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Metagenomic analysis of bacterial community composition among the cave sediments of Indo-
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Burman biodiversity hotspot region
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Surajit De Mandal, Zothansanga and Nachimuthu Senthil Kumar*
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Department of Biotechnology, Mizoram University, Aizawl-796004, Mizoram, India.
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*Corresponding author:
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Email: [email protected]
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Mobile: +91-9436352574
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ABSTRACT
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Caves in Mizoram, Northeast India are potential hotspot diversity regions due to the historical
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significance of the formation of Indo-Burman plateau and also because of their unexplored and
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unknown diversity. High throughput paired end illumina sequencing of V4 region of 16S rRNA
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was performed to systematically evaluate the bacterial community of three caves situated in
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Champhai district of Mizoram, Northeast India. A total of 10,643 operational taxonomic units
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(based on 97% cutoff) comprising 21 bacterial phyla and 21 candidate phyla with a sequencing
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depth of 11, 40013 were found in this study. The overall taxonomic profile obtained by BLAST
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against RDP classifier and Greengene OTU database revealed high diversity within the bacterial
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communities, dominated by Planctomycetes, Actinobacteria, Proteobacteria, Bacteroidetes, and
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Firmicutes, while members of archea were less diverse and mainly comprising of eukaryoarchea.
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Analysis revealed that Farpuk (CFP) cave has low diversity and is mainly dominated by
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actinobacteria (80% reads), whereas diverse communities were found in the caves of Murapuk
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(CMP) and Lamsialpuk (CLP). Analysis of rare and abundant species also revealed that a major
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portion of the identified OTUs were falling under rare biosphere. Significantly, all these caves
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recorded a high number of unclassified OUTs which might represent novel species. Further,
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analysis with whole genome sequencing is needed to validate the novel species as well as to
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determine their functional significance.
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Subjects Biodiversity, Cave Ecology, Microbiology
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Keywords Cave, Indo-Burman plateau, Bacterial diversity, illumina sequencing
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INTRODUCTION
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Indo-Burma region, a part of the 25 global biodiversity hotspots, is one of the richest biomes of
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the world with high species diversity (Myers et al., 2000). This region is spread over 2, 62, 379
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sq. kms and represents the transition zone between the Indian and Indochinese subregions of the
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Oriental biogeographic region (Mani, 1974). This region contains an estimated 9.7% of the
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world’s known endemic plant species and 8.3% of the endemic vertebrate species (Brook et al.,
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2003). Interestingly, not many reports are available on the microbial diversity, particularly from
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Caves, from the Indo-Burma region.
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Caves represent subsurface habitat and are less explored in terms of biodiversity and
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community composition due to environmental and geographical constrains. Lack of
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photosynthesis and limited nutrient source makes the caves an extreme environment to sustain
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life. However, alternative energy in the form of allochthonous organic materials transported from
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the surface through bat, rodents and human activities or by percolating water is utilized by
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certain groups of microorganisms (Barton, 2006). These ecosystems with extreme temperature,
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osmolarity, pressure, and pH forces the inhabitants to undertake diverse and novel metabolic
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pathways for oxidizing reduced metals, fixing gases and for utilization of various aromatic
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compounds. Organic matter helps the formation of secondary microbial communities - usually
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multicolored yellow, grey, white or pink cloddy coadings on carbonate or clay coated walls in
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the form of bioflim with unusual coloration, precipitates, corrosion residues (Barton, 2006).
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Caves also act as long-term reservoirs for endemic as well as allochthonous
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microorganisms (Engel et al., 2010). Earlier studies reported diverse group of microorganisms
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associated with different geological and environmental factors (Adetutu et al., 2011; 2012) and
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have already been implicated in astrobiology, drug discovery and cave conservation studies
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(Northup et al., 2011; Saiz-Jimenez, 2012). These microbial communities also influence the
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formation and preservation of cave deposits by constructive and destructive processes. Cave
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microbes are also important since they act as primary producers, which sustain populations of
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more complex organisms (Barton and Northup 2007).
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Majority of the cave microbial diversity studies have been done using culture dependent
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techniques which can reveal only 1% of the total microorganisms. In recent years, a novel
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methodology is being developed to detect the environmental microorganisms, independently of a
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need for culture based screening. Molecular microbial ecology tools such as denaturing gradient
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gel electrophoresis (DGGE) and clone library analysis are being used by many researchers to
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characterize these uncultured microbes, but these techniques are also not sufficient to analyze the
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entire population in the community (Adetutu et al., 2012). With the advancement of Next
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Generation Sequencing, cave microbial ecology research has also expanded which allows us to
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use culture-independent techniques to reveal further the hidden biodiversity and key process
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happening inside the caves.
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This study involves the use of high throughput illumina sequencing of sediment samples
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collected from caves situated in Indo-Burmese border of Champhai district, Mizoram, Northeast
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India to contribute to better understanding of their microbial community.
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MATERIALS AND METHODS
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Three caves namely, Murapuk (CMP), Lamsialpuk (CLP) and Farpuk (CFP) were
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selected for the present study based on the fact these caves are devoid of any human influence
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and have not been studied yet. Sediment samples were collected from different locations of the
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caves and upon collection; the samples were sieved and preserved at 4°C. No specific permit was
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taken for the sampling since it did not involve any endangered species or protected area.
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Sediment samples were analyzed for carbon and nitrogen content with a CHNS/O analyzer
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(Perkin Elmer, USA) and pH of the sample were measured by pH meter (Table 1).
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Soil community DNA was extracted from 0.5 g of soil sample using the Fast DNA spin
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kit (MP Biomedical, Solon, OH, USA) following the manufacturer’s protocol. DNA
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concentration was quantified using a microplate reader (Molecular device Spectromax 2E). V4
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hypervariable region of the 16S rRNA gene was amplified using 2 µl of each 10 pmol/µl forward
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and
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GGACTACHVGGGTWTCTAAT-3′). The amplification mix contained 5 μL of 40mM dNTP, 5
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μL of 5X Phusion HF reaction buffer, 0.2 μL of 2U/ µl F-540Special Phusion HS DNA
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Polymerase, 5ng input DNA and water to make up the total volume to 25 μL. High throughput
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Illumina Mi-seq sequencing was performed at Scigenome Labs, Cochin, India (Table 2).
reverse
primers
515F
(5′-GTGCCAGCMGCCGCGGTAA-3′)
and
806R
(5′-
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Sequence quality was analyzed according to base quality score distributions, average base
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content per read and GC distribution in the reads. Singletons, the unique OTU that did not cluster
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with other sequence were removed as it might be a result of sequencing errors and can be
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resulted to spurious OTUs. Chimeras were also removed using UCHIME method and pre-
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processed consensus V4 sequences were clustered into Operational Taxonomic Units (OTUs)
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based on their sequence similarity using Uclust program (similarity cutoff=0.97). All the pre-
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processed reads were used to identify the OTUs using QIIME program for constructing a
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representative sequence for each OTUs. The representative sequence was finally aligned to the
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Greengenes core set reference databases using PyNAST program (Caporaso et al., 2010;
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DeSantis et al., 2006). Representative sequence for each OTU was classified using RDP
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classifier and Greengenes OTUs database. Sequences which are not classified were categorized
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as unknown.
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QIIME software was used to calculate Shannon index and Observed species metrices.
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Shannon metric represents observed OTU abundance and estimates for both richness and
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evenness, whereas observed species metric detects unique OTUs present in the sample. In this
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study, the comparison of beta diversity between three bacterial communities (CLP, CMP and
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CFP) was done by calculating the distance matrix using UniFrac approach (Lozupone et al.,
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2005). Weighted UPGMA tree was constructed by performing jackknife test A with 10 replicates
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and each sub-sample containing 1, 00,000 random reads selected from each sample.
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RESULTS AND DISCUSSION
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With an unsuitable geology, caves are the most remote and inaccessible environment for
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research, but are now being considered as a potential biodiversity hotspot due to its unique
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ecological significance. Most of the caves present in Mizoram are of tectonic origin which was
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caused due to tension cleavage of the compact host rock (Gebauer et al., 2001). Since these
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caves are present in extreme conditions, it is assumed that microorganisms living in these caves
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would be mostly novel and undisturbed. Studying this unique habitat provides an opportunity to
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understand global microbial diversity, novel population assemblages, energy dynamics and
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metabolism (Ortiz et al., 2013). Previous study based on caves across the world revealed
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heterotrophic interaction and carbon turnover by Alpha and Betaproteobacteria, Firmicutes and
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Actinobacteria (Macalady et al., 2008; Barton, 2014), although application of next generation
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sequencing technology on these environment suggested to have more information about
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microbial physiology in these caves (Tetu et al., 2013; Ortiz et al., 2014).
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In the present study, we have used paired end illumina sequencing generating 585,434
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raw sequences with 90% of reads having a phred score greater than 30. Illumina method is cost
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effective and provides more detailed taxonomic profiles between samples to be determined
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( Nelson el al., 2014). After quality checking of V4 region of 16s rRNA, reads were clustered
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into Operational Taxonomic Units (OTUs) based on their sequence similarity using Uclust
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program (97% similarity level). A total of 1,140,013 preprocessed reads were clustered into
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10,643 OTUs (operational taxonomical units). Sample library ranges from 259,895(CFP) to
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470,260 (CLP) sequence reads (Table 2). Identification of this huge number of sequence reads is
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a common phenomenon for underground microbial community compared to surface environment
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(Moss et al., 2011; Epure et al., 2014).
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The number of OTUs and Shannon diversity indicates are summarized in Table 3. On the
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basis of the OTUs, CMP has the highest diversity followed closely by CLP. Shannon index also
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showes a high diversity among CMP bacterial community. Rarefaction curve for Observed
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species and Shannon metric are shown in Fig.1. The Observed species metric is only the count of
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unique OTUs identified in the sample. This analysis shows that sample from CMP is more
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diverse than the other two samples. Beta diversity represents the explicit comparison of
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microbial communities based on their composition. Unweighted UniFrac reveals a close
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relationship between these three communities with no difference in distance matrix (Table 4).
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Consensus UPGMA tree with weighted Unifac approach bring the two communities CLP and
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CMP together showing the existing of similar bacteria, while the bacterial community in CFP is
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different from others (Fig.2). This difference may be due to the remote location of the Farpuk
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(CFP) than the other two caves, which probably causes CFP to retain its unique bacterial
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community without any disturbance. The sequences from CFP sample had more than 50%
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singletons in the consensus reads, which are believed to possess no taxonomic information and
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hence deleted for further sequence analysis leading to less diversity representation of the
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bacterial species.
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A total of 21 bacterial phyla and 21 candidate phyla were identified from all the cave
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sediments and were mostly dominated by Actinobacteria, Planctomycetes, Chloroflexi,
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Acidobacteria and Proteobacteria. Relative abundance among the top ten dominated phylum are
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represented in Fig. 3 and Fig. 4. Previous studies recorded diverse groups of actinobacteria in
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caves and their role in colored crystal formation in cave walls and therefore also in constructive
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biomineralization processes (Barton et al., 2001). Our study also detected actinobacteria as the
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most dominating phyla (35.97% of total sequence) with majority of them (244 OTU) falling
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under the order actinomycelales, followed by solirubrobacterales and acidimicrobiales. Other
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orders identified include thermoleophilia, rubrobacterraceae and MMB-A2-108. In our study,
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twelve actinobacteria were identified upto species level: Streptomyces radiopugnans,
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Virgisporangium ochraceum, Actinomadura vinacea, Streptomyces lanatus, Rhodococcus
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fascians, R. fascians, Saccharopolyspora hirsute, Virgisporangium ochraceum, S. mirabilis,
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Actinomadura vinacea and Mycobacterium celatum. In all the cave samples, denovo 3283 were
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most dominated phylotype and BLAST result shows a 100% similarity with the species
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Mycobacterium. Other dominated phylotype include denovo5355, which is closely related with
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Arthrobacter - a member of the GC rich ‘actinomycete’ capable of utilizing a wide and diverse
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range of organic substances as carbon and energy sources such as nicotine, nucleic acids, various
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herbicides and pesticides. Phylum Chloroflexi had the second largest number of sequence
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(13.96%) with 1999 OTUs dominated by the class Ktedonobacteria. Identified genera under
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these phyla include Chloroflexus, FFCH10602, Caldilinea, Ardenscatena, Chloronema and
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Oscillochloris. Most dominated OTUs under this phylum were denovo1827 and denovo 9830
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which were classified under the order thermogemmatisporales and TK10, respectively. Members
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of these phyla were commonly found in most of the caves from other environments such as
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anaerobic thermophiles, filamentous anoxygenic phototrophs, and anaerobic organohalide
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respirers. Our study detects on an average value of 13.76% of all sequences belonging to
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planctomycetes,
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compartmentalization and lack of peptidoglycan in their cell walls. Most of the dominant OUTs
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within this group were classified under the order WD2101 and Gemmatales. This phyla is the
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major abundant members in CMP (22.82% of all read) and CLP (18.43% of all read) samples,
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whereas in CFP it is only 0.03%. Although this phylum is a common member of the cave
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bacterial community, its role in cave is not clear due to limited cultural representative. Few study
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showed their involvement in metabolism of sulfated polysaccharides as well as oxidation of
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ammonia (Schmid et al., 2000; Jetten et al., 2003). Proteobacteria was found to be diverse in all
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the three bacterial communities. A total of 46154 sequences with 497 OTUs were found under
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the subphylum alpha proteobacteria. Major OTUs in this subphylum were classified under the
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order Rhizobiales. Dominant genera within this subphylum were Sphingomonadaceae
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kaistobacter, Bradyrhizobiaceae bradyrhizobium and Hyphomicrobiaceae rhodoplanes.
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Betaproteobacteria were less diverse, only 19 OTUs (2792 sequence) was detected among all the
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samples. Most dominant OTUs within Betaproteobacteria were denovo 360, 2244 and 8071 all
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classified under the genus Burkholderia. Fourty nine OTUs from 3534 sequences grouped under
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gamma proteobacteria, dominated by the genus Dyella. 323 sequences clustering into 37 OUTs
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were classified under the subphyla Deltaproteobacteria. All of OTUs were present in less
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distinct
phylum
of
the
domain
bacteria
having
intracellular
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numbers. The phylum acidobacteria was moderately abundant among the cave samples and
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represented by 11.44% of the total sequence obtained. This phylum consisted of family
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solibacteraceae, koribacteraceae and acidobacteriaceae. Most dominant OTUs under this order
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were denovo7994 and denovo 9544, belonging to the class chloracidobacteria and acidobacteria-
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6 respectively. Two OTUs within this phylum - denovo 6901 and denovo 5227 demonstrate
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close sequence similarity with Candidatus Solibacter usitatus Ellin6076, which are adopted to
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survive under low-nutrient conditions (Ward et al., 2009).
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A total of 33411 reads (CLP=8, CFP=11583 & CMP =21820) comprising 361 OTUs
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were classified within the phylum Armatimonadetes (formerly known as ‘candidate division
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OP10’), a dominant and globally-distributed lineage within this ‘uncultured majority’. All the
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OTUs were classified under the genus fimbriimonas except denovo 1709 which is classified
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under the genus chthonomonas. Only one OTU (denovo 4733) was found in CMP classified
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under the genus Gemmatimonas. Seven OTUs containing 89 reads (CFP53, CLP2, and CMP 34)
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were affiliated with phylum nitrospira with only two detected genus- JG37-AG-70 and
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nitrospira.
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oxidation in nitrite and detected previously in Mexican anchialine caves and Tito Bustillo caves
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(Pohlman et al., 1997; Schabereiter-Gurtner et al., 2002). Our analysis reveals 45 OTUs under
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the phylum bacteroidetes. Taxa classified under the species level were Cytophaga xylanolytica,
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Flavobacterium succinicans, Bacteroides plebeius, Sphingobacterium multivorum and
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Fontibacter flavus. Most dominant OTU classified were under flavisolibacter (denovo1336 and
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denovo3516) and adhaeribacter (denovo8478). There were 334 sequences comprising 19 OTUs
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classified under the phylum Euryarchaeota, dividing into four classes methanomicrobia,
Members of this group are obligate chemolithoautotroph, obtaining energy by
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thermoplasmata, halobacteria and methanobacteria. Identified genera in this phylum include
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Methanocella,
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Methanosarcina, Haloquadratum, Methanobacterium, Methanosaeta, and Methanoplanus.
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However within the phylum Euryarchaeota, none of the OTUs were classified upto the species
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level in all the three cave communities and were found to be present in rare numbers. The
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phylum Crenarchaeota is also present in very low quantity and was clustered into 21 OTUs (total
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read 1698). All the Crenarchaeota were assigned into two classes- MBGA and Thaumarchaeota.
Methanocorpusculum,
Halolamina,
Methanoculleus,
Methanoculleus,
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Our analysis identified twenty one candidate phyla, also known as a bacterial lineage,
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mostly falling within the rare biosphere. The most dominant OTU among the candidate phyla is
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denovo 1407 (read=3094), classified under the phylum AD3, having close sequence similarity
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with the environmental clone LuqGS470001 (Minyard et al., 2012). This clone was originally
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isolated from deep saprolite and saprock which is believed to play a role in weathered minerals
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in deep tropical saprolite and is found to be a common inhabitant of all the analyzed caves
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(Minyard et al., 2012). Other candidate phyla identified in our study includes LD1, MPV, NKB,
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OD, OD1, OD3, TM6, TM7, WS1, WS2, WS3, WWE1, ZB3, BH1, BRC1, FCPU, GAL, GN,
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ZB3 and Kazan3b. Top ten bacterial genera based on OTU number and top ten OTU’s based on
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total read count number among the cave samples is represented in Supplementary Tables S1 and
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S2, respectively. Relative abundance of bacterial diversity from phylum to species is shown in
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Supplementary Fig.’s S1 to S3.
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Illumina sequencing reveals a huge number of phylotype among these caves samples
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belonging to the rare biosphere, which are microorganisms with extremely low abundance (Reid
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et al., 2011). We have selected the criteria for rare (<0.01% of total community) and abundant
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(other than rare species) species based on the previous study (Aravindraja et al., 2013) and
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according to this distribution, the rare species was 75.72-83.01% among the samples, whereas
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the abundant species was 16.98-24.27 % (Fig. 5). Ratio of rare and abundant OTUs among all
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the three samples were similar and within a range of 3.11- 4.88. The most abundant phylotype
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was denovo 6722 classified under actinobacteria and were present in all the three cave samples.
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Fig. 6 shows the unique and shared species among the rare biosphere in all the three samples.
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Venn diagram shows only 171 rare OTUs (1.78%) being shared among the three communities,
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but majority of the rare species in CFP are unique, whereas many common species was observed
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between CFP and CMP. Among abundant species 3.94% is shared by all the three samples.
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Many OTUs among the cave samples were rare in one community but showing as an abundant in
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other community. This showes that different environmental factors prevalent among the caves
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which makes some group to be dormant and become a member of the rare biosphere. These
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members can be active at favorable environmental conditions and become abundant. Common
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identified species among the cave samples were members of the phylum Actinobacteria,
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Firmicutes and Proteobacteria (Table 5). Further analysis with whole genome sequencing will
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reveal the actual role of these rare and abundant phyla present in the cave samples.
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This study provides an in-depth study of unexplored bacterial diversity in cave samples
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of Mizoram with a large number of classified phyla (twenty), candidate phyla (twenty one) and a
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large portion of unclassified bacteria, indicates the possibility the presence of novel species. It is
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found that the classified reads were simultaneously decreased from phylum to species level. The
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two most dominant phylotype were denovo 6722 (11.72%) and denovo 4035 (5.72%) belonging
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to Actinobacteria and Verrucomicrobia, respectively. The remaining phyla present in the
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communities had low (<4%) abundance. The present study revealed a unique bacterial
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community in Farpuk which was mostly classified under uncultured actinobacteria. These
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uncultured species could be a major source of new antibiotics. This analysis also revealed that
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the bacterial diversity is higher in CMP and CFP samples compared to CLP samples. This might
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be due to the fact that CLP is situated in extremely remote place and their diversity is not
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influenced by an exogenous source compared to other or each cave environment might have
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different nutrient composition or ecological condition for specific bacterial taxa.
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ACKNOWLEDGEMENTS
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This research was funded by a grant from the State Biotech Hub sponsored by Department of
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Biotechnology, Govt. of India, New Delhi. We would like to thank Mr. Lalrinhlua for his help in
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sampling.
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Competing Interests
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The authors declare there are no competing interests.
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Author Contributions
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• Surajit De Mandal, Zothansanga and Nachimuthu Senthil Kumar conceived and designed the
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experiments, analyzed the data, wrote the paper, prepared Figures and Tables. Surajit De Mandal
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performed the experiments.
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DNA Deposition
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The following information was supplied and is under process regarding the deposition of DNA
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sequences: EBI Sequence Read Archive, Project Number PRJEB7730 and ERP008676.
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Supplemental Information
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Supplemental information for this article is attached. The file contains 2 Tables and 3 Figures.
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REFERENCES
4
Adetutu EM, Thorpe K, Bourne S, Cao X, Shahsavari E, Kirby G, Ball A.S. 2011.
5
Phylogenetic diversity of fungal communities in areas accessible and not accessible to tourists in
6
Naracoorte Caves. Mycologia 103:959-968 http://dx.doi.org/10.3852/10-256.
7
Adetutu EM, Thorpe K, Shahsavari E, Bourne S, Cao X, Fard RMN, Kirby G, Ball AS.
8
2012. Bacterial community survey of sediments at Naracoorte Caves, Australia. International
9
Journal of Speleology 41(2):137-147.
10
Aravindraja C, Viszwapriya D, Karutha PS. 2013. Ultradeep 16S rRNA Sequencing Analysis
11
of Geographically Similar but Diverse Unexplored Marine Samples Reveal Varied Bacterial
12
Community Composition. PLoS ONE 8(10): e76724 doi:10.1371/journal.pone.0076724.
13
Barton AH, Northup ED. 2007. Geomicrobiology in cave environments: past, current and
14
future perspectives. Journal of Cave and Karst Studies 69:163-178.
15
Barton HA, Spear JR, Pace NR. 2001. Microbial life in the underworld: biogenicity in
16
secondary mineral formations. Geomicrobiology Journal 18:359-368.
17
Barton HA. 2006. Introduction to cave microbiology: a review for the non-specialist: Journal of
18
Cave and Karst Studies 68:43-54.
19
Barton HA. 2014. "Starving Artists: Bacterial Oligotrophic Heterotrophy in Caves." in Life in
20
Extreme Environments: Microbial Life of Cave Systems. ed. D. Wagner. (Berlin, Germany: De
21
Gruyter).
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
PrePrints
15
1
Brook BW, Sodhi NS, Ng PKL. 2003. Catastrophic extinctions follow deforestation in
2
Singapore. Nature 424: 420-424.
3
Caporaso JG, Bittinger K, Bushman FD, DeSantis TZ, Andersen GL, Knight R. 2010a.
4
PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26:266–
5
267.
6
Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer
7
N,Pe˜na AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley
8
RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR,
9
Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J, Knight R. 2010b.
10
QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7:335-
11
336 DOI 10.1038/nmeth.f.303.
12
DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL, Keller K, Huber T, Dalevi D,
13
Hu P, Andersen GL. 2006. Greengenes, a chimera-checked 16S rRNA gene database and
14
workbench compatible with ARB. Applied and Environmental Microbiology 72:5069-5072.
15
16
Engel AS, Meisinger DB, Porter ML, Payn R, Schmid M, Stern LA, Schleifer KH, Lee NM.
17
2010. Linking phylogenetic and functional diversity to nutrient spiraling in microbial mats from
18
Lower Kane Cave (USA). The ISME Journal. 4: 98-110.
19
20
Epure J, Meleg JN, Munteanu CM, Roban RD, Moldovan OT. 2014. Bacterial and Fungal
21
Diversity of Quaternary Cave Sediment Deposits Geomicrobiology Journal 31 :(2)116-127 DOI:
22
10.1080/01490451.2013.815292.
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
PrePrints
16
1
Gebauer HD, Chhakchhuak B, Sootinck N. 2001. Caves of Mizoram (speleological projects in
2
NE-India) 5: 61
3
Jetten MS, Sliekers O, Kuypers M, Dalsgaard T, van Niftrik L, Cirpus I, van de Pas-
4
Schoonen K, Lavik G, Thamdrup B, Le Paslier D, Op den Camp HJ, Hulth S, Nielsen
5
LP, Abma W, Third K, Engström P, Kuenen JG, Jørgensen BB, Canfield DE, Sinninghe
6
Damsté JS, Revsbech NP, Fuerst J, Weissenbach J, Wagner M, Schmidt I, Schmid
7
M, Strous M. 2003. Anaerobic ammonium oxidation by marine and freshwater planctomycete-
8
like bacteria. Applied Microbiology and Biotechnology 63:107-114.
9
Lozupone C, Knight R. 2005. UniFrac: a new phylogenetic method for comparing microbial
10
communities. Applied and Environmental Microbiology 71:8228-8235 DOI 10.1128/ AEM.
11
71.12.8228-8235.2005.
12
Macalady JL, Dattagupta S, Schaperdoth I, Jones DS, Druschel GK, Eastman D. 2008.
13
Niche differentiation among sulfur-oxidizing bacterial populations in cave waters. The ISME
14
Journal 2: 590-601.
15
Mani, MS.1974. Ecology and Biogeography in India- Vol-1, Dr. W. Junk b. v. Publishers, The
16
Hague.
17
Minyard ML, Bruns MA, Liermann LJ, Buss HL, Brantley SL. 2012. Bacterial Associations
18
with Weathering Minerals at the Regolith-Bedrock Interface, Luquillo Experimental Forest,
19
Puerto Rico. Geomicrobiology Journal 29: 792-803.
20
Moss JA, Nocker A, Snyder R.A. 2011. Microbial characteristics of a submerged karst cave
21
system in Northern Florida. Geomicrobiology Journal 28: 719-931.
22
Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J. 2000. Biodiversity
23
hotspots for conservation priorities. Nature 403: 853-858.
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
PrePrints
17
1
Nelson MC, Morrison HG, Benjamino J, Grim SL, Graf J. 2014. Analysis, Optimization and
2
Verification of Illumina-Generated 16S rRNA Gene Amplicon Surveys PLoS ONE 9(4): e94249.
3
Northup DE, Melim LA, Spilde MN, Hathaway JM, Garcia MG, Moya M, Stone FD,
4
Boston PJ, Dapkevicius MLNE, Riquelme C. 2011. Lava cave microbial communities within
5
mats and secondary mineral deposits: Implications for life detection on other planets.
6
Astrobiology 11:601-618.
7
Ortiz M, Legatzki A, Neilson JW, Fryslie B, Nelson WM, Wing RA, Soderlund CA, Pryor
8
BM, Maier RM. 2014. Making a living while starving in the dark: metagenomic insights into
9
the energy dynamics of a carbonate cave. The ISME Journal 8: 478-491.
10
Ortiz M, Neilson JW, Nelson WM, Legatzki A, Byrne A, Yu Y, Wing RA, Soderlund CA,
11
Pryor BM, Pierson LS, Maier RM. 2013. Profiling Bacterial Diversity and Taxonomic
12
Composition on Speleothem Surfaces in Kartchner Caverns, AZ. Microbial Ecology 65:371-383.
13
Pohlman JW, Iliffe TM, Cifuentes LA.1997.A stable isotope study of organic cycling and the
14
ecology of an anchialine cave ecosystem. Marine Ecology Progress Series 155:17-27.
15
Reid A, Buckley M. Washington, DC: American Academy of Microbiology; 2011. The Rare
16
Biosphere: A report from the American Academy of Microbiology.
17
Saiz-Jimenez C. 2012. Microbiological and environmental issues in show caves. World Journal
18
of Microbiology and Biotechnology 28: 2453-2464.
19
Schabereiter-Gurtner C, Saiz-Jimenez C, Piñar G, Lubitz W, Rölleke S. 2002a. Altamira
20
cave Paleolithic paintings harbour partly unknown bacterial communities. FEMS Microbiology
21
Letters 211: 7-11.
22
Schmid
23
JW, Schleifer KH, Wagner M. 2000. Molecular evidence for genus level diversity of bacteria
M, Twachtmann
U, Klein
M, Strous
M, Juretschko
S, Jetten
M, Metzger
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
PrePrints
18
1
capable of catalyzing anaerobic ammonium oxidation. Systematic and Applied Microbiology
2
23: 93-106.
3
Tetu SG, Breakwell K, Elbourne LD, Holmes AJ, Gillings MR, Paulsen I.T. 2013. Life in the
4
dark: metagenomic evidence that a microbial slime community is driven by inorganic nitrogen
5
metabolism. The ISME Journal 7:1227-1236.
6
Ward NL, Challacombe JF, Janssen PH, Henrissat B, Coutinho PM, Wu M, Xie G., Haft
7
DH, Sait M, Badger J, Barabote RD, Bradley B, Brettin TS, Brinkac LM, Bruce D, Creasy
8
T, Daugherty SC, Davidsen TM, Deboy RT, Detter JC, Dodson RJ, Durkin, A.S.,
9
Ganapathy A, Gwinn-Giglio M, Han CS, Khouri H, Kiss H, Kothari SP, Madupu R, Nelson
10
K , Nelson WC , Paulsen I , Penn K , Ren Q , Rosovitz MJ, Selengut JD, Shrivastava S,
11
Sullivan SA, Tapia R, Thompson LS, Watkins KL, Yang Q, Yu C, Zafar N, Zhou L, Kuske
12
C.R. 2009. Three genomes from the phylum Acidobacteria provide insight into their lifestyles in
13
soils. Applied and Environmental Microbiology 75:2046-2056.
14
15
16
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1
Table 1 Details of the cave samples used in the present study.
Name of the Place and
Year of
cave
collection
(Sample
Name)
Latitude
Murapuk
(CMP)
N23°44.295'
Lamsialpuk
(CLP)
Farpuk
(CFP)
PrePrints
2
3
4
Champhai,
Mizoram,
N23°08.019'
Northeast India
(2014)
N23°06.055'
Longitude
Elevation
Humidity
Temperature
(MSL)
(%)
(oC)
pH
C
N
H
(%)
(%)
(%)
E92°39'770'
4927
44
22
7.2
110.50
13.46
30.90
E93°16'896'
4446
40
24
7.2
126.79
13.96
9.96
E92°17'911'
4645
44
23
6.8
40.58
3.19
9.07
5
6
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Table 2 Summary statistics of illumina paired-end reads (V4 region of 16S rRNA gene) used in this study.
CLP
CMP
CFP
Total
Reads
(bp)
PrePrints
Sample
Name
674,406
635,210
690,975
Passed
Conserved
Region
Filter (bp)
617,278
583,165
621,772
Passed
Spacer
(bp)
616,828
582,750
620,134
Passed
Passed
Consensus
After
Read
Mismatch Reads
Singleton
Quality
Filter (bp) (bp)
Removal
Filter (bp)
(bp)
616,738
568,149
568,149
470,943
582,628
538,641
538,641
406,640
619,973
560,239
538,641
262,430
Chimeric PreSequences processed
(bp)
Reads
(bp)
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683
1,252
2,535
470,260
405,388
259,895
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Table 3 Summary of illumina operational taxonomical units (OTUs) and alpha diversity
estimates using QIIME tool.
Sample name
Total OTU
Shannon index
6555
9.30
CLP
4968
9.05
CFP
3108
4.75
PrePrints
CMP
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Table 4 Unweighted UniFrac distance matrix among the cave samples.
CLP
CMP
CFP
CLP
0
0.761516
0.550632
CMP
0.761516
0
0.779452
CFP
0.550632
0.779452
0
Sample
PrePrints
name
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PrePrints
Table 5 Shared and unic taxa (identified upto species level) in the cave samples. Species
with * is present in all the samples
Species in CFP
Actinomadura vinacea*
Clostridium bowmanii*
Clostridium butyricum*
Glaciecola polaris
Mycobacterium celatum
Peredibacter starrii*
Rhodococcus fascians
Saccharopolyspora hirsuta*
Sphingomonas azotifigens*
Stenotrophomonas acidaminiphila
Streptomyces mirabilis*
Thermomonas fusca
Virgisporangium ochraceum*
Species in CLP
Actinomadura vinacea*
Bacillus badius
Bacteroides plebeius
Brevundimonas diminuta
Burkholderia tuberum
Caenispirillum salinarum
Clostridium bifermentans
Clostridium bowmanii*
Clostridium butyricum*
Clostridium perfringens
Clostridium tetani
Clostridium venationis
Coccomyxa subellipsoidea
Cytophaga xylanolytica
Escherichia coli
Flavobacterium succinicans*
Fontibacter flavus
Inquilinus limosus
Kosmotoga mrcj
Luteibacter rhizovicinus
Megamonas hypermegale
Methylobacterium organophilum
Mycobacterium celatum
Paenibacillus chondroitinus
Paenibacillus curdlanolyticus
Peredibacter starrii*
Pseudomonas viridiflava
Saccharopolyspora hirsuta*
Shewanella algae
Singulisphaera rosea
Sphingobacteria multivorum
Sphingomonas azotifigens*
Sphingomonas wittichii
Species in CMP
Actinomadura vinacea*
Afipia felis
Bacillus badius
Burkholderia tuberum
Clostridium acetobutylicum
Clostridium bifermentans
Clostridium bowmanii*
Clostridium butyricum*
Coccomyxa subellipsoidea
Corallococcus exiguus
Desulfosporosinus meridiei
Escherichia coli
Flavobacterium succinicans*
Glaciecola polaris
Leptolyngbya frigida
Luteibacter rhizovicinus
Nevskia ramosa
Paenibacillus chondroitinus
Paenibacillus ginsengarvi
Peredibacter starrii*
Propionispira arboris
Pseudomonas viridiflava
Rhodococcus fascians
Roseomonas mucosa
Saccharopolyspora hirsuta*
Shewanella algae
Sphingobacterium multivorum
Sphingomonas azotifigens*
Streptomyces lanatus
Streptomyces mirabilis*
Syntrichia ruralis
Virgisporangium ochraceum*
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PrePrints
Streptomyces mirabilis*
Streptomyces radiopugnans
Veillonella dispar
Virgisporangium ochraceum*
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Figure 1 Rarefaction analysis of alpha diversity among CMP, CLP and CMP samples. Three different diversity matrix
were used a) Observed number of species, b) Shannon diversity index.
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1
2
PrePrints
3
4
5
Figure 2 Phylogenetic tree based on the distances between sample CLP, CMP and CFP
6
with weighted UniFrac approach.
7
8
9
10
11
12
13
14
15
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2
3
4
Figure 3 Taxonomy classifications of reads at phylum level for the cave samples. Only top
10 enriched class categories are shown in the figure. Classification is performed using RDP
classifier and Greengenes OTUs database.
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6
7
8
9
10
11
12
13
14
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PrePrints
2
3
4
5
6
Figure 4 Taxonomy classifications of OTUs at phylum level for the cave samples. Only
top 10 enriched class categories are shown in the figure. Classification is performed using
RDP classifier and Greengenes OTUs database.
7
8
28
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2
3
Figure 5 Percentage of abundant and rare OTUs among the cave samples
4
5
6
7
8
9
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2
3
Figure 6 Venn diagram showing the unique and shared species among the rare and
abundant OTUs among of the cave samples
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7
8
9
10
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12
13
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