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Article

Impact of Non-Metallic Organic Tanning Agents with a Double-Triazine Structure on the Microbial Community Structure in Wastewater

1
College of Environmental Science and Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
2
College of Bioresources Chemical and Materials Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
*
Author to whom correspondence should be addressed.
Water 2021, 13(17), 2438; https://doi.org/10.3390/w13172438
Received: 14 July 2021 / Revised: 1 September 2021 / Accepted: 2 September 2021 / Published: 4 September 2021
(This article belongs to the Section Wastewater Treatment and Reuse)

Abstract

:
This study examined the correlation between non-metallic organic tanning agents (NOTAs) and wastewater containing double-triazine (bistriazine). Specifically, high-throughput sequencing was used to analyze how the physical and chemical properties of the bistriazine-containing wastewater affected the microbial community. In total, 9995 operating taxonomy units (OTUs) were detected at a similarity level of 97%. According to the taxonomy results and relative bacterial richness, the non-metallic organic tanning agent (NOTA) wastewater communities showed significant differences. Clostridium sensu stricto and Turicibacter bacteria were dominant in NOTA T2, T3, and the blank control (KB) wastewater; Trichococcus and Aeromonas were dominant in NOTA T4; and Pseudomonas was dominant in T2, T4, and the blank control (KB). Thus, the addition of bistriazine ring derivatives altered the composition of the bacterial community. Furthermore, Spearman’s correlation analysis revealed a substantial correlation in the composition of the communities and the physicochemical properties of the wastewater. Acidocella and norank c Cyanobacteria correlated positively with Total Phosphorus (TP) (p ≤ 0.001) and Mycobacterium showed a positively held correlation with pH (p ≤ 0.05). Conversely, norank_f__Elev-16S-1332 indicated a negative correlation with TP, Total Nitrogen (TN), and Total Organic Carbon (TOC) (p ≤ 0.05), whereas Zoogloea correlated negatively with NH3–N, TOC, and TN (p ≤ 0.05). Regarding water quality, a significant correlation between microbial communities and the physicochemical properties of bistriazine wastewater was also observed (p ≤ 0.05). We concluded that Trichococcus, Clostridium sensu stricto, Turicibacter, Pseudomonas, Intestinibacter, Acidocella, Bacillus, and Tolumonas showed a high tolerance for bistriazine wastewater, which could offer insight into its bioremediation.

Graphical Abstract

1. Introduction

Tanning agents commonly used in the leather industry are generally categorized into two types: non-metallic organic tanning agents (NOTAs) with a double-triazine structure and metallic tanning agents [1]. The metallic tanning agents are the most widely used owing to their excellent tanning properties. However, the highly carcinogenic properties of hexavalent chromium are a significant environmental hazard. Therefore, to match the tanning properties of most of the chrome tanning agents while preventing potential toxic element (PTE) pollution, the NOTAs have attracted considerable worldwide research interest in recent decades [2]. Moreover, bistriazine tanning agents come under the NOTA category and exhibit excellent tanning properties [3,4]. Agents that are not fully used up in tanning are discharged into the wastewater, which means that the environmental effect of azine-based compounds, chrome-containing tanning agents of explosives [5], and pesticides [6] on the bacterial composition of tannery wastewater remains a concern.
The negative impact on the diversity of microbial communities from azine-based organics and chromium-containing wastewater in explosives and pesticides has been reported [5,6,7]. This diversity was also negatively affected by microbial community structural alterations and the abundance of bistriazine tanning agents. However, the current study is focused on the adaptation of microorganisms in toxic environments and the stable, effective operation of a tannery wastewater treatment system [8,9]. The eco-friendly performance and application prospects of bistriazine tanning agents in the tannery industry were also studied.
High-throughput sequencing technology deploys bacterial 16S rRNA amplification, which can detect most species without the cultivation of samples [10] and completely analyze microbial communities and their corresponding richness [11] in a river [12] and on land [13].
This work focused on using high-throughput sequencing to analyze the bacterial community composition in wastewater treatment setups containing bistriazine as a non-metallic organic tanning agent. The Canonical Correspondence Analysis (CCA) statistical approach helped us to connect the changes in microbial species with the physicochemical properties of the bistriazine-containing wastewater, thereby elucidating the post-degradational effects on the microbial community structure. Furthermore, changes in the dominant genera structure in bistriazine-containing wastewater were analyzed. The results provide insight into the ecological assessment of bistriazine as a non-metallic organic tanning agent.

2. Materials and Methods

2.1. Collection of Samples

The structures of three NOTAs (T2, T3, and T4) are illustrated in Figure 1. Their degradation rates and properties are listed in Table 1. Wastewater sample collection was done in aeration tanks of the Shaanxi University of Science and Technology. According to the NOTA ratio of 1.5%, one of the 3 NOTAs, which was created in a laboratory, was added to a water sample for five continuous days, aerated for 12 h every day, and then stopped for 12 h. To the water sample was added glucose (0.4 g/L); NH4Cl (0.153 g/L); and K2HPO4 (0.129 g/L). Three water samples of the T2, T3, and T4 tanning agents were set in parallel, and water samples without tanning agents were used as blank controls. The numbers were as follows: T2-1, T2-2, T2-3, T3-1, T3-2, T3-3, T4-1, T4-2, T4-3, KB-1, KB-2, KB-3 (KB = blank control. Three replicas of each water sample were prepared in a 10 mL sealed tube and then preserved in dry ice [14]. The samples were stored at −80 °C until the extraction of DNA.

2.2. Analysis of Physicochemical Properties of Bistriazine Ring Derivative Wastewater

The values of Total Nitrogen (TN), Ammonia Nitrogen (NH3–N), Nitrate (NO3–N), Total Phosphorus (TP), and Total Organic Carbon (TOC) in wastewater with added bistriazine ring derivatives were determined using national standardized methods [15]. TN, NH3–N, NO3–N, and TP were determined by a UV spectrophotometer (UV-1800), TOC was determined by a total organic carbon analyzer (LiquiTOCII, Elementar Trading (Shanghai) Co., Ltd., Shanghai, China), and a PHS-3E pH meter (Sartorious, Gottingen, Germany) was used to compute the pH of corresponding samples.

2.3. Extraction of DNA, Library Preparation, and Sequencing

2.3.1. Extraction of DNA

The prepared water samples were placed on dry ice and sent to Shanghai Meiji Biology (Shanghai, China) for DNA extraction and sequencing. Extraction was performed under the guiding principles of the E.Z.N.A.® soil kit (Omega Bio-tek, Norcross, GA, USA). Nanodrop2000 was employed to detect the purified DNA and its concentration, while the DNA’s quality was determined through 1% agarose gel electrophoresis.

2.3.2. PCR Amplification

Variable regions (V3–V4) were amplified through PCR by using 338F (5′-ACTCCTACGGGAGGCAGCAG-3′) and 806R (5′-GGACTACHVGGGTWTCTAAT-3′) primers [11]. The amplification was carried out in the following steps: pre-denaturation for 3 min at a particular temperature (95 °C); 27 cycles, including 30 s of denaturation at 95 °C; annealing at 55 °C for another 30 s; extension at 72 °C for 30 s; and another extension at 72 °C for 10 min (PCR equipment: ABI GeneAmp® 9700). The amplifying system consisted of 20 μL and 4 μL of 5*FastPfu buffer, 2 μL of 2.5 mM dNTPs, 0.8 μL of primer (5 μM), 0.4 μL of FastPfu polymerase, and a DNA template of 10 ng.

2.3.3. Illumina Miseq Sequencing

Two percent agarose-containing gel was applied to recover the PCR product and an AxyPrep DNA Gel extraction kit (Axygen Biosciences, Union City, CA, USA) was used for purification. For elution, Tris–HCl was used followed by a 2% agarose electrophoretic process for final detection. With the help of QuantiFluor™-ST (Promega, Madison, Wisconsin, USA), the PCR product was detected and quantified. The standardized operational procedure of the Illumina MiSeq platform (Illumina, San Diego, CA, USA) was used to construct amplified and purified fragments to prepare the PE2 × 300 library. For sequencing, Illumina’s MiseqPE300 platform was used (Shanghai Meiji Biomedical Technology Co., Ltd., Shanghai, China).

2.4. Analysis of Alpha and Beta Diversity

To further characterize the bacterial community structure, a bacterial database was constructed from 16S rRNAs amplified in the V3–V4 region from individually pooled DNA samples. Typical operating taxonomy units (OTUs) sequences were obtained by removing the chimera during clustering based on a 97% similarity level. For this analysis, 97% similarity of OTUs or other taxonomy levels were chosen. To analyze the alpha diversity index among dissimilar and irregular samples, Mothur software was used, whereas the illustrations were generated by R language [16]. To study the similar and different relationships of the composition of sample species, and to construct a sample-level clustering tree, clustering analysis was performed on the sample distance matrix. To compare the differences in the wastewater bacterial communities, the corresponding distance matrix prior to beta diversity was calculated regarding each sample’s species richness. The Bray–Curtis procedure was used to measure sample distances. The larger the distance between samples, the greater the difference in bacterial community. Based on the distance matrix, a type clustering analysis was performed. Beta diversity was calculated according to the hierarchical clustering analysis of the beta diversity distance matrix by using Quantitative Insights Into Microbial Ecology (Qiime software (v 1.9.1)) (Ann Arbor, MI, USA) [17]. Subsequently, the tree was drawn using R language.

2.5. Data Processing

The trimmomatic software Fast Length Adjustment of SHort reads (FLASH, Baltimore, MD, USA) was used to control for indigenous sequences and perform splicing. First, a window of about 50 base pairs (bp) was set as a quality control. If a window’s quality was lower than 20, the bp’s back-end sequences were cut from the window’s front end, followed by removal. Second, the splicing of both tails of the sequences was performed according to overlapping bases. Among the overlaps, 0.2 was highest mismatching rate, and the length had to exceed 10 bp.
Third, the splicing of a sequence into each sample was performed with barcodes and primers that were present at each corresponding sequence ending. The barcode needed to match exactly. The primer allowed 2 bases to be mismatched and we removed sequences with fuzzy bases. UPARSE software (Pomona, CA, USA) was used to perform sequence clustering of OTUs at a similarity level of 97% and eliminate solo sequences as well as chimeras throughout clustering. The Silva database (SSU123) was aligned to a 70% threshold to perform species classification and annotate each sequence by using the Ribosomal Database Project (RDP) classifier [18].

2.6. Canonical Correspondence Analysis (CCA) Method

The CCA method was proposed in order to estimate how much of the variation in species data can be accounted for by environmental data, which are highly robust when the presumption of no unimodal response holds [19,20]. This method displays a nonlinear relationship among environmental factors and species and selects better weights for environmental variables. We used CCA with Canonical Community Ordination (CANOCO) for the Windows 5.0 package to measure the correlation between the structure of microbial species and the physicochemical properties of wastewater containing different bistriazine tanning agents. The Monte Carlo unrestricted permutation test was used to assess each variable’s statistical significance, which involved 499 enumerated permutations [21]. The biplot from the CCA analysis signified an overlapping relationship between the microbial communities and the combined physicochemical properties (Table 1). Environmental variants are represented by arrows and their maximum values are on the corresponding arrow tip.

3. Discussion and Results

3.1. Physicochemical Properties of Bistriazine Ring Derivative Wastewater after Degradation

The post-degradation pH, TN, TP, NH3–N, NO3–N, and TOC of the bistriazine ring derivative wastewater are listed in Table 2. The TN, NH3–N, NO3–N, and TOC levels were remarkably higher (p < 0.05) compared with the KB. This might have been due to the addition of C and N. The pH of T3 and the KB was significantly higher (p < 0.05) than that of T2 and T4 because the T3 tanning agent was neutral, whereas the T2 agent was acidic. The TN, NH3–N, NO3–N, and TOC levels in T2 were substantially higher (p < 0.05) compared with the other wastewater samples, which could be ascribed to the introduction of the heterocyclic compound N to the tanning agents and the huge quantity of organic matter (COD). The TP content of the T4 wastewater was significantly lower (p < 0.05) than that of T2 or T3.
Industrial wastewater has been reported to severely affect water quality due to the large amount of nitrogen and phosphorus [22,23]. As depicted in Table 1, the bistriazine-containing wastewater increased the phosphorus (P) and nitrogen (N) content, which was attributed to the high concentration of COD and NH3.

3.2. Analysis of the Structure of the Bacterial Community in the Bistriazine Ring Derivative Wastewater

3.2.1. The Alpha Diversity Analysis of Bacteria

The results of the alpha diversity analysis of the bistriazine ring derivative and KB samples are presented in Table 3. High-quality bacterial sequences with a notable sum of 1,986,686 were obtained. UPARSE software was used to trim the sequences, with a maximum of 58,522 and a minimum of 41,495 sequences in each sample, while the average sequence number was 50,009. All the high-quality readings were categorized by taxonomy (from door to genus) by default. Alpha diversity analysis showed that all the coverages of the tested samples were above 0.992, indicating that the sequencing coverage of the samples was high. Furthermore, an estimate of the bacterial community diversity was made by the Shannon and Simpson indexes, whereas the Chao index was employed to reflect the bacterial community richness, and the number of bacterial OTUs varied from 692 to 923. The Shannon bacterial community diversity index ranged from 2.757 to 3.835, whereas the Simpson and Chao bacterial community diversity indexes ranged from 0.121 to 0.233 and from 826.161 to 1154.254, respectively.
The Shannon index of the T4 wastewater was high, while the Simpson index was relatively low [24], indicating that the T4 wastewater had a high diversity of community species. It was reported in our previous study that microorganisms use non-metallic tanning agents as their carbon source, which leads to an increase in species diversity [16]. As shown in Table 1, the T4 bistriazine ring derivatives had a lower COD concentration and were more suitable for microbial metabolism and growth. When these derivatives were discharged into the wastewater, after a period of adaptation, the microorganisms might have metabolized the organic matter, resulting in an increase in the community species diversity. The index was the highest [24] in the KB as no exogenous pollutants and no bistriazine ring derivatives were introduced. Therefore, the impact on the microbial community structure was small. Although the species richness was higher, most of the microbial species showed a lower abundance due to the lack of nutrients.

3.2.2. Effect of Derivatives Containing Different Bistriazine Rings on the Bacterial Microbial Community in the Wastewater

Figure 2a,b illustrate the relative abundance of bistriazine-containing wastewater bacterial communities with respect to phylum and genera. Figure 2a shows the phylum only with abundance rates higher than 0.01%. A total of six phyla were identified and described: Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, and Cyanobacteria. The dominance of Firmicutes and Proteobacteria was witnessed in T2, T3, T4, and KB wastewater with an average relative abundance of 77.57, 18.58, 58.47, and 32.50% and 41.69, 34.52, 62.30, and 26.76%, respectively. Previous research on the bacterial community composition in wastewater reported the dominance of Firmicutes and Proteobacteria. Xu [12] investigated six sewage treatment plants and studied their bacterial communities. This research proved that the main phyla present in the sewage water were Cyanobacteria, Bacteroidetes, Verrucomicrobia, Proteobacteria, Planctomycetes, and Firmicutes. Wang [25] found Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria to be the main bacteria in a leather wastewater treatment plant. The abundance of Actinobacteria was low in T2 and T3 but higher in T4. Cyanobacteria was almost non-existent in T4, indicating a notable difference in the microbial community composition among the bistriazine ring derivative wastewater samples. Previous reports explained that microorganisms might consume organic matter for growth and reproduction, which can repress the growth of other bacteria [16,26] reported that the presence of Pseudomonas might repress the growth of other bacteria. These results indicate that the addition of bistriazine affects the bacterial community structure in wastewater.
Bacterial genera with a higher abundance (<0.01%) are shown in Figure 2b. In all, 24 genera were identified, and the type and quantity changed according to the different types of bistriazine ring derivatives. Notably, T4 had virtually no unclassified species, whereas the T2, T3, and KB populations ranged from 22.29 to 43.46%.
In T2, the dominant genera were Clostridium sensu stricto (9.93%), Turicibacter (9.59%), Pseudomonas (4.66%), Intestinibacter (4.43%), and Acinetobacter (3.93%). In T3, the abundance of Turicibacter and Clostridium sensu stricto decreased significantly, while that of Acidocella and Bacillus increased. The dominant bacterial genera were Acidocella (18.33%), Turicibacter (6.48%), Clostridium sensu stricto (6.45%), and Bacillus (3.37%). In T4, the dominant genera changed to Trichococcus (36.26%), Aeromonas (13.02%), Pseudomonas (3.38%), and Tolumonas (3.37%). In KB, Lactococcus (23.58%), Clostridium sensu stricto (4.64%), Turicibacter (4.14%), Raoultella (3.81%), Acinetobacter (3.31%), and Pseudomonas (3.21%) were dominant.
The presence of Clostridium, Pseudomonas, and Acidocella had a denitrification effect, whereas the presence of Aeromonas and Raoultella dephosphorized the bistriazine wastewater [16,27,28,29,30]. Lactococcus removed ammonia–nitrogen more effectively, indicating that the bacteria responsible for denitrification were predominant, whereas the bacteria responsible for dephosphorization and the removal of ammonia–nitrogen were dominant in the blank control wastewater. From the above analysis, we concluded that the bacterial community composition of the bistriazine ring derivative-containing wastewater systems was different from that of the blank control, and that the bacterial community structure changed dramatically. Jayamani [31] studied hexahydro-1,3,5-trinitro-1,3,5-triazine (generally called RDX) pollutants that affect the groundwater bacterial community structure and found Pseudomonadaceae and Acinetobacter to be the dominant bacteria. Livermore [32] similarly studied the effects of RDX pollutants and found Pseudomonas to be the dominant bacterial genus.
Figure 3 illustrates the sensitive and tolerant species found in the bistriazine ring derivative wastewater relative to the KB control. The abundance of Lactococcus, Acinetobacter, Raoultella, Dechloromonas, and Zoogloea was higher, whereas that of Trichococcus, Clostridium sensu stricto, Turicibacter, Pseudodomonas, Acidocella, Intestinibacter, Tolumonas, and Bacillus was lower. This indicates that the wastewater was rich in tolerant bacteria that provided suitable survival conditions and promoted bacterial reproduction and growth to maximize bistriazine degradation. For example, Jayakumar [33] reported that Pseudomonas is an effective bacterium for remediating tannery wastewater, and a study by Kapahi [34] reported that Bacillus removed heavy metals. A unique microorganism, Acidocella, also caused degradation. Such sensitive and tolerant bacteria must be investigated further to uncover more bioremediation options.

3.3. β-Diversity Analysis

The results of PCoA, illustrated in Figure 4, show an obvious difference between the bacterial community in the bistriazine ring derivative wastewater and that in the blank control wastewater. The distance between T2 and T3 was less, indicating that these two sample communities had a similar composition, and T4 had a specific microbial community. Isazadeh [35] studied eight activated sludge wastewater treatment plants and specific changes in beta diversity were noted in each wastewater sample.
As shown in Table 1, the COD value of T4 was relatively small, so no significant difference between the high concentrations of organic pollutants (COD) found in T2 and T3 was observed.

3.4. Correlation between Bacterial Community Composition and Physicochemical Properties of Bistriazine Ring Derivative Wastewater

The PcOA results show that the bacterial communities from different wastewater samples clustered according to their type (Figure 4). In Figure 5, the CCA shows a clear correlation between the bacterial community composition and physicochemical properties, the extent and direction of which influenced the degree and significance of the correlation. Previous studies reported how pH, TOC, TN, and TP affected the overall diversity and composition of the microbial community [36,37]. The correlation between the cataloging composition at the OTU level and the physicochemical properties of the bistriazine ring derivative wastewater (pH, TN, TP, NH3–N, NO3–N, and TOC) was determined by CCA (Figure 5). About 55.77% of the cataloging information was explained by the two axes. It was revealed by the CCA that the T4 wastewater samples showed a positive correlation for TN and NH3–N, whereas T2 and T3 showed a positive correlation for NO3–N, pH, and TP. A negative correlation was seen between the KB wastewater samples and TN, NH3–N, and NO3–N. These results are consistent with the changes in the microbiological classification structures. Li [16] reported that, based on a RDA analysis, the physicochemical properties had a significant correlation with the bacterial community structure in wastewater containing tanning agents. This indicates a similar microbial community composition in the same area, confirming that these properties were mainly responsible for affecting the bacterial community composition.
Based on the previous discussion, the primary elements that affected the microbial activity in water were pH, TOC, TN, TP, NO3–N, and NH3–N because of their effect on the overall diversity and bacterial communal structure [36,37,38,39,40] observed the effect of adding ferric nitrate at different levels of pH on the stability and bacterial communal system of sewage sludge. The results obtained by Illumina high-throughput sequencing showed that at an optimal level of pH, this group showed a significant increase in the abundance of the Firmicutes phylum (61.0–96.6%), while an increase in pH decreased it. Huang [41] and others analyzed the dynamics of microbial communities in pig farm wastewater treatment systems and found a negative correlation between Clostridium sensu stricto and NH3–N and TN, whereas a positive correlation was found for Acinetobacter. Yang [42] explored the relationship between bacterial diversity in rain-cellar water and major water quality factors, confirming a positive correlation between Acinetobacter and pH, TN, TP, NO3–N, and NH3–N. Similarly, Zhang [43] studied the relation between environmental factors and microbes in farm wastewater and, using a redundancy analysis (RDA), revealed a correlation. In addition, the NH3–N and TN content had the greatest impact on the environment. Pseudomonas, Bacillus, and Aeromonas correlated positively with pH but negatively with NH3–N and TN. Shi [44] studied how different pH values affected the bacterial communities in river sediments, and the results from Illumina high-throughput sequencing revealed an increase in the comparative richness of the predominant Acidocella with an increase in the pH value from 3 to 5. Carvalho [45] considered the effects of pH on bacterial communities in textile wastewater and the Illumina high-throughput sequencing results indicated that the pH significantly affected the relative abundance of Trichococcus. García-Ruíz [46] considered the effects of pH on the bacterial community structure in a black glue fermentation. The results showed a positive correlation between Intestinibacter and pH; therefore, we concluded that physicochemical properties, such as pH, TOC, NH3–N, NO3–N, TP, and TN, had a strong influence on the bacterial community structure in wastewater containing bistriazine.

3.5. Relationship between Bacterial Richness and Physicochemical Properties of Bistriazine Ring Derivative Wastewater

Spearman’s rank correlation analysis was employed to analyze the correlation between microbial richness and the wastewater’s physicochemical properties (Figure 6). The abundance of Serratia, Lactobacillus, Proteus, Clostridium sensu stricto, unclassified_f__Peptostreptococcaceae, Turicibacter, Ruminococcus_1, and Terrisporobacter positively correlated with the TP content of the bistriazine ring derivative wastewater (p ≤ 0.05), whereas a positive correlation between the abundance of Escherichia–Shigella, Blautia, Bacillus, and Ruminococcus 2 and TP (0.01< p ≤ 0.05) was also observed. Similarly, there was a positive correlation between Acidocella and norank c Cyanobacteria and TP (p ≤ 0.001), while the pH content showed a positive correlation with Mycobacterium and norank_o__JG30-KF-CM45 abundance (p ≤ 0.05). The NO3–N content showed a positive correlation with Norank_o__JG30-KF-CM45 and unclassified_k__norank abundance (p ≤ 0.05). Conversely, norank_f__Elev-16S-1332 abundance displayed a negative correlation with TP, TN, and TOC (p ≤ 0.05). The community richness of Aeromonas, norank_p__Saccharibacteria, Macellibacteroides, Tolumonas, Chryseobacterium, and Rhodobacter showed a negative correlation with TP (p ≤ 0.05). The abundance of norank_f__Anaerolineaceae, norank_o__Acidimicrobiales, norank_o__Pem15, Candidatus microthrix, Flavobacterium, and Simplicispira negatively correlated with TP (0.01 < p ≤ 0.05). Moreover, the abundance of norank_f__Caldilineaceae, Comamonas, Trichococcus, Tessaracoccus, norank_f__Actinomycetaceae, and Propioniciclava correlated negatively with TP (p ≤ 0.001). A negative correlation between Zoogloea abundance and NH3–N, TN, and TOC was observed (p ≤ 0.05). Raoultella abundance exhibited a negative correlation with NO3–N (p ≤ 0.05). The correlation-based heat map analysis showed that the physicochemical properties of the bistriazine ring derivative wastewater had both a favorable and an unfavorable effect on the bacterial community, causing a corresponding effect on its composition.
Five components of wastewater (TN, TP, TOC, NH3–N, and NO3–N) were studied to determine their environmental effect on their bacterial communities [47,48,49]. An increase in TP promoted the growth of Serratia, Lactobacillus, Proteus, Clostridium sensu stricto, unclassified_f__Peptostreptococcaceae, Turicibacter, Ruminococcus 1, Terrisporobacter, Escherichia-Shigella, Blautia, Bacillus, Ruminococcus 2, Acidocella, and norank_c__Cyanobacteria. Similarly, an increase in NO3–N promoted the comparative abundance of unclassified_k__norank and norank_o__JG30-KF-CM45. These results confirm that TN, TP, TOC, NH3–N, and NO3–N, along with pH, induced changes in the bacterial community composition. The relative abundance of various sensitive species might also have been affected by these factors, thereby changing the entire bacterial community composition [49].
NOTAs were found to be more eco-friendly than chromium tanning agents [50,51]. The above analysis revealed that the changes in the bacterial community structure were attributable to the physical and chemical properties of the bistriazine wastewater. Thus, removing the nitrogen and phosphorus and enhancing the growth of active and tolerant bacteria by providing a suitable living environment may constitute an effective wastewater treatment. Further investigation into tolerant bacteria is required to enhance the treatment efficiency.

4. Conclusions

This study investigated the structural changes in bacterial communities in wastewater samples caused by bistriazine non-metallic organically synthesized tanning agents via high-throughput sequencing. Subsequently, the composition of microbial communities was studied. Prominent changes were observed in the structure of the bacterial communities in the wastewater containing various bistriazine NOTAs. Several conclusions can be drawn based on the above discussion and results.
The wastewater’s physicochemical properties (pH, TN, TP, TOC, NH3–N, and NO3–N) significantly affected the microbial community structure. Therefore, with regard to the treatment of tanning wastewater containing bistriazine NOTAs, efficiency might be improved by providing a reasonable living environment to promote microbial growth and reproduction, particularly of active functional bacteria.
The bistriazine-containing wastewater was found to have a relatively high microbial abundance and diversity. Trichococcus, Clostridium sensu stricto, Turicibacter, Pseudomonas, Intestinibacter, Acidocella, Tolumonas, and Bacillus all displayed a high tolerance for the bistriazine NOTAs and can remove nitrogen.
Such resistant microbes should be explored intensively for the bioremediation of bistriazine-containing wastewater.

Author Contributions

H.M. and C.L., methodology; Z.X. and A.H., formal analysis; Z.X. and C.L., writing—review and editing; X.Q., funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding

National Key Research and Development Program of China (2017YFB0308500), The Science and Technology Plan Program of the Weiyang District of Xi’an (201710), The Youth Innovation Team of Shaanxi Universities.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Acknowledgments

Financial aid to support this research was issued by the National Key Research and Development Program of China (2017YFB0308500), The Science and Technology Plan Program of the Weiyang District of Xi’an (201710), and the Youth Innovation Team of Shaanxi Universities.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhang, Z.T.; Gao, W.W.; Dai, G.C.; Li, Z.J. Research progress of non-chromium tanning agents and chrome-free tanning technology based on silicon. China Leather 2019, 48, 21–27. [Google Scholar]
  2. Zhou, Y.L.; He, L.R.; Shi, J.B.; Lin, W. Research Progress of Non-metal Tanning Technology. China Leather 2011, 40, 47–51. [Google Scholar]
  3. Liu, D.; Qiang, X.H.; Hu, Y. Synthesis and Tanning Properties of N, N′-Bis-(4, 6-dichloro-[1,3,5]-triazin-2-yl)-2, 6-diaminohexanic Acid. Fine Chem. 2019, 36, 136–141, 148. [Google Scholar]
  4. Yu, L.D.; Qiang, X.H.; Ma, H.F.; Liu, D.; Chen, B.; Wang, X.K. Synthesis and Tanning Properties of p-(4,6-Dichloro-1,3,5-triazin-2-yloxy) benzenesulfonic Acid Sodium Salt. Fine Chem. 2019, 36, 1415–1421. [Google Scholar]
  5. Cupples, A.M. RDX degrading microbial communities and the prediction of microorganisms responsible for RDX bioremediation. Int. Biodeter. Biodegr. 2013, 85, 260–270. [Google Scholar] [CrossRef]
  6. Jiang, C.; Lu, Y.C.; Xu, J.Y.; Song, Y.; Song, Y.; Zhang, S.H.; Ma, L.Y.; Lu, F.F.; Wang, Y.K.; Yang, H. Activity, biomass and composition of microbial communities and their degradation pathways in exposed propazine soil. Ecotox. Environ. Saf. 2017, 145, 398–407. [Google Scholar] [CrossRef] [PubMed]
  7. Liu, Y.; Jin, R.; Liu, G.; Tian, T.; Zhou, J. Effects of hexavalent chromium on performance, extracellular polymeric substances and microbial community structure of anaerobic activated sludge in a sequencing batch reactor. J. Chem. Technol. Biotechnol. 2017, 92, 2719–2730. [Google Scholar] [CrossRef]
  8. Moga, I.C.; Ardelean, I.; Petrescu, G.; Crǎciun, N.; Popa, R. The potential of biofilms from moving bed bioreactors to increase the efficiency of textile industry wastewater treatment. Ind. Text. 2018, 69, 412–418. [Google Scholar]
  9. Stan-Lotter, H.; Fendrihan, S. Adaption of Microbial Life to Environmental Extremes: Novel Research Results and Application, 2nd ed.; Springer: Berlin/Heidelberg, Germany, 2017; pp. 1–342. [Google Scholar]
  10. Gao, P.P.; Mao, D.Q.; Luo, Y.; Wang, L.M.; Xu, B.J.; Xu, L. Occurrence of sulfonamide and tetracycline-resistant bacteria and resistance genes in aquaculture environment. Water Res. 2012, 46, 2355–2364. [Google Scholar] [CrossRef]
  11. Xu, N.; Tan, G.C.; Wang, H.Y. Effect of biochar additions to soil on nitrogen leaching, microbial biomass and bacterial community structure. Eur. J. Soil Biol. 2016, 74, 1–8. [Google Scholar] [CrossRef]
  12. Xu, H.Z.; Pei, H.Y.; Jin, Y. High-throughput sequencing reveals microbial communities in drinking water treatment sludge from six geographically distributed plants, including potentially toxic cyanobacteria and pathogens. Sci. Total Environ. 2018, 634, 769–779. [Google Scholar] [CrossRef]
  13. Schöler, A.; Jacquiod, S.; Vestergaard, G.; Schulz, S.; Schloter, M. Analysis of soil microbial communities based on amplicon sequencing of marker genes. Biol. Fert. Soils 2017, 53, 485–489. [Google Scholar] [CrossRef]
  14. Tao, K.; Liu, Y.X.; Ke, T.; Zhang, Y.R.; Xiao, L.; Li, S.X.; Wei, S.J.; Chen, L.Z.; Hu, T.S. Patterns of bacterial and archaeal communities in sediments in response to dam construction and sewage discharge in Lhasa River. Ecotox. Environ. Saf. 2019, 178, 195–201. [Google Scholar] [CrossRef] [PubMed]
  15. Wei, F.S. Water and Wastewater Monitoring and Analysis Methods, 4th ed.; China Environment Science Press: Beijing, China, 2002. [Google Scholar]
  16. Li, C.; Pan, G.; Wang, X.; Qiang, X.; Qiang, T. The effects of non-metallic organic tanning agents on the microbial community structure in wastewater. J. Clean. Prod. 2020, 279, 123553. [Google Scholar] [CrossRef]
  17. Wang, J.; Huang, M.; Wang, Q.; Sun, Y.; Zhao, Y.; Huang, Y. LDPE microplastics significantly alter the temporal turnover of soil microbial communities. Sci. Total. Environ. 2020, 726, 138682. [Google Scholar] [CrossRef]
  18. Cole, J.R.; Wang, Q.; Fish, J.A.; Chai, B.L.; McGarrell, D.M.; Sun, Y.N.; Brown, C.T.; Porras-Alfaro, A.; Kuske, C.R.; Tiedje, J.M. Ribosomal Database Project: Data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014, 42, D633–D642. [Google Scholar] [CrossRef] [PubMed][Green Version]
  19. Ter Braak, C.J.F. The analysis of vegetation-environment relationships by canonical correspondence analysis. Vegetatio 1987, 69, 69–77. [Google Scholar] [CrossRef]
  20. Jogman, R.H.G.; ter Braak, C.J.F.; van Tongeren, O.F.R. (Eds.) Data Analysis in Community and Landscape Ecology; Pudoc: Wageningen, The Netherlands, 1995; p. 321. [Google Scholar]
  21. Ter Braak, C.J.F. Interpreting canonical correlation analysis through biplots of structural correlations and weights. Psychometrica 1990, 55, 519–531. [Google Scholar] [CrossRef]
  22. Conley, D.J.; Paerl, H.W.; Howarth, R.W. Controlling Eutrophication: Nitrogen and Phosphorus. Science 2009, 323, 1014–1015. [Google Scholar] [CrossRef]
  23. Hu, W.F.; Lo, W.; Chua, H.; Sin, S.N.; Yu, P.H. Nutrient release and sediment oxygen demand in a eutrophic land-locked embayment in Hong Kong. Environ. Int. 2001, 26, 369–375. [Google Scholar] [CrossRef]
  24. Chen, H.H.; Wan, J.J.; Chen, K.F.; Luo, G.; Fan, J.J.; Clark, J.; Zhang, S.C. Biogas production from Hydrothermal Liquefaction Waste Water (HTLWW): Focusing on the microbial communities as revealed by high-throughput sequencing of full-length 16S rRNA genes. Water Res. 2016, 106, 98–107. [Google Scholar] [CrossRef][Green Version]
  25. Wang, Z.; Zhang, X.X.; Huang, K.L.; Miao, Y.; Shi, P.; Liu, B.; Long, C.; Li, A.M. Metagenomic Profiling of Antibiotic Resistance Genes and Mobile Genetic Elements in a Tannery Wastewater Treatment Plant. PLoS ONE 2013, 8, e76079. [Google Scholar] [CrossRef]
  26. Liu, B.-Y.; Huan, H.-L.; Gu, H.-R.; Xu, N.-X.; Shen, Q.; Ding, C.-L. Dynamics of a microbial community during ensiling and upon aerobic exposure in lactic acid bacteria inoculation-treated and untreated barley silages. Bioresour. Technol. 2019, 273, 212–219. [Google Scholar] [CrossRef] [PubMed]
  27. Zhang, B.; Xu, X.-Y.; Zhu, L. Activated sludge bacterial communities of typical wastewater treatment plants: Distinct genera identification and metabolic potential differential analysis. AMB Express 2018, 8, 184. [Google Scholar] [CrossRef] [PubMed]
  28. Zhang, L.-H.; Tian, R.; Chen, Z.-C.; Guo, J.; Jia, Y. Effects of NaCl salinity on wastewater pollutants removal and microorganism in A2/O technology process. Trans. Chin. Soc. Agric. Eng. 2018, 34, 231–237. [Google Scholar]
  29. Zhang, L.-H.; Zhang, J.-C.; Zeng, G.-M.; Dong, H.-R.; Chen, Y.-N.; Huang, C.; Zhu, Y.; Xu, R.; Cheng, Y.-J.; Hou, K.-J.; et al. Multivariate relationships between microbial communities and environmental variables during co-composting of sewage sludge and agricultural waste in the presence of PVP-AgNPs. Bioresour. Technol. 2018, 261, 10–18. [Google Scholar] [CrossRef]
  30. Yao, Y.; Zhu, J.-R.; Tang, M.; Chen, G.; Huang, J.; Chen, T.; Liao, W. Application of aerobic granular sludge technology on treatment of villages and towns sewage. Res. Environ. Sci. 2018, 31, 379–388. [Google Scholar]
  31. Jayamani, I. Assessing the Biodegradation of Toluene, Ethylbenzene and RDX and the Identification of the Microorganisms Involved Using Stable Isotope Probing and High Throughput Amplicon Sequencing. Ph.D. Thesis, Michigan State University, East Lansing, MI, USA, 2015. [Google Scholar]
  32. Livermore, J.A. Microbial Ecology of a Managed Aquifer near the Iowa Army Ammunition Plant (Middletown, IA). Ph.D. Thesis, The University of Lowa, Iowa City, IA, USA, 2011. [Google Scholar]
  33. Jayakumar, G.C.; Kumar, G.; Tesema, A.F.; Thi NB, D.; Kobayashi, T.; Xu, K. Bioremediation for tanning industry: A future perspective for zero emission. In Management of Hazardous Wastes; IntechOpen: London, UK, 2016. [Google Scholar]
  34. Kapahi, M.; Sachdeva, S. Bioremediation Options for Heavy Metal Pollution. J. Health Pollut. 2019, 9, 191203. [Google Scholar] [CrossRef][Green Version]
  35. Isazadeh, S.; Jauffur, S.; Frigon, D. Bacterial community assembly in activated sludge: Mapping beta diversity across environmental variables. Microbiol. Open 2016, 5, 1050–1060. [Google Scholar] [CrossRef] [PubMed]
  36. Hidaka, T.; Tsushima, I.; Tsumori, J. Comparative analyses of microbial structures and gene copy numbers in the anaerobic digestion of various types of sewage sludge. Bioresour. Technol. 2018, 253, 315–322. [Google Scholar] [CrossRef] [PubMed]
  37. Zhao, C.; Lu, D.; Bai, Y.; Hu, W.; Li, M. Influence of effluent from sewage treatment plants on the biofilm of receiving rivers. Coal Chem. Ind. 2018, 41, 145–148. [Google Scholar]
  38. Shin, S.G.; Koo, T.; Lee, J.; Han, G.; Cho, K.; Kim, W.; Hwang, S. Correlations between bacterial populations and process parameters in four full-scale anaerobic digesters treating sewage sludge. Bioresour. Technol. 2016, 214, 711–721. [Google Scholar] [CrossRef] [PubMed]
  39. Li, X.; Li, Y.Y.; Li, Y.; Wu, J.S. Diversity and distribution of bacteria in a multistage surface flow constructed wetland to treat swine wastewater in sediments. Appl. Microbiol. Biot. 2018, 102, 10755–10765. [Google Scholar] [CrossRef]
  40. Jin, N.B.; Shou, Z.Q.; Yuan, H.P.; Lou, Z.Y.; Zhu, N.W. Effects of ferric nitrate additions under different pH conditions on autothermal thermophilic aerobic digestion for sewage sludge. RSC Adv. 2015, 5, 90127–90134. [Google Scholar] [CrossRef]
  41. Huang, W.; Liu, L.Y.; Wu, M.H.; Chen, L.H.; Lü, X.; Ye, M.F.; Lin, D.Y.; Song, Y.K. Microbial community structure and dynamics in swine wastewater treatment system. J. Environ. Sci. 2019, 39, 839–848. [Google Scholar]
  42. Yang, H.; Zhang, G.Z.; Yang, X.N.; Wu, F.P.; Zhao, W.; Zhang, H.W.; Zhang, X. Microbial Community Structure and Diversity in Cellar Water by 16S rRNA High-throughput Sequencing. Environ. Sci. 2017, 38, 1704–1716. [Google Scholar]
  43. Zhang, H.; Sun, Z.L.; Liu, B.; Xuan, Y.M.; Jiang, M.; Pan, Y.S.; Zhang, Y.M.; Gong, Y.P.; Lu, X.P.; Yu, D.S.; et al. Dynamic changes of microbial communities in Litopenaeus vannamei cultures and the effects of environmental factors. Aquaculture 2016, 455, 97–108. [Google Scholar] [CrossRef]
  44. Shi, M.Y.; Wen, G.C.; Liu, H.F.; Jian, G.D.; Chen, Y.Q. Influence of initial pH on bioleaching of river sediments to achieve deep dehydration. Environ. Sci. Pollut. R 2019, 26, 17183–17194. [Google Scholar] [CrossRef]
  45. Carvalho, J.R.S.; Amaral, F.M.; Florencio, L.; Kato, M.T.; Delforno, T.P.; Gavazza, S. Microaerated UASB reactor treating textile wastewater: The core microbiome and removal of azo dye Direct Black 22. Chemosphere 2020, 242, 125127. [Google Scholar] [CrossRef]
  46. García-Ruíz, M.J.; Castellano-Hinojosa, A.; Armato, C.; González-Martínez, A.; González-López, J.; Osorio, F. Biogas production and microbial community structure in a stable-stage of a two-stage anaerobic digester. Aiche J. 2020, 66, e16807. [Google Scholar] [CrossRef]
  47. Luo, Y.S.; Yao, J.Q.; Wang, X.Y.; Zheng, M.Y.; Guo, D.Y.; Chen, Y.G. Efficient municipal wastewater treatment by oxidation ditch process at low temperature: Bacterial community structure in activated sludge. Sci. Total Environ. 2020, 703, 135031. [Google Scholar] [CrossRef] [PubMed]
  48. Zhai, J.; Rahaman, M.H.; Chen, X.; Xiao, H.W.; Liao, K.S.; Li, X.T.; Duan, C.R.; Zhang, B.Y.; Tao, G.L.; John, Y. New nitrogen removal pathways in a full-scale hybrid constructed wetland proposed from high-throughput sequencing and isotopic tracing results. Ecol. Eng. 2016, 97, 434–443. [Google Scholar] [CrossRef]
  49. Lin, X.; Liu, F.; Hu, X.M. Influence of Acid and Alkali Modified Fly Ash on Handling Ammonia Nitrogen Wastewater by SBR Reactor. J. Northeast. Univ. (Nat. Sci.) 2018, 39, 1783–1787. [Google Scholar]
  50. Eleni, V.; Petros, G. Effects of chromium on activated sludge and on the performance of wastewater treatment plants: A review. Water Res. 2012, 46, 549–570. [Google Scholar]
  51. Miao, Y.; Liao, R.-H.; Zhang, X.-X.; Wang, Y.; Wang, Z.; Shi, P.; Liu, B.; Li, A.-M. Metagenomic insights into Cr(VI) effect on microbial communities and functional genes of an expanded granular sludge bed reactor treating high-nitrate wastewater. Water Res. 2015, 76, 43–52. [Google Scholar] [CrossRef]
Figure 1. Basic structure of non-metallic organic tanning agents (NOTAs).
Figure 1. Basic structure of non-metallic organic tanning agents (NOTAs).
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Figure 2. (a). Diagram of the bacterial community structure at the phylum level. (b). Genus-level bacterial community structure.
Figure 2. (a). Diagram of the bacterial community structure at the phylum level. (b). Genus-level bacterial community structure.
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Figure 3. Bacterial community structure of bistriazine ring derivative wastewater and blank control wastewater.
Figure 3. Bacterial community structure of bistriazine ring derivative wastewater and blank control wastewater.
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Figure 4. Principal component analysis (PCoA) diagram of the 16S rRNA gene sequencing data based on bistriazine ring derivative wastewater.
Figure 4. Principal component analysis (PCoA) diagram of the 16S rRNA gene sequencing data based on bistriazine ring derivative wastewater.
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Figure 5. Correspondence Analysis (CCA) of the 16SrRNA gene sequencing data and physicochemical properties of bistriazine ring derivative wastewater. The arrow direction and size show the extent of the influence, positive and negative, on the microbial community composition.
Figure 5. Correspondence Analysis (CCA) of the 16SrRNA gene sequencing data and physicochemical properties of bistriazine ring derivative wastewater. The arrow direction and size show the extent of the influence, positive and negative, on the microbial community composition.
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Figure 6. Spearman’s rank correlation studies on physicochemical properties and bacterial species abundances. The correlation coefficient is the heat map’s corresponding value, where the value of “r” ranges from −1 to 1; r < 0 indicates a negative correlation, while r > 0 indicates a positive correlation. * means the test was significant at 0.01 < p ≤ 0.05, ** means the test was significant at 0.001 < p ≤ 0.01, *** means the test was significant at p ≤ 0.001.
Figure 6. Spearman’s rank correlation studies on physicochemical properties and bacterial species abundances. The correlation coefficient is the heat map’s corresponding value, where the value of “r” ranges from −1 to 1; r < 0 indicates a negative correlation, while r > 0 indicates a positive correlation. * means the test was significant at 0.01 < p ≤ 0.05, ** means the test was significant at 0.001 < p ≤ 0.01, *** means the test was significant at p ≤ 0.001.
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Table 1. Basic physicochemical parameters of non-metallic organic tanning agents (NOTAs).
Table 1. Basic physicochemical parameters of non-metallic organic tanning agents (NOTAs).
SamplepH(Chemical Oxygen Demand) COD(mg/L)
T21.743.43 × 104
T36.273.69 × 104
T42.122.99 × 104
Table 2. Physicochemical properties of bistriazine ring derivative wastewater after degradation.
Table 2. Physicochemical properties of bistriazine ring derivative wastewater after degradation.
IndexT2T3T4KB
TN/mg·L−10.171 ± 0.000 a0.109 ± 0.001 b0.168 ± 0.002 a0.066 ± 0.001 c
TP/mg·L−10.019 ± 0.000 a0.020 ± 0.001 a0.011 ± 0.000 b0.018 ± 0.000 a
NH3–N/mg·L−152.400 ± 0.444 a45.420 ± 0.414 b51.650 ± 0.628 a39.680 ± 0.100 c
NO3–N/mg·L−10.237 ± 0.007 a0.083 ± 0.003 c0.157 ± 0.003 b0.003 ± 0.003 d
pH1.810 ± 0.006 d6.020 ± 0.009 a2.250 ± 0.006 c5.860 ± 0.010 b
TOC/mg·L−1721.000 ± 0.577 a358.000 ± 1.732 c640.000 ± 1.155 b203.000 ± 1.732 d
Note: Values are displayed as Mean ± Standard Deviation (SD, n = 3). Significant differences among wastewater samples are displayed by different alphabet letters (p < 0.05). TN, Total Nitrogen; TP, Total Phosphorus; NH3–N, Ammonia–Nitrogen; NO3–N, Nitrate–Nitrogen; TOC, Total Organic Carbon.
Table 3. Bacterial Alpha Diversity Index and Sequence Length Table.
Table 3. Bacterial Alpha Diversity Index and Sequence Length Table.
SampleShannonSimpsonChaoCoverageSeq_NumOTUs
T22.7570.233826.1610.99541,495692
T33.2080.1531119.6420.99243,229878
T43.8350.1531102.6650.99458,522923
KB3.5800.1211154.2540.99353,440840
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Xu, Z.; Ma, H.; Hassan, A.; Li, C.; Qiang, X. Impact of Non-Metallic Organic Tanning Agents with a Double-Triazine Structure on the Microbial Community Structure in Wastewater. Water 2021, 13, 2438. https://doi.org/10.3390/w13172438

AMA Style

Xu Z, Ma H, Hassan A, Li C, Qiang X. Impact of Non-Metallic Organic Tanning Agents with a Double-Triazine Structure on the Microbial Community Structure in Wastewater. Water. 2021; 13(17):2438. https://doi.org/10.3390/w13172438

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Xu, Zhifen, Hongrui Ma, Areeb Hassan, Chengtao Li, and Xihuai Qiang. 2021. "Impact of Non-Metallic Organic Tanning Agents with a Double-Triazine Structure on the Microbial Community Structure in Wastewater" Water 13, no. 17: 2438. https://doi.org/10.3390/w13172438

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