as the freshwater LD28 tribe) [65, 66] in Lake Fuxian is more than twice that in Lake Xingyun (Supplementary Table S2). Effects related to methyl nutrition play a role in the global nitrogen cycle [65], with methanol enhancing denitrification. Functions related to the nitrogen cycle (nitrogen/nitrate respiration, nitrogen/nitrate denitrification) were enriched in Lake Fuxian. Despite the hypereutrophic lake being rich in nitrogen, extreme hypoxia under high algal biomass significantly restricted nitrification, consequently limiting denitrification due to the lack of available substrates [67]. Additionally, the enrichment of functions related to ureolysis, human pathogens, and animal parasites or symbionts in Lake Xingyun suggests the presence of exogenous nitrogen input from agricultural non-point sources and other pollutants, posing a potential pathogenic risk for surrounding residents.

Nutrients are the primary factor driving differences in BCCs between Lake Fuxian and Lake Xingyun

Environmental factors exert significant influence on BCCs in Lakes Fuxian and Xingyun. The intricate interplay between the water environment and microorganisms plays a vital role in shaping microbial distribution and ecological functions [68, 69]. Results from the RDA analysis highlight the predominant impact of nutrient-related factors (e.g., CODMn, DOC, PO4-P and NH4-N) compared to physical factors (e.g., WT and DO) on the variations in BCCs between the two lakes (Fig. 5). Carbon, nitrogen, and phosphorus emerge as crucial determinants affecting bacterial community composition, providing the material foundation for bacterial growth and reproduction in aquatic environments [70].

The commonly used CODMn serves as an indicator of organic pollution levels in water, with its concentration significantly influencing microbial community structures in lakes [71, 72]. DOC, which influences the microbial carbon cycle through its effects on microbial respiration and metabolism, plays a pivotal role in shaping bacterial communities [73, 74]. Studies on DOC sources in lakes across different trophic levels suggest that oligotrophic lakes primarily receive endogenous DOC inputs, while eutrophic lakes benefit from both exogenous and endogenous carbon sources [75]. The profound impact of DOC on bacterial community composition is particularly evident in Lake Fuxian (Fig. 5c), where bacteria with high carbon compound utilization, such as the CL500-29 marine group and hgcI clade, dominate, comprising up to 80.3% of the community during the dry season (Supplementary Fig. S1). The oligotrophic conditions of Lake Fuxian emphasize the importance of carbon in shaping bacterial community composition, given the limited availability of organic carbon sources.

Excessive nutrient levels, particularly nitrogen and phosphorus, promote the growth of algae, including cyanobacteria. In eutrophic Lake Xingyun, the growth of abundant Cyanobacteria, mainly Microcystis, is influenced by dissolved nitrogen and phosphorus elements. Cyanobacteria may compete with heterotrophic bacteria for these nutrients during their growth, establishing a cooperative relationship by recruiting beneficial heterotrophic bacteria through the release of extracellular polysaccharides or DOC [76, 77]. Additionally, the proliferation of cyanobacteria significantly increases water pH, affecting nutrient solubility and bioavailability [78], thereby influencing bacterial community dynamics. Bacterial degradation of organic matter further promotes the regeneration of nitrogen and phosphorus nutrients, stimulating cyanobacterial growth [79]. In summary, the complex interplay between algae and bacteria plays a pivotal role in maintaining the dynamic balance of bacterial communities [76, 80].

While our study primarily focuses on the impact of nutrient conditions on bacterial community structure in hypereutrophic Lake Xingyun and oligotrophic Lake Fuxian, it does not extensively address the role of protists and viruses. These microorganisms influence bacterial communities through predation and viral lysis (top-down control) and nutrient regeneration (bottom-up control) [81, 82]. For example, the dominance of the hgcI clade in both lakes in this study (Supplementary Fig. S1) may be related to their adaptive life strategies, which include responses to top-down mechanisms and efficient resource utilization [83]. The varying impacts of these processes in different trophic environments are significant and warrant further investigation. Future studies should incorporate these top-down forces to provide a more comprehensive understanding of bacterial community dynamics in freshwater lakes.

Bacterial networks in hypereutrophic Lake Xingyun are more complex and stable than in oligotrophic Lake Fuxian

Our findings reveal a higher level of complexity in bacterial networks within the hypereutrophic Lake Xingyun compared to the oligotrophic Lake Fuxian (Table 1; Fig. 7). This aligns with a recent study covering oligotrophic to hypereutrophic lakes, which demonstrated a unimodal pattern in bacterioplankton network complexity across increasing trophic state index, peaking at mesotrophic states and subsequently decreasing towards hyper-eutrophic states [17]. Our results support this trend by confirming that bacterial networks are more intricate in hypereutrophic lakes compared to their oligotrophic counterparts.

In recent decades, numerous studies have explored the stability of ecosystems by examining interaction network structures [23, 84]. Prior research has consistently shown a robust positive correlation between network complexity and stability [31], aligning with the macroecological perspective that increased ecosystem complexity leads to enhanced stability [23]. Conversely, in random networks, an elevation in modularity correlates with a higher likelihood of network instability, establishing a negative correlation between the two [85, 86]. Our results indicate a higher modularity in bacterial networks in oligotrophic states (modularity: 0.702 vs. 0.612), predicting a more stable network in hypereutrophic states. Correspondingly, calculations for stability (robustness and vulnerability) support this outcome (Fig. 7). The shorter average path length of the bacterial network in Lake Fuxian suggests greater efficiency in information, energy, and material transfer between species [31]. This implies that the bacterial community in Lake Fuxian can respond more rapidly to perturbations, rendering the community more prone to changes [87]. Therefore, the higher network complexity in hypereutrophic lakes suggests that bacterial stability in such environments may surpass that in oligotrophic lakes. Consequently, oligotrophic lakes may possess weaker resistance to environmental disturbances, rendering their ecosystems more vulnerable.

The response of bacterial species to various perturbations determines community stability, with the average degree reflecting the complex relationships between species interactions [88]. The higher average degree in the hypereutrophic lake indicates more intricate interactions between bacterial communities in Lake Xingyun. Additionally, the heightened negative correlations in bacterial networks in the hypereutrophic state suggest that bacterial communities in Lake Xingyun engage in more competitive relationships with each other. As a typical algal-type lake, the bacteria-algae system in Lake Xingyun forms complex interrelationships, including mutualistic cooperation and competitive exclusion [25], making it more resistant to environmental disturbances. Despite being a hypereutrophic lake experiencing year-round cyanobacterial bloom accumulation [89], coupled with the stability of bacterial communities, the ecological restoration of Lake Xingyun’s ecosystem from a turbid water algal state to a clear water macrophyte state becomes a more challenging endeavor.

Conclusions

In this study, we examined the bacterial communities in two plateau freshwater lakes with contrasting trophic states—oligotrophic and hypereutrophic—in Yunnan, China. Our results revealed that the hypereutrophic lake exhibited higher α-diversity and β-diversity compared to the oligotrophic lake. Distinct differences in bacterial community compositions were observed between the two ecosystems. In the oligotrophic lake, bacterial communities were primarily involved in carbon and nitrogen cycling processes, while in the hypereutrophic lake, greater human activity-related disturbances highlighted potential pathogenic risks. Additionally, the bacterial community network in the hypereutrophic lake was more complex and stable, suggesting challenges for ecological restoration in lakes with severe cyanobacterial blooms.

Data availability

The raw sequence data reported in this paper have been archived in the Genome Sequence Archive at the BIG Data Center (http://bigd.big.ac.cn/gsa) under the accession number CRA008654.

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Acknowledgements

We express our gratitude to Wei Tian, Jingchen Xue, Meng Qu, Keqiang Shao, and Dong Li for their valuable assistance in sample collection and laboratory measurements. Additionally, we appreciate the editors and anonymous reviewers for their constructive suggestions and comments, which have greatly contributed to the refinement of this work.

Funding

This study received financial support from the National Natural Science Foundation of China (grant numbers: 41971062, 42371065) and West Anhui University through the “Scientific Research Start-up Funds for High-level Talents” (WGKQ2022032).

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Authors and Affiliations

  1. College of Biology and Pharmaceutical Engineering, West Anhui University, Lu’an, 237012, China

    Guijuan Xie

  2. Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China

    Guijuan Xie, Yuqing Zhang, Yi Gong, Wenlei Luo & Xiangming Tang

  3. The Third Construction Company of CCCC second Harbor Engineering Co., Ltd, Zhenjiang, 212000, China

    Yuqing Zhang

  4. The Fuxianhu Station of Plateau Deep Lake Field Scientific Observation and Research, Yunnan, 653100, Yuxi, China

    Wenlei Luo

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  1. Guijuan Xie
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Contributions

G.X. and X.T. conceived and conducted the experiment. G.X., Y.Z., Y.G., and W.L. analyzed the results, and wrote the article; X.T. revised the manuscript. All authors reviewed the manuscript. No conflict of interest exists in the submission of this manuscript, and the final manuscript is approved by all authors for publication.

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Correspondence to Xiangming Tang.

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Xie, G., Zhang, Y., Gong, Y. et al. Extreme trophic tales: deciphering bacterial diversity and potential functions in oligotrophic and hypereutrophic lakes. BMC Microbiol 24, 348 (2024). https://doi.org/10.1186/s12866-024-03488-x

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Keywords

BMC Microbiology

ISSN: 1471-2180

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