Article pubs.acs.org/EF
Bacterial Diversity Analysis of Coal Mine Drainage Enriched by Different Energy Sources Yu Yang,*,†,‡ Gaimei Ren,† Zhiyong Peng,† and Xiang Wang† †
Department of Minerals Processing and Bioengineering, and ‡Key Laboratory of Biometallurgy, Ministry of Education, Central South University, 932 South Lushan Road, Changsha 410083, Hunan, People’s Republic of China ABSTRACT: Coal mine drainage (CMD) collected from Meitanba mine (Hunan province, China) was studied. Coal, sulfur, ferrous sulfate, and a mixture of sulfur and ferrous sulfate were employed as selective energy substrates to culture CMD cells, and the bacterial biodiversities of each group were investigated. Four groups, C group [coal (15%, m/v) + CMD], S group [sulfur (10%, m/v) + CMD], SF group [sulfur (5%, m/v) + ferrous sulfate (22.35%, m/v) + CMD], and F group [ferrous sulfate (44.7%, m/v) + CMD] were set up. A total of 211 clones were recovered and evaluated by amplified ribosomal DNA restriction analysis (ARDRA). A total of 26 different ARDRA patterns were obtained and studied as operational taxonomic units (OTUs). The results showed that the F group could not be studied because of its extremely low cell concentration. The S, SF, and C groups all obtain their OTUs of 7, 8, and 11, respectively. Although they had the same original CMD, obvious differences were detected among these three groups. Additionally, Acidithiobacillus spp. represented 79.07% of the bacterial population in the C group. Pseudomonas spp. (11.63%) and Legionella spp. (9.3%) were also detected in the C group, which was different from the S group [Acidithiobacillus spp. (74.99%), Pseudomonas spp. (21.67%), and Legionella spp. (3.33%)]. The compositions of the microbial community structure of the SF group [Acidithiobacillus spp. (98.42%), Pseudomonas spp. (1.54%), Legionella spp. (0%)] was also different from the other two groups, especially the percentage of Acidithiobacillus spp. and Legionella spp. Principal component analysis (PCA) revealed that the distribution of the microbial communities of the C group was more similar to the S group than the SF group. The results showed that iron could actually play an important role in the microbial community structure of CMD, especially for the selection of some important species for coal biodesulfurization.
■
INTRODUCTION In China, about 70% of energy requirements is currently satisfied by burning coal. The main combustion byproduct of coal is sulfur dioxide, one of the most toxic substances polluting the atmosphere. Therefore, many countries focus on how to remove the sulfur in coal.1 A reasonable approach to prevent our environment from sulfur oxides is to reduce the amount of sulfur in coal before combustion.2,3 Physical, chemical, and biological methods are employed to reduce the organic and inorganic sulfur fractions of coal.4 Actually, physical methods always require intensive grinding to liberate pyrite, especially when pyrite is finely disseminated in the coal matrix, which would truly result in the low efficiency process. Also, although chemical methods can remove organic and inorganic sulfur from coal, they need expensive reagents and produce hazardous products and the important substances of the coal could be broken. Among these three methods, the biological method has become the most potential method, with advantages of safety, environmental protection, low consumption, and high efficiency of coal desulfurization.5 The use of microorganisms able to oxidize sulfur compounds present in coals constitutes a clean alternative to remove sulfur from coal, because they promote the oxidative conversion of the reduced forms of sulfur to soluble, easily washed-out compounds.6 Coal mine drainage (CMD) environments are especially interesting because, in general, the low pH of the habitat is the consequence of microbial metabolism and the stable physiological characteristics of the microorganisms from the long-term natural selection. In terms of the sulfur metabolism bacteria in © 2013 American Chemical Society
CMD, they would be closely related with the coal biodesulfurization system. Bacteria of Acidithiobacillus spp. (Acidithiobacillus ferrooxidans and Acidithiobacillus thiooxidans) have been most frequently used for the removal of inorganic sulfur compounds in coal.7,8 It was considered that A. ferrooxidans could oxidize ferrous ion to ferric ion and oxidize the pyrite sulfur in coal, while A. thiooxidans should be able to oxidize sulfur compounds.8 Furthermore, bacteria of Pseudomonas spp. had been reported, which could oxidize organic sulfur compounds in coal and should play an important role in organic sulfur degradation of coal.9 Actually, Acidithiobacillus caldus, a moderately thermophilic and acidophilic sulfur-oxidizing bacterium, which is intensely used in biometallurgy, has been considered to be the most potential strain for coal biodesulfurization.10−13 Recently, A. caldus has been reported as the dominant sulfur-oxidizing bacterium, accounting for the relatively high percentage in bioleaching reactors at 45 °C using molecular ecology techniques.14 From this perspective, A. caldus means more for coal desulfurization. It had been found that the coal pyritic desulfurization with A. caldus was about 47% and the total desulfurization was 19% after processing for 40 days.15 However, biological desulfurization still had some shortcomings, such as a long processing period and low efficiency. In other words, more efficient strains were extremely needed for coal desulfurization. Therefore, the desulfurization mechanism Received: February 20, 2013 Revised: July 20, 2013 Published: July 29, 2013 5552
dx.doi.org/10.1021/ef400214h | Energy Fuels 2013, 27, 5552−5558
Energy & Fuels
Article
and the optimum growth condition of the present strains would certainly be the core for coal biodesulfurization research.15 In addition, coal biodesulfurization in a pilot-plant scale had also been investigated, and the latest results obtained were 45% of pyritic sulfur and 20% of total sulfur in reduction at a pulp density of 10% and 0−0.5 mm particle size within 14 days.16 However, it became an urgent problem about how to culture the bacteria before bioleaching and when to begin the bioprocess along with the bacterial diversity, which should be the key of how to obtain a higher efficiency of coal desulfurization.17 Some certain sulfur compounds had already been found in our previous studies. For example, sodium thiosulfate plays an important role in regulatory proteins of sulfur metabolism in A. ferrooxidans ATCC 23270.18 For industrial application, the culture of bacteria was an open system, because it is impossible that the expanding culture for industrial application is completely sterile. The dominant bacteria of the culturing system with high efficiency for coal desulfurization could be replaced by some needless species in a certain time, enormously affecting the efficiency of the coal desulfurization system. How to keep the open system stable with its community structure features would be the key to ensuring the desulfurizing efficiency. Actually, energy sources always play a more decisive role in the bacterial diversity.19 This study worked on making it easy to culture the bacteria before their working in industry. Usually, sulfur, ferrous sulfate, and a mixture of them had been commonly used for the cultivation of bacteria of Acidithiobacillus spp.20,21 Consequently, the CMD from Meitanba mine was studied with the energy source list above for obtaining the optimal energy sources of bacteria culturing in the coal desulfurization industrial application. In this study, the bacterial diversity of modified CMD samples was investigated. The amplified ribosomal DNA restriction analysis (ARDRA) was employed for the analysis of bacterial diversities enriched in different energy sources.22 Once the microbial community structure from the known energy source group was much more similar to the coal group, we could certainly know more about the metabolism and development of the microorganisms in the coal group and explore a better way for culturing bacteria of industrial coal desulfurization.
■
Table 1. Energy Sources Used in This Experiment group
medium
concentration of energy sources (g L−1)
S group F group SF group C group
sulfur + CMD ferrous sulfate + CMD sulfur + ferrous sulfate + CMD coal + CMD
10 44.7 5 and 22.35, respectively 150
Chemical Composition of the Sample. Elemental analysis on the filtered CMD sample was carried out using inductively coupled plasma−atomic emission spectrometry (ICP−AES). The results were shown in Table 2. The sample temperature was around 18 °C in the daytime, and the pH value was 2.5.
Table 2. Biogeochemical Properties of the CMD Sample elements CMD
pH
Sn (mg/L)
Zn (mg/L)
Sb (mg/L)
Pb (mg/L)
B (mg/L)
2.5 0.01 V Al (mg/L) (mg/L)
0.04 S (mg/L)
0.02 As (mg/L)
0.02 P (mg/L)
0.01 Mg (mg/L)
0.01 Ni (mg/L)
0.3 Fe (mg/L)
23.2 Cr (mg/L)
0.04 Si (mg/L)
0.2 Na (mg/L)
5.3 Ca (mg/L)
elements
0.04 Cu (mg/L)
0.2 La (mg/L)
0.01 K (mg/L)
6.9 Ti (mg/L)
8.5 Zr (mg/L)
53.3 Ba (mg/L)
CMD
0.02
0.01
2.1
0.02
0.01
0.02
elements CMD elements CMD
Drawing of Bacterial Growth Curves. The growth curves of four groups were finished before the ARDRA. CMD cells at the stable period of the growth curves were sampled to extract the genomic DNA for further study. Growth curves for four groups were shown in Figure 1.
EXPERIMENTAL SECTION
Samples. The CMD samples were collected from Meitanba mine (28° 14′ 32″ N, 112° 23′ 45″ E) in Ningxiang, Hunan province, China. It is a large county and has abundant coal. Until now, Meitanba mine has a coal area of 20.7 km2 and has been known as the “coal capital” in Hunan province. All of the CMD samples were collected in December 2011. Seven bottles of CMD samples was collected, and the sample containing CMD cells of the highest concentration was studied. The cell concentration of these samples was detected under a microscope. The preprocessing of the sample was centrifugation at 3000 rpm to remove the suspended substances. The coal was taken from Liupanshui coal mine, Guizhou province, China, with a total sulfur of 4.48%. The coal was ground and screened with a particle size of 0−0.5 mm. The original CMD sample has not been studied because of its relatively small amounts and low concentration of CMD cells. Chemicals. The CMD sample collected from Meitanba mine was studied. Energy sources used in this study were sulfur, ferrous sulfate, a mixture of sulfur and ferrous sulfate, and the coal. Table 1 showed the design of the groups in this study. In each group, the amount of inoculum was 100 mL (equal to the volume of CMD added), incubated in a 150 mL Erlenmeyer flask and shaking at 30 °C and 170 rpm. Each group was carried out in triplicates.
Figure 1. Growth curves of four groups enriched with different energy sources. DNA Extraction, Polymerase Chain Reaction (PCR), Ligation, and Transformation. Total DNA was extracted directly from the cells of four different groups using an E.Z.N.A. Soil DNA Kit (Tiangen, Beijing, China) according to the protocol of the manufacturer. The sample to be analyzed of each group was the sample of the triplicates with the highest cell concentration (the difference among the triplicates of each group was relatively small). The F group could not be studied because of its extremely low concentration of cells. 5553
dx.doi.org/10.1021/ef400214h | Energy Fuels 2013, 27, 5552−5558
Energy & Fuels
Article
The extracted genomic DNA from the three groups was used as a template for PCR. The primer pair employed in this study was bacterial-specific for 16S rRNA gene. The forward primer was 27F, 5′-AGAGTTTGATCCTGGCTCAG-3′, and the reverse primer was 1492R, 5′-TACCTTGTTACGACTT-3′.23 Each PCR mixture of 25 μL contained a reaction mix of 2.5 μL of 10× PCR buffer, 0.5 μL of 1.5 mM MgCl2, 0.5 μL of 200 μM deoxynucleotides (dNTPs), 0.5 μL of 200 nM of each primer, 0.5 μL of 2.5 units Taq polymerase (Tiangen, Beijing, China), 1 μL of template DNA, and 14 μL of sterile Millipore water. The mixture was denatured at 94 °C for 5 min prior to amplification with 32 PCR cycles with the following incubations: 45 s of denaturation at 94 °C, 45 s of annealing at 55 °C, and 90 s of elongation at 72 °C, followed by an extension step at 72 °C for 10 min. The PCR products were gel-purified. The purified products were ligated into pGM-T vector (Tiangen, Beijing, China) and then transformed into Escherichia coli DH5α competent cells with the following step of blue−white selection. Recombinants were cultured on the Luria−Bertani (LB) agar plates containing 50 μg/mL ampicillin, 20 mg/mL X-gal, and 100 mg/mL isopropyl β-D-1-thiogalactopyranoside (IPTG) for overnight incubation at 37 °C. After, a total of 211 white colonies were randomly selected from all tested groups, and the insert fragment were determined by direct PCR using the vector primers (forward primer T7, 5′-TAATACGACTCACTATAGGG-3′, and reverse primer SP6, 5′-TACGATTTAGGTGACACTATAG-3′). ARDRA, Sequencing, and Phylogenetic Analysis. The ARDRA was performed for characterizing the diversity of 16S rRNA gene within the clone libraries. The amplified rRNA PCR products, approximately 1.3 kb, were digested with RsaI and MspI (Thermo Scientific, Canada) overnight at 37 °C in 10 μL volumes of incubation buffer containing 1 μL of 10× NEB buffer, 0.125 μL of RsaI and MspI, 4 μL of PCR products, and sterile Millipore water added up to 10 μL. The resulting ARDRA products were separated by gel electrophoresis in 3.0% agarose, and the ARDRA patterns were visualized by ultraviolet (UV) excitation. The fragment patterns were aligned, and the operational taxonomic units (OTUs) were obtained according to the number and size of these fragment patterns. The representive clones from the dominant OTUs were selected for nucleotide sequence determination. A total of 26 clones were sequenced (Meiji, China), which had also been aligned with the sequences from the BLAST facility of the National Center for Biotechnology Information (www. ncbi.nlm.nih.gov/BLAST), and the following sequence alignment was processed by Clustal 1.8.24 The distance matrix calculation of nucleotide substitution rates and the final phylogenetic tree had been constructed with the MEGA 4.0.25 Statistical Analysis. Principal component analysis (PCA) was performed using the SYSTAT statistical computing package (version 18.0, SPSS, Inc., Chicago, IL) for each group. PCA simultaneously considers many correlated variables and then identifies the lowest number to accurately represent the structure of the data.26,27 In the present study, PCA was used to group or separate stations, which did or did not differ based on their biogeochemical parameters.28 In this study, the PCA of the bacterial community structure of C, S, and SF groups was performed. The rarefaction analysis of all of the bacterial ARDRA patterns was performed with the OriginPro version 8.0 software. An exponential model, y = a[1 − exp(−bx)], was used in OriginPro to fit the curves. Nucleotide Sequence Accession Numbers. The partial 16S rDNA gene sequences for the OTUs in this study were submitted to GenBank, with accession numbers given in Table 3. The nucleotide sequences are available through the DDBJ/EMBL/GenBank nucleotide sequence databases.
Table 3. Nucleotide Sequence Accession Numbers (GenBank) and Alignment Results of the 26 Dominant Clonesa clone
number of clones
accession number
closest relative (accession number)
similarity (%)
C-1
10
KC409003
99
C-2
15
KC409004
C-3
1
KC409005
C-4
2
KC409006
C-5
5
KC409007
C-6
4
KC409008
C-7
6
KC409009
C-8
8
KC409010
C-9
7
KC409011
C-10
15
KC409012
C-11
13
KC409013
S-1
13
KC409014
S-2
14
KC409015
S-3
5
KC409016
S-4
2
KC409017
S-5
17
KC409018
S-6
8
KC409019
S-7
1
KC409020
SF-1
4
KC409021
SF-2
1
KC409022
SF-3
3
KC409023
SF-4
7
KC409024
SF-5
11
KC409025
SF-6
9
KC409026
SF-7
18
KC409027
SF-8
12
KC409028
Pseudomonas spp. G3DM-81 (EU037286) A. caldus strain N39-30-02 (EU499920) A. caldus strain D-1 (DQ335450) A. caldus strain s2 (DQ256484) Acidithiobacillus spp. enrichment culture clone Z-5 (GQ339885) A. caldus strain HV2-2 (HM070043) Thiobacillus caldus (AB023405) Legionella pneumophila strain Alcoy 2300/99 (EU054324) A. thiooxidans strain NBRC13701 (AY830902) A. thiooxidans strain IESL8 (HQ902066) Acidithiobacillus spp. DBS-4 (EU084710) Pseudomonas spp. G3DM-81 (EU037286) A. thiooxidans strain IESL8 (HQ902066) Acidithiobacillus spp. DBS-4 (EU084710) L. pneumophila Corby14_P1_2003 (FR799704) A. caldus strain s2 (DQ256484) Acidithiobacillus albertensis strain BY-0501 (FJ032185) A. caldus strain D-2 (DQ347501) A. caldus strain DX-2 (DQ470072) Pseudomonas spp. GR7 (DQ100464) A. albertensis strain DSM 14366 (NR028982) Acidithiobacillus spp. enrichment culture clone Z-5 (GQ339885) Acidithiobacillus spp. DBS-4 (EU084710) A. thiooxidans ATCC19377 (Y11596) A. thiooxidans strain NBRC13701 (AY830902) Acidithiobacillus spp. SM-2 (DQ675569)
99 99 98 97 99 98 98 99 98 98 98 99 99 99 99 9 99 99 98 99 98 97 96 96 99
a
Clones C-1−C-11, S-1−S-7, and SF-1−SF-8 were isolated from C, S, and SF groups, respectively.
■
bacteria lower than 108 cells/mL, and the genome of the CMD cells had not been extracted because of its extremely low concentration. The extracted genomes of C, S, and SF groups were for further analysis. Among the four groups, bacteria of the S group were in the highest growth rate at first, while bacteria of the SF group were growing slow but later also achieved a certain cell concentration.
RESULTS AND DISCUSSION Growth Properties of Each Group. The cell concentration of each group was detected under a microscope, and the growth curve of bacteria was performed, as shown in Figure 1. From the graph, it could be found that the growth curves were apparently different. The F group kept the number of 5554
dx.doi.org/10.1021/ef400214h | Energy Fuels 2013, 27, 5552−5558
Energy & Fuels
Article
Figure 2. Relative abundances of dominant OTUs in three groups.
For the C group, the amount of bacteria later had up to the highest cell density of 1.2 × 109 cells/mL. Obviously, the C group reached the highest value of the final cell concentration in the four groups, and the secondary value was the S group. The final cell concentration of the SF group was lower than that of the S group but higher than that of the F group. Moreover, the S group reached the highest value of the growth rate, and the C group was next to the S group. The growth rate of the SF group was lower than that of the C group but higher than that of the F group. It was easy for us to find that the elemental sulfur was easier to use and absorb than coal for bacteria. In addition, the groups with the addition of an iron element (SF and F groups) were both lower than the other two groups according to the final cell concentrations and the growth rate by comparison, indicating that iron had made some difference in the development of the bacterial diversity. Actually, some studies indicate that, when the concentration of Fe was higher than 36 mM, it could limit the growth of Acidithiobacillus spp.29 Finally, the F group had not achieved the high cell concentration in the limited growth conditions. ARDRA of 16S rRNA Gene Clone Libraries. The purified 16S rRNA gene fragments were ligated into the vector pGM-T and then transformed into E. coli DH5α competent cells. Positive clones carrying approximately 1.5 kb 16S rRNA gene were identified by digestion with RsaI and MspI to generate restriction profiles. Several representatives of the ARDRA pattern groups from each clone library were selected for sequencing. From each group, 86 (C group), 65 (SF group), and 60 (S group) clones (211 clones in total, as shown in Table 3) were picked randomly, and the PCR products of 16S rRNA gene fragments inserted into these clones were digested by the two restriction enzymes. The ARDRA revealed a high level of genetic diversity in 16S rRNA gene for both samples examined. Of the total 211 clones, 26 OTUs were identified in the three samples. On the basis of restriction patterns derived from ARDRA, 26 clones were selected for sequencing, which were numbered as C-1−C-11, S-1−S-7, and SF-1−SF-8 (shown in Figure 2) from C, S, and SF groups, respectively. According to the ARDRA of 16S rRNA gene, four OTUs (C-1 and S-1, C-4 and S-5, C-10 and S-2, and C-11 and S-3) were common for the C and S groups. Also, only two OTUs (C-5 and SF-4 and C-9 and SF-7) are common in the C and SF groups. There were no common OTUs in the S and SF groups,
and the C group has common OTUs with either of the other two. The total rarefaction analysis of all of the microbial ARDRA patterns in the three groups indicated that the analysis was sufficient to detect the bacterial community diversity, with the results shown in Figure 3.
Figure 3. Rarefaction curve for the different ARDRA patterns of 16S rDNA clones for the three groups compared to theoretical curves.
Comparative Analysis on the Bacterial Diversities of Three Groups. The 16S rRNA gene sequences obtained from the clones coming from three groups were subject to phylogenetic analysis. The total 26 OTUs from these groups were sequenced. The results were shown in Table 3. In the table, 26 clones were listed along with the identified closest neighbor organisms using the neighbor-joining method. Three phylogenetic trees were obtained for three induced groups (shown in Figures 4−6). From the phylogenetic tree of the C group, of the 11 clones, 5 clones were affiliated with A. caldus, representing of 32.56% in the C group. Clones affiliated with Pseudomonas spp. and Legionella spp. were 11.63 and 9.3% in the C group respectively. The clones affiliated with A. thiooxidans were 25.58%. In the S 5555
dx.doi.org/10.1021/ef400214h | Energy Fuels 2013, 27, 5552−5558
Energy & Fuels
Article
Figure 4. Neighbor-joining tree representing the phylogenetic relationship of the most abundant 16S rRNA gene sequences from the C group.
Figure 5. Neighbor-joining tree representing the phylogenetic relationship of the most abundant 16S rRNA gene sequences from the S group. 5556
dx.doi.org/10.1021/ef400214h | Energy Fuels 2013, 27, 5552−5558
Energy & Fuels
Article
Figure 6. Neighbor-joining tree representing the phylogenetic relationship of the most abundant 16S rRNA gene sequences from the SF group.
group, the clones affiliated with A. caldus (30.00%), Pseudomonas spp. (21.67%), Legionella spp. (3.33%), and A. thiooxidans (23.33%) were detected. In the SF group, the clones affiliated with A. caldus (6.15%), Pseudomonas spp. (1.54%), and A. thiooxidans (41.5%) were detected. However, there was no Legionella spp. detected in the SF group. Of the 26 clones in three groups, 8 OTUs were affiliated with A. caldus. Actually, the clones affiliated with A. caldus were 32.56 and 30.00% in the C and S groups, respectively, while it represented 6.15% in the SF group. As shown in Table 3, it was obvious that Acidithiobacillus spp. accounted for the greatest proportion in the three groups, because iron/sulfur-oxidizing bacteria of Acidithiobacillus mainly take FeII or sulfur as energy sources.30 When another sight was taken of the C, S, and SF groups, clones affiliated with A. caldus were 32.56, 30.00, and 6.15%, respectively. The SF group takes the lowest proportion of clones affiliated with A. caldus in the three groups. Moreover, in comparison to the SF group, proportions of more than 10% of the clone affiliated with Pseudomonas spp. were detected in both the C (11.63%) and S (21.67%) groups, while it was only 1.54% in the SF group. It is obvious that the F group could not be studied because of its extremely low cell concentration. In contrast, C, SF, and F groups contained the iron element with a certain amount, while the S group had little iron element existing in the original CMD sample, with iron playing an important role in the growth of Pseudomonas spp. to some extent.31 Also, the percentage of A. caldus in three groups was
different, while it was high in the C and S group, showing that the amount and form of iron compounds make a great difference in the metabolism of A. caldus. PCA was used to reveal the differences in the clone distributions among the three groups. The results are shown in Figure 7. Principal component 1 (PC1) captured 73.250% of
Figure 7. Ordinate plots from PCA based on the distribution of OTUs derived from 16S rDNA phylogenetic analysis of the three groups. 5557
dx.doi.org/10.1021/ef400214h | Energy Fuels 2013, 27, 5552−5558
Energy & Fuels
■
the variation, and principal component 2 (PC2) captured 26.750% of the variation. The results of PCA suggested that the C and S groups were closely related in terms of microbial community structure. The SF group had great difference with the other two groups. The C group had already been proven to have more species than the other two groups. It was more likely to be that the microbial community structure in this group clearly responded to the complicated influence of coal, the group with the most complex chemical composition in the three kinds of energy sources.32
REFERENCES
(1) Shaddix, C. R. Combust. Flame 2012, 159 (10), 3003−3006. (2) Anna, J.; Florian, D.; Mieczysław, K.; Helena, W. Fuel 1995, 74 (5), 725−728. (3) Xuchang, X.; Changhe, C.; Haiyin, Q.; Rong, H.; Changfu, Y.; Guangming, X. Fuel Process. Technol. 2000, 62 (2−3), 153−160. (4) Ivanov, I. P. Solid Fuel Chem. 2007, 10 (1), 3−10. (5) Bhattacharyya, D.; Hsieh, M.; Francis, H.; Kermode, R. I.; Khalid, A. M. Resour., Conserv. Recycl. 1990, 3 (2−3), 81−96. (6) Isabel, C. C.; Marco, A. M. Fuel Process. Technol. 2009, 90, 1099− 1106. (7) Jorjani, E.; Chehreh, C.; Mesroghli, C. Miner. Eng. 2007, 20 (14), 1285−1292. (8) Celin, A.; Sukla, B. V.; Misra, V. N. Fuel 2005, 84 (12−13), 1597−1600. (9) Krawiec, S. Dev. Ind. Microbiol. Ser. 1990, 31, 103−114. (10) Peter, D. G.; Shelly, M. D.; Douglas, E. R. Hydrometallurgy 2003, 71 (1−2), 115−123. (11) Marla, T.; Peter, D. G.; Shelly, M. D.; Douglas, E. R. Int. Congr. Ser. 2004, 1275, 165−172. (12) Bo, F.; Hongbo, Z.; Rubing, Z.; Guanzhou, Q. Int. Biodeterior. Biodegrad. 2008, 62 (2), 109−115. (13) Weimin, Z.; Hongbo, Z.; Minxi, W.; Weiliang, C.; Ailing, X.; Xueduan, L.; Guanzhou, Q. Hydrometallurgy 2009, 96 (4), 333−336. (14) Naoko, O. Appl. Environ. Microbiol. 2003, 69 (4), 1936−1943. (15) Huan, H.; Fenfen, H.; Xiuxiang, T.; Lei, L.; Chenyan, M.; Yidong, Z. Fuel Process. Technol. 2012, 101, 73−77. (16) Eghbali, F.; Ehsani, M. R. Iran. J. Chem. Chem. Eng. 2010, 29 (4), 75−78. (17) Gregory, J. O. Appl. Environ. Microb. 1991, 57, 642−644. (18) Yu, Y.; Jiangjiang, L.; Xiang, W.; Li, Y.; Zhiying, H.; Xueduan, L.; Guanzhou, Q. Proceedings of the 19th International Biohydrometallurgy Symposium; Changsha, China, Sept 18−21, 2011. (19) Adela, G. S.; Antonia, M. H.; Pieter, V. D.; Jennifer, S.; Paloma, P. T.; Amalia, R.; Estrella, D.; Juan, L. R. Microb. Biotechnol. 2013, 6 (1), 36−44. (20) Franzmanna, P. D.; Haddad, C. M.; Hawkes, R. B.; Robertson, W. J.; Plumb, J. J. Miner. Eng. 2005, 18 (13−14), 1304−1314. (21) Klaus, B. FEMS Microbiol. Rev. 1997, 20 (3−4), 591−604. (22) Chowdhury, S. P.; Schmid, M.; Hartmann, A.; Tripathi, A. K. Eur. J. Soil Biol. 2009, 45, 114−122. (23) Dees, P. M.; Ghiorse, W. C. FEMS Microbiol. Ecol. 2001, 35, 207−216. (24) Thompson, J. D.; Gibson, T. J.; Plewniak, F.; Jeanmougin, F.; Higgins, D. G. Nucleic Acids Res. 1997, 25, 4876−4882. (25) Royo, L. J.; Traoré, A.; Tambourá, H. H.; Á lvarez, I.; Kaboré, A.; Fernández, I.; Ouédraogo-Sanou, G.; Toguyeni, A.; Sawadogo, L.; Goyache, F. Anim. Genet. 2007, 40 (3), 344−347. (26) Sharma, S. Applied Multivariate Techniques; Wiley: Hoboken, NJ, 1996. (27) Liu, X.; Tiquia, S. M.; Holguin, G.; Wu, L.; Nold, S. C.; Devol, A. H.; Luo, K.; Palumbo, A. V.; Tiedje, J. M.; Zhou, J. Appl. Environ. Microbiol. 2003, 69, 3549−3560. (28) Yu, Y.; Minxi, W.; Wuyang, S.; Hong, P.; Guanzhou, Q.; Jizhong, Z.; Xueduan, L. Aquat. Microb. Ecol. 2007, 47, 141−151. (29) Gustavo, C.; Cristina, P.; Edgardo, D. Biotechnol. Lett. 1992, 14 (4), 329−334. (30) Patricio, M.; Sebastián, G.; Norimasa, O.; Marko, B.; Pilar, P. Metabolomics 2013, 9 (1), 247−257. (31) Chee, K. L.; Karl, A. H.; Sasha, G. T.; Joyce, E. L.; Ian, T. P. PLoS One 2012, 7 (6), No. e39139. (32) Hortal, A. R.; Hurtado, P.; Martinez, H. B.; Mullins, O. C. Energy Fuels 2007, 21, 2863−2868. (33) Fikret, K. Trends Biotechnol. 1986, 4 (11), 293−297.
■
CONCLUSION This experiment was aimed at exploring the change of bacterial diversities of the CMD enriched with different energy sources. The results showed that great differences had taken place in the four groups, while they were the same CMD sample originally. The results were as follows: first, the F group had the lowest final cell concentration and the lowest cell growth rate, showing that the bacteria of the F group lived in a limited growth condition. Second, differences of the species abundances were detected in the other three groups. OTUs of 11, 7, and 8 were detected in C, S, and SF groups, respectively. Obviously, the amount of OTUs in the C group was significantly higher than the other two groups; one could draw a conclusion that coal was a kind of considerably complex matrix. Third, sequence analysis results showed that Pseudomonas spp. had been detected in the C group, which had a homology of 99% with Pseudomonas aeruginosa strains; these results were consistent with the study that organic sulfur in coal could be removed by Pseudomonas spp.33 Iron could make some difference in the growth of Pseudomonas spp. Finally, PCA showed that the C and S groups were closely related in terms of microbial community structure. The SF group had great difference with the other two groups. The results showed that iron could actually play an important role in the microbial community structure of CMD, especially for the selection of some important species for coal biodesulfurization.
■
Article
AUTHOR INFORMATION
Corresponding Author
*Telephone: +86-731-88877216. Fax: +86-731-88710804. E-mail:
[email protected]. Notes
The authors declare no competing financial interest.
■
ACKNOWLEDGMENTS The present research was carried out under the Program of Hunan Science and Technology Plan (2012FJ4322), the Program of University Students Innovative Training of Central South University (DL12456), and the Program of New Century Excellent Talents in Ministry of Education of China (NCET-07-0869).
■
NOMENCLATURE CMD = coal mine drainage ARDRA = amplified ribosomal DNA restriction analysis OTU = operational taxonomic unit PCA = principal component analysis PCR = polymerase chain reaction ICP−AES = inductively coupled plasma−atomic emission spectrometry 5558
dx.doi.org/10.1021/ef400214h | Energy Fuels 2013, 27, 5552−5558
