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Article

Diversity and Their Response to Environmental Factors of Prokaryotic Ultraplankton in Spring and Summer of Cihu Lake and Xiandao Lake in China

by
Cong Lan
1,2,†,
Lili Sun
1,2,†,
Yihan Hu
1,2,
Yan Zhang
1,2,
Jinjing Xu
1,2,
Heng Ding
1,2,
Rong Tang
1,2,
Jianjun Hou
1,2,*,
Yuntao Li
3 and
Xiaodong Wu
4
1
Huangshi Key Laboratory of Lake Environmental Protection and Sustainable Resource Utilization, Hubei Normal University, Huangshi 435002, China
2
College of Life Sciences, Hubei Normal University, Huangshi 435002, China
3
School of Food and Biological Engineering, Bengbu University, Bengbu 233030, China
4
College of Urban and Environmental Sciences, Hubei Normal University, Huangshi 435002, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sustainability 2023, 15(15), 11532; https://doi.org/10.3390/su151511532
Submission received: 21 June 2023 / Revised: 15 July 2023 / Accepted: 21 July 2023 / Published: 26 July 2023
(This article belongs to the Special Issue Wetlands: Conservation, Management, Restoration and Policy)
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Abstract

:
Ultraplankton plays an important role in the biogeochemical cycles of aquatic ecosystems. Based on 16S rRNA gene sequencing technology, the community structure composition of prokaryotic ultraplankton and its relationship with environmental factors were analyzed. The results showed that Cihu Lake was experiencing eutrophication and that Xiandao Lake was in the process of changing from mesotrophic to oligotrophic conditions. Cihu Lake and Xiandao Lake were regulated primarily by nitrogen nutrients. Proteobacteria, Bacteroidota, Cyanobacteria, and Actinobacteriota were the major phyla of prokaryotic ultraplankton in both lakes. Among them, Cyanobacteria dominate in the summer in Cihu Lake, which can have seasonal cyanobacterial blooms. Seasonal variation significantly affects the diversity and community structure of prokaryotic ultraplankton in the lakes, with temperature and dissolved oxygen being the key environmental factors determining plankton community composition. The PICRUSt functional prediction analysis indicated a higher water purification and exogenous pollution remediation capacity of the microbial communities of Xiandao Lake, as well as in the spring samples of Cihu Lake. In this study, the diversity and spatial–temporal succession patterns of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake were elucidated, providing a useful reference for the lake environmental protection and water eutrophication management in Cihu Lake and Xiandao Lake.

1. Introduction

The scarcity of fresh water has always been a worldwide problem. Not only do people require ample potable water to maintain daily life, but the increasing food production of the future will also have a great demand for agricultural water [1,2,3]. Of the global water resources, 97.5% is salt water and 2.5% is freshwater, while most of the freshwater exists in the form of glaciers and permanent snow. Only 0.26% of the total freshwater is concentrated in rivers, lakes, and reservoirs and is easily accessible for human use [4]. The conservation and utilization of freshwater lakes are particularly important for the maintenance of freshwater ecosystems. One of the major problems facing the global inland waters is the eutrophication of lakes, as the increase in nutrition load and algal bloom leads to the deterioration of water quality and the reduction of plankton diversity in lakes [5,6].
Plankton is an essential component of aquatic ecosystems. Ultraplankton (0.2–5 μm) includes nanoplankton (2–20 μm) with particle sizes below 5 μm and picoplankton (0.2–2 μm). Ultraplankton can be further divided into eukaryotic and prokaryotic components. Free-living prokaryotic plankton (primarily bacteria and archaea) are central members of the microbial loop in the water column and flow into the classical food web through the predation of zooplankton. They are also a principal carbon sink in the plankton community and perform an active role in the inorganic nutrient regeneration in the water column, especially of those planktonic organisms smaller than 1 μm in diameter [7,8]. Several studies have been carried out on prokaryotic plankton, while few studies have targeted those that are ultra-particle in size. Widely distributed and highly reproductive planktonic populations with various metabolic and resource preferences can respond rapidly to environmental changes (nutrients, temperature, and so forth) by altering their community composition and metabolic activities [9,10]. Diversity studies of prokaryotic ultraplankton can help enhance our understanding of the microbial community dynamics within lakes from a unique perspective.
Cihu Lake is the largest urban shallow lake in Huangshi City, Hubei Province, located in the middle reaches of the Yangtze River. Its water quality ranges between China’s National Class IV–V of the surface water standards all year round [11]. Urban lakes are closely related to the daily life of residents and are exposed to strong anthropogenic disturbances, coupled with lower water circulation, thus making them highly susceptible to eutrophication [12,13]. Cyanobacteria could develop toxic blooms under such circumstances, posing a serious threat to the rest of the planktonic community (phytoplankton, zooplankton, and so forth) as well as human health [14,15,16,17]. Xiandao Lake, located in Yangxin County, Huangshi City, China, is a deep-water lake and is an alternate source of drinking water for Huangshi City. Water quality in the lake is sustained at a level above China’s National Class II surface water standard [18]. Xiandao Lake is an artificial reservoir, and its establishment has been significant in providing a reliable water source for urban use, as an element in flood control, irrigation and for many other purposes [19]. With the development of intense urban tourism in recent years, the water quality of Xiandao Lake has significantly declined. Studies of the plankton diversity and their response to environmental factors in Cihu Lake and Xiandao Lake may provide theoretical references for the management of eutrophication in similar lakes and reservoirs.
With the continuous advancements in science and technology, plankton community assessment methods have evolved, and high-throughput sequencing techniques and metagenomic techniques have emerged one after another [20,21]. Compared with the traditional cultivation-dependent approach to investigating plankton diversity, the progress of gene sequencing technology and bioinformatics analysis has greatly improved the efficiency of the research. And there is also a lack of application space for laboratory cultivation techniques for the studies of ultraplankton. At present, numerous studies on plankton diversity have been conducted with high-throughput sequencing technology, and the sequencing technology has been developed to the latest level of third-generation sequencing (TGS) [22,23]. With a wider range of applications and the ability for in-depth exploration of single-cell genomes, gene sequencing has provided a solution for the studies of many uncultured microorganisms and shows a broad development prospect [24].
Using Cihu Lake and Xiandao Lake as the target lakes, in this study the total length of 16S rRNA gene from prokaryotic ultraplankton was sequenced by Pacific Bioscience (PacBio) third-generation sequencing technology. Based on alpha diversity analysis, beta diversity analysis, redundancy analysis (RDA), and others, the community structure composition and spatial–temporal succession patterns of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake were revealed, and the interactions between prokaryotic ultraplankton and environmental factors were also investigated. The results of this study will provide a useful reference for the lake environmental protection and water eutrophication management in Cihu Lake and Xiandao Lake and will be beneficial for promoting the application of third-generation sequencing technology in the studies of ultraplankton.

2. Materials and Methods

2.1. Sample Collection and Determination of Physico-Chemical Indicators

Five sampling sites were established in Cihu Lake and Xiandao Lake, respectively (Figure 1), and two seasons of sampling were conducted in the spring of 2019 (April to May) and the summer of 2019 (July), for a total of 20 samples. The Cihu Lake samples were collected in the spring samples 1 to 5 (CH1spr to CH5spr) and summer samples 1 to 5 (CH1sum to CH5sum), and the Xiandao Lake samples were spring samples 1 to 5 (XDH1spr to XDH5spr) and summer samples 1 to 5 (XDH1sum to XDH5sum). Water samples were collected at a depth of 0.5 m below the water surface using an organic glass water sampler at each sampling site, and the determination of physico-chemical indicators and the filtration of water samples were completed within 24 h after the completion of water samplings. Take 500 mL of water samples and sequentially passed them through a 10 μm sieve and a 5 μm Millipore nuclear membrane in order to filter out the plankton with diameters above 5 μm. After filtration, the water samples were filtered again using a 0.7 μm GF/F membrane (Whatman, Shanghai, China), and what remained on the membranes were the ultraplankton samples with particle sizes of 0.7 to 5 μm. Finally, wrapped the GF/F membranes in tinfoil and stored in a −80 °C ultra-low temperature refrigerator until analysis. Physico-chemical indicators can be divided into six field physico-chemical indicators, including Secchi disc transparency (SD), temperature (Temp), dissolved oxygen (DO), specific conductivity (SPC), total dissolved solids (TDS), salinity (Sal), and five laboratory water chemistry indicators, i.e., total nitrogen (TN), total phosphorus (TP), phosphate (PO43−), chlorophyll a (Chl a), and chemical oxygen demand (CODMn). The physico-chemical indicators of field tests were measured using YSI (EXO3, USA) and Secchi discs, while the determination and analysis of the laboratory water chemistry indicators followed the “China’s environmental quality standards for surface water —GB3838-2002” [25].

2.2. RNA Extraction and 16S rRNA Gene Sequencing

RNA of the samples was extracted by using the Trizol-based method [26]. The membranes of ultraplankton samples were cut up under low-temperature conditions and 1 mL of Trizol was added, then mixed by hand and cooled samples on ice for 10 min. After two repeat extractions were performed with chloroform, an equal volume of isopropanol was added to precipitate the RNA at −20 °C. Washed the RNA precipitate with 1 mL of 75% ethanol and air-dried it at room temperature for 5–10 min. The final RNA was dissolved in 30 µL of de-ionized water and the quality of the RNA was detected by agarose gel electrophoresis. Reverse transcription of RNA samples and amplification of the V1-V9 region of target fragments were carried out by reverse transcription-PCR (RT-PCR). The primer sequences used for the amplification of target fragments were 27F (AGRGTTTGATYNTGGCTCAG) and 1492R (TASGGHTACCTTGTTASGACTT). After passing through agarose gel electrophoresis once again, the target products were recovered and purified via a TIANgel midi purification kit (Tiangen, Beijing, China) and sequenced using PacBio’s single molecule real-time (SMRT) sequencing platform (Biomarker Technologies, Beijing, China).

2.3. Statistical Analyses

Five physico-chemical indicators (SD, TN, TP, Chl a, and CODMn) were selected to evaluate the nutritional status of Cihu Lake and Xiandao Lake by means of the comprehensive trophic level index (TLI) method [27]. FLASH v1.2.11 software was used to splice the raw data obtained from sequencing [28], and Trimmomatic v0.33 software was applied to control the quality of the sequences obtained from splicing [29]. Set a sliding window of 50 bp, cut off the back-end bases from the sliding window if the value of the average quality was lower than 20, and filter the tags which were shorter than 75% of the original tags after quality control. And chimera sequences were removed through UCHIME v8.1 software [30], thus obtaining high-quality tag sequences. After this process, the sequences were clustered at the 97% level of similarity using USEARCH v10.0 to acquire the operational taxonomic unit (OTU) sequences and OTU tables [31]. The OTU sequences were annotated for classification by aligning them with the Silva database (Release132, http://www.arb-silva.de, accessed on 1 September 2020) through RDP Classifier to obtain the species composition data [32,33] (v2.2, http://sourceforge.net/projects/rdpclassifier/, accessed on 1 September 2020). Four alpha diversity indices (Shannon index, Chao1 index, ACE index, and observed species index) were assessed and calculated with Mothur v1.30 software [34]. Beta diversity analyses such as the unweighted pair-group method with arithmetic mean analysis (UPGMA), principal coordinates analysis (PCoA), and analysis of similarities (Anosim) were developed according to the Bray–Curtis distance matrix. Selected species listed in the top 10 of abundance at the species level and took their logarithms to perform the correlation analysis between species and environmental factors with the help of Canoco v5.0. The redundancy analysis (RDA) was chosen according to the gradient length of detrended correspondence analysis (DCA) was smaller than 3. And the environmental variables screened for RDA were based on forward selection under the conditions of p < 0.05. Then mantel test between all species at the species level and environmental factors was conducted using R v4.1.3. Functional prediction of the Kyoto Encyclopedia of Genes and Genomes (KEGG) in prokaryotic ultraplankton from Cihu Lake and Xiandao Lake was carried out by comparing the OTU data information accessed through 16S rRNA gene sequencing using the PICRUSt software. The Kruskal–Wallis nonparametric test was applied to investigate the significant differences between multiple groups of variables. The related statistical analysis was calculated using IBM SPSS Statistics v26.0.

3. Results

3.1. Spatial–Temporal Variation of Physico-Chemical Indicators

Spatial–temporal variation analysis of physico-chemical indicators was performed, and the data were shown in mean ± SEM formats in Table 1. As can be seen from the table, there was greater spatial–temporal heterogeneity among the environmental factors in Cihu Lake and Xiandao Lake, and the spatial variations were much stronger than the temporal variations. In spatial aspects, DO, SPC, TDS, Sal, TN, TP, Chl a, and CODMn were found to be higher in Cihu Lake than in Xiandao Lake during both the spring and summer. Significant differences were particularly marked in summer (p < 0.05), while SD and TN/TP were higher in Xiandao Lake during spring and summer. DO was at a maximum of 13.16 mg/L at site 4 of Cihu Lake and was at a minimum of 8.02 mg/L at site 1 of Xiandao Lake in the summer (p = 0.002). The concentration of Chl a ranged widely from 0.67 to 109.10 μg/L (p = 0.001). Those of Chl a at Cihu Lake were above 50 μg/L and below 10 μg/L at all stations of Xiandao Lake. In temporal aspects, SPC, TDS, Sal, TN, TP, TN/TP, and Chl a were all higher in spring than in summer, only the temperature was higher when compared to spring. Both TN and TP had maximum values in spring at site 4 of Cihu Lake. The averages of TN/TP in spring and summer at Cihu Lake and Xiandao Lake in summer were all lower than 9. The averages of TN/TP in spring at Xiandao Lake varied from 9 to 22.6. Some of the highest concentrations of Chl a were observed in spring at sites 4 and 5 of Cihu Lake. According to “China’s environmental quality standards for surface water” (GB 3838-2002) [25], the lake is Grade I surface water when TN ≤ 0.2 mg/L. In the case of 0.2 < TN ≤ 0.5 mg/L, the lake is Grade II surface water. The water quality of Xiandao Lake was thus judged to be Grade I to II surface water, while Cihu Lake was Grade III surface water (0.5 < TN ≤ 1.0 mg/L). From the perspective of TP, when 0.01 < TP ≤ 0.025 mg/L, the lake is Grade II surface water, and for 0.025 < TP ≤ 0.05 mg/L, the lake is Grade III surface water. And Xiandao Lake was classified as Grade II to III surface water, and Cihu Lake was Grade V surface water (0.1 < TP ≤ 0.2 mg/L).
There were some variations in the nutritional status of Cihu Lake and Xiandao Lake from one season to another (Figure 2). In spring the Cihu Lake was generally in mildly eutrophic status, but some stations reached and even exceeded middle eutrophic standards, with a maximum value of 70.84. All stations of Cihu Lake were in middle eutrophic status during the summer and were more stable. Most of the sample sites in Xiandao Lake were at a mesotrophic level in spring, with only site 1 at an oligotrophic stage with a TLI index was 28.69. Low eutrophic indices and good water quality were found at Xiandao Lake in summer, and the water column as a whole was in an oligotrophic condition.

3.2. Components of Prokaryotic Ultraplankton Communities

At a similarity level of 97%, 762 OTU sequences were obtained by clustering the 16S data of 20 samples from the 2 lakes. There were 136 OTU sequences that overlapped between the 2 seasons in the 2 lakes, with the highest number of unique OTU sequences occurring at Xiandao Lake in spring and followed by Xiandao Lake in summer. All OTU sequences were then taxonomically annotated to produce a total of 21 phyla, 37 classes, 84 orders, 120 families, 205 genera, and 244 species.
In terms of relative abundance composition of species at the phylum level (Figure 3), Proteobacteria (34.06%), Bacteroidota (19.57%), Cyanobacteria (15.30%), and Actinobacteriota (10.59%) were the dominant taxa in the prokaryotic ultraplankton community, accounting for 80% of the total numbers. The composition of the community in spring was similar in Cihu Lake and Xiandao Lake. The most dominant species were Proteobacteria (36.97%, 35.19%) and Bacteroidota (22.42%, 16.98%), and the secondary dominant species were Actinobacteriota (16.34%, 15.58%) and Cyanobacteria (8.24%, 12.17%). The species compositions changed in summer as compared to spring. Cyanobacteria (41.78%) and Proteobacteria (24.83%) were the dominant phyla of Cihu Lake in summer, with Planctomycetota (10.85%) and Bacteroidota (7.62%) being the secondary dominant phyla. The significant dominance of Cyanobacteria in summer may indicate the occurrence of cyanobacterial blooms. The dominant phyla of Xiandao Lake in summer were Proteobacteria (37.78%) and Bacteroidota (29.48%), which were the same as in spring, while the secondary dominant phyla were replaced by Planctomycetota (5.68%) and Actinobacteriota (3.89%). A Venn diagram analysis of species composition was performed at the phylum level (Figure 4), and 15, 18, 19, and 18 phyla were detected in spring and summer at Cihu Lake and Xiandao Lake, respectively, of which 13 phyla were present in all 4 sampling occasions. Campilobacterota and Nitrospirota were found as two unique phyla in the spring in Xiandao Lake. The characteristic phylum in the spring and summer of Cihu Lake was Patescibacteria, and Hydrogenedentes was specific to the summer of Cihu Lake and Xiandao Lake.

3.3. Analysis of Community Diversity

Four alpha diversity indices of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake were calculated from OTU tables. The Shannon index values (Figure 5a) of each sample point varied from 2.93 to 4.79 and there were no significant differences between groups (p > 0.05). Both spring and summer had a higher Shannon index in Xiandao Lake than in Cihu Lake, which suggests that Xiandao Lake had higher community diversity and that the community diversity was greater in spring than summer. In addition, the Chao1 index (Figure 5b), ACE index (Figure 5c), and observed species index (Figure 5d) all showed the same trends, ranked as CHsum > XDHsum > XDHspr > CHspr and were significantly different between groups (p < 0.05). Present results demonstrated that the community richness was highest in summer and lowest in spring at Cihu Lake and that the community richness was always higher in summer than in spring in different water areas.
Analysis of variations in the community structure of prokaryotic ultraplankton at Cihu Lake and Xiandao Lake was based on the Bray–Curtis distance matrix. The result of UPGMA (Figure 6a) showed that the samples of Cihu Lake and Xiandao Lake were clustered into two major groups according to season initially, and then clustered internally on the basis of space. This indicated that the seasonal influence on the community structure was stronger than the spatial one. As in PCoA (Figure 6b), the first axis had an explanatory rate of 36%, and all samples were separated by seasonal differences. The explanatory rate for the second axis was 25%, with samples divided by areas of water, revealing that spatial impacts on community structure were weaker than seasonal influences, which corroborated the results of the clustering analysis. Anosim (Figure 7) illustrated that the inter-group variations were greater than the intra-group ones, and there were significant differences between groups (p = 0.001), particularly in the spring and summer months of Cihu Lake, which had the greatest differences between groups.

3.4. Correlation Analysis between Plankton Communities and Environmental Factors

Selected species ranked among the top 10 in abundance at the species level as the main subjects, combined with three environmental variables DO (p = 0.002), TN (p = 0.004), and Temp (p = 0.036) forward selected from the 11 environmental factors for RDA analysis (Figure 8). Of the RDA ranking, the first two axes accounted for 61.89% and 5.52%, respectively, with a cumulative explanation rate of 67.41%. All three environmental factors were primarily located in the positive direction of the first axis, among which Temp and DO were the most crucial environmental influences, contributing 65.4% of the explanation of the composition of the plankton community and having strong effects on it. Temp and DO have the greatest effect on the plankton distribution of summer samples from Cihu Lake and Xiandao Lake, and TN showed the greatest effect on the plankton distribution of spring samples. Out of the 10 species, Synechococcus_rubescens, Limnohabitans_sp., Acinetobacter_johnsonii, and Rhodoferax_ferrireducens were positively correlated with Temp and DO, while the rest were negatively correlated. TN was positively correlated with all species except for Candidatus_Planktophila_limnetica.
The mantel test of environmental factors and all the prokaryotic ultraplankton at the species level was conducted in Figure 9. On the whole, Temp (r = 0.629, p = 0.001) and DO (r = 0.509, p = 0.001) were the strongest correlated factors for the species composition of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake, which coincide with the results of the RDA analysis. SD (r = 0.332, p = 0.004) and CODMn (r = 0.393, p = 0.002) were secondary correlates, but there were no significant correlations for species compositions with TN (r = 0.128, p = 0.085) and TP (r = 0.050, p = 0.220). Apart from this, all of the environmental factors were highly associated with each other, except for PO43− and Temp, which were weakly correlated. Among the environmental factors, Chl a was significantly and positively associated with TN (r = 0.929, p = 0.001) and TP (r = 0.910, p = 0.001), in contrast to SD which was significantly and negatively associated with TN (r = −0.906, p = 0.001), TP (r = −0.907, p = 0.001), and Chl a (r = −0.970, p = 0.001).

3.5. Functional Prediction of KEGG with PICRUSt

On the basis of the OTU sequence information, PICRUSt was used to match the KEGG database for functional prediction of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake, and six categories of biological metabolic pathways were obtained: metabolism (81.04%), genetic information processing (6.00%), environmental information processing (5.84%), cellular processes (3.00%), human diseases (2.52%) and organismal systems (1.60%), with metabolism being the major component (Figure 10a). For the secondary functional layer analysis, the prokaryotic ultraplankton in Cihu Lake and Xiandao Lake mainly consisted of global and overview maps (42.96%), carbohydrate metabolism (8.84%), amino acid metabolism (7.66%), energy metabolism (4.84%), metabolism of cofactors and vitamins (4.31%), membrane transport (3.14%) and 39 other secondary functions (Figure 10b). The potential functional composition of prokaryotic ultraplankton was similar among the samples. Analysis of inter-group differences in predicted metabolism functions was performed through the STAMP v2.1.3 software (Figure 10c), of which 11 groups had statistically significant differences between samples (p < 0.05). This was primarily due to the mean proportion of most secondary functional layers in the summer of Cihu Lake that were significantly different from the other three samples. Global and overview maps, energy metabolism, metabolism of cofactors and vitamins, and nucleotide metabolism functions were abundant in the summer samples from Cihu Lake, while carbohydrate metabolism, amino acid metabolism, lipid metabolism, and xenobiotics biodegradation and metabolism functional layers were significantly higher in the spring in Cihu Lake and the spring and summer of Xiandao Lake.

4. Discussion

4.1. Nitrogen and Phosphorus Nutritional Regulations of Cihu Lake and Xiandao Lake

In terms of TN, Xiandao Lake was classified as Grade I to II surface water and Cihu Lake was Grade III surface water. From the perspective of TP, Xiandao Lake belonged to surface water categories II to III, while Cihu Lake fell into surface water category V. The water quality of Cihu Lake was much less than that of Xiandao Lake. Cihu Lake was eutrophic and Xiandao Lake was intermediate between mesotrophic and oligotrophic categories. Shallow lakes are generally more susceptible to eutrophication than deep lakes, and eutrophication was the central factor that led to the degradation of water quality at Cihu Lake [35,36]. Nitrogen and phosphorus were readily soluble in waters that could be quickly absorbed and recycled by the plankton in water columns, and the nutrient overload caused by high concentrations of nitrogen and phosphorus is the principal reason for eutrophication in lakes [27].
The ratio of nitrogen and phosphorus (TN/TP) also has a potential influence on the growth of plankton and can be used as an effective tool to identify the nutrient-limiting factors and nutrient structure in the water column [37,38]. As Redfield’s law states [39], the N/P (mass ratio) required for phytoplankton growth and physiological equilibrium is 7:1. When N/P < 9, the lake exhibits nitrogen limitation, when 9 ≤ N/P < 22.6, nitrogen and phosphorus co-limit the lake’s nutrition, and when N/P ≥ 22.6, phosphorus is the primary restrictive element in the lake [40]. Judging from the N/P of the lakes, Cihu Lake and the summer of Xiandao Lake were regulated by nitrogen, and the Xiandao Lake of spring indicates that it is limited by a combination of nitrogen and phosphorous nutrients.

4.2. Changes in the Structural Composition and Diversity of the Plankton Community in Cihu Lake and Xiandao Lake

Proteobacteria (34.06%), Bacteroidota (19.57%), Cyanobacteria (15.30%), and Actinobacteriota (10.59%) were dominant at the phylum level composition of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake, which is consistent with the composition of Wuhan East Lake and Anhui Taihu Lake in China [41,42,43]. Proteobacteria were the most plentiful phylum in the sediment or soil and held a numerical dominance in lakes of different nutritional statuses [44], representing a key population in maintaining the stability of lake ecosystems and also the primary dominant taxon in the prokaryotic ultraplankton communities of Cihu Lake and Xiandao Lake. But under the conditions of high temperature, nutrients, and light availability, Cyanobacteria would be more competitive and would become the dominant phylum in summer within Cihu Lake [45]. This has led to further deterioration and eutrophication in Cihu Lake and a possible outbreak of cyanobacterial blooms. Xiandao Lake had a low nutritional status and could better withstand the adverse effects of warming, thus there were no observable changes in the abundance of Cyanobacteria under the warm conditions [46]. Furthermore, the increased relative abundance of Cyanobacteria could also have an inhibitory effect on the growth of Actinobacteriota to some extent [47]. Studies of the crucial role of Actinobacteriota in freshwater have demonstrated that they were unable to compete with Cyanobacteria for nutrients in conditions of high organic or inorganic nutrients. They may function as sentinel microorganisms for ecological damage that could be a measurable indicator of the ecological quality of freshwater habitats [48]. The occurrence of Actinobacteriota is often associated with nutrient conditions of lower eutrophication and was relatively high in the spring of Cihu Lake and Xiandao Lake [49]. Campilobacterota and Nitrospirota were the unique phyla of Xiandao Lake in spring. The phylum Campilobacterota can mediate the oxidation of sulfur, sulfide, or thiosulfate in the water column and plays an active role in the sulfur cycle [50,51]. Similarly, Nitrospirota is active in the nitrogen cycle of the water column and is the main nitrite-oxidizing bacteria [52]. All of these suggest more oxidizing conditions in the spring of Xiandao Lake. Co-existing bacteria centered around Patescibacteria have a degradation effect on organic compounds produced by anaerobic ammonia-oxidizing bacteria and are involved in maintaining the stability of the anaerobic ammonia-oxidizing ecosystem [53], and they contribute to water purification.
The alpha diversity index is an essential indicator to assess the diversity of microbial communities and reflects the richness and diversity of the community. The community diversity of Cihu Lake and Xiandao Lake in summer was lower than that in spring, and the community richness in spring was lower than that in summer, exhibiting exactly opposite trends. Water temperature changes due to seasonal variations are likely the reasons for this phenomenon. In summer, the increase in water temperature provided a suitable environment for bacterioplankton to reproduce, and the dominant taxon that was formed in the lake, mainly cyanobacteria, greatly hindered the growth of other bacteria through nutrient competition, thus reducing lake community diversity [44,54]. In spring, the changes in water temperature slow the growth of bacterioplankton and form a more stabilized community structure [55,56], thus making the community diversity relatively more complex. At the same time, there were also synchronous changes between the Shannon index of the community and TN/TP, with nitrogen-fixing cyanobacteria predominant under a low N/P ratio [57,58]. Variations of the N/P ratio may also have an effect on the community diversity of prokaryotic ultraplankton and compositional succession.
Apart from this, the UPGMA clustering and PCoA analyses in beta diversity also demonstrated that it was the temporal series rather than the spatial variance that had a significant effect on community structure, and some scholars obtained similar conclusions on the community structure composition of bacterioplankton in urban freshwater lakes [59].

4.3. Driving Factors of Prokaryotic Ultraplankton Diversity in Cihu Lake and Xiandao Lake

Environmental factors were the crucial drivers of changes in the composition of the prokaryotic ultraplankton community. The results obtained in this study on the basis of the RDA and mantel test of correlations indicated that Temp and DO were the strongest associated factors in the community composition at the species level of prokaryotic ultraplankton in both lakes. Comparable conclusions have been reached in studies on the community structure of marine microorganisms [60]. Water temperature is a major seasonal factor, and it has direct and indirect influences on the community structure composition of bacterioplankton [61]. Water temperature is potentially the limiting factor for the development of bacterioplankton, and different bacterioplankton have their own optimum growth temperature preferences. For example, Ilumatobacter_fluminis prefers to be grown at 26 to 31 °C [62], the optimum growth temperature of Algisphaera_agarilytica is around 28 °C [63], and Acinetobacter_johnsonii grows well at 15 to 30 °C [64]. The community composition of bacterioplankton changes in response to seasonal fluctuations in water temperature, thereby directly affecting its community succession. Water temperature can also significantly affect the community structure of phytoplankton and zooplankton, indirectly influencing the community composition of bacterioplankton through bottom-up and top-down regulation [65,66]. Bacterioplankton are composed of aerobic, anaerobic, and facultative anaerobic bacteria, varying in demand for DO in water according to their own characteristics. Previous studies on the composition of bacterioplankton in the Yangtze River and Three Gorges Reservoir have similarly demonstrated that gradient changes in Temp and DO could affect the alteration of bacterioplankton communities in temporal and spatial terms [67,68]. All in all, the influence of physico-chemical factors on the structural composition of the bacterioplankton community is ultimately determined by the strength of the bacterioplankton’s adaptation to the environment.

4.4. Functional Prediction Analysis of Prokaryotic Ultraplankton in Cihu Lake and Xiandao Lake

PICRUSt is an algorithm and software package that provides functional prediction by comparing reference genomic databases of known functions on the basis of OTU datasets such as 16S and metagenome, together with highly accurate (>90% in most cases) [69,70]. Currently, PICRUSt has been applied in functional attributes prediction of microbiomes in the human gastrointestinal tract [71], soil [72], water bodies [73], and other categories. Carbohydrate metabolism and amino acid metabolism are the core metabolic pathways in the microbial community of lakes and play an important role in the basic nutrition and energy-cycling supplies of microorganisms [74]. These were the leading secondary functional layers in the prokaryotic ultraplankton of Cihu Lake and Xiandao Lake. The phylum Bacteroidota is an essential decomposer of algal-derived carbohydrates, a major player in carbohydrate metabolism [75], and also the dominant phylum in Cihu Lake and Xiandao Lake, possibly accounting for the higher abundance of carbohydrate metabolism. Higher proportions of lipid metabolism, xenobiotics biodegradation, and metabolism functions of Xiandao Lake as well as in the spring of Cihu Lake suggested that microbial communities in the samples had a greater capacity for water purification with exogenous pollution remediation [76]. This helps explain the nutritional status results of the lakes. Finally, although the PICRUSt algorithm is able to predict the functions of lake microorganisms, further and systematic studies using technologies such as metagenomic sequencing are still necessary for a more complete understanding.

5. Conclusions

The diversity of the prokaryotic ultraplankton community in Cihu Lake and Xiandao Lake was elucidated by 16S rRNA gene sequencing. The water quality of Cihu Lake as a whole was lower than that of Xiandao Lake. Cyanobacterial blooms may have occurred in the summer of Cihu Lake, and the effective control of nitrogen and phosphorus nutrients is of great importance to the water quality improvement of Cihu Lake. Relatively speaking, the entire water quality of Xiandao Lake was better, and it had a higher water purification and exogenous pollution remediation capacity within the lake. The application of third-generation sequencing technology has provided great convenience for the exploration of ultraplankton, but molecular mechanisms need to be explored in conjunction with metagenomic analyses for a more complete understanding.

Author Contributions

Conceptualization, C.L. and L.S.; methodology, C.L., L.S., Y.H. and Y.Z.; project administration, J.H.; resources, J.H. and X.W.; supervision, J.H.; validation, Y.H., H.D. and R.T.; writing—original draft, C.L. and L.S.; writing—review and editing, Y.Z., J.X. and Y.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (41171045), the Central Government Guided Local Science and Technology Development Project of Hubei Province (No.2017ZYYD008), the Open Foundation of Hubei Key Laboratory of Pollutant Analysis and Reuse Technology (Hubei Normal University) (PA220103), and the Graduate Scientific Research Innovation Project of Hubei Normal University (2023Z032).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

We are grateful to the peer reviewers for their helpful comments on this paper and to the editors for reviewing the contents of the paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Locations of the monitoring sites in Cihu Lake and Xiandao Lake.
Figure 1. Locations of the monitoring sites in Cihu Lake and Xiandao Lake.
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Figure 2. Evaluation of the nutritional status of Cihu Lake and Xiandao Lake in spring and summer.
Figure 2. Evaluation of the nutritional status of Cihu Lake and Xiandao Lake in spring and summer.
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Figure 3. Relative abundance composition of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake at the phylum level.
Figure 3. Relative abundance composition of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake at the phylum level.
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Figure 4. Venn diagram at the phylum level.
Figure 4. Venn diagram at the phylum level.
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Figure 5. Four alpha diversity indices of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake. (a) Shannon index; (b) Chao1 index; (c) ACE index; (d) observed species index. Data with different letters of a or b were significantly different (p < 0.05), while data with the same letters were not significantly different at the 0.05 level (p > 0.05). Data with multiple letters were not significantly different from data with any one of the letters.
Figure 5. Four alpha diversity indices of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake. (a) Shannon index; (b) Chao1 index; (c) ACE index; (d) observed species index. Data with different letters of a or b were significantly different (p < 0.05), while data with the same letters were not significantly different at the 0.05 level (p > 0.05). Data with multiple letters were not significantly different from data with any one of the letters.
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Figure 6. Beta diversity analysis of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake. (a) Analysis of UPGMA; (b) analysis of PCoA.
Figure 6. Beta diversity analysis of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake. (a) Analysis of UPGMA; (b) analysis of PCoA.
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Figure 7. Anosim of between and within groups of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake. The ordinate represents beta distance, the box plot above “All between” represents beta distance data of all samples between groups, and the box plot above “All within” represents beta distance data of all samples within groups.
Figure 7. Anosim of between and within groups of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake. The ordinate represents beta distance, the box plot above “All between” represents beta distance data of all samples between groups, and the box plot above “All within” represents beta distance data of all samples within groups.
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Figure 8. RDA plot between prokaryotic ultraplankton and environmental factors in Cihu Lake and Xiandao Lake. The blue lines represent different species and the red lines represent different environmental factors.
Figure 8. RDA plot between prokaryotic ultraplankton and environmental factors in Cihu Lake and Xiandao Lake. The blue lines represent different species and the red lines represent different environmental factors.
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Figure 9. Mantel test of prokaryotic ultraplankton and environmental factors in Cihu Lake and Xiandao Lake at the species level. “*” means the significance at 0.05 level, and “***” means the significance at 0.001 level.
Figure 9. Mantel test of prokaryotic ultraplankton and environmental factors in Cihu Lake and Xiandao Lake at the species level. “*” means the significance at 0.05 level, and “***” means the significance at 0.001 level.
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Figure 10. Functional prediction analysis of KEGG. (a) Biological metabolic pathways of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake. (b) Secondary functional layer prediction of KEGG. (c) Inter-group differences analysis of Metabolism functions. Data with different letters of a or b were significantly different (p < 0.05), while data with the same letters were not significantly different at the 0.05 level (p > 0.05). Data with multiple letters were not significantly different from data with any one of the letters.
Figure 10. Functional prediction analysis of KEGG. (a) Biological metabolic pathways of prokaryotic ultraplankton in Cihu Lake and Xiandao Lake. (b) Secondary functional layer prediction of KEGG. (c) Inter-group differences analysis of Metabolism functions. Data with different letters of a or b were significantly different (p < 0.05), while data with the same letters were not significantly different at the 0.05 level (p > 0.05). Data with multiple letters were not significantly different from data with any one of the letters.
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Table
Table 1. Spatial–temporal variation of environmental variables during sampling.
Table 1. Spatial–temporal variation of environmental variables during sampling.
CHsprCHsumXDHsprXDHsump
SD (m)0.57 ± 0.08 a0.45 ± 0.01 a2.62 ± 0.33 ab5.06 ± 0.74 b0.001
Temp (℃)22.64 ± 0.28 a29.99 ± 0.43 b25.53 ± 0.06 ac28.49 ± 0.06 bc<0.001
DO (mg/L)9.47 ± 0.50 ab11.78 ± 0.53 a8.94 ± 0.17 ab8.27 ± 0.09 b0.002
SPC (uS/cm)431.38 ± 9.16 a349.74 ± 10.27 ab194.80 ± 4.74 b187.64 ± 2.30 b0.001
TDS (mg/L)280.20 ± 5.98 a224.00 ± 3.51 ab126.6 ± 3.17 b116.20 ± 2.62 b0.001
Sal (psu)0.21 ± 0.00 a0.16 ± 0.00 ab0.09 ± 0.00 b0.08 ± 0.00 b0.001
TN (mg/L)0.88 ± 0.26 a0.63 ± 0.06 a0.27 ± 0.06 ab0.13 ± 0.02 b0.002
TP (mg/L)0.32 ± 0.25 ab0.17 ± 0.01 a0.03 ± 0.00 bc0.02 ± 0.00 c0.001
TN/TP7.88 ± 1.87 a3.75 ± 0.35 a9.42 ± 2.29 a6.48 ± 1.52 a0.217
PO43− (mg/L)0.02 ± 0.01 ab0.04 ± 0.00 a0.04 ± 0.00 a0.01 ± 0.00 b0.003
Chl a (μg/L)55.71 ± 17.12 a55.07 ± 5.42 a6.01 ± 0.52 ab1.01 ± 0.19 b0.001
CODMn (mg/L)6.30 ± 0.51 a7.40 ± 0.12 a2.24 ± 0.06 ab1.89 ± 0.07 b0.001
In the same row, data with different letters of a, b, or c were significantly different (p < 0.05), while data with the same letters were not significantly different at the 0.05 level (p > 0.05). Data with multiple letters were not significantly different from data with any one of the letters.
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MDPI and ACS Style

Lan, C.; Sun, L.; Hu, Y.; Zhang, Y.; Xu, J.; Ding, H.; Tang, R.; Hou, J.; Li, Y.; Wu, X. Diversity and Their Response to Environmental Factors of Prokaryotic Ultraplankton in Spring and Summer of Cihu Lake and Xiandao Lake in China. Sustainability 2023, 15, 11532. https://doi.org/10.3390/su151511532

AMA Style

Lan C, Sun L, Hu Y, Zhang Y, Xu J, Ding H, Tang R, Hou J, Li Y, Wu X. Diversity and Their Response to Environmental Factors of Prokaryotic Ultraplankton in Spring and Summer of Cihu Lake and Xiandao Lake in China. Sustainability. 2023; 15(15):11532. https://doi.org/10.3390/su151511532

Chicago/Turabian Style

Lan, Cong, Lili Sun, Yihan Hu, Yan Zhang, Jinjing Xu, Heng Ding, Rong Tang, Jianjun Hou, Yuntao Li, and Xiaodong Wu. 2023. "Diversity and Their Response to Environmental Factors of Prokaryotic Ultraplankton in Spring and Summer of Cihu Lake and Xiandao Lake in China" Sustainability 15, no. 15: 11532. https://doi.org/10.3390/su151511532

APA Style

Lan, C., Sun, L., Hu, Y., Zhang, Y., Xu, J., Ding, H., Tang, R., Hou, J., Li, Y., & Wu, X. (2023). Diversity and Their Response to Environmental Factors of Prokaryotic Ultraplankton in Spring and Summer of Cihu Lake and Xiandao Lake in China. Sustainability, 15(15), 11532. https://doi.org/10.3390/su151511532

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