1 1 Metagenomic analysis of bacterial community composition among the cave sediments of Indo- 2 Burman biodiversity hotspot region 3 4 Surajit De Mandal, Zothansanga and Nachimuthu Senthil Kumar* 5 Department of Biotechnology, Mizoram University, Aizawl-796004, Mizoram, India. 6 7 s t n i 8 r P 9 e r 10 P 11 12 13 14 15 16 17 *Corresponding author: 18 Email: [email protected] 19 Mobile: +91-9436352574 20 21 22 23 PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.631v1 | CC-BY 4.0 Open Access | rec: 22 Nov 2014, publ: 22 Nov 2014 2 1 ABSTRACT 2 Caves in Mizoram, Northeast India are potential hotspot diversity regions due to the historical 3 significance of the formation of Indo-Burman plateau and also because of their unexplored and 4 unknown diversity. High throughput paired end illumina sequencing of V4 region of 16S rRNA 5 was performed to systematically evaluate the bacterial community of three caves situated in 6 Champhai district of Mizoram, Northeast India. A total of 10,643 operational taxonomic units 7 (based on 97% cutoff) comprising 21 bacterial phyla and 21 candidate phyla with a sequencing s t n i 8 depth of 11, 40013 were found in this study. The overall taxonomic profile obtained by BLAST r P 9 against RDP classifier and Greengene OTU database revealed high diversity within the bacterial e r 10 communities, dominated by Planctomycetes, Actinobacteria, Proteobacteria, Bacteroidetes, and P 11 Firmicutes, while members of archea were less diverse and mainly comprising of eukaryoarchea. 12 Analysis revealed that Farpuk (CFP) cave has low diversity and is mainly dominated by 13 actinobacteria (80% reads), whereas diverse communities were found in the caves of Murapuk 14 (CMP) and Lamsialpuk (CLP). Analysis of rare and abundant species also revealed that a major 15 portion of the identified OTUs were falling under rare biosphere. Significantly, all these caves 16 recorded a high number of unclassified OUTs which might represent novel species. Further, 17 analysis with whole genome sequencing is needed to validate the novel species as well as to 18 determine their functional significance. 19 20 Subjects Biodiversity, Cave Ecology, Microbiology 21 Keywords Cave, Indo-Burman plateau, Bacterial diversity, illumina sequencing 22 23 PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.631v1 | CC-BY 4.0 Open Access | rec: 22 Nov 2014, publ: 22 Nov 2014 3 1 INTRODUCTION 2 Indo-Burma region, a part of the 25 global biodiversity hotspots, is one of the richest biomes of 3 the world with high species diversity (Myers et al., 2000). This region is spread over 2, 62, 379 4 sq. kms and represents the transition zone between the Indian and Indochinese subregions of the 5 Oriental biogeographic region (Mani, 1974). This region contains an estimated 9.7% of the 6 world’s known endemic plant species and 8.3% of the endemic vertebrate species (Brook et al., 7 2003). Interestingly, not many reports are available on the microbial diversity, particularly from s t n i 8 Caves, from the Indo-Burma region. r P e 9 Caves represent subsurface habitat and are less explored in terms of biodiversity and r P 10 community composition due to environmental and geographical constrains. Lack of 11 photosynthesis and limited nutrient source makes the caves an extreme environment to sustain 12 life. However, alternative energy in the form of allochthonous organic materials transported from 13 the surface through bat, rodents and human activities or by percolating water is utilized by 14 certain groups of microorganisms (Barton, 2006). These ecosystems with extreme temperature, 15 osmolarity, pressure, and pH forces the inhabitants to undertake diverse and novel metabolic 16 pathways for oxidizing reduced metals, fixing gases and for utilization of various aromatic 17 compounds. Organic matter helps the formation of secondary microbial communities - usually 18 multicolored yellow, grey, white or pink cloddy coadings on carbonate or clay coated walls in 19 the form of bioflim with unusual coloration, precipitates, corrosion residues (Barton, 2006). 20 Caves also act as long-term reservoirs for endemic as well as allochthonous 21 microorganisms (Engel et al., 2010). Earlier studies reported diverse group of microorganisms 22 associated with different geological and environmental factors (Adetutu et al., 2011; 2012) and 23 have already been implicated in astrobiology, drug discovery and cave conservation studies PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.631v1 | CC-BY 4.0 Open Access | rec: 22 Nov 2014, publ: 22 Nov 2014 4 1 (Northup et al., 2011; Saiz-Jimenez, 2012). These microbial communities also influence the 2 formation and preservation of cave deposits by constructive and destructive processes. Cave 3 microbes are also important since they act as primary producers, which sustain populations of 4 more complex organisms (Barton and Northup 2007). 5 Majority of the cave microbial diversity studies have been done using culture dependent 6 techniques which can reveal only 1% of the total microorganisms. In recent years, a novel s t 7 methodology is being developed to detect the environmental microorganisms, independently of a n i r 8 need for culture based screening. Molecular microbial ecology tools such as denaturing gradient P e 9 gel electrophoresis (DGGE) and clone library analysis are being used by many researchers to r P 10 characterize these uncultured microbes, but these techniques are also not sufficient to analyze the 11 entire population in the community (Adetutu et al., 2012). With the advancement of Next 12 Generation Sequencing, cave microbial ecology research has also expanded which allows us to 13 use culture-independent techniques to reveal further the hidden biodiversity and key process 14 happening inside the caves. 15 This study involves the use of high throughput illumina sequencing of sediment samples 16 collected from caves situated in Indo-Burmese border of Champhai district, Mizoram, Northeast 17 India to contribute to better understanding of their microbial community. 18 19 MATERIALS AND METHODS 20 Three caves namely, Murapuk (CMP), Lamsialpuk (CLP) and Farpuk (CFP) were 21 selected for the present study based on the fact these caves are devoid of any human influence 22 and have not been studied yet. Sediment samples were collected from different locations of the 23 caves and upon collection; the samples were sieved and preserved at 4°C. No specific permit was PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.631v1 | CC-BY 4.0 Open Access | rec: 22 Nov 2014, publ: 22 Nov 2014 5 1 taken for the sampling since it did not involve any endangered species or protected area. 2 Sediment samples were analyzed for carbon and nitrogen content with a CHNS/O analyzer 3 (Perkin Elmer, USA) and pH of the sample were measured by pH meter (Table 1). 4 Soil community DNA was extracted from 0.5 g of soil sample using the Fast DNA spin 5 kit (MP Biomedical, Solon, OH, USA) following the manufacturer’s protocol. DNA 6 concentration was quantified using a microplate reader (Molecular device Spectromax 2E). V4 s t 7 hypervariable region of the 16S rRNA gene was amplified using 2 µl of each 10 pmol/µl forward n i r 8 and reverse primers 515F (5′-GTGCCAGCMGCCGCGGTAA-3′) and 806R (5′- P e 9 GGACTACHVGGGTWTCTAAT-3′). The amplification mix contained 5 μL of 40mM dNTP, 5 r P 10 μL of 5X Phusion HF reaction buffer, 0.2 μL of 2U/ µl F-540Special Phusion HS DNA 11 Polymerase, 5ng input DNA and water to make up the total volume to 25 μL. High throughput 12 Illumina Mi-seq sequencing was performed at Scigenome Labs, Cochin, India (Table 2). 13 14 Sequence quality was analyzed according to base quality score distributions, average base 15 content per read and GC distribution in the reads. Singletons, the unique OTU that did not cluster 16 with other sequence were removed as it might be a result of sequencing errors and can be 17 resulted to spurious OTUs. Chimeras were also removed using UCHIME method and pre- 18 processed consensus V4 sequences were clustered into Operational Taxonomic Units (OTUs) 19 based on their sequence similarity using Uclust program (similarity cutoff=0.97). All the pre- 20 processed reads were used to identify the OTUs using QIIME program for constructing a 21 representative sequence for each OTUs. The representative sequence was finally aligned to the 22 Greengenes core set reference databases using PyNAST program (Caporaso et al., 2010; 23 DeSantis et al., 2006). Representative sequence for each OTU was classified using RDP PeerJ PrePrints | http://dx.doi.org/10.7287/peerj.preprints.631v1 | CC-BY 4.0 Open Access | rec: 22 Nov 2014, publ: 22 Nov 2014 6 1 classifier and Greengenes OTUs database. Sequences which are not classified were categorized 2 as unknown. 3 QIIME software was used to calculate Shannon index and Observed species metrices. 4 Shannon metric represents observed OTU abundance and estimates for both richness and 5 evenness, whereas observed species metric detects unique OTUs present in the sample. In this 6 study, the comparison of beta diversity between three bacterial communities (CLP, CMP and 7 CFP) was done by calculating the distance matrix using UniFrac approach (Lozupone et al., s t n i 8 2005). Weighted UPGMA tree was constructed by performing jackknife test A with 10 replicates r P 9 and each sub-sample containing 1, 00,000 random reads selected from each sample. e r 10 P 11 RESULTS AND DISCUSSION 12 With an unsuitable geology, caves are the most remote and inaccessible environment for 13 research, but are now being considered as a potential biodiversity hotspot due to its unique 14 ecological significance.