Culture-independent metagenomic approach to ...

World J Microbiol Biotechnol (2014) 30:519–528 DOI 10.1007/s11274-013-1467-1

ORIGINAL PAPER

Culture-independent metagenomic approach to characterize the surface and subsurface soil bacterial community in the Brahmaputra valley, Assam, North-East India, an Indo-Burma mega-biodiversity hotspot P. N. Bhattacharyya • B. Tanti • P. Barman D. K. Jha

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Received: 6 February 2013 / Accepted: 16 August 2013 / Published online: 30 August 2013 Ó Springer Science+Business Media Dordrecht 2013

Abstract Soil bacterial communities, which contain the highest level of prokaryotic diversity of any natural environment, are important for ecosystem functioning. A cultureindependent metagenomic approach was employed in the present investigation to characterize the diversity of soil bacterial community composition in five geochemically and hydrologically different surface and subsurface soil habitats of Brahmaputra valley, Assam, North-East India, an IndoBurma mega-biodiversity hotspot. The diversity of soil bacterial community was determined through sequence analysis of 16S–23S intergenic spacer regions (ISR). Polymerase chain reaction (PCR) universal primers, 1406F (50 -TGYACACACCGCCCGT-30 ) and 155r (50 -GGGTT BCATTCRG-30 ) were used for amplification of 16S–23S ribosomal DNA intergenic spacers of bacteria. Amplification resulted in an intense array of PCR products approximately ranging in size from 200 to 900 bp. Clear banding patterns were observed in analysed samples using the primer set in combination. A clear change in microbial ISR profile was observed on visual analysis of gel electrophoresis profiles. Fast alignment database searches of PCR amplicons of 16S–23S ISR sequence data revealed that the isolated sequences resembled five major phylogenetic groups of

P. N. Bhattacharyya D. K. Jha (&) Microbial Ecology Laboratory, Department of Botany, Gauhati University, Guwahati 781014, Assam, India e-mail: [email protected]; [email protected] B. Tanti Cytogenetics and Plant Breeding Laboratory, Department of Botany, Gauhati University, Guwahati 781014, Assam, India P. Barman Department of Biotechnology, Gauhati University, Guwahati 781014, Assam, India

bacteria, namely a-, b- and c-subdivisions of Proteobacteria, Acidobacterium and Comamonadaceae. Keywords Bacterial community Brahmaputra valley Culture-independent metagenomic approach Gel electrophoresis profiles Intergenic spacer regions Proteobacteria Abbreviations ARISA Automated ribosomal intergenic spacer analysis BLAST Basic local alignment search tool EtBr Ethidium bromide FASTA Fast alignment ISR Intergenic spacer region msl Mean sea level NCBI National Center for Biotechnology Information OTU Operational taxonomic units

Introduction The microbial world is the largest unexplored reservoir of biodiversity on earth (up to the last century, the nature and identity of only a tiny fraction, \10 %, of microscopic landscape is known) (Bhardwaj and Neelam 2012). Accordingly, research based on microbial ecology has emerged as an important frontier in present day biological science. Microbial communities such as bacteria, fungi and actinomycetes play significant roles in maintaining the structure, function and overall sustainability of living systems (Briones and Raskin 2003). Soil is the unique natural environment harbouring a good reservoir of bacteria (Fierer and Jackson 2006). Very meagre information, however, is available on true bacterial

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life inhabiting the soil, since most of the studies related to soil microbiology have generally focused exclusively on the surface soil of 10–40 cm (Kennedy and Smith 1995; Tiedje et al. 2001; Martinez-Alonso et al. 2010). To explore more bacterial diversity we need to study the pristine soil locations like subsurface soil. Subsurface soil environments are both physically and chemically heterogeneous and facilitate more bacterial populations with unique properties which could be best exploited in further human welfare. The fact that bacteria are essential for the entire ecosystem, since they perform numerous functions like maintenance of biogeochemical cycles has spurred keen interest by scientists for the exploration of the vast resource of soil bacterial diversity (Madsen 2011). Traditional approach to characterize soil bacterial diversity usually involves the application of a culture-based approach, where a wide variety of culture media and prolonged incubation periods are usually recommended for maximum recovery and identification of diverse microbial groups (Kirk et al. 2004). The culture-based approach is highly biased, meant only for the isolation of a small subset of fast-growing bacterial species (less than 1 % of total bacterial populations) with specialized growth requirements (Tyson and Banfield 2005; Vartoukian et al. 2010). The approach is thus completely unable to detect the dominant uncultivable bacterial communities present in soil (Alain and Querellou 2009), although, the amount of such bacteria has been recorded as significantly higher than the culturable bacteria (Amann et al. 1995). The number of phylogenetic groups recovered using the culture-independent approach is relatively more than those isolated using the culture-dependent approach (Webster et al. 2001). Application of cultureindependent metagenomic approach is, therefore, highly appreciated in relation to exploration of the vast resources of soil microbial communities (Ellis et al. 2003; Fierer et al. 2012). A culture-independent metagenomic approach could facilitate the characterization of soil microbial community structure through direct isolation of total soil microbial DNAs. The technique involves the application of polymerase chain reaction (PCR) to amplify the target regions of DNA directly extracted from soil samples. In metagenomic studies, it is also essential to separate the microbial DNAs from humic substances since the later is known to interfere with the PCR amplification and sequencing reactions (Robe et al. 2003). Recent developments in the use of rDNA homology and conserved features of biosynthetic pathways have made this phylogeny-based metagenomic approach more easy and reliable. Most of the microbial species is now assigned to ‘‘RNA similarity groups’’ (Claesson et al. 2009) for better understanding of the diversity and community dynamics of whole soil microbiota. India is one of the important mega biodiverse countries. North-east India, the bio-geographical gateway of greater

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World J Microbiol Biotechnol (2014) 30:519–528

India, is considered as one of the richest biodiversity hotspot zone and is known for its potential genetic resources all over the world (Meyers et al. 2000). The Brahmaputra valley of Assam with an average elevation of 50–120 m above msl represents a unique and important priority ecoregion in India among 200 global priority ecoregions (Chatterjee et al. 2006). However, unfortunately, due to inaccessibility of some tough terrains and probably due to geographical and political constraints, the soil microflora of this geologically and ecologically important region has not so far been explored properly. This biosphere also seemed as an emerging frontier for the studies of physiological limits of microbial life, microbial mechanisms of action and potentially analogous environment for sustainable human development (Bhattacharyya 2012). To explore more microbial diversity we also need to study the pristine locations like subsurface soil, which would substantially provide valuable information about the soil microbial ecology and the energetics of microbial metabolism in that particular soil habitat. Subsurface microbial isolates at different geological formations (depths), and to a lesser extent, at the same geological formation differ significantly mainly because of different selection pressure at different depths (Balkwill et al. 1989). Agnelli et al. (2004) demonstrated the significance of both the surface and subsurface soil environment in relation to exploration of global microbial diversity. The present investigation has been carried out to understand the bacterial community compositions in the surface and subsurface soil environment of Brahmaputra valley, in relation to habitat change, using a culture-independent metagenomic approach. A direct DNA extraction method was employed to recover the metagenomic DNA. The phylogenetic relationship among different microbial taxa based on their 16S–23S rDNA ISR sequence analyses, were carried out to confirm their taxonomic identity.

Materials and methods Selection, climate and vegetation of the study area The study was conducted in and around the soils of Brahmaputra valley (26°400 N latitude and 92°580 E longitude) (Fig. 1), Assam, North-east India during December 2009–March 2012. Five different locations (land-use systems) such as agricultural land, tea garden, disturbed and undisturbed forests and active flood plain were selected to collect the soil samples. Two spots at each location i.e. a total of 10 sampling points were selected for the collection of soil samples. The study site is characterized by a climate with most rainfall occurring during the summer months (May–July) with relatively little or scanty rainfall during


the winter months. The average annual rainfall varied from 670 to 1100 mm and the mean minimum and maximum soil temperatures ranged in between 17 and 36 °C in this humid tropical climate. The study area is mostly inhabited

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by plants like Alstonia scholaris, Arundo sp., Dalbergia sissoo, Dellinia indica, Dipterocarpus macrocarpus, Gmelina arborea, Mangifera indica, Messua ferrea, Phoebea attenuata, Shorea robusta and Tectona grandis.

Fig. 1 Map showing the sampling points (red dots) in the study area (Brahmaputra valley, Assam, North-east India)

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Collection of soil samples Soil was sampled from three different spots, each of 1–9 cm (surface) and 101–200 cm (subsurface) depths using a sterilized hand auger. Randomly collected soil samples were mixed thoroughly and the composite sample was taken to the laboratory for molecular analysis. The sieved (2 mm mesh) samples were placed in a 2 ml microtubes and stored in a deep-freeze at -80 °C until used. Extraction of soil DNA Metagenomic DNA was extracted directly from the surface and subsurface soils using the following protocol. 0.5 g soil of each sample was extracted with 1.5 ml lysis buffer [100 mM Tris–HCl (pH 8.0), 100 mM phosphate (pH 8.0), 100 mM sodium EDTA (pH 8.0), 1 % CTAB, 1.5 M NaCl and 15 ll proteinase K (10 mg/ml)] in horizontal shaking incubator at 225 rev/min for 30 min at 37 °C. 160 ll of 20 % SDS was then added to the mixed samples. The samples were incubated at 65 °C in hot water bath for 2 h. Gentle vortexing of the samples were made at every 15–20 min during incubation. The samples were centrifuged at 6000 rev/min for 10 min at 25 °C. The supernatant was collected and transferred into 1.5 ml Eppendorf tubes. The supernatant was mixed with an equal volume of phenol, chloroform and isoamyl alcohol (25:24:1) to remove components like protein and amylose. The aqueous phase, so obtained, was collected by centrifuging it at 6000 rev/min for 10 min. 0.6 ml isopropanol was used to precipitate the aqueous phase. It was then stored overnight at 4 °C. Crude DNA was obtained by centrifuging (6000 rev/min) the aqueous phase for 15 min at 4 °C. The precipitate, so obtained, was washed with cold 70 % ethanol and re-suspended in TE buffer (10 mM Tris–HCl, 1 mM EDTA, pH 8.0). The final volume was made up to 50 ll by adding double distilled water (Jia et al. 2006). Quality of isolated DNA was checked by resolving it on 0.8 % agarose gel using Bio-Rad gel electrophoresis unit. 1 kb ladder was used as molar mass marker (Lane 1). Lanes 2–11 were loaded with metagenomic DNA samples of different study locations. The resultant DNA profiles were photographed in Gel documentation system (Gel Logic 212 PRO Carestream, USA) (Fig. 2). PCR amplification of bacterial-specific genes Polymerase chain reaction universal primers 1406F (50 -TG YACACACCGCCCGT-30 ) and 155r (50 -GGGTTBCATTC RG-30 ) were used, to amplify the 16S–23S ribosomal DNA intergenic spacers of bacteria. The universal primers for PCR were selected in accordance with Ikeda et al. (2004a),

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Fig. 2 Metagenomic soil DNA profiles. Lane 1 molar mass marker (1 kb), Lanes 2–11 metagenomic DNA samples of different study locations. Lane 2 and 3 sample of surface and subsurface soil from agricultural land soil, Lane 4 and 5 sample of surface and subsurface soil from undisturbed forest soil, Lane 6 and 7 sample of surface and subsurface soil from tea garden soil, Lane 8 and 9 sample of surface and subsurface soil from disturbed forest soil, Lane 10 and 11 sample of surface and subsurface soil from active flood plain soil

who too used the same primers for soil microbial community analysis using the ribosomal intergenic spacer analysis (RISA). All the PCR reactions were carried out in 50 ll volumes containing 5 ll of 10 X PCR buffer, 1.5 ll of 50 mM MgCl2, 1 ll of a mixture containing each of the dNTPs at a concentration of 2.5 mol/l, each primer at a final concentration of 5.0 pM, 2 ll of DNA containing 100 ng and 1 U of Taq DNA polymerase. The amplification reaction was carried out for 30 cycles. Each cycle comprised of 30 s of denaturation at 94 °C, 30 s of annealing at 52 °C and 2 min of extension at 72 °C. The PCR amplification was preceded by incubation of PCR product at 95 °C for 5 min. After 30 cycles, there was a final extension for 7 min at 72 °C (Tanti et al. 2012). PCR amplifications were performed using C1000 thermal cycler (Bio-Rad, USA, thermal cycler). The resulting PCR products (amplicons) were separated electrophoretically in 0.8 % agarose gel, stained with EtBr (10 mg/ml) in 1 X TAE buffer and photographed in Gel documentation system (Gel Logic 212 PRO Carestream, USA). The amplified DNA products were purified with the help of HiPurATM Quick Gel Purification Kit (MB539, HiMedia Laboratories, Pvt. Ltd, Mumbai, India) after adequate separation and excision of bands from gel. Purity of isolated DNA was checked by resolving it on 0.8 % agarose gel and photographed using Gel documentation system (Gel Logic 212 PRO Carestream, USA).


Phylogeny construction based on DNA sequence data Purified amplified DNA was sequenced using cycle sequencing Kit with DS-35 dye set and automated DNA Sequencer (Applied Biosystems, ABI3730 xl). Bidirectional sequencing was performed for bacteria using ribosomal intergenic spacer-based Universal forward 1406F (50 -TGYACACACCGCCCGT-30 ) and reverse primers 155r (50 -GGGTTBCATTCRG-30 ). Chromatograms were manually checked and visualized using ChromasPro. Chromatograms were converted into FASTA format (Pearson 1991) by codon code aligner. Sequence data was aligned and analysed for identification, confirmation as well as to find the closest homolog by a BLAST (Altschul et al. 1997) search. A web based NCBI BLAST (BLASTn) was performed to observe the homology of the sequences to non-redundant nucleotide databases (nr). Known sequences were selected from BLAST results and downloaded. The significance of BLAST results were tested by expect values (e-value) generated through BLAST search algorithm. Multiple sequence alignment was performed along with the downloaded sequences from GenBank using CLUSTALWx locally. Manual adjustments of the sequences were made wherever necessary. Phylogenetic analysis was performed using the dnaml module of the PHYLIP package. Maximum composite likelihood method (Tamura et al. 2004) was employed to generate the phylogenetic relationship among the sequences. A rooted tree was generated using the drawgram module of the PHYLIP package.

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PCR amplification of the 16S–23S rDNA intergenic region Metagenomic DNA obtained from both the surface and subsurface soil horizons at different land-use systems, revealed identical bands ([1 kb) on 0.8 % agarose gel using Bio-Rad gel electrophoresis unit (Fig. 2). A clear shift in microbial ISR profile was, however, observed on visual analysis of gel electrophoresis profiles, which suggested prominent change in soil microbial community structure across different land-use systems. PCR amplification with the primers 1406F and 155r resulted in PCR products for each of the sample analysed (Fig. 3). From the gel electrophoresis profile, it became evident that agricultural, undisturbed forest and tea garden soils (Lanes 2–7) had several bands in common. Besides, the relative intensities of most of these bands were similar between the lanes. Whereas the amplified bacterial metagenomic DNA profiles of disturbed forest and active flood plain soils (Lanes 8–11) showed unique type of banding patterns. Gel electrophoresis profiles for each of the five land-use systems indicated a clear shift in the bacterial 16S–23S rDNA ISR profiles. Change in bacterial ISR profiles suggested variation in soil bacterial community structure across the study locations throughout different depths. Metagenomic bacterial DNA samples were assigned with the laboratory codes i.e. SSB1 and SSB2 for

Results and discussion Conservation International has identified the North East (NE) India under the Indo-Burma mega biodiversity hotspot zone (Meyers et al. 2000) and thereby indicated the potentiality of this bio-diverse region for the benefit of society. Soil of Brahmaputra Valley, Assam, North-east India (Fig. 1) has distinct physico-chemical features, which creates unique ecological niche for the microbial communities (Bhattacharyya and Jha 2011). Molecular technique based on the analysis of 16S–23S rDNA intergenic region to determine the bacterial diversity in this ecologically diverse, hitherto-unexplored region is, therefore, expected to be a novel approach to generate an interesting data pertaining to microbial community structure. A cultureindependent approach to characterize the microbial community composition in three municipal wastewater treatment plants was made by Cui et al. (2012). Ikeda et al. (2004b) also assessed the bacterial diversity and community dynamics based on amplification of 16S–23S rDNA intergenic region.

Fig. 3 Bacterial PCR amplification for ISR of 16S–23S rDNA genes of metagenomic DNA profiles Lane 1 molar mass marker (100 bp ladder), Lane 2 and 3 surface and subsurface soil samples of agricultural land soil, Lane 4 and 5 surface and subsurface soil samples of undisturbed forest soil, Lane 6 and 7 surface and subsurface soil samples of tea garden soil, Lane 8 and 9 surface and subsurface soil samples of disturbed forest soil, Lane 10 and 11 surface and subsurface soil samples of active flood plain soil

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agricultural, SSB3 and SSB4 for undisturbed forest, SSB5 and SSB6 for tea garden, SSB7 and SSB8 for disturbed forest and SSB9 and SSB10 for active flood plain soils. The ARISA technique to analyse the bacterial community composition in the rumen of lactating dairy cows involved the digestion of purified amplified DNA followed by gel electrophoresis (Palmonari et al. 2010). The unique banding patterns generated on gel could, thus, be used for microbial identification processes or comparison of microbial communities with public databases (Zhou et al. 2011). Sequence analysis and phylogeny construction All the bands were excised for sequencing. The partial sequences ranging about 200–300 nucleotides, include a variable domain that was considered sufficient to distinguish different operational taxonomic units as well as to place each sequence roughly in a phylogenetic tree. The short sequences were, however, not adequate to obtain a definitive identification up to the species level or to resolve precisely all the phylogenetic relationships among the isolated microbial taxa. The phylogenetic analyses were based on successive multiple alignment and reinvestigations on the deep branching patterns of a large phylogenetic tree. According to Sneath (1993) this approach is easier to use and is probably the most reasonable compromise regarding the possible

problems occurring with crossing over. In each phylogenetic analysis, comparisons were restricted to nucleotide positions that were definitely aligned. Some analyses were performed several times with or without small domains that could have reached the point of saturation. Fast alignment database searches of PCR amplicons of 16S–23S ISR sequence data revealed maximum similarity of the isolated bacterial sequences to Proteobacteria. Proteobacteria were also reported as the most dominant members of the bacterial community by Cui et al. (2012), during their investigations on microbial community composition using both culture-dependent and culture-independent approaches. Proteobacteria can significantly utilize nutrients like peptone, glucose and fatty acids that generally get deposited in soil, mainly because of excessive accumulation of sewage sludge. Closest species match along with GenBank accession number based on BLAST; results are summarized in Table 1. Although the majority of the microbial sequences had a high level ([85 %) of similarity with the known bacterial ribosomal ISR sequences in the public databases, the similarity of three bacterial sequences i.e. SSB1, SSB3 and SSB10 in database profiles was recorded as low which ultimately hampered their identification up to the species level. Low level of similarity of microbial sequences to the known ones in public databases strongly suggested their novel occurrence with respect to a given land-use system

Table 1 Two closest species match obtained from FASTA searches between the analysed bacterial sequences and the sequences downloaded from GenBank/EMBL/DDBJ databases Analysed sequences

Oligonucleotides

Closest species match and accession number

Similarity (%)

E value

SSB1

5-0 TGYACACACCGCCCGT-30

Ralstonia solanacearum strain CaRs-Mep [JF523188.1]

83

3e-04

Ralstonia syzygii R24 [FR854086.1]

80

3e-04

Polynucleobacter difficilis [FM208181.1] Polynucleobacter sp. FC1 [AM398081.1]

82 82

0.007 0.007

Nitrosomonas sp. [HQ213727.1]

80

6e-13

Halomonas pacifica [AB367219.1]

80

7e-12

Acidobacteria sp. [DQ829686.1]

82

1e-14

Proteobacterium sp. [GU195789.1]

81

1e-13

Acidobacteria sp. [DQ829679.1]

85

2e-18

Pelobacter sp. [FJ550730.1]

81

9e-16

Proteobacterium sp. [AB491966.1]

91

3e-35

Xanthomonas sp. [HQ213459.1]

90

4e-34

Wautersia basilensis [AJ783973.1]

85

3e-14

Rhodocyclus sp. [FJ552019.1]

77

1e-13

Nitrosomonas sp. [HQ212766.1]

90

5e-33

Xanthomonas sp. [HQ213459.1]

88

2e-30

Burkholderia sp. [AB491963.1]

87

3e-24

Delftia acidovorans [FJ410384.1]

87

3e-24

Dokdonella sp. [HM438452.1] Xanthomonas sp. [FJ552035.1]

77 77

4e-08 1e-08

0

0

SSB2

5 -GGGTTBCCCCATTCRG-3

SSB3

50 -TGYACACACCGCCCGT-30

SSB4 SSB5 SSB6 SSB7 SSB8 SSB9 SSB10

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0


0

0

5 -TGYACACACCGCCCGT-3

0

50 -GGGTTBCCCCATTCRG-30 0

5 -TGYACACACCGCCCGT-3 0


0

0

50 -TGYACACACCGCCCGT-30 0


0


(Demergasso et al. 2008). Topographical complexity and environmental heterogeneity of the Brahmaputra valley might be the possible reason influencing on the overall occurrences of bacterial ribosomal ISR sequences to particular land-use system. To provide more information and accuracy to the identification and phylogenetic placement of the sequences, the two closest species matches in the database were made for proper phylogenetic analysis. Multiple sequence alignment of the bacterial sequences generated by CLUSTALWx showed significant similarity among the metagenome ISR sequence profiles (Fig. 4). The output generated by CLUSTALWx and the phylogenetic tree obtained from the PHYLIP package is shown in Fig. 5. All the positions containing gaps and missing data were eliminated in the final dataset. The highest proportion (40 %) of sequenced PCR bands had more level of sequence similarity to Rhodocyclaceae, Myxococcales, Xanthomonas sp. Dokdonella sp. and

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Proteobacterium sp., belonging to the Proteobacteria group (Fig. 5). The four sequences (Group I) i.e. SSB7 and SSB8 (isolated from the surface and subsurface soils of disturbed forest) and SSB9 and SSB10 (isolated from the surface and subsurface soils of active flood plain) were most closely related to Rhodocyclaceae. The sequences i.e. SSB4 and SSB5 (Group II) obtained from the undisturbed forest subsurface soil and surface soil of tea garden showed similarity with Halomonas pacifica and Wautersia basilensis. Thus, almost all the sequences of group I and group II belonged to the Proteobacteria group and thereby suggested the occurrences of a common type of bacterial community structure at different depths across dissimilar land-use systems. Group III contained three sequences i.e. SSB1, SSB2 and SSB3 which showed closest similarity with Acidobacter sp. The remaining sequence, SSB6 clustered tightly in its phylogeny with b-Proteobacterium group. Although all the sequences mainly belonged to Proteobacteria, some of

Fig. 4 Sequence alignment between the bacterial metagenome ISR profiles. Data for other species were gathered from NCBI. The conserved regions of the gene are demonstrated in different colour

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Fig. 5 The phylogenetic tree based on maximum-likelihood method using PHYLIP package

them also revealed similarities with other bacterial groups like Acidobacteria, gram-positive bacteria and Chlamydiae. Alterations in the vegetation patterns throughout the study locations might have played a significant role in influencing the composition of soil bacterial community structure (Banning et al. 2011). Generally, as per taxonomic identification of bacterial strains, the phylogenetic definition of a species includes strains with approximately 70 % DNA–DNA relatedness or greater (Wayne et al.1987). This threshold values corresponds to 98.7–99 % similarity in case of 16S rRNA gene sequences (Stackebrandt and Ebers 2006). By applying this criterion, to all the intergenic regions of 16S–23S rRNA bacterial sequence (a total no. of 10), in the present

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investigation, similarities of less than 98.7 % was revealed to any known bacteria deposited in the public databases. Thus, in the present investigation, in some cases, the 16S–23S ISR sequences did not match with the sequences in the public databases, that suggested the uniqueness and unidentified bacterial community structure in those considerations which might lead to reveal novel bacterial populations with some unique properties.

Conclusion The present investigation, thus, emphasized the importance of culture-independent metagenomic approach for the


exploration of unexplored microbial gene pool of hithertounexplored surface and subsurface soil habitats in the IndoBurma mega biodiversity hot-spot. Metagenomic sequencing of microbial genetic materials from soil facilitates characterization of the largest proportion of genes. Amplification of 16S–23S ribosomal DNA intergenic spacers of bacteria for metagenome sequence analysis of 16S–23S ISR using universal primers 1406 F and 155 r revealed the isolation of a-, b- and c- subdivisions of Proteobacteria, Acidobacterium and Comamonadaceae. The findings could possibly serve as a basis for future studies on bacterial community structure in natural ecosystems. This novel approach for isolation and characterization of bacterial communities will further strengthen the understanding on the roles played by different microbial groups within an ecosystem. Acknowledgments The authors are thankful to the Department of Science and Technology (DST), Govt. of India, New Delhi for financial assistance in the form of a research project. The authors are also indebted to Mr. Saurov Mahanta for his valuable suggestions in using the Bioinformatics tools for analysis of data. Conflict of interest declared.

No conflict of interest is perceived and none

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