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Identification of the microbial community composition and structure of coal-mine wastewater treatment plants
Microbiological Research 175 (2015) 1–5
Contents lists available at ScienceDirect
Microbiological Research journal homepage: www.elsevier.com/locate/micres
Identification of the microbial community composition and structure of coal-mine wastewater treatment plants Qiao Ma, Yuan-Yuan Qu ∗ , Xu-Wang Zhang, Wen-Li Shen, Zi-Yan Liu, Jing-Wei Wang, Zhao-Jing Zhang, Ji-Ti Zhou Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
a r t i c l e
i n f o
Article history: Received 5 December 2014 Received in revised form 30 December 2014 Accepted 30 December 2014 Available online 23 January 2015 Keywords: Coal-mine wastewater Activated sludge Microbial community Illumina high-throughput sequencing
a b s t r a c t The wastewater from coal-mine industry varies greatly and is resistant to biodegradation for containing large quantities of inorganic and organic pollutants. Microorganisms in activated sludge are responsible for the pollutants’ removal, whereas the microbial community composition and structure are far from understood. In the present study, the sludges from five coal-mine wastewater treatment plants were collected and the microbial communities were analyzed by Illumina high-throughput sequencing. The diversities of these sludges were lower than that of the municipal wastewater treatment systems. The most abundant phylum was Proteobacteria ranging from 63.64% to 96.10%, followed by Bacteroidetes (7.26%), Firmicutes (5.12%), Nitrospira (2.02%), Acidobacteria (1.31%), Actinobacteria (1.30%) and Planctomycetes (0.95%). At genus level, Thiobacillus and Comamonas were the two primary genera in all sludges, other major genera included Azoarcus, Thauera, Pseudomonas, Ohtaekwangia, Nitrosomonas and Nitrospira. Most of these core genera were closely related with aromatic hydrocarbon degradation and denitrification processes. Identification of the microbial communities in coal-mine wastewater treatment plants will be helpful for wastewater management and control. © 2015 Elsevier GmbH. All rights reserved.
1. Introduction Coal is one of the main foundations of Chinese economic development, and China is now the largest coal production and consumption country in the world (Liu and Liu 2010). The large production of coal mining wastes, nearly 15% of coal production, has been a major threat to economy and environment (Bian et al. 2010). Coal-mine wastewater originated from coal cooking, coal gas purification and by-product recovery processes is resistant to biodegradation for encompassing large quantities of inorganic and organic pollutants (Guo and Fu 2010; Kim et al. 2008). Particularly, the existence of high concentrations of ammonium and aromatic hydrocarbons severely inhibit biological treatment and conventional activated sludge process cannot reach pollutant emission standard of national coking chemical industry (Huo et al. 2012). Novel biological processes such as anaerobic–oxic–oxic (A/O/O) and anaerobic–anoxic–oxic–oxic (A/A/O/O) have been applied to improve wastewater treatment efficiency. Regardless of the treatment modes, sludge microbial communities play the crucial roles
∗ Corresponding author. Tel.: +86 411 84706251; fax: +86 84706252. E-mail address: [email protected] (Y.-Y. Qu). http://dx.doi.org/10.1016/j.micres.2014.12.013 0944-5013/© 2015 Elsevier GmbH. All rights reserved.
in pollutant removal processes (Wagner and Loy 2002). Reveal of the diverse microbial communities will undoubtedly provide new insights into wastewater treatment. In this respect, the microbial community of either municipal or industrial wastewater treatment plants has been extensively explored using various biological techniques, such as denaturing gradient gel electrophoresis, terminal-restriction fragment length polymorphism, 16S rRNA gene clone library and fluorescence in situ hybridization analyses. In recent years, the newly developed high-throughput sequencing technology has been successfully applied in this field which can afford us a huge massive of information to identify the entire profile of microbial communities. It was shown that some core genera such as Zoogloea and Prosthecobacter existed in dozens of municipal sludges (Wang et al. 2012; Zhang et al. 2012; Hu et al. 2012). However, distinct community compositions were found in different industrial wastewater treatment plans (Ibarbalz et al. 2013). For the coal-mine industrial sludge, the microbial community composition and structure have not been well investigated. In the present study, the activated sludges of five coal-mine wastewater treatment plants from Shanxi, China were collected and sequenced using Illumina MiSeq platform for the first time. The aim of the study was to comprehensively reveal the microbial composition and structure of coal-mine industrial sludge.
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Q. Ma et al. / Microbiological Research 175 (2015) 1–5
Table 1 Operation parameters for each coal-mine wastewater treatment plant. Name
G1 G2 G3 G4 G5
COD (mg/L)
NH4 -N (mg/L)
Influent
Effluent
Influent
Effluent
3000 1000 4000 700 3000
120 150 300 200 100
250 250 140 450 250
5 10 20 200 15
2. Materials and methods
Flow rate (m3 /h)
Process
20 35 45 25 6
A/A/O/O AO/O A/A/O/O CAS A/O/O
MiSeq platform at the Institute for Environmental Genomics (IEG), University of Oklahoma.
2.1. Coking wastewater treatment plants and sample collection Activated sludge samples were collected from the secondary sedimentation tanks of five coal-mine wastewater treatment plants in Shanxi, China in June 2013. Triplicate samples were collected for each plant and stored at −80 ◦ C before DNA extraction. Two of the plants were operated with A/O/O mode, two with A/A/O/O mode, and the remaining one with conventional activated sludge process (CAS). The detailed parameters for each plant were given in Table 1, of which the plant operated with CAS showed weak NH4 -N removal capacity.
2.2. DNA extraction, PCR amplification and sequencing The genomic DNA was extracted using the protocol by Purkhold et al. (2000). The sludge was centrifuged and resuspended in 650 L DNA extraction buffer in a 2-mL tube. Then, it was successively treated with 60 L lysozyme mixture (37 ◦ C, 60 min), 60 L protease mixture (37 ◦ C, 30 min) and 80 L 20% sodium dodecyl sulfate (37 ◦ C, 90 min). Afterwards, adding 600 L phenol–chloroform–isoamyl alcohol (25:24:1) to the mixture and incubated at 65 ◦ C for 20 min, and the supernatant was extracted using equal volume of chloroform–isoamyl alcohol (24:1). Transferring the aqueous phase into a new tube and precipitating nucleic acids by adding 0.6 volumes of isopropanol with inversion for several times. Finally, washing the pellets with 70% cold ethanol, air-drying and resuspending the pellets in nuclease free water. DNA concentration was measured by Pico Green assay using a FLUOstar OPTIMA fluorescence plate reader (BMG Labtech, Germany). For high-throughput sequencing, the primers 515F (5 GTG CCA GCM GCC GCG GTA A-3 ) and 806R (5 -GGA CTA CHV GGG TWT CTA AT-3 ) were used to amplify the V4 region of the 16S rRNA gene (Bates et al. 2011). The PCR was conducted in a 25 L mixture system containing 0.1 L AccuPrime High Fidelity Taq Polymerase (Invitrogen, USA), 2.5 L of 10× AccuPrime PCR buffer II, 1 L of each primer (10 M), 1 L template DNA and 19.4 L nuclease-free water under the following condition: 94 ◦ C for 1 min; 35 cycles of 94 ◦ C for 20 s, 53 ◦ C for 25 s, and 68 ◦ C for 45 s; and a final extension at 68 ◦ C for 10 min. Each sample was amplified in triplicates. PCR products were pooled, purified through QIAquick Gel Extraction Kit (Qiagen, USA), and quantified by Pico Green analysis. The 16S rRNA high-throughput sequencing was conducted on Illumina
2.3. High-throughput sequencing data analysis After sequencing, the bar-codes and primers were removed, and the paired-end reads were overlapped to assemble the final tag sequences using the Flash program. The failed reads, low quality fragments, sequences containing one or more ambiguous reads (N), and the variable tags shorter than 240 bp were all removed by Mothur. The clean sequences were screened for chimera detection using UCHIME. Operational taxonomic units (OTUs) were selected using CD-HIT at a 97% sequence similarity threshold, and the taxonomic assignment of OTUs was performed by RDP classifier with a confidence cutoff of 0.5. Hierarchical clustering analysis was performed using CLUSTER and visualized using TREEVIEW, and other statistical analyses were performed through the IEG pipeline (http://ieg.ou.edu). The data for each treatment plant were the average values of triplicate measurements. 3. Results and discussion 3.1. Overview of sequencing and microbial diversity The microbial communities of the five coal-mine sludges were analyzed by Illumina high-throughput sequencing and at least 16,000 sequences with average length of 253 bp were obtained for each sample. The average OTU number and ␣-diversity for each sludge were shown in Table 2. The larger Shannon index values, the higher ␣-diversity. On the contrary, the smaller Simpson index values, the higher ␣-diversity. The diversity of these sludges was lower than that of the municipal wastewater treatment systems (Wang et al. 2012; Zhang et al. 2012; Hu et al. 2012; Ibarbalz et al. 2013), which should be due to the complexity and recalcitrance of coking influent. It was obvious that G4 operated with CAS harbored the lowest ␣-diversity (Shannon index 1.83) and richness (OTU number 188). G5 operated with A/O/O had the highest diversity (Shannon index 5.28) and richness (OTU number 1071), suggesting that operation modes were closely related with the microbial diversities. Detrended correspondence analysis (DCA) and hierarchical clustering analysis of all the samples were conducted at the OTU level. It was shown that the triplicate samples of each plant were clustered, proving that the sequencing results were reproducible and reliable (Fig. 1). It could also be concluded that the microbial communities
Table 2 ␣-Diversity of each sludge. Index
G1
G2
G3
G4
G5
Shannon index Simpson index Chao1 OTU
3.58 0.08 766 461
4.05 0.04 867 543
3.12 0.14 922 553
1.83 0.29 354 188
5.28 0.01 1435 1071
Q. Ma et al. / Microbiological Research 175 (2015) 1–5
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Fig. 2. Major phyla of each sludge (sequence percentage >1% in at least one sludge; inner to outer: G1–G5).
3.3. Microbial community structures at the genus level
Fig. 1. Detrended correspondence analysis and hierarchical clustering analysis of all the samples at the OTU level.
were distinct among the five sludges, of which G3 and G4 were sort of similar and clustered together both in DCA and hierarchical clustering plots.
3.2. Microbial community structures at the phylum level In the present study, nearly all the obtained sequences (over 99.99%) were assigned to bacteria thus the following analysis was concentrated on bacteria. Only 2.20% of the sequences were not classified at the phylum level, and the seven major phyla were Proteobacteria (78.86% on average), Bacteroidetes (7.26%), Firmicutes (5.12%), Nitrospira (2.02%), Acidobacteria (1.31%), Actinobacteria (1.30%) and Planctomycetes (0.95%) (Fig. 2). The most abundant phylum was Proteobacteria ranging from 63.64% to 96.10%. Among the Proteobacteria, Betaproteobacteria ranked first with an average percentage of 47.77%, followed by Gammaproteobacteria (15.19%) and Alphaproteobacteria (12.34%). The high proportion of Betaproteobacteria was in accordance with previous studies of industrial sludge and the percentage was higher than that of various municipal wastewater treatment plants and bioreactors (Wagner and Loy 2002; Wang et al. 2012; Zhang et al. 2012). Bacteroidetes, Firmicutes, Acidobacteria, Actinobacteria and Planctomycetes were also reported as the major phyla widespread in wastewater treatment systems, suggesting these bacteria play the key roles in nutrient removal processes.
Only 61 OTUs and 49 genera were shared by all groups, while they occupied 58.50% of the total sequences and 80.53% of the identified sequences (Table 3). Very few proportions of the OTUs (5.66%) and genera (0.43%) were located in one sludge. The relatively high proportion of the shared taxa represented the core status of these bacteria in coal-mine wastewater treatment process. To further analyze the microbial community structures, the major genera of each sludge were given in Fig. 3 and Table 4. It should be noted that 15.26–45.51% of the sequences were not assigned to any genera, suggesting that many novel microbes were existing in the sludge that needed to be further explored. Thiobacillus and Comamonas were the two primary genera in all sludges. Thiobacillus accounted for 16.94% of the total sequences, while it only occupied 3.80% in G4. In G5, it reached 31.25%. Thiobacillus spp. were reported to be the main degraders for thiocyanate and widespread in coking wastewater treatment systems. Meanwhile, they have been widely applied for denitrification processes for the superior denitrification capacity (Beller et al. 2006; Park and Yoo 2009). The percentages of Comamonas in the sludges were very different. It was highly abundant in G3 (29.17%) and G5 (42.39%), but relatively in low abundance in other three groups, i.e. G1 (3.83%), G2 (1.29%) and G4 (4.00%). Comamonas is widespread in various environmental niches. It is especially prominent for the aromatics degradation capacity, such as phenolics, polycyclic and heterocyclic aromatic hydrocarbons. In addition, it was also reported to couple denitrification and nitrification abilities under various aeration conditions (Felföldi et al. 2010; Gumaelius et al. 2001). These two genera were also previously reported to be the key members in steel industrial coking wastewater treatment plants, which contained similar pollutant composition (high concentrations of phenol, polycyclic aromatics, thiocyanate, ammonia and nitrate) with coal-mine wastewater (Ma et al. 2015). Azoarcus and Thauera are the two important denitrifiers reported elsewhere in wastewater treatment systems and often occur together. Moreover, they are also involved in aromatic biodegradation processes (Thomsen et al. 2007; Liu et al. 2006). For example, Mao et al. (2010) reported that Thauera spp. could degrade phenol, methylphenol and indole, and Song et al. (1999) reported that Azoarcus could remove toluene and phenol under denitrification conditions. The percentages of the two genera were all above 1% except in G5 that Azoarcus was only 0.03% and Thauera was below 0.01%. It was shown that 1.91% of total sequences belonged to Pseudomonas. The genus of Pseudomonas harbors a great deal of metabolic diversity, some of which are able to metabolize
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Table 3 Percentages of the shared genera/OTU and their corresponding proportions. Numbers of treatment plants
Numbers of OTUs
Percentage in total sequences (%)
Numbers of genera
Percentage in identified sequences (%)
5 4 3 2 1
61 191 437 1052 2509
58.50 71.49 84.38 94.34 100
49 118 184 304 426
80.53 95.23 97.53 99.57 100
Fig. 3. Major genera of each sludge (sequence percentage >1% in at least two sludges).
Table 4 Major genera with the sequence percentage above 1% in only one sludge. Genus
G1
G2
G3
G4
G5
Tissierella Luteimonas Proteiniclasticum Acinetobacter Aquamicrobium Brevundimonas Gp4 Caldimonas Arcobacter Pusillimonas Gp6 Petrimonas Trichococcus Aquicella Lysobacter Pseudoxanthomonas Lewinella Janibacter Haliscomenobacter Soehngenia Providencia
0.00 0.01 0.00 0.05 0.78 0.01 2.92 1.95 0.02 1.03 0.36 0.00 0.00 1.55 0.01 0.00 0.00 0.00 0.00 0.00 0.00
0.03 0.01 0.02 0.03 0.18 0.00 0.26 0.25 0.07 0.60 1.44 0.01 0.01 0.08 0.00 0.00 0.00 0.01 0.00 0.00 0.00
7.77 0.17 0.40 5.31 0.45 0.02 0.01 0.47 0.05 0.30 0.06 1.02 0.10 0.01 0.49 0.03 0.00 0.06 0.01 0.02 0.00
0.48 8.50 5.86 0.76 0.75 3.40 0.06 0.43 2.46 0.20 0.34 0.35 1.96 0.01 1.08 1.49 1.48 1.38 1.43 0.00 1.04
0.34 0.00 0.10 0.18 2.48 0.02 0.00 0.00 0.32 0.18 0.00 0.80 0.01 0.00 0.00 0.00 0.00 0.00 0.00 1.03 0.01
Q. Ma et al. / Microbiological Research 175 (2015) 1–5
diverse chemical pollutants such as Pseudomonas putida and Pseudomonas alcaligenes (Wu et al. 2011). Although many reports have been available that characterized the aromatic degradation abilities of Pseudomonas, microbial community analysis indicated that it was not the dominant genus in various sludge systems. Nevertheless, the roles of this versatile genus should not be neglected. Nitrosomonas belongs to the typical ammonium-oxidizing bacteria (AOB) and Nitrospira belongs to the typical nitriteoxidizing bacteria (NOB). Nitrosomonas occupied 5.95% and 4.25% in G1 and G2, respectively. It is resistant to the changing environment and has a relatively high growth rate thus it is often detected as the dominant AOB (Siripong and Rittmann 2007). The percentage of Nitrospira reached 7.80% in G2 and 1.52% in G1, but notably lower in other sludges (0.05% in G3, 0.74% in G4 and 0% in G5), suggesting the NOB proportions in the same type wastewater were distinct from each other. In G3 and G5, either AOB or NOB was very low, whereas they still displayed satisfactory ammonia removal performance. Thus we speculated that there should be other bacteria, possibly heterotrophic bacteria, played key roles in nitrification process. Some of the major genera were abundant in only one sludge (Table 4). For example, Tissierella and Acinetobacter occupied 7.77% and 5.31% in G3; Gp4 accounted for 2.92% in G1 and Gp6 accounted for 1.44% in G2. It was clearly shown that over half of these ‘individual major genera’ (11/21) were located in G4, which was operated with CAS mode. It suggested that the function stability of conventional activated sludge system might rely on more functional bacteria. However, it should be noted that most of these genera were uncultured or rarely reported, thus the roles of these major genera in the wastewater treatment process were still not clear. Recently developed metagenome sequencing technology was proven to be an efficient tool for reconstructing the genome and exploring the gene functions of the uncultured species from microbial communities (Albertsen et al. 2013), without doubt the functions and interactions of the complicated microbes would be more and more comprehensible. 4. Conclusion This is the first time to identify the microbial communities of coal-mine wastewater treatment systems by Illumina high-throughput sequencing. Thiobacillus, Comamonas, Azoarcus, Thauera, Pseudomonas, Ohtaekwangia, Nitrosomonas and Nitrospira were detected as the major genera. The microbial communities were quite different from the municipal sludge communities and the understanding of CWWTP microbial communities will be helpful in developing efficient strategies for coal-mine wastewater treatment. Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 21176040), the Program for New Century Excellent Talents in University (No. NCET-13-0077), and the Fundamental Research Funds for the Central Universities (No.
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DUT14YQ107). We thank Jizhong Zhou and Jiayu Qin for Illumina high-throughput sequencing technical help. References Albertsen M, Hugenholtz P, Skarshewski A, Nielsen KL, Tyson GW, Nielsen PH. Genome sequences of rare, uncultured bacteria obtained by differential coverage binning of multiple metagenomes. Nat Biotechnol 2013;31:533–8. Bates ST, Berg-Lyons D, Caporaso JG, Walters WA, Knight R, Fierer N. Examining the global distribution of dominant archaeal populations in soil. ISME J 2011;5:908–17. Beller HR, Chain PS, Letain TE, Chakicherla A, Larimer FW, Richardson PM, et al. The genome sequence of the obligately chemolithoautotrophic, facultatively anaerobic bacterium Thiobacillus denitrificans. J Bacteriol 2006;188:1473–88. Bian Z, Inyang HI, Daniels JL, Otto F, Struthers S. Environmental issues from coal mining and their solutions. Min Sci Technol 2010;20:215–23. Felföldi T, Székely AJ, Gorál R, Barkács K, Scheirich G, András J, et al. Polyphasic bacterial community analysis of an aerobic activated sludge removing phenols and thiocyanate from coke plant effluent. Bioresour Technol 2010;101:3406–14. Gumaelius L, Magnusson G, Pettersson B, Dalhammar G. Comamonas denitrificans sp. nov., an efficient denitrifying bacterium isolated from activated sludge. Int J Syst Evol Microbiol 2001;51:999–1006. Guo ZC, Fu ZX. Current situation of energy consumption and measures taken for energy saving in the iron and steel industry in China. Energy 2010;35:4356–60. Hu M, Wang X, Wen X, Xia Y. Microbial community structures in different wastewater treatment plants as revealed by 454-pyrosequencing analysis. Bioresour Technol 2012;117:72–9. Huo H, Lei Y, Zhang Q, Zhao L, He K. China’s coke industry: recent policies, technology shift, and implication for energy and the environment. Energy Policy 2012;51:397–404. Ibarbalz FM, Figuerola EL, Erijman L. Industrial activated sludge exhibit unique bacterial community composition at high taxonomic ranks. Water Res 2013;47:3854–64. Kim YM, Park D, Lee DS, Park JM. Inhibitory effects of toxic compounds on nitrification process for cokes wastewater treatment. J Hazard Mater 2008;152: 915–21. Liu B, Zhang F, Feng X, Liu Y, Yan X, Zhang X, et al. Thauera and Azoarcus as functionally important genera in a denitrifying quinoline-removal bioreactor as revealed by microbial community structure comparison. FEMS Microbiol Ecol 2006;55:274–86. Liu H, Liu Z. Recycling utilization patterns of coal mining waste in China. Resour Conserv Recycl 2010;54:1331–40. Ma Q, Qu Y, Shen W, Zhang Z, Wang J, Liu Z, et al. Bacterial community compositions of coking wastewater treatment plants in steel industry revealed by Illumina high-throughput sequencing. Bioresour Technol 2015;179:436–43. Mao Y, Zhang X, Xia X, Zhong H, Zhao L. Versatile aromatic compound-degrading capacity and microdiversity of Thauera strains isolated from a coking wastewater treatment bioreactor. J Ind Microbiol Biotechnol 2010;37:927–34. Purkhold U, Pommerening-Röser A, Juretschko S, Schmid MC, Koops HP, Wagner M. Phylogeny of all recognized species of ammonia oxidizers based on comparative 16S rRNA and amoA sequence analysis: implications for molecular diversity surveys. Appl Environ Microbiol 2000;66:5368–82. Park JY, Yoo YJ. Biological nitrate removal in industrial wastewater treatment: which electron donor we can choose. Appl Microbiol Biotechnol 2009;82:415–29. Siripong S, Rittmann BE. Diversity study of nitrifying bacteria in full-scale municipal wastewater treatment plants. Water Res 2007;41:1110–20. Song B, Häggblom MM, Zhou J, Tiedje JM, Palleroni NJ. Taxonomic characterization of denitrifying bacteria that degrade aromatic compounds and description of Azoarcus toluvorans sp. nov. and Azoarcus toluclasticus sp. nov. Int J Syst Bacteriol 1999;49:1129–40. Thomsen TR, Kong Y, Nielsen PH. Ecophysiology of abundant denitrifying bacteria in activated sludge. FEMS Microbiol Ecol 2007;60:370–82. Wagner M, Loy A. Bacterial community composition and function in sewage treatment systems. Curr Opin Biotechnol 2002;13:218–27. Wang X, Hu M, Xia Y, Wen X, Ding K. Pyrosequencing analysis of bacterial diversity in 14 wastewater treatment systems in China. Appl Environ Microbiol 2012;78:7042–7. Wu X, Monchy S, Taghavi S, Zhu W, Ramos J, van der Lelie D. Comparative genomics and functional analysis of niche-specific adaptation in Pseudomonas putida. FEMS Microbial Rev 2011;35:299–323. Zhang T, Shao MF, Ye L. 454 Pyrosequencing reveals bacterial diversity of activated sludge from 14 sewage treatment plants. ISME J 2012;6:1137–47.
Contents lists available at ScienceDirect
Microbiological Research journal homepage: www.elsevier.com/locate/micres
Identification of the microbial community composition and structure of coal-mine wastewater treatment plants Qiao Ma, Yuan-Yuan Qu ∗ , Xu-Wang Zhang, Wen-Li Shen, Zi-Yan Liu, Jing-Wei Wang, Zhao-Jing Zhang, Ji-Ti Zhou Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China
a r t i c l e
i n f o
Article history: Received 5 December 2014 Received in revised form 30 December 2014 Accepted 30 December 2014 Available online 23 January 2015 Keywords: Coal-mine wastewater Activated sludge Microbial community Illumina high-throughput sequencing
a b s t r a c t The wastewater from coal-mine industry varies greatly and is resistant to biodegradation for containing large quantities of inorganic and organic pollutants. Microorganisms in activated sludge are responsible for the pollutants’ removal, whereas the microbial community composition and structure are far from understood. In the present study, the sludges from five coal-mine wastewater treatment plants were collected and the microbial communities were analyzed by Illumina high-throughput sequencing. The diversities of these sludges were lower than that of the municipal wastewater treatment systems. The most abundant phylum was Proteobacteria ranging from 63.64% to 96.10%, followed by Bacteroidetes (7.26%), Firmicutes (5.12%), Nitrospira (2.02%), Acidobacteria (1.31%), Actinobacteria (1.30%) and Planctomycetes (0.95%). At genus level, Thiobacillus and Comamonas were the two primary genera in all sludges, other major genera included Azoarcus, Thauera, Pseudomonas, Ohtaekwangia, Nitrosomonas and Nitrospira. Most of these core genera were closely related with aromatic hydrocarbon degradation and denitrification processes. Identification of the microbial communities in coal-mine wastewater treatment plants will be helpful for wastewater management and control. © 2015 Elsevier GmbH. All rights reserved.
1. Introduction Coal is one of the main foundations of Chinese economic development, and China is now the largest coal production and consumption country in the world (Liu and Liu 2010). The large production of coal mining wastes, nearly 15% of coal production, has been a major threat to economy and environment (Bian et al. 2010). Coal-mine wastewater originated from coal cooking, coal gas purification and by-product recovery processes is resistant to biodegradation for encompassing large quantities of inorganic and organic pollutants (Guo and Fu 2010; Kim et al. 2008). Particularly, the existence of high concentrations of ammonium and aromatic hydrocarbons severely inhibit biological treatment and conventional activated sludge process cannot reach pollutant emission standard of national coking chemical industry (Huo et al. 2012). Novel biological processes such as anaerobic–oxic–oxic (A/O/O) and anaerobic–anoxic–oxic–oxic (A/A/O/O) have been applied to improve wastewater treatment efficiency. Regardless of the treatment modes, sludge microbial communities play the crucial roles
∗ Corresponding author. Tel.: +86 411 84706251; fax: +86 84706252. E-mail address: [email protected] (Y.-Y. Qu). http://dx.doi.org/10.1016/j.micres.2014.12.013 0944-5013/© 2015 Elsevier GmbH. All rights reserved.
in pollutant removal processes (Wagner and Loy 2002). Reveal of the diverse microbial communities will undoubtedly provide new insights into wastewater treatment. In this respect, the microbial community of either municipal or industrial wastewater treatment plants has been extensively explored using various biological techniques, such as denaturing gradient gel electrophoresis, terminal-restriction fragment length polymorphism, 16S rRNA gene clone library and fluorescence in situ hybridization analyses. In recent years, the newly developed high-throughput sequencing technology has been successfully applied in this field which can afford us a huge massive of information to identify the entire profile of microbial communities. It was shown that some core genera such as Zoogloea and Prosthecobacter existed in dozens of municipal sludges (Wang et al. 2012; Zhang et al. 2012; Hu et al. 2012). However, distinct community compositions were found in different industrial wastewater treatment plans (Ibarbalz et al. 2013). For the coal-mine industrial sludge, the microbial community composition and structure have not been well investigated. In the present study, the activated sludges of five coal-mine wastewater treatment plants from Shanxi, China were collected and sequenced using Illumina MiSeq platform for the first time. The aim of the study was to comprehensively reveal the microbial composition and structure of coal-mine industrial sludge.
2
Q. Ma et al. / Microbiological Research 175 (2015) 1–5
Table 1 Operation parameters for each coal-mine wastewater treatment plant. Name
G1 G2 G3 G4 G5
COD (mg/L)
NH4 -N (mg/L)
Influent
Effluent
Influent
Effluent
3000 1000 4000 700 3000
120 150 300 200 100
250 250 140 450 250
5 10 20 200 15
2. Materials and methods
Flow rate (m3 /h)
Process
20 35 45 25 6
A/A/O/O AO/O A/A/O/O CAS A/O/O
MiSeq platform at the Institute for Environmental Genomics (IEG), University of Oklahoma.
2.1. Coking wastewater treatment plants and sample collection Activated sludge samples were collected from the secondary sedimentation tanks of five coal-mine wastewater treatment plants in Shanxi, China in June 2013. Triplicate samples were collected for each plant and stored at −80 ◦ C before DNA extraction. Two of the plants were operated with A/O/O mode, two with A/A/O/O mode, and the remaining one with conventional activated sludge process (CAS). The detailed parameters for each plant were given in Table 1, of which the plant operated with CAS showed weak NH4 -N removal capacity.
2.2. DNA extraction, PCR amplification and sequencing The genomic DNA was extracted using the protocol by Purkhold et al. (2000). The sludge was centrifuged and resuspended in 650 L DNA extraction buffer in a 2-mL tube. Then, it was successively treated with 60 L lysozyme mixture (37 ◦ C, 60 min), 60 L protease mixture (37 ◦ C, 30 min) and 80 L 20% sodium dodecyl sulfate (37 ◦ C, 90 min). Afterwards, adding 600 L phenol–chloroform–isoamyl alcohol (25:24:1) to the mixture and incubated at 65 ◦ C for 20 min, and the supernatant was extracted using equal volume of chloroform–isoamyl alcohol (24:1). Transferring the aqueous phase into a new tube and precipitating nucleic acids by adding 0.6 volumes of isopropanol with inversion for several times. Finally, washing the pellets with 70% cold ethanol, air-drying and resuspending the pellets in nuclease free water. DNA concentration was measured by Pico Green assay using a FLUOstar OPTIMA fluorescence plate reader (BMG Labtech, Germany). For high-throughput sequencing, the primers 515F (5 GTG CCA GCM GCC GCG GTA A-3 ) and 806R (5 -GGA CTA CHV GGG TWT CTA AT-3 ) were used to amplify the V4 region of the 16S rRNA gene (Bates et al. 2011). The PCR was conducted in a 25 L mixture system containing 0.1 L AccuPrime High Fidelity Taq Polymerase (Invitrogen, USA), 2.5 L of 10× AccuPrime PCR buffer II, 1 L of each primer (10 M), 1 L template DNA and 19.4 L nuclease-free water under the following condition: 94 ◦ C for 1 min; 35 cycles of 94 ◦ C for 20 s, 53 ◦ C for 25 s, and 68 ◦ C for 45 s; and a final extension at 68 ◦ C for 10 min. Each sample was amplified in triplicates. PCR products were pooled, purified through QIAquick Gel Extraction Kit (Qiagen, USA), and quantified by Pico Green analysis. The 16S rRNA high-throughput sequencing was conducted on Illumina
2.3. High-throughput sequencing data analysis After sequencing, the bar-codes and primers were removed, and the paired-end reads were overlapped to assemble the final tag sequences using the Flash program. The failed reads, low quality fragments, sequences containing one or more ambiguous reads (N), and the variable tags shorter than 240 bp were all removed by Mothur. The clean sequences were screened for chimera detection using UCHIME. Operational taxonomic units (OTUs) were selected using CD-HIT at a 97% sequence similarity threshold, and the taxonomic assignment of OTUs was performed by RDP classifier with a confidence cutoff of 0.5. Hierarchical clustering analysis was performed using CLUSTER and visualized using TREEVIEW, and other statistical analyses were performed through the IEG pipeline (http://ieg.ou.edu). The data for each treatment plant were the average values of triplicate measurements. 3. Results and discussion 3.1. Overview of sequencing and microbial diversity The microbial communities of the five coal-mine sludges were analyzed by Illumina high-throughput sequencing and at least 16,000 sequences with average length of 253 bp were obtained for each sample. The average OTU number and ␣-diversity for each sludge were shown in Table 2. The larger Shannon index values, the higher ␣-diversity. On the contrary, the smaller Simpson index values, the higher ␣-diversity. The diversity of these sludges was lower than that of the municipal wastewater treatment systems (Wang et al. 2012; Zhang et al. 2012; Hu et al. 2012; Ibarbalz et al. 2013), which should be due to the complexity and recalcitrance of coking influent. It was obvious that G4 operated with CAS harbored the lowest ␣-diversity (Shannon index 1.83) and richness (OTU number 188). G5 operated with A/O/O had the highest diversity (Shannon index 5.28) and richness (OTU number 1071), suggesting that operation modes were closely related with the microbial diversities. Detrended correspondence analysis (DCA) and hierarchical clustering analysis of all the samples were conducted at the OTU level. It was shown that the triplicate samples of each plant were clustered, proving that the sequencing results were reproducible and reliable (Fig. 1). It could also be concluded that the microbial communities
Table 2 ␣-Diversity of each sludge. Index
G1
G2
G3
G4
G5
Shannon index Simpson index Chao1 OTU
3.58 0.08 766 461
4.05 0.04 867 543
3.12 0.14 922 553
1.83 0.29 354 188
5.28 0.01 1435 1071
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Fig. 2. Major phyla of each sludge (sequence percentage >1% in at least one sludge; inner to outer: G1–G5).
3.3. Microbial community structures at the genus level
Fig. 1. Detrended correspondence analysis and hierarchical clustering analysis of all the samples at the OTU level.
were distinct among the five sludges, of which G3 and G4 were sort of similar and clustered together both in DCA and hierarchical clustering plots.
3.2. Microbial community structures at the phylum level In the present study, nearly all the obtained sequences (over 99.99%) were assigned to bacteria thus the following analysis was concentrated on bacteria. Only 2.20% of the sequences were not classified at the phylum level, and the seven major phyla were Proteobacteria (78.86% on average), Bacteroidetes (7.26%), Firmicutes (5.12%), Nitrospira (2.02%), Acidobacteria (1.31%), Actinobacteria (1.30%) and Planctomycetes (0.95%) (Fig. 2). The most abundant phylum was Proteobacteria ranging from 63.64% to 96.10%. Among the Proteobacteria, Betaproteobacteria ranked first with an average percentage of 47.77%, followed by Gammaproteobacteria (15.19%) and Alphaproteobacteria (12.34%). The high proportion of Betaproteobacteria was in accordance with previous studies of industrial sludge and the percentage was higher than that of various municipal wastewater treatment plants and bioreactors (Wagner and Loy 2002; Wang et al. 2012; Zhang et al. 2012). Bacteroidetes, Firmicutes, Acidobacteria, Actinobacteria and Planctomycetes were also reported as the major phyla widespread in wastewater treatment systems, suggesting these bacteria play the key roles in nutrient removal processes.
Only 61 OTUs and 49 genera were shared by all groups, while they occupied 58.50% of the total sequences and 80.53% of the identified sequences (Table 3). Very few proportions of the OTUs (5.66%) and genera (0.43%) were located in one sludge. The relatively high proportion of the shared taxa represented the core status of these bacteria in coal-mine wastewater treatment process. To further analyze the microbial community structures, the major genera of each sludge were given in Fig. 3 and Table 4. It should be noted that 15.26–45.51% of the sequences were not assigned to any genera, suggesting that many novel microbes were existing in the sludge that needed to be further explored. Thiobacillus and Comamonas were the two primary genera in all sludges. Thiobacillus accounted for 16.94% of the total sequences, while it only occupied 3.80% in G4. In G5, it reached 31.25%. Thiobacillus spp. were reported to be the main degraders for thiocyanate and widespread in coking wastewater treatment systems. Meanwhile, they have been widely applied for denitrification processes for the superior denitrification capacity (Beller et al. 2006; Park and Yoo 2009). The percentages of Comamonas in the sludges were very different. It was highly abundant in G3 (29.17%) and G5 (42.39%), but relatively in low abundance in other three groups, i.e. G1 (3.83%), G2 (1.29%) and G4 (4.00%). Comamonas is widespread in various environmental niches. It is especially prominent for the aromatics degradation capacity, such as phenolics, polycyclic and heterocyclic aromatic hydrocarbons. In addition, it was also reported to couple denitrification and nitrification abilities under various aeration conditions (Felföldi et al. 2010; Gumaelius et al. 2001). These two genera were also previously reported to be the key members in steel industrial coking wastewater treatment plants, which contained similar pollutant composition (high concentrations of phenol, polycyclic aromatics, thiocyanate, ammonia and nitrate) with coal-mine wastewater (Ma et al. 2015). Azoarcus and Thauera are the two important denitrifiers reported elsewhere in wastewater treatment systems and often occur together. Moreover, they are also involved in aromatic biodegradation processes (Thomsen et al. 2007; Liu et al. 2006). For example, Mao et al. (2010) reported that Thauera spp. could degrade phenol, methylphenol and indole, and Song et al. (1999) reported that Azoarcus could remove toluene and phenol under denitrification conditions. The percentages of the two genera were all above 1% except in G5 that Azoarcus was only 0.03% and Thauera was below 0.01%. It was shown that 1.91% of total sequences belonged to Pseudomonas. The genus of Pseudomonas harbors a great deal of metabolic diversity, some of which are able to metabolize
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Table 3 Percentages of the shared genera/OTU and their corresponding proportions. Numbers of treatment plants
Numbers of OTUs
Percentage in total sequences (%)
Numbers of genera
Percentage in identified sequences (%)
5 4 3 2 1
61 191 437 1052 2509
58.50 71.49 84.38 94.34 100
49 118 184 304 426
80.53 95.23 97.53 99.57 100
Fig. 3. Major genera of each sludge (sequence percentage >1% in at least two sludges).
Table 4 Major genera with the sequence percentage above 1% in only one sludge. Genus
G1
G2
G3
G4
G5
Tissierella Luteimonas Proteiniclasticum Acinetobacter Aquamicrobium Brevundimonas Gp4 Caldimonas Arcobacter Pusillimonas Gp6 Petrimonas Trichococcus Aquicella Lysobacter Pseudoxanthomonas Lewinella Janibacter Haliscomenobacter Soehngenia Providencia
0.00 0.01 0.00 0.05 0.78 0.01 2.92 1.95 0.02 1.03 0.36 0.00 0.00 1.55 0.01 0.00 0.00 0.00 0.00 0.00 0.00
0.03 0.01 0.02 0.03 0.18 0.00 0.26 0.25 0.07 0.60 1.44 0.01 0.01 0.08 0.00 0.00 0.00 0.01 0.00 0.00 0.00
7.77 0.17 0.40 5.31 0.45 0.02 0.01 0.47 0.05 0.30 0.06 1.02 0.10 0.01 0.49 0.03 0.00 0.06 0.01 0.02 0.00
0.48 8.50 5.86 0.76 0.75 3.40 0.06 0.43 2.46 0.20 0.34 0.35 1.96 0.01 1.08 1.49 1.48 1.38 1.43 0.00 1.04
0.34 0.00 0.10 0.18 2.48 0.02 0.00 0.00 0.32 0.18 0.00 0.80 0.01 0.00 0.00 0.00 0.00 0.00 0.00 1.03 0.01
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diverse chemical pollutants such as Pseudomonas putida and Pseudomonas alcaligenes (Wu et al. 2011). Although many reports have been available that characterized the aromatic degradation abilities of Pseudomonas, microbial community analysis indicated that it was not the dominant genus in various sludge systems. Nevertheless, the roles of this versatile genus should not be neglected. Nitrosomonas belongs to the typical ammonium-oxidizing bacteria (AOB) and Nitrospira belongs to the typical nitriteoxidizing bacteria (NOB). Nitrosomonas occupied 5.95% and 4.25% in G1 and G2, respectively. It is resistant to the changing environment and has a relatively high growth rate thus it is often detected as the dominant AOB (Siripong and Rittmann 2007). The percentage of Nitrospira reached 7.80% in G2 and 1.52% in G1, but notably lower in other sludges (0.05% in G3, 0.74% in G4 and 0% in G5), suggesting the NOB proportions in the same type wastewater were distinct from each other. In G3 and G5, either AOB or NOB was very low, whereas they still displayed satisfactory ammonia removal performance. Thus we speculated that there should be other bacteria, possibly heterotrophic bacteria, played key roles in nitrification process. Some of the major genera were abundant in only one sludge (Table 4). For example, Tissierella and Acinetobacter occupied 7.77% and 5.31% in G3; Gp4 accounted for 2.92% in G1 and Gp6 accounted for 1.44% in G2. It was clearly shown that over half of these ‘individual major genera’ (11/21) were located in G4, which was operated with CAS mode. It suggested that the function stability of conventional activated sludge system might rely on more functional bacteria. However, it should be noted that most of these genera were uncultured or rarely reported, thus the roles of these major genera in the wastewater treatment process were still not clear. Recently developed metagenome sequencing technology was proven to be an efficient tool for reconstructing the genome and exploring the gene functions of the uncultured species from microbial communities (Albertsen et al. 2013), without doubt the functions and interactions of the complicated microbes would be more and more comprehensible. 4. Conclusion This is the first time to identify the microbial communities of coal-mine wastewater treatment systems by Illumina high-throughput sequencing. Thiobacillus, Comamonas, Azoarcus, Thauera, Pseudomonas, Ohtaekwangia, Nitrosomonas and Nitrospira were detected as the major genera. The microbial communities were quite different from the municipal sludge communities and the understanding of CWWTP microbial communities will be helpful in developing efficient strategies for coal-mine wastewater treatment. Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 21176040), the Program for New Century Excellent Talents in University (No. NCET-13-0077), and the Fundamental Research Funds for the Central Universities (No.
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