Bacterial Diversity in Subbituminous Coal and Soil from Coal Mine of South Sumatra, Indonesia

INTRODUCTION

Soil contains many elements that make it a good place for various populations of microorganis ms. The soil diversity is a great resource for biotechnological exploration of novel organisms, products and processes (Torsvik and Øvreås, 2002). The extreme spatial heterogeneity, multiphase nature (including gases, water and solid material) and the chemical co mp lex and biological properties of soil environ ments are thought to contribute the microbial diversity present in soil samples (Daniel, 2005).

One of the layer that is often found as part of soil horizon is coal seam. The coal-surrounding soil, which are soil horizons bordered on coal seam are rich environments like other soil types. Soil horizons may be on the above (coal roof) and lower side (coal floor) of coal seam. Coal is an organic sedimentary rock containing various amounts of carbon, hydrogen, nitrogen, oxygen, and sulfur as well as trace elements including minerals (Speight, 2005). The content of coal in soil may increase soil organic carbon that supports growth of soil microorganisms (Ru mpel and Kogel-Knabner, 2004; Machulla et al., 2005). Hence, coal is expected as potential resource of agents which is used in coal biotechnology itself, such as bioconversion, biodesulfurizat ion, b iosolubilization, and methane production.

*_{Corresponding author:}

Akhm aloka, Ph.D.

Biochemistry Research Group,

Faculty of Mathematics and Natural Science, Institut Teknologi Bandung, Jl. Ganesha 10, Bandung, Indonesia

Em ail:loka@chem.itb.ac.id

Research

International Journal of Integrative Biology

_{A journal for biology beyond borders}

ISSN 0973-8363

Bacterial diversity in subbituminous coal and soil from coal mine of

South Sumatra, Indonesia

Me gga Ratnasari Pikoli

1,3

, Pingkan Aditiawati

1

, De a Indriati Astuti

1

, Akhmaloka

2,* 1

Microbial Biotechnology Research Group, School of Life Science and Technology, Institut Tek nologi Bandung, Bandung, Indonesia ,

2

Biochemistry Research Group, Faculty of Mathematics and Natural Science, Institut Tek nologi Bandung, Bandung, Indonesia,

3 _{Department of Biology, Faculty of Science and Technology, State Islamic Universi ty Syarif} Hidayatullah Jak arta, Indonesia,

Subm itted: 27 Aug. 2013; Revised: 25 Oct. 2013; Accepted: 5 Nov. 2013

Abstract

Coal and soil around coal seam environment provide potential factors that support bacterial life. Study of environmental metagenome to determine relationship among the bacteria that make up the community in coal and soil layers is reported. DNA extractions were performed in three different methods, namely direct extraction, filtration, and filtration with blending. The DNA of coal and soil samples were subject to PCR-amplification to get V5-V6 16S rRNA gene fragments, then separated by denaturing gradient gel electrophoresis (DGGE). Results showed that the indirect methods are advantageous and can detect more bands compared to the direct method. Among the samples tested, a high number of bacterial ribotypes (Shannon diversity index), but low evenness of the bacterial community were observed in the coal samples. Cluster analysis of DGGE bands showed that the coal mixed soil clustered separate from its parent, the coal seam and the soil samples. Phylogenetic trees of their sequences showed that the coal boomed more Firmicutes and Actinobacteria compared to the soil samples. It confirmed that the physico-chemical properties of soil strongly influence evolutionary distance of coal and soil bacteria though they are separated by little physical distance. This is the first report in which the disturbed coal which is represented by coal mixed soil was not artificially reconstructed as a microcosm, and is in a natural situation in which the virgin coal seam is compared to the adjacent virgin soil layers and soil mixed coal as the disturbed soil.

In this study, we examined soil bacterial co mmun ities among soil samples in coal mine in Muara Tigo Besar Utara, South Su matra Prov ince, Indonesia. South Sumat ra has the largest coal reserves in the country, and the highest reserveis of subbituminous coal. By comparing diversity among the adjacent soil layers, coal seam, and mined soil, this study investigated the influence of physico-chemical factors on bacterial diversity. We also compared bacterial diversity in the original coal seam to the excavated, to probe the anthropogenic causes, where the results confirm the concept that disturbance leads to negative effect of diversity, such as species richness and evenness (Laplante and Dero me, 2011; Limberger and Wickham, 2012). Unlike most other research in disturbance ecology which used time-series data to investigate the responses of microbial communities to d isturbances (Gonzales et al., 2011), the present study used spatial data in the presence of coal seam in original horizon and the same coal type as coal mixed soil in mined coal.

The culture-independent method of PCR-based Denaturing Gradient Gel Electophoresis (DGGE) was emp loyed to investigate difference of spatial–scale diversity in soil co mmunities (Valášková and Baldrian,

2009; Ge et al., 2010; Dav ison et al., 2012). By applying DNA extract ion methods directly to soil and coal samples, without prior cultivation, nonculturable organisms are expected to be detected more co mpletely.

Although studies comparing various commercial kits to other methods have shown that DNA yield and purity varies depend on methodology and soil type, it is still unclear to what extent these protocols yield genomic DNA representative of the microbial co mmunity found within soil (Feinstein et al., 2009). Therefore, we conducted three modification protocols of DNA extraction based on proposed by Zhou et al. (1996) to meet the optimu m result in extracting DNA fro m coal and coal-surrounding soils.

MATERIALS AND METHODS

Site description and sampling method

Coal and soil were co llected fro m coal mine located in Muara Tigo Besar Utara, one of coal mine managed by Bukit Asam Co mpany in South Sumatra (Fig.1). Pro ximate analysis and gross calorific value confirmed that the coal type is subbitumonous coal (data not shown). The coal mixed soil was fro m coal mined soil fro m a field where coal had been mined and was left for 9 months when the soil was collected, whereas the other samples came fro m an unexp lored field. These samples are referred as coal seam, coal roof, and coal floor (Table 1 [Supplem entary data]).

Each sample of coal or soil was taken appro ximately 10 cm fro m the wall inside, and collected fro m five random points, as well as the coal mixed soil as suggested by Nakatsu et al. (2000). The five sub-samples were mixed for ho mogenizat ion and then filtered by a 2 mm sieve to produce a co mposite sample. Chemical and physical properties of the soil samples were analyzed at Research Institute for Soil, in Bogor. The coal samples were analy zed by Coal Examination Laboratory, Bukit Asam Company.

DNA extraction and purification

DNA ext raction was performed by direct and indirect methods. The direct method (marked by L) was a slight modification of method described previously by Zhou et al (1996)(described below).

One gram of coal or soil samp le was subject to DNA -extraction wh ich consists of three steps from soft to harder lysis. In the first step, sample in a microtube was incubated in 0.5 ml of extraction buffer (100 mM Tris -HCl [pH 8.0], 100 mM sodium EDTA [p H 8.0], 100 mM sodium phosphate [pH 8.0], 1.5 M NaCl, 1% CTA B), and 50 µ l of proteinase K (10 mg/ ml), by horizontal shaking at 225 rp m for 30 min at 37°C. After the treatment, 50 µl of 20% SDS was added to the sample, and incubated at 65°C water bath for 2 h with gentle end-over-end inversions every 15 min . The mixtu re was centrifuged at 6.000 X g for 10 min at room temperature and transferred into new tube. In the second step, 0.5 ml o f fresh extraction buffer, 50 u l of 20% SDS, and one volume of sterilized sea sands were added to the rest of sample, incubated at 65°C water bath for 15 min, and vortexed for 1 min. Supernatant was collected after centrifugation at 6.000 X g for 10 min at roo m temperature. In the last step, one volume of sterilized glass beads was added to the rest of sample fro m previous steps and vortexed by a cell disruptor for 3 min after incubation in a 65°C water bath for 15 min. Supernatant was collected by the same centrifugation as before. The supernatant collected fro m all steps were extracted by equal volume of phenol, chloroform, isoamyl alcohol (25:24:1, vol/vol). The aqueous phase was recovered by centrifugation and precipitated with 0.6 volu me of isopropanol at room temperature for 1 h. The pellet of crude nucleic acids were obtained by centrifugation at 12.000 X g for 30 min at roo m temperature, and washed twice with 70% ethanol. Dried pellet was dissolved in 100 µ l o f 10 mM Tris-HCl buffer pH 8.

For the indirect method, cells were harvested from the original samp le before DNA ext raction. One g m of coal or soil sample was washed with washing solution. The washing solution comprised of three type of solution, each containing 0.85% saline solution, H2SO4 p H 3 in 0.85% saline solution, and H2SO4 pH 2 in 0.85% saline solution, respectively. At the end of each washing, the solution was centrifuged at 1000 X g for 10 min . Then

the collected supernatant was filtered by vacuu m filter

with 0.22 μm of pore-d iameter to obtain cell pellet. This method is called filter method (marked by F). Another indirect method, called Blending method (marked by B), is similar procedure as the Filter method but the washed sample was blended by a h igh speed blender for 1 min in every last step of washing. Extracted DNA fro m the harvested cells was then continuously treated with the direct method. The extracted DNA fro m either direct or ind irect methods were purified by polyvinylpolypyrrolidone (PVPP) in a spin column, and eluted with 30 µ l of 10 mM Tris-HCl buffer pH 8 to harvest final extract.

PCR-DGGE

Partial frag ments of 16S rRNA gene sequences (V4-V5 hypervariable region) were amplified using the primer set consisting of 408 base pairs (position of 519-536 and 907-926 based on Escherichia coli). The primers used were: forward primer Co m1-F with the sequence of 5'-CA G CGC CA G GGT AAT A C-3', carry ing GC-clamp 5'-CGC CCG CCG CGC GCG GCG GGC CGG GGG GGG GGG GGG CA C, and Co m2-R with the sequence of 5'-CGG TCA ATT CCT TTG A GT TT -3'. (Schwieber and Tebbe, 1998; Sch malenberger et al., 2001; A minin et al., 2008a; A minin et al., 2008b; Morales and Holben, 2009). The reaction mixture (50 µ L) contained 1.0 µ L o f 10 mM each p rimer, 5.0 µ L of 10× Taq bu_ffer, 1.0 µ L of 10 µM deo xyribonucleotide triphosphate mixture, 4.0 µ L of total DNA, and 0.25 µ L of 5U/µ L Taq DNA poly merase. The PCR react ions were carried out as fo llo ws: init ial denaturation at 95°C for 5 min, followed by 30 cycles consisting of

denaturation at 95°C for 1 min, annealing at 51°C fo r 1 min, extension at 72°C fo r 1 min, and a 10-min final extension step at 72°C. The amp licons were subjected to electrophoresis in 1.2% agarose gels. The gel were stained with ethidiu m bro mide and visualized under ultrav iolet light. Each type of sample was ext racted in 3 rep licates separately and then amplified separately.

DGGE was performed with a DCode Universal Mutation Detection System (Bio-Rad) containing TAE buffer. PCR products were loaded onto 7% polyacrylamide gel containing 30 to 80% denaturant gradient of urea and formamide solution. Each sample (40 µ L) consisted of three PCR products and 8 µL of 5× loading dye was loaded onto gels and then run for 12 h at 60°C under 70 V. Each type of sample was loaded into two wells as replicates. After the runs, gels were removed fro m the setup, stained with silver staining, and scanned. In order to identity the bands, each DGGE band was cut separately, and re-amp lified by adding 50 µl sterile deionized water to allo w the DNA to passively diffused in the water at 37°C overnight (Aminin et al., 2008b). Four microliters of the eluted fraction was used as template DNA for PCR again with the primers previously described but without GC-clamp. PCR products were then purified and sequenced commercially (Macrogen, Korea). All of the 16S rRNA sequence genes were deposit in the GenBank with accession number fro m KF545601-KF545614.

Sequences were checked by using Bioedit 7.0.9.0. (Hall, 1999), and then compared with sequences in the National Center for Biotechnology Information data bank using the Blast 2.2.28+ (Zhang et al., 2000, http: //www.ncbi.nlm.nih.gov/BLAST). Align ment of DNA sequences was performed by ClustalW program, and Phylogenetic tree was constructed using the neighbor-joining method with Mega5 (Tamura et al., 2011). All trees were based on the Jukes–Cantor distance and constructed with 1000 bootstrapping.

Data analysis

The soil properties were co mpared each other in pairs by Pearson correlation, subjected to Princ ipal Co mponent Analysis in order to extract the correlated soil properties, and constructed by XLSTAT Version

2013.3.02 (Addinsoft, 1995-2013,

http://www.xlstat.com). The b iplot projection was used to probe the similarity of the soil samp les based on the extracted properties. The scanned DGGE gel was analyzed for band intensity using ImageJ

(http://rsb.info.nih.gov/ij/). DGGE profiles were

quantified to determine total number of bands (S), each peak square (ni) and sum of all the peak square (N) (Fro min et al., 2002). This informat ion was used to calculate the community diversity using three indices: the Shannon index (H) calculated with the fo rmula H = -Ʃ(ni/N) ln(ni/N); the dominance index (c) calculated

with the formula c = Ʃ(ni/N)2_{; and (iii) the evenness} index (e) calculated with the formula e = H/lnS. The indices data were analyzed by multivariate analysis of variance (Manova) at significance level 0.01, followed

by Tukey’s HSD test as post hoc test, by using SPSS 20.0 (IBM Corp., 2011). The similarity between the DGGE p rofiles were determined by calculating Dice similarity coefficient S = 2nAB /(nA+nB), where nA is the number of DGGE bands in line 1, nB is the number of DGGE bands in line 2, and nAB is the number of common DGGE bands (Konstantinov et al., 2003; Sun et al., 2004; Andreetta et al., 2012). Two bands were common if they migrated at the same distance on a gel. Cluster analysis was used to compare the similarity coefficient among DGGE bands, constructed by using the UPGMA algorith m, and analysed by SPSS 20.0 (IBM Corp., 2011).

RESULTS

Coal and soil

properties

Result of pro ximate analysis (Table 2 [Supplem entary data]) showed that the coal

sample was

categorized as brown coal ly ing between subbituminous and lignite, although the value of volatile material and fixed carbon confirmed it as subbituminous coal (Speight, 2005). Sumat ra contains 64% of total coal deposit in Indonesia, where the Bu kit Asam area of South Sumat ra, including the sites (Muara Tigo Besar Utara), are one of the biggest resources with 48.6% coal deposit of Indonesia reserves (Thomas, 2005). Therefore some v irg in coal seam were still found in this area which is yet to be mined(as coal roof and coal floor).

Physical and chemical properties among the soil samples were quite different (Table 3 [Supplem entary data]), although they were separated in only approximately 10-20 cm distance or thickness. However, the analysis on soil particle size showed that the three soil samples were all categorized as clay soil. The pH of the soils was very acidic. It was as a result of the high content of acid-forming cat ion sources, such as Al, Fe and S.

Interaction among soil properties analyzed by Pearson correlation revealed that there were some properties having correlation among each other. There were 18 out of 23 properties having significant correlation (P value 0.05) (data is not shown). In order to exp lain characteristics differences between coal mixed soil and clay soils (i.e. coal roof and coal floor), we used Principal Co mponent Analysis (PCA) to highlight on the significant correlation. The analysis resulted in two factors which represent the whole initial variability of the data (Fig. 2). It exp lained the variability in the original twenty three variables, so the complexity of the data set is reduced by using the two components, without loss of informat ion. Here, we selected a group

Figure 3: Dendogram of cluster analysis of DGGE bands. The dendrogram was constructed using average

linkage (between groups). The numbers above the bands are replication of the lane. All of the compared lanes are separated into three areas (I, II, and III) for phylogenetic analysis.

of soil properties that gave high contribution (the squared cosines of the variables close to 1) to the new factors. The correlation circle showed that the extracted soil properties (total sulfur, C/N rat io, organic carbon, organic nitrogen, total calciu m, pH, and free cation) appear in positive correlation with each other, but negative against total potassium. The b iplot showed that the three soil samp les have different features, based on the extracted properties. The total sulfur increased by increasing organic carbon in soil mixed coal, showing the contribution of coal to the soil.

DGGE band profile

In order to obtain complete diversity, DNA extract ion was carried out with some modificat ions. The DGGE gel of each sample showed similar profile but different band intensity among DNA obtained fro m the three modification methods (Figure 3). Hence, the data obtained from DGGE were processed by considering the extraction method and samp le type as variab les. Assuming that each band represented one operational taxono mical unit (OTU), the nu mber of bands showed richness (S) in each samp le (Table 4 [Supplementary data]). Though not all bands from the DGGE gel were successfully re -amplified, the identity of the sharing line bands showed that they were identical (data not shown). Therefore, it was assumed that any of the different bands represented different OTUs, and vice versa, in the same type of sample.

The DGGE bands fro m d ifferent samp le types and extraction methods were used to calculate Dice similarity coefficient. Analysis of coefficient similarity showed that the bands were grouped in 2 major groups (Fig.3). The coal mixed soil samples were separated fro m other samples in a major group. In the other major group there were t wo minor groups which separated the coal samp les fro m the coal roof and coal floor samp les. Fro m the point of v iew of band similarity, the data suggested that coal seam has different nature fro m the surrounding soil layers, as well as the coal mixed soil as a mixture of soil and coal.

Bacterial diversity

The number and the intensity of DGGE bands were analyzed to probe diversity indices based on Fromin et al., 2002. Analysis of variance (sig.0.01) showed that Shannon index, do minance, and evenness had significant variat ions based on sample type and DNA extraction method (Fig.4). According to the Shannon index, the bacterial co mmun ity fro m coal mixed soil samples had the lowest diversity and not significantly different fro m the coal roof, but significantly d ifferent fro m the coal floor and coal seam with h igher diversity. The Shannon indices of samples obtained from direct method were less compared to that those from the indirect method (Filt ration and Blending). In terms of

the evenness index, evenness of the coal floor and the coal seam were lower co mpared to those of the coal roof and the coal mixed soil. The evenness indices derived fro m the samples with the Blending method were lo wer than those from the Filtrat ion and the direct method. The do minance index was consistent with the evenness index, i.e. the coal mixed soil and the coal roof samples had higher do minance than the coal floor and the coal seam samples.

Bacterial identification and phylogenetic

analysis

In order to facilitate similarity of inter-samp le sequence, the overall DGGE bands were d ivided into three areas as shown in Fig.3. In the first area, the OTUs were separated into two major clusters, Proteobacteria and Firmicutes. The Proteobacteria is do minated by OTU similar to uncultured bacteria, whereas the Firmicutes cluster showed many similarit ies to genera Bacillus. In this area, it was also observed that most of COs showing close relationship to CR samples belong to Beta proteobacteria, whereas the COs having close relationship to CFs were Firmicutes.

In the second area, Actinobacteria begins to be detected. In this area there were some OTUs hard to categorize

(marked as “unknown”) as they were not affiliated to

any well known sequence. Firmicutes in this area do not congregate in cluster, but two OTUs are part of the cluster along with the Proteobacteria. In the third area, the two clusters of sequences clearly separated Actinobacteria fro m the remaining Alpha proteobacteria. Almost one third of the CO in this area were part of the Actinobacteria cluster. Only a s mall part of CR and CF OTUs were in this cluster, moreover, CS was not a part of other. Th is area revealed characteristics that distinguish CO samp les fro m another samples, do minated by h igh-GC sequences. The CS OTUs have the least number of DGGE bands, only spread on areas I and II, where their pro ximit ies were spread among the soil and the coal OTUs .

DISCUSSION

Coal content in coal mixed soil (CS) clearly affected its properties compared to coal roof and coal floor which were higher in silt proportion, and had lower water content, lower bulk density and particle density, higher C/N ratio, and higher total sulfur. The lower bulk density indicated that soil compaction has not occurred yet during mining. However, the open min ing activity as well as the site has resulted in soil mixed with different layers, so that the mined soil went through physical and chemical change, such as lower content of the readily used organic carbon, pH and ability to withstand soil-water (Haering et al., 2004; Rodrigue

and Burger, 2004; Ghose, 2004). Coal has been well known to contain organic and inorganic sulfur, in which the organic sulfur is covalently bound to the coal matrix. In addit ion, active sulfur is produced primarily fro m decomposition of pyrite, sulfate and gaseous sulfur co mpounds in coal co mbined with organic matrix to form new organic sulfur co mpounds (Závodská and Lesny, 2006). The coal mixed soil had the highest dominance index, as a consequence of the bands with h igh intensity. The higher intensity of band, the more dominant is the bacterial ribotype corresponding to the band (Sun et al., 2004).

The dominance index variat ion was depended on the extraction methods, magnitude order of filtration, blending, and direct method. The lack o f an appropriate protocol for all types of soil was due to the differences in each type of soil commun ity members. The differences in the microorganisms on the microbial community sample affect efficiency of cell lysis (Carrig et al., 2007; Yuan et al., 2012). The type of DNA extraction method affects the diversity and commun ity structure in an environmental sample as visualized by PCR-DGGE (İnceoǧlu et al., 2010; Santos et al., 2012). The indirect extraction methods in the present study, particularly processedby Blending method, brought DGGE bands that did not appear in the direct method. This phenomenon is likely due to physical treat ments that is capable of separating cells attached to clay particles which trap the cells. Bacterial cells were known to attach predominantly to s mall particles (<2

μm) and hence remained un attenuated during transport (Muirhead et al., 2006; Dhand et al., 2009). The size of bacteria and clay minerals which are in the same size become the reason for difficulties in separating the cells fro m clay minerals. The amount of cell adsorption increases with pH fro m 2.0 to 3.0 due to the extent of electrostatic properties or surface charges of both the bacterial and mineral surfaces (Jiang et al., 2007; Hong et al., 2012). In this case, sulfuric acid was used as the washing solution, in the pH range resembling the soil samples.

All the data revealed that the coal seam (as the virgin coal) is different fro m the coal mixed soil (as the disturbed coal). Excavated coal seam represented by the coal mixed soil in this research was considered as a disturbance since it has been mixed with other soil strata, like the mineral layer on top, and a layer of topsoil on it. The disturbance occurred is not the same as experimental microcosms usually performed in other studies (Norris et al., 2002; Wertz et al., 2007; Ferrenberg et al., 2013), but this was due to nature, although caused by mining carried out inadvertently by human activity.

Some experiments on microbial ecology showed that disturbance might cause changes in the bacterial community co mposition, thus changes in diversity,

depending on the level of disturbance (Van der Zaan et al., 2010). The rates of recovery and the degree of congruence in the response patterns of commun ity composition and functioning along disturbance gradients depend on functional type and the character of the disturbance (Berga et al., 2012). The structure, diversity and abundance of microbial 16S rRNA genes were h ighly influenced by the concentration and presence of the disturbing factors (Sheik et al., 2012). The diversity of the mined soil leads to change compared to the coal seam, the coal roof, and the coal floor, which are entirely undisturbed soil. However, it appears that the difference in d iversity was due to disturbances much more as a result of changes in physico-chemical p roperties the of soil and coal (Fig.2). The commun ity members persisting in the d isturbed soil had similarity of the genus between the coal seam and the coal mixed soil.

Detection of uncultured bacteria established significant phylogenetic entity indicating the extent of their distribution in the environment hence they may offer a broad diversity of undiscovered biochemical and metabolic novelty (Dojka et al., 2000; Harris et al., 2004). The knowledge on uncultured microbial diversity of 16S rRNA genes is directly fro m natural environments via cultivation-independent approaches (Rappé and Giovannoni, 2003). A lthough the result of cluster analysis (Fig. 3) showed the overall CS was a distinctive cluster, the three phylogenetic trees showed that the successfully sequenced bands of CS were closely related to CO and CF. The results fro m the third area of DGGE band also confirmed that the high-GC bacteria is a group which easily survives in coal environment, for instance the bacteria that play a role in the formation of methane in abandoned coal mines (Beckmann et al., 2011) and abandoned coal power plant soil (Salam et al., 2011). Ho wever the high-GC groups found in the present study in the coal samples belong to Actinomycetes, to the bacterial group associated to soil environ ments (Felske et al., 1996; Thirup et al., 2001).

Most of DGGE bands (71.2%) have been sequenced (Table 5 [Supplem entary data]), whereas the remain ing bands failed to be sequenced. About 28.6% of the sequenced OTU have low similarity (89-96%) to the closest known species, while one-third of them were CO samp les. It seems that those OTUs were possibly exotic in the coal, even most of them were relatively close to uncultured bacteria, but not all bands matching the sequence were identified .

The other interesting ones are the OTUs of CF, the soil sample with the highest content of clay particles in this study. Most of them are uncultured bacteria, while some of them have high abundance in their co mmunity, even OTUs with 99% similarity are not close to the known genus. Bacteria present in the clay layer can be

derived fro m bacteria trapped during sedimentation (Bo ivin-Jahns et al., 1996). It suggests that the extraction method, particu larly the direct method (Zhou et al., 1996), is more suitable to be applied on the high clay soil. It successfully obtained OTUs that might not be obtained by culture method.

Firmicutes, especially Bacillus, has higher abundance of OTU than others in their co mmun ity in the coal sample. It is possible since the genus Bacillus is an r-strategist organism (Sarathchandra et al., 1997). In rich environments of organic matter such as coal, Bacillus easily outperforms other members by intensifying its growth by using abundant organic resources. The high abundance of Bacillus showed that they had been in the favorable physico-chemical conditions in the coal. Moreover, they are well known to have an endospore as defense mechanism of sustainability.

In conclusion, the differences in physical and chemical properties of the coal and the surrounding soil bacterial showed different diversity in their co mmunit ies though they still have genetic relatedness.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgement

This research was hardly carried out without support from PhD Scholarship (Grant Number: BPPS Dikti-ITB 2009) (Beasiswa Pendidikan Pasca Sarjana), Directorate General of Higher Education, M inistry of National Education, Indonesia, and Dissertation Support from State Islamic University Syarif Hidayatullah Jakarta, M inistry of Religious Affairs, Indonesia to M RP. We also appreciated to Ir. Eko Pujiantoro and his staff from PT. Bukit Asam Company..

References

Aminin ALN, Warganegara FM, Aditiawati P and Akhmaloka (2008a) Culture-independent and culture-dependent approaches on microbial community analysis at Gedongsongo analysis at Gedongsongo (GS-2) hot spring. Int. J. Integrative Biol., 2: 145-152. Aminin ALN, Warganegara FM, Aditiawati P and Akhmaloka (2008b) Simple enrichment and independent cultures to expand bacterial community analysis from Gedongsongo hot spring. J. Biosci. Bioeng., 106: 211-214.

Andreetta A, Macci C, Ceccherini MT, Cecchini G, Masciandaro G, Pietramellara G and Carnicelli S (2012) Microbial dynamics in Mediterranean moder humus. Biology and Fertility of Soils, 48: 259-270.

Beckmann S, Lueders T, et al. (2011) Acetogens and acetoclastic methanosarcinales govern methane formation in abandoned coal mines. Appl. Environ. Microbiol., 77(11): 3749-3756.

Berga M, Sze´kely AJ, Langenheder S (2012) Effects of disturbance intensity and frequency on bacterial community composition and function. PLoSONE 7: e36959. doi:10.1371/ journal.pone.0036959 Boivin-Jahns V, Ruimy R, Bianchi A, Daumas S and Christen R (1996) Bacterial diversity in a deep-subsurface clay environment. Appl. Environ. Microbiol., 62: 3405-3412.

Carrigg C, Rice O, Kavanagh S, Collins G, and O’Flaherty V (2007) DNA extraction method affects microbial community profiles from soils and sediment. Appl. Microbial. and Biotechnol., 77: 955-964. Daniel R (2005) The metagenomics of soil. Nat. Rev.Microbiol. 3: 470-478.

Davison J, Öpik M, Zobel M, Vasar M, Metsis M, et al (2012) Communities of arbuscular mycorrhizal fungi detected in forest soil are spatially heterogeneous but do not vary throughout the growing season. PLoSONE 7: e41938. doi:10.1371/journal.pone.0041938 Dhand NK, Toribio JAL and Whittington RJ (2009) Adsorption of Mycobacterium avium subsp. paratuberculosis to soil particles. Appl. Environ. Microbiol., 75: 5581-5585.

Dojka MA, Harris JK and Pace NR (2000) Expanding the known diversity and environmental distribution of an uncultured phylogenetic division of bacteria. Appl. Environ. Microbiol., 66: 1617-1621.

Feinstein LM, Sul WJ and Blackwood CB (2009) Assessment of bias associated with incomplete extraction of microbial DNA from soil. Appl. Environ. Microbiol., 75: 5428-5433.

Felske A, Engelen B, Nübel U and Backhaus H (1996) Direct ribosome isolation from soil to extract bacterial rRNA for community analysis. Appl. Environ. Microbiol., 62: 4162-4167.

Ferrenberg S, O'Neill SP, Knelman JE, Todd B, Duggan S, Bradley D, ... and Nemergut DR (2013) Changes in assembly processes in soil bacterial communities following a wildfire disturbance. The ISME journal.

Fromin N, Hamelin J, Tarnawski S, Roesti D, Jourdain_‐Miserez K, Forestier N, T eyssier‐Cuvelle S, Gillet F, Aragno M and Rossi P (2002) Statistical analysis of denaturing gel electrophoresis (DGE) fingerprinting patterns. Environmental Microbiology 4: 634-643. Ge Y, Chen C, Xu Z, Oren R and He JZ (2010) The spatial factor, rather than elevated CO2, controls the soil bacterial community in a temperate forest ecosystem. Appl. Environ. Microbiol., 76: 7429-7436.

Ghose MK (2004) Effect of opencast mining on soil fertility. J. Sci. Ind. Res., 63: 1006-1009.

Gonzalez A, Clemente JC, et al. (2011) Our microbial selves: what ecology can teach us. EMBO Reports, 12: 775-784.

Haering KC, Daniels WL and Galbraith JM (2004) Appalachian mine soil morphology and properties. Soil Sci. Soc. Am. J., 68(4): 1315-1325.

Hall A (1999) Bioedit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/ NT . Nucl. Acids. Sym p. Ser. 41: 95–98.

Harris JK, Kelley ST and Pace NR (2004) New perspective on uncultured bacterial phylogenetic division OP11. Appl. Environ. Microbiol., 70(2): 845-849.

Hong Z, Rong X, Cai P, Dai K, Liang W, Chen W and Huang Q (2012) Initial adhesion of Bacillus subtilis on soil minerals as related

to their surface properties. European Journal of Soil Science, 63: 457-466.

İnceoǧlu Ö, Hoogwout EF, Hill P and van Elsas JD (2010) Effect of DNA extraction method on the apparent microbial diversity of soil. Appl. Environ. Microbiol., 76: 3378-3382.

Jiang D, Huang Q, Cai P, Rong X and Chen W (2007) Adsorption of Pseudomonas putida on clay minerals and iron oxide. Colloids and Surfaces B: Biointerfaces, 54: 217-221.

Konstantinov SR, Zhu WY, Williams BA, Tamminga S, Vos WM and Akkermans AD (2003) Effect of fermentable carbohydrates on piglet faecal bacterial communities as revealed by denaturing gradient gel electrophoresis analysis of 16S ribosomal DNA. FEMS Microbiology Ecology, 43: 225-235.

Laplante K and Derome N (2011) Parallel changes in the taxonomical structure of bacterial communities exposed to a similar environmental disturbance. Ecology and Evolution 1: 489–501.

Limberger R and Wickham SA (2012) Disturbance and diversity at two spatial scales. Oecologia 168: 785–795. DOI

10.1007/s00442-011-2140-8.

Machulla G, Bruns MA and Scow KM (2005) Microbial properties of mine spoil materials in the initial stages of soil development. Soil Sci. Soc. Am . J., 69(4): 1069-1077.

Morales SE and Holben WE (2009) Empirical testing of 16S rRNA gene PCR primer pairs reveals variance in target specificity and efficacy not suggested by in silico analysis. Appl. Environ. Microbiol.

75: 2677-2683.

Muirhead RW, Collins RP and Bremer PJ (2006) Interaction of Escherichia coli and soil particles in runoff. Appl. Environ. Microbiol., 72: 3406-3411.

Nakatsu CH, Torsvik V and Øvreås L (2000) Soil community analysis using DGGE of 16S rDNA polymerase chain reaction products. Soil Science Society of America Journal, 64(4): 1382-1388. Norris T B, Wraith JM, Castenholz RW and McDermott TR (2002) Soil microbial community structure across a thermal gradient following a geothermal heating event. Appl. Environ. Microbiol., 68: 6300-6309.

Rappé MS and Giovannoni SJ (2003) The uncultured microbial majority. Annual Reviews in Microbiology 57: 369-394.

Rodrigue JA and Burger JA (2004) Forest soil productivity of mined land in the midwestern and eastern coalfield regions. Soil Sci. Soc. Am . J., 68(3): 833-844.

Rumpel C and Kögel-Knabner I (2004) Microbial use of lignite compared to recent plant litter as substrates in reclaimed coal mine soils. Soil Biol. Biochem., 36(1): 67-75.

Salam LB, Obayori OS, Akashoro OS and Okogie GO (2011) Biodegradation of bonny light crude oil by bacteria isolated from contaminated soil. Int. J. Agric. Biol., 13: 245–250.

Santos HF, Carmo FL, Leite DCA, Jesus HE, De Carvalho Maalouf P, Almeida C, Soriano A. U. et al. (2012) Comparison of different protocols for the extraction of microbial DNA from reef corals. Brazilian Journal of Microbiology 43: 517-527.

Sarathchandra SU, Burch G and Cox NR (1997) Growth patterns of bacterial communities in the rhizoplane and rhizosphere of white clover Trifolium repens and perennial ryegrass Lolium perenne L.) in long-term pasture. Applied Soil Ecology, 6: 293-299.

Schmalenberger A, Schwieger F and Tebbe CC (2001) Effect of primers hybridizing to different evolutionarily conserved regions of the small-subunit rRNA gene in PCR-based microbial community analyses and genetic profiling. Appl. Environ. Microbiol. 67: 3557–

3563.

Schwieger F and Tebbe CC (1998) A new approach to utilize PCR– single-strand-conformation polymorphism for 16S rRNA gene-based microbial community analysis. Appl. Environ. Microbiol,. 64: 4870–

4876.

Sheik CS, Mitchell TW, et al. (2012) Exposure of soil microbial communities to chromium and arsenic alters their diversity and structure. PloS One, 7(6), e40059.

Speight JG (2005) Handbook of Coal Analysis. Wiley-Interscience. . 166: 1-19. ISBN 0-471-52273-2.

Sun HY, Deng SP and Raun WR (2004) Bacterial community structure and diversity in a century-old manure-treated agroecosystem. Appl. Environ. Microbiol., 70: 5868-5874.

Tamura K, Peterson D, Peterson N, Stecher G, Nei M, and Kumar S (2011) MEGA5: Molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evol., 28: 2731-2739. doi:10.1093/molbev /msr121.

Thirup L, Johnsen K and Winding A (2001) Succession of indigenous Pseudomonas spp. and Actinomycetes on barley roots affected by the antagonistic strain Pseudomonas fluorescens DR54 and the fungicide imazalil. Appl. Environ. Microbiol., 67(3): 1147-1153.

Torsvik V and Øvreås L (2002) Microbial diversity and function in soil: from genes to ecosystems. Current Opinion in Microbiology 5: 240–245.

Thomas IP (2005) Fuel Resources: Coal. In Barber AJ, Crow MJ and Milsom JS (Editors). Sumatra: Geology, Resources and Tectonic Evolution, Issue 31. T he Geological Society, UK.

Valášková V and Baldrian P (2009) Denaturing gradient gel electrophoresis as a fingerprinting method for the analysis of soil microbial communities. Plant Soil Environ., 55: 413-423.

Van Der Zaan B, Smidt H, De Vos WM, Rijnaarts H and Gerritse J (2010) Stability of the total and functional microbial communities in river sediment mesocosms exposed to anthropogenic disturbances. FEMS Microbiology Ecology, 74: 72-82.

Wertz S, Degrange V, Prosser JI, Poly F, Commeaux C, Guillaumaud N and Le Roux X (2007) Decline of soil microbial diversity does not influence the resistance and resilience of key soil microbial functional groups following a model disturbance. Environmental Microbiology,

9: 2211-2219.

Yuan S, Cohen DB, Ravel J, Abdo Z, Forney LJ (2012) Evaluation of methods for the extraction and purification of DNA from the human

microbiome. PLoSONE 7: e33865.

doi:10.1371/journal.pone.0033865.

Závodská L and Lesný J (2006) Recent development in lignite investigation. HEJ Manuscript no.: ENV-061026-A.

Zhang Z, Schwartz S, Wagner L and Miller W (2000) A greedy algorithm for aligning DNA sequences, J. Comput. Biol., 7: 203-214. Zhou J, Bruns MA and T iedje JM (1996) DNA recovery from soils of diverse composition. Appl. Environ. Microbiol., 62: 316-322.

the collected supernatant was filtered by vacuu m filter with 0.22 μm of pore-d iameter to obtain cell pellet. This method is called filter method (marked by F). Another indirect method, called Blending method (marked by B), is similar procedure as the Filter method but the washed sample was blended by a h igh speed blender for 1 min in every last step of washing. Extracted DNA fro m the harvested cells was then continuously treated with the direct method. The extracted DNA fro m either direct or ind irect methods were purified by polyvinylpolypyrrolidone (PVPP) in a spin column, and eluted with 30 µ l of 10 mM Tris-HCl buffer pH 8 to harvest final extract.

PCR-DGGE

Partial frag ments of 16S rRNA gene sequences (V4-V5 hypervariable region) were amplified using the primer set consisting of 408 base pairs (position of 519-536 and 907-926 based on Escherichia coli). The primers

used were: forward primer Co m1-F with the sequence of 5'-CA G CGC CA G GGT AAT A C-3', carry ing GC-clamp 5'-CGC CCG CCG CGC GCG GCG GGC CGG GGG GGG GGG GGG CA C, and Co m2-R with the sequence of 5'-CGG TCA ATT CCT TTG A GT TT -3'. (Schwieber and Tebbe, 1998; Sch malenberger et al., 2001; A minin et al., 2008a; A minin et al., 2008b; Morales and Holben, 2009). The reaction mixture (50 µ L) contained 1.0 µ L o f 10 mM each p rimer, 5.0 µ L of 10× Taq bu_ffer, 1.0 µ L of 10 µM deo xyribonucleotide triphosphate mixture, 4.0 µ L of total DNA, and 0.25 µ L of 5U/µ L Taq DNA poly merase. The PCR react ions were carried out as fo llo ws: init ial denaturation at 95°C for 5 min, followed by 30 cycles consisting of

denaturation at 95°C for 1 min, annealing at 51°C fo r 1 min, extension at 72°C fo r 1 min, and a 10-min final extension step at 72°C. The amp licons were subjected to electrophoresis in 1.2% agarose gels. The gel were stained with ethidiu m bro mide and visualized under ultrav iolet light. Each type of sample was ext racted in 3 rep licates separately and then amplified separately.

DGGE was performed with a DCode Universal Mutation Detection System (Bio-Rad) containing TAE buffer. PCR products were loaded onto 7% polyacrylamide gel containing 30 to 80% denaturant gradient of urea and formamide solution. Each sample (40 µ L) consisted of three PCR products and 8 µL of 5× loading dye was loaded onto gels and then run for 12 h at 60°C under 70 V. Each type of sample was loaded into two wells as replicates. After the runs, gels were removed fro m the setup, stained with silver staining, and scanned. In order to identity the bands, each DGGE band was cut separately, and re-amp lified by adding 50 µl sterile deionized water to allo w the DNA to passively diffused in the water at 37°C overnight (Aminin et al., 2008b). Four microliters of the eluted fraction was used as template DNA for PCR again with the primers previously described but without GC-clamp. PCR products were then purified and sequenced commercially (Macrogen, Korea). All of the 16S rRNA sequence genes were deposit in the GenBank with accession number fro m KF545601-KF545614.

Sequences were checked by using Bioedit 7.0.9.0. (Hall, 1999), and then compared with sequences in the National Center for Biotechnology Information data bank using the Blast 2.2.28+ (Zhang et al., 2000, http: //www.ncbi.nlm.nih.gov/BLAST). Align ment of DNA sequences was performed by ClustalW program, and Phylogenetic tree was constructed using the neighbor-joining method with Mega5 (Tamura et al., 2011). All trees were based on the Jukes–Cantor distance and constructed with 1000 bootstrapping.

Data analysis

The soil properties were co mpared each other in pairs by Pearson correlation, subjected to Princ ipal Co mponent Analysis in order to extract the correlated soil properties, and constructed by XLSTAT Version

2013.3.02 (Addinsoft, 1995-2013,

http://www.xlstat.com). The b iplot projection was used to probe the similarity of the soil samp les based on the extracted properties. The scanned DGGE gel was analyzed for band intensity using ImageJ (http://rsb.info.nih.gov/ij/). DGGE profiles were quantified to determine total number of bands (S), each peak square (ni) and sum of all the peak square (N) (Fro min et al., 2002). This informat ion was used to calculate the community diversity using three indices: the Shannon index (H) calculated with the fo rmula H = -Ʃ(ni/N) ln(ni/N); the dominance index (c) calculated with the formula c = Ʃ(ni/N)2_{; and (iii) the evenness} index (e) calculated with the formula e = H/lnS. The indices data were analyzed by multivariate analysis of variance (Manova) at significance level 0.01, followed by Tukey’s HSD test as post hoc test, by using SPSS 20.0 (IBM Corp., 2011). The similarity between the DGGE p rofiles were determined by calculating Dice similarity coefficient S = 2nAB /(nA+nB), where nA is the number of DGGE bands in line 1, nB is the number of DGGE bands in line 2, and nAB is the number of common DGGE bands (Konstantinov et al., 2003; Sun

et al., 2004; Andreetta et al., 2012). Two bands were common if they migrated at the same distance on a gel. Cluster analysis was used to compare the similarity coefficient among DGGE bands, constructed by using the UPGMA algorith m, and analysed by SPSS 20.0 (IBM Corp., 2011).

RESULTS

Coal and soil

properties

Result of pro ximate analysis (Table 2

[Supplem entary data])

showed that the coal

sample was

categorized as brown coal ly ing between subbituminous and lignite, although the value of volatile material and fixed carbon confirmed it as subbituminous coal (Speight, 2005). Sumat ra contains 64% of total coal deposit in Indonesia, where the Bu kit Asam area of South Sumat ra, including the sites (Muara Tigo Besar Utara), are one of the biggest resources with 48.6% coal deposit of Indonesia reserves (Thomas, 2005). Therefore some v irg in coal seam were still found in this area which is yet to be mined(as coal roof and coal floor).

Physical and chemical properties among the soil samples were quite different (Table 3 [Supplem entary data]), although they were separated in only

approximately 10-20 cm distance or thickness. However, the analysis on soil particle size showed that the three soil samples were all categorized as clay soil. The pH of the soils was very acidic. It was as a result of the high content of acid-forming cat ion sources, such as Al, Fe and S.

Interaction among soil properties analyzed by Pearson correlation revealed that there were some properties having correlation among each other. There were 18 out of 23 properties having significant correlation (P value 0.05) (data is not shown). In order to exp lain characteristics differences between coal mixed soil and clay soils (i.e. coal roof and coal floor), we used Principal Co mponent Analysis (PCA) to highlight on the significant correlation. The analysis resulted in two factors which represent the whole initial variability of the data (Fig. 2). It exp lained the variability in the original twenty three variables, so the complexity of the data set is reduced by using the two components, without loss of informat ion. Here, we selected a group Figure 3: Dendogram of cluster analysis of DGGE bands. The dendrogram was constructed using average

linkage (between groups). The numbers above the bands are replication of the lane. All of the compared lanes are separated into three areas (I, II, and III) for phylogenetic analysis.

of soil properties that gave high contribution (the squared cosines of the variables close to 1) to the new factors. The correlation circle showed that the extracted soil properties (total sulfur, C/N rat io, organic carbon, organic nitrogen, total calciu m, pH, and free cation) appear in positive correlation with each other, but negative against total potassium. The b iplot showed that the three soil samp les have different features, based on the extracted properties. The total sulfur increased by increasing organic carbon in soil mixed coal, showing the contribution of coal to the soil.

DGGE band profile

In order to obtain complete diversity, DNA extract ion was carried out with some modificat ions. The DGGE gel of each sample showed similar profile but different band intensity among DNA obtained fro m the three modification methods (Figure 3). Hence, the data obtained from DGGE were processed by considering the extraction method and samp le type as variab les. Assuming that each band represented one operational taxono mical unit (OTU), the nu mber of bands showed richness (S) in each samp le (Table 4 [Supplementary data]).

Though not all bands from the DGGE gel were successfully re -amplified, the identity of the sharing line bands showed that they were identical (data not shown). Therefore, it was assumed that any of the different bands represented different OTUs, and vice versa, in the same type of sample.

The DGGE bands fro m d ifferent samp le types and extraction methods were used to calculate Dice similarity coefficient. Analysis of coefficient similarity showed that the bands were grouped in 2 major groups (Fig.3). The coal mixed soil samples were separated fro m other samples in a major group. In the other major group there were t wo minor groups which separated the coal samp les fro m the coal roof and coal floor samp les. Fro m the point of v iew of band similarity, the data suggested that coal seam has different nature fro m the surrounding soil layers, as well as the coal mixed soil as a mixture of soil and coal.

Bacterial diversity

The number and the intensity of DGGE bands were analyzed to probe diversity indices based on Fromin et al., 2002. Analysis of variance (sig.0.01) showed that Shannon index, do minance, and evenness had significant variat ions based on sample type and DNA extraction method (Fig.4). According to the Shannon index, the bacterial co mmun ity fro m coal mixed soil samples had the lowest diversity and not significantly different fro m the coal roof, but significantly d ifferent fro m the coal floor and coal seam with h igher diversity. The Shannon indices of samples obtained from direct method were less compared to that those from the indirect method (Filt ration and Blending). In terms of

the evenness index, evenness of the coal floor and the coal seam were lower co mpared to those of the coal roof and the coal mixed soil. The evenness indices derived fro m the samples with the Blending method were lo wer than those from the Filtrat ion and the direct method. The do minance index was consistent with the evenness index, i.e. the coal mixed soil and the coal roof samples had higher do minance than the coal floor and the coal seam samples.

Bacterial identification and phylogenetic

analysis

In order to facilitate similarity of inter-samp le sequence, the overall DGGE bands were d ivided into three areas as shown in Fig.3. In the first area, the OTUs were separated into two major clusters, Proteobacteria and Firmicutes. The Proteobacteria is do minated by OTU similar to uncultured bacteria, whereas the Firmicutes cluster showed many similarit ies to genera Bacillus. In this area, it was also observed that most of COs showing close relationship to CR samples belong to Beta proteobacteria, whereas the COs having close relationship to CFs were Firmicutes.

In the second area, Actinobacteria begins to be detected. In this area there were some OTUs hard to categorize (marked as “unknown”) as they were not affiliated to any well known sequence. Firmicutes in this area do not congregate in cluster, but two OTUs are part of the cluster along with the Proteobacteria. In the third area, the two clusters of sequences clearly separated Actinobacteria fro m the remaining Alpha proteobacteria. Almost one third of the CO in this area were part of the Actinobacteria cluster. Only a s mall part of CR and CF OTUs were in this cluster, moreover, CS was not a part of other. Th is area revealed characteristics that distinguish CO samp les fro m another samples, do minated by h igh-GC sequences. The CS OTUs have the least number of DGGE bands, only spread on areas I and II, where their pro ximit ies were spread among the soil and the coal OTUs .

DISCUSSION

Coal content in coal mixed soil (CS) clearly affected its properties compared to coal roof and coal floor which were higher in silt proportion, and had lower water content, lower bulk density and particle density, higher C/N ratio, and higher total sulfur. The lower bulk density indicated that soil compaction has not occurred yet during mining. However, the open min ing activity as well as the site has resulted in soil mixed with different layers, so that the mined soil went through physical and chemical change, such as lower content of the readily used organic carbon, pH and ability to withstand soil-water (Haering et al., 2004; Rodrigue

and Burger, 2004; Ghose, 2004). Coal has been well known to contain organic and inorganic sulfur, in which the organic sulfur is covalently bound to the coal matrix. In addit ion, active sulfur is produced primarily fro m decomposition of pyrite, sulfate and gaseous sulfur co mpounds in coal co mbined with organic matrix to form new organic sulfur co mpounds (Závodská and Lesny, 2006). The coal mixed soil had the highest dominance index, as a consequence of the bands with h igh intensity. The higher intensity of band, the more dominant is the bacterial ribotype corresponding to the band (Sun et al., 2004).

The dominance index variat ion was depended on the extraction methods, magnitude order of filtration, blending, and direct method. The lack o f an appropriate protocol for all types of soil was due to the differences in each type of soil commun ity members. The differences in the microorganisms on the microbial community sample affect efficiency of cell lysis (Carrig

et al., 2007; Yuan et al., 2012). The type of DNA

extraction method affects the diversity and commun ity structure in an environmental sample as visualized by PCR-DGGE (İnceoǧluet al., 2010; Santos et al., 2012). The indirect extraction methods in the present study, particularly processedby Blending method, brought DGGE bands that did not appear in the direct method. This phenomenon is likely due to physical treat ments that is capable of separating cells attached to clay particles which trap the cells. Bacterial cells were known to attach predominantly to s mall particles (<2 μm) and hence remained un attenuated during transport (Muirhead et al., 2006; Dhand et al., 2009). The size of bacteria and clay minerals which are in the same size become the reason for difficulties in separating the cells fro m clay minerals. The amount of cell adsorption increases with pH fro m 2.0 to 3.0 due to the extent of electrostatic properties or surface charges of both the bacterial and mineral surfaces (Jiang et al., 2007; Hong

et al., 2012). In this case, sulfuric acid was used as the washing solution, in the pH range resembling the soil samples.

All the data revealed that the coal seam (as the virgin coal) is different fro m the coal mixed soil (as the disturbed coal). Excavated coal seam represented by the coal mixed soil in this research was considered as a disturbance since it has been mixed with other soil strata, like the mineral layer on top, and a layer of topsoil on it. The disturbance occurred is not the same as experimental microcosms usually performed in other studies (Norris et al., 2002; Wertz et al., 2007;

Ferrenberg et al., 2013), but this was due to nature, although caused by mining carried out inadvertently by human activity.

Some experiments on microbial ecology showed that disturbance might cause changes in the bacterial community co mposition, thus changes in diversity,

depending on the level of disturbance (Van der Zaan et al., 2010). The rates of recovery and the degree of congruence in the response patterns of commun ity composition and functioning along disturbance gradients depend on functional type and the character of the disturbance (Berga et al., 2012). The structure,

diversity and abundance of microbial 16S rRNA genes were h ighly influenced by the concentration and presence of the disturbing factors (Sheik et al., 2012). The diversity of the mined soil leads to change compared to the coal seam, the coal roof, and the coal floor, which are entirely undisturbed soil. However, it appears that the difference in d iversity was due to disturbances much more as a result of changes in physico-chemical p roperties the of soil and coal (Fig.2). The commun ity members persisting in the d isturbed soil had similarity of the genus between the coal seam and the coal mixed soil.

Detection of uncultured bacteria established significant phylogenetic entity indicating the extent of their distribution in the environment hence they may offer a broad diversity of undiscovered biochemical and metabolic novelty (Dojka et al., 2000; Harris et al., 2004). The knowledge on uncultured microbial diversity of 16S rRNA genes is directly fro m natural environments via cultivation-independent approaches (Rappé and Giovannoni, 2003). A lthough the result of cluster analysis (Fig. 3) showed the overall CS was a distinctive cluster, the three phylogenetic trees showed that the successfully sequenced bands of CS were closely related to CO and CF. The results fro m the third area of DGGE band also confirmed that the high-GC bacteria is a group which easily survives in coal environment, for instance the bacteria that play a role in the formation of methane in abandoned coal mines (Beckmann et al., 2011) and abandoned coal power

plant soil (Salam et al., 2011). Ho wever the high-GC groups found in the present study in the coal samples belong to Actinomycetes, to the bacterial group associated to soil environ ments (Felske et al., 1996; Thirup et al., 2001).

Most of DGGE bands (71.2%) have been sequenced (Table 5 [Supplem entary data]), whereas the remain ing

bands failed to be sequenced. About 28.6% of the sequenced OTU have low similarity (89-96%) to the closest known species, while one-third of them were CO samp les. It seems that those OTUs were possibly exotic in the coal, even most of them were relatively close to uncultured bacteria, but not all bands matching the sequence were identified .

The other interesting ones are the OTUs of CF, the soil sample with the highest content of clay particles in this study. Most of them are uncultured bacteria, while some of them have high abundance in their co mmunity, even OTUs with 99% similarity are not close to the known genus. Bacteria present in the clay layer can be

derived fro m bacteria trapped during sedimentation (Bo ivin-Jahns et al., 1996). It suggests that the extraction method, particu larly the direct method (Zhou

et al., 1996), is more suitable to be applied on the high clay soil. It successfully obtained OTUs that might not be obtained by culture method.

Firmicutes, especially Bacillus, has higher abundance of OTU than others in their co mmun ity in the coal sample. It is possible since the genus Bacillus is an r-strategist organism (Sarathchandra et al., 1997). In rich environments of organic matter such as coal, Bacillus easily outperforms other members by intensifying its growth by using abundant organic resources. The high abundance of Bacillus showed that they had been in the favorable physico-chemical conditions in the coal. Moreover, they are well known to have an endospore as defense mechanism of sustainability.

In conclusion, the differences in physical and chemical properties of the coal and the surrounding soil bacterial showed different diversity in their co mmunit ies though they still have genetic relatedness.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgement

This research was hardly carried out without support from PhD Scholarship (Grant Number: BPPS Dikti-ITB 2009) (Beasiswa Pendidikan Pasca Sarjana), Directorate General of Higher Education, M inistry of National Education, Indonesia, and Dissertation Support from State Islamic University Syarif Hidayatullah Jakarta, M inistry of Religious Affairs, Indonesia to M RP. We also appreciated to Ir. Eko Pujiantoro and his staff from PT. Bukit Asam Company..

References

Aminin ALN, Warganegara FM, Aditiawati P and Akhmaloka (2008a) Culture-independent and culture-dependent approaches on microbial community analysis at Gedongsongo analysis at Gedongsongo (GS-2) hot spring. Int. J. Integrative Biol., 2: 145-152. Aminin ALN, Warganegara FM, Aditiawati P and Akhmaloka (2008b) Simple enrichment and independent cultures to expand bacterial community analysis from Gedongsongo hot spring. J. Biosci. Bioeng., 106: 211-214.

Andreetta A, Macci C, Ceccherini MT, Cecchini G, Masciandaro G, Pietramellara G and Carnicelli S (2012) Microbial dynamics in Mediterranean moder humus. Biology and Fertility of Soils, 48: 259-270.

Beckmann S, Lueders T, et al. (2011) Acetogens and acetoclastic methanosarcinales govern methane formation in abandoned coal mines. Appl. Environ. Microbiol., 77(11): 3749-3756.

Berga M, Sze´kely AJ, Langenheder S (2012) Effects of disturbance intensity and frequency on bacterial community composition and function. PLoSONE7: e36959. doi:10.1371/ journal.pone.0036959 Boivin-Jahns V, Ruimy R, Bianchi A, Daumas S and Christen R (1996) Bacterial diversity in a deep-subsurface clay environment.

Appl. Environ. Microbiol., 62: 3405-3412.

Carrigg C, Rice O, Kavanagh S, Collins G, and O’Flaherty V (2007)

DNA extraction method affects microbial community profiles from soils and sediment. Appl. Microbial. and Biotechnol., 77: 955-964. Daniel R (2005) The metagenomics of soil. Nat. Rev.Microbiol. 3: 470-478.

Davison J, Öpik M, Zobel M, Vasar M, Metsis M, et al (2012) Communities of arbuscular mycorrhizal fungi detected in forest soil are spatially heterogeneous but do not vary throughout the growing season. PLoSONE 7: e41938. doi:10.1371/journal.pone.0041938 Dhand NK, Toribio JAL and Whittington RJ (2009) Adsorption of Mycobacterium avium subsp. paratuberculosis to soil particles. Appl. Environ. Microbiol., 75: 5581-5585.

Dojka MA, Harris JK and Pace NR (2000) Expanding the known diversity and environmental distribution of an uncultured phylogenetic division of bacteria. Appl. Environ. Microbiol., 66: 1617-1621.

Feinstein LM, Sul WJ and Blackwood CB (2009) Assessment of bias associated with incomplete extraction of microbial DNA from soil.

Appl. Environ. Microbiol., 75: 5428-5433.

Felske A, Engelen B, Nübel U and Backhaus H (1996) Direct ribosome isolation from soil to extract bacterial rRNA for community analysis. Appl. Environ. Microbiol., 62: 4162-4167.

Ferrenberg S, O'Neill SP, Knelman JE, Todd B, Duggan S, Bradley D, ... and Nemergut DR (2013) Changes in assembly processes in soil bacterial communities following a wildfire disturbance. The ISME journal.

Fromin N, Hamelin J, Tarnawski S, Roesti D, Jourdain_‐Miserez K, Forestier N, T eyssier‐Cuvelle S, Gillet F, Aragno M and Rossi P (2002) Statistical analysis of denaturing gel electrophoresis (DGE) fingerprinting patterns. Environmental Microbiology4: 634-643. Ge Y, Chen C, Xu Z, Oren R and He JZ (2010) The spatial factor, rather than elevated CO2, controls the soil bacterial community in a temperate forest ecosystem. Appl. Environ. Microbiol., 76: 7429-7436.

Ghose MK (2004) Effect of opencast mining on soil fertility. J. Sci. Ind. Res., 63: 1006-1009.

Gonzalez A, Clemente JC, et al. (2011) Our microbial selves: what ecology can teach us. EMBO Reports, 12: 775-784.

Haering KC, Daniels WL and Galbraith JM (2004) Appalachian mine soil morphology and properties. Soil Sci. Soc. Am. J., 68(4): 1315-1325.

Hall A (1999) Bioedit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/ NT . Nucl. Acids. Sym p. Ser.41: 95–98.

Harris JK, Kelley ST and Pace NR (2004) New perspective on uncultured bacterial phylogenetic division OP11. Appl. Environ. Microbiol., 70(2): 845-849.

Hong Z, Rong X, Cai P, Dai K, Liang W, Chen W and Huang Q (2012) Initial adhesion of Bacillus subtilis on soil minerals as related

to their surface properties. European Journal of Soil Science, 63: 457-466.

İnceoǧlu Ö, Hoogwout EF, Hill P and van Elsas JD (2010) Effect of

DNA extraction method on the apparent microbial diversity of soil.

Appl. Environ. Microbiol., 76: 3378-3382.

Jiang D, Huang Q, Cai P, Rong X and Chen W (2007) Adsorption of

Pseudomonas putida on clay minerals and iron oxide. Colloids and Surfaces B: Biointerfaces, 54: 217-221.

Konstantinov SR, Zhu WY, Williams BA, Tamminga S, Vos WM and Akkermans AD (2003) Effect of fermentable carbohydrates on piglet faecal bacterial communities as revealed by denaturing gradient gel electrophoresis analysis of 16S ribosomal DNA. FEMS Microbiology Ecology, 43: 225-235.

Laplante K and Derome N (2011) Parallel changes in the taxonomical structure of bacterial communities exposed to a similar environmental disturbance. Ecology and Evolution1: 489–501.

Limberger R and Wickham SA (2012) Disturbance and diversity at two spatial scales. Oecologia168: 785–795. DOI 10.1007/s00442-011-2140-8.

Machulla G, Bruns MA and Scow KM (2005) Microbial properties of mine spoil materials in the initial stages of soil development. Soil Sci. Soc. Am . J., 69(4): 1069-1077.

Morales SE and Holben WE (2009) Empirical testing of 16S rRNA gene PCR primer pairs reveals variance in target specificity and efficacy not suggested by in silico analysis. Appl. Environ. Microbiol. 75: 2677-2683.

Muirhead RW, Collins RP and Bremer PJ (2006) Interaction of Escherichia coli and soil particles in runoff. Appl. Environ. Microbiol., 72: 3406-3411.

Nakatsu CH, Torsvik V and Øvreås L (2000) Soil community analysis using DGGE of 16S rDNA polymerase chain reaction products. Soil Science Society of America Journal, 64(4): 1382-1388. Norris T B, Wraith JM, Castenholz RW and McDermott TR (2002) Soil microbial community structure across a thermal gradient following a geothermal heating event. Appl. Environ. Microbiol., 68: 6300-6309.

Rappé MS and Giovannoni SJ (2003) The uncultured microbial majority. Annual Reviews in Microbiology 57: 369-394.

Rodrigue JA and Burger JA (2004) Forest soil productivity of mined land in the midwestern and eastern coalfield regions. Soil Sci. Soc. Am . J., 68(3): 833-844.

Rumpel C and Kögel-Knabner I (2004) Microbial use of lignite compared to recent plant litter as substrates in reclaimed coal mine soils. Soil Biol. Biochem., 36(1): 67-75.

Salam LB, Obayori OS, Akashoro OS and Okogie GO (2011) Biodegradation of bonny light crude oil by bacteria isolated from contaminated soil. Int. J. Agric. Biol., 13: 245–250.

Santos HF, Carmo FL, Leite DCA, Jesus HE, De Carvalho Maalouf P, Almeida C, Soriano A. U. et al. (2012) Comparison of different protocols for the extraction of microbial DNA from reef corals.

Brazilian Journal of Microbiology43: 517-527.

Sarathchandra SU, Burch G and Cox NR (1997) Growth patterns of bacterial communities in the rhizoplane and rhizosphere of white clover Trifolium repens and perennial ryegrass Lolium perenne L.) in long-term pasture. Applied Soil Ecology, 6: 293-299.

Schmalenberger A, Schwieger F and Tebbe CC (2001) Effect of primers hybridizing to different evolutionarily conserved regions of the small-subunit rRNA gene in PCR-based microbial community analyses and genetic profiling. Appl. Environ. Microbiol. 67: 3557– 3563.

Schwieger F and Tebbe CC (1998) A new approach to utilize PCR–

single-strand-conformation polymorphism for 16S rRNA gene-based microbial community analysis. Appl. Environ. Microbiol,. 64: 4870–

4876.

Sheik CS, Mitchell TW, et al. (2012) Exposure of soil microbial communities to chromium and arsenic alters their diversity and structure. PloS One, 7(6), e40059.

Speight JG (2005) Handbook of Coal Analysis. Wiley-Interscience. . 166: 1-19. ISBN 0-471-52273-2.

Sun HY, Deng SP and Raun WR (2004) Bacterial community structure and diversity in a century-old manure-treated agroecosystem.

Appl. Environ. Microbiol., 70: 5868-5874.

Tamura K, Peterson D, Peterson N, Stecher G, Nei M, and Kumar S (2011) MEGA5: Molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evol., 28: 2731-2739. doi:10.1093/molbev /msr121.

Thirup L, Johnsen K and Winding A (2001) Succession of indigenous

Pseudomonas spp. and Actinomycetes on barley roots affected by the antagonistic strain Pseudomonas fluorescens DR54 and the fungicide imazalil. Appl. Environ. Microbiol., 67(3): 1147-1153.

Torsvik V and Øvreås L (2002) Microbial diversity and function in soil: from genes to ecosystems. Current Opinion in Microbiology5: 240–245.

Thomas IP (2005) Fuel Resources: Coal. In Barber AJ, Crow MJ and Milsom JS (Editors). Sumatra: Geology, Resources and Tectonic Evolution, Issue 31. T he Geological Society, UK.

Valášková V and Baldrian P (2009) Denaturing gradient gel

electrophoresis as a fingerprinting method for the analysis of soil microbial communities. Plant Soil Environ., 55: 413-423.

Van Der Zaan B, Smidt H, De Vos WM, Rijnaarts H and Gerritse J (2010) Stability of the total and functional microbial communities in river sediment mesocosms exposed to anthropogenic disturbances.

FEMS Microbiology Ecology, 74: 72-82.

Wertz S, Degrange V, Prosser JI, Poly F, Commeaux C, Guillaumaud N and Le Roux X (2007) Decline of soil microbial diversity does not influence the resistance and resilience of key soil microbial functional groups following a model disturbance. Environmental Microbiology, 9: 2211-2219.

Yuan S, Cohen DB, Ravel J, Abdo Z, Forney LJ (2012) Evaluation of methods for the extraction and purification of DNA from the human

microbiome. PLoSONE 7: e33865.

doi:10.1371/journal.pone.0033865.

Závodská L and Lesný J (2006) Recent development in lignite investigation. HEJ Manuscript no.: ENV-061026-A.

Zhang Z, Schwartz S, Wagner L and Miller W (2000) A greedy algorithm for aligning DNA sequences, J. Comput. Biol., 7: 203-214. Zhou J, Bruns MA and T iedje JM (1996) DNA recovery from soils of diverse composition. Appl. Environ. Microbiol., 62: 316-322.