Next Article in Journal
Practical Approaches to Transvenous Lead Extraction Procedures—Clinical Case Series
Previous Article in Journal
Valuation of Ecosystem Services Based on EU Carbon Allowances—Optimal Recovery for a Coal Mining Area
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 20
Issue 1
10.3390/ijerph20010382
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Bacterial Community Composition and Function of Tropical River Ecosystem along the Nandu River on Hainan Island, China

by
Jinbiao Li
1,2,3,
Yangni Zhai
1,3,
Guojian Ge
1,3,
Yang Xu
1,3,
Can Wang
1,3,
Anyong Hu
1,3,
Yujie Han
1,3,
Nan Shan
2,3,
Bo Liu
1,3,*,
Jinlin Chen
4 and
Wenlin Wang
2,3,*
1
School of Geographic Science, Nantong University, Nantong 226019, China
2
Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Nanjing 210042, China
3
State Environmental Protection Scientific Observation and Research Station for Ecological Environment of Lake Hulun Wetland, Hulunbuir 021000, China
4
Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China
*
Authors to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2023, 20(1), 382; https://doi.org/10.3390/ijerph20010382
Submission received: 10 September 2022 / Revised: 20 December 2022 / Accepted: 21 December 2022 / Published: 26 December 2022
(This article belongs to the Section Water Science and Technology)
Browse Figures
Versions Notes

Abstract

:
Microorganisms play a pivotal role in nutrient cycling in aquatic ecosystems. Understanding bacterial diversity and its functional composition are crucial for aquatic ecology and protection. We investigated the bacterial community structure using 16S rRNA gene amplicons high-throughput sequencing in this study. Results showed that 105 amplicon sequence variants (ASVs) account for 43.8% of the total sequences shared by the Nandu River’s lower, middle, and upper reach and the Songtao Reservoir. The dominant bacterial phylum in the Nandu River and its connected Songtao Reservoir were Proteobacteria and Actinobacteriota, respectively. The highest Chao1 and Shannon index values were found in the lower reach of the Nandu River. Beta diversity analysis showed the divergence in bacterial communities in the Nandu River and Songtao Reservoir, but not in different reaches. Among the water properties, based on the Mantel test, dissolved oxygen, total nitrogen, and nitrite significantly affected the bacterial communities. The functional profiles predicted by Tax4Fun2 showed that metabolism was the most abundant function group. The relative abundance of genetic information processing was significantly higher in the Songtao Reservoir than in the Nandu River, while the relative abundance of human diseases was significantly lower in the Songtao Reservoir than in the Nandu River. The appearance of the xenobiotics biodegradation and metabolism function group requires us to pay more attention to possible water pollution, especially at the upper reach of the Nandu River.

1. Introduction

Rivers link land and sea and play an essential role in ecosystems’ material circulation and energy flow [1]. Pesticides, heavy metals, and agricultural activities easily pollute water bodies, thereby affecting their ecological function [2,3,4]. On the other hand, water sources such as rivers may be subject to microbial contamination from human or animal feces and wastewater discharge [5,6,7]. At the same time, in aquatic ecosystems, microbial activity is of great importance to ecosystem function [8]. As decomposers and mineralizers, bacteria can decompose organic pollutants into inorganic chemical components and purify water, eventually affecting and regulating the quality of water bodies. Thus, understanding the bacterial diversity and composition of the river system is fundamental to better managing it.
The Nandu River is the largest river on Hainan Island and flows through seven cities and counties. The river is 314 km long and has a discharge of 6099 billion cubic meters, with a population of more than 2 million people living around its drainage area. It is the primary source of living water for Haikou City and other regions. The Nandu River has been polluted to varying degrees due to rapid industrial and economic development, but it has also polluted adjacent coastal waters [9,10,11]. On the other hand, problems such as garbage dumping, sewage disposal, and deforestation are relatively serious at the upper reach of the Nandu River, and this has threatened the water quality. The Songtao Reservoir, located at the upper reach of the Nandu River, is the largest reservoir on Hainan Island, one of the 10 largest reservoirs in China, and it is a significant source of production and domestic water supply for cities such as Danzhou, Lingao, Tunchang, Chengmai, Dingan, Haikou, and Yangpu. Recently, several studies have been conducted evaluating the composition, distribution, and source of heavy metals [12] and nutrients such as nitrogen and phosphorus [10] in the Nandu River. However, no study has assessed the composition and diversity of the bacterial community of the Nandu River.
Many methods have been used to detect and identify microbial in aquatic ecosystems, including culturing, biochemical reactions, and immunological methods [13]. However, most of these methods are difficult and time-consuming [14]. Recently, Next Generation Sequencing (NGS) has provided molecular methods for establishing a baseline for monitoring environmental perturbations. NGS can also help researchers identify possible pathogens in the aquatic systems and discover new water quality bioindicators [15,16].
Factors governing the distribution of bacterial communities vary due to the different locations and environments of the aquatic ecosystems. For instance, Aguilar et al. deemed that climate warming could support a higher diversity of microbial communities based on a study conducted in the Andean plateau lakes [17]. Godoy et al. found that phosphate, ammonia-nitrate, and dissolved oxygen were important factors that structured bacterial communities in a heavily polluted river in Brazil [18]. Moreover, Wang et al. found that besides nutrients, metals could also drive the microbial community composition in urban rivers [19]. However, few studies of the freshwater on Hainan Island, China, have been published to address the bacterial composition and its driving factors. The objectives of this study were to determine: (1) the degree of heterogeneity of bacterial community composition in different reaches of the Nandu River and Songtao Reservoir; (2) the relationship between environmental factors and changes in bacterial composition; (3) the functional diversity of the bacterial communities of the Nandu River and Songtao Reservoir.

2. Materials and Methods

2.1. Sample Collection

The Nandu River is the largest river on Hainan Island, China, and flows from the center to the north of Hainan Island. This island has a tropical marine monsoon climate, with a mean annual precipitation of approximately 1700 mm, mean annual temperature of 23.8 °C, and abundant water resources [20]. Even though the Nandu River has a relatively short flow, its volume of water is large and flow regions are complex. The upper (U), middle (M), and lower (L) reaches of the Nandu River were divided by the Songtao Reservoir Dam and Jiulong Shoal. Water samples were collected in November 2021 from 14 locations of the Nandu River (U1~U3, M1~M4, and L1~L4) and Songtao Reservoir (R1~R3), respectively (Figure 1). For the sampling points, U1~U3 were located at the upper reach, M1~M4 were located at the middle reach, and L1~L4 were located at the lower reach, while R1~R3 were sampled from the Songtao Reservoir.
In order to avoid the potential impact of temperature changes, all 14 samples were collected between 10 am and 2 pm for three days. When sampling, three liters of water from each sampling point were collected and maintained in sterile plastic bottles. The samples were then stored in a cooling box with dry ice filled in it until the filtering process, which occurred no later than 24 h after sampling. For water chemical properties, samples were filtered through 0.45 μm Millipore GS membranes of cellulose ester (47 mm diameter, white, smooth—©Merck KGaA, Darmstadt, Germany). For DNA extraction, each sample was filtered through 0.22 μm Millipore GS membranes of cellulose ester (47 mm diameter, white, smooth—©Merck KGaA, Darmstadt, Germany) [21].

2.2. Water Property

The water quality indexes integrate water temperature, pH, dissolved oxygen (DO), electrical conductivity (EC), turbidity (NTU), total nitrogen (Total N), total phosphorus (Total P), chemical oxygen demand (permanganate index, CODMn), ammoniacal nitrogen (NH4+), nitrate (NO3−), nitrite (NO2−), and phosphate (PO43−). Water temperature, pH, EC, and NTU were determined immediately after sampling. Water temperature, pH, EC, and DO were measured using a digital portable multimeter (Multi 3630, WTW®, Weilheim, Germany). Water turbidity was measured using a portable turbidimeter (2100Q, HACH®, Loveland, CO, USA). Water TN was determined using alkaline potassium persulfate digestion UV spectrophotometric method [22]. Water TP was determined using the ammonium molybdate spectrophotometric method [23]. The permanganate index was determined according to ISO 8467-1993 [24]. The concentrations of NH4+, NO3−, NO2−, and PO43− in the water were analyzed using a continuous flow analyzer (Skala San++, Skalar Analytical B.V., Breda, Netherlands).

2.3. DNA Extraction, PCR Amplification, and Sequencing

Total DNA was extracted from the membranes using the Power Soil DNA Isolation Kit® (MoBio Labs, Inc. Solana Beach, CA, USA) according to the manufacturer’s instructions. In order to assess the success of the extraction, the V3-V4 16S region of bacterial ribosomal RNA was amplified using the primers 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) [25], and visualized on 2% agarose gels in TAE buffer (400 mM Tris, 20 mM glacial acetic acid, 1 mM EDTA). The polymerase chain reaction (PCR) amplification of 16S rRNA gene was performed in a 20 μL volume containing 4 μL 5×FastPfu Buffer, 2 μL 2.5 mM dNTPs, 0.8 μL 5 μM forward primer, 0.8 μL 5 μM reverse primer, 0.4 μL 500 U FastPfu polymerase, 0.2 μL 20 mg mL−1 BSA, and 10 ng template DNA. The final volume was adjusted to 20 μL using ddH2O. The PCR products were extracted from a 2% agarose gel, purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), and the Illumina MiSeq paired-end (PE300) sequencing was performed by Majorbio Biological Pharmaceutical Co., Ltd. (Shanghai, China). The raw amplicons reads were deposited in the NCBI Sequence Read Archive database under BioProject [PRJNA#878943].

2.4. Sequenced Data Processing

The raw reads were processed using the dada2 package in R [26]. The forward and reverse reads were demultiplexed by cutting off the barcode and primer sequences. Sequences with lengths greater than 200 bp and mean quality value ≥20 were retained. The chimeric sequences were removed by using the UCHIME algorithm [27]. Sequences were then clustered into amplicon sequence variants (ASVs). The taxonomic classification of the representative sequence for each ASV was performed using the Ribosomal Database Program classifier against the Silva 138 16S rRNA database [28]. Functional pathways were annotated by using the Tax4Fun2 package [29] in R, which is based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) [30].

2.5. Statistical Analyses

The alpha diversity indexes (Chao1, Shannon) were calculated with the ASV table using the Vegan package (v 2.5.6) in R [31]. Differences in water chemical properties, bacterial community alpha diversity indexes, and phylum relative abundance among the rivers and reservoirs were tested using a one-way analysis of variance (ANOVA) of the linear fixed-effects model and Sidak test with an α of 0.05. The normality of residuals and homogeneity of variance assumptions were checked with Shapiro–Wilk and Levene’s tests, respectively, before conducting ANOVA. Natural base logarithmic transformations were applied to normalize the data if needed. A Venn diagram with shared and unique ASVs was applied to depict the similarities between soil bacterial communities of different groups. Principal coordinates analysis (PCoA) was performed based on the Bray–Curtis distances to visualize the composition of bacterial communities at the ASV level [32]. Permutation multivariate analysis of variance (PERMANOVA, Adonis function in the Vegan R package) with 999 random permutations were conducted to examine if water bacterial composition varies in different reaches and reservoirs of the Nandu River [33]. Canonical correlation analysis (CCA, “cca” function in the Vegan R package) was conducted to assess the correlation between bacterial communities (at the genus rank) and water properties [26]. In addition, Pearson correlation analysis was performed to examine the associations between water chemical properties and the diversity of water bacterial communities. All analyses were conducted using R Software (Version 4.1.3) [34].

3. Results

3.1. Properties of Water Samples

Water properties vary at different reaches of the Nandu River (Table 1). Water temperature was highest in the Songtao Reservoir and has no significant difference among different river reaches. Water DO was significantly higher in the Nandu River than the Songtao Reservoir. Water EC was highest at the lower reach and significantly higher than at the middle reach. Water turbidity was highest at the lower reach and significantly greater than at the upper reach and the reservoir. Water TN and NO3− were significantly higher at the lower reach than at the upper reach and the reservoir. Water NO2− was highest at the lower reach and significantly higher than at other reaches and the Songtao Reservoir. However, water pH, TP, CODMn, NH4+, and PO43− were not significantly different between different reaches and the Songtao Reservoir.

3.2. The Bacterial Diversity of the Surface Water

In total, 389,264 high-quality sequences were obtained from the 14 surface water samples. The rarefaction analysis of ASVs (amplicon sequence variants) at >10,000 reads showed that the diversity of the 14 samples could be well represented (Figure S1). These high-quality sequences were clustered into 2273 ASVs (Figure 2). There were 43.8% shared sequences across the four different sample groups. In contrast, samples from the Songtao Reservoir and the lower reach of the Nandu River had a relatively higher percentage of unique sequences, with values of 3.2% and 3.5%, respectively (Figure 2).
The Chao1 value (richness) of the water samples drawn from the upper reach was the smallest and significantly lower than at other reaches and the Songtao Reservoir (Figure 3A). The Shannon value (diversity) of the water samples drawn from the upper reach was the smallest and significantly lower than that sampled from the lower reach, but has no significant differences with samples drawn from the middle reach and the Songtao Reservoir (Figure 3B).

3.3. Bacterial Community Structure and Composition in the Water Samples

At the phylum rank, on average, the dominant bacterial phyla were Proteobacteria (38.41%, relative abundance, same below), Actinobacteriota (30.88%), Bacteroidota (17.39%), and Cyanobacteria (6.38%), with the relative abundance of the top 10 bacterial phyla accounting for 98.88% of the bacterial sequences across all 14 water samples (Figure 4). However, Proteobacteria and Actinobacteriota were the most dominant phylum in the river and reservoir, respectively (Figure 4). For the top 10 phyla, their relative abundance was significantly different in reservoir samples and some reaches of river samples, such as Proteobacteria, Actinobacteriota, Bacteroidota, Cyanobacteria, and Chloroflexi (Table S1). However, the relative abundance of Firmicutes, Acidobacteriota, Patescibacteria, and Deinococcota had no significant difference in the reservoir and the river (Table S1).
A noticeable difference was observed at the genus rank between river and reservoir samples (Figure 5). The hgcI clade, Limnohabitans, and CL500-29 marine group were the dominant genus in both river and reservoir samples (Figure 5). However, the relative abundance of Novosphingobium, Rhodoluna, Rhizorhapis, and Pseudorhodobacter was found to be high in river samples but not in reservoir samples (Figure 5).
The PCoA plot and PerMANOVA result (F = 3.27, p < 0.001) clearly show a separation between the reservoir and river samples on the first and second axis, which together accounted for 54.0% of the variation (Figure 6).

3.4. Correlation between Bacterial Communities and Environmental Factors

The correlation between environmental variables and dominant phyla indicated that DO was positively correlated with the relative abundance of Proteobacteria and Bacteroidota (Figure 7). DO also negatively correlated with the relative abundance of Bdellovibrionota, Chloroflexi, Cyanobacteria, Gemmatimonadota, Margulisbacteria, Planctomycetota, and SAR324 clade (Marine group B) (Figure 7). Likewise, the NH4+ and PO43− concentrations were negatively correlated with the relative abundance of Fibrobacterota (Figure 7). The Pearson correlation between environmental variables and alpha diversity indexes showed that NTU, TN, NO3−, NO2−, and PO43− were positively correlated with both the Chao1 and Shannon indexes (Figure 7). CCA of the bacterial genus was used to identify their relationship with environmental variables, with the first two CCA axes explaining 45.7% of the total variance in the bacterial composition (at the genus rank). The Mantel test was used to check whether there were significant correlations between environmental variables and the bacterial communities’ distance matrix, DO (p = 0.018), TN (p = 0.050), and NO3− (p = 0.040) identified as significant (Figure 8).

3.5. Bacterial Functional Genes

Tax4Fun2 analysis indicated that the major functional gene groups were related to metabolism (76.08~78.35%), environmental information processing (8.17~8.97%), cellular process (4.47~5.81%), genetic information processing (4.09~5.58%), human diseases (2.59~3.37%), and organismal systems (1.37~1.49%) (Figure 9A). The relative abundance of most predicted functions was significantly different between the Nandu River and Songtao Reservoir. For example, the relative abundance of genetic information processing was significantly higher in the Songtao Reservoir than in the Nandu River, while the relative abundance of human diseases was significantly lower in the Songtao Reservoir than in the Nandu River (Table S2). Under the metabolism category, genes related to global and overview maps, carbohydrate metabolism, amino acid metabolism, energy metabolism, and xenobiotics biodegradation and metabolism had high abundance in all groups. We further investigated the energy metabolism and the xenobiotics biodegradation categories at KEGG level 3, with the results shown using heatmaps (Figure 9B,C). The oxidative phosphorylation, carbon fixation pathways in prokaryotes, methane metabolism, and sulfur metabolism pathways had higher relative abundance than others under the energy metabolism category. The benzoate degradation and aminobenzoate degradation pathways had higher relative abundance than others under the xenobiotics biodegradation category. The upper reach had the highest relative abundance of the benzoate degradation pathway.

4. Discussion

Our results indicated significant differences in the water microbial diversity and its community composition at different reaches of the Nandu River and the Songtao Reservoir. A change in surface water can change microbial communities, leading to changes in water quality and contamination by water-borne pathogens [35]. The upper reach had the lowest bacterial richness and diversity, indicated by the Chao1 and Shannon values, probably due to the water flowing faster and fewer anthropogenic activities delivering fewer organic contaminants into the river. This finding is consistent with Godoy, who reported that reservoirs with less anthropogenic influences had lower Shannon values than rivers in Brazil [18].
Our study found that the four groups (upper, middle, and lower reaches of the Nandu River and the Songtao Reservoir) shared only 105 core ASVs but occupied 43.8% of all sequences (Figure 2). As a result, these core species (ASVs) may be well adapted to tropical surface waters. The dominant bacterial phyla in river samples were Proteobacteria (Figure 4). This is consistent with other studies demonstrating Proteobacteria as a rich bacterial phylum observed in surface water by analysis of the hypervariable regions of the 16S rRNA gene [36,37]. However, we found that Actinobacteriota was the dominant bacterial phylum in reservoir samples. This agrees with previous studies, which found that Actinobacteriota was dominant in lakes [36,38,39]. Actinobacteriota can enter the drinking water reservoir through rainfall or surface runoff, as reported by Zhang et al. [40]. Furthermore, the hgcI clade (Actinobacteriota) had the highest relative abundance in both river and reservoir samples in our study, possibly because it is a common and abundant bacterial clade that is tolerant of a wide range of water conditions [41,42]. On the other hand, the abundance of the hgcI clade communities, has been shown to be positively correlated with solar ultraviolet (UV) radiation [43]. The reservoir locates at a higher elevation and has a wide range of open water surfaces that receive more UV radiation, which might explain why the relative abundance of this genus has higher relative abundance in the reservoir than in the river. Furthermore, a higher relative abundance of Cyanobacteria was found in reservoir samples than in river samples (Figure 4). This could be attributed to reservoirs usually having a stable water environment compared to rivers and favoring eutrophication conditions [44]. However, in our study, both the Nandu River and Songtao Reservoir do not have eutrophication problems, indicating that the nitrogen and phosphorus concentrations were still at a relatively lower level [45] (Table 1).
Water turbidity and nutrients were positively correlated with bacterial Chao1 and Shannon diversity indexes in this study, which confirmed turbidity, or water resident time, could significantly influence microbial diversity, as water resident time could be measured by turbidity [46]. Furthermore, nutrients were also positively correlated with Chao1 and Shannon diversity indexes, indicating the importance of nutrients as the energy source for microbes [47]. Except for nutrients, DO was also important for microbes in the water system, as indicated by the RDA results. These microbes can use DO to decompose organic material and are essential for nutrient recycling in water [48]. However, unlike many other studies that show pH is an essential factor in determining microbial diversity, probably due to the pH only ranging from 7.25~7.50 in our study, it did not cause significant changes in the bacteria community.
Tax4Fun2 is a unique tool that accurately predicts functional profiles of prokaryotic communities from 16S rRNA gene sequences [49]. The relative abundances of functional profiles were not significantly different among different reaches of the Nandu River and the Songtao Reservoir (Figure 9), probably due to the water quality being in good condition. It has been reported that significant differences in functional profiles can be found at sites with different levels of pollution in the same river [50]. However, the appearance of xenobiotics biodegradation and metabolism-related genes indicates the presence of pollutants in the tropical river and reservoir ecosystem. On the other hand, a higher relative abundance of benzoate and aminobenzoate degradation pathways was found in river samples, indicating that the Nandu River may contain more pollutants than the Songtao Reservoir. Some natural or industrial pollutants may enter the river at its upper reaches, and their concentration can be diluted in the reservoir. Thus, more efforts should be made to protect the drinking water resources, especially at the upper reach of the Nandu River, which is one of the sources of the Songtao Reservoir. Furthermore, it provides an opportunity to exploit microbial resources capable of bioremediating waste materials [51].

5. Conclusions

In conclusion, water properties show significant differences between different reaches of the Nandu River and the Songtao Reservoir, especially water temperature, DO, and nutrients. The highest species’ richness and diversity were found in the lower reach of the Nandu River, based on the Chao1 index and Shannon index. The beta diversity analysis indicated the divergence in bacterial communities in the Nandu River and Songtao Reservoir. At the genus rank, the bacterial composition was very similar in the lower and middle reaches of the Nandu River, while the upper reach of the Nandu River was similar to the Songtao Reservoir. Proteobacteria and Actinobacteriota were the most abundant phyla in the Nandu River and Songtao Reservoir, respectively. The hgcI clade showed high relative abundance in all samples. Functional profiles predicted by Tax4Fun2 showed similar patterns in water samples, while the benzoate and aminobenzoate degradation pathways had higher relative abundance in the upper reach of the Nandu River. Our study showed baseline information on the changes in bacterial communities of the water bodies connected to the Nandu River.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph20010382/s1, Figure S1: Rarefaction curves for samples in the Nandu River and Songtao Reservoir; Table S1: Relative abundance of the dominant phyla of water samples drawn from different reaches of the Nandu River and the Songtao Reservoir; Table S2: Relative abundance of the functional genes of water samples drawn from different reaches of the Nandu River and the Songtao Reservoir.

Author Contributions

Conceptualization, J.L. and W.W.; Data curation, J.L.; Funding acquisition, B.L.; Investigation, J.C.; Methodology, A.H.; Project administration, W.W.; Supervision, J.C. and B.L.; Validation, Y.H. and N.S.; Visualization, J.L.; Writing—original draft preparation, J.L.; Writing—review and editing, Y.Z., G.G., Y.X., C.W., A.H., N.S. and B.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Investigation and Evaluation of Ecological Security in Key Watersheds in Hainan Province (HZ2021-1222), the Science and Technology Cooperation Project of Inner Mongolia (2021CG0037), the College Students’ Innovation and Entrepreneurship Training Projects of Jiangsu Province (202210304139Y), the College Students’ Innovation and Entrepreneurship Training Projects of Nantong University (2022181), and the Research Startup Fund of Nantong University (135421621089).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The raw data in this study were deposited in the NCBI Sequence Read Archive database with accession number PRJNA878943.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Giller, P.S.; Giller, P.; Malmqvist, B.; Department of Animal Ecology Bj Rn Malmqvist. The Biology of Streams and Rivers; OUP Oxford: Oxford, UK, 1998. [Google Scholar]
  2. Cooper, C.M. Biological Effects of Agriculturally Derived Surface Water Pollutants on Aquatic Systems—A Review. J. Environ. Qual. 1993, 22, 402–408. [Google Scholar] [CrossRef]
  3. Okereafor, U.; Makhatha, M.; Mekuto, L.; Uche-Okereafor, N.; Sebola, T.; Mavumengwana, V. Toxic Metal Implications on Agricultural Soils, Plants, Animals, Aquatic Life and Human Health. Int. J. Environ. Res. Public Health 2020, 17, 2204. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Rashmi, I.; Roy, T.; Kartika, K.S.; Pal, R.; Coumar, V.; Kala, S.; Shinoji, K.C. Organic and Inorganic Fertilizer Contaminants in Agriculture: Impact on Soil and Water Resources. In Contaminants in Agriculture; Springer: Berlin/Heidelberg, Germany, 2020; pp. 3–41. [Google Scholar]
  5. Cabral, J.P.S. Water Microbiology. Bacterial Pathogens and Water. Int. J. Environ. Res. Public Health 2010, 7, 3657–3703. [Google Scholar] [CrossRef] [PubMed]
  6. Liu, J.; Diamond, J. China’s Environment in a Globalizing World. Nature 2005, 435, 1179–1186. [Google Scholar] [CrossRef] [PubMed]
  7. World Health Organization. Guidelines for Drinking-Water Quality; World Health Organization: Geneva, Switzerland, 2004. [Google Scholar]
  8. Zeglin, L.H. Stream Microbial Diversity in Response to Environmental Changes: Review and Synthesis of Existing Research. Front. Microbiol. 2015, 6, 454. [Google Scholar] [CrossRef] [Green Version]
  9. Department of Hainan Province Ocean and Fishery. Hainan Province Marine Environment Condition Bulletin in 2016. 2017. Available online: http://cn.chinagate.cn/environment/2018-03/17/content_50684722.htm (accessed on 10 August 2022).
  10. Zhang, J.; Zhang, P.; Dai, P.; Lai, J.; Chen, Y. Spatiotemporal Distributions of DIP and the Eutrophication in Hainan Island Adjacent Coastal Water. China Environ. Sci. 2019, 39, 2541–2548. (In Chinese) [Google Scholar]
  11. Zhang, P.; Wei, L.; Lai, J.; Dai, P.; Chen, Y.; Zhang, J. Concentration, Composition and Fluxes of Land-Based Nitrogen and Phosphorus Source Pollutants Input into Zhanjiang Bay in Summer. J. Guangdong Ocean Univ. 2019, 39, 46–55. (In Chinese) [Google Scholar]
  12. Zhao, D.; Wan, S.; Yu, Z.; Huang, J. Distribution, Enrichment and Sources of Heavy Metals in Surface Sediments of Hainan Island Rivers, China. Environ. Earth Sci. 2015, 74, 5097–5110. [Google Scholar] [CrossRef]
  13. Saxena, G.; Bharagava, R.N.; Kaithwas, G.; Raj, A. Microbial Indicators, Pathogens and Methods for Their Monitoring in Water Environment. J. Water Health 2014, 13, 319–339. [Google Scholar] [CrossRef] [Green Version]
  14. Toze, S. PCR and the Detection of Microbial Pathogens in Water and Wastewater. Water Res. 1999, 33, 3545–3556. [Google Scholar] [CrossRef]
  15. Semedo-Aguiar, A.P.; Pereira-Leal, J.B.; Leite, R.B. Microbial Diversity and Toxin Risk in Tropical Freshwater Reservoirs of Cape Verde. Toxins 2018, 10, 186. [Google Scholar] [CrossRef] [Green Version]
  16. Tan, B.; Ng, C.; Nshimyimana, J.P.; Loh, L.L.; Gin, K.Y.-H.; Thompson, J.R. Next-Generation Sequencing (NGS) for Assessment of Microbial Water Quality: Current Progress, Challenges, and Future Opportunities. Front. Microbiol. 2015, 6, 1027. [Google Scholar] [CrossRef]
  17. Aguilar, P.; Dorador, C.; Vila, I.; Sommaruga, R. Bacterioplankton Composition in Tropical High-Elevation Lakes of the Andean Plateau. FEMS Microbiol. Ecol. 2018, 94, fiy004. [Google Scholar] [CrossRef] [Green Version]
  18. Godoy, R.G.; Marcondes, M.A.; Pessôa, R.; Nascimento, A.; Victor, J.R.; Duarte, A.J.d.S.; Clissa, P.B.; Sanabani, S.S. Bacterial Community Composition and Potential Pathogens along the Pinheiros River in the Southeast of Brazil. Sci. Rep. 2020, 10, 9331. [Google Scholar] [CrossRef]
  19. Wang, L.; Zhang, J.; Li, H.; Yang, H.; Peng, C.; Peng, Z.; Lu, L. Shift in the Microbial Community Composition of Surface Water and Sediment along an Urban River. Sci. Total Environ. 2018, 627, 600–612. [Google Scholar] [CrossRef]
  20. Gong, J.; Gao, J.; Fu, Y.; Tang, S.; Cai, Y.; Yang, J.; Wu, H.; Ma, S. Vertical Distribution and Major Influencing Factors of Soil Selenium in Tropical Climate: A Case Study of Chengmai County, Hainan Island. Chemosphere 2023, 312, 137207. [Google Scholar] [CrossRef]
  21. Li, J.; Li, N.; Li, F.; Zou, T.; Yu, S.; Wang, Y.; Qin, S.; Wang, G. Spatial Diversity of Bacterioplankton Communities in Surface Water of Northern South China Sea. PLoS ONE 2014, 9, e113014. [Google Scholar] [CrossRef]
  22. Chen, D.; Ding, J. Study on Influencing Factors of Measurement of Total Nitrogen by Digestion with UV-Alkaline Potassium Persulfate and Reduction with Hydrazine Sulphate Spectrophotometric Method and Application. In Proceedings of the 2008 2nd International Conference on Bioinformatics and Biomedical Engineering, Shanghai, China, 16–18 May 2008; pp. 3223–3227. [Google Scholar]
  23. Yang, C.-X.; Sun, X.-Y.; Liu, B.; Lian, H.-T. Determination of Total Phosphorus in Water Sample by Digital Imaging Colorimetry. Chin. J. Anal. Chem. 2007, 35, 850–853. [Google Scholar] [CrossRef]
  24. International Organization for Standardization. Water Quality—Determination of Permanganate Index; International Organization for Standardization: Geneva, Switzerland, 1993. [Google Scholar]
  25. Feng, Y.; Zhao, Y.; Guo, Y.; Liu, S. Microbial Transcript and Metabolome Analysis Uncover Discrepant Metabolic Pathways in Autotrophic and Mixotrophic Anammox Consortia. Water Res. 2018, 128, 402–411. [Google Scholar] [CrossRef]
  26. Callahan, B.J.; Sankaran, K.; Fukuyama, J.A.; McMurdie, P.J.; Holmes, S.P. Bioconductor Workflow for Microbiome Data Analysis: From Raw Reads to Community Analyses. F1000Res 2016, 5, 1492. [Google Scholar] [CrossRef] [PubMed]
  27. Edgar, R.C.; Haas, B.J.; Clemente, J.C.; Quince, C.; Knight, R. UCHIME Improves Sensitivity and Speed of Chimera Detection. Bioinformatics 2011, 27, 2194–2200. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Quast, C.; Pruesse, E.; Yilmaz, P.; Gerken, J.; Schweer, T.; Yarza, P.; Peplies, J.; Glöckner, F.O. The SILVA Ribosomal RNA Gene Database Project: Improved Data Processing and Web-Based Tools. Nucleic Acids Res. 2013, 41, D590–D596. [Google Scholar] [CrossRef] [PubMed]
  29. Wemheuer, F.; Taylor, J.A.; Daniel, R.; Johnston, E.; Meinicke, P.; Thomas, T.; Wemheuer, B. Tax4Fun2: Prediction of Habitat-Specific Functional Profiles and Functional Redundancy Based on 16S rRNA Gene Sequences. Environ. Microbiome 2020, 15, 11. [Google Scholar] [CrossRef] [PubMed]
  30. Kanehisa, M.; Sato, Y.; Morishima, K. BlastKOALA and GhostKOALA: KEGG Tools for Functional Characterization of Genome and Metagenome Sequences. J. Mol. Biol. 2016, 428, 726–731. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  31. Oksanen, J. Vegan: Ecological Diversity. R Project 2018. Available online: https://cran.r-project.org/web/packages/vegan/gnettes/diversity-vegan.pdf (accessed on 20 April 2022).
  32. Abdi, H.; Williams, L.J. Principal Component Analysis: Principal Component Analysis. WIREs Comp. Stat. 2010, 2, 433–459. [Google Scholar] [CrossRef]
  33. Anderson, M.J. Permutational Multivariate Analysis of Variance (PERMANOVA). In Wiley StatsRef: Statistics Reference Online; American Cancer Society: Atlanta, GA, USA, 2017; pp. 1–15. [Google Scholar]
  34. R Core Team. A Language and Environment for Statistical Computing. R Foundation for Statistical Computing; R Core Team: Vienna, Austria, 2018. [Google Scholar]
  35. Jin, D.; Kong, X.; Cui, B.; Jin, S.; Xie, Y.; Wang, X.; Deng, Y. Bacterial Communities and Potential Waterborne Pathogens within the Typical Urban Surface Waters. Sci. Rep. 2018, 8, 13368. [Google Scholar] [CrossRef] [Green Version]
  36. Betiku, O.C.; Sarjeant, K.C.; Ngatia, L.W.; Aghimien, M.O.; Odewumi, C.O.; Latinwo, L.M. Evaluation of Microbial Diversity of Three Recreational Water Bodies Using 16S rRNA Metagenomic Approach. Sci. Total Environ. 2021, 771, 144773. [Google Scholar] [CrossRef]
  37. Burkert, U.; Warnecke, F.; Babenzien, D.; Zwirnmann, E.; Pernthaler, J. Members of a Readily Enriched β-Proteobacterial Clade Are Common in Surface Waters of a Humic Lake. Appl. Environ. Microbiol. 2003, 69, 6550–6559. [Google Scholar] [CrossRef] [Green Version]
  38. Newton, R.J.; Jones, S.E.; Eiler, A.; McMahon, K.D.; Bertilsson, S. A Guide to the Natural History of Freshwater Lake Bacteria. Microbiol. Mol. Biol. Rev. 2011, 75, 14–49. [Google Scholar] [CrossRef] [Green Version]
  39. Zhang, J.; Ding, X.; Guan, R.; Zhu, C.; Xu, C.; Zhu, B.; Zhang, H.; Xiong, Z.; Xue, Y.; Tu, J. Evaluation of Different 16S rRNA Gene V Regions for Exploring Bacterial Diversity in a Eutrophic Freshwater Lake. Sci. Total Environ. 2018, 618, 1254–1267. [Google Scholar] [CrossRef]
  40. Zhang, H.; Ma, M.; Huang, T.; Miao, Y.; Li, H.; Liu, K.; Yang, W.; Ma, B. Spatial and Temporal Dynamics of Actinobacteria in Drinking Water Reservoirs: Novel Insights into Abundance, Community Structure, and Co-Existence Model. Sci. Total Environ. 2022, 814, 152804. [Google Scholar] [CrossRef]
  41. Liu, J.; Fu, B.; Yang, H.; Zhao, M.; He, B.; Zhang, X.-H. Phylogenetic Shifts of Bacterioplankton Community Composition along the Pearl Estuary: The Potential Impact of Hypoxia and Nutrients. Front. Microbiol. 2015, 6, 64. [Google Scholar] [CrossRef]
  42. Warnecke, F.; Amann, R.; Pernthaler, J. Actinobacterial 16S rRNA Genes from Freshwater Habitats Cluster in Four Distinct Lineages. Environ. Microbiol. 2004, 6, 242–253. [Google Scholar] [CrossRef]
  43. Warnecke, F.; Sommaruga, R.; Sekar, R.; Hofer, J.S.; Pernthaler, J. Abundances, Identity, and Growth State of Actinobacteria in Mountain Lakes of Different UV Transparency. Appl. Environ. Microbiol. 2005, 71, 5551–5559. [Google Scholar] [CrossRef] [Green Version]
  44. Domingues, R.B.; Barbosa, A.B.; Sommer, U.; Galvão, H.M. Phytoplankton Composition, Growth and Production in the Guadiana Estuary (SW Iberia): Unraveling Changes Induced after Dam Construction. Sci. Total Environ. 2012, 416, 300–313. [Google Scholar] [CrossRef]
  45. Gao, G.; Xiao, L.; Lin, Q.; Hu, R.; Lei, L. Structure of phytoplankton functional groups and water quality assessment of main reservoirs in Hainan Province. Ecol. Sci. 2013, 32, 144–150. (In Chinese) [Google Scholar]
  46. Wang, S.; Hou, W.; Jiang, H.; Huang, L.; Dong, H.; Chen, S.; Wang, B.; Chen, Y.; Lin, B.; Deng, Y. Microbial Diversity Accumulates in a Downstream Direction in the Three Gorges Reservoir. J. Environ. Sci. 2021, 101, 156–167. [Google Scholar] [CrossRef]
  47. Pikaar, I.; Matassa, S.; Rabaey, K.; Bodirsky, B.L.; Popp, A.; Herrero, M.; Verstraete, W. Microbes and the Next Nitrogen Revolution. Environ. Sci. Technol. 2017, 51, 7297–7303. [Google Scholar] [CrossRef] [Green Version]
  48. Summers, J.K. Water Quality: Science, Assessments and Policy; Open access peer-reviewed edited volume, IntechOpen Book Series; U.S. Environmental Protection Agency: Washington, DC, USA, 2020. [Google Scholar]
  49. Wemheuer, F.; Taylor, J.A.; Daniel, R.; Johnston, E.; Meinicke, P.; Thomas, T.; Wemheuer, B. Tax4Fun2: A R-Based Tool for the Rapid Prediction of Habitat-Specific Functional Profiles and Functional Redundancy Based on 16S rRNA Gene Marker Gene Sequences. bioRxiv 2018. [Google Scholar] [CrossRef] [Green Version]
  50. Wu, H.; Li, Y.; Zhang, W.; Wang, C.; Wang, P.; Niu, L.; Du, J.; Gao, Y. Bacterial Community Composition and Function Shift with the Aggravation of Water Quality in a Heavily Polluted River. J. Environ. Manag. 2019, 237, 433–441. [Google Scholar] [CrossRef]
  51. Dindhoria, K.; Kumar, S.; Kumar, R. Taxonomic and Functional Analysis of Proglacial Water Bodies of Triloknath Glacier Ecosystem from North-Western Himalayas. Ecol. Inform. 2021, 64, 101365. [Google Scholar] [CrossRef]
Ijerph 20 00382 g001 550
Figure 1. Sampling sites in the Nandu River.
Figure 1. Sampling sites in the Nandu River.
Ijerph 20 00382 g001
Ijerph 20 00382 g002 550
Figure 2. Venn diagram of ASVs and sequences of water sample groups. The numeral data are the ASV numbers and the percentage data are the ratio of the sequence numbers to the total sequence numbers.
Figure 2. Venn diagram of ASVs and sequences of water sample groups. The numeral data are the ASV numbers and the percentage data are the ratio of the sequence numbers to the total sequence numbers.
Ijerph 20 00382 g002
Ijerph 20 00382 g003 550
Figure 3. Bacterial (A) Chao1 and (B) Shannon values of the lower, middle, and upper reaches of the Nandu River and the Songtao Reservoir. Means not sharing any lowercase letter indicating significant differences among different groups.
Figure 3. Bacterial (A) Chao1 and (B) Shannon values of the lower, middle, and upper reaches of the Nandu River and the Songtao Reservoir. Means not sharing any lowercase letter indicating significant differences among different groups.
Ijerph 20 00382 g003
Ijerph 20 00382 g004 550
Figure 4. The relative abundance of dominant bacterial phyla (top 10) in the water samples.
Figure 4. The relative abundance of dominant bacterial phyla (top 10) in the water samples.
Ijerph 20 00382 g004
Ijerph 20 00382 g005 550
Figure 5. Top 40 taxa at genus level of the 14 water samples.
Figure 5. Top 40 taxa at genus level of the 14 water samples.
Ijerph 20 00382 g005
Ijerph 20 00382 g006 550
Figure 6. Principal coordinate analysis (PCoA) plots of bacterial communities in the water.
Figure 6. Principal coordinate analysis (PCoA) plots of bacterial communities in the water.
Ijerph 20 00382 g006
Ijerph 20 00382 g007 550
Figure 7. Correlation between environmental variables and the relative abundance of bacterial phyla and alpha diversity indexes.
Figure 7. Correlation between environmental variables and the relative abundance of bacterial phyla and alpha diversity indexes.
Ijerph 20 00382 g007
Ijerph 20 00382 g008 550
Figure 8. Canonical correspondence analysis (CCA) plots of bacterial communities in the water.
Figure 8. Canonical correspondence analysis (CCA) plots of bacterial communities in the water.
Ijerph 20 00382 g008
Ijerph 20 00382 g009 550
Figure 9. Function prediction obtained by Tax4Fun2. (A) Functional gene pathways at KEGG level2; (B) relative abundance of functions under the energy metabolism category; (C) relative abundance of functions under the xenobiotics’ biodegradation and metabolism category.
Figure 9. Function prediction obtained by Tax4Fun2. (A) Functional gene pathways at KEGG level2; (B) relative abundance of functions under the energy metabolism category; (C) relative abundance of functions under the xenobiotics’ biodegradation and metabolism category.
Ijerph 20 00382 g009
Table
Table 1. Properties of water samples drawn from the Nandu River. Values are means ± SE, means not sharing any lowercase letter indicating significant differences among different groups.
Table 1. Properties of water samples drawn from the Nandu River. Values are means ± SE, means not sharing any lowercase letter indicating significant differences among different groups.
PropertiesReservoirUpperMiddleLower
Temperature (°C)25.87 ± 1.26 a23.57 ± 0.60 ab23.33 ± 1.19 b25.00 ± 0.91 ab
pH7.20 ± 0.30 a7.50 ± 0.48 a7.29 ± 0.15 a7.35 ± 0.29 a
DO (mg L−1)7.05 ± 0.76 b8.53 ± 0.16 a8.47 ± 0.10 a8.08 ± 0.28 a
EC (uS cm−1)81.27 ± 16.31 ab96.37 ± 11.63 ab78.30 ± 19.68 b117.28 ± 11.90 a
Turbidity4.36 ± 2.00 b4.24 ± 1.19 b11.33 ± 2.95 ab17.13 ± 4.85 a
TN (mg L−1)0.86 ± 0.20 c1.84 ± 1.06 bc4.75 ± 2.00 ab6.28 ± 1.68 a
TP (mg L−1)0.10 ± 0.05 a0.13 ± 0.06 a0.15 ± 0.06 a0.17 ± 0.07 a
CODMn (mg L−1)0.75 ± 0.18 a4.69 ± 5.91 a1.28 ± 0.91 a2.88 ± 3.68 a
NH4+ (mg L−1)0.05 ± 0.03 a0.04 ± 0.04 a0.06 ± 0.03 a0.29 ± 0.34 a
NO3− (mg L−1)0.59 ± 0.49 c1.56 ± 1.31 bc4.43 ± 2.04 ab6.07 ± 1.52 a
NO2− (mg L−1)0.02 ± 0.03 b0.01 ± 0.01 b0.03 ± 0.02 b0.09 ± 0.02 a
PO43− (mg L−1)0.03 ± 0.01 a0.02 ± 0.01 a0.08 ± 0.05 a0.14 ± 0.09 a
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Li, J.; Zhai, Y.; Ge, G.; Xu, Y.; Wang, C.; Hu, A.; Han, Y.; Shan, N.; Liu, B.; Chen, J.; et al. Bacterial Community Composition and Function of Tropical River Ecosystem along the Nandu River on Hainan Island, China. Int. J. Environ. Res. Public Health 2023, 20, 382. https://doi.org/10.3390/ijerph20010382

AMA Style

Li J, Zhai Y, Ge G, Xu Y, Wang C, Hu A, Han Y, Shan N, Liu B, Chen J, et al. Bacterial Community Composition and Function of Tropical River Ecosystem along the Nandu River on Hainan Island, China. International Journal of Environmental Research and Public Health. 2023; 20(1):382. https://doi.org/10.3390/ijerph20010382

Chicago/Turabian Style

Li, Jinbiao, Yangni Zhai, Guojian Ge, Yang Xu, Can Wang, Anyong Hu, Yujie Han, Nan Shan, Bo Liu, Jinlin Chen, and et al. 2023. "Bacterial Community Composition and Function of Tropical River Ecosystem along the Nandu River on Hainan Island, China" International Journal of Environmental Research and Public Health 20, no. 1: 382. https://doi.org/10.3390/ijerph20010382

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop